Efficient Sparse Signal Transmission over a Lossy Link Using Compressive Sensing

PubMed Central

Wu, Liantao; Yu, Kai; Cao, Dongyu; Hu, Yuhen; Wang, Zhi

2015-01-01

Reliable data transmission over lossy communication link is expensive due to overheads for error protection. For signals that have inherent sparse structures, compressive sensing (CS) is applied to facilitate efficient sparse signal transmissions over lossy communication links without data compression or error protection. The natural packet loss in the lossy link is modeled as a random sampling process of the transmitted data, and the original signal will be reconstructed from the lossy transmission results using the CS-based reconstruction method at the receiving end. The impacts of packet lengths on transmission efficiency under different channel conditions have been discussed, and interleaving is incorporated to mitigate the impact of burst data loss. Extensive simulations and experiments have been conducted and compared to the traditional automatic repeat request (ARQ) interpolation technique, and very favorable results have been observed in terms of both accuracy of the reconstructed signals and the transmission energy consumption. Furthermore, the packet length effect provides useful insights for using compressed sensing for efficient sparse signal transmission via lossy links. PMID:26287195

Machinery Bearing Fault Diagnosis Using Variational Mode Decomposition and Support Vector Machine as a Classifier

NASA Astrophysics Data System (ADS)

Rama Krishna, K.; Ramachandran, K. I.

2018-02-01

Crack propagation is a major cause of failure in rotating machines. It adversely affects the productivity, safety, and the machining quality. Hence, detecting the crack’s severity accurately is imperative for the predictive maintenance of such machines. Fault diagnosis is an established concept in identifying the faults, for observing the non-linear behaviour of the vibration signals at various operating conditions. In this work, we find the classification efficiencies for both original and the reconstructed vibrational signals. The reconstructed signals are obtained using Variational Mode Decomposition (VMD), by splitting the original signal into three intrinsic mode functional components and framing them accordingly. Feature extraction, feature selection and feature classification are the three phases in obtaining the classification efficiencies. All the statistical features from the original signals and reconstructed signals are found out in feature extraction process individually. A few statistical parameters are selected in feature selection process and are classified using the SVM classifier. The obtained results show the best parameters and appropriate kernel in SVM classifier for detecting the faults in bearings. Hence, we conclude that better results were obtained by VMD and SVM process over normal process using SVM. This is owing to denoising and filtering the raw vibrational signals.

An efficient ASIC implementation of 16-channel on-line recursive ICA processor for real-time EEG system.

PubMed

Fang, Wai-Chi; Huang, Kuan-Ju; Chou, Chia-Ching; Chang, Jui-Chung; Cauwenberghs, Gert; Jung, Tzyy-Ping

2014-01-01

This is a proposal for an efficient very-large-scale integration (VLSI) design, 16-channel on-line recursive independent component analysis (ORICA) processor ASIC for real-time EEG system, implemented with TSMC 40 nm CMOS technology. ORICA is appropriate to be used in real-time EEG system to separate artifacts because of its highly efficient and real-time process features. The proposed ORICA processor is composed of an ORICA processing unit and a singular value decomposition (SVD) processing unit. Compared with previous work [1], this proposed ORICA processor has enhanced effectiveness and reduced hardware complexity by utilizing a deeper pipeline architecture, shared arithmetic processing unit, and shared registers. The 16-channel random signals which contain 8-channel super-Gaussian and 8-channel sub-Gaussian components are used to analyze the dependence of the source components, and the average correlation coefficient is 0.95452 between the original source signals and extracted ORICA signals. Finally, the proposed ORICA processor ASIC is implemented with TSMC 40 nm CMOS technology, and it consumes 15.72 mW at 100 MHz operating frequency.

Cycling excitation process: An ultra efficient and quiet signal amplification mechanism in semiconductor

NASA Astrophysics Data System (ADS)

Liu, Yu-Hsin; Yan, Lujiang; Zhang, Alex Ce; Hall, David; Niaz, Iftikhar Ahmad; Zhou, Yuchun; Sham, L. J.; Lo, Yu-Hwa

2015-08-01

Signal amplification, performed by transistor amplifiers with its merit rated by the efficiency and noise characteristics, is ubiquitous in all electronic systems. Because of transistor thermal noise, an intrinsic signal amplification mechanism, impact ionization was sought after to complement the limits of transistor amplifiers. However, due to the high operation voltage (30-200 V typically), low power efficiency, limited scalability, and, above all, rapidly increasing excess noise with amplification factor, impact ionization has been out of favor for most electronic systems except for a few applications such as avalanche photodetectors and single-photon Geiger detectors. Here, we report an internal signal amplification mechanism based on the principle of the phonon-assisted cycling excitation process (CEP). Si devices using this concept show ultrahigh gain, low operation voltage, CMOS compatibility, and, above all, quantum limit noise performance that is 30 times lower than devices using impact ionization. Established on a unique physical effect of attractive properties, CEP-based devices can potentially revolutionize the fields of semiconductor electronics.

Digital Signal Processing Methods for Ultrasonic Echoes.

PubMed

Sinding, Kyle; Drapaca, Corina; Tittmann, Bernhard

2016-04-28

Digital signal processing has become an important component of data analysis needed in industrial applications. In particular, for ultrasonic thickness measurements the signal to noise ratio plays a major role in the accurate calculation of the arrival time. For this application a band pass filter is not sufficient since the noise level cannot be significantly decreased such that a reliable thickness measurement can be performed. This paper demonstrates the abilities of two regularization methods - total variation and Tikhonov - to filter acoustic and ultrasonic signals. Both of these methods are compared to a frequency based filtering for digitally produced signals as well as signals produced by ultrasonic transducers. This paper demonstrates the ability of the total variation and Tikhonov filters to accurately recover signals from noisy acoustic signals faster than a band pass filter. Furthermore, the total variation filter has been shown to reduce the noise of a signal significantly for signals with clear ultrasonic echoes. Signal to noise ratios have been increased over 400% by using a simple parameter optimization. While frequency based filtering is efficient for specific applications, this paper shows that the reduction of noise in ultrasonic systems can be much more efficient with regularization methods.

Energy-efficient hierarchical processing in the network of wireless intelligent sensors (WISE)

NASA Astrophysics Data System (ADS)

Raskovic, Dejan

Sensor network nodes have benefited from technological advances in the field of wireless communication, processing, and power sources. However, the processing power of microcontrollers is often not sufficient to perform sophisticated processing, while the power requirements of digital signal processing boards or handheld computers are usually too demanding for prolonged system use. We are matching the intrinsic hierarchical nature of many digital signal-processing applications with the natural hierarchy in distributed wireless networks, and building the hierarchical system of wireless intelligent sensors. Our goal is to build a system that will exploit the hierarchical organization to optimize the power consumption and extend battery life for the given time and memory constraints, while providing real-time processing of sensor signals. In addition, we are designing our system to be able to adapt to the current state of the environment, by dynamically changing the algorithm through procedure replacement. This dissertation presents the analysis of hierarchical environment and methods for energy profiling used to evaluate different system design strategies, and to optimize time-effective and energy-efficient processing.

Investigation of signal processing algorithms for an embedded microcontroller-based wearable pulse oximeter.

PubMed

Johnston, W S; Mendelson, Y

2006-01-01

Despite steady progress in the miniaturization of pulse oximeters over the years, significant challenges remain since advanced signal processing must be implemented efficiently in real-time by a relatively small size wearable device. The goal of this study was to investigate several potential digital signal processing algorithms for computing arterial oxygen saturation (SpO(2)) and heart rate (HR) in a battery-operated wearable reflectance pulse oximeter that is being developed in our laboratory for use by medics and first responders in the field. We found that a differential measurement approach, combined with a low-pass filter (LPF), yielded the most suitable signal processing technique for estimating SpO(2), while a signal derivative approach produced the most accurate HR measurements.

Effects of Computer Architecture on FFT (Fast Fourier Transform) Algorithm Performance.

DTIC Science & Technology

1983-12-01

Criteria for Efficient Implementation of FFT Algorithms," IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-30, pp. 107-109, Feb...1982. Burrus, C. S. and P. W. Eschenbacher. "An In-Place, In-Order Prime Factor FFT Algorithm," IEEE Transactions on Acoustics, Speech, and Signal... Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-30, pp. 217-226, Apr. 1982. Control Data Corporation. CDC Cyber 170 Computer Systems

Event-driven processing for hardware-efficient neural spike sorting

NASA Astrophysics Data System (ADS)

Liu, Yan; Pereira, João L.; Constandinou, Timothy G.

2018-02-01

Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.

Cycling excitation process: An ultra efficient and quiet signal amplification mechanism in semiconductor

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

Liu, Yu-Hsin; Yan, Lujiang; Zhang, Alex Ce

2015-08-03

Signal amplification, performed by transistor amplifiers with its merit rated by the efficiency and noise characteristics, is ubiquitous in all electronic systems. Because of transistor thermal noise, an intrinsic signal amplification mechanism, impact ionization was sought after to complement the limits of transistor amplifiers. However, due to the high operation voltage (30-200 V typically), low power efficiency, limited scalability, and, above all, rapidly increasing excess noise with amplification factor, impact ionization has been out of favor for most electronic systems except for a few applications such as avalanche photodetectors and single-photon Geiger detectors. Here, we report an internal signal amplification mechanismmore » based on the principle of the phonon-assisted cycling excitation process (CEP). Si devices using this concept show ultrahigh gain, low operation voltage, CMOS compatibility, and, above all, quantum limit noise performance that is 30 times lower than devices using impact ionization. Established on a unique physical effect of attractive properties, CEP-based devices can potentially revolutionize the fields of semiconductor electronics.« less

Radioastronomic signal processing cores for the SKA radio telescope

NASA Astrophysics Data System (ADS)

Comorett, G.; Chiarucc, S.; Belli, C.

Modern radio telescopes require the processing of wideband signals, with sample rates from tens of MHz to tens of GHz, and are composed from hundreds up to a million of individual antennas. Digital signal processing of these signals include digital receivers (the digital equivalent of the heterodyne receiver), beamformers, channelizers, spectrometers. FPGAs present the advantage of providing a relatively low power consumption, relative to GPUs or dedicated computers, a wide signal data path, and high interconnectivity. Efficient algorithms have been developed for these applications. Here we will review some of the signal processing cores developed for the SKA telescope. The LFAA beamformer/channelizer architecture is based on an oversampling channelizer, where the channelizer output sampling rate and channel spacing can be set independently. This is useful where an overlap between adjacent channels is required to provide an uniform spectral coverage. The architecture allows for an efficient and distributed channelization scheme, with a final resolution corresponding to a million of spectral channels, minimum leakage and high out-of-band rejection. An optimized filter design procedure is used to provide an equiripple response with a very large number of spectral channels. A wideband digital receiver has been designed in order to select the processed bandwidth of the SKA Mid receiver. The receiver extracts a 2.5 MHz bandwidth form a 14 GHz input bandwidth. The design allows for non-integer ratios between the input and output sampling rates, with a resource usage comparable to that of a conventional decimating digital receiver. Finally, some considerations on quantization of radioastronomic signals are presented. Due to the stochastic nature of the signal, quantization using few data bits is possible. Good accuracies and dynamic range are possible even with 2-3 bits, but the nonlinearity in the correlation process must be corrected in post-processing. With at least 6 bits it is possible to have a very linear response of the instrument, with nonlinear terms below 80 dB, providing the signal amplitude is kept within bounds.

A fast discrete S-transform for biomedical signal processing.

PubMed

Brown, Robert A; Frayne, Richard

2008-01-01

Determining the frequency content of a signal is a basic operation in signal and image processing. The S-transform provides both the true frequency and globally referenced phase measurements characteristic of the Fourier transform and also generates local spectra, as does the wavelet transform. Due to this combination, the S-transform has been successfully demonstrated in a variety of biomedical signal and image processing tasks. However, the computational demands of the S-transform have limited its application in medicine to this point in time. This abstract introduces the fast S-transform, a more efficient discrete implementation of the classic S-transform with dramatically reduced computational requirements.

A Study on Signal Group Processing of AUTOSAR COM Module

NASA Astrophysics Data System (ADS)

Lee, Jeong-Hwan; Hwang, Hyun Yong; Han, Tae Man; Ahn, Yong Hak

2013-06-01

In vehicle, there are many ECU(Electronic Control Unit)s, and ECUs are connected to networks such as CAN, LIN, FlexRay, and so on. AUTOSAR COM(Communication) which is a software platform of AUTOSAR(AUTomotive Open System ARchitecture) in the international industry standards of automotive electronic software processes signals and signal groups for data communications between ECUs. Real-time and reliability are very important for data communications in the vehicle. Therefore, in this paper, we analyze functions of signals and signal groups used in COM, and represent that functions of signal group are more efficient than signals in real-time data synchronization and network resource usage between the sender and receiver.

Comparison of two hardware-based hit filtering methods for trackers in high-pileup environments

NASA Astrophysics Data System (ADS)

Gradin, J.; Mårtensson, M.; Brenner, R.

2018-04-01

As experiments in high energy physics aim to measure increasingly rare processes, the experiments continually strive to increase the expected signal yields. In the case of the High Luminosity upgrade of the LHC, the luminosity is raised by increasing the number of simultaneous proton-proton interactions, so-called pile-up. This increases the expected yields of signal and background processes alike. The signal is embedded in a large background of processes that mimic that of signal events. It is therefore imperative for the experiments to develop new triggering methods to effectively distinguish the interesting events from the background. We present a comparison of two methods for filtering detector hits to be used for triggering on particle tracks: one based on a pattern matching technique using Associative Memory (AM) chips and the other based on the Hough transform. Their efficiency and hit rejection are evaluated for proton-proton collisions with varying amounts of pile-up using a simulation of a generic silicon tracking detector. It is found that, while both methods are feasible options for a track trigger with single muon efficiencies around 98–99%, the AM based pattern matching produces a lower number of hit combinations with respect to the Hough transform whilst keeping more of the true signal hits. We also present the effect on the two methods of increasing the amount of support material in the detector and of introducing inefficiencies by deactivating detector modules. The increased support material has negligable effects on the efficiency for both methods, while dropping 5% (10%) of the available modules decreases the efficiency to about 95% (87%) for both methods, irrespective of the amount of pile-up.

Sparse gammatone signal model optimized for English speech does not match the human auditory filters.

PubMed

Strahl, Stefan; Mertins, Alfred

2008-07-18

Evidence that neurosensory systems use sparse signal representations as well as improved performance of signal processing algorithms using sparse signal models raised interest in sparse signal coding in the last years. For natural audio signals like speech and environmental sounds, gammatone atoms have been derived as expansion functions that generate a nearly optimal sparse signal model (Smith, E., Lewicki, M., 2006. Efficient auditory coding. Nature 439, 978-982). Furthermore, gammatone functions are established models for the human auditory filters. Thus far, a practical application of a sparse gammatone signal model has been prevented by the fact that deriving the sparsest representation is, in general, computationally intractable. In this paper, we applied an accelerated version of the matching pursuit algorithm for gammatone dictionaries allowing real-time and large data set applications. We show that a sparse signal model in general has advantages in audio coding and that a sparse gammatone signal model encodes speech more efficiently in terms of sparseness than a sparse modified discrete cosine transform (MDCT) signal model. We also show that the optimal gammatone parameters derived for English speech do not match the human auditory filters, suggesting for signal processing applications to derive the parameters individually for each applied signal class instead of using psychometrically derived parameters. For brain research, it means that care should be taken with directly transferring findings of optimality for technical to biological systems.

Optimal size of stochastic Hodgkin-Huxley neuronal systems for maximal energy efficiency in coding pulse signals

NASA Astrophysics Data System (ADS)

Yu, Lianchun; Liu, Liwei

2014-03-01

The generation and conduction of action potentials (APs) represents a fundamental means of communication in the nervous system and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in transferring pulse signals with APs. By analytically solving a bistable neuron model that mimics the AP generation with a particle crossing the barrier of a double well, we find the optimal number of ion channels that maximizes the energy efficiency of a neuron. We also investigate the energy efficiency of a neuron population in which the input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal number of neurons in neuron population, as well as the number of ion channels in each neuron that maximizes the energy efficiency. The energy efficiency also depends on the characters of the input signals, e.g., the pulse strength and the interpulse intervals. These results are confirmed by computer simulation of the stochastic Hodgkin-Huxley model with a detailed description of the ion channel random gating. We argue that the tradeoff between signal transmission reliability and energy cost may influence the size of the neural systems when energy use is constrained.

Optimal size of stochastic Hodgkin-Huxley neuronal systems for maximal energy efficiency in coding pulse signals.

PubMed

Yu, Lianchun; Liu, Liwei

2014-03-01

The generation and conduction of action potentials (APs) represents a fundamental means of communication in the nervous system and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in transferring pulse signals with APs. By analytically solving a bistable neuron model that mimics the AP generation with a particle crossing the barrier of a double well, we find the optimal number of ion channels that maximizes the energy efficiency of a neuron. We also investigate the energy efficiency of a neuron population in which the input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal number of neurons in neuron population, as well as the number of ion channels in each neuron that maximizes the energy efficiency. The energy efficiency also depends on the characters of the input signals, e.g., the pulse strength and the interpulse intervals. These results are confirmed by computer simulation of the stochastic Hodgkin-Huxley model with a detailed description of the ion channel random gating. We argue that the tradeoff between signal transmission reliability and energy cost may influence the size of the neural systems when energy use is constrained.

Information theoretical assessment of digital imaging systems

NASA Technical Reports Server (NTRS)

John, Sarah; Rahman, Zia-Ur; Huck, Friedrich O.; Reichenbach, Stephen E.

1990-01-01

The end-to-end performance of image gathering, coding, and restoration as a whole is considered. This approach is based on the pivotal relationship that exists between the spectral information density of the transmitted signal and the restorability of images from this signal. The information-theoretical assessment accounts for (1) the information density and efficiency of the acquired signal as a function of the image-gathering system design and the radiance-field statistics, and (2) the improvement in information efficiency and data compression that can be gained by combining image gathering with coding to reduce the signal redundancy and irrelevancy. It is concluded that images can be restored with better quality and from fewer data as the information efficiency of the data is increased. The restoration correctly explains the image gathering and coding processes and effectively suppresses the image-display degradations.

Information theoretical assessment of digital imaging systems

NASA Astrophysics Data System (ADS)

John, Sarah; Rahman, Zia-Ur; Huck, Friedrich O.; Reichenbach, Stephen E.

1990-10-01

The end-to-end performance of image gathering, coding, and restoration as a whole is considered. This approach is based on the pivotal relationship that exists between the spectral information density of the transmitted signal and the restorability of images from this signal. The information-theoretical assessment accounts for (1) the information density and efficiency of the acquired signal as a function of the image-gathering system design and the radiance-field statistics, and (2) the improvement in information efficiency and data compression that can be gained by combining image gathering with coding to reduce the signal redundancy and irrelevancy. It is concluded that images can be restored with better quality and from fewer data as the information efficiency of the data is increased. The restoration correctly explains the image gathering and coding processes and effectively suppresses the image-display degradations.

Parallel heterogeneous architectures for efficient OMP compressive sensing reconstruction

NASA Astrophysics Data System (ADS)

Kulkarni, Amey; Stanislaus, Jerome L.; Mohsenin, Tinoosh

2014-05-01

Compressive Sensing (CS) is a novel scheme, in which a signal that is sparse in a known transform domain can be reconstructed using fewer samples. The signal reconstruction techniques are computationally intensive and have sluggish performance, which make them impractical for real-time processing applications . The paper presents novel architectures for Orthogonal Matching Pursuit algorithm, one of the popular CS reconstruction algorithms. We show the implementation results of proposed architectures on FPGA, ASIC and on a custom many-core platform. For FPGA and ASIC implementation, a novel thresholding method is used to reduce the processing time for the optimization problem by at least 25%. Whereas, for the custom many-core platform, efficient parallelization techniques are applied, to reconstruct signals with variant signal lengths of N and sparsity of m. The algorithm is divided into three kernels. Each kernel is parallelized to reduce execution time, whereas efficient reuse of the matrix operators allows us to reduce area. Matrix operations are efficiently paralellized by taking advantage of blocked algorithms. For demonstration purpose, all architectures reconstruct a 256-length signal with maximum sparsity of 8 using 64 measurements. Implementation on Xilinx Virtex-5 FPGA, requires 27.14 μs to reconstruct the signal using basic OMP. Whereas, with thresholding method it requires 18 μs. ASIC implementation reconstructs the signal in 13 μs. However, our custom many-core, operating at 1.18 GHz, takes 18.28 μs to complete. Our results show that compared to the previous published work of the same algorithm and matrix size, proposed architectures for FPGA and ASIC implementations perform 1.3x and 1.8x respectively faster. Also, the proposed many-core implementation performs 3000x faster than the CPU and 2000x faster than the GPU.

BioSig: The Free and Open Source Software Library for Biomedical Signal Processing

PubMed Central

Vidaurre, Carmen; Sander, Tilmann H.; Schlögl, Alois

2011-01-01

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals. PMID:21437227

BioSig: the free and open source software library for biomedical signal processing.

PubMed

Vidaurre, Carmen; Sander, Tilmann H; Schlögl, Alois

2011-01-01

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.

An Assessment of Behavioral Dynamic Information Processing Measures in Audiovisual Speech Perception

PubMed Central

Altieri, Nicholas; Townsend, James T.

2011-01-01

Research has shown that visual speech perception can assist accuracy in identification of spoken words. However, little is known about the dynamics of the processing mechanisms involved in audiovisual integration. In particular, architecture and capacity, measured using response time methodologies, have not been investigated. An issue related to architecture concerns whether the auditory and visual sources of the speech signal are integrated “early” or “late.” We propose that “early” integration most naturally corresponds to coactive processing whereas “late” integration corresponds to separate decisions parallel processing. We implemented the double factorial paradigm in two studies. First, we carried out a pilot study using a two-alternative forced-choice discrimination task to assess architecture, decision rule, and provide a preliminary assessment of capacity (integration efficiency). Next, Experiment 1 was designed to specifically assess audiovisual integration efficiency in an ecologically valid way by including lower auditory S/N ratios and a larger response set size. Results from the pilot study support a separate decisions parallel, late integration model. Results from both studies showed that capacity was severely limited for high auditory signal-to-noise ratios. However, Experiment 1 demonstrated that capacity improved as the auditory signal became more degraded. This evidence strongly suggests that integration efficiency is vitally affected by the S/N ratio. PMID:21980314

An open-loop system design for deep space signal processing applications

NASA Astrophysics Data System (ADS)

Tang, Jifei; Xia, Lanhua; Mahapatra, Rabi

2018-06-01

A novel open-loop system design with high performance is proposed for space positioning and navigation signal processing. Divided by functions, the system has four modules, bandwidth selectable data recorder, narrowband signal analyzer, time-delay difference of arrival estimator and ANFIS supplement processor. A hardware-software co-design approach is made to accelerate computing capability and improve system efficiency. Embedded with the proposed signal processing algorithms, the designed system is capable of handling tasks with high accuracy over long period of continuous measurements. The experiment results show the Doppler frequency tracking root mean square error during 3 h observation is 0.0128 Hz, while the TDOA residue analysis in correlation power spectrum is 0.1166 rad.

Nano-optomechanical transducer

DOEpatents

Rakich, Peter T; El-Kady, Ihab F; Olsson, Roy H; Su, Mehmet Fatih; Reinke, Charles; Camacho, Ryan; Wang, Zheng; Davids, Paul

2013-12-03

A nano-optomechanical transducer provides ultrabroadband coherent optomechanical transduction based on Mach-wave emission that uses enhanced photon-phonon coupling efficiencies by low impedance effective phononic medium, both electrostriction and radiation pressure to boost and tailor optomechanical forces, and highly dispersive electromagnetic modes that amplify both electrostriction and radiation pressure. The optomechanical transducer provides a large operating bandwidth and high efficiency while simultaneously having a small size and minimal power consumption, enabling a host of transformative phonon and signal processing capabilities. These capabilities include optomechanical transduction via pulsed phonon emission and up-conversion, broadband stimulated phonon emission and amplification, picosecond pulsed phonon lasers, broadband phononic modulators, and ultrahigh bandwidth true time delay and signal processing technologies.

Laser doppler blood flow imaging using a CMOS imaging sensor with on-chip signal processing.

PubMed

He, Diwei; Nguyen, Hoang C; Hayes-Gill, Barrie R; Zhu, Yiqun; Crowe, John A; Gill, Cally; Clough, Geraldine F; Morgan, Stephen P

2013-09-18

The first fully integrated 2D CMOS imaging sensor with on-chip signal processing for applications in laser Doppler blood flow (LDBF) imaging has been designed and tested. To obtain a space efficient design over 64 × 64 pixels means that standard processing electronics used off-chip cannot be implemented. Therefore the analog signal processing at each pixel is a tailored design for LDBF signals with balanced optimization for signal-to-noise ratio and silicon area. This custom made sensor offers key advantages over conventional sensors, viz. the analog signal processing at the pixel level carries out signal normalization; the AC amplification in combination with an anti-aliasing filter allows analog-to-digital conversion with a low number of bits; low resource implementation of the digital processor enables on-chip processing and the data bottleneck that exists between the detector and processing electronics has been overcome. The sensor demonstrates good agreement with simulation at each design stage. The measured optical performance of the sensor is demonstrated using modulated light signals and in vivo blood flow experiments. Images showing blood flow changes with arterial occlusion and an inflammatory response to a histamine skin-prick demonstrate that the sensor array is capable of detecting blood flow signals from tissue.

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

Shaw, Debra J.; Morse, Robert; Todd, Adrian G.

The Ewing Sarcoma (EWS) protein is a ubiquitously expressed RNA processing factor that localises predominantly to the nucleus. However, the mechanism through which EWS enters the nucleus remains unclear, with differing reports identifying three separate import signals within the EWS protein. Here we have utilized a panel of truncated EWS proteins to clarify the reported nuclear localisation signals. We describe three C-terminal domains that are important for efficient EWS nuclear localization: (1) the third RGG-motif; (2) the last 10 amino acids (known as the PY-import motif); and (3) the zinc-finger motif. Although these three domains are involved in nuclear import,more » they are not independently capable of driving the efficient import of a GFP-moiety. However, collectively they form a complex tripartite signal that efficiently drives GFP-import into the nucleus. This study helps clarify the EWS import signal, and the identification of the involvement of both the RGG- and zinc-finger motifs has wide reaching implications.« less

Photon correlation in single-photon frequency upconversion.

PubMed

Gu, Xiaorong; Huang, Kun; Pan, Haifeng; Wu, E; Zeng, Heping

2012-01-30

We experimentally investigated the intensity cross-correlation between the upconverted photons and the unconverted photons in the single-photon frequency upconversion process with multi-longitudinal mode pump and signal sources. In theoretical analysis, with this multi-longitudinal mode of both signal and pump sources system, the properties of the signal photons could also be maintained as in the single-mode frequency upconversion system. Experimentally, based on the conversion efficiency of 80.5%, the joint probability of simultaneously detecting at upconverted and unconverted photons showed an anti-correlation as a function of conversion efficiency which indicated the upconverted photons were one-to-one from the signal photons. While due to the coherent state of the signal photons, the intensity cross-correlation function g(2)(0) was shown to be equal to unity at any conversion efficiency, agreeing with the theoretical prediction. This study will benefit the high-speed wavelength-tunable quantum state translation or photonic quantum interface together with the mature frequency tuning or longitudinal mode selection techniques.

GPU-accelerated algorithms for compressed signals recovery with application to astronomical imagery deblurring

NASA Astrophysics Data System (ADS)

Fiandrotti, Attilio; Fosson, Sophie M.; Ravazzi, Chiara; Magli, Enrico

2018-04-01

Compressive sensing promises to enable bandwidth-efficient on-board compression of astronomical data by lifting the encoding complexity from the source to the receiver. The signal is recovered off-line, exploiting GPUs parallel computation capabilities to speedup the reconstruction process. However, inherent GPU hardware constraints limit the size of the recoverable signal and the speedup practically achievable. In this work, we design parallel algorithms that exploit the properties of circulant matrices for efficient GPU-accelerated sparse signals recovery. Our approach reduces the memory requirements, allowing us to recover very large signals with limited memory. In addition, it achieves a tenfold signal recovery speedup thanks to ad-hoc parallelization of matrix-vector multiplications and matrix inversions. Finally, we practically demonstrate our algorithms in a typical application of circulant matrices: deblurring a sparse astronomical image in the compressed domain.

Information efficiency in visual communication

NASA Astrophysics Data System (ADS)

Alter-Gartenberg, Rachel; Rahman, Zia-ur

1993-08-01

This paper evaluates the quantization process in the context of the end-to-end performance of the visual-communication channel. Results show that the trade-off between data transmission and visual quality revolves around the information in the acquired signal, not around its energy. Improved information efficiency is gained by frequency dependent quantization that maintains the information capacity of the channel and reduces the entropy of the encoded signal. Restorations with energy bit-allocation lose both in sharpness and clarity relative to restorations with information bit-allocation. Thus, quantization with information bit-allocation is preferred for high information efficiency and visual quality in optimized visual communication.

Information efficiency in visual communication

NASA Technical Reports Server (NTRS)

Alter-Gartenberg, Rachel; Rahman, Zia-Ur

1993-01-01

This paper evaluates the quantization process in the context of the end-to-end performance of the visual-communication channel. Results show that the trade-off between data transmission and visual quality revolves around the information in the acquired signal, not around its energy. Improved information efficiency is gained by frequency dependent quantization that maintains the information capacity of the channel and reduces the entropy of the encoded signal. Restorations with energy bit-allocation lose both in sharpness and clarity relative to restorations with information bit-allocation. Thus, quantization with information bit-allocation is preferred for high information efficiency and visual quality in optimized visual communication.

Understanding nitrate uptake, signaling and remobilisation for improving plant nitrogen use efficiency.

PubMed

Kant, Surya

2018-02-01

The majority of terrestrial plants use nitrate as their main source of nitrogen. Nitrate also acts as an important signalling molecule in vital physiological processes required for optimum plant growth and development. Improving nitrate uptake and transport, through activation by nitrate sensing, signalling and regulatory processes, would enhance plant growth, resulting in improved crop yields. The increased remobilisation of nitrate, and assimilated nitrogenous compounds, from source to sink tissues further ensures higher yields and quality. An updated knowledge of various transporters, genes, activators, and microRNAs, involved in nitrate uptake, transport, remobilisation, and nitrate-mediated root growth, is presented. An enhanced understanding of these components will allow for their orchestrated fine tuning in efforts to improving nitrogen use efficiency in plants. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Heavy metal accumulation and signal transduction in herbaceous and woody plants: Paving the way for enhancing phytoremediation efficiency.

PubMed

Luo, Zhi-Bin; He, Jiali; Polle, Andrea; Rennenberg, Heinz

2016-11-01

Heavy metal (HM)-accumulating herbaceous and woody plants are employed for phytoremediation. To develop improved strategies for enhancing phytoremediation efficiency, knowledge of the microstructural, physiological and molecular responses underlying HM-accumulation is required. Here we review the progress in understanding the structural, physiological and molecular mechanisms underlying HM uptake, transport, sequestration and detoxification, as well as the regulation of these processes by signal transduction in response to HM exposure. The significance of genetic engineering for enhancing phytoremediation efficiency is also discussed. In herbaceous plants, HMs are taken up by roots and transported into the root cells via transmembrane carriers for nutritional ions. The HMs absorbed by root cells can be further translocated to the xylem vessels and unloaded into the xylem sap, thereby reaching the aerial parts of plants. HMs can be sequestered in the cell walls, vacuoles and the Golgi apparatuses. Plant roots initially perceive HM stress and trigger the signal transduction, thereby mediating changes at the molecular, physiological, and microstructural level. Signaling molecules such as phytohormones, reactive oxygen species (ROS) and nitric oxide (NO), modulate plant responses to HMs via differentially expressed genes, activation of the antioxidative system and coordinated cross talk among different signaling molecules. A number of genes participated in HM uptake, transport, sequestration and detoxification have been functionally characterized and transformed to target plants for enhancing phytoremediation efficiency. Fast growing woody plants hold an advantage over herbaceous plants for phytoremediation in terms of accumulation of high HM-amounts in their large biomass. Presumably, woody plants accumulate HMs using similar mechanisms as herbaceous counterparts, but the processes of HM accumulation and signal transduction can be more complex in woody plants. Copyright © 2016 Elsevier Inc. All rights reserved.

Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures

NASA Astrophysics Data System (ADS)

Hegazy, Maha A.; Lotfy, Hayam M.; Mowaka, Shereen; Mohamed, Ekram Hany

2016-07-01

Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.

A low-rank matrix recovery approach for energy efficient EEG acquisition for a wireless body area network.

PubMed

Majumdar, Angshul; Gogna, Anupriya; Ward, Rabab

2014-08-25

We address the problem of acquiring and transmitting EEG signals in Wireless Body Area Networks (WBAN) in an energy efficient fashion. In WBANs, the energy is consumed by three operations: sensing (sampling), processing and transmission. Previous studies only addressed the problem of reducing the transmission energy. For the first time, in this work, we propose a technique to reduce sensing and processing energy as well: this is achieved by randomly under-sampling the EEG signal. We depart from previous Compressed Sensing based approaches and formulate signal recovery (from under-sampled measurements) as a matrix completion problem. A new algorithm to solve the matrix completion problem is derived here. We test our proposed method and find that the reconstruction accuracy of our method is significantly better than state-of-the-art techniques; and we achieve this while saving sensing, processing and transmission energy. Simple power analysis shows that our proposed methodology consumes considerably less power compared to previous CS based techniques.

Travelling-wave resonant four-wave mixing breaks the limits of cavity-enhanced all-optical wavelength conversion.

PubMed

Morichetti, Francesco; Canciamilla, Antonio; Ferrari, Carlo; Samarelli, Antonio; Sorel, Marc; Melloni, Andrea

2011-01-01

Wave mixing inside optical resonators, while experiencing a large enhancement of the nonlinear interaction efficiency, suffers from strong bandwidth constraints, preventing its practical exploitation for processing broad-band signals. Here we show that such limits are overcome by the new concept of travelling-wave resonant four-wave mixing (FWM). This approach combines the efficiency enhancement provided by resonant propagation with a wide-band conversion process. Compared with conventional FWM in bare waveguides, it exhibits higher robustness against chromatic dispersion and propagation loss, while preserving transparency to modulation formats. Travelling-wave resonant FWM has been demonstrated in silicon-coupled ring resonators and was exploited to realize a 630-μm-long wavelength converter operating over a wavelength range wider than 60 nm and with 28-dB gain with respect to a bare waveguide of the same physical length. Full compatibility of the travelling-wave resonant FWM with optical signal processing applications has been demonstrated through signal retiming and reshaping at 10 Gb s(-1).

Travelling-wave resonant four-wave mixing breaks the limits of cavity-enhanced all-optical wavelength conversion

PubMed Central

Morichetti, Francesco; Canciamilla, Antonio; Ferrari, Carlo; Samarelli, Antonio; Sorel, Marc; Melloni, Andrea

2011-01-01

Wave mixing inside optical resonators, while experiencing a large enhancement of the nonlinear interaction efficiency, suffers from strong bandwidth constraints, preventing its practical exploitation for processing broad-band signals. Here we show that such limits are overcome by the new concept of travelling-wave resonant four-wave mixing (FWM). This approach combines the efficiency enhancement provided by resonant propagation with a wide-band conversion process. Compared with conventional FWM in bare waveguides, it exhibits higher robustness against chromatic dispersion and propagation loss, while preserving transparency to modulation formats. Travelling-wave resonant FWM has been demonstrated in silicon-coupled ring resonators and was exploited to realize a 630-μm-long wavelength converter operating over a wavelength range wider than 60 nm and with 28-dB gain with respect to a bare waveguide of the same physical length. Full compatibility of the travelling-wave resonant FWM with optical signal processing applications has been demonstrated through signal retiming and reshaping at 10 Gb s−1 PMID:21540838

MIMO signal progressing with RLSCMA algorithm for multi-mode multi-core optical transmission system

NASA Astrophysics Data System (ADS)

Bi, Yuan; Liu, Bo; Zhang, Li-jia; Xin, Xiang-jun; Zhang, Qi; Wang, Yong-jun; Tian, Qing-hua; Tian, Feng; Mao, Ya-ya

2018-01-01

In the process of transmitting signals of multi-mode multi-core fiber, there will be mode coupling between modes. The mode dispersion will also occur because each mode has different transmission speed in the link. Mode coupling and mode dispersion will cause damage to the useful signal in the transmission link, so the receiver needs to deal received signal with digital signal processing, and compensate the damage in the link. We first analyzes the influence of mode coupling and mode dispersion in the process of transmitting signals of multi-mode multi-core fiber, then presents the relationship between the coupling coefficient and dispersion coefficient. Then we carry out adaptive signal processing with MIMO equalizers based on recursive least squares constant modulus algorithm (RLSCMA). The MIMO equalization algorithm offers adaptive equalization taps according to the degree of crosstalk in cores or modes, which eliminates the interference among different modes and cores in space division multiplexing(SDM) transmission system. The simulation results show that the distorted signals are restored efficiently with fast convergence speed.

A Power-Efficient Bio-Potential Acquisition Device with DS-MDE Sensors for Long-Term Healthcare Monitoring Applications

PubMed Central

Chang, Chia-Lin; Chang, Chih-Wei; Huang, Hong-Yi; Hsu, Chen-Ming; Huang, Chia-Hsuan; Chiou, Jin-Chern; Luo, Ching-Hsing

2010-01-01

This work describes a power-efficient bio-potential acquisition device for long-term healthcare applications that is implemented using novel microelectromechanical dry electrodes (MDE) and a low power bio-potential processing chip. Using micromachining technology, an attempt is also made to enhance the sensing reliability and stability by fabricating a diamond-shaped MDE (DS-MDE) that has a satisfactory self-stability capability and superior electric conductivity when attached onto skin without any extra skin tissue injury technology. To acquire differential bio-potentials such as ECG signals, the proposed processing chip fabricated in a standard CMOS process has a high common mode rejection ratio (C.M.R.R.) differential amplifier and a 12-bit analog-to-digital converter (ADC). Use of the proposed system and integrate simple peripheral commercial devices can obtain the ECG signal efficiently without additional skin tissue injury and ensure continuous monitoring more than 70 hours with a 400 mAh battery. PMID:22399907

A power-efficient bio-potential acquisition device with DS-MDE sensors for long-term healthcare monitoring applications.

PubMed

Chang, Chia-Lin; Chang, Chih-Wei; Huang, Hong-Yi; Hsu, Chen-Ming; Huang, Chia-Hsuan; Chiou, Jin-Chern; Luo, Ching-Hsing

2010-01-01

This work describes a power-efficient bio-potential acquisition device for long-term healthcare applications that is implemented using novel microelectromechanical dry electrodes (MDE) and a low power bio-potential processing chip. Using micromachining technology, an attempt is also made to enhance the sensing reliability and stability by fabricating a diamond-shaped MDE (DS-MDE) that has a satisfactory self-stability capability and superior electric conductivity when attached onto skin without any extra skin tissue injury technology. To acquire differential bio-potentials such as ECG signals, the proposed processing chip fabricated in a standard CMOS process has a high common mode rejection ratio (C.M.R.R.) differential amplifier and a 12-bit analog-to-digital converter (ADC). Use of the proposed system and integrate simple peripheral commercial devices can obtain the ECG signal efficiently without additional skin tissue injury and ensure continuous monitoring more than 70 hours with a 400 mAh battery.

Chirp Scaling Algorithms for SAR Processing

NASA Technical Reports Server (NTRS)

Jin, M.; Cheng, T.; Chen, M.

1993-01-01

The chirp scaling SAR processing algorithm is both accurate and efficient. Successful implementation requires proper selection of the interval of output samples, which is a function of the chirp interval, signal sampling rate, and signal bandwidth. Analysis indicates that for both airborne and spaceborne SAR applications in the slant range domain a linear chirp scaling is sufficient. To perform nonlinear interpolation process such as to output ground range SAR images, one can use a nonlinear chirp scaling interpolator presented in this paper.

Multichannel Baseband Processor for Wideband CDMA

NASA Astrophysics Data System (ADS)

Jalloul, Louay M. A.; Lin, Jim

2005-12-01

The system architecture of the cellular base station modem engine (CBME) is described. The CBME is a single-chip multichannel transceiver capable of processing and demodulating signals from multiple users simultaneously. It is optimized to process different classes of code-division multiple-access (CDMA) signals. The paper will show that through key functional system partitioning, tightly coupled small digital signal processing cores, and time-sliced reuse architecture, CBME is able to achieve a high degree of algorithmic flexibility while maintaining efficiency. The paper will also highlight the implementation and verification aspects of the CBME chip design. In this paper, wideband CDMA is used as an example to demonstrate the architecture concept.

Theoretical analysis of terahertz generation from a compact optical parametric oscillator based on adhesive-free-bonded periodically inverted KTiOPO4 plates

NASA Astrophysics Data System (ADS)

Li, Zhongyang; Wang, Silei; Wang, Mengtao; Yuan, Bin; Wang, Weishu

2017-10-01

Terahertz (THz) generation by difference frequency generation (DFG) processes with dual signal waves is theoretically analyzed. The dual signal waves are generated by an optical parametric oscillator (OPO) with periodically inverted KTiOPO4 (KTP) plates based on adhesive-free-bonded (AFB) technology. The phase-matching conditions in a same AFB KTP composite for the OPO generating signals and idlers and for the DFG generating THz wave can be simultaneously satisfied by selecting the thickness of each KTP plate. Moreover, 4-order cascaded DFG processes can be realized in the same AFB KTP composite. The cascaded Stokes interaction processes generating THz photons and the cascaded anti-Stokes interaction processes consuming THz photons are investigated from coupled wave equations. Take an example of 3.106 THz which locates in the vicinity of polariton resonances, THz intensities and quantum conversion efficiencies are calculated. Compared with non-cascaded DFG processes, THz intensities of 3.106 THz in 4-order cascaded DFG processes increase to 5.56 times. When the pump intensity equals 20 MW mm-2, the quantum conversion efficiency of 259% in 4-order cascaded DFG processes can be realized, which exceeds the Manley-Rowe limit.

A de-noising method using the improved wavelet threshold function based on noise variance estimation

NASA Astrophysics Data System (ADS)

Liu, Hui; Wang, Weida; Xiang, Changle; Han, Lijin; Nie, Haizhao

2018-01-01

The precise and efficient noise variance estimation is very important for the processing of all kinds of signals while using the wavelet transform to analyze signals and extract signal features. In view of the problem that the accuracy of traditional noise variance estimation is greatly affected by the fluctuation of noise values, this study puts forward the strategy of using the two-state Gaussian mixture model to classify the high-frequency wavelet coefficients in the minimum scale, which takes both the efficiency and accuracy into account. According to the noise variance estimation, a novel improved wavelet threshold function is proposed by combining the advantages of hard and soft threshold functions, and on the basis of the noise variance estimation algorithm and the improved wavelet threshold function, the research puts forth a novel wavelet threshold de-noising method. The method is tested and validated using random signals and bench test data of an electro-mechanical transmission system. The test results indicate that the wavelet threshold de-noising method based on the noise variance estimation shows preferable performance in processing the testing signals of the electro-mechanical transmission system: it can effectively eliminate the interference of transient signals including voltage, current, and oil pressure and maintain the dynamic characteristics of the signals favorably.

Control of coherent information via on-chip photonic-phononic emitter-receivers.

PubMed

Shin, Heedeuk; Cox, Jonathan A; Jarecki, Robert; Starbuck, Andrew; Wang, Zheng; Rakich, Peter T

2015-03-05

Rapid progress in integrated photonics has fostered numerous chip-scale sensing, computing and signal processing technologies. However, many crucial filtering and signal delay operations are difficult to perform with all-optical devices. Unlike photons propagating at luminal speeds, GHz-acoustic phonons moving at slower velocities allow information to be stored, filtered and delayed over comparatively smaller length-scales with remarkable fidelity. Hence, controllable and efficient coupling between coherent photons and phonons enables new signal processing technologies that greatly enhance the performance and potential impact of integrated photonics. Here we demonstrate a mechanism for coherent information processing based on travelling-wave photon-phonon transduction, which achieves a phonon emit-and-receive process between distinct nanophotonic waveguides. Using this device, physics--which supports GHz frequencies--we create wavelength-insensitive radiofrequency photonic filters with frequency selectivity, narrow-linewidth and high power-handling in silicon. More generally, this emit-receive concept is the impetus for enabling new signal processing schemes.

Fractal Signals & Space-Time Cartoons

NASA Astrophysics Data System (ADS)

Oetama, H. C. Jakob; Maksoed, W. H.

2016-03-01

In ``Theory of Scale Relativity'', 1991- L. Nottale states whereas ``scale relativity is a geometrical & fractal space-time theory''. It took in comparisons to ``a unified, wavelet based framework for efficiently synthetizing, analyzing ∖7 processing several broad classes of fractal signals''-Gregory W. Wornell:``Signal Processing with Fractals'', 1995. Furthers, in Fig 1.1. a simple waveform from statistically scale-invariant random process [ibid.,h 3 ]. Accompanying RLE Technical Report 566 ``Synthesis, Analysis & Processing of Fractal Signals'' as well as from Wornell, Oct 1991 herewith intended to deducts =a Δt + (1 - β Δ t) ...in Petersen, et.al: ``Scale invariant properties of public debt growth'',2010 h. 38006p2 to [1/{1- (2 α (λ) /3 π) ln (λ/r)}depicts in Laurent Nottale,1991, h 24. Acknowledgment devotes to theLates HE. Mr. BrigadierGeneral-TNI[rtd].Prof. Ir. HANDOJO.

Smart integrated microsystems: the energy efficiency challenge (Conference Presentation) (Plenary Presentation)

NASA Astrophysics Data System (ADS)

Benini, Luca

2017-06-01

The "internet of everything" envisions trillions of connected objects loaded with high-bandwidth sensors requiring massive amounts of local signal processing, fusion, pattern extraction and classification. From the computational viewpoint, the challenge is formidable and can be addressed only by pushing computing fabrics toward massive parallelism and brain-like energy efficiency levels. CMOS technology can still take us a long way toward this goal, but technology scaling is losing steam. Energy efficiency improvement will increasingly hinge on architecture, circuits, design techniques such as heterogeneous 3D integration, mixed-signal preprocessing, event-based approximate computing and non-Von-Neumann architectures for scalable acceleration.

Processing Functional Near Infrared Spectroscopy Signal with a Kalman Filter to Assess Working Memory during Simulated Flight.

PubMed

Durantin, Gautier; Scannella, Sébastien; Gateau, Thibault; Delorme, Arnaud; Dehais, Frédéric

2015-01-01

Working memory (WM) is a key executive function for operating aircraft, especially when pilots have to recall series of air traffic control instructions. There is a need to implement tools to monitor WM as its limitation may jeopardize flight safety. An innovative way to address this issue is to adopt a Neuroergonomics approach that merges knowledge and methods from Human Factors, System Engineering, and Neuroscience. A challenge of great importance for Neuroergonomics is to implement efficient brain imaging techniques to measure the brain at work and to design Brain Computer Interfaces (BCI). We used functional near infrared spectroscopy as it has been already successfully tested to measure WM capacity in complex environment with air traffic controllers (ATC), pilots, or unmanned vehicle operators. However, the extraction of relevant features from the raw signal in ecological environment is still a critical issue due to the complexity of implementing real-time signal processing techniques without a priori knowledge. We proposed to implement the Kalman filtering approach, a signal processing technique that is efficient when the dynamics of the signal can be modeled. We based our approach on the Boynton model of hemodynamic response. We conducted a first experiment with nine participants involving a basic WM task to estimate the noise covariances of the Kalman filter. We then conducted a more ecological experiment in our flight simulator with 18 pilots who interacted with ATC instructions (two levels of difficulty). The data was processed with the same Kalman filter settings implemented in the first experiment. This filter was benchmarked with a classical pass-band IIR filter and a Moving Average Convergence Divergence (MACD) filter. Statistical analysis revealed that the Kalman filter was the most efficient to separate the two levels of load, by increasing the observed effect size in prefrontal areas involved in WM. In addition, the use of a Kalman filter increased the performance of the classification of WM levels based on brain signal. The results suggest that Kalman filter is a suitable approach for real-time improvement of near infrared spectroscopy signal in ecological situations and the development of BCI.

Processing Functional Near Infrared Spectroscopy Signal with a Kalman Filter to Assess Working Memory during Simulated Flight

PubMed Central

Durantin, Gautier; Scannella, Sébastien; Gateau, Thibault; Delorme, Arnaud; Dehais, Frédéric

2016-01-01

Working memory (WM) is a key executive function for operating aircraft, especially when pilots have to recall series of air traffic control instructions. There is a need to implement tools to monitor WM as its limitation may jeopardize flight safety. An innovative way to address this issue is to adopt a Neuroergonomics approach that merges knowledge and methods from Human Factors, System Engineering, and Neuroscience. A challenge of great importance for Neuroergonomics is to implement efficient brain imaging techniques to measure the brain at work and to design Brain Computer Interfaces (BCI). We used functional near infrared spectroscopy as it has been already successfully tested to measure WM capacity in complex environment with air traffic controllers (ATC), pilots, or unmanned vehicle operators. However, the extraction of relevant features from the raw signal in ecological environment is still a critical issue due to the complexity of implementing real-time signal processing techniques without a priori knowledge. We proposed to implement the Kalman filtering approach, a signal processing technique that is efficient when the dynamics of the signal can be modeled. We based our approach on the Boynton model of hemodynamic response. We conducted a first experiment with nine participants involving a basic WM task to estimate the noise covariances of the Kalman filter. We then conducted a more ecological experiment in our flight simulator with 18 pilots who interacted with ATC instructions (two levels of difficulty). The data was processed with the same Kalman filter settings implemented in the first experiment. This filter was benchmarked with a classical pass-band IIR filter and a Moving Average Convergence Divergence (MACD) filter. Statistical analysis revealed that the Kalman filter was the most efficient to separate the two levels of load, by increasing the observed effect size in prefrontal areas involved in WM. In addition, the use of a Kalman filter increased the performance of the classification of WM levels based on brain signal. The results suggest that Kalman filter is a suitable approach for real-time improvement of near infrared spectroscopy signal in ecological situations and the development of BCI. PMID:26834607

Dye bias correction in dual-labeled cDNA microarray gene expression measurements.

PubMed Central

Rosenzweig, Barry A; Pine, P Scott; Domon, Olen E; Morris, Suzanne M; Chen, James J; Sistare, Frank D

2004-01-01

A significant limitation to the analytical accuracy and precision of dual-labeled spotted cDNA microarrays is the signal error due to dye bias. Transcript-dependent dye bias may be due to gene-specific differences of incorporation of two distinctly different chemical dyes and the resultant differential hybridization efficiencies of these two chemically different targets for the same probe. Several approaches were used to assess and minimize the effects of dye bias on fluorescent hybridization signals and maximize the experimental design efficiency of a cell culture experiment. Dye bias was measured at the individual transcript level within each batch of simultaneously processed arrays by replicate dual-labeled split-control sample hybridizations and accounted for a significant component of fluorescent signal differences. This transcript-dependent dye bias alone could introduce unacceptably high numbers of both false-positive and false-negative signals. We found that within a given set of concurrently processed hybridizations, the bias is remarkably consistent and therefore measurable and correctable. The additional microarrays and reagents required for paired technical replicate dye-swap corrections commonly performed to control for dye bias could be costly to end users. Incorporating split-control microarrays within a set of concurrently processed hybridizations to specifically measure dye bias can eliminate the need for technical dye swap replicates and reduce microarray and reagent costs while maintaining experimental accuracy and technical precision. These data support a practical and more efficient experimental design to measure and mathematically correct for dye bias. PMID:15033598

N-terminal domain of the dual-targeted pea glutathione reductase signal peptide controls organellar targeting efficiency.

PubMed

Rudhe, Charlotta; Clifton, Rachel; Whelan, James; Glaser, Elzbieta

2002-12-06

Import of nuclear-encoded proteins into mitochondria and chloroplasts is generally organelle specific and its specificity depends on the N-terminal signal peptide. Yet, a group of proteins known as dual-targeted proteins have a targeting peptide capable of leading the mature protein to both organelles. We have investigated the domain structure of the dual-targeted pea glutathione reductase (GR) signal peptide by using N-terminal truncations. A mutant of the GR precursor (pGR) starting with the second methionine residue of the targeting peptide, pGRdelta2-4, directed import into both organelles, negating the possibility that dual import was controlled by the nature of the N terminus. The deletion of the 30 N-terminal residues (pGRdelta2-30) inhibited import efficiency into chloroplasts substantially and almost completely into mitochondria, whereas the removal of only 16 N-terminal amino acid residues (pGRdelta2-16) resulted in the strongly stimulated mitochondrial import without significantly affecting chloroplast import. Furthermore, N-terminal truncations of the signal peptide (pGRdelta2-16 and pGRdelta2-30) greatly stimulated the mitochondrial processing activity measured with the isolated processing peptidase. These results suggest a domain structure for the dual-targeting peptide of pGR and the existence of domains controlling organellar import efficiency therein.

Bacterial biofilms and quorum sensing: fidelity in bioremediation technology.

PubMed

Mangwani, Neelam; Kumari, Supriya; Das, Surajit

Increased contamination of the environment with toxic pollutants has paved the way for efficient strategies which can be implemented for environmental restoration. The major problem with conventional methods used for cleaning of pollutants is inefficiency and high economic costs. Bioremediation is a growing technology having advanced potential of cleaning pollutants. Biofilm formed by various micro-organisms potentially provide a suitable microenvironment for efficient bioremediation processes. High cell density and stress resistance properties of the biofilm environment provide opportunities for efficient metabolism of number of hydrophobic and toxic compounds. Bacterial biofilm formation is often regulated by quorum sensing (QS) which is a population density-based cell-cell communication process via signaling molecules. Numerous signaling molecules such as acyl homoserine lactones, peptides, autoinducer-2, diffusion signaling factors, and α-hydroxyketones have been studied in bacteria. Genetic alteration of QS machinery can be useful to modulate vital characters valuable for environmental applications such as biofilm formation, biosurfactant production, exopolysaccharide synthesis, horizontal gene transfer, catabolic gene expression, motility, and chemotaxis. These qualities are imperative for bacteria during degradation or detoxification of any pollutant. QS signals can be used for the fabrication of engineered biofilms with enhanced degradation kinetics. This review discusses the connection between QS and biofilm formation by bacteria in relation to bioremediation technology.

Free-space microwave-power transmission

NASA Technical Reports Server (NTRS)

Brown, W. C.

1976-01-01

Laboratory-scale wireless transmission of microwave power approaches fifty-four percent efficiency. DC is converted to a 2.45-GHz signal and is transmitted through horn antenna array; microwave signal is received at rectenna and is simultaneously collected and rectified back to dc at receiving sites; dc is then processed for wired distribution.

Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures.

PubMed

Hegazy, Maha A; Lotfy, Hayam M; Mowaka, Shereen; Mohamed, Ekram Hany

2016-07-05

Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations. Copyright © 2016 Elsevier B.V. All rights reserved.

Seismic signal processing on heterogeneous supercomputers

NASA Astrophysics Data System (ADS)

Gokhberg, Alexey; Ermert, Laura; Fichtner, Andreas

2015-04-01

The processing of seismic signals - including the correlation of massive ambient noise data sets - represents an important part of a wide range of seismological applications. It is characterized by large data volumes as well as high computational input/output intensity. Development of efficient approaches towards seismic signal processing on emerging high performance computing systems is therefore essential. Heterogeneous supercomputing systems introduced in the recent years provide numerous computing nodes interconnected via high throughput networks, every node containing a mix of processing elements of different architectures, like several sequential processor cores and one or a few graphical processing units (GPU) serving as accelerators. A typical representative of such computing systems is "Piz Daint", a supercomputer of the Cray XC 30 family operated by the Swiss National Supercomputing Center (CSCS), which we used in this research. Heterogeneous supercomputers provide an opportunity for manifold application performance increase and are more energy-efficient, however they have much higher hardware complexity and are therefore much more difficult to program. The programming effort may be substantially reduced by the introduction of modular libraries of software components that can be reused for a wide class of seismology applications. The ultimate goal of this research is design of a prototype for such library suitable for implementing various seismic signal processing applications on heterogeneous systems. As a representative use case we have chosen an ambient noise correlation application. Ambient noise interferometry has developed into one of the most powerful tools to image and monitor the Earth's interior. Future applications will require the extraction of increasingly small details from noise recordings. To meet this demand, more advanced correlation techniques combined with very large data volumes are needed. This poses new computational problems that require dedicated HPC solutions. The chosen application is using a wide range of common signal processing methods, which include various IIR filter designs, amplitude and phase correlation, computing the analytic signal, and discrete Fourier transforms. Furthermore, various processing methods specific for seismology, like rotation of seismic traces, are used. Efficient implementation of all these methods on the GPU-accelerated systems represents several challenges. In particular, it requires a careful distribution of work between the sequential processors and accelerators. Furthermore, since the application is designed to process very large volumes of data, special attention had to be paid to the efficient use of the available memory and networking hardware resources in order to reduce intensity of data input and output. In our contribution we will explain the software architecture as well as principal engineering decisions used to address these challenges. We will also describe the programming model based on C++ and CUDA that we used to develop the software. Finally, we will demonstrate performance improvements achieved by using the heterogeneous computing architecture. This work was supported by a grant from the Swiss National Supercomputing Centre (CSCS) under project ID d26.

Residual stress evaluation by Barkhausen signals with a magnetic field sensor for high efficiency electrical motors

NASA Astrophysics Data System (ADS)

Tsuchida, Yuji; Enokizono, Masato

2018-04-01

The iron loss of industrial motors increases by residual stress during manufacturing processes. It is very important to make clear the distribution of the residual stress in the motor cores to reduce the iron loss in the motors. Barkhausen signals which occur on electrical steel sheets can be used for the evaluation of the residual stress because they are very sensitive to the material properties. Generally, a B-sensor is used to measure Barkhausen signals, however, we developed a new H-sensor to measure them and applied it into the stress evaluation. It is supposed that the Barkhausen signals by using a H-sensor can be much effective to the residual stress on the electrical steel sheets by referring our results regarding to the stress evaluations. We evaluated the tensile stress of the electrical steel sheets by measuring Barkhausen signals by using our developed H-sensor for high efficiency electrical motors.

[Application of the mixed programming with Labview and Matlab in biomedical signal analysis].

PubMed

Yu, Lu; Zhang, Yongde; Sha, Xianzheng

2011-01-01

This paper introduces the method of mixed programming with Labview and Matlab, and applies this method in a pulse wave pre-processing and feature detecting system. The method has been proved suitable, efficient and accurate, which has provided a new kind of approach for biomedical signal analysis.

Design and Analysis of a Neuromemristive Reservoir Computing Architecture for Biosignal Processing

PubMed Central

Kudithipudi, Dhireesha; Saleh, Qutaiba; Merkel, Cory; Thesing, James; Wysocki, Bryant

2016-01-01

Reservoir computing (RC) is gaining traction in several signal processing domains, owing to its non-linear stateful computation, spatiotemporal encoding, and reduced training complexity over recurrent neural networks (RNNs). Previous studies have shown the effectiveness of software-based RCs for a wide spectrum of applications. A parallel body of work indicates that realizing RNN architectures using custom integrated circuits and reconfigurable hardware platforms yields significant improvements in power and latency. In this research, we propose a neuromemristive RC architecture, with doubly twisted toroidal structure, that is validated for biosignal processing applications. We exploit the device mismatch to implement the random weight distributions within the reservoir and propose mixed-signal subthreshold circuits for energy efficiency. A comprehensive analysis is performed to compare the efficiency of the neuromemristive RC architecture in both digital(reconfigurable) and subthreshold mixed-signal realizations. Both Electroencephalogram (EEG) and Electromyogram (EMG) biosignal benchmarks are used for validating the RC designs. The proposed RC architecture demonstrated an accuracy of 90 and 84% for epileptic seizure detection and EMG prosthetic finger control, respectively. PMID:26869876

Enhanced automatic artifact detection based on independent component analysis and Renyi's entropy.

PubMed

Mammone, Nadia; Morabito, Francesco Carlo

2008-09-01

Artifacts are disturbances that may occur during signal acquisition and may affect their processing. The aim of this paper is to propose a technique for automatically detecting artifacts from the electroencephalographic (EEG) recordings. In particular, a technique based on both Independent Component Analysis (ICA) to extract artifactual signals and on Renyi's entropy to automatically detect them is presented. This technique is compared to the widely known approach based on ICA and the joint use of kurtosis and Shannon's entropy. The novel processing technique is shown to detect on average 92.6% of the artifactual signals against the average 68.7% of the previous technique on the studied available database. Moreover, Renyi's entropy is shown to be able to detect muscle and very low frequency activity as well as to discriminate them from other kinds of artifacts. In order to achieve an efficient rejection of the artifacts while minimizing the information loss, future efforts will be devoted to the improvement of blind artifact separation from EEG in order to ensure a very efficient isolation of the artifactual activity from any signals deriving from other brain tasks.

Efficient Processing of Data for Locating Lightning Strikes

NASA Technical Reports Server (NTRS)

Medelius, Pedro J.; Starr, Stan

2003-01-01

Two algorithms have been devised to increase the efficiency of processing of data in lightning detection and ranging (LDAR) systems so as to enable the accurate location of lightning strikes in real time. In LDAR, the location of a lightning strike is calculated by solving equations for the differences among the times of arrival (DTOAs) of the lightning signals at multiple antennas as functions of the locations of the antennas and the speed of light. The most difficult part of the problem is computing the DTOAs from digitized versions of the signals received by the various antennas. One way (a time-domain approach) to determine the DTOAs is to compute cross-correlations among variously differentially delayed replicas of the digitized signals and to select, as the DTOAs, those differential delays that yield the maximum correlations. Another way (a frequency-domain approach) to determine the DTOAs involves the computation of cross-correlations among Fourier transforms of variously differentially phased replicas of the digitized signals, along with utilization of the relationship among phase difference, time delay, and frequency.

Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines

NASA Astrophysics Data System (ADS)

Ha, Jong M.; Youn, Byeng D.; Oh, Hyunseok; Han, Bongtae; Jung, Yoongho; Park, Jungho

2016-03-01

We propose autocorrelation-based time synchronous averaging (ATSA) to cope with the challenges associated with the current practice of time synchronous averaging (TSA) for planet gears in planetary gearboxes of wind turbine (WT). An autocorrelation function that represents physical interactions between the ring, sun, and planet gears in the gearbox is utilized to define the optimal shape and range of the window function for TSA using actual kinetic responses. The proposed ATSA offers two distinctive features: (1) data-efficient TSA processing and (2) prevention of signal distortion during the TSA process. It is thus expected that an order analysis with the ATSA signals significantly improves the efficiency and accuracy in fault diagnostics of planet gears in planetary gearboxes. Two case studies are presented to demonstrate the effectiveness of the proposed method: an analytical signal from a simulation and a signal measured from a 2 kW WT testbed. It can be concluded from the results that the proposed method outperforms conventional TSA methods in condition monitoring of the planetary gearbox when the amount of available stationary data is limited.

Eddy current testing for blade edge micro cracks of aircraft engine

NASA Astrophysics Data System (ADS)

Zhang, Wei-min; Xu, Min-dong; Gao, Xuan-yi; Jin, Xin; Qin, Feng

2017-10-01

Based on the problems of low detection efficiency in the micro cracks detection of aircraft engine blades, a differential excitation eddy current testing system was designed and developed. The function and the working principle of the system were described, the problems which contained the manufacture method of simulated cracks, signal generating, signal processing and the signal display method were described. The detection test was carried out by taking a certain model aircraft engine blade with simulated cracks as a tested specimen. The test data was processed by digital low-pass filter in the computer and the crack signals of time domain display and Lissajous figure display were acquired. By comparing the test results, it is verified that Lissajous figure display shows better performance compared to time domain display when the crack angle is small. The test results show that the eddy current testing system designed in this paper is feasible to detect the micro cracks on the aeroengine blade and can effectively improve the detection efficiency of micro cracks in the practical detection work.

CORDIC-based digital signal processing (DSP) element for adaptive signal processing

NASA Astrophysics Data System (ADS)

Bolstad, Gregory D.; Neeld, Kenneth B.

1995-04-01

The High Performance Adaptive Weight Computation (HAWC) processing element is a CORDIC based application specific DSP element that, when connected in a linear array, can perform extremely high throughput (100s of GFLOPS) matrix arithmetic operations on linear systems of equations in real time. In particular, it very efficiently performs the numerically intense computation of optimal least squares solutions for large, over-determined linear systems. Most techniques for computing solutions to these types of problems have used either a hard-wired, non-programmable systolic array approach, or more commonly, programmable DSP or microprocessor approaches. The custom logic methods can be efficient, but are generally inflexible. Approaches using multiple programmable generic DSP devices are very flexible, but suffer from poor efficiency and high computation latencies, primarily due to the large number of DSP devices that must be utilized to achieve the necessary arithmetic throughput. The HAWC processor is implemented as a highly optimized systolic array, yet retains some of the flexibility of a programmable data-flow system, allowing efficient implementation of algorithm variations. This provides flexible matrix processing capabilities that are one to three orders of magnitude less expensive and more dense than the current state of the art, and more importantly, allows a realizable solution to matrix processing problems that were previously considered impractical to physically implement. HAWC has direct applications in RADAR, SONAR, communications, and image processing, as well as in many other types of systems.

Stanford Hardware Development Program

NASA Technical Reports Server (NTRS)

Peterson, A.; Linscott, I.; Burr, J.

1986-01-01

Architectures for high performance, digital signal processing, particularly for high resolution, wide band spectrum analysis were developed. These developments are intended to provide instrumentation for NASA's Search for Extraterrestrial Intelligence (SETI) program. The real time signal processing is both formal and experimental. The efficient organization and optimal scheduling of signal processing algorithms were investigated. The work is complemented by efforts in processor architecture design and implementation. A high resolution, multichannel spectrometer that incorporates special purpose microcoded signal processors is being tested. A general purpose signal processor for the data from the multichannel spectrometer was designed to function as the processing element in a highly concurrent machine. The processor performance required for the spectrometer is in the range of 1000 to 10,000 million instructions per second (MIPS). Multiple node processor configurations, where each node performs at 100 MIPS, are sought. The nodes are microprogrammable and are interconnected through a network with high bandwidth for neighboring nodes, and medium bandwidth for nodes at larger distance. The implementation of both the current mutlichannel spectrometer and the signal processor as Very Large Scale Integration CMOS chip sets was commenced.

Application of wavelet filtering and Barker-coded pulse compression hybrid method to air-coupled ultrasonic testing

NASA Astrophysics Data System (ADS)

Zhou, Zhenggan; Ma, Baoquan; Jiang, Jingtao; Yu, Guang; Liu, Kui; Zhang, Dongmei; Liu, Weiping

2014-10-01

Air-coupled ultrasonic testing (ACUT) technique has been viewed as a viable solution in defect detection of advanced composites used in aerospace and aviation industries. However, the giant mismatch of acoustic impedance in air-solid interface makes the transmission efficiency of ultrasound low, and leads to poor signal-to-noise (SNR) ratio of received signal. The utilisation of signal-processing techniques in non-destructive testing is highly appreciated. This paper presents a wavelet filtering and phase-coded pulse compression hybrid method to improve the SNR and output power of received signal. The wavelet transform is utilised to filter insignificant components from noisy ultrasonic signal, and pulse compression process is used to improve the power of correlated signal based on cross-correction algorithm. For the purpose of reasonable parameter selection, different families of wavelets (Daubechies, Symlet and Coiflet) and decomposition level in discrete wavelet transform are analysed, different Barker codes (5-13 bits) are also analysed to acquire higher main-to-side lobe ratio. The performance of the hybrid method was verified in a honeycomb composite sample. Experimental results demonstrated that the proposed method is very efficient in improving the SNR and signal strength. The applicability of the proposed method seems to be a very promising tool to evaluate the integrity of high ultrasound attenuation composite materials using the ACUT.

Investigation of signal models and methods for evaluating structures of processing telecommunication information exchange systems under acoustic noise conditions

NASA Astrophysics Data System (ADS)

Kropotov, Y. A.; Belov, A. A.; Proskuryakov, A. Y.; Kolpakov, A. A.

2018-05-01

The paper considers models and methods for estimating signals during the transmission of information messages in telecommunication systems of audio exchange. One-dimensional probability distribution functions that can be used to isolate useful signals, and acoustic noise interference are presented. An approach to the estimation of the correlation and spectral functions of the parameters of acoustic signals is proposed, based on the parametric representation of acoustic signals and the components of the noise components. The paper suggests an approach to improving the efficiency of interference cancellation and highlighting the necessary information when processing signals from telecommunications systems. In this case, the suppression of acoustic noise is based on the methods of adaptive filtering and adaptive compensation. The work also describes the models of echo signals and the structure of subscriber devices in operational command telecommunications systems.

Real Time Phase Noise Meter Based on a Digital Signal Processor

NASA Technical Reports Server (NTRS)

Angrisani, Leopoldo; D'Arco, Mauro; Greenhall, Charles A.; Schiano Lo Morille, Rosario

2006-01-01

A digital signal-processing meter for phase noise measurement on sinusoidal signals is dealt with. It enlists a special hardware architecture, made up of a core digital signal processor connected to a data acquisition board, and takes advantage of a quadrature demodulation-based measurement scheme, already proposed by the authors. Thanks to an efficient measurement process and an optimized implementation of its fundamental stages, the proposed meter succeeds in exploiting all hardware resources in such an effective way as to gain high performance and real-time operation. For input frequencies up to some hundreds of kilohertz, the meter is capable both of updating phase noise power spectrum while seamlessly capturing the analyzed signal into its memory, and granting as good frequency resolution as few units of hertz.

The effect of nonadiabaticity on the efficiency of quantum memory based on an optical cavity

NASA Astrophysics Data System (ADS)

Veselkova, N. G.; Sokolov, I. V.

2017-07-01

Quantum efficiency is an important characteristic of quantum memory devices that are aimed at recording the quantum state of light signals and its storing and reading. In the case of memory based on an ensemble of cold atoms placed in an optical cavity, the efficiency is restricted, in particular, by relaxation processes in the system of active atomic levels. We show how the effect of the relaxation on the quantum efficiency can be determined in a regime of the memory usage in which the evolution of signals in time is not arbitrarily slow on the scale of the field lifetime in the cavity and when the frequently used approximation of the adiabatic elimination of the quantized cavity mode field cannot be applied. Taking into account the effect of the nonadiabaticity on the memory quality is of interest in view of the fact that, in order to increase the field-medium coupling parameter, a higher cavity quality factor is required, whereas storing and processing of sequences of many signals in the memory implies that their duration is reduced. We consider the applicability of the well-known efficiency estimates via the system cooperativity parameter and estimate a more general form. In connection with the theoretical description of the memory of the given type, we also discuss qualitative differences in the behavior of a random source introduced into the Heisenberg-Langevin equations for atomic variables in the cases of a large and a small number of atoms.

shiftNMFk 1.1: Robust Nonnegative matrix factorization with kmeans clustering and signal shift, for allocation of unknown physical sources, toy version for open sourcing with publications

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

Alexandrov, Boian S.; Lliev, Filip L.; Stanev, Valentin G.

This code is a toy (short) version of CODE-2016-83. From a general perspective, the code represents an unsupervised adaptive machine learning algorithm that allows efficient and high performance de-mixing and feature extraction of a multitude of non-negative signals mixed and recorded by a network of uncorrelated sensor arrays. The code identifies the number of the mixed original signals and their locations. Further, the code also allows deciphering of signals that have been delayed in regards to the mixing process in each sensor. This code is high customizable and it can be efficiently used for a fast macro-analyses of data. Themore » code is applicable to a plethora of distinct problems: chemical decomposition, pressure transient decomposition, unknown sources/signal allocation, EM signal decomposition. An additional procedure for allocation of the unknown sources is incorporated in the code.« less

Control of coherent information via on-chip photonic–phononic emitter–receivers

DOE PAGES

Shin, Heedeuk; Cox, Jonathan A.; Jarecki, Robert; ...

2015-03-05

We report that rapid progress in integrated photonics has fostered numerous chip-scale sensing, computing and signal processing technologies. However, many crucial filtering and signal delay operations are difficult to perform with all-optical devices. Unlike photons propagating at luminal speeds, GHz-acoustic phonons moving at slower velocities allow information to be stored, filtered and delayed over comparatively smaller length-scales with remarkable fidelity. Hence, controllable and efficient coupling between coherent photons and phonons enables new signal processing technologies that greatly enhance the performance and potential impact of integrated photonics. Here we demonstrate a mechanism for coherent information processing based on travelling-wave photon–phonon transduction,more » which achieves a phonon emit-and-receive process between distinct nanophotonic waveguides. Using this device, physics—which supports GHz frequencies—we create wavelength-insensitive radiofrequency photonic filters with frequency selectivity, narrow-linewidth and high power-handling in silicon. More generally, this emit-receive concept is the impetus for enabling new signal processing schemes.« less

Control of coherent information via on-chip photonic–phononic emitter–receivers

PubMed Central

Shin, Heedeuk; Cox, Jonathan A.; Jarecki, Robert; Starbuck, Andrew; Wang, Zheng; Rakich, Peter T.

2015-01-01

Rapid progress in integrated photonics has fostered numerous chip-scale sensing, computing and signal processing technologies. However, many crucial filtering and signal delay operations are difficult to perform with all-optical devices. Unlike photons propagating at luminal speeds, GHz-acoustic phonons moving at slower velocities allow information to be stored, filtered and delayed over comparatively smaller length-scales with remarkable fidelity. Hence, controllable and efficient coupling between coherent photons and phonons enables new signal processing technologies that greatly enhance the performance and potential impact of integrated photonics. Here we demonstrate a mechanism for coherent information processing based on travelling-wave photon–phonon transduction, which achieves a phonon emit-and-receive process between distinct nanophotonic waveguides. Using this device, physics—which supports GHz frequencies—we create wavelength-insensitive radiofrequency photonic filters with frequency selectivity, narrow-linewidth and high power-handling in silicon. More generally, this emit-receive concept is the impetus for enabling new signal processing schemes. PMID:25740405

Always-on low-power optical system for skin-based touchless machine control.

PubMed

Lecca, Michela; Gottardi, Massimo; Farella, Elisabetta; Milosevic, Bojan

2016-06-01

Embedded vision systems are smart energy-efficient devices that capture and process a visual signal in order to extract high-level information about the surrounding observed world. Thanks to these capabilities, embedded vision systems attract more and more interest from research and industry. In this work, we present a novel low-power optical embedded system tailored to detect the human skin under various illuminant conditions. We employ the presented sensor as a smart switch to activate one or more appliances connected to it. The system is composed of an always-on low-power RGB color sensor, a proximity sensor, and an energy-efficient microcontroller (MCU). The architecture of the color sensor allows a hardware preprocessing of the RGB signal, which is converted into the rg space directly on chip reducing the power consumption. The rg signal is delivered to the MCU, where it is classified as skin or non-skin. Each time the signal is classified as skin, the proximity sensor is activated to check the distance of the detected object. If it appears to be in the desired proximity range, the system detects the interaction and switches on/off the connected appliances. The experimental validation of the proposed system on a prototype shows that processing both distance and color remarkably improves the performance of the two separated components. This makes the system a promising tool for energy-efficient, touchless control of machines.

Optical information-processing systems and architectures II; Proceedings of the Meeting, San Diego, CA, July 9-13, 1990

NASA Astrophysics Data System (ADS)

Javidi, Bahram

The present conference discusses topics in the fields of neural networks, acoustooptic signal processing, pattern recognition, phase-only processing, nonlinear signal processing, image processing, optical computing, and optical information processing. Attention is given to the optical implementation of an inner-product neural associative memory, optoelectronic associative recall via motionless-head/parallel-readout optical disk, a compact real-time acoustooptic image correlator, a multidimensional synthetic estimation filter, and a light-efficient joint transform optical correlator. Also discussed are a high-resolution spatial light modulator, compact real-time interferometric Fourier-transform processors, a fast decorrelation algorithm for permutation arrays, the optical interconnection of optical modules, and carry-free optical binary adders.

An efficient quantum algorithm for spectral estimation

NASA Astrophysics Data System (ADS)

Steffens, Adrian; Rebentrost, Patrick; Marvian, Iman; Eisert, Jens; Lloyd, Seth

2017-03-01

We develop an efficient quantum implementation of an important signal processing algorithm for line spectral estimation: the matrix pencil method, which determines the frequencies and damping factors of signals consisting of finite sums of exponentially damped sinusoids. Our algorithm provides a quantum speedup in a natural regime where the sampling rate is much higher than the number of sinusoid components. Along the way, we develop techniques that are expected to be useful for other quantum algorithms as well—consecutive phase estimations to efficiently make products of asymmetric low rank matrices classically accessible and an alternative method to efficiently exponentiate non-Hermitian matrices. Our algorithm features an efficient quantum-classical division of labor: the time-critical steps are implemented in quantum superposition, while an interjacent step, requiring much fewer parameters, can operate classically. We show that frequencies and damping factors can be obtained in time logarithmic in the number of sampling points, exponentially faster than known classical algorithms.

Perception SoC Based on an Ultrasonic Array of Sensors: Efficient DSP Core Implementation and Subsequent Experimental Results

NASA Astrophysics Data System (ADS)

Kassem, A.; Sawan, M.; Boukadoum, M.; Haidar, A.

2005-12-01

We are concerned with the design, implementation, and validation of a perception SoC based on an ultrasonic array of sensors. The proposed SoC is dedicated to ultrasonic echography applications. A rapid prototyping platform is used to implement and validate the new architecture of the digital signal processing (DSP) core. The proposed DSP core efficiently integrates all of the necessary ultrasonic B-mode processing modules. It includes digital beamforming, quadrature demodulation of RF signals, digital filtering, and envelope detection of the received signals. This system handles 128 scan lines and 6400 samples per scan line with a[InlineEquation not available: see fulltext.] angle of view span. The design uses a minimum size lookup memory to store the initial scan information. Rapid prototyping using an ARM/FPGA combination is used to validate the operation of the described system. This system offers significant advantages of portability and a rapid time to market.

Chloroplast-to-nucleus communication

PubMed Central

Chan, Kai Xun; Crisp, Peter Alexander; Estavillo, Gonzalo Martin

2010-01-01

In order for plant cells to function efficiently under different environmental conditions, chloroplastic processes have to be tightly regulated by the nucleus. It is widely believed that there is inter-organelle communication from the chloroplast to the nucleus, called retrograde signaling. Although some pathways of communication have been identified, the actual signals that move between the two cellular compartments are largely unknown. This review provides an overview of retrograde signaling including its importance to the cell, candidate signals, recent advances and current experimental systems. In addition, we highlight the potential of using drought stress as a model for studying retrograde signaling. PMID:21512326

A sequential method for spline approximation with variable knots. [recursive piecewise polynomial signal processing

NASA Technical Reports Server (NTRS)

Mier Muth, A. M.; Willsky, A. S.

1978-01-01

In this paper we describe a method for approximating a waveform by a spline. The method is quite efficient, as the data are processed sequentially. The basis of the approach is to view the approximation problem as a question of estimation of a polynomial in noise, with the possibility of abrupt changes in the highest derivative. This allows us to bring several powerful statistical signal processing tools into play. We also present some initial results on the application of our technique to the processing of electrocardiograms, where the knot locations themselves may be some of the most important pieces of diagnostic information.

Parmeterization of spectra

NASA Technical Reports Server (NTRS)

Cornish, C. R.

1983-01-01

Following reception and analog to digital conversion (A/D) conversion, atmospheric radar backscatter echoes need to be processed so as to obtain desired information about atmospheric processes and to eliminate or minimize contaminating contributions from other sources. Various signal processing techniques have been implemented at mesosphere-stratosphere-troposphere (MST) radar facilities to estimate parameters of interest from received spectra. Such estimation techniques need to be both accurate and sufficiently efficient to be within the capabilities of the particular data-processing system. The various techniques used to parameterize the spectra of received signals are reviewed herein. Noise estimation, electromagnetic interference, data smoothing, correlation, and the Doppler effect are among the specific points addressed.

Signal evaluation environment: a new method for the design of peripheral in-vehicle warning signals.

PubMed

Werneke, Julia; Vollrath, Mark

2011-06-01

An evaluation method called the Signal Evaluation Environment (SEE) was developed for use in the early stages of the design process of peripheral warning signals while driving. Accident analyses have shown that with complex driving situations such as intersections, the visual scan strategies of the driver contribute to overlooking other road users who have the right of way. Salient peripheral warning signals could disrupt these strategies and direct drivers' attention towards these road users. To select effective warning signals, the SEE was developed as a laboratory task requiring visual-cognitive processes similar to those used at intersections. For validation of the SEE, four experiments were conducted using different stimulus characteristics (size, colour contrast, shape, flashing) that influence peripheral vision. The results confirm that the SEE is able to differentiate between the selected stimulus characteristics. The SEE is a useful initial tool for designing peripheral signals, allowing quick and efficient preselection of beneficial signals.

Chloroplast-to-nucleus communication: current knowledge, experimental strategies and relationship to drought stress signaling.

PubMed

Chan, Kai Xun; Crisp, Peter Alexander; Estavillo, Gonzalo Martin; Pogson, Barry James

2010-12-01

In order for plant cells to function efficiently under different environmental conditions, chloroplastic processes have to be tightly regulated by the nucleus. It is widely believed that there is inter-organelle communication from the chloroplast to the nucleus, called retrograde signaling. Although some pathways of communication have been identified, the actual signals that move between the two cellular compartments are largely unknown. This review provides an overview of retrograde signaling including its importance to the cell, candidate signals, recent advances, and current experimental systems. In addition, we highlight the potential of using drought stress as a model for studying retrograde signaling.

Psychoacoustic processing of test signals

NASA Astrophysics Data System (ADS)

Kadlec, Frantisek

2003-10-01

For the quantitative evaluation of electroacoustic system properties and for psychoacoustic testing it is possible to utilize harmonic signals with fixed frequency, sweeping signals, random signals or their combination. This contribution deals with the design of various test signals with emphasis on audible perception. During the digital generation of signals, some additional undesirable frequency components and noise are produced, which are dependent on signal amplitude and sampling frequency. A mathematical analysis describes the origin of this distortion. By proper selection of signal frequency and amplitude it is possible to minimize those undesirable components. An additional step is to minimize the audible perception of this signal distortion by the application of additional noise (dither). For signals intended for listening tests a dither with triangular or Gaussian probability density function was found to be most effective. Signals modified this way may be further improved by the application of noise shaping, which transposes those undesirable products into frequency regions where they are perceived less, according to psychoacoustic principles. The efficiency of individual processing steps was confirmed both by measurements and by listening tests. [Work supported by the Czech Science Foundation.

Digital CODEC for real-time processing of broadcast quality video signals at 1.8 bits/pixel

NASA Technical Reports Server (NTRS)

Shalkhauser, Mary JO; Whyte, Wayne A., Jr.

1989-01-01

Advances in very large-scale integration and recent work in the field of bandwidth efficient digital modulation techniques have combined to make digital video processing technically feasible and potentially cost competitive for broadcast quality television transmission. A hardware implementation was developed for a DPCM-based digital television bandwidth compression algorithm which processes standard NTSC composite color television signals and produces broadcast quality video in real time at an average of 1.8 bits/pixel. The data compression algorithm and the hardware implementation of the CODEC are described, and performance results are provided.

Digital CODEC for real-time processing of broadcast quality video signals at 1.8 bits/pixel

NASA Technical Reports Server (NTRS)

Shalkhauser, Mary JO; Whyte, Wayne A.

1991-01-01

Advances in very large scale integration and recent work in the field of bandwidth efficient digital modulation techniques have combined to make digital video processing technically feasible an potentially cost competitive for broadcast quality television transmission. A hardware implementation was developed for DPCM (differential pulse code midulation)-based digital television bandwidth compression algorithm which processes standard NTSC composite color television signals and produces broadcast quality video in real time at an average of 1.8 bits/pixel. The data compression algorithm and the hardware implementation of the codec are described, and performance results are provided.

Real-time encoding and compression of neuronal spikes by metal-oxide memristors

NASA Astrophysics Data System (ADS)

Gupta, Isha; Serb, Alexantrou; Khiat, Ali; Zeitler, Ralf; Vassanelli, Stefano; Prodromakis, Themistoklis

2016-09-01

Advanced brain-chip interfaces with numerous recording sites bear great potential for investigation of neuroprosthetic applications. The bottleneck towards achieving an efficient bio-electronic link is the real-time processing of neuronal signals, which imposes excessive requirements on bandwidth, energy and computation capacity. Here we present a unique concept where the intrinsic properties of memristive devices are exploited to compress information on neural spikes in real-time. We demonstrate that the inherent voltage thresholds of metal-oxide memristors can be used for discriminating recorded spiking events from background activity and without resorting to computationally heavy off-line processing. We prove that information on spike amplitude and frequency can be transduced and stored in single devices as non-volatile resistive state transitions. Finally, we show that a memristive device array allows for efficient data compression of signals recorded by a multi-electrode array, demonstrating the technology's potential for building scalable, yet energy-efficient on-node processors for brain-chip interfaces.

Real-time encoding and compression of neuronal spikes by metal-oxide memristors

PubMed Central

Gupta, Isha; Serb, Alexantrou; Khiat, Ali; Zeitler, Ralf; Vassanelli, Stefano; Prodromakis, Themistoklis

2016-01-01

Advanced brain-chip interfaces with numerous recording sites bear great potential for investigation of neuroprosthetic applications. The bottleneck towards achieving an efficient bio-electronic link is the real-time processing of neuronal signals, which imposes excessive requirements on bandwidth, energy and computation capacity. Here we present a unique concept where the intrinsic properties of memristive devices are exploited to compress information on neural spikes in real-time. We demonstrate that the inherent voltage thresholds of metal-oxide memristors can be used for discriminating recorded spiking events from background activity and without resorting to computationally heavy off-line processing. We prove that information on spike amplitude and frequency can be transduced and stored in single devices as non-volatile resistive state transitions. Finally, we show that a memristive device array allows for efficient data compression of signals recorded by a multi-electrode array, demonstrating the technology's potential for building scalable, yet energy-efficient on-node processors for brain-chip interfaces. PMID:27666698

Efficient resource allocation scheme for visible-light communication system

NASA Astrophysics Data System (ADS)

Kim, Woo-Chan; Bae, Chi-Sung; Cho, Dong-Ho; Shin, Hong-Seok; Jung, D. K.; Oh, Y. J.

2009-01-01

A visible-light communication utilizing LED has many advantagies such as visibility of information, high SNR (Signal to Noise Ratio), low installation cost, usage of existing illuminators, and high security. Furthermore, exponentially increasing needs and quality of LED have helped the development of visible-light communication. The visibility is the most attractive property in visible-light communication system, but it is difficult to ensure visibility and transmission efficiency simultaneously during initial access because of the small amount of initial access process signals. In this paper, we propose an efficient resource allocation scheme at initial access for ensuring visibility with high resource utilization rate and low data transmission failure rate. The performance has been evaluated through the numerical analysis and simulation results.

A miniature electronic nose system based on an MWNT-polymer microsensor array and a low-power signal-processing chip.

PubMed

Chiu, Shih-Wen; Wu, Hsiang-Chiu; Chou, Ting-I; Chen, Hsin; Tang, Kea-Tiong

2014-06-01

This article introduces a power-efficient, miniature electronic nose (e-nose) system. The e-nose system primarily comprises two self-developed chips, a multiple-walled carbon nanotube (MWNT)-polymer based microsensor array, and a low-power signal-processing chip. The microsensor array was fabricated on a silicon wafer by using standard photolithography technology. The microsensor array comprised eight interdigitated electrodes surrounded by SU-8 "walls," which restrained the material-solvent liquid in a defined area of 650 × 760 μm(2). To achieve a reliable sensor-manufacturing process, we used a two-layer deposition method, coating the MWNTs and polymer film as the first and second layers, respectively. The low-power signal-processing chip included array data acquisition circuits and a signal-processing core. The MWNT-polymer microsensor array can directly connect with array data acquisition circuits, which comprise sensor interface circuitry and an analog-to-digital converter; the signal-processing core consists of memory and a microprocessor. The core executes the program, classifying the odor data received from the array data acquisition circuits. The low-power signal-processing chip was designed and fabricated using the Taiwan Semiconductor Manufacturing Company 0.18-μm 1P6M standard complementary metal oxide semiconductor process. The chip consumes only 1.05 mW of power at supply voltages of 1 and 1.8 V for the array data acquisition circuits and the signal-processing core, respectively. The miniature e-nose system, which used a microsensor array, a low-power signal-processing chip, and an embedded k-nearest-neighbor-based pattern recognition algorithm, was developed as a prototype that successfully recognized the complex odors of tincture, sorghum wine, sake, whisky, and vodka.

Biosensor system-on-a-chip including CMOS-based signal processing circuits and 64 carbon nanotube-based sensors for the detection of a neurotransmitter.

PubMed

Lee, Byung Yang; Seo, Sung Min; Lee, Dong Joon; Lee, Minbaek; Lee, Joohyung; Cheon, Jun-Ho; Cho, Eunju; Lee, Hyunjoong; Chung, In-Young; Park, Young June; Kim, Suhwan; Hong, Seunghun

2010-04-07

We developed a carbon nanotube (CNT)-based biosensor system-on-a-chip (SoC) for the detection of a neurotransmitter. Here, 64 CNT-based sensors were integrated with silicon-based signal processing circuits in a single chip, which was made possible by combining several technological breakthroughs such as efficient signal processing, uniform CNT networks, and biocompatible functionalization of CNT-based sensors. The chip was utilized to detect glutamate, a neurotransmitter, where ammonia, a byproduct of the enzymatic reaction of glutamate and glutamate oxidase on CNT-based sensors, modulated the conductance signals to the CNT-based sensors. This is a major technological advancement in the integration of CNT-based sensors with microelectronics, and this chip can be readily integrated with larger scale lab-on-a-chip (LoC) systems for various applications such as LoC systems for neural networks.

Investigating the neural bases for intra-subject cognitive efficiency changes using functional magnetic resonance imaging

PubMed Central

Rao, Neena K.; Motes, Michael A.; Rypma, Bart

2014-01-01

Several fMRI studies have examined brain regions mediating inter-subject variability in cognitive efficiency, but none have examined regions mediating intra-subject variability in efficiency. Thus, the present study was designed to identify brain regions involved in intra-subject variability in cognitive efficiency via participant-level correlations between trial-level reaction time (RT) and trial-level fMRI BOLD percent signal change on a processing speed task. On each trial, participants indicated whether a digit-symbol probe-pair was present or absent in an array of nine digit-symbol probe-pairs while fMRI data were collected. Deconvolution analyses, using RT time-series models (derived from the proportional scaling of an event-related hemodynamic response function model by trial-level RT), were used to evaluate relationships between trial-level RTs and BOLD percent signal change. Although task-related patterns of activation and deactivation were observed in regions including bilateral occipital, bilateral parietal, portions of the medial wall such as the precuneus, default mode network regions including anterior cingulate, posterior cingulate, bilateral temporal, right cerebellum, and right cuneus, RT-BOLD correlations were observed in a more circumscribed set of regions. Positive RT-BOLD correlations, where fast RTs were associated with lower BOLD percent signal change, were observed in regions including bilateral occipital, bilateral parietal, and the precuneus. RT-BOLD correlations were not observed in the default mode network indicating a smaller set of regions associated with intra-subject variability in cognitive efficiency. The results are discussed in terms of a distributed area of regions that mediate variability in the cognitive efficiency that might underlie processing speed differences between individuals. PMID:25374527

Investigating the neural bases for intra-subject cognitive efficiency changes using functional magnetic resonance imaging.

PubMed

Rao, Neena K; Motes, Michael A; Rypma, Bart

2014-01-01

Several fMRI studies have examined brain regions mediating inter-subject variability in cognitive efficiency, but none have examined regions mediating intra-subject variability in efficiency. Thus, the present study was designed to identify brain regions involved in intra-subject variability in cognitive efficiency via participant-level correlations between trial-level reaction time (RT) and trial-level fMRI BOLD percent signal change on a processing speed task. On each trial, participants indicated whether a digit-symbol probe-pair was present or absent in an array of nine digit-symbol probe-pairs while fMRI data were collected. Deconvolution analyses, using RT time-series models (derived from the proportional scaling of an event-related hemodynamic response function model by trial-level RT), were used to evaluate relationships between trial-level RTs and BOLD percent signal change. Although task-related patterns of activation and deactivation were observed in regions including bilateral occipital, bilateral parietal, portions of the medial wall such as the precuneus, default mode network regions including anterior cingulate, posterior cingulate, bilateral temporal, right cerebellum, and right cuneus, RT-BOLD correlations were observed in a more circumscribed set of regions. Positive RT-BOLD correlations, where fast RTs were associated with lower BOLD percent signal change, were observed in regions including bilateral occipital, bilateral parietal, and the precuneus. RT-BOLD correlations were not observed in the default mode network indicating a smaller set of regions associated with intra-subject variability in cognitive efficiency. The results are discussed in terms of a distributed area of regions that mediate variability in the cognitive efficiency that might underlie processing speed differences between individuals.

Updated energy budgets for neural computation in the neocortex and cerebellum

PubMed Central

Howarth, Clare; Gleeson, Padraig; Attwell, David

2012-01-01

The brain's energy supply determines its information processing power, and generates functional imaging signals. The energy use on the different subcellular processes underlying neural information processing has been estimated previously for the grey matter of the cerebral and cerebellar cortex. However, these estimates need reevaluating following recent work demonstrating that action potentials in mammalian neurons are much more energy efficient than was previously thought. Using this new knowledge, this paper provides revised estimates for the energy expenditure on neural computation in a simple model for the cerebral cortex and a detailed model of the cerebellar cortex. In cerebral cortex, most signaling energy (50%) is used on postsynaptic glutamate receptors, 21% is used on action potentials, 20% on resting potentials, 5% on presynaptic transmitter release, and 4% on transmitter recycling. In the cerebellar cortex, excitatory neurons use 75% and inhibitory neurons 25% of the signaling energy, and most energy is used on information processing by non-principal neurons: Purkinje cells use only 15% of the signaling energy. The majority of cerebellar signaling energy use is on the maintenance of resting potentials (54%) and postsynaptic receptors (22%), while action potentials account for only 17% of the signaling energy use. PMID:22434069

Efficient and Robust Signal Approximations

DTIC Science & Technology

2009-05-01

otherwise. Remark. Permutation matrices are both orthogonal and doubly- stochastic [62]. We will now show how to further simplify the Robust Coding...reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching...Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Keywords: signal processing, image compression, independent component analysis , sparse

Cancer systems biology: signal processing for cancer research

PubMed Central

Yli-Harja, Olli; Ylipää, Antti; Nykter, Matti; Zhang, Wei

2011-01-01

In this editorial we introduce the research paradigms of signal processing in the era of systems biology. Signal processing is a field of science traditionally focused on modeling electronic and communications systems, but recently it has turned to biological applications with astounding results. The essence of signal processing is to describe the natural world by mathematical models and then, based on these models, develop efficient computational tools for solving engineering problems. Here, we underline, with examples, the endless possibilities which arise when the battle-hardened tools of engineering are applied to solve the problems that have tormented cancer researchers. Based on this approach, a new field has emerged, called cancer systems biology. Despite its short history, cancer systems biology has already produced several success stories tackling previously impracticable problems. Perhaps most importantly, it has been accepted as an integral part of the major endeavors of cancer research, such as analyzing the genomic and epigenomic data produced by The Cancer Genome Atlas (TCGA) project. Finally, we show that signal processing and cancer research, two fields that are seemingly distant from each other, have merged into a field that is indeed more than the sum of its parts. PMID:21439242

Study on negative incident photon-to-electron conversion efficiency of quantum dot-sensitized solar cells

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

Li, Chunhui; Wu, Huijue; Zhu, Lifeng

2014-02-15

Recently, negative signals are frequently observed during the measuring process of monochromatic incident photon-to-electron conversion efficiency (IPCE) for sensitized solar cells by DC method. This phenomenon is confusing and hindering the reasonable evaluation of solar cells. Here, cause of negative IPCE values is studied by taking quantum dot-sensitized solar cell (QDSC) as an example, and the accurate measurement method to avoid the negative value is suggested. The negative background signals of QDSC without illumination are found the direct cause of the negative IPCE values by DC method. Ambient noise, significant capacitance characteristics, and uncontrolled electrochemical reaction all can lead tomore » the negative background signals. When the photocurrent response of device under monochromatic light illumination is relatively weak, the actual photocurrent signals will be covered by the negative background signals and the resulting IPCE values will appear negative. To improve the signal-to-noise ratio, quasi-AC method is proposed for IPCE measurement of solar cells with weak photocurrent response based on the idea of replacing the absolute values by the relative values.« less

Three-Level De-Multiplexed Dual-Branch Complex Delta-Sigma Transmitter.

PubMed

Arfi, Anis Ben; Elsayed, Fahmi; Aflaki, Pouya M; Morris, Brad; Ghannouchi, Fadhel M

2018-02-20

In this paper, a dual-branch topology driven by a Delta-Sigma Modulator (DSM) with a complex quantizer, also known as the Complex Delta Sigma Modulator (CxDSM), with a 3-level quantized output signal is proposed. By de-multiplexing the 3-level Delta-Sigma-quantized signal into two bi-level streams, an efficiency enhancement over the operational frequency range is achieved. The de-multiplexed signals drive a dual-branch amplification block composed of two switch-mode back-to-back power amplifiers working at peak power. A signal processing technique known as quantization noise reduction with In-band Filtering (QNRIF) is applied to each of the de-multiplexed streams to boost the overall performances; particularly the Adjacent Channel Leakage Ratio (ACLR). After amplification, the two branches are combined using a non-isolated combiner, preserving the efficiency of the transmitter. A comprehensive study on the operation of this topology and signal characteristics used to drive the dual-branch Switch-Mode Power Amplifiers (SMPAs) was established. Moreover, this work proposes a highly efficient design of the amplification block based on a back-to-back power topology performing a dynamic load modulation exploiting the non-overlapping properties of the de-multiplexed Complex DSM signal. For experimental validation, the proposed de-multiplexed 3-level Delta-Sigma topology was implemented on the BEEcube™ platform followed by the back-to-back Class-E switch-mode power amplification block. The full transceiver is assessed using a 4th-Generation mobile communications standard LTE (Long Term Evolution) standard 1.4 MHz signal with a peak to average power ratio (PAPR) of 8 dB. The dual-branch topology exhibited a good linearity and a coding efficiency of the transmitter chain higher than 72% across the band of frequency from 1.8 GHz to 2.7 GHz.

Digital signal processing techniques for coherent optical communication

NASA Astrophysics Data System (ADS)

Goldfarb, Gilad

Coherent detection with subsequent digital signal processing (DSP) is developed, analyzed theoretically and numerically and experimentally demonstrated in various fiber-optic transmission scenarios. The use of DSP in conjunction with coherent detection unleashes the benefits of coherent detection which rely on the preservaton of full information of the incoming field. These benefits include high receiver sensitivity, the ability to achieve high spectral-efficiency and the use of advanced modulation formats. With the immense advancements in DSP speeds, many of the problems hindering the use of coherent detection in optical transmission systems have been eliminated. Most notably, DSP alleviates the need for hardware phase-locking and polarization tracking, which can now be achieved in the digital domain. The complexity previously associated with coherent detection is hence significantly diminished and coherent detection is once gain considered a feasible detection alternative. In this thesis, several aspects of coherent detection (with or without subsequent DSP) are addressed. Coherent detection is presented as a means to extend the dispersion limit of a duobinary signal using an analog decision-directed phase-lock loop. Analytical bit-error ratio estimation for quadrature phase-shift keying signals is derived. To validate the promise for high spectral efficiency, the orthogonal-wavelength-division multiplexing scheme is suggested. In this scheme the WDM channels are spaced at the symbol rate, thus achieving the spectral efficiency limit. Theory, simulation and experimental results demonstrate the feasibility of this approach. Infinite impulse response filtering is shown to be an efficient alternative to finite impulse response filtering for chromatic dispersion compensation. Theory, design considerations, simulation and experimental results relating to this topic are presented. Interaction between fiber dispersion and nonlinearity remains the last major challenge deterministic effects pose for long-haul optical data transmission. Experimental results which demonstrate the possibility to digitally mitigate both dispersion and nonlinearity are presented. Impairment compensation is achieved using backward propagation by implementing the split-step method. Efficient realizations of the dispersion compensation operator used in this implementation are considered. Infinite-impulse response and wavelet-based filtering are both investigated as a means to reduce the required computational load associated with signal backward-propagation. Possible future research directions conclude this dissertation.

Snore related signals processing in a private cloud computing system.

PubMed

Qian, Kun; Guo, Jian; Xu, Huijie; Zhu, Zhaomeng; Zhang, Gongxuan

2014-09-01

Snore related signals (SRS) have been demonstrated to carry important information about the obstruction site and degree in the upper airway of Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) patients in recent years. To make this acoustic signal analysis method more accurate and robust, big SRS data processing is inevitable. As an emerging concept and technology, cloud computing has motivated numerous researchers and engineers to exploit applications both in academic and industry field, which could have an ability to implement a huge blue print in biomedical engineering. Considering the security and transferring requirement of biomedical data, we designed a system based on private cloud computing to process SRS. Then we set the comparable experiments of processing a 5-hour audio recording of an OSAHS patient by a personal computer, a server and a private cloud computing system to demonstrate the efficiency of the infrastructure we proposed.

Analysis of the times involved in processing and communication in a lower limb simulation system controlled by SEMG

NASA Astrophysics Data System (ADS)

Profumieri, A.; Bonell, C.; Catalfamo, P.; Cherniz, A.

2016-04-01

Virtual reality has been proposed for different applications, including the evaluation of new control strategies and training protocols for upper limb prostheses and for the study of new rehabilitation programs. In this study, a lower limb simulation environment commanded by surface electromyography signals is evaluated. The time delays generated by the acquisition and processing stages for the signals that would command the knee joint, were measured and different acquisition windows were analysed. The subjective perception of the quality of simulation was also evaluated when extra delays were added to the process. The results showed that the acquisition window is responsible for the longest delay. Also, the basic implemented processes allowed for the acquisition of three signal channels for commanding the simulation. Finally, the communication between different applications is arguably efficient, although it depends on the amount of data to be sent.

Ultra-low-power and robust digital-signal-processing hardware for implantable neural interface microsystems.

PubMed

Narasimhan, S; Chiel, H J; Bhunia, S

2011-04-01

Implantable microsystems for monitoring or manipulating brain activity typically require on-chip real-time processing of multichannel neural data using ultra low-power, miniaturized electronics. In this paper, we propose an integrated-circuit/architecture-level hardware design framework for neural signal processing that exploits the nature of the signal-processing algorithm. First, we consider different power reduction techniques and compare the energy efficiency between the ultra-low frequency subthreshold and conventional superthreshold design. We show that the superthreshold design operating at a much higher frequency can achieve comparable energy dissipation by taking advantage of extensive power gating. It also provides significantly higher robustness of operation and yield under large process variations. Next, we propose an architecture level preferential design approach for further energy reduction by isolating the critical computation blocks (with respect to the quality of the output signal) and assigning them higher delay margins compared to the noncritical ones. Possible delay failures under parameter variations are confined to the noncritical components, allowing graceful degradation in quality under voltage scaling. Simulation results using prerecorded neural data from the sea-slug (Aplysia californica) show that the application of the proposed design approach can lead to significant improvement in total energy, without compromising the output signal quality under process variations, compared to conventional design approaches.

Real time SAR processing

NASA Technical Reports Server (NTRS)

Premkumar, A. B.; Purviance, J. E.

1990-01-01

A simplified model for the SAR imaging problem is presented. The model is based on the geometry of the SAR system. Using this model an expression for the entire phase history of the received SAR signal is formulated. From the phase history, it is shown that the range and the azimuth coordinates for a point target image can be obtained by processing the phase information during the intrapulse and interpulse periods respectively. An architecture for a VLSI implementation for the SAR signal processor is presented which generates images in real time. The architecture uses a small number of chips, a new correlation processor, and an efficient azimuth correlation process.

An integrated condition-monitoring method for a milling process using reduced decomposition features

NASA Astrophysics Data System (ADS)

Liu, Jie; Wu, Bo; Wang, Yan; Hu, Youmin

2017-08-01

Complex and non-stationary cutting chatter affects productivity and quality in the milling process. Developing an effective condition-monitoring approach is critical to accurately identify cutting chatter. In this paper, an integrated condition-monitoring method is proposed, where reduced features are used to efficiently recognize and classify machine states in the milling process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition, and Shannon power spectral entropy is calculated to extract features from the decomposed signals. Principal component analysis is adopted to reduce feature size and computational cost. With the extracted feature information, the probabilistic neural network model is used to recognize and classify the machine states, including stable, transition, and chatter states. Experimental studies are conducted, and results show that the proposed method can effectively detect cutting chatter during different milling operation conditions. This monitoring method is also efficient enough to satisfy fast machine state recognition and classification.

Electrocardiogram signal denoising based on a new improved wavelet thresholding

NASA Astrophysics Data System (ADS)

Han, Guoqiang; Xu, Zhijun

2016-08-01

Good quality electrocardiogram (ECG) is utilized by physicians for the interpretation and identification of physiological and pathological phenomena. In general, ECG signals may mix various noises such as baseline wander, power line interference, and electromagnetic interference in gathering and recording process. As ECG signals are non-stationary physiological signals, wavelet transform is investigated to be an effective tool to discard noises from corrupted signals. A new compromising threshold function called sigmoid function-based thresholding scheme is adopted in processing ECG signals. Compared with other methods such as hard/soft thresholding or other existing thresholding functions, the new algorithm has many advantages in the noise reduction of ECG signals. It perfectly overcomes the discontinuity at ±T of hard thresholding and reduces the fixed deviation of soft thresholding. The improved wavelet thresholding denoising can be proved to be more efficient than existing algorithms in ECG signal denoising. The signal to noise ratio, mean square error, and percent root mean square difference are calculated to verify the denoising performance as quantitative tools. The experimental results reveal that the waves including P, Q, R, and S waves of ECG signals after denoising coincide with the original ECG signals by employing the new proposed method.

Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD).

PubMed

Bermúdez Ordoñez, Juan Carlos; Arnaldo Valdés, Rosa María; Gómez Comendador, Fernando

2018-05-16

A significant challenge in global navigation satellite system (GNSS) signal processing is a requirement for a very high sampling rate. The recently-emerging compressed sensing (CS) theory makes processing GNSS signals at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on CS and SVD theories, an algorithm for sampling GNSS signals at a rate much lower than the Nyquist rate and reconstructing the compressed signal is proposed in this research, which is validated after the output from that process still performs signal detection using the standard fast Fourier transform (FFT) parallel frequency space search acquisition. The sparse representation of the GNSS signal is the most important precondition for CS, by constructing a rectangular Toeplitz matrix (TZ) of the transmitted signal, calculating the left singular vectors using SVD from the TZ, to achieve sparse signal representation. Next, obtaining the M-dimensional observation vectors based on the left singular vectors of the SVD, which are equivalent to the sampler operator in standard compressive sensing theory, the signal can be sampled below the Nyquist rate, and can still be reconstructed via ℓ 1 minimization with accuracy using convex optimization. As an added value, there is a GNSS signal acquisition enhancement effect by retaining the useful signal and filtering out noise by projecting the signal into the most significant proper orthogonal modes (PODs) which are the optimal distributions of signal power. The algorithm is validated with real recorded signals, and the results show that the proposed method is effective for sampling, reconstructing intermediate frequency (IF) GNSS signals in the time discrete domain.

Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)

PubMed Central

Arnaldo Valdés, Rosa María; Gómez Comendador, Fernando

2018-01-01

A significant challenge in global navigation satellite system (GNSS) signal processing is a requirement for a very high sampling rate. The recently-emerging compressed sensing (CS) theory makes processing GNSS signals at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on CS and SVD theories, an algorithm for sampling GNSS signals at a rate much lower than the Nyquist rate and reconstructing the compressed signal is proposed in this research, which is validated after the output from that process still performs signal detection using the standard fast Fourier transform (FFT) parallel frequency space search acquisition. The sparse representation of the GNSS signal is the most important precondition for CS, by constructing a rectangular Toeplitz matrix (TZ) of the transmitted signal, calculating the left singular vectors using SVD from the TZ, to achieve sparse signal representation. Next, obtaining the M-dimensional observation vectors based on the left singular vectors of the SVD, which are equivalent to the sampler operator in standard compressive sensing theory, the signal can be sampled below the Nyquist rate, and can still be reconstructed via ℓ1 minimization with accuracy using convex optimization. As an added value, there is a GNSS signal acquisition enhancement effect by retaining the useful signal and filtering out noise by projecting the signal into the most significant proper orthogonal modes (PODs) which are the optimal distributions of signal power. The algorithm is validated with real recorded signals, and the results show that the proposed method is effective for sampling, reconstructing intermediate frequency (IF) GNSS signals in the time discrete domain. PMID:29772731

Bayesian Inference for Signal-Based Seismic Monitoring

NASA Astrophysics Data System (ADS)

Moore, D.

2015-12-01

Traditional seismic monitoring systems rely on discrete detections produced by station processing software, discarding significant information present in the original recorded signal. SIG-VISA (Signal-based Vertically Integrated Seismic Analysis) is a system for global seismic monitoring through Bayesian inference on seismic signals. By modeling signals directly, our forward model is able to incorporate a rich representation of the physics underlying the signal generation process, including source mechanisms, wave propagation, and station response. This allows inference in the model to recover the qualitative behavior of recent geophysical methods including waveform matching and double-differencing, all as part of a unified Bayesian monitoring system that simultaneously detects and locates events from a global network of stations. We demonstrate recent progress in scaling up SIG-VISA to efficiently process the data stream of global signals recorded by the International Monitoring System (IMS), including comparisons against existing processing methods that show increased sensitivity from our signal-based model and in particular the ability to locate events (including aftershock sequences that can tax analyst processing) precisely from waveform correlation effects. We also provide a Bayesian analysis of an alleged low-magnitude event near the DPRK test site in May 2010 [1] [2], investigating whether such an event could plausibly be detected through automated processing in a signal-based monitoring system. [1] Zhang, Miao and Wen, Lianxing. "Seismological Evidence for a Low-Yield Nuclear Test on 12 May 2010 in North Korea". Seismological Research Letters, January/February 2015. [2] Richards, Paul. "A Seismic Event in North Korea on 12 May 2010". CTBTO SnT 2015 oral presentation, video at https://video-archive.ctbto.org/index.php/kmc/preview/partner_id/103/uiconf_id/4421629/entry_id/0_ymmtpps0/delivery/http

Image detection and compression for memory efficient system analysis

NASA Astrophysics Data System (ADS)

Bayraktar, Mustafa

2015-02-01

The advances in digital signal processing have been progressing towards efficient use of memory and processing. Both of these factors can be utilized efficiently by using feasible techniques of image storage by computing the minimum information of image which will enhance computation in later processes. Scale Invariant Feature Transform (SIFT) can be utilized to estimate and retrieve of an image. In computer vision, SIFT can be implemented to recognize the image by comparing its key features from SIFT saved key point descriptors. The main advantage of SIFT is that it doesn't only remove the redundant information from an image but also reduces the key points by matching their orientation and adding them together in different windows of image [1]. Another key property of this approach is that it works on highly contrasted images more efficiently because it`s design is based on collecting key points from the contrast shades of image.

Activation of HIV-1 pre-mRNA 3' processing in vitro requires both an upstream element and TAR.

PubMed Central

Gilmartin, G M; Fleming, E S; Oetjen, J

1992-01-01

The architecture of the human immunodeficiency virus type 1 (HIV-1) genome presents an intriguing dilemma for the 3' processing of viral transcripts--to disregard a canonical 'core' poly(A) site processing signal present at the 5' end of the transcript and yet to utilize efficiently an identical signal that resides at the 3' end of the message. The choice of processing sites in HIV-1 appears to be influenced by two factors: (i) proximity to the cap site, and (ii) sequences upstream of the core poly(A) site. We now demonstrate that an in vivo-defined upstream element that resides within the U3 region, 76 nucleotides upstream of the AAUAAA hexamer, acts specifically to enhance 3' processing at the HIV-1 core poly(A) site in vitro. We furthermore show that efficient in vitro 3' processing requires the RNA stem-loop structure of TAR, which serves to juxtapose spatially the upstream element and the core poly(A) site. An analysis of the stability of 3' processing complexes formed at the HIV-1 poly(A) site in vitro suggests that the upstream element may function by increasing processing complex stability at the core poly(A) site. Images PMID:1425577

Development and verification of signal processing system of avalanche photo diode for the active shields onboard ASTRO-H

NASA Astrophysics Data System (ADS)

Ohno, M.; Kawano, T.; Edahiro, I.; Shirakawa, H.; Ohashi, N.; Okada, C.; Habata, S.; Katsuta, J.; Tanaka, Y.; Takahashi, H.; Mizuno, T.; Fukazawa, Y.; Murakami, H.; Kobayashi, S.; Miyake, K.; Ono, K.; Kato, Y.; Furuta, Y.; Murota, Y.; Okuda, K.; Wada, Y.; Nakazawa, K.; Mimura, T.; Kataoka, J.; Ichinohe, Y.; Uchida, Y.; Katsuragawa, M.; Yoneda, H.; Sato, G.; Sato, R.; Kawaharada, M.; Harayama, A.; Odaka, H.; Hayashi, K.; Ohta, M.; Watanabe, S.; Kokubun, M.; Takahashi, T.; Takeda, S.; Kinoshita, M.; Yamaoka, K.; Tajima, H.; Yatsu, Y.; Uchiyama, H.; Saito, S.; Yuasa, T.; Makishima, K.; ASTRO-H HXI/SGD Team

2016-09-01

The hard X-ray Imager and Soft Gamma-ray Detector onboard ASTRO-H demonstrate high sensitivity to hard X-ray (5-80 keV) and soft gamma-rays (60-600 keV), respectively. To reduce the background, both instruments are actively shielded by large, thick Bismuth Germanate scintillators. We have developed the signal processing system of the avalanche photodiode in the BGO active shields and have demonstrated its effectiveness after assembly in the flight model of the HXI/SGD sensor and after integration into the satellite. The energy threshold achieved is about 150 keV and anti-coincidence efficiency for cosmic-ray events is almost 100%. Installed in the BGO active shield, the developed signal processing system successfully reduces the room background level of the main detector.

A method for compression of intra-cortically-recorded neural signals dedicated to implantable brain-machine interfaces.

PubMed

Shaeri, Mohammad Ali; Sodagar, Amir M

2015-05-01

This paper proposes an efficient data compression technique dedicated to implantable intra-cortical neural recording devices. The proposed technique benefits from processing neural signals in the Discrete Haar Wavelet Transform space, a new spike extraction approach, and a novel data framing scheme to telemeter the recorded neural information to the outside world. Based on the proposed technique, a 64-channel neural signal processor was designed and prototyped as a part of a wireless implantable extra-cellular neural recording microsystem. Designed in a 0.13- μ m standard CMOS process, the 64-channel neural signal processor reported in this paper occupies ∼ 0.206 mm(2) of silicon area, and consumes 94.18 μW when operating under a 1.2-V supply voltage at a master clock frequency of 1.28 MHz.

AnyWave: a cross-platform and modular software for visualizing and processing electrophysiological signals.

PubMed

Colombet, B; Woodman, M; Badier, J M; Bénar, C G

2015-03-15

The importance of digital signal processing in clinical neurophysiology is growing steadily, involving clinical researchers and methodologists. There is a need for crossing the gap between these communities by providing efficient delivery of newly designed algorithms to end users. We have developed such a tool which both visualizes and processes data and, additionally, acts as a software development platform. AnyWave was designed to run on all common operating systems. It provides access to a variety of data formats and it employs high fidelity visualization techniques. It also allows using external tools as plug-ins, which can be developed in languages including C++, MATLAB and Python. In the current version, plug-ins allow computation of connectivity graphs (non-linear correlation h2) and time-frequency representation (Morlet wavelets). The software is freely available under the LGPL3 license. AnyWave is designed as an open, highly extensible solution, with an architecture that permits rapid delivery of new techniques to end users. We have developed AnyWave software as an efficient neurophysiological data visualizer able to integrate state of the art techniques. AnyWave offers an interface well suited to the needs of clinical research and an architecture designed for integrating new tools. We expect this software to strengthen the collaboration between clinical neurophysiologists and researchers in biomedical engineering and signal processing. Copyright © 2015 Elsevier B.V. All rights reserved.

Distributed processing for features improvement in real-time portable medical devices.

PubMed

Mercado, Erwin John Saavedra

2008-01-01

Portable biomedical devices are being developed and incorporated in daily life. Nevertheless, their standalone capacity is diminished due to the lack of processing power required to face such duties as for example, signal artifacts robustness in EKG monitor devices. The following paper presents a multiprocessor architecture made from simple microcontrollers to provide an increase in processing performance, power consumption efficiency and lower cost.

Efficient high light acclimation involves rapid processes at multiple mechanistic levels.

PubMed

Dietz, Karl-Josef

2015-05-01

Like no other chemical or physical parameter, the natural light environment of plants changes with high speed and jumps of enormous intensity. To cope with this variability, photosynthetic organisms have evolved sensing and response mechanisms that allow efficient acclimation. Most signals originate from the chloroplast itself. In addition to very fast photochemical regulation, intensive molecular communication is realized within the photosynthesizing cell, optimizing the acclimation process. Current research has opened up new perspectives on plausible but mostly unexpected complexity in signalling events, crosstalk, and process adjustments. Within seconds and minutes, redox states, levels of reactive oxygen species, metabolites, and hormones change and transmit information to the cytosol, modifying metabolic activity, gene expression, translation activity, and alternative splicing events. Signalling pathways on an intermediate time scale of several minutes to a few hours pave the way for long-term acclimation. Thereby, a new steady state of the transcriptome, proteome, and metabolism is realized within rather short time periods irrespective of the previous acclimation history to shade or sun conditions. This review provides a time line of events during six hours in the 'stressful' life of a plant. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

A Modified Subpulse SAR Processing Procedure Based on the Range-Doppler Algorithm for Synthetic Wideband Waveforms

PubMed Central

Lim, Byoung-Gyun; Woo, Jea-Choon; Lee, Hee-Young; Kim, Young-Soo

2008-01-01

Synthetic wideband waveforms (SWW) combine a stepped frequency CW waveform and a chirp signal waveform to achieve high range resolution without requiring a large bandwidth or the consequent very high sampling rate. If an efficient algorithm like the range-Doppler algorithm (RDA) is used to acquire the SAR images for synthetic wideband signals, errors occur due to approximations, so the images may not show the best possible result. This paper proposes a modified subpulse SAR processing algorithm for synthetic wideband signals which is based on RDA. An experiment with an automobile-based SAR system showed that the proposed algorithm is quite accurate with a considerable improvement in resolution and quality of the obtained SAR image. PMID:27873984

Aerospace Applications Conference, Steamboat Springs, CO, Feb. 1-8, 1986, Digest

NASA Astrophysics Data System (ADS)

The present conference considers topics concerning the projected NASA Space Station's systems, digital signal and data processing applications, and space science and microwave applications. Attention is given to Space Station video and audio subsystems design, clock error, jitter, phase error and differential time-of-arrival in satellite communications, automation and robotics in space applications, target insertion into synthetic background scenes, and a novel scheme for the computation of the discrete Fourier transform on a systolic processor. Also discussed are a novel signal parameter measurement system employing digital signal processing, EEPROMS for spacecraft applications, a unique concurrent processor architecture for high speed simulation of dynamic systems, a dual polarization flat plate antenna, Fresnel diffraction, and ultralinear TWTs for high efficiency satellite communications.

Dissociable brain mechanisms underlying the conscious and unconscious control of behavior.

PubMed

van Gaal, Simon; Lamme, Victor A F; Fahrenfort, Johannes J; Ridderinkhof, K Richard

2011-01-01

Cognitive control allows humans to overrule and inhibit habitual responses to optimize performance in challenging situations. Contradicting traditional views, recent studies suggest that cognitive control processes can be initiated unconsciously. To further capture the relation between consciousness and cognitive control, we studied the dynamics of inhibitory control processes when triggered consciously versus unconsciously in a modified version of the stop task. Attempts to inhibit an imminent response were often successful after unmasked (visible) stop signals. Masked (invisible) stop signals rarely succeeded in instigating overt inhibition but did trigger slowing down of response times. Masked stop signals elicited a sequence of distinct ERP components that were also observed on unmasked stop signals. The N2 component correlated with the efficiency of inhibitory control when elicited by unmasked stop signals and with the magnitude of slowdown when elicited by masked stop signals. Thus, the N2 likely reflects the initiation of inhibitory control, irrespective of conscious awareness. The P3 component was much reduced in amplitude and duration on masked versus unmasked stop trials. These patterns of differences and similarities between conscious and unconscious cognitive control processes are discussed in a framework that differentiates between feedforward and feedback connections in yielding conscious experience.

Remote Entanglement by Coherent Multiplication of Concurrent Quantum Signals

NASA Astrophysics Data System (ADS)

Roy, Ananda; Jiang, Liang; Stone, A. Douglas; Devoret, Michel

2015-10-01

Concurrent remote entanglement of distant, noninteracting quantum entities is a crucial function for quantum information processing. In contrast with the existing protocols which employ the addition of signals to generate entanglement between two remote qubits, the continuous variable protocol we present is based on the multiplication of signals. This protocol can be straightforwardly implemented by a novel Josephson junction mixing circuit. Our scheme would be able to generate provable entanglement even in the presence of practical imperfections: finite quantum efficiency of detectors and undesired photon loss in current state-of-the-art devices.

Sensor-Based Vibration Signal Feature Extraction Using an Improved Composite Dictionary Matching Pursuit Algorithm

PubMed Central

Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

2014-01-01

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective. PMID:25207870

Sensor-based vibration signal feature extraction using an improved composite dictionary matching pursuit algorithm.

PubMed

Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

2014-09-09

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective.

Interpretation of interference signals in label free integrated interferometric biosensors

NASA Astrophysics Data System (ADS)

Heikkinen, Hanna; Wang, Meng; Okkonen, Matti; Hast, Jukka; Myllylä, Risto

2006-02-01

In the future fast, simple and reliable biosensors will be needed to detect various analytes from different biosamples. This is due to fact that the needs of traditional health care are changing. In the future homecare of patients and peoples' responsibility for their own health will increase. Also, different wellness applications need new parameters to be analysed, reducing costs of traditional health care, which are increasing rapidly. One fascinating and promising sensor type for these applications is an integrated optical interferometric immunosensor, which is manufactured using organic materials. The use of organic materials opens up enormous possibilities to develop different biochemical functions. In label free biosensors the measurement is based on detecting changes in refractive index, which typically are in the range of 10 -6-10 -8 [1]. In this research, theoretically generated interferograms are used to compare various signal processing methods. The goal is to develop an efficient method to analyse the interferogram. Different time domain signal processing methods are studied to determine the measuring resolution and efficiency of these methods. A low cost CCD -element is used in detecting the interferogram dynamics. It was found that in most of the signal processing methods the measuring resolution was mainly limited by pixel size. With calculation of Pearson's correlation coefficient, subpixel resolution was achieved which means that nanometer range optical path differences can be measured. This results in the refractive index resolution of the order of 10 -7.

TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach.

PubMed

Elgendi, Mohamed

2016-11-02

Biomedical signals contain features that represent physiological events, and each of these events has peaks. The analysis of biomedical signals for monitoring or diagnosing diseases requires the detection of these peaks, making event detection a crucial step in biomedical signal processing. Many researchers have difficulty detecting these peaks to investigate, interpret and analyze their corresponding events. To date, there is no generic framework that captures these events in a robust, efficient and consistent manner. A new method referred to for the first time as two event-related moving averages ("TERMA") involves event-related moving averages and detects events in biomedical signals. The TERMA framework is flexible and universal and consists of six independent LEGO building bricks to achieve high accuracy detection of biomedical events. Results recommend that the window sizes for the two moving averages ( W 1 and W 2 ) have to follow the inequality ( 8 × W 1 ) ≥ W 2 ≥ ( 2 × W 1 ) . Moreover, TERMA is a simple yet efficient event detector that is suitable for wearable devices, point-of-care devices, fitness trackers and smart watches, compared to more complex machine learning solutions.

Aquaporin-facilitated transmembrane diffusion of hydrogen peroxide.

PubMed

Bienert, Gerd P; Chaumont, François

2014-05-01

Hydrogen peroxide (H2O2) is an important signaling compound that has recently been identified as a new substrate for several members of the aquaporin superfamily in various organisms. Evidence is emerging about the physiological significance of aquaporin-facilitated H2O2 diffusion. This review summarizes current knowledge about aquaporin-facilitated H2O2 diffusion across cellular membranes. It focuses on physicochemical and experimental evidence demonstrating the involvement of aquaporins in the transport of this redox signaling compound and discusses the regulation and structural prerequisites of these channels to transmit this signal. It also provides perspectives about the potential importance of aquaporin-facilitated H2O2 diffusion processes and places this knowledge in the context of the current understanding of transmembrane redox signaling processes. Specific aquaporin isoforms facilitate the passive diffusion of H2O2 across biological membranes and control H2O2 membrane permeability and signaling in living organisms. Redox signaling is a very important process regulating the physiology of cells and organisms in a similar way to the well-characterized hormonal and calcium signaling pathways. Efficient transmembrane diffusion of H2O2, a key molecule in the redox signaling network, requires aquaporins and makes these channels important players in this signaling process. Channel-mediated membrane transport allows the fine adjustment of H2O2 levels in the cytoplasm, intracellular organelles, the apoplast, and the extracellular space, which are essential for it to function as a signal molecule. This article is part of a Special Issue entitled Aquaporins. © 2013.

Imaging synthetic aperture radar

DOEpatents

Burns, Bryan L.; Cordaro, J. Thomas

1997-01-01

A linear-FM SAR imaging radar method and apparatus to produce a real-time image by first arranging the returned signals into a plurality of subaperture arrays, the columns of each subaperture array having samples of dechirped baseband pulses, and further including a processing of each subaperture array to obtain coarse-resolution in azimuth, then fine-resolution in range, and lastly, to combine the processed subapertures to obtain the final fine-resolution in azimuth. Greater efficiency is achieved because both the transmitted signal and a local oscillator signal mixed with the returned signal can be varied on a pulse-to-pulse basis as a function of radar motion. Moreover, a novel circuit can adjust the sampling location and the A/D sample rate of the combined dechirped baseband signal which greatly reduces processing time and hardware. The processing steps include implementing a window function, stabilizing either a central reference point and/or all other points of a subaperture with respect to doppler frequency and/or range as a function of radar motion, sorting and compressing the signals using a standard fourier transforms. The stabilization of each processing part is accomplished with vector multiplication using waveforms generated as a function of radar motion wherein these waveforms may be synthesized in integrated circuits. Stabilization of range migration as a function of doppler frequency by simple vector multiplication is a particularly useful feature of the invention; as is stabilization of azimuth migration by correcting for spatially varying phase errors prior to the application of an autofocus process.

A reprogrammable receiver architecture for wireless signal interception

NASA Astrophysics Data System (ADS)

Yao, Timothy S.

2003-09-01

In this paper, a re-programmable receiver architecture, based on software-defined-radio concept, for wireless signal interception is presented. The radio-frequency (RF) signal that the receiver would like to intercept may come from a terrestrial cellular network or communication satellites, which their carrier frequency are in the range from 800 MHz (civilian mobile) to 15 GHz (Ku band). To intercept signals from such a wide range of frequency in these variant communication systems, the traditional way is to deploy multiple receivers to scan and detect the desired signal. This traditional approach is obviously unattractive due to the cost, efficiency, and accuracy. Instead, we propose a universal receiver, which is software-driven and re-configurable, to intercept signals of interest. The software-defined-radio based receiver first intercepts RF energy of wide spectrum (25MHz) through antenna, performs zero-IF down conversion (homodyne architecture) to baseband, and digital channelizes the baseband signal. The channelization module is a bank of high performance digital filters. The bandwidth of the filter bank is programmable according to the wireless communication protocol under watch. In the baseband processing, high-performance digital signal processors carry out the detection process and microprocessors handle the communication protocols. The baseband processing is also re-configurable for different wireless standards and protocol. The advantages of the software-defined-radio architecture over traditional RF receiver make it a favorable technology for the communication signal interception and surveillance.

Secretory production of a beta-mannanase and a chitosanase using a Lactobacillus plantarum expression system.

PubMed

Sak-Ubol, Suttipong; Namvijitr, Peenida; Pechsrichuang, Phornsiri; Haltrich, Dietmar; Nguyen, Thu-Ha; Mathiesen, Geir; Eijsink, Vincent G H; Yamabhai, Montarop

2016-05-12

Heterologous production of hydrolytic enzymes is important for green and white biotechnology since these enzymes serve as efficient biocatalysts for the conversion of a wide variety of raw materials into value-added products. Lactic acid bacteria are interesting cell factories for the expression of hydrolytic enzymes as many of them are generally recognized as safe and require only a simple cultivation process. We are studying a potentially food-grade expression system for secretion of hydrolytic enzymes into the culture medium, since this enables easy harvesting and purification, while allowing direct use of the enzymes in food applications. We studied overexpression of a chitosanase (CsnA) and a β-mannanase (ManB), from Bacillus licheniformis and Bacillus subtilis, respectively, in Lactobacillus plantarum, using the pSIP system for inducible expression. The enzymes were over-expressed in three forms: without a signal peptide, with their natural signal peptide and with the well-known OmpA signal peptide from Escherichia coli. The total production levels and secretion efficiencies of CsnA and ManB were highest when using the native signal peptides, and both were reduced considerably when using the OmpA signal. At 20 h after induction with 12.5 ng/mL of inducing peptide in MRS media containing 20 g/L glucose, the yields and secretion efficiencies of the proteins with their native signal peptides were 50 kU/L and 84% for ManB, and 79 kU/L and 56% for CsnA, respectively. In addition, to avoid using antibiotics, the erythromycin resistance gene was replaced on the expression plasmid with the alanine racemase (alr) gene, which led to comparable levels of protein production and secretion efficiency in a suitable, alr-deficient L. plantarum host. ManB and CsnA were efficiently produced and secreted in L. plantarum using pSIP-based expression vectors containing either an erythromycin resistance or the alr gene as selection marker.

Coherent detection in optical fiber systems.

PubMed

Ip, Ezra; Lau, Alan Pak Tao; Barros, Daniel J F; Kahn, Joseph M

2008-01-21

The drive for higher performance in optical fiber systems has renewed interest in coherent detection. We review detection methods, including noncoherent, differentially coherent, and coherent detection, as well as a hybrid method. We compare modulation methods encoding information in various degrees of freedom (DOF). Polarization-multiplexed quadrature-amplitude modulation maximizes spectral efficiency and power efficiency, by utilizing all four available DOF, the two field quadratures in the two polarizations. Dual-polarization homodyne or heterodyne downconversion are linear processes that can fully recover the received signal field in these four DOF. When downconverted signals are sampled at the Nyquist rate, compensation of transmission impairments can be performed using digital signal processing (DSP). Linear impairments, including chromatic dispersion and polarization-mode dispersion, can be compensated quasi-exactly using finite impulse response filters. Some nonlinear impairments, such as intra-channel four-wave mixing and nonlinear phase noise, can be compensated partially. Carrier phase recovery can be performed using feedforward methods, even when phase-locked loops may fail due to delay constraints. DSP-based compensation enables a receiver to adapt to time-varying impairments, and facilitates use of advanced forward-error-correction codes. We discuss both single- and multi-carrier system implementations. For a given modulation format, using coherent detection, they offer fundamentally the same spectral efficiency and power efficiency, but may differ in practice, because of different impairments and implementation details. With anticipated advances in analog-to-digital converters and integrated circuit technology, DSP-based coherent receivers at bit rates up to 100 Gbit/s should become practical within the next few years.

Open source cardiology electronic health record development for DIGICARDIAC implementation

NASA Astrophysics Data System (ADS)

Dugarte, Nelson; Medina, Rubén.; Huiracocha, Lourdes; Rojas, Rubén.

2015-12-01

This article presents the development of a Cardiology Electronic Health Record (CEHR) system. Software consists of a structured algorithm designed under Health Level-7 (HL7) international standards. Novelty of the system is the integration of high resolution ECG (HRECG) signal acquisition and processing tools, patient information management tools and telecardiology tools. Acquisition tools are for management and control of the DIGICARDIAC electrocardiograph functions. Processing tools allow management of HRECG signal analysis searching for indicative patterns of cardiovascular pathologies. Telecardiology tools incorporation allows system communication with other health care centers decreasing access time to the patient information. CEHR system was completely developed using open source software. Preliminary results of process validation showed the system efficiency.

Multivariate analysis techniques

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

Bendavid, Josh; Fisher, Wade C.; Junk, Thomas R.

2016-01-01

The end products of experimental data analysis are designed to be simple and easy to understand: hypothesis tests and measurements of parameters. But, the experimental data themselves are voluminous and complex. Furthermore, in modern collider experiments, many petabytes of data must be processed in search of rare new processes which occur together with much more copious background processes that are of less interest to the task at hand. The systematic uncertainties on the background may be larger than the expected signal in many cases. The statistical power of an analysis and its sensitivity to systematic uncertainty can therefore usually bothmore » be improved by separating signal events from background events with higher efficiency and purity.« less

Dual-pumped nondegenerate four-wave mixing in semiconductor laser with a built-in external cavity

NASA Astrophysics Data System (ADS)

Wu, Jian-Wei; Qiu, Qi; Hyub Won, Yong

2017-04-01

In this paper, a semiconductor laser system consisting of a conventional multimode Fabry-Pérot laser diode with a built-in external cavity is presented and demonstrated. More than two resonance modes, whose peak levels are significantly higher than other residual modes, are simultaneously supported and output by adjusting the bias current and operating temperature of the active region. Based on this device, dual-pumped nondegenerate four-wave mixing—in which two pump waves and a single signal wave are simultaneously fed into the laser, and the injection power and wavelength of the injected pump and signal waves are changed—is observed and discussed thoroughly. The results show that while the wavelengths of pump wave A and signal wave S are kept constant, the other pump wave B jumps from about 1535 nm to 1578 nm, generating conversion signals with changed wavelengths. The achieved conversion bandwidth between the primary signal and the converted signal waves is broadly tunable in the range of several terahertz frequencies. Both the conversion efficiency and optical signal-to-noise ratio of the newly generated conversion signals are adopted to evaluate the performance of the proposed four-wave mixing process, and are strongly dependent on the wavelength and power of the injected waves. Here, the attained maximum conversion efficiency and optical signal-to-noise ratio are close to -22 dB and 15 dB, respectively.

Study of sensor spectral responses and data processing algorithms and architectures for onboard feature identification

NASA Technical Reports Server (NTRS)

Huck, F. O.; Davis, R. E.; Fales, C. L.; Aherron, R. M.

1982-01-01

A computational model of the deterministic and stochastic processes involved in remote sensing is used to study spectral feature identification techniques for real-time onboard processing of data acquired with advanced earth-resources sensors. Preliminary results indicate that: Narrow spectral responses are advantageous; signal normalization improves mean-square distance (MSD) classification accuracy but tends to degrade maximum-likelihood (MLH) classification accuracy; and MSD classification of normalized signals performs better than the computationally more complex MLH classification when imaging conditions change appreciably from those conditions during which reference data were acquired. The results also indicate that autonomous categorization of TM signals into vegetation, bare land, water, snow and clouds can be accomplished with adequate reliability for many applications over a reasonably wide range of imaging conditions. However, further analysis is required to develop computationally efficient boundary approximation algorithms for such categorization.

Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum

PubMed Central

Lv, Yong; Zhu, Qinglin; Yuan, Rui

2015-01-01

The nonlocal means (NL-Means) method that has been widely used in the field of image processing in recent years effectively overcomes the limitations of the neighborhood filter and eliminates the artifact and edge problems caused by the traditional image denoising methods. Although NL-Means is very popular in the field of 2D image signal processing, it has not received enough attention in the field of 1D signal processing. This paper proposes a novel approach that diagnoses the fault of a rolling bearing based on fast NL-Means and the envelop spectrum. The parameters of the rolling bearing signals are optimized in the proposed method, which is the key contribution of this paper. This approach is applied to the fault diagnosis of rolling bearing, and the results have shown the efficiency at detecting roller bearing failures. PMID:25585105

Single Channel EEG Artifact Identification Using Two-Dimensional Multi-Resolution Analysis.

PubMed

Taherisadr, Mojtaba; Dehzangi, Omid; Parsaei, Hossein

2017-12-13

As a diagnostic monitoring approach, electroencephalogram (EEG) signals can be decoded by signal processing methodologies for various health monitoring purposes. However, EEG recordings are contaminated by other interferences, particularly facial and ocular artifacts generated by the user. This is specifically an issue during continuous EEG recording sessions, and is therefore a key step in using EEG signals for either physiological monitoring and diagnosis or brain-computer interface to identify such artifacts from useful EEG components. In this study, we aim to design a new generic framework in order to process and characterize EEG recording as a multi-component and non-stationary signal with the aim of localizing and identifying its component (e.g., artifact). In the proposed method, we gather three complementary algorithms together to enhance the efficiency of the system. Algorithms include time-frequency (TF) analysis and representation, two-dimensional multi-resolution analysis (2D MRA), and feature extraction and classification. Then, a combination of spectro-temporal and geometric features are extracted by combining key instantaneous TF space descriptors, which enables the system to characterize the non-stationarities in the EEG dynamics. We fit a curvelet transform (as a MRA method) to 2D TF representation of EEG segments to decompose the given space to various levels of resolution. Such a decomposition efficiently improves the analysis of the TF spaces with different characteristics (e.g., resolution). Our experimental results demonstrate that the combination of expansion to TF space, analysis using MRA, and extracting a set of suitable features and applying a proper predictive model is effective in enhancing the EEG artifact identification performance. We also compare the performance of the designed system with another common EEG signal processing technique-namely, 1D wavelet transform. Our experimental results reveal that the proposed method outperforms 1D wavelet.

Comments on `Area and power efficient DCT architecture for image compression' by Dhandapani and Ramachandran

NASA Astrophysics Data System (ADS)

Cintra, Renato J.; Bayer, Fábio M.

2017-12-01

In [Dhandapani and Ramachandran, "Area and power efficient DCT architecture for image compression", EURASIP Journal on Advances in Signal Processing 2014, 2014:180] the authors claim to have introduced an approximation for the discrete cosine transform capable of outperforming several well-known approximations in literature in terms of additive complexity. We could not verify the above results and we offer corrections for their work.

Multiparameter magnetic inspection system with magnetic field control and plural magnetic transducers

DOEpatents

Jiles, D.C.

1991-04-16

A multiparameter magnetic inspection system is disclosed for providing an efficient and economical way to derive a plurality of independent measurements regarding magnetic properties of the magnetic material under investigation. The plurality of transducers for a plurality of different types of measurements operatively connected to the specimen. The transducers are in turn connected to analytical circuits for converting transducer signals to meaningful measurement signals of the magnetic properties of the specimen. The measurement signals are processed and can be simultaneously communicated to a control component. The measurement signals can also be selectively plotted against one another. The control component operates the functioning of the analytical circuits and operates and controls components to impose magnetic fields of desired characteristics upon the specimen. The system therefore allows contemporaneous or simultaneous derivation of the plurality of different independent magnetic properties of the material which can then be processed to derive characteristics of the material. 1 figure.

Multiparameter magnetic inspection system with magnetic field control and plural magnetic transducers

DOEpatents

Jiles, David C.

1991-04-16

A multiparameter magnetic inspection system for providing an efficient and economical way to derive a plurality of independent measurements regarding magnetic properties of the magnetic material under investigation. The plurality of transducers for a plurality of different types of measurements operatively connected to the specimen. The transducers are in turn connected to analytical circuits for converting transducer signals to meaningful measurement signals of the magnetic properties of the specimen. The measurement signals are processed and can be simultaneously communicated to a control component. The measurement signals can also be selectively plotted against one another. The control component operates the functioning of the analytical circuits and operates and controls components to impose magnetic fields of desired characteristics upon the specimen. The system therefore allows contemporaneous or simultaneous derivation of the plurality of different independent magnetic properties of the material which can then be processed to derive characteristics of the material.

Differential pulse amplitude modulation for multiple-input single-output OWVLC

NASA Astrophysics Data System (ADS)

Yang, S. H.; Kwon, D. H.; Kim, S. J.; Son, Y. H.; Han, S. K.

2015-01-01

White light-emitting diodes (LEDs) are widely used for lighting due to their energy efficiency, eco-friendly, and small size than previously light sources such as incandescent, fluorescent bulbs and so on. Optical wireless visible light communication (OWVLC) based on LED merges lighting and communications in applications such as indoor lighting, traffic signals, vehicles, and underwater communications because LED can be easily modulated. However, physical bandwidth of LED is limited about several MHz by slow time constant of the phosphor and characteristics of device. Therefore, using the simplest modulation format which is non-return-zero on-off-keying (NRZ-OOK), the data rate reaches only to dozens Mbit/s. Thus, to improve the transmission capacity, optical filtering and pre-, post-equalizer are adapted. Also, high-speed wireless connectivity is implemented using spectrally efficient modulation methods: orthogonal frequency division multiplexing (OFDM) or discrete multi-tone (DMT). However, these modulation methods need additional digital signal processing such as FFT and IFFT, thus complexity of transmitter and receiver is increasing. To reduce the complexity of transmitter and receiver, we proposed a novel modulation scheme which is named differential pulse amplitude modulation. The proposed modulation scheme transmits different NRZ-OOK signals with same amplitude and unit time delay using each LED chip, respectively. The `N' parallel signals from LEDs are overlapped and directly detected at optical receiver. Received signal is demodulated by power difference between unit time slots. The proposed scheme can overcome the bandwidth limitation of LEDs and data rate can be improved according to number of LEDs without complex digital signal processing.

Signalling through NK1.1 triggers NK cells to die but induces NK T cells to produce interleukin-4.

PubMed

Asea, A; Stein-Streilein, J

1998-02-01

In vivo inoculation of specific antibody is an accepted protocol for elimination of specific cell populations. Except for anti-CD3 and anti-CD4, it is not known if the depleted cells are eliminated by signalling through the target molecule or through a more non-specific mechanism. C57BL/6 mice were inoculated with anti-natural killer (NK1.1) monoclonal antibody (mAb). Thereafter spleen cells were harvested, stained for both surface and intracellular markers, and analysed by flow cytometry. As early as 2 hr post inoculation, NK cells were signalled to become apoptotic while signalling through the NK1.1 molecule activated NK1.1+ T-cell receptor (TCR)+ (NK T) cells to increase in number, and produce interleukin-4 (IL-4). Anti NK1.1 mAb was less efficient at signalling apoptosis in NK cells when NK T-cell deficient [beta 2-microglobulin beta 2m-deficient] mice were used compared with wild type mice. Efficient apoptotic signalling was restored when beta 2m-deficient mice were reconstituted with NK T cells. NK-specific antibody best signals the apoptotic process in susceptible NK cells when resistant NK T cells are present, activated, and secrete IL-4.

Development of signal processing system of avalanche photo diode for space observations by Astro-H

NASA Astrophysics Data System (ADS)

Ohno, M.; Goto, K.; Hanabata, Y.; Takahashi, H.; Fukazawa, Y.; Yoshino, M.; Saito, T.; Nakamori, T.; Kataoka, J.; Sasano, M.; Torii, S.; Uchiyama, H.; Nakazawa, K.; Watanabe, S.; Kokubun, M.; Ohta, M.; Sato, T.; Takahashi, T.; Tajima, H.

2013-01-01

Astro-H is the sixth Japanese X-ray space observatory which will be launched in 2014. Two of onboard instruments of Astro-H, Hard X-ray Imager and Soft Gamma-ray Detector are surrounded by many number of large Bismuth Germanate (Bi4Ge3O12; BGO) scintillators. Optimum readout system of scintillation lights from these BGOs are essential to reduce the background signals and achieve high performance for main detectors because most of gamma-rays from out of field-of-view of main detectors or radio-isotopes produced inside them due to activation can be eliminated by anti-coincidence technique using BGO signals. We apply Avalanche Photo Diode (APD) for light sensor of these BGO detectors since their compactness and high quantum efficiency make it easy to design such large number of BGO detector system. For signal processing from APDs, digital filter and other trigger logics on the Field-Programmable Gate Array (FPGA) is used instead of discrete analog circuits due to limitation of circuit implementation area on spacecraft. For efficient observations, we have to achieve as low threshold of anti-coincidence signal as possible by utilizing the digital filtering. In addition, such anti-coincident signals should be sent to the main detector within 5 μs to make it in time to veto the A-D conversion. Considering this requirement and constraint from logic size of FPGA, we adopt two types of filter, 8 delay taps filter with only 2 bit precision coefficient and 16 delay taps filter with 8 bit precision coefficient. The data after former simple filter provides anti-coincidence signal quickly in orbit, and the latter filter is used for detail analysis after the data is down-linked.

Experimental demonstration of real-time adaptively modulated DDO-OFDM systems with a high spectral efficiency up to 5.76bit/s/Hz transmission over SMF links.

PubMed

Chen, Ming; He, Jing; Tang, Jin; Wu, Xian; Chen, Lin

2014-07-28

In this paper, a FPGAs-based real-time adaptively modulated 256/64/16QAM-encoded base-band OFDM transceiver with a high spectral efficiency up to 5.76bit/s/Hz is successfully developed, and experimentally demonstrated in a simple intensity-modulated direct-detection optical communication system. Experimental results show that it is feasible to transmit a raw signal bit rate of 7.19Gbps adaptively modulated real-time optical OFDM signal over 20km and 50km single mode fibers (SMFs). The performance comparison between real-time and off-line digital signal processing is performed, and the results show that there is a negligible power penalty. In addition, to obtain the best transmission performance, direct-current (DC) bias voltage for MZM and launch power into optical fiber links are explored in the real-time optical OFDM systems.

Peptides interfering with protein-protein interactions in the ethylene signaling pathway delay tomato fruit ripening

NASA Astrophysics Data System (ADS)

Bisson, Melanie M. A.; Kessenbrock, Mareike; Müller, Lena; Hofmann, Alexander; Schmitz, Florian; Cristescu, Simona M.; Groth, Georg

2016-08-01

The plant hormone ethylene is involved in the regulation of several processes with high importance for agricultural applications, e.g. ripening, aging and senescence. Previous work in our group has identified a small peptide (NOP-1) derived from the nuclear localization signal of the Arabidopsis ethylene regulator ETHYLENE INSENSITIVE-2 (EIN2) C-terminal part as efficient inhibitor of ethylene responses. Here, we show that NOP-1 is also able to efficiently disrupt EIN2-ETR1 complex formation in tomato, indicating that the NOP-1 inhibition mode is conserved across plant species. Surface application of NOP-1 on green tomato fruits delays ripening similar to known inhibitors of ethylene perception (MCP) and ethylene biosynthesis (AVG). Fruits treated with NOP-1 showed similar ethylene production as untreated controls underlining that NOP-1 blocks ethylene signaling by targeting an essential interaction in this pathway, while having no effect on ethylene biosynthesis.

Peptides interfering with protein-protein interactions in the ethylene signaling pathway delay tomato fruit ripening.

PubMed

Bisson, Melanie M A; Kessenbrock, Mareike; Müller, Lena; Hofmann, Alexander; Schmitz, Florian; Cristescu, Simona M; Groth, Georg

2016-08-01

The plant hormone ethylene is involved in the regulation of several processes with high importance for agricultural applications, e.g. ripening, aging and senescence. Previous work in our group has identified a small peptide (NOP-1) derived from the nuclear localization signal of the Arabidopsis ethylene regulator ETHYLENE INSENSITIVE-2 (EIN2) C-terminal part as efficient inhibitor of ethylene responses. Here, we show that NOP-1 is also able to efficiently disrupt EIN2-ETR1 complex formation in tomato, indicating that the NOP-1 inhibition mode is conserved across plant species. Surface application of NOP-1 on green tomato fruits delays ripening similar to known inhibitors of ethylene perception (MCP) and ethylene biosynthesis (AVG). Fruits treated with NOP-1 showed similar ethylene production as untreated controls underlining that NOP-1 blocks ethylene signaling by targeting an essential interaction in this pathway, while having no effect on ethylene biosynthesis.

A New Method for Suppressing Periodic Narrowband Interference Based on the Chaotic van der Pol Oscillator

NASA Astrophysics Data System (ADS)

Lu, Jia; Zhang, Xiaoxing; Xiong, Hao

The chaotic van der Pol oscillator is a powerful tool for detecting defects in electric systems by using online partial discharge (PD) monitoring. This paper focuses on realizing weak PD signal detection in the strong periodic narrowband interference by using high sensitivity to the periodic narrowband interference signals and immunity to white noise and PD signals of chaotic systems. A new approach to removing the periodic narrowband interference by using a van der Pol chaotic oscillator is described by analyzing the motion characteristic of the chaotic oscillator on the basis of the van der Pol equation. Furthermore, the Floquet index for measuring the amplitude of periodic narrowband signals is redefined. The denoising signal processed by the chaotic van der Pol oscillators is further processed by wavelet analysis. Finally, the denoising results verify that the periodic narrowband and white noise interference can be removed efficiently by combining the theory of the chaotic van der Pol oscillator and wavelet analysis.

Variable threshold method for ECG R-peak detection.

PubMed

Kew, Hsein-Ping; Jeong, Do-Un

2011-10-01

In this paper, a wearable belt-type ECG electrode worn around the chest by measuring the real-time ECG is produced in order to minimize the inconvenient in wearing. ECG signal is detected using a potential instrument system. The measured ECG signal is transmits via an ultra low power consumption wireless data communications unit to personal computer using Zigbee-compatible wireless sensor node. ECG signals carry a lot of clinical information for a cardiologist especially the R-peak detection in ECG. R-peak detection generally uses the threshold value which is fixed. There will be errors in peak detection when the baseline changes due to motion artifacts and signal size changes. Preprocessing process which includes differentiation process and Hilbert transform is used as signal preprocessing algorithm. Thereafter, variable threshold method is used to detect the R-peak which is more accurate and efficient than fixed threshold value method. R-peak detection using MIT-BIH databases and Long Term Real-Time ECG is performed in this research in order to evaluate the performance analysis.

Flexible Software Architecture for Visualization and Seismic Data Analysis

NASA Astrophysics Data System (ADS)

Petunin, S.; Pavlov, I.; Mogilenskikh, D.; Podzyuban, D.; Arkhipov, A.; Baturuin, N.; Lisin, A.; Smith, A.; Rivers, W.; Harben, P.

2007-12-01

Research in the field of seismology requires software and signal processing utilities for seismogram manipulation and analysis. Seismologists and data analysts often encounter a major problem in the use of any particular software application specific to seismic data analysis: the tuning of commands and windows to the specific waveforms and hot key combinations so as to fit their familiar informational environment. The ability to modify the user's interface independently from the developer requires an adaptive code structure. An adaptive code structure also allows for expansion of software capabilities such as new signal processing modules and implementation of more efficient algorithms. Our approach is to use a flexible "open" architecture for development of geophysical software. This report presents an integrated solution for organizing a logical software architecture based on the Unix version of the Geotool software implemented on the Microsoft NET 2.0 platform. Selection of this platform greatly expands the variety and number of computers that can implement the software, including laptops that can be utilized in field conditions. It also facilitates implementation of communication functions for seismic data requests from remote databases through the Internet. The main principle of the new architecture for Geotool is that scientists should be able to add new routines for digital waveform analysis via software plug-ins that utilize the basic Geotool display for GUI interaction. The use of plug-ins allows the efficient integration of diverse signal-processing software, including software still in preliminary development, into an organized platform without changing the fundamental structure of that platform itself. An analyst's use of Geotool is tracked via a metadata file so that future studies can reconstruct, and alter, the original signal processing operations. The work has been completed in the framework of a joint Russian- American project.

Applications of Hilbert Spectral Analysis for Speech and Sound Signals

NASA Technical Reports Server (NTRS)

Huang, Norden E.

2003-01-01

A new method for analyzing nonlinear and nonstationary data has been developed, and the natural applications are to speech and sound signals. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time, which give sharp identifications of imbedded structures. This method invention can be used to process all acoustic signals. Specifically, it can process the speech signals for Speech synthesis, Speaker identification and verification, Speech recognition, and Sound signal enhancement and filtering. Additionally, as the acoustical signals from machinery are essentially the way the machines are talking to us. Therefore, the acoustical signals, from the machines, either from sound through air or vibration on the machines, can tell us the operating conditions of the machines. Thus, we can use the acoustic signal to diagnosis the problems of machines.

Binaural noise reduction via cue-preserving MMSE filter and adaptive-blocking-based noise PSD estimation

NASA Astrophysics Data System (ADS)

Azarpour, Masoumeh; Enzner, Gerald

2017-12-01

Binaural noise reduction, with applications for instance in hearing aids, has been a very significant challenge. This task relates to the optimal utilization of the available microphone signals for the estimation of the ambient noise characteristics and for the optimal filtering algorithm to separate the desired speech from the noise. The additional requirements of low computational complexity and low latency further complicate the design. A particular challenge results from the desired reconstruction of binaural speech input with spatial cue preservation. The latter essentially diminishes the utility of multiple-input/single-output filter-and-sum techniques such as beamforming. In this paper, we propose a comprehensive and effective signal processing configuration with which most of the aforementioned criteria can be met suitably. This relates especially to the requirement of efficient online adaptive processing for noise estimation and optimal filtering while preserving the binaural cues. Regarding noise estimation, we consider three different architectures: interaural (ITF), cross-relation (CR), and principal-component (PCA) target blocking. An objective comparison with two other noise PSD estimation algorithms demonstrates the superiority of the blocking-based noise estimators, especially the CR-based and ITF-based blocking architectures. Moreover, we present a new noise reduction filter based on minimum mean-square error (MMSE), which belongs to the class of common gain filters, hence being rigorous in terms of spatial cue preservation but also efficient and competitive for the acoustic noise reduction task. A formal real-time subjective listening test procedure is also developed in this paper. The proposed listening test enables a real-time assessment of the proposed computationally efficient noise reduction algorithms in a realistic acoustic environment, e.g., considering time-varying room impulse responses and the Lombard effect. The listening test outcome reveals that the signals processed by the blocking-based algorithms are significantly preferred over the noisy signal in terms of instantaneous noise attenuation. Furthermore, the listening test data analysis confirms the conclusions drawn based on the objective evaluation.

Silicon trench photodiodes on a wafer for efficient X-ray-to-current signal conversion using side-X-ray-irradiation mode

NASA Astrophysics Data System (ADS)

Ariyoshi, Tetsuya; Takane, Yuta; Iwasa, Jumpei; Sakamoto, Kenji; Baba, Akiyoshi; Arima, Yutaka

2018-04-01

In this paper, we report a direct-conversion-type X-ray sensor composed of trench-structured silicon photodiodes, which achieves a high X-ray-to-current conversion efficiency under side X-ray irradiation. The silicon X-ray sensor with a length of 22.6 mm and a trench depth of 300 µm was fabricated using a single-poly single-metal 0.35 µm process. X-rays with a tube voltage of 80 kV were irradiated along the trench photodiode from the side of the test chip. The theoretical limit of X-ray-to-current conversion efficiency of 83.8% was achieved at a low reverse bias voltage of 25 V. The X-ray-to-electrical signal conversion efficiency of conventional indirect-conversion-type X-ray sensors is about 10%. Therefore, the developed sensor has a conversion efficiency that is about eight times higher than that of conventional sensors. It is expected that the developed X-ray sensor will be able to markedly lower the radiation dose required for X-ray diagnoses.

Study on De-noising Technology of Radar Life Signal

NASA Astrophysics Data System (ADS)

Yang, Xiu-Fang; Wang, Lian-Huan; Ma, Jiang-Fei; Wang, Pei-Pei

2016-05-01

Radar detection is a kind of novel life detection technology, which can be applied to medical monitoring, anti-terrorism and disaster relief street fighting, etc. As the radar life signal is very weak, it is often submerged in the noise. Because of non-stationary and randomness of these clutter signals, it is necessary to denoise efficiently before extracting and separating the useful signal. This paper improves the radar life signal's theoretical model of the continuous wave, does de-noising processing by introducing lifting wavelet transform and determine the best threshold function through comparing the de-noising effects of different threshold functions. The result indicates that both SNR and MSE of the signal are better than the traditional ones by introducing lifting wave transform and using a new improved soft threshold function de-noising method..

Dynamic Synchronous Capture Algorithm for an Electromagnetic Flowmeter.

PubMed

Fanjiang, Yong-Yi; Lu, Shih-Wei

2017-04-10

This paper proposes a dynamic synchronous capture (DSC) algorithm to calculate the flow rate for an electromagnetic flowmeter. The characteristics of the DSC algorithm can accurately calculate the flow rate signal and efficiently convert an analog signal to upgrade the execution performance of a microcontroller unit (MCU). Furthermore, it can reduce interference from abnormal noise. It is extremely steady and independent of fluctuations in the flow measurement. Moreover, it can calculate the current flow rate signal immediately (m/s). The DSC algorithm can be applied to the current general MCU firmware platform without using DSP (Digital Signal Processing) or a high-speed and high-end MCU platform, and signal amplification by hardware reduces the demand for ADC accuracy, which reduces the cost.

Dynamic Synchronous Capture Algorithm for an Electromagnetic Flowmeter

PubMed Central

Fanjiang, Yong-Yi; Lu, Shih-Wei

2017-01-01

This paper proposes a dynamic synchronous capture (DSC) algorithm to calculate the flow rate for an electromagnetic flowmeter. The characteristics of the DSC algorithm can accurately calculate the flow rate signal and efficiently convert an analog signal to upgrade the execution performance of a microcontroller unit (MCU). Furthermore, it can reduce interference from abnormal noise. It is extremely steady and independent of fluctuations in the flow measurement. Moreover, it can calculate the current flow rate signal immediately (m/s). The DSC algorithm can be applied to the current general MCU firmware platform without using DSP (Digital Signal Processing) or a high-speed and high-end MCU platform, and signal amplification by hardware reduces the demand for ADC accuracy, which reduces the cost. PMID:28394306

Coherent detection and digital signal processing for fiber optic communications

NASA Astrophysics Data System (ADS)

Ip, Ezra

The drive towards higher spectral efficiency in optical fiber systems has generated renewed interest in coherent detection. We review different detection methods, including noncoherent, differentially coherent, and coherent detection, as well as hybrid detection methods. We compare the modulation methods that are enabled and their respective performances in a linear regime. An important system parameter is the number of degrees of freedom (DOF) utilized in transmission. Polarization-multiplexed quadrature-amplitude modulation maximizes spectral efficiency and power efficiency as it uses all four available DOF contained in the two field quadratures in the two polarizations. Dual-polarization homodyne or heterodyne downconversion are linear processes that can fully recover the received signal field in these four DOF. When downconverted signals are sampled at the Nyquist rate, compensation of transmission impairments can be performed using digital signal processing (DSP). Software based receivers benefit from the robustness of DSP, flexibility in design, and ease of adaptation to time-varying channels. Linear impairments, including chromatic dispersion (CD) and polarization-mode dispersion (PMD), can be compensated quasi-exactly using finite impulse response filters. In practical systems, sampling the received signal at 3/2 times the symbol rate is sufficient to enable an arbitrary amount of CD and PMD to be compensated for a sufficiently long equalizer whose tap length scales linearly with transmission distance. Depending on the transmitted constellation and the target bit error rate, the analog-to-digital converter (ADC) should have around 5 to 6 bits of resolution. Digital coherent receivers are naturally suited for the implementation of feedforward carrier recovery, which has superior linewidth tolerance than phase-locked loops, and does not suffer from feedback delay constraints. Differential bit encoding can be used to prevent catastrophic receiver failure due to cycle slips. In systems where nonlinear effects are concentrated mostly at fiber locations with small accumulated dispersion, nonlinear phase de-rotation is a low-complexity algorithm that can partially mitigate nonlinear effects. For systems with arbitrary dispersion maps, however, backpropagation is the only universal technique that can jointly compensate dispersion and fiber nonlinearity. Backpropagation requires solving the nonlinear Schrodinger equation at the receiver, and has high computational cost. Backpropagation is most effective when dispersion compensation fibers are removed, and when signal processing is performed at three times oversampling. Backpropagation can improve system performance and increase transmission distance. With anticipated advances in analog-to-digital converters and integrated circuit technology, DSP-based coherent receivers at bit rates up to 100 Gb/s should become practical in the near future.

Conflict and disfluency as aversive signals: context-specific processing adjustments are modulated by affective location associations.

PubMed

Dreisbach, Gesine; Reindl, Anna-Lena; Fischer, Rico

2018-03-01

Context-specific processing adjustments are one signature feature of flexible human action control. However, up to now the precise mechanisms underlying these adjustments are not fully understood. Here it is argued that aversive signals produced by conflict- or disfluency-experience originally motivate such context-specific processing adjustments. We tested whether the efficiency of the aversive conflict signal for control adaptation depends on the affective nature of the context it is presented in. In two experiments, high vs. low proportions of aversive signals (Experiment 1: conflict trials; Experiment 2: disfluent trials) were presented either above or below the screen center. This location manipulation was motivated by existing evidence that verticality is generally associated with affective valence with up being positive and down being negative. From there it was hypothesized that the aversive signals would lose their trigger function for processing adjustments when presented at the lower (i.e., more negative) location. This should then result in a reduced context-specific proportion effect when the high proportion of aversive signals was presented at the lower location. Results fully confirmed the predictions. In both experiments, the location-specific proportion effects were only present when the high proportion of aversive signals occurred at the more positive location above but were reduced (Experiment 1) or even eliminated (Experiment 2) when the high proportion occurred at the more negative location below. This interaction of processing adjustments with affective background contexts can thus be taken as further hint for an affective origin of control adaptations.

Entropy in DNA Double-Strand Break, Detection and Signaling

NASA Astrophysics Data System (ADS)

Zhang, Yang; Schindler, Christina; Heermann, Dieter

2014-03-01

In biology, the term entropy is often understood as a measure of disorder - a restrictive interpretation that can even be misleading. Recently it has become clearer and clearer that entropy, contrary to conventional wisdom, can help to order and guide biological processes in living cells. DNA double-strand breaks (DSBs) are among the most dangerous lesions and efficient damage detection and repair is essential for organism viability. However, what remains unknown is the precise mechanism of targeting the site of damage within billions of intact nucleotides and a crowded nuclear environment, a process which is often referred to as recruitment or signaling. Here we show that the change in entropy associated with inflicting a DSB facilitates the recruitment of damage sensor proteins. By means of computational modeling we found that higher mobility and local chromatin structure accelerate protein association at DSB ends. We compared the effect of different chromatin architectures on protein dynamics and concentrations in the vicinity of DSBs, and related these results to experiments on repair in heterochromatin. Our results demonstrate how entropy contributes to a more efficient damage detection. We identify entropy as the physical basis for DNA double-strand break signaling.

Iridium N-Heterocyclic Carbene Complexes as Efficient Catalysts for Magnetization Transfer from para-Hydrogen

PubMed Central

2011-01-01

While the characterization of materials by NMR is hugely important in the physical and biological sciences, it also plays a vital role in medical imaging. This success is all the more impressive because of the inherently low sensitivity of the method. We establish here that [Ir(H)2(IMes)(py)3]Cl undergoes both pyridine (py) loss as well as the reductive elimination of H2. These reversible processes bring para-H2 and py into contact in a magnetically coupled environment, delivering an 8100-fold increase in 1H NMR signal strength relative to non-hyperpolarized py at 3 T. An apparatus that facilitates signal averaging has been built to demonstrate that the efficiency of this process is controlled by the strength of the magnetic field experienced by the complex during the magnetization transfer step. Thermodynamic and kinetic data combined with DFT calculations reveal the involvement of [Ir(H)2(η2-H2)(IMes)(py)2]+, an unlikely yet key intermediate in the reaction. Deuterium labeling yields an additional 60% improvement in signal, an observation that offers insight into strategies for optimizing this approach. PMID:21469642

Iridium N-heterocyclic carbene complexes as efficient catalysts for magnetization transfer from para-hydrogen.

PubMed

Cowley, Michael J; Adams, Ralph W; Atkinson, Kevin D; Cockett, Martin C R; Duckett, Simon B; Green, Gary G R; Lohman, Joost A B; Kerssebaum, Rainer; Kilgour, David; Mewis, Ryan E

2011-04-27

While the characterization of materials by NMR is hugely important in the physical and biological sciences, it also plays a vital role in medical imaging. This success is all the more impressive because of the inherently low sensitivity of the method. We establish here that [Ir(H)(2)(IMes)(py)(3)]Cl undergoes both pyridine (py) loss as well as the reductive elimination of H(2). These reversible processes bring para-H(2) and py into contact in a magnetically coupled environment, delivering an 8100-fold increase in (1)H NMR signal strength relative to non-hyperpolarized py at 3 T. An apparatus that facilitates signal averaging has been built to demonstrate that the efficiency of this process is controlled by the strength of the magnetic field experienced by the complex during the magnetization transfer step. Thermodynamic and kinetic data combined with DFT calculations reveal the involvement of [Ir(H)(2)(η(2)-H(2))(IMes)(py)(2)](+), an unlikely yet key intermediate in the reaction. Deuterium labeling yields an additional 60% improvement in signal, an observation that offers insight into strategies for optimizing this approach.

Review of smoothing methods for enhancement of noisy data from heavy-duty LHD mining machines

NASA Astrophysics Data System (ADS)

Wodecki, Jacek; Michalak, Anna; Stefaniak, Paweł

2018-01-01

Appropriate analysis of data measured on heavy-duty mining machines is essential for processes monitoring, management and optimization. Some particular classes of machines, for example LHD (load-haul-dump) machines, hauling trucks, drilling/bolting machines etc. are characterized with cyclicity of operations. In those cases, identification of cycles and their segments or in other words - simply data segmentation is a key to evaluate their performance, which may be very useful from the management point of view, for example leading to introducing optimization to the process. However, in many cases such raw signals are contaminated with various artifacts, and in general are expected to be very noisy, which makes the segmentation task very difficult or even impossible. To deal with that problem, there is a need for efficient smoothing methods that will allow to retain informative trends in the signals while disregarding noises and other undesired non-deterministic components. In this paper authors present a review of various approaches to diagnostic data smoothing. Described methods can be used in a fast and efficient way, effectively cleaning the signals while preserving informative deterministic behaviour, that is a crucial to precise segmentation and other approaches to industrial data analysis.

Biophysically Inspired Rational Design of Structured Chimeric Substrates for DNAzyme Cascade Engineering

PubMed Central

Lakin, Matthew R.; Brown, Carl W.; Horwitz, Eli K.; Fanning, M. Leigh; West, Hannah E.; Stefanovic, Darko; Graves, Steven W.

2014-01-01

The development of large-scale molecular computational networks is a promising approach to implementing logical decision making at the nanoscale, analogous to cellular signaling and regulatory cascades. DNA strands with catalytic activity (DNAzymes) are one means of systematically constructing molecular computation networks with inherent signal amplification. Linking multiple DNAzymes into a computational circuit requires the design of substrate molecules that allow a signal to be passed from one DNAzyme to another through programmed biochemical interactions. In this paper, we chronicle an iterative design process guided by biophysical and kinetic constraints on the desired reaction pathways and use the resulting substrate design to implement heterogeneous DNAzyme signaling cascades. A key aspect of our design process is the use of secondary structure in the substrate molecule to sequester a downstream effector sequence prior to cleavage by an upstream DNAzyme. Our goal was to develop a concrete substrate molecule design to achieve efficient signal propagation with maximal activation and minimal leakage. We have previously employed the resulting design to develop high-performance DNAzyme-based signaling systems with applications in pathogen detection and autonomous theranostics. PMID:25347066

Dual Key Speech Encryption Algorithm Based Underdetermined BSS

PubMed Central

Zhao, Huan; Chen, Zuo; Zhang, Xixiang

2014-01-01

When the number of the mixed signals is less than that of the source signals, the underdetermined blind source separation (BSS) is a significant difficult problem. Due to the fact that the great amount data of speech communications and real-time communication has been required, we utilize the intractability of the underdetermined BSS problem to present a dual key speech encryption method. The original speech is mixed with dual key signals which consist of random key signals (one-time pad) generated by secret seed and chaotic signals generated from chaotic system. In the decryption process, approximate calculation is used to recover the original speech signals. The proposed algorithm for speech signals encryption can resist traditional attacks against the encryption system, and owing to approximate calculation, decryption becomes faster and more accurate. It is demonstrated that the proposed method has high level of security and can recover the original signals quickly and efficiently yet maintaining excellent audio quality. PMID:24955430

HEVC for high dynamic range services

NASA Astrophysics Data System (ADS)

Kim, Seung-Hwan; Zhao, Jie; Misra, Kiran; Segall, Andrew

2015-09-01

Displays capable of showing a greater range of luminance values can render content containing high dynamic range information in a way such that the viewers have a more immersive experience. This paper introduces the design aspects of a high dynamic range (HDR) system, and examines the performance of the HDR processing chain in terms of compression efficiency. Specifically it examines the relation between recently introduced Society of Motion Picture and Television Engineers (SMPTE) ST 2084 transfer function and the High Efficiency Video Coding (HEVC) standard. SMPTE ST 2084 is designed to cover the full range of an HDR signal from 0 to 10,000 nits, however in many situations the valid signal range of actual video might be smaller than SMPTE ST 2084 supported range. The above restricted signal range results in restricted range of code values for input video data and adversely impacts compression efficiency. In this paper, we propose a code value remapping method that extends the restricted range code values into the full range code values so that the existing standards such as HEVC may better compress the video content. The paper also identifies related non-normative encoder-only changes that are required for remapping method for a fair comparison with anchor. Results are presented comparing the efficiency of the current approach versus the proposed remapping method for HM-16.2.

Systems Imaging of the Immune Synapse.

PubMed

Ambler, Rachel; Ruan, Xiangtao; Murphy, Robert F; Wülfing, Christoph

2017-01-01

Three-dimensional live cell imaging of the interaction of T cells with antigen-presenting cells (APCs) visualizes the subcellular distributions of signaling intermediates during T cell activation at thousands of resolved positions within a cell. These information-rich maps of local protein concentrations are a valuable resource in understanding T cell signaling. Here, we describe a protocol for the efficient acquisition of such imaging data and their computational processing to create four-dimensional maps of local concentrations. This protocol allows quantitative analysis of T cell signaling as it occurs inside live cells with resolution in time and space across thousands of cells.

Introducing fluorescence resonance energy transfer-based biosensors for the analysis of cAMP-PKA signalling in the fungal pathogen Candida glabrata.

PubMed

Demuyser, Liesbeth; Van Genechten, Wouter; Mizuno, Hideaki; Colombo, Sonia; Van Dijck, Patrick

2018-05-29

The cyclic adenosine monophosphate-protein kinase A (cAMP-PKA) pathway is central to signal transduction in many organisms. In pathogenic fungi such as Candida albicans, this signalling cascade has proven to be involved in several processes, such as virulence, indicating its potential importance in antifungal drug discovery. Candida glabrata is an upcoming pathogen of the same species, yet information regarding the role of cAMP-PKA signalling in virulence is largely lacking. To enable efficient monitoring of cAMP-PKA activity in this pathogen, we here present the usage of two FRET-based biosensors. Both variations in the activity of PKA and the quantity of cAMP can be detected in a time-resolved manner, as we exemplify by glucose-induced activation of the pathway. We also present information on how to adequately process and analyse the data in a mathematically correct and physiologically relevant manner. These sensors will be of great benefit for scientists interested in linking the cAMP-PKA signalling cascade to downstream processes, such as virulence, possibly in a host environment. © 2018 John Wiley & Sons Ltd.

Bilinear Inverse Problems: Theory, Algorithms, and Applications

NASA Astrophysics Data System (ADS)

Ling, Shuyang

We will discuss how several important real-world signal processing problems, such as self-calibration and blind deconvolution, can be modeled as bilinear inverse problems and solved by convex and nonconvex optimization approaches. In Chapter 2, we bring together three seemingly unrelated concepts, self-calibration, compressive sensing and biconvex optimization. We show how several self-calibration problems can be treated efficiently within the framework of biconvex compressive sensing via a new method called SparseLift. More specifically, we consider a linear system of equations y = DAx, where the diagonal matrix D (which models the calibration error) is unknown and x is an unknown sparse signal. By "lifting" this biconvex inverse problem and exploiting sparsity in this model, we derive explicit theoretical guarantees under which both x and D can be recovered exactly, robustly, and numerically efficiently. In Chapter 3, we study the question of the joint blind deconvolution and blind demixing, i.e., extracting a sequence of functions [special characters omitted] from observing only the sum of their convolutions [special characters omitted]. In particular, for the special case s = 1, it becomes the well-known blind deconvolution problem. We present a non-convex algorithm which guarantees exact recovery under conditions that are competitive with convex optimization methods, with the additional advantage of being computationally much more efficient. We discuss several applications of the proposed framework in image processing and wireless communications in connection with the Internet-of-Things. In Chapter 4, we consider three different self-calibration models of practical relevance. We show how their corresponding bilinear inverse problems can be solved by both the simple linear least squares approach and the SVD-based approach. As a consequence, the proposed algorithms are numerically extremely efficient, thus allowing for real-time deployment. Explicit theoretical guarantees and stability theory are derived and the number of sampling complexity is nearly optimal (up to a poly-log factor). Applications in imaging sciences and signal processing are discussed and numerical simulations are presented to demonstrate the effectiveness and efficiency of our approach.

Mathematical approach to recover EEG brain signals with artifacts by means of Gram-Schmidt transform

NASA Astrophysics Data System (ADS)

Runnova, A. E.; Zhuravlev, M. O.; Koronovskiy, A. A.; Hramov, A. E.

2017-04-01

A novel method for removing oculomotor artifacts on electroencephalographical signals is proposed and based on the orthogonal Gram-Schmidt transform using electrooculography data. The method has shown high efficiency removal of artifacts caused by spontaneous movements of the eyeballs (about 95-97% correct remote oculomotor artifacts). This method may be recommended for multi-channel electroencephalography data processing in an automatic on-line in a variety of psycho-physiological experiments.

The software system development for the TAMU real-time fan beam scatterometer data processors

NASA Technical Reports Server (NTRS)

Clark, B. V.; Jean, B. R.

1980-01-01

A software package was designed and written to process in real-time any one quadrature channel pair of radar scatterometer signals form the NASA L- or C-Band radar scatterometer systems. The software was successfully tested in the C-Band processor breadboard hardware using recorded radar and NERDAS (NASA Earth Resources Data Annotation System) signals as the input data sources. The processor development program and the overall processor theory of operation and design are described. The real-time processor software system is documented and the results of the laboratory software tests, and recommendations for the efficient application of the data processing capabilities are presented.

UWB communication receiver feedback loop

DOEpatents

Spiridon, Alex; Benzel, Dave; Dowla, Farid U.; Nekoogar, Faranak; Rosenbury, Erwin T.

2007-12-04

A novel technique and structure that maximizes the extraction of information from reference pulses for UWB-TR receivers is introduced. The scheme efficiently processes an incoming signal to suppress different types of UWB as well as non-UWB interference prior to signal detection. Such a method and system adds a feedback loop mechanism to enhance the signal-to-noise ratio of reference pulses in a conventional TR receiver. Moreover, sampling the second order statistical function such as, for example, the autocorrelation function (ACF) of the received signal and matching it to the ACF samples of the original pulses for each transmitted bit provides a more robust UWB communications method and system in the presence of channel distortions.

Design and DSP implementation of star image acquisition and star point fast acquiring and tracking

NASA Astrophysics Data System (ADS)

Zhou, Guohui; Wang, Xiaodong; Hao, Zhihang

2006-02-01

Star sensor is a special high accuracy photoelectric sensor. Attitude acquisition time is an important function index of star sensor. In this paper, the design target is to acquire 10 samples per second dynamic performance. On the basis of analyzing CCD signals timing and star image processing, a new design and a special parallel architecture for improving star image processing are presented in this paper. In the design, the operation moving the data in expanded windows including the star to the on-chip memory of DSP is arranged in the invalid period of CCD frame signal. During the CCD saving the star image to memory, DSP processes the data in the on-chip memory. This parallelism greatly improves the efficiency of processing. The scheme proposed here results in enormous savings of memory normally required. In the scheme, DSP HOLD mode and CPLD technology are used to make a shared memory between CCD and DSP. The efficiency of processing is discussed in numerical tests. Only in 3.5ms is acquired the five lightest stars in the star acquisition stage. In 43us, the data in five expanded windows including stars are moved into the internal memory of DSP, and in 1.6ms, five star coordinates are achieved in the star tracking stage.

Efficient audio signal processing for embedded systems

NASA Astrophysics Data System (ADS)

Chiu, Leung Kin

As mobile platforms continue to pack on more computational power, electronics manufacturers start to differentiate their products by enhancing the audio features. However, consumers also demand smaller devices that could operate for longer time, hence imposing design constraints. In this research, we investigate two design strategies that would allow us to efficiently process audio signals on embedded systems such as mobile phones and portable electronics. In the first strategy, we exploit properties of the human auditory system to process audio signals. We designed a sound enhancement algorithm to make piezoelectric loudspeakers sound ”richer" and "fuller." Piezoelectric speakers have a small form factor but exhibit poor response in the low-frequency region. In the algorithm, we combine psychoacoustic bass extension and dynamic range compression to improve the perceived bass coming out from the tiny speakers. We also developed an audio energy reduction algorithm for loudspeaker power management. The perceptually transparent algorithm extends the battery life of mobile devices and prevents thermal damage in speakers. This method is similar to audio compression algorithms, which encode audio signals in such a ways that the compression artifacts are not easily perceivable. Instead of reducing the storage space, however, we suppress the audio contents that are below the hearing threshold, therefore reducing the signal energy. In the second strategy, we use low-power analog circuits to process the signal before digitizing it. We designed an analog front-end for sound detection and implemented it on a field programmable analog array (FPAA). The system is an example of an analog-to-information converter. The sound classifier front-end can be used in a wide range of applications because programmable floating-gate transistors are employed to store classifier weights. Moreover, we incorporated a feature selection algorithm to simplify the analog front-end. A machine learning algorithm AdaBoost is used to select the most relevant features for a particular sound detection application. In this classifier architecture, we combine simple "base" analog classifiers to form a strong one. We also designed the circuits to implement the AdaBoost-based analog classifier.

SPICODYN: A Toolbox for the Analysis of Neuronal Network Dynamics and Connectivity from Multi-Site Spike Signal Recordings.

PubMed

Pastore, Vito Paolo; Godjoski, Aleksandar; Martinoia, Sergio; Massobrio, Paolo

2018-01-01

We implemented an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. The software package, named SPICODYN, has been developed as a standalone windows GUI application, using C# programming language with Microsoft Visual Studio based on .NET framework 4.5 development environment. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis. In particular, for inferring network connectivity, a new implementation of the transfer entropy method is presented dealing with multiple time delays (temporal extension) and with multiple binary patterns (high order extension). SPICODYN is specifically tailored to process data coming from different Multi-Electrode Arrays setups, guarantying, in those specific cases, automated processing. The optimized implementation of the Delayed Transfer Entropy and the High-Order Transfer Entropy algorithms, allows performing accurate and rapid analysis on multiple spike trains from thousands of electrodes.

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Matched Filtering and Convolutional Neural Network.

PubMed

Chen, Shuo; Luo, Chenggao; Wang, Hongqiang; Deng, Bin; Cheng, Yongqiang; Zhuang, Zhaowen

2018-04-26

As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. However, there are still two problems in three-dimensional (3D) TCAI. Firstly, the large-scale reference-signal matrix based on meshing the 3D imaging area creates a heavy computational burden, thus leading to unsatisfactory efficiency. Secondly, it is difficult to resolve the target under low signal-to-noise ratio (SNR). In this paper, we propose a 3D imaging method based on matched filtering (MF) and convolutional neural network (CNN), which can reduce the computational burden and achieve high-resolution imaging for low SNR targets. In terms of the frequency-hopping (FH) signal, the original echo is processed with MF. By extracting the processed echo in different spike pulses separately, targets in different imaging planes are reconstructed simultaneously to decompose the global computational complexity, and then are synthesized together to reconstruct the 3D target. Based on the conventional TCAI model, we deduce and build a new TCAI model based on MF. Furthermore, the convolutional neural network (CNN) is designed to teach the MF-TCAI how to reconstruct the low SNR target better. The experimental results demonstrate that the MF-TCAI achieves impressive performance on imaging ability and efficiency under low SNR. Moreover, the MF-TCAI has learned to better resolve the low-SNR 3D target with the help of CNN. In summary, the proposed 3D TCAI can achieve: (1) low-SNR high-resolution imaging by using MF; (2) efficient 3D imaging by downsizing the large-scale reference-signal matrix; and (3) intelligent imaging with CNN. Therefore, the TCAI based on MF and CNN has great potential in applications such as security screening, nondestructive detection, medical diagnosis, etc.

Efficient block processing of long duration biotelemetric brain data for health care monitoring

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

Soumya, I.; Zia Ur Rahman, M., E-mail: mdzr-5@ieee.org; Rama Koti Reddy, D. V.

In real time clinical environment, the brain signals which doctor need to analyze are usually very long. Such a scenario can be made simple by partitioning the input signal into several blocks and applying signal conditioning. This paper presents various block based adaptive filter structures for obtaining high resolution electroencephalogram (EEG) signals, which estimate the deterministic components of the EEG signal by removing noise. To process these long duration signals, we propose Time domain Block Least Mean Square (TDBLMS) algorithm for brain signal enhancement. In order to improve filtering capability, we introduce normalization in the weight update recursion of TDBLMS,more » which results TD-B-normalized-least mean square (LMS). To increase accuracy and resolution in the proposed noise cancelers, we implement the time domain cancelers in frequency domain which results frequency domain TDBLMS and FD-B-Normalized-LMS. Finally, we have applied these algorithms on real EEG signals obtained from human using Emotive Epoc EEG recorder and compared their performance with the conventional LMS algorithm. The results show that the performance of the block based algorithms is superior to the LMS counter-parts in terms of signal to noise ratio, convergence rate, excess mean square error, misadjustment, and coherence.« less

A new OTDR based on probe frequency multiplexing

NASA Astrophysics Data System (ADS)

Lu, Lidong; Liang, Yun; Li, Binglin; Guo, Jinghong; Zhang, Xuping

2013-12-01

Two signal multiplexing methods are proposed and experimentally demonstrated in optical time domain reflectometry (OTDR) for fault location of optical fiber transmission line to obtain high measurement efficiency. Probe signal multiplexing is individually obtained by phase modulation for generation of multi-frequency and time sequential frequency probe pulses. The backscattered Rayleigh light of the multiplexing probe signals is transferred to corresponding heterodyne intermediate frequency (IF) through heterodyning with the single frequency local oscillator (LO). Then the IFs are simultaneously acquired by use of a data acquisition card (DAQ) with sampling rate of 100Msps, and the obtained data are processed by digital band pass filtering (BPF), digital down conversion (DDC) and digital low pass filtering (BPF) procedure. For each probe frequency of the detected signals, the extraction of the time domain reflecting signal power is performed by parallel computing method. For a comprehensive performance comparison with conventional coherent OTDR on the probe frequency multiplexing methods, the potential for enhancement of dynamic range, spatial resolution and measurement time are analyzed and discussed. Experimental results show that by use of the probe frequency multiplexing method, the measurement efficiency of coherent OTDR can be enhanced by nearly 40 times.

TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach

PubMed Central

Elgendi, Mohamed

2016-01-01

Biomedical signals contain features that represent physiological events, and each of these events has peaks. The analysis of biomedical signals for monitoring or diagnosing diseases requires the detection of these peaks, making event detection a crucial step in biomedical signal processing. Many researchers have difficulty detecting these peaks to investigate, interpret and analyze their corresponding events. To date, there is no generic framework that captures these events in a robust, efficient and consistent manner. A new method referred to for the first time as two event-related moving averages (“TERMA”) involves event-related moving averages and detects events in biomedical signals. The TERMA framework is flexible and universal and consists of six independent LEGO building bricks to achieve high accuracy detection of biomedical events. Results recommend that the window sizes for the two moving averages (W1 and W2) have to follow the inequality (8×W1)≥W2≥(2×W1). Moreover, TERMA is a simple yet efficient event detector that is suitable for wearable devices, point-of-care devices, fitness trackers and smart watches, compared to more complex machine learning solutions. PMID:27827852

Psycho-physiological training approach for amputee rehabilitation.

PubMed

Dhal, Chandan; Wahi, Akshat

2015-01-01

Electromyography (EMG) signals are very noisy and difficult to acquire. Conventional techniques involve amplification and filtering through analog circuits, which makes the system very unstable. The surface EMG signals lie in the frequency range of 6Hz to 600Hz, and the dominant range is between the ranges from 20Hz to 150Hz. 1 Our project aimed to analyze an EMG signal effectively over its complete frequency range. To remove these defects, we designed what we think is an easy, effective, and reliable signal processing technique. We did spectrum analysis, so as to perform all the processing such as amplification, filtering, and thresholding on an Arduino Uno board, hence removing the need for analog amplifiers and filtering circuits, which have stability issues. The conversion of time domain to frequency domain of any signal gives a detailed data of the signal set. Our main aim is to use this useful data for an alternative methodology for rehabilitation called a psychophysiological approach to rehabilitation in prosthesis, which can reduce the cost of the myoelectric arm, as well as increase its efficiency. This method allows the user to gain control over their muscle sets in a less stressful environment. Further, we also have described how our approach is viable and can benefit the rehabilitation process. We used our DSP EMG signals to play an online game and showed how this approach can be used in rehabilitation.

Steam generator on-line efficiency monitor

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

Johnson, R.K.; Kaya, A.; Keyes, M.A. IV

1987-08-04

This patent describes a system for automatically and continuously determining the efficiency of a combustion process in a fossil-fuel fired vapor generator for utilization by an automatic load control system that controls the distribution of a system load among a plurality of vapor generators, comprising: a first function generator, connected to an oxygen transducer for sensing the level of excess air in the flue gas, for generating a first signal indicative of the total air supplied for combustion in percent by weight; a second function generator, connected to a combustibles transducer for sensing the level of combustibles in the fluemore » gas, for generating a second signal indicative of the percent combustibles present in the flue gas; means for correcting the first signal, connected to the first and second function generators, when the oxygen transducer is of a type that operates at a temperature level sufficient to cause the unburned combustibles to react with the oxygen present in the flue gas; an ambient air temperature transducer for generating a third signal indicative of the temperature of the ambient air supplied to the vapor generator for combustion.« less

Qualitatively similar processing for own- and other-race faces: Evidence from efficiency and equivalent input noise.

PubMed

Shafai, Fakhri; Oruc, Ipek

2018-02-01

The other-race effect is the finding of diminished performance in recognition of other-race faces compared to those of own-race. It has been suggested that the other-race effect stems from specialized expert processes being tuned exclusively to own-race faces. In the present study, we measured recognition contrast thresholds for own- and other-race faces as well as houses for Caucasian observers. We have factored face recognition performance into two invariant aspects of visual function: efficiency, which is related to neural computations and processing demanded by the task, and equivalent input noise, related to signal degradation within the visual system. We hypothesized that if expert processes are available only to own-race faces, this should translate into substantially greater recognition efficiencies for own-race compared to other-race faces. Instead, we found similar recognition efficiencies for both own- and other-race faces. The other-race effect manifested as increased equivalent input noise. These results argue against qualitatively distinct perceptual processes. Instead they suggest that for Caucasian observers, similar neural computations underlie recognition of own- and other-race faces. Copyright © 2018 Elsevier Ltd. All rights reserved.

Lightning Detection Efficiency Analysis Process: Modeling Based on Empirical Data

NASA Technical Reports Server (NTRS)

Rompala, John T.

2005-01-01

A ground based lightning detection system employs a grid of sensors, which record and evaluate the electromagnetic signal produced by a lightning strike. Several detectors gather information on that signal s strength, time of arrival, and behavior over time. By coordinating the information from several detectors, an event solution can be generated. That solution includes the signal s point of origin, strength and polarity. Determination of the location of the lightning strike uses algorithms based on long used techniques of triangulation. Determination of the event s original signal strength relies on the behavior of the generated magnetic field over distance and time. In general the signal from the event undergoes geometric dispersion and environmental attenuation as it progresses. Our knowledge of that radial behavior together with the strength of the signal received by detecting sites permits an extrapolation and evaluation of the original strength of the lightning strike. It also limits the detection efficiency (DE) of the network. For expansive grids and with a sparse density of detectors, the DE varies widely over the area served. This limits the utility of the network in gathering information on regional lightning strike density and applying it to meteorological studies. A network of this type is a grid of four detectors in the Rondonian region of Brazil. The service area extends over a million square kilometers. Much of that area is covered by rain forests. Thus knowledge of lightning strike characteristics over the expanse is of particular value. I have been developing a process that determines the DE over the region [3]. In turn, this provides a way to produce lightning strike density maps, corrected for DE, over the entire region of interest. This report offers a survey of that development to date and a record of present activity.

Full-duplex bidirectional transmission of 10-Gb/s millimeter-wave QPSK signal in E-band optical wireless link.

PubMed

Fang, Yuan; Yu, Jianjun; Chi, Nan; Xiao, Jiangnan

2014-01-27

We experimentally demonstrated full-duplex bidirectional transmission of 10-Gb/s millimeter-wave (mm-wave) quadrature phase shift keying (QPSK) signal in E-band (71-76 GHz and 81-86 GHz) optical wireless link. Single-mode fibers (SMF) are connected at both sides of the antenna for uplink and downlink which realize 40-km SMF and 2-m wireless link for bidirectional transmission simultaneously. We utilized multi-level modulation format and coherent detection in such E-band optical wireless link for the first time. Mm-wave QPSK signal is generated by photonic technique to increase spectrum efficiency and received signal is coherently detected to improve receiver sensitivity. After the coherent detection, digital signal processing is utilized to compensate impairments of devices and transmission link.

Secretion of CyaA-PrtB and HlyA-PrtB fusion proteins in Escherichia coli: involvement of the glycine-rich repeat domain of Erwinia chrysanthemi protease B.

PubMed Central

Létoffé, S; Wandersman, C

1992-01-01

Protease B from Erwinia chrysanthemi was shown previously to have a C-terminal secretion signal located downstream of a domain that contains six glycine-rich repeats. This domain is conserved in all known bacterial proteins secreted by the signal peptide-independent pathway. The role of these repeats in the secretion process is controversial. We compared the secretion processes of various heterologous polypeptides fused either directly to the signal or separated from it by the glycine-rich domain. Although the repeats are not involved in the secretion of small truncated protease B carboxy-terminal peptides, they are required for the secretion of higher-molecular-weight fusion proteins. Secretion efficiency was also dependent on the size of the passenger polypeptide. Images PMID:1629152

An ultra low power ECG signal processor design for cardiovascular disease detection.

PubMed

Jain, Sanjeev Kumar; Bhaumik, Basabi

2015-08-01

This paper presents an ultra low power ASIC design based on a new cardiovascular disease diagnostic algorithm. This new algorithm based on forward search is designed for real time ECG signal processing. The algorithm is evaluated for Physionet PTB database from the point of view of cardiovascular disease diagnosis. The failed detection rate of QRS complex peak detection of our algorithm ranges from 0.07% to 0.26% for multi lead ECG signal. The ASIC is designed using 130-nm CMOS low leakage process technology. The area of ASIC is 1.21 mm(2). This ASIC consumes only 96 nW at an operating frequency of 1 kHz with a supply voltage of 0.9 V. Due to ultra low power consumption, our proposed ASIC design is most suitable for energy efficient wearable ECG monitoring devices.

Increasing Efficiency at the NTF by Optimizing Model AoA Positioning

NASA Technical Reports Server (NTRS)

Crawford, Bradley L.; Spells, Courtney

2006-01-01

The National Transonic Facility (NTF) at NASA Langley Research Center (LaRC) is a national resource for aeronautical research and development. The government, military and private industries rely on the capability of this facility for realistic flight data. Reducing the operation costs and keeping the NTF affordable is essential for aeronautics research. The NTF is undertaking an effort to reduce the time between data points during a pitch polar. This reduction is being driven by the operating costs of a cryogenic facility. If the time per data point can be reduced, a substantial cost savings can be realized from a reduction in liquid nitrogen (LN2) consumption. It is known that angle-of-attack (AoA) positioning is the longest lead-time item between points. In January 2005 a test was conducted at the NTF to determine the cause of the long lead-time so that an effort could be made to improve efficiency. The AoA signal at the NTF originates from onboard instrumentation then travels through a number of different systems including the signal conditioner, digital voltmeter, and the data system where the AoA angle is calculated. It is then fed into a closed loop control system that sets the model position. Each process along this path adds to the time per data point affecting the efficiency of the data taking process. Due to the nature of the closed loop feed back AoA control and the signal path, it takes approximately 18 seconds to take one pitch pause point with a typical AoA increment. Options are being investigated to reduce the time delay between points by modifying the signal path. These options include: reduced signal filtering, using analog channels instead of a digital volt meter (DVM), re-routing the signal directly to the AoA control computer and implementing new control algorithms. Each of these has potential to reduce the positioning time and together the savings could be significant. These timesaving efforts are essential but must be weighed against possible loss of data quality. For example, a reduction in filtering can introduce noise into the signal and using analog channels could result in some loss of accuracy. Data quality assessments need to be performed concurrently with timesaving techniques since data quality parameters are essential in maintaining facility integrity. This paper will highlight time saving efforts being undertaken or studied at the NTF. It will outline the instrumentation and computer systems involved in setting of the model pitch attitude then suggest changes to the process and discuss how these system changes would effect the time between data points. It also discusses the issue of data quality and how the potential efficiency changes in the system could affect it. Lastly, it will discuss the possibility of using an open loop control system and give some pros and cons of this method.

Optical Signal Processing.

DTIC Science & Technology

1986-10-31

constructed from TeO2 sisting of lenses L6 and L- and a cylindrical lens C- material which is oriented to operate in the slow shear shape the Bragg...to focus the light into a horizontal line for efficient illumination. The Bragg cells are constructed from TeO2 material which is oriented to operate...source is a 10 mW He-Ne laser for which = 632.8 nm. The holographic element was constructed on a SO-120 glass plate with a reference-to-signal beam

Differential effects of ADORA2A gene variations in pre-attentive visual sensory memory subprocesses.

PubMed

Beste, Christian; Stock, Ann-Kathrin; Ness, Vanessa; Epplen, Jörg T; Arning, Larissa

2012-08-01

The ADORA2A gene encodes the adenosine A(2A) receptor that is highly expressed in the striatum where it plays a role in modulating glutamatergic and dopaminergic transmission. Glutamatergic signaling has been suggested to play a pivotal role in cognitive functions related to the pre-attentive processing of external stimuli. Yet, the precise molecular mechanism of these processes is poorly understood. Therefore, we aimed to investigate whether ADORA2A gene variation has modulating effects on visual pre-attentive sensory memory processing. Studying two polymorphisms, rs5751876 and rs2298383, in 199 healthy control subjects who performed a partial-report paradigm, we find that ADORA2A variation is associated with differences in the efficiency of pre-attentive sensory memory sub-processes. We show that especially the initial visual availability of stimulus information is rendered more efficiently in the homozygous rare genotype groups. Processes related to the transfer of information into working memory and the duration of visual sensory (iconic) memory are compromised in the homozygous rare genotype groups. Our results show a differential genotype-dependent modulation of pre-attentive sensory memory sub-processes. Hence, we assume that this modulation may be due to differential effects of increased adenosine A(2A) receptor signaling on glutamatergic transmission and striatal medium spiny neuron (MSN) interaction. Copyright © 2011 Elsevier B.V. and ECNP. All rights reserved.

A Simple Method to Simultaneously Detect and Identify Spikes from Raw Extracellular Recordings.

PubMed

Petrantonakis, Panagiotis C; Poirazi, Panayiota

2015-01-01

The ability to track when and which neurons fire in the vicinity of an electrode, in an efficient and reliable manner can revolutionize the neuroscience field. The current bottleneck lies in spike sorting algorithms; existing methods for detecting and discriminating the activity of multiple neurons rely on inefficient, multi-step processing of extracellular recordings. In this work, we show that a single-step processing of raw (unfiltered) extracellular signals is sufficient for both the detection and identification of active neurons, thus greatly simplifying and optimizing the spike sorting approach. The efficiency and reliability of our method is demonstrated in both real and simulated data.

Neuromorphic sensory systems.

PubMed

Liu, Shih-Chii; Delbruck, Tobi

2010-06-01

Biology provides examples of efficient machines which greatly outperform conventional technology. Designers in neuromorphic engineering aim to construct electronic systems with the same efficient style of computation. This task requires a melding of novel engineering principles with knowledge gleaned from neuroscience. We discuss recent progress in realizing neuromorphic sensory systems which mimic the biological retina and cochlea, and subsequent sensor processing. The main trends are the increasing number of sensors and sensory systems that communicate through asynchronous digital signals analogous to neural spikes; the improved performance and usability of these sensors; and novel sensory processing methods which capitalize on the timing of spikes from these sensors. Experiments using these sensors can impact how we think the brain processes sensory information. 2010 Elsevier Ltd. All rights reserved.

Loss of Acetylcholine Signaling Reduces Cell Clearance Deficiencies in Caenorhabditis elegans.

PubMed

Pinto, Sérgio M; Almendinger, Johann; Cabello, Juan; Hengartner, Michael O

2016-01-01

The ability to eliminate undesired cells by apoptosis is a key mechanism to maintain organismal health and homeostasis. Failure to clear apoptotic cells efficiently can cause autoimmune diseases in mammals. Genetic studies in Caenorhabditis elegans have greatly helped to decipher the regulation of apoptotic cell clearance. In this study, we show that the loss of levamisole-sensitive acetylcholine receptor, but not of a typical neuronal acetylcholine receptor causes a reduction in the number of persistent cell corpses in worms suffering from an engulfment deficiency. This reduction is not caused by impaired or delayed cell death but rather by a partial restoration of the cell clearance capacity. Mutants in acetylcholine turn-over elicit a similar phenotype, implying that acetylcholine signaling is the process responsible for these observations. Surprisingly, tissue specific RNAi suggests that UNC-38, a major component of the levamisole-sensitive receptor, functions in the dying germ cell to influence engulfment efficiency. Animals with loss of acetylcholine receptor exhibit a higher fraction of cell corpses positive for the "eat-me" signal phosphatidylserine. Our results suggest that modulation by ion channels of ion flow across plasma membrane in dying cells can influence the dynamics of phosphatidylserine exposure and thus clearance efficiency.

Proposal and performance analysis on the PDM microwave photonic link for the mm-wave signal with hybrid QAM-MPPM-RZ modulation

NASA Astrophysics Data System (ADS)

Tian, Bo; Zhang, Qi; Ma, Jianxin; Tao, Ying; Shen, Yufei; Wang, Yang; Zhang, Geng; Zhou, Wenmao; Zhao, Yi; Pan, Xiaolong

2018-07-01

A polarization division multiplexed (PDM) microwave photonic link for the millimeter (MM)-wave signal with hybrid modulation scheme is proposed in this paper, which is based on the combination of quadrature amplitude modulation, multi-pulse pulse-position modulation and return to zero modulation (QAM-MPPM-RZ). In this scheme, the two orthogonal polarization states enable simultaneous transmission of four data flows, which can provide different services for users according to the data rate requirement. To generate hybrid QAM-MPPM-RZ mm-wave signal, the QAM mm-wave signal is directly modulated by MPPM-RZ signal without using digital signal processing (DSP) devices, which reduces the overhead of the encoding process. Then, the generated QAM-MPPM-RZ mm-wave signal is transmitted in PDM microwave photonic link based on SSB modulation. The sparsity characteristic of QAM-MPPM-RZ not only improves the power efficiency, but also decreases the degradation caused by the fiber chromatic dispersion. The simulation results show that, under the constraint of the same transmitted data rate, the PDM microwave photonic link with 50 GHz QAM-MPPM-RZ mm-wave signal achieves much lower levels of bit-error rate than ordinary 32-QAM. In addition, the increase of laser linewidth brings no additional impact to the proposed scheme.

Analysis on electronic control unit of continuously variable transmission

NASA Astrophysics Data System (ADS)

Cao, Shuanggui

Continuously variable transmission system can ensure that the engine work along the line of best fuel economy, improve fuel economy, save fuel and reduce harmful gas emissions. At the same time, continuously variable transmission allows the vehicle speed is more smooth and improves the ride comfort. Although the CVT technology has made great development, but there are many shortcomings in the CVT. The CVT system of ordinary vehicles now is still low efficiency, poor starting performance, low transmission power, and is not ideal controlling, high cost and other issues. Therefore, many scholars began to study some new type of continuously variable transmission. The transmission system with electronic systems control can achieve automatic control of power transmission, give full play to the characteristics of the engine to achieve optimal control of powertrain, so the vehicle is always traveling around the best condition. Electronic control unit is composed of the core processor, input and output circuit module and other auxiliary circuit module. Input module collects and process many signals sent by sensor and , such as throttle angle, brake signals, engine speed signal, speed signal of input and output shaft of transmission, manual shift signals, mode selection signals, gear position signal and the speed ratio signal, so as to provide its corresponding processing for the controller core.

Recombinant protein secretion in Pseudozyma flocculosa and Pseudozyma antarctica with a novel signal peptide.

PubMed

Cheng, Yali; Avis, Tyler J; Bolduc, Sébastien; Zhao, Yingyi; Anguenot, Raphaël; Neveu, Bertrand; Labbé, Caroline; Belzile, François; Bélanger, Richard R

2008-12-01

Secretion of recombinant proteins aims to reproduce the correct posttranslational modifications of the expressed protein while simplifying its recovery. In this study, secretion signal sequences from an abundantly secreted 34-kDa protein (P34) from Pseudozyma flocculosa were cloned. The efficiency of these sequences in the secretion of recombinant green fluorescent protein (GFP) was investigated in two Pseudozyma species and compared with other secretion signal sequences, from S. cerevisiae and Pseudozyma spp. The results indicate that various secretion signal sequences were functional and that the P34 signal peptide was the most effective secretion signal sequence in both P. flocculosa and P. antarctica. The cells correctly processed the secretion signal sequences, including P34 signal peptide, and mature GFP was recovered from the culture medium. This is the first report of functional secretion signal sequences in P. flocculosa. These sequences can be used to test the secretion of other recombinant proteins and for studying the secretion pathway in P. flocculosa and P. antarctica.

Improving the signal-to-noise ratio in ultrasound-modulated optical tomography by a lock-in amplifier

NASA Astrophysics Data System (ADS)

Zhu, Lili; Wu, Jingping; Lin, Guimin; Hu, Liangjun; Li, Hui

2016-10-01

With high spatial resolution of ultrasonic location and high sensitivity of optical detection, ultrasound-modulated optical tomography (UOT) is a promising noninvasive biological tissue imaging technology. In biological tissue, the ultrasound-modulated light signals are very weak and are overwhelmed by the strong unmodulated light signals. It is a difficulty and key to efficiently pick out the weak modulated light from strong unmodulated light in UOT. Under the effect of an ultrasonic field, the scattering light intensity presents a periodic variation as the ultrasonic frequency changes. So the modulated light signals would be escape from the high unmodulated light signals, when the modulated light signals and the ultrasonic signal are processed cross correlation operation by a lock-in amplifier and without a chopper. Experimental results indicated that the signal-to-noise ratio of UOT is significantly improved by a lock-in amplifier, and the higher the repetition frequency of pulsed ultrasonic wave, the better the signal-to-noise ratio of UOT.

Digital nonlinearity compensation in high-capacity optical communication systems considering signal spectral broadening effect.

PubMed

Xu, Tianhua; Karanov, Boris; Shevchenko, Nikita A; Lavery, Domaniç; Liga, Gabriele; Killey, Robert I; Bayvel, Polina

2017-10-11

Nyquist-spaced transmission and digital signal processing have proved effective in maximising the spectral efficiency and reach of optical communication systems. In these systems, Kerr nonlinearity determines the performance limits, and leads to spectral broadening of the signals propagating in the fibre. Although digital nonlinearity compensation was validated to be promising for mitigating Kerr nonlinearities, the impact of spectral broadening on nonlinearity compensation has never been quantified. In this paper, the performance of multi-channel digital back-propagation (MC-DBP) for compensating fibre nonlinearities in Nyquist-spaced optical communication systems is investigated, when the effect of signal spectral broadening is considered. It is found that accounting for the spectral broadening effect is crucial for achieving the best performance of DBP in both single-channel and multi-channel communication systems, independent of modulation formats used. For multi-channel systems, the degradation of DBP performance due to neglecting the spectral broadening effect in the compensation is more significant for outer channels. Our work also quantified the minimum bandwidths of optical receivers and signal processing devices to ensure the optimal compensation of deterministic nonlinear distortions.

A novel multireceiver communications system configuration based on optimal estimation theory

NASA Technical Reports Server (NTRS)

Kumar, R.

1990-01-01

A multireceiver configuration for the purpose of carrier arraying and/or signal arraying is presented. Such a problem arises for example, in the NASA Deep Space Network where the same data-modulated signal from a spacecraft is received by a number of geographically separated antennas and the data detection must be efficiently performed on the basis of the various received signals. The proposed configuration is arrived at by formulating the carrier and/or signal arraying problem as an optimal estimation problem. Two specific solutions are proposed. The first solution is to simultaneously and optimally estimate the various phase processes received at different receivers with coupled phase locked loops (PLLs) wherein the individual PLLs acquire and track their respective receivers' phase processes, but are aided by each other in an optimal manner. However, when the phase processes are relatively weakly correlated, and for the case of relatively high values of symbol energy-to-noise spectral density ratio, a novel configuration for combining the data modulated, loop-output signals is proposed. The scheme can be extended to the case of low symbol energy-to-noise case by performing the combining/detection process over a multisymbol period. Such a configuration results in the minimization of the effective radio loss at the combiner output, and thus a maximization of energy per bit to noise-power spectral density ration is achieved.

Paracrine control of tissue regeneration and cell proliferation by Caspase-3

PubMed Central

Boland, K; Flanagan, L; Prehn, J HM

2013-01-01

Executioner caspases such as Caspase-3 and Caspase-7 have long been recognised as the key proteases involved in cell demolition during apoptosis. Caspase activation also modulates signal transduction inside cells, through activation or inactivation of kinases, phosphatases and other signalling molecules. Interestingly, a series of recent studies have demonstrated that caspase activation may also influence signal transduction and gene expression changes in neighbouring cells that themselves did not activate caspases. This review describes the physiological relevance of paracrine Caspase-3 signalling for developmental processes, tissue homeostasis and tissue regeneration, and discusses the role of soluble factors and microparticles in mediating these paracrine activities. While non-cell autonomous control of tissue regeneration by Caspase-3 may represent an important process for maintaining tissue homeostasis, it may limit the efficiency of current cancer therapy by promoting cell proliferation in those cancer cells resistant to radio- or chemotherapy. We discuss recent evidence in support of such a role for Caspase-3, and discuss its therapeutic implication. PMID:23846227

Noise facilitates transcriptional control under dynamic inputs.

PubMed

Kellogg, Ryan A; Tay, Savaş

2015-01-29

Cells must respond sensitively to time-varying inputs in complex signaling environments. To understand how signaling networks process dynamic inputs into gene expression outputs and the role of noise in cellular information processing, we studied the immune pathway NF-κB under periodic cytokine inputs using microfluidic single-cell measurements and stochastic modeling. We find that NF-κB dynamics in fibroblasts synchronize with oscillating TNF signal and become entrained, leading to significantly increased NF-κB oscillation amplitude and mRNA output compared to non-entrained response. Simulations show that intrinsic biochemical noise in individual cells improves NF-κB oscillation and entrainment, whereas cell-to-cell variability in NF-κB natural frequency creates population robustness, together enabling entrainment over a wider range of dynamic inputs. This wide range is confirmed by experiments where entrained cells were measured under all input periods. These results indicate that synergy between oscillation and noise allows cells to achieve efficient gene expression in dynamically changing signaling environments. Copyright © 2015 Elsevier Inc. All rights reserved.

Noncausal telemetry data recovery techniques

NASA Technical Reports Server (NTRS)

Tsou, H.; Lee, R.; Mileant, A.; Hinedi, S.

1995-01-01

Cost efficiency is becoming a major driver in future space missions. Because of the constraints on total cost, including design, implementation, and operation, future spacecraft are limited in terms of their size power and complexity. Consequently, it is expected that future missions will operate on marginal space-to-ground communication links that, in turn, can pose an additional risk on the successful scientific data return of these missions. For low data-rate and low downlink-margin missions, the buffering of the telemetry signal for further signal processing to improve data return is a possible strategy; it has been adopted for the Galileo S-band mission. This article describes techniques used for postprocessing of buffered telemetry signal segments (called gaps) to recover data lost during acquisition and resynchronization. Two methods, one for a closed-loop and the other one for an open-loop configuration, are discussed in this article. Both of them can be used in either forward or backward processing of signal segments, depending on where a gap is specifically situated in a pass.

A high capacity data centre network: simultaneous 4-PAM data at 20 Gbps and 2 GHz phase modulated RF clock signal over a single VCSEL carrier

NASA Astrophysics Data System (ADS)

Isoe, G. M.; Wassin, S.; Gamatham, R. R. G.; Leitch, A. W. R.; Gibbon, T. B.

2017-11-01

Optical fibre communication technologies are playing important roles in data centre networks (DCNs). Techniques for increasing capacity and flexibility for the inter-rack/pod communications in data centres have drawn remarkable attention in recent years. In this work, we propose a low complexity, reliable, alternative technique for increasing DCN capacity and flexibility through multi-signal modulation onto a single mode VCSEL carrier. A 20 Gbps 4-PAM data signal is directly modulated on a single mode 10 GHz bandwidth VCSEL carrier at 1310 nm, therefore, doubling the network bit rate. Carrier spectral efficiency is further maximized by modulating its phase attribute with a 2 GHz reference frequency (RF) clock signal. We, therefore, simultaneously transmit a 20 Gbps 4-PAM data signal and a phase modulated 2 GHz RF signal using a single mode 10 GHz bandwidth VCSEL carrier. It is the first time a single mode 10 GHz bandwidth VCSEL carrier is reported to simultaneously transmit a directly modulated 4-PAM data signal and a phase modulated RF clock signal. A receiver sensitivity of -10. 52 dBm was attained for a 20 Gbps 4-PAM VCSEL transmission. The 2 GHz phase modulated RF clock signal introduced a power budget penalty of 0.21 dB. Simultaneous distribution of both data and timing signals over shared infrastructure significantly increases the aggregated data rate at different optical network units within the DCN, without expensive optics investment. We further demonstrate on the design of a software-defined digital signal processing assisted receiver to efficiently recover the transmitted signal without employing costly receiver hardware.

Convergent optical wired and wireless long-reach access network using high spectral-efficient modulation.

PubMed

Chow, C W; Lin, Y H

2012-04-09

To provide broadband services in a single and low cost perform, the convergent optical wired and wireless access network is promising. Here, we propose and demonstrate a convergent optical wired and wireless long-reach access networks based on orthogonal wavelength division multiplexing (WDM). Both the baseband signal and the radio-over-fiber (ROF) signal are multiplexed and de-multiplexed in optical domain, hence it is simple and the operation speed is not limited by the electronic bottleneck caused by the digital signal processing (DSP). Error-free de-multiplexing and down-conversion can be achieved for all the signals after 60 km (long-reach) fiber transmission. The scalability of the system for higher bit-rate (60 GHz) is also simulated and discussed.

Independent component analysis algorithm FPGA design to perform real-time blind source separation

NASA Astrophysics Data System (ADS)

Meyer-Baese, Uwe; Odom, Crispin; Botella, Guillermo; Meyer-Baese, Anke

2015-05-01

The conditions that arise in the Cocktail Party Problem prevail across many fields creating a need for of Blind Source Separation. The need for BSS has become prevalent in several fields of work. These fields include array processing, communications, medical signal processing, and speech processing, wireless communication, audio, acoustics and biomedical engineering. The concept of the cocktail party problem and BSS led to the development of Independent Component Analysis (ICA) algorithms. ICA proves useful for applications needing real time signal processing. The goal of this research was to perform an extensive study on ability and efficiency of Independent Component Analysis algorithms to perform blind source separation on mixed signals in software and implementation in hardware with a Field Programmable Gate Array (FPGA). The Algebraic ICA (A-ICA), Fast ICA, and Equivariant Adaptive Separation via Independence (EASI) ICA were examined and compared. The best algorithm required the least complexity and fewest resources while effectively separating mixed sources. The best algorithm was the EASI algorithm. The EASI ICA was implemented on hardware with Field Programmable Gate Arrays (FPGA) to perform and analyze its performance in real time.

Parallel Processing Strategies of the Primate Visual System

PubMed Central

Nassi, Jonathan J.; Callaway, Edward M.

2009-01-01

Preface Incoming sensory information is sent to the brain along modality-specific channels corresponding to the five senses. Each of these channels further parses the incoming signals into parallel streams to provide a compact, efficient input to the brain. Ultimately, these parallel input signals must be elaborated upon and integrated within the cortex to provide a unified and coherent percept. Recent studies in the primate visual cortex have greatly contributed to our understanding of how this goal is accomplished. Multiple strategies including retinal tiling, hierarchical and parallel processing and modularity, defined spatially and by cell type-specific connectivity, are all used by the visual system to recover the rich detail of our visual surroundings. PMID:19352403

Heart rate calculation from ensemble brain wave using wavelet and Teager-Kaiser energy operator.

PubMed

Srinivasan, Jayaraman; Adithya, V

2015-01-01

Electroencephalogram (EEG) signal artifacts are caused by various factors, such as, Electro-oculogram (EOG), Electromyogram (EMG), Electrocardiogram (ECG), movement artifact and line interference. The relatively high electrical energy cardiac activity causes EEG artifacts. In EEG signal processing the general approach is to remove the ECG signal. In this paper, we introduce an automated method to extract the ECG signal from EEG using wavelet and Teager-Kaiser energy operator for R-peak enhancement and detection. From the detected R-peaks the heart rate (HR) is calculated for clinical diagnosis. To check the efficiency of our method, we compare the HR calculated from ECG signal recorded in synchronous with EEG. The proposed method yields a mean error of 1.4% for the heart rate and 1.7% for mean R-R interval. The result illustrates that, proposed method can be used for ECG extraction from single channel EEG and used in clinical diagnosis like estimation for stress analysis, fatigue, and sleep stages classification studies as a multi-model system. In addition, this method eliminates the dependence of additional synchronous ECG in extraction of ECG from EEG signal process.

Imaging interferometer using dual broadband quantum well infrared photodetectors

NASA Technical Reports Server (NTRS)

Reininger, F.; Gunapala, S.; Bandara, S.; Grimm, M.; Johnson, D.; Peters, D.; Leland, S.; Liu, J.; Mumolo, J.; Rafol, D.;

Fisher information matrix for branching processes with application to electron-multiplying charge-coupled devices

PubMed Central

Chao, Jerry; Ward, E. Sally; Ober, Raimund J.

2012-01-01

The high quantum efficiency of the charge-coupled device (CCD) has rendered it the imaging technology of choice in diverse applications. However, under extremely low light conditions where few photons are detected from the imaged object, the CCD becomes unsuitable as its readout noise can easily overwhelm the weak signal. An intended solution to this problem is the electron-multiplying charge-coupled device (EMCCD), which stochastically amplifies the acquired signal to drown out the readout noise. Here, we develop the theory for calculating the Fisher information content of the amplified signal, which is modeled as the output of a branching process. Specifically, Fisher information expressions are obtained for a general and a geometric model of amplification, as well as for two approximations of the amplified signal. All expressions pertain to the important scenario of a Poisson-distributed initial signal, which is characteristic of physical processes such as photon detection. To facilitate the investigation of different data models, a “noise coefficient” is introduced which allows the analysis and comparison of Fisher information via a scalar quantity. We apply our results to the problem of estimating the location of a point source from its image, as observed through an optical microscope and detected by an EMCCD. PMID:23049166

Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment

PubMed Central

Wen, Dong; Jia, Peilei; Lian, Qiusheng; Zhou, Yanhong; Lu, Chengbiao

2016-01-01

At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals. PMID:27458376

Enhancement and inhibition of second-harmonic generation and absorption in a negative index cavity.

PubMed

de Ceglia, Domenico; D'Orazio, Antonella; De Sario, Marco; Petruzzelli, Vincenzo; Prudenzano, Francesco; Centini, Marco; Cappeddu, Mirko G; Bloemer, Mark J; Scalora, Michael

2007-02-01

We study second-harmonic generation in a negative-index material cavity. The transmission spectrum shows a bandgap between the electric and magnetic plasma frequencies. The nonlinear process is made efficient by local phase-matching conditions between a forward-propagating pump and a backward-propagating second-harmonic signal. By simultaneously exciting the cavity with counterpropagating pulses, and by varying their relative phase difference, one is able to enhance or inhibit linear absorption and the second-harmonic conversion efficiency.

SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering

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

Iliopoulos, AS; Sun, X; Floros, D

Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well asmore » histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173.« less

Comparative of signal processing techniques for micro-Doppler signature extraction with automotive radar systems

NASA Astrophysics Data System (ADS)

Rodriguez-Hervas, Berta; Maile, Michael; Flores, Benjamin C.

2014-05-01

In recent years, the automotive industry has experienced an evolution toward more powerful driver assistance systems that provide enhanced vehicle safety. These systems typically operate in the optical and microwave regions of the electromagnetic spectrum and have demonstrated high efficiency in collision and risk avoidance. Microwave radar systems are particularly relevant due to their operational robustness under adverse weather or illumination conditions. Our objective is to study different signal processing techniques suitable for extraction of accurate micro-Doppler signatures of slow moving objects in dense urban environments. Selection of the appropriate signal processing technique is crucial for the extraction of accurate micro-Doppler signatures that will lead to better results in a radar classifier system. For this purpose, we perform simulations of typical radar detection responses in common driving situations and conduct the analysis with several signal processing algorithms, including short time Fourier Transform, continuous wavelet or Kernel based analysis methods. We take into account factors such as the relative movement between the host vehicle and the target, and the non-stationary nature of the target's movement. A comparison of results reveals that short time Fourier Transform would be the best approach for detection and tracking purposes, while the continuous wavelet would be the best suited for classification purposes.

The Wnt/β-catenin signaling pathway tips the balance between apoptosis and reprograming of cell fusion hybrids.

PubMed

Lluis, Frederic; Pedone, Elisa; Pepe, Stefano; Cosma, Maria Pia

2010-11-01

Cell-cell fusion contributes to cell differentiation and developmental processes. We have previously showed that activation of Wnt/β-catenin enhances somatic cell reprograming after polyethylene glycol (PEG)-mediated fusion. Here, we show that neural stem cells and ESCs can fuse spontaneously in cocultures, although with very low efficiency (about 2%), as the hybrids undergo apoptosis. In contrast, when Wnt/β-catenin signaling is activated in ESCs and leads to accumulation of low amounts of β-catenin in the nucleus, activated ESCs can reprogram somatic cells with very high efficiency after spontaneous fusion. Furthermore, we also show that different levels of β-catenin accumulation in the ESC nuclei can modulate cell proliferation, although in our experimental setting, cell proliferation does not modulate the reprograming efficiency per se. Overall, the present study provides evidence that spontaneous fusion occurs, while the survival of the reprogramed clones is strictly dependent on induction of a Wnt-mediated reprograming pathway. Copyright © 2010 AlphaMed Press.

Saturation of side-band instabilities in a free-electron laser

NASA Astrophysics Data System (ADS)

Lin, A. T.

The efficiency of a free electron laser is intrinsically limited because the growth of the ponderomotive force produced by the interaction of the rippled magnetic field and the signal wave will eventually trap the electrons. There are a number of approaches for enhancing the efficiency of a free electron laser (FEL). One approach employs a dc field. Most of the efficiency enhancement calculations use a single-mode approximation which prohibits the side band waves to grow. In the present investigation, a particle simulation procedure is employed to demonstrate that the enhancement process is ultimately terminated by the generation of side band instabilities due to the interaction of the trapped electrons and the signal wave. The side band instability will play an important part in determining the maximum output power which can be obtained from a FEL. It is also shown that a considerable improvement in output power can still be achieved by carefully choosing the strength and the turn-on time of the dc electric field.

Zinc Signal in Brain Diseases.

PubMed

Portbury, Stuart D; Adlard, Paul A

2017-11-23

The divalent cation zinc is an integral requirement for optimal cellular processes, whereby it contributes to the function of over 300 enzymes, regulates intracellular signal transduction, and contributes to efficient synaptic transmission in the central nervous system. Given the critical role of zinc in a breadth of cellular processes, its cellular distribution and local tissue level concentrations remain tightly regulated via a series of proteins, primarily including zinc transporter and zinc import proteins. A loss of function of these regulatory pathways, or dietary alterations that result in a change in zinc homeostasis in the brain, can all lead to a myriad of pathological conditions with both acute and chronic effects on function. This review aims to highlight the role of zinc signaling in the central nervous system, where it may precipitate or potentiate diverse issues such as age-related cognitive decline, depression, Alzheimer's disease or negative outcomes following brain injury.

Photonic crystal nanocavity assisted rejection ratio tunable notch microwave photonic filter

PubMed Central

Long, Yun; Xia, Jinsong; Zhang, Yong; Dong, Jianji; Wang, Jian

2017-01-01

Driven by the increasing demand on handing microwave signals with compact device, low power consumption, high efficiency and high reliability, it is highly desired to generate, distribute, and process microwave signals using photonic integrated circuits. Silicon photonics offers a promising platform facilitating ultracompact microwave photonic signal processing assisted by silicon nanophotonic devices. In this paper, we propose, theoretically analyze and experimentally demonstrate a simple scheme to realize ultracompact rejection ratio tunable notch microwave photonic filter (MPF) based on a silicon photonic crystal (PhC) nanocavity with fixed extinction ratio. Using a conventional modulation scheme with only a single phase modulator (PM), the rejection ratio of the presented MPF can be tuned from about 10 dB to beyond 60 dB. Moreover, the central frequency tunable operation in the high rejection ratio region is also demonstrated in the experiment. PMID:28067332

Photonic crystal nanocavity assisted rejection ratio tunable notch microwave photonic filter

NASA Astrophysics Data System (ADS)

Long, Yun; Xia, Jinsong; Zhang, Yong; Dong, Jianji; Wang, Jian

2017-01-01

Driven by the increasing demand on handing microwave signals with compact device, low power consumption, high efficiency and high reliability, it is highly desired to generate, distribute, and process microwave signals using photonic integrated circuits. Silicon photonics offers a promising platform facilitating ultracompact microwave photonic signal processing assisted by silicon nanophotonic devices. In this paper, we propose, theoretically analyze and experimentally demonstrate a simple scheme to realize ultracompact rejection ratio tunable notch microwave photonic filter (MPF) based on a silicon photonic crystal (PhC) nanocavity with fixed extinction ratio. Using a conventional modulation scheme with only a single phase modulator (PM), the rejection ratio of the presented MPF can be tuned from about 10 dB to beyond 60 dB. Moreover, the central frequency tunable operation in the high rejection ratio region is also demonstrated in the experiment.

Photonic crystal nanocavity assisted rejection ratio tunable notch microwave photonic filter.

PubMed

Long, Yun; Xia, Jinsong; Zhang, Yong; Dong, Jianji; Wang, Jian

2017-01-09

Driven by the increasing demand on handing microwave signals with compact device, low power consumption, high efficiency and high reliability, it is highly desired to generate, distribute, and process microwave signals using photonic integrated circuits. Silicon photonics offers a promising platform facilitating ultracompact microwave photonic signal processing assisted by silicon nanophotonic devices. In this paper, we propose, theoretically analyze and experimentally demonstrate a simple scheme to realize ultracompact rejection ratio tunable notch microwave photonic filter (MPF) based on a silicon photonic crystal (PhC) nanocavity with fixed extinction ratio. Using a conventional modulation scheme with only a single phase modulator (PM), the rejection ratio of the presented MPF can be tuned from about 10 dB to beyond 60 dB. Moreover, the central frequency tunable operation in the high rejection ratio region is also demonstrated in the experiment.

Smart Microsystems with Photonic Element and Their Applications to Aerospace Platforms

NASA Technical Reports Server (NTRS)

Adamovsky, G.; Lekki, J.; Sutter, J. K.; Sarkisov, S. S.; Curley, M. J.; Martin, C. E.

2000-01-01

The need to make manufacturing, operation, and support of airborne vehicles safer and more efficient forces engineers and scientists to look for lighter, cheaper, more reliable technologies. Light weight, immunity to EMI, fire safety, high bandwidth, and high signal fidelity have already made photonics in general and fiber optics in particular an extremely attractive medium for communication purposes. With the fiber optics serving as a central nervous system of the vehicle, generation, detection, and processing of the signal occurs at the peripherals that include smart structures and devices. Due to their interdisciplinary nature, photonic technologies cover such diverse areas as optical sensors and actuators, embedded and distributed sensors, sensing schemes and architectures, harnesses and connectors, signal processing and algorithms. The paper includes a brief description of work in the photonic area that is going on at NASA, especially at the Glenn Research Center (GRC).

Onboard Image Processing System for Hyperspectral Sensor

PubMed Central

Hihara, Hiroki; Moritani, Kotaro; Inoue, Masao; Hoshi, Yoshihiro; Iwasaki, Akira; Takada, Jun; Inada, Hitomi; Suzuki, Makoto; Seki, Taeko; Ichikawa, Satoshi; Tanii, Jun

2015-01-01

Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast and small-footprint lossless image compression capability is essential for reducing the size and weight of a sensor system. A fast lossless image compression algorithm has been developed, and is implemented in the onboard correction circuitry of sensitivity and linearity of Complementary Metal Oxide Semiconductor (CMOS) sensors in order to maximize the compression ratio. The employed image compression method is based on Fast, Efficient, Lossless Image compression System (FELICS), which is a hierarchical predictive coding method with resolution scaling. To improve FELICS’s performance of image decorrelation and entropy coding, we apply a two-dimensional interpolation prediction and adaptive Golomb-Rice coding. It supports progressive decompression using resolution scaling while still maintaining superior performance measured as speed and complexity. Coding efficiency and compression speed enlarge the effective capacity of signal transmission channels, which lead to reducing onboard hardware by multiplexing sensor signals into a reduced number of compression circuits. The circuitry is embedded into the data formatter of the sensor system without adding size, weight, power consumption, and fabrication cost. PMID:26404281

Investigation of Microbubble Cavitation-Induced Calcein Release from Cells In Vitro.

PubMed

Maciulevičius, Martynas; Tamošiūnas, Mindaugas; Jakštys, Baltramiejus; Jurkonis, Rytis; Venslauskas, Mindaugas Saulius; Šatkauskas, Saulius

2016-12-01

In the present study, microbubble (MB) cavitation signal analysis was performed together with calcein release evaluation in both pressure and exposure duration domains of the acoustic field. A passive cavitation detection system was used to simultaneously measure MB scattering and attenuation signals for subsequent extraction efficiency relative to MB cavitation activity. The results indicate that the decrease in the efficiency of extraction of calcein molecules from Chinese hamster ovary cells, as well as cell viability, is associated with MB cavitation activity and can be accurately predicted using inertial cavitation doses up to 0.18 V × s (R 2  > 0.9, p < 0.0001). No decrease in additional calcein release or cell viability was observed after complete MB sonodestruction was achieved. This indicates that the optimal exposure duration within which maximal sono-extraction efficiency is obtained coincides with the time necessary to achieve complete MB destruction. These results illustrate the importance of MB inertial cavitation in the sono-extraction process. To our knowledge, this study is the first to (i) investigate small molecule extraction from cells via sonoporation and (ii) relate the extraction process to the quantitative characteristics of MB cavitation acoustic spectra. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Freeze-drying process monitoring using a cold plasma ionization device.

PubMed

Mayeresse, Y; Veillon, R; Sibille, P H; Nomine, C

2007-01-01

A cold plasma ionization device has been designed to monitor freeze-drying processes in situ by monitoring lyophilization chamber moisture content. This plasma device, which consists of a probe that can be mounted directly on the lyophilization chamber, depends upon the ionization of nitrogen and water molecules using a radiofrequency generator and spectrometric signal collection. The study performed on this probe shows that it is steam sterilizable, simple to integrate, reproducible, and sensitive. The limitations include suitable positioning in the lyophilization chamber, calibration, and signal integration. Sensitivity was evaluated in relation to the quantity of vials and the probe positioning, and correlation with existing methods, such as microbalance, was established. These tests verified signal reproducibility through three freeze-drying cycles. Scaling-up studies demonstrated a similar product signature for the same product using pilot-scale and larger-scale equipment. On an industrial scale, the method efficiently monitored the freeze-drying cycle, but in a larger industrial freeze-dryer the signal was slightly modified. This was mainly due to the positioning of the plasma device, in relation to the vapor flow pathway, which is not necessarily homogeneous within the freeze-drying chamber. The plasma tool is a relevant method for monitoring freeze-drying processes and may in the future allow the verification of current thermodynamic freeze-drying models. This plasma technique may ultimately represent a process analytical technology (PAT) approach for the freeze-drying process.

Efficient heart beat detection using embedded system electronics

NASA Astrophysics Data System (ADS)

Ramasamy, Mouli; Oh, Sechang; Varadan, Vijay K.

2014-04-01

The present day bio-technical field concentrates on developing various types of innovative ambulatory and wearable devices to monitor several bio-physical, physio-pathological, bio-electrical and bio-potential factors to assess a human body's health condition without intruding quotidian activities. One of the most important aspects of this evolving technology is monitoring heart beat rate and electrocardiogram (ECG) from which many other subsidiary results can be derived. Conventionally, the devices and systems consumes a lot of power since the acquired signals are always processed on the receiver end. Because of this back end processing, the unprocessed raw data is transmitted resulting in usage of more power, memory and processing time. This paper proposes an innovative technique where the acquired signals are processed by a microcontroller in the front end of the module and just the processed signal is then transmitted wirelessly to the display unit. Therefore, power consumption is considerably reduced and clearer data analysis is performed within the module. This also avoids the need for the user to be educated about usage of the device and signal/system analysis, since only the number of heart beats will displayed at the user end. Additionally, the proposed concept also eradicates the other disadvantages like obtrusiveness, high power consumption and size. To demonstrate the above said factors, a commercial controller board was used to extend the monitoring method by using the saved ECG data from a computer.

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.

Fault tolerance in space-based digital signal processing and switching systems: Protecting up-link processing resources, demultiplexer, demodulator, and decoder

NASA Technical Reports Server (NTRS)

Redinbo, Robert

1994-01-01

Fault tolerance features in the first three major subsystems appearing in the next generation of communications satellites are described. These satellites will contain extensive but efficient high-speed processing and switching capabilities to support the low signal strengths associated with very small aperture terminals. The terminals' numerous data channels are combined through frequency division multiplexing (FDM) on the up-links and are protected individually by forward error-correcting (FEC) binary convolutional codes. The front-end processing resources, demultiplexer, demodulators, and FEC decoders extract all data channels which are then switched individually, multiplexed, and remodulated before retransmission to earth terminals through narrow beam spot antennas. Algorithm based fault tolerance (ABFT) techniques, which relate real number parity values with data flows and operations, are used to protect the data processing operations. The additional checking features utilize resources that can be substituted for normal processing elements when resource reconfiguration is required to replace a failed unit.

Development of high-efficiency power amplifiers for PIP2 (Project X), Phase II

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

Raab, Frederick

The Fermi Lab PIP II (formerly Project X) accelerator will require the generation of over a megawatt of radio-frequency (RF) power at 325 and 650 MHz. This Phase-II SBIR grant developed techniques to generate this RF power efficienly. The basis of this approach is a system comprising high-efficiency RF power amplifiers, high-efficiency class-S modulators to maintain efficiency at all power levels, and low-loss power combiners. A digital signal processor adjusts signal parameters to obtain the maximum efficiency while producing a signal of the desired amplitude and phase. Components of 4-kW prototypes were designed, assembled, and tested. The 500-W modules producemore » signals at 325 MHz with an overall efficiency of 83 percent and signals at 650 MHz with an overall efficiency of 79 percent. This efficiency is nearly double that available from conventional techniques, which makes it possible to cut the power consumption nearly in half. The system is designed to be scalable to the multi-kilowatt level and can be adapted to other DoE applications.« less

Signal recognition efficiencies of artificial neural-network pulse-shape discrimination in HPGe -decay searches

NASA Astrophysics Data System (ADS)

Caldwell, A.; Cossavella, F.; Majorovits, B.; Palioselitis, D.; Volynets, O.

2015-07-01

A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate variations of efficiencies as a function of used training set. It is verified that neural networks are well-suited to identify background pulses in true-coaxial high-purity germanium detectors. The systematic uncertainty on the signal recognition efficiency derived using signal-like evaluation samples from calibration measurements is estimated to be 5 %. This uncertainty is due to differences between signal and calibration samples.

Detection and Processing Techniques of FECG Signal for Fetal Monitoring

PubMed Central

2009-01-01

Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system. PMID:19495912

Brain Signal Variability is Parametrically Modifiable

PubMed Central

Garrett, Douglas D.; McIntosh, Anthony R.; Grady, Cheryl L.

2014-01-01

Moment-to-moment brain signal variability is a ubiquitous neural characteristic, yet remains poorly understood. Evidence indicates that heightened signal variability can index and aid efficient neural function, but it is not known whether signal variability responds to precise levels of environmental demand, or instead whether variability is relatively static. Using multivariate modeling of functional magnetic resonance imaging-based parametric face processing data, we show here that within-person signal variability level responds to incremental adjustments in task difficulty, in a manner entirely distinct from results produced by examining mean brain signals. Using mixed modeling, we also linked parametric modulations in signal variability with modulations in task performance. We found that difficulty-related reductions in signal variability predicted reduced accuracy and longer reaction times within-person; mean signal changes were not predictive. We further probed the various differences between signal variance and signal means by examining all voxels, subjects, and conditions; this analysis of over 2 million data points failed to reveal any notable relations between voxel variances and means. Our results suggest that brain signal variability provides a systematic task-driven signal of interest from which we can understand the dynamic function of the human brain, and in a way that mean signals cannot capture. PMID:23749875

Microwave-to-Optical Conversion in WGM Resonators

NASA Technical Reports Server (NTRS)

Savchenkov, Anatoliy; Strekalov, Dmitry; Yu, Nan; Matsko, Andrey; Maleki, Lute

2008-01-01

Microwave-to-optical frequency converters based on whispering-gallery-mode (WGM) resonators have been proposed as mixers for the input ends of microwave receivers in which, downstream of the input ends, signals would be processed photonically. A frequency converter as proposed (see figure) would exploit the nonlinearity of the electromagnetic response of a WGM resonator made of LiNbO3 or another suitable ferroelectric material. Up-conversion would take place by three-wave mixing in the resonator. The WGM resonator would be de - signed and fabricated to obtain (1) resonance at both the microwave and the optical operating frequencies and (2) phase matching among the input and output microwave and optical signals as described in the immediately preceding article. Because the resonator would be all dielectric there would be no metal electrodes signal losses would be very low and, consequently, the resonance quality factors (Q values) of the microwave and optical fields would be very large. The long lifetimes associated with the large Q values would enable attainment of high efficiency of nonlinear interaction with low saturation power. It is anticipated that efficiency would be especially well enhanced by the combination of optical and microwave resonances in operation at input signal frequencies between 90 and 300 GHz.

Wavelet-based characterization of gait signal for neurological abnormalities.

PubMed

Baratin, E; Sugavaneswaran, L; Umapathy, K; Ioana, C; Krishnan, S

2015-02-01

Studies conducted by the World Health Organization (WHO) indicate that over one billion suffer from neurological disorders worldwide, and lack of efficient diagnosis procedures affects their therapeutic interventions. Characterizing certain pathologies of motor control for facilitating their diagnosis can be useful in quantitatively monitoring disease progression and efficient treatment planning. As a suitable directive, we introduce a wavelet-based scheme for effective characterization of gait associated with certain neurological disorders. In addition, since the data were recorded from a dynamic process, this work also investigates the need for gait signal re-sampling prior to identification of signal markers in the presence of pathologies. To benefit automated discrimination of gait data, certain characteristic features are extracted from the wavelet-transformed signals. The performance of the proposed approach was evaluated using a database consisting of 15 Parkinson's disease (PD), 20 Huntington's disease (HD), 13 Amyotrophic lateral sclerosis (ALS) and 16 healthy control subjects, and an average classification accuracy of 85% is achieved using an unbiased cross-validation strategy. The obtained results demonstrate the potential of the proposed methodology for computer-aided diagnosis and automatic characterization of certain neurological disorders. Copyright © 2015 Elsevier B.V. All rights reserved.

Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings

NASA Astrophysics Data System (ADS)

Miao, Yonghao; Zhao, Ming; Lin, Jing; Lei, Yaguo

2017-08-01

The extraction of periodic impulses, which are the important indicators of rolling bearing faults, from vibration signals is considerably significance for fault diagnosis. Maximum correlated kurtosis deconvolution (MCKD) developed from minimum entropy deconvolution (MED) has been proven as an efficient tool for enhancing the periodic impulses in the diagnosis of rolling element bearings and gearboxes. However, challenges still exist when MCKD is applied to the bearings operating under harsh working conditions. The difficulties mainly come from the rigorous requires for the multi-input parameters and the complicated resampling process. To overcome these limitations, an improved MCKD (IMCKD) is presented in this paper. The new method estimates the iterative period by calculating the autocorrelation of the envelope signal rather than relies on the provided prior period. Moreover, the iterative period will gradually approach to the true fault period through updating the iterative period after every iterative step. Since IMCKD is unaffected by the impulse signals with the high kurtosis value, the new method selects the maximum kurtosis filtered signal as the final choice from all candidates in the assigned iterative counts. Compared with MCKD, IMCKD has three advantages. First, without considering prior period and the choice of the order of shift, IMCKD is more efficient and has higher robustness. Second, the resampling process is not necessary for IMCKD, which is greatly convenient for the subsequent frequency spectrum analysis and envelope spectrum analysis without resetting the sampling rate. Third, IMCKD has a significant performance advantage in diagnosing the bearing compound-fault which expands the application range. Finally, the effectiveness and superiority of IMCKD are validated by a number of simulated bearing fault signals and applying to compound faults and single fault diagnosis of a locomotive bearing.

Physical and Mathematical Questions on Signal Processing in Multibase Phase Direction Finders

NASA Astrophysics Data System (ADS)

Denisov, V. P.; Dubinin, D. V.; Meshcheryakov, A. A.

2018-02-01

Questions on improving the accuracy of multiple-base phase direction finders by rejecting anomalously large errors in the process of resolving the measurement ambiguities are considered. A physical basis is derived and calculated relationships characterizing the efficiency of the proposed solutions are obtained. Results of a computer simulation of a three-base direction finder are analyzed, along with field measurements of a three-base direction finder along near-ground paths.

Fractal Point Process and Queueing Theory and Application to Communication Networks

DTIC Science & Technology

1999-12-31

use of nonlinear dynamics and chaos in the design of innovative analog error-protection codes for com- munications applications. In the chaos...the fol- lowing theses, patent, and papers. 1. A. Narula, M. D. Trott , and G. W. Wornell, "Information-Theoretic Analysis of Multiple-Antenna...Bounds," in Proc. Int. Conf. Dec. Control, (Japan), Dec. 1996. 5. G. W. Wornell and M. D. Trott , "Efficient Signal Processing Tech- niques for

A multi-channel instrumentation system for biosignal recording.

PubMed

Yu, Hong; Li, Pengfei; Xiao, Zhiming; Peng, Chung-Ching; Bashirullah, Rizwan

2008-01-01

This paper reports a highly integrated battery operated multi-channel instrumentation system intended for physiological signal recording. The mixed signal IC has been fabricated in standard 0.5microm 5V 3M-2P CMOS process and features 32 instrumentation amplifiers, four 8b SAR ADCs, a wireless power interface with Li-ion battery charger, low power bidirectional telemetry and FSM controller with power gating control for improved energy efficiency. The chip measures 3.2mm by 4.8mm and dissipates approximately 2.1mW when fully operational.

Single-cell Transcriptome Study as Big Data

PubMed Central

Yu, Pingjian; Lin, Wei

2016-01-01

The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies. PMID:26876720

Fast-Acquisition/Weak-Signal-Tracking GPS Receiver for HEO

NASA Technical Reports Server (NTRS)

Wintemitz, Luke; Boegner, Greg; Sirotzky, Steve

2004-01-01

A report discusses the technical background and design of the Navigator Global Positioning System (GPS) receiver -- . a radiation-hardened receiver intended for use aboard spacecraft. Navigator is capable of weak signal acquisition and tracking as well as much faster acquisition of strong or weak signals with no a priori knowledge or external aiding. Weak-signal acquisition and tracking enables GPS use in high Earth orbits (HEO), and fast acquisition allows for the receiver to remain without power until needed in any orbit. Signal acquisition and signal tracking are, respectively, the processes of finding and demodulating a signal. Acquisition is the more computationally difficult process. Previous GPS receivers employ the method of sequentially searching the two-dimensional signal parameter space (code phase and Doppler). Navigator exploits properties of the Fourier transform in a massively parallel search for the GPS signal. This method results in far faster acquisition times [in the lab, 12 GPS satellites have been acquired with no a priori knowledge in a Low-Earth-Orbit (LEO) scenario in less than one second]. Modeling has shown that Navigator will be capable of acquiring signals down to 25 dB-Hz, appropriate for HEO missions. Navigator is built using the radiation-hardened ColdFire microprocessor and housing the most computationally intense functions in dedicated field-programmable gate arrays. The high performance of the algorithm and of the receiver as a whole are made possible by optimizing computational efficiency and carefully weighing tradeoffs among the sampling rate, data format, and data-path bit width.

Wavelet Filter Banks for Super-Resolution SAR Imaging

NASA Technical Reports Server (NTRS)

Sheybani, Ehsan O.; Deshpande, Manohar; Memarsadeghi, Nargess

2011-01-01

This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem.

The design of multi-core DSP parallel model based on message passing and multi-level pipeline

NASA Astrophysics Data System (ADS)

Niu, Jingyu; Hu, Jian; He, Wenjing; Meng, Fanrong; Li, Chuanrong

2017-10-01

Currently, the design of embedded signal processing system is often based on a specific application, but this idea is not conducive to the rapid development of signal processing technology. In this paper, a parallel processing model architecture based on multi-core DSP platform is designed, and it is mainly suitable for the complex algorithms which are composed of different modules. This model combines the ideas of multi-level pipeline parallelism and message passing, and summarizes the advantages of the mainstream model of multi-core DSP (the Master-Slave model and the Data Flow model), so that it has better performance. This paper uses three-dimensional image generation algorithm to validate the efficiency of the proposed model by comparing with the effectiveness of the Master-Slave and the Data Flow model.

The technique of entropy optimization in motor current signature analysis and its application in the fault diagnosis of gear transmission

NASA Astrophysics Data System (ADS)

Chen, Xiaoguang; Liang, Lin; Liu, Fei; Xu, Guanghua; Luo, Ailing; Zhang, Sicong

2012-05-01

Nowadays, Motor Current Signature Analysis (MCSA) is widely used in the fault diagnosis and condition monitoring of machine tools. However, although the current signal has lower SNR (Signal Noise Ratio), it is difficult to identify the feature frequencies of machine tools from complex current spectrum that the feature frequencies are often dense and overlapping by traditional signal processing method such as FFT transformation. With the study in the Motor Current Signature Analysis (MCSA), it is found that the entropy is of importance for frequency identification, which is associated with the probability distribution of any random variable. Therefore, it plays an important role in the signal processing. In order to solve the problem that the feature frequencies are difficult to be identified, an entropy optimization technique based on motor current signal is presented in this paper for extracting the typical feature frequencies of machine tools which can effectively suppress the disturbances. Some simulated current signals were made by MATLAB, and a current signal was obtained from a complex gearbox of an iron works made in Luxembourg. In diagnosis the MCSA is combined with entropy optimization. Both simulated and experimental results show that this technique is efficient, accurate and reliable enough to extract the feature frequencies of current signal, which provides a new strategy for the fault diagnosis and the condition monitoring of machine tools.

Constraints on drivers for visible light communications emitters based on energy efficiency.

PubMed

Del Campo-Jimenez, Guillermo; Perez-Jimenez, Rafael; Lopez-Hernandez, Francisco Jose

2016-05-02

In this work we analyze the energy efficiency constraints on drivers for Visible light communication (VLC) emitters. This is the main reason why LED is becoming the main source of illumination. We study the effect of the waveform shape and the modulation techniques on the overall energy efficiency of an LED lamp. For a similar level of illumination, we calculate the emitter energy efficiency ratio η (P_LED/P_TOTAL) for different signals. We compare switched and sinusoidal signals and analyze the effect of both OOK and OFDM modulation techniques depending on the power supply adjustment, level of illumination and signal amplitude distortion. Switched and OOK signals present higher energy efficiency behaviors (0.86≤η≤0.95) than sinusoidal and OFDM signals (0.53≤η≤0.79).

Surveillance of Space - Optimal Use of Complementary Sensors for Maximum Efficiency

DTIC Science & Technology

2006-04-01

as track - before - detect [4] have been shown to allow improved sensitivity. This technique employs fast running algorithms and computing power to pre...Multifunction Radar” IEEE Signal Processing Magazine, January 2006. [4] Wallace W R “The Use of Track - Before - Detect in Pulse-Doppler Radar” IEE 490, Radar

Attentional Control in Visual Signal Detection: Effects of Abrupt-Onset and No-Onset Stimuli

ERIC Educational Resources Information Center

Sewell, David K.; Smith, Philip L.

2012-01-01

The attention literature distinguishes two general mechanisms by which attention can benefit performance: gain (or resource) models and orienting (or switching) models. In gain models, processing efficiency is a function of a spatial distribution of capacity or resources; in orienting models, an attentional spotlight must be aligned with the…

AKAP-Lbc enhances cyclic AMP control of the ERK1/2 cascade.

PubMed

Smith, F Donelson; Langeberg, Lorene K; Cellurale, Cristina; Pawson, Tony; Morrison, Deborah K; Davis, Roger J; Scott, John D

2010-12-01

Mitogen-activated protein kinase (MAPK) cascades propagate a variety of cellular activities. Processive relay of signals through RAF-MEK-ERK modulates cell growth and proliferation. Signalling through this ERK cascade is frequently amplified in cancers, and drugs such as sorafenib (which is prescribed to treat renal and hepatic carcinomas) and PLX4720 (which targets melanomas) inhibit RAF kinases. Natural factors that influence ERK1/2 signalling include the second messenger cyclic AMP. However, the mechanisms underlying this cascade have been difficult to elucidate. We demonstrate that the A-kinase-anchoring protein AKAP-Lbc and the scaffolding protein kinase suppressor of Ras (KSR-1) form the core of a signalling network that efficiently relay signals from RAF, through MEK, and on to ERK1/2. AKAP-Lbc functions as an enhancer of ERK signalling by securing RAF in the vicinity of MEK1 and synchronizing protein kinase A (PKA)-mediated phosphorylation of Ser 838 on KSR-1. This offers mechanistic insight into cAMP-responsive control of ERK signalling events.

Boehmenan, a lignan from Hibiscus ficulneus, showed Wnt signal inhibitory activity.

PubMed

Shono, Takumi; Ishikawa, Naoki; Toume, Kazufumi; Arai, Midori A; Ahmed, Firoj; Sadhu, Samir K; Ishibashi, Masami

2015-07-15

The Wnt signal pathway modulates numerous biological processes, and its aberrant activation is related to various diseases. Therefore, inhibition of the Wnt signal may provide an effective (or efficient) strategy for these diseases. Cell-based luciferase assay targeting the Wnt signal (TOP assay) revealed that Hibiscus ficulneus extract inhibited the Wnt signal. The activity-guided isolation of the MeOH extract of H. ficulneus stems yielded four known (1-4) lignans along with myriceric acid (5). Compounds 1-4 potently inhibited the Wnt signal with TOPflash IC50 values of 1.0, 4.5, 6.3, and 1.9 μM, respectively. Compound 1 exhibited cytotoxicity against both Wnt-dependent (HCT116) and Wnt-independent (RKO) cells. Western blot analysis showed that 1 decreased the expression of full, cytosolic and nuclear β-catenin along with c-myc in STF/293 cells. Our results suggested that 1 may have inhibited the Wnt signal by decreasing β-catenin levels. Copyright © 2015 Elsevier Ltd. All rights reserved.

An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm.

PubMed

Qin, Qin; Li, Jianqing; Yue, Yinggao; Liu, Chengyu

2017-01-01

R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.

An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

PubMed Central

Qin, Qin

2017-01-01

R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method. PMID:29104745

An Energy Efficient ECG Signal Processor Detecting Cardiovascular Diseases on Smartphone.

PubMed

Jain, Sanjeev Kumar; Bhaumik, Basabi

2017-04-01

A novel disease diagnostic algorithm for ECG signal processing based on forward search is implemented in Application Specific Integrated Circuit (ASIC) for cardiovascular disease diagnosis on smartphone. An ASIC is fabricated using 130-nm CMOS low leakage process technology. The area of our PQRST ASIC is 1.21 mm 2 . The energy dissipation of PQRST ASIC is 96 pJ with a supply voltage of 0.9 V. The outputs from the ASIC are fed to an Android application that generates diagnostic report and can be sent to a cardiologist via email. The ASIC and Android application are verified for the detection of bundle branch block, hypertrophy, arrhythmia and myocardial infarction using Physionet PTB diagnostic ECG database. The failed detection rate is 0.69%, 0.69%, 0.34% and 1.72% for bundle branch block, hypertrophy, arrhythmia and myocardial infarction respectively. The AV block is detected in all the three patients in the Physionet St. Petersburg arrhythmia database. Our proposed ASIC together with our Android application is the most suitable for an energy efficient wearable cardiovascular disease detection system.

Chip Design Process Optimization Based on Design Quality Assessment

NASA Astrophysics Data System (ADS)

Häusler, Stefan; Blaschke, Jana; Sebeke, Christian; Rosenstiel, Wolfgang; Hahn, Axel

2010-06-01

Nowadays, the managing of product development projects is increasingly challenging. Especially the IC design of ASICs with both analog and digital components (mixed-signal design) is becoming more and more complex, while the time-to-market window narrows at the same time. Still, high quality standards must be fulfilled. Projects and their status are becoming less transparent due to this complexity. This makes the planning and execution of projects rather difficult. Therefore, there is a need for efficient project control. A main challenge is the objective evaluation of the current development status. Are all requirements successfully verified? Are all intermediate goals achieved? Companies often develop special solutions that are not reusable in other projects. This makes the quality measurement process itself less efficient and produces too much overhead. The method proposed in this paper is a contribution to solve these issues. It is applied at a German design house for analog mixed-signal IC design. This paper presents the results of a case study and introduces an optimized project scheduling on the basis of quality assessment results.

An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications

PubMed Central

Sodhro, Ali Hassan; Sodhro, Gul Hassan; Lohano, Sonia; Pirbhulal, Sandeep

2018-01-01

Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone’s life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments. The developed chip collects real-time ECG data with two adopted channels for continuous monitoring of human heart activity. Then, these two channels and the AFE are built into a right leg drive right leg drive (RLD) driver circuit with lead-off detection and medical graded test signal. Human ECG data was collected at 60 beats per minute (BPM) to 120 BPM with 60 Hz noise and considered throughout the experimental set-up. Moreover, notch filter (cutoff frequency 60 Hz), high-pass filter (cutoff frequency 0.67 Hz), and low-pass filter (cutoff frequency 100 Hz) with cut-off frequencies of 60 Hz, 0.67 Hz, and 100 Hz, respectively, were designed with bilinear transformation for rectifying the power-line noise and artifacts while extracting real-time ECG signals. Finally, a transmission power control-based energy-efficient (ETPC) algorithm is proposed, implemented on the hardware and then compared with the several conventional TPC methods. Experimental results reveal that our developed chip collects real-time ECG data efficiently, and the proposed ETPC algorithm achieves higher energy savings of 35.5% with a slightly larger packet loss ratio (PLR) as compared to conventional TPC (e.g., constant TPC, Gao’s, and Xiao’s methods). PMID:29558433

An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications.

PubMed

Sodhro, Ali Hassan; Sangaiah, Arun Kumar; Sodhro, Gul Hassan; Lohano, Sonia; Pirbhulal, Sandeep

2018-03-20

Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone's life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments. The developed chip collects real-time ECG data with two adopted channels for continuous monitoring of human heart activity. Then, these two channels and the AFE are built into a right leg drive right leg drive (RLD) driver circuit with lead-off detection and medical graded test signal. Human ECG data was collected at 60 beats per minute (BPM) to 120 BPM with 60 Hz noise and considered throughout the experimental set-up. Moreover, notch filter (cutoff frequency 60 Hz), high-pass filter (cutoff frequency 0.67 Hz), and low-pass filter (cutoff frequency 100 Hz) with cut-off frequencies of 60 Hz, 0.67 Hz, and 100 Hz, respectively, were designed with bilinear transformation for rectifying the power-line noise and artifacts while extracting real-time ECG signals. Finally, a transmission power control-based energy-efficient (ETPC) algorithm is proposed, implemented on the hardware and then compared with the several conventional TPC methods. Experimental results reveal that our developed chip collects real-time ECG data efficiently, and the proposed ETPC algorithm achieves higher energy savings of 35.5% with a slightly larger packet loss ratio (PLR) as compared to conventional TPC (e.g., constant TPC, Gao's, and Xiao's methods).

Simple simulation training system for short-wave radio station

NASA Astrophysics Data System (ADS)

Tan, Xianglin; Shao, Zhichao; Tu, Jianhua; Qu, Fuqi

2018-04-01

The short-wave radio station is a most important transmission equipment of our signal corps, but in the actual teaching process, which exist the phenomenon of fewer equipment and more students, making the students' short-wave radio operation and practice time is very limited. In order to solve the above problems, to carry out shortwave radio simple simulation training system development is very necessary. This project is developed by combining hardware and software to simulate the voice communication operation and signal principle of shortwave radio station, and can test the signal flow of shortwave radio station. The test results indicate that this system is simple operation, human-machine interface friendly and can improve teaching more efficiency.

Bandwidth Efficient Modulation and Coding Techniques for NASA's Existing Ku/Ka-Band 225 MHz Wide Service

NASA Technical Reports Server (NTRS)

Gioannini, Bryan; Wong, Yen; Wesdock, John

2005-01-01

The National Aeronautics and Space Administration (NASA) has recently established the Tracking and Data Relay Satellite System (TDRSS) K-band Upgrade Project (TKUP), a project intended to enhance the TDRSS Ku-band and Ka-band Single Access Return 225 MHz (Ku/KaSAR-225) data service by adding the capability to process bandwidth efficient signal design and to replace the White Sand Complex (WSC) KSAR high data rate ground equipment and high rate switches which are nearing obsolescence. As a precursor to this project, a modulation and coding study was performed to identify signal structures which maximized the data rate through the Ku/KaSAR-225 channel, minimized the required customer EIRP and ensured acceptable hardware complexity on the customer platform. This paper presents the results and conclusions of the TKUP modulation and coding study.

A new class of weight and WA systems of the Kravchenko-Kaiser functions

NASA Astrophysics Data System (ADS)

Kravchenko, V. F.; Pustovoit, V. I.; Churikov, D. V.

2014-05-01

A new class of weight and WA-systems of the Kravchenko-Kaiser functions which showed its efficiency in various physical applications is proposed and substantiated. This publication consists of three parts. In the first the Kravchenko-Kaiser weight functions are constructed on basis of the theory of atomic functions (AFs) and the Kaiser windows for the first time. In the second part new constructions of analytic WA-systems of the Kravchenko-Kaiser functions are costructed. In the third part their applications to problems of weight averaging of the difference frequency signals are considered. The numerical experiment and the physical analysis of the results for concrete physical models confirmed their efficiency. This class of functions can find wide physical applications in problems of digital signal processing, restoration of images, radar, radiometry, radio astronomy, remote sensing, etc.

Hybrid method based on singular value decomposition and embedded zero tree wavelet technique for ECG signal compression.

PubMed

Kumar, Ranjeet; Kumar, A; Singh, G K

2016-06-01

In the field of biomedical, it becomes necessary to reduce data quantity due to the limitation of storage in real-time ambulatory system and telemedicine system. Research has been underway since very beginning for the development of an efficient and simple technique for longer term benefits. This paper, presents an algorithm based on singular value decomposition (SVD), and embedded zero tree wavelet (EZW) techniques for ECG signal compression which deals with the huge data of ambulatory system. The proposed method utilizes the low rank matrix for initial compression on two dimensional (2-D) ECG data array using SVD, and then EZW is initiated for final compression. Initially, 2-D array construction has key issue for the proposed technique in pre-processing. Here, three different beat segmentation approaches have been exploited for 2-D array construction using segmented beat alignment with exploitation of beat correlation. The proposed algorithm has been tested on MIT-BIH arrhythmia record, and it was found that it is very efficient in compression of different types of ECG signal with lower signal distortion based on different fidelity assessments. The evaluation results illustrate that the proposed algorithm has achieved the compression ratio of 24.25:1 with excellent quality of signal reconstruction in terms of percentage-root-mean square difference (PRD) as 1.89% for ECG signal Rec. 100 and consumes only 162bps data instead of 3960bps uncompressed data. The proposed method is efficient and flexible with different types of ECG signal for compression, and controls quality of reconstruction. Simulated results are clearly illustrate the proposed method can play a big role to save the memory space of health data centres as well as save the bandwidth in telemedicine based healthcare systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Optical feedback-induced light modulation for fiber-based laser ablation.

PubMed

Kang, Hyun Wook

2014-11-01

Optical fibers have been used as a minimally invasive tool in various medical fields. However, due to excessive heat accumulation, the distal end of a fiber often suffers from severe melting or devitrification, leading to the eventual fiber failure during laser treatment. In order to minimize thermal damage at the fiber tip, an optical feedback sensor was developed and tested ex vivo. Porcine kidney tissue was used to evaluate the feasibility of optical feedback in terms of signal activation, ablation performance, and light transmission. Testing various signal thresholds demonstrated that 3 V was relatively appropriate to trigger the feedback sensor and to prevent the fiber deterioration during kidney tissue ablation. Based upon the development of temporal signal signatures, full contact mode rapidly activated the optical feedback sensor possibly due to heat accumulation. Modulated light delivery induced by optical feedback diminished ablation efficiency by 30% in comparison with no feedback case. However, long-term transmission results validated that laser ablation assisted with optical feedback was able to almost consistently sustain light delivery to the tissue as well as ablation efficiency. Therefore, an optical feedback sensor can be a feasible tool to protect optical fiber tips by minimizing debris contamination and delaying thermal damage process and to ensure more efficient and safer laser-induced tissue ablation.

Energy Efficient Communication Using Relationships between Biological Signals for Ubiquitous Health Monitoring

NASA Astrophysics Data System (ADS)

Lee, Songjun; Na, Doosu; Koo, Bonmin

Wireless sensor networks with a star network topology are commonly applied for health monitoring systems. To determine the condition of a patient, sensor nodes are attached to the body to transmit the data to a coordinator. However, this process is inefficient because the coordinator is always communicating with each sensor node resulting in a data processing workload for the coordinator that becomes much greater than that of the sensor nodes. In this paper, a method is proposed to reduce the number of data transmissions from the sensor nodes to the coordinator by establishing a threshold for data from the biological signals to ensure that only relevant information is transmitted. This results in a dramatic reduction in power consumption throughout the entire network.

A new stationary gridline artifact suppression method based on the 2D discrete wavelet transform

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

Tang, Hui, E-mail: corinna@seu.edu.cn; Key Laboratory of Computer Network and Information Integration; Centre de Recherche en Information Biomédicale sino-français, Laboratoire International Associé, Inserm, Université de Rennes 1, Rennes 35000

2015-04-15

Purpose: In digital x-ray radiography, an antiscatter grid is inserted between the patient and the image receptor to reduce scattered radiation. If the antiscatter grid is used in a stationary way, gridline artifacts will appear in the final image. In most of the gridline removal image processing methods, the useful information with spatial frequencies close to that of the gridline is usually lost or degraded. In this study, a new stationary gridline suppression method is designed to preserve more of the useful information. Methods: The method is as follows. The input image is first recursively decomposed into several smaller subimagesmore » using a multiscale 2D discrete wavelet transform. The decomposition process stops when the gridline signal is found to be greater than a threshold in one or several of these subimages using a gridline detection module. An automatic Gaussian band-stop filter is then applied to the detected subimages to remove the gridline signal. Finally, the restored image is achieved using the corresponding 2D inverse discrete wavelet transform. Results: The processed images show that the proposed method can remove the gridline signal efficiently while maintaining the image details. The spectra of a 1D Fourier transform of the processed images demonstrate that, compared with some existing gridline removal methods, the proposed method has better information preservation after the removal of the gridline artifacts. Additionally, the performance speed is relatively high. Conclusions: The experimental results demonstrate the efficiency of the proposed method. Compared with some existing gridline removal methods, the proposed method can preserve more information within an acceptable execution time.« less

Analytical Cost Metrics : Days of Future Past

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

Prajapati, Nirmal; Rajopadhye, Sanjay; Djidjev, Hristo Nikolov

As we move towards the exascale era, the new architectures must be capable of running the massive computational problems efficiently. Scientists and researchers are continuously investing in tuning the performance of extreme-scale computational problems. These problems arise in almost all areas of computing, ranging from big data analytics, artificial intelligence, search, machine learning, virtual/augmented reality, computer vision, image/signal processing to computational science and bioinformatics. With Moore’s law driving the evolution of hardware platforms towards exascale, the dominant performance metric (time efficiency) has now expanded to also incorporate power/energy efficiency. Therefore the major challenge that we face in computing systems researchmore » is: “how to solve massive-scale computational problems in the most time/power/energy efficient manner?”« less

A hybrid MAC protocol design for energy-efficient very-high-throughput millimeter wave, wireless sensor communication networks

NASA Astrophysics Data System (ADS)

Jian, Wei; Estevez, Claudio; Chowdhury, Arshad; Jia, Zhensheng; Wang, Jianxin; Yu, Jianguo; Chang, Gee-Kung

2010-12-01

This paper presents an energy-efficient Medium Access Control (MAC) protocol for very-high-throughput millimeter-wave (mm-wave) wireless sensor communication networks (VHT-MSCNs) based on hybrid multiple access techniques of frequency division multiplexing access (FDMA) and time division multiplexing access (TDMA). An energy-efficient Superframe for wireless sensor communication network employing directional mm-wave wireless access technologies is proposed for systems that require very high throughput, such as high definition video signals, for sensing, processing, transmitting, and actuating functions. Energy consumption modeling for each network element and comparisons among various multi-access technologies in term of power and MAC layer operations are investigated for evaluating the energy-efficient improvement of proposed MAC protocol.

Cellular and molecular mechanisms for the bone response to mechanical loading

NASA Technical Reports Server (NTRS)

Bloomfield, S. A.

2001-01-01

To define the cellular and molecular mechanisms for the osteogenic response of bone to increased loading, several key steps must be defined: sensing of the mechanical signal by cells in bone, transduction of the mechanical signal to a biochemical one, and transmission of that biochemical signal to effector cells. Osteocytes are likely to serve as sensors of loading, probably via interstitial fluid flow produced during loading. Evidence is presented for the role of integrins, the cell's actin cytoskeleton, G proteins, and various intracellular signaling pathways in transducing that mechanical signal to a biochemical one. Nitric oxide, prostaglandins, and insulin-like growth factors all play important roles in these pathways. There is growing evidence for modulation of these mechanotransduction steps by endocrine factors, particularly parathyroid hormone and estrogen. The efficiency of this process is also impaired in the aged animal, yet what remains undefined is at what step mechanotransduction is affected.

Machine learning based Intelligent cognitive network using fog computing

NASA Astrophysics Data System (ADS)

Lu, Jingyang; Li, Lun; Chen, Genshe; Shen, Dan; Pham, Khanh; Blasch, Erik

2017-05-01

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.

Setting the standards for signal transduction research.

PubMed

Saez-Rodriguez, Julio; Alexopoulos, Leonidas G; Stolovitzky, Gustavo

2011-02-15

Major advances in high-throughput technology platforms, coupled with increasingly sophisticated computational methods for systematic data analysis, have provided scientists with tools to better understand the complexity of signaling networks. In this era of massive and diverse data collection, standardization efforts that streamline data gathering, analysis, storage, and sharing are becoming a necessity. Here, we give an overview of current technologies to study signal transduction. We argue that along with the opportunities the new technologies open, their heterogeneous nature poses critical challenges for data handling that are further increased when data are to be integrated in mathematical models. Efficient standardization through markup languages and data annotation is a sine qua non condition for a systems-level analysis of signaling processes. It remains to be seen the extent to which and the speed at which the emerging standardization efforts will be embraced by the signaling community.

Dual-drive Mach-Zehnder modulator-based reconfigurable and transparent spectral conversion for dense wavelength division multiplexing transmissions

NASA Astrophysics Data System (ADS)

Mao, Mingzhi; Qian, Chen; Cao, Bingyao; Zhang, Qianwu; Song, Yingxiong; Wang, Min

2017-09-01

A digital signal process enabled dual-drive Mach-Zehnder modulator (DD-MZM)-based spectral converter is proposed and extensively investigated to realize dynamically reconfigurable and high transparent spectral conversion. As another important innovation point of the paper, to optimize the converter performance, the optimum operation conditions of the proposed converter are deduced, statistically simulated, and experimentally verified. The optimum conditions supported-converter performances are verified by detail numerical simulations and experiments in intensity-modulation and direct-detection-based network in terms of frequency detuning range-dependent conversion efficiency, strict operation transparency for user signal characteristics, impact of parasitic components on the conversion performance, as well as the converted component waveform are almost nondistortion. It is also found that the converter has the high robustness to the input signal power, optical signal-to-noise ratio variations, extinction ratio, and driving signal frequency.

Electrospun amplified fiber optics.

PubMed

Morello, Giovanni; Camposeo, Andrea; Moffa, Maria; Pisignano, Dario

2015-03-11

All-optical signal processing is the focus of much research aiming to obtain effective alternatives to existing data transmission platforms. Amplification of light in fiber optics, such as in Erbium-doped fiber amplifiers, is especially important for efficient signal transmission. However, the complex fabrication methods involving high-temperature processes performed in a highly pure environment slow the fabrication process and make amplified components expensive with respect to an ideal, high-throughput, room temperature production. Here, we report on near-infrared polymer fiber amplifiers working over a band of ∼20 nm. The fibers are cheap, spun with a process entirely carried out at room temperature, and shown to have amplified spontaneous emission with good gain coefficients and low levels of optical losses (a few cm(-1)). The amplification process is favored by high fiber quality and low self-absorption. The found performance metrics appear to be suitable for short-distance operations, and the large variety of commercially available doping dyes might allow for effective multiwavelength operations by electrospun amplified fiber optics.

Software for Acoustic Rendering

NASA Technical Reports Server (NTRS)

Miller, Joel D.

2003-01-01

SLAB is a software system that can be run on a personal computer to simulate an acoustic environment in real time. SLAB was developed to enable computational experimentation in which one can exert low-level control over a variety of signal-processing parameters, related to spatialization, for conducting psychoacoustic studies. Among the parameters that can be manipulated are the number and position of reflections, the fidelity (that is, the number of taps in finite-impulse-response filters), the system latency, and the update rate of the filters. Another goal in the development of SLAB was to provide an inexpensive means of dynamic synthesis of virtual audio over headphones, without need for special-purpose signal-processing hardware. SLAB has a modular, object-oriented design that affords the flexibility and extensibility needed to accommodate a variety of computational experiments and signal-flow structures. SLAB s spatial renderer has a fixed signal-flow architecture corresponding to a set of parallel signal paths from each source to a listener. This fixed architecture can be regarded as a compromise that optimizes efficiency at the expense of complete flexibility. Such a compromise is necessary, given the design goal of enabling computational psychoacoustic experimentation on inexpensive personal computers.

Development of Quenching-qPCR (Q-Q) assay for measuring absolute intracellular cleavage efficiency of ribozyme.

PubMed

Kim, Min Woo; Sun, Gwanggyu; Lee, Jung Hyuk; Kim, Byung-Gee

2018-06-01

Ribozyme (Rz) is a very attractive RNA molecule in metabolic engineering and synthetic biology fields where RNA processing is required as a control unit or ON/OFF signal for its cleavage reaction. In order to use Rz for such RNA processing, Rz must have highly active and specific catalytic activity. However, current methods for assessing the intracellular activity of Rz have limitations such as difficulty in handling and inaccuracies in the evaluation of correct cleavage activity. In this paper, we proposed a simple method to accurately measure the "intracellular cleavage efficiency" of Rz. This method deactivates unwanted activity of Rz which may consistently occur after cell lysis using DNA quenching method, and calculates the cleavage efficiency by analyzing the cleaved fraction of mRNA by Rz from the total amount of mRNA containing Rz via quantitative real-time PCR (qPCR). The proposed method was applied to measure "intracellular cleavage efficiency" of sTRSV, a representative Rz, and its mutant, and their intracellular cleavage efficiencies were calculated as 89% and 93%, respectively. Copyright © 2018 Elsevier Inc. All rights reserved.

A method for discrimination of noise and EMG signal regions recorded during rhythmic behaviors.

PubMed

Ying, Rex; Wall, Christine E

2016-12-08

Analyses of muscular activity during rhythmic behaviors provide critical data for biomechanical studies. Electrical potentials measured from muscles using electromyography (EMG) require discrimination of noise regions as the first step in analysis. An experienced analyst can accurately identify the onset and offset of EMG but this process takes hours to analyze a short (10-15s) record of rhythmic EMG bursts. Existing computational techniques reduce this time but have limitations. These include a universal threshold for delimiting noise regions (i.e., a single signal value for identifying the EMG signal onset and offset), pre-processing using wide time intervals that dampen sensitivity for EMG signal characteristics, poor performance when a low frequency component (e.g., DC offset) is present, and high computational complexity leading to lack of time efficiency. We present a new statistical method and MATLAB script (EMG-Extractor) that includes an adaptive algorithm to discriminate noise regions from EMG that avoids these limitations and allows for multi-channel datasets to be processed. We evaluate the EMG-Extractor with EMG data on mammalian jaw-adductor muscles during mastication, a rhythmic behavior typified by low amplitude onsets/offsets and complex signal pattern. The EMG-Extractor consistently and accurately distinguishes noise from EMG in a manner similar to that of an experienced analyst. It outputs the raw EMG signal region in a form ready for further analysis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Heartbeat detection in multimodal physiological signals using signal quality assessment based on sample entropy.

PubMed

Singh, Omkar; Sunkaria, Ramesh Kumar

2017-12-01

This paper presents a novel technique to identify heartbeats in multimodal data using electrocardiogram (ECG) and arterial blood pressure (ABP) signals. Multiple physiological signals such as ECG, ABP, and Respiration are often recorded in parallel from the activity of heart. These signals generally possess related information as they are generated by the same physical system. The ECG and ABP correspond to the same phenomenon of contraction and relaxation activity of heart. Multiple signals acquired from various sensors are generally processed independently, thus discarding the information from other measurements. In the estimation of heart rate and heart rate variability, the R peaks are generally identified from ECG signal. Efficient detection of R-peaks in electrocardiogram (ECG) is a key component in the estimation of clinically relevant parameters from ECG. However, when the signal is severely affected by undesired artifacts, this becomes a challenging task. Sometimes in clinical environment, other physiological signals reflecting the cardiac activity such as ABP signal are also acquired simultaneously. Under the availability of such multimodal signals, the accuracy of R peak detection methods can be improved using sensor-fusion techniques. In the proposed method, the sample entropy (SampEn) is used as a metric for assessing the noise content in the physiological signal and the R peaks in ECG and the systolic peaks in ABP signals are fused together to enhance the efficiency of heartbeat detection. The proposed method was evaluated on the 100 records from the computing in cardiology challenge 2014 training data set. The performance parameters are: sensitivity (Se) and positive predictivity (PPV). The unimodal R peaks detector achieved: Se gross = 99.40%, PPV gross = 99.29%, Se average = 99.37%, PPV average = 99.29%. Similarly unimodal BP delineator achieved Se gross = 99.93%, PPV gross = 99.99%, Se average = 99.93%, PPV average = 99.99% whereas, the proposed multimodal beat detector achieved: Se gross = 99.65%, PPV gross = 99.91%, Se average = 99.68%, PPV average = 99.91%.

Far-field DOA estimation and source localization for different scenarios in a distributed sensor network

NASA Astrophysics Data System (ADS)

Asgari, Shadnaz

Recent developments in the integrated circuits and wireless communications not only open up many possibilities but also introduce challenging issues for the collaborative processing of signals for source localization and beamforming in an energy-constrained distributed sensor network. In signal processing, various sensor array processing algorithms and concepts have been adopted, but must be further tailored to match the communication and computational constraints. Sometimes the constraints are such that none of the existing algorithms would be an efficient option for the defined problem and as the result; the necessity of developing a new algorithm becomes undeniable. In this dissertation, we present the theoretical and the practical issues of Direction-Of-Arrival (DOA) estimation and source localization using the Approximate-Maximum-Likelihood (AML) algorithm for different scenarios. We first investigate a robust algorithm design for coherent source DOA estimation in a limited reverberant environment. Then, we provide a least-square (LS) solution for source localization based on our newly proposed virtual array model. In another scenario, we consider the determination of the location of a disturbance source which emits both wideband acoustic and seismic signals. We devise an enhanced AML algorithm to process the data collected at the acoustic sensors. For processing the seismic signals, two distinct algorithms are investigated to determine the DOAs. Then, we consider a basic algorithm for fusion of the results yielded by the acoustic and seismic arrays. We also investigate the theoretical and practical issues of DOA estimation in a three-dimensional (3D) scenario. We show that the performance of the proposed 3D AML algorithm converges to the Cramer-Rao Bound. We use the concept of an isotropic array to reduce the complexity of the proposed algorithm by advocating a decoupled 3D version. We also explore a modified version of the decoupled 3D AML algorithm which can be used for DOA estimation with non-isotropic arrays. In this dissertation, for each scenario, efficient numerical implementations of the corresponding AML algorithm are derived and applied into a real-time sensor network testbed. Extensive simulations as well as experimental results are presented to verify the effectiveness of the proposed algorithms.

Advanced Signal Conditioners for Data-Acquisition Systems

NASA Technical Reports Server (NTRS)

Lucena, Angel; Perotti, Jose; Eckhoff, Anthony; Medelius, Pedro

2004-01-01

Signal conditioners embodying advanced concepts in analog and digital electronic circuitry and software have been developed for use in data-acquisition systems that are required to be compact and lightweight, to utilize electric energy efficiently, and to operate with high reliability, high accuracy, and high power efficiency, without intervention by human technicians. These signal conditioners were originally intended for use aboard spacecraft. There are also numerous potential terrestrial uses - especially in the fields of aeronautics and medicine, wherein it is necessary to monitor critical functions. Going beyond the usual analog and digital signal-processing functions of prior signal conditioners, the new signal conditioner performs the following additional functions: It continuously diagnoses its own electronic circuitry, so that it can detect failures and repair itself (as described below) within seconds. It continuously calibrates itself on the basis of a highly accurate and stable voltage reference, so that it can continue to generate accurate measurement data, even under extreme environmental conditions. It repairs itself in the sense that it contains a micro-controller that reroutes signals among redundant components as needed to maintain the ability to perform accurate and stable measurements. It detects deterioration of components, predicts future failures, and/or detects imminent failures by means of a real-time analysis in which, among other things, data on its present state are continuously compared with locally stored historical data. It minimizes unnecessary consumption of electric energy. The design architecture divides the signal conditioner into three main sections: an analog signal section, a digital module, and a power-management section. The design of the analog signal section does not follow the traditional approach of ensuring reliability through total redundancy of hardware: Instead, following an approach called spare parts tool box, the reliability of each component is assessed in terms of such considerations as risks of damage, mean times between failures, and the effects of certain failures on the performance of the signal conditioner as a whole system. Then, fewer or more spares are assigned for each affected component, pursuant to the results of this analysis, in order to obtain the required degree of reliability of the signal conditioner as a whole system. The digital module comprises one or more processors and field-programmable gate arrays, the number of each depending on the results of the aforementioned analysis. The digital module provides redundant control, monitoring, and processing of several analog signals. It is designed to minimize unnecessary consumption of electric energy, including, when possible, going into a low-power "sleep" mode that is implemented in firmware. The digital module communicates with external equipment via a personal-computer serial port. The digital module monitors the "health" of the rest of the signal conditioner by processing defined measurements and/or trends. It automatically makes adjustments to respond to channel failures, compensate for effects of temperature, and maintain calibration.

Simultaneously estimating evolutionary history and repeated traits phylogenetic signal: applications to viral and host phenotypic evolution

PubMed Central

Vrancken, Bram; Lemey, Philippe; Rambaut, Andrew; Bedford, Trevor; Longdon, Ben; Günthard, Huldrych F.; Suchard, Marc A.

2014-01-01

Phylogenetic signal quantifies the degree to which resemblance in continuously-valued traits reflects phylogenetic relatedness. Measures of phylogenetic signal are widely used in ecological and evolutionary research, and are recently gaining traction in viral evolutionary studies. Standard estimators of phylogenetic signal frequently condition on data summary statistics of the repeated trait observations and fixed phylogenetics trees, resulting in information loss and potential bias. To incorporate the observation process and phylogenetic uncertainty in a model-based approach, we develop a novel Bayesian inference method to simultaneously estimate the evolutionary history and phylogenetic signal from molecular sequence data and repeated multivariate traits. Our approach builds upon a phylogenetic diffusion framework that model continuous trait evolution as a Brownian motion process and incorporates Pagel’s λ transformation parameter to estimate dependence among traits. We provide a computationally efficient inference implementation in the BEAST software package. We evaluate the synthetic performance of the Bayesian estimator of phylogenetic signal against standard estimators, and demonstrate the use of our coherent framework to address several virus-host evolutionary questions, including virulence heritability for HIV, antigenic evolution in influenza and HIV, and Drosophila sensitivity to sigma virus infection. Finally, we discuss model extensions that will make useful contributions to our flexible framework for simultaneously studying sequence and trait evolution. PMID:25780554

Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method.

PubMed

Batres-Mendoza, Patricia; Ibarra-Manzano, Mario A; Guerra-Hernandez, Erick I; Almanza-Ojeda, Dora L; Montoro-Sanjose, Carlos R; Romero-Troncoso, Rene J; Rostro-Gonzalez, Horacio

2017-01-01

We present an improvement to the quaternion-based signal analysis (QSA) technique to extract electroencephalography (EEG) signal features with a view to developing real-time applications, particularly in motor imagery (IM) cognitive processes. The proposed methodology (iQSA, improved QSA) extracts features such as the average, variance, homogeneity, and contrast of EEG signals related to motor imagery in a more efficient manner (i.e., by reducing the number of samples needed to classify the signal and improving the classification percentage) compared to the original QSA technique. Specifically, we can sample the signal in variable time periods (from 0.5 s to 3 s, in half-a-second intervals) to determine the relationship between the number of samples and their effectiveness in classifying signals. In addition, to strengthen the classification process a number of boosting-technique-based decision trees were implemented. The results show an 82.30% accuracy rate for 0.5 s samples and 73.16% for 3 s samples. This is a significant improvement compared to the original QSA technique that offered results from 33.31% to 40.82% without sampling window and from 33.44% to 41.07% with sampling window, respectively. We can thus conclude that iQSA is better suited to develop real-time applications.

Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method

PubMed Central

Batres-Mendoza, Patricia; Guerra-Hernandez, Erick I.; Almanza-Ojeda, Dora L.; Montoro-Sanjose, Carlos R.

2017-01-01

We present an improvement to the quaternion-based signal analysis (QSA) technique to extract electroencephalography (EEG) signal features with a view to developing real-time applications, particularly in motor imagery (IM) cognitive processes. The proposed methodology (iQSA, improved QSA) extracts features such as the average, variance, homogeneity, and contrast of EEG signals related to motor imagery in a more efficient manner (i.e., by reducing the number of samples needed to classify the signal and improving the classification percentage) compared to the original QSA technique. Specifically, we can sample the signal in variable time periods (from 0.5 s to 3 s, in half-a-second intervals) to determine the relationship between the number of samples and their effectiveness in classifying signals. In addition, to strengthen the classification process a number of boosting-technique-based decision trees were implemented. The results show an 82.30% accuracy rate for 0.5 s samples and 73.16% for 3 s samples. This is a significant improvement compared to the original QSA technique that offered results from 33.31% to 40.82% without sampling window and from 33.44% to 41.07% with sampling window, respectively. We can thus conclude that iQSA is better suited to develop real-time applications. PMID:29348744

Phase editing as a signal pre-processing step for automated bearing fault detection

NASA Astrophysics Data System (ADS)

Barbini, L.; Ompusunggu, A. P.; Hillis, A. J.; du Bois, J. L.; Bartic, A.

2017-07-01

Scheduled maintenance and inspection of bearing elements in industrial machinery contributes significantly to the operating costs. Savings can be made through automatic vibration-based damage detection and prognostics, to permit condition-based maintenance. However automation of the detection process is difficult due to the complexity of vibration signals in realistic operating environments. The sensitivity of existing methods to the choice of parameters imposes a requirement for oversight from a skilled operator. This paper presents a novel approach to the removal of unwanted vibrational components from the signal: phase editing. The approach uses a computationally-efficient full-band demodulation and requires very little oversight. Its effectiveness is tested on experimental data sets from three different test-rigs, and comparisons are made with two state-of-the-art processing techniques: spectral kurtosis and cepstral pre- whitening. The results from the phase editing technique show a 10% improvement in damage detection rates compared to the state-of-the-art while simultaneously improving on the degree of automation. This outcome represents a significant contribution in the pursuit of fully automatic fault detection.

Threat as a feature in visual semantic object memory.

PubMed

Calley, Clifford S; Motes, Michael A; Chiang, H-Sheng; Buhl, Virginia; Spence, Jeffrey S; Abdi, Hervé; Anand, Raksha; Maguire, Mandy; Estevez, Leonardo; Briggs, Richard; Freeman, Thomas; Kraut, Michael A; Hart, John

2013-08-01

Threatening stimuli have been found to modulate visual processes related to perception and attention. The present functional magnetic resonance imaging (fMRI) study investigated whether threat modulates visual object recognition of man-made and naturally occurring categories of stimuli. Compared with nonthreatening pictures, threatening pictures of real items elicited larger fMRI BOLD signal changes in medial visual cortices extending inferiorly into the temporo-occipital (TO) "what" pathways. This region elicited greater signal changes for threatening items compared to nonthreatening from both the natural-occurring and man-made stimulus supraordinate categories, demonstrating a featural component to these visual processing areas. Two additional loci of signal changes within more lateral inferior TO areas (bilateral BA18 and 19 as well as the right ventral temporal lobe) were detected for a category-feature interaction, with stronger responses to man-made (category) threatening (feature) stimuli than to natural threats. The findings are discussed in terms of visual recognition of processing efficiently or rapidly groups of items that confer an advantage for survival. Copyright © 2012 Wiley Periodicals, Inc.

A Signal Processing Module for the Analysis of Heart Sounds and Heart Murmurs

NASA Astrophysics Data System (ADS)

Javed, Faizan; Venkatachalam, P. A.; H, Ahmad Fadzil M.

2006-04-01

In this paper a Signal Processing Module (SPM) for the computer-aided analysis of heart sounds has been developed. The module reveals important information of cardiovascular disorders and can assist general physician to come up with more accurate and reliable diagnosis at early stages. It can overcome the deficiency of expert doctors in rural as well as urban clinics and hospitals. The module has five main blocks: Data Acquisition & Pre-processing, Segmentation, Feature Extraction, Murmur Detection and Murmur Classification. The heart sounds are first acquired using an electronic stethoscope which has the capability of transferring these signals to the near by workstation using wireless media. Then the signals are segmented into individual cycles as well as individual components using the spectral analysis of heart without using any reference signal like ECG. Then the features are extracted from the individual components using Spectrogram and are used as an input to a MLP (Multiple Layer Perceptron) Neural Network that is trained to detect the presence of heart murmurs. Once the murmur is detected they are classified into seven classes depending on their timing within the cardiac cycle using Smoothed Pseudo Wigner-Ville distribution. The module has been tested with real heart sounds from 40 patients and has proved to be quite efficient and robust while dealing with a large variety of pathological conditions.

Dangerous mating systems: signal complexity, signal content and neural capacity in spiders.

PubMed

Herberstein, M E; Wignall, A E; Hebets, E A; Schneider, J M

2014-10-01

Spiders are highly efficient predators in possession of exquisite sensory capacities for ambushing prey, combined with machinery for launching rapid and determined attacks. As a consequence, any sexually motivated approach carries a risk of ending up as prey rather than as a mate. Sexual selection has shaped courtship to effectively communicate the presence, identity, motivation and/or quality of potential mates, which help ameliorate these risks. Spiders communicate this information via several sensory channels, including mechanical (e.g. vibrational), visual and/or chemical, with examples of multimodal signalling beginning to emerge in the literature. The diverse environments that spiders inhabit have further shaped courtship content and form. While our understanding of spider neurobiology remains in its infancy, recent studies are highlighting the unique and considerable capacities of spiders to process and respond to complex sexual signals. As a result, the dangerous mating systems of spiders are providing important insights into how ecology shapes the evolution of communication systems, with future work offering the potential to link this complex communication with its neural processes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Software system for data management and distributed processing of multichannel biomedical signals.

PubMed

Franaszczuk, P J; Jouny, C C

2004-01-01

The presented software is designed for efficient utilization of cluster of PC computers for signal analysis of multichannel physiological data. The system consists of three main components: 1) a library of input and output procedures, 2) a database storing additional information about location in a storage system, 3) a user interface for selecting data for analysis, choosing programs for analysis, and distributing computing and output data on cluster nodes. The system allows for processing multichannel time series data in multiple binary formats. The description of data format, channels and time of recording are included in separate text files. Definition and selection of multiple channel montages is possible. Epochs for analysis can be selected both manually and automatically. Implementation of a new signal processing procedures is possible with a minimal programming overhead for the input/output processing and user interface. The number of nodes in cluster used for computations and amount of storage can be changed with no major modification to software. Current implementations include the time-frequency analysis of multiday, multichannel recordings of intracranial EEG of epileptic patients as well as evoked response analyses of repeated cognitive tasks.

Playback system designed for X-Band SAR

NASA Astrophysics Data System (ADS)

Yuquan, Liu; Changyong, Dou

2014-03-01

SAR(Synthetic Aperture Radar) has extensive application because it is daylight and weather independent. In particular, X-Band SAR strip map, designed by Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, provides high ground resolution images, at the same time it has a large spatial coverage and a short acquisition time, so it is promising in multi-applications. When sudden disaster comes, the emergency situation acquires radar signal data and image as soon as possible, in order to take action to reduce loss and save lives in the first time. This paper summarizes a type of X-Band SAR playback processing system designed for disaster response and scientific needs. It describes SAR data workflow includes the payload data transmission and reception process. Playback processing system completes signal analysis on the original data, providing SAR level 0 products and quick image. Gigabit network promises radar signal transmission efficiency from recorder to calculation unit. Multi-thread parallel computing and ping pong operation can ensure computation speed. Through gigabit network, multi-thread parallel computing and ping pong operation, high speed data transmission and processing meet the SAR radar data playback real time requirement.

A Subsystem Test Bed for Chinese Spectral Radioheliograph

NASA Astrophysics Data System (ADS)

Zhao, An; Yan, Yihua; Wang, Wei

2014-11-01

The Chinese Spectral Radioheliograph is a solar dedicated radio interferometric array that will produce high spatial resolution, high temporal resolution, and high spectral resolution images of the Sun simultaneously in decimetre and centimetre wave range. Digital processing of intermediate frequency signal is an important part in a radio telescope. This paper describes a flexible and high-speed digital down conversion system for the CSRH by applying complex mixing, parallel filtering, and extracting algorithms to process IF signal at the time of being designed and incorporates canonic-signed digit coding and bit-plane method to improve program efficiency. The DDC system is intended to be a subsystem test bed for simulation and testing for CSRH. Software algorithms for simulation and hardware language algorithms based on FPGA are written which use less hardware resources and at the same time achieve high performances such as processing high-speed data flow (1 GHz) with 10 MHz spectral resolution. An experiment with the test bed is illustrated by using geostationary satellite data observed on March 20, 2014. Due to the easy alterability of the algorithms on FPGA, the data can be recomputed with different digital signal processing algorithms for selecting optimum algorithm.

Clearance of Dying Cells by Phagocytes: Mechanisms and Implications for Disease Pathogenesis.

PubMed

Fond, Aaron M; Ravichandran, Kodi S

The efficient clearance of apoptotic cells is an evolutionarily conserved process crucial for homeostasis in multicellular organisms. The clearance involves a series of steps that ultimately facilitates the recognition of the apoptotic cell by the phagocytes and the subsequent uptake and processing of the corpse. These steps include the phagocyte sensing of "find-me" signals released by the apoptotic cell, recognizing "eat-me" signals displayed on the apoptotic cell surface, and then intracellular signaling within the phagocyte to mediate phagocytic cup formation around the corpse and corpse internalization, and the processing of the ingested contents. The engulfment of apoptotic cells by phagocytes not only eliminates debris from tissues but also produces an anti-inflammatory response that suppresses local tissue inflammation. Conversely, impaired corpse clearance can result in loss of immune tolerance and the development of various inflammation-associated disorders such as autoimmunity, atherosclerosis, and airway inflammation but can also affect cancer progression. Recent studies suggest that the clearance process can also influence antitumor immune responses. In this review, we will discuss how apoptotic cells interact with their engulfing phagocytes to generate important immune responses, and how modulation of such responses can influence pathology.

Combining Image Processing with Signal Processing to Improve Transmitter Geolocation Estimation

DTIC Science & Technology

2014-03-27

transmitter by searching a grid of possible transmitter locations within the image region. At each evaluated grid point, theoretical TDOA values are computed...requires converting the image to a grayscale intensity image. This allows efficient manipulation of data and ease of comparison among pixel values . The...cluster of redundant y values along the top edge of an ideal rectangle. The same is true for the bottom edge, as well as for the x values along the

Reliable and Efficient Parallel Processing Algorithms and Architectures for Modern Signal Processing. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Liu, Kuojuey Ray

1990-01-01

Least-squares (LS) estimations and spectral decomposition algorithms constitute the heart of modern signal processing and communication problems. Implementations of recursive LS and spectral decomposition algorithms onto parallel processing architectures such as systolic arrays with efficient fault-tolerant schemes are the major concerns of this dissertation. There are four major results in this dissertation. First, we propose the systolic block Householder transformation with application to the recursive least-squares minimization. It is successfully implemented on a systolic array with a two-level pipelined implementation at the vector level as well as at the word level. Second, a real-time algorithm-based concurrent error detection scheme based on the residual method is proposed for the QRD RLS systolic array. The fault diagnosis, order degraded reconfiguration, and performance analysis are also considered. Third, the dynamic range, stability, error detection capability under finite-precision implementation, order degraded performance, and residual estimation under faulty situations for the QRD RLS systolic array are studied in details. Finally, we propose the use of multi-phase systolic algorithms for spectral decomposition based on the QR algorithm. Two systolic architectures, one based on triangular array and another based on rectangular array, are presented for the multiphase operations with fault-tolerant considerations. Eigenvectors and singular vectors can be easily obtained by using the multi-pase operations. Performance issues are also considered.

Carotenoid coloration is related to fat digestion efficiency in a wild bird

NASA Astrophysics Data System (ADS)

Madonia, Christina; Hutton, Pierce; Giraudeau, Mathieu; Sepp, Tuul

2017-12-01

Some of the most spectacular visual signals found in the animal kingdom are based on dietarily derived carotenoid pigments (which cannot be produced de novo), with a general assumption that carotenoids are limited resources for wild organisms, causing trade-offs in allocation of carotenoids to different physiological functions and ornamentation. This resource trade-off view has been recently questioned, since the efficiency of carotenoid processing may relax the trade-off between allocation toward condition or ornamentation. This hypothesis has so far received little exploratory support, since studies of digestive efficiency of wild animals are limited due to methodological difficulties. Recently, a method for quantifying the percentage of fat in fecal samples to measure digestive efficiency has been developed in birds. Here, we use this method to test if the intensity of the carotenoid-based coloration predicts digestive efficiency in a wild bird, the house finch ( Haemorhous mexicanus). The redness of carotenoid feather coloration (hue) positively predicted digestion efficiency, with redder birds being more efficient at absorbing fats from seeds. We show for the first time in a wild species that digestive efficiency predicts ornamental coloration. Though not conclusive due to the correlative nature of our study, these results strongly suggest that fat extraction might be a crucial but overlooked process behind many ornamental traits.

DSP code optimization based on cache

NASA Astrophysics Data System (ADS)

Xu, Chengfa; Li, Chengcheng; Tang, Bin

2013-03-01

DSP program's running efficiency on board is often lower than which via the software simulation during the program development, which is mainly resulted from the user's improper use and incomplete understanding of the cache-based memory. This paper took the TI TMS320C6455 DSP as an example, analyzed its two-level internal cache, and summarized the methods of code optimization. Processor can achieve its best performance when using these code optimization methods. At last, a specific algorithm application in radar signal processing is proposed. Experiment result shows that these optimization are efficient.

Methods and Apparatus for Autonomous Robotic Control

NASA Technical Reports Server (NTRS)

Gorshechnikov, Anatoly (Inventor); Livitz, Gennady (Inventor); Versace, Massimiliano (Inventor); Palma, Jesse (Inventor)

2017-01-01

Sensory processing of visual, auditory, and other sensor information (e.g., visual imagery, LIDAR, RADAR) is conventionally based on "stovepiped," or isolated processing, with little interactions between modules. Biological systems, on the other hand, fuse multi-sensory information to identify nearby objects of interest more quickly, more efficiently, and with higher signal-to-noise ratios. Similarly, examples of the OpenSense technology disclosed herein use neurally inspired processing to identify and locate objects in a robot's environment. This enables the robot to navigate its environment more quickly and with lower computational and power requirements.

Kepler Planet Detection Metrics: Per-Target Flux-Level Transit Injection Tests of TPS for Data Release 25

NASA Technical Reports Server (NTRS)

Burke, Christopher J.; Catanzarite, Joseph

2017-01-01

Quantifying the ability of a transiting planet survey to recover transit signals has commonly been accomplished through Monte-Carlo injection of transit signals into the observed data and subsequent running of the signal search algorithm (Gilliland et al., 2000; Weldrake et al., 2005; Burke et al., 2006). In order to characterize the performance of the Kepler pipeline (Twicken et al., 2016; Jenkins et al., 2017) on a sample of over 200,000 stars, two complementary injection and recovery tests are utilized:1. Injection of a single transit signal per target into the image or pixel-level data, hereafter referred to as pixel-level transit injection (PLTI), with subsequent processing through the Photometric Analysis (PA), Presearch Data Conditioning (PDC), Transiting Planet Search (TPS), and Data Validation (DV) modules of the Kepler pipeline. The PLTI quantification of the Kepler pipeline's completeness has been described previously by Christiansen et al. (2015, 2016); the completeness of the final SOC 9.3 Kepler pipeline acting on the Data Release 25 (DR25) light curves is described by Christiansen (2017).2. Injection of multiple transit signals per target into the normalized flux time series data with a subsequent transit search using a stream-lined version of the Transiting Planet Search (TPS) module. This test, hereafter referred to as flux-level transit injection (FLTI), is the subject of this document. By running a heavily modified version of TPS, FLTI is able to perform many injections on selected targets and determine in some detail which injected signals are recoverable. Significant numerical efficiency gains are enabled by precomputing the data conditioning steps at the onset of TPS and limiting the search parameter space (i.e., orbital period, transit duration, and ephemeris zero-point) to a small region around each injected transit signal.The PLTI test has the advantage that it follows transit signals through all processing steps of the Kepler pipeline, and the recovered signals can be further classified as planet candidates or false positives in the exact same manner as detections from the nominal (i.e., observed) pipeline run (Twicken et al., 2016, Thompson et al., in preparation). To date, the PLTI test has been the standard means of measuring pipeline completeness averaged over large samples of targets (Christiansen et al., 2015, 2016; Christiansen, 2017). However, since the PLTI test uses only one injection per target, it does not elucidate individual-target variations in pipeline completeness due to differences in stellar properties or astrophysical variability. Thus, we developed the FLTI test to provide a numerically efficient way to fully map individual targets and explore the performance of the pipeline in greater detail. The FLTI tests thereby allow a thorough validation of the pipeline completeness models (such as window function (Burke and Catanzarite, 2017a), detection efficiency (Burke Catanzarite, 2017b), etc.) across the spectrum of Kepler targets (i.e., various astrophysical phenomena and differences in instrumental noise). Tests during development of the FLTI capability revealed that there are significant target-to-target variations in the detection efficiency.

Spatial Mnemonic Encoding: Theta Power Decreases and Medial Temporal Lobe BOLD Increases Co-Occur during the Usage of the Method of Loci

PubMed Central

Volberg, Gregor; Goldhacker, Markus; Hanslmayr, Simon

2016-01-01

Abstract The method of loci is one, if not the most, efficient mnemonic encoding strategy. This spatial mnemonic combines the core cognitive processes commonly linked to medial temporal lobe (MTL) activity: spatial and associative memory processes. During such processes, fMRI studies consistently demonstrate MTL activity, while electrophysiological studies have emphasized the important role of theta oscillations (3–8 Hz) in the MTL. However, it is still unknown whether increases or decreases in theta power co-occur with increased BOLD signal in the MTL during memory encoding. To investigate this question, we recorded EEG and fMRI separately, while human participants used the spatial method of loci or the pegword method, a similarly associative but nonspatial mnemonic. The more effective spatial mnemonic induced a pronounced theta power decrease source localized to the left MTL compared with the nonspatial associative mnemonic strategy. This effect was mirrored by BOLD signal increases in the MTL. Successful encoding, irrespective of the strategy used, elicited decreases in left temporal theta power and increases in MTL BOLD activity. This pattern of results suggests a negative relationship between theta power and BOLD signal changes in the MTL during memory encoding and spatial processing. The findings extend the well known negative relation of alpha/beta oscillations and BOLD signals in the cortex to theta oscillations in the MTL. PMID:28101523

A robust approach to measuring the detective quantum efficiency of radiographic detectors in a clinical setting

NASA Astrophysics Data System (ADS)

McDonald, Michael C.; Kim, H. K.; Henry, J. R.; Cunningham, I. A.

2012-03-01

The detective quantum efficiency (DQE) is widely accepted as a primary measure of x-ray detector performance in the scientific community. A standard method for measuring the DQE, based on IEC 62220-1, requires the system to have a linear response meaning that the detector output signals are proportional to the incident x-ray exposure. However, many systems have a non-linear response due to characteristics of the detector, or post processing of the detector signals, that cannot be disabled and may involve unknown algorithms considered proprietary by the manufacturer. For these reasons, the DQE has not been considered as a practical candidate for routine quality assurance testing in a clinical setting. In this article we described a method that can be used to measure the DQE of both linear and non-linear systems that employ only linear image processing algorithms. The method was validated on a Cesium Iodide based flat panel system that simultaneously stores a raw (linear) and processed (non-linear) image for each exposure. It was found that the resulting DQE was equivalent to a conventional standards-compliant DQE with measurement precision, and the gray-scale inversion and linear edge enhancement did not affect the DQE result. While not IEC 62220-1 compliant, it may be adequate for QA programs.

Signal Propagation in Proteins and Relation to Equilibrium Fluctuations

PubMed Central

Chennubhotla, Chakra; Bahar, Ivet

2007-01-01

Elastic network (EN) models have been widely used in recent years for describing protein dynamics, based on the premise that the motions naturally accessible to native structures are relevant to biological function. We posit that equilibrium motions also determine communication mechanisms inherent to the network architecture. To this end, we explore the stochastics of a discrete-time, discrete-state Markov process of information transfer across the network of residues. We measure the communication abilities of residue pairs in terms of hit and commute times, i.e., the number of steps it takes on an average to send and receive signals. Functionally active residues are found to possess enhanced communication propensities, evidenced by their short hit times. Furthermore, secondary structural elements emerge as efficient mediators of communication. The present findings provide us with insights on the topological basis of communication in proteins and design principles for efficient signal transduction. While hit/commute times are information-theoretic concepts, a central contribution of this work is to rigorously show that they have physical origins directly relevant to the equilibrium fluctuations of residues predicted by EN models. PMID:17892319

Man-Machine Interface System for Neuromuscular Training and Evaluation Based on EMG and MMG Signals

PubMed Central

de la Rosa, Ramon; Alonso, Alonso; Carrera, Albano; Durán, Ramon; Fernández, Patricia

2010-01-01

This paper presents the UVa-NTS (University of Valladolid Neuromuscular Training System), a multifunction and portable Neuromuscular Training System. The UVa-NTS is designed to analyze the voluntary control of severe neuromotor handicapped patients, their interactive response, and their adaptation to neuromuscular interface systems, such as neural prostheses or domotic applications. Thus, it is an excellent tool to evaluate the residual muscle capabilities in the handicapped. The UVa-NTS is composed of a custom signal conditioning front-end and a computer. The front-end electronics is described thoroughly as well as the overall features of the custom software implementation. The software system is composed of a set of graphical training tools and a processing core. The UVa-NTS works with two classes of neuromuscular signals: the classic myoelectric signals (MES) and, as a novelty, the myomechanic signals (MMS). In order to evaluate the performance of the processing core, a complete analysis has been done to classify its efficiency and to check that it fulfils with the real-time constraints. Tests were performed both with healthy and selected impaired subjects. The adaptation was achieved rapidly, applying a predefined protocol for the UVa-NTS set of training tools. Fine voluntary control was demonstrated to be reached with the myoelectric signals. And the UVa-NTS demonstrated to provide a satisfactory voluntary control when applying the myomechanic signals. PMID:22163515

Man-machine interface system for neuromuscular training and evaluation based on EMG and MMG signals.

PubMed

de la Rosa, Ramon; Alonso, Alonso; Carrera, Albano; Durán, Ramon; Fernández, Patricia

2010-01-01

This paper presents the UVa-NTS (University of Valladolid Neuromuscular Training System), a multifunction and portable Neuromuscular Training System. The UVa-NTS is designed to analyze the voluntary control of severe neuromotor handicapped patients, their interactive response, and their adaptation to neuromuscular interface systems, such as neural prostheses or domotic applications. Thus, it is an excellent tool to evaluate the residual muscle capabilities in the handicapped. The UVa-NTS is composed of a custom signal conditioning front-end and a computer. The front-end electronics is described thoroughly as well as the overall features of the custom software implementation. The software system is composed of a set of graphical training tools and a processing core. The UVa-NTS works with two classes of neuromuscular signals: the classic myoelectric signals (MES) and, as a novelty, the myomechanic signals (MMS). In order to evaluate the performance of the processing core, a complete analysis has been done to classify its efficiency and to check that it fulfils with the real-time constraints. Tests were performed both with healthy and selected impaired subjects. The adaptation was achieved rapidly, applying a predefined protocol for the UVa-NTS set of training tools. Fine voluntary control was demonstrated to be reached with the myoelectric signals. And the UVa-NTS demonstrated to provide a satisfactory voluntary control when applying the myomechanic signals.

Dynamic multiprotein assemblies shape the spatial structure of cell signaling.

PubMed

Nussinov, Ruth; Jang, Hyunbum

2014-01-01

Cell signaling underlies critical cellular decisions. Coordination, efficiency as well as fail-safe mechanisms are key elements. How the cell ensures that these hallmarks are at play are important questions. Cell signaling is often viewed as taking place through discrete and cross-talking pathways; oftentimes these are modularized to emphasize distinct functions. While simple, convenient and clear, such models largely neglect the spatial structure of cell signaling; they also convey inter-modular (or inter-protein) spatial separation that may not exist. Here our thesis is that cell signaling is shaped by a network of multiprotein assemblies. While pre-organized, the assemblies and network are loose and dynamic. They contain transiently-associated multiprotein complexes which are often mediated by scaffolding proteins. They are also typically anchored in the membrane, and their continuum may span the cell. IQGAP1 scaffolding protein which binds proteins including Raf, calmodulin, Mek, Erk, actin, and tens more, with actin shaping B-cell (and likely other) membrane-anchored nanoclusters and allosterically polymerizing in dynamic cytoskeleton formation, and Raf anchoring in the membrane along with Ras, provides a striking example. The multivalent network of dynamic proteins and lipids, with specific interactions forming and breaking, can be viewed as endowing gel-like properties. Collectively, this reasons that efficient, productive and reliable cell signaling takes place primarily through transient, preorganized and cooperative protein-protein interactions spanning the cell rather than stochastic, diffusion-controlled processes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Sparse representation of electrodermal activity with knowledge-driven dictionaries.

PubMed

Chaspari, Theodora; Tsiartas, Andreas; Stein, Leah I; Cermak, Sharon A; Narayanan, Shrikanth S

2015-03-01

Biometric sensors and portable devices are being increasingly embedded into our everyday life, creating the need for robust physiological models that efficiently represent, analyze, and interpret the acquired signals. We propose a knowledge-driven method to represent electrodermal activity (EDA), a psychophysiological signal linked to stress, affect, and cognitive processing. We build EDA-specific dictionaries that accurately model both the slow varying tonic part and the signal fluctuations, called skin conductance responses (SCR), and use greedy sparse representation techniques to decompose the signal into a small number of atoms from the dictionary. Quantitative evaluation of our method considers signal reconstruction, compression rate, and information retrieval measures, that capture the ability of the model to incorporate the main signal characteristics, such as SCR occurrences. Compared to previous studies fitting a predetermined structure to the signal, results indicate that our approach provides benefits across all aforementioned criteria. This paper demonstrates the ability of appropriate dictionaries along with sparse decomposition methods to reliably represent EDA signals and provides a foundation for automatic measurement of SCR characteristics and the extraction of meaningful EDA features.

Rapid Structured Volume Grid Smoothing and Adaption Technique

NASA Technical Reports Server (NTRS)

Alter, Stephen J.

2006-01-01

A rapid, structured volume grid smoothing and adaption technique, based on signal processing methods, was developed and applied to the Shuttle Orbiter at hypervelocity flight conditions in support of the Columbia Accident Investigation. Because of the fast pace of the investigation, computational aerothermodynamicists, applying hypersonic viscous flow solving computational fluid dynamic (CFD) codes, refined and enhanced a grid for an undamaged baseline vehicle to assess a variety of damage scenarios. Of the many methods available to modify a structured grid, most are time-consuming and require significant user interaction. By casting the grid data into different coordinate systems, specifically two computational coordinates with arclength as the third coordinate, signal processing methods are used for filtering the data [Taubin, CG v/29 1995]. Using a reverse transformation, the processed data are used to smooth the Cartesian coordinates of the structured grids. By coupling the signal processing method with existing grid operations within the Volume Grid Manipulator tool, problems related to grid smoothing are solved efficiently and with minimal user interaction. Examples of these smoothing operations are illustrated for reductions in grid stretching and volume grid adaptation. In each of these examples, other techniques existed at the time of the Columbia accident, but the incorporation of signal processing techniques reduced the time to perform the corrections by nearly 60%. This reduction in time to perform the corrections therefore enabled the assessment of approximately twice the number of damage scenarios than previously possible during the allocated investigation time.

Rapid Structured Volume Grid Smoothing and Adaption Technique

NASA Technical Reports Server (NTRS)

Alter, Stephen J.

2004-01-01

A rapid, structured volume grid smoothing and adaption technique, based on signal processing methods, was developed and applied to the Shuttle Orbiter at hypervelocity flight conditions in support of the Columbia Accident Investigation. Because of the fast pace of the investigation, computational aerothermodynamicists, applying hypersonic viscous flow solving computational fluid dynamic (CFD) codes, refined and enhanced a grid for an undamaged baseline vehicle to assess a variety of damage scenarios. Of the many methods available to modify a structured grid, most are time-consuming and require significant user interaction. By casting the grid data into different coordinate systems, specifically two computational coordinates with arclength as the third coordinate, signal processing methods are used for filtering the data [Taubin, CG v/29 1995]. Using a reverse transformation, the processed data are used to smooth the Cartesian coordinates of the structured grids. By coupling the signal processing method with existing grid operations within the Volume Grid Manipulator tool, problems related to grid smoothing are solved efficiently and with minimal user interaction. Examples of these smoothing operations are illustrated for reduction in grid stretching and volume grid adaptation. In each of these examples, other techniques existed at the time of the Columbia accident, but the incorporation of signal processing techniques reduced the time to perform the corrections by nearly 60%. This reduction in time to perform the corrections therefore enabled the assessment of approximately twice the number of damage scenarios than previously possible during the allocated investigation time.

Technical Assessment: Integrated Photonics

DTIC Science & Technology

2015-10-01

in global internet protocol traffic as a function of time by local access technology. Photonics continues to play a critical role in enabling this...communication networks. This has enabled services like the internet , high performance computing, and power-efficient large-scale data centers. The...signal processing, quantum information science, and optics for free space applications. However major obstacles challenge the implementation of

Connecting G protein signaling to chemoattractant-mediated cell polarity and cytoskeletal reorganization.

PubMed

Liu, Youtao; Lacal, Jesus; Firtel, Richard A; Kortholt, Arjan

2018-07-04

The directional movement toward extracellular chemical gradients, a process called chemotaxis, is an important property of cells. Central to eukaryotic chemotaxis is the molecular mechanism by which chemoattractant-mediated activation of G-protein coupled receptors (GPCRs) induces symmetry breaking in the activated downstream signaling pathways. Studies with mainly Dictyostelium and mammalian neutrophils as experimental systems have shown that chemotaxis is mediated by a complex network of signaling pathways. Recently, several labs have used extensive and efficient proteomic approaches to further unravel this dynamic signaling network. Together these studies showed the critical role of the interplay between heterotrimeric G-protein subunits and monomeric G proteins in regulating cytoskeletal rearrangements during chemotaxis. Here we highlight how these proteomic studies have provided greater insight into the mechanisms by which the heterotrimeric G protein cycle is regulated, how heterotrimeric G proteins-induced symmetry breaking is mediated through small G protein signaling, and how symmetry breaking in G protein signaling subsequently induces cytoskeleton rearrangements and cell migration.

Energy-efficient digital and wireless IC design for wireless smart sensing

NASA Astrophysics Data System (ADS)

Zhou, Jun; Huang, Xiongchuan; Wang, Chao; Tae-Hyoung Kim, Tony; Lian, Yong

2017-10-01

Wireless smart sensing is now widely used in various applications such as health monitoring and structural monitoring. In conventional wireless sensor nodes, significant power is consumed in wirelessly transmitting the raw data. Smart sensing adds local intelligence to the sensor node and reduces the amount of wireless data transmission via on-node digital signal processing. While the total power consumption is reduced compared to conventional wireless sensing, the power consumption of the digital processing becomes as dominant as wireless data transmission. This paper reviews the state-of-the-art energy-efficient digital and wireless IC design techniques for reducing the power consumption of the wireless smart sensor node to prolong battery life and enable self-powered applications.

An Efficient Moving Target Detection Algorithm Based on Sparsity-Aware Spectrum Estimation

PubMed Central

Shen, Mingwei; Wang, Jie; Wu, Di; Zhu, Daiyin

2014-01-01

In this paper, an efficient direct data domain space-time adaptive processing (STAP) algorithm for moving targets detection is proposed, which is achieved based on the distinct spectrum features of clutter and target signals in the angle-Doppler domain. To reduce the computational complexity, the high-resolution angle-Doppler spectrum is obtained by finding the sparsest coefficients in the angle domain using the reduced-dimension data within each Doppler bin. Moreover, we will then present a knowledge-aided block-size detection algorithm that can discriminate between the moving targets and the clutter based on the extracted spectrum features. The feasibility and effectiveness of the proposed method are validated through both numerical simulations and raw data processing results. PMID:25222035

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.

Modeling VLF signal modulation during solar flares with GEANT4 Monte Carlo simulation, a simple chemical model and LWPC

NASA Astrophysics Data System (ADS)

Palit, Sourav; Chakrabarti, Sandip Kumar; Pal, Sujay; Basak, Tamal

Extra ionization by X-rays during solar flares affects VLF signal propagation through D-region ionosphere. Ionization produced in the lower ionosphere due to X-ray spectra of solar flares are simulated with an efficient detector simulation program, GEANT4. The balancing between the ionization and loss processes, causing the lower ionosphere to settle back to its undisturbed state is handled with a simple chemical model consisting of four broad species of ion densities. Using the electron densities, modified VLF signal amplitude is then computed with LWPC code. VLF signal along NWC (Australia) to IERC/ICSP (India) propagation path is examined during a M and a X-type solar flares and observational deviations are compared with simulated results. The agreement is found to be excellent.

Introduction.

PubMed

Tepikin, Alexei V

2017-01-01

In the title of this part of the book, the tail is wagging not just in a single dog but multiple dogs; in other words, a single process SOCE (tail) somehow involves a cross talk of (wagging) large and powerful organelle and cellular compartments (dogs). So how is this possible? Is this really necessary? Is the title actually appropriate?SOCE is a rather special process, it allows efficient signaling based on a ubiquitous second messenger (Ca 2+ ) in multiple cell and tissue types, it has specific signaling modality (i.e., some downstream reactions depend specifically on SOCE and not just on global Ca 2+ increase), it is vital for the normal functioning of multiple types of cells and tissues, and when misregulated it induces important pathological processes. The reader hopefully agree that such an important "tail" is more appropriate for a kangaroo than for a Chihuahua and that it has awesome wagging capacity.

Sentential negation modulates inhibition in a stop-signal task. Evidence from behavioral and ERP data.

PubMed

Beltrán, David; Muñetón-Ayala, Mercedes; de Vega, Manuel

2018-04-01

Embodiment theories claim that language meaning involves sensory-motor simulation processes in the brain. A challenge for these theories, however, is to explain how abstract words, such as negations, are processed. In this article, we test the hypothesis that understanding sentential negation (e.g., You will not cut the bread) reuses the neural circuitry of response inhibition. Participants read manual action sentences with either affirmative or negative polarity, embedded in a Stop-Signal paradigm, while their EEG was recorded. The results showed that the inhibition-related N1 and P3 components were enhanced by successful inhibition. Most important, the early N1 amplitude was also modulated by sentence polarity, producing the largest values for successful inhibitions in the context of negative sentences, whereas no polarity effect was found for failing inhibition or go trials. The estimated neural sources for N1 effects revealed activations in the right inferior frontal gyrus, a typical inhibition-related area. Also, the estimated stop-signal reaction time was larger in trials with negative sentences. These results provide strong evidence that action-related negative sentences consume neural resources of response inhibition, resulting in less efficient processing in the Stop-Signal task. Copyright © 2018 Elsevier Ltd. All rights reserved.

Ultrabroadband phased-array radio frequency (RF) receivers based on optical techniques

NASA Astrophysics Data System (ADS)

Overmiller, Brock M.; Schuetz, Christopher A.; Schneider, Garrett; Murakowski, Janusz; Prather, Dennis W.

2014-03-01

Military operations require the ability to locate and identify electronic emissions in the battlefield environment. However, recent developments in radio detection and ranging (RADAR) and communications technology are making it harder to effectively identify such emissions. Phased array systems aid in discriminating emitters in the scene by virtue of their relatively high-gain beam steering and nulling capabilities. For the purpose of locating emitters, we present an approach realize a broadband receiver based on optical processing techniques applied to the response of detectors in conformal antenna arrays. This approach utilizes photonic techniques that enable us to capture, route, and process the incoming signals. Optical modulators convert the incoming signals up to and exceeding 110 GHz with appreciable conversion efficiency and route these signals via fiber optics to a central processing location. This central processor consists of a closed loop phase control system which compensates for phase fluctuations induced on the fibers due to thermal or acoustic vibrations as well as an optical heterodyne approach for signal conversion down to baseband. Our optical heterodyne approach uses injection-locked paired optical sources to perform heterodyne downconversion/frequency identification of the detected emission. Preliminary geolocation and frequency identification testing of electronic emissions has been performed demonstrating the capabilities of our RF receiver.

Alteration of the Cortical Actin Cytoskeleton Deregulates Ca2+ Signaling, Monospermic Fertilization, and Sperm Entry

PubMed Central

Puppo, A.; Chun, Jong T.; Gragnaniello, Giovanni; Garante, Ezio; Santella, Luigia

2008-01-01

Background When preparing for fertilization, oocytes undergo meiotic maturation during which structural changes occur in the endoplasmic reticulum (ER) that lead to a more efficient calcium response. During meiotic maturation and subsequent fertilization, the actin cytoskeleton also undergoes dramatic restructuring. We have recently observed that rearrangements of the actin cytoskeleton induced by actin-depolymerizing agents, or by actin-binding proteins, strongly modulate intracellular calcium (Ca2+) signals during the maturation process. However, the significance of the dynamic changes in F-actin within the fertilized egg has been largely unclear. Methodology/Principal Findings We have measured changes in intracellular Ca2+ signals and F-actin structures during fertilization. We also report the unexpected observation that the conventional antagonist of the InsP3 receptor, heparin, hyperpolymerizes the cortical actin cytoskeleton in postmeiotic eggs. Using heparin and other pharmacological agents that either hypo- or hyperpolymerize the cortical actin, we demonstrate that nearly all aspects of the fertilization process are profoundly affected by the dynamic restructuring of the egg cortical actin cytoskeleton. Conclusions/Significance Our findings identify important roles for subplasmalemmal actin fibers in the process of sperm-egg interaction and in the subsequent events related to fertilization: the generation of Ca2+ signals, sperm penetration, cortical granule exocytosis, and the block to polyspermy. PMID:18974786

Optical signal processing for enabling high-speed, highly spectrally efficient and high capacity optical systems

NASA Astrophysics Data System (ADS)

Fazal, Muhammad Irfan

The unabated demand for more capacity due to the ever-increasing internet traffic dictates that the boundaries of the state of the art maybe pushed to send more data through the network. Traditionally, this need has been satisfied by multiple wavelengths (wavelength division multiplexing), higher order modulation formats and coherent communication (either individually or combined together). WDM has the ability to reduce cost by using multiple channels within the same physical fiber, and with EDFA amplifiers, the need for O-E-O regenerators is eliminated. Moreover the availability of multiple colors allows for wavelength-based routing and network planning. Higher order modulation formats increases the capacity of the link by their ability to encode data in both the phase and amplitude of light, thereby increasing the bits/sec/Hz as compared to simple on-off keyed format. Coherent communications has also emerged as a primary means of transmitting and receiving optical data due to its support of formats that utilize both phase and amplitude to further increase the spectral efficiency of the optical channel, including quadrature amplitude modulation (QAM) and quadrature phase shift keying (QPSK). Polarization multiplexing of channels can double capacity by allowing two channels to share the same wavelength by propagating on orthogonal polarization axis and is easily supported in coherent systems where the polarization tracking can be performed in the digital domain. Furthermore, the forthcoming IEEE 100 Gbit/s Ethernet Standard, 802.3ba, provides greater bandwidth, higher data rates, and supports a mixture of modulation formats. In particular, Pol-MUX QPSK is increasingly becoming the industry's format of choice as the high spectral efficiency allows for 100 Gbit/s transmission while still occupying the current 50 GHz/channel allocation of current 10 Gbit/s OOK fiber systems. In this manner, 100 Gbit/s transfer speeds using current fiber links, amplifiers, and filters may be possible. Recently, interest has increased in exploring the spatial dimension of light to increase capacity, both in fiber as well as free-space communication channels. The orbital angular momentum (OAM) of light, carried by Laguerre-Gaussian (LG) beams have the interesting property that, in theory, an infinite number of OAMs can be transmitted; which due to its inherent orthogonality will not affect each other. Thus, in theory, one can increase the channel capacity arbitrarily. However, in practice, the device dimensions will reduce the number of OAMs used. In addition to advanced modulation formats, it is expected that optical signal processing may play a role in the future development of more efficient optical transmission systems. The hope is that performing signal processing in the optical domain may reduce optical-to-electronic conversion inefficiencies, eliminate bottlenecks and take advantage of the ultrahigh bandwidth inherent in optics. While 40 to 50 Gbit/s electronic components are the peak of commercial technology and 100 Gbit/s capable RF components are still in their infancy, optical signal processing of these high-speed data signals may provide a potential solution. Furthermore, any optical processing system or sub-system must be capable of handling the wide array of data formats and data rates that networks may employ. The work presented in this Ph.D. dissertation attempts at addressing the issue of optical processing for advanced optical modulation formats, and particularly explores the state of the art in increasing the capacity of an optical link by a combination of wavelength/phase/polarization/OAM dimensions of light. Spatial multiplexing and demultiplexing of both coherently and directly detected signals at the 100 Gbit/s Ethernet standard is addressed. The application of a continuously tunable all-optical delay for all-optical functionality like time-slot interchange at high data-rates is presented. Moreover the interplay of chirp generated by differently cross-phase modulation wavelength-convertors based on SOA-MZI with the residual dispersion of a fiber link is studied and the optimal operating conditions are explored

Terahertz generation by difference frequency generation from a compact optical parametric oscillator

NASA Astrophysics Data System (ADS)

Li, Zhongyang; Wang, Silei; Wang, Mengtao; Wang, Weishu

2017-11-01

Terahertz (THz) generation by difference frequency generation (DFG) processes with dual idler waves is theoretically analyzed. The dual idler waves are generated by a compact optical parametric oscillator (OPO) with periodically poled lithium niobate (PPLN). The phase-matching conditions in a same PPLN for the optical parametric oscillation generating signal and idler waves and for the DFG generating THz waves can be simultaneously satisfied by selecting the poling period of PPLN. Moreover, 3-order cascaded DFG processes generating THz waves can be realized in the same PPLN. To take an example of 8.341 THz which locates in the vicinity of polariton resonances, THz intensities and quantum conversion efficiencies are calculated. Compared with non-cascaded DFG processes, THz intensities of 8.341 THz in 3-order cascaded DFG processes increase to 2.57 times. When the pump intensity equals to 20 MW/mm2, the quantum conversion efficiency of 106% in 3-order cascaded DFG processes can be realized, which exceeds the Manley-Rowe limit.

Tongue motor training support system.

PubMed

Sasaki, Makoto; Onishi, Kohei; Nakayama, Atsushi; Kamata, Katsuhiro; Stefanov, Dimitar; Yamaguchi, Masaki

2014-01-01

In this paper, we introduce a new tongue-training system that can be used for improvement of the tongue's range of motion and muscle strength after dysphagia. The training process is organized in game-like manner. Initially, we analyzed surface electromyography (EMG) signals of the suprahyoid muscles of five subjects during tongue-training motions. This test revealed that four types tongue training motions and a swallowing motion could be classified with 93.5% accuracy. Recognized EMG signals during tongue motions were designed to allow control of a mouse cursor via intentional tongue motions. Results demonstrated that simple PC games could be played by tongue motions, achieving in this way efficient, enjoyable and pleasant tongue training. Using the proposed method, dysphagia patients can choose games that suit their preferences and/or state of mind. It is expected that the proposed system will be an efficient tool for long-term tongue motor training and maintaining patients' motivation.

Simultaneous quantitative analysis of olmesartan, amlodipine and hydrochlorothiazide in their combined dosage form utilizing classical and alternating least squares based chemometric methods.

PubMed

Darwish, Hany W; Bakheit, Ahmed H; Abdelhameed, Ali S

2016-03-01

Simultaneous spectrophotometric analysis of a multi-component dosage form of olmesartan, amlodipine and hydrochlorothiazide used for the treatment of hypertension has been carried out using various chemometric methods. Multivariate calibration methods include classical least squares (CLS) executed by net analyte processing (NAP-CLS), orthogonal signal correction (OSC-CLS) and direct orthogonal signal correction (DOSC-CLS) in addition to multivariate curve resolution-alternating least squares (MCR-ALS). Results demonstrated the efficiency of the proposed methods as quantitative tools of analysis as well as their qualitative capability. The three analytes were determined precisely using the aforementioned methods in an external data set and in a dosage form after optimization of experimental conditions. Finally, the efficiency of the models was validated via comparison with the partial least squares (PLS) method in terms of accuracy and precision.

Ultralow-power electronics for biomedical applications.

PubMed

Chandrakasan, Anantha P; Verma, Naveen; Daly, Denis C

2008-01-01

The electronics of a general biomedical device consist of energy delivery, analog-to-digital conversion, signal processing, and communication subsystems. Each of these blocks must be designed for minimum energy consumption. Specific design techniques, such as aggressive voltage scaling, dynamic power-performance management, and energy-efficient signaling, must be employed to adhere to the stringent energy constraint. The constraint itself is set by the energy source, so energy harvesting holds tremendous promise toward enabling sophisticated systems without straining user lifestyle. Further, once harvested, efficient delivery of the low-energy levels, as well as robust operation in the aggressive low-power modes, requires careful understanding and treatment of the specific design limitations that dominate this realm. We outline the performance and power constraints of biomedical devices, and present circuit techniques to achieve complete systems operating down to power levels of microwatts. In all cases, approaches that leverage advanced technology trends are emphasized.

Preliminary Assessment of Detection Efficiency for the Geostationary Lightning Mapper Using Intercomparisons with Ground-Based Systems

NASA Technical Reports Server (NTRS)

Bateman, Monte; Mach, Douglas; Blakeslee, Richard J.; Koshak, William

2018-01-01

As part of the calibration/validation (cal/val) effort for the Geostationary Lightning Mapper (GLM) on GOES-16, we need to assess instrument performance (detection efficiency and accuracy). One major effort is to calculate the detection efficiency of GLM by comparing to multiple ground-based systems. These comparisons will be done pair-wise between GLM and each other source. A complication in this process is that the ground-based systems sense different properties of the lightning signal than does GLM (e.g., RF vs. optical). Also, each system has a different time and space resolution and accuracy. Preliminary results indicate that GLM is performing at or above its specification.

Multi-aperture digital coherent combining for free-space optical communication receivers.

PubMed

Geisler, David J; Yarnall, Timothy M; Stevens, Mark L; Schieler, Curt M; Robinson, Bryan S; Hamilton, Scott A

2016-06-13

Space-to-ground optical communication systems can benefit from reducing the size, weight, and power profiles of space terminals. One way of reducing the required power-aperture product on a space platform is to implement effective, but costly, single-aperture ground terminals with large collection areas. In contrast, we present a ground terminal receiver architecture in which many small less-expensive apertures are efficiently combined to create a large effective aperture while maintaining excellent receiver sensitivity. This is accomplished via coherent detection behind each aperture followed by digitization. The digitized signals are then combined in a digital signal processing chain. Experimental results demonstrate lossless coherent combining of four lasercom signals, at power levels below 0.1 photons/bit/aperture.

A hybrid voice/data modulation for the VHF aeronautical channels

NASA Technical Reports Server (NTRS)

Akos, Dennis M.

1993-01-01

A method of improving the spectral efficiency of the existing Very High Frequency (VHF) Amplitude Modulation (AM) voice communication channels is proposed. The technique is to phase modulate the existing voice amplitude modulated carrier with digital data. This allows the transmission of digital information over an existing AM voice channel with no change to the existing AM signal format. There is no modification to the existing AM receiver to demodulate the voice signal and an additional receiver module can be added for processing of the digital data. The existing VHF AM transmitter requires only a slight modification for the addition of the digital data signal. The past work in the area is summarized and presented together with an improved system design and the proposed implementation.

An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks

PubMed Central

Pirbhulal, Sandeep; Zhang, Heye; Mukhopadhyay, Subhas Chandra; Li, Chunyue; Wang, Yumei; Li, Guanglin; Wu, Wanqing; Zhang, Yuan-Ting

2015-01-01

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption. PMID:26131666

An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks.

PubMed

Pirbhulal, Sandeep; Zhang, Heye; Mukhopadhyay, Subhas Chandra; Li, Chunyue; Wang, Yumei; Li, Guanglin; Wu, Wanqing; Zhang, Yuan-Ting

2015-06-26

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption.

Manyscale Computing for Sensor Processing in Support of Space Situational Awareness

NASA Astrophysics Data System (ADS)

Schmalz, M.; Chapman, W.; Hayden, E.; Sahni, S.; Ranka, S.

2014-09-01

Increasing image and signal data burden associated with sensor data processing in support of space situational awareness implies continuing computational throughput growth beyond the petascale regime. In addition to growing applications data burden and diversity, the breadth, diversity and scalability of high performance computing architectures and their various organizations challenge the development of a single, unifying, practicable model of parallel computation. Therefore, models for scalable parallel processing have exploited architectural and structural idiosyncrasies, yielding potential misapplications when legacy programs are ported among such architectures. In response to this challenge, we have developed a concise, efficient computational paradigm and software called Manyscale Computing to facilitate efficient mapping of annotated application codes to heterogeneous parallel architectures. Our theory, algorithms, software, and experimental results support partitioning and scheduling of application codes for envisioned parallel architectures, in terms of work atoms that are mapped (for example) to threads or thread blocks on computational hardware. Because of the rigor, completeness, conciseness, and layered design of our manyscale approach, application-to-architecture mapping is feasible and scalable for architectures at petascales, exascales, and above. Further, our methodology is simple, relying primarily on a small set of primitive mapping operations and support routines that are readily implemented on modern parallel processors such as graphics processing units (GPUs) and hybrid multi-processors (HMPs). In this paper, we overview the opportunities and challenges of manyscale computing for image and signal processing in support of space situational awareness applications. We discuss applications in terms of a layered hardware architecture (laboratory > supercomputer > rack > processor > component hierarchy). Demonstration applications include performance analysis and results in terms of execution time as well as storage, power, and energy consumption for bus-connected and/or networked architectures. The feasibility of the manyscale paradigm is demonstrated by addressing four principal challenges: (1) architectural/structural diversity, parallelism, and locality, (2) masking of I/O and memory latencies, (3) scalability of design as well as implementation, and (4) efficient representation/expression of parallel applications. Examples will demonstrate how manyscale computing helps solve these challenges efficiently on real-world computing systems.

New algorithms for processing time-series big EEG data within mobile health monitoring systems.

PubMed

Serhani, Mohamed Adel; Menshawy, Mohamed El; Benharref, Abdelghani; Harous, Saad; Navaz, Alramzana Nujum

2017-10-01

Recent advances in miniature biomedical sensors, mobile smartphones, wireless communications, and distributed computing technologies provide promising techniques for developing mobile health systems. Such systems are capable of monitoring epileptic seizures reliably, which are classified as chronic diseases. Three challenging issues raised in this context with regard to the transformation, compression, storage, and visualization of big data, which results from a continuous recording of epileptic seizures using mobile devices. In this paper, we address the above challenges by developing three new algorithms to process and analyze big electroencephalography data in a rigorous and efficient manner. The first algorithm is responsible for transforming the standard European Data Format (EDF) into the standard JavaScript Object Notation (JSON) and compressing the transformed JSON data to decrease the size and time through the transfer process and to increase the network transfer rate. The second algorithm focuses on collecting and storing the compressed files generated by the transformation and compression algorithm. The collection process is performed with respect to the on-the-fly technique after decompressing files. The third algorithm provides relevant real-time interaction with signal data by prospective users. It particularly features the following capabilities: visualization of single or multiple signal channels on a smartphone device and query data segments. We tested and evaluated the effectiveness of our approach through a software architecture model implementing a mobile health system to monitor epileptic seizures. The experimental findings from 45 experiments are promising and efficiently satisfy the approach's objectives in a price of linearity. Moreover, the size of compressed JSON files and transfer times are reduced by 10% and 20%, respectively, while the average total time is remarkably reduced by 67% through all performed experiments. Our approach successfully develops efficient algorithms in terms of processing time, memory usage, and energy consumption while maintaining a high scalability of the proposed solution. Our approach efficiently supports data partitioning and parallelism relying on the MapReduce platform, which can help in monitoring and automatic detection of epileptic seizures. Copyright © 2017 Elsevier B.V. All rights reserved.

Concepts for on board satellite image registration. Volume 4: Impact of data set selection on satellite on board signal processing

NASA Technical Reports Server (NTRS)

Ruedger, W. H.; Aanstoos, J. V.; Snyder, W. E.

1982-01-01

The NASA NEEDS program goals present a requirement for on-board signal processing to achieve user-compatible, information-adaptive data acquisition. This volume addresses the impact of data set selection on data formatting required for efficient telemetering of the acquired satellite sensor data. More specifically, the FILE algorithm developed by Martin-Marietta provides a means for the determination of those pixels from the data stream effects an improvement in the achievable system throughput. It will be seen that based on the lack of statistical stationarity in cloud cover, spatial distribution periods exist where data acquisition rates exceed the throughput capability. The study therefore addresses various approaches to data compression and truncation as applicable to this sensor mission.

A scalable SIMD digital signal processor for high-quality multifunctional printer systems

NASA Astrophysics Data System (ADS)

Kang, Hyeong-Ju; Choi, Yongwoo; Kim, Kimo; Park, In-Cheol; Kim, Jung-Wook; Lee, Eul-Hwan; Gahang, Goo-Soo

2005-01-01

This paper describes a high-performance scalable SIMD digital signal processor (DSP) developed for multifunctional printer systems. The DSP supports a variable number of datapaths to cover a wide range of performance and maintain a RISC-like pipeline structure. Many special instructions suitable for image processing algorithms are included in the DSP. Quad/dual instructions are introduced for 8-bit or 16-bit data, and bit-field extraction/insertion instructions are supported to process various data types. Conditional instructions are supported to deal with complex relative conditions efficiently. In addition, an intelligent DMA block is integrated to align data in the course of data reading. Experimental results show that the proposed DSP outperforms a high-end printer-system DSP by at least two times.

The development of global motion discrimination in school aged children

PubMed Central

Bogfjellmo, Lotte-Guri; Bex, Peter J.; Falkenberg, Helle K.

2014-01-01

Global motion perception matures during childhood and involves the detection of local directional signals that are integrated across space. We examine the maturation of local directional selectivity and global motion integration with an equivalent noise paradigm applied to direction discrimination. One hundred and three observers (6–17 years) identified the global direction of motion in a 2AFC task. The 8° central stimuli consisted of 100 dots of 10% Michelson contrast moving 2.8°/s or 9.8°/s. Local directional selectivity and global sampling efficiency were estimated from direction discrimination thresholds as a function of external directional noise, speed, and age. Direction discrimination thresholds improved gradually until the age of 14 years (linear regression, p < 0.05) for both speeds. This improvement was associated with a gradual increase in sampling efficiency (linear regression, p < 0.05), with no significant change in internal noise. Direction sensitivity was lower for dots moving at 2.8°/s than at 9.8°/s for all ages (paired t test, p < 0.05) and is mainly due to lower sampling efficiency. Global motion perception improves gradually during development and matures by age 14. There was no change in internal noise after the age of 6, suggesting that local direction selectivity is mature by that age. The improvement in global motion perception is underpinned by a steady increase in the efficiency with which direction signals are pooled, suggesting that global motion pooling processes mature for longer and later than local motion processing. PMID:24569985

Fast contactless vibrating structure characterization using real time field programmable gate array-based digital signal processing: demonstrations with a passive wireless acoustic delay line probe and vision.

PubMed

Goavec-Mérou, G; Chrétien, N; Friedt, J-M; Sandoz, P; Martin, G; Lenczner, M; Ballandras, S

2014-01-01

Vibrating mechanical structure characterization is demonstrated using contactless techniques best suited for mobile and rotating equipments. Fast measurement rates are achieved using Field Programmable Gate Array (FPGA) devices as real-time digital signal processors. Two kinds of algorithms are implemented on FPGA and experimentally validated in the case of the vibrating tuning fork. A first application concerns in-plane displacement detection by vision with sampling rates above 10 kHz, thus reaching frequency ranges above the audio range. A second demonstration concerns pulsed-RADAR cooperative target phase detection and is applied to radiofrequency acoustic transducers used as passive wireless strain gauges. In this case, the 250 ksamples/s refresh rate achieved is only limited by the acoustic sensor design but not by the detection bandwidth. These realizations illustrate the efficiency, interest, and potentialities of FPGA-based real-time digital signal processing for the contactless interrogation of passive embedded probes with high refresh rates.

Dense fibrillar collagen is a potent inducer of invadopodia via a specific signaling network

PubMed Central

Swatkoski, Stephen; Matsumoto, Kazue; Campbell, Catherine B.; Petrie, Ryan J.; Dimitriadis, Emilios K.; Li, Xin; Mueller, Susette C.; Bugge, Thomas H.; Gucek, Marjan

2015-01-01

Cell interactions with the extracellular matrix (ECM) can regulate multiple cellular activities and the matrix itself in dynamic, bidirectional processes. One such process is local proteolytic modification of the ECM. Invadopodia of tumor cells are actin-rich proteolytic protrusions that locally degrade matrix molecules and mediate invasion. We report that a novel high-density fibrillar collagen (HDFC) matrix is a potent inducer of invadopodia, both in carcinoma cell lines and in primary human fibroblasts. In carcinoma cells, HDFC matrix induced formation of invadopodia via a specific integrin signaling pathway that did not require growth factors or even altered gene and protein expression. In contrast, phosphoproteomics identified major changes in a complex phosphosignaling network with kindlin2 serine phosphorylation as a key regulatory element. This kindlin2-dependent signal transduction network was required for efficient induction of invadopodia on dense fibrillar collagen and for local degradation of collagen. This novel phosphosignaling mechanism regulates cell surface invadopodia via kindlin2 for local proteolytic remodeling of the ECM. PMID:25646088

WGM-Based Photonic Local Oscillators and Modulators

NASA Technical Reports Server (NTRS)

Matsko, Andrey; Maleki, Lute; Iltchenko, Vladimir; Savchenkov, Anatoliy

2007-01-01

Photonic local oscillators and modulators that include whispering-gallery mode (WGM) optical resonators have been proposed as power-efficient devices for generating and detecting radiation at frequencies of the order of a terahertz. These devices are intended especially to satisfy anticipated needs for receivers capable of detecting lowpower, narrow-band terahertz signals to be used for sensing substances of interest in scientific and military applications. At present, available terahertz-signal detectors are power-inefficient and do not afford the spectral and amplitude resolution needed for detecting such signals. The proposed devices would not be designed according to the conventional approach of direct detection of terahertz radiation. Instead, terahertz radiation would first be up-converted into the optical domain, wherein signals could be processed efficiently by photonic means and detected by optical photodetectors, which are more efficient than are photodetectors used in conventional direct detection of terahertz radiation. The photonic devices used to effect the up-conversion would include a tunable optical local oscillator and a novel electro-optical modulator. A local oscillator according to the proposal would be a WGM-based modelocked laser operating at a desired pulserepetition rate of the order of a terahertz. The oscillator would include a terahertz optical filter based on a WGM microresonator, a fiber-optic delay line, an optical amplifier (which could be either a semiconductor optical amplifier or an erbium-doped optical fiberamplifier), and a WGM Ka-band modulator. The terahertz repetition rate would be obtained through harmonic mode locking: for example, by modulating the light at a frequency of 33 GHz and locking each 33d optical mode, one would create a 1.089-THz pulse train. The high resonance quality factors (Q values) of WGM optical resonators should make it possible to decrease signal-generation threshold power levels significantly below those of other optical-signal-generation devices.

Pump RIN-induced impairments in unrepeatered transmission systems using distributed Raman amplifier.

PubMed

Cheng, Jingchi; Tang, Ming; Lau, Alan Pak Tao; Lu, Chao; Wang, Liang; Dong, Zhenhua; Bilal, Syed Muhammad; Fu, Songnian; Shum, Perry Ping; Liu, Deming

2015-05-04

High spectral efficiency modulation format based unrepeatered transmission systems using distributed Raman amplifier (DRA) have attracted much attention recently. To enhance the reach and optimize system performance, careful design of DRA is required based on the analysis of various types of impairments and their balance. In this paper, we study various pump RIN induced distortions on high spectral efficiency modulation formats. The vector theory of both 1st and higher-order stimulated Raman scattering (SRS) effect using Jones-matrix formalism is presented. The pump RIN will induce three types of distortion on high spectral efficiency signals: intensity noise stemming from SRS, phase noise stemming from cross phase modulation (XPM), and polarization crosstalk stemming from cross polarization modulation (XPolM). An analytical model for the statistical property of relative phase noise (RPN) in higher order DRA without dealing with complex vector theory is derived. The impact of pump RIN induced impairments are analyzed in polarization-multiplexed (PM)-QPSK and PM-16QAM-based unrepeatered systems simulations using 1st, 2nd and 3rd-order forward pumped Raman amplifier. It is shown that at realistic RIN levels, negligible impairments will be induced to PM-QPSK signals in 1st and 2nd order DRA, while non-negligible impairments will occur in 3rd order case. PM-16QAM signals suffer more penalties compared to PM-QPSK with the same on-off gain where both 2nd and 3rd order DRA will cause non-negligible performance degradations. We also investigate the performance of digital signal processing (DSP) algorithms to mitigate such impairments.

A new perspective on binaural integration using response time methodology: super capacity revealed in conditions of binaural masking release.

PubMed

Lentz, Jennifer J; He, Yuan; Townsend, James T

2014-01-01

This study applied reaction-time based methods to assess the workload capacity of binaural integration by comparing reaction time (RT) distributions for monaural and binaural tone-in-noise detection tasks. In the diotic contexts, an identical tone + noise stimulus was presented to each ear. In the dichotic contexts, an identical noise was presented to each ear, but the tone was presented to one of the ears 180° out of phase with respect to the other ear. Accuracy-based measurements have demonstrated a much lower signal detection threshold for the dichotic vs. the diotic conditions, but accuracy-based techniques do not allow for assessment of system dynamics or resource allocation across time. Further, RTs allow comparisons between these conditions at the same signal-to-noise ratio. Here, we apply a reaction-time based capacity coefficient, which provides an index of workload efficiency and quantifies the resource allocations for single ear vs. two ear presentations. We demonstrate that the release from masking generated by the addition of an identical stimulus to one ear is limited-to-unlimited capacity (efficiency typically less than 1), consistent with less gain than would be expected by probability summation. However, the dichotic presentation leads to a significant increase in workload capacity (increased efficiency)-most specifically at lower signal-to-noise ratios. These experimental results provide further evidence that configural processing plays a critical role in binaural masking release, and that these mechanisms may operate more strongly when the signal stimulus is difficult to detect, albeit still with nearly 100% accuracy.

A new perspective on binaural integration using response time methodology: super capacity revealed in conditions of binaural masking release

PubMed Central

Lentz, Jennifer J.; He, Yuan; Townsend, James T.

2014-01-01

This study applied reaction-time based methods to assess the workload capacity of binaural integration by comparing reaction time (RT) distributions for monaural and binaural tone-in-noise detection tasks. In the diotic contexts, an identical tone + noise stimulus was presented to each ear. In the dichotic contexts, an identical noise was presented to each ear, but the tone was presented to one of the ears 180° out of phase with respect to the other ear. Accuracy-based measurements have demonstrated a much lower signal detection threshold for the dichotic vs. the diotic conditions, but accuracy-based techniques do not allow for assessment of system dynamics or resource allocation across time. Further, RTs allow comparisons between these conditions at the same signal-to-noise ratio. Here, we apply a reaction-time based capacity coefficient, which provides an index of workload efficiency and quantifies the resource allocations for single ear vs. two ear presentations. We demonstrate that the release from masking generated by the addition of an identical stimulus to one ear is limited-to-unlimited capacity (efficiency typically less than 1), consistent with less gain than would be expected by probability summation. However, the dichotic presentation leads to a significant increase in workload capacity (increased efficiency)—most specifically at lower signal-to-noise ratios. These experimental results provide further evidence that configural processing plays a critical role in binaural masking release, and that these mechanisms may operate more strongly when the signal stimulus is difficult to detect, albeit still with nearly 100% accuracy. PMID:25202254

Global scale stratospheric processes as measured by the infrasound IMS network

NASA Astrophysics Data System (ADS)

Le Pichon, A.; Ceranna, L.; Kechut, P.

2012-04-01

IMS infrasound array data are routinely processed at the International Data Center (IDC). The wave parameters of the detected signals are estimated with the Progressive Multi-Channel Correlation method (PMCC). This new implementation of the PMCC algorithm allows the full frequency range of interest (0.01-5 Hz) to be processed efficiently in a single computational run. We have processed continuous recordings from 41 certified IMS stations from 2005 to 2010. We show that microbaroms are the dominant source of signals and are near-continuously globally detected. The observed azimuthal seasonal trend correlates well with the variation of the effective sound speed ratio which is a proxy for the combined effects of refraction due to sound speed gradients and advection due to along-path wind on infrasound propagation. A general trend in signal backazimuth is observed between winter and summer, driven by the seasonal reversal of the stratospheric winds. Combined with propagation modeling, we show that such an analysis enables a characterization of the wind and temperature structure above the stratosphere and may provide detailed information on upper atmospheric processes (e.g., large-scale planetary waves, stratospheric warming effects). We correlate perturbations and deviations from the seasonal trend to short time-scale variability of the atmosphere. We discuss the potential benefit of long-term infrasound monitoring to infer stratospheric processes for the first time on a global scale.

Design and implementation of novel nonlinear processes in bulk and waveguide periodic structures

NASA Astrophysics Data System (ADS)

Kajal, Meenu

The telecommunication networks are facing increasing demand to implement all-optical network infrastructure for enabling the wide deployment of new triple play high-speed services (e.g. IPTV, Video On Demand, Voice over IP). One of the challenges with such video broadcasting applications is that these are much more distributed and multi-point in nature unlike the traditional point-to-point communication networks. Currently deployed high-speed electronic components in the optical networks are incapable of handling the unprecedented bandwidth demand for real-time multimedia based broadcasting. The solution essentially lies in increasing the transparency of networks i.e. by replacing high speed signal processing electronics with all-optical signal processors capable of performing signal manipulations such as wavelength switching, time and wavelength division multiplexing, optical pulse compression etc. all in optical domain. This thesis aims at providing an all-optical solution for broadband wavelength conversion and tunable broadcasting, a crucial optical network component, based on quasi-phase-matched wave mixing in nonlinear materials. The quasi phase matching (QPM) technique allows phase matching in long crystal lengths by employing domain-inverted gratings to periodically reverse the sign of nonlinearity, known as periodic poling. This results into new frequency components with high conversion efficiency and has been successfully implemented towards various processes such as second harmonic generation (SHG), sum- and difference- frequency generation (SFG and DFG). Conventionally, the optical networks has an operation window of ˜35 nm centered at 1.55 mum, known as C-band. The wavelength conversion of a signal channel in C-band to an output channel also in the C-band has been demonstrated in periodically poled lithium niobate (PPLN) waveguides via the process of difference frequency mixing, cascaded SHG/DFG and cascaded SFG/DFG. While a DFG process utilized a pump wavelength in 775nm regime, it suffered from low efficiency due to mode mismatch between the pump and the signal wavelengths; whereas the technique based on cSHG/DFG or cSFG/DFG eliminated the mode mismatch problem with pump(s) lying in the 1.55 mum wavelength regime. In this thesis, for the first time a flattened bandwidth of cSFG/DFG have been experimentally realized by slight detuning of the pump wavelengths from their phase matching condition. Moreover, employing two closely spaced pumps in a cSFG/DFG process in a PPLN waveguide, a signal has been broadcast to three idlers in C-band. Although a uniform period PPLN grating increases efficiency by the use of highest nonlinearity tensor coefficient via QPM, it suffers from the limitation of a narrow bandwidth of frequency doubling. The narrow bandwidth restricts the choice of pump wavelengths in a cascaded conversion process and consequently the converted signal wavelength is also fixed for a given signal wavelength. Enhancing the frequency doubling bandwidth is necessary for mainly two reasons: firstly, to achieve the tunability of wavelength conversion of a signal to any channel in the communication band; and secondly, to broadcast a signal to several channels simultaneously by employing multiple pump lasers within its broad bandwidth. The first engineered PPLN device proposed and demonstrated in this thesis for broadband wavelength conversion has an aperiodic domain in the center of an otherwise periodic grating. This phase-shifted or aperiodic (a-) PPLN has a dual-peak SH response with an increase in bandwidth compared to a uniform PPLN. It has also been shown that using temperature tuning, the phase matching conditions of the aPPLN can be varied and its SH bandwidth can be further enhanced. The triple-idler broadcasting is shown and for the first time, the idlers are tuned across 40 channels in C-band with flexible location and mutual spacing in the WDM grid assisted with pump detuning and temperature tuning. Although the temperature-tuning scheme solves the problem of narrow SH bandwidth and tunability of conversion, the slow speed of temperature change makes it inadequate for ultra-fast WDM applications. Therefore, a temperature-independent broadband device has been demonstrated for the first time in this dissertation, using a step-chirped grating (SCG), which has an inherent 30-nm SH bandwidth overlapping the C-band. This device obviates the need of temperature tuning and leads to tunable wavelength conversion and flexible broadcasting. Employing a single tuned pump wavelength in the SC-PPLN, conversion of a signal in C-band to tunable dual idlers via cSHG/DFG process is demonstrated for the first time. Also by taking advantage of the broad SH-SF bandwidth, for the first time, agile broadcasting of a signal to seven idlers spanning across C-band with variable position in the grid is realized based on cSHG/DFG and cSFG/DFG processes. By tuning the two pump wavelengths over less than 6 nm, broadcasting is achieved across ˜70 WDM channels within the 50 GHz spacing WDM grid. (Abstract shortened by UMI.).

Seismo-acoustic analysis of the near quarry blasts using Plostina small aperture array

NASA Astrophysics Data System (ADS)

Ghica, Daniela; Stancu, Iulian; Ionescu, Constantin

2013-04-01

Seismic and acoustic signals are important to recognize different type of industrial blasting sources in order to discriminate between them and natural earthquakes. We have analyzed the seismic events listed in the Romanian catalogue (Romplus) for the time interval between 2011 and 2012, and occurred in the Dobrogea region, in order to determine detection seismo-acoustic signals of quarry blasts by Plostina array stations. Dobrogea is known as a seismic region characterized by crustal earthquakes with low magnitudes; at the same time, over 40 quarry mines are located in the area, being sources of blasts recorded both with the seismic and infrasound sensors of the Romanian Seismic Network. Plostina seismo-acoustic array, deployed in the central part of Romania, consists of 7 seismic sites (3C broad-band instruments and accelerometers) collocated with 7 infrasound instruments. The array is particularly used for the seismic monitoring of the local and regional events, as well as for the detection of infrasonic signals produced by various sources. Considering the characteristics of the infrasound sensors (frequency range, dynamic, sensibility), the array proved its efficiency in observing the signals produced by explosions, mine explosion and quarry blasts. The quarry mines included for this study cover distances of two hundreds of kilometers from the station and routinely generate explosions that are detected as seismic and infrasonic signals with Plostina array. The combined seismo-acoustic analysis uses two types of detectors for signal identification: one, applied for the seismic signal identification, is based on array processing techniques (beamforming and frequency-wave number analysis), while the other one, which is used for infrasound detection and characterization, is the automatic detector DFX-PMCC (Progressive Multi-Channel Correlation Method). Infrasonic waves generated by quarry blasts have frequencies ranging from 0.05 Hz up to at least 6 Hz and amplitudes below 5 Pa. Seismic data analysis shows that the frequency range of the signals are above 2 Hz. Surface explosions such as quarry blasts are useful sources for checking detection and location efficiency, when seismic measurements are added. The process is crucial for discrimination purposes and for establishing of a set of ground-truth infrasound events. Ground truth information plays a key role in the interpretation of infrasound signals, by including near-field observations from industrial blasts.

Reduction of Phase Ambiguity in an Offset-QPSK Receiver

NASA Technical Reports Server (NTRS)

Berner, Jeff; Kinman, Peter

2004-01-01

Proposed modifications of an offset-quadri-phase-shift keying (offset-QPSK) transmitter and receiver would reduce the amount of signal processing that must be done in the receiver to resolve the QPSK fourfold phase ambiguity. Resolution of the phase ambiguity is necessary in order to synchronize, with the received carrier signal, the signal generated by a local oscillator in a carrier-tracking loop in the receiver. Without resolution of the fourfold phase ambiguity, the loop could lock to any of four possible phase points, only one of which has the proper phase relationship with the carrier. The proposal applies, more specifically, to an offset-QPSK receiver that contains a carrier-tracking loop like that shown in Figure 1. This carrier-tracking loop does not resolve or reduce the phase ambiguity. A carrier-tracking loop of a different design optimized for the reception of offset QPSK could reduce the phase ambiguity from fourfold to twofold, but would be more complex. Alternatively, one could resolve the fourfold phase ambiguity by use of differential coding in the transmitter, at a cost of reduced power efficiency. The proposed modifications would make it possible to reduce the fourfold phase ambiguity to twofold, with no loss in power efficiency and only relatively simple additional signal-processing steps in the transmitter and receiver. The twofold phase ambiguity would then be resolved by use of a unique synchronization word, as is commonly done in binary phase-shift keying (BPSK). Although the mathematical and signal-processing principles underlying the modifications are too complex to explain in detail here, the modifications themselves would be relatively simple and are best described with the help of simple block diagrams (see Figure 2). In the transmitter, one would add a unit that would periodically invert bits going into the QPSK modulator; in the receiver, one would add a unit that would effect different but corresponding inversions of bits coming out of the QPSK demodulator. The net effect of all the inversions would be that depending on which lock point the carrier-tracking loop had selected, all the output bits would be either inverted or non-inverted together; hence, the ambiguity would be reduced from fourfold to twofold, as desired.

Multiresolution wavelet analysis for efficient analysis, compression and remote display of long-term physiological signals.

PubMed

Khuan, L Y; Bister, M; Blanchfield, P; Salleh, Y M; Ali, R A; Chan, T H

2006-06-01

Increased inter-equipment connectivity coupled with advances in Web technology allows ever escalating amounts of physiological data to be produced, far too much to be displayed adequately on a single computer screen. The consequence is that large quantities of insignificant data will be transmitted and reviewed. This carries an increased risk of overlooking vitally important transients. This paper describes a technique to provide an integrated solution based on a single algorithm for the efficient analysis, compression and remote display of long-term physiological signals with infrequent short duration, yet vital events, to effect a reduction in data transmission and display cluttering and to facilitate reliable data interpretation. The algorithm analyses data at the server end and flags significant events. It produces a compressed version of the signal at a lower resolution that can be satisfactorily viewed in a single screen width. This reduced set of data is initially transmitted together with a set of 'flags' indicating where significant events occur. Subsequent transmissions need only involve transmission of flagged data segments of interest at the required resolution. Efficient processing and code protection with decomposition alone is novel. The fixed transmission length method ensures clutter-less display, irrespective of the data length. The flagging of annotated events in arterial oxygen saturation, electroencephalogram and electrocardiogram illustrates the generic property of the algorithm. Data reduction of 87% to 99% and improved displays are demonstrated.

Selective suppression of CARS signal with three-beam competing stimulated Raman scattering processes.

PubMed

Choi, Dae Sik; Rao, B Jayachander; Kim, Doyeon; Shim, Sang-Hee; Rhee, Hanju; Cho, Minhaeng

2018-06-14

Coherent Raman scattering spectroscopy and microscopy are useful methods for studying the chemical and biological structures of molecules with Raman-active modes. In particular, coherent anti-Stokes Raman scattering (CARS) microscopy, which is a label-free method capable of imaging structures by displaying the vibrational contrast of the molecules, has been widely used. However, the lack of a technique for switching-off the CARS signal has prevented the development of the super-resolution Raman imaging method. Here, we demonstrate that a selective suppression of the CARS signal is possible by using a three-beam double stimulated Raman scattering (SRS) scheme; the three beams are the pump, Stokes, and depletion lights in order of frequency. Both pump-Stokes and pump-depletion beam pairs can generate SRS processes by tuning their beat frequencies to match two different vibrational modes, then two CARS signals induced by pump-Stokes-pump and pump-depletion-pump interactions can be generated, where the two CARS signals are coupled with each other because they both involve interactions with the common pump beam. Herein, we show that as the intensity of the depletion beam is increased, one can selectively suppress the pump-Stokes-pump CARS signal because the pump-depletion SRS depletes the pump photons. A detailed theoretical description of the coupled differential equations for the three incident fields and the generated CARS signal fields is presented. Taking benzene as a molecular system, we obtained a maximum CARS suppression efficiency of about 97% with our experimental scheme, where the ring breathing mode of the benzene is associated with pump-Stokes-pump CARS, while the C-H stretching mode is associated with the competing pump-depletion SRS process. We anticipate that this selective switching-off scheme will be of use in developing super-resolution label-free CARS microscopy.

Auditory processing efficiency deficits in children with developmental language impairments

NASA Astrophysics Data System (ADS)

Hartley, Douglas E. H.; Moore, David R.

2002-12-01

The ``temporal processing hypothesis'' suggests that individuals with specific language impairments (SLIs) and dyslexia have severe deficits in processing rapidly presented or brief sensory information, both within the auditory and visual domains. This hypothesis has been supported through evidence that language-impaired individuals have excess auditory backward masking. This paper presents an analysis of masking results from several studies in terms of a model of temporal resolution. Results from this modeling suggest that the masking results can be better explained by an ``auditory efficiency'' hypothesis. If impaired or immature listeners have a normal temporal window, but require a higher signal-to-noise level (poor processing efficiency), this hypothesis predicts the observed small deficits in the simultaneous masking task, and the much larger deficits in backward and forward masking tasks amongst those listeners. The difference in performance on these masking tasks is predictable from the compressive nonlinearity of the basilar membrane. The model also correctly predicts that backward masking (i) is more prone to training effects, (ii) has greater inter- and intrasubject variability, and (iii) increases less with masker level than do other masking tasks. These findings provide a new perspective on the mechanisms underlying communication disorders and auditory masking.

β1 integrin signaling promotes neuronal migration along vascular scaffolds in the post-stroke brain.

PubMed

Fujioka, Teppei; Kaneko, Naoko; Ajioka, Itsuki; Nakaguchi, Kanako; Omata, Taichi; Ohba, Honoka; Fässler, Reinhard; García-Verdugo, José Manuel; Sekiguchi, Kiyotoshi; Matsukawa, Noriyuki; Sawamoto, Kazunobu

2017-02-01

Cerebral ischemic stroke is a main cause of chronic disability. However, there is currently no effective treatment to promote recovery from stroke-induced neurological symptoms. Recent studies suggest that after stroke, immature neurons, referred to as neuroblasts, generated in a neurogenic niche, the ventricular-subventricular zone, migrate toward the injured area, where they differentiate into mature neurons. Interventions that increase the number of neuroblasts distributed at and around the lesion facilitate neuronal repair in rodent models for ischemic stroke, suggesting that promoting neuroblast migration in the post-stroke brain could improve efficient neuronal regeneration. To move toward the lesion, neuroblasts form chain-like aggregates and migrate along blood vessels, which are thought to increase their migration efficiency. However, the molecular mechanisms regulating these migration processes are largely unknown. Here we studied the role of β1-class integrins, transmembrane receptors for extracellular matrix proteins, in these migrating neuroblasts. We found that the neuroblast chain formation and blood vessel-guided migration critically depend on β1 integrin signaling. β1 integrin facilitated the adhesion of neuroblasts to laminin and the efficient translocation of their soma during migration. Moreover, artificial laminin-containing scaffolds promoted neuroblast chain formation and migration toward the injured area. These data suggest that laminin signaling via β1 integrin supports vasculature-guided neuronal migration to efficiently supply neuroblasts to injured areas. This study also highlights the importance of vascular scaffolds for cell migration in development and regeneration. Copyright © 2017 3-V Biosciences. Published by Elsevier B.V. All rights reserved.

ENCoRE: an efficient software for CRISPR screens identifies new players in extrinsic apoptosis.

PubMed

Trümbach, Dietrich; Pfeiffer, Susanne; Poppe, Manuel; Scherb, Hagen; Doll, Sebastian; Wurst, Wolfgang; Schick, Joel A

2017-11-25

As CRISPR/Cas9 mediated screens with pooled guide libraries in somatic cells become increasingly established, an unmet need for rapid and accurate companion informatics tools has emerged. We have developed a lightweight and efficient software to easily manipulate large raw next generation sequencing datasets derived from such screens into informative relational context with graphical support. The advantages of the software entitled ENCoRE (Easy NGS-to-Gene CRISPR REsults) include a simple graphical workflow, platform independence, local and fast multithreaded processing, data pre-processing and gene mapping with custom library import. We demonstrate the capabilities of ENCoRE to interrogate results from a pooled CRISPR cellular viability screen following Tumor Necrosis Factor-alpha challenge. The results not only identified stereotypical players in extrinsic apoptotic signaling but two as yet uncharacterized members of the extrinsic apoptotic cascade, Smg7 and Ces2a. We further validated and characterized cell lines containing mutations in these genes against a panel of cell death stimuli and involvement in p53 signaling. In summary, this software enables bench scientists with sensitive data or without access to informatic cores to rapidly interpret results from large scale experiments resulting from pooled CRISPR/Cas9 library screens.

Integrated optical signal processing with magnetostatic waves

NASA Technical Reports Server (NTRS)

Fisher, A. D.; Lee, J. N.

1984-01-01

Magneto-optical devices based on Bragg diffraction of light by magnetostatic waves (MSW's) offer the potential of large time-bandwidth optical signal processing at microwave frequencies of 1 to 20 GHz and higher. A thin-film integrated-optical configuration, with the interacting MSW and guided-optical wave both propagating in a common ferrite layer, is necessary to avoid shape-factor demagnetization effects. The underlying theory of the MSW-optical interaction is outlined, including the development of expressions for optical diffraction efficiency as a function of MSW power and other relevant parameters. Bradd diffraction of guided-optical waves by transversely-propagating magnetostatic waves and collinear TE/TM mode conversion included by MSW's have been demonstrated in yttrium iron garnet (YIG) thin films. Diffraction levels as large as 4% (7 mm interaction length) and a modulation dynamic range of approx 30 dB have been observed. Advantages of these MSW-based devices over the analogous acousto-optical devices include: much greater operating frequencies, tunability of the MSW dispersion relation by varying either the RF frequency or the applied bias magnetic field, simple broad-band MSW transducer structures (e.g., a single stripline), and the potential for very high diffraction efficiencies.

A metrics for soil hydrological processes and their intrinsic dimensionality in heterogeneous systems

NASA Astrophysics Data System (ADS)

Lischeid, G.; Hohenbrink, T.; Schindler, U.

2012-04-01

Hydrology is based on the observation that catchments process input signals, e.g., precipitation, in a highly deterministic way. Thus, the Darcy or the Richards equation can be applied to model water fluxes in the saturated or vadose zone, respectively. Soils and aquifers usually exhibit substantial spatial heterogeneities at different scales that can, in principle, be represented by corresponding parameterisations of the models. In practice, however, data are hardly available at the required spatial resolution, and accounting for observed heterogeneities of soil and aquifer structure renders models very time and CPU consuming. We hypothesize that the intrinsic dimensionality of soil hydrological processes, which is induced by spatial heterogeneities, actually is very low and that soil hydrological processes in heterogeneous soils follow approximately the same trajectory. That means, the way how the soil transforms any hydrological input signals is the same for different soil textures and structures. Different soils differ only with respect to the extent of transformation of input signals. In a first step, we analysed the output of a soil hydrological model, based on the Richards equation, for homogeneous soils down to 5 m depth for different soil textures. A matrix of time series of soil matrix potential and soil water content at 10 cm depth intervals was set up. The intrinsic dimensionality of that matrix was assessed using the Correlation Dimension and a non-linear principal component approach. The latter provided a metrics for the extent of transformation ("damping") of the input signal. In a second step, model outputs for heterogeneous soils were analysed. In a last step, the same approaches were applied to 55 time series of observed soil water content from 15 sites and different depths. In all cases, the intrinsic dimensionality in fact was very close to unity, confirming our hypothesis. The metrics provided a very efficient tool to quantify the observed behaviour, depending on depth and soil heterogeneity: Different soils differed primarily with respect to the extent of damping per depth interval rather than to the kind of damping. We will show how that metrics can be used in a very efficient way for representing soil heterogeneities in simulation models.

State Recognition of Bone Drilling Based on Acoustic Emission in Pedicle Screw Operation.

PubMed

Guan, Fengqing; Sun, Yu; Qi, Xiaozhi; Hu, Ying; Yu, Gang; Zhang, Jianwei

2018-05-09

Pedicle drilling is an important step in pedicle screw fixation and the most significant challenge in this operation is how to determine a key point in the transition region between cancellous and inner cortical bone. The purpose of this paper is to find a method to achieve the recognition for the key point. After acquiring acoustic emission (AE) signals during the drilling process, this paper proposed a novel frequency distribution-based algorithm (FDB) to analyze the AE signals in the frequency domain after certain processes. Then we select a specific frequency domain of the signal for standard operations and choose a fitting function to fit the obtained sequence. Characters of the fitting function are extracted as outputs for identification of different bone layers. The results, which are obtained by detecting force signal and direct measurement, are given in the paper. Compared with the results above, the results obtained by AE signals are distinguishable for different bone layers and are more accurate and precise. The results of the algorithm are trained and identified by a neural network and the recognition rate reaches 84.2%. The proposed method is proved to be efficient and can be used for bone layer identification in pedicle screw fixation.

Signal processing to evaluate parameters affecting SPE for multi-residue analysis of personal care products.

PubMed

Pietrogrande, Maria Chiara; Basaglia, Giulia; Dondi, Francesco

2009-05-01

This paper discusses the development of a comprehensive method for the simultaneous analysis of personal care products (PCPs) based on SPE and GC-MS. The method was developed on 29 target compounds to represent PCPs belonging to different chemical classes: surfactants in detergents (alkyl benzenes), fragrances in cosmetics (nitro and polycyclic musks), antioxidants and preservatives (phenols), plasticizers (phthalates) displaying a wide range of volatility, polarity, water solubility. In addition to the conventional C(18) stationary phase, a surface modified styrene divinylbenzene polymeric phase (Strata X SPE cartridge) has been investigated as suitable for the simultaneous extraction of several PCPs with polar and non-polar characteristics. For both sorbents different solvent compositions and eluting conditions were tested and compared in order to achieve high extraction efficiency for as many sample components as possible. Comparison of the behavior of the two cartridges reveals that, overall, Strata-X provides better efficiency with extraction recovery higher than 70% for most of the PCPs investigated. The best results were obtained under the following operative conditions: an evaporation temperature of 40 degrees C, elution on Strata-X cartridge using a volume of 15 mL of ethyl acetate (EA) as solvent and operating with slow flow rate (-10 KPa). In addition to the conventional method based on peak integration, a chemometric approach based on the computation of the experimental autocovariance function (EACVF(tot)) was applied to the complex GC-MS signal: the percentage recovery and information on peak abundance distribution can be evaluated for each procedure step. The PC-based signal processing proved very helpful in assisting the development of the analytical procedure, since it saves labor and time and increases result reliability in handling GC complex signals.

Extended Logic Intelligent Processing System for a Sensor Fusion Processor Hardware

NASA Technical Reports Server (NTRS)

Stoica, Adrian; Thomas, Tyson; Li, Wei-Te; Daud, Taher; Fabunmi, James

2000-01-01

The paper presents the hardware implementation and initial tests from a low-power, highspeed reconfigurable sensor fusion processor. The Extended Logic Intelligent Processing System (ELIPS) is described, which combines rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor signals in compact low power VLSI. The development of the ELIPS concept is being done to demonstrate the interceptor functionality which particularly underlines the high speed and low power requirements. The hardware programmability allows the processor to reconfigure into different machines, taking the most efficient hardware implementation during each phase of information processing. Processing speeds of microseconds have been demonstrated using our test hardware.

Fractional Gaussian noise-enhanced information capacity of a nonlinear neuron model with binary signal input

NASA Astrophysics Data System (ADS)

Gao, Feng-Yin; Kang, Yan-Mei; Chen, Xi; Chen, Guanrong

2018-05-01

This paper reveals the effect of fractional Gaussian noise with Hurst exponent H ∈(1 /2 ,1 ) on the information capacity of a general nonlinear neuron model with binary signal input. The fGn and its corresponding fractional Brownian motion exhibit long-range, strong-dependent increments. It extends standard Brownian motion to many types of fractional processes found in nature, such as the synaptic noise. In the paper, for the subthreshold binary signal, sufficient conditions are given based on the "forbidden interval" theorem to guarantee the occurrence of stochastic resonance, while for the suprathreshold binary signal, the simulated results show that additive fGn with Hurst exponent H ∈(1 /2 ,1 ) could increase the mutual information or bits count. The investigation indicated that the synaptic noise with the characters of long-range dependence and self-similarity might be the driving factor for the efficient encoding and decoding of the nervous system.

Seismic and acoustic signal identification algorithms

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

LADD,MARK D.; ALAM,M. KATHLEEN; SLEEFE,GERARD E.

2000-04-03

This paper will describe an algorithm for detecting and classifying seismic and acoustic signals for unattended ground sensors. The algorithm must be computationally efficient and continuously process a data stream in order to establish whether or not a desired signal has changed state (turned-on or off). The paper will focus on describing a Fourier based technique that compares the running power spectral density estimate of the data to a predetermined signature in order to determine if the desired signal has changed state. How to establish the signature and the detection thresholds will be discussed as well as the theoretical statisticsmore » of the algorithm for the Gaussian noise case with results from simulated data. Actual seismic data results will also be discussed along with techniques used to reduce false alarms due to the inherent nonstationary noise environments found with actual data.« less

In vivo photoacoustic monitoring of photosensitizer in skin: application to dosimetry for antibacterial photodynamic treatment

NASA Astrophysics Data System (ADS)

Hirao, Akihiro; Sato, Shunichi; Saitoh, Daizoh; Shinomiya, Nariyoshi; Ashida, Hiroshi; Obara, Minoru

2009-02-01

To obtain efficient antibacterial photodynamic effect in traumatic injuries such as burns, depth-resolved dosimetry of photosensitizer is required. In this study, we performed dual-wavelength photoacoustic (PA) measurement for rat burned skins injected with a photosensitizer. As a photosensitizer, methylene blue (MB) or porfimer sodium was injected into the subcutaneous tissue in rats with deep dermal burn. The wound was irradiated with red (665 nm or 630 nm) pulsed light to excite photosensitizers and green (532 nm) pulsed light to excite blood in the tissue; the latter signal was used to eliminate blood-associated component involved in the former signal. Acoustic attenuation was also compensated from the photosensitizer-associated PA signals. These signal processing was effective to obtain high-contrast image of a photosensitizer in the tissue. Behaviors of MB and porfimer sodium in the tissue were compared.

Quantum-enabled temporal and spectral mode conversion of microwave signals

PubMed Central

Andrews, R. W.; Reed, A. P.; Cicak, K.; Teufel, J. D.; Lehnert, K. W.

2015-01-01

Electromagnetic waves are ideal candidates for transmitting information in a quantum network as they can be routed rapidly and efficiently between locations using optical fibres or microwave cables. Yet linking quantum-enabled devices with cables has proved difficult because most cavity or circuit quantum electrodynamics systems used in quantum information processing can only absorb and emit signals with a specific frequency and temporal envelope. Here we show that the temporal and spectral content of microwave-frequency electromagnetic signals can be arbitrarily manipulated with a flexible aluminium drumhead embedded in a microwave circuit. The aluminium drumhead simultaneously forms a mechanical oscillator and a tunable capacitor. This device offers a way to build quantum microwave networks using separate and otherwise mismatched components. Furthermore, it will enable the preparation of non-classical states of motion by capturing non-classical microwave signals prepared by the most coherent circuit quantum electrodynamics systems. PMID:26617386

Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals

NASA Astrophysics Data System (ADS)

Lu, Siliang; Wang, Xiaoxian; He, Qingbo; Liu, Fang; Liu, Yongbin

2016-12-01

Transient signal analysis (TSA) has been proven an effective tool for motor bearing fault diagnosis, but has yet to be applied in processing bearing fault signals with variable rotating speed. In this study, a new TSA-based angular resampling (TSAAR) method is proposed for fault diagnosis under speed fluctuation condition via sound signal analysis. By applying the TSAAR method, the frequency smearing phenomenon is eliminated and the fault characteristic frequency is exposed in the envelope spectrum for bearing fault recognition. The TSAAR method can accurately estimate the phase information of the fault-induced impulses using neither complicated time-frequency analysis techniques nor external speed sensors, and hence it provides a simple, flexible, and data-driven approach that realizes variable-speed motor bearing fault diagnosis. The effectiveness and efficiency of the proposed TSAAR method are verified through a series of simulated and experimental case studies.

VLSI implementation of a new LMS-based algorithm for noise removal in ECG signal

NASA Astrophysics Data System (ADS)

Satheeskumaran, S.; Sabrigiriraj, M.

2016-06-01

Least mean square (LMS)-based adaptive filters are widely deployed for removing artefacts in electrocardiogram (ECG) due to less number of computations. But they posses high mean square error (MSE) under noisy environment. The transform domain variable step-size LMS algorithm reduces the MSE at the cost of computational complexity. In this paper, a variable step-size delayed LMS adaptive filter is used to remove the artefacts from the ECG signal for improved feature extraction. The dedicated digital Signal processors provide fast processing, but they are not flexible. By using field programmable gate arrays, the pipelined architectures can be used to enhance the system performance. The pipelined architecture can enhance the operation efficiency of the adaptive filter and save the power consumption. This technique provides high signal-to-noise ratio and low MSE with reduced computational complexity; hence, it is a useful method for monitoring patients with heart-related problem.

Low-power, high-speed 1-bit inexact Full Adder cell designs applicable to low-energy image processing

NASA Astrophysics Data System (ADS)

Zareei, Zahra; Navi, Keivan; Keshavarziyan, Peiman

2018-03-01

In this paper, three novel low-power and high-speed 1-bit inexact Full Adder cell designs are presented based on current mode logic in 32 nm carbon nanotube field effect transistor technology for the first time. The circuit-level figures of merits, i.e. power, delay and power-delay product as well as application-level metric such as error distance, are considered to assess the efficiency of the proposed cells over their counterparts. The effect of voltage scaling and temperature variation on the proposed cells is studied using HSPICE tool. Moreover, using MATLAB tool, the peak signal to noise ratio of the proposed cells is evaluated in an image-processing application referred to as motion detector. Simulation results confirm the efficiency of the proposed cells.

Fiber-fed time-resolved photoluminescence for reduced process feedback time on thin-film photovoltaics

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

Repins, I. L.; Egaas, B.; Mansfield, L. M.

2015-01-15

Fiber-fed time-resolved photoluminescence is demonstrated as a tool for immediate process feedback after deposition of the absorber layer for CuIn{sub x}Ga{sub 1-x}Se{sub 2} and Cu{sub 2}ZnSnSe{sub 4} photovoltaic devices. The technique uses a simplified configuration compared to typical laboratory time-resolved photoluminescence in the delivery of the exciting beam, signal collection, and electronic components. Correlation of instrument output with completed device efficiency is demonstrated over a large sample set. The extraction of the instrument figure of merit, depending on both the initial luminescence intensity and its time decay, is explained and justified. Limitations in the prediction of device efficiency by thismore » method, including surface effect, are demonstrated and discussed.« less

CHAM: weak signals detection through a new multivariate algorithm for process control

NASA Astrophysics Data System (ADS)

Bergeret, François; Soual, Carole; Le Gratiet, B.

2016-10-01

Derivatives technologies based on core CMOS processes are significantly aggressive in term of design rules and process control requirements. Process control plan is a derived from Process Assumption (PA) calculations which result in a design rule based on known process variability capabilities, taking into account enough margin to be safe not only for yield but especially for reliability. Even though process assumptions are calculated with a 4 sigma known process capability margin, efficient and competitive designs are challenging the process especially for derivatives technologies in 40 and 28nm nodes. For wafer fab process control, PA are declined in monovariate (layer1 CD, layer2 CD, layer2 to layer1 overlay, layer3 CD etc….) control charts with appropriated specifications and control limits which all together are securing the silicon. This is so far working fine but such system is not really sensitive to weak signals coming from interactions of multiple key parameters (high layer2 CD combined with high layer3 CD as an example). CHAM is a software using an advanced statistical algorithm specifically designed to detect small signals, especially when there are many parameters to control and when the parameters can interact to create yield issues. In this presentation we will first present the CHAM algorithm, then the case-study on critical dimensions, with the results, and we will conclude on future work. This partnership between Ippon and STM is part of E450LMDAP, European project dedicated to metrology and lithography development for future technology nodes, especially 10nm.

Research Based on the Acoustic Emission of Wind Power Tower Drum Dynamic Monitoring Technology

NASA Astrophysics Data System (ADS)

Zhang, Penglin; Sang, Yuan; Xu, Yaxing; Zhao, Zhiqiang

Wind power tower drum is one of the key components of the wind power equipment. Whether the wind tower drum performs safety directly affects the efficiency, life, and performance of wind power equipment. Wind power tower drum in the process of manufacture, installation, and operation may lead to injury, and the wind load and gravity load and long-term factors such as poor working environment under the action of crack initiation or distortion, which eventually result in the instability or crack of the wind power tower drum and cause huge economic losses. Thus detecting the wind power tower drum crack damage and instability is especially important. In this chapter, acoustic emission is used to monitor the whole process of wind power tower drum material Q345E steel tensile test at first, and processing and analysis tensile failure signal of the material. And then based on the acoustic emission testing technology to the dynamic monitoring of wind power tower drum, the overall detection and evaluation of the existence of active defects in the whole structure, and the acoustic emission signals collected for processing and analysis, we could preliminarily master the wind tower drum mechanism of acoustic emission source. The acoustic emission is a kind of online, efficient, and economic method, which has very broad prospects for work. The editorial committee of nondestructive testing qualification and certification of personnel teaching material of science and technology industry of national defense, "Acoustic emission testing" (China Machine Press, 2005.1).

Distributed Peer-to-Peer Target Tracking in Wireless Sensor Networks

PubMed Central

Wang, Xue; Wang, Sheng; Bi, Dao-Wei; Ma, Jun-Jie

2007-01-01

Target tracking is usually a challenging application for wireless sensor networks (WSNs) because it is always computation-intensive and requires real-time processing. This paper proposes a practical target tracking system based on the auto regressive moving average (ARMA) model in a distributed peer-to-peer (P2P) signal processing framework. In the proposed framework, wireless sensor nodes act as peers that perform target detection, feature extraction, classification and tracking, whereas target localization requires the collaboration between wireless sensor nodes for improving the accuracy and robustness. For carrying out target tracking under the constraints imposed by the limited capabilities of the wireless sensor nodes, some practically feasible algorithms, such as the ARMA model and the 2-D integer lifting wavelet transform, are adopted in single wireless sensor nodes due to their outstanding performance and light computational burden. Furthermore, a progressive multi-view localization algorithm is proposed in distributed P2P signal processing framework considering the tradeoff between the accuracy and energy consumption. Finally, a real world target tracking experiment is illustrated. Results from experimental implementations have demonstrated that the proposed target tracking system based on a distributed P2P signal processing framework can make efficient use of scarce energy and communication resources and achieve target tracking successfully.

Cytoplasmic utilization of human immunodeficiency virus type 1 genomic RNA is not dependent on a nuclear interaction with gag.

PubMed

Grewe, Bastian; Hoffmann, Bianca; Ohs, Inga; Blissenbach, Maik; Brandt, Sabine; Tippler, Bettina; Grunwald, Thomas; Uberla, Klaus

2012-03-01

In some retroviruses, such as Rous sarcoma virus and prototype foamy virus, Gag proteins are known to shuttle between the nucleus and the cytoplasm and are implicated in nuclear export of the viral genomic unspliced RNA (gRNA) for subsequent encapsidation. A similar function has been proposed for human immunodeficiency virus type 1 (HIV-1) Gag based on the identification of nuclear localization and export signals. However, the ability of HIV-1 Gag to transit through the nucleus has never been confirmed. In addition, the lentiviral Rev protein promotes efficient nuclear gRNA export, and previous reports indicate a cytoplasmic interaction between Gag and gRNA. Therefore, functional effects of HIV-1 Gag on gRNA and its usage were explored. Expression of gag in the absence of Rev was not able to increase cytoplasmic gRNA levels of subgenomic, proviral, or lentiviral vector constructs, and gene expression from genomic reporter plasmids could not be induced by Gag provided in trans. Furthermore, Gag lacking the reported nuclear localization and export signals was still able to mediate an efficient packaging process. Although small amounts of Gag were detectable in the nuclei of transfected cells, a Crm1-dependent nuclear export signal in Gag could not be confirmed. Thus, our study does not provide any evidence for a nuclear function of HIV-1 Gag. The encapsidation process of HIV-1 therefore clearly differs from that of Rous sarcoma virus and prototype foamy virus.

An Ultra-Wideband Cross-Correlation Radiometer for Mesoscopic Experiments

NASA Astrophysics Data System (ADS)

Toonen, Ryan; Haselby, Cyrus; Qin, Hua; Eriksson, Mark; Blick, Robert

2007-03-01

We have designed, built and tested a cross-correlation radiometer for detecting statistical order in the quantum fluctuations of mesoscopic experiments at sub-Kelvin temperatures. Our system utilizes a fully analog front-end--operating over the X- and Ku-bands (8 to 18 GHz)--for computing the cross-correlation function. Digital signal processing techniques are used to provide robustness against instrumentation drifts and offsets. The economized version of our instrument can measure, with sufficient correlation efficiency, noise signals having power levels as low as 10 fW. We show that, if desired, we can improve this performance by including cryogenic preamplifiers which boost the signal-to-noise ratio near the signal source. By adding a few extra components, we can measure both the real and imaginary parts of the cross-correlation function--improving the overall signal-to-noise ratio by a factor of sqrt[2]. We demonstrate the utility of our cross-correlator with noise power measurements from a quantum point contact.

Direct bit detection receiver noise performance analysis for 32-PSK and 64-PSK modulated signals

NASA Astrophysics Data System (ADS)

Ahmed, Iftikhar

1987-12-01

Simple two channel receivers for 32-PSK and 64-PSK modulated signals have been proposed which allow digital data (namely bits), to be recovered directly instead of the traditional approach of symbol detection followed by symbol to bit mappings. This allows for binary rather than M-ary receiver decisions, reduces the amount of signal processing operations and permits parallel recovery of the bits. The noise performance of these receivers quantified by the Bit Error Rate (BER) assuming an Additive White Gaussian Noise interference model is evaluated as a function of Eb/No, the signal to noise ratio, and transmitted phase angles of the signals. The performance results of the direct bit detection receivers (DBDR) when compared to that of convectional phase measurement receivers demonstrate that DBDR's are optimum in BER sense. The simplicity of the receiver implementations and the BER of the delivered data make DBDR's attractive for high speed, spectrally efficient digital communication systems.

Measuring the labeling efficiency of pseudocontinuous arterial spin labeling.

PubMed

Chen, Zhensen; Zhang, Xingxing; Yuan, Chun; Zhao, Xihai; van Osch, Matthias J P

2017-05-01

Optimization and validation of a sequence for measuring the labeling efficiency of pseudocontinuous arterial spin labeling (pCASL) perfusion MRI. The proposed sequence consists of a labeling module and a single slice Look-Locker echo planar imaging readout. A model-based algorithm was used to calculate labeling efficiency from the signal acquired from the main brain-feeding arteries. Stability of the labeling efficiency measurement was evaluated with regard to the use of cardiac triggering, flow compensation and vein signal suppression. Accuracy of the measurement was assessed by comparing the measured labeling efficiency to mean brain pCASL signal intensity over a wide range of flip angles as applied in the pCASL labeling. Simulations show that the proposed algorithm can effectively calculate labeling efficiency when correcting for T1 relaxation of the blood spins. Use of cardiac triggering and vein signal suppression improved stability of the labeling efficiency measurement, while flow compensation resulted in little improvement. The measured labeling efficiency was found to be linearly (R = 0.973; P < 0.001) related to brain pCASL signal intensity over a wide range of pCASL flip angles. The optimized labeling efficiency sequence provides robust artery-specific labeling efficiency measurement within a short acquisition time (∼30 s), thereby enabling improved accuracy of pCASL CBF quantification. Magn Reson Med 77:1841-1852, 2017. © 2016 International Society for Magnetic Resonance in Medicine Magn Reson Med 77:1841-1852, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Power control electronics for cryogenic instrumentation

NASA Technical Reports Server (NTRS)

Ray, Biswajit; Gerber, Scott S.; Patterson, Richard L.; Myers, Ira T.

1995-01-01

In order to achieve a high-efficiency high-density cryogenic instrumentation system, the power processing electronics should be placed in the cold environment along with the sensors and signal-processing electronics. The typical instrumentation system requires low voltage dc usually obtained from processing line frequency ac power. Switch-mode power conversion topologies such as forward, flyback, push-pull, and half-bridge are used for high-efficiency power processing using pulse-width modulation (PWM) or resonant control. This paper presents several PWM and multiresonant power control circuits, implemented using commercially available CMOS and BiCMOS integrated circuits, and their performance at liquid-nitrogen temperature (77 K) as compared to their room temperature (300 K) performance. The operation of integrated circuits at cryogenic temperatures results in an improved performance in terms of increased speed, reduced latch-up susceptibility, reduced leakage current, and reduced thermal noise. However, the switching noise increased at 77 K compared to 300 K. The power control circuits tested in the laboratory did successfully restart at 77 K.

Statistical process control using optimized neural networks: a case study.

PubMed

Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

2014-09-01

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Missile signal processing common computer architecture for rapid technology upgrade

NASA Astrophysics Data System (ADS)

Rabinkin, Daniel V.; Rutledge, Edward; Monticciolo, Paul

2004-10-01

Interceptor missiles process IR images to locate an intended target and guide the interceptor towards it. Signal processing requirements have increased as the sensor bandwidth increases and interceptors operate against more sophisticated targets. A typical interceptor signal processing chain is comprised of two parts. Front-end video processing operates on all pixels of the image and performs such operations as non-uniformity correction (NUC), image stabilization, frame integration and detection. Back-end target processing, which tracks and classifies targets detected in the image, performs such algorithms as Kalman tracking, spectral feature extraction and target discrimination. In the past, video processing was implemented using ASIC components or FPGAs because computation requirements exceeded the throughput of general-purpose processors. Target processing was performed using hybrid architectures that included ASICs, DSPs and general-purpose processors. The resulting systems tended to be function-specific, and required custom software development. They were developed using non-integrated toolsets and test equipment was developed along with the processor platform. The lifespan of a system utilizing the signal processing platform often spans decades, while the specialized nature of processor hardware and software makes it difficult and costly to upgrade. As a result, the signal processing systems often run on outdated technology, algorithms are difficult to update, and system effectiveness is impaired by the inability to rapidly respond to new threats. A new design approach is made possible three developments; Moore's Law - driven improvement in computational throughput; a newly introduced vector computing capability in general purpose processors; and a modern set of open interface software standards. Today's multiprocessor commercial-off-the-shelf (COTS) platforms have sufficient throughput to support interceptor signal processing requirements. This application may be programmed under existing real-time operating systems using parallel processing software libraries, resulting in highly portable code that can be rapidly migrated to new platforms as processor technology evolves. Use of standardized development tools and 3rd party software upgrades are enabled as well as rapid upgrade of processing components as improved algorithms are developed. The resulting weapon system will have a superior processing capability over a custom approach at the time of deployment as a result of a shorter development cycles and use of newer technology. The signal processing computer may be upgraded over the lifecycle of the weapon system, and can migrate between weapon system variants enabled by modification simplicity. This paper presents a reference design using the new approach that utilizes an Altivec PowerPC parallel COTS platform. It uses a VxWorks-based real-time operating system (RTOS), and application code developed using an efficient parallel vector library (PVL). A quantification of computing requirements and demonstration of interceptor algorithm operating on this real-time platform are provided.

Vimentin coordinates fibroblast proliferation and keratinocyte differentiation in wound healing via TGF-β–Slug signaling

PubMed Central

Cheng, Fang; Shen, Yue; Mohanasundaram, Ponnuswamy; Lindström, Michelle; Ivaska, Johanna; Ny, Tor; Eriksson, John E.

2016-01-01

Vimentin has been shown to be involved in wound healing, but its functional contribution to this process is poorly understood. Here we describe a previously unrecognized function of vimentin in coordinating fibroblast proliferation and keratinocyte differentiation during wound healing. Loss of vimentin led to a severe deficiency in fibroblast growth, which in turn inhibited the activation of two major initiators of epithelial–mesenchymal transition (EMT), TGF-β1 signaling and the Zinc finger transcriptional repressor protein Slug, in vimentin-deficient (VIM−/−) wounds. Correspondingly, VIM−/− wounds exhibited loss of EMT-like keratinocyte activation, limited keratinization, and slow reepithelialization. Furthermore, the fibroblast deficiency abolished collagen accumulation in the VIM−/− wounds. Vimentin reconstitution in VIM−/− fibroblasts restored both their proliferation and TGF-β1 production. Similarly, restoring paracrine TGF-β–Slug–EMT signaling reactivated the transdifferentiation of keratinocytes, reviving their migratory properties, a critical feature for efficient healing. Our results demonstrate that vimentin orchestrates the healing by controlling fibroblast proliferation, TGF-β1–Slug signaling, collagen accumulation, and EMT processing, all of which in turn govern the required keratinocyte activation. PMID:27466403

A computational model of conditioning inspired by Drosophila olfactory system.

PubMed

Faghihi, Faramarz; Moustafa, Ahmed A; Heinrich, Ralf; Wörgötter, Florentin

2017-03-01

Recent studies have demonstrated that Drosophila melanogaster (briefly Drosophila) can successfully perform higher cognitive processes including second order olfactory conditioning. Understanding the neural mechanism of this behavior can help neuroscientists to unravel the principles of information processing in complex neural systems (e.g. the human brain) and to create efficient and robust robotic systems. In this work, we have developed a biologically-inspired spiking neural network which is able to execute both first and second order conditioning. Experimental studies demonstrated that volume signaling (e.g. by the gaseous transmitter nitric oxide) contributes to memory formation in vertebrates and invertebrates including insects. Based on the existing knowledge of odor encoding in Drosophila, the role of retrograde signaling in memory function, and the integration of synaptic and non-synaptic neural signaling, a neural system is implemented as Simulated fly. Simulated fly navigates in a two-dimensional environment in which it receives odors and electric shocks as sensory stimuli. The model suggests some experimental research on retrograde signaling to investigate neural mechanisms of conditioning in insects and other animals. Moreover, it illustrates a simple strategy to implement higher cognitive capabilities in machines including robots. Copyright © 2016 Elsevier Ltd. All rights reserved.

A long distance voice transmission system based on the white light LED

NASA Astrophysics Data System (ADS)

Tian, Chunyu; Wei, Chang; Wang, Yulian; Wang, Dachi; Yu, Benli; Xu, Feng

2017-10-01

A long distance voice transmission system based on a visible light communication technology (VLCT) is proposed in the paper. Our proposed system includes transmitter, receiver and the voice signal processing of single chip microcomputer. In the compact-sized LED transmitter, we use on-off-keying and not-return-to-zero (OOK-NRZ) to easily realize high speed modulation, and then systematic complexity is reduced. A voice transmission system, which possesses the properties of the low-noise and wide modulation band, is achieved by the design of high efficiency receiving optical path and using filters to reduce noise from the surrounding light. To improve the speed of the signal processing, we use single chip microcomputer to code and decode voice signal. Furthermore, serial peripheral interface (SPI) is adopted to accurately transmit voice signal data. The test results of our proposed system show that the transmission distance of this system is more than100 meters with the maximum data rate of 1.5 Mbit/s and a SNR of 30dB. This system has many advantages, such as simple construction, low cost and strong practicality. Therefore, it has extensive application prospect in the fields of the emergency communication and indoor wireless communication, etc.

Do not let death do us part: 'find-me' signals in communication between dying cells and the phagocytes.

PubMed

Medina, C B; Ravichandran, K S

2016-06-01

The turnover and clearance of cells is an essential process that is part of many physiological and pathological processes. Improper or deficient clearance of apoptotic cells can lead to excessive inflammation and autoimmune disease. The steps involved in cell clearance include: migration of the phagocyte toward the proximity of the dying cells, specific recognition and internalization of the dying cell, and degradation of the corpse. The ability of phagocytes to recognize and react to dying cells to perform efficient and immunologically silent engulfment has been well-characterized in vitro and in vivo. However, how apoptotic cells themselves initiate the corpse removal and also influence the cells within the neighboring environment during clearance was less understood. Recent exciting observations suggest that apoptotic cells can attract phagocytes through the regulated release of 'find-me' signals. More recent studies also suggest that these find-me signals can have additional roles outside of phagocyte attraction to help orchestrate engulfment. This review will discuss our current understanding of the different find-me signals released by apoptotic cells, how they may be relevant in vivo, and their additional roles in facilitating engulfment.

Do not let death do us part: ‘find-me' signals in communication between dying cells and the phagocytes

PubMed Central

Medina, C B; Ravichandran, K S

2016-01-01

The turnover and clearance of cells is an essential process that is part of many physiological and pathological processes. Improper or deficient clearance of apoptotic cells can lead to excessive inflammation and autoimmune disease. The steps involved in cell clearance include: migration of the phagocyte toward the proximity of the dying cells, specific recognition and internalization of the dying cell, and degradation of the corpse. The ability of phagocytes to recognize and react to dying cells to perform efficient and immunologically silent engulfment has been well-characterized in vitro and in vivo. However, how apoptotic cells themselves initiate the corpse removal and also influence the cells within the neighboring environment during clearance was less understood. Recent exciting observations suggest that apoptotic cells can attract phagocytes through the regulated release of ‘find-me' signals. More recent studies also suggest that these find-me signals can have additional roles outside of phagocyte attraction to help orchestrate engulfment. This review will discuss our current understanding of the different find-me signals released by apoptotic cells, how they may be relevant in vivo, and their additional roles in facilitating engulfment. PMID:26891690

The R-package eseis - A toolbox to weld geomorphic, seismologic, spatial, and time series analysis

NASA Astrophysics Data System (ADS)

Dietze, Michael

2017-04-01

Environmental seismology is the science of investigating the seismic signals that are emitted by Earth surface processes. This emerging field provides unique opportunities to identify, locate, track and inspect a wide range of the processes that shape our planet. Modern broadband seismometers are sensitive enough to detect signals from sources as weak as wind interacting with the ground and as powerful as collapsing mountains. This places the field of environmental seismology at the seams of many geoscientific disciplines and requires integration of a series of specialised analysis techniques. R provides the perfect environment for this challenge. The package eseis uses the foundations laid by a series of existing packages and data types tailored to solve specialised problems (e.g., signal, sp, rgdal, Rcpp, matrixStats) and thus provides access to efficiently handling large streams of seismic data (> 300 million samples per station and day). It supports standard data formats (mseed, sac), preparation techniques (deconvolution, filtering, rotation), processing methods (spectra, spectrograms, event picking, migration for localisation) and data visualisation. Thus, eseis provides a seamless approach to the entire workflow of environmental seismology and passes the output to related analysis fields with temporal, spatial and modelling focus in R.

SNIa detection in the SNLS photometric analysis using Morphological Component Analysis

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

Möller, A.; Ruhlmann-Kleider, V.; Neveu, J.

2015-04-01

Detection of supernovae (SNe) and, more generally, of transient events in large surveys can provide numerous false detections. In the case of a deferred processing of survey images, this implies reconstructing complete light curves for all detections, requiring sizable processing time and resources. Optimizing the detection of transient events is thus an important issue for both present and future surveys. We present here the optimization done in the SuperNova Legacy Survey (SNLS) for the 5-year data deferred photometric analysis. In this analysis, detections are derived from stacks of subtracted images with one stack per lunation. The 3-year analysis provided 300,000more » detections dominated by signals of bright objects that were not perfectly subtracted. Allowing these artifacts to be detected leads not only to a waste of resources but also to possible signal coordinate contamination. We developed a subtracted image stack treatment to reduce the number of non SN-like events using morphological component analysis. This technique exploits the morphological diversity of objects to be detected to extract the signal of interest. At the level of our subtraction stacks, SN-like events are rather circular objects while most spurious detections exhibit different shapes. A two-step procedure was necessary to have a proper evaluation of the noise in the subtracted image stacks and thus a reliable signal extraction. We also set up a new detection strategy to obtain coordinates with good resolution for the extracted signal. SNIa Monte-Carlo (MC) generated images were used to study detection efficiency and coordinate resolution. When tested on SNLS 3-year data this procedure decreases the number of detections by a factor of two, while losing only 10% of SN-like events, almost all faint ones. MC results show that SNIa detection efficiency is equivalent to that of the original method for bright events, while the coordinate resolution is improved.« less

G-CNV: A GPU-Based Tool for Preparing Data to Detect CNVs with Read-Depth Methods.

PubMed

Manconi, Andrea; Manca, Emanuele; Moscatelli, Marco; Gnocchi, Matteo; Orro, Alessandro; Armano, Giuliano; Milanesi, Luciano

2015-01-01

Copy number variations (CNVs) are the most prevalent types of structural variations (SVs) in the human genome and are involved in a wide range of common human diseases. Different computational methods have been devised to detect this type of SVs and to study how they are implicated in human diseases. Recently, computational methods based on high-throughput sequencing (HTS) are increasingly used. The majority of these methods focus on mapping short-read sequences generated from a donor against a reference genome to detect signatures distinctive of CNVs. In particular, read-depth based methods detect CNVs by analyzing genomic regions with significantly different read-depth from the other ones. The pipeline analysis of these methods consists of four main stages: (i) data preparation, (ii) data normalization, (iii) CNV regions identification, and (iv) copy number estimation. However, available tools do not support most of the operations required at the first two stages of this pipeline. Typically, they start the analysis by building the read-depth signal from pre-processed alignments. Therefore, third-party tools must be used to perform most of the preliminary operations required to build the read-depth signal. These data-intensive operations can be efficiently parallelized on graphics processing units (GPUs). In this article, we present G-CNV, a GPU-based tool devised to perform the common operations required at the first two stages of the analysis pipeline. G-CNV is able to filter low-quality read sequences, to mask low-quality nucleotides, to remove adapter sequences, to remove duplicated read sequences, to map the short-reads, to resolve multiple mapping ambiguities, to build the read-depth signal, and to normalize it. G-CNV can be efficiently used as a third-party tool able to prepare data for the subsequent read-depth signal generation and analysis. Moreover, it can also be integrated in CNV detection tools to generate read-depth signals.

Brain signal variability is parametrically modifiable.

PubMed

Garrett, Douglas D; McIntosh, Anthony R; Grady, Cheryl L

2014-11-01

Moment-to-moment brain signal variability is a ubiquitous neural characteristic, yet remains poorly understood. Evidence indicates that heightened signal variability can index and aid efficient neural function, but it is not known whether signal variability responds to precise levels of environmental demand, or instead whether variability is relatively static. Using multivariate modeling of functional magnetic resonance imaging-based parametric face processing data, we show here that within-person signal variability level responds to incremental adjustments in task difficulty, in a manner entirely distinct from results produced by examining mean brain signals. Using mixed modeling, we also linked parametric modulations in signal variability with modulations in task performance. We found that difficulty-related reductions in signal variability predicted reduced accuracy and longer reaction times within-person; mean signal changes were not predictive. We further probed the various differences between signal variance and signal means by examining all voxels, subjects, and conditions; this analysis of over 2 million data points failed to reveal any notable relations between voxel variances and means. Our results suggest that brain signal variability provides a systematic task-driven signal of interest from which we can understand the dynamic function of the human brain, and in a way that mean signals cannot capture. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Fundamental performance differences between CMOS and CCD imagers: Part II

NASA Astrophysics Data System (ADS)

Janesick, James; Andrews, James; Tower, John; Grygon, Mark; Elliott, Tom; Cheng, John; Lesser, Michael; Pinter, Jeff

2007-09-01

A new class of CMOS imagers that compete with scientific CCDs is presented. The sensors are based on deep depletion backside illuminated technology to achieve high near infrared quantum efficiency and low pixel cross-talk. The imagers deliver very low read noise suitable for single photon counting - Fano-noise limited soft x-ray applications. Digital correlated double sampling signal processing necessary to achieve low read noise performance is analyzed and demonstrated for CMOS use. Detailed experimental data products generated by different pixel architectures (notably 3TPPD, 5TPPD and 6TPG designs) are presented including read noise, charge capacity, dynamic range, quantum efficiency, charge collection and transfer efficiency and dark current generation. Radiation damage data taken for the imagers is also reported.

Induction of Suppressor of Cytokine Signaling-3 by Herpes Simplex Virus Type 1 Contributes to Inhibition of the Interferon Signaling Pathway

PubMed Central

Yokota, Shin-ichi; Yokosawa, Noriko; Okabayashi, Tamaki; Suzutani, Tatsuo; Miura, Shunsuke; Jimbow, Kowichi; Fujii, Nobuhiro

2004-01-01

We showed previously that herpes simplex virus type 1 (HSV-1) suppresses the interferon (IFN) signaling pathway during the early infection stage in the human amnion cell line FL. HSV-1 inhibits the IFN-induced phosphorylation of Janus kinases (JAK) in infected FL cells. In the present study, we showed that the suppressor of cytokine signaling-3 (SOCS3), a host negative regulator of the JAK/STAT pathway, is rapidly induced in FL cells after HSV-1 infection. Maximal levels of SOCS3 protein were detected at around 1 to 2 h after infection. This is consistent with the occurrence of HSV-1-mediated inhibition of IFN-induced JAK phosphorylation. The HSV-1 wild-type strain VR3 induced SOCS3 more efficiently than did mutants that are defective in UL41 or UL13 and that are hyperresponsive to IFN. Induction of the IRF-7 protein and transcriptional activation of IFN-α4, which occur in a JAK/STAT pathway-dependent manner, were poorly induced by VR3 but efficiently induced by the mutant viruses. In contrast, phosphorylation of IRF-3 and transcriptional activation of IFN-β, which are JAK/STAT pathway-independent process, were equally well induced by the wild-type strain and the mutants. In conclusion, the SOCS3 protein appears to be mainly responsible for the suppression of IFN signaling and IFN production that occurs during HSV-1 infection. PMID:15163721

Application of independent component analysis for speech-music separation using an efficient score function estimation

NASA Astrophysics Data System (ADS)

Pishravian, Arash; Aghabozorgi Sahaf, Masoud Reza

2012-12-01

In this paper speech-music separation using Blind Source Separation is discussed. The separating algorithm is based on the mutual information minimization where the natural gradient algorithm is used for minimization. In order to do that, score function estimation from observation signals (combination of speech and music) samples is needed. The accuracy and the speed of the mentioned estimation will affect on the quality of the separated signals and the processing time of the algorithm. The score function estimation in the presented algorithm is based on Gaussian mixture based kernel density estimation method. The experimental results of the presented algorithm on the speech-music separation and comparing to the separating algorithm which is based on the Minimum Mean Square Error estimator, indicate that it can cause better performance and less processing time

Brain Signal Variability Differentially Affects Cognitive Flexibility and Cognitive Stability.

PubMed

Armbruster-Genç, Diana J N; Ueltzhöffer, Kai; Fiebach, Christian J

2016-04-06

Recent research yielded the intriguing conclusion that, in healthy adults, higher levels of variability in neuronal processes are beneficial for cognitive functioning. Beneficial effects of variability in neuronal processing can also be inferred from neurocomputational theories of working memory, albeit this holds only for tasks requiring cognitive flexibility. However, cognitive stability, i.e., the ability to maintain a task goal in the face of irrelevant distractors, should suffer under high levels of brain signal variability. To directly test this prediction, we studied both behavioral and brain signal variability during cognitive flexibility (i.e., task switching) and cognitive stability (i.e., distractor inhibition) in a sample of healthy human subjects and developed an efficient and easy-to-implement analysis approach to assess BOLD-signal variability in event-related fMRI task paradigms. Results show a general positive effect of neural variability on task performance as assessed by accuracy measures. However, higher levels of BOLD-signal variability in the left inferior frontal junction area result in reduced error rate costs during task switching and thus facilitate cognitive flexibility. In contrast, variability in the same area has a detrimental effect on cognitive stability, as shown in a negative effect of variability on response time costs during distractor inhibition. This pattern was mirrored at the behavioral level, with higher behavioral variability predicting better task switching but worse distractor inhibition performance. Our data extend previous results on brain signal variability by showing a differential effect of brain signal variability that depends on task context, in line with predictions from computational theories. Recent neuroscientific research showed that the human brain signal is intrinsically variable and suggested that this variability improves performance. Computational models of prefrontal neural networks predict differential effects of variability for different behavioral situations requiring either cognitive flexibility or stability. However, this hypothesis has so far not been put to an empirical test. In this study, we assessed cognitive flexibility and cognitive stability, and, besides a generally positive effect of neural variability on accuracy measures, we show that neural variability in a prefrontal brain area at the inferior frontal junction is differentially associated with performance: higher levels of variability are beneficial for the effectiveness of task switching (cognitive flexibility) but detrimental for the efficiency of distractor inhibition (cognitive stability). Copyright © 2016 the authors 0270-6474/16/363978-10$15.00/0.

The role of microbial signals in plant growth and development

PubMed Central

Ortíz-Castro, Randy; Contreras-Cornejo, Hexon Angel; Macías-Rodríguez, Lourdes

2009-01-01

Plant growth and development involves a tight coordination of the spatial and temporal organization of cell division, cell expansion and cell differentiation. Orchestration of these events requires the exchange of signaling molecules between the root and shoot, which can be affected by both biotic and abiotic factors. The interactions that occur between plants and their associated microorganisms have long been of interest, as knowledge of these processes could lead to the development of novel agricultural applications. Plants produce a wide range of organic compounds including sugars, organic acids and vitamins, which can be used as nutrients or signals by microbial populations. On the other hand, microorganisms release phytohormones, small molecules or volatile compounds, which may act directly or indirectly to activate plant immunity or regulate plant growth and morphogenesis. In this review, we focus on recent developments in the identification of signals from free-living bacteria and fungi that interact with plants in a beneficial way. Evidence has accumulated indicating that classic plant signals such as auxins and cytokinins can be produced by microorganisms to efficiently colonize the root and modulate root system architecture. Other classes of signals, including N-acyl-L-homoserine lactones, which are used by bacteria for cell-to-cell communication, can be perceived by plants to modulate gene expression, metabolism and growth. Finally, we discuss the role played by volatile organic compounds released by certain plant growth-promoting rhizobacteria in plant immunity and developmental processes. The picture that emerges is one in which plants and microbes communicate themselves through transkingdom signaling systems involving classic and novel signals. PMID:19820333

Dual Rate Adaptive Control for an Industrial Heat Supply Process Using Signal Compensation Approach

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

Chai, Tianyou; Jia, Yao; Wang, Hong

The industrial heat supply process (HSP) is a highly nonlinear cascaded process which uses a steam valve opening as its control input, the steam flow-rate as its inner loop output and the supply water temperature as its outer loop output. The relationship between the heat exchange rate and the model parameters, such as steam density, entropy, and fouling correction factor and heat exchange efficiency are unknown and nonlinear. Moreover, these model parameters vary in line with steam pressure, ambient temperature and the residuals caused by the quality variations of the circulation water. When the steam pressure and the ambient temperaturemore » are of high values and are subjected to frequent external random disturbances, the supply water temperature and the steam flow-rate would interact with each other and fluctuate a lot. This is also true when the process exhibits unknown characteristic variations of the process dynamics caused by the unexpected changes of the heat exchange residuals. As a result, it is difficult to control the supply water temperature and the rates of changes of steam flow-rate well inside their targeted ranges. In this paper, a novel compensation signal based dual rate adaptive controller is developed by representing the unknown variations of dynamics as unmodeled dynamics. In the proposed controller design, such a compensation signal is constructed and added onto the control signal obtained from the linear deterministic model based feedback control design. Such a compensation signal aims at eliminating the unmodeled dynamics and the rate of changes of the currently sample unmodeled dynamics. A successful industrial application is carried out, where it has been shown that both the supply water temperature and the rate of the changes of the steam flow-rate can be controlled well inside their targeted ranges when the process is subjected to unknown variations of its dynamics.« less

The research of knitting needle status monitoring setup

NASA Astrophysics Data System (ADS)

Liu, Lu; Liao, Xiao-qing; Zhu, Yong-kang; Yang, Wei; Zhang, Pei; Zhao, Yong-kai; Huang, Hui-jie

2013-09-01

In textile production, quality control and testing is the key to ensure the process and improve the efficiency. Defect of the knitting needles is the main factor affecting the quality of the appearance of textiles. Defect detection method based on machine vision and image processing technology is universal. This approach does not effectively identify the defect generated by damaged knitting needles and raise the alarm. We developed a knitting needle status monitoring setup using optical imaging, photoelectric detection and weak signal processing technology to achieve real-time monitoring of weaving needles' position. Depending on the shape of the knitting needle, we designed a kind of Glass Optical Fiber (GOF) light guides with a rectangular port used for transmission of the signal light. To be able to capture the signal of knitting needles accurately, we adopt a optical 4F system which has better imaging quality and simple structure and there is a rectangle image on the focal plane after the system. When a knitting needle passes through position of the rectangle image, the reflected light from needle surface will back to the GOF light guides along the same optical system. According to the intensity of signals, the computer control unit distinguish that the knitting needle is broken or curving. The experimental results show that this system can accurately detect the broken needles and the curving needles on the knitting machine in operating condition.

A New Signaling Architecture THREP with Autonomous Radio-Link Control for Wireless Communications Systems

NASA Astrophysics Data System (ADS)

Hirono, Masahiko; Nojima, Toshio

This paper presents a new signaling architecture for radio-access control in wireless communications systems. Called THREP (for THREe-phase link set-up Process), it enables systems with low-cost configurations to provide tetherless access and wide-ranging mobility by using autonomous radio-link controls for fast cell searching and distributed call management. A signaling architecture generally consists of a radio-access part and a service-entity-access part. In THREP, the latter part is divided into two steps: preparing a communication channel, and sustaining it. Access control in THREP is thus composed of three separated parts, or protocol phases. The specifications of each phase are determined independently according to system requirements. In the proposed architecture, the first phase uses autonomous radio-link control because we want to construct low-power indoor wireless communications systems. Evaluation of channel usage efficiency and hand-over loss probability in the personal handy-phone system (PHS) shows that THREP makes the radio-access sub-system operations in a practical application model highly efficient, and the results of a field experiment show that THREP provides sufficient protection against severe fast CNR degradation in practical indoor propagation environments.

Hybrid indium phosphide-on-silicon nanolaser diode

NASA Astrophysics Data System (ADS)

Crosnier, Guillaume; Sanchez, Dorian; Bouchoule, Sophie; Monnier, Paul; Beaudoin, Gregoire; Sagnes, Isabelle; Raj, Rama; Raineri, Fabrice

2017-04-01

The most-awaited convergence of microelectronics and photonics promises to bring about a revolution for on-chip data communications and processing. Among all the optoelectronic devices to be developed, power-efficient nanolaser diodes able to be integrated densely with silicon photonics and electronics are essential to convert electrical data into the optical domain. Here, we report a demonstration of ultracompact laser diodes based on one-dimensional (1D) photonic crystal (PhC) nanocavities made in InP nanoribs heterogeneously integrated on a silicon-waveguide circuitry. The specific nanorib design enables an efficient electrical injection of carriers in the nanocavity without spoiling its optical properties. Room-temperature continuous-wave (CW) single-mode operation is obtained with a low current threshold of 100 µA. Laser emission at 1.56 µm in the silicon waveguides is obtained with wall-plug efficiencies greater than 10%. This result opens up exciting avenues for constructing optical networks at the submillimetre scale for on-chip interconnects and signal processing.

Transmissible Gastroenteritis Coronavirus Genome Packaging Signal Is Located at the 5′ End of the Genome and Promotes Viral RNA Incorporation into Virions in a Replication-Independent Process

PubMed Central

Morales, Lucia; Mateos-Gomez, Pedro A.; Capiscol, Carmen; del Palacio, Lorena; Sola, Isabel

2013-01-01

Preferential RNA packaging in coronaviruses involves the recognition of viral genomic RNA, a crucial process for viral particle morphogenesis mediated by RNA-specific sequences, known as packaging signals. An essential packaging signal component of transmissible gastroenteritis coronavirus (TGEV) has been further delimited to the first 598 nucleotides (nt) from the 5′ end of its RNA genome, by using recombinant viruses transcribing subgenomic mRNA that included potential packaging signals. The integrity of the entire sequence domain was necessary because deletion of any of the five structural motifs defined within this region abrogated specific packaging of this viral RNA. One of these RNA motifs was the stem-loop SL5, a highly conserved motif in coronaviruses located at nucleotide positions 106 to 136. Partial deletion or point mutations within this motif also abrogated packaging. Using TGEV-derived defective minigenomes replicated in trans by a helper virus, we have shown that TGEV RNA packaging is a replication-independent process. Furthermore, the last 494 nt of the genomic 3′ end were not essential for packaging, although this region increased packaging efficiency. TGEV RNA sequences identified as necessary for viral genome packaging were not sufficient to direct packaging of a heterologous sequence derived from the green fluorescent protein gene. These results indicated that TGEV genome packaging is a complex process involving many factors in addition to the identified RNA packaging signal. The identification of well-defined RNA motifs within the TGEV RNA genome that are essential for packaging will be useful for designing packaging-deficient biosafe coronavirus-derived vectors and providing new targets for antiviral therapies. PMID:23966403

Improved particle swarm optimization algorithm for android medical care IOT using modified parameters.

PubMed

Sung, Wen-Tsai; Chiang, Yen-Chun

2012-12-01

This study examines wireless sensor network with real-time remote identification using the Android study of things (HCIOT) platform in community healthcare. An improved particle swarm optimization (PSO) method is proposed to efficiently enhance physiological multi-sensors data fusion measurement precision in the Internet of Things (IOT) system. Improved PSO (IPSO) includes: inertia weight factor design, shrinkage factor adjustment to allow improved PSO algorithm data fusion performance. The Android platform is employed to build multi-physiological signal processing and timely medical care of things analysis. Wireless sensor network signal transmission and Internet links allow community or family members to have timely medical care network services.

Distributed Environment Control Using Wireless Sensor/Actuator Networks for Lighting Applications

PubMed Central

Nakamura, Masayuki; Sakurai, Atsushi; Nakamura, Jiro

2009-01-01

We propose a decentralized algorithm to calculate the control signals for lights in wireless sensor/actuator networks. This algorithm uses an appropriate step size in the iterative process used for quickly computing the control signals. We demonstrate the accuracy and efficiency of this approach compared with the penalty method by using Mote-based mesh sensor networks. The estimation error of the new approach is one-eighth as large as that of the penalty method with one-fifth of its computation time. In addition, we describe our sensor/actuator node for distributed lighting control based on the decentralized algorithm and demonstrate its practical efficacy. PMID:22291525

A Novel Approach to Noise-Filtering Based on a Gain-Scheduling Neural Network Architecture

NASA Technical Reports Server (NTRS)

Troudet, T.; Merrill, W.

1994-01-01

A gain-scheduling neural network architecture is proposed to enhance the noise-filtering efficiency of feedforward neural networks, in terms of both nominal performance and robustness. The synergistic benefits of the proposed architecture are demonstrated and discussed in the context of the noise-filtering of signals that are typically encountered in aerospace control systems. The synthesis of such a gain-scheduled neurofiltering provides the robustness of linear filtering, while preserving the nominal performance advantage of conventional nonlinear neurofiltering. Quantitative performance and robustness evaluations are provided for the signal processing of pitch rate responses to typical pilot command inputs for a modern fighter aircraft model.

Hierarchical Self Assembly of Patterns from the Robinson Tilings: DNA Tile Design in an Enhanced Tile Assembly Model

PubMed Central

Padilla, Jennifer E.; Liu, Wenyan; Seeman, Nadrian C.

2012-01-01

We introduce a hierarchical self assembly algorithm that produces the quasiperiodic patterns found in the Robinson tilings and suggest a practical implementation of this algorithm using DNA origami tiles. We modify the abstract Tile Assembly Model, (aTAM), to include active signaling and glue activation in response to signals to coordinate the hierarchical assembly of Robinson patterns of arbitrary size from a small set of tiles according to the tile substitution algorithm that generates them. Enabling coordinated hierarchical assembly in the aTAM makes possible the efficient encoding of the recursive process of tile substitution. PMID:23226722

Hierarchical Self Assembly of Patterns from the Robinson Tilings: DNA Tile Design in an Enhanced Tile Assembly Model.

PubMed

Padilla, Jennifer E; Liu, Wenyan; Seeman, Nadrian C

2012-06-01

We introduce a hierarchical self assembly algorithm that produces the quasiperiodic patterns found in the Robinson tilings and suggest a practical implementation of this algorithm using DNA origami tiles. We modify the abstract Tile Assembly Model, (aTAM), to include active signaling and glue activation in response to signals to coordinate the hierarchical assembly of Robinson patterns of arbitrary size from a small set of tiles according to the tile substitution algorithm that generates them. Enabling coordinated hierarchical assembly in the aTAM makes possible the efficient encoding of the recursive process of tile substitution.

Improved EEG Event Classification Using Differential Energy.

PubMed

Harati, A; Golmohammadi, M; Lopez, S; Obeid, I; Picone, J

2015-12-01

Feature extraction for automatic classification of EEG signals typically relies on time frequency representations of the signal. Techniques such as cepstral-based filter banks or wavelets are popular analysis techniques in many signal processing applications including EEG classification. In this paper, we present a comparison of a variety of approaches to estimating and postprocessing features. To further aid in discrimination of periodic signals from aperiodic signals, we add a differential energy term. We evaluate our approaches on the TUH EEG Corpus, which is the largest publicly available EEG corpus and an exceedingly challenging task due to the clinical nature of the data. We demonstrate that a variant of a standard filter bank-based approach, coupled with first and second derivatives, provides a substantial reduction in the overall error rate. The combination of differential energy and derivatives produces a 24 % absolute reduction in the error rate and improves our ability to discriminate between signal events and background noise. This relatively simple approach proves to be comparable to other popular feature extraction approaches such as wavelets, but is much more computationally efficient.

HIV-1 Activates T Cell Signaling Independently of Antigen to Drive Viral Spread.

PubMed

Len, Alice C L; Starling, Shimona; Shivkumar, Maitreyi; Jolly, Clare

2017-01-24

HIV-1 spreads between CD4 T cells most efficiently through virus-induced cell-cell contacts. To test whether this process potentiates viral spread by activating signaling pathways, we developed an approach to analyze the phosphoproteome in infected and uninfected mixed-population T cells using differential metabolic labeling and mass spectrometry. We discovered HIV-1-induced activation of signaling networks during viral spread encompassing over 200 cellular proteins. Strikingly, pathways downstream of the T cell receptor were the most significantly activated, despite the absence of canonical antigen-dependent stimulation. The importance of this pathway was demonstrated by the depletion of proteins, and we show that HIV-1 Env-mediated cell-cell contact, the T cell receptor, and the Src kinase Lck were essential for signaling-dependent enhancement of viral dissemination. This study demonstrates that manipulation of signaling at immune cell contacts by HIV-1 is essential for promoting virus replication and defines a paradigm for antigen-independent T cell signaling. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Direct position determination for digital modulation signals based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Yu, Wan-Ting; Yu, Hong-yi; Du, Jian-Ping; Wang, Ding

2018-04-01

The Direct Position Determination (DPD) algorithm has been demonstrated to achieve a better accuracy with known signal waveforms. However, the signal waveform is difficult to be completely known in the actual positioning process. To solve the problem, we proposed a DPD method for digital modulation signals based on improved particle swarm optimization algorithm. First, a DPD model is established for known modulation signals and a cost function is obtained on symbol estimation. Second, as the optimization of the cost function is a nonlinear integer optimization problem, an improved Particle Swarm Optimization (PSO) algorithm is considered for the optimal symbol search. Simulations are carried out to show the higher position accuracy of the proposed DPD method and the convergence of the fitness function under different inertia weight and population size. On the one hand, the proposed algorithm can take full advantage of the signal feature to improve the positioning accuracy. On the other hand, the improved PSO algorithm can improve the efficiency of symbol search by nearly one hundred times to achieve a global optimal solution.

Fusiform Gyrus Dysfunction is Associated with Perceptual Processing Efficiency to Emotional Faces in Adolescent Depression: A Model-Based Approach.

PubMed

Ho, Tiffany C; Zhang, Shunan; Sacchet, Matthew D; Weng, Helen; Connolly, Colm G; Henje Blom, Eva; Han, Laura K M; Mobayed, Nisreen O; Yang, Tony T

2016-01-01

While the extant literature has focused on major depressive disorder (MDD) as being characterized by abnormalities in processing affective stimuli (e.g., facial expressions), little is known regarding which specific aspects of cognition influence the evaluation of affective stimuli, and what are the underlying neural correlates. To investigate these issues, we assessed 26 adolescents diagnosed with MDD and 37 well-matched healthy controls (HCL) who completed an emotion identification task of dynamically morphing faces during functional magnetic resonance imaging (fMRI). We analyzed the behavioral data using a sequential sampling model of response time (RT) commonly used to elucidate aspects of cognition in binary perceptual decision making tasks: the Linear Ballistic Accumulator (LBA) model. Using a hierarchical Bayesian estimation method, we obtained group-level and individual-level estimates of LBA parameters on the facial emotion identification task. While the MDD and HCL groups did not differ in mean RT, accuracy, or group-level estimates of perceptual processing efficiency (i.e., drift rate parameter of the LBA), the MDD group showed significantly reduced responses in left fusiform gyrus compared to the HCL group during the facial emotion identification task. Furthermore, within the MDD group, fMRI signal in the left fusiform gyrus during affective face processing was significantly associated with greater individual-level estimates of perceptual processing efficiency. Our results therefore suggest that affective processing biases in adolescents with MDD are characterized by greater perceptual processing efficiency of affective visual information in sensory brain regions responsible for the early processing of visual information. The theoretical, methodological, and clinical implications of our results are discussed.

Fusiform Gyrus Dysfunction is Associated with Perceptual Processing Efficiency to Emotional Faces in Adolescent Depression: A Model-Based Approach

PubMed Central

Ho, Tiffany C.; Zhang, Shunan; Sacchet, Matthew D.; Weng, Helen; Connolly, Colm G.; Henje Blom, Eva; Han, Laura K. M.; Mobayed, Nisreen O.; Yang, Tony T.

2016-01-01

While the extant literature has focused on major depressive disorder (MDD) as being characterized by abnormalities in processing affective stimuli (e.g., facial expressions), little is known regarding which specific aspects of cognition influence the evaluation of affective stimuli, and what are the underlying neural correlates. To investigate these issues, we assessed 26 adolescents diagnosed with MDD and 37 well-matched healthy controls (HCL) who completed an emotion identification task of dynamically morphing faces during functional magnetic resonance imaging (fMRI). We analyzed the behavioral data using a sequential sampling model of response time (RT) commonly used to elucidate aspects of cognition in binary perceptual decision making tasks: the Linear Ballistic Accumulator (LBA) model. Using a hierarchical Bayesian estimation method, we obtained group-level and individual-level estimates of LBA parameters on the facial emotion identification task. While the MDD and HCL groups did not differ in mean RT, accuracy, or group-level estimates of perceptual processing efficiency (i.e., drift rate parameter of the LBA), the MDD group showed significantly reduced responses in left fusiform gyrus compared to the HCL group during the facial emotion identification task. Furthermore, within the MDD group, fMRI signal in the left fusiform gyrus during affective face processing was significantly associated with greater individual-level estimates of perceptual processing efficiency. Our results therefore suggest that affective processing biases in adolescents with MDD are characterized by greater perceptual processing efficiency of affective visual information in sensory brain regions responsible for the early processing of visual information. The theoretical, methodological, and clinical implications of our results are discussed. PMID:26869950

Real-Time Signal Processing Systems

DTIC Science & Technology

1992-10-29

Programmer’s Model 50 15. Synchronization 67 16. Parameter Passage to Routines VIA Stacks 68 17. Typical VPH Activity Flow Chart 70 18. CPH...computing facilities to take advantage of cost effective solutions. A proliferation of different microprocessors and development systems spread among the... activities are completed, the roles of the VPH memory banks are reversed. This function-swapping is the primary reason, for the efficiency and high

Multi-Beam Radio Frequency (RF) Aperture Arrays Using Multiplierless Approximate Fast Fourier Transform (FFT)

DTIC Science & Technology

2017-08-01

filtering, correlation and radio- astronomy . In this report approximate transforms that closely follow the DFT have been studied and found. The approximate...communications, data networks, sensor networks, cognitive radio, radar and beamforming, imaging, filtering, correlation and radio- astronomy . FFTs efficiently...public release; distribution is unlimited. 4.3 Digital Hardware and Design Architectures Collaboration for Astronomy Signal Processing and Electronics

Sparse Representation for Color Image Restoration (PREPRINT)

DTIC Science & Technology

2006-10-01

as a universal denoiser of images, which learns the posterior from the given image in a way inspired by the Lempel - Ziv universal compression ...such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data . In...describe the data source. Such a model becomes paramount when developing algorithms for processing these signals. In this context, Markov-Random-Field

Long-range eye tracking: A feasibility study

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

Jayaweera, S.K.; Lu, Shin-yee

1994-08-24

The design considerations for a long-range Purkinje effects based video tracking system using current technology is presented. Past work, current experiments, and future directions are thoroughly discussed, with an emphasis on digital signal processing techniques and obstacles. It has been determined that while a robust, efficient, long-range, and non-invasive eye tracking system will be difficult to develop, such as a project is indeed feasible.

Efficiency of the human observer detecting random signals in random backgrounds

PubMed Central

Park, Subok; Clarkson, Eric; Kupinski, Matthew A.; Barrett, Harrison H.

2008-01-01

The efficiencies of the human observer and the channelized-Hotelling observer relative to the ideal observer for signal-detection tasks are discussed. Both signal-known-exactly (SKE) tasks and signal-known-statistically (SKS) tasks are considered. Signal location is uncertain for the SKS tasks, and lumpy backgrounds are used for background uncertainty in both cases. Markov chain Monte Carlo methods are employed to determine ideal-observer performance on the detection tasks. Psychophysical studies are conducted to compute human-observer performance on the same tasks. Efficiency is computed as the squared ratio of the detectabilities of the observer of interest to the ideal observer. Human efficiencies are approximately 2.1% and 24%, respectively, for the SKE and SKS tasks. The results imply that human observers are not affected as much as the ideal observer by signal-location uncertainty even though the ideal observer outperforms the human observer for both tasks. Three different simplified pinhole imaging systems are simulated, and the humans and the model observers rank the systems in the same order for both the SKE and the SKS tasks. PMID:15669610

Signaling efficiency of Gαq through its effectors p63RhoGEF and GEFT depends on their subcellular location.

PubMed

Goedhart, Joachim; van Unen, Jakobus; Adjobo-Hermans, Merel J W; Gadella, Theodorus W J

2013-01-01

The p63RhoGEF and GEFT proteins are encoded by the same gene and both members of the Dbl family of guanine nucleotide exchange factors. These proteins can be activated by the heterotrimeric G-protein subunit Gαq. We show that p63RhoGEF is located at the plasma membrane, whereas GEFT is confined to the cytoplasm. Live-cell imaging studies yielded quantitative information on diffusion coefficients, association rates and encounter times of GEFT and p63RhoGEF. Calcium signaling was examined as a measure of the signal transmission, revealing more efficient signaling through the membrane-associated p63RhoGEF. A rapamycin dependent recruitment system was used to dynamically alter the subcellular location and concentration of GEFT, showing efficient signaling through GEFT only upon membrane recruitment. Together, our results show efficient signal transmission through membrane located effectors, and highlight a role for increased concentration rather than increased encounter times due to membrane localization in the Gαq mediated pathways to p63RhoGEF and PLCβ.

Power-efficient method for IM-DD optical transmission of multiple OFDM signals.

PubMed

Effenberger, Frank; Liu, Xiang

2015-05-18

We propose a power-efficient method for transmitting multiple frequency-division multiplexed (FDM) orthogonal frequency-division multiplexing (OFDM) signals in intensity-modulation direct-detection (IM-DD) optical systems. This method is based on quadratic soft clipping in combination with odd-only channel mapping. We show, both analytically and experimentally, that the proposed approach is capable of improving the power efficiency by about 3 dB as compared to conventional FDM OFDM signals under practical bias conditions, making it a viable solution in applications such as optical fiber-wireless integrated systems where both IM-DD optical transmission and OFDM signaling are important.

Prefrontal activity during working memory is modulated by the interaction of variation in CB1 and COX2 coding genes and correlates with frequency of cannabis use.

PubMed

Taurisano, Paolo; Antonucci, Linda A; Fazio, Leonardo; Rampino, Antonio; Romano, Raffaella; Porcelli, Annamaria; Masellis, Rita; Colizzi, Marco; Quarto, Tiziana; Torretta, Silvia; Di Giorgio, Annabella; Pergola, Giulio; Bertolino, Alessandro; Blasi, Giuseppe

2016-08-01

The CB1 cannabinoid receptor is targeted in the brain by endocannabinoids under physiological conditions as well as by delta9-tetrahydrocannabinol under cannabis use. Furthermore, its signaling appears to affect brain cognitive processing. Recent findings highlight a crucial role of cyclooxygenase-2 (COX-2) in the mechanism of intraneuronal CB1 signaling transduction, while others indicate that two single nucleotide polymorphisms (SNPs) (rs1406977 and rs20417) modulate expression of CB1 (CNR1) and COX-2 (PTGS2) coding genes, respectively. Here, our aim was to use fMRI to investigate in healthy humans whether these SNPs interact in modulating prefrontal activity during working memory processing and if this modulation is linked with cannabis use. We recruited 242 healthy subjects genotyped for CNR1 rs1406977 and PTGS2 rs20417 that performed the N-back working memory task during fMRI and were interviewed using the Cannabis Experience Questionnaire (CEQ). We found that the interaction between CNR1 rs1406977 and PTGS2 rs20417 is associated with dorsolateral prefrontal cortex (DLPFC) activity such that specific genotype configurations (CNR1 C carriers/PTGS2 C carriers and CNR1 TT/PTGS2 GG) predict lower cortical response versus others in spite of similar behavioral accuracy. Furthermore, DLPFC activity in the cluster associated with the CNR1 by PTGS2 interaction was negatively correlated with behavioral efficiency and positively correlated with frequency of cannabis use in cannabis users. These results suggest that a genetically modulated balancing of signaling within the CB1-COX-2 pathway may reflect on more or less efficient patterns of prefrontal activity during working memory. Frequency of cannabis use may be a factor for further modulation of CNR1/PTGS2-mediated cortical processing associated with this cognitive process. Copyright © 2016 Elsevier Ltd. All rights reserved.

Automated processing of label-free Raman microscope images of macrophage cells with standardized regression for high-throughput analysis.

PubMed

Milewski, Robert J; Kumagai, Yutaro; Fujita, Katsumasa; Standley, Daron M; Smith, Nicholas I

2010-11-19

Macrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively. We show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest. The computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without compromise in image quality or information loss in associated spectra. These results motivate further use of label free microscopy techniques in real-time imaging of live immune cells.

A cost-effective line-based light-balancing technique using adaptive processing.

PubMed

Hsia, Shih-Chang; Chen, Ming-Huei; Chen, Yu-Min

2006-09-01

The camera imaging system has been widely used; however, the displaying image appears to have an unequal light distribution. This paper presents novel light-balancing techniques to compensate uneven illumination based on adaptive signal processing. For text image processing, first, we estimate the background level and then process each pixel with nonuniform gain. This algorithm can balance the light distribution while keeping a high contrast in the image. For graph image processing, the adaptive section control using piecewise nonlinear gain is proposed to equalize the histogram. Simulations show that the performance of light balance is better than the other methods. Moreover, we employ line-based processing to efficiently reduce the memory requirement and the computational cost to make it applicable in real-time systems.

Smith predictor with sliding mode control for processes with large dead times

NASA Astrophysics Data System (ADS)

Mehta, Utkal; Kaya, İbrahim

2017-11-01

The paper discusses the Smith Predictor scheme with Sliding Mode Controller (SP-SMC) for processes with large dead times. This technique gives improved load-disturbance rejection with optimum input control signal variations. A power rate reaching law is incorporated in the sporadic part of sliding mode control such that the overall performance recovers meaningfully. The proposed scheme obtains parameter values by satisfying a new performance index which is based on biobjective constraint. In simulation study, the efficiency of the method is evaluated for robustness and transient performance over reported techniques.

A Signal Processing Approach with a Smooth Empirical Mode Decomposition to Reveal Hidden Trace of Corrosion in Highly Contaminated Guided Wave Signals for Concrete-Covered Pipes.

PubMed

Rostami, Javad; Chen, Jingming; Tse, Peter W

2017-02-07

Ultrasonic guided waves have been extensively applied for non-destructive testing of plate-like structures particularly pipes in past two decades. In this regard, if a structure has a simple geometry, obtained guided waves' signals are easy to explain. However, any small degree of complexity in the geometry such as contacting with other materials may cause an extra amount of complication in the interpretation of guided wave signals. The problem deepens if defects have irregular shapes such as natural corrosion. Signal processing techniques that have been proposed for guided wave signals' analysis are generally good for simple signals obtained in a highly controlled experimental environment. In fact, guided wave signals in a real situation such as the existence of natural corrosion in wall-covered pipes are much more complicated. Considering pipes in residential buildings that pass through concrete walls, in this paper we introduced Smooth Empirical Mode Decomposition (SEMD) to efficiently separate overlapped guided waves. As empirical mode decomposition (EMD) which is a good candidate for analyzing non-stationary signals, suffers from some shortcomings, wavelet transform was adopted in the sifting stage of EMD to improve its outcome in SEMD. However, selection of mother wavelet that suits best for our purpose plays an important role. Since in guided wave inspection, the incident waves are well known and are usually tone-burst signals, we tailored a complex tone-burst signal to be used as our mother wavelet. In the sifting stage of EMD, wavelet de-noising was applied to eliminate unwanted frequency components from each IMF. SEMD greatly enhances the performance of EMD in guided wave analysis for highly contaminated signals. In our experiment on concrete covered pipes with natural corrosion, this method not only separates the concrete wall indication clearly in time domain signal, a natural corrosion with complex geometry that was hidden and located inside the concrete section was successfully exposed.

Ciliates learn to diagnose and correct classical error syndromes in mating strategies

PubMed Central

Clark, Kevin B.

2013-01-01

Preconjugal ciliates learn classical repetition error-correction codes to safeguard mating messages and replies from corruption by “rivals” and local ambient noise. Because individual cells behave as memory channels with Szilárd engine attributes, these coding schemes also might be used to limit, diagnose, and correct mating-signal errors due to noisy intracellular information processing. The present study, therefore, assessed whether heterotrich ciliates effect fault-tolerant signal planning and execution by modifying engine performance, and consequently entropy content of codes, during mock cell–cell communication. Socially meaningful serial vibrations emitted from an ambiguous artificial source initiated ciliate behavioral signaling performances known to advertise mating fitness with varying courtship strategies. Microbes, employing calcium-dependent Hebbian-like decision making, learned to diagnose then correct error syndromes by recursively matching Boltzmann entropies between signal planning and execution stages via “power” or “refrigeration” cycles. All eight serial contraction and reversal strategies incurred errors in entropy magnitude by the execution stage of processing. Absolute errors, however, subtended expected threshold values for single bit-flip errors in three-bit replies, indicating coding schemes protected information content throughout signal production. Ciliate preparedness for vibrations selectively and significantly affected the magnitude and valence of Szilárd engine performance during modal and non-modal strategy corrective cycles. But entropy fidelity for all replies mainly improved across learning trials as refinements in engine efficiency. Fidelity neared maximum levels for only modal signals coded in resilient three-bit repetition error-correction sequences. Together, these findings demonstrate microbes can elevate survival/reproductive success by learning to implement classical fault-tolerant information processing in social contexts. PMID:23966987

Major transcriptome re-organisation and abrupt changes in signalling, cell cycle and chromatin regulation at neural differentiation in vivo.

PubMed

Olivera-Martinez, Isabel; Schurch, Nick; Li, Roman A; Song, Junfang; Halley, Pamela A; Das, Raman M; Burt, Dave W; Barton, Geoffrey J; Storey, Kate G

2014-08-01

Here, we exploit the spatial separation of temporal events of neural differentiation in the elongating chick body axis to provide the first analysis of transcriptome change in progressively more differentiated neural cell populations in vivo. Microarray data, validated against direct RNA sequencing, identified: (1) a gene cohort characteristic of the multi-potent stem zone epiblast, which contains neuro-mesodermal progenitors that progressively generate the spinal cord; (2) a major transcriptome re-organisation as cells then adopt a neural fate; and (3) increasing diversity as neural patterning and neuron production begin. Focussing on the transition from multi-potent to neural state cells, we capture changes in major signalling pathways, uncover novel Wnt and Notch signalling dynamics, and implicate new pathways (mevalonate pathway/steroid biogenesis and TGFβ). This analysis further predicts changes in cellular processes, cell cycle, RNA-processing and protein turnover as cells acquire neural fate. We show that these changes are conserved across species and provide biological evidence for reduced proteasome efficiency and a novel lengthening of S phase. This latter step may provide time for epigenetic events to mediate large-scale transcriptome re-organisation; consistent with this, we uncover simultaneous downregulation of major chromatin modifiers as the neural programme is established. We further demonstrate that transcription of one such gene, HDAC1, is dependent on FGF signalling, making a novel link between signals that control neural differentiation and transcription of a core regulator of chromatin organisation. Our work implicates new signalling pathways and dynamics, cellular processes and epigenetic modifiers in neural differentiation in vivo, identifying multiple new potential cellular and molecular mechanisms that direct differentiation. © 2014. Published by The Company of Biologists Ltd.

Multimodal and Multi-tissue Measures of Connectivity Revealed by Joint Independent Component Analysis.

PubMed

Franco, Alexandre R; Ling, Josef; Caprihan, Arvind; Calhoun, Vince D; Jung, Rex E; Heileman, Gregory L; Mayer, Andrew R

2008-12-01

The human brain functions as an efficient system where signals arising from gray matter are transported via white matter tracts to other regions of the brain to facilitate human behavior. However, with a few exceptions, functional and structural neuroimaging data are typically optimized to maximize the quantification of signals arising from a single source. For example, functional magnetic resonance imaging (FMRI) is typically used as an index of gray matter functioning whereas diffusion tensor imaging (DTI) is typically used to determine white matter properties. While it is likely that these signals arising from different tissue sources contain complementary information, the signal processing algorithms necessary for the fusion of neuroimaging data across imaging modalities are still in a nascent stage. In the current paper we present a data-driven method for combining measures of functional connectivity arising from gray matter sources (FMRI resting state data) with different measures of white matter connectivity (DTI). Specifically, a joint independent component analysis (J-ICA) was used to combine these measures of functional connectivity following intensive signal processing and feature extraction within each of the individual modalities. Our results indicate that one of the most predominantly used measures of functional connectivity (activity in the default mode network) is highly dependent on the integrity of white matter connections between the two hemispheres (corpus callosum) and within the cingulate bundles. Importantly, the discovery of this complex relationship of connectivity was entirely facilitated by the signal processing and fusion techniques presented herein and could not have been revealed through separate analyses of both data types as is typically performed in the majority of neuroimaging experiments. We conclude by discussing future applications of this technique to other areas of neuroimaging and examining potential limitations of the methods.

Wavelet transform analysis of transient signals: the seismogram and the electrocardiogram

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

Anant, K.S.

1997-06-01

In this dissertation I quantitatively demonstrate how the wavelet transform can be an effective mathematical tool for the analysis of transient signals. The two key signal processing applications of the wavelet transform, namely feature identification and representation (i.e., compression), are shown by solving important problems involving the seismogram and the electrocardiogram. The seismic feature identification problem involved locating in time the P and S phase arrivals. Locating these arrivals accurately (particularly the S phase) has been a constant issue in seismic signal processing. In Chapter 3, I show that the wavelet transform can be used to locate both the Pmore » as well as the S phase using only information from single station three-component seismograms. This is accomplished by using the basis function (wave-let) of the wavelet transform as a matching filter and by processing information across scales of the wavelet domain decomposition. The `pick` time results are quite promising as compared to analyst picks. The representation application involved the compression of the electrocardiogram which is a recording of the electrical activity of the heart. Compression of the electrocardiogram is an important problem in biomedical signal processing due to transmission and storage limitations. In Chapter 4, I develop an electrocardiogram compression method that applies vector quantization to the wavelet transform coefficients. The best compression results were obtained by using orthogonal wavelets, due to their ability to represent a signal efficiently. Throughout this thesis the importance of choosing wavelets based on the problem at hand is stressed. In Chapter 5, I introduce a wavelet design method that uses linear prediction in order to design wavelets that are geared to the signal or feature being analyzed. The use of these designed wavelets in a test feature identification application led to positive results. The methods developed in this thesis; the feature identification methods of Chapter 3, the compression methods of Chapter 4, as well as the wavelet design methods of Chapter 5, are general enough to be easily applied to other transient signals.« less

Automatic efficiency optimization of an axial compressor with adjustable inlet guide vanes

NASA Astrophysics Data System (ADS)

Li, Jichao; Lin, Feng; Nie, Chaoqun; Chen, Jingyi

2012-04-01

The inlet attack angle of rotor blade reasonably can be adjusted with the change of the stagger angle of inlet guide vane (IGV); so the efficiency of each condition will be affected. For the purpose to improve the efficiency, the DSP (Digital Signal Processor) controller is designed to adjust the stagger angle of IGV automatically in order to optimize the efficiency at any operating condition. The A/D signal collection includes inlet static pressure, outlet static pressure, outlet total pressure, rotor speed and torque signal, the efficiency can be calculated in the DSP, and the angle signal for the stepping motor which control the IGV will be sent out from the D/A. Experimental investigations are performed in a three-stage, low-speed axial compressor with variable inlet guide vanes. It is demonstrated that the DSP designed can well adjust the stagger angle of IGV online, the efficiency under different conditions can be optimized. This establishment of DSP online adjustment scheme may provide a practical solution for improving performance of multi-stage axial flow compressor when its operating condition is varied.

Cyclic interconversion of methionine containing peptide between oxidized and reduced phases monitored by reversed-phase HPLC and ESI-MS/MS.

PubMed

Jin, Yulong; Huang, Yanyan; Xie, Yunfeng; Hu, Wenbing; Wang, Fuyi; Liu, Guoquan; Zhao, Rui

2012-01-30

The cyclic oxidation and reduction of methionine (Met) containing peptides and proteins play important roles in biological system. This work was contributed to analysis the cyclic oxidation and reduction processes of a methionine containing peptide which is very likely to relate in the cell signal transduction pathways. To mimic the biological oxidation condition, hydrogen peroxide was used as the reactive oxygen species to oxidize the peptide. Reversed-phase high-performance liquid chromatography and mass spectrometry were employed to monitor the reactions and characterize the structural changes of the products. A rapid reduction procedure was developed by simply using KI as the reductant, which is green and highly efficient. By investigation of the cyclic oxidation and reduction process, our work provides a new perspective to study the function and mechanism of Met containing peptides and proteins during cell signaling processes as well as diseases. Copyright © 2011 Elsevier B.V. All rights reserved.

Carbon nanotube-based three-dimensional monolithic optoelectronic integrated system

NASA Astrophysics Data System (ADS)

Liu, Yang; Wang, Sheng; Liu, Huaping; Peng, Lian-Mao

2017-06-01

Single material-based monolithic optoelectronic integration with complementary metal oxide semiconductor-compatible signal processing circuits is one of the most pursued approaches in the post-Moore era to realize rapid data communication and functional diversification in a limited three-dimensional space. Here, we report an electrically driven carbon nanotube-based on-chip three-dimensional optoelectronic integrated circuit. We demonstrate that photovoltaic receivers, electrically driven transmitters and on-chip electronic circuits can all be fabricated using carbon nanotubes via a complementary metal oxide semiconductor-compatible low-temperature process, providing a seamless integration platform for realizing monolithic three-dimensional optoelectronic integrated circuits with diversified functionality such as the heterogeneous AND gates. These circuits can be vertically scaled down to sub-30 nm and operates in photovoltaic mode at room temperature. Parallel optical communication between functional layers, for example, bottom-layer digital circuits and top-layer memory, has been demonstrated by mapping data using a 2 × 2 transmitter/receiver array, which could be extended as the next generation energy-efficient signal processing paradigm.

Two-step frequency conversion for connecting distant quantum memories by transmission through an optical fiber

NASA Astrophysics Data System (ADS)

Tamura, Shuhei; Ikeda, Kohei; Okamura, Kotaro; Yoshii, Kazumichi; Hong, Feng-Lei; Horikiri, Tomoyuki; Kosaka, Hideo

2018-06-01

Long-distance quantum communication requires entanglement between distant quantum memories. For this purpose, photon transmission is necessary to connect the distant memories. Here, for the first time, we develop a two-step frequency conversion process (from a visible wavelength to a telecommunication wavelength and back) involving the use of independent two-frequency conversion media where the target quantum memories are nitrogen-vacancy centers in diamonds (with an emission/absorption wavelength of 637.2 nm), and experimentally characterize the performance of this process acting on light from an attenuated CW laser. A total conversion efficiency of approximately 7% is achieved. The noise generated in the frequency conversion processes is measured, and the signal-to-noise ratio is estimated for a single photon signal emitted by a nitrogen-vacancy (NV) center. The developed frequency conversion system has future applications via transmission through a long optical fiber channel at a telecommunication wavelength for a quantum repeater network.

Reprint of Design of synthetic microbial communities for biotechnological production processes.

PubMed

Jagmann, Nina; Philipp, Bodo

2014-12-20

In their natural habitats microorganisms live in multi-species communities, in which the community members exhibit complex metabolic interactions. In contrast, biotechnological production processes catalyzed by microorganisms are usually carried out with single strains in pure cultures. A number of production processes, however, may be more efficiently catalyzed by the concerted action of microbial communities. This review will give an overview of organismic interactions between microbial cells and of biotechnological applications of microbial communities. It focuses on synthetic microbial communities that consist of microorganisms that have been genetically engineered. Design principles for such synthetic communities will be exemplified based on plausible scenarios for biotechnological production processes. These design principles comprise interspecific metabolic interactions via cross-feeding, regulation by interspecific signaling processes via metabolites and autoinducing signal molecules, and spatial structuring of synthetic microbial communities. In particular, the implementation of metabolic interdependencies, of positive feedback regulation and of inducible cell aggregation and biofilm formation will be outlined. Synthetic microbial communities constitute a viable extension of the biotechnological application of metabolically engineered single strains and enlarge the scope of microbial production processes. Copyright © 2014 Elsevier B.V. All rights reserved.

Efficient ASK-assisted system for expression and purification of plant F-box proteins.

PubMed

Li, Haiou; Yao, Ruifeng; Ma, Sui; Hu, Shuai; Li, Suhua; Wang, Yupei; Yan, Chun; Xie, Daoxin; Yan, Jianbin

2017-11-01

Ubiquitin-mediated protein degradation plays an essential role in plant growth and development as well as responses to environmental and endogenous signals. F-box protein is one of the key components of the SCF (SKP1-CUL1-F-box protein) E3 ubiquitin ligase complex, which recruit specific substrate proteins for subsequent ubiquitination and 26S proteasome-mediated degradation to regulate developmental processes and signaling networks. However, it is not easy to obtain purified F-box proteins with high activity due to their unstable protein structures. Here, we found that Arabidopsis SKP-like proteins (ASKs) can significantly improve soluble expression of F-box proteins and maintain their bioactivity. We established an efficient ASK-assisted method to express and purify plant F-box proteins. The method meets a broad range of criteria required for the biochemical analysis or protein crystallization of plant F-box proteins. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

Enhanced light extraction of plastic scintillator using large-area photonic crystal structures fabricated by hot embossing.

PubMed

Chen, Xueye; Liu, Bo; Wu, Qiang; Zhu, Zhichao; Zhu, Jingtao; Gu, Mu; Chen, Hong; Liu, Jinliang; Chen, Liang; Ouyang, Xiaoping

2018-04-30

Plastic scintillators are widely used in various radiation measurement systems. However, detection efficiency and signal-to-noise are limited due to the total internal reflection, especially for weak signal detection situations. In the present investigation, large-area photonic crystals consisting of an array of periodic truncated cone holes were prepared based on hot embossing technology aiming at coupling with the surface of plastic scintillator to improve the light extraction efficiency and directionality control. The experimental results show that a maximum enhancement of 64% at 25° emergence angle along Γ-M orientation and a maximum enhancement of 58% at 20° emergence angle along Γ-K orientation were obtained. The proposed fabrication method of photonic crystal scintillator can avoid complicated pattern transfer processes used in most traditional methods, leading to a simple, economical method for large-area preparation. The photonic crystal scintillator demonstrated in this work is of great value for practical applications of nuclear radiation detection.

Antenna-coupled photon emission from hexagonal boron nitride tunnel junctions.

PubMed

Parzefall, M; Bharadwaj, P; Jain, A; Taniguchi, T; Watanabe, K; Novotny, L

2015-12-01

The ultrafast conversion of electrical signals to optical signals at the nanoscale is of fundamental interest for data processing, telecommunication and optical interconnects. However, the modulation bandwidths of semiconductor light-emitting diodes are limited by the spontaneous recombination rate of electron-hole pairs, and the footprint of electrically driven ultrafast lasers is too large for practical on-chip integration. A metal-insulator-metal tunnel junction approaches the ultimate size limit of electronic devices and its operating speed is fundamentally limited only by the tunnelling time. Here, we study the conversion of electrons (localized in vertical gold-hexagonal boron nitride-gold tunnel junctions) to free-space photons, mediated by resonant slot antennas. Optical antennas efficiently bridge the size mismatch between nanoscale volumes and far-field radiation and strongly enhance the electron-photon conversion efficiency. We achieve polarized, directional and resonantly enhanced light emission from inelastic electron tunnelling and establish a novel platform for studying the interaction of electrons with strongly localized electromagnetic fields.

Efficient high-performance ultrasound beamforming using oversampling

NASA Astrophysics Data System (ADS)

Freeman, Steven R.; Quick, Marshall K.; Morin, Marc A.; Anderson, R. C.; Desilets, Charles S.; Linnenbrink, Thomas E.; O'Donnell, Matthew

1998-05-01

High-performance and efficient beamforming circuitry is very important in large channel count clinical ultrasound systems. Current state-of-the-art digital systems using multi-bit analog to digital converters (A/Ds) have matured to provide exquisite image quality with moderate levels of integration. A simplified oversampling beamforming architecture has been proposed that may a low integration of delta-sigma A/Ds onto the same chip as digital delay and processing circuitry to form a monolithic ultrasound beamformer. Such a beamformer may enable low-power handheld scanners for high-end systems with very large channel count arrays. This paper presents an oversampling beamformer architecture that generates high-quality images using very simple; digitization, delay, and summing circuits. Additional performance may be obtained with this oversampled system for narrow bandwidth excitations by mixing the RF signal down in frequency to a range where the electronic signal to nose ratio of the delta-sigma A/D is optimized. An oversampled transmit beamformer uses the same delay circuits as receive and eliminates the need for separate transmit function generators.

A CMOS-Compatible, Low-Noise ISFET Based on High Efficiency Ion-Modulated Lateral-Bipolar Conduction

PubMed Central

Chang, Sheng-Ren; Chen, Hsin

2009-01-01

Ion-sensitive, field-effect transistors (ISFET) have been useful biosensors in many applications. However, the signal-to-noise ratio of the ISFET is limited by its intrinsic, low-frequency noise. This paper presents an ISFET capable of utilizing lateral-bipolar conduction to reduce low-frequency noise. With a particular layout design, the conduction efficiency is further enhanced. Moreover, the ISFET is compatible with the standard CMOS technology. All materials above the gate-oxide are removed by simple, die-level post-CMOS process, allowing ions to modulate the lateral-bipolar current directly. By varying the gate-to-bulk voltage, the operation mode of the ISFET is controlled effectively, so is the noise performance measured and compared. Finally, the biasing conditions preferable for different low-noise applications are identified. Under the identified biasing condition, the signal-to-noise ratio of the ISFET as a pH sensor is proved to be improved by more than five times. PMID:22408508

Wavelet multiresolution complex network for decoding brain fatigued behavior from P300 signals

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Wang, Zi-Bo; Yang, Yu-Xuan; Li, Shan; Dang, Wei-Dong; Mao, Xiao-Qian

2018-09-01

Brain-computer interface (BCI) enables users to interact with the environment without relying on neural pathways and muscles. P300 based BCI systems have been extensively used to achieve human-machine interaction. However, the appearance of fatigue symptoms during operation process leads to the decline in classification accuracy of P300. Characterizing brain cognitive process underlying normal and fatigue conditions constitutes a problem of vital importance in the field of brain science. We in this paper propose a novel wavelet decomposition based complex network method to efficiently analyze the P300 signals recorded in the image stimulus test based on classical 'Oddball' paradigm. Initially, multichannel EEG signals are decomposed into wavelet coefficient series. Then we construct complex network by treating electrodes as nodes and determining the connections according to the 2-norm distances between wavelet coefficient series. The analysis of topological structure and statistical index indicates that the properties of brain network demonstrate significant distinctions between normal status and fatigue status. More specifically, the brain network reconfiguration in response to the cognitive task in fatigue status is reflected as the enhancement of the small-worldness.

Effects of pre- and pro-sequence of thaumatin on the secretion by Pichia pastoris.

PubMed

Ide, Nobuyuki; Masuda, Tetsuya; Kitabatake, Naofumi

2007-11-23

Thaumatin is a 22-kDa sweet-tasting protein containing eight disulfide bonds. When thaumatin is expressed in Pichia pastoris using the thaumatin cDNA fused with both the alpha-factor signal sequence and the Kex2 protease cleavage site from Saccharomyces cerevisiae, the N-terminal sequence of the secreted thaumatin molecule is not processed correctly. To examine the role of the thaumatin cDNA-encoded N-terminal pre-sequence and C-terminal pro-sequence on the processing of thaumatin and efficiency of thaumatin production in P. pastoris, four expression plasmids with different pre-sequence and pro-sequence were constructed and transformed into P. pastoris. The transformants containing pre-thaumatin gene that has the native plant signal, secreted thaumatin molecules in the medium. The N-terminal amino acid sequence of the secreted thaumatin molecule was processed correctly. The production yield of thaumatin was not affected by the C-terminal pro-sequence, and the pro-sequence was not processed in P. pastoris, indicating that pro-sequence is not necessary for thaumatin synthesis.

Multi-Element CZT Array for Nuclear Safeguards Applications

NASA Astrophysics Data System (ADS)

Kwak, S.-W.; Lee, A.-R.; Shin, J.-K.; Park, U.-R.; Park, S.; Kim, Y.; Chung, H.

2016-12-01

Due to its electronic properties, a cadmium zinc telluride (CZT) detector has been used as a hand-held portable nuclear measurement instrument. However, a CZT detector has low detection efficiency because of a limitation of its single crystal growth. To address its low efficiency, we have constructed a portable four-CZT array based gamma-ray spectrometer consisting of a CZT array, electronics for signal processing and software. Its performance has been characterized in terms of energy resolution and detection efficiency using radioactive sources and nuclear materials. Experimental results showed that the detection efficiency of the four-CZT array based gamma-ray spectrometer was much higher than that of a single CZT detector in the array. The FWHMs of the CZT array were 9, 18, and 21 keV at 185.7, 662, and 1,332 keV, respectively. Some gamma-rays in a range of 100 keV to 200 keV were not clear in a single crystal detector while those from the CZT array system were observed to be clear. The energy resolution of the CZT array system was only slightely worse than those of the single CZT detectors. By combining several single crystals and summing signals from each single detector at a digital electronic circuit, the detection efficiency of a CZT array system increased without degradation of its energy resolution. The technique outlined in this paper shows a very promising method for designing a CZT-based gamma-ray spectroscopy that overcomes the fundamental limitations of a small volume CZT detector.

Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques.

PubMed

Cui, De-Mi; Yan, Weizhong; Wang, Xiao-Quan; Lu, Lie-Min

2017-10-25

Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts only. For foundation construction sites where the number of piles to be tested is large, it may take days before the expert can complete interpreting all of the piles and delivering the integrity assessment report. Techniques that can automate test signal interpretation, thus shortening the LSPIT's turnaround time, are of great business value and are in great need. Motivated by this need, in this paper, we develop a computer-aided reflectogram interpretation (CARI) methodology that can interpret a large number of LSPIT signals quickly and consistently. The methodology, built on advanced signal processing and machine learning technologies, can be used to assist the experts in performing both qualitative and quantitative interpretation of LSPIT signals. Specifically, the methodology can ease experts' interpretation burden by screening all test piles quickly and identifying a small number of suspected piles for experts to perform manual, in-depth interpretation. We demonstrate the methodology's effectiveness using the LSPIT signals collected from a number of real-world pile construction sites. The proposed methodology can potentially enhance LSPIT and make it even more efficient and effective in quality control of deep foundation construction.

A hierarchical approach for online temporal lobe seizure detection in long-term intracranial EEG recordings

NASA Astrophysics Data System (ADS)

Liang, Sheng-Fu; Chen, Yi-Chun; Wang, Yu-Lin; Chen, Pin-Tzu; Yang, Chia-Hsiang; Chiueh, Herming

2013-08-01

Objective. Around 1% of the world's population is affected by epilepsy, and nearly 25% of patients cannot be treated effectively by available therapies. The presence of closed-loop seizure-triggered stimulation provides a promising solution for these patients. Realization of fast, accurate, and energy-efficient seizure detection is the key to such implants. In this study, we propose a two-stage on-line seizure detection algorithm with low-energy consumption for temporal lobe epilepsy (TLE). Approach. Multi-channel signals are processed through independent component analysis and the most representative independent component (IC) is automatically selected to eliminate artifacts. Seizure-like intracranial electroencephalogram (iEEG) segments are fast detected in the first stage of the proposed method and these seizures are confirmed in the second stage. The conditional activation of the second-stage signal processing reduces the computational effort, and hence energy, since most of the non-seizure events are filtered out in the first stage. Main results. Long-term iEEG recordings of 11 patients who suffered from TLE were analyzed via leave-one-out cross validation. The proposed method has a detection accuracy of 95.24%, a false alarm rate of 0.09/h, and an average detection delay time of 9.2 s. For the six patients with mesial TLE, a detection accuracy of 100.0%, a false alarm rate of 0.06/h, and an average detection delay time of 4.8 s can be achieved. The hierarchical approach provides a 90% energy reduction, yielding effective and energy-efficient implementation for real-time epileptic seizure detection. Significance. An on-line seizure detection method that can be applied to monitor continuous iEEG signals of patients who suffered from TLE was developed. An IC selection strategy to automatically determine the most seizure-related IC for seizure detection was also proposed. The system has advantages of (1) high detection accuracy, (2) low false alarm, (3) short detection latency, and (4) energy-efficient design for hardware implementation.

A Signal Processing Approach with a Smooth Empirical Mode Decomposition to Reveal Hidden Trace of Corrosion in Highly Contaminated Guided Wave Signals for Concrete-Covered Pipes

PubMed Central

Rostami, Javad; Chen, Jingming; Tse, Peter W.

2017-01-01

Ultrasonic guided waves have been extensively applied for non-destructive testing of plate-like structures particularly pipes in past two decades. In this regard, if a structure has a simple geometry, obtained guided waves’ signals are easy to explain. However, any small degree of complexity in the geometry such as contacting with other materials may cause an extra amount of complication in the interpretation of guided wave signals. The problem deepens if defects have irregular shapes such as natural corrosion. Signal processing techniques that have been proposed for guided wave signals’ analysis are generally good for simple signals obtained in a highly controlled experimental environment. In fact, guided wave signals in a real situation such as the existence of natural corrosion in wall-covered pipes are much more complicated. Considering pipes in residential buildings that pass through concrete walls, in this paper we introduced Smooth Empirical Mode Decomposition (SEMD) to efficiently separate overlapped guided waves. As empirical mode decomposition (EMD) which is a good candidate for analyzing non-stationary signals, suffers from some shortcomings, wavelet transform was adopted in the sifting stage of EMD to improve its outcome in SEMD. However, selection of mother wavelet that suits best for our purpose plays an important role. Since in guided wave inspection, the incident waves are well known and are usually tone-burst signals, we tailored a complex tone-burst signal to be used as our mother wavelet. In the sifting stage of EMD, wavelet de-noising was applied to eliminate unwanted frequency components from each IMF. SEMD greatly enhances the performance of EMD in guided wave analysis for highly contaminated signals. In our experiment on concrete covered pipes with natural corrosion, this method not only separates the concrete wall indication clearly in time domain signal, a natural corrosion with complex geometry that was hidden and located inside the concrete section was successfully exposed. PMID:28178220

NeuroPigPen: A Scalable Toolkit for Processing Electrophysiological Signal Data in Neuroscience Applications Using Apache Pig

PubMed Central

Sahoo, Satya S.; Wei, Annan; Valdez, Joshua; Wang, Li; Zonjy, Bilal; Tatsuoka, Curtis; Loparo, Kenneth A.; Lhatoo, Samden D.

2016-01-01

The recent advances in neurological imaging and sensing technologies have led to rapid increase in the volume, rate of data generation, and variety of neuroscience data. This “neuroscience Big data” represents a significant opportunity for the biomedical research community to design experiments using data with greater timescale, large number of attributes, and statistically significant data size. The results from these new data-driven research techniques can advance our understanding of complex neurological disorders, help model long-term effects of brain injuries, and provide new insights into dynamics of brain networks. However, many existing neuroinformatics data processing and analysis tools were not built to manage large volume of data, which makes it difficult for researchers to effectively leverage this available data to advance their research. We introduce a new toolkit called NeuroPigPen that was developed using Apache Hadoop and Pig data flow language to address the challenges posed by large-scale electrophysiological signal data. NeuroPigPen is a modular toolkit that can process large volumes of electrophysiological signal data, such as Electroencephalogram (EEG), Electrocardiogram (ECG), and blood oxygen levels (SpO2), using a new distributed storage model called Cloudwave Signal Format (CSF) that supports easy partitioning and storage of signal data on commodity hardware. NeuroPigPen was developed with three design principles: (a) Scalability—the ability to efficiently process increasing volumes of data; (b) Adaptability—the toolkit can be deployed across different computing configurations; and (c) Ease of programming—the toolkit can be easily used to compose multi-step data processing pipelines using high-level programming constructs. The NeuroPigPen toolkit was evaluated using 750 GB of electrophysiological signal data over a variety of Hadoop cluster configurations ranging from 3 to 30 Data nodes. The evaluation results demonstrate that the toolkit is highly scalable and adaptable, which makes it suitable for use in neuroscience applications as a scalable data processing toolkit. As part of the ongoing extension of NeuroPigPen, we are developing new modules to support statistical functions to analyze signal data for brain connectivity research. In addition, the toolkit is being extended to allow integration with scientific workflow systems. NeuroPigPen is released under BSD license at: https://sites.google.com/a/case.edu/neuropigpen/. PMID:27375472

NeuroPigPen: A Scalable Toolkit for Processing Electrophysiological Signal Data in Neuroscience Applications Using Apache Pig.

PubMed

Sahoo, Satya S; Wei, Annan; Valdez, Joshua; Wang, Li; Zonjy, Bilal; Tatsuoka, Curtis; Loparo, Kenneth A; Lhatoo, Samden D

2016-01-01

The recent advances in neurological imaging and sensing technologies have led to rapid increase in the volume, rate of data generation, and variety of neuroscience data. This "neuroscience Big data" represents a significant opportunity for the biomedical research community to design experiments using data with greater timescale, large number of attributes, and statistically significant data size. The results from these new data-driven research techniques can advance our understanding of complex neurological disorders, help model long-term effects of brain injuries, and provide new insights into dynamics of brain networks. However, many existing neuroinformatics data processing and analysis tools were not built to manage large volume of data, which makes it difficult for researchers to effectively leverage this available data to advance their research. We introduce a new toolkit called NeuroPigPen that was developed using Apache Hadoop and Pig data flow language to address the challenges posed by large-scale electrophysiological signal data. NeuroPigPen is a modular toolkit that can process large volumes of electrophysiological signal data, such as Electroencephalogram (EEG), Electrocardiogram (ECG), and blood oxygen levels (SpO2), using a new distributed storage model called Cloudwave Signal Format (CSF) that supports easy partitioning and storage of signal data on commodity hardware. NeuroPigPen was developed with three design principles: (a) Scalability-the ability to efficiently process increasing volumes of data; (b) Adaptability-the toolkit can be deployed across different computing configurations; and (c) Ease of programming-the toolkit can be easily used to compose multi-step data processing pipelines using high-level programming constructs. The NeuroPigPen toolkit was evaluated using 750 GB of electrophysiological signal data over a variety of Hadoop cluster configurations ranging from 3 to 30 Data nodes. The evaluation results demonstrate that the toolkit is highly scalable and adaptable, which makes it suitable for use in neuroscience applications as a scalable data processing toolkit. As part of the ongoing extension of NeuroPigPen, we are developing new modules to support statistical functions to analyze signal data for brain connectivity research. In addition, the toolkit is being extended to allow integration with scientific workflow systems. NeuroPigPen is released under BSD license at: https://sites.google.com/a/case.edu/neuropigpen/.

Design of Efficient Mirror Adder in Quantum- Dot Cellular Automata

NASA Astrophysics Data System (ADS)

Mishra, Prashant Kumar; Chattopadhyay, Manju K.

2018-03-01

Lower power consumption is an essential demand for portable multimedia system using digital signal processing algorithms and architectures. Quantum dot cellular automata (QCA) is a rising nano technology for the development of high performance ultra-dense low power digital circuits. QCA based several efficient binary and decimal arithmetic circuits are implemented, however important improvements are still possible. This paper demonstrate Mirror Adder circuit design in QCA. We present comparative study of mirror adder cells designed using conventional CMOS technique and mirror adder cells designed using quantum-dot cellular automata. QCA based mirror adders are better in terms of area by order of three.

A power-efficient switchable CML driver at 10 Gbps

NASA Astrophysics Data System (ADS)

Peipei, Chen; Lei, Li; Huihua, Liu

2016-02-01

High static power limits the application of conventional current-mode logic(CML). This paper presents a power-efficient switchable CML driver, which achieves a significant current saving by 75% compared with conventional ones. Implemented in the 130 nm CMOS technology process, the proposed CML driver just occupies an area about 0.003 mm2 and provides a robust differential signal of 1600 mV for 10 Gbps optical line terminal (OLT) with a total current of 10 mA. The peak-to-peak jitter is about 4 ps (0.04TUI) and the offset voltage is 347.2 mV @ 1600 mVPP.

Demonstration of nonreciprocity in a microwave cavity optomechanical circuit

NASA Astrophysics Data System (ADS)

Peterson, Gabriel; Lecocq, Florent; Kotler, Shlomi; Cicak, Katarina; Simmonds, Raymond; Aumentado, Jose; Teufel, John

The ability to engineer nonreciprocal interactions is essential for many applications including quantum signal processing and quantum transduction. While attributes such as high efficiency and low added noise are always beneficial, for quantum applications these metrics are crucial. Here we present recent experimental results on a parametric, nonreciprocal microwave circuit based on the optomechanical interaction between a superconducting microwave resonator and a mechanically compliant vacuum gap capacitor. Unlike standard Faraday-based circulators, this parametric interaction does not require magnetic fields, and the direction of circulation can be controlled dynamically in situ. Looking forward, such devices could enable programmable, high-efficiency connections between disparate nodes of a quantum network.

Efficient Surface Enhanced Raman Scattering substrates from femtosecond laser based fabrication

NASA Astrophysics Data System (ADS)

Parmar, Vinod; Kanaujia, Pawan K.; Bommali, Ravi Kumar; Vijaya Prakash, G.

2017-10-01

A fast and simple femtosecond laser based methodology for efficient Surface Enhanced Raman Scattering (SERS) substrate fabrication has been proposed. Both nano scaffold silicon (black silicon) and gold nanoparticles (Au-NP) are fabricated by femtosecond laser based technique for mass production. Nano rough silicon scaffold enables large electromagnetic fields for the localized surface plasmons from decorated metallic nanoparticles. Thus giant enhancement (approximately in the order of 104) of Raman signal arises from the mixed effects of electron-photon-phonon coupling, even at nanomolar concentrations of test organic species (Rhodamine 6G). Proposed process demonstrates the low-cost and label-less application ability from these large-area SERS substrates.

Association rule mining in the US Vaccine Adverse Event Reporting System (VAERS).

PubMed

Wei, Lai; Scott, John

2015-09-01

Spontaneous adverse event reporting systems are critical tools for monitoring the safety of licensed medical products. Commonly used signal detection algorithms identify disproportionate product-adverse event pairs and may not be sensitive to more complex potential signals. We sought to develop a computationally tractable multivariate data-mining approach to identify product-multiple adverse event associations. We describe an application of stepwise association rule mining (Step-ARM) to detect potential vaccine-symptom group associations in the US Vaccine Adverse Event Reporting System. Step-ARM identifies strong associations between one vaccine and one or more adverse events. To reduce the number of redundant association rules found by Step-ARM, we also propose a clustering method for the post-processing of association rules. In sample applications to a trivalent intradermal inactivated influenza virus vaccine and to measles, mumps, rubella, and varicella (MMRV) vaccine and in simulation studies, we find that Step-ARM can detect a variety of medically coherent potential vaccine-symptom group signals efficiently. In the MMRV example, Step-ARM appears to outperform univariate methods in detecting a known safety signal. Our approach is sensitive to potentially complex signals, which may be particularly important when monitoring novel medical countermeasure products such as pandemic influenza vaccines. The post-processing clustering algorithm improves the applicability of the approach as a screening method to identify patterns that may merit further investigation. Copyright © 2015 John Wiley & Sons, Ltd.

Sequential Bayesian geoacoustic inversion for mobile and compact source-receiver configuration.

PubMed

Carrière, Olivier; Hermand, Jean-Pierre

2012-04-01

Geoacoustic characterization of wide areas through inversion requires easily deployable configurations including free-drifting platforms, underwater gliders and autonomous vehicles, typically performing repeated transmissions during their course. In this paper, the inverse problem is formulated as sequential Bayesian filtering to take advantage of repeated transmission measurements. Nonlinear Kalman filters implement a random-walk model for geometry and environment and an acoustic propagation code in the measurement model. Data from MREA/BP07 sea trials are tested consisting of multitone and frequency-modulated signals (bands: 0.25-0.8 and 0.8-1.6 kHz) received on a shallow vertical array of four hydrophones 5-m spaced drifting over 0.7-1.6 km range. Space- and time-coherent processing are applied to the respective signal types. Kalman filter outputs are compared to a sequence of global optimizations performed independently on each received signal. For both signal types, the sequential approach is more accurate but also more efficient. Due to frequency diversity, the processing of modulated signals produces a more stable tracking. Although an extended Kalman filter provides comparable estimates of the tracked parameters, the ensemble Kalman filter is necessary to properly assess uncertainty. In spite of mild range dependence and simplified bottom model, all tracked geoacoustic parameters are consistent with high-resolution seismic profiling, core logging P-wave velocity, and previous inversion results with fixed geometries.

Adaptive regularization network based neural modeling paradigm for nonlinear adaptive estimation of cerebral evoked potentials.

PubMed

Zhang, Jian-Hua; Böhme, Johann F

2007-11-01

In this paper we report an adaptive regularization network (ARN) approach to realizing fast blind separation of cerebral evoked potentials (EPs) from background electroencephalogram (EEG) activity with no need to make any explicit assumption on the statistical (or deterministic) signal model. The ARNs are proposed to construct nonlinear EEG and EP signal models. A novel adaptive regularization training (ART) algorithm is proposed to improve the generalization performance of the ARN. Two adaptive neural modeling methods based on the ARN are developed and their implementation and performance analysis are also presented. The computer experiments using simulated and measured visual evoked potential (VEP) data have shown that the proposed ARN modeling paradigm yields computationally efficient and more accurate VEP signal estimation owing to its intrinsic model-free and nonlinear processing characteristics.

Toward high fidelity spectral sensing and RF signal processing in silicon photonic and nano-opto-mechanical platforms

NASA Astrophysics Data System (ADS)

Siddiqui, Aleem; Reinke, Charles; Shin, Heedeuk; Jarecki, Robert L.; Starbuck, Andrew L.; Rakich, Peter

2017-05-01

The performance of electronic systems for radio-frequency (RF) spectrum analysis is critical for agile radar and communications systems, ISR (intelligence, surveillance, and reconnaissance) operations in challenging electromagnetic (EM) environments, and EM-environment situational awareness. While considerable progress has been made in size, weight, and power (SWaP) and performance metrics in conventional RF technology platforms, fundamental limits make continued improvements increasingly difficult. Alternatively, we propose employing cascaded transduction processes in a chip-scale nano-optomechanical system (NOMS) to achieve a spectral sensor with exceptional signal-linearity, high dynamic range, narrow spectral resolution and ultra-fast sweep times. By leveraging the optimal capabilities of photons and phonons, the system we pursue in this work has performance metrics scalable well beyond the fundamental limitations inherent to all electronic systems. In our device architecture, information processing is performed on wide-bandwidth RF-modulated optical signals by photon-mediated phononic transduction of the modulation to the acoustical-domain for narrow-band filtering, and then back to the optical-domain by phonon-mediated phase modulation (the reverse process). Here, we rely on photonics to efficiently distribute signals for parallel processing, and on phononics for effective and flexible RF-frequency manipulation. This technology is used to create RF-filters that are insensitive to the optical wavelength, with wide center frequency bandwidth selectivity (1-100GHz), ultra-narrow filter bandwidth (1-100MHz), and high dynamic range (70dB), which we will present. Additionally, using this filter as a building block, we will discuss current results and progress toward demonstrating a multichannel-filter with a bandwidth of < 10MHz per channel, while minimizing cumulative optical/acoustic/optical transduced insertion-loss to ideally < 10dB. These proposed metric represent significant improvements over RF-platforms.

Radiation Hardened, Modulator ASIC for High Data Rate Communications

NASA Technical Reports Server (NTRS)

McCallister, Ron; Putnam, Robert; Andro, Monty; Fujikawa, Gene

2000-01-01

Satellite-based telecommunication services are challenged by the need to generate down-link power levels adequate to support high quality (BER approx. equals 10(exp 12)) links required for modem broadband data services. Bandwidth-efficient Nyquist signaling, using low values of excess bandwidth (alpha), can exhibit large peak-to-average-power ratio (PAPR) values. High PAPR values necessitate high-power amplifier (HPA) backoff greater than the PAPR, resulting in unacceptably low HPA efficiency. Given the high cost of on-board prime power, this inefficiency represents both an economical burden, and a constraint on the rates and quality of data services supportable from satellite platforms. Constant-envelope signals offer improved power-efficiency, but only by imposing a severe bandwidth-efficiency penalty. This paper describes a radiation- hardened modulator which can improve satellite-based broadband data services by combining the bandwidth-efficiency of low-alpha Nyquist signals with high power-efficiency (negligible HPA backoff).

Racial bias shapes social reinforcement learning.

PubMed

Lindström, Björn; Selbing, Ida; Molapour, Tanaz; Olsson, Andreas

2014-03-01

Both emotional facial expressions and markers of racial-group belonging are ubiquitous signals in social interaction, but little is known about how these signals together affect future behavior through learning. To address this issue, we investigated how emotional (threatening or friendly) in-group and out-group faces reinforced behavior in a reinforcement-learning task. We asked whether reinforcement learning would be modulated by intergroup attitudes (i.e., racial bias). The results showed that individual differences in racial bias critically modulated reinforcement learning. As predicted, racial bias was associated with more efficiently learned avoidance of threatening out-group individuals. We used computational modeling analysis to quantitatively delimit the underlying processes affected by social reinforcement. These analyses showed that racial bias modulates the rate at which exposure to threatening out-group individuals is transformed into future avoidance behavior. In concert, these results shed new light on the learning processes underlying social interaction with racial-in-group and out-group individuals.

Overarching framework for data-based modelling

NASA Astrophysics Data System (ADS)

Schelter, Björn; Mader, Malenka; Mader, Wolfgang; Sommerlade, Linda; Platt, Bettina; Lai, Ying-Cheng; Grebogi, Celso; Thiel, Marco

2014-02-01

One of the main modelling paradigms for complex physical systems are networks. When estimating the network structure from measured signals, typically several assumptions such as stationarity are made in the estimation process. Violating these assumptions renders standard analysis techniques fruitless. We here propose a framework to estimate the network structure from measurements of arbitrary non-linear, non-stationary, stochastic processes. To this end, we propose a rigorous mathematical theory that underlies this framework. Based on this theory, we present a highly efficient algorithm and the corresponding statistics that are immediately sensibly applicable to measured signals. We demonstrate its performance in a simulation study. In experiments of transitions between vigilance stages in rodents, we infer small network structures with complex, time-dependent interactions; this suggests biomarkers for such transitions, the key to understand and diagnose numerous diseases such as dementia. We argue that the suggested framework combines features that other approaches followed so far lack.

Application of wavelet and Fuorier transforms as powerful alternatives for derivative spectrophotometry in analysis of binary mixtures: A comparative study

NASA Astrophysics Data System (ADS)

Hassan, Said A.; Abdel-Gawad, Sherif A.

2018-02-01

Two signal processing methods, namely, Continuous Wavelet Transform (CWT) and the second was Discrete Fourier Transform (DFT) were introduced as alternatives to the classical Derivative Spectrophotometry (DS) in analysis of binary mixtures. To show the advantages of these methods, a comparative study was performed on a binary mixture of Naltrexone (NTX) and Bupropion (BUP). The methods were compared by analyzing laboratory prepared mixtures of the two drugs. By comparing performance of the three methods, it was proved that CWT and DFT methods are more efficient and advantageous in analysis of mixtures with overlapped spectra than DS. The three signal processing methods were adopted for the quantification of NTX and BUP in pure and tablet forms. The adopted methods were validated according to the ICH guideline where accuracy, precision and specificity were found to be within appropriate limits.

Terabit bandwidth-adaptive transmission using low-complexity format-transparent digital signal processing.

PubMed

Zhuge, Qunbi; Morsy-Osman, Mohamed; Chagnon, Mathieu; Xu, Xian; Qiu, Meng; Plant, David V

2014-02-10

In this paper, we propose a low-complexity format-transparent digital signal processing (DSP) scheme for next generation flexible and energy-efficient transceiver. It employs QPSK symbols as the training and pilot symbols for the initialization and tracking stage of the receiver-side DSP, respectively, for various modulation formats. The performance is numerically and experimentally evaluated in a dual polarization (DP) 11 Gbaud 64QAM system. Employing the proposed DSP scheme, we conduct a system-level study of Tb/s bandwidth-adaptive superchannel transmissions with flexible modulation formats including QPSK, 8QAM and 16QAM. The spectrum bandwidth allocation is realized in the digital domain instead of turning on/off sub-channels, which improves the performance of higher order QAM. Various transmission distances ranging from 240 km to 6240 km are demonstrated with a colorless detection for hardware complexity reduction.

Poised Regeneration of Zebrafish Melanocytes Involves Direct Differentiation and Concurrent Replenishment of Tissue-Resident Progenitor Cells.

PubMed

Iyengar, Sharanya; Kasheta, Melissa; Ceol, Craig J

2015-06-22

Efficient regeneration following injury is critical for maintaining tissue function and enabling organismal survival. Cells reconstituting damaged tissue are often generated from resident stem or progenitor cells or from cells that have dedifferentiated and become proliferative. While lineage-tracing studies have defined cellular sources of regeneration in many tissues, the process by which these cells execute the regenerative process is largely obscure. Here, we have identified tissue-resident progenitor cells that mediate regeneration of zebrafish stripe melanocytes and defined how these cells reconstitute pigmentation. Nearly all regeneration melanocytes arise through direct differentiation of progenitor cells. Wnt signaling is activated prior to differentiation, and inhibition of Wnt signaling impairs regeneration. Additional progenitors divide symmetrically to sustain the pool of progenitor cells. Combining direct differentiation with symmetric progenitor divisions may serve as a means to rapidly repair injured tissue while preserving the capacity to regenerate. Copyright © 2015 Elsevier Inc. All rights reserved.

Avionics-compatible video facial cognizer for detection of pilot incapacitation.

PubMed

Steffin, Morris

2006-01-01

High-acceleration loss of consciousness is a serious problem for military pilots. In this laboratory, a video cognizer has been developed that in real time detects facial changes closely coupled to the onset of loss of consciousness. Efficient algorithms are compatible with video digital signal processing hardware and are thus configurable on an autonomous single board that generates alarm triggers to activate autopilot, and is avionics-compatible.

Viscoelastic property identification from waveform reconstruction

NASA Astrophysics Data System (ADS)

Leymarie, N.; Aristégui, C.; Audoin, B.; Baste, S.

2002-05-01

An inverse method is proposed for the determination of the viscoelastic properties of material plates from the plane-wave transmitted acoustic field. Innovations lie in a two-step inversion scheme based on the well-known maximum-likelihood principle with an analytic signal formulation. In addition, establishing the analytical formulations of the plate transmission coefficient we implement an efficient and slightly noise-sensitive process suited to both very thin plates and strongly dispersive media.

Natural Communication with Computers. Volume 1. Speech Understanding Research at BBN

DTIC Science & Technology

1974-12-01

Signal Processing Concurrently with the incremental simulation experiments used to develop insights into the organization of the control component and...us to seek an alternative organization for our phonemic dictionary. There is also the potential problem of new words being used to name new places... organize the lexicon for maximization of efficient retrieval by taking advantage of phonetic, syntactic and semantic relationsnips. Work has already

Design and evaluation of a wireless sensor network based aircraft strength testing system.

PubMed

Wu, Jian; Yuan, Shenfang; Zhou, Genyuan; Ji, Sai; Wang, Zilong; Wang, Yang

2009-01-01

The verification of aerospace structures, including full-scale fatigue and static test programs, is essential for structure strength design and evaluation. However, the current overall ground strength testing systems employ a large number of wires for communication among sensors and data acquisition facilities. The centralized data processing makes test programs lack efficiency and intelligence. Wireless sensor network (WSN) technology might be expected to address the limitations of cable-based aeronautical ground testing systems. This paper presents a wireless sensor network based aircraft strength testing (AST) system design and its evaluation on a real aircraft specimen. In this paper, a miniature, high-precision, and shock-proof wireless sensor node is designed for multi-channel strain gauge signal conditioning and monitoring. A cluster-star network topology protocol and application layer interface are designed in detail. To verify the functionality of the designed wireless sensor network for strength testing capability, a multi-point WSN based AST system is developed for static testing of a real aircraft undercarriage. Based on the designed wireless sensor nodes, the wireless sensor network is deployed to gather, process, and transmit strain gauge signals and monitor results under different static test loads. This paper shows the efficiency of the wireless sensor network based AST system, compared to a conventional AST system.

Design and Evaluation of a Wireless Sensor Network Based Aircraft Strength Testing System

PubMed Central

Wu, Jian; Yuan, Shenfang; Zhou, Genyuan; Ji, Sai; Wang, Zilong; Wang, Yang

2009-01-01

The verification of aerospace structures, including full-scale fatigue and static test programs, is essential for structure strength design and evaluation. However, the current overall ground strength testing systems employ a large number of wires for communication among sensors and data acquisition facilities. The centralized data processing makes test programs lack efficiency and intelligence. Wireless sensor network (WSN) technology might be expected to address the limitations of cable-based aeronautical ground testing systems. This paper presents a wireless sensor network based aircraft strength testing (AST) system design and its evaluation on a real aircraft specimen. In this paper, a miniature, high-precision, and shock-proof wireless sensor node is designed for multi-channel strain gauge signal conditioning and monitoring. A cluster-star network topology protocol and application layer interface are designed in detail. To verify the functionality of the designed wireless sensor network for strength testing capability, a multi-point WSN based AST system is developed for static testing of a real aircraft undercarriage. Based on the designed wireless sensor nodes, the wireless sensor network is deployed to gather, process, and transmit strain gauge signals and monitor results under different static test loads. This paper shows the efficiency of the wireless sensor network based AST system, compared to a conventional AST system. PMID:22408521

A label-free and high-efficient GO-based aptasensor for cancer cells based on cyclic enzymatic signal amplification.

PubMed

Xiao, Kunyi; Liu, Juan; Chen, Hui; Zhang, Song; Kong, Jilie

2017-05-15

A label-free and high-efficient graphene oxide (GO)-based aptasensor was developed for the detection of low quantity cancer cells based on cell-triggered cyclic enzymatic signal amplification (CTCESA). In the absence of target cells, hairpin aptamer probes (HAPs) and dye-labeled linker DNAs stably coexisted in solution, and the fluorescence was quenched by the GO-based FÖrster resonance energy transfer (FRET) process. In the presence of target cells, the specific binding of HAPs with the target cells triggered a conformational alternation, which resulted in linker DNA complementary pairing and cleavage by nicking endonuclease-strand scission cycles. Consequently, more cleaved fragments of linker DNAs with more the terminal labeled dyes could show the enhanced fluorescence because these cleaved DNA fragments hardly combine with GOs and prevent the FRET process. Fluorescence analysis demonstrated that this GO-based aptasensor exhibited selective and sensitive response to the presence of target CCRF-CEM cells in the concentration range from 50 to 10 5 cells. The detection limit of this method was 25 cells, which was approximately 20 times lower than the detection limit of normal fluorescence aptasensors without amplification. With high sensitivity and specificity, it provided a simple and cost-effective approach for early cancer diagnosis. Copyright © 2016 Elsevier B.V. All rights reserved.

A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings

PubMed Central

Liu, Jie; Hu, Youmin; Wu, Bo; Wang, Yan; Xie, Fengyun

2017-01-01

The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD). Parameters of the VMD, in the form of generalized intervals, provide a concise representation for aleatory and epistemic uncertainty and improve the robustness of identification. The multi-scale permutation entropy method is applied to extract state features from the decomposed signals in different operating conditions. Traditional principal component analysis is adopted to reduce feature size and computational cost. With the extracted features’ information, the generalized hidden Markov model, based on generalized interval probability, is used to recognize and classify the fault types and fault severity levels. Finally, the experiment results show that the proposed method is effective at recognizing and classifying the fault types and fault severity levels of rolling bearings. This monitoring method is also efficient enough to quantify the two uncertainty components. PMID:28524088

Brightness checkerboard lattice method for the calibration of the coaxial reverse Hartmann test

NASA Astrophysics Data System (ADS)

Li, Xinji; Hui, Mei; Li, Ning; Hu, Shinan; Liu, Ming; Kong, Lingqin; Dong, Liquan; Zhao, Yuejin

2018-01-01

The coaxial reverse Hartmann test (RHT) is widely used in the measurement of large aspheric surfaces as an auxiliary method for interference measurement, because of its large dynamic range, highly flexible test with low frequency of surface errors, and low cost. And the accuracy of the coaxial RHT depends on the calibration. However, the calibration process remains inefficient, and the signal-to-noise ratio limits the accuracy of the calibration. In this paper, brightness checkerboard lattices were used to replace the traditional dot matrix. The brightness checkerboard method can reduce the number of dot matrix projections in the calibration process, thus improving efficiency. An LCD screen displayed a brightness checkerboard lattice, in which the brighter checkerboard and the darker checkerboard alternately arranged. Based on the image on the detector, the relationship between the rays at certain angles and the photosensitive positions of the detector coordinates can be obtained. And a differential de-noising method can effectively reduce the impact of noise on the measurement results. Simulation and experimentation proved the feasibility of the method. Theoretical analysis and experimental results show that the efficiency of the brightness checkerboard lattices is about four times that of the traditional dot matrix, and the signal-to-noise ratio of the calibration is significantly improved.

Sensitive Infrared Signal Detection by Upconversion Technique

NASA Technical Reports Server (NTRS)

Wong, Teh-Hwa; Yu, Jirong; Bai, Yingxin; Johnson, William; Chen, Songsheng; Petros, Mulugeta; Singh, Upendra N.

2014-01-01

We demonstrated upconversion assisted detection of a 2.05-micron signal by sum frequency generation to generate a 700-nm light using a bulk periodically poled lithium niobate crystal. The achieved 94% intrinsic upconversion efficiency and 22.58% overall detection efficiency at a pW level of 2.05 micron pave the path to detect extremely weak infrared (IR) signals for remote sensing applications.

Quantitative Förster resonance energy transfer analysis for kinetic determinations of SUMO-specific protease.

PubMed

Liu, Yan; Song, Yang; Madahar, Vipul; Liao, Jiayu

2012-03-01

Förster resonance energy transfer (FRET) technology has been widely used in biological and biomedical research, and it is a very powerful tool for elucidating protein interactions in either dynamic or steady state. SUMOylation (the process of SUMO [small ubiquitin-like modifier] conjugation to substrates) is an important posttranslational protein modification with critical roles in multiple biological processes. Conjugating SUMO to substrates requires an enzymatic cascade. Sentrin/SUMO-specific proteases (SENPs) act as an endopeptidase to process the pre-SUMO or as an isopeptidase to deconjugate SUMO from its substrate. To fully understand the roles of SENPs in the SUMOylation cycle, it is critical to understand their kinetics. Here, we report a novel development of a quantitative FRET-based protease assay for SENP1 kinetic parameter determination. The assay is based on the quantitative analysis of the FRET signal from the total fluorescent signal at acceptor emission wavelength, which consists of three components: donor (CyPet-SUMO1) emission, acceptor (YPet) emission, and FRET signal during the digestion process. Subsequently, we developed novel theoretical and experimental procedures to determine the kinetic parameters, k(cat), K(M), and catalytic efficiency (k(cat)/K(M)) of catalytic domain SENP1 toward pre-SUMO1. Importantly, the general principles of this quantitative FRET-based protease kinetic determination can be applied to other proteases. Copyright © 2011 Elsevier Inc. All rights reserved.

Molecular Mechanism of Signal Perception and Integration by the Innate Immune Sensor Retinoic Acid-inducible Gene-I (RIG-I)*

PubMed Central

Binder, Marco; Eberle, Florian; Seitz, Stefan; Mücke, Norbert; Hüber, Christian M.; Kiani, Narsis; Kaderali, Lars; Lohmann, Volker; Dalpke, Alexander; Bartenschlager, Ralf

2011-01-01

RIG-I is a major innate immune sensor for viral infection, triggering an interferon (IFN)-mediated antiviral response upon cytosolic detection of viral RNA. Double-strandedness and 5′-terminal triphosphates were identified as motifs required to elicit optimal immunological signaling. However, very little is known about the response dynamics of the RIG-I pathway, which is crucial for the ability of the cell to react to diverse classes of viral RNA while maintaining self-tolerance. In the present study, we addressed the molecular mechanism of RIG-I signal detection and its translation into pathway activation. By employing highly quantitative methods, we could establish the length of the double-stranded RNA (dsRNA) to be the most critical determinant of response strength. Size exclusion chromatography and direct visualization in scanning force microscopy suggested that this was due to cooperative oligomerization of RIG-I along dsRNA. The initiation efficiency of this oligomerization process critically depended on the presence of high affinity motifs, like a 5′-triphosphate. It is noteworthy that for dsRNA longer than 200 bp, internal initiation could effectively compensate for a lack of terminal triphosphates. In summary, our data demonstrate a very flexible response behavior of the RIG-I pathway, in which sensing and integration of at least two distinct signals, initiation efficiency and double strand length, allow the host cell to mount an antiviral response that is tightly adjusted to the type of the detected signal, such as viral genomes, replication intermediates, or small by-products. PMID:21659521

Spin dynamics of light-induced charge separation in composites of semiconducting polymers and PC60BM revealed using Q-band pulse EPR.

PubMed

Lukina, E A; Suturina, E; Reijerse, E; Lubitz, W; Kulik, L V

2017-08-23

Light-induced processes in composites of semiconducting polymers and fullerene derivatives have been widely studied due to their usage as active layers of organic solar cells. However the process of charge separation under light illumination - the key process of an organic solar cell is not well understood yet. Here we report a Q-band pulse electron paramagnetic resonance study of composites of the fullerene derivative PC 60 BM ([6,6]-phenyl-C 61 -butyric acid methyl ester) with different p-type semiconducting polymers regioregular and regiorandom P3HT (poly(3-hexylthiophene-2,5-diyl), MEH-PPV (poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene]), PCDTBT (poly[N-9'-heptadecanyl-2,7-carbazole-alt-5,5-(4',7'-di-2-thienyl-2',1',3'-benzothiadiazole)]), PTB7 (poly({4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b']dithiophene-2,6-diyl}{3-fluoro-2-[(2-ethylhexyl)carbonyl]thieno[3,4-b]thiophenediyl}))), resulting in a detailed description of the in-phase laser flash-induced electron spin echo (ESE) signal. We found that in organic donor-acceptor composites the laser flash simultaneously induces species of two types: a polymer˙ + /fullerene˙ - spin-correlated polaron pair (SCPP) with an initial singlet spin state and (nearly) free polymer˙ + and fullerene˙ - species with non-equilibrium spin polarization. Species of the first type (SCPP) are well-known for polymer/fullerene blends and are usually associated with a charge-separated state. Also, spin polarization of long-living free species (polarons in deep traps) is affected by the laser flash, which is the third contribution to the flash-induced ESE signal. A protocol for extracting the in-phase ESE signal of the SCPP based on the dependence of the microwave nutation frequency on the strength of the spin coupling within the polaron pair was developed. Nutation experiments revealed an unusual pattern of the SCPP in RR-P3HT/PC 60 BM composites, from which the strength of the exchange interaction between the polymer˙ + and fullerene˙ - was extracted. In composites with low-efficient polymers the contribution of the SCPP to the in-phase ESE signal is high, while in composites with high-efficient polymers it is low. This finding can be used as a selection criterion of charge separation efficiency in the polymer/fullerene composites.

Effect of Resting-State fNIRS Scanning Duration on Functional Brain Connectivity and Graph Theory Metrics of Brain Network.

PubMed

Geng, Shujie; Liu, Xiangyu; Biswal, Bharat B; Niu, Haijing

2017-01-01

As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity.

Intrinsic retrieval efficiency for quantum memories: A three-dimensional theory of light interaction with an atomic ensemble

NASA Astrophysics Data System (ADS)

Gujarati, Tanvi P.; Wu, Yukai; Duan, Luming

2018-03-01

Duan-Lukin-Cirac-Zoller quantum repeater protocol, which was proposed to realize long distance quantum communication, requires usage of quantum memories. Atomic ensembles interacting with optical beams based on off-resonant Raman scattering serve as convenient on-demand quantum memories. Here, a complete free space, three-dimensional theory of the associated read and write process for this quantum memory is worked out with the aim of understanding intrinsic retrieval efficiency. We develop a formalism to calculate the transverse mode structure for the signal and the idler photons and use the formalism to study the intrinsic retrieval efficiency under various configurations. The effects of atomic density fluctuations and atomic motion are incorporated by numerically simulating this system for a range of realistic experimental parameters. We obtain results that describe the variation in the intrinsic retrieval efficiency as a function of the memory storage time for skewed beam configuration at a finite temperature, which provides valuable information for optimization of the retrieval efficiency in experiments.

Lightweight DC-DC Converter with Partial Power Processing and MPPT for a Solar Powered Aircraft

NASA Astrophysics Data System (ADS)

Diab-Marzouk, Ahmad

A lightweight dc-dc partial power processing converter is demonstrated for solar aerospace applications. A system-level model is conceived to determine conformity to payload and target distance objectives, with the Solarship aircraft used as an application example. The concept of partial power processing is utilized to realize a high efficiency lightweight converter that performs Max Peak Power Tracking (MPPT) to transfer power from the aircraft solar array to the high-voltage battery bus. The isolated Cuk is determined to be a suitable converter topology for the application. A small-signal model is derived for control design. The operation of a 400V, 2.7 kW prototype is verified at high frequency (200 kHz), high efficiency (> 98%), small mass (0.604 kg), and uses no electrolytic capacitors. MPPT operation is verified on a 376 V commercial solar installation at The University of Toronto. The prototype serves as an enabling technology for solar aerospace applications.

A method for reduction of Acoustic Emission (AE) data with application in machine failure detection and diagnosis

NASA Astrophysics Data System (ADS)

Vicuña, Cristián Molina; Höweler, Christoph

2017-12-01

The use of AE in machine failure diagnosis has increased over the last years. Most AE-based failure diagnosis strategies use digital signal processing and thus require the sampling of AE signals. High sampling rates are required for this purpose (e.g. 2 MHz or higher), leading to streams of large amounts of data. This situation is aggravated if fine resolution and/or multiple sensors are required. These facts combine to produce bulky data, typically in the range of GBytes, for which sufficient storage space and efficient signal processing algorithms are required. This situation probably explains why, in practice, AE-based methods consist mostly in the calculation of scalar quantities such as RMS and Kurtosis, and the analysis of their evolution in time. While the scalar-based approach offers the advantage of maximum data reduction; it has the disadvantage that most part of the information contained in the raw AE signal is lost unrecoverably. This work presents a method offering large data reduction, while keeping the most important information conveyed by the raw AE signal, useful for failure detection and diagnosis. The proposed method consist in the construction of a synthetic, unevenly sampled signal which envelopes the AE bursts present on the raw AE signal in a triangular shape. The constructed signal - which we call TriSignal - also permits the estimation of most scalar quantities typically used for failure detection. But more importantly, it contains the information of the time of occurrence of the bursts, which is key for failure diagnosis. Lomb-Scargle normalized periodogram is used to construct the TriSignal spectrum, which reveals the frequency content of the TriSignal and provides the same information as the classic AE envelope. The paper includes application examples in planetary gearbox and low-speed rolling element bearing.

The Influence of Gravito-Inertial Force on Sensorimotor Integration and Reflexive Responses

NASA Technical Reports Server (NTRS)

Curthoys, Ian S.; Guedry, Fred E.; Merfeld, Daniel M.; Watt, Doug G. D.; Tomko, David L.; Wade, Charles E. (Technical Monitor)

1994-01-01

Sensorimotor responses (e.g.. eye movements, spinal reflexes, etc depend upon the interpretation of the neural signals from the sensory systems. Since neural signals from the otoliths may represent either tilt (gravity) or translation (linear inertial force), sensory signals from the otolith organs are necessarily somewhat ambiguous. Therefore. the neural responses to changing otolith signals depend upon the context of the stimulation (e.g- active vs. passive, relative orientation of gravity, etc.) as well as upon other sensory signals (e.g., vision. canals, etc.). This session will focus upon the -role -played by the sensory signals from the otolith organs in producing efficient sensorimotor and behavioral responses. Curthoys will show the influence of the peripheral anatomy and physiology. Tomko will discuss the influence of tilt and translational otolith signals on eye movements. Merfeld will demonstrate the rate otolith organs play during the interaction of sensory signals from the canals and otoliths. Watt will show the influence of the otoliths on spinal/postural responses. Guedry will discuss the contribution of vestibular information to "path of movement"' perception and to the development of a stable vertical reference. Sensorimotor responses to the ambiguous inertial force stimulation provide an important tool to investigate how the nervous system processes patterns of sensory information and yields functional sensorimotor responses.

Clustering social cues to determine social signals: developing learning algorithms using the "n-most likely states" approach

NASA Astrophysics Data System (ADS)

Best, Andrew; Kapalo, Katelynn A.; Warta, Samantha F.; Fiore, Stephen M.

2016-05-01

Human-robot teaming largely relies on the ability of machines to respond and relate to human social signals. Prior work in Social Signal Processing has drawn a distinction between social cues (discrete, observable features) and social signals (underlying meaning). For machines to attribute meaning to behavior, they must first understand some probabilistic relationship between the cues presented and the signal conveyed. Using data derived from a study in which participants identified a set of salient social signals in a simulated scenario and indicated the cues related to the perceived signals, we detail a learning algorithm, which clusters social cue observations and defines an "N-Most Likely States" set for each cluster. Since multiple signals may be co-present in a given simulation and a set of social cues often maps to multiple social signals, the "N-Most Likely States" approach provides a dramatic improvement over typical linear classifiers. We find that the target social signal appears in a "3 most-likely signals" set with up to 85% probability. This results in increased speed and accuracy on large amounts of data, which is critical for modeling social cognition mechanisms in robots to facilitate more natural human-robot interaction. These results also demonstrate the utility of such an approach in deployed scenarios where robots need to communicate with human teammates quickly and efficiently. In this paper, we detail our algorithm, comparative results, and offer potential applications for robot social signal detection and machine-aided human social signal detection.

Design of a portable noninvasive photoacoustic glucose monitoring system integrated laser diode excitation with annular array detection

NASA Astrophysics Data System (ADS)

Zeng, Lvming; Liu, Guodong; Yang, Diwu; Ren, Zhong; Huang, Zhen

2008-12-01

A near-infrared photoacoustic glucose monitoring system, which is integrated dual-wavelength pulsed laser diode excitation with eight-element planar annular array detection technique, is designed and fabricated during this study. It has the characteristics of nonivasive, inexpensive, portable, accurate location, and high signal-to-noise ratio. In the system, the exciting source is based on two laser diodes with wavelengths of 905 nm and 1550 nm, respectively, with optical pulse energy of 20 μJ and 6 μJ. The laser beam is optically focused and jointly projected to a confocal point with a diameter of 0.7 mm approximately. A 7.5 MHz 8-element annular array transducer with a hollow structure is machined to capture photoacoustic signal in backward mode. The captured signals excitated from blood glucose are processed with a synthetic focusing algorithm to obtain high signal-to-noise ratio and accurate location over a range of axial detection depth. The custom-made transducer with equal area elements is coaxially collimated with the laser source to improve the photoacoustic excite/receive efficiency. In the paper, we introduce the photoacoustic theory, receive/process technique, and design method of the portable noninvasive photoacoustic glucose monitoring system, which can potentially be developed as a powerful diagnosis and treatment tool for diabetes mellitus.

All-optical control of light on a graphene-on-silicon nitride chip using thermo-optic effect.

PubMed

Qiu, Ciyuan; Yang, Yuxing; Li, Chao; Wang, Yifang; Wu, Kan; Chen, Jianping

2017-12-06

All-optical signal processing avoids the conversion between optical signals and electronic signals and thus has the potential to achieve a power efficient photonic system. Micro-scale all-optical devices for light manipulation are the key components in the all-optical signal processing and have been built on the semiconductor platforms (e.g., silicon and III-V semiconductors). However, the two-photon absorption (TPA) effect and the free-carrier absorption (FCA) effect in these platforms deteriorate the power handling and limit the capability to realize complex functions. Instead, silicon nitride (Si 3 N 4 ) provides a possibility to realize all-optical large-scale integrated circuits due to its insulator nature without TPA and FCA. In this work, we investigate the physical dynamics of all-optical control on a graphene-on-Si 3 N 4 chip based on thermo-optic effect. In the experimental demonstration, a switching response time constant of 253.0 ns at a switching energy of ~50 nJ is obtained with a device dimension of 60 μm × 60 μm, corresponding to a figure of merit (FOM) of 3.0 nJ mm. Detailed coupled-mode theory based analysis on the thermo-optic effect of the device has been performed.

Real-Time and Secure Wireless Health Monitoring

PubMed Central

Dağtaş, S.; Pekhteryev, G.; Şahinoğlu, Z.; Çam, H.; Challa, N.

2008-01-01

We present a framework for a wireless health monitoring system using wireless networks such as ZigBee. Vital signals are collected and processed using a 3-tiered architecture. The first stage is the mobile device carried on the body that runs a number of wired and wireless probes. This device is also designed to perform some basic processing such as the heart rate and fatal failure detection. At the second stage, further processing is performed by a local server using the raw data transmitted by the mobile device continuously. The raw data is also stored at this server. The processed data as well as the analysis results are then transmitted to the service provider center for diagnostic reviews as well as storage. The main advantages of the proposed framework are (1) the ability to detect signals wirelessly within a body sensor network (BSN), (2) low-power and reliable data transmission through ZigBee network nodes, (3) secure transmission of medical data over BSN, (4) efficient channel allocation for medical data transmission over wireless networks, and (5) optimized analysis of data using an adaptive architecture that maximizes the utility of processing and computational capacity at each platform. PMID:18497866

Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques.

PubMed

Sutha, P; Jayanthi, V E

2017-12-08

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods.The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with Daubechies wavelet, employed along with de noising techniques for the extraction of fetal Electrocardiogram.Both the methods are having good sensitivity and accuracy. In adaptive method the sensitivity is 96.83, accuracy 89.87, wavelet sensitivity is 95.97 and accuracy is 88.5. Additionally, time domain parameters from the plot of heart rate variability of mother and fetus are analyzed.

Triboelectric Nanogenerator Enabled Body Sensor Network for Self-Powered Human Heart-Rate Monitoring.

PubMed

Lin, Zhiming; Chen, Jun; Li, Xiaoshi; Zhou, Zhihao; Meng, Keyu; Wei, Wei; Yang, Jin; Wang, Zhong Lin

2017-09-26

Heart-rate monitoring plays a critical role in personal healthcare management. A low-cost, noninvasive, and user-friendly heart-rate monitoring system is highly desirable. Here, a self-powered wireless body sensor network (BSN) system is developed for heart-rate monitoring via integration of a downy-structure-based triboelectric nanogenerator (D-TENG), a power management circuit, a heart-rate sensor, a signal processing unit, and Bluetooth module for wireless data transmission. By converting the inertia energy of human walking into electric power, a maximum power of 2.28 mW with total conversion efficiency of 57.9% was delivered at low operation frequency, which is capable of immediately and sustainably driving the highly integrated BSN system. The acquired heart-rate signal by the sensor would be processed in the signal process circuit, sent to an external device via the Bluetooth module, and displayed on a personal cell phone in a real-time manner. Moreover, by combining a TENG-based generator and a TENG-based sensor, an all-TENG-based wireless BSN system was developed, realizing continuous and self-powered heart-rate monitoring. This work presents a potential method for personal heart-rate monitoring, featured as being self-powered, cost-effective, noninvasive, and user-friendly.

An improved PSO-SVM model for online recognition defects in eddy current testing

NASA Astrophysics Data System (ADS)

Liu, Baoling; Hou, Dibo; Huang, Pingjie; Liu, Banteng; Tang, Huayi; Zhang, Wubo; Chen, Peihua; Zhang, Guangxin

2013-12-01

Accurate and rapid recognition of defects is essential for structural integrity and health monitoring of in-service device using eddy current (EC) non-destructive testing. This paper introduces a novel model-free method that includes three main modules: a signal pre-processing module, a classifier module and an optimisation module. In the signal pre-processing module, a kind of two-stage differential structure is proposed to suppress the lift-off fluctuation that could contaminate the EC signal. In the classifier module, multi-class support vector machine (SVM) based on one-against-one strategy is utilised for its good accuracy. In the optimisation module, the optimal parameters of classifier are obtained by an improved particle swarm optimisation (IPSO) algorithm. The proposed IPSO technique can improve convergence performance of the primary PSO through the following strategies: nonlinear processing of inertia weight, introductions of the black hole and simulated annealing model with extremum disturbance. The good generalisation ability of the IPSO-SVM model has been validated through adding additional specimen into the testing set. Experiments show that the proposed algorithm can achieve higher recognition accuracy and efficiency than other well-known classifiers and the superiorities are more obvious with less training set, which contributes to online application.

Simultaneous excitation system for efficient guided wave structural health monitoring

NASA Astrophysics Data System (ADS)

Hua, Jiadong; Michaels, Jennifer E.; Chen, Xin; Lin, Jing

2017-10-01

Many structural health monitoring systems utilize guided wave transducer arrays for defect detection and localization. Signals are usually acquired using the ;pitch-catch; method whereby each transducer is excited in turn and the response is received by the remaining transducers. When extensive signal averaging is performed, the data acquisition process can be quite time-consuming, especially for metallic components that require a low repetition rate to allow signals to die out. Such a long data acquisition time is particularly problematic if environmental and operational conditions are changing while data are being acquired. To reduce the total data acquisition time, proposed here is a methodology whereby multiple transmitters are simultaneously triggered, and each transmitter is driven with a unique excitation. The simultaneously transmitted waves are captured by one or more receivers, and their responses are processed by dispersion-compensated filtering to extract the response from each individual transmitter. The excitation sequences are constructed by concatenating a series of chirps whose start and stop frequencies are randomly selected from a specified range. The process is optimized using a Monte-Carlo approach to select sequences with impulse-like autocorrelations and relatively flat cross-correlations. The efficacy of the proposed methodology is evaluated by several metrics and is experimentally demonstrated with sparse array imaging of simulated damage.

Electroencephalographic compression based on modulated filter banks and wavelet transform.

PubMed

Bazán-Prieto, Carlos; Cárdenas-Barrera, Julián; Blanco-Velasco, Manuel; Cruz-Roldán, Fernando

2011-01-01

Due to the large volume of information generated in an electroencephalographic (EEG) study, compression is needed for storage, processing or transmission for analysis. In this paper we evaluate and compare two lossy compression techniques applied to EEG signals. It compares the performance of compression schemes with decomposition by filter banks or wavelet Packets transformation, seeking the best value for compression, best quality and more efficient real time implementation. Due to specific properties of EEG signals, we propose a quantization stage adapted to the dynamic range of each band, looking for higher quality. The results show that the compressor with filter bank performs better than transform methods. Quantization adapted to the dynamic range significantly enhances the quality.

ON THE RADIO DETECTION OF MULTIPLE-EXOMOON SYSTEMS DUE TO PLASMA TORUS SHARING

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

Noyola, J. P.; Satyal, S.; Musielak, Z. E., E-mail: jpnoyola@uta.edu, E-mail: ssatyal@uta.edu, E-mail: zmusielak@uta.edu

2016-04-20

The idea of single exomoon detection due to the radio emissions caused by its interaction with the host exoplanet is extended to multiple-exomoon systems. The characteristic radio emissions are made possible in part by plasma from the exomoon’s own ionosphere. In this work, it is demonstrated that neighboring exomoons and the exoplanetary magnetosphere could also provide enough plasma to generate a detectable signal. In particular, the plasma-torus-sharing phenomenon is found to be particularly well suited to facilitate the radio detection of plasma-deficient exomoons. The efficiency of this process is evaluated, and the predicted power and frequency of the resulting radiomore » signals are presented.« less

Online frequency estimation with applications to engine and generator sets

NASA Astrophysics Data System (ADS)

Manngård, Mikael; Böling, Jari M.

2017-07-01

Frequency and spectral analysis based on the discrete Fourier transform is a fundamental task in signal processing and machine diagnostics. This paper aims at presenting computationally efficient methods for real-time estimation of stationary and time-varying frequency components in signals. A brief survey of the sliding time window discrete Fourier transform and Goertzel filter is presented, and two filter banks consisting of: (i) sliding time window Goertzel filters (ii) infinite impulse response narrow bandpass filters are proposed for estimating instantaneous frequencies. The proposed methods show excellent results on both simulation studies and on a case study using angular speed data measurements of the crankshaft of a marine diesel engine-generator set.

Wavelet analysis to decompose a vibration simulation signal to improve pre-distribution testing of packaging

NASA Astrophysics Data System (ADS)

Griffiths, K. R.; Hicks, B. J.; Keogh, P. S.; Shires, D.

2016-08-01

In general, vehicle vibration is non-stationary and has a non-Gaussian probability distribution; yet existing testing methods for packaging design employ Gaussian distributions to represent vibration induced by road profiles. This frequently results in over-testing and/or over-design of the packaging to meet a specification and correspondingly leads to wasteful packaging and product waste, which represent 15bn per year in the USA and €3bn per year in the EU. The purpose of the paper is to enable a measured non-stationary acceleration signal to be replaced by a constructed signal that includes as far as possible any non-stationary characteristics from the original signal. The constructed signal consists of a concatenation of decomposed shorter duration signals, each having its own kurtosis level. Wavelet analysis is used for the decomposition process into inner and outlier signal components. The constructed signal has a similar PSD to the original signal, without incurring excessive acceleration levels. This allows an improved and more representative simulated input signal to be generated that can be used on the current generation of shaker tables. The wavelet decomposition method is also demonstrated experimentally through two correlation studies. It is shown that significant improvements over current international standards for packaging testing are achievable; hence the potential for more efficient packaging system design is possible.

Alternative Splicing and Cross-Talk with Light Signaling.

PubMed

Cheng, You-Liang; Tu, Shih-Long

2018-06-01

Alternative splicing (AS) is the main source of proteome diversity that in large part contributes to the complexity of eukaryotes. Recent global analysis of AS with RNA sequencing has revealed that AS is prevalent in plants, particularly when responding to environmental changes. Light is one of the most important environmental factors for plant growth and development. To optimize light absorption, plants evolve complex photoreceptors and signaling systems to regulate gene expression and biological processes in the cell. Genome-wide analyses have shown that light induces intensive AS in plants. However, the biochemical mechanisms of light regulating AS remain poorly understood. In this review, we aim to discuss recent progress in investigating the functions of AS, discovery of cross-talk between AS and light signaling, and the potential mechanism of light-regulated AS. Understanding how light signaling regulates the efficiency of AS and the biological significance of light-regulated AS in plant systems will provide new insights into the adaptation of plants to their environment and, ultimately, crop improvement.

Stimfit: quantifying electrophysiological data with Python

PubMed Central

Guzman, Segundo J.; Schlögl, Alois; Schmidt-Hieber, Christoph

2013-01-01

Intracellular electrophysiological recordings provide crucial insights into elementary neuronal signals such as action potentials and synaptic currents. Analyzing and interpreting these signals is essential for a quantitative understanding of neuronal information processing, and requires both fast data visualization and ready access to complex analysis routines. To achieve this goal, we have developed Stimfit, a free software package for cellular neurophysiology with a Python scripting interface and a built-in Python shell. The program supports most standard file formats for cellular neurophysiology and other biomedical signals through the Biosig library. To quantify and interpret the activity of single neurons and communication between neurons, the program includes algorithms to characterize the kinetics of presynaptic action potentials and postsynaptic currents, estimate latencies between pre- and postsynaptic events, and detect spontaneously occurring events. We validate and benchmark these algorithms, give estimation errors, and provide sample use cases, showing that Stimfit represents an efficient, accessible and extensible way to accurately analyze and interpret neuronal signals. PMID:24600389

Detection of the ODMR signal of a nitrogen vacancy centre in nanodiamond in propagating surface plasmons

NASA Astrophysics Data System (ADS)

Al-Baiaty, Zahraa; Cumming, Benjamin P.; Gan, Xiaosong; Gu, Min

2018-02-01

We demonstrate that the optically detected magnetic resonance (ODMR) signal of a nitrogen vacancy (NV) centre can be coupled to propagating surface plasmons for the detection of the NV centre spin states, and of external magnetic fields. By coupling the spin dependent luminescence signal of a NV centre in a nanodiamond (ND) to a chemically synthesized silver nanowire, we demonstrate the readout of the ODMR signal as a reduction in the surface plasmon polariton intensity, with improved contrast in comparison to the emission from the NV centre. Furthermore, on the application of a permanent magnetic field from zero to 13 G, we demonstrate that the Zeeman splitting of the magnetic spin states of the nitrogen vacancy centre ground states can also be detected in the coupled surface plasmons. This is an important step in the development of a compact on-chip information processing system utilizing the nitrogen vacancy in nanodiamond as an on-chip source with efficient magnetometry sensing properties.

Anxiety dissociates the adaptive functions of sensory and motor response enhancements to social threats

PubMed Central

El Zein, Marwa; Wyart, Valentin; Grèzes, Julie

2015-01-01

Efficient detection and reaction to negative signals in the environment is essential for survival. In social situations, these signals are often ambiguous and can imply different levels of threat for the observer, thereby making their recognition susceptible to contextual cues – such as gaze direction when judging facial displays of emotion. However, the mechanisms underlying such contextual effects remain poorly understood. By computational modeling of human behavior and electrical brain activity, we demonstrate that gaze direction enhances the perceptual sensitivity to threat-signaling emotions – anger paired with direct gaze, and fear paired with averted gaze. This effect arises simultaneously in ventral face-selective and dorsal motor cortices at 200 ms following face presentation, dissociates across individuals as a function of anxiety, and does not reflect increased attention to threat-signaling emotions. These findings reveal that threat tunes neural processing in fast, selective, yet attention-independent fashion in sensory and motor systems, for different adaptive purposes. DOI: http://dx.doi.org/10.7554/eLife.10274.001 PMID:26712157

SVD compression for magnetic resonance fingerprinting in the time domain.

PubMed

McGivney, Debra F; Pierre, Eric; Ma, Dan; Jiang, Yun; Saybasili, Haris; Gulani, Vikas; Griswold, Mark A

2014-12-01

Magnetic resonance (MR) fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition, which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.

Instrument-independent analysis of music by means of the continuous wavelet transform

NASA Astrophysics Data System (ADS)

Olmo, Gabriella; Dovis, Fabio; Benotto, Paolo; Calosso, Claudio; Passaro, Pierluigi

1999-10-01

This paper deals with the problem of automatic recognition of music. Segments of digitized music are processed by means of a Continuous Wavelet Transform, properly chosen so as to match the spectral characteristics of the signal. In order to achieve a good time-scale representation of the signal components a novel wavelet has been designed suited to the musical signal features. particular care has been devoted towards an efficient implementation, which operates in the frequency domain, and includes proper segmentation and aliasing reduction techniques to make the analysis of long signals feasible. The method achieves very good performance in terms of both time and frequency selectivity, and can yield the estimate and the localization in time of both the fundamental frequency and the main harmonics of each tone. The analysis is used as a preprocessing step for a recognition algorithm, which we show to be almost independent on the instrument reproducing the sounds. Simulations are provided to demonstrate the effectiveness of the proposed method.

Signal identification in acoustic emission monitoring of fatigue cracking in steel bridges

NASA Astrophysics Data System (ADS)

Yu, Jianguo P.; Ziehl, Paul; Pollock, Adrian

2012-04-01

Signal identification including noise filtering and reduction of acquired signals is needed to achieve efficient and accurate data interpretation for remote acoustic emission (AE) monitoring of in-service steel bridges. Noise filtering may ensure that genuine hits from crack growth are involved in the estimation of fatigue damage and remaining fatigue life. Reduction of the data quantity is desirable for the sensing system to conserve energy in the data transmission and processing procedures. Identification and categorization of acquired signals is a promising approach to effectively filter and reduce AE data in the application of bridge monitoring. In this study an investigation on waveform features (time domain and frequency domain) and relevant filters is carried out using the results from AE monitored fatigue tests. It is verified that duration-amplitude (D-A) filters are effective to discriminate against noise for results of steel fatigue tests. The study is helpful to find an appropriate AE data filtering protocol for field implementations.

SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain

PubMed Central

McGivney, Debra F.; Pierre, Eric; Ma, Dan; Jiang, Yun; Saybasili, Haris; Gulani, Vikas; Griswold, Mark A.

2016-01-01

Magnetic resonance fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition (SVD), which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously. PMID:25029380

Orbital-angular-momentum mode-group multiplexed transmission over a graded-index ring-core fiber based on receive diversity and maximal ratio combining

NASA Astrophysics Data System (ADS)

Zhang, Junwei; Zhu, Guoxuan; Liu, Jie; Wu, Xiong; Zhu, Jiangbo; Du, Cheng; Luo, Wenyong; Chen, Yujie; Yu, Siyuan

2018-02-01

An orbital-angular-momentum (OAM) mode-group multiplexing (MGM) scheme based on a graded-index ring-core fiber (GIRCF) is proposed, in which a single-input two-output (or receive diversity) architecture is designed for each MG channel and simple digital signal processing (DSP) is utilized to adaptively resist the mode partition noise resulting from random intra-group mode crosstalk. There is no need of complex multiple-input multiple-output (MIMO) equalization in this scheme. Furthermore, the signal-to-noise ratio (SNR) of the received signals can be improved if a simple maximal ratio combining (MRC) technique is employed on the receiver side to efficiently take advantage of the diversity gain of receiver. Intensity-modulated direct-detection (IM-DD) systems transmitting three OAM mode groups with total 100-Gb/s discrete multi-tone (DMT) signals over a 1-km GIRCF and two OAM mode groups with total 40-Gb/s DMT signals over an 18-km GIRCF are experimentally demonstrated, respectively, to confirm the feasibility of our proposed OAM-MGM scheme.

Top-Down Inhibition of BMP Signaling Enables Robust Induction of hPSCs Into Neural Crest in Fully Defined, Xeno-free Conditions.

PubMed

Hackland, James O S; Frith, Tom J R; Thompson, Oliver; Marin Navarro, Ana; Garcia-Castro, Martin I; Unger, Christian; Andrews, Peter W

2017-10-10

Defects in neural crest development have been implicated in many human disorders, but information about human neural crest formation mostly depends on extrapolation from model organisms. Human pluripotent stem cells (hPSCs) can be differentiated into in vitro counterparts of the neural crest, and some of the signals known to induce neural crest formation in vivo are required during this process. However, the protocols in current use tend to produce variable results, and there is no consensus as to the precise signals required for optimal neural crest differentiation. Using a fully defined culture system, we have now found that the efficient differentiation of hPSCs to neural crest depends on precise levels of BMP signaling, which are vulnerable to fluctuations in endogenous BMP production. We present a method that controls for this phenomenon and could be applied to other systems where endogenous signaling can also affect the outcome of differentiation protocols. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Topics in the Detection of Gravitational Waves from Compact Binary Inspirals

NASA Astrophysics Data System (ADS)

Kapadia, Shasvath Jagat

Orbiting compact binaries - such as binary black holes, binary neutron stars and neutron star-black hole binaries - are among the most promising sources of gravitational waves observable by ground-based interferometric detectors. Despite numerous sophisticated engineering techniques, the gravitational wave signals will be buried deep within noise generated by various instrumental and environmental processes, and need to be extracted via a signal processing technique referred to as matched filtering. Matched filtering requires large banks of signal templates that are faithful representations of the true gravitational waveforms produced by astrophysical binaries. The accurate and efficient production of templates is thus crucial to the success of signal processing and data analysis. To that end, the dissertation presents a numerical technique that calibrates existing analytical (Post-Newtonian) waveforms, which are relatively inexpensive, to more accurate fiducial waveforms that are computationally expensive to generate. The resulting waveform family is significantly more accurate than the analytical waveforms, without incurring additional computational costs of production. Certain kinds of transient background noise artefacts, called "glitches'', can masquerade as gravitational wave signals for short durations and throw-off the matched-filter algorithm. Identifying glitches from true gravitational wave signals is a highly non-trivial exercise in data analysis which has been attempted with varying degrees of success. We present here a machine-learning based approach that exploits the various attributes of glitches and signals within detector data to provide a classification scheme that is a significant improvement over previous methods. The dissertation concludes by investigating the possibility of detecting a non-linear DC imprint, called the Christodoulou memory, produced in the arms of ground-based interferometers by the recently detected gravitational waves. The memory, which is even smaller in amplitude than the primary (detected) gravitational waves, will almost certainly not be seen in the current detection event. Nevertheless, future space-based detectors will likely be sensitive enough to observe the memory.

Ras GTPases Modulate Morphogenesis, Sporulation and Cellulase Gene Expression in the Cellulolytic Fungus Trichoderma reesei

PubMed Central

Zhang, Jiwei; Zhang, Yanmei; Zhong, Yaohua; Qu, Yinbo; Wang, Tianhong

2012-01-01

Background The model cellulolytic fungus Trichoderma reesei (teleomorph Hypocrea jecorina) is capable of responding to environmental cues to compete for nutrients in its natural saprophytic habitat despite its genome encodes fewer degradative enzymes. Efficient signalling pathways in perception and interpretation of environmental signals are indispensable in this process. Ras GTPases represent a kind of critical signal proteins involved in signal transduction and regulation of gene expression. In T. reesei the genome contains two Ras subfamily small GTPases TrRas1 and TrRas2 homologous to Ras1 and Ras2 from S. cerevisiae, but their functions remain unknown. Methodology/Principal Findings Here, we have investigated the roles of GTPases TrRas1 and TrRas2 during fungal morphogenesis and cellulase gene expression. We show that both TrRas1 and TrRas2 play important roles in some cellular processes such as polarized apical growth, hyphal branch formation, sporulation and cAMP level adjustment, while TrRas1 is more dominant in these processes. Strikingly, we find that TrRas2 is involved in modulation of cellulase gene expression. Deletion of TrRas2 results in considerably decreased transcription of cellulolytic genes upon growth on cellulose. Although the strain carrying a constitutively activated TrRas2G16V allele exhibits increased cellulase gene transcription, the cbh1 and cbh2 expression in this mutant still strictly depends on cellulose, indicating TrRas2 does not directly mediate the transmission of the cellulose signal. In addition, our data suggest that the effect of TrRas2 on cellulase gene is exerted through regulation of transcript abundance of cellulase transcription factors such as Xyr1, but the influence is independent of cAMP signalling pathway. Conclusions/Significance Together, these findings elucidate the functions for Ras signalling of T. reesei in cellular morphogenesis, especially in cellulase gene expression, which contribute to deciphering the powerful competitive ability of plant cell wall degrading fungi in nature. PMID:23152805

Using nonlocal means to separate cardiac and respiration sounds

NASA Astrophysics Data System (ADS)

Rudnitskii, A. G.

2014-11-01

The paper presents the results of applying nonlocal means (NLMs) approach in the problem of separating respiration and cardiac sounds in a signal recorded on a human chest wall. The performance of the algorithm was tested both by simulated and real signals. As a quantitative efficiency measure of NLM filtration, the angle of divergence between isolated and reference signal was used. It is shown that for a wide range of signal-to-noise ratios, the algorithm makes it possible to efficiently solve this problem of separating cardiac and respiration sounds in the sum signal recorded on a human chest wall.

Real-Time Digital Signal Processing Based on FPGAs for Electronic Skin Implementation †

PubMed Central

Ibrahim, Ali; Gastaldo, Paolo; Chible, Hussein; Valle, Maurizio

2017-01-01

Enabling touch-sensing capability would help appliances understand interaction behaviors with their surroundings. Many recent studies are focusing on the development of electronic skin because of its necessity in various application domains, namely autonomous artificial intelligence (e.g., robots), biomedical instrumentation, and replacement prosthetic devices. An essential task of the electronic skin system is to locally process the tactile data and send structured information either to mimic human skin or to respond to the application demands. The electronic skin must be fabricated together with an embedded electronic system which has the role of acquiring the tactile data, processing, and extracting structured information. On the other hand, processing tactile data requires efficient methods to extract meaningful information from raw sensor data. Machine learning represents an effective method for data analysis in many domains: it has recently demonstrated its effectiveness in processing tactile sensor data. In this framework, this paper presents the implementation of digital signal processing based on FPGAs for tactile data processing. It provides the implementation of a tensorial kernel function for a machine learning approach. Implementation results are assessed by highlighting the FPGA resource utilization and power consumption. Results demonstrate the feasibility of the proposed implementation when real-time classification of input touch modalities are targeted. PMID:28287448

Real-Time Digital Signal Processing Based on FPGAs for Electronic Skin Implementation.

PubMed

Ibrahim, Ali; Gastaldo, Paolo; Chible, Hussein; Valle, Maurizio

2017-03-10

Enabling touch-sensing capability would help appliances understand interaction behaviors with their surroundings. Many recent studies are focusing on the development of electronic skin because of its necessity in various application domains, namely autonomous artificial intelligence (e.g., robots), biomedical instrumentation, and replacement prosthetic devices. An essential task of the electronic skin system is to locally process the tactile data and send structured information either to mimic human skin or to respond to the application demands. The electronic skin must be fabricated together with an embedded electronic system which has the role of acquiring the tactile data, processing, and extracting structured information. On the other hand, processing tactile data requires efficient methods to extract meaningful information from raw sensor data. Machine learning represents an effective method for data analysis in many domains: it has recently demonstrated its effectiveness in processing tactile sensor data. In this framework, this paper presents the implementation of digital signal processing based on FPGAs for tactile data processing. It provides the implementation of a tensorial kernel function for a machine learning approach. Implementation results are assessed by highlighting the FPGA resource utilization and power consumption. Results demonstrate the feasibility of the proposed implementation when real-time classification of input touch modalities are targeted.

A 6.45 μW Self-Powered SoC With Integrated Energy-Harvesting Power Management and ULP Asymmetric Radios for Portable Biomedical Systems.

PubMed

Roy, Abhishek; Klinefelter, Alicia; Yahya, Farah B; Chen, Xing; Gonzalez-Guerrero, Luisa Patricia; Lukas, Christopher J; Kamakshi, Divya Akella; Boley, James; Craig, Kyle; Faisal, Muhammad; Oh, Seunghyun; Roberts, Nathan E; Shakhsheer, Yousef; Shrivastava, Aatmesh; Vasudevan, Dilip P; Wentzloff, David D; Calhoun, Benton H

2015-12-01

This paper presents a batteryless system-on-chip (SoC) that operates off energy harvested from indoor solar cells and/or thermoelectric generators (TEGs) on the body. Fabricated in a commercial 0.13 μW process, this SoC sensing platform consists of an integrated energy harvesting and power management unit (EH-PMU) with maximum power point tracking, multiple sensing modalities, programmable core and a low power microcontroller with several hardware accelerators to enable energy-efficient digital signal processing, ultra-low-power (ULP) asymmetric radios for wireless transmission, and a 100 nW wake-up radio. The EH-PMU achieves a peak end-to-end efficiency of 75% delivering power to a 100 μA load. In an example motion detection application, the SoC reads data from an accelerometer through SPI, processes it, and sends it over the radio. The SPI and digital processing consume only 2.27 μW, while the integrated radio consumes 4.18 μW when transmitting at 187.5 kbps for a total of 6.45 μW.

High-Speed Photonic Reservoir Computing Using a Time-Delay-Based Architecture: Million Words per Second Classification

NASA Astrophysics Data System (ADS)

Larger, Laurent; Baylón-Fuentes, Antonio; Martinenghi, Romain; Udaltsov, Vladimir S.; Chembo, Yanne K.; Jacquot, Maxime

2017-01-01

Reservoir computing, originally referred to as an echo state network or a liquid state machine, is a brain-inspired paradigm for processing temporal information. It involves learning a "read-out" interpretation for nonlinear transients developed by high-dimensional dynamics when the latter is excited by the information signal to be processed. This novel computational paradigm is derived from recurrent neural network and machine learning techniques. It has recently been implemented in photonic hardware for a dynamical system, which opens the path to ultrafast brain-inspired computing. We report on a novel implementation involving an electro-optic phase-delay dynamics designed with off-the-shelf optoelectronic telecom devices, thus providing the targeted wide bandwidth. Computational efficiency is demonstrated experimentally with speech-recognition tasks. State-of-the-art speed performances reach one million words per second, with very low word error rate. Additionally, to record speed processing, our investigations have revealed computing-efficiency improvements through yet-unexplored temporal-information-processing techniques, such as simultaneous multisample injection and pitched sampling at the read-out compared to information "write-in".

Iterative filtering decomposition based on local spectral evolution kernel

PubMed Central

Wang, Yang; Wei, Guo-Wei; Yang, Siyang

2011-01-01

The synthesizing information, achieving understanding, and deriving insight from increasingly massive, time-varying, noisy and possibly conflicting data sets are some of most challenging tasks in the present information age. Traditional technologies, such as Fourier transform and wavelet multi-resolution analysis, are inadequate to handle all of the above-mentioned tasks. The empirical model decomposition (EMD) has emerged as a new powerful tool for resolving many challenging problems in data processing and analysis. Recently, an iterative filtering decomposition (IFD) has been introduced to address the stability and efficiency problems of the EMD. Another data analysis technique is the local spectral evolution kernel (LSEK), which provides a near prefect low pass filter with desirable time-frequency localizations. The present work utilizes the LSEK to further stabilize the IFD, and offers an efficient, flexible and robust scheme for information extraction, complexity reduction, and signal and image understanding. The performance of the present LSEK based IFD is intensively validated over a wide range of data processing tasks, including mode decomposition, analysis of time-varying data, information extraction from nonlinear dynamic systems, etc. The utility, robustness and usefulness of the proposed LESK based IFD are demonstrated via a large number of applications, such as the analysis of stock market data, the decomposition of ocean wave magnitudes, the understanding of physiologic signals and information recovery from noisy images. The performance of the proposed method is compared with that of existing methods in the literature. Our results indicate that the LSEK based IFD improves both the efficiency and the stability of conventional EMD algorithms. PMID:22350559

Asymmetry in Signal Oscillations Contributes to Efficiency of Periodic Systems.

PubMed

Bae, Seul-A; Acevedo, Alison; Androulakis, Ioannis P

2016-01-01

Oscillations are an important feature of cellular signaling that result from complex combinations of positive- and negative-feedback loops. The encoding and decoding mechanisms of oscillations based on amplitude and frequency have been extensively discussed in the literature in the context of intercellular and intracellular signaling. However, the fundamental questions of whether and how oscillatory signals offer any competitive advantages-and, if so, what-have not been fully answered. We investigated established oscillatory mechanisms and designed a study to analyze the oscillatory characteristics of signaling molecules and system output in an effort to answer these questions. Two classic oscillators, Goodwin and PER, were selected as the model systems, and corresponding no-feedback models were created for each oscillator to discover the advantage of oscillating signals. Through simulating the original oscillators and the matching no-feedback models, we show that oscillating systems have the capability to achieve better resource-to-output efficiency, and we identify oscillatory characteristics that lead to improved efficiency.

Efficient detection of a CW signal with a linear frequency drift

NASA Technical Reports Server (NTRS)

Swarztrauber, Paul N.; Bailey, David H.

1989-01-01

An efficient method is presented for the detection of a continuous wave (CW) signal with a frequency drift that is linear in time. Signals of this type occur in transmissions between any two locations that are accelerating relative to one another, e.g., transmissions from the Voyager spacecraft. We assume that both the frequency and the drift are unknown. We also assume that the signal is weak compared to the Gaussian noise. The signal is partitioned into subsequences whose discrete Fourier transforms provide a sequence of instantaneous spectra at equal time intervals. These spectra are then accumulated with a shift that is proportional to time. When the shift is equal to the frequency drift, the signal to noise ratio increases and detection occurs. Here, we show how to compute these accumulations for many shifts in an efficient manner using a variety of Fast Fourier Transformations (FFT). Computing time is proportional to L log L where L is the length of the time series.

Infrared Signal Detection by Upconversion Technique

NASA Technical Reports Server (NTRS)

Wong, Teh-Hwa; Yu, Jirong; Bai, Yingxin; Johnson, William E.

2014-01-01

We demonstrated up-conversion assisted detection of a 2.05-micron signal by using a bulk periodically poled Lithium niobate crystal. The 94% intrinsic up-conversion efficiency and 22.58% overall detection efficiency at pW level of 2.05-micron was achieved.

Retained energy-based coding for EEG signals.

PubMed

Bazán-Prieto, Carlos; Blanco-Velasco, Manuel; Cárdenas-Barrera, Julián; Cruz-Roldán, Fernando

2012-09-01

The recent use of long-term records in electroencephalography is becoming more frequent due to its diagnostic potential and the growth of novel signal processing methods that deal with these types of recordings. In these cases, the considerable volume of data to be managed makes compression necessary to reduce the bit rate for transmission and storage applications. In this paper, a new compression algorithm specifically designed to encode electroencephalographic (EEG) signals is proposed. Cosine modulated filter banks are used to decompose the EEG signal into a set of subbands well adapted to the frequency bands characteristic of the EEG. Given that no regular pattern may be easily extracted from the signal in time domain, a thresholding-based method is applied for quantizing samples. The method of retained energy is designed for efficiently computing the threshold in the decomposition domain which, at the same time, allows the quality of the reconstructed EEG to be controlled. The experiments are conducted over a large set of signals taken from two public databases available at Physionet and the results show that the compression scheme yields better compression than other reported methods. Copyright © 2011 IPEM. Published by Elsevier Ltd. All rights reserved.