Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons.

PubMed

Zhuang, Yuan; Yang, Jun; Li, You; Qi, Longning; El-Sheimy, Naser

2016-04-26

Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target's location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of <2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than <3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than <3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of <3.88 m at 90% of the time, which performs 49.58% more accurate than <8.00 m from the PM + EKF algorithm and 21.41% better than <4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment.

Estimation of key parameters in adaptive neuron model according to firing patterns based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Yuan, Chunhua; Wang, Jiang; Yi, Guosheng

2017-03-01

Estimation of ion channel parameters is crucial to spike initiation of neurons. The biophysical neuron models have numerous ion channel parameters, but only a few of them play key roles in the firing patterns of the models. So we choose three parameters featuring the adaptation in the Ermentrout neuron model to be estimated. However, the traditional particle swarm optimization (PSO) algorithm is still easy to fall into local optimum and has the premature convergence phenomenon in the study of some problems. In this paper, we propose an improved method that uses a concave function and dynamic logistic chaotic mapping mixed to adjust the inertia weights of the fitness value, effectively improve the global convergence ability of the algorithm. The perfect predicting firing trajectories of the rebuilt model using the estimated parameters prove that only estimating a few important ion channel parameters can establish the model well and the proposed algorithm is effective. Estimations using two classic PSO algorithms are also compared to the improved PSO to verify that the algorithm proposed in this paper can avoid local optimum and quickly converge to the optimal value. The results provide important theoretical foundations for building biologically realistic neuron models.

Rainfall Estimates from the TMI and the SSM/I

NASA Technical Reports Server (NTRS)

Hong, Ye; Kummerow, Christian D.; Olson, William S.; Viltard, Nicolas

1999-01-01

The Tropical Rainfall Measuring Mission (TRMM), which is a joint Japan-U.S. Earth observing satellite, has been successfully launched from Japan on November 27, 1997. The main purpose of the TRMM is to measure quantitatively rainfall over the tropics for the research of climate and weather. One of three rainfall measuring instruments abroad the TRMM is the high resolution TRMM Microwave Imager (TMI). The TMI instrument is essentially the copy of the SSM/I with a dual-polarized pair of 10.7 GHz channels added to increase the dynamic range of rainfall estimates. In addition, the 21.3 GHz water vapor absorption channel is designed in the TMI as opposed to the 22.235 GHz in the SSM/I to avoid saturation in the tropics. This paper will present instantaneous rain rates estimated from the coincident TMI and SSM/I observations. The algorithm for estimating instantaneous rainfall rates from both sensors is the Goddard Profiling algorithm (Gprof). The Gprof algorithm is a physically based, multichannel rainfall retrieval algorithm, The algorithm is very portable and can be used for various sensors with different channels and resolutions. The comparison of rain rates estimated from TMI and SSM/I on the same rain regions will be performed. The results from the comparison and the insight of tile retrieval algorithm will be given.

Side-information-dependent correlation channel estimation in hash-based distributed video coding.

PubMed

Deligiannis, Nikos; Barbarien, Joeri; Jacobs, Marc; Munteanu, Adrian; Skodras, Athanassios; Schelkens, Peter

2012-04-01

In the context of low-cost video encoding, distributed video coding (DVC) has recently emerged as a potential candidate for uplink-oriented applications. This paper builds on a concept of correlation channel (CC) modeling, which expresses the correlation noise as being statistically dependent on the side information (SI). Compared with classical side-information-independent (SII) noise modeling adopted in current DVC solutions, it is theoretically proven that side-information-dependent (SID) modeling improves the Wyner-Ziv coding performance. Anchored in this finding, this paper proposes a novel algorithm for online estimation of the SID CC parameters based on already decoded information. The proposed algorithm enables bit-plane-by-bit-plane successive refinement of the channel estimation leading to progressively improved accuracy. Additionally, the proposed algorithm is included in a novel DVC architecture that employs a competitive hash-based motion estimation technique to generate high-quality SI at the decoder. Experimental results corroborate our theoretical gains and validate the accuracy of the channel estimation algorithm. The performance assessment of the proposed architecture shows remarkable and consistent coding gains over a germane group of state-of-the-art distributed and standard video codecs, even under strenuous conditions, i.e., large groups of pictures and highly irregular motion content.

Efficient algorithms for solution of interference cancellation and channel estimation for mobile OFDM system

NASA Astrophysics Data System (ADS)

Fan, Tong-liang; Wen, Yu-cang; Kadri, Chaibou

Orthogonal frequency-division multiplexing (OFDM) is robust against frequency selective fading because of the increase of the symbol duration. However, the time-varying nature of the channel causes inter-carrier interference (ICI) which destroys the orthogonal of sub-carriers and degrades the system performance severely. To alleviate the detrimental effect of ICI, there is a need for ICI mitigation within one OFDM symbol. We propose an iterative Inter-Carrier Interference (ICI) estimation and cancellation technique for OFDM systems based on regularized constrained total least squares. In the proposed scheme, ICI aren't treated as additional additive white Gaussian noise (AWGN). The effect of Inter-Carrier Interference (ICI) and inter-symbol interference (ISI) on channel estimation is regarded as perturbation of channel. We propose a novel algorithm for channel estimation o based on regularized constrained total least squares. Computer simulations show that significant improvement can be obtained by the proposed scheme in fast fading channels.

An Algorithm for Obtaining the Distribution of 1-Meter Lightning Channel Segment Altitudes for Application in Lightning NOx Production Estimation

NASA Technical Reports Server (NTRS)

Peterson, Harold; Koshak, William J.

2009-01-01

An algorithm has been developed to estimate the altitude distribution of one-meter lightning channel segments. The algorithm is required as part of a broader objective that involves improving the lightning NOx emission inventories of both regional air quality and global chemistry/climate models. The algorithm was tested and applied to VHF signals detected by the North Alabama Lightning Mapping Array (NALMA). The accuracy of the algorithm was characterized by comparing algorithm output to the plots of individual discharges whose lengths were computed by hand; VHF source amplitude thresholding and smoothing were applied to optimize results. Several thousands of lightning flashes within 120 km of the NALMA network centroid were gathered from all four seasons, and were analyzed by the algorithm. The mean, standard deviation, and median statistics were obtained for all the flashes, the ground flashes, and the cloud flashes. One-meter channel segment altitude distributions were also obtained for the different seasons.

A simple algorithm for distance estimation without radar and stereo vision based on the bionic principle of bee eyes

NASA Astrophysics Data System (ADS)

Khamukhin, A. A.

2017-02-01

Simple navigation algorithms are needed for small autonomous unmanned aerial vehicles (UAVs). These algorithms can be implemented in a small microprocessor with low power consumption. This will help to reduce the weight of the UAVs computing equipment and to increase the flight range. The proposed algorithm uses only the number of opaque channels (ommatidia in bees) through which a target can be seen by moving an observer from location 1 to 2 toward the target. The distance estimation is given relative to the distance between locations 1 and 2. The simple scheme of an appositional compound eye to develop calculation formula is proposed. The distance estimation error analysis shows that it decreases with an increase of the total number of opaque channels to a certain limit. An acceptable error of about 2 % is achieved with the angle of view from 3 to 10° when the total number of opaque channels is 21600.

Self-calibrated multiple-echo acquisition with radial trajectories using the conjugate gradient method (SMART-CG).

PubMed

Jung, Youngkyoo; Samsonov, Alexey A; Bydder, Mark; Block, Walter F

2011-04-01

To remove phase inconsistencies between multiple echoes, an algorithm using a radial acquisition to provide inherent phase and magnitude information for self correction was developed. The information also allows simultaneous support for parallel imaging for multiple coil acquisitions. Without a separate field map acquisition, a phase estimate from each echo in multiple echo train was generated. When using a multiple channel coil, magnitude and phase estimates from each echo provide in vivo coil sensitivities. An algorithm based on the conjugate gradient method uses these estimates to simultaneously remove phase inconsistencies between echoes, and in the case of multiple coil acquisition, simultaneously provides parallel imaging benefits. The algorithm is demonstrated on single channel, multiple channel, and undersampled data. Substantial image quality improvements were demonstrated. Signal dropouts were completely removed and undersampling artifacts were well suppressed. The suggested algorithm is able to remove phase cancellation and undersampling artifacts simultaneously and to improve image quality of multiecho radial imaging, the important technique for fast three-dimensional MRI data acquisition. Copyright © 2011 Wiley-Liss, Inc.

Self-calibrated Multiple-echo Acquisition with Radial Trajectories using the Conjugate Gradient Method (SMART-CG)

PubMed Central

Jung, Youngkyoo; Samsonov, Alexey A; Bydder, Mark; Block, Walter F.

2011-01-01

Purpose To remove phase inconsistencies between multiple echoes, an algorithm using a radial acquisition to provide inherent phase and magnitude information for self correction was developed. The information also allows simultaneous support for parallel imaging for multiple coil acquisitions. Materials and Methods Without a separate field map acquisition, a phase estimate from each echo in multiple echo train was generated. When using a multiple channel coil, magnitude and phase estimates from each echo provide in-vivo coil sensitivities. An algorithm based on the conjugate gradient method uses these estimates to simultaneously remove phase inconsistencies between echoes, and in the case of multiple coil acquisition, simultaneously provides parallel imaging benefits. The algorithm is demonstrated on single channel, multiple channel, and undersampled data. Results Substantial image quality improvements were demonstrated. Signal dropouts were completely removed and undersampling artifacts were well suppressed. Conclusion The suggested algorithm is able to remove phase cancellation and undersampling artifacts simultaneously and to improve image quality of multiecho radial imaging, the important technique for fast 3D MRI data acquisition. PMID:21448967

Real-time image dehazing using local adaptive neighborhoods and dark-channel-prior

NASA Astrophysics Data System (ADS)

Valderrama, Jesus A.; Díaz-Ramírez, Víctor H.; Kober, Vitaly; Hernandez, Enrique

2015-09-01

A real-time algorithm for single image dehazing is presented. The algorithm is based on calculation of local neighborhoods of a hazed image inside a moving window. The local neighborhoods are constructed by computing rank-order statistics. Next the dark-channel-prior approach is applied to the local neighborhoods to estimate the transmission function of the scene. By using the suggested approach there is no need for applying a refining algorithm to the estimated transmission such as the soft matting algorithm. To achieve high-rate signal processing the proposed algorithm is implemented exploiting massive parallelism on a graphics processing unit (GPU). Computer simulation results are carried out to test the performance of the proposed algorithm in terms of dehazing efficiency and speed of processing. These tests are performed using several synthetic and real images. The obtained results are analyzed and compared with those obtained with existing dehazing algorithms.

Single-channel mixed signal blind source separation algorithm based on multiple ICA processing

NASA Astrophysics Data System (ADS)

Cheng, Xiefeng; Li, Ji

2017-01-01

Take separating the fetal heart sound signal from the mixed signal that get from the electronic stethoscope as the research background, the paper puts forward a single-channel mixed signal blind source separation algorithm based on multiple ICA processing. Firstly, according to the empirical mode decomposition (EMD), the single-channel mixed signal get multiple orthogonal signal components which are processed by ICA. The multiple independent signal components are called independent sub component of the mixed signal. Then by combining with the multiple independent sub component into single-channel mixed signal, the single-channel signal is expanded to multipath signals, which turns the under-determined blind source separation problem into a well-posed blind source separation problem. Further, the estimate signal of source signal is get by doing the ICA processing. Finally, if the separation effect is not very ideal, combined with the last time's separation effect to the single-channel mixed signal, and keep doing the ICA processing for more times until the desired estimated signal of source signal is get. The simulation results show that the algorithm has good separation effect for the single-channel mixed physiological signals.

Improving Pulse Rate Measurements during Random Motion Using a Wearable Multichannel Reflectance Photoplethysmograph.

PubMed

Warren, Kristen M; Harvey, Joshua R; Chon, Ki H; Mendelson, Yitzhak

2016-03-07

Photoplethysmographic (PPG) waveforms are used to acquire pulse rate (PR) measurements from pulsatile arterial blood volume. PPG waveforms are highly susceptible to motion artifacts (MA), limiting the implementation of PR measurements in mobile physiological monitoring devices. Previous studies have shown that multichannel photoplethysmograms can successfully acquire diverse signal information during simple, repetitive motion, leading to differences in motion tolerance across channels. In this paper, we investigate the performance of a custom-built multichannel forehead-mounted photoplethysmographic sensor under a variety of intense motion artifacts. We introduce an advanced multichannel template-matching algorithm that chooses the channel with the least motion artifact to calculate PR for each time instant. We show that for a wide variety of random motion, channels respond differently to motion artifacts, and the multichannel estimate outperforms single-channel estimates in terms of motion tolerance, signal quality, and PR errors. We have acquired 31 data sets consisting of PPG waveforms corrupted by random motion and show that the accuracy of PR measurements achieved was increased by up to 2.7 bpm when the multichannel-switching algorithm was compared to individual channels. The percentage of PR measurements with error ≤ 5 bpm during motion increased by 18.9% when the multichannel switching algorithm was compared to the mean PR from all channels. Moreover, our algorithm enables automatic selection of the best signal fidelity channel at each time point among the multichannel PPG data.

Evaluation of Dynamic Channel and Power Assignment for Cognitive Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Syed A. Ahmad; Umesh Shukla; Ryan E. Irwin

2011-03-01

In this paper, we develop a unifying optimization formulation to describe the Dynamic Channel and Power Assignment (DCPA) problem and evaluation method for comparing DCPA algorithms. DCPA refers to the allocation of transmit power and frequency channels to links in a cognitive network so as to maximize the total number of feasible links while minimizing the aggregate transmit power. We apply our evaluation method to five algorithms representative of DCPA used in literature. This comparison illustrates the tradeoffs between control modes (centralized versus distributed) and channel/power assignment techniques. We estimate the complexity of each algorithm. Through simulations, we evaluate themore » effectiveness of the algorithms in achieving feasible link allocations in the network, as well as their power efficiency. Our results indicate that, when few channels are available, the effectiveness of all algorithms is comparable and thus the one with smallest complexity should be selected. The Least Interfering Channel and Iterative Power Assignment (LICIPA) algorithm does not require cross-link gain information, has the overall lowest run time, and highest feasibility ratio of all the distributed algorithms; however, this comes at a cost of higher average power per link.« less

Road-Aided Ground Slowly Moving Target 2D Motion Estimation for Single-Channel Synthetic Aperture Radar.

PubMed

Wang, Zhirui; Xu, Jia; Huang, Zuzhen; Zhang, Xudong; Xia, Xiang-Gen; Long, Teng; Bao, Qian

2016-03-16

To detect and estimate ground slowly moving targets in airborne single-channel synthetic aperture radar (SAR), a road-aided ground moving target indication (GMTI) algorithm is proposed in this paper. First, the road area is extracted from a focused SAR image based on radar vision. Second, after stationary clutter suppression in the range-Doppler domain, a moving target is detected and located in the image domain via the watershed method. The target's position on the road as well as its radial velocity can be determined according to the target's offset distance and traffic rules. Furthermore, the target's azimuth velocity is estimated based on the road slope obtained via polynomial fitting. Compared with the traditional algorithms, the proposed method can effectively cope with slowly moving targets partly submerged in a stationary clutter spectrum. In addition, the proposed method can be easily extended to a multi-channel system to further improve the performance of clutter suppression and motion estimation. Finally, the results of numerical experiments are provided to demonstrate the effectiveness of the proposed algorithm.

Estimation of Boreal Forest Biomass Using Spaceborne SAR Systems

NASA Technical Reports Server (NTRS)

Saatchi, Sassan; Moghaddam, Mahta

1995-01-01

In this paper, we report on the use of a semiempirical algorithm derived from a two layer radar backscatter model for forest canopies. The model stratifies the forest canopy into crown and stem layers, separates the structural and biometric attributes of the canopy. The structural parameters are estimated by training the model with polarimetric SAR (synthetic aperture radar) data acquired over homogeneous stands with known above ground biomass. Given the structural parameters, the semi-empirical algorithm has four remaining parameters, crown biomass, stem biomass, surface soil moisture, and surface rms height that can be estimated by at least four independent SAR measurements. The algorithm has been used to generate biomass maps over the entire images acquired by JPL AIRSAR and SIR-C SAR systems. The semi-empirical algorithms are then modified to be used by single frequency radar systems such as ERS-1, JERS-1, and Radarsat. The accuracy. of biomass estimation from single channel radars is compared with the case when the channels are used together in synergism or in a polarimetric system.

An improved dehazing algorithm of aerial high-definition image

NASA Astrophysics Data System (ADS)

Jiang, Wentao; Ji, Ming; Huang, Xiying; Wang, Chao; Yang, Yizhou; Li, Tao; Wang, Jiaoying; Zhang, Ying

2016-01-01

For unmanned aerial vehicle(UAV) images, the sensor can not get high quality images due to fog and haze weather. To solve this problem, An improved dehazing algorithm of aerial high-definition image is proposed. Based on the model of dark channel prior, the new algorithm firstly extracts the edges from crude estimated transmission map and expands the extracted edges. Then according to the expended edges, the algorithm sets a threshold value to divide the crude estimated transmission map into different areas and makes different guided filter on the different areas compute the optimized transmission map. The experimental results demonstrate that the performance of the proposed algorithm is substantially the same as the one based on dark channel prior and guided filter. The average computation time of the new algorithm is around 40% of the one as well as the detection ability of UAV image is improved effectively in fog and haze weather.

A channel estimation scheme for MIMO-OFDM systems

NASA Astrophysics Data System (ADS)

He, Chunlong; Tian, Chu; Li, Xingquan; Zhang, Ce; Zhang, Shiqi; Liu, Chaowen

2017-08-01

In view of the contradiction of the time-domain least squares (LS) channel estimation performance and the practical realization complexity, a reduced complexity channel estimation method for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) based on pilot is obtained. This approach can transform the complexity of MIMO-OFDM channel estimation problem into a simple single input single output-orthogonal frequency division multiplexing (SISO-OFDM) channel estimation problem and therefore there is no need for large matrix pseudo-inverse, which greatly reduces the complexity of algorithms. Simulation results show that the bit error rate (BER) performance of the obtained method with time orthogonal training sequences and linear minimum mean square error (LMMSE) criteria is better than that of time-domain LS estimator and nearly optimal performance.

Performance Analysis for Channel Estimation With 1-Bit ADC and Unknown Quantization Threshold

NASA Astrophysics Data System (ADS)

Stein, Manuel S.; Bar, Shahar; Nossek, Josef A.; Tabrikian, Joseph

2018-05-01

In this work, the problem of signal parameter estimation from measurements acquired by a low-complexity analog-to-digital converter (ADC) with $1$-bit output resolution and an unknown quantization threshold is considered. Single-comparator ADCs are energy-efficient and can be operated at ultra-high sampling rates. For analysis of such systems, a fixed and known quantization threshold is usually assumed. In the symmetric case, i.e., zero hard-limiting offset, it is known that in the low signal-to-noise ratio (SNR) regime the signal processing performance degrades moderately by ${2}/{\\pi}$ ($-1.96$ dB) when comparing to an ideal $\\infty$-bit converter. Due to hardware imperfections, low-complexity $1$-bit ADCs will in practice exhibit an unknown threshold different from zero. Therefore, we study the accuracy which can be obtained with receive data processed by a hard-limiter with unknown quantization level by using asymptotically optimal channel estimation algorithms. To characterize the estimation performance of these nonlinear algorithms, we employ analytic error expressions for different setups while modeling the offset as a nuisance parameter. In the low SNR regime, we establish the necessary condition for a vanishing loss due to missing offset knowledge at the receiver. As an application, we consider the estimation of single-input single-output wireless channels with inter-symbol interference and validate our analysis by comparing the analytic and experimental performance of the studied estimation algorithms. Finally, we comment on the extension to multiple-input multiple-output channel models.

Array signal recovery algorithm for a single-RF-channel DBF array

NASA Astrophysics Data System (ADS)

Zhang, Duo; Wu, Wen; Fang, Da Gang

2016-12-01

An array signal recovery algorithm based on sparse signal reconstruction theory is proposed for a single-RF-channel digital beamforming (DBF) array. A single-RF-channel antenna array is a low-cost antenna array in which signals are obtained from all antenna elements by only one microwave digital receiver. The spatially parallel array signals are converted into time-sequence signals, which are then sampled by the system. The proposed algorithm uses these time-sequence samples to recover the original parallel array signals by exploiting the second-order sparse structure of the array signals. Additionally, an optimization method based on the artificial bee colony (ABC) algorithm is proposed to improve the reconstruction performance. Using the proposed algorithm, the motion compensation problem for the single-RF-channel DBF array can be solved effectively, and the angle and Doppler information for the target can be simultaneously estimated. The effectiveness of the proposed algorithms is demonstrated by the results of numerical simulations.

Estimating the beam attenuation coefficient in coastal waters from AVHRR imagery

NASA Astrophysics Data System (ADS)

Gould, Richard W.; Arnone, Robert A.

1997-09-01

This paper presents an algorithm to estimate particle beam attenuation at 660 nm ( cp660) in coastal areas using the red and near-infrared channels of the NOAA AVHRR satellite sensor. In situ reflectance spectra and cp660 measurements were collected at 23 stations in Case I and II waters during an April 1993 cruise in the northern Gulf of Mexico. The reflectance spectra were weighted by the spectral response of the AVHRR sensor and integrated over the channel 1 waveband to estimate the atmospherically corrected signal recorded by the satellite. An empirical relationship between integrated reflectance and cp660 values was derived with a linear correlation coefficient of 0.88. Because the AVHRR sensor requires a strong channel 1 signal, the algorithm is applicable in highly turbid areas ( cp660 > 1.5 m -1) where scattering from suspended sediment strongly controls the shape and magnitude of the red (550-650 nm) reflectance spectrum. The algorithm was tested on a data set collected 2 years later in different coastal waters in the northern Gulf of Mexico and satellite estimates of cp660 averaged within 37% of measured values. Application of the algorithm provides daily images of nearshore regions at 1 km resolution for evaluating processes affecting ocean color distribution patterns (tides, winds, currents, river discharge). Further validation and refinement of the algorithm are in progress to permit quantitative application in other coastal areas. Published by Elsevier Science Ltd

Evaluation of the operational SAR based Baltic sea ice concentration products

NASA Astrophysics Data System (ADS)

Karvonen, Juha

Sea ice concentration is an important ice parameter both for weather and climate modeling and sea ice navigation. We have developed an fully automated algorithm for sea ice concentration retrieval using dual-polarized ScanSAR wide mode RADARSAT-2 data. RADARSAT-2 is a C-band SAR instrument enabling dual-polarized acquisition in ScanSAR mode. The swath width for the RADARSAT-2 ScanSAR mode is about 500 km, making it very suitable for operational sea ice monitoring. The polarization combination used in our concentration estimation is HH/HV. The SAR data is first preprocessed, the preprocessing consists of geo-rectification to Mercator projection, incidence angle correction fro both the polarization channels. and SAR mosaicking. After preprocessing a segmentation is performed for the SAR mosaics, and some single-channel and dual-channel features are computed for each SAR segment. Finally the SAR concentration is estimated based on these segment-wise features. The algorithm is similar as introduced in Karvonen 2014. The ice concentration is computed daily using a daily RADARSAT-2 SAR mosaic as its input, and it thus gives the concentration estimated at each Baltic Sea location based on the most recent SAR data at the location. The algorithm has been run in an operational test mode since January 2014. We present evaluation of the SAR-based concentration estimates for the Baltic ice season 2014 by comparing the SAR results with gridded the Finnish Ice Service ice charts and ice concentration estimates from a radiometer algorithm (AMSR-2 Bootstrap algorithm results). References: J. Karvonen, Baltic Sea Ice Concentration Estimation Based on C-Band Dual-Polarized SAR Data, IEEE Transactions on Geoscience and Remote Sensing, in press, DOI: 10.1109/TGRS.2013.2290331, 2014.

Sea Turtles Geolocalization in the Indian Ocean: An Over Sea Radio Channel framework integrating a trilateration technique

NASA Astrophysics Data System (ADS)

Guegan, Loic; Murad, Nour Mohammad; Bonhommeau, Sylvain

2018-03-01

This paper deals with the modeling of the over sea radio channel and aims to establish sea turtles localization off the coast of Reunion Island, and also on Europa Island in the Mozambique Channel. In order to model this radio channel, a framework measurement protocol is proposed. The over sea measured channel is integrated to the localization algorithm to estimate the turtle trajectory based on Power of Arrival (PoA) technique compared to GPS localization. Moreover, cross correlation tool is used to characterize the over sea propagation channel. First measurement of the radio channel on the Reunion Island coast combine to the POA algorithm show an error of 18 m for 45% of the approximated points.

A novel time of arrival estimation algorithm using an energy detector receiver in MMW systems

NASA Astrophysics Data System (ADS)

Liang, Xiaolin; Zhang, Hao; Lyu, Tingting; Xiao, Han; Gulliver, T. Aaron

2017-12-01

This paper presents a new time of arrival (TOA) estimation technique using an improved energy detection (ED) receiver based on the empirical mode decomposition (EMD) in an impulse radio (IR) 60 GHz millimeter wave (MMW) system. A threshold is employed via analyzing the characteristics of the received energy values with an extreme learning machine (ELM). The effect of the channel and integration period on the TOA estimation is evaluated. Several well-known ED-based TOA algorithms are used to compare with the proposed technique. It is shown that this ELM-based technique has lower TOA estimation error compared to other approaches and provides robust performance with the IEEE 802.15.3c channel models.

Bandwidth efficient channel estimation method for airborne hyperspectral data transmission in sparse doubly selective communication channels

NASA Astrophysics Data System (ADS)

Vahidi, Vahid; Saberinia, Ebrahim; Regentova, Emma E.

2017-10-01

A channel estimation (CE) method based on compressed sensing (CS) is proposed to estimate the sparse and doubly selective (DS) channel for hyperspectral image transmission from unmanned aircraft vehicles to ground stations. The proposed method contains three steps: (1) the priori estimate of the channel by orthogonal matching pursuit (OMP), (2) calculation of the linear minimum mean square error (LMMSE) estimate of the received pilots given the estimated channel, and (3) estimate of the complex amplitudes and Doppler shifts of the channel using the enhanced received pilot data applying a second round of a CS algorithm. The proposed method is named DS-LMMSE-OMP, and its performance is evaluated by simulating transmission of AVIRIS hyperspectral data via the communication channel and assessing their fidelity for the automated analysis after demodulation. The performance of the DS-LMMSE-OMP approach is compared with that of two other state-of-the-art CE methods. The simulation results exhibit up to 8-dB figure of merit in the bit error rate and 50% improvement in the hyperspectral image classification accuracy.

Improving PERSIANN-CCS rain estimation using probabilistic approach and multi-sensors information

NASA Astrophysics Data System (ADS)

Karbalaee, N.; Hsu, K. L.; Sorooshian, S.; Kirstetter, P.; Hong, Y.

2016-12-01

This presentation discusses the recent implemented approaches to improve the rainfall estimation from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System (PERSIANN-CCS). PERSIANN-CCS is an infrared (IR) based algorithm being integrated in the IMERG (Integrated Multi-Satellite Retrievals for the Global Precipitation Mission GPM) to create a precipitation product in 0.1x0.1degree resolution over the chosen domain 50N to 50S every 30 minutes. Although PERSIANN-CCS has a high spatial and temporal resolution, it overestimates or underestimates due to some limitations.PERSIANN-CCS can estimate rainfall based on the extracted information from IR channels at three different temperature threshold levels (220, 235, and 253k). This algorithm relies only on infrared data to estimate rainfall indirectly from this channel which cause missing the rainfall from warm clouds and false estimation for no precipitating cold clouds. In this research the effectiveness of using other channels of GOES satellites such as visible and water vapors has been investigated. By using multi-sensors the precipitation can be estimated based on the extracted information from multiple channels. Also, instead of using the exponential function for estimating rainfall from cloud top temperature, the probabilistic method has been used. Using probability distributions of precipitation rates instead of deterministic values has improved the rainfall estimation for different type of clouds.

A robust pseudo-inverse spectral filter applied to the Earth Radiation Budget Experiment (ERBE) scanning channels

NASA Technical Reports Server (NTRS)

Avis, L. M.; Green, R. N.; Suttles, J. T.; Gupta, S. K.

1984-01-01

Computer simulations of a least squares estimator operating on the ERBE scanning channels are discussed. The estimator is designed to minimize the errors produced by nonideal spectral response to spectrally varying and uncertain radiant input. The three ERBE scanning channels cover a shortwave band a longwave band and a ""total'' band from which the pseudo inverse spectral filter estimates the radiance components in the shortwave band and a longwave band. The radiance estimator draws on instantaneous field of view (IFOV) scene type information supplied by another algorithm of the ERBE software, and on a priori probabilistic models of the responses of the scanning channels to the IFOV scene types for given Sun scene spacecraft geometry. It is found that the pseudoinverse spectral filter is stable, tolerant of errors in scene identification and in channel response modeling, and, in the absence of such errors, yields minimum variance and essentially unbiased radiance estimates.

A Comparative Study of Co-Channel Interference Suppression Techniques

NASA Technical Reports Server (NTRS)

Hamkins, Jon; Satorius, Ed; Paparisto, Gent; Polydoros, Andreas

1997-01-01

We describe three methods of combatting co-channel interference (CCI): a cross-coupled phase-locked loop (CCPLL); a phase-tracking circuit (PTC), and joint Viterbi estimation based on the maximum likelihood principle. In the case of co-channel FM-modulated voice signals, the CCPLL and PTC methods typically outperform the maximum likelihood estimators when the modulation parameters are dissimilar. However, as the modulation parameters become identical, joint Viterbi estimation provides for a more robust estimate of the co-channel signals and does not suffer as much from "signal switching" which especially plagues the CCPLL approach. Good performance for the PTC requires both dissimilar modulation parameters and a priori knowledge of the co-channel signal amplitudes. The CCPLL and joint Viterbi estimators, on the other hand, incorporate accurate amplitude estimates. In addition, application of the joint Viterbi algorithm to demodulating co-channel digital (BPSK) signals in a multipath environment is also discussed. It is shown in this case that if the interference is sufficiently small, a single trellis model is most effective in demodulating the co-channel signals.

Electrode channel selection based on backtracking search optimization in motor imagery brain-computer interfaces.

PubMed

Dai, Shengfa; Wei, Qingguo

2017-01-01

Common spatial pattern algorithm is widely used to estimate spatial filters in motor imagery based brain-computer interfaces. However, use of a large number of channels will make common spatial pattern tend to over-fitting and the classification of electroencephalographic signals time-consuming. To overcome these problems, it is necessary to choose an optimal subset of the whole channels to save computational time and improve the classification accuracy. In this paper, a novel method named backtracking search optimization algorithm is proposed to automatically select the optimal channel set for common spatial pattern. Each individual in the population is a N-dimensional vector, with each component representing one channel. A population of binary codes generate randomly in the beginning, and then channels are selected according to the evolution of these codes. The number and positions of 1's in the code denote the number and positions of chosen channels. The objective function of backtracking search optimization algorithm is defined as the combination of classification error rate and relative number of channels. Experimental results suggest that higher classification accuracy can be achieved with much fewer channels compared to standard common spatial pattern with whole channels.

IDMA-Based MAC Protocol for Satellite Networks with Consideration on Channel Quality

PubMed Central

2014-01-01

In order to overcome the shortcomings of existing medium access control (MAC) protocols based on TDMA or CDMA in satellite networks, interleave division multiple access (IDMA) technique is introduced into satellite communication networks. Therefore, a novel wide-band IDMA MAC protocol based on channel quality is proposed in this paper, consisting of a dynamic power allocation algorithm, a rate adaptation algorithm, and a call admission control (CAC) scheme. Firstly, the power allocation algorithm combining the technique of IDMA SINR-evolution and channel quality prediction is developed to guarantee high power efficiency even in terrible channel conditions. Secondly, the effective rate adaptation algorithm, based on accurate channel information per timeslot and by the means of rate degradation, can be realized. What is more, based on channel quality prediction, the CAC scheme, combining the new power allocation algorithm, rate scheduling, and buffering strategies together, is proposed for the emerging IDMA systems, which can support a variety of traffic types, and offering quality of service (QoS) requirements corresponding to different priority levels. Simulation results show that the new wide-band IDMA MAC protocol can make accurate estimation of available resource considering the effect of multiuser detection (MUD) and QoS requirements of multimedia traffic, leading to low outage probability as well as high overall system throughput. PMID:25126592

Multichannel Speech Enhancement Based on Generalized Gamma Prior Distribution with Its Online Adaptive Estimation

NASA Astrophysics Data System (ADS)

Dat, Tran Huy; Takeda, Kazuya; Itakura, Fumitada

We present a multichannel speech enhancement method based on MAP speech spectral magnitude estimation using a generalized gamma model of speech prior distribution, where the model parameters are adapted from actual noisy speech in a frame-by-frame manner. The utilization of a more general prior distribution with its online adaptive estimation is shown to be effective for speech spectral estimation in noisy environments. Furthermore, the multi-channel information in terms of cross-channel statistics are shown to be useful to better adapt the prior distribution parameters to the actual observation, resulting in better performance of speech enhancement algorithm. We tested the proposed algorithm in an in-car speech database and obtained significant improvements of the speech recognition performance, particularly under non-stationary noise conditions such as music, air-conditioner and open window.

Modeling, Real-Time Estimation, and Identification of UWB Indoor Wireless Channels

DOE PAGES

Olama, Mohammed M.; Djouadi, Seddik M.; Li, Yanyan; ...

2013-01-01

Stochastic differential equations (SDEs) are used to model ultrawideband (UWB) indoor wireless channels. We show that the impulse responses for time-varying indoor wireless channels can be approximated in a mean-square sense as close as desired by impulse responses that can be realized by SDEs. The state variables represent the inphase and quadrature components of the UWB channel. The expected maximization and extended Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Both resolvable and nonresolvable multipath received signals are considered and represented as small-scaled Nakagami fading. Themore » proposed models together with the estimation algorithm are tested using UWB indoor measurement data demonstrating the method’s viability and the results are presented.« less

Blind color isolation for color-channel-based fringe pattern profilometry using digital projection

NASA Astrophysics Data System (ADS)

Hu, Yingsong; Xi, Jiangtao; Chicharo, Joe; Yang, Zongkai

2007-08-01

We present an algorithm for estimating the color demixing matrix based on the color fringe patterns captured from the reference plane or the surface of the object. The advantage of this algorithm is that it is a blind approach to calculating the demixing matrix in the sense that no extra images are required for color calibration before performing profile measurement. Simulation and experimental results convince us that the proposed algorithm can significantly reduce the influence of the color cross talk and at the same time improve the measurement accuracy of the color-channel-based phase-shifting profilometry.

Prioritized packet video transmission over time-varying wireless channel using proactive FEC

NASA Astrophysics Data System (ADS)

Kumwilaisak, Wuttipong; Kim, JongWon; Kuo, C.-C. Jay

2000-12-01

Quality of video transmitted over time-varying wireless channels relies heavily on the coordinated effort to cope with both channel and source variations dynamically. Given the priority of each source packet and the estimated channel condition, an adaptive protection scheme based on joint source-channel criteria is investigated via proactive forward error correction (FEC). With proactive FEC in Reed Solomon (RS)/Rate-compatible punctured convolutional (RCPC) codes, we study a practical algorithm to match the relative priority of source packets and instantaneous channel conditions. The channel condition is estimated to capture the long-term fading effect in terms of the averaged SNR over a preset window. Proactive protection is performed for each packet based on the joint source-channel criteria with special attention to the accuracy, time-scale match, and feedback delay of channel status estimation. The overall gain of the proposed protection mechanism is demonstrated in terms of the end-to-end wireless video performance.

Doppler-shift estimation of flat underwater channel using data-aided least-square approach

NASA Astrophysics Data System (ADS)

Pan, Weiqiang; Liu, Ping; Chen, Fangjiong; Ji, Fei; Feng, Jing

2015-06-01

In this paper we proposed a dada-aided Doppler estimation method for underwater acoustic communication. The training sequence is non-dedicate, hence it can be designed for Doppler estimation as well as channel equalization. We assume the channel has been equalized and consider only flat-fading channel. First, based on the training symbols the theoretical received sequence is composed. Next the least square principle is applied to build the objective function, which minimizes the error between the composed and the actual received signal. Then an iterative approach is applied to solve the least square problem. The proposed approach involves an outer loop and inner loop, which resolve the channel gain and Doppler coefficient, respectively. The theoretical performance bound, i.e. the Cramer-Rao Lower Bound (CRLB) of estimation is also derived. Computer simulations results show that the proposed algorithm achieves the CRLB in medium to high SNR cases.

An Off-Grid Turbo Channel Estimation Algorithm for Millimeter Wave Communications.

PubMed

Han, Lingyi; Peng, Yuexing; Wang, Peng; Li, Yonghui

2016-09-22

The bandwidth shortage has motivated the exploration of the millimeter wave (mmWave) frequency spectrum for future communication networks. To compensate for the severe propagation attenuation in the mmWave band, massive antenna arrays can be adopted at both the transmitter and receiver to provide large array gains via directional beamforming. To achieve such array gains, channel estimation (CE) with high resolution and low latency is of great importance for mmWave communications. However, classic super-resolution subspace CE methods such as multiple signal classification (MUSIC) and estimation of signal parameters via rotation invariant technique (ESPRIT) cannot be applied here due to RF chain constraints. In this paper, an enhanced CE algorithm is developed for the off-grid problem when quantizing the angles of mmWave channel in the spatial domain where off-grid problem refers to the scenario that angles do not lie on the quantization grids with high probability, and it results in power leakage and severe reduction of the CE performance. A new model is first proposed to formulate the off-grid problem. The new model divides the continuously-distributed angle into a quantized discrete grid part, referred to as the integral grid angle, and an offset part, termed fractional off-grid angle. Accordingly, an iterative off-grid turbo CE (IOTCE) algorithm is proposed to renew and upgrade the CE between the integral grid part and the fractional off-grid part under the Turbo principle. By fully exploiting the sparse structure of mmWave channels, the integral grid part is estimated by a soft-decoding based compressed sensing (CS) method called improved turbo compressed channel sensing (ITCCS). It iteratively updates the soft information between the linear minimum mean square error (LMMSE) estimator and the sparsity combiner. Monte Carlo simulations are presented to evaluate the performance of the proposed method, and the results show that it enhances the angle detection resolution greatly.

Robust transceiver design for reciprocal M × N interference channel based on statistical linearization approximation

NASA Astrophysics Data System (ADS)

Mayvan, Ali D.; Aghaeinia, Hassan; Kazemi, Mohammad

2017-12-01

This paper focuses on robust transceiver design for throughput enhancement on the interference channel (IC), under imperfect channel state information (CSI). In this paper, two algorithms are proposed to improve the throughput of the multi-input multi-output (MIMO) IC. Each transmitter and receiver has, respectively, M and N antennas and IC operates in a time division duplex mode. In the first proposed algorithm, each transceiver adjusts its filter to maximize the expected value of signal-to-interference-plus-noise ratio (SINR). On the other hand, the second algorithm tries to minimize the variances of the SINRs to hedge against the variability due to CSI error. Taylor expansion is exploited to approximate the effect of CSI imperfection on mean and variance. The proposed robust algorithms utilize the reciprocity of wireless networks to optimize the estimated statistical properties in two different working modes. Monte Carlo simulations are employed to investigate sum rate performance of the proposed algorithms and the advantage of incorporating variation minimization into the transceiver design.

Soft Sensing of Non-Newtonian Fluid Flow in Open Venturi Channel Using an Array of Ultrasonic Level Sensors—AI Models and Their Validations

PubMed Central

Viumdal, Håkon; Mylvaganam, Saba

2017-01-01

In oil and gas and geothermal installations, open channels followed by sieves for removal of drill cuttings, are used to monitor the quality and quantity of the drilling fluids. Drilling fluid flow rate is difficult to measure due to the varying flow conditions (e.g., wavy, turbulent and irregular) and the presence of drilling cuttings and gas bubbles. Inclusion of a Venturi section in the open channel and an array of ultrasonic level sensors above it at locations in the vicinity of and above the Venturi constriction gives the varying levels of the drilling fluid in the channel. The time series of the levels from this array of ultrasonic level sensors are used to estimate the drilling fluid flow rate, which is compared with Coriolis meter measurements. Fuzzy logic, neural networks and support vector regression algorithms applied to the data from temporal and spatial ultrasonic level measurements of the drilling fluid in the open channel give estimates of its flow rate with sufficient reliability, repeatability and uncertainty, providing a novel soft sensing of an important process variable. Simulations, cross-validations and experimental results show that feedforward neural networks with the Bayesian regularization learning algorithm provide the best flow rate estimates. Finally, the benefits of using this soft sensing technique combined with Venturi constriction in open channels are discussed. PMID:29072595

Validation of MODIS-derived bidirectional reflectivity retrieval algorithm in mid-infrared channel with field measurements.

PubMed

Tang, Bo-Hui; Wu, Hua-; Li, Zhao-Liang; Nerry, Françoise

2012-07-30

This work addressed the validation of the MODIS-derived bidirectional reflectivity retrieval algorithm in mid-infrared (MIR) channel, proposed by Tang and Li [Int. J. Remote Sens. 29, 4907 (2008)], with ground-measured data, which were collected from a field campaign that took place in June 2004 at the ONERA (Office National d'Etudes et de Recherches Aérospatiales) center of Fauga-Mauzac, on the PIRRENE (Programme Interdisciplinaire de Recherche sur la Radiométrie en Environnement Extérieur) experiment site [Opt. Express 15, 12464 (2007)]. The leaving-surface spectral radiances measured by a BOMEM (MR250 Series) Fourier transform interferometer were used to calculate the ground brightness temperatures with the combination of the inversion of the Planck function and the spectral response functions of MODIS channels 22 and 23, and then to estimate the ground brightness temperature without the contribution of the solar direct beam and the bidirectional reflectivity by using Tang and Li's proposed algorithm. On the other hand, the simultaneously measured atmospheric profiles were used to obtain the atmospheric parameters and then to calculate the ground brightness temperature without the contribution of the solar direct beam, based on the atmospheric radiative transfer equation in the MIR region. Comparison of those two kinds of brightness temperature obtained by two different methods indicated that the Root Mean Square Error (RMSE) between the brightness temperatures estimated respectively using Tang and Li's algorithm and the atmospheric radiative transfer equation is 1.94 K. In addition, comparison of the hemispherical-directional reflectances derived by Tang and Li's algorithm with those obtained from the field measurements showed that the RMSE is 0.011, which indicates that Tang and Li's algorithm is feasible to retrieve the bidirectional reflectivity in MIR channel from MODIS data.

Performance of the split-symbol moments SNR estimator in the presence of inter-symbol interference

NASA Technical Reports Server (NTRS)

Shah, B.; Hinedi, S.

1989-01-01

The Split-Symbol Moments Estimator (SSME) is an algorithm that is designed to estimate symbol signal-to-noise ratio (SNR) in the presence of additive white Gaussian noise (AWGN). The performance of the SSME algorithm in band-limited channels is examined. The effects of the resulting inter-symbol interference (ISI) are quantified. All results obtained are in closed form and can be easily evaluated numerically for performance prediction purposes. Furthermore, they are validated through digital simulations.

Estimating Angle-of-Arrival and Time-of-Flight for Multipath Components Using WiFi Channel State Information.

PubMed

Ahmed, Afaz Uddin; Arablouei, Reza; Hoog, Frank de; Kusy, Branislav; Jurdak, Raja; Bergmann, Neil

2018-05-29

Channel state information (CSI) collected during WiFi packet transmissions can be used for localization of commodity WiFi devices in indoor environments with multipath propagation. To this end, the angle of arrival (AoA) and time of flight (ToF) for all dominant multipath components need to be estimated. A two-dimensional (2D) version of the multiple signal classification (MUSIC) algorithm has been shown to solve this problem using 2D grid search, which is computationally expensive and is therefore not suited for real-time localisation. In this paper, we propose using a modified matrix pencil (MMP) algorithm instead. Specifically, we show that the AoA and ToF estimates can be found independently of each other using the one-dimensional (1D) MMP algorithm and the results can be accurately paired to obtain the AoA⁻ToF pairs for all multipath components. Thus, the 2D estimation problem reduces to running 1D estimation multiple times, substantially reducing the computational complexity. We identify and resolve the problem of degenerate performance when two or more multipath components have the same AoA. In addition, we propose a packet aggregation model that uses the CSI data from multiple packets to improve the performance under noisy conditions. Simulation results show that our algorithm achieves two orders of magnitude reduction in the computational time over the 2D MUSIC algorithm while achieving similar accuracy. High accuracy and low computation complexity of our approach make it suitable for applications that require location estimation to run on resource-constrained embedded devices in real time.

Weather, Climate, and Society: New Demands on Science and Services

NASA Technical Reports Server (NTRS)

2010-01-01

A new algorithm has been constructed to estimate the path length of lightning channels for the purpose of improving the model predictions of lightning NOx in both regional air quality and global chemistry/climate models. This algorithm was tested and applied to VHF signals detected by the North Alabama Lightning Mapping Array (NALMA). The accuracy of the algorithm was characterized by comparing algorithm output to the plots of individual discharges whose lengths were computed by hand. Several thousands of lightning flashes within 120 km of the NALMA network centroid were gathered from all four seasons, and were analyzed by the algorithm. The mean, standard deviation, and median statistics were obtained for all the flashes, the ground flashes, and the cloud flashes. Channel length distributions were also obtained for the different seasons.

Mode Shape Estimation Algorithms Under Ambient Conditions: A Comparative Review

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dosiek, Luke; Zhou, Ning; Pierre, John W.

Abstract—This paper provides a comparative review of five existing ambient electromechanical mode shape estimation algorithms, i.e., the Transfer Function (TF), Spectral, Frequency Domain Decomposition (FDD), Channel Matching, and Subspace Methods. It is also shown that the TF Method is a general approach to estimating mode shape and that the Spectral, FDD, and Channel Matching Methods are actually special cases of it. Additionally, some of the variations of the Subspace Method are reviewed and the Numerical algorithm for Subspace State Space System IDentification (N4SID) is implemented. The five algorithms are then compared using data simulated from a 17-machine model of themore » Western Electricity Coordinating Council (WECC) under ambient conditions with both low and high damping, as well as during the case where ambient data is disrupted by an oscillatory ringdown. The performance of the algorithms is compared using the statistics from Monte Carlo Simulations and results from measured WECC data, and a discussion of the practical issues surrounding their implementation, including cases where power system probing is an option, is provided. The paper concludes with some recommendations as to the appropriate use of the various techniques. Index Terms—Electromechanical mode shape, small-signal stability, phasor measurement units (PMU), system identification, N4SID, subspace.« less

Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO

NASA Astrophysics Data System (ADS)

Gao, Zhen; Dai, Linglong; Wang, Zhaocheng; Chen, Sheng

2015-12-01

This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a non-orthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. Additionally, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the non-orthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramer-Rao lower bound of the proposed scheme, which enlightens us to design the non-orthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.

High resolution time of arrival estimation for a cooperative sensor system

NASA Astrophysics Data System (ADS)

Morhart, C.; Biebl, E. M.

2010-09-01

Distance resolution of cooperative sensors is limited by the signal bandwidth. For the transmission mainly lower frequency bands are used which are more narrowband than classical radar frequencies. To compensate this resolution problem the combination of a pseudo-noise coded pulse compression system with superresolution time of arrival estimation is proposed. Coded pulsecompression allows secure and fast distance measurement in multi-user scenarios which can easily be adapted for data transmission purposes (Morhart and Biebl, 2009). Due to the lack of available signal bandwidth the measurement accuracy degrades especially in multipath scenarios. Superresolution time of arrival algorithms can improve this behaviour by estimating the channel impulse response out of a band-limited channel view. For the given test system the implementation of a MUSIC algorithm permitted a two times better distance resolution as the standard pulse compression.

A hybrid frame concealment algorithm for H.264/AVC.

PubMed

Yan, Bo; Gharavi, Hamid

2010-01-01

In packet-based video transmissions, packets loss due to channel errors may result in the loss of the whole video frame. Recently, many error concealment algorithms have been proposed in order to combat channel errors; however, most of the existing algorithms can only deal with the loss of macroblocks and are not able to conceal the whole missing frame. In order to resolve this problem, in this paper, we have proposed a new hybrid motion vector extrapolation (HMVE) algorithm to recover the whole missing frame, and it is able to provide more accurate estimation for the motion vectors of the missing frame than other conventional methods. Simulation results show that it is highly effective and significantly outperforms other existing frame recovery methods.

The analysis of polar clouds from AVHRR satellite data using pattern recognition techniques

NASA Technical Reports Server (NTRS)

Smith, William L.; Ebert, Elizabeth

1990-01-01

The cloud cover in a set of summertime and wintertime AVHRR data from the Arctic and Antarctic regions was analyzed using a pattern recognition algorithm. The data were collected by the NOAA-7 satellite on 6 to 13 Jan. and 1 to 7 Jul. 1984 between 60 deg and 90 deg north and south latitude in 5 spectral channels, at the Global Area Coverage (GAC) resolution of approximately 4 km. This data embodied a Polar Cloud Pilot Data Set which was analyzed by a number of research groups as part of a polar cloud algorithm intercomparison study. This study was intended to determine whether the additional information contained in the AVHRR channels (beyond the standard visible and infrared bands on geostationary satellites) could be effectively utilized in cloud algorithms to resolve some of the cloud detection problems caused by low visible and thermal contrasts in the polar regions. The analysis described makes use of a pattern recognition algorithm which estimates the surface and cloud classification, cloud fraction, and surface and cloudy visible (channel 1) albedo and infrared (channel 4) brightness temperatures on a 2.5 x 2.5 deg latitude-longitude grid. In each grid box several spectral and textural features were computed from the calibrated pixel values in the multispectral imagery, then used to classify the region into one of eighteen surface and/or cloud types using the maximum likelihood decision rule. A slightly different version of the algorithm was used for each season and hemisphere because of differences in categories and because of the lack of visible imagery during winter. The classification of the scene is used to specify the optimal AVHRR channel for separating clear and cloudy pixels using a hybrid histogram-spatial coherence method. This method estimates values for cloud fraction, clear and cloudy albedos and brightness temperatures in each grid box. The choice of a class-dependent AVHRR channel allows for better separation of clear and cloudy pixels than does a global choice of a visible and/or infrared threshold. The classification also prevents erroneous estimates of large fractional cloudiness in areas of cloudfree snow and sea ice. The hybrid histogram-spatial coherence technique and the advantages of first classifying a scene in the polar regions are detailed. The complete Polar Cloud Pilot Data Set was analyzed and the results are presented and discussed.

A complex valued radial basis function network for equalization of fast time varying channels.

PubMed

Gan, Q; Saratchandran, P; Sundararajan, N; Subramanian, K R

1999-01-01

This paper presents a complex valued radial basis function (RBF) network for equalization of fast time varying channels. A new method for calculating the centers of the RBF network is given. The method allows fixing the number of RBF centers even as the equalizer order is increased so that a good performance is obtained by a high-order RBF equalizer with small number of centers. Simulations are performed on time varying channels using a Rayleigh fading channel model to compare the performance of our RBF with an adaptive maximum-likelihood sequence estimator (MLSE) consisting of a channel estimator and a MLSE implemented by the Viterbi algorithm. The results show that the RBF equalizer produces superior performance with less computational complexity.

An enhanced multi-channel bacterial foraging optimization algorithm for MIMO communication system

NASA Astrophysics Data System (ADS)

Palanimuthu, Senthilkumar Jayalakshmi; Muthial, Chandrasekaran

2017-04-01

Channel estimation and optimisation are the main challenging tasks in Multi Input Multi Output (MIMO) wireless communication systems. In this work, a Multi-Channel Bacterial Foraging Optimization Algorithm approach is proposed for the selection of antenna in a transmission area. The main advantage of this method is, it reduces the loss of bandwidth during data transmission effectively. Here, we considered the channel estimation and optimisation for improving the transmission speed and reducing the unused bandwidth. Initially, the message is given to the input of the communication system. Then, the symbol mapping process is performed for converting the message into signals. It will be encoded based on the space-time encoding technique. Here, the single signal is divided into multiple signals and it will be given to the input of space-time precoder. Hence, the multiplexing is applied to transmission channel estimation. In this paper, the Rayleigh channel is selected based on the bandwidth range. This is the Gaussian distribution type channel. Then, the demultiplexing is applied on the obtained signal that is the reverse function of multiplexing, which splits the combined signal arriving from a medium into the original information signal. Furthermore, the long-term evolution technique is used for scheduling the time to channels during transmission. Here, the hidden Markov model technique is employed to predict the status information of the channel. Finally, the signals are decoded and the reconstructed signal is obtained after performing the scheduling process. The experimental results evaluate the performance of the proposed MIMO communication system in terms of bit error rate, mean squared error, average throughput, outage capacity and signal to interference noise ratio.

A review of channel selection algorithms for EEG signal processing

NASA Astrophysics Data System (ADS)

Alotaiby, Turky; El-Samie, Fathi E. Abd; Alshebeili, Saleh A.; Ahmad, Ishtiaq

2015-12-01

Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.

Estimation of cloud optical thickness by processing SEVIRI images and implementing a semi analytical cloud property retrieval algorithm

NASA Astrophysics Data System (ADS)

Pandey, P.; De Ridder, K.; van Lipzig, N.

2009-04-01

Clouds play a very important role in the Earth's climate system, as they form an intermediate layer between Sun and the Earth. Satellite remote sensing systems are the only means to provide information about clouds on large scales. The geostationary satellite, Meteosat Second Generation (MSG) has onboard an imaging radiometer, the Spinning Enhanced Visible and Infrared Imager (SEVIRI). SEVIRI is a 12 channel imager, with 11 channels observing the earth's full disk with a temporal resolution of 15 min and spatial resolution of 3 km at nadir, and a high resolution visible (HRV) channel. The visible channels (0.6 µm and 0.81 µm) and near infrared channel (1.6µm) of SEVIRI are being used to retrieve the cloud optical thickness (COT). The study domain is over Europe covering the region between 35°N - 70°N and 10°W - 30°E. SEVIRI level 1.5 images over this domain are being acquired from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) archive. The processing of this imagery, involves a number of steps before estimating the COT. The steps involved in pre-processing are as follows. First, the digital count number is acquired from the imagery. Image geo-coding is performed in order to relate the pixel positions to the corresponding longitude and latitude. Solar zenith angle is determined as a function of latitude and time. The radiometric conversion is done using the values of offsets and slopes of each band. The values of radiance obtained are then used to calculate the reflectance for channels in the visible spectrum using the information of solar zenith angle. An attempt is made to estimate the COT from the observed radiances. A semi analytical algorithm [Kokhanovsky et al., 2003] is implemented for the estimation of cloud optical thickness from the visible spectrum of light intensity reflected from clouds. The asymptotical solution of the radiative transfer equation, for clouds with large optical thickness, is the basis of this algorithm. The two visible channels of SEVIRI are used to find the COT and the near infra red channel to estimate the effective radius of droplets. Estimation of COT using a semi analytical scheme, which doesn't involve the conventional look-up table approach, is the aim of this work and henceforth, vertically integrated liquid water (w) or ice water content will be retrieved. The COT estimated and w obtained, will be compared with the values obtained from other approaches and will be validated with in situ measurements. Corresponding author address: Praveen Pandey, VITO - Flemish Institute for Technological Research, Boeretang 200, B 2400, Mol, Belgium. E-mail: praveen.pandey@vito.be

Robust Rate Maximization for Heterogeneous Wireless Networks under Channel Uncertainties

PubMed Central

Xu, Yongjun; Hu, Yuan; Li, Guoquan

2018-01-01

Heterogeneous wireless networks are a promising technology in next generation wireless communication networks, which has been shown to efficiently reduce the blind area of mobile communication and improve network coverage compared with the traditional wireless communication networks. In this paper, a robust power allocation problem for a two-tier heterogeneous wireless networks is formulated based on orthogonal frequency-division multiplexing technology. Under the consideration of imperfect channel state information (CSI), the robust sum-rate maximization problem is built while avoiding sever cross-tier interference to macrocell user and maintaining the minimum rate requirement of each femtocell user. To be practical, both of channel estimation errors from the femtocells to the macrocell and link uncertainties of each femtocell user are simultaneously considered in terms of outage probabilities of users. The optimization problem is analyzed under no CSI feedback with some cumulative distribution function and partial CSI with Gaussian distribution of channel estimation error. The robust optimization problem is converted into the convex optimization problem which is solved by using Lagrange dual theory and subgradient algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm by the impact of channel uncertainties on the system performance. PMID:29466315

Simulation of devices mobility to estimate wireless channel quality metrics in 5G networks

NASA Astrophysics Data System (ADS)

Orlov, Yu.; Fedorov, S.; Samuylov, A.; Gaidamaka, Yu.; Molchanov, D.

2017-07-01

The problem of channel quality estimation for devices in a wireless 5G network is formulated. As a performance metrics of interest we choose the signal-to-interference-plus-noise ratio, which depends essentially on the distance between the communicating devices. A model with a plurality of moving devices in a bounded three-dimensional space and a simulation algorithm to determine the distances between the devices for a given motion model are devised.

Sequential Total Variation Denoising for the Extraction of Fetal ECG from Single-Channel Maternal Abdominal ECG

PubMed Central

Lee, Kwang Jin; Lee, Boreom

2016-01-01

Fetal heart rate (FHR) is an important determinant of fetal health. Cardiotocography (CTG) is widely used for measuring the FHR in the clinical field. However, fetal movement and blood flow through the maternal blood vessels can critically influence Doppler ultrasound signals. Moreover, CTG is not suitable for long-term monitoring. Therefore, researchers have been developing algorithms to estimate the FHR using electrocardiograms (ECGs) from the abdomen of pregnant women. However, separating the weak fetal ECG signal from the abdominal ECG signal is a challenging problem. In this paper, we propose a method for estimating the FHR using sequential total variation denoising and compare its performance with that of other single-channel fetal ECG extraction methods via simulation using the Fetal ECG Synthetic Database (FECGSYNDB). Moreover, we used real data from PhysioNet fetal ECG databases for the evaluation of the algorithm performance. The R-peak detection rate is calculated to evaluate the performance of our algorithm. Our approach could not only separate the fetal ECG signals from the abdominal ECG signals but also accurately estimate the FHR. PMID:27376296

Sequential Total Variation Denoising for the Extraction of Fetal ECG from Single-Channel Maternal Abdominal ECG.

PubMed

Lee, Kwang Jin; Lee, Boreom

2016-07-01

Fetal heart rate (FHR) is an important determinant of fetal health. Cardiotocography (CTG) is widely used for measuring the FHR in the clinical field. However, fetal movement and blood flow through the maternal blood vessels can critically influence Doppler ultrasound signals. Moreover, CTG is not suitable for long-term monitoring. Therefore, researchers have been developing algorithms to estimate the FHR using electrocardiograms (ECGs) from the abdomen of pregnant women. However, separating the weak fetal ECG signal from the abdominal ECG signal is a challenging problem. In this paper, we propose a method for estimating the FHR using sequential total variation denoising and compare its performance with that of other single-channel fetal ECG extraction methods via simulation using the Fetal ECG Synthetic Database (FECGSYNDB). Moreover, we used real data from PhysioNet fetal ECG databases for the evaluation of the algorithm performance. The R-peak detection rate is calculated to evaluate the performance of our algorithm. Our approach could not only separate the fetal ECG signals from the abdominal ECG signals but also accurately estimate the FHR.

Rainfall Estimation over the Nile Basin using an Adapted Version of the SCaMPR Algorithm

NASA Astrophysics Data System (ADS)

Habib, E. H.; Kuligowski, R. J.; Elshamy, M. E.; Ali, M. A.; Haile, A.; Amin, D.; Eldin, A.

2011-12-01

Management of Egypt's Aswan High Dam is critical not only for flood control on the Nile but also for ensuring adequate water supplies for most of Egypt since rainfall is scarce over the vast majority of its land area. However, reservoir inflow is driven by rainfall over Sudan, Ethiopia, Uganda, and several other countries from which routine rain gauge data are sparse. Satellite-derived estimates of rainfall offer a much more detailed and timely set of data to form a basis for decisions on the operation of the dam. A single-channel infrared algorithm is currently in operational use at the Egyptian Nile Forecast Center (NFC). This study reports on the adaptation of a multi-spectral, multi-instrument satellite rainfall estimation algorithm (Self-Calibrating Multivariate Precipitation Retrieval, SCaMPR) for operational application over the Nile Basin. The algorithm uses a set of rainfall predictors from multi-spectral Infrared cloud top observations and self-calibrates them to a set of predictands from Microwave (MW) rain rate estimates. For application over the Nile Basin, the SCaMPR algorithm uses multiple satellite IR channels recently available to NFC from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Microwave rain rates are acquired from multiple sources such as SSM/I, SSMIS, AMSU, AMSR-E, and TMI. The algorithm has two main steps: rain/no-rain separation using discriminant analysis, and rain rate estimation using stepwise linear regression. We test two modes of algorithm calibration: real-time calibration with continuous updates of coefficients with newly coming MW rain rates, and calibration using static coefficients that are derived from IR-MW data from past observations. We also compare the SCaMPR algorithm to other global-scale satellite rainfall algorithms (e.g., 'Tropical Rainfall Measuring Mission (TRMM) and other sources' (TRMM-3B42) product, and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) CMORPH product. The algorithm has several potential future applications such as: improving the performance accuracy of hydrologic forecasting models over the Nile Basin, and utilizing the enhanced rainfall datasets and better-calibrated hydrologic models to assess the impacts of climate change on the region's water availability.

Long-term analysis of aerosol optical depth over Northeast Asia using a satellite-based measurement: MI Yonsei Aerosol Retrieval Algorithm (YAER)

NASA Astrophysics Data System (ADS)

Kim, Mijin; Kim, Jhoon; Yoon, Jongmin; Chung, Chu-Yong; Chung, Sung-Rae

2017-04-01

In 2010, the Korean geostationary earth orbit (GEO) satellite, the Communication, Ocean, and Meteorological Satellite (COMS), was launched including the Meteorological Imager (MI). The MI measures atmospheric condition over Northeast Asia (NEA) using a single visible channel centered at 0.675 μm and four IR channels at 3.75, 6.75, 10.8, 12.0 μm. The visible measurement can also be utilized for the retrieval of aerosol optical properties (AOPs). Since the GEO satellite measurement has an advantage for continuous monitoring of AOPs, we can analyze the spatiotemporal variation of the aerosol using the MI observations over NEA. Therefore, we developed an algorithm to retrieve aerosol optical depth (AOD) using the visible observation of MI, and named as MI Yonsei Aerosol Retrieval Algorithm (YAER). In this study, we investigated the accuracy of MI YAER AOD by comparing the values with the long-term products of AERONET sun-photometer. The result showed that the MI AODs were significantly overestimated than the AERONET values over bright surface in low AOD case. Because the MI visible channel centered at red color range, contribution of aerosol signal to the measured reflectance is relatively lower than the surface contribution. Therefore, the AOD error in low AOD case over bright surface can be a fundamental limitation of the algorithm. Meanwhile, an assumption of background aerosol optical depth (BAOD) could result in the retrieval uncertainty, also. To estimate the surface reflectance by considering polluted air condition over the NEA, we estimated the BAOD from the MODIS dark target (DT) aerosol products by pixel. The satellite-based AOD retrieval, however, largely depends on the accuracy of the surface reflectance estimation especially in low AOD case, and thus, the BAOD could include the uncertainty in surface reflectance estimation of the satellite-based retrieval. Therefore, we re-estimated the BAOD using the ground-based sun-photometer measurement, and investigated the effects of the BAOD assumption. The satellite-based BAOD was significantly higher than the ground-based value over urban area, and thus, resulted in the underestimation of surface reflectance and the overestimation of AOD. The error analysis of the MI AOD also showed sensitivity to cloud contamination, clearly. Therefore, improvements of cloud masking process in the developed single channel MI algorithm as well as the modification of the surface reflectance estimation will be required for the future study.

Non-contact cardiac pulse rate estimation based on web-camera

NASA Astrophysics Data System (ADS)

Wang, Yingzhi; Han, Tailin

2015-12-01

In this paper, we introduce a new methodology of non-contact cardiac pulse rate estimation based on the imaging Photoplethysmography (iPPG) and blind source separation. This novel's approach can be applied to color video recordings of the human face and is based on automatic face tracking along with blind source separation of the color channels into RGB three-channel component. First of all, we should do some pre-processings of the data which can be got from color video such as normalization and sphering. We can use spectrum analysis to estimate the cardiac pulse rate by Independent Component Analysis (ICA) and JADE algorithm. With Bland-Altman and correlation analysis, we compared the cardiac pulse rate extracted from videos recorded by a basic webcam to a Commercial pulse oximetry sensors and achieved high accuracy and correlation. Root mean square error for the estimated results is 2.06bpm, which indicates that the algorithm can realize the non-contact measurements of cardiac pulse rate.

Improved Atmospheric Soundings and Error Estimates from Analysis of AIRS/AMSU Data

NASA Technical Reports Server (NTRS)

Susskind, Joel

2007-01-01

The AIRS Science Team Version 5.0 retrieval algorithm became operational at the Goddard DAAC in July 2007 generating near real-time products from analysis of AIRS/AMSU sounding data. This algorithm contains many significant theoretical advances over the AIRS Science Team Version 4.0 retrieval algorithm used previously. Three very significant developments of Version 5 are: 1) the development and implementation of an improved Radiative Transfer Algorithm (RTA) which allows for accurate treatment of non-Local Thermodynamic Equilibrium (non-LTE) effects on shortwave sounding channels; 2) the development of methodology to obtain very accurate case by case product error estimates which are in turn used for quality control; and 3) development of an accurate AIRS only cloud clearing and retrieval system. These theoretical improvements taken together enabled a new methodology to be developed which further improves soundings in partially cloudy conditions, without the need for microwave observations in the cloud clearing step as has been done previously. In this methodology, longwave C02 channel observations in the spectral region 700 cm-' to 750 cm-' are used exclusively for cloud clearing purposes, while shortwave C02 channels in the spectral region 2195 cm-' to 2395 cm-' are used for temperature sounding purposes. The new methodology for improved error estimates and their use in quality control is described briefly and results are shown indicative of their accuracy. Results are also shown of forecast impact experiments assimilating AIRS Version 5.0 retrieval products in the Goddard GEOS 5 Data Assimilation System using different quality control thresholds.

Demodulation Algorithms for the Ofdm Signals in the Time- and Frequency-Scattering Channels

NASA Astrophysics Data System (ADS)

Bochkov, G. N.; Gorokhov, K. V.; Kolobkov, A. V.

2016-06-01

We consider a method based on the generalized maximum-likelihood rule for solving the problem of reception of the signals with orthogonal frequency division multiplexing of their harmonic components (OFDM signals) in the time- and frequency-scattering channels. The coherent and incoherent demodulators effectively using the time scattering due to the fast fading of the signal are developed. Using computer simulation, we performed comparative analysis of the proposed algorithms and well-known signal-reception algorithms with equalizers. The proposed symbolby-symbol detector with decision feedback and restriction of the number of searched variants is shown to have the best bit-error-rate performance. It is shown that under conditions of the limited accuracy of estimating the communication-channel parameters, the incoherent OFDMsignal detectors with differential phase-shift keying can ensure a better bit-error-rate performance compared with the coherent OFDM-signal detectors with absolute phase-shift keying.

FDD Massive MIMO Channel Estimation With Arbitrary 2D-Array Geometry

NASA Astrophysics Data System (ADS)

Dai, Jisheng; Liu, An; Lau, Vincent K. N.

2018-05-01

This paper addresses the problem of downlink channel estimation in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. The existing methods usually exploit hidden sparsity under a discrete Fourier transform (DFT) basis to estimate the cdownlink channel. However, there are at least two shortcomings of these DFT-based methods: 1) they are applicable to uniform linear arrays (ULAs) only, since the DFT basis requires a special structure of ULAs, and 2) they always suffer from a performance loss due to the leakage of energy over some DFT bins. To deal with the above shortcomings, we introduce an off-grid model for downlink channel sparse representation with arbitrary 2D-array antenna geometry, and propose an efficient sparse Bayesian learning (SBL) approach for the sparse channel recovery and off-grid refinement. The main idea of the proposed off-grid method is to consider the sampled grid points as adjustable parameters. Utilizing an in-exact block majorization-minimization (MM) algorithm, the grid points are refined iteratively to minimize the off-grid gap. Finally, we further extend the solution to uplink-aided channel estimation by exploiting the angular reciprocity between downlink and uplink channels, which brings enhanced recovery performance.

Correction of clipped pixels in color images.

PubMed

Xu, Di; Doutre, Colin; Nasiopoulos, Panos

2011-03-01

Conventional images store a very limited dynamic range of brightness. The true luma in the bright area of such images is often lost due to clipping. When clipping changes the R, G, B color ratios of a pixel, color distortion also occurs. In this paper, we propose an algorithm to enhance both the luma and chroma of the clipped pixels. Our method is based on the strong chroma spatial correlation between clipped pixels and their surrounding unclipped area. After identifying the clipped areas in the image, we partition the clipped areas into regions with similar chroma, and estimate the chroma of each clipped region based on the chroma of its surrounding unclipped region. We correct the clipped R, G, or B color channels based on the estimated chroma and the unclipped color channel(s) of the current pixel. The last step involves smoothing of the boundaries between regions of different clipping scenarios. Both objective and subjective experimental results show that our algorithm is very effective in restoring the color of clipped pixels. © 2011 IEEE

Recent Progress in Development of SWOT River Discharge Algorithms

NASA Astrophysics Data System (ADS)

Pavelsky, Tamlin M.; Andreadis, Konstantinos; Biancamaria, Sylvian; Durand, Michael; Moller, Dewlyn; Rodriguez, Enersto; Smith, Laurence C.

2013-09-01

The Surface Water and Ocean Topography (SWOT) Mission is a satellite mission under joint development by NASA and CNES. The mission will use interferometric synthetic aperture radar technology to continuously map, for the first time, water surface elevations and water surface extents in rivers, lakes, and oceans at high spatial resolutions. Among the primary goals of SWOT is the accurate retrieval of river discharge directly from SWOT measurements. Although it is central to the SWOT mission, discharge retrieval represents a substantial challenge due to uncertainties in SWOT measurements and because traditional discharge algorithms are not optimized for SWOT-like measurements. However, recent work suggests that SWOT may also have unique strengths that can be exploited to yield accurate estimates of discharge. A NASA-sponsored workshop convened June 18-20, 2012 at the University of North Carolina focused on progress and challenges in developing SWOT-specific discharge algorithms. Workshop participants agreed that the only viable approach to discharge estimation will be based on a slope-area scaling method such as Manning's equation, but modified slightly to reflect the fact that SWOT will estimate reach-averaged rather than cross- sectional discharge. While SWOT will provide direct measurements of some key parameters such as width and slope, others such as baseflow depth and channel roughness must be estimated. Fortunately, recent progress has suggested several algorithms that may allow the simultaneous estimation of these quantities from SWOT observations by using multitemporal observations over several adjacent reaches. However, these algorithms will require validation, which will require the collection of new field measurements, airborne imagery from AirSWOT (a SWOT analogue), and compilation of global datasets of channel roughness, river width, and other relevant variables.

Channel-parameter estimation for satellite-to-submarine continuous-variable quantum key distribution

NASA Astrophysics Data System (ADS)

Guo, Ying; Xie, Cailang; Huang, Peng; Li, Jiawei; Zhang, Ling; Huang, Duan; Zeng, Guihua

2018-05-01

This paper deals with a channel-parameter estimation for continuous-variable quantum key distribution (CV-QKD) over a satellite-to-submarine link. In particular, we focus on the channel transmittances and the excess noise which are affected by atmospheric turbulence, surface roughness, zenith angle of the satellite, wind speed, submarine depth, etc. The estimation method is based on proposed algorithms and is applied to low-Earth orbits using the Monte Carlo approach. For light at 550 nm with a repetition frequency of 1 MHz, the effects of the estimated parameters on the performance of the CV-QKD system are assessed by a simulation by comparing the secret key bit rate in the daytime and at night. Our results show the feasibility of satellite-to-submarine CV-QKD, providing an unconditionally secure approach to achieve global networks for underwater communications.

AATSR Based Volcanic Ash Plume Top Height Estimation

NASA Astrophysics Data System (ADS)

Virtanen, Timo H.; Kolmonen, Pekka; Sogacheva, Larisa; Sundstrom, Anu-Maija; Rodriguez, Edith; de Leeuw, Gerrit

2015-11-01

The AATSR Correlation Method (ACM) height estimation algorithm is presented. The algorithm uses Advanced Along Track Scanning Radiometer (AATSR) satellite data to detect volcanic ash plumes and to estimate the plume top height. The height estimate is based on the stereo-viewing capability of the AATSR instrument, which allows to determine the parallax between the satellite's nadir and 55◦ forward views, and thus the corresponding height. AATSR provides an advantage compared to other stereo-view satellite instruments: with AATSR it is possible to detect ash plumes using brightness temperature difference between thermal infrared (TIR) channels centered at 11 and 12 μm. The automatic ash detection makes the algorithm efficient in processing large quantities of data: the height estimate is calculated only for the ash-flagged pixels. Besides ash plumes, the algorithm can be applied to any elevated feature with sufficient contrast to the background, such as smoke and dust plumes and clouds. The ACM algorithm can be applied to the Sea and Land Surface Temperature Radiometer (SLSTR), scheduled for launch at the end of 2015.

An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks.

PubMed

Lei, Chunyang; Bie, Hongxia; Fang, Gengfa; Zhang, Xuekun

2015-12-03

Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT), Machine-to-Machine (M2M) communications, Vehicular-to-Vehicular (V2V) communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than 95 % of the theoretical optimal throughput and 0 . 97 of fairness index especially in dynamic and dense networks.

Underwater image enhancement through depth estimation based on random forest

NASA Astrophysics Data System (ADS)

Tai, Shen-Chuan; Tsai, Ting-Chou; Huang, Jyun-Han

2017-11-01

Light absorption and scattering in underwater environments can result in low-contrast images with a distinct color cast. This paper proposes a systematic framework for the enhancement of underwater images. Light transmission is estimated using the random forest algorithm. RGB values, luminance, color difference, blurriness, and the dark channel are treated as features in training and estimation. Transmission is calculated using an ensemble machine learning algorithm to deal with a variety of conditions encountered in underwater environments. A color compensation and contrast enhancement algorithm based on depth information was also developed with the aim of improving the visual quality of underwater images. Experimental results demonstrate that the proposed scheme outperforms existing methods with regard to subjective visual quality as well as objective measurements.

An isometric muscle force estimation framework based on a high-density surface EMG array and an NMF algorithm

NASA Astrophysics Data System (ADS)

Huang, Chengjun; Chen, Xiang; Cao, Shuai; Qiu, Bensheng; Zhang, Xu

2017-08-01

Objective. To realize accurate muscle force estimation, a novel framework is proposed in this paper which can extract the input of the prediction model from the appropriate activation area of the skeletal muscle. Approach. Surface electromyographic (sEMG) signals from the biceps brachii muscle during isometric elbow flexion were collected with a high-density (HD) electrode grid (128 channels) and the external force at three contraction levels was measured at the wrist synchronously. The sEMG envelope matrix was factorized into a matrix of basis vectors with each column representing an activation pattern and a matrix of time-varying coefficients by a nonnegative matrix factorization (NMF) algorithm. The activation pattern with the highest activation intensity, which was defined as the sum of the absolute values of the time-varying coefficient curve, was considered as the major activation pattern, and its channels with high weighting factors were selected to extract the input activation signal of a force estimation model based on the polynomial fitting technique. Main results. Compared with conventional methods using the whole channels of the grid, the proposed method could significantly improve the quality of force estimation and reduce the electrode number. Significance. The proposed method provides a way to find proper electrode placement for force estimation, which can be further employed in muscle heterogeneity analysis, myoelectric prostheses and the control of exoskeleton devices.

Digital Self-Interference Cancellation for Asynchronous In-Band Full-Duplex Underwater Acoustic Communication.

PubMed

Qiao, Gang; Gan, Shuwei; Liu, Songzuo; Ma, Lu; Sun, Zongxin

2018-05-24

To improve the throughput of underwater acoustic (UWA) networking, the In-band full-duplex (IBFD) communication is one of the most vital pieces of research. The major drawback of IBFD-UWA communication is Self-Interference (SI). This paper presents a digital SI cancellation algorithm for asynchronous IBFD-UWA communication system. We focus on two issues: one is asynchronous communication dissimilar to IBFD radio communication, the other is nonlinear distortion caused by power amplifier (PA). First, we discuss asynchronous IBFD-UWA signal model with the nonlinear distortion of PA. Then, we design a scheme for asynchronous IBFD-UWA communication utilizing the non-overlapping region between SI and intended signal to estimate the nonlinear SI channel. To cancel the nonlinear distortion caused by PA, we propose an Over-Parameterization based Recursive Least Squares (RLS) algorithm (OPRLS) to estimate the nonlinear SI channel. Furthermore, we present the OPRLS with a sparse constraint to estimate the SI channel, which reduces the requirement of the length of the non-overlapping region. Finally, we verify our concept through simulation and the pool experiment. Results demonstrate that the proposed digital SI cancellation scheme can cancel SI efficiently.

A Distributed Transmission Rate Adjustment Algorithm in Heterogeneous CSMA/CA Networks

PubMed Central

Xie, Shuanglong; Low, Kay Soon; Gunawan, Erry

2015-01-01

Distributed transmission rate tuning is important for a wide variety of IEEE 802.15.4 network applications such as industrial network control systems. Such systems often require each node to sustain certain throughput demand in order to guarantee the system performance. It is thus essential to determine a proper transmission rate that can meet the application requirement and compensate for network imperfections (e.g., packet loss). Such a tuning in a heterogeneous network is difficult due to the lack of modeling techniques that can deal with the heterogeneity of the network as well as the network traffic changes. In this paper, a distributed transmission rate tuning algorithm in a heterogeneous IEEE 802.15.4 CSMA/CA network is proposed. Each node uses the results of clear channel assessment (CCA) to estimate the busy channel probability. Then a mathematical framework is developed to estimate the on-going heterogeneous traffics using the busy channel probability at runtime. Finally a distributed algorithm is derived to tune the transmission rate of each node to accurately meet the throughput requirement. The algorithm does not require modifications on IEEE 802.15.4 MAC layer and it has been experimentally implemented and extensively tested using TelosB nodes with the TinyOS protocol stack. The results reveal that the algorithm is accurate and can satisfy the throughput demand. Compared with existing techniques, the algorithm is fully distributed and thus does not require any central coordination. With this property, it is able to adapt to traffic changes and re-adjust the transmission rate to the desired level, which cannot be achieved using the traditional modeling techniques. PMID:25822140

Distortion outage minimization in Nakagami fading using limited feedback

NASA Astrophysics Data System (ADS)

Wang, Chih-Hong; Dey, Subhrakanti

2011-12-01

We focus on a decentralized estimation problem via a clustered wireless sensor network measuring a random Gaussian source where the clusterheads amplify and forward their received signals (from the intra-cluster sensors) over orthogonal independent stationary Nakagami fading channels to a remote fusion center that reconstructs an estimate of the original source. The objective of this paper is to design clusterhead transmit power allocation policies to minimize the distortion outage probability at the fusion center, subject to an expected sum transmit power constraint. In the case when full channel state information (CSI) is available at the clusterhead transmitters, the optimization problem can be shown to be convex and is solved exactly. When only rate-limited channel feedback is available, we design a number of computationally efficient sub-optimal power allocation algorithms to solve the associated non-convex optimization problem. We also derive an approximation for the diversity order of the distortion outage probability in the limit when the average transmission power goes to infinity. Numerical results illustrate that the sub-optimal power allocation algorithms perform very well and can close the outage probability gap between the constant power allocation (no CSI) and full CSI-based optimal power allocation with only 3-4 bits of channel feedback.

Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) Data

PubMed Central

Tang, Bohui; Bi, Yuyun; Li, Zhao-Liang; Xia, Jun

2008-01-01

On the basis of the radiative transfer theory, this paper addressed the estimate of Land Surface Temperature (LST) from the Chinese first operational geostationary meteorological satellite-FengYun-2C (FY-2C) data in two thermal infrared channels (IR1, 10.3-11.3 μm and IR2, 11.5-12.5 μm), using the Generalized Split-Window (GSW) algorithm proposed by Wan and Dozier (1996). The coefficients in the GSW algorithm corresponding to a series of overlapping ranging of the mean emissivity, the atmospheric Water Vapor Content (WVC), and the LST were derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer model MODTRAN 4 over a wide range of atmospheric and surface conditions. The simulation analysis showed that the LST could be estimated by the GSW algorithm with the Root Mean Square Error (RMSE) less than 1 K for the sub-ranges with the Viewing Zenith Angle (VZA) less than 30° or for the sub-rangs with VZA less than 60° and the atmospheric WVC less than 3.5 g/cm2 provided that the Land Surface Emissivities (LSEs) are known. In order to determine the range for the optimum coefficients of the GSW algorithm, the LSEs could be derived from the data in MODIS channels 31 and 32 provided by MODIS/Terra LST product MOD11B1, or be estimated either according to the land surface classification or using the method proposed by Jiang et al. (2006); and the WVC could be obtained from MODIS total precipitable water product MOD05, or be retrieved using Li et al.' method (2003). The sensitivity and error analyses in term of the uncertainty of the LSE and WVC as well as the instrumental noise were performed. In addition, in order to compare the different formulations of the split-window algorithms, several recently proposed split-window algorithms were used to estimate the LST with the same simulated FY-2C data. The result of the intercomparsion showed that most of the algorithms give comparable results. PMID:27879744

A parameter estimation algorithm for LFM/BPSK hybrid modulated signal intercepted by Nyquist folding receiver

NASA Astrophysics Data System (ADS)

Qiu, Zhaoyang; Wang, Pei; Zhu, Jun; Tang, Bin

2016-12-01

Nyquist folding receiver (NYFR) is a novel ultra-wideband receiver architecture which can realize wideband receiving with a small amount of equipment. Linear frequency modulated/binary phase shift keying (LFM/BPSK) hybrid modulated signal is a novel kind of low probability interception signal with wide bandwidth. The NYFR is an effective architecture to intercept the LFM/BPSK signal and the LFM/BPSK signal intercepted by the NYFR will add the local oscillator modulation. A parameter estimation algorithm for the NYFR output signal is proposed. According to the NYFR prior information, the chirp singular value ratio spectrum is proposed to estimate the chirp rate. Then, based on the output self-characteristic, matching component function is designed to estimate Nyquist zone (NZ) index. Finally, matching code and subspace method are employed to estimate the phase change points and code length. Compared with the existing methods, the proposed algorithm has a better performance. It also has no need to construct a multi-channel structure, which means the computational complexity for the NZ index estimation is small. The simulation results demonstrate the efficacy of the proposed algorithm.

Development, Validation, and Potential Enhancements to the Second-Generation Operational Aerosol Product at the National Environmental Satellite, Data, and Information Service of the National Oceanic and Atmospheric Administration

NASA Technical Reports Server (NTRS)

Stowe, Larry L.; Ignatov, Alexander M.; Singh, Ramdas R.

1997-01-01

A revised (phase 2) single-channel algorithm for aerosol optical thickness, tau(sup A)(sub SAT), retrieval over oceans from radiances in channel 1 (0.63 microns) of the Advanced Very High Resolution Radiometer (AVHRR) has been implemented at the National Oceanic and Atmospheric Administration's National Environmental Satellite Data and Information Service for the NOAA 14 satellite launched December 30, 1994. It is based on careful validation of its operational predecessor (phase 1 algorithm), implemented for NOAA 14 in 1989. Both algorithms scale the upward satellite radiances in cloud-free conditions to aerosol optical thickness using an updated radiative transfer model of the ocean and atmosphere. Application of the phase 2 algorithm to three matchup Sun-photometer and satellite data sets, one with NOAA 9 in 1988 and two with NOAA 11 in 1989 and 1991, respectively, show systematic error is less than 10%, with a random error of sigma(sub tau) approx. equal 0.04. First results of tau(sup A)(sub SAT) retrievals from NOAA 14 using the phase 2 algorithm, and from checking its internal consistency, are presented. The potential two-channel (phase 3) algorithm for the retrieval of an aerosol size parameter, such as the Junge size distribution exponent, by adding either channel 2 (0.83 microns) from the current AVHRR instrument, or a 1.6-microns channel to be available on the Tropical Rainfall Measurement Mission and the NOAA-KLM satellites by 1997 is under investigation. The possibility of using this additional information in the retrieval of a more accurate estimate of aerosol optical thickness is being explored.

Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of an Enhanced System

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2008-01-01

In this paper, an enhanced on-line diagnostic system which utilizes dual-channel sensor measurements is developed for the aircraft engine application. The enhanced system is composed of a nonlinear on-board engine model (NOBEM), the hybrid Kalman filter (HKF) algorithm, and fault detection and isolation (FDI) logic. The NOBEM provides the analytical third channel against which the dual-channel measurements are compared. The NOBEM is further utilized as part of the HKF algorithm which estimates measured engine parameters. Engine parameters obtained from the dual-channel measurements, the NOBEM, and the HKF are compared against each other. When the discrepancy among the signals exceeds a tolerance level, the FDI logic determines the cause of discrepancy. Through this approach, the enhanced system achieves the following objectives: 1) anomaly detection, 2) component fault detection, and 3) sensor fault detection and isolation. The performance of the enhanced system is evaluated in a simulation environment using faults in sensors and components, and it is compared to an existing baseline system.

Plumes and Blooms: Observations, Analysis and Modeling for SIMBIOS

NASA Technical Reports Server (NTRS)

Maritorena, S.; Siegel, D. A.; Nelson, N. B.

2004-01-01

The goal of the Plumes and Blooms (PnB) project is to develop, validate and apply to imagery state-of-the-art ocean color algorithms for quantifying sediment plumes and phytoplankton blooms for the Case II environment of the Santa Barbara Channel. We conduct monthly to twice-monthly transect observations across the Santa Barbara Channel to develop an algorithm development and product validation data set. A primary goal is the use the PnB field data set to objectively tune semi-analytical models of ocean color for this site and apply them using available satellite imagery (SeaWiFS and MODIS). However, the comparison between PnB field observations and satellite estimates of primary products has been disappointing. We find that field estimates of water-leaving radiance correspond poorly to satellite estimates for both SeaWiFS and MODIS local area coverage imagery. We believe this is due to poor atmospheric correction due to complex mixtures of aerosol types found in these near-coastal regions.

Efficient Implementation of a Symbol Timing Estimator for Broadband PLC.

PubMed

Nombela, Francisco; García, Enrique; Mateos, Raúl; Hernández, Álvaro

2015-08-21

Broadband Power Line Communications (PLC) have taken advantage of the research advances in multi-carrier modulations to mitigate frequency selective fading, and their adoption opens up a myriad of applications in the field of sensory and automation systems, multimedia connectivity or smart spaces. Nonetheless, the use of these multi-carrier modulations, such as Wavelet-OFDM, requires a highly accurate symbol timing estimation for reliably recovering of transmitted data. Furthermore, the PLC channel presents some particularities that prevent the direct use of previous synchronization algorithms proposed in wireless communication systems. Therefore more research effort should be involved in the design and implementation of novel and robust synchronization algorithms for PLC, thus enabling real-time synchronization. This paper proposes a symbol timing estimator for broadband PLC based on cross-correlation with multilevel complementary sequences or Zadoff-Chu sequences and its efficient implementation in a FPGA; the obtained results show a 90% of success rate in symbol timing estimation for a certain PLC channel model and a reduced resource consumption for its implementation in a Xilinx Kyntex FPGA.

Robust Transceiver Design for Multiuser MIMO Downlink with Channel Uncertainties

NASA Astrophysics Data System (ADS)

Miao, Wei; Li, Yunzhou; Chen, Xiang; Zhou, Shidong; Wang, Jing

This letter addresses the problem of robust transceiver design for the multiuser multiple-input-multiple-output (MIMO) downlink where the channel state information at the base station (BS) is imperfect. A stochastic approach which minimizes the expectation of the total mean square error (MSE) of the downlink conditioned on the channel estimates under a total transmit power constraint is adopted. The iterative algorithm reported in [2] is improved to handle the proposed robust optimization problem. Simulation results show that our proposed robust scheme effectively reduces the performance loss due to channel uncertainties and outperforms existing methods, especially when the channel errors of the users are different.

Multichannel blind iterative image restoration.

PubMed

Sroubek, Filip; Flusser, Jan

2003-01-01

Blind image deconvolution is required in many applications of microscopy imaging, remote sensing, and astronomical imaging. Unfortunately in a single-channel framework, serious conceptual and numerical problems are often encountered. Very recently, an eigenvector-based method (EVAM) was proposed for a multichannel framework which determines perfectly convolution masks in a noise-free environment if channel disparity, called co-primeness, is satisfied. We propose a novel iterative algorithm based on recent anisotropic denoising techniques of total variation and a Mumford-Shah functional with the EVAM restoration condition included. A linearization scheme of half-quadratic regularization together with a cell-centered finite difference discretization scheme is used in the algorithm and provides a unified approach to the solution of total variation or Mumford-Shah. The algorithm performs well even on very noisy images and does not require an exact estimation of mask orders. We demonstrate capabilities of the algorithm on synthetic data. Finally, the algorithm is applied to defocused images taken with a digital camera and to data from astronomical ground-based observations of the Sun.

Method for optimizing channelized quadratic observers for binary classification of large-dimensional image datasets

PubMed Central

Kupinski, M. K.; Clarkson, E.

2015-01-01

We present a new method for computing optimized channels for channelized quadratic observers (CQO) that is feasible for high-dimensional image data. The method for calculating channels is applicable in general and optimal for Gaussian distributed image data. Gradient-based algorithms for determining the channels are presented for five different information-based figures of merit (FOMs). Analytic solutions for the optimum channels for each of the five FOMs are derived for the case of equal mean data for both classes. The optimum channels for three of the FOMs under the equal mean condition are shown to be the same. This result is critical since some of the FOMs are much easier to compute. Implementing the CQO requires a set of channels and the first- and second-order statistics of channelized image data from both classes. The dimensionality reduction from M measurements to L channels is a critical advantage of CQO since estimating image statistics from channelized data requires smaller sample sizes and inverting a smaller covariance matrix is easier. In a simulation study we compare the performance of ideal and Hotelling observers to CQO. The optimal CQO channels are calculated using both eigenanalysis and a new gradient-based algorithm for maximizing Jeffrey's divergence (J). Optimal channel selection without eigenanalysis makes the J-CQO on large-dimensional image data feasible. PMID:26366764

Real-time estimation of horizontal gaze angle by saccade integration using in-ear electrooculography

PubMed Central

2018-01-01

The manuscript proposes and evaluates a real-time algorithm for estimating eye gaze angle based solely on single-channel electrooculography (EOG), which can be obtained directly from the ear canal using conductive ear moulds. In contrast to conventional high-pass filtering, we used an algorithm that calculates absolute eye gaze angle via statistical analysis of detected saccades. The estimated eye positions of the new algorithm were still noisy. However, the performance in terms of Pearson product-moment correlation coefficients was significantly better than the conventional approach in some instances. The results suggest that in-ear EOG signals captured with conductive ear moulds could serve as a basis for light-weight and portable horizontal eye gaze angle estimation suitable for a broad range of applications. For instance, for hearing aids to steer the directivity of microphones in the direction of the user’s eye gaze. PMID:29304120

Real-time estimation of horizontal gaze angle by saccade integration using in-ear electrooculography.

PubMed

Hládek, Ľuboš; Porr, Bernd; Brimijoin, W Owen

2018-01-01

The manuscript proposes and evaluates a real-time algorithm for estimating eye gaze angle based solely on single-channel electrooculography (EOG), which can be obtained directly from the ear canal using conductive ear moulds. In contrast to conventional high-pass filtering, we used an algorithm that calculates absolute eye gaze angle via statistical analysis of detected saccades. The estimated eye positions of the new algorithm were still noisy. However, the performance in terms of Pearson product-moment correlation coefficients was significantly better than the conventional approach in some instances. The results suggest that in-ear EOG signals captured with conductive ear moulds could serve as a basis for light-weight and portable horizontal eye gaze angle estimation suitable for a broad range of applications. For instance, for hearing aids to steer the directivity of microphones in the direction of the user's eye gaze.

Wind Retrievals under Rain for Passive Satellite Microwave Radiometers and its Applications to Hurricane Tracking

NASA Technical Reports Server (NTRS)

Meissner, Thomas; Wentz, Frank J.

2008-01-01

We have developed an algorithm that retrieves wind speed under rain using C-hand and X-band channels of passive microwave satellite radiometers. The spectral difference of the brightness temperature signals due to wind or rain allows to find channel combinations that are sufficiently sensitive to wind speed but little or not sensitive to rain. We &ve trained a statistical algorithm that applies under hurricane conditions and is able to measure wind speeds in hurricanes to an estimated accuracy of about 2 m/s. We have also developed a global algorithm, that is less accurate but can be applied under all conditions. Its estimated accuracy is between 2 and 5 mls, depending on wind speed and rain rate. We also extend the wind speed region in our model for the wind induced sea surface emissivity from currently 20 m/s to 40 mls. The data indicate that the signal starts to saturate above 30 mls. Finally, we make an assessment of the performance of wind direction retrievals from polarimetric radiometers as function of wind speed and rain rate

Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users.

PubMed

Goehring, Tobias; Bolner, Federico; Monaghan, Jessica J M; van Dijk, Bas; Zarowski, Andrzej; Bleeck, Stefan

2017-02-01

Speech understanding in noisy environments is still one of the major challenges for cochlear implant (CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks (NNSE) for improving speech intelligibility in noise for CI users. The algorithm decomposes the noisy speech signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the neural network to produce an estimation of which frequency channels contain more perceptually important information (higher signal-to-noise ratio, SNR). This estimate is used to attenuate noise-dominated and retain speech-dominated CI channels for electrical stimulation, as in traditional n-of-m CI coding strategies. The proposed algorithm was evaluated by measuring the speech-in-noise performance of 14 CI users using three types of background noise. Two NNSE algorithms were compared: a speaker-dependent algorithm, that was trained on the target speaker used for testing, and a speaker-independent algorithm, that was trained on different speakers. Significant improvements in the intelligibility of speech in stationary and fluctuating noises were found relative to the unprocessed condition for the speaker-dependent algorithm in all noise types and for the speaker-independent algorithm in 2 out of 3 noise types. The NNSE algorithms used noise-specific neural networks that generalized to novel segments of the same noise type and worked over a range of SNRs. The proposed algorithm has the potential to improve the intelligibility of speech in noise for CI users while meeting the requirements of low computational complexity and processing delay for application in CI devices. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

The profile algorithm for microwave delay estimation from water vapor radiometer data

NASA Technical Reports Server (NTRS)

Robinson, Steven E.

1988-01-01

A new algorithm has been developed for the estimation of tropospheric microwave path delays from water vapor radiometer (WVR) data, which does not require site and weather dependent empirical parameters to produce accuracy better than 0.3 cm of delay. Instead of taking the conventional linear approach, the new algorithm first uses the observables with an emission model to determine an approximate form of the vertical water vapor distribution, which is then explicitly integrated to estimate wet path delays in a second step. The intrinsic accuracy of this algorithm, excluding uncertainties caused by the radiometers and the emission model, has been examined for two channel WVR data using path delays and corresponding simulated observables computed from archived radiosonde data. It is found that annual rms errors for a wide range of sites average 0.18 cm in the absence of clouds, 0.22 cm in cloudy weather, and 0.19 cm overall. In clear weather, the new algorithm's accuracy is comparable to the best that can be obtained from conventional linear algorithms, while in cloudy weather it offers a 35 percent improvement.

A new algorithm for microwave delay estimation from water vapor radiometer data

NASA Technical Reports Server (NTRS)

Robinson, S. E.

1986-01-01

A new algorithm has been developed for the estimation of tropospheric microwave path delays from water vapor radiometer (WVR) data, which does not require site and weather dependent empirical parameters to produce high accuracy. Instead of taking the conventional linear approach, the new algorithm first uses the observables with an emission model to determine an approximate form of the vertical water vapor distribution which is then explicitly integrated to estimate wet path delays, in a second step. The intrinsic accuracy of this algorithm has been examined for two channel WVR data using path delays and stimulated observables computed from archived radiosonde data. It is found that annual RMS errors for a wide range of sites are in the range from 1.3 mm to 2.3 mm, in the absence of clouds. This is comparable to the best overall accuracy obtainable from conventional linear algorithms, which must be tailored to site and weather conditions using large radiosonde data bases. The new algorithm's accuracy and flexibility are indications that it may be a good candidate for almost all WVR data interpretation.

Improved Determination of Surface and Atmospheric Temperatures Using Only Shortwave AIRS Channels: The AIRS Version 6 Retrieval Algorithm

NASA Technical Reports Server (NTRS)

Susskind, Joel; Blaisdell, John; Iredell, Lena

2010-01-01

AIRS was launched on EOS Aqua on May 4, 2002 together with ASMU-A and HSB to form a next generation polar orbiting infrared and microwave atmosphere sounding system (Pagano et al 2003). The theoretical approach used to analyze AIRS/AMSU/HSB data in the presence of clouds in the AIRS Science Team Version 3 at-launch algorithm, and that used in the Version 4 post-launch algorithm, have been published previously. Significant theoretical and practical improvements have been made in the analysis of AIRS/AMSU data since the Version 4 algorithm. Most of these have already been incorporated in the AIRS Science Team Version 5 algorithm (Susskind et al 2010), now being used operationally at the Goddard DISC. The AIRS Version 5 retrieval algorithm contains three significant improvements over Version 4. Improved physics in Version 5 allowed for use of AIRS clear column radiances (R(sub i)) in the entire 4.3 micron CO2 absorption band in the retrieval of temperature profiles T(p) during both day and night. Tropospheric sounding 15 micron CO2 observations were used primarily in the generation of clear column radiances (R(sub i)) for all channels. This new approach allowed for the generation of accurate Quality Controlled values of R(sub i) and T(p) under more stressing cloud conditions. Secondly, Version 5 contained a new methodology to provide accurate case-by-case error estimates for retrieved geophysical parameters and for channel-by-channel clear column radiances. Thresholds of these error estimates are used in a new approach for Quality Control. Finally, Version 5 contained for the first time an approach to provide AIRS soundings in partially cloudy conditions that does not require use of any microwave data. This new AIRS Only sounding methodology was developed as a backup to AIRS Version 5 should the AMSU-A instrument fail. Susskind et al 2010 shows that Version 5 AIRS Only sounding are only slightly degraded from the AIRS/AMSU soundings, even at large fractional cloud cover.

Improved Soundings and Error Estimates using AIRS/AMSU Data

NASA Technical Reports Server (NTRS)

Susskind, Joel

2006-01-01

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The sounding goals of AIRS are to produce 1 km tropospheric layer mean temperatures with an rms error of 1 K, and layer precipitable water with an rms error of 20 percent, in cases with up to 80 percent effective cloud cover. The basic theory used to analyze AIRS/AMSU/HSB data in the presence of clouds, called the at-launch algorithm, and a post-launch algorithm which differed only in the minor details from the at-launch algorithm, have been described previously. The post-launch algorithm, referred to as AIRS Version 4.0, has been used by the Goddard DAAC to analyze and distribute AIRS retrieval products. In this paper we show progress made toward the AIRS Version 5.0 algorithm which will be used by the Goddard DAAC starting late in 2006. A new methodology has been developed to provide accurate case by case error estimates for retrieved geophysical parameters and for the channel by channel cloud cleared radiances used to derive the geophysical parameters from the AIRS/AMSU observations. These error estimates are in turn used for quality control of the derived geophysical parameters and clear column radiances. Improvements made to the retrieval algorithm since Version 4.0 are described as well as results comparing Version 5.0 retrieval accuracy and spatial coverage with those obtained using Version 4.0.

Estimating the Volterra Series Transfer Function over coherent optical OFDM for efficient monitoring of the fiber channel nonlinearity.

PubMed

Shulkind, Gal; Nazarathy, Moshe

2012-12-17

We present an efficient method for system identification (nonlinear channel estimation) of third order nonlinear Volterra Series Transfer Function (VSTF) characterizing the four-wave-mixing nonlinear process over a coherent OFDM fiber link. Despite the seemingly large number of degrees of freedom in the VSTF (cubic in the number of frequency points) we identified a compressed VSTF representation which does not entail loss of information. Additional slightly lossy compression may be obtained by discarding very low power VSTF coefficients associated with regions of destructive interference in the FWM phased array effect. Based on this two-staged VSTF compressed representation, we develop a robust and efficient algorithm of nonlinear system identification (optical performance monitoring) estimating the VSTF by transmission of an extended training sequence over the OFDM link, performing just a matrix-vector multiplication at the receiver by a pseudo-inverse matrix which is pre-evaluated offline. For 512 (1024) frequency samples per channel, the VSTF measurement takes less than 1 (10) msec to complete with computational complexity of one real-valued multiply-add operation per time sample. Relative to a naïve exhaustive three-tone-test, our algorithm is far more tolerant of ASE additive noise and its acquisition time is orders of magnitude faster.

A survey of the state of the art and focused research in range systems, task 2

NASA Technical Reports Server (NTRS)

Yao, K.

1986-01-01

Contract generated publications are compiled which describe the research activities for the reporting period. Study topics include: equivalent configurations of systolic arrays; least squares estimation algorithms with systolic array architectures; modeling and equilization of nonlinear bandlimited satellite channels; and least squares estimation and Kalman filtering by systolic arrays.

Power optimization of digital baseband WCDMA receiver components on algorithmic and architectural level

NASA Astrophysics Data System (ADS)

Schämann, M.; Bücker, M.; Hessel, S.; Langmann, U.

2008-05-01

High data rates combined with high mobility represent a challenge for the design of cellular devices. Advanced algorithms are required which result in higher complexity, more chip area and increased power consumption. However, this contrasts to the limited power supply of mobile devices. This presentation discusses the application of an HSDPA receiver which has been optimized regarding power consumption with the focus on the algorithmic and architectural level. On algorithmic level the Rake combiner, Prefilter-Rake equalizer and MMSE equalizer are compared regarding their BER performance. Both equalizer approaches provide a significant increase of performance for high data rates compared to the Rake combiner which is commonly used for lower data rates. For both equalizer approaches several adaptive algorithms are available which differ in complexity and convergence properties. To identify the algorithm which achieves the required performance with the lowest power consumption the algorithms have been investigated using SystemC models regarding their performance and arithmetic complexity. Additionally, for the Prefilter Rake equalizer the power estimations of a modified Griffith (LMS) and a Levinson (RLS) algorithm have been compared with the tool ORINOCO supplied by ChipVision. The accuracy of this tool has been verified with a scalable architecture of the UMTS channel estimation described both in SystemC and VHDL targeting a 130 nm CMOS standard cell library. An architecture combining all three approaches combined with an adaptive control unit is presented. The control unit monitors the current condition of the propagation channel and adjusts parameters for the receiver like filter size and oversampling ratio to minimize the power consumption while maintaining the required performance. The optimization strategies result in a reduction of the number of arithmetic operations up to 70% for single components which leads to an estimated power reduction of up to 40% while the BER performance is not affected. This work utilizes SystemC and ORINOCO for the first estimation of power consumption in an early step of the design flow. Thereby algorithms can be compared in different operating modes including the effects of control units. Here an algorithm having higher peak complexity and power consumption but providing more flexibility showed less consumption for normal operating modes compared to the algorithm which is optimized for peak performance.

Weighted least squares techniques for improved received signal strength based localization.

PubMed

Tarrío, Paula; Bernardos, Ana M; Casar, José R

2011-01-01

The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location. In this paper, we propose the use of weighted multilateration techniques to gain robustness with respect to these inaccuracies, reducing the dependency of having an optimal channel model. In particular, we propose two weighted least squares techniques based on the standard hyperbolic and circular positioning algorithms that specifically consider the accuracies of the different measurements to obtain a better estimation of the position. These techniques are compared to the standard hyperbolic and circular positioning techniques through both numerical simulations and an exhaustive set of real experiments on different types of wireless networks (a wireless sensor network, a WiFi network and a Bluetooth network). The algorithms not only produce better localization results with a very limited overhead in terms of computational cost but also achieve a greater robustness to inaccuracies in channel modeling.

Weighted Least Squares Techniques for Improved Received Signal Strength Based Localization

PubMed Central

Tarrío, Paula; Bernardos, Ana M.; Casar, José R.

2011-01-01

The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location. In this paper, we propose the use of weighted multilateration techniques to gain robustness with respect to these inaccuracies, reducing the dependency of having an optimal channel model. In particular, we propose two weighted least squares techniques based on the standard hyperbolic and circular positioning algorithms that specifically consider the accuracies of the different measurements to obtain a better estimation of the position. These techniques are compared to the standard hyperbolic and circular positioning techniques through both numerical simulations and an exhaustive set of real experiments on different types of wireless networks (a wireless sensor network, a WiFi network and a Bluetooth network). The algorithms not only produce better localization results with a very limited overhead in terms of computational cost but also achieve a greater robustness to inaccuracies in channel modeling. PMID:22164092

Expeditious reconciliation for practical quantum key distribution

NASA Astrophysics Data System (ADS)

Nakassis, Anastase; Bienfang, Joshua C.; Williams, Carl J.

2004-08-01

The paper proposes algorithmic and environmental modifications to the extant reconciliation algorithms within the BB84 protocol so as to speed up reconciliation and privacy amplification. These algorithms have been known to be a performance bottleneck 1 and can process data at rates that are six times slower than the quantum channel they serve2. As improvements in single-photon sources and detectors are expected to improve the quantum channel throughput by two or three orders of magnitude, it becomes imperative to improve the performance of the classical software. We developed a Cascade-like algorithm that relies on a symmetric formulation of the problem, error estimation through the segmentation process, outright elimination of segments with many errors, Forward Error Correction, recognition of the distinct data subpopulations that emerge as the algorithm runs, ability to operate on massive amounts of data (of the order of 1 Mbit), and a few other minor improvements. The data from the experimental algorithm we developed show that by operating on massive arrays of data we can improve software performance by better than three orders of magnitude while retaining nearly as many bits (typically more than 90%) as the algorithms that were designed for optimal bit retention.

Evolutionary Algorithm Based Feature Optimization for Multi-Channel EEG Classification.

PubMed

Wang, Yubo; Veluvolu, Kalyana C

2017-01-01

The most BCI systems that rely on EEG signals employ Fourier based methods for time-frequency decomposition for feature extraction. The band-limited multiple Fourier linear combiner is well-suited for such band-limited signals due to its real-time applicability. Despite the improved performance of these techniques in two channel settings, its application in multiple-channel EEG is not straightforward and challenging. As more channels are available, a spatial filter will be required to eliminate the noise and preserve the required useful information. Moreover, multiple-channel EEG also adds the high dimensionality to the frequency feature space. Feature selection will be required to stabilize the performance of the classifier. In this paper, we develop a new method based on Evolutionary Algorithm (EA) to solve these two problems simultaneously. The real-valued EA encodes both the spatial filter estimates and the feature selection into its solution and optimizes it with respect to the classification error. Three Fourier based designs are tested in this paper. Our results show that the combination of Fourier based method with covariance matrix adaptation evolution strategy (CMA-ES) has the best overall performance.

Nonlinear Blind Compensation for Array Signal Processing Application

PubMed Central

Ma, Hong; Jin, Jiang; Zhang, Hua

2018-01-01

Recently, nonlinear blind compensation technique has attracted growing attention in array signal processing application. However, due to the nonlinear distortion stemming from array receiver which consists of multi-channel radio frequency (RF) front-ends, it is too difficult to estimate the parameters of array signal accurately. A novel nonlinear blind compensation algorithm aims at the nonlinearity mitigation of array receiver and its spurious-free dynamic range (SFDR) improvement, which will be more precise to estimate the parameters of target signals such as their two-dimensional directions of arrival (2-D DOAs). Herein, the suggested method is designed as follows: the nonlinear model parameters of any channel of RF front-end are extracted to synchronously compensate the nonlinear distortion of the entire receiver. Furthermore, a verification experiment on the array signal from a uniform circular array (UCA) is adopted to testify the validity of our approach. The real-world experimental results show that the SFDR of the receiver is enhanced, leading to a significant improvement of the 2-D DOAs estimation performance for weak target signals. And these results demonstrate that our nonlinear blind compensation algorithm is effective to estimate the parameters of weak array signal in concomitance with strong jammers. PMID:29690571

Receiver IQ mismatch estimation in PDM CO-OFDM system using training symbol

NASA Astrophysics Data System (ADS)

Peng, Dandan; Ma, Xiurong; Yao, Xin; Zhang, Haoyuan

2017-07-01

Receiver in-phase/quadrature (IQ) mismatch is hard to mitigate at the receiver via using conventional method in polarization division multiplexed (PDM) coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. In this paper, a novel training symbol structure is proposed to estimate IQ mismatch and channel distortion. Combined this structure with Gram Schmidt orthogonalization procedure (GSOP) algorithm, we can get lower bit error rate (BER). Meanwhile, based on this structure one estimation method is deduced in frequency domain which can achieve the estimation of IQ mismatch and channel distortion independently and improve the system performance obviously. Numerical simulation shows that the proposed two methods have better performance than compared method at 100 Gb/s after 480 km fiber transmission. Besides, the calculation complexity is also analyzed.

Efficient Processing of Acoustic Signals for High Rate Information Transmission over Sparse Underwater Channels

DTIC Science & Technology

2016-09-02

the fractionally-spaced channel estimators and the short feedforward equalizer filters . Receiver algorithm is applied to real data transmitted at 10...multichannel decision-feedback equalizer (DFE)[1]. This receiver consists of a bank of adaptive feedforwad filters , one per array element, followed by a...decision-feedback filter . It has been implemented in the prototype high-rate acoustic modem developed at the Woods Hole Oceanographic Institution, and

[The progress in retrieving land surface temperature based on thermal infrared and microwave remote sensing technologies].

PubMed

Zhang, Jia-Hua; Li, Xin; Yao, Feng-Mei; Li, Xian-Hua

2009-08-01

Land surface temperature (LST) is an important parameter in the study on the exchange of substance and energy between land surface and air for the land surface physics process at regional and global scales. Many applications of satellites remotely sensed data must provide exact and quantificational LST, such as drought, high temperature, forest fire, earthquake, hydrology and the vegetation monitor, and the models of global circulation and regional climate also need LST as input parameter. Therefore, the retrieval of LST using remote sensing technology becomes one of the key tasks in quantificational remote sensing study. Normally, in the spectrum bands, the thermal infrared (TIR, 3-15 microm) and microwave bands (1 mm-1 m) are important for retrieval of the LST. In the present paper, firstly, several methods for estimating the LST on the basis of thermal infrared (TIR) remote sensing were synthetically reviewed, i. e., the LST measured with an ground-base infrared thermometer, the LST retrieval from mono-window algorithm (MWA), single-channel algorithm (SCA), split-window techniques (SWT) and multi-channels algorithm(MCA), single-channel & multi-angle algorithm and multi-channels algorithm & multi-angle algorithm, and retrieval method of land surface component temperature using thermal infrared remotely sensed satellite observation. Secondly, the study status of land surface emissivity (epsilon) was presented. Thirdly, in order to retrieve LST for all weather conditions, microwave remotely sensed data, instead of thermal infrared data, have been developed recently, and the LST retrieval method from passive microwave remotely sensed data was also introduced. Finally, the main merits and shortcomings of different kinds of LST retrieval methods were discussed, respectively.

Blind equalization with criterion with memory nonlinearity

NASA Astrophysics Data System (ADS)

Chen, Yuanjie; Nikias, Chrysostomos L.; Proakis, John G.

1992-06-01

Blind equalization methods usually combat the linear distortion caused by a nonideal channel via a transversal filter, without resorting to the a priori known training sequences. We introduce a new criterion with memory nonlinearity (CRIMNO) for the blind equalization problem. The basic idea of this criterion is to augment the Godard [or constant modulus algorithm (CMA)] cost function with additional terms that penalize the autocorrelations of the equalizer outputs. Several variations of the CRIMNO algorithms are derived, with the variations dependent on (1) whether the empirical averages or the single point estimates are used to approximate the expectations, (2) whether the recent or the delayed equalizer coefficients are used, and (3) whether the weights applied to the autocorrelation terms are fixed or are allowed to adapt. Simulation experiments show that the CRIMNO algorithm, and especially its adaptive weight version, exhibits faster convergence speed than the Godard (or CMA) algorithm. Extensions of the CRIMNO criterion to accommodate the case of correlated inputs to the channel are also presented.

Adaptive Wiener filter super-resolution of color filter array images.

PubMed

Karch, Barry K; Hardie, Russell C

2013-08-12

Digital color cameras using a single detector array with a Bayer color filter array (CFA) require interpolation or demosaicing to estimate missing color information and provide full-color images. However, demosaicing does not specifically address fundamental undersampling and aliasing inherent in typical camera designs. Fast non-uniform interpolation based super-resolution (SR) is an attractive approach to reduce or eliminate aliasing and its relatively low computational load is amenable to real-time applications. The adaptive Wiener filter (AWF) SR algorithm was initially developed for grayscale imaging and has not previously been applied to color SR demosaicing. Here, we develop a novel fast SR method for CFA cameras that is based on the AWF SR algorithm and uses global channel-to-channel statistical models. We apply this new method as a stand-alone algorithm and also as an initialization image for a variational SR algorithm. This paper presents the theoretical development of the color AWF SR approach and applies it in performance comparisons to other SR techniques for both simulated and real data.

Design of a practical model-observer-based image quality assessment method for CT imaging systems

NASA Astrophysics Data System (ADS)

Tseng, Hsin-Wu; Fan, Jiahua; Cao, Guangzhi; Kupinski, Matthew A.; Sainath, Paavana

2014-03-01

The channelized Hotelling observer (CHO) is a powerful method for quantitative image quality evaluations of CT systems and their image reconstruction algorithms. It has recently been used to validate the dose reduction capability of iterative image-reconstruction algorithms implemented on CT imaging systems. The use of the CHO for routine and frequent system evaluations is desirable both for quality assurance evaluations as well as further system optimizations. The use of channels substantially reduces the amount of data required to achieve accurate estimates of observer performance. However, the number of scans required is still large even with the use of channels. This work explores different data reduction schemes and designs a new approach that requires only a few CT scans of a phantom. For this work, the leave-one-out likelihood (LOOL) method developed by Hoffbeck and Landgrebe is studied as an efficient method of estimating the covariance matrices needed to compute CHO performance. Three different kinds of approaches are included in the study: a conventional CHO estimation technique with a large sample size, a conventional technique with fewer samples, and the new LOOL-based approach with fewer samples. The mean value and standard deviation of area under ROC curve (AUC) is estimated by shuffle method. Both simulation and real data results indicate that an 80% data reduction can be achieved without loss of accuracy. This data reduction makes the proposed approach a practical tool for routine CT system assessment.

F-8C adaptive control law refinement and software development

NASA Technical Reports Server (NTRS)

Hartmann, G. L.; Stein, G.

1981-01-01

An explicit adaptive control algorithm based on maximum likelihood estimation of parameters was designed. To avoid iterative calculations, the algorithm uses parallel channels of Kalman filters operating at fixed locations in parameter space. This algorithm was implemented in NASA/DFRC's Remotely Augmented Vehicle (RAV) facility. Real-time sensor outputs (rate gyro, accelerometer, surface position) are telemetered to a ground computer which sends new gain values to an on-board system. Ground test data and flight records were used to establish design values of noise statistics and to verify the ground-based adaptive software.

Cloud screening Coastal Zone Color Scanner images using channel 5

NASA Technical Reports Server (NTRS)

Eckstein, B. A.; Simpson, J. J.

1991-01-01

Clouds are removed from Coastal Zone Color Scanner (CZCS) data using channel 5. Instrumentation problems require pre-processing of channel 5 before an intelligent cloud-screening algorithm can be used. For example, at intervals of about 16 lines, the sensor records anomalously low radiances. Moreover, the calibration equation yields negative radiances when the sensor records zero counts, and pixels corrupted by electronic overshoot must also be excluded. The remaining pixels may then be used in conjunction with the procedure of Simpson and Humphrey to determine the CZCS cloud mask. These results plus in situ observations of phytoplankton pigment concentration show that pre-processing and proper cloud-screening of CZCS data are necessary for accurate satellite-derived pigment concentrations. This is especially true in the coastal margins, where pigment content is high and image distortion associated with electronic overshoot is also present. The pre-processing algorithm is critical to obtaining accurate global estimates of pigment from spacecraft data.

Time-domain digital pre-equalization for band-limited signals based on receiver-side adaptive equalizers.

PubMed

Zhang, Junwen; Yu, Jianjun; Chi, Nan; Chien, Hung-Chang

2014-08-25

We theoretically and experimentally investigate a time-domain digital pre-equalization (DPEQ) scheme for bandwidth-limited optical coherent communication systems, which is based on feedback of channel characteristics from the receiver-side blind and adaptive equalizers, such as least-mean-squares (LMS) algorithm and constant or multi- modulus algorithms (CMA, MMA). Based on the proposed DPEQ scheme, we theoretically and experimentally study its performance in terms of various channel conditions as well as resolutions for channel estimation, such as filtering bandwidth, taps length, and OSNR. Using a high speed 64-GSa/s DAC in cooperation with the proposed DPEQ technique, we successfully synthesized band-limited 40-Gbaud signals in modulation formats of polarization-diversion multiplexed (PDM) quadrature phase shift keying (QPSK), 8-quadrature amplitude modulation (QAM) and 16-QAM, and significant improvement in both back-to-back and transmission BER performances are also demonstrated.

Comparisons of Monthly Oceanic Rainfall Derived from TMI and SSM/I

NASA Technical Reports Server (NTRS)

Chang, A. T. C.; Chiu, L. S.; Meng, J.; Wilheit, T. T.; Kummerow, C. D.

1999-01-01

A technique for estimating monthly oceanic rainfall rate using multi-channel microwave measurements has been developed. There are three prominent features of this algorithm. First, the knowledge of the form of the rainfall intensity probability density function used to augment the measurements. Second, utilizing a linear combination of the 19.35 and 22.235 GHz channels to de-emphasize the effect of water vapor. Third, an objective technique has been developed to estimate the rain layer thickness from the 19.35 and 22.235 GHz brightness temperature histograms. This technique is applied to the SSM/I data since 1987 to infer monthly rainfall for the Global Precipitation Climatology Project (GPCP). A modified version of this algorithm is now being applied to the TRMM Microwave Imager (TMI) data. TMI data with better spatial resolution and 24 hour sampling (vs. sun-synchronized sampling, which is limited to two narrow intervals of local solar time for DMSP satellites) prompt us to study the similarity and difference between these two rainfall estimates. Six months of rainfall data (January to June 1998) are used in this study. Means and standard deviations are calculated. Paired student t-tests are administrated to evaluate the differences between rainfall estimates from SSM/I and TMI data. Their differences are discussed in the context of global satellite rainfall estimation.

Setting the scene for SWOT: global maps of river reach hydrodynamic variables

NASA Astrophysics Data System (ADS)

Schumann, Guy J.-P.; Durand, Michael; Pavelsky, Tamlin; Lion, Christine; Allen, George

2017-04-01

Credible and reliable characterization of discharge from the Surface Water and Ocean Topography (SWOT) mission using the Manning-based algorithms needs a prior estimate constraining reach-scale channel roughness, base flow and river bathymetry. For some places, any one of those variables may exist locally or even regionally as a measurement, which is often only at a station, or sometimes as a basin-wide model estimate. However, to date none of those exist at the scale required for SWOT and thus need to be mapped at a continental scale. The prior estimates will be employed for producing initial discharge estimates, which will be used as starting-guesses for the various Manning-based algorithms, to be refined using the SWOT measurements themselves. A multitude of reach-scale variables were derived, including Landsat-based width, SRTM slope and accumulation area. As a possible starting point for building the prior database of low flow, river bathymetry and channel roughness estimates, we employed a variety of sources, including data from all GRDC records, simulations from the long-time runs of the global water balance model (WBM), and reach-based calculations from hydraulic geometry relationships as well as Manning's equation. Here, we present the first global maps of this prior database with some initial validation, caveats and prospective uses.

Estimation of color filter array data from JPEG images for improved demosaicking

NASA Astrophysics Data System (ADS)

Feng, Wei; Reeves, Stanley J.

2006-02-01

On-camera demosaicking algorithms are necessarily simple and therefore do not yield the best possible images. However, off-camera demosaicking algorithms face the additional challenge that the data has been compressed and therefore corrupted by quantization noise. We propose a method to estimate the original color filter array (CFA) data from JPEG-compressed images so that more sophisticated (and better) demosaicking schemes can be applied to get higher-quality images. The JPEG image formation process, including simple demosaicking, color space transformation, chrominance channel decimation and DCT, is modeled as a series of matrix operations followed by quantization on the CFA data, which is estimated by least squares. An iterative method is used to conserve memory and speed computation. Our experiments show that the mean square error (MSE) with respect to the original CFA data is reduced significantly using our algorithm, compared to that of unprocessed JPEG and deblocked JPEG data.

Post-processing procedure for industrial quantum key distribution systems

NASA Astrophysics Data System (ADS)

Kiktenko, Evgeny; Trushechkin, Anton; Kurochkin, Yury; Fedorov, Aleksey

2016-08-01

We present algorithmic solutions aimed on post-processing procedure for industrial quantum key distribution systems with hardware sifting. The main steps of the procedure are error correction, parameter estimation, and privacy amplification. Authentication of classical public communication channel is also considered.

Retrieval of tropospheric aerosol properties over land from visible and near-infrared spectral reflectance: Application over Maryland

NASA Astrophysics Data System (ADS)

Levy, Robert Carroll

Aerosols are major components of the Earth's global climate system, affecting the radiation budget and cloud processes of the atmosphere. When located near the surface, high concentrations lead to lowered visibility, increased health problems and generally reduced quality of life for the human population. Over the United States mid-Atlantic region, aerosol pollution is a problem mainly during the summer. Satellites, such as the MODerate Imaging Spectrometer (MODIS), from their vantage point above the atmosphere, provide unprecedented coverage of global and regional aerosols over land. During MODIS' eight-year operation, exhaustive data validation and analyses have shown how the algorithm should be improved. This dissertation describes the development of the 'second-generation' operational algorithm for retrieval of global tropospheric aerosol properties over dark land surfaces, from MODIS-observed spectral reflectance. New understanding about global aerosol properties, land surface reflectance characteristics, and radiative transfer properties were learned in the process. This new operational algorithm performs a simultaneous inversion of reflectance in two visible channels (0.47 and 0.66 mum) and one shortwave infrared channel (2.12 mum), thereby having increased sensitivity to coarse aerosol. Inversion of the three channels retrieves the aerosol optical depth (tau) at 0.55 mum, the percentage of non-dust (fine model) aerosol (eta) and the surface reflectance. This algorithm is applied globally, and retrieves tau that is highly correlated (y = 0.02 + 1.0x, R=0.9) with ground-based sunphotometer measurements. The new algorithm estimates the global, over-land, long-term averaged tau ˜ 0.21, a 25% reduction from previous MODIS estimates. This leads to reducing estimates of global, non-desert, over-land aerosol direct radiative effect (all aerosols) by 1.7 W·m-2 (0.5 W·m-2 over the entire globe), which significantly impacts assessment of aerosol direct radiative forcing (contribution from anthropogenic aerosols only). Over the U.S. mid-Atlantic region, validated retrievals of tau (an integrated column property) can help to estimate surface PM2.5 concentration, a monitored criteria air quality property. The 3-dimensional aerosol loading in the region is characterized using aircraft measurements and the Community Multi-scale Air Quality Model (CMAQ) model, leading to some convergence of observed quantities and modeled processes.

An algorithm for estimating aerosol optical depth from HIMAWARI-8 data over Ocean

NASA Astrophysics Data System (ADS)

Lee, Kwon Ho

2016-04-01

The paper presents currently developing algorithm for aerosol detection and retrieval over ocean for the next generation geostationary satellite, HIMAWARI-8. Enhanced geostationary remote sensing observations are now enables for aerosol retrieval of dust, smoke, and ash, which began a new era of geostationary aerosol observations. Sixteen channels of the Advanced HIMAWARI Imager (AHI) onboard HIMAWARI-8 offer capabilities for aerosol remote sensing similar to those currently provided by the Moderate Resolution Imaging Spectroradiometer (MODIS). Aerosols were estimated in detection processing from visible and infrared channel radiances, and in retrieval processing using the inversion-optimization of satellite-observed radiances with those calculated from radiative transfer model. The retrievals are performed operationally every ten minutes for pixel sizes of ~8 km. The algorithm currently under development uses a multichannel approach to estimate the effective radius, aerosol optical depth (AOD) simultaneously. The instantaneous retrieved AOD is evaluated by the MODIS level 2 operational aerosol products (C006), and the daily retrieved AOD was compared with ground-based measurements from the AERONET databases. The results show that the detection of aerosol and estimated AOD are in good agreement with the MODIS data and ground measurements with a correlation coefficient of ˜0.90 and a bias of 4%. These results suggest that the proposed method applied to the HIMAWARI-8 satellite data can accurately estimate continuous AOD. Acknowledgments This work was supported by "Development of Geostationary Meteorological Satellite Ground Segment(NMSC-2014-01)" program funded by National Meteorological Satellite Centre(NMSC) of Korea Meteorological Administration(KMA).

Transmission over UWB channels with OFDM system using LDPC coding

NASA Astrophysics Data System (ADS)

Dziwoki, Grzegorz; Kucharczyk, Marcin; Sulek, Wojciech

2009-06-01

Hostile wireless environment requires use of sophisticated signal processing methods. The paper concerns on Ultra Wideband (UWB) transmission over Personal Area Networks (PAN) including MB-OFDM specification of physical layer. In presented work the transmission system with OFDM modulation was connected with LDPC encoder/decoder. Additionally the frame and bit error rate (FER and BER) of the system was decreased using results from the LDPC decoder in a kind of turbo equalization algorithm for better channel estimation. Computational block using evolutionary strategy, from genetic algorithms family, was also used in presented system. It was placed after SPA (Sum-Product Algorithm) decoder and is conditionally turned on in the decoding process. The result is increased effectiveness of the whole system, especially lower FER. The system was tested with two types of LDPC codes, depending on type of parity check matrices: randomly generated and constructed deterministically, optimized for practical decoder architecture implemented in the FPGA device.

FPGA implementation of image dehazing algorithm for real time applications

NASA Astrophysics Data System (ADS)

Kumar, Rahul; Kaushik, Brajesh Kumar; Balasubramanian, R.

2017-09-01

Weather degradation such as haze, fog, mist, etc. severely reduces the effective range of visual surveillance. This degradation is a spatially varying phenomena, which makes this problem non trivial. Dehazing is an essential preprocessing stage in applications such as long range imaging, border security, intelligent transportation system, etc. However, these applications require low latency of the preprocessing block. In this work, single image dark channel prior algorithm is modified and implemented for fast processing with comparable visual quality of the restored image/video. Although conventional single image dark channel prior algorithm is computationally expensive, it yields impressive results. Moreover, a two stage image dehazing architecture is introduced, wherein, dark channel and airlight are estimated in the first stage. Whereas, transmission map and intensity restoration are computed in the next stages. The algorithm is implemented using Xilinx Vivado software and validated by using Xilinx zc702 development board, which contains an Artix7 equivalent Field Programmable Gate Array (FPGA) and ARM Cortex A9 dual core processor. Additionally, high definition multimedia interface (HDMI) has been incorporated for video feed and display purposes. The results show that the dehazing algorithm attains 29 frames per second for the image resolution of 1920x1080 which is suitable of real time applications. The design utilizes 9 18K_BRAM, 97 DSP_48, 6508 FFs and 8159 LUTs.

Error control techniques for satellite and space communications

NASA Technical Reports Server (NTRS)

Costello, D. J., Jr.

1986-01-01

High rate concatenated coding systems with trellis inner codes and Reed-Solomon (RS) outer codes for application in satellite communication systems are considered. Two types of inner codes are studied: high rate punctured binary convolutional codes which result in overall effective information rates between 1/2 and 1 bit per channel use; and bandwidth efficient signal space trellis codes which can achieve overall effective information rates greater than 1 bit per channel use. Channel capacity calculations with and without side information performed for the concatenated coding system. Concatenated coding schemes are investigated. In Scheme 1, the inner code is decoded with the Viterbi algorithm and the outer RS code performs error-correction only (decoding without side information). In scheme 2, the inner code is decoded with a modified Viterbi algorithm which produces reliability information along with the decoded output. In this algorithm, path metrics are used to estimate the entire information sequence, while branch metrics are used to provide the reliability information on the decoded sequence. This information is used to erase unreliable bits in the decoded output. An errors-and-erasures RS decoder is then used for the outer code. These two schemes are proposed for use on NASA satellite channels. Results indicate that high system reliability can be achieved with little or no bandwidth expansion.

Rainfall Estimation over the Nile Basin using Multi-Spectral, Multi- Instrument Satellite Techniques

NASA Astrophysics Data System (ADS)

Habib, E.; Kuligowski, R.; Sazib, N.; Elshamy, M.; Amin, D.; Ahmed, M.

2012-04-01

Management of Egypt's Aswan High Dam is critical not only for flood control on the Nile but also for ensuring adequate water supplies for most of Egypt since rainfall is scarce over the vast majority of its land area. However, reservoir inflow is driven by rainfall over Sudan, Ethiopia, Uganda, and several other countries from which routine rain gauge data are sparse. Satellite- derived estimates of rainfall offer a much more detailed and timely set of data to form a basis for decisions on the operation of the dam. A single-channel infrared (IR) algorithm is currently in operational use at the Egyptian Nile Forecast Center (NFC). In this study, the authors report on the adaptation of a multi-spectral, multi-instrument satellite rainfall estimation algorithm (Self- Calibrating Multivariate Precipitation Retrieval, SCaMPR) for operational application by NFC over the Nile Basin. The algorithm uses a set of rainfall predictors that come from multi-spectral Infrared cloud top observations and self-calibrate them to a set of predictands that come from the more accurate, but less frequent, Microwave (MW) rain rate estimates. For application over the Nile Basin, the SCaMPR algorithm uses multiple satellite IR channels that have become recently available to NFC from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Microwave rain rates are acquired from multiple sources such as the Special Sensor Microwave/Imager (SSM/I), the Special Sensor Microwave Imager and Sounder (SSMIS), the Advanced Microwave Sounding Unit (AMSU), the Advanced Microwave Scanning Radiometer on EOS (AMSR-E), and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The algorithm has two main steps: rain/no-rain separation using discriminant analysis, and rain rate estimation using stepwise linear regression. We test two modes of algorithm calibration: real- time calibration with continuous updates of coefficients with newly coming MW rain rates, and calibration using static coefficients that are derived from IR-MW data from past observations. We also compare the SCaMPR algorithm to other global-scale satellite rainfall algorithms (e.g., 'Tropical Rainfall Measuring Mission (TRMM) and other sources' (TRMM-3B42) product, and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) CMORPH product. The algorithm has several potential future applications such as: improving the performance accuracy of hydrologic forecasting models over the Nile Basin, and utilizing the enhanced rainfall datasets and better-calibrated hydrologic models to assess the impacts of climate change on the region's water availability using global circulation models and regional climate models.

Advanced Physiological Estimation of Cognitive Status. Part 2

DTIC Science & Technology

2011-05-24

Neurofeedback Algorithms and Gaze Controller EEG Sensor System g.USBamp *, ** • internal 24-bit ADC and digital signal processor • 16 channels (expandable...SUBJECT TERMS EEG eye-tracking mental state estimation machine learning Leonard J. Trejo Pacific Development and Technology LLC 999 Commercial St. Palo...fatigue, overload) Technology Transfer Opportunity Technology from PDT – Methods to acquire various physiological signals ( EEG , EOG, EMG, ECG, etc

A Decentralized Eigenvalue Computation Method for Spectrum Sensing Based on Average Consensus

NASA Astrophysics Data System (ADS)

Mohammadi, Jafar; Limmer, Steffen; Stańczak, Sławomir

2016-07-01

This paper considers eigenvalue estimation for the decentralized inference problem for spectrum sensing. We propose a decentralized eigenvalue computation algorithm based on the power method, which is referred to as generalized power method GPM; it is capable of estimating the eigenvalues of a given covariance matrix under certain conditions. Furthermore, we have developed a decentralized implementation of GPM by splitting the iterative operations into local and global computation tasks. The global tasks require data exchange to be performed among the nodes. For this task, we apply an average consensus algorithm to efficiently perform the global computations. As a special case, we consider a structured graph that is a tree with clusters of nodes at its leaves. For an accelerated distributed implementation, we propose to use computation over multiple access channel (CoMAC) as a building block of the algorithm. Numerical simulations are provided to illustrate the performance of the two algorithms.

Cascade and parallel combination (CPC) of adaptive filters for estimating heart rate during intensive physical exercise from photoplethysmographic signal

PubMed Central

Islam, Mohammad Tariqul; Tanvir Ahmed, Sk.; Zabir, Ishmam; Shahnaz, Celia

2018-01-01

Photoplethysmographic (PPG) signal is getting popularity for monitoring heart rate in wearable devices because of simplicity of construction and low cost of the sensor. The task becomes very difficult due to the presence of various motion artefacts. In this study, an algorithm based on cascade and parallel combination (CPC) of adaptive filters is proposed in order to reduce the effect of motion artefacts. First, preliminary noise reduction is performed by averaging two channel PPG signals. Next in order to reduce the effect of motion artefacts, a cascaded filter structure consisting of three cascaded adaptive filter blocks is developed where three-channel accelerometer signals are used as references to motion artefacts. To further reduce the affect of noise, a scheme based on convex combination of two such cascaded adaptive noise cancelers is introduced, where two widely used adaptive filters namely recursive least squares and least mean squares filters are employed. Heart rates are estimated from the noise reduced PPG signal in spectral domain. Finally, an efficient heart rate tracking algorithm is designed based on the nature of the heart rate variability. The performance of the proposed CPC method is tested on a widely used public database. It is found that the proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach. PMID:29515812

Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems

NASA Astrophysics Data System (ADS)

Rao, Xiongbin; Lau, Vincent K. N.

2014-06-01

To fully utilize the spatial multiplexing gains or array gains of massive MIMO, the channel state information must be obtained at the transmitter side (CSIT). However, conventional CSIT estimation approaches are not suitable for FDD massive MIMO systems because of the overwhelming training and feedback overhead. In this paper, we consider multi-user massive MIMO systems and deploy the compressive sensing (CS) technique to reduce the training as well as the feedback overhead in the CSIT estimation. The multi-user massive MIMO systems exhibits a hidden joint sparsity structure in the user channel matrices due to the shared local scatterers in the physical propagation environment. As such, instead of naively applying the conventional CS to the CSIT estimation, we propose a distributed compressive CSIT estimation scheme so that the compressed measurements are observed at the users locally, while the CSIT recovery is performed at the base station jointly. A joint orthogonal matching pursuit recovery algorithm is proposed to perform the CSIT recovery, with the capability of exploiting the hidden joint sparsity in the user channel matrices. We analyze the obtained CSIT quality in terms of the normalized mean absolute error, and through the closed-form expressions, we obtain simple insights into how the joint channel sparsity can be exploited to improve the CSIT recovery performance.

On a Chirplet Transform Based Method for Co-channel Voice Separation

NASA Astrophysics Data System (ADS)

Dugnol, B.; Fernández, C.; Galiano, G.; Velasco, J.

We use signal and image theory based algorithms to produce estimations of the number of wolves emitting howls or barks in a given field recording as an individuals counting alternative to the traditional trace collecting methodologies. We proceed in two steps. Firstly, we clean and enhance the signal by using PDE based image processing algorithms applied to the signal spectrogram. Secondly, assuming that the wolves chorus may be modelled as an addition of nonlinear chirps, we use the quadratic energy distribution corresponding to the Chirplet Transform of the signal to produce estimates of the corresponding instantaneous frequencies, chirp-rates and amplitudes at each instant of the recording. We finally establish suitable criteria to decide how such estimates are connected in time.

Studies of the net surface radiative flux from satellite radiances during FIFE

NASA Technical Reports Server (NTRS)

Frouin, Robert

1993-01-01

Studies of the net surface radiative flux from satellite radiances during First ISLSCP Field Experiment (FIFE) are presented. Topics covered include: radiative transfer model validation; calibration of VISSR and AVHRR solar channels; development and refinement of algorithms to estimate downward solar and terrestrial irradiances at the surface, including photosynthetically available radiation (PAR) and surface albedo; verification of these algorithms using in situ measurements; production of maps of shortwave irradiance, surface albedo, and related products; analysis of the temporal variability of shortwave irradiance over the FIFE site; development of a spectroscopy technique to estimate atmospheric total water vapor amount; and study of optimum linear combinations of visible and near-infrared reflectances for estimating the fraction of PAR absorbed by plants.

Improved Temperature Sounding and Quality Control Methodology Using AIRS/AMSU Data: The AIRS Science Team Version 5 Retrieval Algorithm

NASA Technical Reports Server (NTRS)

Susskind, Joel; Blaisdell, John M.; Iredell, Lena; Keita, Fricky

2009-01-01

This paper describes the AIRS Science Team Version 5 retrieval algorithm in terms of its three most significant improvements over the methodology used in the AIRS Science Team Version 4 retrieval algorithm. Improved physics in Version 5 allows for use of AIRS clear column radiances in the entire 4.3 micron CO2 absorption band in the retrieval of temperature profiles T(p) during both day and night. Tropospheric sounding 15 micron CO2 observations are now used primarily in the generation of clear column radiances .R(sub i) for all channels. This new approach allows for the generation of more accurate values of .R(sub i) and T(p) under most cloud conditions. Secondly, Version 5 contains a new methodology to provide accurate case-by-case error estimates for retrieved geophysical parameters and for channel-by-channel clear column radiances. Thresholds of these error estimates are used in a new approach for Quality Control. Finally, Version 5 also contains for the first time an approach to provide AIRS soundings in partially cloudy conditions that does not require use of any microwave data. This new AIRS Only sounding methodology, referred to as AIRS Version 5 AO, was developed as a backup to AIRS Version 5 should the AMSU-A instrument fail. Results are shown comparing the relative performance of the AIRS Version 4, Version 5, and Version 5 AO for the single day, January 25, 2003. The Goddard DISC is now generating and distributing products derived using the AIRS Science Team Version 5 retrieval algorithm. This paper also described the Quality Control flags contained in the DISC AIRS/AMSU retrieval products and their intended use for scientific research purposes.

Comparison and optimization of in silico algorithms for predicting the pathogenicity of sodium channel variants in epilepsy.

PubMed

Holland, Katherine D; Bouley, Thomas M; Horn, Paul S

2017-07-01

Variants in neuronal voltage-gated sodium channel α-subunits genes SCN1A, SCN2A, and SCN8A are common in early onset epileptic encephalopathies and other autosomal dominant childhood epilepsy syndromes. However, in clinical practice, missense variants are often classified as variants of uncertain significance when missense variants are identified but heritability cannot be determined. Genetic testing reports often include results of computational tests to estimate pathogenicity and the frequency of that variant in population-based databases. The objective of this work was to enhance clinicians' understanding of results by (1) determining how effectively computational algorithms predict epileptogenicity of sodium channel (SCN) missense variants; (2) optimizing their predictive capabilities; and (3) determining if epilepsy-associated SCN variants are present in population-based databases. This will help clinicians better understand the results of indeterminate SCN test results in people with epilepsy. Pathogenic, likely pathogenic, and benign variants in SCNs were identified using databases of sodium channel variants. Benign variants were also identified from population-based databases. Eight algorithms commonly used to predict pathogenicity were compared. In addition, logistic regression was used to determine if a combination of algorithms could better predict pathogenicity. Based on American College of Medical Genetic Criteria, 440 variants were classified as pathogenic or likely pathogenic and 84 were classified as benign or likely benign. Twenty-eight variants previously associated with epilepsy were present in population-based gene databases. The output provided by most computational algorithms had a high sensitivity but low specificity with an accuracy of 0.52-0.77. Accuracy could be improved by adjusting the threshold for pathogenicity. Using this adjustment, the Mendelian Clinically Applicable Pathogenicity (M-CAP) algorithm had an accuracy of 0.90 and a combination of algorithms increased the accuracy to 0.92. Potentially pathogenic variants are present in population-based sources. Most computational algorithms overestimate pathogenicity; however, a weighted combination of several algorithms increased classification accuracy to >0.90. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

Application of a soft computing technique in predicting the percentage of shear force carried by walls in a rectangular channel with non-homogeneous roughness.

PubMed

Khozani, Zohreh Sheikh; Bonakdari, Hossein; Zaji, Amir Hossein

2016-01-01

Two new soft computing models, namely genetic programming (GP) and genetic artificial algorithm (GAA) neural network (a combination of modified genetic algorithm and artificial neural network methods) were developed in order to predict the percentage of shear force in a rectangular channel with non-homogeneous roughness. The ability of these methods to estimate the percentage of shear force was investigated. Moreover, the independent parameters' effectiveness in predicting the percentage of shear force was determined using sensitivity analysis. According to the results, the GP model demonstrated superior performance to the GAA model. A comparison was also made between the GP program determined as the best model and five equations obtained in prior research. The GP model with the lowest error values (root mean square error ((RMSE) of 0.0515) had the best function compared with the other equations presented for rough and smooth channels as well as smooth ducts. The equation proposed for rectangular channels with rough boundaries (RMSE of 0.0642) outperformed the prior equations for smooth boundaries.

Information geometry of Gaussian channels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Monras, Alex; CNR-INFM Coherentia, Napoli; CNISM Unita di Salerno

2010-06-15

We define a local Riemannian metric tensor in the manifold of Gaussian channels and the distance that it induces. We adopt an information-geometric approach and define a metric derived from the Bures-Fisher metric for quantum states. The resulting metric inherits several desirable properties from the Bures-Fisher metric and is operationally motivated by distinguishability considerations: It serves as an upper bound to the attainable quantum Fisher information for the channel parameters using Gaussian states, under generic constraints on the physically available resources. Our approach naturally includes the use of entangled Gaussian probe states. We prove that the metric enjoys some desirablemore » properties like stability and covariance. As a by-product, we also obtain some general results in Gaussian channel estimation that are the continuous-variable analogs of previously known results in finite dimensions. We prove that optimal probe states are always pure and bounded in the number of ancillary modes, even in the presence of constraints on the reduced state input in the channel. This has experimental and computational implications. It limits the complexity of optimal experimental setups for channel estimation and reduces the computational requirements for the evaluation of the metric: Indeed, we construct a converging algorithm for its computation. We provide explicit formulas for computing the multiparametric quantum Fisher information for dissipative channels probed with arbitrary Gaussian states and provide the optimal observables for the estimation of the channel parameters (e.g., bath couplings, squeezing, and temperature).« less

Data Assimilation Experiments Using Quality Controlled AIRS Version 5 Temperature Soundings

NASA Technical Reports Server (NTRS)

Susskind, Joel

2009-01-01

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains a number of significant improvements over Version 4. Two very significant improvements are described briefly below. 1) The AIRS Science Team Radiative Transfer Algorithm (RTA) has now been upgraded to accurately account for effects of non-local thermodynamic equilibrium on the AIRS observations. This allows for use of AIRS observations in the entire 4.3 micron CO2 absorption band in the retrieval algorithm during both day and night. Following theoretical considerations, tropospheric temperature profile information is obtained almost exclusively from clear column radiances in the 4.3 micron CO2 band in the AIRS Version 5 temperature profile retrieval step. These clear column radiances are a derived product that are indicative of radiances AIRS channels would have seen if the field of view were completely clear. Clear column radiances for all channels are determined using tropospheric sounding 15 micron CO2 observations. This approach allows for the generation of accurate values of clear column radiances and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by-channel clear column radiances. These error estimates are used for quality control of the retrieved products. Based on error estimate thresholds, each temperature profiles is assigned a characteristic pressure, pg, down to which the profile is characterized as good for use for data assimilation purposes. We have conducted forecast impact experiments assimilating AIRS quality controlled temperature profiles using the NASA GEOS-5 data assimilation system, consisting of the NCEP GSI analysis coupled with the NASA FVGCM, at a spatial resolution of 0.5 deg by 0.5 deg. Assimilation of Quality Controlled AIRS temperature profiles down to pg resulted in significantly improved forecast skill compared to that obtained from experiments when all data used operationally by NCEP, except for AIRS data, is assimilated. These forecasts were also significantly better than to those obtained when AIRS radiances (rather than temperature profiles) are assimilated, which is the way AIRS data is used operationally by NCEP and ECMWF.

Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) Data.

PubMed

Tang, Bohui; Bi, Yuyun; Li, Zhao-Liang; Xia, Jun

2008-02-14

On the basis of the radiative transfer theory, this paper addressed the estimate ofLand Surface Temperature (LST) from the Chinese first operational geostationarymeteorological satellite-FengYun-2C (FY-2C) data in two thermal infrared channels (IR1,10.3-11.3 μ m and IR2, 11.5-12.5 μ m ), using the Generalized Split-Window (GSW)algorithm proposed by Wan and Dozier (1996). The coefficients in the GSW algorithmcorresponding to a series of overlapping ranging of the mean emissivity, the atmosphericWater Vapor Content (WVC), and the LST were derived using a statistical regressionmethod from the numerical values simulated with an accurate atmospheric radiativetransfer model MODTRAN 4 over a wide range of atmospheric and surface conditions.The simulation analysis showed that the LST could be estimated by the GSW algorithmwith the Root Mean Square Error (RMSE) less than 1 K for the sub-ranges with theViewing Zenith Angle (VZA) less than 30° or for the sub-rangs with VZA less than 60°and the atmospheric WVC less than 3.5 g/cm² provided that the Land Surface Emissivities(LSEs) are known. In order to determine the range for the optimum coefficients of theGSW algorithm, the LSEs could be derived from the data in MODIS channels 31 and 32 provided by MODIS/Terra LST product MOD11B1, or be estimated either according tothe land surface classification or using the method proposed by Jiang et al. (2006); and theWVC could be obtained from MODIS total precipitable water product MOD05, or beretrieved using Li et al.' method (2003). The sensitivity and error analyses in term of theuncertainty of the LSE and WVC as well as the instrumental noise were performed. Inaddition, in order to compare the different formulations of the split-window algorithms,several recently proposed split-window algorithms were used to estimate the LST with thesame simulated FY-2C data. The result of the intercomparsion showed that most of thealgorithms give comparable results.

Filter bank common spatial patterns in mental workload estimation.

PubMed

Arvaneh, Mahnaz; Umilta, Alberto; Robertson, Ian H

2015-01-01

EEG-based workload estimation technology provides a real time means of assessing mental workload. Such technology can effectively enhance the performance of the human-machine interaction and the learning process. When designing workload estimation algorithms, a crucial signal processing component is the feature extraction step. Despite several studies on this field, the spatial properties of the EEG signals were mostly neglected. Since EEG inherently has a poor spacial resolution, features extracted individually from each EEG channel may not be sufficiently efficient. This problem becomes more pronounced when we use low-cost but convenient EEG sensors with limited stability which is the case in practical scenarios. To address this issue, in this paper, we introduce a filter bank common spatial patterns algorithm combined with a feature selection method to extract spatio-spectral features discriminating different mental workload levels. To evaluate the proposed algorithm, we carry out a comparative analysis between two representative types of working memory tasks using data recorded from an Emotiv EPOC headset which is a mobile low-cost EEG recording device. The experimental results showed that the proposed spatial filtering algorithm outperformed the state-of-the algorithms in terms of the classification accuracy.

Real time estimation of generation, extinction and flow of muscle fibre action potentials in high density surface EMG.

PubMed

Mesin, Luca

2015-02-01

Developing a real time method to estimate generation, extinction and propagation of muscle fibre action potentials from bi-dimensional and high density surface electromyogram (EMG). A multi-frame generalization of an optical flow technique including a source term is considered. A model describing generation, extinction and propagation of action potentials is fit to epochs of surface EMG. The algorithm is tested on simulations of high density surface EMG (inter-electrode distance equal to 5mm) from finite length fibres generated using a multi-layer volume conductor model. The flow and source term estimated from interference EMG reflect the anatomy of the muscle, i.e. the direction of the fibres (2° of average estimation error) and the positions of innervation zone and tendons under the electrode grid (mean errors of about 1 and 2mm, respectively). The global conduction velocity of the action potentials from motor units under the detection system is also obtained from the estimated flow. The processing time is about 1 ms per channel for an epoch of EMG of duration 150 ms. A new real time image processing algorithm is proposed to investigate muscle anatomy and activity. Potential applications are proposed in prosthesis control, automatic detection of optimal channels for EMG index extraction and biofeedback. Copyright © 2014 Elsevier Ltd. All rights reserved.

RAC-multi: reader anti-collision algorithm for multichannel mobile RFID networks.

PubMed

Shin, Kwangcheol; Song, Wonil

2010-01-01

At present, RFID is installed on mobile devices such as mobile phones or PDAs and provides a means to obtain information about objects equipped with an RFID tag over a multi-channeled telecommunication networks. To use mobile RFIDs, reader collision problems should be addressed given that readers are continuously moving. Moreover, in a multichannel environment for mobile RFIDs, interference between adjacent channels should be considered. This work first defines a new concept of a reader collision problem between adjacent channels and then suggests a novel reader anti-collision algorithm for RFID readers that use multiple channels. To avoid interference with adjacent channels, the suggested algorithm separates data channels into odd and even numbered channels and allocates odd-numbered channels first to readers. It also sets an unused channel between the control channel and data channels to ensure that control messages and the signal of the adjacent channel experience no interference. Experimental results show that suggested algorithm shows throughput improvements ranging from 29% to 46% for tag identifications compared to the GENTLE reader anti-collision algorithm for multichannel RFID networks.

RAC-Multi: Reader Anti-Collision Algorithm for Multichannel Mobile RFID Networks

PubMed Central

Shin, Kwangcheol; Song, Wonil

2010-01-01

At present, RFID is installed on mobile devices such as mobile phones or PDAs and provides a means to obtain information about objects equipped with an RFID tag over a multi-channeled telecommunication networks. To use mobile RFIDs, reader collision problems should be addressed given that readers are continuously moving. Moreover, in a multichannel environment for mobile RFIDs, interference between adjacent channels should be considered. This work first defines a new concept of a reader collision problem between adjacent channels and then suggests a novel reader anti-collision algorithm for RFID readers that use multiple channels. To avoid interference with adjacent channels, the suggested algorithm separates data channels into odd and even numbered channels and allocates odd-numbered channels first to readers. It also sets an unused channel between the control channel and data channels to ensure that control messages and the signal of the adjacent channel experience no interference. Experimental results show that suggested algorithm shows throughput improvements ranging from 29% to 46% for tag identifications compared to the GENTLE reader anti-collision algorithm for multichannel RFID networks. PMID:22315528

Flight data processing with the F-8 adaptive algorithm

NASA Technical Reports Server (NTRS)

Hartmann, G.; Stein, G.; Petersen, K.

1977-01-01

An explicit adaptive control algorithm based on maximum likelihood estimation of parameters has been designed for NASA's DFBW F-8 aircraft. To avoid iterative calculations, the algorithm uses parallel channels of Kalman filters operating at fixed locations in parameter space. This algorithm has been implemented in NASA/DFRC's Remotely Augmented Vehicle (RAV) facility. Real-time sensor outputs (rate gyro, accelerometer and surface position) are telemetered to a ground computer which sends new gain values to an on-board system. Ground test data and flight records were used to establish design values of noise statistics and to verify the ground-based adaptive software. The software and its performance evaluation based on flight data are described

Characterization of a symbol rate timing recovery technique for a 2B1Q digital receiver

NASA Astrophysics Data System (ADS)

Aboulnasr, T.; Hage, M.; Sayar, B.; Aly, S.

1994-02-01

This paper presents a study of several implementations of the Mueller and Muller symbol rate timing recovery algorithm for ISDN transmission over digital subscriber loops (DSL). Implementations of this algorithm using various estimates of a specified timing function are investigated. It will be shown that despite the fact that all of the estimates considered are derived based on one set of conditions, their performance varies widely in a real system. The intrinsic properties of these estimates are first analyzed, then their performance on real subscriber loops is studied through extensive simulations of a practical digital receiver. The effect of various system parameters such as channel distortion and additive noise are included. Possible sources of convergence problems are also identified and corrective action proposed.

Real-time spectrum estimation–based dual-channel speech-enhancement algorithm for cochlear implant

PubMed Central

2012-01-01

Background Improvement of the cochlear implant (CI) front-end signal acquisition is needed to increase speech recognition in noisy environments. To suppress the directional noise, we introduce a speech-enhancement algorithm based on microphone array beamforming and spectral estimation. The experimental results indicate that this method is robust to directional mobile noise and strongly enhances the desired speech, thereby improving the performance of CI devices in a noisy environment. Methods The spectrum estimation and the array beamforming methods were combined to suppress the ambient noise. The directivity coefficient was estimated in the noise-only intervals, and was updated to fit for the mobile noise. Results The proposed algorithm was realized in the CI speech strategy. For actual parameters, we use Maxflat filter to obtain fractional sampling points and cepstrum method to differentiate the desired speech frame and the noise frame. The broadband adjustment coefficients were added to compensate the energy loss in the low frequency band. Discussions The approximation of the directivity coefficient is tested and the errors are discussed. We also analyze the algorithm constraint for noise estimation and distortion in CI processing. The performance of the proposed algorithm is analyzed and further be compared with other prevalent methods. Conclusions The hardware platform was constructed for the experiments. The speech-enhancement results showed that our algorithm can suppresses the non-stationary noise with high SNR. Excellent performance of the proposed algorithm was obtained in the speech enhancement experiments and mobile testing. And signal distortion results indicate that this algorithm is robust with high SNR improvement and low speech distortion. PMID:23006896

Correlator data analysis for the array feed compensation system

NASA Technical Reports Server (NTRS)

Iijima, B.; Fort, D.; Vilnrotter, V.

1994-01-01

The real-time array feed compensation system is currently being evaluated at DSS 13. This system recovers signal-to-noise ratio (SNR) loss due to mechanical antenna deformations by using an array of seven Ka-band (33.7-GHz) horns to collect the defocused signal fields. The received signals are downconverted and digitized, in-phase and quadrature samples are generated, and combining weights are applied before the samples are recombined. It is shown that when optimum combining weights are employed, the SNR of the combined signal approaches the sum of the channel SNR's. The optimum combining weights are estimated directly from the signals in each channel by the Real-Time Block 2 (RTB2) correlator; since it was designed for very-long-baseline interferometer (VLBI) applications, it can process broadband signals as well as tones to extract the required weight estimates. The estimation algorithms for the optimum combining weights are described for tones and broadband sources. Data recorded in correlator output files can also be used off-line to estimate combiner performance by estimating the SNR in each channel, which was done for data taken during a Jupiter track at DSS 13.

Real-time single image dehazing based on dark channel prior theory and guided filtering

NASA Astrophysics Data System (ADS)

Zhang, Zan

2017-10-01

Images and videos taken outside the foggy day are serious degraded. In order to restore degraded image taken in foggy day and overcome traditional Dark Channel prior algorithms problems of remnant fog in edge, we propose a new dehazing method.We first find the fog area in the dark primary color map to obtain the estimated value of the transmittance using quadratic tree. Then we regard the gray-scale image after guided filtering as atmospheric light map and remove haze based on it. Box processing and image down sampling technology are also used to improve the processing speed. Finally, the atmospheric light scattering model is used to restore the image. A plenty of experiments show that algorithm is effective, efficient and has a wide range of application.

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.

PubMed

Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

2015-01-01

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs

PubMed Central

Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

2015-01-01

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network. PMID:26571042

ERAASR: an algorithm for removing electrical stimulation artifacts from multielectrode array recordings

NASA Astrophysics Data System (ADS)

O'Shea, Daniel J.; Shenoy, Krishna V.

2018-04-01

Objective. Electrical stimulation is a widely used and effective tool in systems neuroscience, neural prosthetics, and clinical neurostimulation. However, electrical artifacts evoked by stimulation prevent the detection of spiking activity on nearby recording electrodes, which obscures the neural population response evoked by stimulation. We sought to develop a method to clean artifact-corrupted electrode signals recorded on multielectrode arrays in order to recover the underlying neural spiking activity. Approach. We created an algorithm, which performs estimation and removal of array artifacts via sequential principal components regression (ERAASR). This approach leverages the similar structure of artifact transients, but not spiking activity, across simultaneously recorded channels on the array, across pulses within a train, and across trials. The ERAASR algorithm requires no special hardware, imposes no requirements on the shape of the artifact or the multielectrode array geometry, and comprises sequential application of straightforward linear methods with intuitive parameters. The approach should be readily applicable to most datasets where stimulation does not saturate the recording amplifier. Main results. The effectiveness of the algorithm is demonstrated in macaque dorsal premotor cortex using acute linear multielectrode array recordings and single electrode stimulation. Large electrical artifacts appeared on all channels during stimulation. After application of ERAASR, the cleaned signals were quiescent on channels with no spontaneous spiking activity, whereas spontaneously active channels exhibited evoked spikes which closely resembled spontaneously occurring spiking waveforms. Significance. We hope that enabling simultaneous electrical stimulation and multielectrode array recording will help elucidate the causal links between neural activity and cognition and facilitate naturalistic sensory protheses.

Improved Surface Parameter Retrievals using AIRS/AMSU Data

NASA Technical Reports Server (NTRS)

Susskind, Joel; Blaisdell, John

2008-01-01

The AIRS Science Team Version 5.0 retrieval algorithm became operational at the Goddard DAAC in July 2007 generating near real-time products from analysis of AIRS/AMSU sounding data. This algorithm contains many significant theoretical advances over the AIRS Science Team Version 4.0 retrieval algorithm used previously. Two very significant developments of Version 5 are: 1) the development and implementation of an improved Radiative Transfer Algorithm (RTA) which allows for accurate treatment of non-Local Thermodynamic Equilibrium (non-LTE) effects on shortwave sounding channels; and 2) the development of methodology to obtain very accurate case by case product error estimates which are in turn used for quality control. These theoretical improvements taken together enabled a new methodology to be developed which further improves soundings in partially cloudy conditions. In this methodology, longwave C02 channel observations in the spectral region 700 cm(exp -1) to 750 cm(exp -1) are used exclusively for cloud clearing purposes, while shortwave C02 channels in the spectral region 2195 cm(exp -1) 2395 cm(exp -1) are used for temperature sounding purposes. This allows for accurate temperature soundings under more difficult cloud conditions. This paper further improves on the methodology used in Version 5 to derive surface skin temperature and surface spectral emissivity from AIRS/AMSU observations. Now, following the approach used to improve tropospheric temperature profiles, surface skin temperature is also derived using only shortwave window channels. This produces improved surface parameters, both day and night, compared to what was obtained in Version 5. These in turn result in improved boundary layer temperatures and retrieved total O3 burden.

Accounting for one-channel depletion improves missing value imputation in 2-dye microarray data.

PubMed

Ritz, Cecilia; Edén, Patrik

2008-01-19

For 2-dye microarray platforms, some missing values may arise from an un-measurably low RNA expression in one channel only. Information of such "one-channel depletion" is so far not included in algorithms for imputation of missing values. Calculating the mean deviation between imputed values and duplicate controls in five datasets, we show that KNN-based imputation gives a systematic bias of the imputed expression values of one-channel depleted spots. Evaluating the correction of this bias by cross-validation showed that the mean square deviation between imputed values and duplicates were reduced up to 51%, depending on dataset. By including more information in the imputation step, we more accurately estimate missing expression values.

Deconvolution of continuous paleomagnetic data from pass-through magnetometer: A new algorithm to restore geomagnetic and environmental information based on realistic optimization

NASA Astrophysics Data System (ADS)

Oda, Hirokuni; Xuan, Chuang

2014-10-01

development of pass-through superconducting rock magnetometers (SRM) has greatly promoted collection of paleomagnetic data from continuous long-core samples. The output of pass-through measurement is smoothed and distorted due to convolution of magnetization with the magnetometer sensor response. Although several studies could restore high-resolution paleomagnetic signal through deconvolution of pass-through measurement, difficulties in accurately measuring the magnetometer sensor response have hindered the application of deconvolution. We acquired reliable sensor response of an SRM at the Oregon State University based on repeated measurements of a precisely fabricated magnetic point source. In addition, we present an improved deconvolution algorithm based on Akaike's Bayesian Information Criterion (ABIC) minimization, incorporating new parameters to account for errors in sample measurement position and length. The new algorithm was tested using synthetic data constructed by convolving "true" paleomagnetic signal containing an "excursion" with the sensor response. Realistic noise was added to the synthetic measurement using Monte Carlo method based on measurement noise distribution acquired from 200 repeated measurements of a u-channel sample. Deconvolution of 1000 synthetic measurements with realistic noise closely resembles the "true" magnetization, and successfully restored fine-scale magnetization variations including the "excursion." Our analyses show that inaccuracy in sample measurement position and length significantly affects deconvolution estimation, and can be resolved using the new deconvolution algorithm. Optimized deconvolution of 20 repeated measurements of a u-channel sample yielded highly consistent deconvolution results and estimates of error in sample measurement position and length, demonstrating the reliability of the new deconvolution algorithm for real pass-through measurements.

Sleep Quality Estimation based on Chaos Analysis for Heart Rate Variability

NASA Astrophysics Data System (ADS)

Fukuda, Toshio; Wakuda, Yuki; Hasegawa, Yasuhisa; Arai, Fumihito; Kawaguchi, Mitsuo; Noda, Akiko

In this paper, we propose an algorithm to estimate sleep quality based on a heart rate variability using chaos analysis. Polysomnography(PSG) is a conventional and reliable system to diagnose sleep disorder and to evaluate its severity and therapeatic effect, by estimating sleep quality based on multiple channels. However, a recording process requires a lot of time and a controlled environment for measurement and then an analyzing process of PSG data is hard work because the huge sensed data should be manually evaluated. On the other hand, it is focused that some people make a mistake or cause an accident due to lost of regular sleep and of homeostasis these days. Therefore a simple home system for checking own sleep is required and then the estimation algorithm for the system should be developed. Therefore we propose an algorithm to estimate sleep quality based only on a heart rate variability which can be measured by a simple sensor such as a pressure sensor and an infrared sensor in an uncontrolled environment, by experimentally finding the relationship between chaos indices and sleep quality. The system including the estimation algorithm can inform patterns and quality of own daily sleep to a user, and then the user can previously arranges his life schedule, pays more attention based on sleep results and consult with a doctor.

GOME Total Ozone and Calibration Error Derived Usign Version 8 TOMS Algorithm

NASA Technical Reports Server (NTRS)

Gleason, J.; Wellemeyer, C.; Qin, W.; Ahn, C.; Gopalan, A.; Bhartia, P.

2003-01-01

The Global Ozone Monitoring Experiment (GOME) is a hyper-spectral satellite instrument measuring the ultraviolet backscatter at relatively high spectral resolution. GOME radiances have been slit averaged to emulate measurements of the Total Ozone Mapping Spectrometer (TOMS) made at discrete wavelengths and processed using the new TOMS Version 8 Ozone Algorithm. Compared to Differential Optical Absorption Spectroscopy (DOAS) techniques based on local structure in the Huggins Bands, the TOMS uses differential absorption between a pair of wavelengths including the local stiucture as well as the background continuum. This makes the TOMS Algorithm more sensitive to ozone, but it also makes the algorithm more sensitive to instrument calibration errors. While calibration adjustments are not needed for the fitting techniques like the DOAS employed in GOME algorithms, some adjustment is necessary when applying the TOMS Algorithm to GOME. Using spectral discrimination at near ultraviolet wavelength channels unabsorbed by ozone, the GOME wavelength dependent calibration drift is estimated and then checked using pair justification. In addition, the day one calibration offset is estimated based on the residuals of the Version 8 TOMS Algorithm. The estimated drift in the 2b detector of GOME is small through the first four years and then increases rapidly to +5% in normalized radiance at 331 nm relative to 385 nm by mid 2000. The lb detector appears to be quite well behaved throughout this time period.

Evaluation of stochastic differential equation approximation of ion channel gating models.

PubMed

Bruce, Ian C

2009-04-01

Fox and Lu derived an algorithm based on stochastic differential equations for approximating the kinetics of ion channel gating that is simpler and faster than "exact" algorithms for simulating Markov process models of channel gating. However, the approximation may not be sufficiently accurate to predict statistics of action potential generation in some cases. The objective of this study was to develop a framework for analyzing the inaccuracies and determining their origin. Simulations of a patch of membrane with voltage-gated sodium and potassium channels were performed using an exact algorithm for the kinetics of channel gating and the approximate algorithm of Fox & Lu. The Fox & Lu algorithm assumes that channel gating particle dynamics have a stochastic term that is uncorrelated, zero-mean Gaussian noise, whereas the results of this study demonstrate that in many cases the stochastic term in the Fox & Lu algorithm should be correlated and non-Gaussian noise with a non-zero mean. The results indicate that: (i) the source of the inaccuracy is that the Fox & Lu algorithm does not adequately describe the combined behavior of the multiple activation particles in each sodium and potassium channel, and (ii) the accuracy does not improve with increasing numbers of channels.

Aerosol Correction for Remotely Sensed Sea Surface Temperatures From the NOAA AVHRR: Phase II

NASA Astrophysics Data System (ADS)

Nalli, N. R.; Ignatov, A.

2002-05-01

For over two decades, the National Oceanic and Atmospheric Administration (NOAA) has produced global retrievals of sea surface temperature (SST) using infrared (IR) data from the Advanced Very High Resolution Radiometer (AVHRR). The standard multichannel retrieval algorithms are derived from regression analyses of AVHRR window channel brightness temperatures against in situ buoy measurements under non-cloudy conditions thus providing a correction for IR attenuation due to molecular water vapor absorption. However, for atmospheric conditions with elevated aerosol levels (e.g., arising from dust, biomass burning and volcanic eruptions), such algorithms lead to significant negative biases in SST because of IR attenuation arising from aerosol absorption and scattering. This research presents the development of a 2nd-phase aerosol correction algorithm for daytime AVHRR SST. To accomplish this, a long-term (1990-1998), global AVHRR-buoy matchup database was created by merging the Pathfinder Atmospheres (PATMOS) and Oceans (PFMDB) data sets. The merged data are unique in that they include multi-year, global daytime estimates of aerosol optical depth (AOD) derived from AVHRR channels 1 and 2 (0.63 and 0.83 μ m, respectively), along with an effective Angstrom exponent derived from the AOD retrievals (Ignatov and Nalli, 2002). Recent enhancements in the aerosol data constitute an improvement over the Phase I algorithm (Nalli and Stowe, 2002) which relied only on channel 1 AOD and the ratio of normalized reflectance from channels 1 and 2. The Angstrom exponent and channel 2 AOD provide important statistical information about the particle size distribution of the aerosol. The SST bias can be parametrically expressed as a function of observed AVHRR channels 1 and 2 slant-path AOD, normalized reflectance ratio and the Angstrom exponent. Based upon these empirical relationships, aerosol correction equations are then derived for the daytime multichannel and nonlinear SST (MCSST and NLSST) algorithms. Separate sets of coefficients are utilized for two aerosol modes, these being stratospheric/tropospheric (e.g., volcanic aerosol) and tropospheric (e.g., dust, smoke). The algorithms are subsequently applied to retrospective PATMOS data to demonstrate the potential for climate applications. The minimization of cold biases in the AVHRR SST, as demonstrated in this work, should improve its overall utility for the general user community.

MIMO to LS-MIMO: A road to realization of 5G

NASA Astrophysics Data System (ADS)

Koppati, Naveena; Pavani, K.; Sharma, Dinesh; Sharma, Purnima K.

2017-07-01

MIMO means multiple inputs multiple outputs. As it refers MIMO is a RF technology used in many new technologies these days to increase link capacity and spectral efficiency. MIMO is used in Wi-Fi, LTE, 4G, 5G and other wireless technologies. This paper describes the earlier history of MIMO-OFDM and the antenna beam forming development in MIMO and types of MIMO. Also this treatise describes several decoding algorithms. The MIMO combined with OFDM increases the channel capacity. But the main problem is in estimating the transmitted signal from the received signal. So the channel knowledge is to be known in estimating the channel capacity. The advancement in MIMO-OFDM is Massive MIMO which is beneficial in providing additional data capacity in the increased traffic environment is described. In this memoir various application scenarios of LS-MIMO which increases the capacity are discussed.

Software algorithm and hardware design for real-time implementation of new spectral estimator

PubMed Central

2014-01-01

Background Real-time spectral analyzers can be difficult to implement for PC computer-based systems because of the potential for high computational cost, and algorithm complexity. In this work a new spectral estimator (NSE) is developed for real-time analysis, and compared with the discrete Fourier transform (DFT). Method Clinical data in the form of 216 fractionated atrial electrogram sequences were used as inputs. The sample rate for acquisition was 977 Hz, or approximately 1 millisecond between digital samples. Real-time NSE power spectra were generated for 16,384 consecutive data points. The same data sequences were used for spectral calculation using a radix-2 implementation of the DFT. The NSE algorithm was also developed for implementation as a real-time spectral analyzer electronic circuit board. Results The average interval for a single real-time spectral calculation in software was 3.29 μs for NSE versus 504.5 μs for DFT. Thus for real-time spectral analysis, the NSE algorithm is approximately 150× faster than the DFT. Over a 1 millisecond sampling period, the NSE algorithm had the capability to spectrally analyze a maximum of 303 data channels, while the DFT algorithm could only analyze a single channel. Moreover, for the 8 second sequences, the NSE spectral resolution in the 3-12 Hz range was 0.037 Hz while the DFT spectral resolution was only 0.122 Hz. The NSE was also found to be implementable as a standalone spectral analyzer board using approximately 26 integrated circuits at a cost of approximately $500. The software files used for analysis are included as a supplement, please see the Additional files 1 and 2. Conclusions The NSE real-time algorithm has low computational cost and complexity, and is implementable in both software and hardware for 1 millisecond updates of multichannel spectra. The algorithm may be helpful to guide radiofrequency catheter ablation in real time. PMID:24886214

An Algorithm For Climate-Quality Atmospheric Profiling Continuity From EOS Aqua To Suomi-NPP

NASA Astrophysics Data System (ADS)

Moncet, J. L.

2015-12-01

We will present results from an algorithm that is being developed to produce climate-quality atmospheric profiling earth system data records (ESDRs) for application to hyperspectral sounding instrument data from Suomi-NPP, EOS Aqua, and other spacecraft. The current focus is on data from the S-NPP Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) instruments as well as the Atmospheric InfraRed Sounder (AIRS) on EOS Aqua. The algorithm development at Atmospheric and Environmental Research (AER) has common heritage with the optimal estimation (OE) algorithm operationally processing S-NPP data in the Interface Data Processing Segment (IDPS), but the ESDR algorithm has a flexible, modular software structure to support experimentation and collaboration and has several features adapted to the climate orientation of ESDRs. Data record continuity benefits from the fact that the same algorithm can be applied to different sensors, simply by providing suitable configuration and data files. The radiative transfer component uses an enhanced version of optimal spectral sampling (OSS) with updated spectroscopy, treatment of emission that is not in local thermodynamic equilibrium (non-LTE), efficiency gains with "global" optimal sampling over all channels, and support for channel selection. The algorithm is designed for adaptive treatment of clouds, with capability to apply "cloud clearing" or simultaneous cloud parameter retrieval, depending on conditions. We will present retrieval results demonstrating the impact of a new capability to perform the retrievals on sigma or hybrid vertical grid (as opposed to a fixed pressure grid), which particularly affects profile accuracy over land with variable terrain height and with sharp vertical structure near the surface. In addition, we will show impacts of alternative treatments of regularization of the inversion. While OE algorithms typically implement regularization by using background estimates from climatological or numerical forecast data, those sources are problematic for climate applications due to the imprint of biases from past climate analyses or from model error.

Reliable video transmission over fading channels via channel state estimation

NASA Astrophysics Data System (ADS)

Kumwilaisak, Wuttipong; Kim, JongWon; Kuo, C.-C. Jay

2000-04-01

Transmission of continuous media such as video over time- varying wireless communication channels can benefit from the use of adaptation techniques in both source and channel coding. An adaptive feedback-based wireless video transmission scheme is investigated in this research with special emphasis on feedback-based adaptation. To be more specific, an interactive adaptive transmission scheme is developed by letting the receiver estimate the channel state information and send it back to the transmitter. By utilizing the feedback information, the transmitter is capable of adapting the level of protection by changing the flexible RCPC (rate-compatible punctured convolutional) code ratio depending on the instantaneous channel condition. The wireless channel is modeled as a fading channel, where the long-term and short- term fading effects are modeled as the log-normal fading and the Rayleigh flat fading, respectively. Then, its state (mainly the long term fading portion) is tracked and predicted by using an adaptive LMS (least mean squares) algorithm. By utilizing the delayed feedback on the channel condition, the adaptation performance of the proposed scheme is first evaluated in terms of the error probability and the throughput. It is then extended to incorporate variable size packets of ITU-T H.263+ video with the error resilience option. Finally, the end-to-end performance of wireless video transmission is compared against several non-adaptive protection schemes.

Performance comparison between packet and continuous data transmission using two adaptive equalizers in shallow water

NASA Astrophysics Data System (ADS)

Yoon, Jong Rak; Park, Kyu-Chil; Park, Jihyun

2015-07-01

Transmitted signals are markedly affected by sea surface and bottom boundaries in shallow water. The time variant reflection signals from such boundaries characterize the channel as a frequency-selective fading channel and cause intersymbol interference (ISI) in underwater acoustic communication. A channel-estimate-based equalizer is usually adopted to compensate for the reflected signals under this kind of acoustic channel. In this study, we apply two approaches for packet and continuous data transmission of the quadrature phase shift keying (QPSK) system. One is the use of a two-dimensional (2D) rotation matrix in a non-frequency-selective channel. The other is the use of two equalizers of types — the feed forward equalizer (FFE) and decision-directed equalizer (DDE) — with a normalized least mean square (NLMS) algorithm in a frequency-selective channel. The percentage improvement of packet transmission is notably better than that of continuous transmission.

Adaptive recurrence quantum entanglement distillation for two-Kraus-operator channels

NASA Astrophysics Data System (ADS)

Ruan, Liangzhong; Dai, Wenhan; Win, Moe Z.

2018-05-01

Quantum entanglement serves as a valuable resource for many important quantum operations. A pair of entangled qubits can be shared between two agents by first preparing a maximally entangled qubit pair at one agent, and then sending one of the qubits to the other agent through a quantum channel. In this process, the deterioration of entanglement is inevitable since the noise inherent in the channel contaminates the qubit. To address this challenge, various quantum entanglement distillation (QED) algorithms have been developed. Among them, recurrence algorithms have advantages in terms of implementability and robustness. However, the efficiency of recurrence QED algorithms has not been investigated thoroughly in the literature. This paper puts forth two recurrence QED algorithms that adapt to the quantum channel to tackle the efficiency issue. The proposed algorithms have guaranteed convergence for quantum channels with two Kraus operators, which include phase-damping and amplitude-damping channels. Analytical results show that the convergence speed of these algorithms is improved from linear to quadratic and one of the algorithms achieves the optimal speed. Numerical results confirm that the proposed algorithms significantly improve the efficiency of QED.

System Design for Nano-Network Communications

NASA Astrophysics Data System (ADS)

ShahMohammadian, Hoda

The potential applications of nanotechnology in a wide range of areas necessities nano-networking research. Nano-networking is a new type of networking which has emerged by applying nanotechnology to communication theory. Therefore, this dissertation presents a framework for physical layer communications in a nano-network and addresses some of the pressing unsolved challenges in designing a molecular communication system. The contribution of this dissertation is proposing well-justified models for signal propagation, noise sources, optimum receiver design and synchronization in molecular communication channels. The design of any communication system is primarily based on the signal propagation channel and noise models. Using the Brownian motion and advection molecular statistics, separate signal propagation and noise models are presented for diffusion-based and flow-based molecular communication channels. It is shown that the corrupting noise of molecular channels is uncorrelated and non-stationary with a signal dependent magnitude. The next key component of any communication system is the reception and detection process. This dissertation provides a detailed analysis of the effect of the ligand-receptor binding mechanism on the received signal, and develops the first optimal receiver design for molecular communications. The bit error rate performance of the proposed receiver is evaluated and the impact of medium motion on the receiver performance is investigated. Another important feature of any communication system is synchronization. In this dissertation, the first blind synchronization algorithm is presented for the molecular communication channels. The proposed algorithm uses a non-decision directed maximum likelihood criterion for estimating the channel delay. The Cramer-Rao lower bound is also derived and the performance of the proposed synchronization algorithm is evaluated by investigating its mean square error.

On the more accurate channel model and positioning based on time-of-arrival for visible light localization

NASA Astrophysics Data System (ADS)

Amini, Changeez; Taherpour, Abbas; Khattab, Tamer; Gazor, Saeed

2017-01-01

This paper presents an improved propagation channel model for the visible light in indoor environments. We employ this model to derive an enhanced positioning algorithm using on the relation between the time-of-arrivals (TOAs) and the distances for two cases either by assuming known or unknown transmitter and receiver vertical distances. We propose two estimators, namely the maximum likelihood estimator and an estimator by employing the method of moments. To have an evaluation basis for these methods, we calculate the Cramer-Rao lower bound (CRLB) for the performance of the estimations. We show that the proposed model and estimations result in a superior performance in positioning when the transmitter and receiver are perfectly synchronized in comparison to the existing state-of-the-art counterparts. Moreover, the corresponding CRLB of the proposed model represents almost about 20 dB reduction in the localization error bound in comparison with the previous model for some practical scenarios.

Multiple estimation channel decoupling and optimization method based on inverse system

NASA Astrophysics Data System (ADS)

Wu, Peng; Mu, Rongjun; Zhang, Xin; Deng, Yanpeng

2018-03-01

This paper addressed the intelligent autonomous navigation request of intelligent deformation missile, based on the intelligent deformation missile dynamics and kinematics modeling, navigation subsystem solution method and error modeling, and then focuses on the corresponding data fusion and decision fusion technology, decouples the sensitive channel of the filter input through the inverse system of design dynamics to reduce the influence of sudden change of the measurement information on the filter input. Then carrying out a series of simulation experiments, which verified the feasibility of the inverse system decoupling algorithm effectiveness.

Super-Nyquist shaping and processing technologies for high-spectral-efficiency optical systems

NASA Astrophysics Data System (ADS)

Jia, Zhensheng; Chien, Hung-Chang; Zhang, Junwen; Dong, Ze; Cai, Yi; Yu, Jianjun

2013-12-01

The implementations of super-Nyquist pulse generation, both in a digital field using a digital-to-analog converter (DAC) or an optical filter at transmitter side, are introduced. Three corresponding signal processing algorithms at receiver are presented and compared for high spectral-efficiency (SE) optical systems employing the spectral prefiltering. Those algorithms are designed for the mitigation towards inter-symbol-interference (ISI) and inter-channel-interference (ICI) impairments by the bandwidth constraint, including 1-tap constant modulus algorithm (CMA) and 3-tap maximum likelihood sequence estimation (MLSE), regular CMA and digital filter with 2-tap MLSE, and constant multi-modulus algorithm (CMMA) with 2-tap MLSE. The principles and prefiltering tolerance are given through numerical and experimental results.

Evaluation of bio-optical algorithms to remotely sense marine primary production from space

NASA Technical Reports Server (NTRS)

Berthelot, Beatrice; Deschamps, Pierre-Yves

1994-01-01

In situ bio-optical measurements from several oceanographic campaigns were analyzed to derive a direct relationship between water column primary production P (sub t) ocean color as expressed by the ratio of reflectances R (sub 1) at 440 nm and R (sub 3) at 550 nm and photosynthetically available radiation (PAR). The study is restricted to the Morel case I waters for which the following algorithm is proposed: log (P(sub f)) = -4.286 - 1.390 log (R(sub 1)/R(sub3)) + 0.621 log (PAR), with P(sub t) in g C m(exp -2)/d and PAR in J m(exp -2)/d. Using this algorithm the rms accuracy of primary production estimate is 0.17 on a logarithmic scale, i.e., a factor of 1.5. Using spectral reflectance measurements in the entire visible spectral range, the central wavelength, spectral bandwidth, and radiometric noise level requirements are investigated for the channels to be used by an ocean color space mission dedicated to estimating global marine primary production and the associated carbon fluxes. Nearly all the useful information is provided by two channels centered at 440 nm and 550 nm, but the accuracy of primary production estimate appears weakly sensitive to spectral bandwidth, which, consequently, may be enlarged by several tens of nanometers. The sensitivity to radiometric noise, on the contrary, is strong, and a noise equivalent reflectance of 0.005 degraded the accuracy on the primary production estimate by a factor 2 (0.14-0.25 on a logarithmic scale). The results should be applicable to evaluating the primary production of oligotrophic and mesotrophic waters, which constitute most of the open ocean.

UV Reconstruction Algorithm And Diurnal Cycle Variability

NASA Astrophysics Data System (ADS)

Curylo, Aleksander; Litynska, Zenobia; Krzyscin, Janusz; Bogdanska, Barbara

2009-03-01

UV reconstruction is a method of estimation of surface UV with the use of available actinometrical and aerological measurements. UV reconstruction is necessary for the study of long-term UV change. A typical series of UV measurements is not longer than 15 years, which is too short for trend estimation. The essential problem in the reconstruction algorithm is the good parameterization of clouds. In our previous algorithm we used an empirical relation between Cloud Modification Factor (CMF) in global radiation and CMF in UV. The CMF is defined as the ratio between measured and modelled irradiances. Clear sky irradiance was calculated with a solar radiative transfer model. In the proposed algorithm, the time variability of global radiation during the diurnal cycle is used as an additional source of information. For elaborating an improved reconstruction algorithm relevant data from Legionowo [52.4 N, 21.0 E, 96 m a.s.l], Poland were collected with the following instruments: NILU-UV multi channel radiometer, Kipp&Zonen pyranometer, radiosonde profiles of ozone, humidity and temperature. The proposed algorithm has been used for reconstruction of UV at four Polish sites: Mikolajki, Kolobrzeg, Warszawa-Bielany and Zakopane since the early 1960s. Krzyscin's reconstruction of total ozone has been used in the calculations.

Multi-frequency Phase Unwrap from Noisy Data: Adaptive Least Squares Approach

NASA Astrophysics Data System (ADS)

Katkovnik, Vladimir; Bioucas-Dias, José

2010-04-01

Multiple frequency interferometry is, basically, a phase acquisition strategy aimed at reducing or eliminating the ambiguity of the wrapped phase observations or, equivalently, reducing or eliminating the fringe ambiguity order. In multiple frequency interferometry, the phase measurements are acquired at different frequencies (or wavelengths) and recorded using the corresponding sensors (measurement channels). Assuming that the absolute phase to be reconstructed is piece-wise smooth, we use a nonparametric regression technique for the phase reconstruction. The nonparametric estimates are derived from a local least squares criterion, which, when applied to the multifrequency data, yields denoised (filtered) phase estimates with extended ambiguity (periodized), compared with the phase ambiguities inherent to each measurement frequency. The filtering algorithm is based on local polynomial (LPA) approximation for design of nonlinear filters (estimators) and adaptation of these filters to unknown smoothness of the spatially varying absolute phase [9]. For phase unwrapping, from filtered periodized data, we apply the recently introduced robust (in the sense of discontinuity preserving) PUMA unwrapping algorithm [1]. Simulations give evidence that the proposed algorithm yields state-of-the-art performance for continuous as well as for discontinues phase surfaces, enabling phase unwrapping in extraordinary difficult situations when all other algorithms fail.

New distributed fusion filtering algorithm based on covariances over sensor networks with random packet dropouts

NASA Astrophysics Data System (ADS)

Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J.

2017-07-01

This paper studies the distributed fusion estimation problem from multisensor measured outputs perturbed by correlated noises and uncertainties modelled by random parameter matrices. Each sensor transmits its outputs to a local processor over a packet-erasure channel and, consequently, random losses may occur during transmission. Different white sequences of Bernoulli variables are introduced to model the transmission losses. For the estimation, each lost output is replaced by its estimator based on the information received previously, and only the covariances of the processes involved are used, without requiring the signal evolution model. First, a recursive algorithm for the local least-squares filters is derived by using an innovation approach. Then, the cross-correlation matrices between any two local filters is obtained. Finally, the distributed fusion filter weighted by matrices is obtained from the local filters by applying the least-squares criterion. The performance of the estimators and the influence of both sensor uncertainties and transmission losses on the estimation accuracy are analysed in a numerical example.

Estimation of phytoplankton production from space: current status and future potential of satellite remote sensing.

PubMed

Joint; Groom

2000-07-30

A new generation of ocean colour satellites is now operational, with frequent observation of the global ocean. This paper reviews the potential to estimate marine primary production from satellite images. The procedures involved in retrieving estimates of phytoplankton biomass, as pigment concentrations, are discussed. Algorithms are applied to SeaWiFS ocean colour data to indicate seasonal variations in phytoplankton biomass in the Celtic Sea, on the continental shelf to the south west of the UK. Algorithms to estimate primary production rates from chlorophyll concentration are compared and the advantages and disadvantage discussed. The simplest algorithms utilise correlations between chlorophyll concentration and production rate and one equation is used to estimate daily primary production rates for the western English Channel and Celtic Sea; these estimates compare favourably with published values. Primary production for the central Celtic Sea in the period April to September inclusive is estimated from SeaWiFS data to be 102 gC m(-2) in 1998 and 93 gC m(-2) in 1999; published estimates, based on in situ incubations, are ca. 80 gC m(-2). The satellite data demonstrate large variations in primary production between 1998 and 1999, with a significant increase in late summer in 1998 which did not occur in 1999. Errors are quantified for the estimation of primary production from simple algorithms based on satellite-derived chlorophyll concentration. These data show the potential to obtain better estimates of marine primary production than are possible with ship-based methods, with the ability to detect short-lived phytoplankton blooms. In addition, the potential to estimate new production from satellite data is discussed.

Effective pore size and radius of capture for K(+) ions in K-channels.

PubMed

Moldenhauer, Hans; Díaz-Franulic, Ignacio; González-Nilo, Fernando; Naranjo, David

2016-02-02

Reconciling protein functional data with crystal structure is arduous because rare conformations or crystallization artifacts occur. Here we present a tool to validate the dimensions of open pore structures of potassium-selective ion channels. We used freely available algorithms to calculate the molecular contour of the pore to determine the effective internal pore radius (r(E)) in several K-channel crystal structures. r(E) was operationally defined as the radius of the biggest sphere able to enter the pore from the cytosolic side. We obtained consistent r(E) estimates for MthK and Kv1.2/2.1 structures, with r(E) = 5.3-5.9 Å and r(E) = 4.5-5.2 Å, respectively. We compared these structural estimates with functional assessments of the internal mouth radii of capture (r(C)) for two electrophysiological counterparts, the large conductance calcium activated K-channel (r(C) = 2.2 Å) and the Shaker Kv-channel (r(C) = 0.8 Å), for MthK and Kv1.2/2.1 structures, respectively. Calculating the difference between r(E) and r(C), produced consistent size radii of 3.1-3.7 Å and 3.6-4.4 Å for hydrated K(+) ions. These hydrated K(+) estimates harmonize with others obtained with diverse experimental and theoretical methods. Thus, these findings validate MthK and the Kv1.2/2.1 structures as templates for open BK and Kv-channels, respectively.

Progressive transmission of images over fading channels using rate-compatible LDPC codes.

PubMed

Pan, Xiang; Banihashemi, Amir H; Cuhadar, Aysegul

2006-12-01

In this paper, we propose a combined source/channel coding scheme for transmission of images over fading channels. The proposed scheme employs rate-compatible low-density parity-check codes along with embedded image coders such as JPEG2000 and set partitioning in hierarchical trees (SPIHT). The assignment of channel coding rates to source packets is performed by a fast trellis-based algorithm. We examine the performance of the proposed scheme over correlated and uncorrelated Rayleigh flat-fading channels with and without side information. Simulation results for the expected peak signal-to-noise ratio of reconstructed images, which are within 1 dB of the capacity upper bound over a wide range of channel signal-to-noise ratios, show considerable improvement compared to existing results under similar conditions. We also study the sensitivity of the proposed scheme in the presence of channel estimation error at the transmitter and demonstrate that under most conditions our scheme is more robust compared to existing schemes.

An FPGA-Based Real-Time Maximum Likelihood 3D Position Estimation for a Continuous Crystal PET Detector

NASA Astrophysics Data System (ADS)

Wang, Yonggang; Xiao, Yong; Cheng, Xinyi; Li, Deng; Wang, Liwei

2016-02-01

For the continuous crystal-based positron emission tomography (PET) detector built in our lab, a maximum likelihood algorithm adapted for implementation on a field programmable gate array (FPGA) is proposed to estimate the three-dimensional (3D) coordinate of interaction position with the single-end detected scintillation light response. The row-sum and column-sum readout scheme organizes the 64 channels of photomultiplier (PMT) into eight row signals and eight column signals to be readout for X- and Y-coordinates estimation independently. By the reference events irradiated in a known oblique angle, the probability density function (PDF) for each depth-of-interaction (DOI) segment is generated, by which the reference events in perpendicular irradiation are assigned to DOI segments for generating the PDFs for X and Y estimation in each DOI layer. Evaluated by the experimental data, the algorithm achieves an average X resolution of 1.69 mm along the central X-axis, and DOI resolution of 3.70 mm over the whole thickness (0-10 mm) of crystal. The performance improvements from 2D estimation to the 3D algorithm are also presented. Benefiting from abundant resources of FPGA and a hierarchical storage arrangement, the whole algorithm can be implemented into a middle-scale FPGA. By a parallel structure in pipelines, the 3D position estimator on the FPGA can achieve a processing throughput of 15 M events/s, which is sufficient for the requirement of real-time PET imaging.

Underwater Electromagnetic Sensor Networks, Part II: Localization and Network Simulations

PubMed Central

Zazo, Javier; Valcarcel Macua, Sergio; Zazo, Santiago; Pérez, Marina; Pérez-Álvarez, Iván; Jiménez, Eugenio; Cardona, Laura; Brito, Joaquín Hernández; Quevedo, Eduardo

2016-01-01

In the first part of the paper, we modeled and characterized the underwater radio channel in shallow waters. In the second part, we analyze the application requirements for an underwater wireless sensor network (U-WSN) operating in the same environment and perform detailed simulations. We consider two localization applications, namely self-localization and navigation aid, and propose algorithms that work well under the specific constraints associated with U-WSN, namely low connectivity, low data rates and high packet loss probability. We propose an algorithm where the sensor nodes collaboratively estimate their unknown positions in the network using a low number of anchor nodes and distance measurements from the underwater channel. Once the network has been self-located, we consider a node estimating its position for underwater navigation communicating with neighboring nodes. We also propose a communication system and simulate the whole electromagnetic U-WSN in the Castalia simulator to evaluate the network performance, including propagation impairments (e.g., noise, interference), radio parameters (e.g., modulation scheme, bandwidth, transmit power), hardware limitations (e.g., clock drift, transmission buffer) and complete MAC and routing protocols. We also explain the changes that have to be done to Castalia in order to perform the simulations. In addition, we propose a parametric model of the communication channel that matches well with the results from the first part of this paper. Finally, we provide simulation results for some illustrative scenarios. PMID:27999309

Joint source-channel coding for motion-compensated DCT-based SNR scalable video.

PubMed

Kondi, Lisimachos P; Ishtiaq, Faisal; Katsaggelos, Aggelos K

2002-01-01

In this paper, we develop an approach toward joint source-channel coding for motion-compensated DCT-based scalable video coding and transmission. A framework for the optimal selection of the source and channel coding rates over all scalable layers is presented such that the overall distortion is minimized. The algorithm utilizes universal rate distortion characteristics which are obtained experimentally and show the sensitivity of the source encoder and decoder to channel errors. The proposed algorithm allocates the available bit rate between scalable layers and, within each layer, between source and channel coding. We present the results of this rate allocation algorithm for video transmission over a wireless channel using the H.263 Version 2 signal-to-noise ratio (SNR) scalable codec for source coding and rate-compatible punctured convolutional (RCPC) codes for channel coding. We discuss the performance of the algorithm with respect to the channel conditions, coding methodologies, layer rates, and number of layers.

Routing channels in VLSI layout

NASA Astrophysics Data System (ADS)

Cai, Hong

A number of algorithms for the automatic routing of interconnections in Very Large Scale Integration (VLSI) building-block layouts are presented. Algorithms for the topological definition of channels, the global routing and the geometrical definition of channels are presented. In contrast to traditional approaches the definition and ordering of the channels is done after the global routing. This approach has the advantage that global routing information can be taken into account to select the optimal channel structure. A polynomial algorithm for the channel definition and ordering problem is presented. The existence of a conflict-free channel structure is guaranteed by enforcing a sliceable placement. Algorithms for finding the shortest connection path are described. A separate algorithm is developed for the power net routing, because the two power nets must be planarly routed with variable wire width. An integrated placement and routing system for generating building-block layout is briefly described. Some experimental results and design experiences in using the system are also presented. Very good results are obtained.

An automatic algorithm for blink-artifact suppression based on iterative template matching: application to single channel recording of cortical auditory evoked potentials

NASA Astrophysics Data System (ADS)

Valderrama, Joaquin T.; de la Torre, Angel; Van Dun, Bram

2018-02-01

Objective. Artifact reduction in electroencephalogram (EEG) signals is usually necessary to carry out data analysis appropriately. Despite the large amount of denoising techniques available with a multichannel setup, there is a lack of efficient algorithms that remove (not only detect) blink-artifacts from a single channel EEG, which is of interest in many clinical and research applications. This paper describes and evaluates the iterative template matching and suppression (ITMS), a new method proposed for detecting and suppressing the artifact associated with the blink activity from a single channel EEG. Approach. The approach of ITMS consists of (a) an iterative process in which blink-events are detected and the blink-artifact waveform of the analyzed subject is estimated, (b) generation of a signal modeling the blink-artifact, and (c) suppression of this signal from the raw EEG. The performance of ITMS is compared with the multi-window summation of derivatives within a window (MSDW) technique using both synthesized and real EEG data. Main results. Results suggest that ITMS presents an adequate performance in detecting and suppressing blink-artifacts from a single channel EEG. When applied to the analysis of cortical auditory evoked potentials (CAEPs), ITMS provides a significant quality improvement in the resulting responses, i.e. in a cohort of 30 adults, the mean correlation coefficient improved from 0.37 to 0.65 when the blink-artifacts were detected and suppressed by ITMS. Significance. ITMS is an efficient solution to the problem of denoising blink-artifacts in single-channel EEG applications, both in clinical and research fields. The proposed ITMS algorithm is stable; automatic, since it does not require human intervention; low-invasive, because the EEG segments not contaminated by blink-artifacts remain unaltered; and easy to implement, as can be observed in the Matlab script implemeting the algorithm provided as supporting material.

Simplified Antenna Group Determination of RS Overhead Reduced Massive MIMO for Wireless Sensor Networks.

PubMed

Lee, Byung Moo

2017-12-29

Massive multiple-input multiple-output (MIMO) systems can be applied to support numerous internet of things (IoT) devices using its excessive amount of transmitter (TX) antennas. However, one of the big obstacles for the realization of the massive MIMO system is the overhead of reference signal (RS), because the number of RS is proportional to the number of TX antennas and/or related user equipments (UEs). It has been already reported that antenna group-based RS overhead reduction can be very effective to the efficient operation of massive MIMO, but the method of deciding the number of antennas needed in each group is at question. In this paper, we propose a simplified determination scheme of the number of antennas needed in each group for RS overhead reduced massive MIMO to support many IoT devices. Supporting many distributed IoT devices is a framework to configure wireless sensor networks. Our contribution can be divided into two parts. First, we derive simple closed-form approximations of the achievable spectral efficiency (SE) by using zero-forcing (ZF) and matched filtering (MF) precoding for the RS overhead reduced massive MIMO systems with channel estimation error. The closed-form approximations include a channel error factor that can be adjusted according to the method of the channel estimation. Second, based on the closed-form approximation, we present an efficient algorithm determining the number of antennas needed in each group for the group-based RS overhead reduction scheme. The algorithm depends on the exact inverse functions of the derived closed-form approximations of SE. It is verified with theoretical analysis and simulation that the proposed algorithm works well, and thus can be used as an important tool for massive MIMO systems to support many distributed IoT devices.

Simplified Antenna Group Determination of RS Overhead Reduced Massive MIMO for Wireless Sensor Networks

PubMed Central

2017-01-01

Massive multiple-input multiple-output (MIMO) systems can be applied to support numerous internet of things (IoT) devices using its excessive amount of transmitter (TX) antennas. However, one of the big obstacles for the realization of the massive MIMO system is the overhead of reference signal (RS), because the number of RS is proportional to the number of TX antennas and/or related user equipments (UEs). It has been already reported that antenna group-based RS overhead reduction can be very effective to the efficient operation of massive MIMO, but the method of deciding the number of antennas needed in each group is at question. In this paper, we propose a simplified determination scheme of the number of antennas needed in each group for RS overhead reduced massive MIMO to support many IoT devices. Supporting many distributed IoT devices is a framework to configure wireless sensor networks. Our contribution can be divided into two parts. First, we derive simple closed-form approximations of the achievable spectral efficiency (SE) by using zero-forcing (ZF) and matched filtering (MF) precoding for the RS overhead reduced massive MIMO systems with channel estimation error. The closed-form approximations include a channel error factor that can be adjusted according to the method of the channel estimation. Second, based on the closed-form approximation, we present an efficient algorithm determining the number of antennas needed in each group for the group-based RS overhead reduction scheme. The algorithm depends on the exact inverse functions of the derived closed-form approximations of SE. It is verified with theoretical analysis and simulation that the proposed algorithm works well, and thus can be used as an important tool for massive MIMO systems to support many distributed IoT devices. PMID:29286339

Blind Channel Equalization Using Constrained Generalized Pattern Search Optimization and Reinitialization Strategy

NASA Astrophysics Data System (ADS)

Zaouche, Abdelouahib; Dayoub, Iyad; Rouvaen, Jean Michel; Tatkeu, Charles

2008-12-01

We propose a global convergence baud-spaced blind equalization method in this paper. This method is based on the application of both generalized pattern optimization and channel surfing reinitialization. The potentially used unimodal cost function relies on higher- order statistics, and its optimization is achieved using a pattern search algorithm. Since the convergence to the global minimum is not unconditionally warranted, we make use of channel surfing reinitialization (CSR) strategy to find the right global minimum. The proposed algorithm is analyzed, and simulation results using a severe frequency selective propagation channel are given. Detailed comparisons with constant modulus algorithm (CMA) are highlighted. The proposed algorithm performances are evaluated in terms of intersymbol interference, normalized received signal constellations, and root mean square error vector magnitude. In case of nonconstant modulus input signals, our algorithm outperforms significantly CMA algorithm with full channel surfing reinitialization strategy. However, comparable performances are obtained for constant modulus signals.

Detection Optimization of the Progressive Multi-Channel Correlation Algorithm Used in Infrasound Nuclear Treaty Monitoring

DTIC Science & Technology

2013-03-01

82 4.3.2 Bayes Decision Criteria and Risk Minimization ............................................ 86...on the globe. In its mission to achieve information superiority, AFTAC has historically combined data garnered from seismic and infrasound networks...to improve location estimates for nuclear events. For instance, underground explosions produce seismic waves that can couple into the atmosphere

Channel Acquisition for Massive MIMO-OFDM With Adjustable Phase Shift Pilots

NASA Astrophysics Data System (ADS)

You, Li; Gao, Xiqi; Swindlehurst, A. Lee; Zhong, Wen

2016-03-01

We propose adjustable phase shift pilots (APSPs) for channel acquisition in wideband massive multiple-input multiple-output (MIMO) systems employing orthogonal frequency division multiplexing (OFDM) to reduce the pilot overhead. Based on a physically motivated channel model, we first establish a relationship between channel space-frequency correlations and the channel power angle-delay spectrum in the massive antenna array regime, which reveals the channel sparsity in massive MIMO-OFDM. With this channel model, we then investigate channel acquisition, including channel estimation and channel prediction, for massive MIMO-OFDM with APSPs. We show that channel acquisition performance in terms of sum mean square error can be minimized if the user terminals' channel power distributions in the angle-delay domain can be made non-overlapping with proper phase shift scheduling. A simplified pilot phase shift scheduling algorithm is developed based on this optimal channel acquisition condition. The performance of APSPs is investigated for both one symbol and multiple symbol data models. Simulations demonstrate that the proposed APSP approach can provide substantial performance gains in terms of achievable spectral efficiency over the conventional phase shift orthogonal pilot approach in typical mobility scenarios.

Subarray Processing for Projection-based RFI Mitigation in Radio Astronomical Interferometers

NASA Astrophysics Data System (ADS)

Burnett, Mitchell C.; Jeffs, Brian D.; Black, Richard A.; Warnick, Karl F.

2018-04-01

Radio Frequency Interference (RFI) is a major problem for observations in Radio Astronomy (RA). Adaptive spatial filtering techniques such as subspace projection are promising candidates for RFI mitigation; however, for radio interferometric imaging arrays, these have primarily been used in engineering demonstration experiments rather than mainstream scientific observations. This paper considers one reason that adoption of such algorithms is limited: RFI decorrelates across the interferometric array because of long baseline lengths. This occurs when the relative RFI time delay along a baseline is large compared to the frequency channel inverse bandwidth used in the processing chain. Maximum achievable excision of the RFI is limited by covariance matrix estimation error when identifying interference subspace parameters, and decorrelation of the RFI introduces errors that corrupt the subspace estimate, rendering subspace projection ineffective over the entire array. In this work, we present an algorithm that overcomes this challenge of decorrelation by applying subspace projection via subarray processing (SP-SAP). Each subarray is designed to have a set of elements with high mutual correlation in the interferer for better estimation of subspace parameters. In an RFI simulation scenario for the proposed ngVLA interferometric imaging array with 15 kHz channel bandwidth for correlator processing, we show that compared to the former approach of applying subspace projection on the full array, SP-SAP improves mitigation of the RFI on the order of 9 dB. An example of improved image synthesis and reduced RFI artifacts for a simulated image “phantom” using the SP-SAP algorithm is presented.

On Several Fundamental Problems of Optimization, Estimation, and Scheduling in Wireless Communications

NASA Astrophysics Data System (ADS)

Gao, Qian

For both the conventional radio frequency and the comparably recent optical wireless communication systems, extensive effort from the academia had been made in improving the network spectrum efficiency and/or reducing the error rate. To achieve these goals, many fundamental challenges such as power efficient constellation design, nonlinear distortion mitigation, channel training design, network scheduling and etc. need to be properly addressed. In this dissertation, novel schemes are proposed accordingly to deal with specific problems falling in category of these challenges. Rigorous proofs and analyses are provided for each of our work to make a fair comparison with the corresponding peer works to clearly demonstrate the advantages. The first part of this dissertation considers a multi-carrier optical wireless system employing intensity modulation (IM) and direct detection (DD). A block-wise constellation design is presented, which treats the DC-bias that conventionally used solely for biasing purpose as an information basis. Our scheme, we term it MSM-JDCM, takes advantage of the compactness of sphere packing in a higher dimensional space, and in turn power efficient constellations are obtained by solving an advanced convex optimization problem. Besides the significant power gains, the MSM-JDCM has many other merits such as being capable of mitigating nonlinear distortion by including a peak-to-power ratio (PAPR) constraint, minimizing inter-symbol-interference (ISI) caused by frequency-selective fading with a novel precoder designed and embedded, and further reducing the bit-error-rate (BER) by combining with an optimized labeling scheme. The second part addresses several optimization problems in a multi-color visible light communication system, including power efficient constellation design, joint pre-equalizer and constellation design, and modeling of different structured channels with cross-talks. Our novel constellation design scheme, termed CSK-Advanced, is compared with the conventional decoupled system with the same spectrum efficiency to demonstrate the power efficiency. Crucial lighting requirements are included as optimization constraints. To control non-linear distortion, the optical peak-to-average-power ratio (PAPR) of LEDs can be individually constrained. With a SVD-based pre-equalizer designed and employed, our scheme can achieve lower BER than counterparts applying zero-forcing (ZF) or linear minimum-mean-squared-error (LMMSE) based post-equalizers. Besides, a binary switching algorithm (BSA) is applied to improve BER performance. The third part looks into a problem of two-phase channel estimation in a relayed wireless network. The channel estimates in every phase are obtained by the linear minimum mean squared error (LMMSE) method. Inaccurate estimate of the relay to destination (RtD) channel in phase 1 could affect estimate of the source to relay (StR) channel in phase 2, which is made erroneous. We first derive a close-form expression for the averaged Bayesian mean-square estimation error (ABMSE) for both phase estimates in terms of the length of source and relay training slots, based on which an iterative searching algorithm is then proposed that optimally allocates training slots to the two phases such that estimation errors are balanced. Analysis shows how the ABMSE of the StD channel estimation varies with the lengths of relay training and source training slots, the relay amplification gain, and the channel prior information respectively. The last part deals with a transmission scheduling problem in a uplink multiple-input-multiple-output (MIMO) wireless network. Code division multiple access (CDMA) is assumed as a multiple access scheme and pseudo-random codes are employed for different users. We consider a heavy traffic scenario, in which each user always has packets to transmit in the scheduled time slots. If the relay is scheduled for transmission together with users, then it operates in a full-duplex mode, where the packets previously collected from users are transmitted to the destination while new packets are being collected from users. A novel expression of throughput is first derived and then used to develop a scheduling algorithm to maximize the throughput. Our full-duplex scheduling is compared with a half-duplex scheduling, random access, and time division multiple access (TDMA), and simulation results illustrate its superiority. Throughput gains due to employment of both MIMO and CDMA are observed.

Software-Defined Architectures for Spectrally Efficient Cognitive Networking in Extreme Environments

NASA Astrophysics Data System (ADS)

Sklivanitis, Georgios

The objective of this dissertation is the design, development, and experimental evaluation of novel algorithms and reconfigurable radio architectures for spectrally efficient cognitive networking in terrestrial, airborne, and underwater environments. Next-generation wireless communication architectures and networking protocols that maximize spectrum utilization efficiency in congested/contested or low-spectral availability (extreme) communication environments can enable a rich body of applications with unprecedented societal impact. In recent years, underwater wireless networks have attracted significant attention for military and commercial applications including oceanographic data collection, disaster prevention, tactical surveillance, offshore exploration, and pollution monitoring. Unmanned aerial systems that are autonomously networked and fully mobile can assist humans in extreme or difficult-to-reach environments and provide cost-effective wireless connectivity for devices without infrastructure coverage. Cognitive radio (CR) has emerged as a promising technology to maximize spectral efficiency in dynamically changing communication environments by adaptively reconfiguring radio communication parameters. At the same time, the fast developing technology of software-defined radio (SDR) platforms has enabled hardware realization of cognitive radio algorithms for opportunistic spectrum access. However, existing algorithmic designs and protocols for shared spectrum access do not effectively capture the interdependencies between radio parameters at the physical (PHY), medium-access control (MAC), and network (NET) layers of the network protocol stack. In addition, existing off-the-shelf radio platforms and SDR programmable architectures are far from fulfilling runtime adaptation and reconfiguration across PHY, MAC, and NET layers. Spectrum allocation in cognitive networks with multi-hop communication requirements depends on the location, network traffic load, and interference profile at each network node. As a result, the development and implementation of algorithms and cross-layer reconfigurable radio platforms that can jointly treat space, time, and frequency as a unified resource to be dynamically optimized according to inter- and intra-network interference constraints is of fundamental importance. In the next chapters, we present novel algorithmic and software/hardware implementation developments toward the deployment of spectrally efficient terrestrial, airborne, and underwater wireless networks. In Chapter 1 we review the state-of-art in commercially available SDR platforms, describe their software and hardware capabilities, and classify them based on their ability to enable rapid prototyping and advance experimental research in wireless networks. Chapter 2 discusses system design and implementation details toward real-time evaluation of a software-radio platform for all-spectrum cognitive channelization in the presence of narrowband or wideband primary stations. All-spectrum channelization is achieved by designing maximum signal-to-interference-plus-noise ratio (SINR) waveforms that span the whole continuum of the device-accessible spectrum, while satisfying peak power and interference temperature (IT) constraints for the secondary and primary users, respectively. In Chapter 3, we introduce the concept of all-spectrum channelization based on max-SINR optimized sparse-binary waveforms, we propose optimal and suboptimal waveform design algorithms, and evaluate their SINR and bit-error-rate (BER) performance in an SDR testbed. Chapter 4 considers the problem of channel estimation with minimal pilot signaling in multi-cell multi-user multi-input multi-output (MIMO) systems with very large antenna arrays at the base station, and proposes a least-squares (LS)-type algorithm that iteratively extracts channel and data estimates from a short record of data measurements. Our algorithmic developments toward spectrally-efficient cognitive networking through joint optimization of channel access code-waveforms and routes in a multi-hop network are described in Chapter 5. Algorithmic designs are software optimized on heterogeneous multi-core general-purpose processor (GPP)-based SDR architectures by leveraging a novel software-radio framework that offers self-optimization and real-time adaptation capabilities at the PHY, MAC, and NET layers of the network protocol stack. Our system design approach is experimentally validated under realistic conditions in a large-scale hybrid ground-air testbed deployment. Chapter 6 reviews the state-of-art in software and hardware platforms for underwater wireless networking and proposes a software-defined acoustic modem prototype that enables (i) cognitive reconfiguration of PHY/MAC parameters, and (ii) cross-technology communication adaptation. The proposed modem design is evaluated in terms of effective communication data rate in both water tank and lake testbed setups. In Chapter 7, we present a novel receiver configuration for code-waveform-based multiple-access underwater communications. The proposed receiver is fully reconfigurable and executes (i) all-spectrum cognitive channelization, and (ii) combined synchronization, channel estimation, and demodulation. Experimental evaluation in terms of SINR and BER show that all-spectrum channelization is a powerful proposition for underwater communications. At the same time, the proposed receiver design can significantly enhance bandwidth utilization. Finally, in Chapter 8, we focus on challenging practical issues that arise in underwater acoustic sensor network setups where co-located multi-antenna sensor deployment is not feasible due to power, computation, and hardware limitations, and design, implement, and evaluate an underwater receiver structure that accounts for multiple carrier frequency and timing offsets in virtual (distributed) MIMO underwater systems.

Computer Aided Synthesis or Measurement Schemes for Telemetry applications

DTIC Science & Technology

1997-09-02

5.2.5. Frame structure generation The algorithm generating the frame structure should take as inputs the sampling frequency requirements of the channels...these channels into the frame structure. Generally there can be a lot of ways to divide channels among groups. The algorithm implemented in...groups) first. The algorithm uses the function "try_permutation" recursively to distribute channels among the groups, and the function "try_subtable

EDMC: An enhanced distributed multi-channel anti-collision algorithm for RFID reader system

NASA Astrophysics Data System (ADS)

Zhang, YuJing; Cui, Yinghua

2017-05-01

In this paper, we proposes an enhanced distributed multi-channel reader anti-collision algorithm for RFID environments which is based on the distributed multi-channel reader anti-collision algorithm for RFID environments (called DiMCA). We proposes a monitor method to decide whether reader receive the latest control news after it selected the data channel. The simulation result shows that it improves interrogation delay.

Evaluation of Skin Temperatures Retrieved from GOES-8

NASA Technical Reports Server (NTRS)

Suggs, Ronnie, J.; Jedlovec, G. J.; Lapenta, W. M.; Haines, S. L.

2000-01-01

Skin temperatures derived from geostationary satellites have the potential of providing the temporal and spatial resolution needed for model assimilation. To adequately assess the potential improvements in numerical model forecasts that can be made by assimilating satellite data, an estimate of the accuracy of the skin temperature product is necessary. A particular skin temperature algorithm, the Physical Split Window Technique, that uses the longwave infrared channels of the GOES Imager has shown promise in recent model assimilation studies to provide land surface temperatures with reasonable accuracy. A comparison of retrieved GOES-8 skin temperatures from this algorithm with in situ measurements is presented. Various retrieval algorithm issues are addressed including surface emissivity

A Robust Dynamic Heart-Rate Detection Algorithm Framework During Intense Physical Activities Using Photoplethysmographic Signals

PubMed Central

Song, Jiajia; Li, Dan; Ma, Xiaoyuan; Teng, Guowei; Wei, Jianming

2017-01-01

Dynamic accurate heart-rate (HR) estimation using a photoplethysmogram (PPG) during intense physical activities is always challenging due to corruption by motion artifacts (MAs). It is difficult to reconstruct a clean signal and extract HR from contaminated PPG. This paper proposes a robust HR-estimation algorithm framework that uses one-channel PPG and tri-axis acceleration data to reconstruct the PPG and calculate the HR based on features of the PPG and spectral analysis. Firstly, the signal is judged by the presence of MAs. Then, the spectral peaks corresponding to acceleration data are filtered from the periodogram of the PPG when MAs exist. Different signal-processing methods are applied based on the amount of remaining PPG spectral peaks. The main MA-removal algorithm (NFEEMD) includes the repeated single-notch filter and ensemble empirical mode decomposition. Finally, HR calibration is designed to ensure the accuracy of HR tracking. The NFEEMD algorithm was performed on the 23 datasets from the 2015 IEEE Signal Processing Cup Database. The average estimation errors were 1.12 BPM (12 training datasets), 2.63 BPM (10 testing datasets) and 1.87 BPM (all 23 datasets), respectively. The Pearson correlation was 0.992. The experiment results illustrate that the proposed algorithm is not only suitable for HR estimation during continuous activities, like slow running (13 training datasets), but also for intense physical activities with acceleration, like arm exercise (10 testing datasets). PMID:29068403

Estimation of anomaly location and size using electrical impedance tomography.

PubMed

Kwon, Ohin; Yoon, Jeong Rock; Seo, Jin Keun; Woo, Eung Je; Cho, Young Gu

2003-01-01

We developed a new algorithm that estimates locations and sizes of anomalies in electrically conducting medium based on electrical impedance tomography (EIT) technique. When only the boundary current and voltage measurements are available, it is not practically feasible to reconstruct accurate high-resolution cross-sectional conductivity or resistivity images of a subject. In this paper, we focus our attention on the estimation of locations and sizes of anomalies with different conductivity values compared with the background tissues. We showed the performance of the algorithm from experimental results using a 32-channel EIT system and saline phantom. With about 1.73% measurement error in boundary current-voltage data, we found that the minimal size (area) of the detectable anomaly is about 0.72% of the size (area) of the phantom. Potential applications include the monitoring of impedance related physiological events and bubble detection in two-phase flow. Since this new algorithm requires neither any forward solver nor time-consuming minimization process, it is fast enough for various real-time applications in medicine and nondestructive testing.

Decoupling PI Controller Design for a Normal Conducting RF Cavity Using a Recursive LEVENBERG-MARQUARDT Algorithm

NASA Astrophysics Data System (ADS)

Kwon, Sung-il; Lynch, M.; Prokop, M.

2005-02-01

This paper addresses the system identification and the decoupling PI controller design for a normal conducting RF cavity. Based on the open-loop measurement data of an SNS DTL cavity, the open-loop system's bandwidths and loop time delays are estimated by using batched least square. With the identified system, a PI controller is designed in such a way that it suppresses the time varying klystron droop and decouples the In-phase and Quadrature of the cavity field. The Levenberg-Marquardt algorithm is applied for nonlinear least squares to obtain the optimal PI controller parameters. The tuned PI controller gains are downloaded to the low-level RF system by using channel access. The experiment of the closed-loop system is performed and the performance is investigated. The proposed tuning method is running automatically in real time interface between a host computer with controller hardware through ActiveX Channel Access.

Pattern recognition analysis of polar clouds during summer and winter

NASA Technical Reports Server (NTRS)

Ebert, Elizabeth E.

1992-01-01

A pattern recognition algorithm is demonstrated which classifies eighteen surface and cloud types in high-latitude AVHRR imagery based on several spectral and textural features, then estimates the cloud properties (fractional coverage, albedo, and brightness temperature) using a hybrid histogram and spatial coherence technique. The summertime version of the algorithm uses both visible and infrared data (AVHRR channels 1-4), while the wintertime version uses only infrared data (AVHRR channels 3-5). Three days of low-resolution AVHRR imagery from the Arctic and Antarctic during January and July 1984 were analyzed for cloud type and fractional coverage. The analysis showed significant amounts of high cloudiness in the Arctic during one day in winter. The Antarctic summer scene was characterized by heavy cloud cover in the southern ocean and relatively clear conditions in the continental interior. A large region of extremely low brightness temperatures in East Antarctica during winter suggests the presence of polar stratospheric cloud.

Algorithm for Automatic Segmentation of Nuclear Boundaries in Cancer Cells in Three-Channel Luminescent Images

NASA Astrophysics Data System (ADS)

Lisitsa, Y. V.; Yatskou, M. M.; Apanasovich, V. V.; Apanasovich, T. V.

2015-09-01

We have developed an algorithm for segmentation of cancer cell nuclei in three-channel luminescent images of microbiological specimens. The algorithm is based on using a correlation between fluorescence signals in the detection channels for object segmentation, which permits complete automation of the data analysis procedure. We have carried out a comparative analysis of the proposed method and conventional algorithms implemented in the CellProfiler and ImageJ software packages. Our algorithm has an object localization uncertainty which is 2-3 times smaller than for the conventional algorithms, with comparable segmentation accuracy.

Uncertainty analysis technique for OMEGA Dante measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

May, M. J.; Widmann, K.; Sorce, C.

2010-10-15

The Dante is an 18 channel x-ray filtered diode array which records the spectrally and temporally resolved radiation flux from various targets (e.g., hohlraums, etc.) at x-ray energies between 50 eV and 10 keV. It is a main diagnostic installed on the OMEGA laser facility at the Laboratory for Laser Energetics, University of Rochester. The absolute flux is determined from the photometric calibration of the x-ray diodes, filters and mirrors, and an unfold algorithm. Understanding the errors on this absolute measurement is critical for understanding hohlraum energetic physics. We present a new method for quantifying the uncertainties on the determinedmore » flux using a Monte Carlo parameter variation technique. This technique combines the uncertainties in both the unfold algorithm and the error from the absolute calibration of each channel into a one sigma Gaussian error function. One thousand test voltage sets are created using these error functions and processed by the unfold algorithm to produce individual spectra and fluxes. Statistical methods are applied to the resultant set of fluxes to estimate error bars on the measurements.« less

Uncertainty Analysis Technique for OMEGA Dante Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

May, M J; Widmann, K; Sorce, C

2010-05-07

The Dante is an 18 channel X-ray filtered diode array which records the spectrally and temporally resolved radiation flux from various targets (e.g. hohlraums, etc.) at X-ray energies between 50 eV to 10 keV. It is a main diagnostics installed on the OMEGA laser facility at the Laboratory for Laser Energetics, University of Rochester. The absolute flux is determined from the photometric calibration of the X-ray diodes, filters and mirrors and an unfold algorithm. Understanding the errors on this absolute measurement is critical for understanding hohlraum energetic physics. We present a new method for quantifying the uncertainties on the determinedmore » flux using a Monte-Carlo parameter variation technique. This technique combines the uncertainties in both the unfold algorithm and the error from the absolute calibration of each channel into a one sigma Gaussian error function. One thousand test voltage sets are created using these error functions and processed by the unfold algorithm to produce individual spectra and fluxes. Statistical methods are applied to the resultant set of fluxes to estimate error bars on the measurements.« less

Arctic Sea ice studies with passive microwave satellite observations

NASA Technical Reports Server (NTRS)

Cavalieri, D. J.

1988-01-01

The objectives of this research are: (1) to improve sea ice concentration determinations from passive microwave space observations; (2) to study the role of Arctic polynyas in the production of sea ice and the associated salinization of Arctic shelf water; and (3) to study large scale sea ice variability in the polar oceans. The strategy is to analyze existing data sets and data acquired from both the DMSP SSM/I and recently completed aircraft underflights. Special attention will be given the high resolution 85.5 GHz SSM/I channels for application to thin ice algorithms and processes studies. Analysis of aircraft and satellite data sets is expected to provide a basis for determining the potential of the SSM/I high frequency channels for improving sea ice algorithms and for investigating oceanic processes. Improved sea ice algorithms will aid the study of Arctic coastal polynyas which in turn will provide a better understanding of the role of these polynyas in maintaining the Arctic watermass structure. Analysis of satellite and archived meteorological data sets will provide improved estimates of annual, seasonal and shorter-term sea ice variability.

An ICA based MIMO-OFDM VLC scheme

NASA Astrophysics Data System (ADS)

Jiang, Fangqing; Deng, Honggui; Xiao, Wei; Tao, Shaohua; Zhu, Kaicheng

2015-07-01

In this paper, we propose a novel ICA based MIMO-OFDM VLC scheme, where ICA is applied to convert the MIMO-OFDM channel into several SISO-OFDM channels to reduce computational complexity in channel estimation, without any spectral overhead. Besides, the FM is first investigated to further modulate the OFDM symbols to eliminate the correlation of the signals, so as to improve the separation performance of the ICA algorithm. In the 4×4MIMO-OFDM VLC simulation experiment, LOS path and NLOS paths are both considered, each transmitting signal at 100 Mb/s. Simulation results show that the BER of the proposed scheme reaches the 10-5 level at SNR=20 dB, which is a large improvement compared to the traditional schemes.

Plumes and Blooms: Modeling the Case II Waters of the Santa Barbara Channel. Chapter 15

NASA Technical Reports Server (NTRS)

Siegel, D. A.; Maritorena, S.; Nelson, N. B.

2003-01-01

The goal of the Plumes and Blooms (PnB) project is to develop, validate and apply to imagery state-of-the-art ocean color algorithms for quantifying sediment plumes and phytoplankton blooms for the Case II environment of the Santa Barbara Channel. We conduct monthly to twice-monthly transect observations across the Santa Barbara Channel to develop an algorithm development and product validation data set. The PnB field program started in the summer of 1996. At each of the 7 PnB stations, a complete verification bio-geo-optical data set is collected. Included are redundant measures of apparent optical properties (remote sensing reflectance and diffuse attenuation spectra), as well as in situ profiles of spectral absorption, beam attenuation and backscattering coefficients. Water samples are analyzed for component in vivo absorption spectra, fluorometric chlorophyll, phytoplankton pigment (by the SDSU CHORS laboratory), and inorganic nutrient concentrations. A primary goal is to use the PnB field data set to objectively tune semi-analytical models of ocean color for this site and apply them using available satellite imagery (SeaWiFS and MODIS). In support of this goal, we have also been addressing SeaWiFS ocean color and AVHRR SST imagery. We also are using the PnB data set to address time/space variability of water masses in the Santa Barbara Channel and its relationship to the 1997/1998 El Nino. However, the comparison between PnB field observations and satellite estimates of primary products has been disappointing. We find that field estimates of water-leaving radiance, L(sub wN)(lambda), correspond poorly to satellite estimates for both SeaWiFS and MODIS local area coverage imagery. We believe this is due to poor atmospheric correction due to complex mixtures of aerosol types found in these near-coastal regions. Last, we remain active in outreach activities.

Use of GOES, SSM/I, TRMM Satellite Measurements Estimating Water Budget Variations in Gulf of Mexico - Caribbean Sea Basins

NASA Technical Reports Server (NTRS)

Smith, Eric A.

2004-01-01

This study presents results from a multi-satellite/multi-sensor retrieval system designed to obtain the atmospheric water budget over the open ocean. A combination of 3ourly-sampled monthly datasets derived from the GOES-8 5-channel Imager, the TRMM TMI radiometer, and the DMSP 7-channel passive microwave radiometers (SSM/I) have been acquired for the combined Gulf of Mexico-Caribbean Sea basin. Whereas the methodology has been tested over this basin, the retrieval system is designed for portability to any open-ocean region. Algorithm modules using the different datasets to retrieve individual geophysical parameters needed in the water budget equation are designed in a manner that takes advantage of the high temporal resolution of the GOES-8 measurements, as well as the physical relationships inherent to the TRMM and SSM/I passive microwave measurements in conjunction with water vapor, cloud liquid water, and rainfall. The methodology consists of retrieving the precipitation, surface evaporation, and vapor-cloud water storage terms in the atmospheric water balance equation from satellite techniques, with the water vapor advection term being obtained as the residue needed for balance. Thus, the intent is to develop a purely satellite-based method for obtaining the full set of terms in the atmospheric water budget equation without requiring in situ sounding information on the wind profile. The algorithm is validated by cross-checking all the algorithm components through multiple- algorithm retrieval intercomparisons. A further check on the validation is obtained by directly comparing water vapor transports into the targeted basin diagnosed from the satellite algorithms to those obtained observationally from a network of land-based upper air stations that nearly uniformly surround the basin, although it is fair to say that these checks are more effective m identifying problems in estimating vapor transports from a leaky operational radiosonde network than in verifying the transport estimates determined from the satellite algorithm system Total columnar atmospheric water budget results are presented for an extended annual cycle consisting of the months of October-97, January-98, April-98, July-98,October-98, and January 1999. These results are used to emphasize the changing relationship in E-P, as well as in the varying roles of storage and advection in balancing E-P both on daily and monthly time scales and on localized and basin space scales. Results from the algorithm-to-algorithm intercomparisons are also presented in the context of sensitivity testing to help understand the intrinsic uncertainties in evaluating the water budget terms by an all-satellite algorithm approach.

Effective pore size and radius of capture for K+ ions in K-channels

PubMed Central

Moldenhauer, Hans; Díaz-Franulic, Ignacio; González-Nilo, Fernando; Naranjo, David

2016-01-01

Reconciling protein functional data with crystal structure is arduous because rare conformations or crystallization artifacts occur. Here we present a tool to validate the dimensions of open pore structures of potassium-selective ion channels. We used freely available algorithms to calculate the molecular contour of the pore to determine the effective internal pore radius (rE) in several K-channel crystal structures. rE was operationally defined as the radius of the biggest sphere able to enter the pore from the cytosolic side. We obtained consistent rE estimates for MthK and Kv1.2/2.1 structures, with rE = 5.3–5.9 Å and rE = 4.5–5.2 Å, respectively. We compared these structural estimates with functional assessments of the internal mouth radii of capture (rC) for two electrophysiological counterparts, the large conductance calcium activated K-channel (rC = 2.2 Å) and the Shaker Kv-channel (rC = 0.8 Å), for MthK and Kv1.2/2.1 structures, respectively. Calculating the difference between rE and rC, produced consistent size radii of 3.1–3.7 Å and 3.6–4.4 Å for hydrated K+ ions. These hydrated K+ estimates harmonize with others obtained with diverse experimental and theoretical methods. Thus, these findings validate MthK and the Kv1.2/2.1 structures as templates for open BK and Kv-channels, respectively. PMID:26831782

Blind ICA detection based on second-order cone programming for MC-CDMA systems

NASA Astrophysics Data System (ADS)

Jen, Chih-Wei; Jou, Shyh-Jye

2014-12-01

The multicarrier code division multiple access (MC-CDMA) technique has received considerable interest for its potential application to future wireless communication systems due to its high data rate. A common problem regarding the blind multiuser detectors used in MC-CDMA systems is that they are extremely sensitive to the complex channel environment. Besides, the perturbation of colored noise may negatively affect the performance of the system. In this paper, a new coherent detection method will be proposed, which utilizes the modified fast independent component analysis (FastICA) algorithm, based on approximate negentropy maximization that is subject to the second-order cone programming (SOCP) constraint. The aim of the proposed coherent detection is to provide robustness against small-to-medium channel estimation mismatch (CEM) that may arise from channel frequency response estimation error in the MC-CDMA system, which is modulated by downlink binary phase-shift keying (BPSK) under colored noise. Noncoherent demodulation schemes are preferable to coherent demodulation schemes, as the latter are difficult to implement over time-varying fading channels. Differential phase-shift keying (DPSK) is therefore the natural choice for an alternative modulation scheme. Furthermore, the new blind differential SOCP-based ICA (SOCP-ICA) detection without channel estimation and compensation will be proposed to combat Doppler spread caused by time-varying fading channels in the DPSK-modulated MC-CDMA system under colored noise. In this paper, numerical simulations are used to illustrate the robustness of the proposed blind coherent SOCP-ICA detector against small-to-medium CEM and to emphasize the advantage of the blind differential SOCP-ICA detector in overcoming Doppler spread.

Sound source tracking device for telematic spatial sound field reproduction

NASA Astrophysics Data System (ADS)

Cardenas, Bruno

This research describes an algorithm that localizes sound sources for use in telematic applications. The localization algorithm is based on amplitude differences between various channels of a microphone array of directional shotgun microphones. The amplitude differences will be used to locate multiple performers and reproduce their voices, which were recorded at close distance with lavalier microphones, spatially corrected using a loudspeaker rendering system. In order to track multiple sound sources in parallel the information gained from the lavalier microphones will be utilized to estimate the signal-to-noise ratio between each performer and the concurrent performers.

Research Supporting Satellite Communications Technology

NASA Technical Reports Server (NTRS)

Horan Stephen; Lyman, Raphael

2005-01-01

This report describes the second year of research effort under the grant Research Supporting Satellite Communications Technology. The research program consists of two major projects: Fault Tolerant Link Establishment and the design of an Auto-Configurable Receiver. The Fault Tolerant Link Establishment protocol is being developed to assist the designers of satellite clusters to manage the inter-satellite communications. During this second year, the basic protocol design was validated with an extensive testing program. After this testing was completed, a channel error model was added to the protocol to permit the effects of channel errors to be measured. This error generation was used to test the effects of channel errors on Heartbeat and Token message passing. The C-language source code for the protocol modules was delivered to Goddard Space Flight Center for integration with the GSFC testbed. The need for a receiver autoconfiguration capability arises when a satellite-to-ground transmission is interrupted due to an unexpected event, the satellite transponder may reset to an unknown state and begin transmitting in a new mode. During Year 2, we completed testing of these algorithms when noise-induced bit errors were introduced. We also developed and tested an algorithm for estimating the data rate, assuming an NRZ-formatted signal corrupted with additive white Gaussian noise, and we took initial steps in integrating both algorithms into the SDR test bed at GSFC.

High-precision approach to localization scheme of visible light communication based on artificial neural networks and modified genetic algorithms

NASA Astrophysics Data System (ADS)

Guan, Weipeng; Wu, Yuxiang; Xie, Canyu; Chen, Hao; Cai, Ye; Chen, Yingcong

2017-10-01

An indoor positioning algorithm based on visible light communication (VLC) is presented. This algorithm is used to calculate a three-dimensional (3-D) coordinate of an indoor optical wireless environment, which includes sufficient orders of multipath reflections from reflecting surfaces of the room. Leveraging the global optimization ability of the genetic algorithm (GA), an innovative framework for 3-D position estimation based on a modified genetic algorithm is proposed. Unlike other techniques using VLC for positioning, the proposed system can achieve indoor 3-D localization without making assumptions about the height or acquiring the orientation angle of the mobile terminal. Simulation results show that an average localization error of less than 1.02 cm can be achieved. In addition, in most VLC-positioning systems, the effect of reflection is always neglected and its performance is limited by reflection, which makes the results not so accurate for a real scenario and the positioning errors at the corners are relatively larger than other places. So, we take the first-order reflection into consideration and use artificial neural network to match the model of a nonlinear channel. The studies show that under the nonlinear matching of direct and reflected channels the average positioning errors of four corners decrease from 11.94 to 0.95 cm. The employed algorithm is emerged as an effective and practical method for indoor localization and outperform other existing indoor wireless localization approaches.

Lossless compression algorithm for multispectral imagers

NASA Astrophysics Data System (ADS)

Gladkova, Irina; Grossberg, Michael; Gottipati, Srikanth

2008-08-01

Multispectral imaging is becoming an increasingly important tool for monitoring the earth and its environment from space borne and airborne platforms. Multispectral imaging data consists of visible and IR measurements from a scene across space and spectrum. Growing data rates resulting from faster scanning and finer spatial and spectral resolution makes compression an increasingly critical tool to reduce data volume for transmission and archiving. Research for NOAA NESDIS has been directed to finding for the characteristics of satellite atmospheric Earth science Imager sensor data what level of Lossless compression ratio can be obtained as well as appropriate types of mathematics and approaches that can lead to approaching this data's entropy level. Conventional lossless do not achieve the theoretical limits for lossless compression on imager data as estimated from the Shannon entropy. In a previous paper, the authors introduce a lossless compression algorithm developed for MODIS as a proxy for future NOAA-NESDIS satellite based Earth science multispectral imagers such as GOES-R. The algorithm is based on capturing spectral correlations using spectral prediction, and spatial correlations with a linear transform encoder. In decompression, the algorithm uses a statistically computed look up table to iteratively predict each channel from a channel decompressed in the previous iteration. In this paper we present a new approach which fundamentally differs from our prior work. In this new approach, instead of having a single predictor for each pair of bands we introduce a piecewise spatially varying predictor which significantly improves the compression results. Our new algorithm also now optimizes the sequence of channels we use for prediction. Our results are evaluated by comparison with a state of the art wavelet based image compression scheme, Jpeg2000. We present results on the 14 channel subset of the MODIS imager, which serves as a proxy for the GOES-R imager. We will also show results of the algorithm for on NOAA AVHRR data and data from SEVIRI. The algorithm is designed to be adapted to the wide range of multispectral imagers and should facilitate distribution of data throughout globally. This compression research is managed by Roger Heymann, PE of OSD NOAA NESDIS Engineering, in collaboration with the NOAA NESDIS STAR Research Office through Mitch Goldberg, Tim Schmit, Walter Wolf.

Estimating Extracellular Spike Waveforms from CA1 Pyramidal Cells with Multichannel Electrodes

PubMed Central

Molden, Sturla; Moldestad, Olve; Storm, Johan F.

2013-01-01

Extracellular (EC) recordings of action potentials from the intact brain are embedded in background voltage fluctuations known as the “local field potential” (LFP). In order to use EC spike recordings for studying biophysical properties of neurons, the spike waveforms must be separated from the LFP. Linear low-pass and high-pass filters are usually insufficient to separate spike waveforms from LFP, because they have overlapping frequency bands. Broad-band recordings of LFP and spikes were obtained with a 16-channel laminar electrode array (silicone probe). We developed an algorithm whereby local LFP signals from spike-containing channel were modeled using locally weighted polynomial regression analysis of adjoining channels without spikes. The modeled LFP signal was subtracted from the recording to estimate the embedded spike waveforms. We tested the method both on defined spike waveforms added to LFP recordings, and on in vivo-recorded extracellular spikes from hippocampal CA1 pyramidal cells in anaesthetized mice. We show that the algorithm can correctly extract the spike waveforms embedded in the LFP. In contrast, traditional high-pass filters failed to recover correct spike shapes, albeit produceing smaller standard errors. We found that high-pass RC or 2-pole Butterworth filters with cut-off frequencies below 12.5 Hz, are required to retrieve waveforms comparable to our method. The method was also compared to spike-triggered averages of the broad-band signal, and yielded waveforms with smaller standard errors and less distortion before and after the spike. PMID:24391714

Optimal and robust control of transition

NASA Technical Reports Server (NTRS)

Bewley, T. R.; Agarwal, R.

1996-01-01

Optimal and robust control theories are used to determine feedback control rules that effectively stabilize a linearly unstable flow in a plane channel. Wall transpiration (unsteady blowing/suction) with zero net mass flux is used as the control. Control algorithms are considered that depend both on full flowfield information and on estimates of that flowfield based on wall skin-friction measurements only. The development of these control algorithms accounts for modeling errors and measurement noise in a rigorous fashion; these disturbances are considered in both a structured (Gaussian) and unstructured ('worst case') sense. The performance of these algorithms is analyzed in terms of the eigenmodes of the resulting controlled systems, and the sensitivity of individual eigenmodes to both control and observation is quantified.

Arctic Sea Ice Parameters from AMSR-E Data using Two Techniques, and Comparisons with Sea Ice from SSM

NASA Technical Reports Server (NTRS)

Comiso, Josefino C.; Parkinson, Claire L.

2007-01-01

We use two algorithms to process AMSR-E data in order to determine algorithm dependence, if any, on the estimates of sea ice concentration, ice extent and area, and trends and to evaluate how AMSR-E data compare with historical SSM/I data. The monthly ice concentrations derived from the two algorithms from AMSR-E data (the AMSR-E Bootstrap Algorithm, or ABA, and the enhanced NASA Team algorithm, or NT2) differ on average by about 1 to 3%, with data from the consolidated ice region being generally comparable for ABA and NT2 retrievals while data in the marginal ice zones and thin ice regions show higher values when the NT2 algorithm is used. The ice extents and areas derived separately from AMSR-E using these two algorithms are, however, in good agreement, with the differences (ABA-NT2) being about 6.6 x 10(exp 4) square kilometers on average for ice extents and -6.6 x 10(exp 4) square kilometers for ice area which are small compared to mean seasonal values of 10.5 x 10(exp 6) and 9.8 x 10(exp 6) for ice extent and area: respectively. Likewise, extents and areas derived from the same algorithm but from AMSR-E and SSM/I data are consistent but differ by about -24.4 x 10(exp 4) square kilometers and -13.9 x 10(exp 4) square kilometers, respectively. The discrepancies are larger with the estimates of extents than area mainly because of differences in channel selection and sensor resolutions. Trends in extent during the AMSR-E era were also estimated and results from all three data sets are shown to be in good agreement (within errors).

Two algorithms for neural-network design and training with application to channel equalization.

PubMed

Sweatman, C Z; Mulgrew, B; Gibson, G J

1998-01-01

We describe two algorithms for designing and training neural-network classifiers. The first, the linear programming slab algorithm (LPSA), is motivated by the problem of reconstructing digital signals corrupted by passage through a dispersive channel and by additive noise. It constructs a multilayer perceptron (MLP) to separate two disjoint sets by using linear programming methods to identify network parameters. The second, the perceptron learning slab algorithm (PLSA), avoids the computational costs of linear programming by using an error-correction approach to identify parameters. Both algorithms operate in highly constrained parameter spaces and are able to exploit symmetry in the classification problem. Using these algorithms, we develop a number of procedures for the adaptive equalization of a complex linear 4-quadrature amplitude modulation (QAM) channel, and compare their performance in a simulation study. Results are given for both stationary and time-varying channels, the latter based on the COST 207 GSM propagation model.

Mapping ephemeral stream networks in desert environments using very-high-spatial-resolution multispectral remote sensing

DOE PAGES

Hamada, Yuki; O'Connor, Ben L.; Orr, Andrew B.; ...

2016-03-26

In this paper, understanding the spatial patterns of ephemeral streams is crucial for understanding how hydrologic processes influence the abundance and distribution of wildlife habitats in desert regions. Available methods for mapping ephemeral streams at the watershed scale typically underestimate the size of channel networks. Although remote sensing is an effective means of collecting data and obtaining information on large, inaccessible areas, conventional techniques for extracting channel features are not sufficient in regions that have small topographic gradients and subtle target-background spectral contrast. By using very high resolution multispectral imagery, we developed a new algorithm that applies landscape information tomore » map ephemeral channels in desert regions of the Southwestern United States where utility-scale solar energy development is occurring. Knowledge about landscape features and structures was integrated into the algorithm using a series of spectral transformation and spatial statistical operations to integrate information about landscape features and structures. The algorithm extracted ephemeral stream channels at a local scale, with the result that approximately 900% more ephemeral streams was identified than what were identified by using the U.S. Geological Survey’s National Hydrography Dataset. The accuracy of the algorithm in detecting channel areas was as high as 92%, and its accuracy in delineating channel center lines was 91% when compared to a subset of channel networks that were digitized by using the very high resolution imagery. Although the algorithm captured stream channels in desert landscapes across various channel sizes and forms, it often underestimated stream headwaters and channels obscured by bright soils and sparse vegetation. While further improvement is warranted, the algorithm provides an effective means of obtaining detailed information about ephemeral streams, and it could make a significant contribution toward improving the hydrological modelling of desert environments.« less

Mapping ephemeral stream networks in desert environments using very-high-spatial-resolution multispectral remote sensing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamada, Yuki; O'Connor, Ben L.; Orr, Andrew B.

In this paper, understanding the spatial patterns of ephemeral streams is crucial for understanding how hydrologic processes influence the abundance and distribution of wildlife habitats in desert regions. Available methods for mapping ephemeral streams at the watershed scale typically underestimate the size of channel networks. Although remote sensing is an effective means of collecting data and obtaining information on large, inaccessible areas, conventional techniques for extracting channel features are not sufficient in regions that have small topographic gradients and subtle target-background spectral contrast. By using very high resolution multispectral imagery, we developed a new algorithm that applies landscape information tomore » map ephemeral channels in desert regions of the Southwestern United States where utility-scale solar energy development is occurring. Knowledge about landscape features and structures was integrated into the algorithm using a series of spectral transformation and spatial statistical operations to integrate information about landscape features and structures. The algorithm extracted ephemeral stream channels at a local scale, with the result that approximately 900% more ephemeral streams was identified than what were identified by using the U.S. Geological Survey’s National Hydrography Dataset. The accuracy of the algorithm in detecting channel areas was as high as 92%, and its accuracy in delineating channel center lines was 91% when compared to a subset of channel networks that were digitized by using the very high resolution imagery. Although the algorithm captured stream channels in desert landscapes across various channel sizes and forms, it often underestimated stream headwaters and channels obscured by bright soils and sparse vegetation. While further improvement is warranted, the algorithm provides an effective means of obtaining detailed information about ephemeral streams, and it could make a significant contribution toward improving the hydrological modelling of desert environments.« less

Retinex based low-light image enhancement using guided filtering and variational framework

NASA Astrophysics Data System (ADS)

Zhang, Shi; Tang, Gui-jin; Liu, Xiao-hua; Luo, Su-huai; Wang, Da-dong

2018-03-01

A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV color space to get the V channel. Next, the illuminations are respectively estimated by the guided filtering and the variational framework on the V channel and combined into a new illumination by average gradient. The new reflectance is calculated using V channel and the new illumination. Then a new V channel obtained by multiplying the new illumination and reflectance is processed with contrast limited adaptive histogram equalization (CLAHE). Finally, the new image in HSV space is converted back to RGB space to obtain the enhanced image. Experimental results show that the proposed method has better subjective quality and objective quality than existing methods.

Systematic Differences between Satellite-Based Presipitation Climatologies over the Tropical Oceans

NASA Technical Reports Server (NTRS)

Robertson, Frankin R.; Fitzjarrald, Dan; McCaul, Eugene W.

1999-01-01

Since the beginning of the World Climate Research Program's Global Precipitation Climatology Project (GPCP) satellite remote sensing of precipitation has made dramatic improvements, particularly for tropical regions. Data from microwave and infrared sensors now form the most critical input to precipitation data sets and can be calibrated with surface gauges to so that the strengths of each data source can be maximized in some statistically optimal sense. It is clear however that there still remain significant uncertainties with satellite precipitation retrievals which limit their usefulness for many purposes. Systematic differences i'A tropical precipitation estimates have been brought to light in comparison activities such as the GPCP Algorithm Intercomparison Project and more recent Wetnet Precipitation Intercomparison Project 3. These uncertainties are assuming more importance because of the demands for validation associated with global climate modeling and data assimilation methodologies. The objective of the present study is to determine the physical basis for systematic differences in spatial structure of tropical precipitation as portrayed by several different satellite-based data sets. The study is limited to oceanic regions only and deals primarily with aspects of spatial variability. We are specifically interested in why MSU channel 1 and GPI precipitation differences are so striking over the Eastern Pacific ITCZ and why they both differ from other microwave emission-based precipitation estimates from SSM/I and a scattering-based deep convective ice index from MSU channel 2. Our results to date have shown that MSU channel I precipitation estimates are biased high over the Eastern Pacific ITCZ because of two factors: (1) the hypersensitivity of this frequency to cloud water in contrast to falling rain drops, and (2) unaccounted for scattering effects by precipitation-size ice which depresses the signal of the liquid water emission. Likewise, cold cloud top climatologies such as the GPI show an excess (a deficit) in estimated rainfall over the E. Pacific ITCZ (Warm Pool region). We show that these algorithms need to account for regionally varying heights (or temperatures) at which tropical convection detrains to form cirrus shields. A second objective we pursue is to identify variations in the macroscale cloud physical and thermodynamic properties of precipitation regimes" and relate these differences to tropical dynamical mechanisms of tropical heat and moisture balance. Finally, we interpret the algorithm differences and their associations with tropical dynamics in terms of WCRP GPCP goals for constructing precipitation climatologies.

Investigating methods to estimate melting event parameters over Arctic sea- ice using SSM/I, OKEAN, and RADARSAT Data

NASA Astrophysics Data System (ADS)

Belchansky, G.; Eremeev, V.; Mordvintsev, I.; Platonov, N.; Douglas, D.

The melting events (early melt, melt onset, melt ponding, freeze-up onset) over Arctic sea-ice area are critical for climate and global change studies. They are combined with accuracy of surface energy balances estimates (due to contrasts in the short wave albedo of snow and ice, open water or melt ponds) and drives a number of important processes (onset of snow melt, thawing of boreal forest, etc). M icrowave measurements identify seasonal transition zones due to large differences in emissivity during melt onset, melt ponding and freeze-up periods. This report presents near coincident observation of backscatter cross section (0 ) and brightness temperature (Tb) from Russian OKEAN 01 satellite series, backscatter cross section (0) from RADARSAT-1, brightness temperatures (Tbs) from SSM/I sensors, and near-surface temperature derived from the International Arctic Buoy Program data (IABP) (Belchansky and Douglas, 2000, 2002). To determine the melt duration (time of freeze-up onset minus time of melt onset) passive and active microwave methods were developed. These methods used differences between SSM /I 19.3GHz,H and SSM/I 37.0 GHz, H channels (SSM/I Tb), OKEAN 0 (9.52GHz, VV) and Tb (37.47 GHz, H) channels, RADARSAT-1 0 (5.3GHz, HH), and a threshold technique. An evolution of the SSM/I Tb, OKEAN-01 0 and Tb, RADARSAT ScanSAR 0, MEAN ( 0), SD(0) and SD(0 ) / MEAN(0 ) as function of time was investigated along FY and MY dominant type ice areas during January 1996 through December 1998. The SSM/I, OKEAN and RADARSAT melt onset and freeze up onset algorithms were constructed. The SSM/I algorithm was based- on analysis of the SSM/I Tb. The OKEAN and RADARSAT ScanSAR algorithms were based, respectively, on analysis of OKEAN 0 and Tb of MY and FY sea ice at each MY and FY ice region (200 km by 200 km) determined in OKEAN imagery prior to melting period and changes in RADARSAT SD(0 ) / MEAN(0) of sea-ice during different stages of melting processes at each ice site (75 km by 75 km) determined prior to spring period in ScanSAR imagery. The averaged 12-h near surface temperatures derived from the IABP wer e used to analyze changes in the SSM/I Tb, OKEAN 0 and OKEAN Tb, RADARSAT SD(0) / MEAN(0), and to estimate respective thresholds associated with the melt onset and freeze-up onset. To highlight the sources of differences among various sensors results were compared to understand how the average the melt onset, melt duration and freeze-up onset estimates varied between different instruments and algorithms. A discrepancy in estimates resulted due to the nature of active and passive microwave measurements, frequency and polarization, number of channels, temperature and emissivity effects, and algorithm types. Higher spatial resolution of OKEAN-01 and RADARSAT-1 SAR was an important characteristic for obtaining better estimates of melting parameters. The SSM/ data provide a spatial resolution with global coverageI suitable for circulation models. Therefore OKEAN-01 and RADARSAT measurements can complement SSM/I data. These studies contribute to the growing body of documentation about the levels of disparity obtained when Arctic seasonal transition parameters are calculated using various types of satellite sensors and algorithms. ACKNOWLEDGEMENTS This work was carried out with the support from the International Arctic Research Center and Cooperative Institute for Arctic Research (IARC/CIFAR), University of Alaska Fairbanks. We would like to acknowledge the Alaska SAR Facility (Fairbanks), the National Snow and Ice Data Center (University of Colorado), and the Global Hydrology Resource Center, respectively, for providing RADARSAT images, the DMSP SSM/I Daily Polar Gridded Tb and Sea Ice Concentrations, the single-pass SSM/I brightness temperature data. REFERENCES Belchansky, G. I. and Douglas, D. C. (2000). Classification methods for monitoring Arctic sea-ice using OKEAN passive / active two-channel microwave data. J. Remote Sensing of Environment, Elsevier Science, New York. 73 (3): 307 -322. Belchansky, G. I. and Douglas, D. C. (2002). Seasonal comparisons of sea ice concentration estimates derived from SSM /I, OKEAN, and RADARSAT data. J. Remote Sensing of Environment, Elsevier Science, New York, 81 (1): 67-81.

Object tracking algorithm based on the color histogram probability distribution

NASA Astrophysics Data System (ADS)

Li, Ning; Lu, Tongwei; Zhang, Yanduo

2018-04-01

In order to resolve tracking failure resulted from target's being occlusion and follower jamming caused by objects similar to target in the background, reduce the influence of light intensity. This paper change HSV and YCbCr color channel correction the update center of the target, continuously updated image threshold self-adaptive target detection effect, Clustering the initial obstacles is roughly range, shorten the threshold range, maximum to detect the target. In order to improve the accuracy of detector, this paper increased the Kalman filter to estimate the target state area. The direction predictor based on the Markov model is added to realize the target state estimation under the condition of background color interference and enhance the ability of the detector to identify similar objects. The experimental results show that the improved algorithm more accurate and faster speed of processing.

An Underlay Communication Channel for 5G Cognitive Mesh Networks: Packet Design, Implementation, Analysis, and Experimental Results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tarek Haddadin; Stephen Andrew Laraway; Arslan Majid

This paper proposes and presents the design and implementation of an underlay communication channel (UCC) for 5G cognitive mesh networks. The UCC builds its waveform based on filter bank multicarrier spread spectrum (FB-MCSS) signaling. The use of this novel spread spectrum signaling allows the device-to-device (D2D) user equipments (UEs) to communicate at a level well below noise temperature and hence, minimize taxation on macro-cell/small-cell base stations and their UEs in 5G wireless systems. Moreover, the use of filter banks allows us to avoid those portions of the spectrum that are in use by macro-cell and small-cell users. Hence, both D2D-to-cellularmore » and cellular-to-D2D interference will be very close to none. We propose a specific packet for UCC and develop algorithms for packet detection, timing acquisition and tracking, as well as channel estimation and equalization. We also present the detail of an implementation of the proposed transceiver on a software radio platform and compare our experimental results with those from a theoretical analysis of our packet detection algorithm.« less

Comparative intelligibility investigation of single-channel noise-reduction algorithms for Chinese, Japanese, and English.

PubMed

Li, Junfeng; Yang, Lin; Zhang, Jianping; Yan, Yonghong; Hu, Yi; Akagi, Masato; Loizou, Philipos C

2011-05-01

A large number of single-channel noise-reduction algorithms have been proposed based largely on mathematical principles. Most of these algorithms, however, have been evaluated with English speech. Given the different perceptual cues used by native listeners of different languages including tonal languages, it is of interest to examine whether there are any language effects when the same noise-reduction algorithm is used to process noisy speech in different languages. A comparative evaluation and investigation is taken in this study of various single-channel noise-reduction algorithms applied to noisy speech taken from three languages: Chinese, Japanese, and English. Clean speech signals (Chinese words and Japanese words) were first corrupted by three types of noise at two signal-to-noise ratios and then processed by five single-channel noise-reduction algorithms. The processed signals were finally presented to normal-hearing listeners for recognition. Intelligibility evaluation showed that the majority of noise-reduction algorithms did not improve speech intelligibility. Consistent with a previous study with the English language, the Wiener filtering algorithm produced small, but statistically significant, improvements in intelligibility for car and white noise conditions. Significant differences between the performances of noise-reduction algorithms across the three languages were observed.

An Optical Flow-Based Full Reference Video Quality Assessment Algorithm.

PubMed

K, Manasa; Channappayya, Sumohana S

2016-06-01

We present a simple yet effective optical flow-based full-reference video quality assessment (FR-VQA) algorithm for assessing the perceptual quality of natural videos. Our algorithm is based on the premise that local optical flow statistics are affected by distortions and the deviation from pristine flow statistics is proportional to the amount of distortion. We characterize the local flow statistics using the mean, the standard deviation, the coefficient of variation (CV), and the minimum eigenvalue ( λ min ) of the local flow patches. Temporal distortion is estimated as the change in the CV of the distorted flow with respect to the reference flow, and the correlation between λ min of the reference and of the distorted patches. We rely on the robust multi-scale structural similarity index for spatial quality estimation. The computed temporal and spatial distortions, thus, are then pooled using a perceptually motivated heuristic to generate a spatio-temporal quality score. The proposed method is shown to be competitive with the state-of-the-art when evaluated on the LIVE SD database, the EPFL Polimi SD database, and the LIVE Mobile HD database. The distortions considered in these databases include those due to compression, packet-loss, wireless channel errors, and rate-adaptation. Our algorithm is flexible enough to allow for any robust FR spatial distortion metric for spatial distortion estimation. In addition, the proposed method is not only parameter-free but also independent of the choice of the optical flow algorithm. Finally, we show that the replacement of the optical flow vectors in our proposed method with the much coarser block motion vectors also results in an acceptable FR-VQA algorithm. Our algorithm is called the flow similarity index.

Estimation of optical properties of aerosols and bidirectional reflectance from PARASOL/POLDER data over land

NASA Astrophysics Data System (ADS)

Kusaka, Takashi; Miyazaki, Go

2014-10-01

When monitoring target areas covered with vegetation from a satellite, it is very useful to estimate the vegetation index using the surface anisotropic reflectance, which is dependent on both solar and viewing geometries, from satellite data. In this study, the algorithm for estimating optical properties of atmospheric aerosols such as the optical thickness (τ), the refractive index (Nr), the mixing ratio of small particles in the bimodal log-normal distribution function (C) and the bidirectional reflectance (R) from only the radiance and polarization at the 865nm channel received by the PARASOL/POLDER is described. Parameters of the bimodal log-normal distribution function: mean radius, r1, standard deviation, σ1, of fine aerosols, and r2, σ2 of coarse aerosols were fixed, and these values were estimated from monthly averaged size distribution at AERONET sites managed by NASA near the target area. Moreover, it is assumed that the contribution of the surface reflectance with directional anisotropy to the polarized radiance received by the satellite is small because it is shown from our ground-based polarization measurements of light ray reflected by the grassland that degrees of polarization of the reflected light by the grassland are very low values at the 865nm channel. First aerosol properties were estimated from only the polarized radiance and then the bidirectional reflectance given by the Ross-Li BRDF model was estimated from only the total radiance at target areas in PARASOL/POLDER data over the Japanese islands taken on April 28, 2012 and April 25, 2010. The estimated optical thickness of aerosols was checked with those given in AERONET sites and the estimated parameters of BRDF were compared with those of vegetation measured from the radio-controlled helicopter. Consequently, it is shown that the algorithm described in the present study provides reasonable values for aerosol properties and surface bidirectional reflectance.

A source number estimation method for single optical fiber sensor

NASA Astrophysics Data System (ADS)

Hu, Junpeng; Huang, Zhiping; Su, Shaojing; Zhang, Yimeng; Liu, Chunwu

2015-10-01

The single-channel blind source separation (SCBSS) technique makes great significance in many fields, such as optical fiber communication, sensor detection, image processing and so on. It is a wide range application to realize blind source separation (BSS) from a single optical fiber sensor received data. The performance of many BSS algorithms and signal process methods will be worsened with inaccurate source number estimation. Many excellent algorithms have been proposed to deal with the source number estimation in array signal process which consists of multiple sensors, but they can not be applied directly to the single sensor condition. This paper presents a source number estimation method dealing with the single optical fiber sensor received data. By delay process, this paper converts the single sensor received data to multi-dimension form. And the data covariance matrix is constructed. Then the estimation algorithms used in array signal processing can be utilized. The information theoretic criteria (ITC) based methods, presented by AIC and MDL, Gerschgorin's disk estimation (GDE) are introduced to estimate the source number of the single optical fiber sensor's received signal. To improve the performance of these estimation methods at low signal noise ratio (SNR), this paper make a smooth process to the data covariance matrix. By the smooth process, the fluctuation and uncertainty of the eigenvalues of the covariance matrix are reduced. Simulation results prove that ITC base methods can not estimate the source number effectively under colored noise. The GDE method, although gets a poor performance at low SNR, but it is able to accurately estimate the number of sources with colored noise. The experiments also show that the proposed method can be applied to estimate the source number of single sensor received data.

Development of a 3D Stream Network and Topography for Improved Large-Scale Hydraulic Modeling

NASA Astrophysics Data System (ADS)

Saksena, S.; Dey, S.; Merwade, V.

2016-12-01

Most digital elevation models (DEMs) used for hydraulic modeling do not include channel bed elevations. As a result, the DEMs are complimented with additional bathymetric data for accurate hydraulic simulations. Existing methods to acquire bathymetric information through field surveys or through conceptual models are limited to reach-scale applications. With an increasing focus on large scale hydraulic modeling of rivers, a framework to estimate and incorporate bathymetry for an entire stream network is needed. This study proposes an interpolation-based algorithm to estimate bathymetry for a stream network by modifying the reach-based empirical River Channel Morphology Model (RCMM). The effect of a 3D stream network that includes river bathymetry is then investigated by creating a 1D hydraulic model (HEC-RAS) and 2D hydrodynamic model (Integrated Channel and Pond Routing) for the Upper Wabash River Basin in Indiana, USA. Results show improved simulation of flood depths and storage in the floodplain. Similarly, the impact of river bathymetry incorporation is more significant in the 2D model as compared to the 1D model.

SMOS/SMAP Synergy for SMAP Level 2 Soil Moisture Algorithm Evaluation

NASA Technical Reports Server (NTRS)

Bindlish, Rajat; Jackson, Thomas J.; Zhao, Tianjie; Cosh, Michael; Chan, Steven; O'Neill, Peggy; Njoku, Eni; Colliander, Andreas; Kerr, Yann

2011-01-01

Soil Moisture Active Passive (SMAP) satellite has been proposed to provide global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolutions, respectively. SMAP would also provide a radiometer-only soil moisture product at 40-km spatial resolution. This product and the supporting brightness temperature observations are common to both SMAP and European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. As a result, there are opportunities for synergies between the two missions. These include exploiting the data for calibration and validation and establishing longer term L-band brightness temperature and derived soil moisture products. In this investigation we will be using SMOS brightness temperature, ancillary data, and soil moisture products to develop and evaluate a candidate SMAP L2 passive soil moisture retrieval algorithm. This work will begin with evaluations based on the SMOS product grids and ancillary data sets and transition to those that will be used by SMAP. An important step in this analysis is reprocessing the multiple incidence angle observations provided by SMOS to a global brightness temperature product that simulates the constant 40 degree incidence angle observations that SMAP will provide. The reprocessed brightness temperature data provide a basis for evaluating different SMAP algorithm alternatives. Several algorithms are being considered for the SMAP radiometer-only soil moisture retrieval. In this first phase, we utilized only the Single Channel Algorithm (SCA), which is based on the radiative transfer equation and uses the channel that is most sensitive to soil moisture (H-pol). Brightness temperature is corrected sequentially for the effects of temperature, vegetation, roughness (dynamic ancillary data sets) and soil texture (static ancillary data set). European Centre for Medium-Range Weather Forecasts (ECMWF) estimates of soil temperature for the top layer (as provided as part of the SMOS ancillary data) were used to correct for surface temperature effects and to derive microwave emissivity. ECMWF data were also used for precipitation forecasts, presence of snow, and frozen ground. Vegetation options are described below. One year of soil moisture observations from a set of four watersheds in the U.S. were used to evaluate four different retrieval methodologies: (1) SMOS soil moisture estimates (version 400), (2) SeA soil moisture estimates using the SMOS/SMAP data with SMOS estimated vegetation optical depth, which is part of the SMOS level 2 product, (3) SeA soil moisture estimates using the SMOS/SMAP data and the MODIS-based vegetation climatology data, and (4) SeA soil moisture estimates using the SMOS/SMAP data and actual MODIS observations. The use of SMOS real-world global microwave observations and the analyses described here will help in the development and selection of different land surface parameters and ancillary observations needed for the SMAP soil moisture algorithms. These investigations will greatly improve the quality and reliability of this SMAP product at launch.

Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network

PubMed Central

Lin, Kai; Wang, Di; Hu, Long

2016-01-01

With the development of wireless technology, the widespread use of 5G is already an irreversible trend, and millimeter-wave sensor networks are becoming more and more common. However, due to the high degree of complexity and bandwidth bottlenecks, the millimeter-wave sensor network still faces numerous problems. In this paper, we propose a novel content-based multi-channel network coding algorithm, which uses the functions of data fusion, multi-channel and network coding to improve the data transmission; the algorithm is referred to as content-based multi-channel network coding (CMNC). The CMNC algorithm provides a fusion-driven model based on the Dempster-Shafer (D-S) evidence theory to classify the sensor nodes into different classes according to the data content. By using the result of the classification, the CMNC algorithm also provides the channel assignment strategy and uses network coding to further improve the quality of data transmission in the millimeter-wave sensor network. Extensive simulations are carried out and compared to other methods. Our simulation results show that the proposed CMNC algorithm can effectively improve the quality of data transmission and has better performance than the compared methods. PMID:27376302

Algorithm for Simulating Atmospheric Turbulence and Aeroelastic Effects on Simulator Motion Systems

NASA Technical Reports Server (NTRS)

Ercole, Anthony V.; Cardullo, Frank M.; Kelly, Lon C.; Houck, Jacob A.

2012-01-01

Atmospheric turbulence produces high frequency accelerations in aircraft, typically greater than the response to pilot input. Motion system equipped flight simulators must present cues representative of the aircraft response to turbulence in order to maintain the integrity of the simulation. Currently, turbulence motion cueing produced by flight simulator motion systems has been less than satisfactory because the turbulence profiles have been attenuated by the motion cueing algorithms. This report presents a new turbulence motion cueing algorithm, referred to as the augmented turbulence channel. Like the previous turbulence algorithms, the output of the channel only augments the vertical degree of freedom of motion. This algorithm employs a parallel aircraft model and an optional high bandwidth cueing filter. Simulation of aeroelastic effects is also an area where frequency content must be preserved by the cueing algorithm. The current aeroelastic implementation uses a similar secondary channel that supplements the primary motion cue. Two studies were conducted using the NASA Langley Visual Motion Simulator and Cockpit Motion Facility to evaluate the effect of the turbulence channel and aeroelastic model on pilot control input. Results indicate that the pilot is better correlated with the aircraft response, when the augmented channel is in place.

Refining atmosphere light to improve the dark channel prior algorithm

NASA Astrophysics Data System (ADS)

Gan, Ling; Li, Dagang; Zhou, Can

2017-05-01

The defogging image gotten through dark channel prior algorithm has some shortcomings, such like color distortion, dimmer light and detail-loss near the observer. The main reasons are that the atmosphere light is estimated as one value and its change in different scene depth is not considered. So we modeled the atmosphere, one parameter of the defogging model. Firstly, we scatter the atmosphere light into equivalent point and build discrete model of the light. Secondly, we build some rough and possible models through analyzing the relationship between the atmosphere light and the medium transmission. Finally, by analyzing the results of many experiments qualitatively and quantitatively, we get the selected and optimized model. Although using this method causes the time-consuming to increase slightly, the evaluations, histogram correlation coefficient and peak signal-to-noise ratio are improved significantly and the defogging result is more conformed to human visual. And the color and the details near the observer in the defogging image are better than that achieved by the primal method.

Joint Source-Channel Coding by Means of an Oversampled Filter Bank Code

NASA Astrophysics Data System (ADS)

Marinkovic, Slavica; Guillemot, Christine

2006-12-01

Quantized frame expansions based on block transforms and oversampled filter banks (OFBs) have been considered recently as joint source-channel codes (JSCCs) for erasure and error-resilient signal transmission over noisy channels. In this paper, we consider a coding chain involving an OFB-based signal decomposition followed by scalar quantization and a variable-length code (VLC) or a fixed-length code (FLC). This paper first examines the problem of channel error localization and correction in quantized OFB signal expansions. The error localization problem is treated as an[InlineEquation not available: see fulltext.]-ary hypothesis testing problem. The likelihood values are derived from the joint pdf of the syndrome vectors under various hypotheses of impulse noise positions, and in a number of consecutive windows of the received samples. The error amplitudes are then estimated by solving the syndrome equations in the least-square sense. The message signal is reconstructed from the corrected received signal by a pseudoinverse receiver. We then improve the error localization procedure by introducing a per-symbol reliability information in the hypothesis testing procedure of the OFB syndrome decoder. The per-symbol reliability information is produced by the soft-input soft-output (SISO) VLC/FLC decoders. This leads to the design of an iterative algorithm for joint decoding of an FLC and an OFB code. The performance of the algorithms developed is evaluated in a wavelet-based image coding system.

Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties

NASA Astrophysics Data System (ADS)

Yang, Shuangming; Deng, Bin; Wang, Jiang; Li, Huiyan; Liu, Chen; Fietkiewicz, Chris; Loparo, Kenneth A.

2017-01-01

Real-time estimation of dynamical characteristics of thalamocortical cells, such as dynamics of ion channels and membrane potentials, is useful and essential in the study of the thalamus in Parkinsonian state. However, measuring the dynamical properties of ion channels is extremely challenging experimentally and even impossible in clinical applications. This paper presents and evaluates a real-time estimation system for thalamocortical hidden properties. For the sake of efficiency, we use a field programmable gate array for strictly hardware-based computation and algorithm optimization. In the proposed system, the FPGA-based unscented Kalman filter is implemented into a conductance-based TC neuron model. Since the complexity of TC neuron model restrains its hardware implementation in parallel structure, a cost efficient model is proposed to reduce the resource cost while retaining the relevant ionic dynamics. Experimental results demonstrate the real-time capability to estimate thalamocortical hidden properties with high precision under both normal and Parkinsonian states. While it is applied to estimate the hidden properties of the thalamus and explore the mechanism of the Parkinsonian state, the proposed method can be useful in the dynamic clamp technique of the electrophysiological experiments, the neural control engineering and brain-machine interface studies.

Retrieved Vertical Profiles of Latent Heat Release Using TRMM Rainfall Products

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Olson, W. S.; Meneghini, R.; Yang, S.; Simpson, J.; Kummerow, C.; Smith, E.

2000-01-01

This paper represents the first attempt to use TRMM rainfall information to estimate the four dimensional latent heating structure over the global tropics for February 1998. The mean latent heating profiles over six oceanic regions (TOGA COARE IFA, Central Pacific, S. Pacific Convergence Zone, East Pacific, Indian Ocean and Atlantic Ocean) and three continental regions (S. America, Central Africa and Australia) are estimated and studied. The heating profiles obtained from the results of diagnostic budget studies over a broad range of geographic locations are used to provide comparisons and indirect validation for the heating algorithm estimated heating profiles. Three different latent heating algorithms, the Goddard Convective-Stratiform (CSH) heating, the Goddard Profiling (GPROF) heating, and the Hydrometeor heating (HH) are used and their results are intercompared. The horizontal distribution or patterns of latent heat release from the three different heating retrieval methods are quite similar. They all can identify the areas of major convective activity (i.e., a well defined ITCZ in the Pacific, a distinct SPCZ) in the global tropics. The magnitude of their estimated latent heating release is also not in bad agreement with each other and with those determined from diagnostic budget studies. However, the major difference among these three heating retrieval algorithms is the altitude of the maximum heating level. The CSH algorithm estimated heating profiles only show one maximum heating level, and the level varies between convective activity from various geographic locations. These features are in good agreement with diagnostic budget studies. By contrast, two maximum heating levels were found using the GPROF heating and HH algorithms. The latent heating profiles estimated from all three methods can not show cooling between active convective events. We also examined the impact of different TMI (Multi-channel Passive Microwave Sensor) and PR (Precipitation Radar) rainfall information on latent heating structures.

Algorithm Development of a Telephone Signal Conditioner for the Wideband Integrated Network.

DTIC Science & Technology

1981-12-31

Fig. 25c presents the smoothI I ~~~- -. !... " -.... . . 89 -4 C 0 4.) -41 41 04 4) 4.) 04 r4 90 ., 0 .4) 4J 4.) > :$- 91 4-4 44 󈧭 4-4 󈧰J 44 04 N &Q...Channel Estimation May 25, 1979 4. John D. Markel, Steven B. Davis, and Ted Applebaum A Methodology for Studying Telephone Amplitude Distortion Effects

Infrared traffic image enhancement algorithm based on dark channel prior and gamma correction

NASA Astrophysics Data System (ADS)

Zheng, Lintao; Shi, Hengliang; Gu, Ming

2017-07-01

The infrared traffic image acquired by the intelligent traffic surveillance equipment has low contrast, little hierarchical differences in perceptions of image and the blurred vision effect. Therefore, infrared traffic image enhancement, being an indispensable key step, is applied to nearly all infrared imaging based traffic engineering applications. In this paper, we propose an infrared traffic image enhancement algorithm that is based on dark channel prior and gamma correction. In existing research dark channel prior, known as a famous image dehazing method, here is used to do infrared image enhancement for the first time. Initially, in the proposed algorithm, the original degraded infrared traffic image is transformed with dark channel prior as the initial enhanced result. A further adjustment based on the gamma curve is needed because initial enhanced result has lower brightness. Comprehensive validation experiments reveal that the proposed algorithm outperforms the current state-of-the-art algorithms.

TOPEX/POSEIDON microwave radiometer performance and in-flight calibration

NASA Technical Reports Server (NTRS)

Ruf, C. S.; Keihm, Stephen J.; Subramanya, B.; Janssen, Michael A.

1994-01-01

Results of the in-flight calibration and performance evaluation campaign for the TOPEX/POSEIDON microwave radiometer (TMR) are presented. Intercomparisons are made between TMR and various sources of ground truth, including ground-based microwave water vapor radiometers, radiosondes, global climatological models, special sensor microwave imager data over the Amazon rain forest, and models of clear, calm, subpolar ocean regions. After correction for preflight errors in the processing of thermal/vacuum data, relative channel offsets in the open ocean TMR brightness temperatures were noted at the approximately = 1 K level for the three TMR frequencies. Larger absolute offsets of 6-9 K over the rain forest indicated a approximately = 5% gain error in the three channel calibrations. This was corrected by adjusting the antenna pattern correction (APC) algorithm. AS 10% scale error in the TMR path delay estimates, relative to coincident radiosondes, was corrected in part by the APC adjustment and in part by a 5% modification to the value assumed for the 22.235 FGHz water vapor line strength in the path delay retrieval algorithm. After all in-flight corrections to the calibration, TMR global retrieval accuracy for the wet tropospheric range correction is estimated at 1.1 cm root mean square (RMS) with consistent peformance under clear, cloudy, and windy conditions.

Correcting infrared satellite estimates of sea surface temperature for atmospheric water vapor attenuation

NASA Technical Reports Server (NTRS)

Emery, William J.; Yu, Yunyue; Wick, Gary A.; Schluessel, Peter; Reynolds, Richard W.

1994-01-01

A new satellite sea surface temperature (SST) algorithm is developed that uses nearly coincident measurements from the microwave special sensor microwave imager (SSM/I) to correct for atmospheric moisture attenuation of the infrared signal from the advanced very high resolution radiometer (AVHRR). This new SST algorithm is applied to AVHRR imagery from the South Pacific and Norwegian seas, which are then compared with simultaneous in situ (ship based) measurements of both skin and bulk SST. In addition, an SST algorithm using a quadratic product of the difference between the two AVHRR thermal infrared channels is compared with the in situ measurements. While the quadratic formulation provides a considerable improvement over the older cross product (CPSST) and multichannel (MCSST) algorithms, the SSM/I corrected SST (called the water vapor or WVSST) shows overall smaller errors when compared to both the skin and bulk in situ SST observations. Applied to individual AVHRR images, the WVSST reveals an SST difference pattern (CPSST-WVSST) similar in shape to the water vapor structure while the CPSST-quadratic SST difference appears unrelated in pattern to the nearly coincident water vapor pattern. An application of the WVSST to week-long composites of global area coverage (GAC) AVHRR data demonstrates again the manner in which the WVSST corrects the AVHRR for atmospheric moisture attenuation. By comparison the quadratic SST method underestimates the SST corrections in the lower latitudes and overestimates the SST in th e higher latitudes. Correlations between the AVHRR thermal channel differences and the SSM/I water vapor demonstrate the inability of the channel difference to represent water vapor in the midlatitude and high latitudes during summer. Compared against drifting buoy data the WVSST and the quadratic SST both exhibit the same general behavior with the relatively small differences with the buoy temperatures.

Ion flux through membrane channels--an enhanced algorithm for the Poisson-Nernst-Planck model.

PubMed

Dyrka, Witold; Augousti, Andy T; Kotulska, Malgorzata

2008-09-01

A novel algorithmic scheme for numerical solution of the 3D Poisson-Nernst-Planck model is proposed. The algorithmic improvements are universal and independent of the detailed physical model. They include three major steps: an adjustable gradient-based step value, an adjustable relaxation coefficient, and an optimized segmentation of the modeled space. The enhanced algorithm significantly accelerates the speed of computation and reduces the computational demands. The theoretical model was tested on a regular artificial channel and validated on a real protein channel-alpha-hemolysin, proving its efficiency. (c) 2008 Wiley Periodicals, Inc.

Data Assimilation Experiments using Quality Controlled AIRS Version 5 Temperature Soundings

NASA Technical Reports Server (NTRS)

SUsskind, Joel

2008-01-01

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains two significant improvements over Version 4: 1) Improved physics allows for use of AIRS observations in the entire 4.3 pm C02 absorption band in the retrieval of temperature profile T(p) during both day and night. Tropospheric sounding 15 pm C02 observations are now used primarily in the generation of cloud cleared radiances Ri. This approach allows for the generation of accurate values of Ri and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by- channel error estimates for Ri. These error estimates are used for quality control of the retrieved products. We have conducted forecast impact experiments assimilating AIRS temperature profiles with different levels of quality control using the NASA GEOS-5 data assimilation system. Assimilation of quality controlled T(p) resulted in significantly improved forecast skill compared to that obtained from analyses obtained when all data used operationally by NCEP, except for AIRS data, is assimilated. We also conducted an experiment assimilating AIRS radiances uncontaminated by clouds, as done Operationally by ECMWF and NCEP. Forecasts resulting from assimilated AIRS radiances were of poorer quality than those obtained assimilating AIRS temperatures.

Performance analysis of cross-layer design with average PER constraint over MIMO fading channels

NASA Astrophysics Data System (ADS)

Dang, Xiaoyu; Liu, Yan; Yu, Xiangbin

2015-12-01

In this article, a cross-layer design (CLD) scheme for multiple-input and multiple-output system with the dual constraints of imperfect feedback and average packet error rate (PER) is presented, which is based on the combination of the adaptive modulation and the automatic repeat request protocols. The design performance is also evaluated over wireless Rayleigh fading channel. With the constraint of target PER and average PER, the optimum switching thresholds (STs) for attaining maximum spectral efficiency (SE) are developed. An effective iterative algorithm for finding the optimal STs is proposed via Lagrange multiplier optimisation. With different thresholds available, the analytical expressions of the average SE and PER are provided for the performance evaluation. To avoid the performance loss caused by the conventional single estimate, multiple outdated estimates (MOE) method, which utilises multiple previous channel estimation information, is presented for CLD to improve the system performance. It is shown that numerical simulations for average PER and SE are in consistent with the theoretical analysis and that the developed CLD with average PER constraint can meet the target PER requirement and show better performance in comparison with the conventional CLD with instantaneous PER constraint. Especially, the CLD based on the MOE method can obviously increase the system SE and reduce the impact of feedback delay greatly.

GPM Precipitation Estimates over the Walnut Gulch Experimental Watershed/LTAR site in Southeastern Arizona

NASA Astrophysics Data System (ADS)

Goodrich, D. C.; Tan, J.; Petersen, W. A.; Unkrich, C. C.; Demaria, E. M.; Hazenberg, P.; Lakshmi, V.

2017-12-01

Precipitation profiles from the GPM Core Observatory Dual-frequency Precipitation Radar (DPR) form part of the a priori database used in GPM Goddard Profiling (GPROF) algorithm passive microwave radiometer retrievals of rainfall. The GPROF retrievals are in turn used as high quality precipitation estimates in gridded products such as IMERG. Due to the variability in and high surface emissivity of land surfaces, GPROF performs precipitation retrievals as a function of surface classes. As such, different surface types may possess different error characteristics, especially over arid regions where high quality ground measurements are often lacking. Importantly, the emissive properties of land also result in GPROF rainfall estimates being driven primarily by the higher frequency radiometer channels (e.g., > 89 GHz) where precipitation signals are most sensitive to coupling between the ice-phase and rainfall production. In this study, we evaluate the rainfall estimates from the Ku channel of the DPR as well as GPROF estimates from various passive microwave sensors. Our evaluation is conducted at the level of individual satellite pixels (5 to 15 km in diameter), against a dense network of weighing rain gauges (90 in 150 km2) in the USDA-ARS Walnut Gulch Experimental Watershed and Long-Term Agroecosystem Research (LTAR) site in southeastern Arizona. The multiple gauges in each satellite pixel and precise accumulation about the overpass time allow a spatially and temporally representative comparison between the satellite estimates and ground reference. Over Walnut Gulch, both the Ku and GPROF estimates are challenged to delineate between rain and no-rain. Probabilities of detection are relatively high, but false alarm ratios are also high. The rain intensities possess a negative bias across nearly all sensors. It is likely that storm types, arid conditions and the highly variable precipitation regime present a challenge to both rainfall retrieval algorithms. An array of ground-based sensors is being deployed during the 2017 monsoon season to better understand possible reasons for this discrepancy.

Localization algorithms for micro-channel x-ray telescope on board SVOM space mission

NASA Astrophysics Data System (ADS)

Gosset, L.; Götz, D.; Osborne, J.; Willingale, R.

2016-07-01

SVOM is a French-Chinese space mission to be launched in 2021, whose goal is the study of Gamma-Ray Bursts, the most powerful stellar explosions in the Universe. The Micro-channel X-ray Telescope (MXT) is an X-ray focusing telescope, on board SVOM, with a field of view of 1 degree (working in the 0.2-10 keV energy band), dedicated to the rapid follow-up of the Gamma-Ray Bursts counterparts and to their precise localization (smaller than 2 arc minutes). In order to reduce the optics mass and to have an angular resolution of few arc minutes, a "lobster-Eye" configuration has been chosen. Using a numerical model of the MXT Point Spread Function (PSF) we simulated MXT observations of point sources in order to develop and test different localization algorithms to be implemented on board MXT. We included preliminary estimations of the instrumental and sky background. The algorithms on board have to be a combination of speed and precision (the brightest sources are expected to be localized at a precision better than 10 arc seconds in the MXT reference frame). We present the comparison between different methods such as barycentre, PSF fitting in one or two dimensions. The temporal performance of the algorithms is being tested using the X-ray afterglow data base of the XRT telescope on board the NASA Swift satellite.

A Pseudorange Measurement Scheme Based on Snapshot for Base Station Positioning Receivers.

PubMed

Mo, Jun; Deng, Zhongliang; Jia, Buyun; Bian, Xinmei

2017-12-01

Digital multimedia broadcasting signal is promised to be a wireless positioning signal. This paper mainly studies a multimedia broadcasting technology, named China mobile multimedia broadcasting (CMMB), in the context of positioning. Theoretical and practical analysis on the CMMB signal suggests that the existing CMMB signal does not have the meter positioning capability. So, the CMMB system has been modified to achieve meter positioning capability by multiplexing the CMMB signal and pseudo codes in the same frequency band. The time difference of arrival (TDOA) estimation method is used in base station positioning receivers. Due to the influence of a complex fading channel and the limited bandwidth of receivers, the regular tracking method based on pseudo code ranging is difficult to provide continuous and accurate TDOA estimations. A pseudorange measurement scheme based on snapshot is proposed to solve the problem. This algorithm extracts the TDOA estimation from the stored signal fragments, and utilizes the Taylor expansion of the autocorrelation function to improve the TDOA estimation accuracy. Monte Carlo simulations and real data tests show that the proposed algorithm can significantly reduce the TDOA estimation error for base station positioning receivers, and then the modified CMMB system achieves meter positioning accuracy.

Development of GK-2A cloud optical and microphysical properties retrieval algorithm

NASA Astrophysics Data System (ADS)

Yang, Y.; Yum, S. S.; Um, J.

2017-12-01

Cloud and aerosol radiative forcing is known to be one of the the largest uncertainties in climate change prediction. To reduce this uncertainty, remote sensing observation of cloud radiative and microphysical properties have been used since 1970s and the corresponding remote sensing techniques and instruments have been developed. As a part of such effort, Geo-KOMPSAT-2A (Geostationary Korea Multi-Purpose Satellite-2A, GK-2A) will be launched in 2018. On the GK-2A, the Advanced Meteorological Imager (AMI) is primary instrument which have 3 visible, 3 near-infrared, and 10 infrared channels. To retrieve optical and microphysical properties of clouds using AMI measurements, the preliminary version of new cloud retrieval algorithm for GK-2A was developed and several validation tests were conducted. This algorithm retrieves cloud optical thickness (COT), cloud effective radius (CER), liquid water path (LWP), and ice water path (IWP), so we named this algorithm as Daytime Cloud Optical thickness, Effective radius and liquid and ice Water path (DCOEW). The DCOEW uses cloud reflectance at visible and near-infrared channels as input data. An optimal estimation (OE) approach that requires appropriate a-priori values and measurement error information is used to retrieve COT and CER. LWP and IWP are calculated using empirical relationships between COT/CER and cloud water path that were determined previously. To validate retrieved cloud properties, we compared DCOEW output data with other operational satellite data. For COT and CER validation, we used two different data sets. To compare algorithms that use cloud reflectance at visible and near-IR channels as input data, MODIS MYD06 cloud product was selected. For the validation with cloud products that are based on microwave measurements, COT(2B-TAU)/CER(2C-ICE) data retrieved from CloudSat cloud profiling radar (W-band, 94 GHz) was used. For cloud water path validation, AMSR-2 Level-3 Cloud liquid water data was used. Detailed results will be shown at the conference.

Color enhancement and image defogging in HSI based on Retinex model

NASA Astrophysics Data System (ADS)

Gao, Han; Wei, Ping; Ke, Jun

2015-08-01

Retinex is a luminance perceptual algorithm based on color consistency. It has a good performance in color enhancement. But in some cases, the traditional Retinex algorithms, both Single-Scale Retinex(SSR) and Multi-Scale Retinex(MSR) in RGB color space, do not work well and will cause color deviation. To solve this problem, we present improved SSR and MSR algorithms. Compared to other Retinex algorithms, we implement Retinex algorithms in HSI(Hue, Saturation, Intensity) color space, and use a parameter αto improve quality of the image. Moreover, the algorithms presented in this paper has a good performance in image defogging. Contrasted with traditional Retinex algorithms, we use intensity channel to obtain reflection information of an image. The intensity channel is processed using a Gaussian center-surround image filter to get light information, which should be removed from intensity channel. After that, we subtract the light information from intensity channel to obtain the reflection image, which only includes the attribute of the objects in image. Using the reflection image and a parameter α, which is an arbitrary scale factor set manually, we improve the intensity channel, and complete the color enhancement. Our experiments show that this approach works well compared with existing methods for color enhancement. Besides a better performance in color deviation problem and image defogging, a visible improvement in the image quality for human contrast perception is also observed.

A new approach to increase the two-dimensional detection probability of CSI algorithm for WAS-GMTI mode

NASA Astrophysics Data System (ADS)

Yan, H.; Zheng, M. J.; Zhu, D. Y.; Wang, H. T.; Chang, W. S.

2015-07-01

When using clutter suppression interferometry (CSI) algorithm to perform signal processing in a three-channel wide-area surveillance radar system, the primary concern is to effectively suppress the ground clutter. However, a portion of moving target's energy is also lost in the process of channel cancellation, which is often neglected in conventional applications. In this paper, we firstly investigate the two-dimensional (radial velocity dimension and squint angle dimension) residual amplitude of moving targets after channel cancellation with CSI algorithm. Then, a new approach is proposed to increase the two-dimensional detection probability of moving targets by reserving the maximum value of the three channel cancellation results in non-uniformly spaced channel system. Besides, theoretical expression of the false alarm probability with the proposed approach is derived in the paper. Compared with the conventional approaches in uniformly spaced channel system, simulation results validate the effectiveness of the proposed approach. To our knowledge, it is the first time that the two-dimensional detection probability of CSI algorithm is studied.

Assessment of SMOS Soil Moisture Retrieval Parameters Using Tau-Omega Algorithms for Soil Moisture Deficit Estimation

NASA Technical Reports Server (NTRS)

Srivastava, Prashant K.; Han, Dawei; Rico-Ramirez, Miguel A.; O'Neill, Peggy; Islam, Tanvir; Gupta, Manika

2014-01-01

Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale.

Passive microwave remote sensing of rainfall with SSM/I: Algorithm development and implementation

NASA Technical Reports Server (NTRS)

Ferriday, James G.; Avery, Susan K.

1994-01-01

A physically based algorithm sensitive to emission and scattering is used to estimate rainfall using the Special Sensor Microwave/Imager (SSM/I). The algorithm is derived from radiative transfer calculations through an atmospheric cloud model specifying vertical distributions of ice and liquid hydrometeors as a function of rain rate. The algorithm is structured in two parts: SSM/I brightness temperatures are screened to detect rainfall and are then used in rain-rate calculation. The screening process distinguishes between nonraining background conditions and emission and scattering associated with hydrometeors. Thermometric temperature and polarization thresholds determined from the radiative transfer calculations are used to detect rain, whereas the rain-rate calculation is based on a linear function fit to a linear combination of channels. Separate calculations for ocean and land account for different background conditions. The rain-rate calculation is constructed to respond to both emission and scattering, reduce extraneous atmospheric and surface effects, and to correct for beam filling. The resulting SSM/I rain-rate estimates are compared to three precipitation radars as well as to a dynamically simulated rainfall event. Global estimates from the SSM/I algorithm are also compared to continental and shipboard measurements over a 4-month period. The algorithm is found to accurately describe both localized instantaneous rainfall events and global monthly patterns over both land and ovean. Over land the 4-month mean difference between SSM/I and the Global Precipitation Climatology Center continental rain gauge database is less than 10%. Over the ocean, the mean difference between SSM/I and the Legates and Willmott global shipboard rain gauge climatology is less than 20%.

Separation of irradiance and reflectance from observed color images by logarithmical nonlinear diffusion process

NASA Astrophysics Data System (ADS)

Saito, Takahiro; Takahashi, Hiromi; Komatsu, Takashi

2006-02-01

The Retinex theory was first proposed by Land, and deals with separation of irradiance from reflectance in an observed image. The separation problem is an ill-posed problem. Land and others proposed various Retinex separation algorithms. Recently, Kimmel and others proposed a variational framework that unifies the previous Retinex algorithms such as the Poisson-equation-type Retinex algorithms developed by Horn and others, and presented a Retinex separation algorithm with the time-evolution of a linear diffusion process. However, the Kimmel's separation algorithm cannot achieve physically rational separation, if true irradiance varies among color channels. To cope with this problem, we introduce a nonlinear diffusion process into the time-evolution. Moreover, as to its extension to color images, we present two approaches to treat color channels: the independent approach to treat each color channel separately and the collective approach to treat all color channels collectively. The latter approach outperforms the former. Furthermore, we apply our separation algorithm to a high quality chroma key in which before combining a foreground frame and a background frame into an output image a color of each pixel in the foreground frame are spatially adaptively corrected through transformation of the separated irradiance. Experiments demonstrate superiority of our separation algorithm over the Kimmel's separation algorithm.

The performance of the new enhanced-resolution satellite passive microwave dataset applied for snow water equivalent estimation

NASA Astrophysics Data System (ADS)

Pan, J.; Durand, M. T.; Jiang, L.; Liu, D.

2017-12-01

The newly-processed NASA MEaSures Calibrated Enhanced-Resolution Brightness Temperature (CETB) reconstructed using antenna measurement response function (MRF) is considered to have significantly improved fine-resolution measurements with better georegistration for time-series observations and equivalent field of view (FOV) for frequencies with the same monomial spatial resolution. We are looking forward to its potential for the global snow observing purposes, and therefore aim to test its performance for characterizing snow properties, especially the snow water equivalent (SWE) in large areas. In this research, two candidate SWE algorithms will be tested in China for the years between 2005 to 2010 using the reprocessed TB from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), with the results to be evaluated using the daily snow depth measurements at over 700 national synoptic stations. One of the algorithms is the SWE retrieval algorithm used for the FengYun (FY) - 3 Microwave Radiation Imager. This algorithm uses the multi-channel TB to calculate SWE for three major snow regions in China, with the coefficients adapted for different land cover types. The second algorithm is the newly-established Bayesian Algorithm for SWE Estimation with Passive Microwave measurements (BASE-PM). This algorithm uses the physically-based snow radiative transfer model to find the histogram of most-likely snow property that matches the multi-frequency TB from 10.65 to 90 GHz. It provides a rough estimation of snow depth and grain size at the same time and showed a 30 mm SWE RMS error using the ground radiometer measurements at Sodankyla. This study will be the first attempt to test it spatially for satellite. The use of this algorithm benefits from the high resolution and the spatial consistency between frequencies embedded in the new dataset. This research will answer three questions. First, to what extent can CETB increase the heterogeneity in the mapped SWE? Second, will the SWE estimation error statistics be improved using this high-resolution dataset? Third, how will the SWE retrieval accuracy be improved using CETB and the new SWE retrieval techniques?

Atmospheric Compensation and Surface Temperature and Emissivity Retrieval with LWIR Hyperspectral Imagery

NASA Astrophysics Data System (ADS)

Pieper, Michael

Accurate estimation or retrieval of surface emissivity spectra from long-wave infrared (LWIR) or Thermal Infrared (TIR) hyperspectral imaging data acquired by airborne or space-borne sensors is necessary for many scientific and defense applications. The at-aperture radiance measured by the sensor is a function of the ground emissivity and temperature, modified by the atmosphere. Thus the emissivity retrieval process consists of two interwoven steps: atmospheric compensation (AC) to retrieve the ground radiance from the measured at-aperture radiance and temperature-emissivity separation (TES) to separate the temperature and emissivity from the ground radiance. In-scene AC (ISAC) algorithms use blackbody-like materials in the scene, which have a linear relationship between their ground radiances and at-aperture radiances determined by the atmospheric transmission and upwelling radiance. Using a clear reference channel to estimate the ground radiance, a linear fitting of the at-aperture radiance and estimated ground radiance is done to estimate the atmospheric parameters. TES algorithms for hyperspectral imaging data assume that the emissivity spectra for solids are smooth compared to the sharp features added by the atmosphere. The ground temperature and emissivity are found by finding the temperature that provides the smoothest emissivity estimate. In this thesis we develop models to investigate the sensitivity of AC and TES to the basic assumptions enabling their performance. ISAC assumes that there are perfect blackbody pixels in a scene and that there is a clear channel, which is never the case. The developed ISAC model explains how the quality of blackbody-like pixels affect the shape of atmospheric estimates and the clear channel assumption affects their magnitude. Emissivity spectra for solids usually have some roughness. The TES model identifies four sources of error: the smoothing error of the emissivity spectrum, the emissivity error from using the incorrect temperature, and the errors caused by sensor noise and wavelength calibration. The ways these errors interact determines the overall TES performance. Since the AC and TES processes are interwoven, any errors in AC are transferred to TES and the final temperature and emissivity estimates. Combining the two models, shape errors caused by the blackbody assumption are transferred to the emissivity estimates, where magnitude errors from the clear channel assumption are compensated by TES temperature induced emissivity errors. The ability for the temperature induced error to compensate for such atmospheric errors makes it difficult to determine the correct atmospheric parameters for a scene. With these models we are able to determine the expected quality of estimated emissivity spectra based on the quality of blackbody-like materials on the ground, the emissivity of the materials being searched for, and the properties of the sensor. The quality of material emissivity spectra is a key factor in determining detection performance for a material in a scene.

Belief propagation decoding of quantum channels by passing quantum messages

NASA Astrophysics Data System (ADS)

Renes, Joseph M.

2017-07-01

The belief propagation (BP) algorithm is a powerful tool in a wide range of disciplines from statistical physics to machine learning to computational biology, and is ubiquitous in decoding classical error-correcting codes. The algorithm works by passing messages between nodes of the factor graph associated with the code and enables efficient decoding of the channel, in some cases even up to the Shannon capacity. Here we construct the first BP algorithm which passes quantum messages on the factor graph and is capable of decoding the classical-quantum channel with pure state outputs. This gives explicit decoding circuits whose number of gates is quadratic in the code length. We also show that this decoder can be modified to work with polar codes for the pure state channel and as part of a decoder for transmitting quantum information over the amplitude damping channel. These represent the first explicit capacity-achieving decoders for non-Pauli channels.

1D-VAR Retrieval Using Superchannels

NASA Technical Reports Server (NTRS)

Liu, Xu; Zhou, Daniel; Larar, Allen; Smith, William L.; Schluessel, Peter; Mango, Stephen; SaintGermain, Karen

2008-01-01

Since modern ultra-spectral remote sensors have thousands of channels, it is difficult to include all of them in a 1D-var retrieval system. We will describe a physical inversion algorithm, which includes all available channels for the atmospheric temperature, moisture, cloud, and surface parameter retrievals. Both the forward model and the inversion algorithm compress the channel radiances into super channels. These super channels are obtained by projecting the radiance spectra onto a set of pre-calculated eigenvectors. The forward model provides both super channel properties and jacobian in EOF space directly. For ultra-spectral sensors such as Infrared Atmospheric Sounding Interferometer (IASI) and the NPOESS Airborne Sounder Testbed Interferometer (NAST), a compression ratio of more than 80 can be achieved, leading to a significant reduction in computations involved in an inversion process. Results will be shown applying the algorithm to real IASI and NAST data.

Experiences with serial and parallel algorithms for channel routing using simulated annealing

NASA Technical Reports Server (NTRS)

Brouwer, Randall Jay

1988-01-01

Two algorithms for channel routing using simulated annealing are presented. Simulated annealing is an optimization methodology which allows the solution process to back up out of local minima that may be encountered by inappropriate selections. By properly controlling the annealing process, it is very likely that the optimal solution to an NP-complete problem such as channel routing may be found. The algorithm presented proposes very relaxed restrictions on the types of allowable transformations, including overlapping nets. By freeing that restriction and controlling overlap situations with an appropriate cost function, the algorithm becomes very flexible and can be applied to many extensions of channel routing. The selection of the transformation utilizes a number of heuristics, still retaining the pseudorandom nature of simulated annealing. The algorithm was implemented as a serial program for a workstation, and a parallel program designed for a hypercube computer. The details of the serial implementation are presented, including many of the heuristics used and some of the resulting solutions.

Blind Channel Equalization with Colored Source Based on Constrained Optimization Methods

NASA Astrophysics Data System (ADS)

Wang, Yunhua; DeBrunner, Linda; DeBrunner, Victor; Zhou, Dayong

2008-12-01

Tsatsanis and Xu have applied the constrained minimum output variance (CMOV) principle to directly blind equalize a linear channel—a technique that has proven effective with white inputs. It is generally assumed in the literature that their CMOV method can also effectively equalize a linear channel with a colored source. In this paper, we prove that colored inputs will cause the equalizer to incorrectly converge due to inadequate constraints. We also introduce a new blind channel equalizer algorithm that is based on the CMOV principle, but with a different constraint that will correctly handle colored sources. Our proposed algorithm works for channels with either white or colored inputs and performs equivalently to the trained minimum mean-square error (MMSE) equalizer under high SNR. Thus, our proposed algorithm may be regarded as an extension of the CMOV algorithm proposed by Tsatsanis and Xu. We also introduce several methods to improve the performance of our introduced algorithm in the low SNR condition. Simulation results show the superior performance of our proposed methods.

HF band filter bank multi-carrier spread spectrum

DOE Office of Scientific and Technical Information (OSTI.GOV)

Laraway, Stephen Andrew; Moradi, Hussein; Farhang-Boroujeny, Behrouz

Abstract—This paper describes modifications to the filter bank multicarrier spread spectrum (FB-MC-SS) system, that was presented in [1] and [2], to enable transmission of this waveform in the HF skywave channel. FB-MC-SS is well suited for the HF channel because it performs well in channels with frequency selective fading and interference. This paper describes new algorithms for packet detection, timing recovery and equalization that are suitable for the HF channel. Also, an algorithm for optimizing the peak to average power ratio (PAPR) of the FBMC- SS waveform is presented. Application of this algorithm results in a waveform with low PAPR.more » Simulation results using a wide band HF channel model demonstrate the robustness of this system over a wide range of delay and Doppler spreads.« less

CFTLB: a novel cross-layer fault tolerant and load balancing protocol for WMN

NASA Astrophysics Data System (ADS)

Krishnaveni, N. N.; Chitra, K.

2017-12-01

Wireless mesh network (WMN) forms a wireless backbone framework for multi-hop transmission among the routers and clients in the extensible coverage area. To improve the throughput of WMNs with multiple gateways (GWs), several issues related to GW selection, load balancing and frequent link failures due to the presence of dynamic obstacles and channel interference should be addressed. This paper presents a novel cross-layer fault tolerant and load balancing (CFTLB) protocol to overcome the issues in WMN. Initially, the neighbour GW is searched and channel load is calculated. The GW having least channel load is selected which is estimated during the arrival of the new node. The proposed algorithm finds the alternate GWs and calculates the channel availability under high loading scenarios. If the current load in the GW is high, another GW is found and channel availability is calculated. Besides, it initiates the channel switching and establishes the communication with the mesh client effectively. The utilisation of hashing technique in proposed CFTLB verifies the status of the packets and achieves better performance in terms of router average throughput, throughput, average channel access time and lower end-to-end delay, communication overhead and average data loss in the channel compared to the existing protocols.

Validation of Infrared Azimuthal Model as Applied to GOES Data Over the ARM SGP

NASA Technical Reports Server (NTRS)

Gambheer, Arvind V.; Doelling, David R.; Spangenberg, Douglas A.; Minnis, Patrick

2004-01-01

The goal of this research is to identify and reduce the GOES-8 IR temperature biases, induced by a fixed geostationary position, during the course of a day. In this study, the same CERES LW window channel model is applied to GOES-8 IR temperatures during clear days over the Atmospheric Radiation Measurement-Southern Great Plains Central Facility (SCF). The model-adjusted and observed IR temperatures are compared with topof- the-atmosphere (TOA) estimated temperatures derived from a radiative transfer algorithm based on the atmospheric profile and surface radiometer measurements. This algorithm can then be incorporated to derive more accurate Ts from real-time satellite operational products.

Optimal space communications techniques. [discussion of video signals and delta modulation

NASA Technical Reports Server (NTRS)

Schilling, D. L.

1974-01-01

The encoding of video signals using the Song Adaptive Delta Modulator (Song ADM) is discussed. The video signals are characterized as a sequence of pulses having arbitrary height and width. Although the ADM is suited to tracking signals having fast rise times, it was found that the DM algorithm (which permits an exponential rise for estimating an input step) results in a large overshoot and an underdamped response to the step. An overshoot suppression algorithm which significantly reduces the ringing while not affecting the rise time is presented along with formuli for the rise time and the settling time. Channel errors and their effect on the DM encoded bit stream were investigated.

Algorithms and uncertainties for the determination of multispectral irradiance components and aerosol optical depth from a shipborne rotating shadowband radiometer

NASA Astrophysics Data System (ADS)

Witthuhn, Jonas; Deneke, Hartwig; Macke, Andreas; Bernhard, Germar

2017-03-01

The 19-channel rotating shadowband radiometer GUVis-3511 built by Biospherical Instruments provides automated shipborne measurements of the direct, diffuse and global spectral irradiance components without a requirement for platform stabilization. Several direct sun products, including spectral direct beam transmittance, aerosol optical depth, Ångström exponent and precipitable water, can be derived from these observations. The individual steps of the data analysis are described, and the different sources of uncertainty are discussed. The total uncertainty of the observed direct beam transmittances is estimated to be about 4 % for most channels within a 95 % confidence interval for shipborne operation. The calibration is identified as the dominating contribution to the total uncertainty. A comparison of direct beam transmittance with those obtained from a Cimel sunphotometer at a land site and a manually operated Microtops II sunphotometer on a ship is presented. Measurements deviate by less than 3 and 4 % on land and on ship, respectively, for most channels and in agreement with our previous uncertainty estimate. These numbers demonstrate that the instrument is well suited for shipborne operation, and the applied methods for motion correction work accurately. Based on spectral direct beam transmittance, aerosol optical depth can be retrieved with an uncertainty of 0.02 for all channels within a 95 % confidence interval. The different methods to account for Rayleigh scattering and gas absorption in our scheme and in the Aerosol Robotic Network processing for Cimel sunphotometers lead to minor deviations. Relying on the cross calibration of the 940 nm water vapor channel with the Cimel sunphotometer, the column amount of precipitable water can be estimated with an uncertainty of ±0.034 cm.

Dereverberation and denoising based on generalized spectral subtraction by multi-channel LMS algorithm using a small-scale microphone array

NASA Astrophysics Data System (ADS)

Wang, Longbiao; Odani, Kyohei; Kai, Atsuhiko

2012-12-01

A blind dereverberation method based on power spectral subtraction (SS) using a multi-channel least mean squares algorithm was previously proposed to suppress the reverberant speech without additive noise. The results of isolated word speech recognition experiments showed that this method achieved significant improvements over conventional cepstral mean normalization (CMN) in a reverberant environment. In this paper, we propose a blind dereverberation method based on generalized spectral subtraction (GSS), which has been shown to be effective for noise reduction, instead of power SS. Furthermore, we extend the missing feature theory (MFT), which was initially proposed to enhance the robustness of additive noise, to dereverberation. A one-stage dereverberation and denoising method based on GSS is presented to simultaneously suppress both the additive noise and nonstationary multiplicative noise (reverberation). The proposed dereverberation method based on GSS with MFT is evaluated on a large vocabulary continuous speech recognition task. When the additive noise was absent, the dereverberation method based on GSS with MFT using only 2 microphones achieves a relative word error reduction rate of 11.4 and 32.6% compared to the dereverberation method based on power SS and the conventional CMN, respectively. For the reverberant and noisy speech, the dereverberation and denoising method based on GSS achieves a relative word error reduction rate of 12.8% compared to the conventional CMN with GSS-based additive noise reduction method. We also analyze the effective factors of the compensation parameter estimation for the dereverberation method based on SS, such as the number of channels (the number of microphones), the length of reverberation to be suppressed, and the length of the utterance used for parameter estimation. The experimental results showed that the SS-based method is robust in a variety of reverberant environments for both isolated and continuous speech recognition and under various parameter estimation conditions.

Plumes and Blooms: Observations, Analysis and Modeling for SIMBIOS

NASA Technical Reports Server (NTRS)

Siegel, D. A.; Maritorena, S.; Nelson, N. B.

2003-01-01

The goal of the Plumes and Blooms (PnB) project is to develop, validate and apply to imagery state-of-theart ocean color algorithms for quantifying sediment plumes and phytoplankton blooms for the Case I1 environment of the Santa Barbara Channel. We conduct monthly to twice-monthly transect observations across the Santa Barbara Channel to develop an algorithm development and product validation data set. The PnB field program started in the summer of 1996. At each of the 7 PnB stations, a complete verification bio-geo-optical data set is collected. Included are redundant measures of apparent optical properties (remote sensing reflectance and diffuse attenuation spectra), as well as in situ profiles of spectral absorption, beam attenuation and backscattering coefficients. Water samples are analyzed for component in vivo absorption spectra, fluorometric chlorophyll, phytoplankton pigment (by the SDSU CHORS laboratory), and inorganic nutrient concentrations (Table 1). A primary goal is to use the PnB field data set to objectively tune semi-analytical models of ocean color for this site and apply them using available satellite imagery (SeaWiFS and MODIS). In support of this goal, we have also been addressing SeaWiFS ocean color and AVHRR SST imagery (Otero and Siegel, 2003). We also are using the PnB data set to address time/space variability of water masses in the Santa Barbara Channel and its relationship to the 1997/1998 El Niiio. However, the comparison between PnB field observations and satellite estimates of primary products has been disappointing. We find that field estimates of water-leaving radiance, LwN(h), correspond poorly to satellite estimates for both SeaWiFS and MODIS local area coverage imagery. We believe this is due to poor atmospheric correction due to complex mixtures of aerosol types found in these near-coastal regions. Last, we remain active in outreach activities.

Performance analysis of an OAM multiplexing-based MIMO FSO system over atmospheric turbulence using space-time coding with channel estimation.

PubMed

Zhang, Yan; Wang, Ping; Guo, Lixin; Wang, Wei; Tian, Hongxin

2017-08-21

The average bit error rate (ABER) performance of an orbital angular momentum (OAM) multiplexing-based free-space optical (FSO) system with multiple-input multiple-output (MIMO) architecture has been investigated over atmospheric turbulence considering channel estimation and space-time coding. The impact of different types of space-time coding, modulation orders, turbulence strengths, receive antenna numbers on the transmission performance of this OAM-FSO system is also taken into account. On the basis of the proposed system model, the analytical expressions of the received signals carried by the k-th OAM mode of the n-th receive antenna for the vertical bell labs layered space-time (V-Blast) and space-time block codes (STBC) are derived, respectively. With the help of channel estimator carrying out with least square (LS) algorithm, the zero-forcing criterion with ordered successive interference cancellation criterion (ZF-OSIC) equalizer of V-Blast scheme and Alamouti decoder of STBC scheme are adopted to mitigate the performance degradation induced by the atmospheric turbulence. The results show that the ABERs obtained by channel estimation have excellent agreement with those of turbulence phase screen simulations. The ABERs of this OAM multiplexing-based MIMO system deteriorate with the increase of turbulence strengths. And both V-Blast and STBC schemes can significantly improve the system performance by mitigating the distortions of atmospheric turbulence as well as additive white Gaussian noise (AWGN). In addition, the ABER performances of both space-time coding schemes can be further enhanced by increasing the number of receive antennas for the diversity gain and STBC outperforms V-Blast in this system for data recovery. This work is beneficial to the OAM FSO system design.

Retrieval of volcanic ash height from satellite-based infrared measurements

NASA Astrophysics Data System (ADS)

Zhu, Lin; Li, Jun; Zhao, Yingying; Gong, He; Li, Wenjie

2017-05-01

A new algorithm for retrieving volcanic ash cloud height from satellite-based measurements is presented. This algorithm, which was developed in preparation for China's next-generation meteorological satellite (FY-4), is based on volcanic ash microphysical property simulation and statistical optimal estimation theory. The MSG satellite's main payload, a 12-channel Spinning Enhanced Visible and Infrared Imager, was used as proxy data to test this new algorithm. A series of eruptions of Iceland's Eyjafjallajökull volcano during April to May 2010 and the Puyehue-Cordón Caulle volcanic complex eruption in the Chilean Andes on 16 June 2011 were selected as two typical cases for evaluating the algorithm under various meteorological backgrounds. Independent volcanic ash simulation training samples and satellite-based Cloud-Aerosol Lidar with Orthogonal Polarization data were used as validation data. It is demonstrated that the statistically based volcanic ash height algorithm is able to rapidly retrieve volcanic ash heights, globally. The retrieved ash heights show comparable accuracy with both independent training data and the lidar measurements, which is consistent with previous studies. However, under complicated background, with multilayers in vertical scale, underlying stratus clouds tend to have detrimental effects on the final retrieval accuracy. This is an unresolved problem, like many other previously published methods using passive satellite sensors. Compared with previous studies, the FY-4 ash height algorithm is independent of simultaneous atmospheric profiles, providing a flexible way to estimate volcanic ash height using passive satellite infrared measurements.

Random-access algorithms for multiuser computer communication networks. Doctoral thesis, 1 September 1986-31 August 1988

DOE Office of Scientific and Technical Information (OSTI.GOV)

Papantoni-Kazakos, P.; Paterakis, M.

1988-07-01

For many communication applications with time constraints (e.g., transmission of packetized voice messages), a critical performance measure is the percentage of messages transmitted within a given amount of time after their generation at the transmitting station. This report presents a random-access algorithm (RAA) suitable for time-constrained applications. Performance analysis demonstrates that significant message-delay improvement is attained at the expense of minimal traffic loss. Also considered is the case of noisy channels. The noise effect appears at erroneously observed channel feedback. Error sensitivity analysis shows that the proposed random-access algorithm is insensitive to feedback channel errors. Window Random-Access Algorithms (RAAs) aremore » considered next. These algorithms constitute an important subclass of Multiple-Access Algorithms (MAAs); they are distributive, and they attain high throughput and low delays by controlling the number of simultaneously transmitting users.« less

Dual Channel S-Band Frequency Modulated Continuous Wave Through-Wall Radar Imaging

PubMed Central

Oh, Daegun; Kim, Sunwoo; Chong, Jong-Wha

2018-01-01

This article deals with the development of a dual channel S-Band frequency-modulated continuous wave (FMCW) system for a through-the-wall imaging (TWRI) system. Most existing TWRI systems using FMCW were developed for synthetic aperture radar (SAR) which has many drawbacks such as the need for several antenna elements and movement of the system. Our implemented TWRI system comprises a transmitting antenna and two receiving antennas, resulting in a significant reduction of the number of antenna elements. Moreover, a proposed algorithm for range-angle-Doppler 3D estimation based on a 3D shift invariant structure is utilized in our implemented dual channel S-band FMCW TWRI system. Indoor and outdoor experiments were conducted to image the scene beyond a wall for water targets and person targets, respectively. The experimental results demonstrate that high-quality imaging can be achieved under both experimental scenarios. PMID:29361777

Comparing Binaural Pre-processing Strategies III

PubMed Central

Warzybok, Anna; Ernst, Stephan M. A.

2015-01-01

A comprehensive evaluation of eight signal pre-processing strategies, including directional microphones, coherence filters, single-channel noise reduction, binaural beamformers, and their combinations, was undertaken with normal-hearing (NH) and hearing-impaired (HI) listeners. Speech reception thresholds (SRTs) were measured in three noise scenarios (multitalker babble, cafeteria noise, and single competing talker). Predictions of three common instrumental measures were compared with the general perceptual benefit caused by the algorithms. The individual SRTs measured without pre-processing and individual benefits were objectively estimated using the binaural speech intelligibility model. Ten listeners with NH and 12 HI listeners participated. The participants varied in age and pure-tone threshold levels. Although HI listeners required a better signal-to-noise ratio to obtain 50% intelligibility than listeners with NH, no differences in SRT benefit from the different algorithms were found between the two groups. With the exception of single-channel noise reduction, all algorithms showed an improvement in SRT of between 2.1 dB (in cafeteria noise) and 4.8 dB (in single competing talker condition). Model predictions with binaural speech intelligibility model explained 83% of the measured variance of the individual SRTs in the no pre-processing condition. Regarding the benefit from the algorithms, the instrumental measures were not able to predict the perceptual data in all tested noise conditions. The comparable benefit observed for both groups suggests a possible application of noise reduction schemes for listeners with different hearing status. Although the model can predict the individual SRTs without pre-processing, further development is necessary to predict the benefits obtained from the algorithms at an individual level. PMID:26721922

Monthly-Diurnal Water Budget Variability Over Gulf of Mexico-Caribbean Sea Basin from Satellite Observations

NASA Technical Reports Server (NTRS)

Smith, E. A.; Santos, P.

2006-01-01

This study presents results from a multi-satellite/multi-sensor retrieval system design d to obtain the atmospheric water budget over the open ocean. A combination of hourly-sampled monthly datasets derived from the GOES-8 5-channel Imager, the TRMM TMI radiometer, and the DMSP 7-channel passive microwave radiometers (SSM/I) have been acquired for the combined Gulf of Mexico-Caribbean Sea basin. Whereas the methodology has been tested over this basin, the retrieval system is designed for portability to any open-ocean region. Algorithm modules using the different datasets to retrieve individual geophysical parameters needed in the water budget equation are designed in a manner that takes advantage of the high temporal resolution of the GOES-8 measurements, as well as the physical relationships inherent to the TRMM and SSM/I passive microwave measurements in conjunction with water vapor, cloud liquid water, and rainfall. The methodology consists of retrieving the precipitation, surface evaporation, and vapor-cloud water storage terms in the atmospheric water balance equation from satellite techniques, with the water vapor advection term being obtained as the residue needed for balance. Thus, the intent is to develop a purely satellite-based method for obtaining the full set of terms in the atmospheric water budget equation without requiring in situ sounding information on the wind profile. The algorithm is validated by cross-checking all the algorithm components through multiple-algorithm retrieval intercomparisons. A further check on the validation is obtained by directly comparing water vapor transports into the targeted basin diagnosed from the satellite algorithms to those obtained observationally from a network of land-based upper air stations that nearly uniformly surround the basin, although it is fair to say that these checks are more effective in identifying problems in estimating vapor transports from a "leaky" operational radiosonde network than in verifying the transport estimates determined from the satellite algorithm system. Total columnar atmospheric water budget results are presented for an extended annual cycle consisting of the months of October-97, January-98, April-98, July-98,October-98, and January- 1999. These results are used to emphasize the changing relationship in E-P, as well as in the varying roles of storage and advection in balancing E-P both on daily and monthly time scales and on localized and basin space scales. Results from the algorithm-to-algorithm intercomparisons are also presented in the context of sensitivity testing to help understand the intrinsic uncertainties in evaluating the water budget terms by an all-satellite algorithm approach.

Automated Interval velocity picking for Atlantic Multi-Channel Seismic Data

NASA Astrophysics Data System (ADS)

Singh, Vishwajit

2016-04-01

This paper described the challenge in developing and testing a fully automated routine for measuring interval velocities from multi-channel seismic data. Various approaches are employed for generating an interactive algorithm picking interval velocity for continuous 1000-5000 normal moveout (NMO) corrected gather and replacing the interpreter's effort for manual picking the coherent reflections. The detailed steps and pitfalls for picking the interval velocities from seismic reflection time measurements are describe in these approaches. Key ingredients these approaches utilized for velocity analysis stage are semblance grid and starting model of interval velocity. Basin-Hopping optimization is employed for convergence of the misfit function toward local minima. SLiding-Overlapping Window (SLOW) algorithm are designed to mitigate the non-linearity and ill- possessedness of root-mean-square velocity. Synthetic data case studies addresses the performance of the velocity picker generating models perfectly fitting the semblance peaks. A similar linear relationship between average depth and reflection time for synthetic model and estimated models proposed picked interval velocities as the starting model for the full waveform inversion to project more accurate velocity structure of the subsurface. The challenges can be categorized as (1) building accurate starting model for projecting more accurate velocity structure of the subsurface, (2) improving the computational cost of algorithm by pre-calculating semblance grid to make auto picking more feasible.

Experimental demonstration of iterative post-equalization algorithm for 37.5-Gbaud PM-16QAM quad-carrier Terabit superchannel.

PubMed

Jia, Zhensheng; Chien, Hung-Chang; Cai, Yi; Yu, Jianjun; Zhang, Chengliang; Li, Junjie; Ma, Yiran; Shang, Dongdong; Zhang, Qi; Shi, Sheping; Wang, Huitao

2015-02-09

We experimentally demonstrate a quad-carrier 1-Tb/s solution with 37.5-GBaud PM-16QAM signal over 37.5-GHz optical grid at 6.7 b/s/Hz net spectral efficiency. Digital Nyquist pulse shaping at the transmitter and post-equalization at the receiver are employed to mitigate the impairments of joint inter-symbol-interference (ISI) and inter-channel-interference (ICI) symbol degradation. The post-equalization algorithms consist of one sample/symbol based decision-directed least mean square (DD-LMS) adaptive filter, digital post filter and maximum likelihood sequence estimation (MLSE), and a positive iterative process among them. By combining these algorithms, the improvement as much as 4-dB OSNR (0.1nm) at SD-FEC limit (Q(2) = 6.25 corresponding to BER = 2.0e-2) is obtained when compared to no such post-equalization process, and transmission over 820-km EDFA-only standard single-mode fiber (SSMF) link is achieved for two 1.2-Tb/s signals with the averaged Q(2) factor larger than 6.5 dB for all sub-channels. Additionally, 50-GBaud 16QAM operating at 1.28 samples/symbol in a DAC is also investigated and successful transmission over 410-km SSMF link is achieved at 62.5-GHz optical grid.

Optimization of spectral bands for hyperspectral remote sensing of forest vegetation

NASA Astrophysics Data System (ADS)

Dmitriev, Egor V.; Kozoderov, Vladimir V.

2013-10-01

Optimization principles of accounting for the most informative spectral channels in hyperspectral remote sensing data processing serve to enhance the efficiency of the employed high-productive computers. The problem of pattern recognition of the remotely sensed land surface objects with the accent on the forests is outlined from the point of view of the spectral channels optimization on the processed hyperspectral images. The relevant computational procedures are tested using the images obtained by the produced in Russia hyperspectral camera that was installed on a gyro-stabilized platform to conduct the airborne flight campaigns. The Bayesian classifier is used for the pattern recognition of the forests with different tree species and age. The probabilistically optimal algorithm constructed on the basis of the maximum likelihood principle is described to minimize the probability of misclassification given by this classifier. The classification error is the major category to estimate the accuracy of the applied algorithm by the known holdout cross-validation method. Details of the related techniques are presented. Results are shown of selecting the spectral channels of the camera while processing the images having in mind radiometric distortions that diminish the classification accuracy. The spectral channels are selected of the obtained subclasses extracted from the proposed validation techniques and the confusion matrices are constructed that characterize the age composition of the classified pine species as well as the broad age-class recognition for the pine and birch species with the fully illuminated parts of their crowns.

Estimating Cloud optical thickness from SEVIRI, for air quality research, by implementing a semi-analytical cloud retrieval algorithm

NASA Astrophysics Data System (ADS)

Pandey, Praveen; De Ridder, Koen; van Looy, Stijn; van Lipzig, Nicole

2010-05-01

Clouds play an important role in Earth's climate system. As they affect radiation hence photolysis rate coefficients (ozone formation),they also affect the air quality at the surface of the earth. Thus, a satellite remote sensing technique is used to retrieve the cloud properties for air quality research. The geostationary satellite, Meteosat Second Generation (MSG) has onboard, the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The channels in the wavelength 0.6 µm and 1.64 µm are used to retrieve cloud optical thickness (COT). The study domain is over Europe covering a region between 35°N-70°N and 5°W-30°E, centred over Belgium. The steps involved in pre-processing the EUMETSAT level 1.5 images are described, which includes, acquisition of digital count number, radiometric conversion using offsets and slopes, estimation of radiance and calculation of reflectance. The Sun-earth-satellite geometry also plays an important role. A semi-analytical cloud retrieval algorithm (Kokhanovsky et al., 2003) is implemented for the estimation of COT. This approach doesn't involve the conventional look-up table approach, hence it makes the retrieval independent of numerical radiative transfer solutions. The semi-analytical algorithm is implemented on a monthly dataset of SEVIRI level 1.5 images. Minimum reflectance in the visible channel, at each pixel, during the month is accounted as the surface albedo of the pixel. Thus, monthly variation of COT over the study domain is prepared. The result so obtained, is compared with the COT products of Satellite Application Facility on Climate Monitoring (CM SAF). Henceforth, an approach to assimilate the COT for air quality research is presented. Address of corresponding author: Praveen Pandey, VITO- Flemish Institute for Technological Research, Boeretang 200, B 2400, Mol, Belgium E-mail: praveen.pandey@vito.be

Analysis of MASTER Thermal Data in the Greeley Area of the Front Range Urban Corridor, Colorado--Delineation of Sites for Infrastructure Resource Characterization

USGS Publications Warehouse

Livo, K. Eric; Watson, Ken

2002-01-01

Sand and soils southwest of Greeley, Colorado, were characterized for mineral composition and industrial quality. Radi-ance data from the thermal channels of the MASTER simulator were calibrated using estimated atmospheric parameters. Chan-nel emissivities were approximated using an estimated ground temperature. Subsequently, a decorrelation algorithm was used to calculate inverse wave emissivity images. Six soil classes, one vegetation class, water, and several small classes were defined using an unsupervised classification algorithm. Ground covered by each of the derived emissivity spectral classes was studied using color-infrared air photos, color-infrared composite MAS-TER data, geologic maps, NASA/JPL Airborne Visible and Infra-red Imaging Spectrometer (AVIRIS) data, and field examination. Spectral classes were characterized by their responses and related to their mineral content through field examination. Classes with a minimum at channel 44, and having a similar spectral shape to quartz, field checked as containing abundant quartz. Classes with a minimum at channel 45, and having a spectral shape similar to the sheet minerals, were found in the field to contain abundant mica and clay. Sandy soil was found to have a positive slope at the longer wavelengths; the more clay rich soils had a negative slope. Spectra with a strong downturn at channel 50 generally indicated low vegetation cover, whereas an upturn indicated more vegetation cover. Mapping revealed a range of classified soils with varying amounts of quartz, silt, clay, and plant humus. Sand and gravel operations along the St. Vrain River, gravel lots, and some fields spectrally classified as quartz-rich sands were confirmed through field examination. Other fields mapped as sandy soils, ranging from quartz-rich sandy soil to quartz-rich silt-sand soil with clay. Flood plains mapped as sandy-silty-organic-rich clay. The city of Greeley contained all classes of materials, with the sand classes mapping as various types of asphalt. Abundant quartz gravel was apparent within the asphalt during field check-ing. The clay classes mapped silt-clay soils in areas of irrigated grass landscaping, some fields, and roofing materials.

A hierarchical exact accelerated stochastic simulation algorithm

NASA Astrophysics Data System (ADS)

Orendorff, David; Mjolsness, Eric

2012-12-01

A new algorithm, "HiER-leap" (hierarchical exact reaction-leaping), is derived which improves on the computational properties of the ER-leap algorithm for exact accelerated simulation of stochastic chemical kinetics. Unlike ER-leap, HiER-leap utilizes a hierarchical or divide-and-conquer organization of reaction channels into tightly coupled "blocks" and is thereby able to speed up systems with many reaction channels. Like ER-leap, HiER-leap is based on the use of upper and lower bounds on the reaction propensities to define a rejection sampling algorithm with inexpensive early rejection and acceptance steps. But in HiER-leap, large portions of intra-block sampling may be done in parallel. An accept/reject step is used to synchronize across blocks. This method scales well when many reaction channels are present and has desirable asymptotic properties. The algorithm is exact, parallelizable and achieves a significant speedup over the stochastic simulation algorithm and ER-leap on certain problems. This algorithm offers a potentially important step towards efficient in silico modeling of entire organisms.

Algorithmic complexity of quantum capacity

NASA Astrophysics Data System (ADS)

Oskouei, Samad Khabbazi; Mancini, Stefano

2018-04-01

We analyze the notion of quantum capacity from the perspective of algorithmic (descriptive) complexity. To this end, we resort to the concept of semi-computability in order to describe quantum states and quantum channel maps. We introduce algorithmic entropies (like algorithmic quantum coherent information) and derive relevant properties for them. Then we show that quantum capacity based on semi-computable concept equals the entropy rate of algorithmic coherent information, which in turn equals the standard quantum capacity. Thanks to this, we finally prove that the quantum capacity, for a given semi-computable channel, is limit computable.

Improving Forecast Skill by Assimilation of Quality Controlled AIRS Version 5 Temperature Soundings

NASA Technical Reports Server (NTRS)

Susskind, Joel; Reale, Oreste

2009-01-01

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains two significant improvements over Version 4: 1) Improved physics allows for use of AIRS observations in the entire 4.3 micron CO2 absorption band in the retrieval of temperature profile T(p) during both day and night. Tropospheric sounding 15 micron CO2 observations are now used primarily in the generation of cloud cleared radiances R(sub i). This approach allows for the generation of accurate values of R(sub i) and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by-channel error estimates for R(sub i). These error estimates are used for Quality Control of the retrieved products. We have conducted forecast impact experiments assimilating AIRS temperature profiles with different levels of Quality Control using the NASA GEOS-5 data assimilation system. Assimilation of Quality Controlled T(p) resulted in significantly improved forecast skill compared to that obtained from analyses obtained when all data used operationally by NCEP, except for AIRS data, is assimilated. We also conducted an experiment assimilating AIRS radiances uncontaminated by clouds, as done operationally by ECMWF and NCEP. Forecast resulting from assimilated AIRS radiances were of poorer quality than those obtained assimilating AIRS temperatures.

Coordinated Beamforming for MISO Interference Channel: Complexity Analysis and Efficient Algorithms

DTIC Science & Technology

2010-01-01

Algorithm The cyclic coordinate descent algorithm is also known as the nonlinear Gauss - Seidel iteration [32]. There are several studies of this type of...vkρ(vi−1). It can be shown that the above BB gradient projection direction is always a descent direction. The R-linear convergence of the BB method has...KKT solution ) of the inexact pricing algorithm for MISO interference channel. The latter is interesting since the convergence of the original pricing

Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia

NASA Technical Reports Server (NTRS)

Park, Sang Seo; Kim, Jhoon; Lee, Jaehwa; Lee, Sukjo; Kim, Jeong Soo; Chang, Lim Seok; Ou, Steve

2014-01-01

A new dust detection algorithm is developed by combining the results of multiple dust detectionmethods using IR channels onboard the MODerate resolution Imaging Spectroradiometer (MODIS). Brightness Temperature Difference (BTD) between two wavelength channels has been used widely in previous dust detection methods. However, BTDmethods have limitations in identifying the offset values of the BTDto discriminate clear-sky areas. The current algorithm overcomes the disadvantages of previous dust detection methods by considering the Brightness Temperature Ratio (BTR) values of the dual wavelength channels with 30-day composite, the optical properties of the dust particles, the variability of surface properties, and the cloud contamination. Therefore, the current algorithm shows improvements in detecting the dust loaded region over land during daytime. Finally, the confidence index of the current dust algorithm is shown in 10 × 10 pixels of the MODIS observations. From January to June, 2006, the results of the current algorithm are within 64 to 81% of those found using the fine mode fraction (FMF) and aerosol index (AI) from the MODIS and Ozone Monitoring Instrument (OMI). The agreement between the results of the current algorithm and the OMI AI over the non-polluted land also ranges from 60 to 67% to avoid errors due to the anthropogenic aerosol. In addition, the developed algorithm shows statistically significant results at four AErosol RObotic NETwork (AERONET) sites in East Asia.

Liquid water path retrieval using the lowest frequency channels of Fengyun-3C Microwave Radiation Imager (MWRI)

NASA Astrophysics Data System (ADS)

Tang, Fei; Zou, Xiaolei

2017-12-01

The Microwave Radiation Imager (MWRI) on board Chinese Fengyun-3 (FY-3) satellites provides measurements at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz with both horizontal and vertical polarization channels. Brightness temperature measurements of those channels with their central frequencies higher than 19 GHz from satellite-based microwave imager radiometers had traditionally been used to retrieve cloud liquid water path (LWP) over ocean. The results show that the lowest frequency channels are the most appropriate for retrieving LWP when its values are large. Therefore, a modified LWP retrieval algorithm is developed for retrieving LWP of different magnitudes involving not only the high frequency channels but also the lowest frequency channels of FY-3 MWRI. The theoretical estimates of the LWP retrieval errors are between 0.11 and 0.06 mm for 10.65- and 18.7-GHz channels and between 0.02 and 0.04 mm for 36.5- and 89.0-GHz channels. It is also shown that the brightness temperature observations at 10.65 GHz can be utilized to better retrieve the LWP greater than 3 mm in the eyewall region of Super Typhoon Neoguri (2014). The spiral structure of clouds within and around Typhoon Neoguri can be well captured by combining the LWP retrievals from different frequency channels.

Multiple sound source localization using gammatone auditory filtering and direct sound componence detection

NASA Astrophysics Data System (ADS)

Chen, Huaiyu; Cao, Li

2017-06-01

In order to research multiple sound source localization with room reverberation and background noise, we analyze the shortcomings of traditional broadband MUSIC and ordinary auditory filtering based broadband MUSIC method, then a new broadband MUSIC algorithm with gammatone auditory filtering of frequency component selection control and detection of ascending segment of direct sound componence is proposed. The proposed algorithm controls frequency component within the interested frequency band in multichannel bandpass filter stage. Detecting the direct sound componence of the sound source for suppressing room reverberation interference is also proposed, whose merits are fast calculation and avoiding using more complex de-reverberation processing algorithm. Besides, the pseudo-spectrum of different frequency channels is weighted by their maximum amplitude for every speech frame. Through the simulation and real room reverberation environment experiments, the proposed method has good performance. Dynamic multiple sound source localization experimental results indicate that the average absolute error of azimuth estimated by the proposed algorithm is less and the histogram result has higher angle resolution.

Algorithm theoretical baseline for formaldehyde retrievals from S5P TROPOMI and from the QA4ECV project

NASA Astrophysics Data System (ADS)

De Smedt, Isabelle; Theys, Nicolas; Yu, Huan; Danckaert, Thomas; Lerot, Christophe; Compernolle, Steven; Van Roozendael, Michel; Richter, Andreas; Hilboll, Andreas; Peters, Enno; Pedergnana, Mattia; Loyola, Diego; Beirle, Steffen; Wagner, Thomas; Eskes, Henk; van Geffen, Jos; Folkert Boersma, Klaas; Veefkind, Pepijn

2018-04-01

On board the Copernicus Sentinel-5 Precursor (S5P) platform, the TROPOspheric Monitoring Instrument (TROPOMI) is a double-channel, nadir-viewing grating spectrometer measuring solar back-scattered earthshine radiances in the ultraviolet, visible, near-infrared, and shortwave infrared with global daily coverage. In the ultraviolet range, its spectral resolution and radiometric performance are equivalent to those of its predecessor OMI, but its horizontal resolution at true nadir is improved by an order of magnitude. This paper introduces the formaldehyde (HCHO) tropospheric vertical column retrieval algorithm implemented in the S5P operational processor and comprehensively describes its various retrieval steps. Furthermore, algorithmic improvements developed in the framework of the EU FP7-project QA4ECV are described for future updates of the processor. Detailed error estimates are discussed in the light of Copernicus user requirements and needs for validation are highlighted. Finally, verification results based on the application of the algorithm to OMI measurements are presented, demonstrating the performances expected for TROPOMI.

Fast state estimation subject to random data loss in discrete-time nonlinear stochastic systems

NASA Astrophysics Data System (ADS)

Mahdi Alavi, S. M.; Saif, Mehrdad

2013-12-01

This paper focuses on the design of the standard observer in discrete-time nonlinear stochastic systems subject to random data loss. By the assumption that the system response is incrementally bounded, two sufficient conditions are subsequently derived that guarantee exponential mean-square stability and fast convergence of the estimation error for the problem at hand. An efficient algorithm is also presented to obtain the observer gain. Finally, the proposed methodology is employed for monitoring the Continuous Stirred Tank Reactor (CSTR) via a wireless communication network. The effectiveness of the designed observer is extensively assessed by using an experimental tested-bed that has been fabricated for performance evaluation of the over wireless-network estimation techniques under realistic radio channel conditions.

Snowfall Rate Retrieval using NPP ATMS Passive Microwave Measurements

NASA Technical Reports Server (NTRS)

Meng, Huan; Ferraro, Ralph; Kongoli, Cezar; Wang, Nai-Yu; Dong, Jun; Zavodsky, Bradley; Yan, Banghua; Zhao, Limin

2014-01-01

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2014). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The ATMS SFR product is validated against radar and gauge snowfall data and shows that the ATMS algorithm outperforms the AMSU/MHS SFR.

Seasonal comparisons of sea ice concentration estimates derived from SSM/I, OKEAN, and RADARSAT data

USGS Publications Warehouse

Belchansky, Gennady I.; Douglas, David C.

2002-01-01

The Special Sensor Microwave Imager (SSM/I) microwave satellite radiometer and its predecessor SMMR are primary sources of information for global sea ice and climate studies. However, comparisons of SSM/I, Landsat, AVHRR, and ERS-1 synthetic aperture radar (SAR) have shown substantial seasonal and regional differences in their estimates of sea ice concentration. To evaluate these differences, we compared SSM/I estimates of sea ice coverage derived with the NASA Team and Bootstrap algorithms to estimates made using RADARSAT, and OKEAN-01 satellite sensor data. The study area included the Barents Sea, Kara Sea, Laptev Sea, and adjacent parts of the Arctic Ocean, during October 1995 through October 1999. Ice concentration estimates from spatially and temporally near-coincident imagery were calculated using independent algorithms for each sensor type. The OKEAN algorithm implemented the satellite's two-channel active (radar) and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter. The RADARSAT algorithm utilized a segmentation approach of the measured radar backscatter, and the SSM/I ice concentrations were derived at National Snow and Ice Data Center (NSIDC) using the NASA Team and Bootstrap algorithms. Seasonal and monthly differences between SSM/I, OKEAN, and RADARSAT ice concentrations were calculated and compared. Overall, total sea ice concentration estimates derived independently from near-coincident RADARSAT, OKEAN-01, and SSM/I satellite imagery demonstrated mean differences of less than 5.5% (S.D.<9.5%) during the winter period. Differences between the SSM/I NASA Team and the SSM/I Bootstrap concentrations were no more than 3.1% (S.D.<5.4%) during this period. RADARSAT and OKEAN-01 data both yielded higher total ice concentrations than the NASA Team and the Bootstrap algorithms. The Bootstrap algorithm yielded higher total ice concentrations than the NASA Team algorithm. Total ice concentrations derived from OKEAN-01 and SSM/I satellite imagery were highly correlated during winter, spring, and fall, with mean differences of less than 8.1% (S.D.<15%) for the NASA Team algorithm, and less than 2.8% (S.D.<13.8%) for the Bootstrap algorithm. Respective differences between SSM/I NASA Team and SSM/I Bootstrap total concentrations were less than 5.3% (S.D.<6.9%). Monthly mean differences between SSM/I and OKEAN differed annually by less than 6%, with smaller differences primarily in winter. The NASA Team and Bootstrap algorithms underestimated the total sea ice concentrations relative to the RADARSAT ScanSAR no more than 3.0% (S.D.<9%) and 1.2% (S.D.<7.5%) during cold months, and no more than 12% and 7% during summer, respectively. ScanSAR tended to estimate higher ice concentrations for ice concentrations greater than 50%, when compared to SSM/I during all months. ScanSAR underestimated total sea ice concentration by 2% compared to the OKEAN-01 algorithm during cold months, and gave an overestimation by 2% during spring and summer months. Total NASA Team and Bootstrap sea ice concentration estimates derived from coincident SSM/I and OKEAN-01 data demonstrated mean differences of no more than 5.3% (S.D.<7%), 3.1% (S.D.<5.5%), 2.0% (S.D.<5.5%), and 7.3% (S.D.<10%) for fall, winter, spring, and summer periods, respectively. Large disagreements were observed between the OKEAN and NASA Team results in spring and summer for estimates of the first-year (FY) and multiyear (MY) age classes. The OKEAN-01 algorithm and data tended to estimate, on average, lower concentrations of young or FY ice and higher concentrations of total and MY ice for all months and seasons. Our results contribute to the growing body of documentation about the levels of disparity obtained when seasonal sea ice concentrations are estimated using various types of satellite data and algorithms.

Super-resolution for imagery from integrated microgrid polarimeters.

PubMed

Hardie, Russell C; LeMaster, Daniel A; Ratliff, Bradley M

2011-07-04

Imagery from microgrid polarimeters is obtained by using a mosaic of pixel-wise micropolarizers on a focal plane array (FPA). Each distinct polarization image is obtained by subsampling the full FPA image. Thus, the effective pixel pitch for each polarization channel is increased and the sampling frequency is decreased. As a result, aliasing artifacts from such undersampling can corrupt the true polarization content of the scene. Here we present the first multi-channel multi-frame super-resolution (SR) algorithms designed specifically for the problem of image restoration in microgrid polarization imagers. These SR algorithms can be used to address aliasing and other degradations, without sacrificing field of view or compromising optical resolution with an anti-aliasing filter. The new SR methods are designed to exploit correlation between the polarimetric channels. One of the new SR algorithms uses a form of regularized least squares and has an iterative solution. The other is based on the faster adaptive Wiener filter SR method. We demonstrate that the new multi-channel SR algorithms are capable of providing significant enhancement of polarimetric imagery and that they outperform their independent channel counterparts.

Blind information-theoretic multiuser detection algorithms for DS-CDMA and WCDMA downlink systems.

PubMed

Waheed, Khuram; Salem, Fathi M

2005-07-01

Code division multiple access (CDMA) is based on the spread-spectrum technology and is a dominant air interface for 2.5G, 3G, and future wireless networks. For the CDMA downlink, the transmitted CDMA signals from the base station (BS) propagate through a noisy multipath fading communication channel before arriving at the receiver of the user equipment/mobile station (UE/MS). Classical CDMA single-user detection (SUD) algorithms implemented in the UE/MS receiver do not provide the required performance for modern high data-rate applications. In contrast, multi-user detection (MUD) approaches require a lot of a priori information not available to the UE/MS. In this paper, three promising adaptive Riemannian contra-variant (or natural) gradient based user detection approaches, capable of handling the highly dynamic wireless environments, are proposed. The first approach, blind multiuser detection (BMUD), is the process of simultaneously estimating multiple symbol sequences associated with all the users in the downlink of a CDMA communication system using only the received wireless data and without any knowledge of the user spreading codes. This approach is applicable to CDMA systems with relatively short spreading codes but becomes impractical for systems using long spreading codes. We also propose two other adaptive approaches, namely, RAKE -blind source recovery (RAKE-BSR) and RAKE-principal component analysis (RAKE-PCA) that fuse an adaptive stage into a standard RAKE receiver. This adaptation results in robust user detection algorithms with performance exceeding the linear minimum mean squared error (LMMSE) detectors for both Direct Sequence CDMA (DS-CDMA) and wide-band CDMA (WCDMA) systems under conditions of congestion, imprecise channel estimation and unmodeled multiple access interference (MAI).

Machine learning algorithms for meteorological event classification in the coastal area using in-situ data

NASA Astrophysics Data System (ADS)

Sokolov, Anton; Gengembre, Cyril; Dmitriev, Egor; Delbarre, Hervé

2017-04-01

The problem is considered of classification of local atmospheric meteorological events in the coastal area such as sea breezes, fogs and storms. The in-situ meteorological data as wind speed and direction, temperature, humidity and turbulence are used as predictors. Local atmospheric events of 2013-2014 were analysed manually to train classification algorithms in the coastal area of English Channel in Dunkirk (France). Then, ultrasonic anemometer data and LIDAR wind profiler data were used as predictors. A few algorithms were applied to determine meteorological events by local data such as a decision tree, the nearest neighbour classifier, a support vector machine. The comparison of classification algorithms was carried out, the most important predictors for each event type were determined. It was shown that in more than 80 percent of the cases machine learning algorithms detect the meteorological class correctly. We expect that this methodology could be applied also to classify events by climatological in-situ data or by modelling data. It allows estimating frequencies of each event in perspective of climate change.

Fetal ECG extraction via Type-2 adaptive neuro-fuzzy inference systems.

PubMed

Ahmadieh, Hajar; Asl, Babak Mohammadzadeh

2017-04-01

We proposed a noninvasive method for separating the fetal ECG (FECG) from maternal ECG (MECG) by using Type-2 adaptive neuro-fuzzy inference systems. The method can extract FECG components from abdominal signal by using one abdominal channel, including maternal and fetal cardiac signals and other environmental noise signals, and one chest channel. The proposed algorithm detects the nonlinear dynamics of the mother's body. So, the components of the MECG are estimated from the abdominal signal. By subtracting estimated mother cardiac signal from abdominal signal, fetal cardiac signal can be extracted. This algorithm was applied on synthetic ECG signals generated based on the models developed by McSharry et al. and Behar et al. and also on DaISy real database. In environments with high uncertainty, our method performs better than the Type-1 fuzzy method. Specifically, in evaluation of the algorithm with the synthetic data based on McSharry model, for input signals with SNR of -5dB, the SNR of the extracted FECG was improved by 38.38% in comparison with the Type-1 fuzzy method. Also, the results show that increasing the uncertainty or decreasing the input SNR leads to increasing the percentage of the improvement in SNR of the extracted FECG. For instance, when the SNR of the input signal decreases to -30dB, our proposed algorithm improves the SNR of the extracted FECG by 71.06% with respect to the Type-1 fuzzy method. The same results were obtained on synthetic data based on Behar model. Our results on real database reflect the success of the proposed method to separate the maternal and fetal heart signals even if their waves overlap in time. Moreover, the proposed algorithm was applied to the simulated fetal ECG with ectopic beats and achieved good results in separating FECG from MECG. The results show the superiority of the proposed Type-2 neuro-fuzzy inference method over the Type-1 neuro-fuzzy inference and the polynomial networks methods, which is due to its capability to capture the nonlinearities of the model better. Copyright © 2017 Elsevier B.V. All rights reserved.

Sniffer Channel Selection for Monitoring Wireless LANs

NASA Astrophysics Data System (ADS)

Song, Yuan; Chen, Xian; Kim, Yoo-Ah; Wang, Bing; Chen, Guanling

Wireless sniffers are often used to monitor APs in wireless LANs (WLANs) for network management, fault detection, traffic characterization, and optimizing deployment. It is cost effective to deploy single-radio sniffers that can monitor multiple nearby APs. However, since nearby APs often operate on orthogonal channels, a sniffer needs to switch among multiple channels to monitor its nearby APs. In this paper, we formulate and solve two optimization problems on sniffer channel selection. Both problems require that each AP be monitored by at least one sniffer. In addition, one optimization problem requires minimizing the maximum number of channels that a sniffer listens to, and the other requires minimizing the total number of channels that the sniffers listen to. We propose a novel LP-relaxation based algorithm, and two simple greedy heuristics for the above two optimization problems. Through simulation, we demonstrate that all the algorithms are effective in achieving their optimization goals, and the LP-based algorithm outperforms the greedy heuristics.

Constrained maximum consistency multi-path mitigation

NASA Astrophysics Data System (ADS)

Smith, George B.

2003-10-01

Blind deconvolution algorithms can be useful as pre-processors for signal classification algorithms in shallow water. These algorithms remove the distortion of the signal caused by multipath propagation when no knowledge of the environment is available. A framework in which filters that produce signal estimates from each data channel that are as consistent with each other as possible in a least-squares sense has been presented [Smith, J. Acoust. Soc. Am. 107 (2000)]. This framework provides a solution to the blind deconvolution problem. One implementation of this framework yields the cross-relation on which EVAM [Gurelli and Nikias, IEEE Trans. Signal Process. 43 (1995)] and Rietsch [Rietsch, Geophysics 62(6) (1997)] processing are based. In this presentation, partially blind implementations that have good noise stability properties are compared using Classification Operating Characteristics (CLOC) analysis. [Work supported by ONR under Program Element 62747N and NRL, Stennis Space Center, MS.

Fast and robust segmentation in the SDO-AIA era

NASA Astrophysics Data System (ADS)

Verbeeck, Cis; Delouille, Véronique; Mampaey, Benjamin; Hochedez, Jean-François; Boyes, David; Barra, Vincent

Solar images from the Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamics Ob-servatory (SDO) will flood the solar physics community with a wealth of information on solar variability, of great importance both in solar physics and in view of Space Weather applica-tions. Obtaining this information, however, requires the ability to automatically process large amounts of data in an objective fashion. In previous work, we have proposed a multi-channel unsupervised spatially-constrained multi-channel fuzzy clustering algorithm (SPoCA) that automatically segments EUV solar images into Active Regions (AR), Coronal Holes (CH), and Quiet Sun (QS). This algorithm will run in near real time on AIA data as part of the SDO Feature Finding Project, a suite of software pipeline modules for automated feature recognition and analysis for the imagery from SDO. After having corrected for the limb brightening effect, SPoCA computes an optimal clustering with respect to the regions of interest using fuzzy logic on a quality criterion to manage the various noises present in the images and the imprecision in the definition of the above regions. Next, the algorithm applies a morphological opening operation, smoothing the cluster edges while preserving their general shape. The process is fast and automatic. A lower size limit is used to distinguish AR from Bright Points. As the algorithm segments the coronal images according to their brightness, it might happen that an AR is detected as several disjoint pieces, if the brightness in between is somewhat lower. Morphological dilation is employed to reconstruct the AR themselves from their constituent pieces. Combining SPoCA's detection of AR, CH, and QS on subsequent images allows automatic tracking and naming of any region of interest. In the SDO software pipeline, SPoCA will auto-matically populate the Heliophysics Events Knowledgebase(HEK) with Active Region events. Further, the algorithm has a huge potential for correct and automatic identification of AR, CH, and QS in any study that aims to address properties of those specific regions in the corona. SPoCA is now ready and waiting to tackle solar cycle 24 using SDO data. While we presently apply SPoCA to EUV data, the method is generic enough to allow the introduction of other channels or data, e.g., Differential Emission Measure (DEM) maps. Because of the unprecedented challenges brought up by the quantity of SDO data, European partners have gathered within an ISSI team on `Mining and Exploiting the NASA Solar Dynam-ics Observatory data in Europe' (a.k.a. Soldyneuro). Its aim is to provide automated feature recognition algorithms for scanning the SDO archive, as well as conducting scientific studies that combine different algorithm's outputs. Within the Soldyneuro project, we will use data from the EUV Variability Experiment (EVE) spectrometer in order to estimate the full Sun DEM. This DEM will next be used to estimate the total flux from AIA images so as to provide a validation for the calibration of AIA.

Development of a global LAI estimation algorithm for JAXA's new earth observation satellite sensor, GCOM-C/SGLI

NASA Astrophysics Data System (ADS)

Ono, Y.; Murakami, H.; Kobayashi, H.; Nasahara, K. N.; Kajiwara, K.; Honda, Y.

2014-12-01

Leaf Area Index (LAI) is defined as the one-side green leaf area per unit ground surface area. Global LAI products, such as MOD15 (Terra&Aqua/MODIS) and CYCLOPES (SPOT/VEGETATION) are used for many global terrestrial carbon models. Japan Aerospace eXploration Agency (JAXA) is planning to launch GCOM-C (Global Change Observation Mission-Climate) which carries SGLI (Second-generation GLobal Imager) in the Japanese Fiscal Year 2017. SGLI has the features, such as 17-channel from near ultraviolet to thermal infrared, 250-m spatial resolution, polarization, and multi-angle (nadir and ±45-deg. along-track slant) observation. In the GCOM-C/SGLI land science team, LAI is scheduled to be generated from GCOM-C/SGLI observation data as a standard product (daily 250-m). In extisting algorithms, LAI is estimated by the reverse analysis of vegetation radiative transfer models (RTMs) using multi-spectral and mono-angle observation data. Here, understory layer in vegetation RTMs is assumed by plane parallel (green leaves + soil) which set up arbitrary understroy LAI. However, actual understory consists of various elements, such as green leaves, dead leaves, branches, soil, and snow. Therefore, if understory in vegetation RTMs differs from reality, it will cause an error of LAI to estimate. This report describes an algorithm which estimates LAI in consideration of the influence of understory using GCOM-C/SGLI multi-spectral and multi-angle observation data.

Optimization of digital breast tomosynthesis (DBT) acquisition parameters for human observers: effect of reconstruction algorithms

NASA Astrophysics Data System (ADS)

Zeng, Rongping; Badano, Aldo; Myers, Kyle J.

2017-04-01

We showed in our earlier work that the choice of reconstruction methods does not affect the optimization of DBT acquisition parameters (angular span and number of views) using simulated breast phantom images in detecting lesions with a channelized Hotelling observer (CHO). In this work we investigate whether the model-observer based conclusion is valid when using humans to interpret images. We used previously generated DBT breast phantom images and recruited human readers to find the optimal geometry settings associated with two reconstruction algorithms, filtered back projection (FBP) and simultaneous algebraic reconstruction technique (SART). The human reader results show that image quality trends as a function of the acquisition parameters are consistent between FBP and SART reconstructions. The consistent trends confirm that the optimization of DBT system geometry is insensitive to the choice of reconstruction algorithm. The results also show that humans perform better in SART reconstructed images than in FBP reconstructed images. In addition, we applied CHOs with three commonly used channel models, Laguerre-Gauss (LG) channels, square (SQR) channels and sparse difference-of-Gaussian (sDOG) channels. We found that LG channels predict human performance trends better than SQR and sDOG channel models for the task of detecting lesions in tomosynthesis backgrounds. Overall, this work confirms that the choice of reconstruction algorithm is not critical for optimizing DBT system acquisition parameters.

Linear regression models and k-means clustering for statistical analysis of fNIRS data.

PubMed

Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro

2015-02-01

We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets.

Linear regression models and k-means clustering for statistical analysis of fNIRS data

PubMed Central

Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro

2015-01-01

We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets. PMID:25780751

A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks

PubMed Central

Wang, Hao; Wang, Shilian; Zhang, Eryang; Zou, Jianbin

2016-01-01

Underwater Acoustic Sensor Networks (UASNs) have attracted increasing interest in recent years due to their extensive commercial and military applications. However, the harsh underwater channel causes many challenges for the design of reliable underwater data transport protocol. In this paper, we propose an energy efficient data transport protocol based on network coding and hybrid automatic repeat request (NCHARQ) to ensure reliability, efficiency and availability in UASNs. Moreover, an adaptive window length estimation algorithm is designed to optimize the throughput and energy consumption tradeoff. The algorithm can adaptively change the code rate and can be insensitive to the environment change. Extensive simulations and analysis show that NCHARQ significantly reduces energy consumption with short end-to-end delay. PMID:27618044

Estimation of flow properties using surface deformation and head data: A trajectory-based approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vasco, D.W.

2004-07-12

A trajectory-based algorithm provides an efficient and robust means to infer flow properties from surface deformation and head data. The algorithm is based upon the concept of an ''arrival time'' of a drawdown front, which is defined as the time corresponding to the maximum slope of the drawdown curve. The technique involves three steps: the inference of head changes as a function of position and time, the use of the estimated head changes to define arrival times, and the inversion of the arrival times for flow properties. Trajectories, computed from the output of a numerical simulator, are used to relatemore » the drawdown arrival times to flow properties. The inversion algorithm is iterative, requiring one reservoir simulation for each iteration. The method is applied to data from a set of 14 tiltmeters, located at the Raymond Quarry field site in California. Using the technique, I am able to image a high-conductivity channel which extends to the south of the pumping well. The presence of th is permeable pathway is supported by an analysis of earlier cross-well transient pressure test data.« less

Investigation of a novel image segmentation method dedicated to forest fire applications

NASA Astrophysics Data System (ADS)

Rudz, S.; Chetehouna, K.; Hafiane, A.; Laurent, H.; Séro-Guillaume, O.

2013-07-01

To face fire it is crucial to understand its behaviour in order to maximize fighting means. To achieve this task, the development of a metrological tool is necessary for estimating both geometrical and physical parameters involved in forest fire modelling. A key parameter is to estimate fire positions accurately. In this paper an image processing tool especially dedicated to an accurate extraction of fire from an image is presented. In this work, the clustering on several colour spaces is investigated and it appears that the blue chrominance Cb from the YCbCr colour space is the most appropriate. As a consequence, a new segmentation algorithm dedicated to forest fire applications has been built using first an optimized k-means clustering in the Cb-channel and then some properties of fire pixels in the RGB colour space. Next, the performance of the proposed method is evaluated using three supervised evaluation criteria and then compared to other existing segmentation algorithms in the literature. Finally a conclusion is drawn, assessing the good behaviour of the developed algorithm. This paper is dedicated to the memory of Dr Olivier Séro-Guillaume (1950-2013), CNRS Research Director.

Hue-preserving and saturation-improved color histogram equalization algorithm.

PubMed

Song, Ki Sun; Kang, Hee; Kang, Moon Gi

2016-06-01

In this paper, an algorithm is proposed to improve contrast and saturation without color degradation. The local histogram equalization (HE) method offers better performance than the global HE method, whereas the local HE method sometimes produces undesirable results due to the block-based processing. The proposed contrast-enhancement (CE) algorithm reflects the characteristics of the global HE method in the local HE method to avoid the artifacts, while global and local contrasts are enhanced. There are two ways to apply the proposed CE algorithm to color images. One is luminance processing methods, and the other one is each channel processing methods. However, these ways incur excessive or reduced saturation and color degradation problems. The proposed algorithm solves these problems by using channel adaptive equalization and similarity of ratios between the channels. Experimental results show that the proposed algorithm enhances contrast and saturation while preserving the hue and producing better performance than existing methods in terms of objective evaluation metrics.

A theory of local learning, the learning channel, and the optimality of backpropagation.

PubMed

Baldi, Pierre; Sadowski, Peter

2016-11-01

In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying the space of local learning rules is obtained by first specifying the nature of the local variables, and then the functional form that ties them together into each learning rule. Such a framework enables also the systematic discovery of new learning rules and exploration of relationships between learning rules and group symmetries. We study polynomial local learning rules stratified by their degree and analyze their behavior and capabilities in both linear and non-linear units and networks. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex input-output functions requires local deep learning where target information is communicated to the deep layers through a backward learning channel. The nature of the communicated information about the targets and the structure of the learning channel partition the space of learning algorithms. For any learning algorithm, the capacity of the learning channel can be defined as the number of bits provided about the error gradient per weight, divided by the number of required operations per weight. We estimate the capacity associated with several learning algorithms and show that backpropagation outperforms them by simultaneously maximizing the information rate and minimizing the computational cost. This result is also shown to be true for recurrent networks, by unfolding them in time. The theory clarifies the concept of Hebbian learning, establishes the power and limitations of local learning rules, introduces the learning channel which enables a formal analysis of the optimality of backpropagation, and explains the sparsity of the space of learning rules discovered so far. Copyright © 2016 Elsevier Ltd. All rights reserved.

Demonstration of a small programmable quantum computer with atomic qubits.

PubMed

Debnath, S; Linke, N M; Figgatt, C; Landsman, K A; Wright, K; Monroe, C

2016-08-04

Quantum computers can solve certain problems more efficiently than any possible conventional computer. Small quantum algorithms have been demonstrated on multiple quantum computing platforms, many specifically tailored in hardware to implement a particular algorithm or execute a limited number of computational paths. Here we demonstrate a five-qubit trapped-ion quantum computer that can be programmed in software to implement arbitrary quantum algorithms by executing any sequence of universal quantum logic gates. We compile algorithms into a fully connected set of gate operations that are native to the hardware and have a mean fidelity of 98 per cent. Reconfiguring these gate sequences provides the flexibility to implement a variety of algorithms without altering the hardware. As examples, we implement the Deutsch-Jozsa and Bernstein-Vazirani algorithms with average success rates of 95 and 90 per cent, respectively. We also perform a coherent quantum Fourier transform on five trapped-ion qubits for phase estimation and period finding with average fidelities of 62 and 84 per cent, respectively. This small quantum computer can be scaled to larger numbers of qubits within a single register, and can be further expanded by connecting several such modules through ion shuttling or photonic quantum channels.

Demonstration of a small programmable quantum computer with atomic qubits

NASA Astrophysics Data System (ADS)

Debnath, S.; Linke, N. M.; Figgatt, C.; Landsman, K. A.; Wright, K.; Monroe, C.

2016-08-01

Quantum computers can solve certain problems more efficiently than any possible conventional computer. Small quantum algorithms have been demonstrated on multiple quantum computing platforms, many specifically tailored in hardware to implement a particular algorithm or execute a limited number of computational paths. Here we demonstrate a five-qubit trapped-ion quantum computer that can be programmed in software to implement arbitrary quantum algorithms by executing any sequence of universal quantum logic gates. We compile algorithms into a fully connected set of gate operations that are native to the hardware and have a mean fidelity of 98 per cent. Reconfiguring these gate sequences provides the flexibility to implement a variety of algorithms without altering the hardware. As examples, we implement the Deutsch-Jozsa and Bernstein-Vazirani algorithms with average success rates of 95 and 90 per cent, respectively. We also perform a coherent quantum Fourier transform on five trapped-ion qubits for phase estimation and period finding with average fidelities of 62 and 84 per cent, respectively. This small quantum computer can be scaled to larger numbers of qubits within a single register, and can be further expanded by connecting several such modules through ion shuttling or photonic quantum channels.

A novel cost-effective parallel narrowband ANC system with local secondary-path estimation

NASA Astrophysics Data System (ADS)

Delegà, Riccardo; Bernasconi, Giancarlo; Piroddi, Luigi

2017-08-01

Many noise reduction applications are targeted at multi-tonal disturbances. Active noise control (ANC) solutions for such problems are generally based on the combination of multiple adaptive notch filters. Both the performance and the computational cost are negatively affected by an increase in the number of controlled frequencies. In this work we study a different modeling approach for the secondary path, based on the estimation of various small local models in adjacent frequency subbands, that greatly reduces the impact of reference-filtering operations in the ANC algorithm. Furthermore, in combination with a frequency-specific step size tuning method it provides a balanced attenuation performance over the whole controlled frequency range (and particularly in the high end of the range). Finally, the use of small local models is greatly beneficial for the reactivity of the online secondary path modeling algorithm when the characteristics of the acoustic channels are time-varying. Several simulations are provided to illustrate the positive features of the proposed method compared to other well-known techniques.

An improved algorithm for small and cool fire detection using MODIS data: A preliminary study in the southeastern United States

Treesearch

Wanting Wang; John J. Qu; Xianjun Hao; Yongqiang Liu; William T. Sommers

2006-01-01

Traditional fire detection algorithms mainly rely on hot spot detection using thermal infrared (TIR) channels with fixed or contextual thresholds. Three solar reflectance channels (0.65 μm, 0.86 μm, and 2.1 μm) were recently adopted into the MODIS version 4 contextual algorithm to improve the active fire detection. In the southeastern United...

Optimal Golomb Ruler Sequences Generation for Optical WDM Systems: A Novel Parallel Hybrid Multi-objective Bat Algorithm

NASA Astrophysics Data System (ADS)

Bansal, Shonak; Singh, Arun Kumar; Gupta, Neena

2017-02-01

In real-life, multi-objective engineering design problems are very tough and time consuming optimization problems due to their high degree of nonlinearities, complexities and inhomogeneity. Nature-inspired based multi-objective optimization algorithms are now becoming popular for solving multi-objective engineering design problems. This paper proposes original multi-objective Bat algorithm (MOBA) and its extended form, namely, novel parallel hybrid multi-objective Bat algorithm (PHMOBA) to generate shortest length Golomb ruler called optimal Golomb ruler (OGR) sequences at a reasonable computation time. The OGRs found their application in optical wavelength division multiplexing (WDM) systems as channel-allocation algorithm to reduce the four-wave mixing (FWM) crosstalk. The performances of both the proposed algorithms to generate OGRs as optical WDM channel-allocation is compared with other existing classical computing and nature-inspired algorithms, including extended quadratic congruence (EQC), search algorithm (SA), genetic algorithms (GAs), biogeography based optimization (BBO) and big bang-big crunch (BB-BC) optimization algorithms. Simulations conclude that the proposed parallel hybrid multi-objective Bat algorithm works efficiently as compared to original multi-objective Bat algorithm and other existing algorithms to generate OGRs for optical WDM systems. The algorithm PHMOBA to generate OGRs, has higher convergence and success rate than original MOBA. The efficiency improvement of proposed PHMOBA to generate OGRs up to 20-marks, in terms of ruler length and total optical channel bandwidth (TBW) is 100 %, whereas for original MOBA is 85 %. Finally the implications for further research are also discussed.

Integrated segmentation of cellular structures

NASA Astrophysics Data System (ADS)

Ajemba, Peter; Al-Kofahi, Yousef; Scott, Richard; Donovan, Michael; Fernandez, Gerardo

2011-03-01

Automatic segmentation of cellular structures is an essential step in image cytology and histology. Despite substantial progress, better automation and improvements in accuracy and adaptability to novel applications are needed. In applications utilizing multi-channel immuno-fluorescence images, challenges include misclassification of epithelial and stromal nuclei, irregular nuclei and cytoplasm boundaries, and over and under-segmentation of clustered nuclei. Variations in image acquisition conditions and artifacts from nuclei and cytoplasm images often confound existing algorithms in practice. In this paper, we present a robust and accurate algorithm for jointly segmenting cell nuclei and cytoplasm using a combination of ideas to reduce the aforementioned problems. First, an adaptive process that includes top-hat filtering, Eigenvalues-of-Hessian blob detection and distance transforms is used to estimate the inverse illumination field and correct for intensity non-uniformity in the nuclei channel. Next, a minimum-error-thresholding based binarization process and seed-detection combining Laplacian-of-Gaussian filtering constrained by a distance-map-based scale selection is used to identify candidate seeds for nuclei segmentation. The initial segmentation using a local maximum clustering algorithm is refined using a minimum-error-thresholding technique. Final refinements include an artifact removal process specifically targeted at lumens and other problematic structures and a systemic decision process to reclassify nuclei objects near the cytoplasm boundary as epithelial or stromal. Segmentation results were evaluated using 48 realistic phantom images with known ground-truth. The overall segmentation accuracy exceeds 94%. The algorithm was further tested on 981 images of actual prostate cancer tissue. The artifact removal process worked in 90% of cases. The algorithm has now been deployed in a high-volume histology analysis application.

EPIC/DSCOVR's Oxygen Absorption Channels: A Cloud Profiling Information Content Analysis

NASA Astrophysics Data System (ADS)

Davis, A. B.; Merlin, G.; Labonnote, L. C.; Cornet, C.; Dubuisson, P.; Ferlay, N.; Parol, F.; Riedi, J.; Yang, Y.

2016-12-01

EPIC/DSCOVR has several spectral channels dedicated to cloud characterization, most notably O2 A- and B-band. Differential optical absorption spectroscopy (DOAS) ratios of in-band and reference channels are less prone to calibration error than the 4 individual signals. Using these ratios, we have replicated for mono-directional (quasi-backscattering) EPIC observations the recent cloud information content analysis by Merlin et al. (AMT-D,8:12709-12758,2015) that was focused on A-band-only but multi-angle observations by POLDER in the past, by AirMSPI in the present, and by 3MI and MAIA in the future. The methodology is based on extensive forward 1D radiative transfer (RT) computations using the ARTDECO model that implements a k-distribution technique for the absorbing (in-band) channels. These synthetic signals are combined into a Bayesian Rodgers-type framework for estimating posterior uncertainty on retrieved quantities. Recall that this formalism calls explicitly for: (1) estimates of instrument error, and (2) prior uncertainty on the retrieved quantities, to which we add (3) reasonable estimates of uncertainty in the non- or otherwise-retrieved properties. Wide ranges of cloud top heights (CTHs) and cloud geometrical thicknesses (CGTs) are examined for a representative selection of cloud optical thicknesses (COTs), solar angles, and surface reflectances. We found that CTH should be reliably retrieved from EPIC data under most circumstances as long as COT can be inferred from non-absorbing channels, and the bias from in-cloud absorption is removed. However, CGT will be hard to determine unless CTH is constrained by independent means. EPIC has several UV channels that could be brought to bear. These findings conflict those of Yang et al. (JQSRT,122:141-149,2013), so we also revisit that more preliminary study that did not account for a realistic level of residual instrument noise in the DOAS ratios. In conclusion, we believe that the present information content analysis will inform the EPIC/DSCOVR Level 2 algorithm development team about what cloud properties to target using the A/B-band channels, depending on the availability of other cloud information.

Information theoretic analysis of canny edge detection in visual communication

NASA Astrophysics Data System (ADS)

Jiang, Bo; Rahman, Zia-ur

2011-06-01

In general edge detection evaluation, the edge detectors are examined, analyzed, and compared either visually or with a metric for specific an application. This analysis is usually independent of the characteristics of the image-gathering, transmission and display processes that do impact the quality of the acquired image and thus, the resulting edge image. We propose a new information theoretic analysis of edge detection that unites the different components of the visual communication channel and assesses edge detection algorithms in an integrated manner based on Shannon's information theory. The edge detection algorithm here is considered to achieve high performance only if the information rate from the scene to the edge approaches the maximum possible. Thus, by setting initial conditions of the visual communication system as constant, different edge detection algorithms could be evaluated. This analysis is normally limited to linear shift-invariant filters so in order to examine the Canny edge operator in our proposed system, we need to estimate its "power spectral density" (PSD). Since the Canny operator is non-linear and shift variant, we perform the estimation for a set of different system environment conditions using simulations. In our paper we will first introduce the PSD of the Canny operator for a range of system parameters. Then, using the estimated PSD, we will assess the Canny operator using information theoretic analysis. The information-theoretic metric is also used to compare the performance of the Canny operator with other edge-detection operators. This also provides a simple tool for selecting appropriate edgedetection algorithms based on system parameters, and for adjusting their parameters to maximize information throughput.

A fast color image enhancement algorithm based on Max Intensity Channel

PubMed Central

Sun, Wei; Han, Long; Guo, Baolong; Jia, Wenyan; Sun, Mingui

2014-01-01

In this paper, we extend image enhancement techniques based on the retinex theory imitating human visual perception of scenes containing high illumination variations. This extension achieves simultaneous dynamic range modification, color consistency, and lightness rendition without multi-scale Gaussian filtering which has a certain halo effect. The reflection component is analyzed based on the illumination and reflection imaging model. A new prior named Max Intensity Channel (MIC) is implemented assuming that the reflections of some points in the scene are very high in at least one color channel. Using this prior, the illumination of the scene is obtained directly by performing a gray-scale closing operation and a fast cross-bilateral filtering on the MIC of the input color image. Consequently, the reflection component of each RGB color channel can be determined from the illumination and reflection imaging model. The proposed algorithm estimates the illumination component which is relatively smooth and maintains the edge details in different regions. A satisfactory color rendition is achieved for a class of images that do not satisfy the gray-world assumption implicit to the theoretical foundation of the retinex. Experiments are carried out to compare the new method with several spatial and transform domain methods. Our results indicate that the new method is superior in enhancement applications, improves computation speed, and performs well for images with high illumination variations than other methods. Further comparisons of images from National Aeronautics and Space Administration and a wearable camera eButton have shown a high performance of the new method with better color restoration and preservation of image details. PMID:25110395

A fast color image enhancement algorithm based on Max Intensity Channel.

PubMed

Sun, Wei; Han, Long; Guo, Baolong; Jia, Wenyan; Sun, Mingui

2014-03-30

In this paper, we extend image enhancement techniques based on the retinex theory imitating human visual perception of scenes containing high illumination variations. This extension achieves simultaneous dynamic range modification, color consistency, and lightness rendition without multi-scale Gaussian filtering which has a certain halo effect. The reflection component is analyzed based on the illumination and reflection imaging model. A new prior named Max Intensity Channel (MIC) is implemented assuming that the reflections of some points in the scene are very high in at least one color channel. Using this prior, the illumination of the scene is obtained directly by performing a gray-scale closing operation and a fast cross-bilateral filtering on the MIC of the input color image. Consequently, the reflection component of each RGB color channel can be determined from the illumination and reflection imaging model. The proposed algorithm estimates the illumination component which is relatively smooth and maintains the edge details in different regions. A satisfactory color rendition is achieved for a class of images that do not satisfy the gray-world assumption implicit to the theoretical foundation of the retinex. Experiments are carried out to compare the new method with several spatial and transform domain methods. Our results indicate that the new method is superior in enhancement applications, improves computation speed, and performs well for images with high illumination variations than other methods. Further comparisons of images from National Aeronautics and Space Administration and a wearable camera eButton have shown a high performance of the new method with better color restoration and preservation of image details.

A fast color image enhancement algorithm based on Max Intensity Channel

NASA Astrophysics Data System (ADS)

Sun, Wei; Han, Long; Guo, Baolong; Jia, Wenyan; Sun, Mingui

2014-03-01

In this paper, we extend image enhancement techniques based on the retinex theory imitating human visual perception of scenes containing high illumination variations. This extension achieves simultaneous dynamic range modification, color consistency, and lightness rendition without multi-scale Gaussian filtering which has a certain halo effect. The reflection component is analyzed based on the illumination and reflection imaging model. A new prior named Max Intensity Channel (MIC) is implemented assuming that the reflections of some points in the scene are very high in at least one color channel. Using this prior, the illumination of the scene is obtained directly by performing a gray-scale closing operation and a fast cross-bilateral filtering on the MIC of the input color image. Consequently, the reflection component of each RGB color channel can be determined from the illumination and reflection imaging model. The proposed algorithm estimates the illumination component which is relatively smooth and maintains the edge details in different regions. A satisfactory color rendition is achieved for a class of images that do not satisfy the gray-world assumption implicit to the theoretical foundation of the retinex. Experiments are carried out to compare the new method with several spatial and transform domain methods. Our results indicate that the new method is superior in enhancement applications, improves computation speed, and performs well for images with high illumination variations than other methods. Further comparisons of images from National Aeronautics and Space Administration and a wearable camera eButton have shown a high performance of the new method with better color restoration and preservation of image details.

Digital Detection and Processing of Multiple Quadrature Harmonics for EPR Spectroscopy

PubMed Central

Ahmad, R.; Som, S.; Kesselring, E.; Kuppusamy, P.; Zweier, J.L.; Potter, L.C.

2010-01-01

A quadrature digital receiver and associated signal estimation procedure are reported for L-band electron paramagnetic resonance (EPR) spectroscopy. The approach provides simultaneous acquisition and joint processing of multiple harmonics in both in-phase and out-of-phase channels. The digital receiver, based on a high-speed dual-channel analog-to-digital converter, allows direct digital down-conversion with heterodyne processing using digital capture of the microwave reference signal. Thus, the receiver avoids noise and nonlinearity associated with analog mixers. Also, the architecture allows for low-Q anti-alias filtering and does not require the sampling frequency to be time-locked to the microwave reference. A noise model applicable for arbitrary contributions of oscillator phase noise is presented, and a corresponding maximum-likelihood estimator of unknown parameters is also reported. The signal processing is applicable for Lorentzian lineshape under nonsaturating conditions. The estimation is carried out using a convergent iterative algorithm capable of jointly processing the in-phase and out-of-phase data in the presence of phase noise and unknown microwave phase. Cramér-Rao bound analysis and simulation results demonstrate a significant reduction in linewidth estimation error using quadrature detection, for both low and high values of phase noise. EPR spectroscopic data are also reported for illustration. PMID:20971667

Digital detection and processing of multiple quadrature harmonics for EPR spectroscopy.

PubMed

Ahmad, R; Som, S; Kesselring, E; Kuppusamy, P; Zweier, J L; Potter, L C

2010-12-01

A quadrature digital receiver and associated signal estimation procedure are reported for L-band electron paramagnetic resonance (EPR) spectroscopy. The approach provides simultaneous acquisition and joint processing of multiple harmonics in both in-phase and out-of-phase channels. The digital receiver, based on a high-speed dual-channel analog-to-digital converter, allows direct digital down-conversion with heterodyne processing using digital capture of the microwave reference signal. Thus, the receiver avoids noise and nonlinearity associated with analog mixers. Also, the architecture allows for low-Q anti-alias filtering and does not require the sampling frequency to be time-locked to the microwave reference. A noise model applicable for arbitrary contributions of oscillator phase noise is presented, and a corresponding maximum-likelihood estimator of unknown parameters is also reported. The signal processing is applicable for Lorentzian lineshape under nonsaturating conditions. The estimation is carried out using a convergent iterative algorithm capable of jointly processing the in-phase and out-of-phase data in the presence of phase noise and unknown microwave phase. Cramér-Rao bound analysis and simulation results demonstrate a significant reduction in linewidth estimation error using quadrature detection, for both low and high values of phase noise. EPR spectroscopic data are also reported for illustration. Copyright © 2010 Elsevier Inc. All rights reserved.

Efficient estimation of ideal-observer performance in classification tasks involving high-dimensional complex backgrounds

PubMed Central

Park, Subok; Clarkson, Eric

2010-01-01

The Bayesian ideal observer is optimal among all observers and sets an absolute upper bound for the performance of any observer in classification tasks [Van Trees, Detection, Estimation, and Modulation Theory, Part I (Academic, 1968).]. Therefore, the ideal observer should be used for objective image quality assessment whenever possible. However, computation of ideal-observer performance is difficult in practice because this observer requires the full description of unknown, statistical properties of high-dimensional, complex data arising in real life problems. Previously, Markov-chain Monte Carlo (MCMC) methods were developed by Kupinski et al. [J. Opt. Soc. Am. A 20, 430(2003) ] and by Park et al. [J. Opt. Soc. Am. A 24, B136 (2007) and IEEE Trans. Med. Imaging 28, 657 (2009) ] to estimate the performance of the ideal observer and the channelized ideal observer (CIO), respectively, in classification tasks involving non-Gaussian random backgrounds. However, both algorithms had the disadvantage of long computation times. We propose a fast MCMC for real-time estimation of the likelihood ratio for the CIO. Our simulation results show that our method has the potential to speed up ideal-observer performance in tasks involving complex data when efficient channels are used for the CIO. PMID:19884916

Channel modeling, signal processing and coding for perpendicular magnetic recording

NASA Astrophysics Data System (ADS)

Wu, Zheng

With the increasing areal density in magnetic recording systems, perpendicular recording has replaced longitudinal recording to overcome the superparamagnetic limit. Studies on perpendicular recording channels including aspects of channel modeling, signal processing and coding techniques are presented in this dissertation. To optimize a high density perpendicular magnetic recording system, one needs to know the tradeoffs between various components of the system including the read/write transducers, the magnetic medium, and the read channel. We extend the work by Chaichanavong on the parameter optimization for systems via design curves. Different signal processing and coding techniques are studied. Information-theoretic tools are utilized to determine the acceptable region for the channel parameters when optimal detection and linear coding techniques are used. Our results show that a considerable gain can be achieved by the optimal detection and coding techniques. The read-write process in perpendicular magnetic recording channels includes a number of nonlinear effects. Nonlinear transition shift (NLTS) is one of them. The signal distortion induced by NLTS can be reduced by write precompensation during data recording. We numerically evaluate the effect of NLTS on the read-back signal and examine the effectiveness of several write precompensation schemes in combating NLTS in a channel characterized by both transition jitter noise and additive white Gaussian electronics noise. We also present an analytical method to estimate the bit-error-rate and use it to help determine the optimal write precompensation values in multi-level precompensation schemes. We propose a mean-adjusted pattern-dependent noise predictive (PDNP) detection algorithm for use on the channel with NLTS. We show that this detector can offer significant improvements in bit-error-rate (BER) compared to conventional Viterbi and PDNP detectors. Moreover, the system performance can be further improved by combining the new detector with a simple write precompensation scheme. Soft-decision decoding for algebraic codes can improve performance for magnetic recording systems. In this dissertation, we propose two soft-decision decoding methods for tensor-product parity codes. We also present a list decoding algorithm for generalized error locating codes.

Improvement in detection of small wildfires

NASA Astrophysics Data System (ADS)

Sleigh, William J.

1991-12-01

Detecting and imaging small wildfires with an Airborne Scanner is done against generally high background levels. The Airborne Scanner System used is a two-channel thermal IR scanner, with one channel selected for imaging the terrain and the other channel sensitive to hotter targets. If a relationship can be determined between the two channels that quantifies the background signal for hotter targets, then an algorithm can be determined that removes the background signal in that channel leaving only the fire signal. The relationship can be determined anywhere between various points in the signal processing of the radiometric data from the radiometric input to the quantized output of the system. As long as only linear operations are performed on the signal, the relationship will only depend on the system gain and offsets within the range of interest. The algorithm can be implemented either by using a look-up table or performing the calculation in the system computer. The current presentation will describe the algorithm, its derivation, and its implementation in the Firefly Wildfire Detection System by means of an off-the-shelf commercial scanner. Improvement over the previous algorithm used and the margin gained for improving the imaging of the terrain will be demonstrated.

Improvement in detection of small wildfires

NASA Technical Reports Server (NTRS)

Sleigh, William J.

1991-01-01

Detecting and imaging small wildfires with an Airborne Scanner is done against generally high background levels. The Airborne Scanner System used is a two-channel thermal IR scanner, with one channel selected for imaging the terrain and the other channel sensitive to hotter targets. If a relationship can be determined between the two channels that quantifies the background signal for hotter targets, then an algorithm can be determined that removes the background signal in that channel leaving only the fire signal. The relationship can be determined anywhere between various points in the signal processing of the radiometric data from the radiometric input to the quantized output of the system. As long as only linear operations are performed on the signal, the relationship will only depend on the system gain and offsets within the range of interest. The algorithm can be implemented either by using a look-up table or performing the calculation in the system computer. The current presentation will describe the algorithm, its derivation, and its implementation in the Firefly Wildfire Detection System by means of an off-the-shelf commercial scanner. Improvement over the previous algorithm used and the margin gained for improving the imaging of the terrain will be demonstrated.

Cross contrast multi-channel image registration using image synthesis for MR brain images.

PubMed

Chen, Min; Carass, Aaron; Jog, Amod; Lee, Junghoon; Roy, Snehashis; Prince, Jerry L

2017-02-01

Multi-modal deformable registration is important for many medical image analysis tasks such as atlas alignment, image fusion, and distortion correction. Whereas a conventional method would register images with different modalities using modality independent features or information theoretic metrics such as mutual information, this paper presents a new framework that addresses the problem using a two-channel registration algorithm capable of using mono-modal similarity measures such as sum of squared differences or cross-correlation. To make it possible to use these same-modality measures, image synthesis is used to create proxy images for the opposite modality as well as intensity-normalized images from each of the two available images. The new deformable registration framework was evaluated by performing intra-subject deformation recovery, intra-subject boundary alignment, and inter-subject label transfer experiments using multi-contrast magnetic resonance brain imaging data. Three different multi-channel registration algorithms were evaluated, revealing that the framework is robust to the multi-channel deformable registration algorithm that is used. With a single exception, all results demonstrated improvements when compared against single channel registrations using the same algorithm with mutual information. Copyright © 2016 Elsevier B.V. All rights reserved.

Uplink transmit beamforming design for SINR maximization with full multiuser channel state information

NASA Astrophysics Data System (ADS)

Xi, Songnan; Zoltowski, Michael D.

2008-04-01

Multiuser multiple-input multiple-output (MIMO) systems are considered in this paper. We continue our research on uplink transmit beamforming design for multiple users under the assumption that the full multiuser channel state information, which is the collection of the channel state information between each of the users and the base station, is known not only to the receiver but also to all the transmitters. We propose an algorithm for designing optimal beamforming weights in terms of maximizing the signal-to-interference-plus-noise ratio (SINR). Through statistical modeling, we decouple the original mathematically intractable optimization problem and achieved a closed-form solution. As in our previous work, the minimum mean-squared error (MMSE) receiver with successive interference cancellation (SIC) is adopted for multiuser detection. The proposed scheme is compared with an existing jointly optimized transceiver design, referred to as the joint transceiver in this paper, and our previously proposed eigen-beamforming algorithm. Simulation results demonstrate that our algorithm, with much less computational burden, accomplishes almost the same performance as the joint transceiver for spatially independent MIMO channel and even better performance for spatially correlated MIMO channels. And it always works better than our previously proposed eigen beamforming algorithm.

Denoising Algorithm for CFA Image Sensors Considering Inter-Channel Correlation.

PubMed

Lee, Min Seok; Park, Sang Wook; Kang, Moon Gi

2017-05-28

In this paper, a spatio-spectral-temporal filter considering an inter-channel correlation is proposed for the denoising of a color filter array (CFA) sequence acquired by CCD/CMOS image sensors. Owing to the alternating under-sampled grid of the CFA pattern, the inter-channel correlation must be considered in the direct denoising process. The proposed filter is applied in the spatial, spectral, and temporal domain, considering the spatio-tempo-spectral correlation. First, nonlocal means (NLM) spatial filtering with patch-based difference (PBD) refinement is performed by considering both the intra-channel correlation and inter-channel correlation to overcome the spatial resolution degradation occurring with the alternating under-sampled pattern. Second, a motion-compensated temporal filter that employs inter-channel correlated motion estimation and compensation is proposed to remove the noise in the temporal domain. Then, a motion adaptive detection value controls the ratio of the spatial filter and the temporal filter. The denoised CFA sequence can thus be obtained without motion artifacts. Experimental results for both simulated and real CFA sequences are presented with visual and numerical comparisons to several state-of-the-art denoising methods combined with a demosaicing method. Experimental results confirmed that the proposed frameworks outperformed the other techniques in terms of the objective criteria and subjective visual perception in CFA sequences.

Highlights of TOMS Version 9 Total Ozone Algorithm

NASA Technical Reports Server (NTRS)

Bhartia, Pawan; Haffner, David

2012-01-01

The fundamental basis of TOMS total ozone algorithm was developed some 45 years ago by Dave and Mateer. It was designed to estimate total ozone from satellite measurements of the backscattered UV radiances at few discrete wavelengths in the Huggins ozone absorption band (310-340 nm). Over the years, as the need for higher accuracy in measuring total ozone from space has increased, several improvements to the basic algorithms have been made. They include: better correction for the effects of aerosols and clouds, an improved method to account for the variation in shape of ozone profiles with season, latitude, and total ozone, and a multi-wavelength correction for remaining profile shape errors. These improvements have made it possible to retrieve total ozone with just 3 spectral channels of moderate spectral resolution (approx. 1 nm) with accuracy comparable to state-of-the-art spectral fitting algorithms like DOAS that require high spectral resolution measurements at large number of wavelengths. One of the deficiencies of the TOMS algorithm has been that it doesn't provide an error estimate. This is a particular problem in high latitudes when the profile shape errors become significant and vary with latitude, season, total ozone, and instrument viewing geometry. The primary objective of the TOMS V9 algorithm is to account for these effects in estimating the error bars. This is done by a straightforward implementation of the Rodgers optimum estimation method using a priori ozone profiles and their error covariances matrices constructed using Aura MLS and ozonesonde data. The algorithm produces a vertical ozone profile that contains 1-2.5 pieces of information (degrees of freedom of signal) depending upon solar zenith angle (SZA). The profile is integrated to obtain the total column. We provide information that shows the altitude range in which the profile is best determined by the measurements. One can use this information in data assimilation and analysis. A side benefit of this algorithm is that it is considerably simpler than the present algorithm that uses a database of 1512 profiles to retrieve total ozone. These profiles are tedious to construct and modify. Though conceptually similar to the SBUV V8 algorithm that was developed about a decade ago, the SBUV and TOMS V9 algorithms differ in detail. The TOMS algorithm uses 3 wavelengths to retrieve the profile while the SBUV algorithm uses 6-9 wavelengths, so TOMS provides less profile information. However both algorithms have comparable total ozone information and TOMS V9 can be easily adapted to use additional wavelengths from instruments like GOME, OMI and OMPS to provide better profile information at smaller SZAs. The other significant difference between the two algorithms is that while the SBUV algorithm has been optimized for deriving monthly zonal means by making an appropriate choice of the a priori error covariance matrix, the TOMS algorithm has been optimized for tracking short-term variability using month and latitude dependent covariance matrices.

Channel coding for underwater acoustic single-carrier CDMA communication system

NASA Astrophysics Data System (ADS)

Liu, Lanjun; Zhang, Yonglei; Zhang, Pengcheng; Zhou, Lin; Niu, Jiong

2017-01-01

CDMA is an effective multiple access protocol for underwater acoustic networks, and channel coding can effectively reduce the bit error rate (BER) of the underwater acoustic communication system. For the requirements of underwater acoustic mobile networks based on CDMA, an underwater acoustic single-carrier CDMA communication system (UWA/SCCDMA) based on the direct-sequence spread spectrum is proposed, and its channel coding scheme is studied based on convolution, RA, Turbo and LDPC coding respectively. The implementation steps of the Viterbi algorithm of convolutional coding, BP and minimum sum algorithms of RA coding, Log-MAP and SOVA algorithms of Turbo coding, and sum-product algorithm of LDPC coding are given. An UWA/SCCDMA simulation system based on Matlab is designed. Simulation results show that the UWA/SCCDMA based on RA, Turbo and LDPC coding have good performance such that the communication BER is all less than 10-6 in the underwater acoustic channel with low signal to noise ratio (SNR) from -12 dB to -10dB, which is about 2 orders of magnitude lower than that of the convolutional coding. The system based on Turbo coding with Log-MAP algorithm has the best performance.

Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis.

PubMed

Somers, Ben; Bertrand, Alexander

2016-12-01

Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. We apply a distributed canonical correlation analysis (CCA-)based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.

Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis

NASA Astrophysics Data System (ADS)

Somers, Ben; Bertrand, Alexander

2016-12-01

Objective. Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. Approach. We apply a distributed canonical correlation analysis (CCA-)based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. Main results. While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Significance. Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.

Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network.

PubMed

Choi, Sangil; Park, Jong Hyuk

2016-12-02

Wireless mesh networks (WMNs) have been considered as one of the key technologies for the configuration of wireless machines since they emerged. In a WMN, wireless routers provide multi-hop wireless connectivity between hosts in the network and also allow them to access the Internet via gateway devices. Wireless routers are typically equipped with multiple radios operating on different channels to increase network throughput. Multicast is a form of communication that delivers data from a source to a set of destinations simultaneously. It is used in a number of applications, such as distributed games, distance education, and video conferencing. In this study, we address a channel assignment problem for multicast in multi-radio multi-channel WMNs. In a multi-radio multi-channel WMN, two nearby nodes will interfere with each other and cause a throughput decrease when they transmit on the same channel. Thus, an important goal for multicast channel assignment is to reduce the interference among networked devices. We have developed a minimum interference channel assignment (MICA) algorithm for multicast that accurately models the interference relationship between pairs of multicast tree nodes using the concept of the interference factor and assigns channels to tree nodes to minimize interference within the multicast tree. Simulation results show that MICA achieves higher throughput and lower end-to-end packet delay compared with an existing channel assignment algorithm named multi-channel multicast (MCM). In addition, MICA achieves much lower throughput variation among the destination nodes than MCM.

Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network

PubMed Central

Choi, Sangil; Park, Jong Hyuk

2016-01-01

Wireless mesh networks (WMNs) have been considered as one of the key technologies for the configuration of wireless machines since they emerged. In a WMN, wireless routers provide multi-hop wireless connectivity between hosts in the network and also allow them to access the Internet via gateway devices. Wireless routers are typically equipped with multiple radios operating on different channels to increase network throughput. Multicast is a form of communication that delivers data from a source to a set of destinations simultaneously. It is used in a number of applications, such as distributed games, distance education, and video conferencing. In this study, we address a channel assignment problem for multicast in multi-radio multi-channel WMNs. In a multi-radio multi-channel WMN, two nearby nodes will interfere with each other and cause a throughput decrease when they transmit on the same channel. Thus, an important goal for multicast channel assignment is to reduce the interference among networked devices. We have developed a minimum interference channel assignment (MICA) algorithm for multicast that accurately models the interference relationship between pairs of multicast tree nodes using the concept of the interference factor and assigns channels to tree nodes to minimize interference within the multicast tree. Simulation results show that MICA achieves higher throughput and lower end-to-end packet delay compared with an existing channel assignment algorithm named multi-channel multicast (MCM). In addition, MICA achieves much lower throughput variation among the destination nodes than MCM. PMID:27918438

Algorithms development for the GEM-based detection system

NASA Astrophysics Data System (ADS)

Czarski, T.; Chernyshova, M.; Malinowski, K.; Pozniak, K. T.; Kasprowicz, G.; Kolasinski, P.; Krawczyk, R.; Wojenski, A.; Zabolotny, W.

2016-09-01

The measurement system based on GEM - Gas Electron Multiplier detector - is developed for soft X-ray diagnostics of tokamak plasmas. The multi-channel setup is designed for estimation of the energy and the position distribution of an Xray source. The focal measuring issue is the charge cluster identification by its value and position estimation. The fast and accurate mode of the serial data acquisition is applied for the dynamic plasma diagnostics. The charge clusters are counted in the space determined by 2D position, charge value and time intervals. Radiation source characteristics are presented by histograms for a selected range of position, time intervals and cluster charge values corresponding to the energy spectra.

Nonlinear phase noise tolerance for coherent optical systems using soft-decision-aided ML carrier phase estimation enhanced with constellation partitioning

NASA Astrophysics Data System (ADS)

Li, Yan; Wu, Mingwei; Du, Xinwei; Xu, Zhuoran; Gurusamy, Mohan; Yu, Changyuan; Kam, Pooi-Yuen

2018-02-01

A novel soft-decision-aided maximum likelihood (SDA-ML) carrier phase estimation method and its simplified version, the decision-aided and soft-decision-aided maximum likelihood (DA-SDA-ML) methods are tested in a nonlinear phase noise-dominant channel. The numerical performance results show that both the SDA-ML and DA-SDA-ML methods outperform the conventional DA-ML in systems with constant-amplitude modulation formats. In addition, modified algorithms based on constellation partitioning are proposed. With partitioning, the modified SDA-ML and DA-SDA-ML are shown to be useful for compensating the nonlinear phase noise in multi-level modulation systems.

Maximum likelihood positioning algorithm for high-resolution PET scanners

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gross-Weege, Nicolas, E-mail: nicolas.gross-weege@pmi.rwth-aachen.de, E-mail: schulz@pmi.rwth-aachen.de; Schug, David; Hallen, Patrick

2016-06-15

Purpose: In high-resolution positron emission tomography (PET), lightsharing elements are incorporated into typical detector stacks to read out scintillator arrays in which one scintillator element (crystal) is smaller than the size of the readout channel. In order to identify the hit crystal by means of the measured light distribution, a positioning algorithm is required. One commonly applied positioning algorithm uses the center of gravity (COG) of the measured light distribution. The COG algorithm is limited in spatial resolution by noise and intercrystal Compton scatter. The purpose of this work is to develop a positioning algorithm which overcomes this limitation. Methods:more » The authors present a maximum likelihood (ML) algorithm which compares a set of expected light distributions given by probability density functions (PDFs) with the measured light distribution. Instead of modeling the PDFs by using an analytical model, the PDFs of the proposed ML algorithm are generated assuming a single-gamma-interaction model from measured data. The algorithm was evaluated with a hot-rod phantom measurement acquired with the preclinical HYPERION II {sup D} PET scanner. In order to assess the performance with respect to sensitivity, energy resolution, and image quality, the ML algorithm was compared to a COG algorithm which calculates the COG from a restricted set of channels. The authors studied the energy resolution of the ML and the COG algorithm regarding incomplete light distributions (missing channel information caused by detector dead time). Furthermore, the authors investigated the effects of using a filter based on the likelihood values on sensitivity, energy resolution, and image quality. Results: A sensitivity gain of up to 19% was demonstrated in comparison to the COG algorithm for the selected operation parameters. Energy resolution and image quality were on a similar level for both algorithms. Additionally, the authors demonstrated that the performance of the ML algorithm is less prone to missing channel information. A likelihood filter visually improved the image quality, i.e., the peak-to-valley increased up to a factor of 3 for 2-mm-diameter phantom rods by rejecting 87% of the coincidences. A relative improvement of the energy resolution of up to 12.8% was also measured rejecting 91% of the coincidences. Conclusions: The developed ML algorithm increases the sensitivity by correctly handling missing channel information without influencing energy resolution or image quality. Furthermore, the authors showed that energy resolution and image quality can be improved substantially by rejecting events that do not comply well with the single-gamma-interaction model, such as Compton-scattered events.« less

A New Statistical Model of Electroencephalogram Noise Spectra for Real-Time Brain-Computer Interfaces.

PubMed

Paris, Alan; Atia, George K; Vosoughi, Azadeh; Berman, Stephen A

2017-08-01

A characteristic of neurological signal processing is high levels of noise from subcellular ion channels up to whole-brain processes. In this paper, we propose a new model of electroencephalogram (EEG) background periodograms, based on a family of functions which we call generalized van der Ziel-McWhorter (GVZM) power spectral densities (PSDs). To the best of our knowledge, the GVZM PSD function is the only EEG noise model that has relatively few parameters, matches recorded EEG PSD's with high accuracy from 0 to over 30 Hz, and has approximately 1/f θ behavior in the midfrequencies without infinities. We validate this model using three approaches. First, we show how GVZM PSDs can arise in a population of ion channels at maximum entropy equilibrium. Second, we present a class of mixed autoregressive models, which simulate brain background noise and whose periodograms are asymptotic to the GVZM PSD. Third, we present two real-time estimation algorithms for steady-state visual evoked potential (SSVEP) frequencies, and analyze their performance statistically. In pairwise comparisons, the GVZM-based algorithms showed statistically significant accuracy improvement over two well-known and widely used SSVEP estimators. The GVZM noise model can be a useful and reliable technique for EEG signal processing. Understanding EEG noise is essential for EEG-based neurology and applications such as real-time brain-computer interfaces, which must make accurate control decisions from very short data epochs. The GVZM approach represents a successful new paradigm for understanding and managing this neurological noise.

Enabling vendor independent photoacoustic imaging systems with asynchronous laser source

NASA Astrophysics Data System (ADS)

Wu, Yixuan; Zhang, Haichong K.; Boctor, Emad M.

2018-02-01

Channel data acquisition, and synchronization between laser excitation and PA signal acquisition, are two fundamental hardware requirements for photoacoustic (PA) imaging. Unfortunately, however, neither is equipped by most clinical ultrasound scanners. Therefore, less economical specialized research platforms are used in general, which hinders a smooth clinical transition of PA imaging. In previous studies, we have proposed an algorithm to achieve PA imaging using ultrasound post-beamformed (USPB) RF data instead of channel data. This work focuses on enabling clinical ultrasound scanners to implement PA imaging, without requiring synchronization between the laser excitation and PA signal acquisition. Laser synchronization is inherently consisted of two aspects: frequency and phase information. We synchronize without communicating the laser and the ultrasound scanner by investigating USPB images of a point-target phantom in two steps. First, frequency information is estimated by solving a nonlinear optimization problem, under the assumption that the segmented wave-front can only be beamformed into a single spot when synchronization is achieved. Second, after making frequencies of two systems identical, phase delay is estimated by optimizing the image quality while varying phase value. The proposed method is validated through simulation, by manually adding both frequency and phase errors, then applying the proposed algorithm to correct errors and reconstruct PA images. Compared with the ground truth, simulation results indicate that the remaining errors in frequency correction and phase correction are 0.28% and 2.34%, respectively, which affirm the potential of overcoming hardware barriers on PA imaging through software solution.

Accuracy of sea ice temperature derived from the advanced very high resolution radiometer

NASA Technical Reports Server (NTRS)

Yu, Y.; Rothrock, D. A.; Lindsay, R. W.

1995-01-01

The accuracy of Arctic sea ice surface temperatures T(sub s) dericed from advanced very high resolution radiometer (AVHRR) thermal channels is evaluated in the cold seasons by comparing them with surface air temperatures T(sub air) from drifting buoys and ice stations. We use three different estimates of satellite surface temperatures, a direct estimate from AVHRR channel 4 with only correction for the snow surface emissivity but not for the atmosphere, a single-channel regression of T(sub s) with T(sub air), and Key and Haefliger's (1992) polar multichannel algorithm. We find no measurable bias in any of these estimates and few differences in their statistics. The similar performance of all three methods indicates that an atmospheric water vapor correction is not important for the dry winter atmosphere in the central Arctic, given the other sources of error that remain in both the satellite and the comparison data. A record of drifting station data shows winter air temperature to be 1.4 C warmer than the snow surface temperature. `Correcting' air temperatures to skin temperature by subtracting this amount implies that satellite T(sub s) estimates are biased warm with respect to skin temperature by about this amount. A case study with low-flying aircraft data suggests that ice crystal precipitation can cause satellite estimates of T(sub s) to be several degrees warmer than radiometric measurements taken close to the surface, presumably below the ice crystal precipitation layer. An analysis in which errors are assumed to exist in all measurements, not just the satellite measurements, gives a standard deviation in the satellite estimates of 0.9 C, about half the standard deviation of 1.7 C estimated by assigning all the variation between T(sub s) and T(sub air) to errors in T(sub s).

Statewide Groundwater Recharge Modeling in New Mexico

NASA Astrophysics Data System (ADS)

Xu, F.; Cadol, D.; Newton, B. T.; Phillips, F. M.

2017-12-01

It is crucial to understand the rate and distribution of groundwater recharge in New Mexico because it not only largely defines a limit for water availability in this semi-arid state, but also is the least understood aspect of the state's water budget. With the goal of estimating groundwater recharge statewide, we are developing the Evapotranspiration and Recharge Model (ETRM), which uses existing spatial datasets to model the daily soil water balance over the state at a resolution of 250 m cell. The input datasets includes PRISM precipitation data, MODIS Normalized Difference Vegetation Index (NDVI), NRCS soils data, state geology data and reference ET estimates produced by Gridded Atmospheric Data downscalinG and Evapotranspiration Tools (GADGET). The current estimated recharge presents diffuse recharge only, not focused recharge as in channels or playas. Direct recharge measurements are challenging and rare, therefore we estimate diffuse recharge using a water balance approach. The ETRM simulated runoff amount was compared with USGS gauged discharge in four selected ephemeral channels: Mogollon Creek, Zuni River, the Rio Puerco above Bernardo, and the Rio Puerco above Arroyo Chico. Result showed that focused recharge is important, and basin characteristics can be linked with watershed hydrological response. As the sparse instruments in NM provide limited help in improving estimation of focused recharge by linking basin characteristics, the Walnut Gulch Experimental Watershed, which is one of the most densely gauged and monitored semiarid rangeland watershed for hydrology research purpose, is now being modeled with ETRM. Higher spatial resolution of field data is expected to enable detailed comparison of model recharge results with measured transmission losses in ephemeral channels. The final ETRM product will establish an algorithm to estimate the groundwater recharge as a water budget component of the entire state of New Mexico. Reference ET estimated by GADGET suggests 10% - 22% increase by the end of this century under IPCC AR4 A2 emission scenario. ETRM will help water planning for the state to face drought brought by the climate change.

Angular and Seasonal Variation of Spectral Surface Reflectance Ratios: Implications for the Remote Sensing of Aerosol over Land

NASA Technical Reports Server (NTRS)

Remer, L. A.; Wald, A. E.; Kaufman, Y. J.

1999-01-01

We obtain valuable information on the angular and seasonal variability of surface reflectance using a hand-held spectrometer from a light aircraft. The data is used to test a procedure that allows us to estimate visible surface reflectance from the longer wavelength 2.1 micrometer channel (mid-IR). Estimating or avoiding surface reflectance in the visible is a vital first step in most algorithms that retrieve aerosol optical thickness over land targets. The data indicate that specular reflection found when viewing targets from the forward direction can severely corrupt the relationships between the visible and 2.1 micrometer reflectance that were derived from nadir data. There is a month by month variation in the ratios between the visible and the mid-IR, weakly correlated to the Normalized Difference Vegetation Index (NDVI). If specular reflection is not avoided, the errors resulting from estimating surface reflectance from the mid-IR exceed the acceptable limit of DELTA-rho approximately 0.01 in roughly 40% of the cases, using the current algorithm. This is reduced to 25% of the cases if specular reflection is avoided. An alternative method that uses path radiance rather than explicitly estimating visible surface reflectance results in similar errors. The two methods have different strengths and weaknesses that require further study.

3D Target Localization of Modified 3D MUSIC for a Triple-Channel K-Band Radar.

PubMed

Li, Ying-Chun; Choi, Byunggil; Chong, Jong-Wha; Oh, Daegun

2018-05-20

In this paper, a modified 3D multiple signal classification (MUSIC) algorithm is proposed for joint estimation of range, azimuth, and elevation angles of K-band radar with a small 2 × 2 horn antenna array. Three channels of the 2 × 2 horn antenna array are utilized as receiving channels, and the other one is a transmitting antenna. The proposed modified 3D MUSIC is designed to make use of a stacked autocorrelation matrix, whose element matrices are related to each other in the spatial domain. An augmented 2D steering vector based on the stacked autocorrelation matrix is proposed for the modified 3D MUSIC, instead of the conventional 3D steering vector. The effectiveness of the proposed modified 3D MUSIC is verified through implementation with a K-band frequency-modulated continuous-wave (FMCW) radar with the 2 × 2 horn antenna array through a variety of experiments in a chamber.

Improving Video Based Heart Rate Monitoring.

PubMed

Lin, Jian; Rozado, David; Duenser, Andreas

2015-01-01

Non-contact measurements of cardiac pulse can provide robust measurement of heart rate (HR) without the annoyance of attaching electrodes to the body. In this paper we explore a novel and reliable method to carry out video-based HR estimation and propose various performance improvement over existing approaches. The investigated method uses Independent Component Analysis (ICA) to detect the underlying HR signal from a mixed source signal present in the RGB channels of the image. The original ICA algorithm was implemented and several modifications were explored in order to determine which one could be optimal for accurate HR estimation. Using statistical analysis, we compared the cardiac pulse rate estimation from the different methods under comparison on the extracted videos to a commercially available oximeter. We found that some of these methods are quite effective and efficient in terms of improving accuracy and latency of the system. We have made the code of our algorithms openly available to the scientific community so that other researchers can explore how to integrate video-based HR monitoring in novel health technology applications. We conclude by noting that recent advances in video-based HR monitoring permit computers to be aware of a user's psychophysiological status in real time.

Pseudorange Measurement Method Based on AIS Signals.

PubMed

Zhang, Jingbo; Zhang, Shufang; Wang, Jinpeng

2017-05-22

In order to use the existing automatic identification system (AIS) to provide additional navigation and positioning services, a complete pseudorange measurements solution is presented in this paper. Through the mathematical analysis of the AIS signal, the bit-0-phases in the digital sequences were determined as the timestamps. Monte Carlo simulation was carried out to compare the accuracy of the zero-crossing and differential peak, which are two timestamp detection methods in the additive white Gaussian noise (AWGN) channel. Considering the low-speed and low-dynamic motion characteristics of ships, an optimal estimation method based on the minimum mean square error is proposed to improve detection accuracy. Furthermore, the α difference filter algorithm was used to achieve the fusion of the optimal estimation results of the two detection methods. The results show that the algorithm can greatly improve the accuracy of pseudorange estimation under low signal-to-noise ratio (SNR) conditions. In order to verify the effectiveness of the scheme, prototypes containing the measurement scheme were developed and field tests in Xinghai Bay of Dalian (China) were performed. The test results show that the pseudorange measurement accuracy was better than 28 m (σ) without any modification of the existing AIS system.

Pseudorange Measurement Method Based on AIS Signals

PubMed Central

Zhang, Jingbo; Zhang, Shufang; Wang, Jinpeng

2017-01-01

In order to use the existing automatic identification system (AIS) to provide additional navigation and positioning services, a complete pseudorange measurements solution is presented in this paper. Through the mathematical analysis of the AIS signal, the bit-0-phases in the digital sequences were determined as the timestamps. Monte Carlo simulation was carried out to compare the accuracy of the zero-crossing and differential peak, which are two timestamp detection methods in the additive white Gaussian noise (AWGN) channel. Considering the low-speed and low-dynamic motion characteristics of ships, an optimal estimation method based on the minimum mean square error is proposed to improve detection accuracy. Furthermore, the α difference filter algorithm was used to achieve the fusion of the optimal estimation results of the two detection methods. The results show that the algorithm can greatly improve the accuracy of pseudorange estimation under low signal-to-noise ratio (SNR) conditions. In order to verify the effectiveness of the scheme, prototypes containing the measurement scheme were developed and field tests in Xinghai Bay of Dalian (China) were performed. The test results show that the pseudorange measurement accuracy was better than 28 m (σ) without any modification of the existing AIS system. PMID:28531153

Detection of Multiple Innervation Zones from Multi-Channel Surface EMG Recordings with Low Signal-to-Noise Ratio Using Graph-Cut Segmentation.

PubMed

Marateb, Hamid Reza; Farahi, Morteza; Rojas, Monica; Mañanas, Miguel Angel; Farina, Dario

2016-01-01

Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies.

Detection of Multiple Innervation Zones from Multi-Channel Surface EMG Recordings with Low Signal-to-Noise Ratio Using Graph-Cut Segmentation

PubMed Central

Farahi, Morteza; Rojas, Monica; Mañanas, Miguel Angel; Farina, Dario

2016-01-01

Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies. PMID:27978535

Estimation of Dynamical Parameters in Atmospheric Data Sets

NASA Technical Reports Server (NTRS)

Wenig, Mark O.

2004-01-01

In this study a new technique is used to derive dynamical parameters out of atmospheric data sets. This technique, called the structure tensor technique, can be used to estimate dynamical parameters such as motion, source strengths, diffusion constants or exponential decay rates. A general mathematical framework was developed for the direct estimation of the physical parameters that govern the underlying processes from image sequences. This estimation technique can be adapted to the specific physical problem under investigation, so it can be used in a variety of applications in trace gas, aerosol, and cloud remote sensing. The fundamental algorithm will be extended to the analysis of multi- channel (e.g. multi trace gas) image sequences and to provide solutions to the extended aperture problem. In this study sensitivity studies have been performed to determine the usability of this technique for data sets with different resolution in time and space and different dimensions.

Demosaicking of noisy Bayer-sampled color images with least-squares luma-chroma demultiplexing and noise level estimation.

PubMed

Jeon, Gwanggil; Dubois, Eric

2013-01-01

This paper adapts the least-squares luma-chroma demultiplexing (LSLCD) demosaicking method to noisy Bayer color filter array (CFA) images. A model is presented for the noise in white-balanced gamma-corrected CFA images. A method to estimate the noise level in each of the red, green, and blue color channels is then developed. Based on the estimated noise parameters, one of a finite set of configurations adapted to a particular level of noise is selected to demosaic the noisy data. The noise-adaptive demosaicking scheme is called LSLCD with noise estimation (LSLCD-NE). Experimental results demonstrate state-of-the-art performance over a wide range of noise levels, with low computational complexity. Many results with several algorithms, noise levels, and images are presented on our companion web site along with software to allow reproduction of our results.

A three-dimensional spectral algorithm for simulations of transition and turbulence

NASA Technical Reports Server (NTRS)

Zang, T. A.; Hussaini, M. Y.

1985-01-01

A spectral algorithm for simulating three dimensional, incompressible, parallel shear flows is described. It applies to the channel, to the parallel boundary layer, and to other shear flows with one wall bounded and two periodic directions. Representative applications to the channel and to the heated boundary layer are presented.

Improved event positioning in a gamma ray detector using an iterative position-weighted centre-of-gravity algorithm.

PubMed

Liu, Chen-Yi; Goertzen, Andrew L

2013-07-21

An iterative position-weighted centre-of-gravity algorithm was developed and tested for positioning events in a silicon photomultiplier (SiPM)-based scintillation detector for positron emission tomography. The algorithm used a Gaussian-based weighting function centred at the current estimate of the event location. The algorithm was applied to the signals from a 4 × 4 array of SiPM detectors that used individual channel readout and a LYSO:Ce scintillator array. Three scintillator array configurations were tested: single layer with 3.17 mm crystal pitch, matched to the SiPM size; single layer with 1.5 mm crystal pitch; and dual layer with 1.67 mm crystal pitch and a ½ crystal offset in the X and Y directions between the two layers. The flood histograms generated by this algorithm were shown to be superior to those generated by the standard centre of gravity. The width of the Gaussian weighting function of the algorithm was optimized for different scintillator array setups. The optimal width of the Gaussian curve was found to depend on the amount of light spread. The algorithm required less than 20 iterations to calculate the position of an event. The rapid convergence of this algorithm will readily allow for implementation on a front-end detector processing field programmable gate array for use in improved real-time event positioning and identification.

Wearable EEG via lossless compression.

PubMed

Dufort, Guillermo; Favaro, Federico; Lecumberry, Federico; Martin, Alvaro; Oliver, Juan P; Oreggioni, Julian; Ramirez, Ignacio; Seroussi, Gadiel; Steinfeld, Leonardo

2016-08-01

This work presents a wearable multi-channel EEG recording system featuring a lossless compression algorithm. The algorithm, based in a previously reported algorithm by the authors, exploits the existing temporal correlation between samples at different sampling times, and the spatial correlation between different electrodes across the scalp. The low-power platform is able to compress, by a factor between 2.3 and 3.6, up to 300sps from 64 channels with a power consumption of 176μW/ch. The performance of the algorithm compares favorably with the best compression rates reported up to date in the literature.

Atmospheric parameterization schemes for satellite cloud property retrieval during FIRE IFO 2

NASA Technical Reports Server (NTRS)

Titlow, James; Baum, Bryan A.

1993-01-01

Satellite cloud retrieval algorithms generally require atmospheric temperature and humidity profiles to determine such cloud properties as pressure and height. For instance, the CO2 slicing technique called the ratio method requires the calculation of theoretical upwelling radiances both at the surface and a prescribed number (40) of atmospheric levels. This technique has been applied to data from, for example, the High Resolution Infrared Radiometer Sounder (HIRS/2, henceforth HIRS) flown aboard the NOAA series of polar orbiting satellites and the High Resolution Interferometer Sounder (HIS). In this particular study, four NOAA-11 HIRS channels in the 15-micron region are used. The ratio method may be applied to various channel combinations to estimate cloud top heights using channels in the 15-mu m region. Presently, the multispectral, multiresolution (MSMR) scheme uses 4 HIRS channel combination estimates for mid- to high-level cloud pressure retrieval and Advanced Very High Resolution Radiometer (AVHRR) data for low-level (is greater than 700 mb) cloud level retrieval. In order to determine theoretical upwelling radiances, atmospheric temperature and water vapor profiles must be provided as well as profiles of other radiatively important gas absorber constituents such as CO2, O3, and CH4. The assumed temperature and humidity profiles have a large effect on transmittance and radiance profiles, which in turn are used with HIRS data to calculate cloud pressure, and thus cloud height and temperature. For large spatial scale satellite data analysis, atmospheric parameterization schemes for cloud retrieval algorithms are usually based on a gridded product such as that provided by the European Center for Medium Range Weather Forecasting (ECMWF) or the National Meteorological Center (NMC). These global, gridded products prescribe temperature and humidity profiles for a limited number of pressure levels (up to 14) in a vertical atmospheric column. The FIRE IFO 2 experiment provides an opportunity to investigate current atmospheric profile parameterization schemes, compare satellite cloud height results using both gridded products (ECMWF) and high vertical resolution sonde data from the National Weather Service (NWS) and Cross Chain Loran Atmospheric Sounding System (CLASS), and suggest modifications in atmospheric parameterization schemes based on these results.

An algorithm for the estimation of bounds on the emissivity and temperatures from thermal multispectral airborne remotely sensed data

NASA Technical Reports Server (NTRS)

Jaggi, S.; Quattrochi, D.; Baskin, R.

1992-01-01

The effective flux incident upon the detectors of a thermal sensor, after it has been corrected for atmospheric effects, is a function of a non-linear combination of the emissivity of the target for that channel and the temperature of the target. The sensor system cannot separate the contribution from the emissivity and the temperature that constitute the flux value. A method that estimates the bounds on these temperatures and emissivities from thermal data is described. This method is then tested with remotely sensed data obtained from NASA's Thermal Infrared Multispectral Scanner (TIMS) - a 6 channel thermal sensor. Since this is an under-determined set of equations i.e. there are 7 unknowns (6 emissivities and 1 temperature) and 6 equations (corresponding to the 6 channel fluxes), there exist theoretically an infinite combination of values of emissivities and temperature that can satisfy these equations. Using some realistic bounds on the emissivities, bounds on the temperature are calculated. These bounds on the temperature are refined to estimate a tighter bound on the emissivity of the source. An error analysis is also carried out to quantitatively determine the extent of uncertainty introduced in the estimate of these parameters. This method is useful only when a realistic set of bounds can be obtained for the emissivities of the data. In the case of water the lower and upper bounds were set at 0.97 and 1.00 respectively. Five flights were flown in succession at altitudes of 2 km (low), 6 km (mid), 12 km (high), and then back again at 6 km and 2 km. The area selected with the Ross Barnett reservoir near Jackson, Mississippi. The mission was flown during the predawn hours of 1 Feb. 1992. Radiosonde data was collected for that duration to profile the characteristics of the atmosphere. Ground truth temperatures using thermometers and radiometers were also obtained over an area of the reservoir. The results of two independent runs of the radiometer data averaged 7.03 plus or minus .70 for the first run and 7.31 plus or minus .88 for the second run. The results of the algorithm yield a temperature of 7.68 for the low altitude data to 8.73 for the high altitude data.

Based on the CSI regional segmentation indoor localization algorithm

NASA Astrophysics Data System (ADS)

Zeng, Xi; Lin, Wei; Lan, Jingwei

2017-08-01

To solve the problem of high cost and low accuracy, the method of Channel State Information (CSI) regional segmentation are proposed in the indoor positioning. Because Channel State Information (CSI) stability, and effective against multipath effect, we used the Channel State Information (CSI) to segment location area. The method Acquisition CSI the influence of different link to pinpoint the location of the area. Then the method can improve the accuracy of positioning, and reduce the cost of the fingerprint localization algorithm.

On Channel-Discontinuity-Constraint Routing in Wireless Networks☆

PubMed Central

Sankararaman, Swaminathan; Efrat, Alon; Ramasubramanian, Srinivasan; Agarwal, Pankaj K.

2011-01-01

Multi-channel wireless networks are increasingly deployed as infrastructure networks, e.g. in metro areas. Network nodes frequently employ directional antennas to improve spatial throughput. In such networks, between two nodes, it is of interest to compute a path with a channel assignment for the links such that the path and link bandwidths are the same. This is achieved when any two consecutive links are assigned different channels, termed as “Channel-Discontinuity-Constraint” (CDC). CDC-paths are also useful in TDMA systems, where, preferably, consecutive links are assigned different time-slots. In the first part of this paper, we develop a t-spanner for CDC-paths using spatial properties; a sub-network containing O(n/θ) links, for any θ > 0, such that CDC-paths increase in cost by at most a factor t = (1−2 sin (θ/2))−2. We propose a novel distributed algorithm to compute the spanner using an expected number of O(n log n) fixed-size messages. In the second part, we present a distributed algorithm to find minimum-cost CDC-paths between two nodes using O(n2) fixed-size messages, by developing an extension of Edmonds’ algorithm for minimum-cost perfect matching. In a centralized implementation, our algorithm runs in O(n2) time improving the previous best algorithm which requires O(n3) running time. Moreover, this running time improves to O(n/θ) when used in conjunction with the spanner developed. PMID:24443646

Experimental Results and Issues on Equalization for Nonlinear Memory Channel: Pre-Cursor Enhanced Ram-DFE Canceler

NASA Technical Reports Server (NTRS)

Yuan, Lu; LeBlanc, James

1998-01-01

This thesis investigates the effects of the High Power Amplifier (HPA) and the filters over a satellite or telemetry channel. The Volterra series expression is presented for the nonlinear channel with memory, and the algorithm is based on the finite-state machine model. A RAM-based algorithm operating on the receiver side, Pre-cursor Enhanced RAM-FSE Canceler (PERC) is developed. A high order modulation scheme , 16-QAM is used for simulation, the results show that PERC provides an efficient and reliable method to transmit data on the bandlimited nonlinear channel. The contribution of PERC algorithm is that it includes both pre-cursors and post-cursors as the RAM address lines, and suggests a new way to make decision on the pre-addresses. Compared with the RAM-DFE structure that only includes post- addresses, the BER versus Eb/NO performance of PERC is substantially enhanced. Experiments are performed for PERC algorithms with different parameters on AWGN channels, and the results are compared and analyzed. The investigation of this thesis includes software simulation and hardware verification. Hardware is setup to collect actual TWT data. Simulation on both the software-generated data and the real-world data are performed. Practical limitations are considered for the hardware collected data. Simulation results verified the reliability of the PERC algorithm. This work was conducted at NMSU in the Center for Space Telemetering and Telecommunications Systems in the Klipsch School of Electrical and Computer Engineering Department.

Extraction of fetal ECG signal by an improved method using extended Kalman smoother framework from single channel abdominal ECG signal.

PubMed

Panigrahy, D; Sahu, P K

2017-03-01

This paper proposes a five-stage based methodology to extract the fetal electrocardiogram (FECG) from the single channel abdominal ECG using differential evolution (DE) algorithm, extended Kalman smoother (EKS) and adaptive neuro fuzzy inference system (ANFIS) framework. The heart rate of the fetus can easily be detected after estimation of the fetal ECG signal. The abdominal ECG signal contains fetal ECG signal, maternal ECG component, and noise. To estimate the fetal ECG signal from the abdominal ECG signal, removal of the noise and the maternal ECG component presented in it is necessary. The pre-processing stage is used to remove the noise from the abdominal ECG signal. The EKS framework is used to estimate the maternal ECG signal from the abdominal ECG signal. The optimized parameters of the maternal ECG components are required to develop the state and measurement equation of the EKS framework. These optimized maternal ECG parameters are selected by the differential evolution algorithm. The relationship between the maternal ECG signal and the available maternal ECG component in the abdominal ECG signal is nonlinear. To estimate the actual maternal ECG component present in the abdominal ECG signal and also to recognize this nonlinear relationship the ANFIS is used. Inputs to the ANFIS framework are the output of EKS and the pre-processed abdominal ECG signal. The fetal ECG signal is computed by subtracting the output of ANFIS from the pre-processed abdominal ECG signal. Non-invasive fetal ECG database and set A of 2013 physionet/computing in cardiology challenge database (PCDB) are used for validation of the proposed methodology. The proposed methodology shows a sensitivity of 94.21%, accuracy of 90.66%, and positive predictive value of 96.05% from the non-invasive fetal ECG database. The proposed methodology also shows a sensitivity of 91.47%, accuracy of 84.89%, and positive predictive value of 92.18% from the set A of PCDB.

Bayesian sparse channel estimation

NASA Astrophysics Data System (ADS)

Chen, Chulong; Zoltowski, Michael D.

2012-05-01

In Orthogonal Frequency Division Multiplexing (OFDM) systems, the technique used to estimate and track the time-varying multipath channel is critical to ensure reliable, high data rate communications. It is recognized that wireless channels often exhibit a sparse structure, especially for wideband and ultra-wideband systems. In order to exploit this sparse structure to reduce the number of pilot tones and increase the channel estimation quality, the application of compressed sensing to channel estimation is proposed. In this article, to make the compressed channel estimation more feasible for practical applications, it is investigated from a perspective of Bayesian learning. Under the Bayesian learning framework, the large-scale compressed sensing problem, as well as large time delay for the estimation of the doubly selective channel over multiple consecutive OFDM symbols, can be avoided. Simulation studies show a significant improvement in channel estimation MSE and less computing time compared to the conventional compressed channel estimation techniques.

Adaptation of a cubic smoothing spline algortihm for multi-channel data stitching at the National Ignition Facility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, C; Adcock, A; Azevedo, S

2010-12-28

Some diagnostics at the National Ignition Facility (NIF), including the Gamma Reaction History (GRH) diagnostic, require multiple channels of data to achieve the required dynamic range. These channels need to be stitched together into a single time series, and they may have non-uniform and redundant time samples. We chose to apply the popular cubic smoothing spline technique to our stitching problem because we needed a general non-parametric method. We adapted one of the algorithms in the literature, by Hutchinson and deHoog, to our needs. The modified algorithm and the resulting code perform a cubic smoothing spline fit to multiple datamore » channels with redundant time samples and missing data points. The data channels can have different, time-varying, zero-mean white noise characteristics. The method we employ automatically determines an optimal smoothing level by minimizing the Generalized Cross Validation (GCV) score. In order to automatically validate the smoothing level selection, the Weighted Sum-Squared Residual (WSSR) and zero-mean tests are performed on the residuals. Further, confidence intervals, both analytical and Monte Carlo, are also calculated. In this paper, we describe the derivation of our cubic smoothing spline algorithm. We outline the algorithm and test it with simulated and experimental data.« less

Progress towards NASA MODIS and Suomi NPP Cloud Property Data Record Continuity

NASA Astrophysics Data System (ADS)

Platnick, S.; Meyer, K.; Holz, R.; Ackerman, S. A.; Heidinger, A.; Wind, G.; Platnick, S. E.; Wang, C.; Marchant, B.; Frey, R.

2017-12-01

The Suomi NPP VIIRS imager provides an opportunity to extend the 17+ year EOS MODIS climate data record into the next generation operational era. Similar to MODIS, VIIRS provides visible through IR observations at moderate spatial resolution with a 1330 LT equatorial crossing consistent with the MODIS on the Aqua platform. However, unlike MODIS, VIIRS lacks key water vapor and CO2 absorbing channels used for high cloud detection and cloud-top property retrievals. In addition, there is a significant mismatch in the spectral location of the 2.2 μm shortwave-infrared channels used for cloud optical/microphysical retrievals and cloud thermodynamic phase. Given these instrument differences between MODIS EOS and VIIRS S-NPP/JPSS, a merged MODIS-VIIRS cloud record to serve the science community in the coming decades requires different algorithm approaches than those used for MODIS alone. This new approach includes two parallel efforts: (1) Imager-only algorithms with only spectral channels common to VIIRS and MODIS (i.e., eliminate use of MODIS CO2 and NIR/IR water vapor channels). Since the algorithms are run with similar spectral observations, they provide a basis for establishing a continuous cloud data record across the two imagers. (2) Merged imager and sounder measurements (i.e.., MODIS-AIRS, VIIRS-CrIS) in lieu of higher-spatial resolution MODIS absorption channels absent on VIIRS. The MODIS-VIIRS continuity algorithm for cloud optical property retrievals leverages heritage algorithms that produce the existing MODIS cloud mask (MOD35), optical and microphysical properties product (MOD06), and the NOAA AWG Cloud Height Algorithm (ACHA). We discuss our progress towards merging the MODIS observational record with VIIRS in order to generate cloud optical property climate data record continuity across the observing systems. In addition, we summarize efforts to reconcile apparent radiometric biases between analogous imager channels, a critical consideration for obtaining inter-sensor climate data record continuity.

Progress towards MODIS and VIIRS Cloud Optical Property Data Record Continuity

NASA Astrophysics Data System (ADS)

Meyer, K.; Platnick, S. E.; Wind, G.; Amarasinghe, N.; Holz, R.; Ackerman, S. A.; Heidinger, A. K.

2016-12-01

The launch of Suomi NPP in the fall of 2011 began the next generation of U.S. operational polar orbiting Earth observations, and its VIIRS imager provides an opportunity to extend the 15+ year climate data record of MODIS EOS. Similar to MODIS, VIIRS provides visible through IR observations at moderate spatial resolution with a 1330 LT equatorial crossing consistent with the MODIS on the Aqua platform. However, unlike MODIS, VIIRS lacks key water vapor and CO2 absorbing channels used for high cloud detection and cloud-top property retrievals, and there is a significant change in the spectral location of the 2.1μm shortwave-infrared channel used for cloud optical/microphysical retrievals and cloud thermodynamic phase. Given these instrument differences between MODIS EOS and VIIRS S-NPP/JPSS, we discuss our progress towards merging the MODIS observational record with VIIRS in order to generate cloud optical property climate data record continuity across the observing systems. The MODIS-VIIRS continuity algorithm for cloud optical property retrievals leverages heritage algorithms that produce the existing MODIS cloud optical and microphysical properties product (MOD06); the NOAA AWG/CLAVR-x cloud-top property algorithm and a common MODIS-VIIRS cloud mask feed into the optical property algorithm. To account for the different channel sets of MODIS and VIIRS, each algorithm nominally uses a subset of channels common to both imagers. Data granule and aggregated examples for the current version of the continuity algorithm (MODAWG) will be shown. In addition, efforts to reconcile apparent radiometric biases between analogous channels of the two imagers, a critical consideration for obtaining inter-sensor climate data record continuity, will be discussed.

An Image-Based Algorithm for Precise and Accurate High Throughput Assessment of Drug Activity against the Human Parasite Trypanosoma cruzi

PubMed Central

Moraes, Carolina Borsoi; Yang, Gyongseon; Kang, Myungjoo; Freitas-Junior, Lucio H.; Hansen, Michael A. E.

2014-01-01

We present a customized high content (image-based) and high throughput screening algorithm for the quantification of Trypanosoma cruzi infection in host cells. Based solely on DNA staining and single-channel images, the algorithm precisely segments and identifies the nuclei and cytoplasm of mammalian host cells as well as the intracellular parasites infecting the cells. The algorithm outputs statistical parameters including the total number of cells, number of infected cells and the total number of parasites per image, the average number of parasites per infected cell, and the infection ratio (defined as the number of infected cells divided by the total number of cells). Accurate and precise estimation of these parameters allow for both quantification of compound activity against parasites, as well as the compound cytotoxicity, thus eliminating the need for an additional toxicity-assay, hereby reducing screening costs significantly. We validate the performance of the algorithm using two known drugs against T.cruzi: Benznidazole and Nifurtimox. Also, we have checked the performance of the cell detection with manual inspection of the images. Finally, from the titration of the two compounds, we confirm that the algorithm provides the expected half maximal effective concentration (EC50) of the anti-T. cruzi activity. PMID:24503652

Characterization and error analysis of an operational retrieval algorithm for estimating column ozone and aerosol properties from ground-based ultra-violet irradiance measurements

NASA Astrophysics Data System (ADS)

Taylor, Thomas E.; L'Ecuyer, Tristan; Slusser, James; Stephens, Graeme; Krotkov, Nick; Davis, John; Goering, Christian

2005-08-01

Extensive sensitivity and error characteristics of a recently developed optimal estimation retrieval algorithm which simultaneously determines aerosol optical depth (AOD), aerosol single scatter albedo (SSA) and total ozone column (TOC) from ultra-violet irradiances are described. The algorithm inverts measured diffuse and direct irradiances at 7 channels in the UV spectral range obtained from the United States Department of Agriculture's (USDA) UV-B Monitoring and Research Program's (UVMRP) network of 33 ground-based UV-MFRSR instruments to produce aerosol optical properties and TOC at all seven wavelengths. Sensitivity studies of the Tropospheric Ultra-violet/Visible (TUV) radiative transfer model performed for various operating modes (Delta-Eddington versus n-stream Discrete Ordinate) over domains of AOD, SSA, TOC, asymmetry parameter and surface albedo show that the solutions are well constrained. Realistic input error budgets and diagnostic and error outputs from the retrieval are analyzed to demonstrate the atmospheric conditions under which the retrieval provides useful and significant results. After optimizing the algorithm for the USDA site in Panther Junction, Texas the retrieval algorithm was run on a cloud screened set of irradiance measurements for the month of May 2003. Comparisons to independently derived AOD's are favorable with root mean square (RMS) differences of about 3% to 7% at 300nm and less than 1% at 368nm, on May 12 and 22, 2003. This retrieval method will be used to build an aerosol climatology and provide ground-truthing of satellite measurements by running it operationally on the USDA UV network database.

Quantifying short-lived events in multistate ionic current measurements.

PubMed

Balijepalli, Arvind; Ettedgui, Jessica; Cornio, Andrew T; Robertson, Joseph W F; Cheung, Kin P; Kasianowicz, John J; Vaz, Canute

2014-02-25

We developed a generalized technique to characterize polymer-nanopore interactions via single channel ionic current measurements. Physical interactions between analytes, such as DNA, proteins, or synthetic polymers, and a nanopore cause multiple discrete states in the current. We modeled the transitions of the current to individual states with an equivalent electrical circuit, which allowed us to describe the system response. This enabled the estimation of short-lived states that are presently not characterized by existing analysis techniques. Our approach considerably improves the range and resolution of single-molecule characterization with nanopores. For example, we characterized the residence times of synthetic polymers that are three times shorter than those estimated with existing algorithms. Because the molecule's residence time follows an exponential distribution, we recover nearly 20-fold more events per unit time that can be used for analysis. Furthermore, the measurement range was extended from 11 monomers to as few as 8. Finally, we applied this technique to recover a known sequence of single-stranded DNA from previously published ion channel recordings, identifying discrete current states with subpicoampere resolution.

Energy-Efficient Transmissions for Remote Wireless Sensor Networks: An Integrated HAP/Satellite Architecture for Emergency Scenarios

PubMed Central

Dong, Feihong; Li, Hongjun; Gong, Xiangwu; Liu, Quan; Wang, Jingchao

2015-01-01

A typical application scenario of remote wireless sensor networks (WSNs) is identified as an emergency scenario. One of the greatest design challenges for communications in emergency scenarios is energy-efficient transmission, due to scarce electrical energy in large-scale natural and man-made disasters. Integrated high altitude platform (HAP)/satellite networks are expected to optimally meet emergency communication requirements. In this paper, a novel integrated HAP/satellite (IHS) architecture is proposed, and three segments of the architecture are investigated in detail. The concept of link-state advertisement (LSA) is designed in a slow flat Rician fading channel. The LSA is received and processed by the terminal to estimate the link state information, which can significantly reduce the energy consumption at the terminal end. Furthermore, the transmission power requirements of the HAPs and terminals are derived using the gradient descent and differential equation methods. The energy consumption is modeled at both the source and system level. An innovative and adaptive algorithm is given for the energy-efficient path selection. The simulation results validate the effectiveness of the proposed adaptive algorithm. It is shown that the proposed adaptive algorithm can significantly improve energy efficiency when combined with the LSA and the energy consumption estimation. PMID:26404292

Energy-Efficient Transmissions for Remote Wireless Sensor Networks: An Integrated HAP/Satellite Architecture for Emergency Scenarios.

PubMed

Dong, Feihong; Li, Hongjun; Gong, Xiangwu; Liu, Quan; Wang, Jingchao

2015-09-03

A typical application scenario of remote wireless sensor networks (WSNs) is identified as an emergency scenario. One of the greatest design challenges for communications in emergency scenarios is energy-efficient transmission, due to scarce electrical energy in large-scale natural and man-made disasters. Integrated high altitude platform (HAP)/satellite networks are expected to optimally meet emergency communication requirements. In this paper, a novel integrated HAP/satellite (IHS) architecture is proposed, and three segments of the architecture are investigated in detail. The concept of link-state advertisement (LSA) is designed in a slow flat Rician fading channel. The LSA is received and processed by the terminal to estimate the link state information, which can significantly reduce the energy consumption at the terminal end. Furthermore, the transmission power requirements of the HAPs and terminals are derived using the gradient descent and differential equation methods. The energy consumption is modeled at both the source and system level. An innovative and adaptive algorithm is given for the energy-efficient path selection. The simulation results validate the effectiveness of the proposed adaptive algorithm. It is shown that the proposed adaptive algorithm can significantly improve energy efficiency when combined with the LSA and the energy consumption estimation.

Optimal Signal Processing of Frequency-Stepped CW Radar Data

NASA Technical Reports Server (NTRS)

Ybarra, Gary A.; Wu, Shawkang M.; Bilbro, Griff L.; Ardalan, Sasan H.; Hearn, Chase P.; Neece, Robert T.

1995-01-01

An optimal signal processing algorithm is derived for estimating the time delay and amplitude of each scatterer reflection using a frequency-stepped CW system. The channel is assumed to be composed of abrupt changes in the reflection coefficient profile. The optimization technique is intended to maximize the target range resolution achievable from any set of frequency-stepped CW radar measurements made in such an environment. The algorithm is composed of an iterative two-step procedure. First, the amplitudes of the echoes are optimized by solving an overdetermined least squares set of equations. Then, a nonlinear objective function is scanned in an organized fashion to find its global minimum. The result is a set of echo strengths and time delay estimates. Although this paper addresses the specific problem of resolving the time delay between the first two echoes, the derivation is general in the number of echoes. Performance of the optimization approach is illustrated using measured data obtained from an HP-X510 network analyzer. It is demonstrated that the optimization approach offers a significant resolution enhancement over the standard processing approach that employs an IFFT. Degradation in the performance of the algorithm due to suboptimal model order selection and the effects of additive white Gaussion noise are addressed.

Optimal Signal Processing of Frequency-Stepped CW Radar Data

NASA Technical Reports Server (NTRS)

Ybarra, Gary A.; Wu, Shawkang M.; Bilbro, Griff L.; Ardalan, Sasan H.; Hearn, Chase P.; Neece, Robert T.

1995-01-01

An optimal signal processing algorithm is derived for estimating the time delay and amplitude of each scatterer reflection using a frequency-stepped CW system. The channel is assumed to be composed of abrupt changes in the reflection coefficient profile. The optimization technique is intended to maximize the target range resolution achievable from any set of frequency-stepped CW radar measurements made in such an environment. The algorithm is composed of an iterative two-step procedure. First, the amplitudes of the echoes are optimized by solving an overdetermined least squares set of equations. Then, a nonlinear objective function is scanned in an organized fashion to find its global minimum. The result is a set of echo strengths and time delay estimates. Although this paper addresses the specific problem of resolving the time delay between the two echoes, the derivation is general in the number of echoes. Performance of the optimization approach is illustrated using measured data obtained from an HP-851O network analyzer. It is demonstrated that the optimization approach offers a significant resolution enhancement over the standard processing approach that employs an IFFT. Degradation in the performance of the algorithm due to suboptimal model order selection and the effects of additive white Gaussion noise are addressed.

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

NASA Astrophysics Data System (ADS)

Ebtehaj, Isa; Bonakdari, Hossein

2017-12-01

Since the flow entering a sewer contains solid matter, deposition at the bottom of the channel is inevitable. It is difficult to understand the complex, three-dimensional mechanism of sediment transport in sewer pipelines. Therefore, a method to estimate the limiting velocity is necessary for optimal designs. Due to the inability of gradient-based algorithms to train Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for non-deposition sediment transport prediction, a new hybrid ANFIS method based on a differential evolutionary algorithm (ANFIS-DE) is developed. The training and testing performance of ANFIS-DE is evaluated using a wide range of dimensionless parameters gathered from the literature. The input combination used to estimate the densimetric Froude number ( Fr) parameters includes the volumetric sediment concentration ( C V ), ratio of median particle diameter to hydraulic radius ( d/R), ratio of median particle diameter to pipe diameter ( d/D) and overall friction factor of sediment ( λ s ). The testing results are compared with the ANFIS model and regression-based equation results. The ANFIS-DE technique predicted sediment transport at limit of deposition with lower root mean square error (RMSE = 0.323) and mean absolute percentage of error (MAPE = 0.065) and higher accuracy ( R 2 = 0.965) than the ANFIS model and regression-based equations.

Extraction of incident irradiance from LWIR hyperspectral imagery

NASA Astrophysics Data System (ADS)

Lahaie, Pierre

2014-10-01

The atmospheric correction of thermal hyperspectral imagery can be separated in two distinct processes: Atmospheric Compensation (AC) and Temperature and Emissivity separation (TES). TES requires for input at each pixel, the ground leaving radiance and the atmospheric downwelling irradiance, which are the outputs of the AC process. The extraction from imagery of the downwelling irradiance requires assumptions about some of the pixels' nature, the sensor and the atmosphere. Another difficulty is that, often the sensor's spectral response is not well characterized. To deal with this unknown, we defined a spectral mean operator that is used to filter the ground leaving radiance and a computation of the downwelling irradiance from MODTRAN. A user will select a number of pixels in the image for which the emissivity is assumed to be known. The emissivity of these pixels is assumed to be smooth and that the only spectrally fast varying variable in the downwelling irradiance. Using these assumptions we built an algorithm to estimate the downwelling irradiance. The algorithm is used on all the selected pixels. The estimated irradiance is the average on the spectral channels of the resulting computation. The algorithm performs well in simulation and results are shown for errors in the assumed emissivity and for errors in the atmospheric profiles. The sensor noise influences mainly the required number of pixels.

Super-channel oriented routing, spectrum and core assignment under crosstalk limit in spatial division multiplexing elastic optical networks

NASA Astrophysics Data System (ADS)

Zhao, Yongli; Zhu, Ye; Wang, Chunhui; Yu, Xiaosong; Liu, Chuan; Liu, Binglin; Zhang, Jie

2017-07-01

With the capacity increasing in optical networks enabled by spatial division multiplexing (SDM) technology, spatial division multiplexing elastic optical networks (SDM-EONs) attract much attention from both academic and industry. Super-channel is an important type of service provisioning in SDM-EONs. This paper focuses on the issue of super-channel construction in SDM-EONs. Mixed super-channel oriented routing, spectrum and core assignment (MS-RSCA) algorithm is proposed in SDM-EONs considering inter-core crosstalk. Simulation results show that MS-RSCA can improve spectrum resource utilization and reduce blocking probability significantly compared with the baseline RSCA algorithms.

Ant Colony Optimization Algorithm for Centralized Dynamic Channel Allocation in Multi-Cell OFDMA Systems

NASA Astrophysics Data System (ADS)

Kim, Hyo-Su; Kim, Dong-Hoi

The dynamic channel allocation (DCA) scheme in multi-cell systems causes serious inter-cell interference (ICI) problem to some existing calls when channels for new calls are allocated. Such a problem can be addressed by advanced centralized DCA design that is able to minimize ICI. Thus, in this paper, a centralized DCA is developed for the downlink of multi-cell orthogonal frequency division multiple access (OFDMA) systems with full spectral reuse. However, in practice, as the search space of channel assignment for centralized DCA scheme in multi-cell systems grows exponentially with the increase of the number of required calls, channels, and cells, it becomes an NP-hard problem and is currently too complicated to find an optimum channel allocation. In this paper, we propose an ant colony optimization (ACO) based DCA scheme using a low-complexity ACO algorithm which is a kind of heuristic algorithm in order to solve the aforementioned problem. Simulation results demonstrate significant performance improvements compared to the existing schemes in terms of the grade of service (GoS) performance and the forced termination probability of existing calls without degrading the system performance of the average throughput.

Simple, Fast and Accurate Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States

PubMed Central

Orio, Patricio; Soudry, Daniel

2012-01-01

Background The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise) with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system. The effects that channel noise can have on neural dynamics are generally studied using numerical simulations of stochastic models. Algorithms based on discrete Markov Chains (MC) seem to be the most reliable and trustworthy, but even optimized algorithms come with a non-negligible computational cost. Diffusion Approximation (DA) methods use Stochastic Differential Equations (SDE) to approximate the behavior of a number of MCs, considerably speeding up simulation times. However, model comparisons have suggested that DA methods did not lead to the same results as in MC modeling in terms of channel noise statistics and effects on excitability. Recently, it was shown that the difference arose because MCs were modeled with coupled gating particles, while the DA was modeled using uncoupled gating particles. Implementations of DA with coupled particles, in the context of a specific kinetic scheme, yielded similar results to MC. However, it remained unclear how to generalize these implementations to different kinetic schemes, or whether they were faster than MC algorithms. Additionally, a steady state approximation was used for the stochastic terms, which, as we show here, can introduce significant inaccuracies. Main Contributions We derived the SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable – allowing an easy, transparent and efficient DA implementation, avoiding unnecessary approximations. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods, except when short time steps or low channel numbers were used. PMID:22629320

A Fast and Accurate Sparse Continuous Signal Reconstruction by Homotopy DCD with Non-Convex Regularization

PubMed Central

Wang, Tianyun; Lu, Xinfei; Yu, Xiaofei; Xi, Zhendong; Chen, Weidong

2014-01-01

In recent years, various applications regarding sparse continuous signal recovery such as source localization, radar imaging, communication channel estimation, etc., have been addressed from the perspective of compressive sensing (CS) theory. However, there are two major defects that need to be tackled when considering any practical utilization. The first issue is off-grid problem caused by the basis mismatch between arbitrary located unknowns and the pre-specified dictionary, which would make conventional CS reconstruction methods degrade considerably. The second important issue is the urgent demand for low-complexity algorithms, especially when faced with the requirement of real-time implementation. In this paper, to deal with these two problems, we have presented three fast and accurate sparse reconstruction algorithms, termed as HR-DCD, Hlog-DCD and Hlp-DCD, which are based on homotopy, dichotomous coordinate descent (DCD) iterations and non-convex regularizations, by combining with the grid refinement technique. Experimental results are provided to demonstrate the effectiveness of the proposed algorithms and related analysis. PMID:24675758

Improvements in Night-Time Low Cloud Detection and MODIS-Style Cloud Optical Properties from MSG SEVIRI

NASA Technical Reports Server (NTRS)

Wind, Galina (Gala); Platnick, Steven; Riedi, Jerome

2011-01-01

The MODIS cloud optical properties algorithm (MOD06IMYD06 for Terra and Aqua MODIS, respectively) slated for production in Data Collection 6 has been adapted to execute using available channels on MSG SEVIRI. Available MODIS-style retrievals include IR Window-derived cloud top properties, using the new Collection 6 cloud top properties algorithm, cloud optical thickness from VISINIR bands, cloud effective radius from 1.6 and 3.7Jlm and cloud ice/water path. We also provide pixel-level uncertainty estimate for successful retrievals. It was found that at nighttime the SEVIRI cloud mask tends to report unnaturally low cloud fraction for marine stratocumulus clouds. A correction algorithm that improves detection of such clouds has been developed. We will discuss the improvements to nighttime low cloud detection for SEVIRI and show examples and comparisons with MODIS and CALIPSO. We will also show examples of MODIS-style pixel-level (Level-2) cloud retrievals for SEVIRI with comparisons to MODIS.

Design and simulation of sensor networks for tracking Wifi users in outdoor urban environments

NASA Astrophysics Data System (ADS)

Thron, Christopher; Tran, Khoi; Smith, Douglas; Benincasa, Daniel

2017-05-01

We present a proof-of-concept investigation into the use of sensor networks for tracking of WiFi users in outdoor urban environments. Sensors are fixed, and are capable of measuring signal power from users' WiFi devices. We derive a maximum likelihood estimate for user location based on instantaneous sensor power measurements. The algorithm takes into account the effects of power control, and is self-calibrating in that the signal power model used by the location algorithm is adjusted and improved as part of the operation of the network. Simulation results to verify the system's performance are presented. The simulation scenario is based on a 1.5 km2 area of lower Manhattan, The self-calibration mechanism was verified for initial rms (root mean square) errors of up to 12 dB in the channel power estimates: rms errors were reduced by over 60% in 300 track-hours, in systems with limited power control. Under typical operating conditions with (without) power control, location rms errors are about 8.5 (5) meters with 90% accuracy within 9 (13) meters, for both pedestrian and vehicular users. The distance error distributions for smaller distances (<30 m) are well-approximated by an exponential distribution, while the distributions for large distance errors have fat tails. The issue of optimal sensor placement in the sensor network is also addressed. We specify a linear programming algorithm for determining sensor placement for networks with reduced number of sensors. In our test case, the algorithm produces a network with 18.5% fewer sensors with comparable accuracy estimation performance. Finally, we discuss future research directions for improving the accuracy and capabilities of sensor network systems in urban environments.

Task-based detectability comparison of exponential transformation of free-response operating characteristic (EFROC) curve and channelized Hotelling observer (CHO)

NASA Astrophysics Data System (ADS)

Khobragade, P.; Fan, Jiahua; Rupcich, Franco; Crotty, Dominic J.; Gilat Schmidt, Taly

2016-03-01

This study quantitatively evaluated the performance of the exponential transformation of the free-response operating characteristic curve (EFROC) metric, with the Channelized Hotelling Observer (CHO) as a reference. The CHO has been used for image quality assessment of reconstruction algorithms and imaging systems and often it is applied to study the signal-location-known cases. The CHO also requires a large set of images to estimate the covariance matrix. In terms of clinical applications, this assumption and requirement may be unrealistic. The newly developed location-unknown EFROC detectability metric is estimated from the confidence scores reported by a model observer. Unlike the CHO, EFROC does not require a channelization step and is a non-parametric detectability metric. There are few quantitative studies available on application of the EFROC metric, most of which are based on simulation data. This study investigated the EFROC metric using experimental CT data. A phantom with four low contrast objects: 3mm (14 HU), 5mm (7HU), 7mm (5 HU) and 10 mm (3 HU) was scanned at dose levels ranging from 25 mAs to 270 mAs and reconstructed using filtered backprojection. The area under the curve values for CHO (AUC) and EFROC (AFE) were plotted with respect to different dose levels. The number of images required to estimate the non-parametric AFE metric was calculated for varying tasks and found to be less than the number of images required for parametric CHO estimation. The AFE metric was found to be more sensitive to changes in dose than the CHO metric. This increased sensitivity and the assumption of unknown signal location may be useful for investigating and optimizing CT imaging methods. Future work is required to validate the AFE metric against human observers.

The analysis to understand temporal variation and long-range transport of aerosol over Northeast-Asia Using COMS, MI

NASA Astrophysics Data System (ADS)

KIM, M.; Kim, J.

2016-12-01

Numerous efforts to retrieve aerosol optical properties (AOPs) using satellite measurements have been accumulated for decades, resulted in several qualified data which can be used for the analysis of spatiotemporal characteristics of AOPs. However, the limitation in the instrument lifetime restricts temporal window of the analysis of long-term AOPs variation. In this point of view, single channel algorithm, which uses a single visible channel to retrieve aerosol optical depth (AOD), has an advantage to extent the time domain of the analysis. The Korean geostationary earth orbit (GEO) satellite, the Communication, Ocean and Meteorological Satellite (COMS) includes the single channel Meteorological Imager (MI), which can also be utilized for the retrieval of AOPs. Since the GEO satellite measurement has an advantage for continuous monitoring of AOPs over Northeast Asia, we can analyze the spatiotemporal characteristic of the aerosol using MI observations. In this study, we investigate the trend of AOD and also discuss the impact of long-range transport of aerosol on the temporal variation. Since the year 2010 when the COMS was launched, AODs over Northeast China and Yellow Sea region show 3.02 % and 2.74 % decrease per year, respectively, which are significant trends in spite of only 5-year short period. The decreasing behavior seems associated with the recent decreasing frequency of dust event over the region. But other Northeast Asia regions do not show clear temporal change. The accuracy of retrieved AOD can relates to the uncertainty of this trend analysis. According to the error analysis, cloud contamination and error in bright surface reflectance results in the accuracy of AOD. Therefore, improvements of cloud masking process and surface reflectance estimation in the developed single channel MI algorithm will be required for the future study.

Cloud Particle Size and Water/Ice Ratio Estimation using the DMSP SSMIS Sounder

NASA Astrophysics Data System (ADS)

Peng, G. S.; Fote, A. A.; Wu, D. L.; Boucher, D. J.; Thomas, B. H.; Kishi, A. M.

2008-12-01

The Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager/Sounder (SSMIS) is a next-generation passive conically scanning microwave radiometer. It combines both imaging and sounding capabilities of current operational instruments, SSM/I, SSM/T-1 and SSM/T-2. It also improves the capability of temperature sounding by providing profiles from the surface up to 70 km altitude with higher spatial resolutions (~37.5 for lower air and ~75 km for upper air). DMSP Flight 17 launched on 4 November 2006 from Vandenberg Air Force Base carrying the second SSMIS sounder. During the SSMIS Cal/Val period, cold patches were observed in the 50-55 GHz temperature sounding channels at low latitudes. Cold patches were also more apparent in the horizontal polarization (H- pol) than the Vertical polarization (V-pol) channels. A difference in sensitivity of the H-pol and V-pol channels gives the ratio of water to ice in the clouds. Subsequent investigation showed that these patches appeared in the 91.6 GHz channels but not the 37 GHz channels. This information, together with the theoretical scattering efficiency for spherical particles of various sizes, gives an upper bound of < 2 mm diameter for water and ice particles that may not be detected by SSMIS operational 'cloud clearing' algorithms.

Sensor Transmission Power Schedule for Smart Grids

NASA Astrophysics Data System (ADS)

Gao, C.; Huang, Y. H.; Li, J.; Liu, X. D.

2017-11-01

Smart grid has attracted much attention by the requirement of new generation renewable energy. Nowadays, the real-time state estimation, with the help of phasor measurement unit, plays an important role to keep smart grid stable and efficient. However, the limitation of the communication channel is not considered by related work. Considering the familiar limited on-board batteries wireless sensor in smart grid, transmission power schedule is designed in this paper, which minimizes energy consumption with proper EKF filtering performance requirement constrain. Based on the event-triggered estimation theory, the filtering algorithm is also provided to utilize the information contained in the power schedule. Finally, its feasibility and performance is demonstrated using the standard IEEE 39-bus system with phasor measurement units (PMUs).

Optimal signal constellation design for ultra-high-speed optical transport in the presence of nonlinear phase noise.

PubMed

Liu, Tao; Djordjevic, Ivan B

2014-12-29

In this paper, we first describe an optimal signal constellation design algorithm suitable for the coherent optical channels dominated by the linear phase noise. Then, we modify this algorithm to be suitable for the nonlinear phase noise dominated channels. In optimization procedure, the proposed algorithm uses the cumulative log-likelihood function instead of the Euclidian distance. Further, an LDPC coded modulation scheme is proposed to be used in combination with signal constellations obtained by proposed algorithm. Monte Carlo simulations indicate that the LDPC-coded modulation schemes employing the new constellation sets, obtained by our new signal constellation design algorithm, outperform corresponding QAM constellations significantly in terms of transmission distance and have better nonlinearity tolerance.

A family of variable step-size affine projection adaptive filter algorithms using statistics of channel impulse response

NASA Astrophysics Data System (ADS)

Shams Esfand Abadi, Mohammad; AbbasZadeh Arani, Seyed Ali Asghar

2011-12-01

This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection algorithm (VSS-APA), the VSS selective partial update NLMS (VSS-SPU-NLMS), the VSS-SPU-APA, and the VSS selective regressor APA (VSS-SR-APA). In VSS-SPU adaptive algorithms the filter coefficients are partially updated which reduce the computational complexity. In VSS-SR-APA, the optimal selection of input regressors is performed during the adaptation. The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms through several simulations in system identification scenario.

A Stereo Dual-Channel Dynamic Programming Algorithm for UAV Image Stitching

PubMed Central

Chen, Ruizhi; Zhang, Weilong; Li, Deren; Liao, Xuan; Zhang, Peng

2017-01-01

Dislocation is one of the major challenges in unmanned aerial vehicle (UAV) image stitching. In this paper, we propose a new algorithm for seamlessly stitching UAV images based on a dynamic programming approach. Our solution consists of two steps: Firstly, an image matching algorithm is used to correct the images so that they are in the same coordinate system. Secondly, a new dynamic programming algorithm is developed based on the concept of a stereo dual-channel energy accumulation. A new energy aggregation and traversal strategy is adopted in our solution, which can find a more optimal seam line for image stitching. Our algorithm overcomes the theoretical limitation of the classical Duplaquet algorithm. Experiments show that the algorithm can effectively solve the dislocation problem in UAV image stitching, especially for the cases in dense urban areas. Our solution is also direction-independent, which has better adaptability and robustness for stitching images. PMID:28885547

A Stereo Dual-Channel Dynamic Programming Algorithm for UAV Image Stitching.

PubMed

Li, Ming; Chen, Ruizhi; Zhang, Weilong; Li, Deren; Liao, Xuan; Wang, Lei; Pan, Yuanjin; Zhang, Peng

2017-09-08

Dislocation is one of the major challenges in unmanned aerial vehicle (UAV) image stitching. In this paper, we propose a new algorithm for seamlessly stitching UAV images based on a dynamic programming approach. Our solution consists of two steps: Firstly, an image matching algorithm is used to correct the images so that they are in the same coordinate system. Secondly, a new dynamic programming algorithm is developed based on the concept of a stereo dual-channel energy accumulation. A new energy aggregation and traversal strategy is adopted in our solution, which can find a more optimal seam line for image stitching. Our algorithm overcomes the theoretical limitation of the classical Duplaquet algorithm. Experiments show that the algorithm can effectively solve the dislocation problem in UAV image stitching, especially for the cases in dense urban areas. Our solution is also direction-independent, which has better adaptability and robustness for stitching images.

Power amplifier linearization technique with IQ imbalance and crosstalk compensation for broadband MIMO-OFDM transmitters

NASA Astrophysics Data System (ADS)

Gregorio, Fernando; Cousseau, Juan; Werner, Stefan; Riihonen, Taneli; Wichman, Risto

2011-12-01

The design of predistortion techniques for broadband multiple input multiple output-OFDM (MIMO-OFDM) systems raises several implementation challenges. First, the large bandwidth of the OFDM signal requires the introduction of memory effects in the PD model. In addition, it is usual to consider an imbalanced in-phase and quadrature (IQ) modulator to translate the predistorted baseband signal to RF. Furthermore, the coupling effects, which occur when the MIMO paths are implemented in the same reduced size chipset, cannot be avoided in MIMO transceivers structures. This study proposes a MIMO-PD system that linearizes the power amplifier response and compensates nonlinear crosstalk and IQ imbalance effects for each branch of the multiantenna system. Efficient recursive algorithms are presented to estimate the complete MIMO-PD coefficients. The algorithms avoid the high computational complexity in previous solutions based on least squares estimation. The performance of the proposed MIMO-PD structure is validated by simulations using a two-transmitter antenna MIMO system. Error vector magnitude and adjacent channel power ratio are evaluated showing significant improvement compared with conventional MIMO-PD systems.

Performance Analysis of Iterative Channel Estimation and Multiuser Detection in Multipath DS-CDMA Channels

NASA Astrophysics Data System (ADS)

Li, Husheng; Betz, Sharon M.; Poor, H. Vincent

2007-05-01

This paper examines the performance of decision feedback based iterative channel estimation and multiuser detection in channel coded aperiodic DS-CDMA systems operating over multipath fading channels. First, explicit expressions describing the performance of channel estimation and parallel interference cancellation based multiuser detection are developed. These results are then combined to characterize the evolution of the performance of a system that iterates among channel estimation, multiuser detection and channel decoding. Sufficient conditions for convergence of this system to a unique fixed point are developed.

Using Elman recurrent neural networks with conjugate gradient algorithm in determining the anesthetic the amount of anesthetic medicine to be applied.

PubMed

Güntürkün, Rüştü

2010-08-01

In this study, Elman recurrent neural networks have been defined by using conjugate gradient algorithm in order to determine the depth of anesthesia in the continuation stage of the anesthesia and to estimate the amount of medicine to be applied at that moment. The feed forward neural networks are also used for comparison. The conjugate gradient algorithm is compared with back propagation (BP) for training of the neural Networks. The applied artificial neural network is composed of three layers, namely the input layer, the hidden layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. EEG data has been recorded with Nihon Kohden 9200 brand 22-channel EEG device. The international 8-channel bipolar 10-20 montage system (8 TB-b system) has been used in assembling the recording electrodes. EEG data have been recorded by being sampled once in every 2 milliseconds. The artificial neural network has been designed so as to have 60 neurons in the input layer, 30 neurons in the hidden layer and 1 neuron in the output layer. The values of the power spectral density (PSD) of 10-second EEG segments which correspond to the 1-50 Hz frequency range; the ratio of the total power of PSD values of the EEG segment at that moment in the same range to the total of PSD values of EEG segment taken prior to the anesthesia.

Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah

PubMed Central

Tian, Le; Latré, Steven

2017-01-01

IEEE 802.11ah, marketed as Wi-Fi HaLow, extends Wi-Fi to the sub-1 GHz spectrum. Through a number of physical layer (PHY) and media access control (MAC) optimizations, it aims to bring greatly increased range, energy-efficiency, and scalability. This makes 802.11ah the perfect candidate for providing connectivity to Internet of Things (IoT) devices. One of these new features, referred to as the Restricted Access Window (RAW), focuses on improving scalability in highly dense deployments. RAW divides stations into groups and reduces contention and collisions by only allowing channel access to one group at a time. However, the standard does not dictate how to determine the optimal RAW grouping parameters. The optimal parameters depend on the current network conditions, and it has been shown that incorrect configuration severely impacts throughput, latency and energy efficiency. In this paper, we propose a traffic-adaptive RAW optimization algorithm (TAROA) to adapt the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks in which each sensor transmits packets with a certain (predictable) frequency and may change the transmission frequency over time. The TAROA algorithm is executed at each target beacon transmission time (TBTT), and it first estimates the packet transmission interval of each station only based on packet transmission information obtained by access point (AP) during the last beacon interval. Then, TAROA determines the RAW parameters and assigns stations to RAW slots based on this estimated transmission frequency. The simulation results show that, compared to enhanced distributed channel access/distributed coordination function (EDCA/DCF), the TAROA algorithm can highly improve the performance of IEEE 802.11ah dense networks in terms of throughput, especially when hidden nodes exist, although it does not always achieve better latency performance. This paper contributes with a practical approach to optimizing RAW grouping under dynamic traffic in real time, which is a major leap towards applying RAW mechanism in real-life IoT networks. PMID:28677617

Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah.

PubMed

Tian, Le; Khorov, Evgeny; Latré, Steven; Famaey, Jeroen

2017-07-04

IEEE 802.11ah, marketed as Wi-Fi HaLow, extends Wi-Fi to the sub-1 GHz spectrum. Through a number of physical layer (PHY) and media access control (MAC) optimizations, it aims to bring greatly increased range, energy-efficiency, and scalability. This makes 802.11ah the perfect candidate for providing connectivity to Internet of Things (IoT) devices. One of these new features, referred to as the Restricted Access Window (RAW), focuses on improving scalability in highly dense deployments. RAW divides stations into groups and reduces contention and collisions by only allowing channel access to one group at a time. However, the standard does not dictate how to determine the optimal RAW grouping parameters. The optimal parameters depend on the current network conditions, and it has been shown that incorrect configuration severely impacts throughput, latency and energy efficiency. In this paper, we propose a traffic-adaptive RAW optimization algorithm (TAROA) to adapt the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks in which each sensor transmits packets with a certain (predictable) frequency and may change the transmission frequency over time. The TAROA algorithm is executed at each target beacon transmission time (TBTT), and it first estimates the packet transmission interval of each station only based on packet transmission information obtained by access point (AP) during the last beacon interval. Then, TAROA determines the RAW parameters and assigns stations to RAW slots based on this estimated transmission frequency. The simulation results show that, compared to enhanced distributed channel access/distributed coordination function (EDCA/DCF), the TAROA algorithm can highly improve the performance of IEEE 802.11ah dense networks in terms of throughput, especially when hidden nodes exist, although it does not always achieve better latency performance. This paper contributes with a practical approach to optimizing RAW grouping under dynamic traffic in real time, which is a major leap towards applying RAW mechanism in real-life IoT networks.

Impact of spatial resolution on cirrus infrared satellite retrievals in the presence of cloud heterogeneity

NASA Astrophysics Data System (ADS)

Fauchez, T.; Platnick, S. E.; Meyer, K.; Zhang, Z.; Cornet, C.; Szczap, F.; Dubuisson, P.

2015-12-01

Cirrus clouds are an important part of the Earth radiation budget but an accurate assessment of their role remains highly uncertain. Cirrus optical properties such as Cloud Optical Thickness (COT) and ice crystal effective particle size are often retrieved with a combination of Visible/Near InfraRed (VNIR) and ShortWave-InfraRed (SWIR) reflectance channels. Alternatively, Thermal InfraRed (TIR) techniques, such as the Split Window Technique (SWT), have demonstrated better accuracy for thin cirrus effective radius retrievals with small effective radii. However, current global operational algorithms for both retrieval methods assume that cloudy pixels are horizontally homogeneous (Plane Parallel Approximation (PPA)) and independent (Independent Pixel Approximation (IPA)). The impact of these approximations on ice cloud retrievals needs to be understood and, as far as possible, corrected. Horizontal heterogeneity effects in the TIR spectrum are mainly dominated by the PPA bias that primarily depends on the COT subpixel heterogeneity; for solar reflectance channels, in addition to the PPA bias, the IPA can lead to significant retrieval errors due to a significant photon horizontal transport between cloudy columns, as well as brightening and shadowing effects that are more difficult to quantify. Furthermore TIR retrievals techniques have demonstrated better retrieval accuracy for thin cirrus having small effective radii over solar reflectance techniques. The TIR range is thus particularly relevant in order to characterize, as accurately as possible, thin cirrus clouds. Heterogeneity effects in the TIR are evaluated as a function of spatial resolution in order to estimate the optimal spatial resolution for TIR retrieval applications. These investigations are performed using a cirrus 3D cloud generator (3DCloud), a 3D radiative transfer code (3DMCPOL), and two retrieval algorithms, namely the operational MODIS retrieval algorithm (MOD06) and a research-level SWT algorithm.

Robust THP Transceiver Designs for Multiuser MIMO Downlink with Imperfect CSIT

NASA Astrophysics Data System (ADS)

Ubaidulla, P.; Chockalingam, A.

2009-12-01

We present robust joint nonlinear transceiver designs for multiuser multiple-input multiple-output (MIMO) downlink in the presence of imperfections in the channel state information at the transmitter (CSIT). The base station (BS) is equipped with multiple transmit antennas, and each user terminal is equipped with one or more receive antennas. The BS employs Tomlinson-Harashima precoding (THP) for interuser interference precancellation at the transmitter. We consider robust transceiver designs that jointly optimize the transmit THP filters and receive filter for two models of CSIT errors. The first model is a stochastic error (SE) model, where the CSIT error is Gaussian-distributed. This model is applicable when the CSIT error is dominated by channel estimation error. In this case, the proposed robust transceiver design seeks to minimize a stochastic function of the sum mean square error (SMSE) under a constraint on the total BS transmit power. We propose an iterative algorithm to solve this problem. The other model we consider is a norm-bounded error (NBE) model, where the CSIT error can be specified by an uncertainty set. This model is applicable when the CSIT error is dominated by quantization errors. In this case, we consider a worst-case design. For this model, we consider robust (i) minimum SMSE, (ii) MSE-constrained, and (iii) MSE-balancing transceiver designs. We propose iterative algorithms to solve these problems, wherein each iteration involves a pair of semidefinite programs (SDPs). Further, we consider an extension of the proposed algorithm to the case with per-antenna power constraints. We evaluate the robustness of the proposed algorithms to imperfections in CSIT through simulation, and show that the proposed robust designs outperform nonrobust designs as well as robust linear transceiver designs reported in the recent literature.

Backward Channel Protection Based on Randomized Tree-Walking Algorithm and Its Analysis for Securing RFID Tag Information and Privacy

NASA Astrophysics Data System (ADS)

Choi, Wonjoon; Yoon, Myungchul; Roh, Byeong-Hee

Eavesdropping on backward channels in RFID environments may cause severe privacy problems because it means the exposure of personal information related to tags that each person has. However, most existing RFID tag security schemes are focused on the forward channel protections. In this paper, we propose a simple but effective method to solve the backward channel eavesdropping problem based on Randomized-tree walking algorithm for securing tag ID information and privacy in RFID-based applications. In order to show the efficiency of the proposed scheme, we derive two performance models for the cases when CRC is used and not used. It is shown that the proposed method can lower the probability of eavesdropping on backward channels near to ‘0.’

Comparison of neural network applications for channel assignment in cellular TDMA networks and dynamically sectored PCS networks

NASA Astrophysics Data System (ADS)

Hortos, William S.

1997-04-01

The use of artificial neural networks (NNs) to address the channel assignment problem (CAP) for cellular time-division multiple access and code-division multiple access networks has previously been investigated by this author and many others. The investigations to date have been based on a hexagonal cell structure established by omnidirectional antennas at the base stations. No account was taken of the use of spatial isolation enabled by directional antennas to reduce interference between mobiles. Any reduction in interference translates into increased capacity and consequently alters the performance of the NNs. Previous studies have sought to improve the performance of Hopfield- Tank network algorithms and self-organizing feature map algorithms applied primarily to static channel assignment (SCA) for cellular networks that handle uniformly distributed, stationary traffic in each cell for a single type of service. The resulting algorithms minimize energy functions representing interference constraint and ad hoc conditions that promote convergence to optimal solutions. While the structures of the derived neural network algorithms (NNAs) offer the potential advantages of inherent parallelism and adaptability to changing system conditions, this potential has yet to be fulfilled the CAP for emerging mobile networks. The next-generation communication infrastructures must accommodate dynamic operating conditions. Macrocell topologies are being refined to microcells and picocells that can be dynamically sectored by adaptively controlled, directional antennas and programmable transceivers. These networks must support the time-varying demands for personal communication services (PCS) that simultaneously carry voice, data and video and, thus, require new dynamic channel assignment (DCA) algorithms. This paper examines the impact of dynamic cell sectoring and geometric conditioning on NNAs developed for SCA in omnicell networks with stationary traffic to improve the metrics of convergence rate and call blocking. Genetic algorithms (GAs) are also considered in PCS networks as a means to overcome the known weakness of Hopfield NNAs in determining global minima. The resulting GAs for DCA in PCS networks are compared to improved DCA algorithms based on Hopfield NNs for stationary cellular networks. Algorithm performance is compared on the basis of rate of convergence, blocking probability, analytic complexity, and parametric sensitivity to transient traffic demands and channel interference.

Radiative Transfer Modeling and Retrievals for Advanced Hyperspectral Sensors

NASA Technical Reports Server (NTRS)

Liu, Xu; Zhou, Daniel K.; Larar, Allen M.; Smith, William L., Sr.; Mango, Stephen A.

2009-01-01

A novel radiative transfer model and a physical inversion algorithm based on principal component analysis will be presented. Instead of dealing with channel radiances, the new approach fits principal component scores of these quantities. Compared to channel-based radiative transfer models, the new approach compresses radiances into a much smaller dimension making both forward modeling and inversion algorithm more efficient.

Path lumping: An efficient algorithm to identify metastable path channels for conformational dynamics of multi-body systems

NASA Astrophysics Data System (ADS)

Meng, Luming; Sheong, Fu Kit; Zeng, Xiangze; Zhu, Lizhe; Huang, Xuhui

2017-07-01

Constructing Markov state models from large-scale molecular dynamics simulation trajectories is a promising approach to dissect the kinetic mechanisms of complex chemical and biological processes. Combined with transition path theory, Markov state models can be applied to identify all pathways connecting any conformational states of interest. However, the identified pathways can be too complex to comprehend, especially for multi-body processes where numerous parallel pathways with comparable flux probability often coexist. Here, we have developed a path lumping method to group these parallel pathways into metastable path channels for analysis. We define the similarity between two pathways as the intercrossing flux between them and then apply the spectral clustering algorithm to lump these pathways into groups. We demonstrate the power of our method by applying it to two systems: a 2D-potential consisting of four metastable energy channels and the hydrophobic collapse process of two hydrophobic molecules. In both cases, our algorithm successfully reveals the metastable path channels. We expect this path lumping algorithm to be a promising tool for revealing unprecedented insights into the kinetic mechanisms of complex multi-body processes.

Radial magnetic resonance imaging (MRI) using a rotating radiofrequency (RF) coil at 9.4 T.

PubMed

Li, Mingyan; Weber, Ewald; Jin, Jin; Hugger, Thimo; Tesiram, Yasvir; Ullmann, Peter; Stark, Simon; Fuentes, Miguel; Junge, Sven; Liu, Feng; Crozier, Stuart

2018-02-01

The rotating radiofrequency coil (RRFC) has been developed recently as an alternative approach to multi-channel phased-array coils. The single-element RRFC avoids inter-channel coupling and allows a larger coil element with better B 1 field penetration when compared with an array counterpart. However, dedicated image reconstruction algorithms require accurate estimation of temporally varying coil sensitivities to remove artefacts caused by coil rotation. Various methods have been developed to estimate unknown sensitivity profiles from a few experimentally measured sensitivity maps, but these methods become problematic when the RRFC is used as a transceiver coil. In this work, a novel and practical radial encoding method is introduced for the RRFC to facilitate image reconstruction without the measurement or estimation of rotation-dependent sensitivity profiles. Theoretical analyses suggest that the rotation-dependent sensitivities of the RRFC can be used to create a uniform profile with careful choice of sampling positions and imaging parameters. To test this new imaging method, dedicated electronics were designed and built to control the RRFC speed and hence positions in synchrony with imaging parameters. High-quality phantom and animal images acquired on a 9.4 T pre-clinical scanner demonstrate the feasibility and potential of this new RRFC method. Copyright © 2017 John Wiley & Sons, Ltd.

Impact of High Power Interference Sources in Planning and Deployment of Wireless Sensor Networks and Devices in the 2.4 GHz Frequency Band in Heterogeneous Environments

PubMed Central

Iturri, Peio López; Nazábal, Juan Antonio; Azpilicueta, Leire; Rodriguez, Pablo; Beruete, Miguel; Fernández-Valdivielso, Carlos; Falcone, Francisco

2012-01-01

In this work, the impact of radiofrequency radiation leakage from microwave ovens and its effect on 802.15.4 ZigBee-compliant wireless sensor networks operating in the 2.4 GHz Industrial Scientific Medical (ISM) band is analyzed. By means of a novel radioplanning approach, based on electromagnetic field simulation of a microwave oven and determination of equivalent radiation sources applied to an in-house developed 3D ray launching algorithm, estimation of the microwave oven's power leakage is obtained for the complete volume of an indoor scenario. The magnitude and the variable nature of the interference is analyzed and the impact in the radio link quality in operating wireless sensors is estimated and compared with radio channel measurements as well as packet measurements. The measurement results reveal the importance of selecting an adequate 802.15.4 channel, as well as the Wireless Sensor Network deployment strategy within this type of environment, in order to optimize energy consumption and increase the overall network performance. The proposed method enables one to estimate potential interference effects in devices operating within the 2.4 GHz band in the complete scenario, prior to wireless sensor network deployment, which can aid in achieving the most optimal network topology. PMID:23202228

A three-dimensional muscle activity imaging technique for assessing pelvic muscle function

NASA Astrophysics Data System (ADS)

Zhang, Yingchun; Wang, Dan; Timm, Gerald W.

2010-11-01

A novel multi-channel surface electromyography (EMG)-based three-dimensional muscle activity imaging (MAI) technique has been developed by combining the bioelectrical source reconstruction approach and subject-specific finite element modeling approach. Internal muscle activities are modeled by a current density distribution and estimated from the intra-vaginal surface EMG signals with the aid of a weighted minimum norm estimation algorithm. The MAI technique was employed to minimally invasively reconstruct electrical activity in the pelvic floor muscles and urethral sphincter from multi-channel intra-vaginal surface EMG recordings. A series of computer simulations were conducted to evaluate the performance of the present MAI technique. With appropriate numerical modeling and inverse estimation techniques, we have demonstrated the capability of the MAI technique to accurately reconstruct internal muscle activities from surface EMG recordings. This MAI technique combined with traditional EMG signal analysis techniques is being used to study etiologic factors associated with stress urinary incontinence in women by correlating functional status of muscles characterized from the intra-vaginal surface EMG measurements with the specific pelvic muscle groups that generated these signals. The developed MAI technique described herein holds promise for eliminating the need to place needle electrodes into muscles to obtain accurate EMG recordings in some clinical applications.

A good performance watermarking LDPC code used in high-speed optical fiber communication system

NASA Astrophysics Data System (ADS)

Zhang, Wenbo; Li, Chao; Zhang, Xiaoguang; Xi, Lixia; Tang, Xianfeng; He, Wenxue

2015-07-01

A watermarking LDPC code, which is a strategy designed to improve the performance of the traditional LDPC code, was introduced. By inserting some pre-defined watermarking bits into original LDPC code, we can obtain a more correct estimation about the noise level in the fiber channel. Then we use them to modify the probability distribution function (PDF) used in the initial process of belief propagation (BP) decoding algorithm. This algorithm was tested in a 128 Gb/s PDM-DQPSK optical communication system and results showed that the watermarking LDPC code had a better tolerances to polarization mode dispersion (PMD) and nonlinearity than that of traditional LDPC code. Also, by losing about 2.4% of redundancy for watermarking bits, the decoding efficiency of the watermarking LDPC code is about twice of the traditional one.

Automatic channel trimming for control systems: A concept

NASA Technical Reports Server (NTRS)

Vandervoort, R. J.; Sykes, H. A.

1977-01-01

Set of bias signals added to channel inputs automatically normalize differences between channels. Algorithm and second feedback loop compute trim biases. Concept could be applied to regulators and multichannel servosystems for remote manipulators in undersea mining.

Experimental Assessment of Different Receiver Structures for Underwater Acoustic Communications over Multipath Channels

PubMed Central

Zhang, Guosong; Hovem, Jens M.; Dong, Hefeng

2012-01-01

Underwater communication channels are often complicated, and in particular multipath propagation may cause intersymbol interference (ISI). This paper addresses how to remove ISI, and evaluates the performance of three different receiver structures and their implementations. Using real data collected in a high-frequency (10–14 kHz) field experiment, the receiver structures are evaluated by off-line data processing. The three structures are multichannel decision feedback equalizer (DFE), passive time reversal receiver (passive-phase conjugation (PPC) with a single channel DFE), and the joint PPC with multichannel DFE. In sparse channels, dominant arrivals represent the channel information, and the matching pursuit (MP) algorithm which exploits the channel sparseness has been investigated for PPC processing. In the assessment, it is found that: (1) it is advantageous to obtain spatial gain using the adaptive multichannel combining scheme; and (2) the MP algorithm improves the performance of communications using PPC processing. PMID:22438755

Muscle-tendon units localization and activation level analysis based on high-density surface EMG array and NMF algorithm

NASA Astrophysics Data System (ADS)

Huang, Chengjun; Chen, Xiang; Cao, Shuai; Zhang, Xu

2016-12-01

Objective. Some skeletal muscles can be subdivided into smaller segments called muscle-tendon units (MTUs). The purpose of this paper is to propose a framework to locate the active region of the corresponding MTUs within a single skeletal muscle and to analyze the activation level varieties of different MTUs during a dynamic motion task. Approach. Biceps brachii and gastrocnemius were selected as targeted muscles and three dynamic motion tasks were designed and studied. Eight healthy male subjects participated in the data collection experiments, and 128-channel surface electromyographic (sEMG) signals were collected with a high-density sEMG electrode grid (a grid consists of 8 rows and 16 columns). Then the sEMG envelopes matrix was factorized into a matrix of weighting vectors and a matrix of time-varying coefficients by nonnegative matrix factorization algorithm. Main results. The experimental results demonstrated that the weightings vectors, which represent invariant pattern of muscle activity across all channels, could be used to estimate the location of MTUs and the time-varying coefficients could be used to depict the variation of MTUs activation level during dynamic motion task. Significance. The proposed method provides one way to analyze in-depth the functional state of MTUs during dynamic tasks and thus can be employed on multiple noteworthy sEMG-based applications such as muscle force estimation, muscle fatigue research and the control of myoelectric prostheses. This work was supported by the National Nature Science Foundation of China under Grant 61431017 and 61271138.

Maximum-likelihood estimation of channel-dependent trial-to-trial variability of auditory evoked brain responses in MEG

PubMed Central

2014-01-01

Background We propose a mathematical model for multichannel assessment of the trial-to-trial variability of auditory evoked brain responses in magnetoencephalography (MEG). Methods Following the work of de Munck et al., our approach is based on the maximum likelihood estimation and involves an approximation of the spatio-temporal covariance of the contaminating background noise by means of the Kronecker product of its spatial and temporal covariance matrices. Extending the work of de Munck et al., where the trial-to-trial variability of the responses was considered identical to all channels, we evaluate it for each individual channel. Results Simulations with two equivalent current dipoles (ECDs) with different trial-to-trial variability, one seeded in each of the auditory cortices, were used to study the applicability of the proposed methodology on the sensor level and revealed spatial selectivity of the trial-to-trial estimates. In addition, we simulated a scenario with neighboring ECDs, to show limitations of the method. We also present an illustrative example of the application of this methodology to real MEG data taken from an auditory experimental paradigm, where we found hemispheric lateralization of the habituation effect to multiple stimulus presentation. Conclusions The proposed algorithm is capable of reconstructing lateralization effects of the trial-to-trial variability of evoked responses, i.e. when an ECD of only one hemisphere habituates, whereas the activity of the other hemisphere is not subject to habituation. Hence, it may be a useful tool in paradigms that assume lateralization effects, like, e.g., those involving language processing. PMID:24939398

Multi-carrier Communications over Time-varying Acoustic Channels

NASA Astrophysics Data System (ADS)

Aval, Yashar M.

Acoustic communication is an enabling technology for many autonomous undersea systems, such as those used for ocean monitoring, offshore oil and gas industry, aquaculture, or port security. There are three main challenges in achieving reliable high-rate underwater communication: the bandwidth of acoustic channels is extremely limited, the propagation delays are long, and the Doppler distortions are more pronounced than those found in wireless radio channels. In this dissertation we focus on assessing the fundamental limitations of acoustic communication, and designing efficient signal processing methods that cam overcome these limitations. We address the fundamental question of acoustic channel capacity (achievable rate) for single-input-multi-output (SIMO) acoustic channels using a per-path Rician fading model, and focusing on two scenarios: narrowband channels where the channel statistics can be approximated as frequency- independent, and wideband channels where the nominal path loss is frequency-dependent. In each scenario, we compare several candidate power allocation techniques, and show that assigning uniform power across all frequencies for the first scenario, and assigning uniform power across a selected frequency-band for the second scenario, are the best practical choices in most cases, because the long propagation delay renders the feedback information outdated for power allocation based on the estimated channel response. We quantify our results using the channel information extracted form the 2010 Mobile Acoustic Communications Experiment (MACE'10). Next, we focus on achieving reliable high-rate communication over underwater acoustic channels. Specifically, we investigate orthogonal frequency division multiplexing (OFDM) as the state-of-the-art technique for dealing with frequency-selective multipath channels, and propose a class of methods that compensate for the time-variation of the underwater acoustic channel. These methods are based on multiple-FFT demodulation, and are implemented as partial (P), shaped (S), fractional (F), and Taylor series expansion (T) FFT demodulation. They replace the conventional FFT demodulation with a few FFTs and a combiner. The input to each FFT is a specific transformation of the input signal (P,S,F,T), while the combiner performs weighted summation of the FFT outputs. We design an adaptive algorithm of stochastic gradient type to learn the combiner weights for coherent and differentially coherent detection. The algorithm is cast into the framework of multiple receiving elements to take advantage of spatial diversity. Synthetic data, as well as experimental data from the MACE'10 experiment are used to demonstrate the performance of the proposed methods, showing significant improvement over conventional detection techniques with or without inter-carrier interference equalization (5 dB--7 dB on average over multiple hours), as well as improved bandwidth efficiency.

The TileCal Online Energy Estimation for the Next LHC Operation Period

NASA Astrophysics Data System (ADS)

Sotto-Maior Peralva, B.; ATLAS Collaboration

2015-05-01

The ATLAS Tile Calorimeter (TileCal) is the detector used in the reconstruction of hadrons, jets and missing transverse energy from the proton-proton collisions at the Large Hadron Collider (LHC). It covers the central part of the ATLAS detector (|η| < 1.6). The energy deposited by the particles is read out by approximately 5,000 cells, with double readout channels. The signal provided by the readout electronics for each channel is digitized at 40 MHz and its amplitude is estimated by an optimal filtering algorithm, which expects a single signal with a well-defined shape. However, the LHC luminosity is expected to increase leading to pile-up that deforms the signal of interest. Due to limited resources, the current hardware setup, which is based on Digital Signal Processors (DSP), does not allow the implementation of sophisticated energy estimation methods that deal with the pile-up. Therefore, the technique to be employed for online energy estimation in TileCal for next LHC operation period must be based on fast filters such as the Optimal Filter (OF) and the Matched Filter (MF). Both the OF and MF methods envisage the use of the background second order statistics in its design, more precisely the covariance matrix. However, the identity matrix has been used to describe this quantity. Although this approximation can be valid for low luminosity LHC, it leads to biased estimators under pile- up conditions. Since most of the TileCal cell present low occupancy, the pile-up, which is often modeled by a non-Gaussian distribution, can be seen as outlier events. Consequently, the classical covariance matrix estimation does not describe correctly the second order statistics of the background for the majority of the events, as this approach is very sensitive to outliers. As a result, the OF (or MF) coefficients are miscalculated leading to a larger variance and biased energy estimator. This work evaluates the usage of a robust covariance estimator, namely the Minimum Covariance Determinant (MCD) algorithm, to be applied in the OF design. The goal of the MCD estimator is to find a number of observations whose classical covariance matrix has the lowest determinant. Hence, this procedure avoids taking into account low likelihood events to describe the background. It is worth mentioning that the background covariance matrix as well as the OF coefficients for each TileCal channel are computed offline and stored for both online and offline use. In order to evaluate the impact of the MCD estimator on the performance of the OF, simulated data sets were used. Different average numbers of interactions per bunch crossing and bunch spacings were tested. The results show that the estimation of the background covariance matrix through MCD improves significantly the final energy resolution with respect to the identity matrix which is currently used. Particularly, for high occupancy cells, the final energy resolution is improved by more than 20%. Moreover, the use of the classical covariance matrix degrades the energy resolution for the majority of TileCal cells.

Optimalisation of remote sensing algorithm in mapping of chlorophyl-a concentration at Pasuruan coastal based on surface reflectance images of Aqua Modis

NASA Astrophysics Data System (ADS)

Wibisana, H.; Zainab, S.; Dara K., A.

2018-01-01

Chlorophyll-a is one of the parameters used to detect the presence of fish populations, as well as one of the parameters to state the quality of a water. Research on chlorophyll concentrations has been extensively investigated as well as with chlorophyll-a mapping using remote sensing satellites. Mapping of chlorophyll concentration is used to obtain an optimal picture of the condition of waters that is often used as a fishing area by the fishermen. The role of remote sensing is a technological breakthrough in broadly monitoring the condition of waters. And in the process to get a complete picture of the aquatic conditions it would be used an algorithm that can provide an image of the concentration of chlorophyll at certain points scattered in the research area of capture fisheries. Remote sensing algorithms have been widely used by researchers to detect the presence of chlorophyll content, where the channels corresponding to the mapping of chlorophyll -concentrations from Landsat 8 images are canals 4, 3 and 2. With multiple channels from Landsat-8 satellite imagery used for chlorophyll detection, optimum algorithmic search can be formulated to obtain maximum results of chlorophyll-a concentration in the research area. From the calculation of remote sensing algorithm hence can be known the suitable algorithm for condition at coast of Pasuruan, where green channel give good enough correlation equal to R2 = 0,853 with algorithm for Chlorophyll-a (mg / m3) = 0,093 (R (-0) Red - 3,7049, from this result it can be concluded that there is a good correlation of the green channel that can illustrate the concentration of chlorophyll scattered along the coast of Pasuruan

Constrained low-rank matrix estimation: phase transitions, approximate message passing and applications

NASA Astrophysics Data System (ADS)

Lesieur, Thibault; Krzakala, Florent; Zdeborová, Lenka

2017-07-01

This article is an extended version of previous work of Lesieur et al (2015 IEEE Int. Symp. on Information Theory Proc. pp 1635-9 and 2015 53rd Annual Allerton Conf. on Communication, Control and Computing (IEEE) pp 680-7) on low-rank matrix estimation in the presence of constraints on the factors into which the matrix is factorized. Low-rank matrix factorization is one of the basic methods used in data analysis for unsupervised learning of relevant features and other types of dimensionality reduction. We present a framework to study the constrained low-rank matrix estimation for a general prior on the factors, and a general output channel through which the matrix is observed. We draw a parallel with the study of vector-spin glass models—presenting a unifying way to study a number of problems considered previously in separate statistical physics works. We present a number of applications for the problem in data analysis. We derive in detail a general form of the low-rank approximate message passing (Low-RAMP) algorithm, that is known in statistical physics as the TAP equations. We thus unify the derivation of the TAP equations for models as different as the Sherrington-Kirkpatrick model, the restricted Boltzmann machine, the Hopfield model or vector (xy, Heisenberg and other) spin glasses. The state evolution of the Low-RAMP algorithm is also derived, and is equivalent to the replica symmetric solution for the large class of vector-spin glass models. In the section devoted to result we study in detail phase diagrams and phase transitions for the Bayes-optimal inference in low-rank matrix estimation. We present a typology of phase transitions and their relation to performance of algorithms such as the Low-RAMP or commonly used spectral methods.

Plume Detection and Plume Top Height Estimation using SLSTR

NASA Astrophysics Data System (ADS)

Virtanen, Timo H.; Kolmonen, Pekka; Sogacheva, Larisa; Rodriguez, Edith; Saponaro, Giulia; de Leeuw, Gerrit

2017-04-01

We present preliminary results on ash and desert dust plume detection and plume top height estimates based on satellite data from the Sea and Land Surface Temperature Radiometer (SLSTR) aboard Sentinel-3, launched in 2016. The methods are based on the previously developed AATSR Correlation Method (ACM) height estimation algorithm, which utilized the data of the preceding similar instrument, Advanced Along Track Scanning Radiometer (AATSR). The height estimate is based on the stereo-viewing capability of SLSTR, which allows to determine the parallax between the satellite's 55° backward and nadir views, and thus the corresponding height. The ash plume detection is based on the brightness temperature difference between between thermal infrared (TIR) channels centered at 11 and 12 μm, which show characteristic signals for both desert dust and ash plumes. The SLSTR instrument provides a unique combination of dual-view capability and a wavelength range from visible to thermal infrared, rendering it an ideal instrument for this work. Accurate information on the volcanic ash position is important for air traffic safety. The ACM algorithm can provide valuable data of both horizontal and vertical ash dispersion. These data may be useful for comparisons with other volcanic ash and desert dust retrieval methods and dispersion models. The current work is being carried out as part of the H2020 project EUNADICS-AV ("European Natural Disaster Coordination and Information System for Aviation"), which started in October 2016.

Principal components technique analysis for vegetation and land use discrimination. [Brazilian cerrados

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Formaggio, A. R.; Dossantos, J. R.; Dias, L. A. V.

1984-01-01

Automatic pre-processing technique called Principal Components (PRINCO) in analyzing LANDSAT digitized data, for land use and vegetation cover, on the Brazilian cerrados was evaluated. The chosen pilot area, 223/67 of MSS/LANDSAT 3, was classified on a GE Image-100 System, through a maximum-likehood algorithm (MAXVER). The same procedure was applied to the PRINCO treated image. PRINCO consists of a linear transformation performed on the original bands, in order to eliminate the information redundancy of the LANDSAT channels. After PRINCO only two channels were used thus reducing computer effort. The original channels and the PRINCO channels grey levels for the five identified classes (grassland, "cerrado", burned areas, anthropic areas, and gallery forest) were obtained through the MAXVER algorithm. This algorithm also presented the average performance for both cases. In order to evaluate the results, the Jeffreys-Matusita distance (JM-distance) between classes was computed. The classification matrix, obtained through MAXVER, after a PRINCO pre-processing, showed approximately the same average performance in the classes separability.

Extraction of tidal channel networks from airborne scanning laser altimetry

NASA Astrophysics Data System (ADS)

Mason, David C.; Scott, Tania R.; Wang, Hai-Jing

Tidal channel networks are important features of the inter-tidal zone, and play a key role in tidal propagation and in the evolution of salt marshes and tidal flats. The study of their morphology is currently an active area of research, and a number of theories related to networks have been developed which require validation using dense and extensive observations of network forms and cross-sections. The conventional method of measuring networks is cumbersome and subjective, involving manual digitisation of aerial photographs in conjunction with field measurement of channel depths and widths for selected parts of the network. This paper describes a semi-automatic technique developed to extract networks from high-resolution LiDAR data of the inter-tidal zone. A multi-level knowledge-based approach has been implemented, whereby low-level algorithms first extract channel fragments based mainly on image properties then a high-level processing stage improves the network using domain knowledge. The approach adopted at low level uses multi-scale edge detection to detect channel edges, then associates adjacent anti-parallel edges together to form channels. The higher level processing includes a channel repair mechanism. The algorithm may be extended to extract networks from aerial photographs as well as LiDAR data. Its performance is illustrated using LiDAR data of two study sites, the River Ems, Germany and the Venice Lagoon. For the River Ems data, the error of omission for the automatic channel extractor is 26%, partly because numerous small channels are lost because they fall below the edge threshold, though these are less than 10 cm deep and unlikely to be hydraulically significant. The error of commission is lower, at 11%. For the Venice Lagoon data, the error of omission is 14%, but the error of commission is 42%, due partly to the difficulty of interpreting channels in these natural scenes. As a benchmark, previous work has shown that this type of algorithm specifically designed for extracting tidal networks from LiDAR data is able to achieve substantially improved results compared with those obtained using standard algorithms for drainage network extraction from Digital Terrain Models.

LS Channel Estimation and Signal Separation for UHF RFID Tag Collision Recovery on the Physical Layer.

PubMed

Duan, Hanjun; Wu, Haifeng; Zeng, Yu; Chen, Yuebin

2016-03-26

In a passive ultra-high frequency (UHF) radio-frequency identification (RFID) system, tag collision is generally resolved on a medium access control (MAC) layer. However, some of collided tag signals could be recovered on a physical (PHY) layer and, thus, enhance the identification efficiency of the RFID system. For the recovery on the PHY layer, channel estimation is a critical issue. Good channel estimation will help to recover the collided signals. Existing channel estimates work well for two collided tags. When the number of collided tags is beyond two, however, the existing estimates have more estimation errors. In this paper, we propose a novel channel estimate for the UHF RFID system. It adopts an orthogonal matrix based on the information of preambles which is known for a reader and applies a minimum-mean-square-error (MMSE) criterion to estimate channels. From the estimated channel, we could accurately separate the collided signals and recover them. By means of numerical results, we show that the proposed estimate has lower estimation errors and higher separation efficiency than the existing estimates.

An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm

PubMed Central

Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

2015-01-01

A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction. PMID:26287193

An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm.

PubMed

Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

2015-08-13

A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.

Communications protocol

NASA Technical Reports Server (NTRS)

Zhou, Xiaoming (Inventor); Baras, John S. (Inventor)

2010-01-01

The present invention relates to an improved communications protocol which increases the efficiency of transmission in return channels on a multi-channel slotted Alohas system by incorporating advanced error correction algorithms, selective retransmission protocols and the use of reserved channels to satisfy the retransmission requests.

Low-Complexity User Selection for Rate Maximization in MIMO Broadcast Channels with Downlink Beamforming

PubMed Central

Silva, Adão; Gameiro, Atílio

2014-01-01

We present in this work a low-complexity algorithm to solve the sum rate maximization problem in multiuser MIMO broadcast channels with downlink beamforming. Our approach decouples the user selection problem from the resource allocation problem and its main goal is to create a set of quasiorthogonal users. The proposed algorithm exploits physical metrics of the wireless channels that can be easily computed in such a way that a null space projection power can be approximated efficiently. Based on the derived metrics we present a mathematical model that describes the dynamics of the user selection process which renders the user selection problem into an integer linear program. Numerical results show that our approach is highly efficient to form groups of quasiorthogonal users when compared to previously proposed algorithms in the literature. Our user selection algorithm achieves a large portion of the optimum user selection sum rate (90%) for a moderate number of active users. PMID:24574928

A proposed study of multiple scattering through clouds up to 1 THz

NASA Technical Reports Server (NTRS)

Gerace, G. C.; Smith, E. K.

1992-01-01

A rigorous computation of the electromagnetic field scattered from an atmospheric liquid water cloud is proposed. The recent development of a fast recursive algorithm (Chew algorithm) for computing the fields scattered from numerous scatterers now makes a rigorous computation feasible. A method is presented for adapting this algorithm to a general case where there are an extremely large number of scatterers. It is also proposed to extend a new binary PAM channel coding technique (El-Khamy coding) to multiple levels with non-square pulse shapes. The Chew algorithm can be used to compute the transfer function of a cloud channel. Then the transfer function can be used to design an optimum El-Khamy code. In principle, these concepts can be applied directly to the realistic case of a time-varying cloud (adaptive channel coding and adaptive equalization). A brief review is included of some preliminary work on cloud dispersive effects on digital communication signals and on cloud liquid water spectra and correlations.

A noise resistant symmetric key cryptosystem based on S8 S-boxes and chaotic maps

NASA Astrophysics Data System (ADS)

Hussain, Iqtadar; Anees, Amir; Aslam, Muhammad; Ahmed, Rehan; Siddiqui, Nasir

2018-04-01

In this manuscript, we have proposed an encryption algorithm to encrypt any digital data. The proposed algorithm is primarily based on the substitution-permutation in which the substitution process is performed by the S 8 Substitution boxes. The proposed algorithm incorporates three different chaotic maps. We have analysed the behaviour of chaos by secure communication in great length, and accordingly, we have applied those chaotic sequences in the proposed encryption algorithm. The simulation and statistical results revealed that the proposed encryption scheme is secure against different attacks. Moreover, the encryption scheme can tolerate the channel noise as well; if the encrypted data is corrupted by the unauthenticated user or by the channel noise, the decryption can still be successfully done with some distortion. The overall results confirmed that the presented work has good cryptographic features, low computational complexity and resistant to the channel noise which makes it suitable for low profile mobile applications.

Systolic Signal Processor/High Frequency Direction Finding

DTIC Science & Technology

1990-10-01

MUSIC ) algorithm and the finite impulse response (FIR) filter onto the testbed hardware was supported by joint sponsorship of the block and major bid...computational throughput. The systolic implementations of a four-channel finite impulse response (FIR) filter and multiple signal classification ( MUSIC ... MUSIC ) algorithm was mated to a bank of finite impulse response (FIR) filters and a four-channel data acquisition subsystem. A complete description

A New, More Physically Based Algorithm, for Retrieving Aerosol Properties over Land from MODIS

NASA Technical Reports Server (NTRS)

Levy, Robert C.; Kaufman, Yoram J.; Remer, Lorraine A.; Mattoo, Shana

2004-01-01

The MOD Imaging Spectrometer (MODIS) has been successfully retrieving aerosol properties, beginning in early 2000 from Terra and from mid 2002 from Aqua. Over land, the retrieval algorithm makes use of three MODIS channels, in the blue, red and infrared wavelengths. As part of the validation exercises, retrieved spectral aerosol optical thickness (AOT) has been compared via scatterplots against spectral AOT measured by the global Aerosol Robotic NETwork (AERONET). On one hand, global and long term validation looks promising, with two-thirds (average plus and minus one standard deviation) of all points falling between published expected error bars. On the other hand, regression of these points shows a positive y-offset and a slope less than 1.0. For individual regions, such as along the U.S. East Coast, the offset and slope are even worse. Here, we introduce an overhaul of the algorithm for retrieving aerosol properties over land. Some well-known weaknesses in the current aerosol retrieval from MODIS include: a) rigid assumptions about the underlying surface reflectance, b) limited aerosol models to choose from, c) simplified (scalar) radiative transfer (RT) calculations used to simulate satellite observations, and d) assumption that aerosol is transparent in the infrared channel. The new algorithm attempts to address all four problems: a) The new algorithm will include surface type information, instead of fixed ratios of the reflectance in the visible channels to the mid-IR reflectance. b) It will include updated aerosol optical properties to reflect the growing aerosol retrieved from eight-plus years of AERONE". operation. c) The effects of polarization will be including using vector RT calculations. d) Most importantly, the new algorithm does not assume that aerosol is transparent in the infrared channel. It will be an inversion of reflectance observed in the three channels (blue, red, and infrared), rather than iterative single channel retrievals. Thus, this new formulation of the MODIS aerosol retrieval over land includes more physically based surface, aerosol and radiative transfer with fewer potentially erroneous assumptions.

Efficient universal quantum channel simulation in IBM's cloud quantum computer

NASA Astrophysics Data System (ADS)

Wei, Shi-Jie; Xin, Tao; Long, Gui-Lu

2018-07-01

The study of quantum channels is an important field and promises a wide range of applications, because any physical process can be represented as a quantum channel that transforms an initial state into a final state. Inspired by the method of performing non-unitary operators by the linear combination of unitary operations, we proposed a quantum algorithm for the simulation of the universal single-qubit channel, described by a convex combination of "quasi-extreme" channels corresponding to four Kraus operators, and is scalable to arbitrary higher dimension. We demonstrated the whole algorithm experimentally using the universal IBM cloud-based quantum computer and studied the properties of different qubit quantum channels. We illustrated the quantum capacity of the general qubit quantum channels, which quantifies the amount of quantum information that can be protected. The behavior of quantum capacity in different channels revealed which types of noise processes can support information transmission, and which types are too destructive to protect information. There was a general agreement between the theoretical predictions and the experiments, which strongly supports our method. By realizing the arbitrary qubit channel, this work provides a universally- accepted way to explore various properties of quantum channels and novel prospect for quantum communication.

A Channelization-Based DOA Estimation Method for Wideband Signals

PubMed Central

Guo, Rui; Zhang, Yue; Lin, Qianqiang; Chen, Zengping

2016-01-01

In this paper, we propose a novel direction of arrival (DOA) estimation method for wideband signals with sensor arrays. The proposed method splits the wideband array output into multiple frequency sub-channels and estimates the signal parameters using a digital channelization receiver. Based on the output sub-channels, a channelization-based incoherent signal subspace method (Channelization-ISM) and a channelization-based test of orthogonality of projected subspaces method (Channelization-TOPS) are proposed. Channelization-ISM applies narrowband signal subspace methods on each sub-channel independently. Then the arithmetic mean or geometric mean of the estimated DOAs from each sub-channel gives the final result. Channelization-TOPS measures the orthogonality between the signal and the noise subspaces of the output sub-channels to estimate DOAs. The proposed channelization-based method isolates signals in different bandwidths reasonably and improves the output SNR. It outperforms the conventional ISM and TOPS methods on estimation accuracy and dynamic range, especially in real environments. Besides, the parallel processing architecture makes it easy to implement on hardware. A wideband digital array radar (DAR) using direct wideband radio frequency (RF) digitization is presented. Experiments carried out in a microwave anechoic chamber with the wideband DAR are presented to demonstrate the performance. The results verify the effectiveness of the proposed method. PMID:27384566

A novel LTE scheduling algorithm for green technology in smart grid.

PubMed

Hindia, Mohammad Nour; Reza, Ahmed Wasif; Noordin, Kamarul Ariffin; Chayon, Muhammad Hasibur Rashid

2015-01-01

Smart grid (SG) application is being used nowadays to meet the demand of increasing power consumption. SG application is considered as a perfect solution for combining renewable energy resources and electrical grid by means of creating a bidirectional communication channel between the two systems. In this paper, three SG applications applicable to renewable energy system, namely, distribution automation (DA), distributed energy system-storage (DER) and electrical vehicle (EV), are investigated in order to study their suitability in Long Term Evolution (LTE) network. To compensate the weakness in the existing scheduling algorithms, a novel bandwidth estimation and allocation technique and a new scheduling algorithm are proposed. The technique allocates available network resources based on application's priority, whereas the algorithm makes scheduling decision based on dynamic weighting factors of multi-criteria to satisfy the demands (delay, past average throughput and instantaneous transmission rate) of quality of service. Finally, the simulation results demonstrate that the proposed mechanism achieves higher throughput, lower delay and lower packet loss rate for DA and DER as well as provide a degree of service for EV. In terms of fairness, the proposed algorithm shows 3%, 7 % and 9% better performance compared to exponential rule (EXP-Rule), modified-largest weighted delay first (M-LWDF) and exponential/PF (EXP/PF), respectively.

A Novel LTE Scheduling Algorithm for Green Technology in Smart Grid

PubMed Central

Hindia, Mohammad Nour; Reza, Ahmed Wasif; Noordin, Kamarul Ariffin; Chayon, Muhammad Hasibur Rashid

2015-01-01

Smart grid (SG) application is being used nowadays to meet the demand of increasing power consumption. SG application is considered as a perfect solution for combining renewable energy resources and electrical grid by means of creating a bidirectional communication channel between the two systems. In this paper, three SG applications applicable to renewable energy system, namely, distribution automation (DA), distributed energy system-storage (DER) and electrical vehicle (EV), are investigated in order to study their suitability in Long Term Evolution (LTE) network. To compensate the weakness in the existing scheduling algorithms, a novel bandwidth estimation and allocation technique and a new scheduling algorithm are proposed. The technique allocates available network resources based on application’s priority, whereas the algorithm makes scheduling decision based on dynamic weighting factors of multi-criteria to satisfy the demands (delay, past average throughput and instantaneous transmission rate) of quality of service. Finally, the simulation results demonstrate that the proposed mechanism achieves higher throughput, lower delay and lower packet loss rate for DA and DER as well as provide a degree of service for EV. In terms of fairness, the proposed algorithm shows 3%, 7 % and 9% better performance compared to exponential rule (EXP-Rule), modified-largest weighted delay first (M-LWDF) and exponential/PF (EXP/PF), respectively. PMID:25830703

A RSSI-based parameter tracking strategy for constrained position localization

NASA Astrophysics Data System (ADS)

Du, Jinze; Diouris, Jean-François; Wang, Yide

2017-12-01

In this paper, a received signal strength indicator (RSSI)-based parameter tracking strategy for constrained position localization is proposed. To estimate channel model parameters, least mean squares method (LMS) is associated with the trilateration method. In the context of applications where the positions are constrained on a grid, a novel tracking strategy is proposed to determine the real position and obtain the actual parameters in the monitored region. Based on practical data acquired from a real localization system, an experimental channel model is constructed to provide RSSI values and verify the proposed tracking strategy. Quantitative criteria are given to guarantee the efficiency of the proposed tracking strategy by providing a trade-off between the grid resolution and parameter variation. The simulation results show a good behavior of the proposed tracking strategy in the presence of space-time variation of the propagation channel. Compared with the existing RSSI-based algorithms, the proposed tracking strategy exhibits better localization accuracy but consumes more calculation time. In addition, a tracking test is performed to validate the effectiveness of the proposed tracking strategy.

Spatial variation in automated burst suppression detection in pharmacologically induced coma.

PubMed

An, Jingzhi; Jonnalagadda, Durga; Moura, Valdery; Purdon, Patrick L; Brown, Emery N; Westover, M Brandon

2015-01-01

Burst suppression is actively studied as a control signal to guide anesthetic dosing in patients undergoing medically induced coma. The ability to automatically identify periods of EEG suppression and compactly summarize the depth of coma using the burst suppression probability (BSP) is crucial to effective and safe monitoring and control of medical coma. Current literature however does not explicitly account for the potential variation in burst suppression parameters across different scalp locations. In this study we analyzed standard 19-channel EEG recordings from 8 patients with refractory status epilepticus who underwent pharmacologically induced burst suppression as medical treatment for refractory seizures. We found that although burst suppression is generally considered a global phenomenon, BSP obtained using a previously validated algorithm varies systematically across different channels. A global representation of information from individual channels is proposed that takes into account the burst suppression characteristics recorded at multiple electrodes. BSP computed from this representative burst suppression pattern may be more resilient to noise and a better representation of the brain state of patients. Multichannel data integration may enhance the reliability of estimates of the depth of medical coma.

Adaptive channel estimation for soft decision decoding over non-Gaussian optical channel

NASA Astrophysics Data System (ADS)

Xiang, Jing-song; Miao, Tao-tao; Huang, Sheng; Liu, Huan-lin

2016-10-01

An adaptive priori likelihood ratio (LLR) estimation method is proposed over non-Gaussian channel in the intensity modulation/direct detection (IM/DD) optical communication systems. Using the nonparametric histogram and the weighted least square linear fitting in the tail regions, the LLR is estimated and used for the soft decision decoding of the low-density parity-check (LDPC) codes. This method can adapt well to the three main kinds of intensity modulation/direct detection (IM/DD) optical channel, i.e., the chi-square channel, the Webb-Gaussian channel and the additive white Gaussian noise (AWGN) channel. The performance penalty of channel estimation is neglected.

[Comparisons and analysis of the spectral response functions' difference between FY-2E's and FY2C's split window channels].

PubMed

Zhang, Yong; Li, Yuan; Rong, Zhi-Guo

2010-06-01

Remote sensors' channel spectral response function (SRF) was one of the key factors to influence the quantitative products' inversion algorithm, accuracy and the geophysical characteristics. Aiming at the adjustments of FY-2E's split window channels' SRF, detailed comparisons between the FY-2E and FY-2C corresponding channels' SRF differences were carried out based on three data collections: the NOAA AVHRR corresponding channels' calibration look up tables, field measured water surface radiance and atmospheric profiles at Lake Qinghai and radiance calculated from the PLANK function within all dynamic range of FY-2E/C. The results showed that the adjustments of FY-2E's split window channels' SRF would result in the spectral range's movements and influence the inversion algorithms of some ground quantitative products. On the other hand, these adjustments of FY-2E SRFs would increase the brightness temperature differences between FY-2E's two split window channels within all dynamic range relative to FY-2C's. This would improve the inversion ability of FY-2E's split window channels.

Origin of heterogeneous spiking patterns from continuously distributed ion channel densities: a computational study in spinal dorsal horn neurons.

PubMed

Balachandar, Arjun; Prescott, Steven A

2018-05-01

Distinct spiking patterns may arise from qualitative differences in ion channel expression (i.e. when different neurons express distinct ion channels) and/or when quantitative differences in expression levels qualitatively alter the spike generation process. We hypothesized that spiking patterns in neurons of the superficial dorsal horn (SDH) of spinal cord reflect both mechanisms. We reproduced SDH neuron spiking patterns by varying densities of K V 1- and A-type potassium conductances. Plotting the spiking patterns that emerge from different density combinations revealed spiking-pattern regions separated by boundaries (bifurcations). This map suggests that certain spiking pattern combinations occur when the distribution of potassium channel densities straddle boundaries, whereas other spiking patterns reflect distinct patterns of ion channel expression. The former mechanism may explain why certain spiking patterns co-occur in genetically identified neuron types. We also present algorithms to predict spiking pattern proportions from ion channel density distributions, and vice versa. Neurons are often classified by spiking pattern. Yet, some neurons exhibit distinct patterns under subtly different test conditions, which suggests that they operate near an abrupt transition, or bifurcation. A set of such neurons may exhibit heterogeneous spiking patterns not because of qualitative differences in which ion channels they express, but rather because quantitative differences in expression levels cause neurons to operate on opposite sides of a bifurcation. Neurons in the spinal dorsal horn, for example, respond to somatic current injection with patterns that include tonic, single, gap, delayed and reluctant spiking. It is unclear whether these patterns reflect five cell populations (defined by distinct ion channel expression patterns), heterogeneity within a single population, or some combination thereof. We reproduced all five spiking patterns in a computational model by varying the densities of a low-threshold (K V 1-type) potassium conductance and an inactivating (A-type) potassium conductance and found that single, gap, delayed and reluctant spiking arise when the joint probability distribution of those channel densities spans two intersecting bifurcations that divide the parameter space into quadrants, each associated with a different spiking pattern. Tonic spiking likely arises from a separate distribution of potassium channel densities. These results argue in favour of two cell populations, one characterized by tonic spiking and the other by heterogeneous spiking patterns. We present algorithms to predict spiking pattern proportions based on ion channel density distributions and, conversely, to estimate ion channel density distributions based on spiking pattern proportions. The implications for classifying cells based on spiking pattern are discussed. © 2018 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.

GEONEX: algorithm development and validation of Gross Primary Production from geostationary satellites

NASA Astrophysics Data System (ADS)

Hashimoto, H.; Wang, W.; Ganguly, S.; Li, S.; Michaelis, A.; Higuchi, A.; Takenaka, H.; Nemani, R. R.

2017-12-01

New geostationary sensors such as the AHI (Advanced Himawari Imager on Himawari-8) and the ABI (Advanced Baseline Imager on GOES-16) have the potential to advance ecosystem modeling particularly of diurnally varying phenomenon through frequent observations. These sensors have similar channels as in MODIS (MODerate resolution Imaging Spectroradiometer), and allow us to utilize the knowledge and experience in MODIS data processing. Here, we developed sub-hourly Gross Primary Production (GPP) algorithm, leverating the MODIS 17 GPP algorithm. We run the model at 1-km resolution over Japan and Australia using geo-corrected AHI data. Solar radiation was directly calculated from AHI using a neural network technique. The other necessary climate data were derived from weather stations and other satellite data. The sub-hourly estimates of GPP were first compared with ground-measured GPP at various Fluxnet sites. We also compared the AHI GPP with MODIS 17 GPP, and analyzed the differences in spatial patterns and the effect of diurnal changes in climate forcing. The sub-hourly GPP products require massive storage and strong computational power. We use NEX (NASA Earth Exchange) facility to produce the GPP products. This GPP algorithm can be applied to other geostationary satellites including GOES-16 in future.

Numerical Simulation of 3-D Supersonic Viscous Flow in an Experimental MHD Channel

NASA Technical Reports Server (NTRS)

Kato, Hiromasa; Tannehill, John C.; Gupta, Sumeet; Mehta, Unmeel B.

2004-01-01

The 3-D supersonic viscous flow in an experimental MHD channel has been numerically simulated. The experimental MHD channel is currently in operation at NASA Ames Research Center. The channel contains a nozzle section, a center section, and an accelerator section where magnetic and electric fields can be imposed on the flow. In recent tests, velocity increases of up to 40% have been achieved in the accelerator section. The flow in the channel is numerically computed using a new 3-D parabolized Navier-Stokes (PNS) algorithm that has been developed to efficiently compute MHD flows in the low magnetic Reynolds number regime. The MHD effects are modeled by introducing source terms into the PNS equations which can then be solved in a very e5uent manner. To account for upstream (elliptic) effects, the flowfield can be computed using multiple streamwise sweeps with an iterated PNS algorithm. The new algorithm has been used to compute two test cases that match the experimental conditions. In both cases, magnetic and electric fields are applied to the flow. The computed results are in good agreement with the available experimental data.

Centralized Routing and Scheduling Using Multi-Channel System Single Transceiver in 802.16d

NASA Astrophysics Data System (ADS)

Al-Hemyari, A.; Noordin, N. K.; Ng, Chee Kyun; Ismail, A.; Khatun, S.

This paper proposes a cross-layer optimized strategy that reduces the effect of interferences from neighboring nodes within a mesh networks. This cross-layer design relies on the routing information in network layer and the scheduling table in medium access control (MAC) layer. A proposed routing algorithm in network layer is exploited to find the best route for all subscriber stations (SS). Also, a proposed centralized scheduling algorithm in MAC layer is exploited to assign a time slot for each possible node transmission. The cross-layer optimized strategy is using multi-channel single transceiver and single channel single transceiver systems for WiMAX mesh networks (WMNs). Each node in WMN has a transceiver that can be tuned to any available channel for eliminating the secondary interference. Among the considered parameters in the performance analysis are interference from the neighboring nodes, hop count to the base station (BS), number of children per node, slot reuse, load balancing, quality of services (QoS), and node identifier (ID). Results show that the proposed algorithms significantly improve the system performance in terms of length of scheduling, channel utilization ratio (CUR), system throughput, and average end to end transmission delay.

Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information.

PubMed

Wang, Chundong; Zhu, Likun; Gong, Liangyi; Zhao, Zhentang; Yang, Lei; Liu, Zheli; Cheng, Xiaochun

2018-03-15

With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless networks.

Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information

PubMed Central

Wang, Chundong; Zhao, Zhentang; Yang, Lei; Liu, Zheli; Cheng, Xiaochun

2018-01-01

With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless networks. PMID:29543773

A Technique for Remote Sensing of Suspended Sediments and Shallow Coastal Waters Using MODIS Visible and Near-IR Channels

NASA Technical Reports Server (NTRS)

Li, Rong-Rong; Kaufman, Yoram J.

2002-01-01

We have developed an algorithm to detect suspended sediments and shallow coastal waters using imaging data acquired with the Moderate Resolution Imaging SpectroRadiometer (MODIS). The MODIS instruments on board the NASA Terra and Aqua Spacecrafts are equipped with one set of narrow channels located in a wide 0.4 - 2.5 micron spectral range. These channels were designed primarily for remote sensing of the land surface and atmosphere. We have found that the set of land and cloud channels are also quite useful for remote sensing of the bright coastal waters. We have developed an empirical algorithm, which uses the narrow MODIS channels in this wide spectral range, for identifying areas with suspended sediments in turbid waters and shallow waters with bottom reflections. In our algorithm, we take advantage of the strong water absorption at wavelengths longer than 1 micron that does not allow illumination of sediments in the water or a shallow ocean floor. MODIS data acquired over the east coast of China, west coast of Africa, Arabian Sea, Mississippi Delta, and west coast of Florida are used in this study.

A Technique For Remote Sensing Of Suspended Sediments And Shallow Coastal Waters Using MODIS Visible and Near-IR Channels

NASA Astrophysics Data System (ADS)

Li, R.; Kaufman, Y.

2002-12-01

ABSTRACT We have developed an algorithm to detect suspended sediments and shallow coastal waters using imaging data acquired with the Moderate Resolution Imaging SpectroRadiometer (MODIS). The MODIS instruments on board the NASA Terra and Aqua Spacecrafts are equipped with one set of narrow channels located in a wide 0.4 - 2.5 micron spectral range. These channels were designed primarily for remote sensing of the land surface and atmosphere. We have found that the set of land and cloud channels are also quite useful for remote sensing of the bright coastal waters. We have developed an empirical algorithm, which uses the narrow MODIS channels in this wide spectral range, for identifying areas with suspended sediments in turbid waters and shallow waters with bottom reflections. In our algorithm, we take advantage of the strong water absorption at wavelengths longer than 1 æm that does not allow illumination of sediments in the water or a shallow ocean floor. MODIS data acquired over the east coast of China, west coast of Africa, Arabian Sea, Mississippi Delta, and west coast of Florida are used in this study.

Scheduling Algorithms for Maximizing Throughput with Zero-Forcing Beamforming in a MIMO Wireless System

NASA Astrophysics Data System (ADS)

Foronda, Augusto; Ohta, Chikara; Tamaki, Hisashi

Dirty paper coding (DPC) is a strategy to achieve the region capacity of multiple input multiple output (MIMO) downlink channels and a DPC scheduler is throughput optimal if users are selected according to their queue states and current rates. However, DPC is difficult to implement in practical systems. One solution, zero-forcing beamforming (ZFBF) strategy has been proposed to achieve the same asymptotic sum rate capacity as that of DPC with an exhaustive search over the entire user set. Some suboptimal user group selection schedulers with reduced complexity based on ZFBF strategy (ZFBF-SUS) and proportional fair (PF) scheduling algorithm (PF-ZFBF) have also been proposed to enhance the throughput and fairness among the users, respectively. However, they are not throughput optimal, fairness and throughput decrease if each user queue length is different due to different users channel quality. Therefore, we propose two different scheduling algorithms: a throughput optimal scheduling algorithm (ZFBF-TO) and a reduced complexity scheduling algorithm (ZFBF-RC). Both are based on ZFBF strategy and, at every time slot, the scheduling algorithms have to select some users based on user channel quality, user queue length and orthogonality among users. Moreover, the proposed algorithms have to produce the rate allocation and power allocation for the selected users based on a modified water filling method. We analyze the schedulers complexity and numerical results show that ZFBF-RC provides throughput and fairness improvements compared to the ZFBF-SUS and PF-ZFBF scheduling algorithms.

Image defog algorithm based on open close filter and gradient domain recursive bilateral filter

NASA Astrophysics Data System (ADS)

Liu, Daqian; Liu, Wanjun; Zhao, Qingguo; Fei, Bowen

2017-11-01

To solve the problems of fuzzy details, color distortion, low brightness of the image obtained by the dark channel prior defog algorithm, an image defog algorithm based on open close filter and gradient domain recursive bilateral filter, referred to as OCRBF, was put forward. The algorithm named OCRBF firstly makes use of weighted quad tree to obtain more accurate the global atmospheric value, then exploits multiple-structure element morphological open and close filter towards the minimum channel map to obtain a rough scattering map by dark channel prior, makes use of variogram to correct the transmittance map,and uses gradient domain recursive bilateral filter for the smooth operation, finally gets recovery images by image degradation model, and makes contrast adjustment to get bright, clear and no fog image. A large number of experimental results show that the proposed defog method in this paper can be good to remove the fog , recover color and definition of the fog image containing close range image, image perspective, the image including the bright areas very well, compared with other image defog algorithms,obtain more clear and natural fog free images with details of higher visibility, what's more, the relationship between the time complexity of SIDA algorithm and the number of image pixels is a linear correlation.

Improved Surface and Tropospheric Temperatures Determined Using Only Shortwave Channels: The AIRS Science Team Version-6 Retrieval Algorithm

NASA Technical Reports Server (NTRS)

Susskind, Joel; Blaisdell, John; Iredell, Lena

2011-01-01

The Goddard DISC has generated products derived from AIRS/AMSU-A observations, starting from September 2002 when the AIRS instrument became stable, using the AIRS Science Team Version-5 retrieval algorithm. The AIRS Science Team Version-6 retrieval algorithm will be finalized in September 2011. This paper describes some of the significant improvements contained in the Version-6 retrieval algorithm, compared to that used in Version-5, with an emphasis on the improvement of atmospheric temperature profiles, ocean and land surface skin temperatures, and ocean and land surface spectral emissivities. AIRS contains 2378 spectral channels covering portions of the spectral region 650 cm(sup -1) (15.38 micrometers) - 2665 cm(sup -1) (3.752 micrometers). These spectral regions contain significant absorption features from two CO2 absorption bands, the 15 micrometers (longwave) CO2 band, and the 4.3 micrometers (shortwave) CO2 absorption band. There are also two atmospheric window regions, the 12 micrometer - 8 micrometer (longwave) window, and the 4.17 micrometer - 3.75 micrometer (shortwave) window. Historically, determination of surface and atmospheric temperatures from satellite observations was performed using primarily observations in the longwave window and CO2 absorption regions. According to cloud clearing theory, more accurate soundings of both surface skin and atmospheric temperatures can be obtained under partial cloud cover conditions if one uses observations in longwave channels to determine coefficients which generate cloud cleared radiances R(sup ^)(sub i) for all channels, and uses R(sup ^)(sub i) only from shortwave channels in the determination of surface and atmospheric temperatures. This procedure is now being used in the AIRS Version-6 Retrieval Algorithm. Results are presented for both daytime and nighttime conditions showing improved Version-6 surface and atmospheric soundings under partial cloud cover.

Second-order Poisson Nernst-Planck solver for ion channel transport

PubMed Central

Zheng, Qiong; Chen, Duan; Wei, Guo-Wei

2010-01-01

The Poisson Nernst-Planck (PNP) theory is a simplified continuum model for a wide variety of chemical, physical and biological applications. Its ability of providing quantitative explanation and increasingly qualitative predictions of experimental measurements has earned itself much recognition in the research community. Numerous computational algorithms have been constructed for the solution of the PNP equations. However, in the realistic ion-channel context, no second order convergent PNP algorithm has ever been reported in the literature, due to many numerical obstacles, including discontinuous coefficients, singular charges, geometric singularities, and nonlinear couplings. The present work introduces a number of numerical algorithms to overcome the abovementioned numerical challenges and constructs the first second-order convergent PNP solver in the ion-channel context. First, a Dirichlet to Neumann mapping (DNM) algorithm is designed to alleviate the charge singularity due to the protein structure. Additionally, the matched interface and boundary (MIB) method is reformulated for solving the PNP equations. The MIB method systematically enforces the interface jump conditions and achieves the second order accuracy in the presence of complex geometry and geometric singularities of molecular surfaces. Moreover, two iterative schemes are utilized to deal with the coupled nonlinear equations. Furthermore, extensive and rigorous numerical validations are carried out over a number of geometries, including a sphere, two proteins and an ion channel, to examine the numerical accuracy and convergence order of the present numerical algorithms. Finally, application is considered to a real transmembrane protein, the Gramicidin A channel protein. The performance of the proposed numerical techniques is tested against a number of factors, including mesh sizes, diffusion coefficient profiles, iterative schemes, ion concentrations, and applied voltages. Numerical predictions are compared with experimental measurements. PMID:21552336

Optimal Refueling Pattern Search for a CANDU Reactor Using a Genetic Algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quang Binh, DO; Gyuhong, ROH; Hangbok, CHOI

2006-07-01

This paper presents the results from the application of genetic algorithms to a refueling optimization of a Canada deuterium uranium (CANDU) reactor. This work aims at making a mathematical model of the refueling optimization problem including the objective function and constraints and developing a method based on genetic algorithms to solve the problem. The model of the optimization problem and the proposed method comply with the key features of the refueling strategy of the CANDU reactor which adopts an on-power refueling operation. In this study, a genetic algorithm combined with an elitism strategy was used to automatically search for themore » refueling patterns. The objective of the optimization was to maximize the discharge burn-up of the refueling bundles, minimize the maximum channel power, or minimize the maximum change in the zone controller unit (ZCU) water levels. A combination of these objectives was also investigated. The constraints include the discharge burn-up, maximum channel power, maximum bundle power, channel power peaking factor and the ZCU water level. A refueling pattern that represents the refueling rate and channels was coded by a one-dimensional binary chromosome, which is a string of binary numbers 0 and 1. A computer program was developed in FORTRAN 90 running on an HP 9000 workstation to conduct the search for the optimal refueling patterns for a CANDU reactor at the equilibrium state. The results showed that it was possible to apply genetic algorithms to automatically search for the refueling channels of the CANDU reactor. The optimal refueling patterns were compared with the solutions obtained from the AUTOREFUEL program and the results were consistent with each other. (authors)« less

Spatial and Temporal Varying Thresholds for Cloud Detection in Satellite Imagery

NASA Technical Reports Server (NTRS)

Jedlovec, Gary; Haines, Stephanie

2007-01-01

A new cloud detection technique has been developed and applied to both geostationary and polar orbiting satellite imagery having channels in the thermal infrared and short wave infrared spectral regions. The bispectral composite threshold (BCT) technique uses only the 11 micron and 3.9 micron channels, and composite imagery generated from these channels, in a four-step cloud detection procedure to produce a binary cloud mask at single pixel resolution. A unique aspect of this algorithm is the use of 20-day composites of the 11 micron and the 11 - 3.9 micron channel difference imagery to represent spatially and temporally varying clear-sky thresholds for the bispectral cloud tests. The BCT cloud detection algorithm has been applied to GOES and MODIS data over the continental United States over the last three years with good success. The resulting products have been validated against "truth" datasets (generated by the manual determination of the sky conditions from available satellite imagery) for various seasons from the 2003-2005 periods. The day and night algorithm has been shown to determine the correct sky conditions 80-90% of the time (on average) over land and ocean areas. Only a small variation in algorithm performance occurs between day-night, land-ocean, and between seasons. The algorithm performs least well. during he winter season with only 80% of the sky conditions determined correctly. The algorithm was found to under-determine clouds at night and during times of low sun angle (in geostationary satellite data) and tends to over-determine the presence of clouds during the day, particularly in the summertime. Since the spectral tests use only the short- and long-wave channels common to most multispectral scanners; the application of the BCT technique to a variety of satellite sensors including SEVERI should be straightforward and produce similar performance results.

Unsupervised Learning for Monaural Source Separation Using Maximization–Minimization Algorithm with Time–Frequency Deconvolution †

PubMed Central

Bouridane, Ahmed; Ling, Bingo Wing-Kuen

2018-01-01

This paper presents an unsupervised learning algorithm for sparse nonnegative matrix factor time–frequency deconvolution with optimized fractional β-divergence. The β-divergence is a group of cost functions parametrized by a single parameter β. The Itakura–Saito divergence, Kullback–Leibler divergence and Least Square distance are special cases that correspond to β=0, 1, 2, respectively. This paper presents a generalized algorithm that uses a flexible range of β that includes fractional values. It describes a maximization–minimization (MM) algorithm leading to the development of a fast convergence multiplicative update algorithm with guaranteed convergence. The proposed model operates in the time–frequency domain and decomposes an information-bearing matrix into two-dimensional deconvolution of factor matrices that represent the spectral dictionary and temporal codes. The deconvolution process has been optimized to yield sparse temporal codes through maximizing the likelihood of the observations. The paper also presents a method to estimate the fractional β value. The method is demonstrated on separating audio mixtures recorded from a single channel. The paper shows that the extraction of the spectral dictionary and temporal codes is significantly more efficient by using the proposed algorithm and subsequently leads to better source separation performance. Experimental tests and comparisons with other factorization methods have been conducted to verify its efficacy. PMID:29702629

Low complexity adaptive equalizers for underwater acoustic communications

NASA Astrophysics Data System (ADS)

Soflaei, Masoumeh; Azmi, Paeiz

2014-08-01

Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm (APA), selective regressor APA(SR-APA), family of selective partial update (SPU) algorithms, family of set-membership (SM) algorithms and selective partial update selective regressor APA (SPU-SR-APA) are compared with conventional algorithms such as the least mean square (LMS) in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE) of SR-APA, SPU-APA, SPU-normalized least mean square (SPU-NLMS), SPU-SR-APA, SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.

Range resolution improvement in passive bistatic radars using nested FM channels and least squares approach

NASA Astrophysics Data System (ADS)

Arslan, Musa T.; Tofighi, Mohammad; Sevimli, Rasim A.; ćetin, Ahmet E.

2015-05-01

One of the main disadvantages of using commercial broadcasts in a Passive Bistatic Radar (PBR) system is the range resolution. Using multiple broadcast channels to improve the radar performance is offered as a solution to this problem. However, it suffers from detection performance due to the side-lobes that matched filter creates for using multiple channels. In this article, we introduce a deconvolution algorithm to suppress the side-lobes. The two-dimensional matched filter output of a PBR is further analyzed as a deconvolution problem. The deconvolution algorithm is based on making successive projections onto the hyperplanes representing the time delay of a target. Resulting iterative deconvolution algorithm is globally convergent because all constraint sets are closed and convex. Simulation results in an FM based PBR system are presented.

Numerical simulations of piecewise deterministic Markov processes with an application to the stochastic Hodgkin-Huxley model.

PubMed

Ding, Shaojie; Qian, Min; Qian, Hong; Zhang, Xuejuan

2016-12-28

The stochastic Hodgkin-Huxley model is one of the best-known examples of piecewise deterministic Markov processes (PDMPs), in which the electrical potential across a cell membrane, V(t), is coupled with a mesoscopic Markov jump process representing the stochastic opening and closing of ion channels embedded in the membrane. The rates of the channel kinetics, in turn, are voltage-dependent. Due to this interdependence, an accurate and efficient sampling of the time evolution of the hybrid stochastic systems has been challenging. The current exact simulation methods require solving a voltage-dependent hitting time problem for multiple path-dependent intensity functions with random thresholds. This paper proposes a simulation algorithm that approximates an alternative representation of the exact solution by fitting the log-survival function of the inter-jump dwell time, H(t), with a piecewise linear one. The latter uses interpolation points that are chosen according to the time evolution of the H(t), as the numerical solution to the coupled ordinary differential equations of V(t) and H(t). This computational method can be applied to all PDMPs. Pathwise convergence of the approximated sample trajectories to the exact solution is proven, and error estimates are provided. Comparison with a previous algorithm that is based on piecewise constant approximation is also presented.

Decoding communities in networks

NASA Astrophysics Data System (ADS)

Radicchi, Filippo

2018-02-01

According to a recent information-theoretical proposal, the problem of defining and identifying communities in networks can be interpreted as a classical communication task over a noisy channel: memberships of nodes are information bits erased by the channel, edges and nonedges in the network are parity bits introduced by the encoder but degraded through the channel, and a community identification algorithm is a decoder. The interpretation is perfectly equivalent to the one at the basis of well-known statistical inference algorithms for community detection. The only difference in the interpretation is that a noisy channel replaces a stochastic network model. However, the different perspective gives the opportunity to take advantage of the rich set of tools of coding theory to generate novel insights on the problem of community detection. In this paper, we illustrate two main applications of standard coding-theoretical methods to community detection. First, we leverage a state-of-the-art decoding technique to generate a family of quasioptimal community detection algorithms. Second and more important, we show that the Shannon's noisy-channel coding theorem can be invoked to establish a lower bound, here named as decodability bound, for the maximum amount of noise tolerable by an ideal decoder to achieve perfect detection of communities. When computed for well-established synthetic benchmarks, the decodability bound explains accurately the performance achieved by the best community detection algorithms existing on the market, telling us that only little room for their improvement is still potentially left.

Decoding communities in networks.

PubMed

Radicchi, Filippo

2018-02-01

According to a recent information-theoretical proposal, the problem of defining and identifying communities in networks can be interpreted as a classical communication task over a noisy channel: memberships of nodes are information bits erased by the channel, edges and nonedges in the network are parity bits introduced by the encoder but degraded through the channel, and a community identification algorithm is a decoder. The interpretation is perfectly equivalent to the one at the basis of well-known statistical inference algorithms for community detection. The only difference in the interpretation is that a noisy channel replaces a stochastic network model. However, the different perspective gives the opportunity to take advantage of the rich set of tools of coding theory to generate novel insights on the problem of community detection. In this paper, we illustrate two main applications of standard coding-theoretical methods to community detection. First, we leverage a state-of-the-art decoding technique to generate a family of quasioptimal community detection algorithms. Second and more important, we show that the Shannon's noisy-channel coding theorem can be invoked to establish a lower bound, here named as decodability bound, for the maximum amount of noise tolerable by an ideal decoder to achieve perfect detection of communities. When computed for well-established synthetic benchmarks, the decodability bound explains accurately the performance achieved by the best community detection algorithms existing on the market, telling us that only little room for their improvement is still potentially left.

Secure Fusion Estimation for Bandwidth Constrained Cyber-Physical Systems Under Replay Attacks.

PubMed

Chen, Bo; Ho, Daniel W C; Hu, Guoqiang; Yu, Li; Bo Chen; Ho, Daniel W C; Guoqiang Hu; Li Yu; Chen, Bo; Ho, Daniel W C; Hu, Guoqiang; Yu, Li

2018-06-01

State estimation plays an essential role in the monitoring and supervision of cyber-physical systems (CPSs), and its importance has made the security and estimation performance a major concern. In this case, multisensor information fusion estimation (MIFE) provides an attractive alternative to study secure estimation problems because MIFE can potentially improve estimation accuracy and enhance reliability and robustness against attacks. From the perspective of the defender, the secure distributed Kalman fusion estimation problem is investigated in this paper for a class of CPSs under replay attacks, where each local estimate obtained by the sink node is transmitted to a remote fusion center through bandwidth constrained communication channels. A new mathematical model with compensation strategy is proposed to characterize the replay attacks and bandwidth constrains, and then a recursive distributed Kalman fusion estimator (DKFE) is designed in the linear minimum variance sense. According to different communication frameworks, two classes of data compression and compensation algorithms are developed such that the DKFEs can achieve the desired performance. Several attack-dependent and bandwidth-dependent conditions are derived such that the DKFEs are secure under replay attacks. An illustrative example is given to demonstrate the effectiveness of the proposed methods.

On-Board Event-Based State Estimation for Trajectory Approaching and Tracking of a Vehicle

PubMed Central

Martínez-Rey, Miguel; Espinosa, Felipe; Gardel, Alfredo; Santos, Carlos

2015-01-01

For the problem of pose estimation of an autonomous vehicle using networked external sensors, the processing capacity and battery consumption of these sensors, as well as the communication channel load should be optimized. Here, we report an event-based state estimator (EBSE) consisting of an unscented Kalman filter that uses a triggering mechanism based on the estimation error covariance matrix to request measurements from the external sensors. This EBSE generates the events of the estimator module on-board the vehicle and, thus, allows the sensors to remain in stand-by mode until an event is generated. The proposed algorithm requests a measurement every time the estimation distance root mean squared error (DRMS) value, obtained from the estimator's covariance matrix, exceeds a threshold value. This triggering threshold can be adapted to the vehicle's working conditions rendering the estimator even more efficient. An example of the use of the proposed EBSE is given, where the autonomous vehicle must approach and follow a reference trajectory. By making the threshold a function of the distance to the reference location, the estimator can halve the use of the sensors with a negligible deterioration in the performance of the approaching maneuver. PMID:26102489

Assessment of remotely sensed chlorophyll-a concentration in Guanabara Bay, Brazil

NASA Astrophysics Data System (ADS)

Oliveira, Eduardo N.; Fernandes, Alexandre M.; Kampel, Milton; Cordeiro, Renato C.; Brandini, Nilva; Vinzon, Susana B.; Grassi, Renata M.; Pinto, Fernando N.; Fillipo, Alessandro M.; Paranhos, Rodolfo

2016-04-01

The Guanabara Bay (GB) is an estuarine system in the metropolitan region of Rio de Janeiro (Brazil), with a surface area of ˜346 km2 threatened by anthropogenic pressure. Remote sensing can provide frequent data for studies and monitoring of water quality parameters, such as chlorophyll-a concentration (Chl-a). Different combination of Medium Resolution Imaging Spectrometer (MERIS) remote sensing reflectance band ratios were used to estimate Chl-a. Standard algorithms such as Ocean Color 3-band, Ocean Color-4 band, fluorescence line height, and maximum chlorophyll index were also tested. The MERIS Chl-a estimates were statistically compared with a dataset of in situ Chl-a (2002 to 2012). Good correlations were obtained with the use of green, red, and near-infrared bands. The best performing algorithm was based on the red (665 nm) and green (560 nm) band ratio, named "RG3" algorithm (r2=0.71, chl-a=62,565*x1.6118). The RG3 was applied to a time series of MERIS images (2003- to 2012). The GB has a high temporal and spatial variability of Chl-a, with highest values found in the wet season (October to March) and in some of the most internal regions of the estuary. Lowest concentrations are found in the central circulation channel due to the flushing of ocean water masses promoted by pumping tide.

The Locus analytical framework for indoor localization and tracking applications

NASA Astrophysics Data System (ADS)

Segou, Olga E.; Thomopoulos, Stelios C. A.

2015-05-01

Obtaining location information can be of paramount importance in the context of pervasive and context-aware computing applications. Many systems have been proposed to date, e.g. GPS that has been proven to offer satisfying results in outdoor areas. The increased effect of large and small scale fading in indoor environments, however, makes localization a challenge. This is particularly reflected in the multitude of different systems that have been proposed in the context of indoor localization (e.g. RADAR, Cricket etc). The performance of such systems is often validated on vastly different test beds and conditions, making performance comparisons difficult and often irrelevant. The Locus analytical framework incorporates algorithms from multiple disciplines such as channel modeling, non-uniform random number generation, computational geometry, localization, tracking and probabilistic modeling etc. in order to provide: (a) fast and accurate signal propagation simulation, (b) fast experimentation with localization and tracking algorithms and (c) an in-depth analysis methodology for estimating the performance limits of any Received Signal Strength localization system. Simulation results for the well-known Fingerprinting and Trilateration algorithms are herein presented and validated with experimental data collected in real conditions using IEEE 802.15.4 ZigBee modules. The analysis shows that the Locus framework accurately predicts the underlying distribution of the localization error and produces further estimates of the system's performance limitations (in a best-case/worst-case scenario basis).

New algorithms for microwave measurements of ocean winds

NASA Technical Reports Server (NTRS)

Wentz, F. J.; Peteherych, S.

1984-01-01

Improved second generation wind algorithms are used to process the three month SEASAT SMMR and SASS data sets. The new algorithms are derived without using in situ anemometer measurements. All known biases in the sensors prime measurements are removed, and the algorithms prime model functions are internally self-consistent. The computed SMMR and SASS winds are collocated and compared on a 150 km cell-by-cell basis, giving a total of 115444 wind comparisons. The comparisons are done using three different sets of SMMR channels. When the 6.6H SMMR channel is used for wind retrieval, the SMMR and SASS winds agree to within 1.3 m/s over the SASS primary swath. At nadir where the radar cross section is less sensitive to wind, the agreement degrades to 1.9 m/s. The agreement is very good for winds from 0 to 15 m/s. Above 15 m/s, the off-nadir SASS winds are consistently lower than the SMMR winds, while at nadir the high SASS winds are greater than SMMR's. When 10.7H is used for the SMMR wind channel, the SMMR/SASS wind comparisons are not quite as good. When the frequency of the wind channel is increased to 18 GHz, the SMMR/SASS agreement substantially degrades to about 5 m/s.

MT's algorithm: A new algorithm to search for the optimum set of modulation indices for simultaneous range, command, and telemetry

NASA Technical Reports Server (NTRS)

Nguyen, Tien Manh

1989-01-01

MT's algorithm was developed as an aid in the design of space telecommunications systems when utilized with simultaneous range/command/telemetry operations. This algorithm provides selection of modulation indices for: (1) suppression of undesired signals to achieve desired link performance margins and/or to allow for a specified performance degradation in the data channel (command/telemetry) due to the presence of undesired signals (interferers); and (2) optimum power division between the carrier, the range, and the data channel. A software program using this algorithm was developed for use with MathCAD software. This software program, called the MT program, provides the computation of optimum modulation indices for all possible cases that are recommended by the Consultative Committee on Space Data System (CCSDS) (with emphasis on the squarewave, NASA/JPL ranging system).

Aerosol correction for remotely sensed sea surface temperatures from the National Oceanic and Atmospheric Administration advanced very high resolution radiometer

NASA Astrophysics Data System (ADS)

Nalli, Nicholas R.; Stowe, Larry L.

2002-10-01

This research presents the first-phase derivation and implementation of daytime aerosol correction algorithms for remotely sensed sea surface temperature (SST) from the advanced very high resolution radiometer (AVHRR) instrument flown onboard NOAA polar orbiting satellites. To accomplish this, a long-term (1990-1998), global AVHRR-buoy match-up database was created by merging the NOAA/NASA Pathfinder Atmospheres and Pathfinder Oceans data sets. The merged data set is unique in that it includes daytime estimates of aerosol optical depth (AOD) derived from AVHRR channel 1 (0.63 μm) under global conditions of significant aerosol loading. Histograms of retrieved AOD reveal monomodal, lognormal distributions for both tropospheric and stratospheric aerosol modes. It is then shown empirically that the SST depression caused under each aerosol mode can be expressed as a linear function in two predictors, these being the slant path AOD retrieved from AVHRR channel 1 along with the ratio of channels 1 and 2 normalized reflectances. On the basis of these relationships, parametric equations are derived to provide an aerosol correction for retrievals from the daytime NOAA operational multichannel and nonlinear SST algorithms. Separate sets of coefficients are utilized for two aerosol modes: tropospheric (i.e., dust, smoke, haze) and stratospheric/tropospheric (i.e., following a major volcanic eruption). The equations are shown to significantly reduce retrieved SST bias using an independent set of match-ups. Eliminating aerosol-induced bias in both real-time and retrospective processing will enhance the utility of the AVHRR SST for the general user community and in climate research.

Classification methods for monitoring Arctic sea ice using OKEAN passive/active two-channel microwave data

USGS Publications Warehouse

Belchansky, Gennady I.; Douglas, David C.

2000-01-01

This paper presents methods for classifying Arctic sea ice using both passive and active (2-channel) microwave imagery acquired by the Russian OKEAN 01 polar-orbiting satellite series. Methods and results are compared to sea ice classifications derived from nearly coincident Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) image data of the Barents, Kara, and Laptev Seas. The Russian OKEAN 01 satellite data were collected over weekly intervals during October 1995 through December 1997. Methods are presented for calibrating, georeferencing and classifying the raw active radar and passive microwave OKEAN 01 data, and for correcting the OKEAN 01 microwave radiometer calibration wedge based on concurrent 37 GHz horizontal polarization SSM/I brightness temperature data. Sea ice type and ice concentration algorithms utilized OKEAN's two-channel radar and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter, together with a priori knowledge about the scattering parameters and natural emissivities of basic sea ice types. OKEAN 01 data and algorithms tended to classify lower concentrations of young or first-year sea ice when concentrations were less than 60%, and to produce higher concentrations of multi-year sea ice when concentrations were greater than 40%, when compared to estimates produced from SSM/I data. Overall, total sea ice concentration maps derived independently from OKEAN 01, SSM/I, and AVHRR satellite imagery were all highly correlated, with uniform biases, and mean differences in total ice concentration of less than four percent (sd<15%).

Investigation of adaptive filtering and MDL mitigation based on space-time block-coding for spatial division multiplexed coherent receivers

NASA Astrophysics Data System (ADS)

Weng, Yi; He, Xuan; Yao, Wang; Pacheco, Michelle C.; Wang, Junyi; Pan, Zhongqi

2017-07-01

In this paper, we explored the performance of space-time block-coding (STBC) assisted multiple-input multiple-output (MIMO) scheme for modal dispersion and mode-dependent loss (MDL) mitigation in spatial-division multiplexed optical communication systems, whereas the weight matrices of frequency-domain equalization (FDE) were updated heuristically using decision-directed recursive least squares (RLS) algorithm for convergence and channel estimation. The proposed STBC-RLS algorithm can achieve 43.6% enhancement on convergence rate over conventional least mean squares (LMS) for quadrature phase-shift keying (QPSK) signals with merely 16.2% increase in hardware complexity. The overall optical signal to noise ratio (OSNR) tolerance can be improved via STBC by approximately 3.1, 4.9, 7.8 dB for QPSK, 16-quadrature amplitude modulation (QAM) and 64-QAM with respective bit-error-rates (BER) and minimum-mean-square-error (MMSE).

Modeling Paragenesis: Erosion Opposite to Gravity in Cave Channels

NASA Astrophysics Data System (ADS)

Cooper, M. P.; Covington, M. D.

2017-12-01

Sediment plays an important role in bedrock channels, providing both tools and cover that influence patterns of bed erosion. It has also been shown that sediment load influences bedrock channel width, with increased sediment leading to wider channels. A variety of models have been developed to explore these effects. In caves, it is hypothesized that sediments covering the floors of fully flooded channels that are forming beneath the water table (phreatic zone) can force dissolution upwards towards the water table, leading to upward erosion balanced by gradual deposition of sediment within the channel bottom. This strange process is termed paragenesis, and while there are conceptual and experimental models of the process, no prior mathematical models of cave passage evolution has captured these effects. Consequently, there is little quantitative understanding of the processes that drive paragenesis and how they link to the morphology of the cave channels that develop. We adapt a previously developed algorithm for estimating boundary shear stress within channels with free-surface flows to enable calculation of boundary shear stress in pipe-full conditions. This model successfully duplicates scaling relationships in surface channels, and geometries of caves formed in the phreatic zone such as phreatic tubes. Once sediment flux is incorporated the model successfully duplicates the hypothesized processes of paragenetic gallery formation: the cover effect prevents dissolution in the direction of gravity; passages are enlarged upwards reducing the sediment transport capacity; sediment is deposited and the process drives a continuing feedback loop. Simulations reveal that equilibrium paragenetic channel widths scale with both sediment flux and discharge. Unlike in open channel settings, increased sediment load actually narrows paragenetic channels. The cross section evolution model also reveals that the existence of equilibrium widths in such galleries requires erosion to scale with shear stress, suggesting a role of either mechanical erosion or transport limited dissolution. These types of erosion contrast with current numerical models of speleogenesis, where chemically limited dissolution, a process independent of shear stress, is predicted to occur in most turbulent flow settings.

Monitoring inter-channel nonlinearity based on differential pilot

NASA Astrophysics Data System (ADS)

Wang, Wanli; Yang, Aiying; Guo, Peng; Lu, Yueming; Qiao, Yaojun

2018-06-01

We modify and simplify the inter-channel nonlinearity (NL) estimation method by using differential pilot. Compared to previous works, the inter-channel NL estimation method we propose has much lower complexity and does not need modification of the transmitter. The performance of inter-channel NL monitoring with different launch power is tested. For both QPSK and 16QAM systems with 9 channels, the estimation error of inter-channel NL is lower than 1 dB when the total launch power is bigger than 12 dBm after 1000 km optical transmission. At last, we compare our inter-channel NL estimation method with other methods.

Added value of far-infrared radiometry for remote sensing of ice clouds

NASA Astrophysics Data System (ADS)

Libois, Quentin; Blanchet, Jean-Pierre

2017-06-01

Several cloud retrieval algorithms based on satellite observations in the infrared have been developed in the last decades. However, these observations only cover the midinfrared (MIR, λ < 15 μm) part of the spectrum, and none are available in the far-infrared (FIR, λ≥ 15 μm). Using the optimal estimation method, we show that adding a few FIR channels to existing spaceborne radiometers would significantly improve their ability to retrieve ice cloud radiative properties. For clouds encountered in the polar regions and the upper troposphere, where the atmosphere is sufficiently transparent in the FIR, using FIR channels would reduce by more than 50% the uncertainties on retrieved values of optical thickness, effective particle diameter, and cloud top altitude. Notably, this would extend the range of applicability of current retrieval methods to the polar regions and to clouds with large optical thickness, where MIR algorithms perform poorly. The high performance of solar reflection-based algorithms would thus be reached in nighttime conditions. Since the sensitivity of ice cloud thermal emission to effective particle diameter is approximately 5 times larger in the FIR than in the MIR, using FIR observations is a promising venue for studying ice cloud microphysics and precipitation processes. This is highly relevant for cirrus clouds and convective towers. This is also essential to study precipitation in the driest regions of the atmosphere, where strong feedbacks are at play between clouds and water vapor. The deployment in the near future of a FIR spaceborne radiometer is technologically feasible and should be strongly supported.

Performance evaluation of spatial compounding in the presence of aberration and adaptive imaging

NASA Astrophysics Data System (ADS)

Dahl, Jeremy J.; Guenther, Drake; Trahey, Gregg E.

2003-05-01

Spatial compounding has been used for years to reduce speckle in ultrasonic images and to resolve anatomical features hidden behind the grainy appearance of speckle. Adaptive imaging restores image contrast and resolution by compensating for beamforming errors caused by tissue-induced phase errors. Spatial compounding represents a form of incoherent imaging, whereas adaptive imaging attempts to maintain a coherent, diffraction-limited aperture in the presence of aberration. Using a Siemens Antares scanner, we acquired single channel RF data on a commercially available 1-D probe. Individual channel RF data was acquired on a cyst phantom in the presence of a near field electronic phase screen. Simulated data was also acquired for both a 1-D and a custom built 8x96, 1.75-D probe (Tetrad Corp.). The data was compounded using a receive spatial compounding algorithm; a widely used algorithm because it takes advantage of parallel beamforming to avoid reductions in frame rate. Phase correction was also performed by using a least mean squares algorithm to estimate the arrival time errors. We present simulation and experimental data comparing the performance of spatial compounding to phase correction in contrast and resolution tasks. We evaluate spatial compounding and phase correction, and combinations of the two methods, under varying aperture sizes, aperture overlaps, and aberrator strength to examine the optimum configuration and conditions in which spatial compounding will provide a similar or better result than adaptive imaging. We find that, in general, phase correction is hindered at high aberration strengths and spatial frequencies, whereas spatial compounding is helped by these aberrators.

NPP ATMS Snowfall Rate Product

NASA Technical Reports Server (NTRS)

Meng, Huan; Ferraro, Ralph; Kongoli, Cezar; Wang, Nai-Yu; Dong, Jun; Zavodsky, Bradley; Yan, Banghua

2015-01-01

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2015). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. NCEP CMORPH analysis has shown that integration of ATMS SFR has improved the performance of CMORPH-Snow. The ATMS SFR product is also being assessed at several NWS Weather Forecast Offices for its usefulness in weather forecast.

Using LiDAR to Estimate Surface Erosion Volumes within the Post-storm 2012 Bagley Fire

NASA Astrophysics Data System (ADS)

Mikulovsky, R. P.; De La Fuente, J. A.; Mondry, Z. J.

2014-12-01

The total post-storm 2012 Bagley fire sediment budget of the Squaw Creek watershed in the Shasta-Trinity National Forest was estimated using many methods. A portion of the budget was quantitatively estimated using LiDAR. Simple workflows were designed to estimate the eroded volume's of debris slides, fill failures, gullies, altered channels and streams. LiDAR was also used to estimate depositional volumes. Thorough manual mapping of large erosional features using the ArcGIS 10.1 Geographic Information System was required as these mapped features determined the eroded volume boundaries in 3D space. The 3D pre-erosional surface for each mapped feature was interpolated based on the boundary elevations. A surface difference calculation was run using the estimated pre-erosional surfaces and LiDAR surfaces to determine volume of sediment potentially delivered into the stream system. In addition, cross sections of altered channels and streams were taken using stratified random selection based on channel gradient and stream order respectively. The original pre-storm surfaces of channel features were estimated using the cross sections and erosion depth criteria. Open source software Inkscape was used to estimate cross sectional areas for randomly selected channel features and then averaged for each channel gradient and stream order classes. The average areas were then multiplied by the length of each class to estimate total eroded altered channel and stream volume. Finally, reservoir and in-channel depositional volumes were estimated by mapping channel forms and generating specific reservoir elevation zones associated with depositional events. The in-channel areas and zones within the reservoir were multiplied by estimated and field observed sediment thicknesses to attain a best guess sediment volume. In channel estimates included re-occupying stream channel cross sections established before the fire. Once volumes were calculated, other erosion processes of the Bagley sedimentation study, such as surface soil erosion were combined to estimate the total fire and storm sediment budget for the Squaw Creek watershed. The LiDAR-based measurement workflows can be easily applied to other sediment budget studies using one high resolution LiDAR dataset.

Estimating Contrail Climate Effects from Satellite Data

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Duda, David P.; Palikonda, Rabindra; Bedka, Sarah T.; Boeke, Robyn; Khlopenkov, Konstantin; Chee, Thad; Bedka, Kristopher T.

2011-01-01

An automated contrail detection algorithm (CDA) is developed to exploit six of the infrared channels on the 1-km MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites. The CDA is refined and balanced using visual error analysis. It is applied to MODIS data taken by Terra and Aqua over the United States during 2006 and 2008. The results are consistent with flight track data, but differ markedly from earlier analyses. Contrail coverage is a factor of 4 less than other retrievals and the retrieved contrail optical depths and radiative forcing are smaller by approx.30%. The discrepancies appear to be due to the inability to detect wider, older contrails that comprise a significant amount of the contrail coverage. An example of applying the algorithm to MODIS data over the entire Northern Hemisphere is also presented. Overestimates of contrail coverage are apparent in some tropical regions. Methods for improving the algorithm are discussed and are to be implemented before analyzing large amounts of Northern Hemisphere data. The results should be valuable for guiding and validating climate models seeking to account for aviation effects on climate.

Machine learning algorithms for the prediction of hERG and CYP450 binding in drug development.

PubMed

Klon, Anthony E

2010-07-01

The cost of developing new drugs is estimated at approximately $1 billion; the withdrawal of a marketed compound due to toxicity can result in serious financial loss for a pharmaceutical company. There has been a greater interest in the development of in silico tools that can identify compounds with metabolic liabilities before they are brought to market. The two largest classes of machine learning (ML) models, which will be discussed in this review, have been developed to predict binding to the human ether-a-go-go related gene (hERG) ion channel protein and the various CYP isoforms. Being able to identify potentially toxic compounds before they are made would greatly reduce the number of compound failures and the costs associated with drug development. This review summarizes the state of modeling hERG and CYP binding towards this goal since 2003 using ML algorithms. A wide variety of ML algorithms that are comparable in their overall performance are available. These ML methods may be applied regularly in discovery projects to flag compounds with potential metabolic liabilities.

Sifting Through SDO's AIA Cosmic Ray Hits to Find Treasure

NASA Astrophysics Data System (ADS)

Kirk, M. S.; Thompson, B. J.; Viall, N. M.; Young, P. R.

2017-12-01

The Solar Dynamics Observatory's Atmospheric Imaging Assembly (SDO AIA) has revolutionized solar imaging with its high temporal and spatial resolution, unprecedented spatial and temporal coverage, and seven EUV channels. Automated algorithms routinely clean these images to remove cosmic ray intensity spikes as a part of its preprocessing algorithm. We take a novel approach to survey the entire set of AIA "spike" data to identify and group compact brightenings across the entire SDO mission. The AIA team applies a de-spiking algorithm to remove magnetospheric particle impacts on the CCD cameras, but it has been found that compact, intense solar brightenings are often removed as well. We use the spike database to mine the data and form statistics on compact solar brightenings without having to process large volumes of full-disk AIA data. There are approximately 3 trillion "spiked pixels" removed from images over the mission to date. We estimate that 0.001% of those are of solar origin and removed by mistake, giving us a pre-segmented dataset of 30 million events. We explore the implications of these statistics and the physical qualities of the "spikes" of solar origin.

Viterbi equalization for long-distance, high-speed underwater laser communication

NASA Astrophysics Data System (ADS)

Hu, Siqi; Mi, Le; Zhou, Tianhua; Chen, Weibiao

2017-07-01

In long-distance, high-speed underwater laser communication, because of the strong absorption and scattering processes, the laser pulse is stretched with the increase in communication distance and the decrease in water clarity. The maximum communication bandwidth is limited by laser-pulse stretching. Improving the communication rate increases the intersymbol interference (ISI). To reduce the effect of ISI, the Viterbi equalization (VE) algorithm is used to estimate the maximum-likelihood receiving sequence. The Monte Carlo method is used to simulate the stretching of the received laser pulse and the maximum communication rate at a wavelength of 532 nm in Jerlov IB and Jerlov II water channels with communication distances of 80, 100, and 130 m, respectively. The high-data rate communication performance for the VE and hard-decision algorithms is compared. The simulation results show that the VE algorithm can be used to reduce the ISI by selecting the minimum error path. The trade-off between the high-data rate communication performance and minor bit-error rate performance loss makes VE a promising option for applications in long-distance, high-speed underwater laser communication systems.

Simulation of 3-D Nonequilibrium Seeded Air Flow in the NASA-Ames MHD Channel

NASA Technical Reports Server (NTRS)

Gupta, Sumeet; Tannehill, John C.; Mehta, Unmeel B.

2004-01-01

The 3-D nonequilibrium seeded air flow in the NASA-Ames experimental MHD channel has been numerically simulated. The channel contains a nozzle section, a center section, and an accelerator section where magnetic and electric fields can be imposed on the flow. In recent tests, velocity increases of up to 40% have been achieved in the accelerator section. The flow in the channel is numerically computed us ing a 3-D parabolized Navier-Stokes (PNS) algorithm that has been developed to efficiently compute MHD flows in the low magnetic Reynolds number regime: The MHD effects are modeled by introducing source terms into the PNS equations which can then be solved in a very efficient manner. The algorithm has been extended in the present study to account for nonequilibrium seeded air flows. The electrical conductivity of the flow is determined using the program of Park. The new algorithm has been used to compute two test cases that match the experimental conditions. In both cases, magnetic and electric fields are applied to the seeded flow. The computed results are in good agreement with the experimental data.

Aerosol Retrievals over the Ocean using Channel 1 and 2 AVHRR Data: A Sensitivity Analysis and Preliminary Results

NASA Technical Reports Server (NTRS)

Mishchenko, Michael I.; Geogdzhayev, Igor V.; Cairns, Brian; Rossow, William B.; Lacis, Andrew A.

1999-01-01

This paper outlines the methodology of interpreting channel 1 and 2 AVHRR radiance data over the oceans and describes a detailed analysis of the sensitivity of monthly averages of retrieved aerosol parameters to the assumptions made in different retrieval algorithms. The analysis is based on using real AVHRR data and exploiting accurate numerical techniques for computing single and multiple scattering and spectral absorption of light in the vertically inhomogeneous atmosphere-ocean system. We show that two-channel algorithms can be expected to provide significantly more accurate and less biased retrievals of the aerosol optical thickness than one-channel algorithms and that imperfect cloud screening and calibration uncertainties are by far the largest sources of errors in the retrieved aerosol parameters. Both underestimating and overestimating aerosol absorption as well as the potentially strong variability of the real part of the aerosol refractive index may lead to regional and/or seasonal biases in optical thickness retrievals. The Angstrom exponent appears to be the most invariant aerosol size characteristic and should be retrieved along with optical thickness as the second aerosol parameter.

Channel Estimation for Filter Bank Multicarrier Systems in Low SNR Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Driggs, Jonathan; Sibbett, Taylor; Moradiy, Hussein

Channel estimation techniques are crucial for reliable communications. This paper is concerned with channel estimation in a filter bank multicarrier spread spectrum (FBMCSS) system. We explore two channel estimator options: (i) a method that makes use of a periodic preamble and mimics the channel estimation techniques that are widely used in OFDM-based systems; and (ii) a method that stays within the traditional realm of filter bank signal processing. For the case where the channel noise is white, both methods are analyzed in detail and their performance is compared against their respective Cramer-Rao Lower Bounds (CRLB). Advantages and disadvantages of themore » two methods under different channel conditions are given to provide insight to the reader as to when one will outperform the other.« less

Performance of convolutional codes on fading channels typical of planetary entry missions

NASA Technical Reports Server (NTRS)

Modestino, J. W.; Mui, S. Y.; Reale, T. J.

1974-01-01

The performance of convolutional codes in fading channels typical of the planetary entry channel is examined in detail. The signal fading is due primarily to turbulent atmospheric scattering of the RF signal transmitted from an entry probe through a planetary atmosphere. Short constraint length convolutional codes are considered in conjunction with binary phase-shift keyed modulation and Viterbi maximum likelihood decoding, and for longer constraint length codes sequential decoding utilizing both the Fano and Zigangirov-Jelinek (ZJ) algorithms are considered. Careful consideration is given to the modeling of the channel in terms of a few meaningful parameters which can be correlated closely with theoretical propagation studies. For short constraint length codes the bit error probability performance was investigated as a function of E sub b/N sub o parameterized by the fading channel parameters. For longer constraint length codes the effect was examined of the fading channel parameters on the computational requirements of both the Fano and ZJ algorithms. The effects of simple block interleaving in combatting the memory of the channel is explored, using the analytic approach or digital computer simulation.

Optimal block cosine transform image coding for noisy channels

NASA Technical Reports Server (NTRS)

Vaishampayan, V.; Farvardin, N.

1986-01-01

The two dimensional block transform coding scheme based on the discrete cosine transform was studied extensively for image coding applications. While this scheme has proven to be efficient in the absence of channel errors, its performance degrades rapidly over noisy channels. A method is presented for the joint source channel coding optimization of a scheme based on the 2-D block cosine transform when the output of the encoder is to be transmitted via a memoryless design of the quantizers used for encoding the transform coefficients. This algorithm produces a set of locally optimum quantizers and the corresponding binary code assignment for the assumed transform coefficient statistics. To determine the optimum bit assignment among the transform coefficients, an algorithm was used based on the steepest descent method, which under certain convexity conditions on the performance of the channel optimized quantizers, yields the optimal bit allocation. Comprehensive simulation results for the performance of this locally optimum system over noisy channels were obtained and appropriate comparisons against a reference system designed for no channel error were rendered.

Miniature open channel scrubbers for gas collection.

PubMed

Toda, Kei; Koga, Tomoko; Tanaka, Toshinori; Ohira, Shin-Ichi; Berg, Jordan M; Dasgupta, Purnendu K

2010-10-15

An open channel scrubber is proposed as a miniature fieldable gas collector. The device is 100mm in length, 26 mm in width and 22 mm in thickness. The channel bottom is rendered hydrophilic and liquid flows as a thin layer on the bottom. Air sample flows atop the appropriately chosen flowing liquid film and analyte molecules are absorbed into the liquid. There is no membrane at the air-liquid interface: they contact directly each other. Analyte species collected over a 10 min interval are determined by fluorometric flow analysis or ion chromatography. A calculation algorithm was developed to estimate the collection efficiency a priori; experimental and simulated results agreed well. The characteristics of the open channel scrubber are discussed in this paper from both theoretical and experimental points of view. In addition to superior collection efficiencies at relatively high sample air flow rates, this geometry is particularly attractive that there is no change in collection performance due to membrane fouling. We demonstrate field use for analysis of ambient SO(2) near an active volcano. This is basic investigation of membraneless miniature scrubber and is expected to lead development of an excellent micro-gas analysis system integrated with a detector for continuous measurements. Copyright © 2010 Elsevier B.V. All rights reserved.

Fast convergent frequency-domain MIMO equalizer for few-mode fiber communication systems

NASA Astrophysics Data System (ADS)

He, Xuan; Weng, Yi; Wang, Junyi; Pan, Z.

2018-02-01

Space division multiplexing using few-mode fibers has been extensively explored to sustain the continuous traffic growth. In few-mode fiber optical systems, both spatial and polarization modes are exploited to transmit parallel channels, thus increasing the overall capacity. However, signals on spatial channels inevitably suffer from the intrinsic inter-modal coupling and large accumulated differential mode group delay (DMGD), which causes spatial modes de-multiplex even harder. Many research articles have demonstrated that frequency domain adaptive multi-input multi-output (MIMO) equalizer can effectively compensate the DMGD and demultiplex the spatial channels with digital signal processing (DSP). However, the large accumulated DMGD usually requires a large number of training blocks for the initial convergence of adaptive MIMO equalizers, which will decrease the overall system efficiency and even degrade the equalizer performance in fast-changing optical channels. Least mean square (LMS) algorithm is always used in MIMO equalization to dynamically demultiplex the spatial signals. We have proposed to use signal power spectral density (PSD) dependent method and noise PSD directed method to improve the convergence speed of adaptive frequency domain LMS algorithm. We also proposed frequency domain recursive least square (RLS) algorithm to further increase the convergence speed of MIMO equalizer at cost of greater hardware complexity. In this paper, we will compare the hardware complexity and convergence speed of signal PSD dependent and noise power directed algorithms against the conventional frequency domain LMS algorithm. In our numerical study of a three-mode 112 Gbit/s PDM-QPSK optical system with 3000 km transmission, the noise PSD directed and signal PSD dependent methods could improve the convergence speed by 48.3% and 36.1% respectively, at cost of 17.2% and 10.7% higher hardware complexity. We will also compare the frequency domain RLS algorithm against conventional frequency domain LMS algorithm. Our numerical study shows that, in a three-mode 224 Gbit/s PDM-16-QAM system with 3000 km transmission, the RLS algorithm could improve the convergence speed by 53.7% over conventional frequency domain LMS algorithm.

Modulation Depth Estimation and Variable Selection in State-Space Models for Neural Interfaces

PubMed Central

Hochberg, Leigh R.; Donoghue, John P.; Brown, Emery N.

2015-01-01

Rapid developments in neural interface technology are making it possible to record increasingly large signal sets of neural activity. Various factors such as asymmetrical information distribution and across-channel redundancy may, however, limit the benefit of high-dimensional signal sets, and the increased computational complexity may not yield corresponding improvement in system performance. High-dimensional system models may also lead to overfitting and lack of generalizability. To address these issues, we present a generalized modulation depth measure using the state-space framework that quantifies the tuning of a neural signal channel to relevant behavioral covariates. For a dynamical system, we develop computationally efficient procedures for estimating modulation depth from multivariate data. We show that this measure can be used to rank neural signals and select an optimal channel subset for inclusion in the neural decoding algorithm. We present a scheme for choosing the optimal subset based on model order selection criteria. We apply this method to neuronal ensemble spike-rate decoding in neural interfaces, using our framework to relate motor cortical activity with intended movement kinematics. With offline analysis of intracortical motor imagery data obtained from individuals with tetraplegia using the BrainGate neural interface, we demonstrate that our variable selection scheme is useful for identifying and ranking the most information-rich neural signals. We demonstrate that our approach offers several orders of magnitude lower complexity but virtually identical decoding performance compared to greedy search and other selection schemes. Our statistical analysis shows that the modulation depth of human motor cortical single-unit signals is well characterized by the generalized Pareto distribution. Our variable selection scheme has wide applicability in problems involving multisensor signal modeling and estimation in biomedical engineering systems. PMID:25265627

Extension of a GIS procedure for calculating the RUSLE equation LS factor

NASA Astrophysics Data System (ADS)

Zhang, Hongming; Yang, Qinke; Li, Rui; Liu, Qingrui; Moore, Demie; He, Peng; Ritsema, Coen J.; Geissen, Violette

2013-03-01

The Universal Soil Loss Equation (USLE) and revised USLE (RUSLE) are often used to estimate soil erosion at regional landscape scales, however a major limitation is the difficulty in extracting the LS factor. The geographic information system-based (GIS-based) methods which have been developed for estimating the LS factor for USLE and RUSLE also have limitations. The unit contributing area-based estimation method (UCA) converts slope length to unit contributing area for considering two-dimensional topography, however is not able to predict the different zones of soil erosion and deposition. The flowpath and cumulative cell length-based method (FCL) overcomes this disadvantage but does not consider channel networks and flow convergence in two-dimensional topography. The purpose of this research was to overcome these limitations and extend the FCL method through inclusion of channel networks and convergence flow. We developed LS-TOOL in Microsoft's.NET environment using C♯ with a user-friendly interface. Comparing the LS factor calculated with the three methodologies (UCA, FCL and LS-TOOL), LS-TOOL delivers encouraging results. In particular, LS-TOOL uses breaks in slope identified from the DEM to locate soil erosion and deposition zones, channel networks and convergence flow areas. Comparing slope length and LS factor values generated using LS-TOOL with manual methods, LS-TOOL corresponds more closely with the reality of the Xiannangou catchment than results using UCA or FCL. The LS-TOOL algorithm can automatically calculate slope length, slope steepness, L factor, S factor, and LS factors, providing the results as ASCII files which can be easily used in some GIS software. This study is an important step forward in conducting more accurate large area erosion evaluation.

Optimal Power Allocation for CC-HARQ-based Cognitive Radio with Statistical CSI in Nakagami Slow Fading Channels

NASA Astrophysics Data System (ADS)

Xu, Ding; Li, Qun

2017-01-01

This paper addresses the power allocation problem for cognitive radio (CR) based on hybrid-automatic-repeat-request (HARQ) with chase combining (CC) in Nakagamimslow fading channels. We assume that, instead of the perfect instantaneous channel state information (CSI), only the statistical CSI is available at the secondary user (SU) transmitter. The aim is to minimize the SU outage probability under the primary user (PU) interference outage constraint. Using the Lagrange multiplier method, an iterative and recursive algorithm is derived to obtain the optimal power allocation for each transmission round. Extensive numerical results are presented to illustrate the performance of the proposed algorithm.

Robust vector quantization for noisy channels

NASA Technical Reports Server (NTRS)

Demarca, J. R. B.; Farvardin, N.; Jayant, N. S.; Shoham, Y.

1988-01-01

The paper briefly discusses techniques for making vector quantizers more tolerant to tranmsission errors. Two algorithms are presented for obtaining an efficient binary word assignment to the vector quantizer codewords without increasing the transmission rate. It is shown that about 4.5 dB gain over random assignment can be achieved with these algorithms. It is also proposed to reduce the effects of error propagation in vector-predictive quantizers by appropriately constraining the response of the predictive loop. The constrained system is shown to have about 4 dB of SNR gain over an unconstrained system in a noisy channel, with a small loss of clean-channel performance.

A Novel Method for gamma - text{photons} Depth-of-Interaction Detection in Monolithic Scintillation Crystals

NASA Astrophysics Data System (ADS)

Pani, Roberto; Bettiol, Marco; Preziosi, Enrico; Borrazzo, Cristian; Pellegrini, Rosanna; González, Antonio J.; Conde, Pablo; Cinti, Maria Nerina; Fabbri, Andrea; Di Castro, Elisabetta; Majewski, Stan

2016-10-01

Achieved spatial resolution of the PET systems is often limited by the parallax error due to the lack of information about the Depth of Interaction (DoI) inside the crystal of the incoming 511 keV annihilation photons. The smaller the diameter of the PET ring and the thicker the scintillator are, the more this error affects imaging performance. In this work, a DoI calculator suitable for monolithic scintillation crystals and based on the shape of the scintillation light distribution at the photodetector surface has been proposed. To test the estimator performance, a test PET module with a 50 × 50 × 20 mm monolithic LYSO crystal coupled to a 12 × 12 SiPM array has been employed. In addition, for calibration and validation of the method, Geant4 simulations have been also used. The key result of the application of the proposed DoI estimator is obtaining a continuous DoI estimation with an average DoI resolution of about 5 mm in the 20 mm-thick crystal. Benefiting from the DoI estimation capabilities of the method, it has been also possible to achieve additional important goals, first of all reducing the parallax error. First, because the scintillation light collection varies as a function of the 3D position of the interaction of the annihilation photon inside the crystal, a method to correct this response variation via a proper 3D look-up-table is proposed. This has led to an improvement of about 35% in energy resolution. Moreover, a DoI-dependent position algorithm has been proposed, allowing an improvement of both planar (X-Y) position linearity and planar spatial resolution. This algorithm is specifically developed for the rows/columns multi-channel readout logic, that reduces the number of independent channels from N × N to N + N, where N is the number of SiPM photodetection elements (12 in our case) in each row and column. This development was performed in the framework of the MindView PET/MilI brain imaging project.

Automatic detection and classification of artifacts in single-channel EEG.

PubMed

Olund, Thomas; Duun-Henriksen, Jonas; Kjaer, Troels W; Sorensen, Helge B D

2014-01-01

Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated. The detection algorithm yield an average sensitivity and specificity above 95% for both the subject-specific and generic models. The classification algorithm show a mean accuracy of 78 and 64% for the subject-specific and generic model, respectively. The classification model was additionally validated on a reference dataset with similar results.

Evaluation of Origin Ensemble algorithm for image reconstruction for pixelated solid-state detectors with large number of channels

NASA Astrophysics Data System (ADS)

Kolstein, M.; De Lorenzo, G.; Mikhaylova, E.; Chmeissani, M.; Ariño, G.; Calderón, Y.; Ozsahin, I.; Uzun, D.

2013-04-01

The Voxel Imaging PET (VIP) Pathfinder project intends to show the advantages of using pixelated solid-state technology for nuclear medicine applications. It proposes designs for Positron Emission Tomography (PET), Positron Emission Mammography (PEM) and Compton gamma camera detectors with a large number of signal channels (of the order of 106). For PET scanners, conventional algorithms like Filtered Back-Projection (FBP) and Ordered Subset Expectation Maximization (OSEM) are straightforward to use and give good results. However, FBP presents difficulties for detectors with limited angular coverage like PEM and Compton gamma cameras, whereas OSEM has an impractically large time and memory consumption for a Compton gamma camera with a large number of channels. In this article, the Origin Ensemble (OE) algorithm is evaluated as an alternative algorithm for image reconstruction. Monte Carlo simulations of the PET design are used to compare the performance of OE, FBP and OSEM in terms of the bias, variance and average mean squared error (MSE) image quality metrics. For the PEM and Compton camera designs, results obtained with OE are presented.

Characterizing the SWOT discharge error budget on the Sacramento River, CA

NASA Astrophysics Data System (ADS)

Yoon, Y.; Durand, M. T.; Minear, J. T.; Smith, L.; Merry, C. J.

2013-12-01

The Surface Water and Ocean Topography (SWOT) is an upcoming satellite mission (2020 year) that will provide surface-water elevation and surface-water extent globally. One goal of SWOT is the estimation of river discharge directly from SWOT measurements. SWOT discharge uncertainty is due to two sources. First, SWOT cannot measure channel bathymetry and determine roughness coefficient data necessary for discharge calculations directly; these parameters must be estimated from the measurements or from a priori information. Second, SWOT measurement errors directly impact the discharge estimate accuracy. This study focuses on characterizing parameter and measurement uncertainties for SWOT river discharge estimation. A Bayesian Markov Chain Monte Carlo scheme is used to calculate parameter estimates, given the measurements of river height, slope and width, and mass and momentum constraints. The algorithm is evaluated using simulated both SWOT and AirSWOT (the airborne version of SWOT) observations over seven reaches (about 40 km) of the Sacramento River. The SWOT and AirSWOT observations are simulated by corrupting the ';true' HEC-RAS hydraulic modeling results with the instrument error. This experiment answers how unknown bathymetry and roughness coefficients affect the accuracy of the river discharge algorithm. From the experiment, the discharge error budget is almost completely dominated by unknown bathymetry and roughness; 81% of the variance error is explained by uncertainties in bathymetry and roughness. Second, we show how the errors in water surface, slope, and width observations influence the accuracy of discharge estimates. Indeed, there is a significant sensitivity to water surface, slope, and width errors due to the sensitivity of bathymetry and roughness to measurement errors. Increasing water-surface error above 10 cm leads to a corresponding sharper increase of errors in bathymetry and roughness. Increasing slope error above 1.5 cm/km leads to a significant degradation due to direct error in the discharge estimates. As the width error increases past 20%, the discharge error budget is dominated by the width error. Above two experiments are performed based on AirSWOT scenarios. In addition, we explore the sensitivity of the algorithm to the SWOT scenarios.

Comparison of Nimbus-7 SMMR and GOES-1 VISSR Atmospheric Liquid Water Content.

NASA Astrophysics Data System (ADS)

Lojou, Jean-Yves; Frouin, Robert; Bernard, René

1991-02-01

Vertically integrated atmospheric liquid water content derived from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) brightness temperatures and from GOES-1 Visible and Infrared Spin-Scan Radiometer (VISSR) radiances in the visible are compared over the Indian Ocean during MONEX (monsoon experiment). In the retrieval procedure, Wilheit and Chang' algorithm and Stephens' parameterization schemes are applied to the SMMR and VISSR data, respectively. The results indicate that in the 0-100 mg cm2 range of liquid water content considered, the correlation coefficient between the two types of estimates is 0.83 (0.81- 0.85 at the 99 percent confidence level). The Wilheit and Chang algorithm, however, yields values lower than those obtained with Stephens's schemes by 24.5 mg cm2 on the average, and occasionally the SMMR-based values are negative. Alternative algorithms are proposed for use with SMMR data, which eliminate the bias, augment the correlation coefficient, and reduce the rms difference. These algorithms include using the Witheit and Chang formula with modified coefficients (multilinear regression), the Wilheit and Chang formula with the same coefficients but different equivalent atmospheric temperatures for each channel (temperature bias adjustment), and a second-order polynomial in brightness temperatures at 18, 21, and 37 GHz (polynomial development). When applied to a dataset excluded from the regressionn dataset, the multilinear regression algorithm provides the best results, namely a 0.91 correlation coefficient, a 5.2 mg cm2 (residual) difference, and a 2.9 mg cm2 bias. Simply shifting the liquid water content predicted by the Wilheit and Chang algorithm does not yield as good comparison statistics, indicating that the occasional negative values are not due only to a bias. The more accurate SMMR-derived liquid water content allows one to better evaluate cloud transmittance in the solar spectrum, at least in the area and during the period analyzed. Combining this cloud transmittance with a clear sky model would provide ocean surface insulation estimates from SMMR data alone.

ICESat-2 bathymetry: an empirical feasibility assessment using MABEL

NASA Astrophysics Data System (ADS)

Forfinski, Nick; Parrish, Christopher

2016-10-01

The feasibility of deriving bathymetry from data acquired with ATLAS, the photon-counting lidar on NASA's upcoming ICESat-2 satellite, is assessed empirically by examining data from NASA's airborne ICESat-2 simulator, MABEL. The primary objectives of ICESat-2 will be to measure ice-sheet elevations, sea-ice thickness, and global biomass. However, the 6-beam, green-wavelength photon-counting lidar, combined with the 91-day repeat period and near-polar orbit, may provide unique opportunities to measure coastal bathymetry in remote, poorly-mapped areas of the globe. The study focuses on high-probability bottom returns in Keweenaw Bay, Lake Superior, acquired during the "Transit to KPMD" MABEL mission in August, 2012 at an AGL altitude of 20,000 m. Water-surface and bottom returns were identified and manually classified using MABEL Viewer, an in-house prototype data-explorer web application. Water-surface returns were observed in 12 green channels, and bottom returns were observed in 10 channels. Comparing each channel's mean water-surface elevation to a regional NOAA Nowcast water-level estimate revealed channel-specific elevation biases that were corrected for in our bathymetry estimation procedure. Additionally, a first-order refraction correction was applied to each bottom return. Agreement between the refraction-corrected depth profile and NOAA data acquired two years earlier by Fugro LADS with the LADS Mk II airborne system indicates that MABEL reliably detected bathymetry in depths up to 8 m, with an RMS difference of 0.7 m. In addition to feeding coastal bathymetry models, MABEL (and potentially ICESat-2 ATLAS) has the potential to seed algorithms for bathymetry retrieval from passive, multispectral satellite imagery by providing reference depths.

High capacity low delay packet broadcasting multiaccess schemes for satellite repeater systems

NASA Astrophysics Data System (ADS)

Bose, S. K.

1980-12-01

Demand assigned packet radio schemes using satellite repeaters can achieve high capacities but often exhibit relatively large delays under low traffic conditions when compared to random access. Several schemes which improve delay performance at low traffic but which have high capacity are presented and analyzed. These schemes allow random acess attempts by users, who are waiting for channel assignments. The performance of these are considered in the context of a multiple point communication system carrying fixed length messages between geographically distributed (ground) user terminals which are linked via a satellite repeater. Channel assignments are done following a BCC queueing discipline by a (ground) central controller on the basis of requests correctly received over a collision type access channel. In TBACR Scheme A, some of the forward message channels are set aside for random access transmissions; the rest are used in a demand assigned mode. Schemes B and C operate all their forward message channels in a demand assignment mode but, by means of appropriate algorithms for trailer channel selection, allow random access attempts on unassigned channels. The latter scheme also introduces framing and slotting of the time axis to implement a more efficient algorithm for trailer channel selection than the former.

Estimating Soil Moisture Using Polsar Data: a Machine Learning Approach

NASA Astrophysics Data System (ADS)

Khedri, E.; Hasanlou, M.; Tabatabaeenejad, A.

2017-09-01

Soil moisture is an important parameter that affects several environmental processes. This parameter has many important functions in numerous sciences including agriculture, hydrology, aerology, flood prediction, and drought occurrence. However, field procedures for moisture calculations are not feasible in a vast agricultural region territory. This is due to the difficulty in calculating soil moisture in vast territories and high-cost nature as well as spatial and local variability of soil moisture. Polarimetric synthetic aperture radar (PolSAR) imaging is a powerful tool for estimating soil moisture. These images provide a wide field of view and high spatial resolution. For estimating soil moisture, in this study, a model of support vector regression (SVR) is proposed based on obtained data from AIRSAR in 2003 in C, L, and P channels. In this endeavor, sequential forward selection (SFS) and sequential backward selection (SBS) are evaluated to select suitable features of polarized image dataset for high efficient modeling. We compare the obtained data with in-situ data. Output results show that the SBS-SVR method results in higher modeling accuracy compared to SFS-SVR model. Statistical parameters obtained from this method show an R2 of 97% and an RMSE of lower than 0.00041 (m3/m3) for P, L, and C channels, which has provided better accuracy compared to other feature selection algorithms.

A high data rate universal lattice decoder on FPGA

NASA Astrophysics Data System (ADS)

Ma, Jing; Huang, Xinming; Kura, Swapna

2005-06-01

This paper presents the architecture design of a high data rate universal lattice decoder for MIMO channels on FPGA platform. A phost strategy based lattice decoding algorithm is modified in this paper to reduce the complexity of the closest lattice point search. The data dependency of the improved algorithm is examined and a parallel and pipeline architecture is developed with the iterative decoding function on FPGA and the division intensive channel matrix preprocessing on DSP. Simulation results demonstrate that the improved lattice decoding algorithm provides better bit error rate and less iteration number compared with the original algorithm. The system prototype of the decoder shows that it supports data rate up to 7Mbit/s on a Virtex2-1000 FPGA, which is about 8 times faster than the original algorithm on FPGA platform and two-orders of magnitude better than its implementation on a DSP platform.

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope

PubMed Central

2017-01-01

Rapid automatic detection of the fiducial points—namely, the P wave, QRS complex, and T wave—is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs. PMID:29065613

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope.

PubMed

Park, Jeong-Seon; Lee, Sang-Woong; Park, Unsang

2017-01-01

Rapid automatic detection of the fiducial points-namely, the P wave, QRS complex, and T wave-is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.

Feedback power control strategies in wireless sensor networks with joint channel decoding.

PubMed

Abrardo, Andrea; Ferrari, Gianluigi; Martalò, Marco; Perna, Fabio

2009-01-01

In this paper, we derive feedback power control strategies for block-faded multiple access schemes with correlated sources and joint channel decoding (JCD). In particular, upon the derivation of the feasible signal-to-noise ratio (SNR) region for the considered multiple access schemes, i.e., the multidimensional SNR region where error-free communications are, in principle, possible, two feedback power control strategies are proposed: (i) a classical feedback power control strategy, which aims at equalizing all link SNRs at the access point (AP), and (ii) an innovative optimized feedback power control strategy, which tries to make the network operational point fall in the feasible SNR region at the lowest overall transmit energy consumption. These strategies will be referred to as "balanced SNR" and "unbalanced SNR," respectively. While they require, in principle, an unlimited power control range at the sources, we also propose practical versions with a limited power control range. We preliminary consider a scenario with orthogonal links and ideal feedback. Then, we analyze the robustness of the proposed power control strategies to possible non-idealities, in terms of residual multiple access interference and noisy feedback channels. Finally, we successfully apply the proposed feedback power control strategies to a limiting case of the class of considered multiple access schemes, namely a central estimating officer (CEO) scenario, where the sensors observe noisy versions of a common binary information sequence and the AP's goal is to estimate this sequence by properly fusing the soft-output information output by the JCD algorithm.

Retrieving Volcanic SO2 from the 4-UV channels on DSCOVR/EPIC

NASA Astrophysics Data System (ADS)

Fisher, B. L.; Krotkov, N. A.; Carn, S. A.; Taylor, S.; Li, C.; Bhartia, P. K.; Huang, L. K.; Haffner, D. P.

2017-12-01

Since arriving at the L1 Lagrange point in June 2015, the Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) has been collecting continuous full disk images of the sunlit earth from a distance of 1.5 million km. EPIC is a 10-band spectroradiometer that that has a field of view (FoV) at the earth's surface of about 25 km, providing a unique opportunity to observe the initial appearance and evolution of SO2 plumes from volcanic eruptions at about 90 minute temporal resolution. Our algorithm uses the 317.5, 325, 340 and 388 nm UV channels on EPIC to retrieve volcanic SO2, total column ozone, Lambertian equivalent reflectivity and its spectral dependence. The MS_SO2 algorithm has been successfully applied to the data from legacy and current NASA missions (e.g., Nimbus7/TOMS, SNPP/OMPS, and Aura/OMI). The separation between ozone and SO2 is possible due differences in the cross sections at the two shortest UV channels. The images for each spectral channel are not perfectly aligned due to the earth's rotation, geo-rectification, cloud noise, exposure time and spacecraft jitter. These issues introduce additional noise, for a multi-channel inversion. In this presentation, we describe some modifications to the algorithm that attempt to account for these issues. By comparing the plume areas, mass tonnage and peak SO2 values from other low earth observing satellites, it is shown that the algorithm significantly improves the identification of the plume, while eliminating false positives.

Channel estimation in few mode fiber mode division multiplexing transmission system

NASA Astrophysics Data System (ADS)

Hei, Yongqiang; Li, Li; Li, Wentao; Li, Xiaohui; Shi, Guangming

2018-03-01

It is abundantly clear that obtaining the channel state information (CSI) is of great importance for the equalization and detection in coherence receivers. However, to the best of the authors' knowledge, in most of the existing literatures, CSI is assumed to be perfectly known at the receiver. So far, few literature discusses the effects of imperfect CSI on MDM system performance caused by channel estimation. Motivated by that, in this paper, the channel estimation in few mode fiber (FMF) mode division multiplexing (MDM) system is investigated, in which two classical channel estimation methods, i.e., least square (LS) method and minimum mean square error (MMSE) method, are discussed with the assumption of the spatially white noise lumped at the receiver side of MDM system. Both the capacity and BER performance of MDM system affected by mode-dependent gain or loss (MDL) with different channel estimation errors have been studied. Simulation results show that the capacity and BER performance can be further deteriorated in MDM system by the channel estimation, and an 1e-3 variance of channel estimation error is acceptable in MDM system with 0-6 dB MDL values.

Effects of signal artefacts on electroencephalography spectral power during sleep: quantifying the effectiveness of automated artefact-rejection algorithms.

PubMed

Liu, Jianbo; Ramakrishnan, Sridhar; Laxminarayan, Srinivas; Neal, Maxwell; Cashmere, David J; Germain, Anne; Reifman, Jaques

2018-02-01

Electroencephalography (EEG) recordings during sleep are often contaminated by muscle and ocular artefacts, which can affect the results of spectral power analyses significantly. However, the extent to which these artefacts affect EEG spectral power across different sleep states has not been quantified explicitly. Consequently, the effectiveness of automated artefact-rejection algorithms in minimizing these effects has not been characterized fully. To address these issues, we analysed standard 10-channel EEG recordings from 20 subjects during one night of sleep. We compared their spectral power when the recordings were contaminated by artefacts and after we removed them by visual inspection or by using automated artefact-rejection algorithms. During both rapid eye movement (REM) and non-REM (NREM) sleep, muscle artefacts contaminated no more than 5% of the EEG data across all channels. However, they corrupted delta, beta and gamma power levels substantially by up to 126, 171 and 938%, respectively, relative to the power level computed from artefact-free data. Although ocular artefacts were infrequent during NREM sleep, they affected up to 16% of the frontal and temporal EEG channels during REM sleep, primarily corrupting delta power by up to 33%. For both REM and NREM sleep, the automated artefact-rejection algorithms matched power levels to within ~10% of the artefact-free power level for each EEG channel and frequency band. In summary, although muscle and ocular artefacts affect only a small fraction of EEG data, they affect EEG spectral power significantly. This suggests the importance of using artefact-rejection algorithms before analysing EEG data. © 2017 European Sleep Research Society.

Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.

PubMed

Nie, Jingxin; Xue, Zhong; Liu, Tianming; Young, Geoffrey S; Setayesh, Kian; Guo, Lei; Wong, Stephen T C

2009-09-01

A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.

Environment-specific noise suppression for improved speech intelligibility by cochlear implant users.

PubMed

Hu, Yi; Loizou, Philipos C

2010-06-01

Attempts to develop noise-suppression algorithms that can significantly improve speech intelligibility in noise by cochlear implant (CI) users have met with limited success. This is partly because algorithms were sought that would work equally well in all listening situations. Accomplishing this has been quite challenging given the variability in the temporal/spectral characteristics of real-world maskers. A different approach is taken in the present study focused on the development of environment-specific noise suppression algorithms. The proposed algorithm selects a subset of the envelope amplitudes for stimulation based on the signal-to-noise ratio (SNR) of each channel. Binary classifiers, trained using data collected from a particular noisy environment, are first used to classify the mixture envelopes of each channel as either target-dominated (SNR>or=0 dB) or masker-dominated (SNR<0 dB). Only target-dominated channels are subsequently selected for stimulation. Results with CI listeners indicated substantial improvements (by nearly 44 percentage points at 5 dB SNR) in intelligibility with the proposed algorithm when tested with sentences embedded in three real-world maskers. The present study demonstrated that the environment-specific approach to noise reduction has the potential to restore speech intelligibility in noise to a level near to that attained in quiet.

The Error Structure of the SMAP Single and Dual Channel Soil Moisture Retrievals

NASA Astrophysics Data System (ADS)

Dong, Jianzhi; Crow, Wade T.; Bindlish, Rajat

2018-01-01

Knowledge of the temporal error structure for remotely sensed surface soil moisture retrievals can improve our ability to exploit them for hydrologic and climate studies. This study employs a triple collocation analysis to investigate both the total variance and temporal autocorrelation of errors in Soil Moisture Active and Passive (SMAP) products generated from two separate soil moisture retrieval algorithms, the vertically polarized brightness temperature-based single-channel algorithm (SCA-V, the current baseline SMAP algorithm) and the dual-channel algorithm (DCA). A key assumption made in SCA-V is that real-time vegetation opacity can be accurately captured using only a climatology for vegetation opacity. Results demonstrate that while SCA-V generally outperforms DCA, SCA-V can produce larger total errors when this assumption is significantly violated by interannual variability in vegetation health and biomass. Furthermore, larger autocorrelated errors in SCA-V retrievals are found in areas with relatively large vegetation opacity deviations from climatological expectations. This implies that a significant portion of the autocorrelated error in SCA-V is attributable to the violation of its vegetation opacity climatology assumption and suggests that utilizing a real (as opposed to climatological) vegetation opacity time series in the SCA-V algorithm would reduce the magnitude of autocorrelated soil moisture retrieval errors.

FPGA implementation of low complexity LDPC iterative decoder

NASA Astrophysics Data System (ADS)

Verma, Shivani; Sharma, Sanjay

2016-07-01

Low-density parity-check (LDPC) codes, proposed by Gallager, emerged as a class of codes which can yield very good performance on the additive white Gaussian noise channel as well as on the binary symmetric channel. LDPC codes have gained lots of importance due to their capacity achieving property and excellent performance in the noisy channel. Belief propagation (BP) algorithm and its approximations, most notably min-sum, are popular iterative decoding algorithms used for LDPC and turbo codes. The trade-off between the hardware complexity and the decoding throughput is a critical factor in the implementation of the practical decoder. This article presents introduction to LDPC codes and its various decoding algorithms followed by realisation of LDPC decoder by using simplified message passing algorithm and partially parallel decoder architecture. Simplified message passing algorithm has been proposed for trade-off between low decoding complexity and decoder performance. It greatly reduces the routing and check node complexity of the decoder. Partially parallel decoder architecture possesses high speed and reduced complexity. The improved design of the decoder possesses a maximum symbol throughput of 92.95 Mbps and a maximum of 18 decoding iterations. The article presents implementation of 9216 bits, rate-1/2, (3, 6) LDPC decoder on Xilinx XC3D3400A device from Spartan-3A DSP family.

Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous).

PubMed

Hemmateenejad, Bahram; Akhond, Morteza; Miri, Ramin; Shamsipur, Mojtaba

2003-01-01

A QSAR algorithm, principal component-genetic algorithm-artificial neural network (PC-GA-ANN), has been applied to a set of newly synthesized calcium channel blockers, which are of special interest because of their role in cardiac diseases. A data set of 124 1,4-dihydropyridines bearing different ester substituents at the C-3 and C-5 positions of the dihydropyridine ring and nitroimidazolyl, phenylimidazolyl, and methylsulfonylimidazolyl groups at the C-4 position with known Ca(2+) channel binding affinities was employed in this study. Ten different sets of descriptors (837 descriptors) were calculated for each molecule. The principal component analysis was used to compress the descriptor groups into principal components. The most significant descriptors of each set were selected and used as input for the ANN. The genetic algorithm (GA) was used for the selection of the best set of extracted principal components. A feed forward artificial neural network with a back-propagation of error algorithm was used to process the nonlinear relationship between the selected principal components and biological activity of the dihydropyridines. A comparison between PC-GA-ANN and routine PC-ANN shows that the first model yields better prediction ability.

MIMO channel estimation and evaluation for airborne traffic surveillance in cellular networks

NASA Astrophysics Data System (ADS)

Vahidi, Vahid; Saberinia, Ebrahim

2018-01-01

A channel estimation (CE) procedure based on compressed sensing is proposed to estimate the multiple-input multiple-output sparse channel for traffic data transmission from drones to ground stations. The proposed procedure consists of an offline phase and a real-time phase. In the offline phase, a pilot arrangement method, which considers the interblock and block mutual coherence simultaneously, is proposed. The real-time phase contains three steps. At the first step, it obtains the priori estimate of the channel by block orthogonal matching pursuit; afterward, it utilizes that estimated channel to calculate the linear minimum mean square error of the received pilots. Finally, the block compressive sampling matching pursuit utilizes the enhanced received pilots to estimate the channel more accurately. The performance of the CE procedure is evaluated by simulating the transmission of traffic data through the communication channel and evaluating its fidelity for car detection after demodulation. Simulation results indicate that the proposed CE technique enhances the performance of the car detection in a traffic image considerably.

Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data

NASA Astrophysics Data System (ADS)

Biswas, Rahul; Blackburn, Lindy; Cao, Junwei; Essick, Reed; Hodge, Kari Alison; Katsavounidis, Erotokritos; Kim, Kyungmin; Kim, Young-Min; Le Bigot, Eric-Olivier; Lee, Chang-Hwan; Oh, John J.; Oh, Sang Hoon; Son, Edwin J.; Tao, Ye; Vaulin, Ruslan; Wang, Xiaoge

2013-09-01

The sensitivity of searches for astrophysical transients in data from the Laser Interferometer Gravitational-wave Observatory (LIGO) is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high enough rate such that accidental coincidence across multiple detectors is non-negligible. These “glitches” can easily be mistaken for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational waves. We apply machine-learning algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. Noise sources may produce artifacts in these auxiliary channels as well as the gravitational-wave channel. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; high dimensionality is an area where MLAs are particularly well suited. We demonstrate the feasibility and applicability of three different MLAs: artificial neural networks, support vector machines, and random forests. These classifiers identify and remove a substantial fraction of the glitches present in two different data sets: four weeks of LIGO’s fourth science run and one week of LIGO’s sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth-science-run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar performance to the benchmark algorithm, the ordered veto list, which is optimized to detect pairwise correlations between transients in LIGO auxiliary channels and glitches in the gravitational-wave data. This suggests that most of the useful information currently extracted from the auxiliary channels is already described by this model. Future performance gains are thus likely to involve additional sources of information, rather than improvements in the classification algorithms themselves. We discuss several plausible sources of such new information as well as the ways of propagating it through the classifiers into gravitational-wave searches.

Communication Channel Estimation and Waveform Design: Time Delay Estimation on Parallel, Flat Fading Channels

DTIC Science & Technology

2010-02-01

channels, so the channel gain is known on each realization and used in a coherent matched filter; and (c) Rayleigh channels with noncoherent matched...gain is known on each realization and used in a coherent matched filter (channel model 1A); and (c) Rayleigh channels with noncoherent matched filters...filters, averaged over Rayleigh channel realizations (channel model 1A). (b) Noncoherent matched filters with Rayleigh fading (channel model 3). MSEs are

Channel Training for Analog FDD Repeaters: Optimal Estimators and Cramér-Rao Bounds

NASA Astrophysics Data System (ADS)

Wesemann, Stefan; Marzetta, Thomas L.

2017-12-01

For frequency division duplex channels, a simple pilot loop-back procedure has been proposed that allows the estimation of the UL & DL channels at an antenna array without relying on any digital signal processing at the terminal side. For this scheme, we derive the maximum likelihood (ML) estimators for the UL & DL channel subspaces, formulate the corresponding Cram\\'er-Rao bounds and show the asymptotic efficiency of both (SVD-based) estimators by means of Monte Carlo simulations. In addition, we illustrate how to compute the underlying (rank-1) SVD with quadratic time complexity by employing the power iteration method. To enable power control for the data transmission, knowledge of the channel gains is needed. Assuming that the UL & DL channels have on average the same gain, we formulate the ML estimator for the channel norm, and illustrate its robustness against strong noise by means of simulations.