Robust High-Capacity Audio Watermarking Based on FFT Amplitude Modification

NASA Astrophysics Data System (ADS)

Fallahpour, Mehdi; Megías, David

This paper proposes a novel robust audio watermarking algorithm to embed data and extract it in a bit-exact manner based on changing the magnitudes of the FFT spectrum. The key point is selecting a frequency band for embedding based on the comparison between the original and the MP3 compressed/decompressed signal and on a suitable scaling factor. The experimental results show that the method has a very high capacity (about 5kbps), without significant perceptual distortion (ODG about -0.25) and provides robustness against common audio signal processing such as added noise, filtering and MPEG compression (MP3). Furthermore, the proposed method has a larger capacity (number of embedded bits to number of host bits rate) than recent image data hiding methods.

A robust color signal processing with wide dynamic range WRGB CMOS image sensor

NASA Astrophysics Data System (ADS)

Kawada, Shun; Kuroda, Rihito; Sugawa, Shigetoshi

2011-01-01

We have developed a robust color reproduction methodology by a simple calculation with a new color matrix using the formerly developed wide dynamic range WRGB lateral overflow integration capacitor (LOFIC) CMOS image sensor. The image sensor was fabricated through a 0.18 μm CMOS technology and has a 45 degrees oblique pixel array, the 4.2 μm effective pixel pitch and the W pixels. A W pixel was formed by replacing one of the two G pixels in the Bayer RGB color filter. The W pixel has a high sensitivity through the visible light waveband. An emerald green and yellow (EGY) signal is generated from the difference between the W signal and the sum of RGB signals. This EGY signal mainly includes emerald green and yellow lights. These colors are difficult to be reproduced accurately by the conventional simple linear matrix because their wave lengths are in the valleys of the spectral sensitivity characteristics of the RGB pixels. A new linear matrix based on the EGY-RGB signal was developed. Using this simple matrix, a highly accurate color processing with a large margin to the sensitivity fluctuation and noise has been achieved.

A study of swing-curve physics in diffraction-based overlay

NASA Astrophysics Data System (ADS)

Bhattacharyya, Kaustuve; den Boef, Arie; Storms, Greet; van Heijst, Joost; Noot, Marc; An, Kevin; Park, Noh-Kyoung; Jeon, Se-Ra; Oh, Nang-Lyeom; McNamara, Elliott; van de Mast, Frank; Oh, SeungHwa; Lee, Seung Yoon; Hwang, Chan; Lee, Kuntack

2016-03-01

With the increase of process complexity in advanced nodes, the requirements of process robustness in overlay metrology continues to tighten. Especially with the introduction of newer materials in the film-stack along with typical stack variations (thickness, optical properties, profile asymmetry etc.), the signal formation physics in diffraction-based overlay (DBO) becomes an important aspect to apply in overlay metrology target and recipe selection. In order to address the signal formation physics, an effort is made towards studying the swing-curve phenomena through wavelength and polarizations on production stacks using simulations as well as experimental technique using DBO. The results provide a wealth of information on target and recipe selection for robustness. Details from simulation and measurements will be reported in this technical publication.

Robust statistical methods for impulse noise suppressing of spread spectrum induced polarization data, with application to a mine site, Gansu province, China

NASA Astrophysics Data System (ADS)

Liu, Weiqiang; Chen, Rujun; Cai, Hongzhu; Luo, Weibin

2016-12-01

In this paper, we investigated the robust processing of noisy spread spectrum induced polarization (SSIP) data. SSIP is a new frequency domain induced polarization method that transmits pseudo-random m-sequence as source current where m-sequence is a broadband signal. The potential information at multiple frequencies can be obtained through measurement. Removing the noise is a crucial problem for SSIP data processing. Considering that if the ordinary mean stack and digital filter are not capable of reducing the impulse noise effectively in SSIP data processing, the impact of impulse noise will remain in the complex resistivity spectrum that will affect the interpretation of profile anomalies. We implemented a robust statistical method to SSIP data processing. The robust least-squares regression is used to fit and remove the linear trend from the original data before stacking. The robust M estimate is used to stack the data of all periods. The robust smooth filter is used to suppress the residual noise for data after stacking. For robust statistical scheme, the most appropriate influence function and iterative algorithm are chosen by testing the simulated data to suppress the outliers' influence. We tested the benefits of the robust SSIP data processing using examples of SSIP data recorded in a test site beside a mine in Gansu province, China.

A Soft Sensor for Bioprocess Control Based on Sequential Filtering of Metabolic Heat Signals

PubMed Central

Paulsson, Dan; Gustavsson, Robert; Mandenius, Carl-Fredrik

2014-01-01

Soft sensors are the combination of robust on-line sensor signals with mathematical models for deriving additional process information. Here, we apply this principle to a microbial recombinant protein production process in a bioreactor by exploiting bio-calorimetric methodology. Temperature sensor signals from the cooling system of the bioreactor were used for estimating the metabolic heat of the microbial culture and from that the specific growth rate and active biomass concentration were derived. By applying sequential digital signal filtering, the soft sensor was made more robust for industrial practice with cultures generating low metabolic heat in environments with high noise level. The estimated specific growth rate signal obtained from the three stage sequential filter allowed controlled feeding of substrate during the fed-batch phase of the production process. The biomass and growth rate estimates from the soft sensor were also compared with an alternative sensor probe and a capacitance on-line sensor, for the same variables. The comparison showed similar or better sensitivity and lower variability for the metabolic heat soft sensor suggesting that using permanent temperature sensors of a bioreactor is a realistic and inexpensive alternative for monitoring and control. However, both alternatives are easy to implement in a soft sensor, alone or in parallel. PMID:25264951

A soft sensor for bioprocess control based on sequential filtering of metabolic heat signals.

PubMed

Paulsson, Dan; Gustavsson, Robert; Mandenius, Carl-Fredrik

2014-09-26

Soft sensors are the combination of robust on-line sensor signals with mathematical models for deriving additional process information. Here, we apply this principle to a microbial recombinant protein production process in a bioreactor by exploiting bio-calorimetric methodology. Temperature sensor signals from the cooling system of the bioreactor were used for estimating the metabolic heat of the microbial culture and from that the specific growth rate and active biomass concentration were derived. By applying sequential digital signal filtering, the soft sensor was made more robust for industrial practice with cultures generating low metabolic heat in environments with high noise level. The estimated specific growth rate signal obtained from the three stage sequential filter allowed controlled feeding of substrate during the fed-batch phase of the production process. The biomass and growth rate estimates from the soft sensor were also compared with an alternative sensor probe and a capacitance on-line sensor, for the same variables. The comparison showed similar or better sensitivity and lower variability for the metabolic heat soft sensor suggesting that using permanent temperature sensors of a bioreactor is a realistic and inexpensive alternative for monitoring and control. However, both alternatives are easy to implement in a soft sensor, alone or in parallel.

Robust signal recovery using the prolate spherical wave functions and maximum correntropy criterion

NASA Astrophysics Data System (ADS)

Zou, Cuiming; Kou, Kit Ian

2018-05-01

Signal recovery is one of the most important problem in signal processing. This paper proposes a novel signal recovery method based on prolate spherical wave functions (PSWFs). PSWFs are a kind of special functions, which have been proved having good performance in signal recovery. However, the existing PSWFs based recovery methods used the mean square error (MSE) criterion, which depends on the Gaussianity assumption of the noise distributions. For the non-Gaussian noises, such as impulsive noise or outliers, the MSE criterion is sensitive, which may lead to large reconstruction error. Unlike the existing PSWFs based recovery methods, our proposed PSWFs based recovery method employs the maximum correntropy criterion (MCC), which is independent of the noise distribution. The proposed method can reduce the impact of the large and non-Gaussian noises. The experimental results on synthetic signals with various types of noises show that the proposed MCC based signal recovery method has better robust property against various noises compared to other existing methods.

Anomaly Detection of Electromyographic Signals.

PubMed

Ijaz, Ahsan; Choi, Jongeun

2018-04-01

In this paper, we provide a robust framework to detect anomalous electromyographic (EMG) signals and identify contamination types. As a first step for feature selection, optimally selected Lawton wavelets transform is applied. Robust principal component analysis (rPCA) is then performed on these wavelet coefficients to obtain features in a lower dimension. The rPCA based features are used for constructing a self-organizing map (SOM). Finally, hierarchical clustering is applied on the SOM that separates anomalous signals residing in the smaller clusters and breaks them into logical units for contamination identification. The proposed methodology is tested using synthetic and real world EMG signals. The synthetic EMG signals are generated using a heteroscedastic process mimicking desired experimental setups. A sub-part of these synthetic signals is introduced with anomalies. These results are followed with real EMG signals introduced with synthetic anomalies. Finally, a heterogeneous real world data set is used with known quality issues under an unsupervised setting. The framework provides recall of 90% (± 3.3) and precision of 99%(±0.4).

O-Linked-N-Acetylglucosamine Cycling and Insulin Signaling Are Required for the Glucose Stress Response in Caenorhabditis elegans

PubMed Central

Mondoux, Michelle A.; Love, Dona C.; Ghosh, Salil K.; Fukushige, Tetsunari; Bond, Michelle; Weerasinghe, Gayani R.; Hanover, John A.; Krause, Michael W.

2011-01-01

In a variety of organisms, including worms, flies, and mammals, glucose homeostasis is maintained by insulin-like signaling in a robust network of opposing and complementary signaling pathways. The hexosamine signaling pathway, terminating in O-linked-N-acetylglucosamine (O-GlcNAc) cycling, is a key sensor of nutrient status and has been genetically linked to the regulation of insulin signaling in Caenorhabditis elegans. Here we demonstrate that O-GlcNAc cycling and insulin signaling are both essential components of the C. elegans response to glucose stress. A number of insulin-dependent processes were found to be sensitive to glucose stress, including fertility, reproductive timing, and dauer formation, yet each of these differed in their threshold of sensitivity to glucose excess. Our findings suggest that O-GlcNAc cycling and insulin signaling are both required for a robust and adaptable response to glucose stress, but these two pathways show complex and interdependent roles in the maintenance of glucose–insulin homeostasis. PMID:21441213

Oscillation-Induced Signal Transmission and Gating in Neural Circuits

PubMed Central

Jahnke, Sven; Memmesheimer, Raoul-Martin; Timme, Marc

2014-01-01

Reliable signal transmission constitutes a key requirement for neural circuit function. The propagation of synchronous pulse packets through recurrent circuits is hypothesized to be one robust form of signal transmission and has been extensively studied in computational and theoretical works. Yet, although external or internally generated oscillations are ubiquitous across neural systems, their influence on such signal propagation is unclear. Here we systematically investigate the impact of oscillations on propagating synchrony. We find that for standard, additive couplings and a net excitatory effect of oscillations, robust propagation of synchrony is enabled in less prominent feed-forward structures than in systems without oscillations. In the presence of non-additive coupling (as mediated by fast dendritic spikes), even balanced oscillatory inputs may enable robust propagation. Here, emerging resonances create complex locking patterns between oscillations and spike synchrony. Interestingly, these resonances make the circuits capable of selecting specific pathways for signal transmission. Oscillations may thus promote reliable transmission and, in co-action with dendritic nonlinearities, provide a mechanism for information processing by selectively gating and routing of signals. Our results are of particular interest for the interpretation of sharp wave/ripple complexes in the hippocampus, where previously learned spike patterns are replayed in conjunction with global high-frequency oscillations. We suggest that the oscillations may serve to stabilize the replay. PMID:25503492

Robust Connectivity in Sensory and Ad Hoc Network

DTIC Science & Technology

2011-02-01

as the prior probability is π0 = 0.8, the error probability should be capped at 0.2. This seemingly pathological result is due to the fact that the...publications and is the author of the book Multirate and Wavelet Signal Processing (Academic Press, 1998). His research interests include multiscale signal and

Robust detection-isolation-accommodation for sensor failures

NASA Technical Reports Server (NTRS)

Weiss, J. L.; Pattipati, K. R.; Willsky, A. S.; Eterno, J. S.; Crawford, J. T.

1985-01-01

The results of a one year study to: (1) develop a theory for Robust Failure Detection and Identification (FDI) in the presence of model uncertainty, (2) develop a design methodology which utilizes the robust FDI ththeory, (3) apply the methodology to a sensor FDI problem for the F-100 jet engine, and (4) demonstrate the application of the theory to the evaluation of alternative FDI schemes are presented. Theoretical results in statistical discrimination are used to evaluate the robustness of residual signals (or parity relations) in terms of their usefulness for FDI. Furthermore, optimally robust parity relations are derived through the optimization of robustness metrics. The result is viewed as decentralization of the FDI process. A general structure for decentralized FDI is proposed and robustness metrics are used for determining various parameters of the algorithm.

Demodulation signal processing in multiphoton imaging

NASA Astrophysics Data System (ADS)

Fisher, Walter G.; Wachter, Eric A.; Piston, David W.

2002-06-01

Multiphoton laser scanning microscopy offers numerous advantages, but sensitivity can be seriously affected by contamination from ambient room light. Typically, this forces experiments to be performed in an absolutely dark room. Since mode-locked lasers are used to generate detectable signals, signal-processing can be used to avoid such problems by taking advantage of the pulsed characteristics of such lasers. Demodulation of the fluorescence signal generated at the mode-locked frequency can result in significant reduction of interference from ambient noise sources. Such demodulation can be readily adapted to existing microscopes by inserting appropriate processor circuitry between the detector and data collection system, yielding a more robust microscope.

Robust estimators for speech enhancement in real environments

NASA Astrophysics Data System (ADS)

Sandoval-Ibarra, Yuma; Diaz-Ramirez, Victor H.; Kober, Vitaly

2015-09-01

Common statistical estimators for speech enhancement rely on several assumptions about stationarity of speech signals and noise. These assumptions may not always valid in real-life due to nonstationary characteristics of speech and noise processes. We propose new estimators based on existing estimators by incorporation of computation of rank-order statistics. The proposed estimators are better adapted to non-stationary characteristics of speech signals and noise processes. Through computer simulations we show that the proposed estimators yield a better performance in terms of objective metrics than that of known estimators when speech signals are contaminated with airport, babble, restaurant, and train-station noise.

Wireless Source Localization and Signal Collection from an Airborne Symmetric Line Array Sensor Network

DTIC Science & Technology

2014-09-01

band signal samples by taking the ratio of (166) and (165) as     2 2 /2 /2 sin sin coscos g g g g gg cQ cI eE n E n e...processors,” EEE Trans. Acoust. Speech Signal Process., vol. 31, no. 6, pp. 1378–1393, Dec. 1983. [10] J. Li, P. Stoica and Z. Wang, “On robust

Intracortical multiplication of thalamocortical signals in mouse auditory cortex.

PubMed

Li, Ling-yun; Li, Ya-tang; Zhou, Mu; Tao, Huizhong W; Zhang, Li I

2013-09-01

Cortical processing of sensory information begins with the transformation of thalamically relayed signals. We optogenetically silenced intracortical circuits to isolate thalamic inputs to layer 4 neurons and found that intracortical excitation linearly amplified thalamocortical responses underlying frequency and direction selectivity, with spectral range and tuning preserved, and prolonged the response duration. This signal pre-amplification and prolongation enhanced the salience of thalamocortically relayed information and ensured its robust, faithful and more persistent representation.

A robust functional-data-analysis method for data recovery in multichannel sensor systems.

PubMed

Sun, Jian; Liao, Haitao; Upadhyaya, Belle R

2014-08-01

Multichannel sensor systems are widely used in condition monitoring for effective failure prevention of critical equipment or processes. However, loss of sensor readings due to malfunctions of sensors and/or communication has long been a hurdle to reliable operations of such integrated systems. Moreover, asynchronous data sampling and/or limited data transmission are usually seen in multiple sensor channels. To reliably perform fault diagnosis and prognosis in such operating environments, a data recovery method based on functional principal component analysis (FPCA) can be utilized. However, traditional FPCA methods are not robust to outliers and their capabilities are limited in recovering signals with strongly skewed distributions (i.e., lack of symmetry). This paper provides a robust data-recovery method based on functional data analysis to enhance the reliability of multichannel sensor systems. The method not only considers the possibly skewed distribution of each channel of signal trajectories, but is also capable of recovering missing data for both individual and correlated sensor channels with asynchronous data that may be sparse as well. In particular, grand median functions, rather than classical grand mean functions, are utilized for robust smoothing of sensor signals. Furthermore, the relationship between the functional scores of two correlated signals is modeled using multivariate functional regression to enhance the overall data-recovery capability. An experimental flow-control loop that mimics the operation of coolant-flow loop in a multimodular integral pressurized water reactor is used to demonstrate the effectiveness and adaptability of the proposed data-recovery method. The computational results illustrate that the proposed method is robust to outliers and more capable than the existing FPCA-based method in terms of the accuracy in recovering strongly skewed signals. In addition, turbofan engine data are also analyzed to verify the capability of the proposed method in recovering non-skewed signals.

Correlation processing for correction of phase distortions in subaperture imaging.

PubMed

Tavh, B; Karaman, M

1999-01-01

Ultrasonic subaperture imaging combines synthetic aperture and phased array approaches and permits low-cost systems with improved image quality. In subaperture processing, a large array is synthesized using echo signals collected from a number of receive subapertures by multiple firings of a phased transmit subaperture. Tissue inhomogeneities and displacements in subaperture imaging may cause significant phase distortions on received echo signals. Correlation processing on reference echo signals can be used for correction of the phase distortions, for which the accuracy and robustness are critically limited by the signal correlation. In this study, we explore correlation processing techniques for adaptive subaperture imaging with phase correction for motion and tissue inhomogeneities. The proposed techniques use new subaperture data acquisition schemes to produce reference signal sets with improved signal correlation. The experimental test results were obtained using raw radio frequency (RF) data acquired from two different phantoms with 3.5 MHz, 128-element transducer array. The results show that phase distortions can effectively be compensated by the proposed techniques in real-time adaptive subaperture imaging.

Instantaneous and Frequency-Warped Signal Processing Techniques for Auditory Source Separation.

NASA Astrophysics Data System (ADS)

Wang, Avery Li-Chun

This thesis summarizes several contributions to the areas of signal processing and auditory source separation. The philosophy of Frequency-Warped Signal Processing is introduced as a means for separating the AM and FM contributions to the bandwidth of a complex-valued, frequency-varying sinusoid p (n), transforming it into a signal with slowly-varying parameters. This transformation facilitates the removal of p (n) from an additive mixture while minimizing the amount of damage done to other signal components. The average winding rate of a complex-valued phasor is explored as an estimate of the instantaneous frequency. Theorems are provided showing the robustness of this measure. To implement frequency tracking, a Frequency-Locked Loop algorithm is introduced which uses the complex winding error to update its frequency estimate. The input signal is dynamically demodulated and filtered to extract the envelope. This envelope may then be remodulated to reconstruct the target partial, which may be subtracted from the original signal mixture to yield a new, quickly-adapting form of notch filtering. Enhancements to the basic tracker are made which, under certain conditions, attain the Cramer -Rao bound for the instantaneous frequency estimate. To improve tracking, the novel idea of Harmonic -Locked Loop tracking, using N harmonically constrained trackers, is introduced for tracking signals, such as voices and certain musical instruments. The estimated fundamental frequency is computed from a maximum-likelihood weighting of the N tracking estimates, making it highly robust. The result is that harmonic signals, such as voices, can be isolated from complex mixtures in the presence of other spectrally overlapping signals. Additionally, since phase information is preserved, the resynthesized harmonic signals may be removed from the original mixtures with relatively little damage to the residual signal. Finally, a new methodology is given for designing linear-phase FIR filters which require a small fraction of the computational power of conventional FIR implementations. This design strategy is based on truncated and stabilized IIR filters. These signal-processing methods have been applied to the problem of auditory source separation, resulting in voice separation from complex music that is significantly better than previous results at far lower computational cost.

The logic of communication: roles for mobile transcription factors in plants.

PubMed

Long, Yuchen; Scheres, Ben; Blilou, Ikram

2015-02-01

Mobile transcription factors play many roles in plant development. Here, we compare the use of mobile transcription factors as signals with some canonical signal transduction processes in prokaryotes and eukaryotes. After an initial survey, we focus on the SHORT-ROOT pathway in Arabidopsis roots to show that, despite the simplicity of the concept of mobile transcription factor signalling, many lines of evidence reveal a surprising complexity in control mechanisms linked to this process. We argue that these controls bestow precision, robustness, and versatility on mobile transcription factor signalling. © 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 Survey on Optimal Signal Processing Techniques Applied to Improve the Performance of Mechanical Sensors in Automotive Applications

PubMed Central

Hernandez, Wilmar

2007-01-01

In this paper a survey on recent applications of optimal signal processing techniques to improve the performance of mechanical sensors is made. Here, a comparison between classical filters and optimal filters for automotive sensors is made, and the current state of the art of the application of robust and optimal control and signal processing techniques to the design of the intelligent (or smart) sensors that today's cars need is presented through several experimental results that show that the fusion of intelligent sensors and optimal signal processing techniques is the clear way to go. However, the switch between the traditional methods of designing automotive sensors and the new ones cannot be done overnight because there are some open research issues that have to be solved. This paper draws attention to one of the open research issues and tries to arouse researcher's interest in the fusion of intelligent sensors and optimal signal processing techniques.

Model-based ultrasound temperature visualization during and following HIFU exposure.

PubMed

Ye, Guoliang; Smith, Penny Probert; Noble, J Alison

2010-02-01

This paper describes the application of signal processing techniques to improve the robustness of ultrasound feedback for displaying changes in temperature distribution in treatment using high-intensity focused ultrasound (HIFU), especially at the low signal-to-noise ratios that might be expected in in vivo abdominal treatment. Temperature estimation is based on the local displacements in ultrasound images taken during HIFU treatment, and a method to improve robustness to outliers is introduced. The main contribution of the paper is in the application of a Kalman filter, a statistical signal processing technique, which uses a simple analytical temperature model of heat dispersion to improve the temperature estimation from the ultrasound measurements during and after HIFU exposure. To reduce the sensitivity of the method to previous assumptions on the material homogeneity and signal-to-noise ratio, an adaptive form is introduced. The method is illustrated using data from HIFU exposure of ex vivo bovine liver. A particular advantage of the stability it introduces is that the temperature can be visualized not only in the intervals between HIFU exposure but also, for some configurations, during the exposure itself. 2010 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

A robust signal processing method for quantitative high-cycle fatigue crack monitoring using soft elastomeric capacitor sensors

NASA Astrophysics Data System (ADS)

Kong, Xiangxiong; Li, Jian; Collins, William; Bennett, Caroline; Laflamme, Simon; Jo, Hongki

2017-04-01

A large-area electronics (LAE) strain sensor, termed soft elastomeric capacitor (SEC), has shown great promise in fatigue crack monitoring. The SEC is able to monitor strain changes over a mesoscale structural surface and endure large deformations without being damaged under cracking. Previous tests verified that the SEC is able to detect, localize, and monitor fatigue crack activities under low-cycle fatigue loading. In this paper, to examine the SEC's capability of monitoring high-cycle fatigue cracks, a compact specimen is tested under cyclic tension, designed to ensure realistic crack opening sizes representative of those in real steel bridges. To overcome the difficulty of low signal amplitude and relatively high noise level under high-cycle fatigue loading, a robust signal processing method is proposed to convert the measured capacitance time history from the SEC sensor to power spectral densities (PSD) in the frequency domain, such that signal's peak-to-peak amplitude can be extracted at the dominant loading frequency. A crack damage indicator is proposed as the ratio between the square root of the amplitude of PSD and load range. Results show that the crack damage indicator offers consistent indication of crack growth.

Robust iterative learning control for multi-phase batch processes: an average dwell-time method with 2D convergence indexes

NASA Astrophysics Data System (ADS)

Wang, Limin; Shen, Yiteng; Yu, Jingxian; Li, Ping; Zhang, Ridong; Gao, Furong

2018-01-01

In order to cope with system disturbances in multi-phase batch processes with different dimensions, a hybrid robust control scheme of iterative learning control combined with feedback control is proposed in this paper. First, with a hybrid iterative learning control law designed by introducing the state error, the tracking error and the extended information, the multi-phase batch process is converted into a two-dimensional Fornasini-Marchesini (2D-FM) switched system with different dimensions. Second, a switching signal is designed using the average dwell-time method integrated with the related switching conditions to give sufficient conditions ensuring stable running for the system. Finally, the minimum running time of the subsystems and the control law gains are calculated by solving the linear matrix inequalities. Meanwhile, a compound 2D controller with robust performance is obtained, which includes a robust extended feedback control for ensuring the steady-state tracking error to converge rapidly. The application on an injection molding process displays the effectiveness and superiority of the proposed strategy.

Robust real-time extraction of respiratory signals from PET list-mode data

NASA Astrophysics Data System (ADS)

Salomon, André; Zhang, Bin; Olivier, Patrick; Goedicke, Andreas

2018-06-01

Respiratory motion, which typically cannot simply be suspended during PET image acquisition, affects lesions’ detection and quantitative accuracy inside or in close vicinity to the lungs. Some motion compensation techniques address this issue via pre-sorting (‘binning’) of the acquired PET data into a set of temporal gates, where each gate is assumed to be minimally affected by respiratory motion. Tracking respiratory motion is typically realized using dedicated hardware (e.g. using respiratory belts and digital cameras). Extracting respiratory signals directly from the acquired PET data simplifies the clinical workflow as it avoids handling additional signal measurement equipment. We introduce a new data-driven method ‘combined local motion detection’ (CLMD). It uses the time-of-flight (TOF) information provided by state-of-the-art PET scanners in order to enable real-time respiratory signal extraction without additional hardware resources. CLMD applies center-of-mass detection in overlapping regions based on simple back-positioned TOF event sets acquired in short time frames. Following a signal filtering and quality-based pre-selection step, the remaining extracted individual position information over time is then combined to generate a global respiratory signal. The method is evaluated using seven measured FDG studies from single and multiple scan positions of the thorax region, and it is compared to other software-based methods regarding quantitative accuracy and statistical noise stability. Correlation coefficients around 90% between the reference and the extracted signal have been found for those PET scans where motion affected features such as tumors or hot regions were present in the PET field-of-view. For PET scans with a quarter of typically applied radiotracer doses, the CLMD method still provides similar high correlation coefficients which indicates its robustness to noise. Each CLMD processing needed less than 0.4 s in total on a standard multi-core CPU and thus provides a robust and accurate approach enabling real-time processing capabilities using standard PC hardware.

Efficient and robust computation of PDF features from diffusion MR signal.

PubMed

Assemlal, Haz-Edine; Tschumperlé, David; Brun, Luc

2009-10-01

We present a method for the estimation of various features of the tissue micro-architecture using the diffusion magnetic resonance imaging. The considered features are designed from the displacement probability density function (PDF). The estimation is based on two steps: first the approximation of the signal by a series expansion made of Gaussian-Laguerre and Spherical Harmonics functions; followed by a projection on a finite dimensional space. Besides, we propose to tackle the problem of the robustness to Rician noise corrupting in-vivo acquisitions. Our feature estimation is expressed as a variational minimization process leading to a variational framework which is robust to noise. This approach is very flexible regarding the number of samples and enables the computation of a large set of various features of the local tissues structure. We demonstrate the effectiveness of the method with results on both synthetic phantom and real MR datasets acquired in a clinical time-frame.

Noise suppression methods for robust speech processing

NASA Astrophysics Data System (ADS)

Boll, S. F.; Ravindra, H.; Randall, G.; Armantrout, R.; Power, R.

1980-05-01

Robust speech processing in practical operating environments requires effective environmental and processor noise suppression. This report describes the technical findings and accomplishments during this reporting period for the research program funded to develop real time, compressed speech analysis synthesis algorithms whose performance in invariant under signal contamination. Fulfillment of this requirement is necessary to insure reliable secure compressed speech transmission within realistic military command and control environments. Overall contributions resulting from this research program include the understanding of how environmental noise degrades narrow band, coded speech, development of appropriate real time noise suppression algorithms, and development of speech parameter identification methods that consider signal contamination as a fundamental element in the estimation process. This report describes the current research and results in the areas of noise suppression using the dual input adaptive noise cancellation using the short time Fourier transform algorithms, articulation rate change techniques, and a description of an experiment which demonstrated that the spectral subtraction noise suppression algorithm can improve the intelligibility of 2400 bps, LPC 10 coded, helicopter speech by 10.6 point.

Robust control of dielectric elastomer diaphragm actuator for human pulse signal tracking

NASA Astrophysics Data System (ADS)

Ye, Zhihang; Chen, Zheng; Asmatulu, Ramazan; Chan, Hoyin

2017-08-01

Human pulse signal tracking is an emerging technology that is needed in traditional Chinese medicine. However, soft actuation with multi-frequency tracking capability is needed for tracking human pulse signal. Dielectric elastomer (DE) is one type of soft actuating that has great potential in human pulse signal tracking. In this paper, a DE diaphragm actuator was designed and fabricated to track human pulse pressure signal. A physics-based and control-oriented model has been developed to capture the dynamic behavior of DE diaphragm actuator. Using the physical model, an H-infinity robust control was designed for the actuator to reject high-frequency sensing noises and disturbances. The robust control was then implemented in real-time to track a multi-frequency signal, which verified the tracking capability and robustness of the control system. In the human pulse signal tracking test, a human pulse signal was measured at the City University of Hong Kong and then was tracked using DE actuator at Wichita State University in the US. Experimental results have verified that the DE actuator with its robust control is capable of tracking human pulse signal.

Robust Foot Clearance Estimation Based on the Integration of Foot-Mounted IMU Acceleration Data

PubMed Central

Benoussaad, Mourad; Sijobert, Benoît; Mombaur, Katja; Azevedo Coste, Christine

2015-01-01

This paper introduces a method for the robust estimation of foot clearance during walking, using a single inertial measurement unit (IMU) placed on the subject’s foot. The proposed solution is based on double integration and drift cancellation of foot acceleration signals. The method is insensitive to misalignment of IMU axes with respect to foot axes. Details are provided regarding calibration and signal processing procedures. Experimental validation was performed on 10 healthy subjects under three walking conditions: normal, fast and with obstacles. Foot clearance estimation results were compared to measurements from an optical motion capture system. The mean error between them is significantly less than 15% under the various walking conditions. PMID:26703622

Arduino-based noise robust online heart-rate detection.

PubMed

Das, Sangita; Pal, Saurabh; Mitra, Madhuchhanda

2017-04-01

This paper introduces a noise robust real time heart rate detection system from electrocardiogram (ECG) data. An online data acquisition system is developed to collect ECG signals from human subjects. Heart rate is detected using window-based autocorrelation peak localisation technique. A low-cost Arduino UNO board is used to implement the complete automated process. The performance of the system is compared with PC-based heart rate detection technique. Accuracy of the system is validated through simulated noisy ECG data with various levels of signal to noise ratio (SNR). The mean percentage error of detected heart rate is found to be 0.72% for the noisy database with five different noise levels.

Improved Signal Processing Technique Leads to More Robust Self Diagnostic Accelerometer System

NASA Technical Reports Server (NTRS)

Tokars, Roger; Lekki, John; Jaros, Dave; Riggs, Terrence; Evans, Kenneth P.

2010-01-01

The self diagnostic accelerometer (SDA) is a sensor system designed to actively monitor the health of an accelerometer. In this case an accelerometer is considered healthy if it can be determined that it is operating correctly and its measurements may be relied upon. The SDA system accomplishes this by actively monitoring the accelerometer for a variety of failure conditions including accelerometer structural damage, an electrical open circuit, and most importantly accelerometer detachment. In recent testing of the SDA system in emulated engine operating conditions it has been found that a more robust signal processing technique was necessary. An improved accelerometer diagnostic technique and test results of the SDA system utilizing this technique are presented here. Furthermore, the real time, autonomous capability of the SDA system to concurrently compensate for effects from real operating conditions such as temperature changes and mechanical noise, while monitoring the condition of the accelerometer health and attachment, will be demonstrated.

Propagation of kinetic uncertainties through a canonical topology of the TLR4 signaling network in different regions of biochemical reaction space

PubMed Central

2010-01-01

Background Signal transduction networks represent the information processing systems that dictate which dynamical regimes of biochemical activity can be accessible to a cell under certain circumstances. One of the major concerns in molecular systems biology is centered on the elucidation of the robustness properties and information processing capabilities of signal transduction networks. Achieving this goal requires the establishment of causal relations between the design principle of biochemical reaction systems and their emergent dynamical behaviors. Methods In this study, efforts were focused in the construction of a relatively well informed, deterministic, non-linear dynamic model, accounting for reaction mechanisms grounded on standard mass action and Hill saturation kinetics, of the canonical reaction topology underlying Toll-like receptor 4 (TLR4)-mediated signaling events. This signaling mechanism has been shown to be deployed in macrophages during a relatively short time window in response to lypopolysaccharyde (LPS) stimulation, which leads to a rapidly mounted innate immune response. An extensive computational exploration of the biochemical reaction space inhabited by this signal transduction network was performed via local and global perturbation strategies. Importantly, a broad spectrum of biologically plausible dynamical regimes accessible to the network in widely scattered regions of parameter space was reconstructed computationally. Additionally, experimentally reported transcriptional readouts of target pro-inflammatory genes, which are actively modulated by the network in response to LPS stimulation, were also simulated. This was done with the main goal of carrying out an unbiased statistical assessment of the intrinsic robustness properties of this canonical reaction topology. Results Our simulation results provide convincing numerical evidence supporting the idea that a canonical reaction mechanism of the TLR4 signaling network is capable of performing information processing in a robust manner, a functional property that is independent of the signaling task required to be executed. Nevertheless, it was found that the robust performance of the network is not solely determined by its design principle (topology), but this may be heavily dependent on the network's current position in biochemical reaction space. Ultimately, our results enabled us the identification of key rate limiting steps which most effectively control the performance of the system under diverse dynamical regimes. Conclusions Overall, our in silico study suggests that biologically relevant and non-intuitive aspects on the general behavior of a complex biomolecular network can be elucidated only when taking into account a wide spectrum of dynamical regimes attainable by the system. Most importantly, this strategy provides the means for a suitable assessment of the inherent variational constraints imposed by the structure of the system when systematically probing its parameter space. PMID:20230643

Fiber optic sensor for continuous health monitoring in CFRP composite materials

NASA Astrophysics Data System (ADS)

Rippert, Laurent; Papy, Jean-Michel; Wevers, Martine; Van Huffel, Sabine

2002-07-01

An intensity modulated sensor, based on the microbending concept, has been incorporated in laminates produced from a C/epoxy prepreg. Pencil lead break tests (Hsu-Neilsen sources) and tensile tests have been performed on this material. In this research study, fibre optic sensors will be proven to offer an alternative for the robust piezoelectric transducers used for Acoustic Emission (AE) monitoring. The main emphasis has been put on the use of advanced signal processing techniques based on time-frequency analysis. The signal Short Time Fourier Transform (STFT) has been computed and several robust noise reduction algorithms, such as Wiener adaptive filtering, improved spectral subtraction filtering, and Singular Value Decomposition (SVD) -based filtering, have been applied. An energy and frequency -based detection criterion is put forward to detect transient signals that can be correlated with Modal Acoustic Emission (MAE) results and thus damage in the composite material. There is a strong indication that time-frequency analysis and the Hankel Total Least Squares (HTLS) method can also be used for damage characterization. This study shows that the signal from a quite simple microbend optical sensor contains information on the elastic energy released whenever damage is being introduced in the host material by mechanical loading. Robust algorithms can be used to retrieve and analyze this information.

Robust real-time extraction of respiratory signals from PET list-mode data.

PubMed

Salomon, Andre; Zhang, Bin; Olivier, Patrick; Goedicke, Andreas

2018-05-01

Respiratory motion, which typically cannot simply be suspended during PET image acquisition, affects lesions' detection and quantitative accuracy inside or in close vicinity to the lungs. Some motion compensation techniques address this issue via pre-sorting ("binning") of the acquired PET data into a set of temporal gates, where each gate is assumed to be minimally affected by respiratory motion. Tracking respiratory motion is typically realized using dedicated hardware (e.g. using respiratory belts and digital cameras). Extracting respiratory signalsdirectly from the acquired PET data simplifies the clinical workflow as it avoids to handle additional signal measurement equipment. We introduce a new data-driven method "Combined Local Motion Detection" (CLMD). It uses the Time-of-Flight (TOF) information provided by state-of-the-art PET scanners in order to enable real-time respiratory signal extraction without additional hardware resources. CLMD applies center-of-mass detection in overlapping regions based on simple back-positioned TOF event sets acquired in short time frames. Following a signal filtering and quality-based pre-selection step, the remaining extracted individual position information over time is then combined to generate a global respiratory signal. The method is evaluated using 7 measured FDG studies from single and multiple scan positions of the thorax region, and it is compared to other software-based methods regarding quantitative accuracy and statistical noise stability. Correlation coefficients around 90% between the reference and the extracted signal have been found for those PET scans where motion affected features such as tumors or hot regions were present in the PET field-of-view. For PET scans with a quarter of typically applied radiotracer doses, the CLMD method still provides similar high correlation coefficients which indicates its robustness to noise. Each CLMD processing needed less than 0.4s in total on a standard multi-core CPU and thus provides a robust and accurate approach enabling real-time processing capabilities using standard PC hardware. © 2018 Institute of Physics and Engineering in Medicine.

Maxwell's demon in biochemical signal transduction with feedback loop

PubMed Central

Ito, Sosuke; Sagawa, Takahiro

2015-01-01

Signal transduction in living cells is vital to maintain life itself, where information transfer in noisy environment plays a significant role. In a rather different context, the recent intensive research on ‘Maxwell's demon'—a feedback controller that utilizes information of individual molecules—have led to a unified theory of information and thermodynamics. Here we combine these two streams of research, and show that the second law of thermodynamics with information reveals the fundamental limit of the robustness of signal transduction against environmental fluctuations. Especially, we find that the degree of robustness is quantitatively characterized by an informational quantity called transfer entropy. Our information-thermodynamic approach is applicable to biological communication inside cells, in which there is no explicit channel coding in contrast to artificial communication. Our result could open up a novel biophysical approach to understand information processing in living systems on the basis of the fundamental information–thermodynamics link. PMID:26099556

Different designs of kinase-phosphatase interactions and phosphatase sequestration shapes the robustness and signal flow in the MAPK cascade

PubMed Central

2012-01-01

Background The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal. Results We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade’s robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade’s output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases’ sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases. Conclusions Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade’s kinase-phosphatase interaction design. PMID:22748295

Synchronization ability of coupled cell-cycle oscillators in changing environments

PubMed Central

2012-01-01

Background The biochemical oscillator that controls periodic events during the Xenopus embryonic cell cycle is centered on the activity of CDKs, and the cell cycle is driven by a protein circuit that is centered on the cyclin-dependent protein kinase CDK1 and the anaphase-promoting complex (APC). Many studies have been conducted to confirm that the interactions in the cell cycle can produce oscillations and predict behaviors such as synchronization, but much less is known about how the various elaborations and collective behavior of the basic oscillators can affect the robustness of the system. Therefore, in this study, we investigate and model a multi-cell system of the Xenopus embryonic cell cycle oscillators that are coupled through a common complex protein, and then analyze their synchronization ability under four different external stimuli, including a constant input signal, a square-wave periodic signal, a sinusoidal signal and a noise signal. Results Through bifurcation analysis and numerical simulations, we obtain synchronization intervals of the sensitive parameters in the individual oscillator and the coupling parameters in the coupled oscillators. Then, we analyze the effects of these parameters on the synchronization period and amplitude, and find interesting phenomena, e.g., there are two synchronization intervals with activation coefficient in the Hill function of the activated CDK1 that activates the Plk1, and different synchronization intervals have distinct influences on the synchronization period and amplitude. To quantify the speediness and robustness of the synchronization, we use two quantities, the synchronization time and the robustness index, to evaluate the synchronization ability. More interestingly, we find that the coupled system has an optimal signal strength that maximizes the synchronization index under different external stimuli. Simulation results also show that the ability and robustness of the synchronization for the square-wave periodic signal of cyclin synthesis is strongest in comparison to the other three different signals. Conclusions These results suggest that the reaction process in which the activated cyclin-CDK1 activates the Plk1 has a very important influence on the synchronization ability of the coupled system, and the square-wave periodic signal of cyclin synthesis is more conducive to the synchronization and robustness of the coupled cell-cycle oscillators. Our study provides insight into the internal mechanisms of the cell cycle system and helps to generate hypotheses for further research. PMID:23046815

Software algorithms for false alarm reduction in LWIR hyperspectral chemical agent detection

NASA Astrophysics Data System (ADS)

Manolakis, D.; Model, J.; Rossacci, M.; Zhang, D.; Ontiveros, E.; Pieper, M.; Seeley, J.; Weitz, D.

2008-04-01

The long-wave infrared (LWIR) hyperpectral sensing modality is one that is often used for the problem of detection and identification of chemical warfare agents (CWA) which apply to both military and civilian situations. The inherent nature and complexity of background clutter dictates a need for sophisticated and robust statistical models which are then used in the design of optimum signal processing algorithms that then provide the best exploitation of hyperspectral data to ultimately make decisions on the absence or presence of potentially harmful CWAs. This paper describes the basic elements of an automated signal processing pipeline developed at MIT Lincoln Laboratory. In addition to describing this signal processing architecture in detail, we briefly describe the key signal models that form the foundation of these algorithms as well as some spatial processing techniques used for false alarm mitigation. Finally, we apply this processing pipeline to real data measured by the Telops FIRST hyperspectral (FIRST) sensor to demonstrate its practical utility for the user community.

Robust detection of heartbeats using association models from blood pressure and EEG signals.

PubMed

Jeon, Taegyun; Yu, Jongmin; Pedrycz, Witold; Jeon, Moongu; Lee, Boreom; Lee, Byeongcheol

2016-01-15

The heartbeat is fundamental cardiac activity which is straightforwardly detected with a variety of measurement techniques for analyzing physiological signals. Unfortunately, unexpected noise or contaminated signals can distort or cut out electrocardiogram (ECG) signals in practice, misleading the heartbeat detectors to report a false heart rate or suspend itself for a considerable length of time in the worst case. To deal with the problem of unreliable heartbeat detection, PhysioNet/CinC suggests a challenge in 2014 for developing robust heart beat detectors using multimodal signals. This article proposes a multimodal data association method that supplements ECG as a primary input signal with blood pressure (BP) and electroencephalogram (EEG) as complementary input signals when input signals are unreliable. If the current signal quality index (SQI) qualifies ECG as a reliable input signal, our method applies QRS detection to ECG and reports heartbeats. Otherwise, the current SQI selects the best supplementary input signal between BP and EEG after evaluating the current SQI of BP. When BP is chosen as a supplementary input signal, our association model between ECG and BP enables us to compute their regular intervals, detect characteristics BP signals, and estimate the locations of the heartbeat. When both ECG and BP are not qualified, our fusion method resorts to the association model between ECG and EEG that allows us to apply an adaptive filter to ECG and EEG, extract the QRS candidates, and report heartbeats. The proposed method achieved an overall score of 86.26 % for the test data when the input signals are unreliable. Our method outperformed the traditional method, which achieved 79.28 % using QRS detector and BP detector from PhysioNet. Our multimodal signal processing method outperforms the conventional unimodal method of taking ECG signals alone for both training and test data sets. To detect the heartbeat robustly, we have proposed a novel multimodal data association method of supplementing ECG with a variety of physiological signals and accounting for the patient-specific lag between different pulsatile signals and ECG. Multimodal signal detectors and data-fusion approaches such as those proposed in this article can reduce false alarms and improve patient monitoring.

Decomposing Multifractal Crossovers

PubMed Central

Nagy, Zoltan; Mukli, Peter; Herman, Peter; Eke, Andras

2017-01-01

Physiological processes—such as, the brain's resting-state electrical activity or hemodynamic fluctuations—exhibit scale-free temporal structuring. However, impacts common in biological systems such as, noise, multiple signal generators, or filtering by transport function, result in multimodal scaling that cannot be reliably assessed by standard analytical tools that assume unimodal scaling. Here, we present two methods to identify breakpoints or crossovers in multimodal multifractal scaling functions. These methods incorporate the robust iterative fitting approach of the focus-based multifractal formalism (FMF). The first approach (moment-wise scaling range adaptivity) allows for a breakpoint-based adaptive treatment that analyzes segregated scale-invariant ranges. The second method (scaling function decomposition method, SFD) is a crossover-based design aimed at decomposing signal constituents from multimodal scaling functions resulting from signal addition or co-sampling, such as, contamination by uncorrelated fractals. We demonstrated that these methods could handle multimodal, mono- or multifractal, and exact or empirical signals alike. Their precision was numerically characterized on ideal signals, and a robust performance was demonstrated on exemplary empirical signals capturing resting-state brain dynamics by near infrared spectroscopy (NIRS), electroencephalography (EEG), and blood oxygen level-dependent functional magnetic resonance imaging (fMRI-BOLD). The NIRS and fMRI-BOLD low-frequency fluctuations were dominated by a multifractal component over an underlying biologically relevant random noise, thus forming a bimodal signal. The crossover between the EEG signal components was found at the boundary between the δ and θ bands, suggesting an independent generator for the multifractal δ rhythm. The robust implementation of the SFD method should be regarded as essential in the seamless processing of large volumes of bimodal fMRI-BOLD imaging data for the topology of multifractal metrics free of the masking effect of the underlying random noise. PMID:28798694

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.

Robust fundamental frequency estimation in sustained vowels: Detailed algorithmic comparisons and information fusion with adaptive Kalman filtering

PubMed Central

Tsanas, Athanasios; Zañartu, Matías; Little, Max A.; Fox, Cynthia; Ramig, Lorraine O.; Clifford, Gari D.

2014-01-01

There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F0) of speech signals. This study examines ten F0 estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F0 in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F0 estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F0 estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F0 estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F0 estimation is required. PMID:24815269

DOA Finding with Support Vector Regression Based Forward-Backward Linear Prediction.

PubMed

Pan, Jingjing; Wang, Yide; Le Bastard, Cédric; Wang, Tianzhen

2017-05-27

Direction-of-arrival (DOA) estimation has drawn considerable attention in array signal processing, particularly with coherent signals and a limited number of snapshots. Forward-backward linear prediction (FBLP) is able to directly deal with coherent signals. Support vector regression (SVR) is robust with small samples. This paper proposes the combination of the advantages of FBLP and SVR in the estimation of DOAs of coherent incoming signals with low snapshots. The performance of the proposed method is validated with numerical simulations in coherent scenarios, in terms of different angle separations, numbers of snapshots, and signal-to-noise ratios (SNRs). Simulation results show the effectiveness of the proposed method.

Signal transduction and amplification through enzyme-triggered ligand release and accelerated catalysis.

PubMed

Goggins, Sean; Marsh, Barrie J; Lubben, Anneke T; Frost, Christopher G

2015-08-01

Signal transduction and signal amplification are both important mechanisms used within biological signalling pathways. Inspired by this process, we have developed a signal amplification methodology that utilises the selectivity and high activity of enzymes in combination with the robustness and generality of an organometallic catalyst, achieving a hybrid biological and synthetic catalyst cascade. A proligand enzyme substrate was designed to selectively self-immolate in the presence of the enzyme to release a ligand that can bind to a metal pre-catalyst and accelerate the rate of a transfer hydrogenation reaction. Enzyme-triggered catalytic signal amplification was then applied to a range of catalyst substrates demonstrating that signal amplification and signal transduction can both be achieved through this methodology.

A new approach for improving reliability of personal navigation devices under harsh GNSS signal conditions.

PubMed

Dhital, Anup; Bancroft, Jared B; Lachapelle, Gérard

2013-11-07

In natural and urban canyon environments, Global Navigation Satellite System (GNSS) signals suffer from various challenges such as signal multipath, limited or lack of signal availability and poor geometry. Inertial sensors are often employed to improve the solution continuity under poor GNSS signal quality and availability conditions. Various fault detection schemes have been proposed in the literature to detect and remove biased GNSS measurements to obtain a more reliable navigation solution. However, many of these methods are found to be sub-optimal and often lead to unavailability of reliability measures, mostly because of the improper characterization of the measurement errors. A robust filtering architecture is thus proposed which assumes a heavy-tailed distribution for the measurement errors. Moreover, the proposed filter is capable of adapting to the changing GNSS signal conditions such as when moving from open sky conditions to deep canyons. Results obtained by processing data collected in various GNSS challenged environments show that the proposed scheme provides a robust navigation solution without having to excessively reject usable measurements. The tests reported herein show improvements of nearly 15% and 80% for position accuracy and reliability, respectively, when applying the above approach.

A New Approach for Improving Reliability of Personal Navigation Devices under Harsh GNSS Signal Conditions

PubMed Central

Dhital, Anup; Bancroft, Jared B.; Lachapelle, Gérard

2013-01-01

In natural and urban canyon environments, Global Navigation Satellite System (GNSS) signals suffer from various challenges such as signal multipath, limited or lack of signal availability and poor geometry. Inertial sensors are often employed to improve the solution continuity under poor GNSS signal quality and availability conditions. Various fault detection schemes have been proposed in the literature to detect and remove biased GNSS measurements to obtain a more reliable navigation solution. However, many of these methods are found to be sub-optimal and often lead to unavailability of reliability measures, mostly because of the improper characterization of the measurement errors. A robust filtering architecture is thus proposed which assumes a heavy-tailed distribution for the measurement errors. Moreover, the proposed filter is capable of adapting to the changing GNSS signal conditions such as when moving from open sky conditions to deep canyons. Results obtained by processing data collected in various GNSS challenged environments show that the proposed scheme provides a robust navigation solution without having to excessively reject usable measurements. The tests reported herein show improvements of nearly 15% and 80% for position accuracy and reliability, respectively, when applying the above approach. PMID:24212120

Asymmetric Dual-Band Tracking Technique for Optimal Joint Processing of BDS B1I and B1C Signals

PubMed Central

Wang, Chuhan; Cui, Xiaowei; Ma, Tianyi; Lu, Mingquan

2017-01-01

Along with the rapid development of the Global Navigation Satellite System (GNSS), satellite navigation signals have become more diversified, complex, and agile in adapting to increasing market demands. Various techniques have been developed for processing multiple navigation signals to achieve better performance in terms of accuracy, sensitivity, and robustness. This paper focuses on a technique for processing two signals with separate but adjacent center frequencies, such as B1I and B1C signals in the BeiDou global system. The two signals may differ in modulation scheme, power, and initial phase relation and can be processed independently by user receivers; however, the propagation delays of the two signals from a satellite are nearly identical as they are modulated on adjacent frequencies, share the same reference clock, and undergo nearly identical propagation paths to the receiver, resulting in strong coherence between the two signals. Joint processing of these signals can achieve optimal measurement performance due to the increased Gabor bandwidth and power. In this paper, we propose a universal scheme of asymmetric dual-band tracking (ASYM-DBT) to take advantage of the strong coherence, the increased Gabor bandwidth, and power of the two signals in achieving much-reduced thermal noise and more accurate ranging results when compared with the traditional single-band algorithm. PMID:29035350

Asymmetric Dual-Band Tracking Technique for Optimal Joint Processing of BDS B1I and B1C Signals.

PubMed

Wang, Chuhan; Cui, Xiaowei; Ma, Tianyi; Zhao, Sihao; Lu, Mingquan

2017-10-16

Along with the rapid development of the Global Navigation Satellite System (GNSS), satellite navigation signals have become more diversified, complex, and agile in adapting to increasing market demands. Various techniques have been developed for processing multiple navigation signals to achieve better performance in terms of accuracy, sensitivity, and robustness. This paper focuses on a technique for processing two signals with separate but adjacent center frequencies, such as B1I and B1C signals in the BeiDou global system. The two signals may differ in modulation scheme, power, and initial phase relation and can be processed independently by user receivers; however, the propagation delays of the two signals from a satellite are nearly identical as they are modulated on adjacent frequencies, share the same reference clock, and undergo nearly identical propagation paths to the receiver, resulting in strong coherence between the two signals. Joint processing of these signals can achieve optimal measurement performance due to the increased Gabor bandwidth and power. In this paper, we propose a universal scheme of asymmetric dual-band tracking (ASYM-DBT) to take advantage of the strong coherence, the increased Gabor bandwidth, and power of the two signals in achieving much-reduced thermal noise and more accurate ranging results when compared with the traditional single-band algorithm.

Bio-Inspired Microsystem for Robust Genetic Assay Recognition

PubMed Central

Lue, Jaw-Chyng; Fang, Wai-Chi

2008-01-01

A compact integrated system-on-chip (SoC) architecture solution for robust, real-time, and on-site genetic analysis has been proposed. This microsystem solution is noise-tolerable and suitable for analyzing the weak fluorescence patterns from a PCR prepared dual-labeled DNA microchip assay. In the architecture, a preceding VLSI differential logarithm microchip is designed for effectively computing the logarithm of the normalized input fluorescence signals. A posterior VLSI artificial neural network (ANN) processor chip is used for analyzing the processed signals from the differential logarithm stage. A single-channel logarithmic circuit was fabricated and characterized. A prototype ANN chip with unsupervised winner-take-all (WTA) function was designed, fabricated, and tested. An ANN learning algorithm using a novel sigmoid-logarithmic transfer function based on the supervised backpropagation (BP) algorithm is proposed for robustly recognizing low-intensity patterns. Our results show that the trained new ANN can recognize low-fluorescence patterns better than an ANN using the conventional sigmoid function. PMID:18566679

A powerful and robust test in genetic association studies.

PubMed

Cheng, Kuang-Fu; Lee, Jen-Yu

2014-01-01

There are several well-known single SNP tests presented in the literature for detecting gene-disease association signals. Having in place an efficient and robust testing process across all genetic models would allow a more comprehensive approach to analysis. Although some studies have shown that it is possible to construct such a test when the variants are common and the genetic model satisfies certain conditions, the model conditions are too restrictive and in general difficult to verify. In this paper, we propose a powerful and robust test without assuming any model restrictions. Our test is based on the selected 2 × 2 tables derived from the usual 2 × 3 table. By signals from these tables, we show through simulations across a wide range of allele frequencies and genetic models that this approach may produce a test which is almost uniformly most powerful in the analysis of low- and high-frequency variants. Two cancer studies are used to demonstrate applications of the proposed test. © 2014 S. Karger AG, Basel.

Automatic Classification of volcano-seismic events based on Deep Neural Networks.

NASA Astrophysics Data System (ADS)

Titos Luzón, M.; Bueno Rodriguez, A.; Garcia Martinez, L.; Benitez, C.; Ibáñez, J. M.

2017-12-01

Seismic monitoring of active volcanoes is a popular remote sensing technique to detect seismic activity, often associated to energy exchanges between the volcano and the environment. As a result, seismographs register a wide range of volcano-seismic signals that reflect the nature and underlying physics of volcanic processes. Machine learning and signal processing techniques provide an appropriate framework to analyze such data. In this research, we propose a new classification framework for seismic events based on deep neural networks. Deep neural networks are composed by multiple processing layers, and can discover intrinsic patterns from the data itself. Internal parameters can be initialized using a greedy unsupervised pre-training stage, leading to an efficient training of fully connected architectures. We aim to determine the robustness of these architectures as classifiers of seven different types of seismic events recorded at "Volcán de Fuego" (Colima, Mexico). Two deep neural networks with different pre-training strategies are studied: stacked denoising autoencoder and deep belief networks. Results are compared to existing machine learning algorithms (SVM, Random Forest, Multilayer Perceptron). We used 5 LPC coefficients over three non-overlapping segments as training features in order to characterize temporal evolution, avoid redundancy and encode the signal, regardless of its duration. Experimental results show that deep architectures can classify seismic events with higher accuracy than classical algorithms, attaining up to 92% recognition accuracy. Pre-training initialization helps these models to detect events that occur simultaneously in time (such explosions and rockfalls), increase robustness against noisy inputs, and provide better generalization. These results demonstrate deep neural networks are robust classifiers, and can be deployed in real-environments to monitor the seismicity of restless volcanoes.

Dual regulation of gene expression mediated by extended MAPK activation and salicylic acid contributes to robust innate immunity in Arabidopsis thaliana.

PubMed

Tsuda, Kenichi; Mine, Akira; Bethke, Gerit; Igarashi, Daisuke; Botanga, Christopher J; Tsuda, Yayoi; Glazebrook, Jane; Sato, Masanao; Katagiri, Fumiaki

2013-01-01

Network robustness is a crucial property of the plant immune signaling network because pathogens are under a strong selection pressure to perturb plant network components to dampen plant immune responses. Nevertheless, modulation of network robustness is an area of network biology that has rarely been explored. While two modes of plant immunity, Effector-Triggered Immunity (ETI) and Pattern-Triggered Immunity (PTI), extensively share signaling machinery, the network output is much more robust against perturbations during ETI than PTI, suggesting modulation of network robustness. Here, we report a molecular mechanism underlying the modulation of the network robustness in Arabidopsis thaliana. The salicylic acid (SA) signaling sector regulates a major portion of the plant immune response and is important in immunity against biotrophic and hemibiotrophic pathogens. In Arabidopsis, SA signaling was required for the proper regulation of the vast majority of SA-responsive genes during PTI. However, during ETI, regulation of most SA-responsive genes, including the canonical SA marker gene PR1, could be controlled by SA-independent mechanisms as well as by SA. The activation of the two immune-related MAPKs, MPK3 and MPK6, persisted for several hours during ETI but less than one hour during PTI. Sustained MAPK activation was sufficient to confer SA-independent regulation of most SA-responsive genes. Furthermore, the MPK3 and SA signaling sectors were compensatory to each other for inhibition of bacterial growth as well as for PR1 expression during ETI. These results indicate that the duration of the MAPK activation is a critical determinant for modulation of robustness of the immune signaling network. Our findings with the plant immune signaling network imply that the robustness level of a biological network can be modulated by the activities of network components.

Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation.

PubMed

Azami, Hamed; Escudero, Javier

2016-05-01

Signal segmentation and spike detection are two important biomedical signal processing applications. Often, non-stationary signals must be segmented into piece-wise stationary epochs or spikes need to be found among a background of noise before being further analyzed. Permutation entropy (PE) has been proposed to evaluate the irregularity of a time series. PE is conceptually simple, structurally robust to artifacts, and computationally fast. It has been extensively used in many applications, but it has two key shortcomings. First, when a signal is symbolized using the Bandt-Pompe procedure, only the order of the amplitude values is considered and information regarding the amplitudes is discarded. Second, in the PE, the effect of equal amplitude values in each embedded vector is not addressed. To address these issues, we propose a new entropy measure based on PE: the amplitude-aware permutation entropy (AAPE). AAPE is sensitive to the changes in the amplitude, in addition to the frequency, of the signals thanks to it being more flexible than the classical PE in the quantification of the signal motifs. To demonstrate how the AAPE method can enhance the quality of the signal segmentation and spike detection, a set of synthetic and realistic synthetic neuronal signals, electroencephalograms and neuronal data are processed. We compare the performance of AAPE in these problems against state-of-the-art approaches and evaluate the significance of the differences with a repeated ANOVA with post hoc Tukey's test. In signal segmentation, the accuracy of AAPE-based method is higher than conventional segmentation methods. AAPE also leads to more robust results in the presence of noise. The spike detection results show that AAPE can detect spikes well, even when presented with single-sample spikes, unlike PE. For multi-sample spikes, the changes in AAPE are larger than in PE. We introduce a new entropy metric, AAPE, that enables us to consider amplitude information in the formulation of PE. The AAPE algorithm can be used in almost every irregularity-based application in various signal and image processing fields. We also made freely available the Matlab code of the AAPE. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

Robustness of cortical and subcortical processing in the presence of natural masking sounds.

PubMed

Beetz, M Jerome; García-Rosales, Francisco; Kössl, Manfred; Hechavarría, Julio C

2018-05-01

Processing of ethologically relevant stimuli could be interfered by non-relevant stimuli. Animals have behavioral adaptations to reduce signal interference. It is largely unexplored whether the behavioral adaptations facilitate neuronal processing of relevant stimuli. Here, we characterize behavioral adaptations in the presence of biotic noise in the echolocating bat Carollia perspicillata and we show that the behavioral adaptations could facilitate neuronal processing of biosonar information. According to the echolocation behavior, bats need to extract their own signals in the presence of vocalizations from conspecifics. With playback experiments, we demonstrate that C. perspicillata increases the sensory acquisition rate by emitting groups of echolocation calls when flying in noisy environments. Our neurophysiological results from the auditory midbrain and cortex show that the high sensory acquisition rate does not vastly increase neuronal suppression and that the response to an echolocation sequence is partially preserved in the presence of biosonar signals from conspecifics.

See-through Detection and 3D Reconstruction Using Terahertz Leaky-Wave Radar Based on Sparse Signal Processing

NASA Astrophysics Data System (ADS)

Murata, Koji; Murano, Kosuke; Watanabe, Issei; Kasamatsu, Akifumi; Tanaka, Toshiyuki; Monnai, Yasuaki

2018-02-01

We experimentally demonstrate see-through detection and 3D reconstruction using terahertz leaky-wave radar based on sparse signal processing. The application of terahertz waves to radar has received increasing attention in recent years for its potential to high-resolution and see-through detection. Among others, the implementation using a leaky-wave antenna is promising for compact system integration with beam steering capability based on frequency sweep. However, the use of a leaky-wave antenna poses a challenge on signal processing. Since a leaky-wave antenna combines the entire signal captured by each part of the aperture into a single output, the conventional array signal processing assuming access to a respective antenna element is not applicable. In this paper, we apply an iterative recovery algorithm "CoSaMP" to signals acquired with terahertz leaky-wave radar for clutter mitigation and aperture synthesis. We firstly demonstrate see-through detection of target location even when the radar is covered with an opaque screen, and therefore, the radar signal is disturbed by clutter. Furthermore, leveraging the robustness of the algorithm against noise, we also demonstrate 3D reconstruction of distributed targets by synthesizing signals collected from different orientations. The proposed approach will contribute to the smart implementation of terahertz leaky-wave radar.

Taguchi Method Applied in Optimization of Shipley SJR 5740 Positive Resist Deposition

NASA Technical Reports Server (NTRS)

Hui, A.; Blosiu, J. O.; Wiberg, D. V.

1998-01-01

Taguchi Methods of Robust Design presents a way to optimize output process performance through an organized set of experiments by using orthogonal arrays. Analysis of variance and signal-to-noise ratio is used to evaluate the contribution of each of the process controllable parameters in the realization of the process optimization. In the photoresist deposition process, there are numerous controllable parameters that can affect the surface quality and thickness of the final photoresist layer.

Robust, open-source removal of systematics in Kepler data

NASA Astrophysics Data System (ADS)

Aigrain, S.; Parviainen, H.; Roberts, S.; Reece, S.; Evans, T.

2017-10-01

We present ARC2 (Astrophysically Robust Correction 2), an open-source python-based systematics-correction pipeline, to correct for the Kepler prime mission long-cadence light curves. The ARC2 pipeline identifies and corrects any isolated discontinuities in the light curves and then removes trends common to many light curves. These trends are modelled using the publicly available co-trending basis vectors, within an (approximate) Bayesian framework with 'shrinkage' priors to minimize the risk of overfitting and the injection of any additional noise into the corrected light curves, while keeping any astrophysical signals intact. We show that the ARC2 pipeline's performance matches that of the standard Kepler PDC-MAP data products using standard noise metrics, and demonstrate its ability to preserve astrophysical signals using injection tests with simulated stellar rotation and planetary transit signals. Although it is not identical, the ARC2 pipeline can thus be used as an open-source alternative to PDC-MAP, whenever the ability to model the impact of the systematics removal process on other kinds of signal is important.

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

PubMed Central

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

2017-01-01

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

A Robust Zero-Watermarking Algorithm for Audio

NASA Astrophysics Data System (ADS)

Chen, Ning; Zhu, Jie

2007-12-01

In traditional watermarking algorithms, the insertion of watermark into the host signal inevitably introduces some perceptible quality degradation. Another problem is the inherent conflict between imperceptibility and robustness. Zero-watermarking technique can solve these problems successfully. Instead of embedding watermark, the zero-watermarking technique extracts some essential characteristics from the host signal and uses them for watermark detection. However, most of the available zero-watermarking schemes are designed for still image and their robustness is not satisfactory. In this paper, an efficient and robust zero-watermarking technique for audio signal is presented. The multiresolution characteristic of discrete wavelet transform (DWT), the energy compression characteristic of discrete cosine transform (DCT), and the Gaussian noise suppression property of higher-order cumulant are combined to extract essential features from the host audio signal and they are then used for watermark recovery. Simulation results demonstrate the effectiveness of our scheme in terms of inaudibility, detection reliability, and robustness.

Evolution of robustness in the signaling network of Pristionchus vulva development

PubMed Central

Zauner, Hans; Sommer, Ralf J.

2007-01-01

Robustness to environmental or genetic perturbation, like any other trait, is affected by evolutionary change. However, direct studies on the interplay of robustness and evolvability are limited and require experimental microevolutionary studies of developmental processes. One system in which such microevolutionary studies can be performed is vulva development in the nematode Pristionchus pacificus. Three vulval precursor cells respond to redundant cell–cell interactions, including signals from the gonad and the epidermal cell P8.p. Interestingly, P. pacificus P8.p is involved in cell fate specification of the future vulva cells by lateral inhibition but is incompetent to respond to the inductive signal from the gonad itself. These functional properties of P8.p are unknown from other nematodes, such as Caenorhabditis elegans. We began an experimental and genetic analysis of the microevolution of P8.p function. We show that vulva misspecification events differ between Pristionchus strains and species. Similarly, lateral inhibition and developmental competence of P8.p evolved within the genus Pristionchus and between natural isolates of P. pacificus. Surprisingly, in some recombinant inbred lines of two distinct P. pacificus isolates, P8.p gained competence to form vulva tissue, a trait that was never observed in P. pacificus isolates. Our results suggest differences in developmental stability between natural isolates, and we hypothesize that the remarkable evolvability of redundant cell–cell interactions allows for adaptive evolution of robustness to developmental noise. PMID:17551021

A Bibliography on Non-Gaussian Signal Processing: 1971-1980.

DTIC Science & Technology

1980-08-20

Narrowband Acoustic Signals in Noise," DDC Report AD-A069 829, May 1979. 9. Evans, James, Kersten , Paul, Kurz, Ludwik, "Robustized Recursive Esti...Kazakos, D., and Papantoni-Kazakos, P., "Nonparanietric Methods in Communication Systems," Marcel Dekker, Inc., New York, 1977. 17. Kersten , P., Kurz...ARRAY PFOCESSING 1. Adams , S.L.; Doubek, J.W., "Frequency Coherence and Time Cbherence in Random Multipath Channels", DDC Peport, PD-A047 456, August

A structural model of the VEGF signalling pathway: emergence of robustness and redundancy properties.

PubMed

Lignet, Floriane; Calvez, Vincent; Grenier, Emmanuel; Ribba, Benjamin

2013-02-01

The vascular endothelial growth factor (VEGF) is known as one of the main promoter of angiogenesis - the process of blood vessel formation. Angiogenesis has been recognized as a key stage for cancer development and metastasis. In this paper, we propose a structural model of the main molecular pathways involved in the endothelial cells response to VEGF stimuli. The model, built on qualitative information from knowledge databases, is composed of 38 ordinary differential equations with 78 parameters and focuses on the signalling driving endothelial cell proliferation, migration and resistance to apoptosis. Following a VEGF stimulus, the model predicts an increase of proliferation and migration capability, and a decrease in the apoptosis activity. Model simulations and sensitivity analysis highlight the emergence of robustness and redundancy properties of the pathway. If further calibrated and validated, this model could serve as tool to analyse and formulate new hypothesis on th e VEGF signalling cascade and its role in cancer development and treatment.

Robust extrema features for time-series data analysis.

PubMed

Vemulapalli, Pramod K; Monga, Vishal; Brennan, Sean N

2013-06-01

The extraction of robust features for comparing and analyzing time series is a fundamentally important problem. Research efforts in this area encompass dimensionality reduction using popular signal analysis tools such as the discrete Fourier and wavelet transforms, various distance metrics, and the extraction of interest points from time series. Recently, extrema features for analysis of time-series data have assumed increasing significance because of their natural robustness under a variety of practical distortions, their economy of representation, and their computational benefits. Invariably, the process of encoding extrema features is preceded by filtering of the time series with an intuitively motivated filter (e.g., for smoothing), and subsequent thresholding to identify robust extrema. We define the properties of robustness, uniqueness, and cardinality as a means to identify the design choices available in each step of the feature generation process. Unlike existing methods, which utilize filters "inspired" from either domain knowledge or intuition, we explicitly optimize the filter based on training time series to optimize robustness of the extracted extrema features. We demonstrate further that the underlying filter optimization problem reduces to an eigenvalue problem and has a tractable solution. An encoding technique that enhances control over cardinality and uniqueness is also presented. Experimental results obtained for the problem of time series subsequence matching establish the merits of the proposed algorithm.

Design and Processing of a Novel Chaos-Based Stepped Frequency Synthesized Wideband Radar Signal.

PubMed

Zeng, Tao; Chang, Shaoqiang; Fan, Huayu; Liu, Quanhua

2018-03-26

The linear stepped frequency and linear frequency shift keying (FSK) signal has been widely used in radar systems. However, such linear modulation signals suffer from the range-Doppler coupling that degrades radar multi-target resolution. Moreover, the fixed frequency-hopping or frequency-coded sequence can be easily predicted by the interception receiver in the electronic countermeasures (ECM) environments, which limits radar anti-jamming performance. In addition, the single FSK modulation reduces the radar low probability of intercept (LPI) performance, for it cannot achieve a large time-bandwidth product. To solve such problems, we propose a novel chaos-based stepped frequency (CSF) synthesized wideband signal in this paper. The signal introduces chaotic frequency hopping between the coherent stepped frequency pulses, and adopts a chaotic frequency shift keying (CFSK) and phase shift keying (PSK) composited coded modulation in a subpulse, called CSF-CFSK/PSK. Correspondingly, the processing method for the signal has been proposed. According to our theoretical analyses and the simulations, the proposed signal and processing method achieve better multi-target resolution and LPI performance. Furthermore, flexible modulation is able to increase the robustness against identification of the interception receiver and improve the anti-jamming performance of the radar.

A Robust Random Forest-Based Approach for Heart Rate Monitoring Using Photoplethysmography Signal Contaminated by Intense Motion Artifacts.

PubMed

Ye, Yalan; He, Wenwen; Cheng, Yunfei; Huang, Wenxia; Zhang, Zhilin

2017-02-16

The estimation of heart rate (HR) based on wearable devices is of interest in fitness. Photoplethysmography (PPG) is a promising approach to estimate HR due to low cost; however, it is easily corrupted by motion artifacts (MA). In this work, a robust approach based on random forest is proposed for accurately estimating HR from the photoplethysmography signal contaminated by intense motion artifacts, consisting of two stages. Stage 1 proposes a hybrid method to effectively remove MA with a low computation complexity, where two MA removal algorithms are combined by an accurate binary decision algorithm whose aim is to decide whether or not to adopt the second MA removal algorithm. Stage 2 proposes a random forest-based spectral peak-tracking algorithm, whose aim is to locate the spectral peak corresponding to HR, formulating the problem of spectral peak tracking into a pattern classification problem. Experiments on the PPG datasets including 22 subjects used in the 2015 IEEE Signal Processing Cup showed that the proposed approach achieved the average absolute error of 1.65 beats per minute (BPM) on the 22 PPG datasets. Compared to state-of-the-art approaches, the proposed approach has better accuracy and robustness to intense motion artifacts, indicating its potential use in wearable sensors for health monitoring and fitness tracking.

Design optimization for cost and quality: The robust design approach

NASA Technical Reports Server (NTRS)

Unal, Resit

1990-01-01

Designing reliable, low cost, and operable space systems has become the key to future space operations. Designing high quality space systems at low cost is an economic and technological challenge to the designer. A systematic and efficient way to meet this challenge is a new method of design optimization for performance, quality, and cost, called Robust Design. Robust Design is an approach for design optimization. It consists of: making system performance insensitive to material and subsystem variation, thus allowing the use of less costly materials and components; making designs less sensitive to the variations in the operating environment, thus improving reliability and reducing operating costs; and using a new structured development process so that engineering time is used most productively. The objective in Robust Design is to select the best combination of controllable design parameters so that the system is most robust to uncontrollable noise factors. The robust design methodology uses a mathematical tool called an orthogonal array, from design of experiments theory, to study a large number of decision variables with a significantly small number of experiments. Robust design also uses a statistical measure of performance, called a signal-to-noise ratio, from electrical control theory, to evaluate the level of performance and the effect of noise factors. The purpose is to investigate the Robust Design methodology for improving quality and cost, demonstrate its application by the use of an example, and suggest its use as an integral part of space system design process.

Correction of complex nonlinear signal response from a pixel array detector

PubMed Central

van Driel, Tim Brandt; Herrmann, Sven; Carini, Gabriella; Nielsen, Martin Meedom; Lemke, Henrik Till

2015-01-01

The pulsed free-electron laser light sources represent a new challenge to photon area detectors due to the intrinsic spontaneous X-ray photon generation process that makes single-pulse detection necessary. Intensity fluctuations up to 100% between individual pulses lead to high linearity requirements in order to distinguish small signal changes. In real detectors, signal distortions as a function of the intensity distribution on the entire detector can occur. Here a robust method to correct this nonlinear response in an area detector is presented for the case of exposures to similar signals. The method is tested for the case of diffuse scattering from liquids where relevant sub-1% signal changes appear on the same order as artifacts induced by the detector electronics. PMID:25931072

Correction of complex nonlinear signal response from a pixel array detector.

PubMed

van Driel, Tim Brandt; Herrmann, Sven; Carini, Gabriella; Nielsen, Martin Meedom; Lemke, Henrik Till

2015-05-01

The pulsed free-electron laser light sources represent a new challenge to photon area detectors due to the intrinsic spontaneous X-ray photon generation process that makes single-pulse detection necessary. Intensity fluctuations up to 100% between individual pulses lead to high linearity requirements in order to distinguish small signal changes. In real detectors, signal distortions as a function of the intensity distribution on the entire detector can occur. Here a robust method to correct this nonlinear response in an area detector is presented for the case of exposures to similar signals. The method is tested for the case of diffuse scattering from liquids where relevant sub-1% signal changes appear on the same order as artifacts induced by the detector electronics.

Neurons in the human amygdala encode face identity, but not gaze direction.

PubMed

Mormann, Florian; Niediek, Johannes; Tudusciuc, Oana; Quesada, Carlos M; Coenen, Volker A; Elger, Christian E; Adolphs, Ralph

2015-11-01

The amygdala is important for face processing, and direction of eye gaze is one of the most socially salient facial signals. Recording from over 200 neurons in the amygdala of neurosurgical patients, we found robust encoding of the identity of neutral-expression faces, but not of their direction of gaze. Processing of gaze direction may rely on a predominantly cortical network rather than the amygdala.

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

van Driel, Tim Brandt; Herrmann, Sven; Carini, Gabriella

The pulsed free-electron laser light sources represent a new challenge to photon area detectors due to the intrinsic spontaneous X-ray photon generation process that makes single-pulse detection necessary. Intensity fluctuations up to 100% between individual pulses lead to high linearity requirements in order to distinguish small signal changes. In real detectors, signal distortions as a function of the intensity distribution on the entire detector can occur. Here a robust method to correct this nonlinear response in an area detector is presented for the case of exposures to similar signals. The method is tested for the case of diffuse scattering frommore » liquids where relevant sub-1% signal changes appear on the same order as artifacts induced by the detector electronics.« less

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.

Robust watermark technique using masking and Hermite transform.

PubMed

Coronel, Sandra L Gomez; Ramírez, Boris Escalante; Mosqueda, Marco A Acevedo

2016-01-01

The following paper evaluates a watermark algorithm designed for digital images by using a perceptive mask and a normalization process, thus preventing human eye detection, as well as ensuring its robustness against common processing and geometric attacks. The Hermite transform is employed because it allows a perfect reconstruction of the image, while incorporating human visual system properties; moreover, it is based on the Gaussian functions derivates. The applied watermark represents information of the digital image proprietor. The extraction process is blind, because it does not require the original image. The following techniques were utilized in the evaluation of the algorithm: peak signal-to-noise ratio, the structural similarity index average, the normalized crossed correlation, and bit error rate. Several watermark extraction tests were performed, with against geometric and common processing attacks. It allowed us to identify how many bits in the watermark can be modified for its adequate extraction.

Robust technology and system for management of sucker rod pumping units in oil wells

NASA Astrophysics Data System (ADS)

Aliev, T. A.; Rzayev, A. H.; Guluyev, G. A.; Alizada, T. A.; Rzayeva, N. E.

2018-01-01

We propose a technology for calculating the robust, normalized correlation functions of the signal from the force sensor on the rod string attached to the hanger of the sucker rod pumping unit. The robust normalized correlation functions are used to form sets of informative attribute combinations, each of which corresponds to a technical condition of the sucker rod pumping unit. We demonstrate how these sets can be used to solve identification and management problems in the oil production process in real time using inexpensive controllers. The results obtained from using the system on real objects are also presented in this paper. It was determined that the energy saved and prolonged overhaul period substantially increased the cost-effectiveness.

Boundary layer noise subtraction in hydrodynamic tunnel using robust principal component analysis.

PubMed

Amailland, Sylvain; Thomas, Jean-Hugh; Pézerat, Charles; Boucheron, Romuald

2018-04-01

The acoustic study of propellers in a hydrodynamic tunnel is of paramount importance during the design process, but can involve significant difficulties due to the boundary layer noise (BLN). Indeed, advanced denoising methods are needed to recover the acoustic signal in case of poor signal-to-noise ratio. The technique proposed in this paper is based on the decomposition of the wall-pressure cross-spectral matrix (CSM) by taking advantage of both the low-rank property of the acoustic CSM and the sparse property of the BLN CSM. Thus, the algorithm belongs to the class of robust principal component analysis (RPCA), which derives from the widely used principal component analysis. If the BLN is spatially decorrelated, the proposed RPCA algorithm can blindly recover the acoustical signals even for negative signal-to-noise ratio. Unfortunately, in a realistic case, acoustic signals recorded in a hydrodynamic tunnel show that the noise may be partially correlated. A prewhitening strategy is then considered in order to take into account the spatially coherent background noise. Numerical simulations and experimental results show an improvement in terms of BLN reduction in the large hydrodynamic tunnel. The effectiveness of the denoising method is also investigated in the context of acoustic source localization.

Reproducibility study of whole-brain 1H spectroscopic imaging with automated quantification.

PubMed

Gu, Meng; Kim, Dong-Hyun; Mayer, Dirk; Sullivan, Edith V; Pfefferbaum, Adolf; Spielman, Daniel M

2008-09-01

A reproducibility study of proton MR spectroscopic imaging ((1)H-MRSI) of the human brain was conducted to evaluate the reliability of an automated 3D in vivo spectroscopic imaging acquisition and associated quantification algorithm. A PRESS-based pulse sequence was implemented using dualband spectral-spatial RF pulses designed to fully excite the singlet resonances of choline (Cho), creatine (Cre), and N-acetyl aspartate (NAA) while simultaneously suppressing water and lipids; 1% of the water signal was left to be used as a reference signal for robust data processing, and additional lipid suppression was obtained using adiabatic inversion recovery. Spiral k-space trajectories were used for fast spectral and spatial encoding yielding high-quality spectra from 1 cc voxels throughout the brain with a 13-min acquisition time. Data were acquired with an 8-channel phased-array coil and optimal signal-to-noise ratio (SNR) for the combined signals was achieved using a weighting based on the residual water signal. Automated quantification of the spectrum of each voxel was performed using LCModel. The complete study consisted of eight healthy adult subjects to assess intersubject variations and two subjects scanned six times each to assess intrasubject variations. The results demonstrate that reproducible whole-brain (1)H-MRSI data can be robustly obtained with the proposed methods.

Non linear processes modulated by low doses of radiation exposure

NASA Astrophysics Data System (ADS)

Mariotti, Luca; Ottolenghi, Andrea; Alloni, Daniele; Babini, Gabriele; Morini, Jacopo; Baiocco, Giorgio

The perturbation induced by radiation impinging on biological targets can stimulate the activation of several different pathways, spanning from the DNA damage processing to intra/extra -cellular signalling. In the mechanistic investigation of radiobiological damage this complex “system” response (e.g. omics, signalling networks, micro-environmental modifications, etc.) has to be taken into account, shifting from a focus on the DNA molecule solely to a systemic/collective view. An additional complication comes from the finding that the individual response of each of the involved processes is often not linear as a function of the dose. In this context, a systems biology approach to investigate the effects of low dose irradiations on intra/extra-cellular signalling will be presented, where low doses of radiation act as a mild perturbation of a robustly interconnected network. Results obtained through a multi-level investigation of both DNA damage repair processes (e.g. gamma-H2AX response) and of the activation kinetics for intra/extra cellular signalling pathways (e.g. NFkB activation) show that the overall cell response is dominated by non-linear processes - such as negative feedbacks - leading to possible non equilibrium steady states and to a poor signal-to-noise ratio. Together with experimental data of radiation perturbed pathways, different modelling approaches will be also discussed.

Low cost MATLAB-based pulse oximeter for deployment in research and development applications.

PubMed

Shokouhian, M; Morling, R C S; Kale, I

2013-01-01

Problems such as motion artifact and effects of ambient lights have forced developers to design different signal processing techniques and algorithms to increase the reliability and accuracy of the conventional pulse oximeter device. To evaluate the robustness of these techniques, they are applied either to recorded data or are implemented on chip to be applied to real-time data. Recorded data is the most common method of evaluating however it is not as reliable as real-time measurements. On the other hand, hardware implementation can be both expensive and time consuming. This paper presents a low cost MATLAB-based pulse oximeter that can be used for rapid evaluation of newly developed signal processing techniques and algorithms. Flexibility to apply different signal processing techniques, providing both processed and unprocessed data along with low implementation cost are the important features of this design which makes it ideal for research and development purposes, as well as commercial, hospital and healthcare application.

Online tracking of instantaneous frequency and amplitude of dynamical system response

NASA Astrophysics Data System (ADS)

Frank Pai, P.

2010-05-01

This paper presents a sliding-window tracking (SWT) method for accurate tracking of the instantaneous frequency and amplitude of arbitrary dynamic response by processing only three (or more) most recent data points. Teager-Kaiser algorithm (TKA) is a well-known four-point method for online tracking of frequency and amplitude. Because finite difference is used in TKA, its accuracy is easily destroyed by measurement and/or signal-processing noise. Moreover, because TKA assumes the processed signal to be a pure harmonic, any moving average in the signal can destroy the accuracy of TKA. On the other hand, because SWT uses a constant and a pair of windowed regular harmonics to fit the data and estimate the instantaneous frequency and amplitude, the influence of any moving average is eliminated. Moreover, noise filtering is an implicit capability of SWT when more than three data points are used, and this capability increases with the number of processed data points. To compare the accuracy of SWT and TKA, Hilbert-Huang transform is used to extract accurate time-varying frequencies and amplitudes by processing the whole data set without assuming the signal to be harmonic. Frequency and amplitude trackings of different amplitude- and frequency-modulated signals, vibrato in music, and nonlinear stationary and non-stationary dynamic signals are studied. Results show that SWT is more accurate, robust, and versatile than TKA for online tracking of frequency and amplitude.

Robustness, evolvability, and the logic of genetic regulation.

PubMed

Payne, Joshua L; Moore, Jason H; Wagner, Andreas

2014-01-01

In gene regulatory circuits, the expression of individual genes is commonly modulated by a set of regulating gene products, which bind to a gene's cis-regulatory region. This region encodes an input-output function, referred to as signal-integration logic, that maps a specific combination of regulatory signals (inputs) to a particular expression state (output) of a gene. The space of all possible signal-integration functions is vast and the mapping from input to output is many-to-one: For the same set of inputs, many functions (genotypes) yield the same expression output (phenotype). Here, we exhaustively enumerate the set of signal-integration functions that yield identical gene expression patterns within a computational model of gene regulatory circuits. Our goal is to characterize the relationship between robustness and evolvability in the signal-integration space of regulatory circuits, and to understand how these properties vary between the genotypic and phenotypic scales. Among other results, we find that the distributions of genotypic robustness are skewed, so that the majority of signal-integration functions are robust to perturbation. We show that the connected set of genotypes that make up a given phenotype are constrained to specific regions of the space of all possible signal-integration functions, but that as the distance between genotypes increases, so does their capacity for unique innovations. In addition, we find that robust phenotypes are (i) evolvable, (ii) easily identified by random mutation, and (iii) mutationally biased toward other robust phenotypes. We explore the implications of these latter observations for mutation-based evolution by conducting random walks between randomly chosen source and target phenotypes. We demonstrate that the time required to identify the target phenotype is independent of the properties of the source phenotype.

Robustness, Evolvability, and the Logic of Genetic Regulation

PubMed Central

Moore, Jason H.; Wagner, Andreas

2014-01-01

In gene regulatory circuits, the expression of individual genes is commonly modulated by a set of regulating gene products, which bind to a gene’s cis-regulatory region. This region encodes an input-output function, referred to as signal-integration logic, that maps a specific combination of regulatory signals (inputs) to a particular expression state (output) of a gene. The space of all possible signal-integration functions is vast and the mapping from input to output is many-to-one: for the same set of inputs, many functions (genotypes) yield the same expression output (phenotype). Here, we exhaustively enumerate the set of signal-integration functions that yield idential gene expression patterns within a computational model of gene regulatory circuits. Our goal is to characterize the relationship between robustness and evolvability in the signal-integration space of regulatory circuits, and to understand how these properties vary between the genotypic and phenotypic scales. Among other results, we find that the distributions of genotypic robustness are skewed, such that the majority of signal-integration functions are robust to perturbation. We show that the connected set of genotypes that make up a given phenotype are constrained to specific regions of the space of all possible signal-integration functions, but that as the distance between genotypes increases, so does their capacity for unique innovations. In addition, we find that robust phenotypes are (i) evolvable, (ii) easily identified by random mutation, and (iii) mutationally biased toward other robust phenotypes. We explore the implications of these latter observations for mutation-based evolution by conducting random walks between randomly chosen source and target phenotypes. We demonstrate that the time required to identify the target phenotype is independent of the properties of the source phenotype. PMID:23373974

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment

PubMed Central

Rundo, Francesco; Ortis, Alessandro

2018-01-01

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG “combo” pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting “clean” PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach. PMID:29385774

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment.

PubMed

Rundo, Francesco; Conoci, Sabrina; Ortis, Alessandro; Battiato, Sebastiano

2018-01-30

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG "combo" pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting "clean" PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.

Robust nanofabrication of monolayer MoS2 islands with strong photoluminescence enhancement via local anodic oxidation

NASA Astrophysics Data System (ADS)

Fernandes, Thales F. D.; Gadelha, Andreij de C.; Barboza, Ana P. M.; Paniago, Roberto M.; Campos, Leonardo C.; Soares Guimarães, Paulo S.; de Assis, Pierre-Louis; Neves, Bernardo R. A.

2018-04-01

In this work, we demonstrate the nanofabrication of monolayer MoS2 islands using local anodic oxidation of few-layer and bulk MoS2 flakes. The nanofabricated islands present true monolayer Raman signal and photoluminescence intensity up to two orders of magnitude larger than that of a pristine monolayer. This technique is robust enough to result in monolayer islands without the need of meticulously fine-tuning the oxidation process, thus providing a fast and reliable way of creating monolayer regions with enhanced optical properties and with controllable size, shape, and position.

On the robustness of SAC silencing in closed mitosis

NASA Astrophysics Data System (ADS)

Ruth, Donovan; Liu, Jian

Mitosis equally partitions sister chromatids to two daughter cells. This is achieved by properly attaching these chromatids via their kinetochores to microtubules that emanate from the spindle poles. Once the last kinetochore is properly attached, the spindle microtubules pull the sister chromatids apart. Due to the dynamic nature of microtubules, however, kinetochore-microtubule attachment often goes wrong. When this erroneous attachment occurs, it locally activates an ensemble of proteins, called the spindle assembly checkpoint proteins (SAC), which halts the mitotic progression until all the kinetochores are properly attached by spindle microtubules. The timing of SAC silencing thus determines the fidelity of chromosome segregation. We previously established a spatiotemporal model that addresses the robustness of SAC silencing in open mitosis for the first time. Here, we focus on closed mitosis by examining yeast mitosis as a model system. Though much experimental work has been done to study the SAC in cells undergoing closed mitosis, the processes responsible are not well understood. We leverage and extend our previous model to study SAC silencing mechanism in closed mitosis. We show that a robust signal of the SAC protein accumulation at the spindle pole body can be achieved. This signal is a nonlinear increasing function of number of kinetochore-microtubule attachments, and can thus serve as a robust trigger to time the SAC silencing. Together, our mechanism provides a unified framework across species that ensures robust SAC silencing and fidelity of chromosome segregation in mitosis. Intramural research program in NHLBI at NIH.

Optimization and automation of quantitative NMR data extraction.

PubMed

Bernstein, Michael A; Sýkora, Stan; Peng, Chen; Barba, Agustín; Cobas, Carlos

2013-06-18

NMR is routinely used to quantitate chemical species. The necessary experimental procedures to acquire quantitative data are well-known, but relatively little attention has been applied to data processing and analysis. We describe here a robust expert system that can be used to automatically choose the best signals in a sample for overall concentration determination and determine analyte concentration using all accepted methods. The algorithm is based on the complete deconvolution of the spectrum which makes it tolerant of cases where signals are very close to one another and includes robust methods for the automatic classification of NMR resonances and molecule-to-spectrum multiplets assignments. With the functionality in place and optimized, it is then a relatively simple matter to apply the same workflow to data in a fully automatic way. The procedure is desirable for both its inherent performance and applicability to NMR data acquired for very large sample sets.

Fluorescence lifetime assays: current advances and applications in drug discovery.

PubMed

Pritz, Stephan; Doering, Klaus; Woelcke, Julian; Hassiepen, Ulrich

2011-06-01

Fluorescence lifetime assays complement the portfolio of established assay formats available in drug discovery, particularly with the recent advances in microplate readers and the commercial availability of novel fluorescent labels. Fluorescence lifetime assists in lowering complexity of compound screening assays, affording a modular, toolbox-like approach to assay development and yielding robust homogeneous assays. To date, materials and procedures have been reported for biochemical assays on proteases, as well as on protein kinases and phosphatases. This article gives an overview of two assay families, distinguished by the origin of the fluorescence signal modulation. The pharmaceutical industry demands techniques with a robust, integrated compound profiling process and short turnaround times. Fluorescence lifetime assays have already helped the drug discovery field, in this sense, by enhancing productivity during the hit-to-lead and lead optimization phases. Future work will focus on covering other biochemical molecular modifications by investigating the detailed photo-physical mechanisms underlying the fluorescence signal.

Automated smoother for the numerical decoupling of dynamics models.

PubMed

Vilela, Marco; Borges, Carlos C H; Vinga, Susana; Vasconcelos, Ana Tereza R; Santos, Helena; Voit, Eberhard O; Almeida, Jonas S

2007-08-21

Structure identification of dynamic models for complex biological systems is the cornerstone of their reverse engineering. Biochemical Systems Theory (BST) offers a particularly convenient solution because its parameters are kinetic-order coefficients which directly identify the topology of the underlying network of processes. We have previously proposed a numerical decoupling procedure that allows the identification of multivariate dynamic models of complex biological processes. While described here within the context of BST, this procedure has a general applicability to signal extraction. Our original implementation relied on artificial neural networks (ANN), which caused slight, undesirable bias during the smoothing of the time courses. As an alternative, we propose here an adaptation of the Whittaker's smoother and demonstrate its role within a robust, fully automated structure identification procedure. In this report we propose a robust, fully automated solution for signal extraction from time series, which is the prerequisite for the efficient reverse engineering of biological systems models. The Whittaker's smoother is reformulated within the context of information theory and extended by the development of adaptive signal segmentation to account for heterogeneous noise structures. The resulting procedure can be used on arbitrary time series with a nonstationary noise process; it is illustrated here with metabolic profiles obtained from in-vivo NMR experiments. The smoothed solution that is free of parametric bias permits differentiation, which is crucial for the numerical decoupling of systems of differential equations. The method is applicable in signal extraction from time series with nonstationary noise structure and can be applied in the numerical decoupling of system of differential equations into algebraic equations, and thus constitutes a rather general tool for the reverse engineering of mechanistic model descriptions from multivariate experimental time series.

Investigation on changes of modularity and robustness by edge-removal mutations in signaling networks.

PubMed

Truong, Cong-Doan; Kwon, Yung-Keun

2017-12-21

Biological networks consisting of molecular components and interactions are represented by a graph model. There have been some studies based on that model to analyze a relationship between structural characteristics and dynamical behaviors in signaling network. However, little attention has been paid to changes of modularity and robustness in mutant networks. In this paper, we investigated the changes of modularity and robustness by edge-removal mutations in three signaling networks. We first observed that both the modularity and robustness increased on average in the mutant network by the edge-removal mutations. However, the modularity change was negatively correlated with the robustness change. This implies that it is unlikely that both the modularity and the robustness values simultaneously increase by the edge-removal mutations. Another interesting finding is that the modularity change was positively correlated with the degree, the number of feedback loops, and the edge betweenness of the removed edges whereas the robustness change was negatively correlated with them. We note that these results were consistently observed in randomly structure networks. Additionally, we identified two groups of genes which are incident to the highly-modularity-increasing and the highly-robustness-decreasing edges with respect to the edge-removal mutations, respectively, and observed that they are likely to be central by forming a connected component of a considerably large size. The gene-ontology enrichment of each of these gene groups was significantly different from the rest of genes. Finally, we showed that the highly-robustness-decreasing edges can be promising edgetic drug-targets, which validates the usefulness of our analysis. Taken together, the analysis of changes of robustness and modularity against edge-removal mutations can be useful to unravel novel dynamical characteristics underlying in signaling networks.

Blind source separation by sparse decomposition

NASA Astrophysics Data System (ADS)

Zibulevsky, Michael; Pearlmutter, Barak A.

2000-04-01

The blind source separation problem is to extract the underlying source signals from a set of their linear mixtures, where the mixing matrix is unknown. This situation is common, eg in acoustics, radio, and medical signal processing. We exploit the property of the sources to have a sparse representation in a corresponding signal dictionary. Such a dictionary may consist of wavelets, wavelet packets, etc., or be obtained by learning from a given family of signals. Starting from the maximum a posteriori framework, which is applicable to the case of more sources than mixtures, we derive a few other categories of objective functions, which provide faster and more robust computations, when there are an equal number of sources and mixtures. Our experiments with artificial signals and with musical sounds demonstrate significantly better separation than other known techniques.

Correction of complex nonlinear signal response from a pixel array detector

DOE PAGES

van Driel, Tim Brandt; Herrmann, Sven; Carini, Gabriella; ...

2015-04-22

The pulsed free-electron laser light sources represent a new challenge to photon area detectors due to the intrinsic spontaneous X-ray photon generation process that makes single-pulse detection necessary. Intensity fluctuations up to 100% between individual pulses lead to high linearity requirements in order to distinguish small signal changes. In real detectors, signal distortions as a function of the intensity distribution on the entire detector can occur. Here a robust method to correct this nonlinear response in an area detector is presented for the case of exposures to similar signals. The method is tested for the case of diffuse scattering frommore » liquids where relevant sub-1% signal changes appear on the same order as artifacts induced by the detector electronics.« less

Modularized Smad-regulated TGFβ signaling pathway.

PubMed

Li, Yongfeng; Wang, Minli; Carra, Claudio; Cucinotta, Francis A

2012-12-01

The transforming Growth Factor β (TGFβ) signaling pathway is a prominent regulatory signaling pathway controlling various important cellular processes. TGFβ signaling can be induced by several factors including ionizing radiation. The pathway is regulated in a negative feedback loop through promoting the nuclear import of the regulatory Smads and a subsequent expression of inhibitory Smad7, that forms ubiquitin ligase with Smurf2, targeting active TGFβ receptors for degradation. In this work, we proposed a mathematical model to study the Smad-regulated TGFβ signaling pathway. By modularization, we are able to analyze mathematically each component subsystem and recover the nonlinear dynamics of the entire network system. Meanwhile the excitability, a common feature observed in the biological systems, in the TGFβ signaling pathway is discussed and supported as well by numerical simulation, indicating the robustness of the model. Published by Elsevier Inc.

Developing a robust wireless sensor network structure for environmental sensing

NASA Astrophysics Data System (ADS)

Zhang, Z.; Oroza, C.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.

2013-12-01

The American River Hydrologic Observatory is being strategically deployed as a real-time ground-based measurement network that delivers accurate and timely information on snow conditions and other hydrologic attributes with a previously unheard of granularity of time and space. The basin-scale network involves 18 sub-networks set out at physiographically representative locations spanning the seasonally snow-covered half of the 5000 km2 American river basin. Each sub-network, covering about a 1-km2 area, consists of 10 wirelessly networked sensing nodes that continuously measure and telemeter temperature, and snow depth; plus selected locations are equipped with sensors for relative humidity, solar radiation, and soil moisture at several depths. The sensor locations were chosen to maximize the variance sampled for snow depth within the basin. Network design and deployment involves an iterative but efficient process. After sensor-station locations are determined, a robust network of interlinking sensor stations and signal repeaters must be constructed to route sensor data to a central base station with a two-way communicable data uplink. Data can then be uploaded from site to remote servers in real time through satellite and cell modems. Signal repeaters are placed for robustness of a self-healing network with redundant signal paths to the base station. Manual, trial-and-error heuristic approaches for node placement are inefficient and labor intensive. In that approach field personnel must restructure the network in real time and wait for new network statistics to be calculated at the base station before finalizing a placement, acting without knowledge of the global topography or overall network structure. We show how digital elevation plus high-definition aerial photographs to give foliage coverage can optimize planning of signal repeater placements and guarantee a robust network structure prior to the physical deployment. We can also 'stress test' the final network by simulating the failure of an individual node and investigating the effect and the self-healing ability of the stressed network. The resulting sensor network can survive temporary service interruption from a small subset of signal repeaters and sensor stations. The robustness and the resilient of the network performance ensure the integrity of the dataset and the real-time transmissibility during harsh conditions.

Whisker Contact Detection of Rodents Based on Slow and Fast Mechanical Inputs

PubMed Central

Claverie, Laure N.; Boubenec, Yves; Debrégeas, Georges; Prevost, Alexis M.; Wandersman, Elie

2017-01-01

Rodents use their whiskers to locate nearby objects with an extreme precision. To perform such tasks, they need to detect whisker/object contacts with a high temporal accuracy. This contact detection is conveyed by classes of mechanoreceptors whose neural activity is sensitive to either slow or fast time varying mechanical stresses acting at the base of the whiskers. We developed a biomimetic approach to separate and characterize slow quasi-static and fast vibrational stress signals acting on a whisker base in realistic exploratory phases, using experiments on both real and artificial whiskers. Both slow and fast mechanical inputs are successfully captured using a mechanical model of the whisker. We present and discuss consequences of the whisking process in purely mechanical terms and hypothesize that free whisking in air sets a mechanical threshold for contact detection. The time resolution and robustness of the contact detection strategies based on either slow or fast stress signals are determined. Contact detection based on the vibrational signal is faster and more robust to exploratory conditions than the slow quasi-static component, although both slow/fast components allow localizing the object. PMID:28119582

Vestibular blueprint in early vertebrates.

PubMed

Straka, Hans; Baker, Robert

2013-11-19

Central vestibular neurons form identifiable subgroups within the boundaries of classically outlined octavolateral nuclei in primitive vertebrates that are distinct from those processing lateral line, electrosensory, and auditory signals. Each vestibular subgroup exhibits a particular morpho-physiological property that receives origin-specific sensory inputs from semicircular canal and otolith organs. Behaviorally characterized phenotypes send discrete axonal projections to extraocular, spinal, and cerebellar targets including other ipsi- and contralateral vestibular nuclei. The anatomical locations of vestibuloocular and vestibulospinal neurons correlate with genetically defined hindbrain compartments that are well conserved throughout vertebrate evolution though some variability exists in fossil and extant vertebrate species. The different vestibular subgroups exhibit a robust sensorimotor signal processing complemented with a high degree of vestibular and visual adaptive plasticity.

Robust Modulo Remaindering and Applications in Radar and Sensor Signal Processing

DTIC Science & Technology

2015-08-27

Chinese Remainder Theorem in FDD Systems, Science China -- Information Sciences, vol.55, no.7, pp. 1605 -1616, July 2012. 3) Y. Liu, X.-G. Xia, and H. L...Sciences, vol.55, no.7, pp. 1605 -1616, July 2012. 3) Y. Liu, X.-G. Xia, and H. L. Zhang, Distributed Space-Time Coding for Full-DuplexAsynchronous

Application of Frame Theory in Intelligent Packet-Based Communication Networks

NASA Astrophysics Data System (ADS)

Escobar-Moreira, León A.

2007-09-01

Frames are a stable and redundant representation of signals in a Hilbert space that have been used in signal processing because of their resilience to additive noise, quantization error, and their robustness to losses in packet-based networks [1,2]. Depending on the number of erasures (losses), there are some considerations to be taken into account in order to optimize the frame design. Further discussions will explain the innate characteristics of frames to include intelligence on the packet-based communication devices (routers) to increase their performance under different channel behaviors.

Damage localization by statistical evaluation of signal-processed mode shapes

NASA Astrophysics Data System (ADS)

Ulriksen, M. D.; Damkilde, L.

2015-07-01

Due to their inherent, ability to provide structural information on a local level, mode shapes and t.lieir derivatives are utilized extensively for structural damage identification. Typically, more or less advanced mathematical methods are implemented to identify damage-induced discontinuities in the spatial mode shape signals, hereby potentially facilitating damage detection and/or localization. However, by being based on distinguishing damage-induced discontinuities from other signal irregularities, an intrinsic deficiency in these methods is the high sensitivity towards measurement, noise. The present, article introduces a damage localization method which, compared to the conventional mode shape-based methods, has greatly enhanced robustness towards measurement, noise. The method is based on signal processing of spatial mode shapes by means of continuous wavelet, transformation (CWT) and subsequent, application of a generalized discrete Teager-Kaiser energy operator (GDTKEO) to identify damage-induced mode shape discontinuities. In order to evaluate whether the identified discontinuities are in fact, damage-induced, outlier analysis of principal components of the signal-processed mode shapes is conducted on the basis of T2-statistics. The proposed method is demonstrated in the context, of analytical work with a free-vibrating Euler-Bernoulli beam under noisy conditions.

Working Memory and Hearing Aid Processing: Literature Findings, Future Directions, and Clinical Applications

PubMed Central

Souza, Pamela; Arehart, Kathryn; Neher, Tobias

2015-01-01

Working memory—the ability to process and store information—has been identified as an important aspect of speech perception in difficult listening environments. Working memory can be envisioned as a limited-capacity system which is engaged when an input signal cannot be readily matched to a stored representation or template. This “mismatch” is expected to occur more frequently when the signal is degraded. Because working memory capacity varies among individuals, those with smaller capacity are expected to demonstrate poorer speech understanding when speech is degraded, such as in background noise. However, it is less clear whether (and how) working memory should influence practical decisions, such as hearing treatment. Here, we consider the relationship between working memory capacity and response to specific hearing aid processing strategies. Three types of signal processing are considered, each of which will alter the acoustic signal: fast-acting wide-dynamic range compression, which smooths the amplitude envelope of the input signal; digital noise reduction, which may inadvertently remove speech signal components as it suppresses noise; and frequency compression, which alters the relationship between spectral peaks. For fast-acting wide-dynamic range compression, a growing body of data suggests that individuals with smaller working memory capacity may be more susceptible to such signal alterations, and may receive greater amplification benefit with “low alteration” processing. While the evidence for a relationship between wide-dynamic range compression and working memory appears robust, the effects of working memory on perceptual response to other forms of hearing aid signal processing are less clear cut. We conclude our review with a discussion of the opportunities (and challenges) in translating information on individual working memory into clinical treatment, including clinically feasible measures of working memory. PMID:26733899

Electrochemical Probing through a Redox Capacitor To Acquire Chemical Information on Biothiols

PubMed Central

2016-01-01

The acquisition of chemical information is a critical need for medical diagnostics, food/environmental monitoring, and national security. Here, we report an electrochemical information processing approach that integrates (i) complex electrical inputs/outputs, (ii) mediators to transduce the electrical I/O into redox signals that can actively probe the chemical environment, and (iii) a redox capacitor that manipulates signals for information extraction. We demonstrate the capabilities of this chemical information processing strategy using biothiols because of the emerging importance of these molecules in medicine and because their distinct chemical properties allow evaluation of hypothesis-driven information probing. We show that input sequences can be tailored to probe for chemical information both qualitatively (step inputs probe for thiol-specific signatures) and quantitatively. Specifically, we observed picomolar limits of detection and linear responses to concentrations over 5 orders of magnitude (1 pM–0.1 μM). This approach allows the capabilities of signal processing to be extended for rapid, robust, and on-site analysis of chemical information. PMID:27385047

Electrochemical Probing through a Redox Capacitor To Acquire Chemical Information on Biothiols.

PubMed

Liu, Zhengchun; Liu, Yi; Kim, Eunkyoung; Bentley, William E; Payne, Gregory F

2016-07-19

The acquisition of chemical information is a critical need for medical diagnostics, food/environmental monitoring, and national security. Here, we report an electrochemical information processing approach that integrates (i) complex electrical inputs/outputs, (ii) mediators to transduce the electrical I/O into redox signals that can actively probe the chemical environment, and (iii) a redox capacitor that manipulates signals for information extraction. We demonstrate the capabilities of this chemical information processing strategy using biothiols because of the emerging importance of these molecules in medicine and because their distinct chemical properties allow evaluation of hypothesis-driven information probing. We show that input sequences can be tailored to probe for chemical information both qualitatively (step inputs probe for thiol-specific signatures) and quantitatively. Specifically, we observed picomolar limits of detection and linear responses to concentrations over 5 orders of magnitude (1 pM-0.1 μM). This approach allows the capabilities of signal processing to be extended for rapid, robust, and on-site analysis of chemical information.

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

Robust QRS peak detection by multimodal information fusion of ECG and blood pressure signals.

PubMed

Ding, Quan; Bai, Yong; Erol, Yusuf Bugra; Salas-Boni, Rebeca; Zhang, Xiaorong; Hu, Xiao

2016-11-01

QRS peak detection is a challenging problem when ECG signal is corrupted. However, additional physiological signals may also provide information about the QRS position. In this study, we focus on a unique benchmark provided by PhysioNet/Computing in Cardiology Challenge 2014 and Physiological Measurement focus issue: robust detection of heart beats in multimodal data, which aimed to explore robust methods for QRS detection in multimodal physiological signals. A dataset of 200 training and 210 testing records are used, where the testing records are hidden for evaluating the performance only. An information fusion framework for robust QRS detection is proposed by leveraging existing ECG and ABP analysis tools and combining heart beats derived from different sources. Results show that our approach achieves an overall accuracy of 90.94% and 88.66% on the training and testing datasets, respectively. Furthermore, we observe expected performance at each step of the proposed approach, as an evidence of the effectiveness of our approach. Discussion on the limitations of our approach is also provided.

Analysis of a dynamic model of guard cell signaling reveals the stability of signal propagation

NASA Astrophysics Data System (ADS)

Gan, Xiao; Albert, RéKa

Analyzing the long-term behaviors (attractors) of dynamic models of biological systems can provide valuable insight into biological phenotypes and their stability. We identified the long-term behaviors of a multi-level, 70-node discrete dynamic model of the stomatal opening process in plants. We reduce the model's huge state space by reducing unregulated nodes and simple mediator nodes, and by simplifying the regulatory functions of selected nodes while keeping the model consistent with experimental observations. We perform attractor analysis on the resulting 32-node reduced model by two methods: 1. converting it into a Boolean model, then applying two attractor-finding algorithms; 2. theoretical analysis of the regulatory functions. We conclude that all nodes except two in the reduced model have a single attractor; and only two nodes can admit oscillations. The multistability or oscillations do not affect the stomatal opening level in any situation. This conclusion applies to the original model as well in all the biologically meaningful cases. We further demonstrate the robustness of signal propagation by showing that a large percentage of single-node knockouts does not affect the stomatal opening level. Thus, we conclude that the complex structure of this signal transduction network provides multiple information propagation pathways while not allowing extensive multistability or oscillations, resulting in robust signal propagation. Our innovative combination of methods offers a promising way to analyze multi-level models.

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.

Ultra-stable long distance optical frequency distribution using the Internet fiber network.

PubMed

Lopez, Olivier; Haboucha, Adil; Chanteau, Bruno; Chardonnet, Christian; Amy-Klein, Anne; Santarelli, Giorgio

2012-10-08

We report an optical link of 540 km for ultrastable frequency distribution over the Internet fiber network. The stable frequency optical signal is processed enabling uninterrupted propagation on both directions. The robustness and the performance of the link are enhanced by a cost effective fully automated optoelectronic station. This device is able to coherently regenerate the return optical signal with a heterodyne optical phase locking of a low noise laser diode. Moreover the incoming signal polarization variation are tracked and processed in order to maintain beat note amplitudes within the operation range. Stable fibered optical interferometer enables optical detection of the link round trip phase signal. The phase-noise compensated link shows a fractional frequency instability in 10 Hz bandwidth of 5 × 10(-15) at one second measurement time and 2 × 10(-19) at 30,000 s. This work is a significant step towards a sustainable wide area ultrastable optical frequency distribution and comparison network.

Inertial processing of vestibulo-ocular signals

NASA Technical Reports Server (NTRS)

Hess, B. J.; Angelaki, D. E.

1999-01-01

New evidence for a central resolution of gravito-inertial signals has been recently obtained by analyzing the properties of the vestibulo-ocular reflex (VOR) in response to combined lateral translations and roll tilts of the head. It is found that the VOR generates robust compensatory horizontal eye movements independent of whether or not the interaural translatory acceleration component is canceled out by a gravitational acceleration component due to simultaneous roll-tilt. This response property of the VOR depends on functional semicircular canals, suggesting that the brain uses both otolith and semicircular canal signals to estimate head motion relative to inertial space. Vestibular information about dynamic head attitude relative to gravity is the basis for computing head (and body) angular velocity relative to inertial space. Available evidence suggests that the inertial vestibular system controls both head attitude and velocity with respect to a gravity-centered reference frame. The basic computational principles underlying the inertial processing of otolith and semicircular canal afferent signals are outlined.

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

DWT-Based High Capacity Audio Watermarking

NASA Astrophysics Data System (ADS)

Fallahpour, Mehdi; Megías, David

This letter suggests a novel high capacity robust audio watermarking algorithm by using the high frequency band of the wavelet decomposition, for which the human auditory system (HAS) is not very sensitive to alteration. The main idea is to divide the high frequency band into frames and then, for embedding, the wavelet samples are changed based on the average of the relevant frame. The experimental results show that the method has very high capacity (about 5.5kbps), without significant perceptual distortion (ODG in [-1, 0] and SNR about 33dB) and provides robustness against common audio signal processing such as added noise, filtering, echo and MPEG compression (MP3).

Discrete wavelet-aided delineation of PCG signal events via analysis of an area curve length-based decision statistic.

PubMed

Homaeinezhad, M R; Atyabi, S A; Daneshvar, E; Ghaffari, A; Tahmasebi, M

2010-12-01

The aim of this study is to describe a robust unified framework for segmentation of the phonocardiogram (PCG) signal sounds based on the false-alarm probability (FAP) bounded segmentation of a properly calculated detection measure. To this end, first the original PCG signal is appropriately pre-processed and then, a fixed sample size sliding window is moved on the pre-processed signal. In each slid, the area under the excerpted segment is multiplied by its curve-length to generate the Area Curve Length (ACL) metric to be used as the segmentation decision statistic (DS). Afterwards, histogram parameters of the nonlinearly enhanced DS metric are used for regulation of the α-level Neyman-Pearson classifier for FAP-bounded delineation of the PCG events. The proposed method was applied to all 85 records of Nursing Student Heart Sounds database (NSHSDB) including stenosis, insufficiency, regurgitation, gallop, septal defect, split sound, rumble, murmur, clicks, friction rub and snap disorders with different sampling frequencies. Also, the method was applied to the records obtained from an electronic stethoscope board designed for fulfillment of this study in the presence of high-level power-line noise and external disturbing sounds and as the results, no false positive (FP) or false negative (FN) errors were detected. High noise robustness, acceptable detection-segmentation accuracy of PCG events in various cardiac system conditions, and having no parameters dependency to the acquisition sampling frequency can be mentioned as the principal virtues and abilities of the proposed ACL-based PCG events detection-segmentation algorithm.

Signaling mechanisms underlying the robustness and tunability of the plant immune network

PubMed Central

Kim, Yungil; Tsuda, Kenichi; Igarashi, Daisuke; Hillmer, Rachel A.; Sakakibara, Hitoshi; Myers, Chad L.; Katagiri, Fumiaki

2014-01-01

Summary How does robust and tunable behavior emerge in a complex biological network? We sought to understand this for the signaling network controlling pattern-triggered immunity (PTI) in Arabidopsis. A dynamic network model containing four major signaling sectors, the jasmonate, ethylene, PAD4, and salicylate sectors, which together explain up to 80% of the PTI level, was built using data for dynamic sector activities and PTI levels under exhaustive combinatorial sector perturbations. Our regularized multiple regression model had a high level of predictive power and captured known and unexpected signal flows in the network. The sole inhibitory sector in the model, the ethylene sector, was central to the network robustness via its inhibition of the jasmonate sector. The model's multiple input sites linked specific signal input patterns varying in strength and timing to different network response patterns, indicating a mechanism enabling tunability. PMID:24439900

A method for detecting nonlinear determinism in normal and epileptic brain EEG signals.

PubMed

Meghdadi, Amir H; Fazel-Rezai, Reza; Aghakhani, Yahya

2007-01-01

A robust method of detecting determinism for short time series is proposed and applied to both healthy and epileptic EEG signals. The method provides a robust measure of determinism through characterizing the trajectories of the signal components which are obtained through singular value decomposition. Robustness of the method is shown by calculating proposed index of determinism at different levels of white and colored noise added to a simulated chaotic signal. The method is shown to be able to detect determinism at considerably high levels of additive noise. The method is then applied to both intracranial and scalp EEG recordings collected in different data sets for healthy and epileptic brain signals. The results show that for all of the studied EEG data sets there is enough evidence of determinism. The determinism is more significant for intracranial EEG recordings particularly during seizure activity.

Nonlinear microscopy of collagen fibers

NASA Astrophysics Data System (ADS)

Strupler, M.; Pena, A.-M.; Hernest, M.; Tharaux, P.-L.; Fabre, A.; Marchal-Somme, J.; Crestani, B.; Débarre, D.; Martin, J.-L.; Beaurepaire, E.; Schanne-Klein, M.-C.

2007-02-01

We used intrinsic Second Harmonic Generation (SHG) by fibrillar collagen to visualize the three-dimensional architecture of collagen fibrosis at the micrometer scale using laser scanning nonlinear microscopy. We showed that SHG signals are highly specific to fibrillar collagen and provide a sensitive probe of the micrometer-scale structural organization of collagen in tissues. Moreover, recording simultaneously other nonlinear optical signals in a multimodal setup, we visualized the tissue morphology using Two-Photon Excited Fluorescence (2PEF) signals from endogenous chromophores such as NADH or elastin. We then compared different methods to determine accurate indexes of collagen fibrosis using nonlinear microscopy, given that most collagen fibrils are smaller than the microscope resolution and that second harmonic generation is a coherent process. In order to define a robust method to process our three-dimensional images, we either calculated the fraction of the images occupied by a significant SHG signal, or averaged SHG signal intensities. We showed that these scores provide an estimation of the extension of renal and pulmonary fibrosis in murine models, and that they clearly sort out the fibrotic mice.

Statistical Techniques for Signal Processing

DTIC Science & Technology

1993-01-12

functions and extended influence functions of the associated underlying estimators. An interesting application of the influence function and its...and related filter smtctures. While the influence function is best known for its role in characterizing the robustness of estimators. the mathematical...statistics can be designed and analyzed for performance using the influence function as a tool. In particular, we have examined the mean-median

Design and Training of Limited-Interconnect Architectures

DTIC Science & Technology

1991-07-16

and signal processing. Neuromorphic (brain like) models, allow an alternative for achieving real-time operation tor such tasks, while having a...compact and robust architecture. Neuromorphic models consist of interconnections of simple computational nodes. In this approach, each node computes a...operational performance. I1. Research Objectives The research objectives were: 1. Development of on- chip local training rules specifically designed for

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation.

PubMed

Segreto, Tiziana; Caggiano, Alessandra; Karam, Sara; Teti, Roberto

2017-12-12

Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation

PubMed Central

Segreto, Tiziana; Karam, Sara; Teti, Roberto

2017-01-01

Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions. PMID:29231864

Design of an embedded inverse-feedforward biomolecular tracking controller for enzymatic reaction processes.

PubMed

Foo, Mathias; Kim, Jongrae; Sawlekar, Rucha; Bates, Declan G

2017-04-06

Feedback control is widely used in chemical engineering to improve the performance and robustness of chemical processes. Feedback controllers require a 'subtractor' that is able to compute the error between the process output and the reference signal. In the case of embedded biomolecular control circuits, subtractors designed using standard chemical reaction network theory can only realise one-sided subtraction, rendering standard controller design approaches inadequate. Here, we show how a biomolecular controller that allows tracking of required changes in the outputs of enzymatic reaction processes can be designed and implemented within the framework of chemical reaction network theory. The controller architecture employs an inversion-based feedforward controller that compensates for the limitations of the one-sided subtractor that generates the error signals for a feedback controller. The proposed approach requires significantly fewer chemical reactions to implement than alternative designs, and should have wide applicability throughout the fields of synthetic biology and biological engineering.

Using Design Capability Indices to Satisfy Ranged Sets of Design Requirements

NASA Technical Reports Server (NTRS)

Chen, Wei; Allen, Janet K.; Simpson, Timothy W.; Mistree, Farrokh

1996-01-01

For robust design it is desirable to allow the design requirements to vary within a certain range rather than setting point targets. This is particularly important during the early stages of design when little is known about the system and its requirements. Toward this end, design capability indices are developed in this paper to assess the capability of a family of designs, represented by a range of top-level design specifications, to satisfy a ranged set of design requirements. Design capability indices are based on process capability indices from statistical process control and provide a single objective, alternate approach to the use of Taguchi's signal-to- noise ratio which is often used for robust design. Successful implementation of design capability indices ensures that a family of designs conforms to a given ranged set of design requirements. To demonstrate an application and the usefulness of design capability indices, the design of a solar powered irrigation system is presented. Our focus in this paper is on the development and implementation of design capability indices as an alternate approach to the use of the signal-to-noise ratio and not on the results of the example problem, per se.

Interferometric side scan sonar and data fusion

NASA Astrophysics Data System (ADS)

Sintes, Christophe R.; Solaiman, Basel

2000-04-01

This paper concerns the possibilities of sea bottom imaging and altitude determining of each imaged point. The performances of new side scan sonars which are able to image the sea bottom with a high definition and are able to evaluate the relief with the same definition derive from an interferometric multisensor system. The drawbacks concern the precision of the numerical altitude model. One way to improve the measurements precision is to merge all the information issued from the multi-sensors system. This leads to increase the Signal to Noise Ratio (SNR) and the robustness of the used method. The aim of this paper is to clearly demonstrate the ability to derive benefits of all information issued from the three arrays side scan sonar by merging: (1) the three phase signals obtained at the output of the sensors, (2) this same set of data after the application of different processing methods, and (3) the a priori relief contextual information. The key idea the proposed fusion technique is to exploit the strength and the weaknesses of each data element in the fusion of process so that the global SNR will be improved as well as the robustness to hostile noisy environments.

Spatio-temporal diffusion of dynamic PET images

NASA Astrophysics Data System (ADS)

Tauber, C.; Stute, S.; Chau, M.; Spiteri, P.; Chalon, S.; Guilloteau, D.; Buvat, I.

2011-10-01

Positron emission tomography (PET) images are corrupted by noise. This is especially true in dynamic PET imaging where short frames are required to capture the peak of activity concentration after the radiotracer injection. High noise results in a possible bias in quantification, as the compartmental models used to estimate the kinetic parameters are sensitive to noise. This paper describes a new post-reconstruction filter to increase the signal-to-noise ratio in dynamic PET imaging. It consists in a spatio-temporal robust diffusion of the 4D image based on the time activity curve (TAC) in each voxel. It reduces the noise in homogeneous areas while preserving the distinct kinetics in regions of interest corresponding to different underlying physiological processes. Neither anatomical priors nor the kinetic model are required. We propose an automatic selection of the scale parameter involved in the diffusion process based on a robust statistical analysis of the distances between TACs. The method is evaluated using Monte Carlo simulations of brain activity distributions. We demonstrate the usefulness of the method and its superior performance over two other post-reconstruction spatial and temporal filters. Our simulations suggest that the proposed method can be used to significantly increase the signal-to-noise ratio in dynamic PET imaging.

Taguchi experimental design to determine the taste quality characteristic of candied carrot

NASA Astrophysics Data System (ADS)

Ekawati, Y.; Hapsari, A. A.

2018-03-01

Robust parameter design is used to design product that is robust to noise factors so the product’s performance fits the target and delivers a better quality. In the process of designing and developing the innovative product of candied carrot, robust parameter design is carried out using Taguchi Method. The method is used to determine an optimal quality design. The optimal quality design is based on the process and the composition of product ingredients that are in accordance with consumer needs and requirements. According to the identification of consumer needs from the previous research, quality dimensions that need to be assessed are the taste and texture of the product. The quality dimension assessed in this research is limited to the taste dimension. Organoleptic testing is used for this assessment, specifically hedonic testing that makes assessment based on consumer preferences. The data processing uses mean and signal to noise ratio calculation and optimal level setting to determine the optimal process/composition of product ingredients. The optimal value is analyzed using confirmation experiments to prove that proposed product match consumer needs and requirements. The result of this research is identification of factors that affect the product taste and the optimal quality of product according to Taguchi Method.

Robust QRS detection for HRV estimation from compressively sensed ECG measurements for remote health-monitoring systems.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2018-03-15

To present a new compressive sensing (CS)-based method for the acquisition of ECG signals and for robust estimation of heart-rate variability (HRV) parameters from compressively sensed measurements with high compression ratio. CS is used in the biosensor to compress the ECG signal. Estimation of the locations of QRS segments is carried out by applying two algorithms on the compressed measurements. The first algorithm reconstructs the ECG signal by enforcing a block-sparse structure on the first-order difference of the signal, so the transient QRS segments are significantly emphasized on the first-order difference of the signal. Multiple block-divisions of the signals are carried out with various block lengths, and multiple reconstructed signals are combined to enhance the robustness of the localization of the QRS segments. The second algorithm removes errors in the locations of QRS segments by applying low-pass filtering and morphological operations. The proposed CS-based method is found to be effective for the reconstruction of ECG signals by enforcing transient QRS structures on the first-order difference of the signal. It is demonstrated to be robust not only to high compression ratio but also to various artefacts present in ECG signals acquired by using on-body wireless sensors. HRV parameters computed by using the QRS locations estimated from the signals reconstructed with a compression ratio as high as 90% are comparable with that computed by using QRS locations estimated by using the Pan-Tompkins algorithm. The proposed method is useful for the realization of long-term HRV monitoring systems by using CS-based low-power wireless on-body biosensors.

A Robust Image Watermarking in the Joint Time-Frequency Domain

NASA Astrophysics Data System (ADS)

Öztürk, Mahmut; Akan, Aydın; Çekiç, Yalçın

2010-12-01

With the rapid development of computers and internet applications, copyright protection of multimedia data has become an important problem. Watermarking techniques are proposed as a solution to copyright protection of digital media files. In this paper, a new, robust, and high-capacity watermarking method that is based on spatiofrequency (SF) representation is presented. We use the discrete evolutionary transform (DET) calculated by the Gabor expansion to represent an image in the joint SF domain. The watermark is embedded onto selected coefficients in the joint SF domain. Hence, by combining the advantages of spatial and spectral domain watermarking methods, a robust, invisible, secure, and high-capacity watermarking method is presented. A correlation-based detector is also proposed to detect and extract any possible watermarks on an image. The proposed watermarking method was tested on some commonly used test images under different signal processing attacks like additive noise, Wiener and Median filtering, JPEG compression, rotation, and cropping. Simulation results show that our method is robust against all of the attacks.

Signal processing for order 10 PM accuracy displacement metrology in real-world scientific applications

NASA Astrophysics Data System (ADS)

Halverson, Peter G.; Loya, Frank M.

2017-11-01

Projects such as the Space Interferometry Mission (SIM) [1] and Terrestrial Planet Finder (TPF) [2] rely heavily on sub-nanometer accuracy metrology systems to define their optical paths and geometries. The James Web Space Telescope (JWST) is using this metrology in a cryogenic dilatometer for characterizing material properties (thermal expansion, creep) of optical materials. For all these projects, a key issue has been the reliability and stability of the electronics that convert displacement metrology signals into real-time distance determinations. A particular concern is the behavior of the electronics in situations where laser heterodyne signals are weak or noisy and subject to abrupt Doppler shifts due to vibrations or the slewing of motorized optics. A second concern is the long-term (hours to days) stability of the distance measurements under conditions of drifting laser power and ambient temperature. This paper describes heterodyne displacement metrology gauge signal processing methods that achieve satisfactory robustness against low signal strength and spurious signals, and good long-term stability. We have a proven displacement-measuring approach that is useful not only to space-optical projects at JPL, but also to the wider field of distance measurements.

Multi-step EMG Classification Algorithm for Human-Computer Interaction

NASA Astrophysics Data System (ADS)

Ren, Peng; Barreto, Armando; Adjouadi, Malek

A three-electrode human-computer interaction system, based on digital processing of the Electromyogram (EMG) signal, is presented. This system can effectively help disabled individuals paralyzed from the neck down to interact with computers or communicate with people through computers using point-and-click graphic interfaces. The three electrodes are placed on the right frontalis, the left temporalis and the right temporalis muscles in the head, respectively. The signal processing algorithm used translates the EMG signals during five kinds of facial movements (left jaw clenching, right jaw clenching, eyebrows up, eyebrows down, simultaneous left & right jaw clenching) into five corresponding types of cursor movements (left, right, up, down and left-click), to provide basic mouse control. The classification strategy is based on three principles: the EMG energy of one channel is typically larger than the others during one specific muscle contraction; the spectral characteristics of the EMG signals produced by the frontalis and temporalis muscles during different movements are different; the EMG signals from adjacent channels typically have correlated energy profiles. The algorithm is evaluated on 20 pre-recorded EMG signal sets, using Matlab simulations. The results show that this method provides improvements and is more robust than other previous approaches.

Blind adaptive equalization of polarization-switched QPSK modulation.

PubMed

Millar, David S; Savory, Seb J

2011-04-25

Coherent detection in combination with digital signal processing has recently enabled significant progress in the capacity of optical communications systems. This improvement has enabled detection of optimum constellations for optical signals in four dimensions. In this paper, we propose and investigate an algorithm for the blind adaptive equalization of one such modulation format: polarization-switched quaternary phase shift keying (PS-QPSK). The proposed algorithm, which includes both blind initialization and adaptation of the equalizer, is found to be insensitive to the input polarization state and demonstrates highly robust convergence in the presence of PDL, DGD and polarization rotation.

A VHDL Core for Intrinsic Evolution of Discrete Time Filters with Signal Feedback

NASA Technical Reports Server (NTRS)

Gwaltney, David A.; Dutton, Kenneth

2005-01-01

The design of an Evolvable Machine VHDL Core is presented, representing a discrete-time processing structure capable of supporting control system applications. This VHDL Core is implemented in an FPGA and is interfaced with an evolutionary algorithm implemented in firmware on a Digital Signal Processor (DSP) to create an evolvable system platform. The salient features of this architecture are presented. The capability to implement IIR filter structures is presented along with the results of the intrinsic evolution of a filter. The robustness of the evolved filter design is tested and its unique characteristics are described.

Molecular mechanisms governing differential robustness of development and environmental responses in plants

PubMed Central

Lachowiec, Jennifer; Queitsch, Christine; Kliebenstein, Daniel J.

2016-01-01

Background Robustness to genetic and environmental perturbation is a salient feature of multicellular organisms. Loss of developmental robustness can lead to severe phenotypic defects and fitness loss. However, perfect robustness, i.e. no variation at all, is evolutionarily unfit as organisms must be able to change phenotype to properly respond to changing environments and biotic challenges. Plasticity is the ability to adjust phenotypes predictably in response to specific environmental stimuli, which can be considered a transient shift allowing an organism to move from one robust phenotypic state to another. Plants, as sessile organisms that undergo continuous development, are particularly dependent on an exquisite fine-tuning of the processes that balance robustness and plasticity to maximize fitness. Scope and Conclusions This paper reviews recently identified mechanisms, both systems-level and molecular, that modulate robustness, and discusses their implications for the optimization of plant fitness. Robustness in living systems arises from the structure of genetic networks, the specific molecular functions of the underlying genes, and their interactions. This very same network responsible for the robustness of specific developmental states also has to be built such that it enables plastic yet robust shifts in response to environmental changes. In plants, the interactions and functions of signal transduction pathways activated by phytohormones and the tendency for plants to tolerate whole-genome duplications, tandem gene duplication and hybridization are emerging as major regulators of robustness in development. Despite their obvious implications for plant evolution and plant breeding, the mechanistic underpinnings by which plants modulate precise levels of robustness, plasticity and evolvability in networks controlling different phenotypes are under-studied. PMID:26473020

A dynamic multi-channel speech enhancement system for distributed microphones in a car environment

NASA Astrophysics Data System (ADS)

Matheja, Timo; Buck, Markus; Fingscheidt, Tim

2013-12-01

Supporting multiple active speakers in automotive hands-free or speech dialog applications is an interesting issue not least due to comfort reasons. Therefore, a multi-channel system for enhancement of speech signals captured by distributed distant microphones in a car environment is presented. Each of the potential speakers in the car has a dedicated directional microphone close to his position that captures the corresponding speech signal. The aim of the resulting overall system is twofold: On the one hand, a combination of an arbitrary pre-defined subset of speakers' signals can be performed, e.g., to create an output signal in a hands-free telephone conference call for a far-end communication partner. On the other hand, annoying cross-talk components from interfering sound sources occurring in multiple different mixed output signals are to be eliminated, motivated by the possibility of other hands-free applications being active in parallel. The system includes several signal processing stages. A dedicated signal processing block for interfering speaker cancellation attenuates the cross-talk components of undesired speech. Further signal enhancement comprises the reduction of residual cross-talk and background noise. Subsequently, a dynamic signal combination stage merges the processed single-microphone signals to obtain appropriate mixed signals at the system output that may be passed to applications such as telephony or a speech dialog system. Based on signal power ratios between the particular microphone signals, an appropriate speaker activity detection and therewith a robust control mechanism of the whole system is presented. The proposed system may be dynamically configured and has been evaluated for a car setup with four speakers sitting in the car cabin disturbed in various noise conditions.

Self-organized synchronization of digital phase-locked loops with delayed coupling in theory and experiment

PubMed Central

Wetzel, Lucas; Jörg, David J.; Pollakis, Alexandros; Rave, Wolfgang; Fettweis, Gerhard; Jülicher, Frank

2017-01-01

Self-organized synchronization occurs in a variety of natural and technical systems but has so far only attracted limited attention as an engineering principle. In distributed electronic systems, such as antenna arrays and multi-core processors, a common time reference is key to coordinate signal transmission and processing. Here we show how the self-organized synchronization of mutually coupled digital phase-locked loops (DPLLs) can provide robust clocking in large-scale systems. We develop a nonlinear phase description of individual and coupled DPLLs that takes into account filter impulse responses and delayed signal transmission. Our phase model permits analytical expressions for the collective frequencies of synchronized states, the analysis of stability properties and the time scale of synchronization. In particular, we find that signal filtering introduces stability transitions that are not found in systems without filtering. To test our theoretical predictions, we designed and carried out experiments using networks of off-the-shelf DPLL integrated circuitry. We show that the phase model can quantitatively predict the existence, frequency, and stability of synchronized states. Our results demonstrate that mutually delay-coupled DPLLs can provide robust and self-organized synchronous clocking in electronic systems. PMID:28207779

The pulse-pair algorithm as a robust estimator of turbulent weather spectral parameters using airborne pulse Doppler radar

NASA Technical Reports Server (NTRS)

Baxa, Ernest G., Jr.; Lee, Jonggil

1991-01-01

The pulse pair method for spectrum parameter estimation is commonly used in pulse Doppler weather radar signal processing since it is economical to implement and can be shown to be a maximum likelihood estimator. With the use of airborne weather radar for windshear detection, the turbulent weather and strong ground clutter return spectrum differs from that assumed in its derivation, so the performance robustness of the pulse pair technique must be understood. Here, the effect of radar system pulse to pulse phase jitter and signal spectrum skew on the pulse pair algorithm performance is discussed. Phase jitter effect may be significant when the weather return signal to clutter ratio is very low and clutter rejection filtering is attempted. The analysis can be used to develop design specifications for airborne radar system phase stability. It is also shown that the weather return spectrum skew can cause a significant bias in the pulse pair mean windspeed estimates, and that the poly pulse pair algorithm can reduce this bias. It is suggested that use of a spectrum mode estimator may be more appropriate in characterizing the windspeed within a radar range resolution cell for detection of hazardous windspeed gradients.

Aeroservoelastic Model Validation and Test Data Analysis of the F/A-18 Active Aeroelastic Wing

NASA Technical Reports Server (NTRS)

Brenner, Martin J.; Prazenica, Richard J.

2003-01-01

Model validation and flight test data analysis require careful consideration of the effects of uncertainty, noise, and nonlinearity. Uncertainty prevails in the data analysis techniques and results in a composite model uncertainty from unmodeled dynamics, assumptions and mechanics of the estimation procedures, noise, and nonlinearity. A fundamental requirement for reliable and robust model development is an attempt to account for each of these sources of error, in particular, for model validation, robust stability prediction, and flight control system development. This paper is concerned with data processing procedures for uncertainty reduction in model validation for stability estimation and nonlinear identification. F/A-18 Active Aeroelastic Wing (AAW) aircraft data is used to demonstrate signal representation effects on uncertain model development, stability estimation, and nonlinear identification. Data is decomposed using adaptive orthonormal best-basis and wavelet-basis signal decompositions for signal denoising into linear and nonlinear identification algorithms. Nonlinear identification from a wavelet-based Volterra kernel procedure is used to extract nonlinear dynamics from aeroelastic responses, and to assist model development and uncertainty reduction for model validation and stability prediction by removing a class of nonlinearity from the uncertainty.

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning

PubMed Central

Yang, Zhutian; Qiu, Wei; Sun, Hongjian; Nallanathan, Arumugam

2016-01-01

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches. PMID:26927111

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning.

PubMed

Yang, Zhutian; Qiu, Wei; Sun, Hongjian; Nallanathan, Arumugam

2016-02-25

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches.

A comparative robustness evaluation of feedforward neurofilters

NASA Technical Reports Server (NTRS)

Troudet, Terry; Merrill, Walter

1993-01-01

A comparative performance and robustness analysis is provided for feedforward neurofilters trained with back propagation to filter additive white noise. The signals used in this analysis are simulated pitch rate responses to typical pilot command inputs for a modern fighter aircraft model. Various configurations of nonlinear and linear neurofilters are trained to estimate exact signal values from input sequences of noisy sampled signal values. In this application, nonlinear neurofiltering is found to be more efficient than linear neurofiltering in removing the noise from responses of the nominal vehicle model, whereas linear neurofiltering is found to be more robust in the presence of changes in the vehicle dynamics. The possibility of enhancing neurofiltering through hybrid architectures based on linear and nonlinear neuroprocessing is therefore suggested as a way of taking advantage of the robustness of linear neurofiltering, while maintaining the nominal performance advantage of nonlinear neurofiltering.

A Robust High-Performance GPS L1 Receiver with Single-stage Quadrature Redio-Frequency Circuit

NASA Astrophysics Data System (ADS)

Liu, Jianghua; Xu, Weilin; Wan, Qinq; Liu, Tianci

2018-03-01

A low power current reuse single-stage quadrature raido-frequency part (SQRF) is proposed for GPS L1 receiver in 180nm CMOS process. The proposed circuit consists of LNA, Mixer, QVCO, is called the QLMV cell. A two blocks stacked topology is adopted in this design. The parallel QVCO and mixer placed on the top forms the upper stacked block, and the LNA placed on the bottom forms the other stacked block. The two blocks share the current and achieve low power performance. To improve the stability, a float current source is proposed. The float current isolated the local oscillation signal and the input RF signal, which bring the whole circuit robust high-performance. The result shows conversion gain is 34 dB, noise figure is three dB, the phase noise is -110 dBc/Hz at 1MHz and IIP3 is -20 dBm. The proposed circuit dissipated 1.7mW with 1 V supply voltage.

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.

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.

N-Channel field-effect transistors with floating gates for extracellular recordings.

PubMed

Meyburg, Sven; Goryll, Michael; Moers, Jürgen; Ingebrandt, Sven; Böcker-Meffert, Simone; Lüth, Hans; Offenhäusser, Andreas

2006-01-15

A field-effect transistor (FET) for recording extracellular signals from electrogenic cells is presented. The so-called floating gate architecture combines a complementary metal oxide semiconductor (CMOS)-type n-channel transistor with an independent sensing area. This concept allows the transistor and sensing area to be optimised separately. The devices are robust and can be reused several times. The noise level of the devices was smaller than of comparable non-metallised gate FETs. In addition to the usual drift of FET devices, we observed a long-term drift that has to be controlled for future long-term measurements. The device performance for extracellular signal recording was tested using embryonic rat cardiac myocytes cultured on fibronectin-coated chips. The extracellular cell signals were recorded before and after the addition of the cardioactive isoproterenol. The signal shapes of the measured action potentials were comparable to the non-metallised gate FETs previously used in similar experiments. The fabrication of the devices involved the process steps of standard CMOS that were necessary to create n-channel transistors. The implementation of a complete CMOS process would facilitate the integration of the logical circuits necessary for signal pre-processing on a chip, which is a prerequisite for a greater number of sensor spots in future layouts.

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.

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.

Vestibular signals in primate cortex for self-motion perception.

PubMed

Gu, Yong

2018-04-21

The vestibular peripheral organs in our inner ears detect transient motion of the head in everyday life. This information is sent to the central nervous system for automatic processes such as vestibulo-ocular reflexes, balance and postural control, and higher cognitive functions including perception of self-motion and spatial orientation. Recent neurophysiological studies have discovered a prominent vestibular network in the primate cerebral cortex. Many of the areas involved are multisensory: their neurons are modulated by both vestibular signals and visual optic flow, potentially facilitating more robust heading estimation through cue integration. Combining psychophysics, computation, physiological recording and causal manipulation techniques, recent work has addressed both the encoding and decoding of vestibular signals for self-motion perception. Copyright © 2018. Published by Elsevier Ltd.

Chemical genetics and regeneration.

PubMed

Sengupta, Sumitra; Zhang, Liyun; Mumm, Jeff S

2015-01-01

Regeneration involves interactions between multiple signaling pathways acting in a spatially and temporally complex manner. As signaling pathways are highly conserved, understanding how regeneration is controlled in animal models exhibiting robust regenerative capacities should aid efforts to stimulate repair in humans. One way to discover molecular regulators of regeneration is to alter gene/protein function and quantify effect(s) on the regenerative process: dedifferentiation/reprograming, stem/progenitor proliferation, migration/remodeling, progenitor cell differentiation and resolution. A powerful approach for applying this strategy to regenerative biology is chemical genetics, the use of small-molecule modulators of specific targets or signaling pathways. Here, we review advances that have been made using chemical genetics for hypothesis-focused and discovery-driven studies aimed at furthering understanding of how regeneration is controlled.

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

PubMed Central

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Oliva, Aude

2017-01-01

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100 ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250 ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how spatial layout representations emerge in the human brain. PMID:27039703

Optical temperature compensation schemes of spectral modulation sensors for aircraft engine control

NASA Astrophysics Data System (ADS)

Berkcan, Ertugrul

1993-02-01

Optical temperature compensation schemes for the ratiometric interrogation of spectral modulation sensors for source temperature robustness are presented. We have obtained better than 50 - 100X decrease of the temperature coefficient of the sensitivity using these types of compensation. We have also developed a spectrographic interrogation scheme that provides increased source temperature robustness; this affords a significantly improved accuracy over FADEC temperature ranges as well as temperature coefficient of the sensitivity that is substantially and further reduced. This latter compensation scheme can be integrated in a small E/O package including the detection, analog and digital signal processing. We find that these interrogation schemes can be used within a detector spatially multiplexed architecture.

Towards a Video Passive Content Fingerprinting Method for Partial-Copy Detection Robust against Non-Simulated Attacks

PubMed Central

2016-01-01

Passive content fingerprinting is widely used for video content identification and monitoring. However, many challenges remain unsolved especially for partial-copies detection. The main challenge is to find the right balance between the computational cost of fingerprint extraction and fingerprint dimension, without compromising detection performance against various attacks (robustness). Fast video detection performance is desirable in several modern applications, for instance, in those where video detection involves the use of large video databases or in applications requiring real-time video detection of partial copies, a process whose difficulty increases when videos suffer severe transformations. In this context, conventional fingerprinting methods are not fully suitable to cope with the attacks and transformations mentioned before, either because the robustness of these methods is not enough or because their execution time is very high, where the time bottleneck is commonly found in the fingerprint extraction and matching operations. Motivated by these issues, in this work we propose a content fingerprinting method based on the extraction of a set of independent binary global and local fingerprints. Although these features are robust against common video transformations, their combination is more discriminant against severe video transformations such as signal processing attacks, geometric transformations and temporal and spatial desynchronization. Additionally, we use an efficient multilevel filtering system accelerating the processes of fingerprint extraction and matching. This multilevel filtering system helps to rapidly identify potential similar video copies upon which the fingerprint process is carried out only, thus saving computational time. We tested with datasets of real copied videos, and the results show how our method outperforms state-of-the-art methods regarding detection scores. Furthermore, the granularity of our method makes it suitable for partial-copy detection; that is, by processing only short segments of 1 second length. PMID:27861492

Self-tuning bistable parametric feedback oscillator: Near-optimal amplitude maximization without model information

NASA Astrophysics Data System (ADS)

Braun, David J.; Sutas, Andrius; Vijayakumar, Sethu

2017-01-01

Theory predicts that parametrically excited oscillators, tuned to operate under resonant condition, are capable of large-amplitude oscillation useful in diverse applications, such as signal amplification, communication, and analog computation. However, due to amplitude saturation caused by nonlinearity, lack of robustness to model uncertainty, and limited sensitivity to parameter modulation, these oscillators require fine-tuning and strong modulation to generate robust large-amplitude oscillation. Here we present a principle of self-tuning parametric feedback excitation that alleviates the above-mentioned limitations. This is achieved using a minimalistic control implementation that performs (i) self-tuning (slow parameter adaptation) and (ii) feedback pumping (fast parameter modulation), without sophisticated signal processing past observations. The proposed approach provides near-optimal amplitude maximization without requiring model-based control computation, previously perceived inevitable to implement optimal control principles in practical application. Experimental implementation of the theory shows that the oscillator self-tunes itself near to the onset of dynamic bifurcation to achieve extreme sensitivity to small resonant parametric perturbations. As a result, it achieves large-amplitude oscillations by capitalizing on the effect of nonlinearity, despite substantial model uncertainties and strong unforeseen external perturbations. We envision the present finding to provide an effective and robust approach to parametric excitation when it comes to real-world application.

Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

PubMed Central

Sivaraks, Haemwaan

2015-01-01

Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods. PMID:25688284

A Spatio-Temporal Model of Notch Signalling in the Zebrafish Segmentation Clock: Conditions for Synchronised Oscillatory Dynamics

PubMed Central

Terry, Alan J.; Sturrock, Marc; Dale, J. Kim; Maroto, Miguel; Chaplain, Mark A. J.

2011-01-01

In the vertebrate embryo, tissue blocks called somites are laid down in head-to-tail succession, a process known as somitogenesis. Research into somitogenesis has been both experimental and mathematical. For zebrafish, there is experimental evidence for oscillatory gene expression in cells in the presomitic mesoderm (PSM) as well as evidence that Notch signalling synchronises the oscillations in neighbouring PSM cells. A biological mechanism has previously been proposed to explain these phenomena. Here we have converted this mechanism into a mathematical model of partial differential equations in which the nuclear and cytoplasmic diffusion of protein and mRNA molecules is explictly considered. By performing simulations, we have found ranges of values for the model parameters (such as diffusion and degradation rates) that yield oscillatory dynamics within PSM cells and that enable Notch signalling to synchronise the oscillations in two touching cells. Our model contains a Hill coefficient that measures the co-operativity between two proteins (Her1, Her7) and three genes (her1, her7, deltaC) which they inhibit. This coefficient appears to be bounded below by the requirement for oscillations in individual cells and bounded above by the requirement for synchronisation. Consistent with experimental data and a previous spatially non-explicit mathematical model, we have found that signalling can increase the average level of Her1 protein. Biological pattern formation would be impossible without a certain robustness to variety in cell shape and size; our results possess such robustness. Our spatially-explicit modelling approach, together with new imaging technologies that can measure intracellular protein diffusion rates, is likely to yield significant new insight into somitogenesis and other biological processes. PMID:21386903

A spatio-temporal model of Notch signalling in the zebrafish segmentation clock: conditions for synchronised oscillatory dynamics.

PubMed

Terry, Alan J; Sturrock, Marc; Dale, J Kim; Maroto, Miguel; Chaplain, Mark A J

2011-02-28

In the vertebrate embryo, tissue blocks called somites are laid down in head-to-tail succession, a process known as somitogenesis. Research into somitogenesis has been both experimental and mathematical. For zebrafish, there is experimental evidence for oscillatory gene expression in cells in the presomitic mesoderm (PSM) as well as evidence that Notch signalling synchronises the oscillations in neighbouring PSM cells. A biological mechanism has previously been proposed to explain these phenomena. Here we have converted this mechanism into a mathematical model of partial differential equations in which the nuclear and cytoplasmic diffusion of protein and mRNA molecules is explicitly considered. By performing simulations, we have found ranges of values for the model parameters (such as diffusion and degradation rates) that yield oscillatory dynamics within PSM cells and that enable Notch signalling to synchronise the oscillations in two touching cells. Our model contains a Hill coefficient that measures the co-operativity between two proteins (Her1, Her7) and three genes (her1, her7, deltaC) which they inhibit. This coefficient appears to be bounded below by the requirement for oscillations in individual cells and bounded above by the requirement for synchronisation. Consistent with experimental data and a previous spatially non-explicit mathematical model, we have found that signalling can increase the average level of Her1 protein. Biological pattern formation would be impossible without a certain robustness to variety in cell shape and size; our results possess such robustness. Our spatially-explicit modelling approach, together with new imaging technologies that can measure intracellular protein diffusion rates, is likely to yield significant new insight into somitogenesis and other biological processes.

All solid state mid-infrared dual-comb spectroscopy platform based on QCL technology

NASA Astrophysics Data System (ADS)

Hugi, Andreas; Geiser, Markus; Villares, Gustavo; Cappelli, Francesco; Blaser, Stephane; Faist, Jérôme

2015-01-01

We develop a spectroscopy platform for industrial applications based on semiconductor quantum cascade laser (QCL) frequency combs. The platform's key features will be an unmatched combination of bandwidth of 100 cm-1, resolution of 100 kHz, speed of ten to hundreds of μs as well as size and robustness, opening doors to beforehand unreachable markets. The sensor can be built extremely compact and robust since the laser source is an all-electrically pumped semiconductor optical frequency comb and no mechanical elements are required. However, the parallel acquisition of dual-comb spectrometers comes at the price of enormous data-rates. For system scalability, robustness and optical simplicity we use free-running QCL combs. Therefore no complicated optical locking mechanisms are required. To reach high signal-to-noise ratios, we develop an algorithm, which is based on combination of coherent and non-coherent averaging. This algorithm is specifically optimized for free-running and small footprint, therefore high-repetition rate, comb sources. As a consequence, our system generates data-rates of up to 3.2 GB/sec. These data-rates need to be reduced by several orders of magnitude in real-time in order to be useful for spectral fitting algorithms. We present the development of a data-treatment solution, which reaches a single-channel throughput of 22% using a standard laptop-computer. Using a state-of-the art desktop computer, the throughput is increased to 43%. This is combined with a data-acquisition board to a stand-alone data processing unit, allowing real-time industrial process observation and continuous averaging to achieve highest signal fidelity.

Separable spectro-temporal Gabor filter bank features: Reducing the complexity of robust features for automatic speech recognition.

PubMed

Schädler, Marc René; Kollmeier, Birger

2015-04-01

To test if simultaneous spectral and temporal processing is required to extract robust features for automatic speech recognition (ASR), the robust spectro-temporal two-dimensional-Gabor filter bank (GBFB) front-end from Schädler, Meyer, and Kollmeier [J. Acoust. Soc. Am. 131, 4134-4151 (2012)] was de-composed into a spectral one-dimensional-Gabor filter bank and a temporal one-dimensional-Gabor filter bank. A feature set that is extracted with these separate spectral and temporal modulation filter banks was introduced, the separate Gabor filter bank (SGBFB) features, and evaluated on the CHiME (Computational Hearing in Multisource Environments) keywords-in-noise recognition task. From the perspective of robust ASR, the results showed that spectral and temporal processing can be performed independently and are not required to interact with each other. Using SGBFB features permitted the signal-to-noise ratio (SNR) to be lowered by 1.2 dB while still performing as well as the GBFB-based reference system, which corresponds to a relative improvement of the word error rate by 12.8%. Additionally, the real time factor of the spectro-temporal processing could be reduced by more than an order of magnitude. Compared to human listeners, the SNR needed to be 13 dB higher when using Mel-frequency cepstral coefficient features, 11 dB higher when using GBFB features, and 9 dB higher when using SGBFB features to achieve the same recognition performance.

Method for siting detectors within a facility

DOEpatents

Gleason, Nathaniel Jeremy Meyer

2007-12-11

A method, system and article of manufacture of siting one or more detectors in a facility represented with zones are provided. Signals S.sub.i,j representing an effect in zone j in response to a release of contaminant in zone i for one or more flow conditions are provided. A candidate architecture has one or more candidate zones. A limiting case signal is determined for each flow condition for multiple candidate architectures. The limiting case signal is a smallest system signal of multiple system signals associated with a release in a zone. Each system signal is a maximum one of the signals representing the effect in the candidate zones from the release in one zone for the flow condition. For each candidate architecture, a robust limiting case signal is determined based on a minimum of the limiting case signals. One candidate architecture is selected based on the robust limiting case signals.

Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system

NASA Astrophysics Data System (ADS)

Gadsden, S. Andrew; Kirubarajan, T.

2017-05-01

Signal processing techniques are prevalent in a wide range of fields: control, target tracking, telecommunications, robotics, fault detection and diagnosis, and even stock market analysis, to name a few. Although first introduced in the 1950s, the most popular method used for signal processing and state estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties of the SVSF are explored in an effort to detect and diagnosis faults in an electromechanical system. The results are compared with the KF method, and future work is discussed.

Coherent and Noncoherent Joint Processing of Sonar for Detection of Small Targets in Shallow Water.

PubMed

Pan, Xiang; Jiang, Jingning; Li, Si; Ding, Zhenping; Pan, Chen; Gong, Xianyi

2018-04-10

A coherent-noncoherent joint processing framework is proposed for active sonar to combine diversity gain and beamforming gain for detection of a small target in shallow water environments. Sonar utilizes widely-spaced arrays to sense environments and illuminate a target of interest from multiple angles. Meanwhile, it exploits spatial diversity for time-reversal focusing to suppress reverberation, mainly strong bottom reverberation. For enhancement of robustness of time-reversal focusing, an adaptive iterative strategy is utilized in the processing framework. A probing signal is firstly transmitted and echoes of a likely target are utilized as steering vectors for the second transmission. With spatial diversity, target bearing and range are estimated using a broadband signal model. Numerical simulations show that the novel sonar outperforms the traditional phased-array sonar due to benefits of spatial diversity. The effectiveness of the proposed framework has been validated by localization of a small target in at-lake experiments.

Rapid neural discrimination of communicative gestures

PubMed Central

Carlson, Thomas A.

2015-01-01

Humans are biased toward social interaction. Behaviorally, this bias is evident in the rapid effects that self-relevant communicative signals have on attention and perceptual systems. The processing of communicative cues recruits a wide network of brain regions, including mentalizing systems. Relatively less work, however, has examined the timing of the processing of self-relevant communicative cues. In the present study, we used multivariate pattern analysis (decoding) approach to the analysis of magnetoencephalography (MEG) to study the processing dynamics of social-communicative actions. Twenty-four participants viewed images of a woman performing actions that varied on a continuum of communicative factors including self-relevance (to the participant) and emotional valence, while their brain activity was recorded using MEG. Controlling for low-level visual factors, we found early discrimination of emotional valence (70 ms) and self-relevant communicative signals (100 ms). These data offer neural support for the robust and rapid effects of self-relevant communicative cues on behavior. PMID:24958087

Correction of contaminated yaw rate signal and estimation of sensor bias for an electric vehicle under normal driving conditions

NASA Astrophysics Data System (ADS)

Zhang, Guoguang; Yu, Zitian; Wang, Junmin

2017-03-01

Yaw rate is a crucial signal for the motion control systems of ground vehicles. Yet it may be contaminated by sensor bias. In order to correct the contaminated yaw rate signal and estimate the sensor bias, a robust gain-scheduling observer is proposed in this paper. First of all, a two-degree-of-freedom (2DOF) vehicle lateral and yaw dynamic model is presented, and then a Luenberger-like observer is proposed. To make the observer more applicable to real vehicle driving operations, a 2DOF vehicle model with uncertainties on the coefficients of tire cornering stiffness is employed. Further, a gain-scheduling approach and a robustness enhancement are introduced, leading to a robust gain-scheduling observer. Sensor bias detection mechanism is also designed. Case studies are conducted using an electric ground vehicle to assess the performance of signal correction and sensor bias estimation under difference scenarios.

Nonlinear control for a class of hydraulic servo system.

PubMed

Yu, Hong; Feng, Zheng-jin; Wang, Xu-yong

2004-11-01

The dynamics of hydraulic systems are highly nonlinear and the system may be subjected to non-smooth and discontinuous nonlinearities due to directional change of valve opening, friction, etc. Aside from the nonlinear nature of hydraulic dynamics, hydraulic servo systems also have large extent of model uncertainties. To address these challenging issues, a robust state-feedback controller is designed by employing backstepping design technique such that the system output tracks a given signal arbitrarily well, and all signals in the closed-loop system remain bounded. Moreover, a relevant disturbance attenuation inequality is satisfied by the closed-loop signals. Compared with previously proposed robust controllers, this paper's robust controller based on backstepping recursive design method is easier to design, and is more suitable for implementation.

Engineering artificial cells by combining HeLa-based cell-free expression and ultra-thin double emulsion template

PubMed Central

Ho, Kwun Yin; Murray, Victoria L.; Liu, Allen P.

2015-01-01

Generation of artificial cells provides the bridge needed to cover the gap between studying the complexity of biological processes in whole cells and studying these same processes in an in vitro reconstituted system. Artificial cells are defined as the encapsulation of biologically active material in a biological or synthetic membrane. Here, we describe a robust and general method to produce artificial cells for the purpose of mimicking one or more behaviors of a cell. A microfluidic double emulsion system is used to encapsulate a mammalian cell free expression system that is able to express membrane proteins into the bilayer or soluble proteins inside the vesicles. The development of a robust platform that allows the assembly of artificial cells is valuable in understanding subcellular functions and emergent behaviors in a more cell-like environment as well as for creating novel signaling pathways to achieve specific cellular behaviors. PMID:25997354

Wireless brain-machine interface using EEG and EOG: brain wave classification and robot control

NASA Astrophysics Data System (ADS)

Oh, Sechang; Kumar, Prashanth S.; Kwon, Hyeokjun; Varadan, Vijay K.

2012-04-01

A brain-machine interface (BMI) links a user's brain activity directly to an external device. It enables a person to control devices using only thought. Hence, it has gained significant interest in the design of assistive devices and systems for people with disabilities. In addition, BMI has also been proposed to replace humans with robots in the performance of dangerous tasks like explosives handling/diffusing, hazardous materials handling, fire fighting etc. There are mainly two types of BMI based on the measurement method of brain activity; invasive and non-invasive. Invasive BMI can provide pristine signals but it is expensive and surgery may lead to undesirable side effects. Recent advances in non-invasive BMI have opened the possibility of generating robust control signals from noisy brain activity signals like EEG and EOG. A practical implementation of a non-invasive BMI such as robot control requires: acquisition of brain signals with a robust wearable unit, noise filtering and signal processing, identification and extraction of relevant brain wave features and finally, an algorithm to determine control signals based on the wave features. In this work, we developed a wireless brain-machine interface with a small platform and established a BMI that can be used to control the movement of a robot by using the extracted features of the EEG and EOG signals. The system records and classifies EEG as alpha, beta, delta, and theta waves. The classified brain waves are then used to define the level of attention. The acceleration and deceleration or stopping of the robot is controlled based on the attention level of the wearer. In addition, the left and right movements of eye ball control the direction of the robot.

Role of a transductional-transcriptional processor complex involving MyD88 and IRF-7 in Toll-like receptor signaling

PubMed Central

Honda, Kenya; Yanai, Hideyuki; Mizutani, Tatsuaki; Negishi, Hideo; Shimada, Naoya; Suzuki, Nobutaka; Ohba, Yusuke; Takaoka, Akinori; Yeh, Wen-Chen; Taniguchi, Tadatsugu

2004-01-01

Toll-like receptor (TLR) activation is central to immunity, wherein the activation of the TLR9 subfamily members TLR9 and TLR7 results in the robust induction of type I IFNs (IFN-α/β) by means of the MyD88 adaptor protein. However, it remains unknown how the TLR signal “input” can be processed through MyD88 to “output” the induction of the IFN genes. Here, we demonstrate that the transcription factor IRF-7 interacts with MyD88 to form a complex in the cytoplasm. We provide evidence that this complex also involves IRAK4 and TRAF6 and provides the foundation for the TLR9-dependent activation of the IFN genes. The complex defined in this study represents an example of how the coupling of the signaling adaptor and effector kinase molecules together with the transcription factor regulate the processing of an extracellular signal to evoke its versatile downstream transcriptional events in a cell. Thus, we propose that this molecular complex may function as a cytoplasmic transductional-transcriptional processor. PMID:15492225

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

Sampling Technique for Robust Odorant Detection Based on MIT RealNose Data

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2012-01-01

This technique enhances the detection capability of the autonomous Real-Nose system from MIT to detect odorants and their concentrations in noisy and transient environments. The lowcost, portable system with low power consumption will operate at high speed and is suited for unmanned and remotely operated long-life applications. A deterministic mathematical model was developed to detect odorants and calculate their concentration in noisy environments. Real data from MIT's NanoNose was examined, from which a signal conditioning technique was proposed to enable robust odorant detection for the RealNose system. Its sensitivity can reach to sub-part-per-billion (sub-ppb). A Space Invariant Independent Component Analysis (SPICA) algorithm was developed to deal with non-linear mixing that is an over-complete case, and it is used as a preprocessing step to recover the original odorant sources for detection. This approach, combined with the Cascade Error Projection (CEP) Neural Network algorithm, was used to perform odorant identification. Signal conditioning is used to identify potential processing windows to enable robust detection for autonomous systems. So far, the software has been developed and evaluated with current data sets provided by the MIT team. However, continuous data streams are made available where even the occurrence of a new odorant is unannounced and needs to be noticed by the system autonomously before its unambiguous detection. The challenge for the software is to be able to separate the potential valid signal from the odorant and from the noisy transition region when the odorant is just introduced.

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.

An automated image processing routine for segmentation of cell cytoplasms in high-resolution autofluorescence images

NASA Astrophysics Data System (ADS)

Walsh, Alex J.; Skala, Melissa C.

2014-02-01

The heterogeneity of genotypes and phenotypes within cancers is correlated with disease progression and drug-resistant cellular sub-populations. Therefore, robust techniques capable of probing majority and minority cell populations are important both for cancer diagnostics and therapy monitoring. Herein, we present a modified CellProfiler routine to isolate cytoplasmic fluorescence signal on a single cell level from high resolution auto-fluorescence microscopic images.

Saturation-Transfer Difference (STD) NMR: A Simple and Fast Method for Ligand Screening and Characterization of Protein Binding

ERIC Educational Resources Information Center

Viegas, Aldino; Manso, Joao; Nobrega, Franklin L.; Cabrita, Eurico J.

2011-01-01

Saturation transfer difference (STD) NMR has emerged as one of the most popular ligand-based NMR techniques for the study of protein-ligand interactions. The success of this technique is a consequence of its robustness and the fact that it is focused on the signals of the ligand, without any need of processing NMR information about the receptor…

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.

A novel DTI-QA tool: Automated metric extraction exploiting the sphericity of an agar filled phantom.

PubMed

Chavez, Sofia; Viviano, Joseph; Zamyadi, Mojdeh; Kingsley, Peter B; Kochunov, Peter; Strother, Stephen; Voineskos, Aristotle

2018-02-01

To develop a quality assurance (QA) tool (acquisition guidelines and automated processing) for diffusion tensor imaging (DTI) data using a common agar-based phantom used for fMRI QA. The goal is to produce a comprehensive set of automated, sensitive and robust QA metrics. A readily available agar phantom was scanned with and without parallel imaging reconstruction. Other scanning parameters were matched to the human scans. A central slab made up of either a thick slice or an average of a few slices, was extracted and all processing was performed on that image. The proposed QA relies on the creation of two ROIs for processing: (i) a preset central circular region of interest (ccROI) and (ii) a signal mask for all images in the dataset. The ccROI enables computation of average signal for SNR calculations as well as average FA values. The production of the signal masks enables automated measurements of eddy current and B0 inhomogeneity induced distortions by exploiting the sphericity of the phantom. Also, the signal masks allow automated background localization to assess levels of Nyquist ghosting. The proposed DTI-QA was shown to produce eleven metrics which are robust yet sensitive to image quality changes within site and differences across sites. It can be performed in a reasonable amount of scan time (~15min) and the code for automated processing has been made publicly available. A novel DTI-QA tool has been proposed. It has been applied successfully on data from several scanners/platforms. The novelty lies in the exploitation of the sphericity of the phantom for distortion measurements. Other novel contributions are: the computation of an SNR value per gradient direction for the diffusion weighted images (DWIs) and an SNR value per non-DWI, an automated background detection for the Nyquist ghosting measurement and an error metric reflecting the contribution of EPI instability to the eddy current induced shape changes observed for DWIs. Copyright © 2017 Elsevier Inc. All rights reserved.

Robustness of remote stress detection from visible spectrum recordings

NASA Astrophysics Data System (ADS)

Kaur, Balvinder; Moses, Sophia; Luthra, Megha; Ikonomidou, Vasiliki N.

2016-05-01

In our recent work, we have shown that it is possible to extract high fidelity timing information of the cardiac pulse wave from visible spectrum videos, which can then be used as a basis for stress detection. In that approach, we used both heart rate variability (HRV) metrics and the differential pulse transit time (dPTT) as indicators of the presence of stress. One of the main concerns in this analysis is its robustness in the presence of noise, as the remotely acquired signal that we call blood wave (BW) signal is degraded with respect to the signal acquired using contact sensors. In this work, we discuss the robustness of our metrics in the presence of multiplicative noise. Specifically, we study the effects of subtle motion due to respiration and changes in illumination levels due to light flickering on the BW signal, the HRV-driven features, and the dPTT. Our sensitivity study involved both Monte Carlo simulations and experimental data from human facial videos, and indicates that our metrics are robust even under moderate amounts of noise. Generated results will help the remote stress detection community with developing requirements for visual spectrum based stress detection systems.

``Seeing'' electroencephalogram through the skull: imaging prefrontal cortex with fast optical signal

NASA Astrophysics Data System (ADS)

Medvedev, Andrei V.; Kainerstorfer, Jana M.; Borisov, Sergey V.; Gandjbakhche, Amir H.; Vanmeter, John

2010-11-01

Near-infrared spectroscopy is a novel imaging technique potentially sensitive to both brain hemodynamics (slow signal) and neuronal activity (fast optical signal, FOS). The big challenge of measuring FOS noninvasively lies in the presumably low signal-to-noise ratio. Thus, detectability of the FOS has been controversially discussed. We present reliable detection of FOS from 11 individuals concurrently with electroencephalogram (EEG) during a Go-NoGo task. Probes were placed bilaterally over prefrontal cortex. Independent component analysis (ICA) was used for artifact removal. Correlation coefficient in the best correlated FOS-EEG ICA pairs was highly significant (p < 10-8), and event-related optical signal (EROS) was found in all subjects. Several EROS components were similar to the event-related potential (ERP) components. The most robust ``optical N200'' at t = 225 ms coincided with the N200 ERP; both signals showed significant difference between targets and nontargets, and their timing correlated with subject's reaction time. Correlation between FOS and EEG even in single trials provides further evidence that at least some FOS components ``reflect'' electrical brain processes directly. The data provide evidence for the early involvement of prefrontal cortex in rapid object recognition. EROS is highly localized and can provide cost-effective imaging tools for cortical mapping of cognitive processes.

“Seeing” electroencephalogram through the skull: imaging prefrontal cortex with fast optical signal

PubMed Central

Medvedev, Andrei V.; Kainerstorfer, Jana M.; Borisov, Sergey V.; Gandjbakhche, Amir H.; VanMeter, John

2010-01-01

Near-infrared spectroscopy is a novel imaging technique potentially sensitive to both brain hemodynamics (slow signal) and neuronal activity (fast optical signal, FOS). The big challenge of measuring FOS noninvasively lies in the presumably low signal-to-noise ratio. Thus, detectability of the FOS has been controversially discussed. We present reliable detection of FOS from 11 individuals concurrently with electroencephalogram (EEG) during a Go-NoGo task. Probes were placed bilaterally over prefrontal cortex. Independent component analysis (ICA) was used for artifact removal. Correlation coefficient in the best correlated FOS–EEG ICA pairs was highly significant (p < 10−8), and event-related optical signal (EROS) was found in all subjects. Several EROS components were similar to the event-related potential (ERP) components. The most robust “optical N200” at t = 225 ms coincided with the N200 ERP; both signals showed significant difference between targets and nontargets, and their timing correlated with subject’s reaction time. Correlation between FOS and EEG even in single trials provides further evidence that at least some FOS components “reflect” electrical brain processes directly. The data provide evidence for the early involvement of prefrontal cortex in rapid object recognition. EROS is highly localized and can provide cost-effective imaging tools for cortical mapping of cognitive processes. PMID:21198150

Revisiting the X:A signal that specifies Caenorhabditis elegans sexual fate.

PubMed

Gladden, John M; Farboud, Behnom; Meyer, Barbara J

2007-11-01

In Caenorhabditis elegans, sex is determined by the opposing actions of X-signal elements (XSEs) and autosomal signal elements (ASEs), which communicate the ratio of X chromosomes to sets of autosomes (X:A signal). This study delves more deeply into the mechanism by which XSEs transmit X chromosome dose. We determined the relative contributions of individual XSEs to the X:A signal and showed the order of XSE strength to be sex-1 > sex-2 > fox-1 > ceh-39 >/= region 1 XSE. sex-1 exerts a more potent influence on sex determination and dosage compensation than any other XSE by functioning in two separate capacities in the pathway: sex-1 acts upstream as an XSE to repress xol-1 and downstream as an activator of hermaphrodite development and dosage compensation. Furthermore, the process of dosage compensation affects expression of the very XSEs that control it; XSEs become fully dosage compensated once sex is determined. The X:A signal is then equivalent between XO and XX animals, causing sexual differentiation to be controlled by genes downstream of xol-1 in the sex-determination pathway. Prior to the onset of dosage compensation, the difference in XSE expression between XX and XO embryos appears to be greater than twofold, making X chromosome counting a robust process.

A blood pressure monitor with robust noise reduction system under linear cuff inflation and deflation.

PubMed

Usuda, Takashi; Kobayashi, Naoki; Takeda, Sunao; Kotake, Yoshifumi

2010-01-01

We have developed the non-invasive blood pressure monitor which can measure the blood pressure quickly and robustly. This monitor combines two measurement mode: the linear inflation and the linear deflation. On the inflation mode, we realized a faster measurement with rapid inflation rate. On the deflation mode, we realized a robust noise reduction. When there is neither noise nor arrhythmia, the inflation mode incorporated on this monitor provides precise, quick and comfortable measurement. Once the inflation mode fails to calculate appropriate blood pressure due to body movement or arrhythmia, then the monitor switches automatically to the deflation mode and measure blood pressure by using digital signal processing as wavelet analysis, filter bank, filter combined with FFT and Inverse FFT. The inflation mode succeeded 2440 measurements out of 3099 measurements (79%) in an operating room and a rehabilitation room. The new designed blood pressure monitor provides the fastest measurement for patient with normal circulation and robust measurement for patients with body movement or severe arrhythmia. Also this fast measurement method provides comfortableness for patients.

Direct Interactions Between Gli3, Wnt8b, and Fgfs Underlie Patterning of the Dorsal Telencephalon.

PubMed

Hasenpusch-Theil, Kerstin; Watson, Julia A; Theil, Thomas

2017-02-01

A key step in the development of the cerebral cortex is a patterning process, which subdivides the telencephalon into several molecularly distinct domains and is critical for cortical arealization. This process is dependent on a complex network of interactions between signaling molecules of the Fgf and Wnt gene families and the Gli3 transcription factor gene, but a better knowledge of the molecular basis of the interplay between these factors is required to gain a deeper understanding of the genetic circuitry underlying telencephalic patterning. Using DNA-binding and reporter gene assays, we here investigate the possibility that Gli3 and these signaling molecules interact by directly regulating each other's expression. We show that Fgf signaling is required for Wnt8b enhancer activity in the cortical hem, whereas Wnt/β-catenin signaling represses Fgf17 forebrain enhancer activity. In contrast, Fgf and Wnt/β-catenin signaling cooperate to regulate Gli3 expression. Taken together, these findings indicate that mutual interactions between Gli3, Wnt8b, and Fgf17 are crucial elements of the balance between these factors thereby conferring robustness to the patterning process. Hence, our study provides a framework for understanding the genetic circuitry underlying telencephalic patterning and how defects in this process can affect the formation of cortical areas. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Design and implementation of a sigma delta technology based pulse oximeter's acquisition stage

NASA Astrophysics Data System (ADS)

Rossi, E. E.; Peñalva, A.; Schaumburg, F.

2011-12-01

Pulse oximetry is a widely used tool in medical practice for estimating patient's fraction of hemoglobin bonded to oxygen. Conventional oximetry presents limitations when changes in the baseline, or low amplitude of signals involved occur. The aim of this paper is to simultaneously solve these constraints and to simplify the circuitry needed, by using ΣΔ technology. For this purpose, a board for the acquisition of the needed signals was developed, together with a PC managed software which controls it, and displays and processes in real time the information acquired. Also laboratory and field tests where designed and executed to verify the performance of this equipment in adverse situations. A simple, robust and economic instrument was achieved, capable of obtaining signals even in situations where conventional oximetry fails.

Drastic disorder-induced reduction of signal amplification in scale-free networks.

PubMed

Chacón, Ricardo; Martínez, Pedro J

2015-07-01

Understanding information transmission across a network is a fundamental task for controlling and manipulating both biological and manmade information-processing systems. Here we show how topological resonant-like amplification effects in scale-free networks of signaling devices are drastically reduced when phase disorder in the external signals is considered. This is demonstrated theoretically by means of a starlike network of overdamped bistable systems, and confirmed numerically by simulations of scale-free networks of such systems. The taming effect of the phase disorder is found to be sensitive to the amplification's strength, while the topology-induced amplification mechanism is robust against this kind of quenched disorder in the sense that it does not significantly change the values of the coupling strength where amplification is maximum in its absence.

Useful signals from motor cortex

PubMed Central

Schwartz, Andrew B

2007-01-01

Historically, the motor cortical function has been explained as a funnel to muscle activation. This invokes the idea that motor cortical neurons, or ‘upper motoneurons’, directly cause muscle contraction just like spinal motoneurons. Thus, the motor cortex and muscle activity are inextricably entwined like a puppet master and his marionette. Recently, this concept has been challenged by current experimentation showing that many behavioural aspects of action are represented in motor cortical activity. Although this activity may still be related to muscle activation, the relation between the two is likely to be indirect and complex, whereas the relation between cortical activity and kinematic parameters is simple and robust. These findings show how to extract useful signals that help explain the underlying process that generates behaviour and to harness these signals for potentially therapeutic applications. PMID:17255162

Adaptive variational mode decomposition method for signal processing based on mode characteristic

NASA Astrophysics Data System (ADS)

Lian, Jijian; Liu, Zhuo; Wang, Haijun; Dong, Xiaofeng

2018-07-01

Variational mode decomposition is a completely non-recursive decomposition model, where all the modes are extracted concurrently. However, the model requires a preset mode number, which limits the adaptability of the method since a large deviation in the number of mode set will cause the discard or mixing of the mode. Hence, a method called Adaptive Variational Mode Decomposition (AVMD) was proposed to automatically determine the mode number based on the characteristic of intrinsic mode function. The method was used to analyze the simulation signals and the measured signals in the hydropower plant. Comparisons have also been conducted to evaluate the performance by using VMD, EMD and EWT. It is indicated that the proposed method has strong adaptability and is robust to noise. It can determine the mode number appropriately without modulation even when the signal frequencies are relatively close.

Cell-Surface Bound Nonreceptors and Signaling Morphogen Gradients

PubMed Central

Wan, Frederic Y.M.

2013-01-01

The patterning of many developing tissues is orchestrated by gradients of signaling morphogens. Included among the molecular events that drive the formation of morphogen gradients are a variety of elaborate regulatory interactions. Such interactions are thought to make gradients robust, i.e. insensitive to change in the face of genetic or environmental perturbations. But just how this is accomplished is a major unanswered question. Recently extensive numerical simulations suggest that robustness of signaling gradients can be achieved through morphogen degradation mediated by cell surface bound non-signaling receptor molecules (or nonreceptors for short) such as heparan sulfate proteoglycans (HSPG). The present paper provides a mathematical validation of the results from the aforementioned numerical experiments. Extension of a basic extracellular model to include reversible binding with nonreceptors synthesized at a prescribed rate and mediated morphogen degradation shows that the signaling gradient diminishes with increasing concentration of cell-surface nonreceptors. Perturbation and asymptotic solutions obtained for i) low (receptor and nonreceptor) occupancy, and ii) high nonreceptor concntration permit more explicit delineation of the effects of nonreceptors on signaling gradients and facilitate the identification of scenarios in which the presence of nonreceptors may or may not be effective in promoting robustness. PMID:25232201

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.

The devil is in the detail: Quantifying vocal variation in a complex, multi-levelled, and rapidly evolving display.

PubMed

Garland, Ellen C; Rendell, Luke; Lilley, Matthew S; Poole, M Michael; Allen, Jenny; Noad, Michael J

2017-07-01

Identifying and quantifying variation in vocalizations is fundamental to advancing our understanding of processes such as speciation, sexual selection, and cultural evolution. The song of the humpback whale (Megaptera novaeangliae) presents an extreme example of complexity and cultural evolution. It is a long, hierarchically structured vocal display that undergoes constant evolutionary change. Obtaining robust metrics to quantify song variation at multiple scales (from a sound through to population variation across the seascape) is a substantial challenge. Here, the authors present a method to quantify song similarity at multiple levels within the hierarchy. To incorporate the complexity of these multiple levels, the calculation of similarity is weighted by measurements of sound units (lower levels within the display) to bridge the gap in information between upper and lower levels. Results demonstrate that the inclusion of weighting provides a more realistic and robust representation of song similarity at multiple levels within the display. This method permits robust quantification of cultural patterns and processes that will also contribute to the conservation management of endangered humpback whale populations, and is applicable to any hierarchically structured signal sequence.

CNT/PDMS composite flexible dry electrodes for long-term ECG monitoring.

PubMed

Jung, Ha-Chul; Moon, Jin-Hee; Baek, Dong-Hyun; Lee, Jae-Hee; Choi, Yoon-Young; Hong, Joung-Sook; Lee, Sang-Hoon

2012-05-01

We fabricated a carbon nanotube (CNT)/ polydimethylsiloxane (PDMS) composite-based dry ECG electrode that can be readily connected to conventional ECG devices, and showed its long-term wearable monitoring capability and robustness to motion and sweat. While the dispersion of CNTs in PDMS is challenging, we optimized the process to disperse untreated CNTs within PDMS by mechanical force only. The electrical and mechanical characteristics of the CNT/PDMS electrode were tested according to the concentration of CNTs and its thickness. The performances of ECG electrodes were evaluated by using 36 types of electrodes which were fabricated with different concentrations of CNTs, and with a differing diameter and thickness. The ECG signals were obtained by using electrodes of diverse sizes to observe the effects of motion and sweat, and the proposed electrode was shown to be robust to both factors. The CNT concentration and diameter of the electrodes were critical parameters in obtaining high-quality ECG signals. The electrode was shown to be biocompatible from the cytotoxicity test. A seven-day continuous wearability test showed that the quality of the ECG signal did not degrade over time, and skin reactions such as itching or erythema were not observed. This electrode could be used for the long-term measurement of other electrical biosignals for ubiquitous health monitoring including EMG, EEG, and ERG.

Probabilistic n/γ discrimination with robustness against outliers for use in neutron profile monitors

NASA Astrophysics Data System (ADS)

Uchida, Y.; Takada, E.; Fujisaki, A.; Kikuchi, T.; Ogawa, K.; Isobe, M.

2017-08-01

A method to stochastically discriminate neutron and γ-ray signals measured with a stilbene organic scintillator is proposed. Each pulse signal was stochastically categorized into two groups: neutron and γ-ray. In previous work, the Expectation Maximization (EM) algorithm was used with the assumption that the measured data followed a Gaussian mixture distribution. It was shown that probabilistic discrimination between these groups is possible. Moreover, by setting the initial parameters for the Gaussian mixture distribution with a k-means algorithm, the possibility of automatic discrimination was demonstrated. In this study, the Student's t-mixture distribution was used as a probabilistic distribution with the EM algorithm to improve the robustness against the effect of outliers caused by pileup of the signals. To validate the proposed method, the figures of merit (FOMs) were compared for the EM algorithm assuming a t-mixture distribution and a Gaussian mixture distribution. The t-mixture distribution resulted in an improvement of the FOMs compared with the Gaussian mixture distribution. The proposed data processing technique is a promising tool not only for neutron and γ-ray discrimination in fusion experiments but also in other fields, for example, homeland security, cancer therapy with high energy particles, nuclear reactor decommissioning, pattern recognition, and so on.

Mass Spectrometric Quantification of N-Linked Glycans by Reference to Exogenous Standards.

PubMed

Mehta, Nickita; Porterfield, Mindy; Struwe, Weston B; Heiss, Christian; Azadi, Parastoo; Rudd, Pauline M; Tiemeyer, Michael; Aoki, Kazuhiro

2016-09-02

Environmental and metabolic processes shape the profile of glycoprotein glycans expressed by cells, whether in culture, developing tissues, or mature organisms. Quantitative characterization of glycomic changes associated with these conditions has been achieved historically by reductive coupling of oligosaccharides to various fluorophores following release from glycoprotein and subsequent HPLC or capillary electrophoretic separation. Such labeling-based approaches provide a robust means of quantifying glycan amount based on fluorescence yield. Mass spectrometry, on the other hand, has generally been limited to relative quantification in which the contribution of the signal intensity for an individual glycan is expressed as a percent of the signal intensity summed over the total profile. Relative quantification has been valuable for highlighting changes in glycan expression between samples; sensitivity is high, and structural information can be derived by fragmentation. We have investigated whether MS-based glycomics is amenable to absolute quantification by referencing signal intensities to well-characterized oligosaccharide standards. We report the qualification of a set of N-linked oligosaccharide standards by NMR, HPLC, and MS. We also demonstrate the dynamic range, sensitivity, and recovery from complex biological matrices for these standards in their permethylated form. Our results indicate that absolute quantification for MS-based glycomic analysis is reproducible and robust utilizing currently available glycan standards.

The Analysis of Design of Robust Nonlinear Estimators and Robust Signal Coding Schemes.

DTIC Science & Technology

1982-09-16

b - )’/ 12. between uniform and nonuniform quantizers. For the nonuni- Proof: If b - acca then form quantizer we can expect the mean-square error to...in the window greater than or equal to the value at We define f7 ’(s) as the n-times filtered signal p + 1; consequently, point p + 1 is the median and

Utilization of a balanced steady state free precession signal model for improved fat/water decomposition.

PubMed

Henze Bancroft, Leah C; Strigel, Roberta M; Hernando, Diego; Johnson, Kevin M; Kelcz, Frederick; Kijowski, Richard; Block, Walter F

2016-03-01

Chemical shift based fat/water decomposition methods such as IDEAL are frequently used in challenging imaging environments with large B0 inhomogeneity. However, they do not account for the signal modulations introduced by a balanced steady state free precession (bSSFP) acquisition. Here we demonstrate improved performance when the bSSFP frequency response is properly incorporated into the multipeak spectral fat model used in the decomposition process. Balanced SSFP allows for rapid imaging but also introduces a characteristic frequency response featuring periodic nulls and pass bands. Fat spectral components in adjacent pass bands will experience bulk phase offsets and magnitude modulations that change the expected constructive and destructive interference between the fat spectral components. A bSSFP signal model was incorporated into the fat/water decomposition process and used to generate images of a fat phantom, and bilateral breast and knee images in four normal volunteers at 1.5 Tesla. Incorporation of the bSSFP signal model into the decomposition process improved the performance of the fat/water decomposition. Incorporation of this model allows rapid bSSFP imaging sequences to use robust fat/water decomposition methods such as IDEAL. While only one set of imaging parameters were presented, the method is compatible with any field strength or repetition time. © 2015 Wiley Periodicals, Inc.

Automated sleep stage detection with a classical and a neural learning algorithm--methodological aspects.

PubMed

Schwaibold, M; Schöchlin, J; Bolz, A

2002-01-01

For classification tasks in biosignal processing, several strategies and algorithms can be used. Knowledge-based systems allow prior knowledge about the decision process to be integrated, both by the developer and by self-learning capabilities. For the classification stages in a sleep stage detection framework, three inference strategies were compared regarding their specific strengths: a classical signal processing approach, artificial neural networks and neuro-fuzzy systems. Methodological aspects were assessed to attain optimum performance and maximum transparency for the user. Due to their effective and robust learning behavior, artificial neural networks could be recommended for pattern recognition, while neuro-fuzzy systems performed best for the processing of contextual information.

Minimal Network Topologies for Signal Processing during Collective Cell Chemotaxis.

PubMed

Yue, Haicen; Camley, Brian A; Rappel, Wouter-Jan

2018-06-19

Cell-cell communication plays an important role in collective cell migration. However, it remains unclear how cells in a group cooperatively process external signals to determine the group's direction of motion. Although the topology of signaling pathways is vitally important in single-cell chemotaxis, the signaling topology for collective chemotaxis has not been systematically studied. Here, we combine mathematical analysis and simulations to find minimal network topologies for multicellular signal processing in collective chemotaxis. We focus on border cell cluster chemotaxis in the Drosophila egg chamber, in which responses to several experimental perturbations of the signaling network are known. Our minimal signaling network includes only four elements: a chemoattractant, the protein Rac (indicating cell activation), cell protrusion, and a hypothesized global factor responsible for cell-cell interaction. Experimental data on cell protrusion statistics allows us to systematically narrow the number of possible topologies from more than 40,000,000 to only six minimal topologies with six interactions between the four elements. This analysis does not require a specific functional form of the interactions, and only qualitative features are needed; it is thus robust to many modeling choices. Simulations of a stochastic biochemical model of border cell chemotaxis show that the qualitative selection procedure accurately determines which topologies are consistent with the experiment. We fit our model for all six proposed topologies; each produces results that are consistent with all experimentally available data. Finally, we suggest experiments to further discriminate possible pathway topologies. Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

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.

Statistical methods for change-point detection in surface temperature records

NASA Astrophysics Data System (ADS)

Pintar, A. L.; Possolo, A.; Zhang, N. F.

2013-09-01

We describe several statistical methods to detect possible change-points in a time series of values of surface temperature measured at a meteorological station, and to assess the statistical significance of such changes, taking into account the natural variability of the measured values, and the autocorrelations between them. These methods serve to determine whether the record may suffer from biases unrelated to the climate signal, hence whether there may be a need for adjustments as considered by M. J. Menne and C. N. Williams (2009) "Homogenization of Temperature Series via Pairwise Comparisons", Journal of Climate 22 (7), 1700-1717. We also review methods to characterize patterns of seasonality (seasonal decomposition using monthly medians or robust local regression), and explain the role they play in the imputation of missing values, and in enabling robust decompositions of the measured values into a seasonal component, a possible climate signal, and a station-specific remainder. The methods for change-point detection that we describe include statistical process control, wavelet multi-resolution analysis, adaptive weights smoothing, and a Bayesian procedure, all of which are applicable to single station records.

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks.

PubMed

Martin Cichy, Radoslaw; Khosla, Aditya; Pantazis, Dimitrios; Oliva, Aude

2017-06-01

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how spatial layout representations emerge in the human brain. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

CameraHRV: robust measurement of heart rate variability using a camera

NASA Astrophysics Data System (ADS)

Pai, Amruta; Veeraraghavan, Ashok; Sabharwal, Ashutosh

2018-02-01

The inter-beat-interval (time period of the cardiac cycle) changes slightly for every heartbeat; this variation is measured as Heart Rate Variability (HRV). HRV is presumed to occur due to interactions between the parasym- pathetic and sympathetic nervous system. Therefore, it is sometimes used as an indicator of the stress level of an individual. HRV also reveals some clinical information about cardiac health. Currently, HRV is accurately measured using contact devices such as a pulse oximeter. However, recent research in the field of non-contact imaging Photoplethysmography (iPPG) has made vital sign measurements using just the video recording of any exposed skin (such as a person's face) possible. The current signal processing methods for extracting HRV using peak detection perform well for contact-based systems but have poor performance for the iPPG signals. The main reason for this poor performance is the fact that current methods are sensitive to large noise sources which are often present in iPPG data. Further, current methods are not robust to motion artifacts that are common in iPPG systems. We developed a new algorithm, CameraHRV, for robustly extracting HRV even in low SNR such as is common with iPPG recordings. CameraHRV combined spatial combination and frequency demodulation to obtain HRV from the instantaneous frequency of the iPPG signal. CameraHRV outperforms other current methods of HRV estimation. Ground truth data was obtained from FDA-approved pulse oximeter for validation purposes. CameraHRV on iPPG data showed an error of 6 milliseconds for low motion and varying skin tone scenarios. The improvement in error was 14%. In case of high motion scenarios like reading, watching and talking, the error was 10 milliseconds.

GPS Imaging of Time-Dependent Seasonal Strain in Central California

NASA Astrophysics Data System (ADS)

Kraner, M.; Hammond, W. C.; Kreemer, C.; Borsa, A. A.; Blewitt, G.

2016-12-01

Recently, studies are suggesting that crustal deformation can be time-dependent and nontectonic. Continuous global positioning system (cGPS) measurements are now showing how steady long-term deformation can be influenced by factors such as fluctuations in loading and temperature variations. Here we model the seasonal time-dependent dilatational and shear strain in Central California, specifically surrounding the Parkfield region and try to uncover the sources of these deformation patterns. We use 8 years of cGPS data (2008 - 2016) processed by the Nevada Geodetic Laboratory and carefully select the cGPS stations for our analysis based on the vertical position of cGPS time series during the drought period. In building our strain model, we first detrend the selected station time series using a set of velocities from the robust MIDAS trend estimator. This estimation algorithm is a robust approach that is insensitive to common problems such as step discontinuities, outliers, and seasonality. We use these detrended time series to estimate the median cGPS positions for each month of the 8-year period and filter displacement differences between these monthly median positions using a filtering technique called "GPS Imaging." This technique improves the overall robustness and spatial resolution of the input displacements for the strain model. We then model our dilatational and shear strain field for each month of time series. We also test a variety of a priori constraints, which controls the style of faulting within the strain model. Upon examining our strain maps, we find that a seasonal strain signal exists in Central California. We investigate how this signal compares to thermoelastic, hydrologic, and atmospheric loading models during the 8-year period. We additionally determine whether the drought played a role in influencing the seasonal signal.

Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations

NASA Astrophysics Data System (ADS)

García Plaza, E.; Núñez López, P. J.

2018-01-01

The wavelet packet transform method decomposes a time signal into several independent time-frequency signals called packets. This enables the temporary location of transient events occurring during the monitoring of the cutting processes, which is advantageous in monitoring condition and fault diagnosis. This paper proposes the monitoring of surface roughness using a single low cost sensor that is easily implemented in numerical control machine tools in order to make on-line decisions on workpiece surface finish quality. Packet feature extraction in vibration signals was applied to correlate the sensor signals to measured surface roughness. For the successful application of the WPT method, mother wavelets, packet decomposition level, and appropriate packet selection methods should be considered, but are poorly understood aspects in the literature. In this novel contribution, forty mother wavelets, optimal decomposition level, and packet reduction methods were analysed, as well as identifying the effective frequency range providing the best packet feature extraction for monitoring surface finish. The results show that mother wavelet biorthogonal 4.4 in decomposition level L3 with the fusion of the orthogonal vibration components (ax + ay + az) were the best option in the vibration signal and surface roughness correlation. The best packets were found in the medium-high frequency DDA (6250-9375 Hz) and high frequency ADA (9375-12500 Hz) ranges, and the feed acceleration component ay was the primary source of information. The packet reduction methods forfeited packets with relevant features to the signal, leading to poor results for the prediction of surface roughness. WPT is a robust vibration signal processing method for the monitoring of surface roughness using a single sensor without other information sources, satisfactory results were obtained in comparison to other processing methods with a low computational cost.

Comparison of fMRI paradigms assessing visuospatial processing: Robustness and reproducibility

PubMed Central

Herholz, Peer; Zimmermann, Kristin M.; Westermann, Stefan; Frässle, Stefan; Jansen, Andreas

2017-01-01

The development of brain imaging techniques, in particular functional magnetic resonance imaging (fMRI), made it possible to non-invasively study the hemispheric lateralization of cognitive brain functions in large cohorts. Comprehensive models of hemispheric lateralization are, however, still missing and should not only account for the hemispheric specialization of individual brain functions, but also for the interactions among different lateralized cognitive processes (e.g., language and visuospatial processing). This calls for robust and reliable paradigms to study hemispheric lateralization for various cognitive functions. While numerous reliable imaging paradigms have been developed for language, which represents the most prominent left-lateralized brain function, the reliability of imaging paradigms investigating typically right-lateralized brain functions, such as visuospatial processing, has received comparatively less attention. In the present study, we aimed to establish an fMRI paradigm that robustly and reliably identifies right-hemispheric activation evoked by visuospatial processing in individual subjects. In a first study, we therefore compared three frequently used paradigms for assessing visuospatial processing and evaluated their utility to robustly detect right-lateralized brain activity on a single-subject level. In a second study, we then assessed the test-retest reliability of the so-called Landmark task–the paradigm that yielded the most robust results in study 1. At the single-voxel level, we found poor reliability of the brain activation underlying visuospatial attention. This suggests that poor signal-to-noise ratios can become a limiting factor for test-retest reliability. This represents a common detriment of fMRI paradigms investigating visuospatial attention in general and therefore highlights the need for careful considerations of both the possibilities and limitations of the respective fMRI paradigm–in particular, when being interested in effects at the single-voxel level. Notably, however, when focusing on the reliability of measures of hemispheric lateralization (which was the main goal of study 2), we show that hemispheric dominance (quantified by the lateralization index, LI, with |LI| >0.4) of the evoked activation could be robustly determined in more than 62% and, if considering only two categories (i.e., left, right), in more than 93% of our subjects. Furthermore, the reliability of the lateralization strength (LI) was “fair” to “good”. In conclusion, our results suggest that the degree of right-hemispheric dominance during visuospatial processing can be reliably determined using the Landmark task, both at the group and single-subject level, while at the same time stressing the need for future refinements of experimental paradigms and more sophisticated fMRI data acquisition techniques. PMID:29059201

Fast perceptual image hash based on cascade algorithm

NASA Astrophysics Data System (ADS)

Ruchay, Alexey; Kober, Vitaly; Yavtushenko, Evgeniya

2017-09-01

In this paper, we propose a perceptual image hash algorithm based on cascade algorithm, which can be applied in image authentication, retrieval, and indexing. Image perceptual hash uses for image retrieval in sense of human perception against distortions caused by compression, noise, common signal processing and geometrical modifications. The main disadvantage of perceptual hash is high time expenses. In the proposed cascade algorithm of image retrieval initializes with short hashes, and then a full hash is applied to the processed results. Computer simulation results show that the proposed hash algorithm yields a good performance in terms of robustness, discriminability, and time expenses.

A fast, robust algorithm for power line interference cancellation in neural recording.

PubMed

Keshtkaran, Mohammad Reza; Yang, Zhi

2014-04-01

Power line interference may severely corrupt neural recordings at 50/60 Hz and harmonic frequencies. The interference is usually non-stationary and can vary in frequency, amplitude and phase. To retrieve the gamma-band oscillations at the contaminated frequencies, it is desired to remove the interference without compromising the actual neural signals at the interference frequency bands. In this paper, we present a robust and computationally efficient algorithm for removing power line interference from neural recordings. The algorithm includes four steps. First, an adaptive notch filter is used to estimate the fundamental frequency of the interference. Subsequently, based on the estimated frequency, harmonics are generated by using discrete-time oscillators, and then the amplitude and phase of each harmonic are estimated by using a modified recursive least squares algorithm. Finally, the estimated interference is subtracted from the recorded data. The algorithm does not require any reference signal, and can track the frequency, phase and amplitude of each harmonic. When benchmarked with other popular approaches, our algorithm performs better in terms of noise immunity, convergence speed and output signal-to-noise ratio (SNR). While minimally affecting the signal bands of interest, the algorithm consistently yields fast convergence (<100 ms) and substantial interference rejection (output SNR >30 dB) in different conditions of interference strengths (input SNR from -30 to 30 dB), power line frequencies (45-65 Hz) and phase and amplitude drifts. In addition, the algorithm features a straightforward parameter adjustment since the parameters are independent of the input SNR, input signal power and the sampling rate. A hardware prototype was fabricated in a 65 nm CMOS process and tested. Software implementation of the algorithm has been made available for open access at https://github.com/mrezak/removePLI. The proposed algorithm features a highly robust operation, fast adaptation to interference variations, significant SNR improvement, low computational complexity and memory requirement and straightforward parameter adjustment. These features render the algorithm suitable for wearable and implantable sensor applications, where reliable and real-time cancellation of the interference is desired.

A fast, robust algorithm for power line interference cancellation in neural recording

NASA Astrophysics Data System (ADS)

Keshtkaran, Mohammad Reza; Yang, Zhi

2014-04-01

Objective. Power line interference may severely corrupt neural recordings at 50/60 Hz and harmonic frequencies. The interference is usually non-stationary and can vary in frequency, amplitude and phase. To retrieve the gamma-band oscillations at the contaminated frequencies, it is desired to remove the interference without compromising the actual neural signals at the interference frequency bands. In this paper, we present a robust and computationally efficient algorithm for removing power line interference from neural recordings. Approach. The algorithm includes four steps. First, an adaptive notch filter is used to estimate the fundamental frequency of the interference. Subsequently, based on the estimated frequency, harmonics are generated by using discrete-time oscillators, and then the amplitude and phase of each harmonic are estimated by using a modified recursive least squares algorithm. Finally, the estimated interference is subtracted from the recorded data. Main results. The algorithm does not require any reference signal, and can track the frequency, phase and amplitude of each harmonic. When benchmarked with other popular approaches, our algorithm performs better in terms of noise immunity, convergence speed and output signal-to-noise ratio (SNR). While minimally affecting the signal bands of interest, the algorithm consistently yields fast convergence (<100 ms) and substantial interference rejection (output SNR >30 dB) in different conditions of interference strengths (input SNR from -30 to 30 dB), power line frequencies (45-65 Hz) and phase and amplitude drifts. In addition, the algorithm features a straightforward parameter adjustment since the parameters are independent of the input SNR, input signal power and the sampling rate. A hardware prototype was fabricated in a 65 nm CMOS process and tested. Software implementation of the algorithm has been made available for open access at https://github.com/mrezak/removePLI. Significance. The proposed algorithm features a highly robust operation, fast adaptation to interference variations, significant SNR improvement, low computational complexity and memory requirement and straightforward parameter adjustment. These features render the algorithm suitable for wearable and implantable sensor applications, where reliable and real-time cancellation of the interference is desired.

On robust parameter estimation in brain-computer interfacing

NASA Astrophysics Data System (ADS)

Samek, Wojciech; Nakajima, Shinichi; Kawanabe, Motoaki; Müller, Klaus-Robert

2017-12-01

Objective. The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain-computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal. Typical structural elements in BCI data are the trials consisting of a few hundred EEG samples and indicating the start and end of a task. Approach. This work discusses the parameter estimation problem in BCI and introduces a novel hierarchical view on robustness which naturally comprises different types of outlierness occurring in structured data. Furthermore, the class of minimum divergence estimators is reviewed and a robust mean and covariance estimator for structured data is derived and evaluated with simulations and on a benchmark data set. Main results. The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters. Significance. Since parameter estimation is an integral part of various machine learning algorithms, the presented techniques are applicable to many problems beyond BCI.

Control of a small working robot on a large flexible manipulator for suppressing vibrations: Development of a robust control law for flexible robot and it's stability analysis

NASA Technical Reports Server (NTRS)

Soo, Han Lee

1991-01-01

Researchers developed a robust control law for slow motions for the accurate trajectory control of a flexible robot. The control law does not need larger velocity gains than position gains, which some researchers need to ensure the stability of a rigid robot. Initial experimentation for the Small Articulated Manipulator (SAM) shows that control laws that use smaller velocity gains are more robust to signal noise than the control laws that use larger velocity gains. Researchers analyzed the stability of the composite control law, the robust control for the slow motion, and the strain rate feedback for the fast control. The stability analysis was done by using a quadratic Liapunov function. Researchers found that the flexible motion of links could be controlled by relating the input force to the flexible signals which are sensed at the near tip of each link. The signals are contaminated by the time delayed input force. However, the effect of the time delayed input force can be reduced by giving a certain configuration to the SAM.

A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems.

PubMed

Kazemi, Mahdi; Arefi, Mohammad Mehdi

2017-03-01

In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Robust myoelectric signal detection based on stochastic resonance using multiple-surface-electrode array made of carbon nanotube composite paper

NASA Astrophysics Data System (ADS)

Shirata, Kento; Inden, Yuki; Kasai, Seiya; Oya, Takahide; Hagiwara, Yosuke; Kaeriyama, Shunichi; Nakamura, Hideyuki

2016-04-01

We investigated the robust detection of surface electromyogram (EMG) signals based on the stochastic resonance (SR) phenomenon, in which the response to weak signals is optimized by adding noise, combined with multiple surface electrodes. Flexible carbon nanotube composite paper (CNT-cp) was applied to the surface electrode, which showed good performance that is comparable to that of conventional Ag/AgCl electrodes. The SR-based EMG signal system integrating an 8-Schmitt-trigger network and the multiple-CNT-cp-electrode array successfully detected weak EMG signals even when the subject’s body is in the motion, which was difficult to achieve using the conventional technique. The feasibility of the SR-based EMG detection technique was confirmed by demonstrating its applicability to robot hand control.

Techniques for blade tip clearance measurements with capacitive probes

NASA Astrophysics Data System (ADS)

Steiner, Alexander

2000-07-01

This article presents a proven but advantageous concept for blade tip clearance evaluation in turbomachinery. The system is based on heavy duty probes and a high frequency (HF) and amplifying electronic unit followed by a signal processing unit. Measurements are taken under high temperature and other severe conditions such as ionization. Every single blade can be observed. The signals are digitally filtered and linearized in real time. The electronic set-up is highly integrated. Miniaturized versions of the electronic units exist. The small and robust units can be used in turbo engines in flight. With several probes at different angles in one radial plane further information is available. Shaft eccentricity or blade oscillations can be calculated.

Mobile/android application for QRS detection using zero cross method

NASA Astrophysics Data System (ADS)

Rizqyawan, M. I.; Simbolon, A. I.; Suhendra, M. A.; Amri, M. F.; Kusumandari, D. E.

2018-03-01

In automatic ECG signal processing, one of the main topics of research is QRS complex detection. Detecting correct QRS complex or R peak is important since it is used to measure several other ECG metrics. One of the robust methods for QRS detection is Zero Cross method. This method uses an addition of high-frequency signal and zero crossing count to detect QRS complex which has a low-frequency oscillation. This paper presents an application of QRS detection using Zero Cross algorithm in the Android-based system. The performance of the algorithm in the mobile environment is measured. The result shows that this method is suitable for real-time QRS detection in a mobile application.

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.

Ionization Electron Signal Processing in Single Phase LArTPCs II. Data/Simulation Comparison and Performance in MicroBooNE

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

Adams, C.; et al.

The single-phase liquid argon time projection chamber (LArTPC) provides a large amount of detailed information in the form of fine-grained drifted ionization charge from particle traces. To fully utilize this information, the deposited charge must be accurately extracted from the raw digitized waveforms via a robust signal processing chain. Enabled by the ultra-low noise levels associated with cryogenic electronics in the MicroBooNE detector, the precise extraction of ionization charge from the induction wire planes in a single-phase LArTPC is qualitatively demonstrated on MicroBooNE data with event display images, and quantitatively demonstrated via waveform-level and track-level metrics. Improved performance of inductionmore » plane calorimetry is demonstrated through the agreement of extracted ionization charge measurements across different wire planes for various event topologies. In addition to the comprehensive waveform-level comparison of data and simulation, a calibration of the cryogenic electronics response is presented and solutions to various MicroBooNE-specific TPC issues are discussed. This work presents an important improvement in LArTPC signal processing, the foundation of reconstruction and therefore physics analyses in MicroBooNE.« less

A Robust Distributed Multipoint Fiber Optic Gas Sensor System Based on AGC Amplifier Structure.

PubMed

Zhu, Cunguang; Wang, Rende; Tao, Xuechen; Wang, Guangwei; Wang, Pengpeng

2016-07-28

A harsh environment-oriented distributed multipoint fiber optic gas sensor system realized by automatic gain control (AGC) technology is proposed. To improve the photoelectric signal reliability, the electronic variable gain can be modified in real time by an AGC closed-loop feedback structure to compensate for optical transmission loss which is caused by the fiber bend loss or other reasons. The deviation of the system based on AGC structure is below 4.02% when photoelectric signal decays due to fiber bending loss for bending radius of 5 mm, which is 20 times lower than the ordinary differential system. In addition, the AGC circuit with the same electric parameters can keep the baseline intensity of signals in different channels of the distributed multipoint sensor system at the same level. This avoids repetitive calibrations and streamlines the installation process.

Dkk2/Frzb in the dermal papillae regulates feather regeneration

PubMed Central

Chu, Qiqi; Cai, Linyan; Fu, Yu; Chen, Xi; Yan, Zhipeng; Lin, Xiang; Zhou, Guixuan; Han, Hao; Widelitz, Randall B.; Chuong, Cheng-ming; Wu, Wei; Yue, Zhicao

2015-01-01

Avian feathers have robust growth and regeneration capability. To evaluate the contribution of signaling molecules and pathways in these processes, we profiled gene expression in the feather follicle using an absolute quantification approach. We identified hundreds of genes that mark specific components of the feather follicle: the dermal papillae (DP) which controls feather regeneration and axis formation, the pulp mesenchyme (Pp) which is derived from DP cells and nourishes the feather follicle, and the ramogenic zone epithelium (Erz) where a feather starts to branch. The feather DP is enriched in BMP/TGF-β signaling molecules and inhibitors for Wnt signaling including Dkk2/Frzb. Wnt ligands are mainly expressed in the feather epithelium and pulp. We find that while Wnt signaling is required for the maintenance of DP marker gene expression and feather regeneration, excessive Wnt signaling delays regeneration and reduces pulp formation. Manipulating Dkk2/Frzb expression by lentiviral-mediated overexpression, shRNA-knockdown, or by antibody neutralization resulted in dual feather axes formation. Our results suggest that the Wnt signaling in the proximal feather follicle is fine-tuned to accommodate feather regeneration and axis formation. PMID:24463139

Coherent and Noncoherent Joint Processing of Sonar for Detection of Small Targets in Shallow Water

PubMed Central

Jiang, Jingning; Li, Si; Ding, Zhenping; Pan, Chen; Gong, Xianyi

2018-01-01

A coherent-noncoherent joint processing framework is proposed for active sonar to combine diversity gain and beamforming gain for detection of a small target in shallow water environments. Sonar utilizes widely-spaced arrays to sense environments and illuminate a target of interest from multiple angles. Meanwhile, it exploits spatial diversity for time-reversal focusing to suppress reverberation, mainly strong bottom reverberation. For enhancement of robustness of time-reversal focusing, an adaptive iterative strategy is utilized in the processing framework. A probing signal is firstly transmitted and echoes of a likely target are utilized as steering vectors for the second transmission. With spatial diversity, target bearing and range are estimated using a broadband signal model. Numerical simulations show that the novel sonar outperforms the traditional phased-array sonar due to benefits of spatial diversity. The effectiveness of the proposed framework has been validated by localization of a small target in at-lake experiments. PMID:29642637

Rapid neural discrimination of communicative gestures.

PubMed

Redcay, Elizabeth; Carlson, Thomas A

2015-04-01

Humans are biased toward social interaction. Behaviorally, this bias is evident in the rapid effects that self-relevant communicative signals have on attention and perceptual systems. The processing of communicative cues recruits a wide network of brain regions, including mentalizing systems. Relatively less work, however, has examined the timing of the processing of self-relevant communicative cues. In the present study, we used multivariate pattern analysis (decoding) approach to the analysis of magnetoencephalography (MEG) to study the processing dynamics of social-communicative actions. Twenty-four participants viewed images of a woman performing actions that varied on a continuum of communicative factors including self-relevance (to the participant) and emotional valence, while their brain activity was recorded using MEG. Controlling for low-level visual factors, we found early discrimination of emotional valence (70 ms) and self-relevant communicative signals (100 ms). These data offer neural support for the robust and rapid effects of self-relevant communicative cues on behavior. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score.

PubMed

Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G

2014-09-01

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score

NASA Astrophysics Data System (ADS)

Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G.

2014-09-01

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

Stochastic Resonance in Signal Detection and Human Perception

DTIC Science & Technology

2006-07-05

learning scheme performing a stochastic gradient ascent on the SNR to determine the optimal noise level based on the samples from the process. Rather than...produce some SR effect in threshold neurons and a new statistically robust learning law was proposed to find the optimal noise level. [McDonnell...Ultimately, we know that it is the brain that responds to a visual stimulus causing neurons to fire. Conceivably if we understood the effect of the noise PDF

Robust low-frequency spread-spectrum navigation system

DOEpatents

Smith, Stephen F [Loudon, TN; Moore, James A [Powell, TN

2012-01-03

Methods and apparatus are described for a navigation system. A process includes providing a plurality of transmitters distributed throughout a desired coverage area; locking the plurality of transmitters to a common timing reference; transmitting a signal from each of the plurality of transmitters. An apparatus includes a plurality of transmitters distributed throughout a desired coverage area; wherein each of the plurality of transmitters comprises a packet generator; and wherein the plurality of transmitters are locked to a common timing reference.

Robust low-frequency spread-spectrum navigation system

DOEpatents

Smith, Stephen F [Loudon, TN; Moore, James A [Powell, TN

2011-01-25

Methods and apparatus are described for a navigation system. A process includes providing a plurality of transmitters distributed throughout a desired coverage area; locking the plurality of transmitters to a common timing reference; transmitting a signal from each of the plurality of transmitters. An apparatus includes a plurality of transmitters distributed throughout a desired coverage area; wherein each of the plurality of transmitters comprises a packet generator; and wherein the plurality of transmitters are locked to a common timing reference.

Robust low-frequency spread-spectrum navigation system

DOEpatents

Smith, Stephen F; Moore, James A

2012-10-30

Methods and apparatus are described for a navigation system. A process includes providing a plurality of transmitters distributed throughout a desired coverage area; locking the plurality of transmitters to a common timing reference; transmitting a signal from each of the plurality of transmitters. An apparatus includes a plurality of transmitters distributed throughout a desired coverage area; wherein each of the plurality of transmitters comprises a packet generator; and wherein the plurality of transmitters are locked to a common timing reference.

Robust low-frequency spread-spectrum navigation system

DOEpatents

Smith, Stephen F [Loudon, TN; Moore, James A [Powell, TN

2009-12-01

Methods and apparatus are described for a navigation system. A process includes providing a plurality of transmitters distributed throughout a desired coverage area; locking the plurality of transmitters to a common timing reference; transmitting a signal from each of the plurality of transmitters. An apparatus includes a plurality of transmitters distributed throughout a desired coverage area; wherein each of the plurality of transmitters comprises a packet generator; and wherein the plurality of transmitters are locked to a common timing reference.

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.

A Novel Texture-Quantization-Based Reversible Multiple Watermarking Scheme Applied to Health Information System.

PubMed

Turuk, Mousami; Dhande, Ashwin

2018-04-01

The recent innovations in information and communication technologies have appreciably changed the panorama of health information system (HIS). These advances provide new means to process, handle, and share medical images and also augment the medical image security issues in terms of confidentiality, reliability, and integrity. Digital watermarking has emerged as new era that offers acceptable solutions to the security issues in HIS. Texture is a significant feature to detect the embedding sites in an image, which further leads to substantial improvement in the robustness. However, considering the perspective of digital watermarking, this feature has received meager attention in the reported literature. This paper exploits the texture property of an image and presents a novel hybrid texture-quantization-based approach for reversible multiple watermarking. The watermarked image quality has been accessed by peak signal to noise ratio (PSNR), structural similarity measure (SSIM), and universal image quality index (UIQI), and the obtained results are superior to the state-of-the-art methods. The algorithm has been evaluated on a variety of medical imaging modalities (CT, MRA, MRI, US) and robustness has been verified, considering various image processing attacks including JPEG compression. The proposed scheme offers additional security using repetitive embedding of BCH encoded watermarks and ADM encrypted ECG signal. Experimental results achieved a maximum of 22,616 bits hiding capacity with PSNR of 53.64 dB.

SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal

PubMed Central

Cherkassky, Vladimir; Lee, Jieun; Veber, Brandon; Patterson, Edward E.; Brinkmann, Benjamin H.; Worrell, Gregory A.

2017-01-01

Objective This paper describes a data-analytic modeling approach for prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. Methods Our work emphasizes understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during pre-processing and post-processing are considered and investigated for their effect on seizure prediction accuracy. Results Our empirical results show that the proposed SVM-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90–100%, and the false-positive rate is about 0–0.3 times per day. The results also suggest good prediction is subject-specific (dog or human), in agreement with earlier studies. Conclusion Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5–7 seizures. Significance The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages. PMID:27362758

Determinants of cell-to-cell variability in protein kinase signaling.

PubMed

Jeschke, Matthias; Baumgärtner, Stephan; Legewie, Stefan

2013-01-01

Cells reliably sense environmental changes despite internal and external fluctuations, but the mechanisms underlying robustness remain unclear. We analyzed how fluctuations in signaling protein concentrations give rise to cell-to-cell variability in protein kinase signaling using analytical theory and numerical simulations. We characterized the dose-response behavior of signaling cascades by calculating the stimulus level at which a pathway responds ('pathway sensitivity') and the maximal activation level upon strong stimulation. Minimal kinase cascades with gradual dose-response behavior show strong variability, because the pathway sensitivity and the maximal activation level cannot be simultaneously invariant. Negative feedback regulation resolves this trade-off and coordinately reduces fluctuations in the pathway sensitivity and maximal activation. Feedbacks acting at different levels in the cascade control different aspects of the dose-response curve, thereby synergistically reducing the variability. We also investigated more complex, ultrasensitive signaling cascades capable of switch-like decision making, and found that these can be inherently robust to protein concentration fluctuations. We describe how the cell-to-cell variability of ultrasensitive signaling systems can be actively regulated, e.g., by altering the expression of phosphatase(s) or by feedback/feedforward loops. Our calculations reveal that slow transcriptional negative feedback loops allow for variability suppression while maintaining switch-like decision making. Taken together, we describe design principles of signaling cascades that promote robustness. Our results may explain why certain signaling cascades like the yeast pheromone pathway show switch-like decision making with little cell-to-cell variability.

BCI Competition IV – Data Set I: Learning Discriminative Patterns for Self-Paced EEG-Based Motor Imagery Detection

PubMed Central

Zhang, Haihong; Guan, Cuntai; Ang, Kai Keng; Wang, Chuanchu

2012-01-01

Detecting motor imagery activities versus non-control in brain signals is the basis of self-paced brain-computer interfaces (BCIs), but also poses a considerable challenge to signal processing due to the complex and non-stationary characteristics of motor imagery as well as non-control. This paper presents a self-paced BCI based on a robust learning mechanism that extracts and selects spatio-spectral features for differentiating multiple EEG classes. It also employs a non-linear regression and post-processing technique for predicting the time-series of class labels from the spatio-spectral features. The method was validated in the BCI Competition IV on Dataset I where it produced the lowest prediction error of class labels continuously. This report also presents and discusses analysis of the method using the competition data set. PMID:22347153

An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission

NASA Astrophysics Data System (ADS)

Aye, S. A.; Heyns, P. S.

2017-02-01

This paper proposes an optimal Gaussian process regression (GPR) for the prediction of remaining useful life (RUL) of slow speed bearings based on a novel degradation assessment index obtained from acoustic emission signal. The optimal GPR is obtained from an integration or combination of existing simple mean and covariance functions in order to capture the observed trend of the bearing degradation as well the irregularities in the data. The resulting integrated GPR model provides an excellent fit to the data and improves over the simple GPR models that are based on simple mean and covariance functions. In addition, it achieves a low percentage error prediction of the remaining useful life of slow speed bearings. These findings are robust under varying operating conditions such as loading and speed and can be applied to nonlinear and nonstationary machine response signals useful for effective preventive machine maintenance purposes.

Information content and cross-talk in biological signal transduction: An information theory study

NASA Astrophysics Data System (ADS)

Prasad, Ashok; Lyons, Samanthe

2014-03-01

Biological cells respond to chemical cues provided by extra-cellular chemical signals, but many of these chemical signals and the pathways they activate interfere and overlap with one another. How well cells can distinguish between interfering extra-cellular signals is thus an important question in cellular signal transduction. Here we use information theory with stochastic simulations of networks to address the question of what happens to total information content when signals interfere. We find that both total information transmitted by the biological pathway, as well as its theoretical capacity to discriminate between overlapping signals, are relatively insensitive to cross-talk between the extracellular signals, until significantly high levels of cross-talk have been reached. This robustness of information content against cross-talk requires that the average amplitude of the signals are large. We predict that smaller systems, as exemplified by simple phosphorylation relays (two-component systems) in bacteria, should be significantly much less robust against cross-talk. Our results suggest that mammalian signal transduction can tolerate a high amount of cross-talk without degrading information content, while smaller bacterial systems cannot.

Feedback, Mass Conservation and Reaction Kinetics Impact the Robustness of Cellular Oscillations

PubMed Central

Baum, Katharina; Kofahl, Bente; Steuer, Ralf; Wolf, Jana

2016-01-01

Oscillations occur in a wide variety of cellular processes, for example in calcium and p53 signaling responses, in metabolic pathways or within gene-regulatory networks, e.g. the circadian system. Since it is of central importance to understand the influence of perturbations on the dynamics of these systems a number of experimental and theoretical studies have examined their robustness. The period of circadian oscillations has been found to be very robust and to provide reliable timing. For intracellular calcium oscillations the period has been shown to be very sensitive and to allow for frequency-encoded signaling. We here apply a comprehensive computational approach to study the robustness of period and amplitude of oscillatory systems. We employ different prototype oscillator models and a large number of parameter sets obtained by random sampling. This framework is used to examine the effect of three design principles on the sensitivities towards perturbations of the kinetic parameters. We find that a prototype oscillator with negative feedback has lower period sensitivities than a prototype oscillator relying on positive feedback, but on average higher amplitude sensitivities. For both oscillator types, the use of Michaelis-Menten instead of mass action kinetics in all degradation and conversion reactions leads to an increase in period as well as amplitude sensitivities. We observe moderate changes in sensitivities if replacing mass conversion reactions by purely regulatory reactions. These insights are validated for a set of established models of various cellular rhythms. Overall, our work highlights the importance of reaction kinetics and feedback type for the variability of period and amplitude and therefore for the establishment of predictive models. PMID:28027301

Robust snow avalanche detection using machine learning on infrasonic array data

NASA Astrophysics Data System (ADS)

Thüring, Thomas; Schoch, Marcel; van Herwijnen, Alec; Schweizer, Jürg

2014-05-01

Snow avalanches may threaten people and infrastructure in mountain areas. Automated detection of avalanche activity would be highly desirable, in particular during times of poor visibility, to improve hazard assessment, but also to monitor the effectiveness of avalanche control by explosives. In the past, a variety of remote sensing techniques and instruments for the automated detection of avalanche activity have been reported, which are based on radio waves (radar), seismic signals (geophone), optical signals (imaging sensor) or infrasonic signals (microphone). Optical imagery enables to assess avalanche activity with very high spatial resolution, however it is strongly weather dependent. Radar and geophone-based detection typically provide robust avalanche detection for all weather conditions, but are very limited in the size of the monitoring area. On the other hand, due to the long propagation distance of infrasound through air, the monitoring area of infrasonic sensors can cover a large territory using a single sensor (or an array). In addition, they are by far more cost effective than radars or optical imaging systems. Unfortunately, the reliability of infrasonic sensor systems has so far been rather low due to the strong variation of ambient noise (e.g. wind) causing a high false alarm rate. We analyzed the data collected by a low-cost infrasonic array system consisting of four sensors for the automated detection of avalanche activity at Lavin in the eastern Swiss Alps. A comparably large array aperture (~350m) allows highly accurate time delay estimations of signals which arrive at different times at the sensors, enabling precise source localization. An array of four sensors is sufficient for the time resolved source localization of signals in full 3D space, which is an excellent method to anticipate true avalanche activity. Robust avalanche detection is then achieved by using machine learning methods such as support vector machines. The system is initially trained by using characteristic data features from known avalanche and non-avalanche events. Data features are obtained from output signals of the source localization algorithm or from Fourier or time domain processing and support the learning phase of the system. A significantly improved detection rate as well as a reduction of the false alarm rate was achieved compared to previous approaches.

Multiframe video coding for improved performance over wireless channels.

PubMed

Budagavi, M; Gibson, J D

2001-01-01

We propose and evaluate a multi-frame extension to block motion compensation (BMC) coding of videoconferencing-type video signals for wireless channels. The multi-frame BMC (MF-BMC) coder makes use of the redundancy that exists across multiple frames in typical videoconferencing sequences to achieve additional compression over that obtained by using the single frame BMC (SF-BMC) approach, such as in the base-level H.263 codec. The MF-BMC approach also has an inherent ability of overcoming some transmission errors and is thus more robust when compared to the SF-BMC approach. We model the error propagation process in MF-BMC coding as a multiple Markov chain and use Markov chain analysis to infer that the use of multiple frames in motion compensation increases robustness. The Markov chain analysis is also used to devise a simple scheme which randomizes the selection of the frame (amongst the multiple previous frames) used in BMC to achieve additional robustness. The MF-BMC coders proposed are a multi-frame extension of the base level H.263 coder and are found to be more robust than the base level H.263 coder when subjected to simulated errors commonly encountered on wireless channels.

Spectro-Temporal Electrocardiogram Analysis for Noise-Robust Heart Rate and Heart Rate Variability Measurement

PubMed Central

Tobón, Diana P.; Jayaraman, Srinivasan

2017-01-01

The last few years has seen a proliferation of wearable electrocardiogram (ECG) devices in the market with applications in fitness tracking, patient monitoring, athletic performance assessment, stress and fatigue detection, and biometrics, to name a few. The majority of these applications rely on the computation of the heart rate (HR) and the so-called heart rate variability (HRV) index via time-, frequency-, or non-linear-domain approaches. Wearable/portable devices, however, are highly susceptible to artifacts, particularly those resultant from movement. These artifacts can hamper HR/HRV measurement, thus pose a serious threat to cardiac monitoring applications. While current solutions rely on ECG enhancement as a pre-processing step prior to HR/HRV calculation, existing artifact removal algorithms still perform poorly under extremely noisy scenarios. To overcome this limitation, we take an alternate approach and propose the use of a spectro-temporal ECG signal representation that we show separates cardiac components from artifacts. More specifically, by quantifying the rate-of-change of ECG spectral components over time, we show that heart rate estimates can be reliably obtained even in extremely noisy signals, thus bypassing the need for ECG enhancement. With such HR measurements in hands, we then propose a new noise-robust HRV index termed MD-HRV (modulation-domain HRV) computed as the standard deviation of the obtained HR values. Experiments with synthetic ECG signals corrupted at various different signal-to-noise levels, as well as recorded noisy signals show the proposed measure outperforming several HRV benchmark parameters computed post wavelet-based enhancement. These findings suggest that the proposed HR measures and derived MD-HRV metric are well-suited for ambulant cardiac monitoring applications, particularly those involving intense movement (e.g., elite athletic training). PMID:29255653

Leptin as a nutritional signal regulating appetite and reproductive processes in seasonally-breeding ruminants.

PubMed

Zieba, D A; Szczesna, M; Klocek-Gorka, B; Williams, G L

2008-12-01

Photoperiod and nutrition both exert major influences on reproduction. Thus, it seems axiomatic that seasonal rhythms in ovulation are influenced by nutrition. In this context, leptin is one of the most important hormonal signals involved in the control of energy homeostasis, feeding behavior and reproductive function in mammals. However, the number of published investigations establishing a functional interaction between leptin and photoperiodism in seasonal breeders is limited. In common with most seasonally-breeding mammals, sheep exhibit robust circannual cycles in body weight and reproduction, which are driven mainly by changes in day-length. Recently, attention has focused on the role of leptin in this process, particularly in its roles as a major peripheral signal controlling appetite, melatonin and prolactin secretion. The purpose herein is to review current concepts in the overall biology of leptin, to summarize its influence on the hypothalamic-pituitary axis, and to highlight recent developments in our understanding of its interaction with season in regulating appetite, body weight and reproduction in seasonally-breeding mammals. The latter observations may be important in delineating states of leptin resistance and obesity in humans.

Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

PubMed Central

Wang, Deng; Miao, Duoqian; Blohm, Gunnar

2012-01-01

Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. PMID:23087607

A large-scale and robust dynamic MRM study of colorectal cancer biomarkers.

PubMed

You, Jia; Kao, Athit; Dillon, Roslyn; Croner, Lisa J; Benz, Ryan; Blume, John E; Wilcox, Bruce

2018-06-25

Over the past 20 years, mass spectrometry (MS) has emerged as a dynamic tool for proteomics biomarker discovery. However, published MS biomarker candidates often do not translate to the clinic, failing during attempts at independent replication. The cause can be shortcomings in study design, sample quality, assay quantitation, and/or quality/process control. To address these shortcomings, we developed an MS workflow in accordance with Tier 2 measurement requirements for targeted peptides, defined by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) "fit-for-purpose" approach, using dynamic multiple reaction monitoring (dMRM) which measures specific peptide transitions during predefined retention time (RT) windows. We describe the development of a robust multipex dMRM assay measuring 641 proteotypic peptides from 392 colorectal cancer (CRC) related proteins, and the procedures to track and handle sample processing and instrument variation over a four-month study, during which the assay measured blood samples from 1045 patients with CRC symptoms. After data collection, transitions were filtered by signal quality metrics before entering receiver operating characteristic (ROC) analysis. The results demonstrated CRC signal carried by 127 proteins in the symptomatic population. The workflow might be further developed to build Tier 1 assays for clinical tests identifying symptomatic individuals at elevated risk of CRC. We developed a dMRM MS method with the rigor of a Tier 2 assay as defined by the CPTAC 'fit for purpose approach' [1]. Using quality and process control procedures, the assay was used to quantify 641 proteotypic peptides representing 392 CRC-related proteins in plasma from 1045 CRC-symptomatic patients. To our knowledge, this is the largest MRM method applied to the largest study to date. The results showed that 127 of the proteins carried univariate CRC signal in the symptomatic population. This large number of single biomarkers bodes well for future development of multivariate classifiers to distinguish CRC in the symptomatic population. Copyright © 2018. Published by Elsevier B.V.

Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design

PubMed Central

Lotte, Fabien; Larrue, Florian; Mühl, Christian

2013-01-01

While recent research on Brain-Computer Interfaces (BCI) has highlighted their potential for many applications, they remain barely used outside laboratories. The main reason is their lack of robustness. Indeed, with current BCI, mental state recognition is usually slow and often incorrect. Spontaneous BCI (i.e., mental imagery-based BCI) often rely on mutual learning efforts by the user and the machine, with BCI users learning to produce stable ElectroEncephaloGraphy (EEG) patterns (spontaneous BCI control being widely acknowledged as a skill) while the computer learns to automatically recognize these EEG patterns, using signal processing. Most research so far was focused on signal processing, mostly neglecting the human in the loop. However, how well the user masters the BCI skill is also a key element explaining BCI robustness. Indeed, if the user is not able to produce stable and distinct EEG patterns, then no signal processing algorithm would be able to recognize them. Unfortunately, despite the importance of BCI training protocols, they have been scarcely studied so far, and used mostly unchanged for years. In this paper, we advocate that current human training approaches for spontaneous BCI are most likely inappropriate. We notably study instructional design literature in order to identify the key requirements and guidelines for a successful training procedure that promotes a good and efficient skill learning. This literature study highlights that current spontaneous BCI user training procedures satisfy very few of these requirements and hence are likely to be suboptimal. We therefore identify the flaws in BCI training protocols according to instructional design principles, at several levels: in the instructions provided to the user, in the tasks he/she has to perform, and in the feedback provided. For each level, we propose new research directions that are theoretically expected to address some of these flaws and to help users learn the BCI skill more efficiently. PMID:24062669

An adaptive spatio-temporal Gaussian filter for processing cardiac optical mapping data.

PubMed

Pollnow, S; Pilia, N; Schwaderlapp, G; Loewe, A; Dössel, O; Lenis, G

2018-06-04

Optical mapping is widely used as a tool to investigate cardiac electrophysiology in ex vivo preparations. Digital filtering of fluorescence-optical data is an important requirement for robust subsequent data analysis and still a challenge when processing data acquired from thin mammalian myocardium. Therefore, we propose and investigate the use of an adaptive spatio-temporal Gaussian filter for processing optical mapping signals from these kinds of tissue usually having low signal-to-noise ratio (SNR). We demonstrate how filtering parameters can be chosen automatically without additional user input. For systematic comparison of this filter with standard filtering methods from the literature, we generated synthetic signals representing optical recordings from atrial myocardium of a rat heart with varying SNR. Furthermore, all filter methods were applied to experimental data from an ex vivo setup. Our developed filter outperformed the other filter methods regarding local activation time detection at SNRs smaller than 3 dB which are typical noise ratios expected in these signals. At higher SNRs, the proposed filter performed slightly worse than the methods from literature. In conclusion, the proposed adaptive spatio-temporal Gaussian filter is an appropriate tool for investigating fluorescence-optical data with low SNR. The spatio-temporal filter parameters were automatically adapted in contrast to the other investigated filters. Copyright © 2018 Elsevier Ltd. All rights reserved.

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar

PubMed Central

Shin, Young Hoon; Seo, Jiwon

2016-01-01

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker’s vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing. PMID:27801867

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar.

PubMed

Shin, Young Hoon; Seo, Jiwon

2016-10-29

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker's vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing.

High frequency source localization in a shallow ocean sound channel using frequency difference matched field processing.

PubMed

Worthmann, Brian M; Song, H C; Dowling, David R

2015-12-01

Matched field processing (MFP) is an established technique for source localization in known multipath acoustic environments. Unfortunately, in many situations, particularly those involving high frequency signals, imperfect knowledge of the actual propagation environment prevents accurate propagation modeling and source localization via MFP fails. For beamforming applications, this actual-to-model mismatch problem was mitigated through a frequency downshift, made possible by a nonlinear array-signal-processing technique called frequency difference beamforming [Abadi, Song, and Dowling (2012). J. Acoust. Soc. Am. 132, 3018-3029]. Here, this technique is extended to conventional (Bartlett) MFP using simulations and measurements from the 2011 Kauai Acoustic Communications MURI experiment (KAM11) to produce ambiguity surfaces at frequencies well below the signal bandwidth where the detrimental effects of mismatch are reduced. Both the simulation and experimental results suggest that frequency difference MFP can be more robust against environmental mismatch than conventional MFP. In particular, signals of frequency 11.2 kHz-32.8 kHz were broadcast 3 km through a 106-m-deep shallow ocean sound channel to a sparse 16-element vertical receiving array. Frequency difference MFP unambiguously localized the source in several experimental data sets with average peak-to-side-lobe ratio of 0.9 dB, average absolute-value range error of 170 m, and average absolute-value depth error of 10 m.

Measurement of fast-changing low velocities by photonic Doppler velocimetry

NASA Astrophysics Data System (ADS)

Song, Hongwei; Wu, Xianqian; Huang, Chenguang; Wei, Yangpeng; Wang, Xi

2012-07-01

Despite the increasing popularity of photonic Doppler velocimetry (PDV) in shock wave experiments, its capability of capturing low particle velocities while changing rapidly is still questionable. The paper discusses the performance of short time Fourier transform (STFT) and continuous wavelet transform (CWT) in processing fringe signals of fast-changing low velocities measured by PDV. Two typical experiments are carried out to evaluate the performance. In the laser shock peening test, the CWT gives a better interpretation to the free surface velocity history, where the elastic precursor, main plastic wave, and elastic release wave can be clearly identified. The velocities of stress waves, Hugoniot elastic limit, and the amplitude of shock pressure induced by laser can be obtained from the measurement. In the Kolsky-bar based tests, both methods show validity of processing the longitudinal velocity signal of incident bar, whereas CWT improperly interprets the radial velocity of the shocked sample at the beginning period, indicating the sensitiveness of the CWT to the background noise. STFT is relatively robust in extracting waveforms of low signal-to-noise ratio. Data processing method greatly affects the temporal resolution and velocity resolution of a given fringe signal, usually CWT demonstrates a better local temporal resolution and velocity resolution, due to its adaptability to the local frequency, also due to the finer time-frequency product according to the uncertainty principle.

Measurement of fast-changing low velocities by photonic Doppler velocimetry.

PubMed

Song, Hongwei; Wu, Xianqian; Huang, Chenguang; Wei, Yangpeng; Wang, Xi

2012-07-01

Despite the increasing popularity of photonic Doppler velocimetry (PDV) in shock wave experiments, its capability of capturing low particle velocities while changing rapidly is still questionable. The paper discusses the performance of short time Fourier transform (STFT) and continuous wavelet transform (CWT) in processing fringe signals of fast-changing low velocities measured by PDV. Two typical experiments are carried out to evaluate the performance. In the laser shock peening test, the CWT gives a better interpretation to the free surface velocity history, where the elastic precursor, main plastic wave, and elastic release wave can be clearly identified. The velocities of stress waves, Hugoniot elastic limit, and the amplitude of shock pressure induced by laser can be obtained from the measurement. In the Kolsky-bar based tests, both methods show validity of processing the longitudinal velocity signal of incident bar, whereas CWT improperly interprets the radial velocity of the shocked sample at the beginning period, indicating the sensitiveness of the CWT to the background noise. STFT is relatively robust in extracting waveforms of low signal-to-noise ratio. Data processing method greatly affects the temporal resolution and velocity resolution of a given fringe signal, usually CWT demonstrates a better local temporal resolution and velocity resolution, due to its adaptability to the local frequency, also due to the finer time-frequency product according to the uncertainty principle.

Measurement of fast-changing low velocities by photonic Doppler velocimetry

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

Song Hongwei; Wu Xianqian; Huang Chenguang

2012-07-15

Despite the increasing popularity of photonic Doppler velocimetry (PDV) in shock wave experiments, its capability of capturing low particle velocities while changing rapidly is still questionable. The paper discusses the performance of short time Fourier transform (STFT) and continuous wavelet transform (CWT) in processing fringe signals of fast-changing low velocities measured by PDV. Two typical experiments are carried out to evaluate the performance. In the laser shock peening test, the CWT gives a better interpretation to the free surface velocity history, where the elastic precursor, main plastic wave, and elastic release wave can be clearly identified. The velocities of stressmore » waves, Hugoniot elastic limit, and the amplitude of shock pressure induced by laser can be obtained from the measurement. In the Kolsky-bar based tests, both methods show validity of processing the longitudinal velocity signal of incident bar, whereas CWT improperly interprets the radial velocity of the shocked sample at the beginning period, indicating the sensitiveness of the CWT to the background noise. STFT is relatively robust in extracting waveforms of low signal-to-noise ratio. Data processing method greatly affects the temporal resolution and velocity resolution of a given fringe signal, usually CWT demonstrates a better local temporal resolution and velocity resolution, due to its adaptability to the local frequency, also due to the finer time-frequency product according to the uncertainty principle.« less

MIMO-OFDM signal optimization for SAR imaging radar

NASA Astrophysics Data System (ADS)

Baudais, J.-Y.; Méric, S.; Riché, V.; Pottier, É.

2016-12-01

This paper investigates the optimization of the coded orthogonal frequency division multiplexing (OFDM) transmitted signal in a synthetic aperture radar (SAR) context. We propose to design OFDM signals to achieve range ambiguity mitigation. Indeed, range ambiguities are well known to be a limitation for SAR systems which operates with pulsed transmitted signal. The ambiguous reflected signal corresponding to one pulse is then detected when the radar has already transmitted the next pulse. In this paper, we demonstrate that the range ambiguity mitigation is possible by using orthogonal transmitted wave as OFDM pulses. The coded OFDM signal is optimized through genetic optimization procedures based on radar image quality parameters. Moreover, we propose to design a multiple-input multiple-output (MIMO) configuration to enhance the noise robustness of a radar system and this configuration is mainly efficient in the case of using orthogonal waves as OFDM pulses. The results we obtain show that OFDM signals outperform conventional radar chirps for range ambiguity suppression and for robustness enhancement in 2 ×2 MIMO configuration.

Multiple cis-acting signals, some weak by necessity, collectively direct robust transport of oskar mRNA to the oocyte.

PubMed

Ryu, Young Hee; Kenny, Andrew; Gim, Youme; Snee, Mark; Macdonald, Paul M

2017-09-15

Localization of mRNAs can involve multiple steps, each with its own cis -acting localization signals and transport factors. How is the transition between different steps orchestrated? We show that the initial step in localization of Drosophila oskar mRNA - transport from nurse cells to the oocyte - relies on multiple cis -acting signals. Some of these are binding sites for the translational control factor Bruno, suggesting that Bruno plays an additional role in mRNA transport. Although transport of oskar mRNA is essential and robust, the localization activity of individual transport signals is weak. Notably, increasing the strength of individual transport signals, or adding a strong transport signal, disrupts the later stages of oskar mRNA localization. We propose that the oskar transport signals are weak by necessity; their weakness facilitates transfer of the oskar mRNA from the oocyte transport machinery to the machinery for posterior localization. © 2017. Published by The Company of Biologists Ltd.

Gas chromatography - mass spectrometry data processing made easy.

PubMed

Johnsen, Lea G; Skou, Peter B; Khakimov, Bekzod; Bro, Rasmus

2017-06-23

Evaluation of GC-MS data may be challenging due to the high complexity of data including overlapped, embedded, retention time shifted and low S/N ratio peaks. In this work, we demonstrate a new approach, PARAFAC2 based Deconvolution and Identification System (PARADISe), for processing raw GC-MS data. PARADISe is a computer platform independent freely available software incorporating a number of newly developed algorithms in a coherent framework. It offers a solution for analysts dealing with complex chromatographic data. It allows extraction of chemical/metabolite information directly from the raw data. Using PARADISe requires only few inputs from the analyst to process GC-MS data and subsequently converts raw netCDF data files into a compiled peak table. Furthermore, the method is generally robust towards minor variations in the input parameters. The method automatically performs peak identification based on deconvoluted mass spectra using integrated NIST search engine and generates an identification report. In this paper, we compare PARADISe with AMDIS and ChromaTOF in terms of peak quantification and show that PARADISe is more robust to user-defined settings and that these are easier (and much fewer) to set. PARADISe is based on non-proprietary scientifically evaluated approaches and we here show that PARADISe can handle more overlapping signals, lower signal-to-noise peaks and do so in a manner that requires only about an hours worth of work regardless of the number of samples. We also show that there are no non-detects in PARADISe, meaning that all compounds are detected in all samples. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Interrelationship of canonical and non-canonical Wnt signalling pathways in chronic metabolic diseases.

PubMed

Ackers, Ian; Malgor, Ramiro

2018-01-01

Chronic diseases account for approximately 45% of all deaths in developed countries and are particularly prevalent in countries with the most sophisticated and robust public health systems. Chronic metabolic diseases, specifically lifestyle-related diseases pertaining to diet and exercise, continue to be difficult to treat clinically. The most prevalent of these chronic metabolic diseases include obesity, diabetes, non-alcoholic fatty liver disease, chronic kidney disease and cardiovascular disease and will be the focus of this review. Wnt proteins are highly conserved glycoproteins best known for their role in development and homeostasis of tissues. Given the importance of Wnt signalling in homeostasis, aberrant Wnt signalling likely regulates metabolic processes and may contribute to the development of chronic metabolic diseases. Expression of Wnt proteins and dysfunctional Wnt signalling has been reported in multiple chronic diseases. It is interesting to speculate about an interrelationship between the Wnt signalling pathways as a potential pathological mechanism in chronic metabolic diseases. The aim of this review is to summarize reported findings on the contrasting roles of Wnt signalling in lifestyle-related chronic metabolic diseases; specifically, the contribution of Wnt signalling to lipid accumulation, fibrosis and chronic low-grade inflammation.

Quantum neural network-based EEG filtering for a brain-computer interface.

PubMed

Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

2014-02-01

A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

Robust sub-shot-noise measurement via Rabi-Josephson oscillations in bimodal Bose-Einstein condensates

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

Tikhonenkov, I.; Vardi, A.; Moore, M. G.

2011-06-15

Mach-Zehnder atom interferometry requires hold-time phase squeezing to attain readout accuracy below the standard quantum limit. This increases its sensitivity to phase diffusion, restoring shot-noise scaling of the optimal signal-to-noise ratio in the presence of interactions. The contradiction between the preparations required for readout accuracy and robustness to interactions is removed by monitoring Rabi-Josephson oscillations instead of relative-phase oscillations during signal acquisition. Optimizing the signal-to-noise ratio with a Gaussian squeezed input, we find that hold-time number squeezing satisfies both demands and that sub-shot-noise scaling is retained even for strong interactions.

Chaos based encryption system for encrypting electroencephalogram signals.

PubMed

Lin, Chin-Feng; Shih, Shun-Han; Zhu, Jin-De

2014-05-01

In the paper, we use the Microsoft Visual Studio Development Kit and C# programming language to implement a chaos-based electroencephalogram (EEG) encryption system involving three encryption levels. A chaos logic map, initial value, and bifurcation parameter for the map were used to generate Level I chaos-based EEG encryption bit streams. Two encryption-level parameters were added to these elements to generate Level II chaos-based EEG encryption bit streams. An additional chaotic map and chaotic address index assignment process was used to implement the Level III chaos-based EEG encryption system. Eight 16-channel EEG Vue signals were tested using the encryption system. The encryption was the most rapid and robust in the Level III system. The test yielded superior encryption results, and when the correct deciphering parameter was applied, the EEG signals were completely recovered. However, an input parameter error (e.g., a 0.00001 % initial point error) causes chaotic encryption bit streams, preventing the recovery of 16-channel EEG Vue signals.

BMP Signaling in Astrocytes Downregulates EGFR to Modulate Survival and Maturation

PubMed Central

Scholze, Anja R.; Foo, Lynette C.; Mulinyawe, Sara; Barres, Ben A.

2014-01-01

Astrocytes constitute a major cell population in the brain with a myriad of essential functions, yet we know remarkably little about the signaling pathways and mechanisms that direct astrocyte maturation. To explore the signals regulating astrocyte development, we prospectively purified and cultured immature postnatal rodent astrocytes. We identified fibroblast growth factors (FGFs) and bone morphogenetic proteins (BMPs) as robust trophic factors for immature astrocytes. We showed that astrocytes respond directly to BMPs via phosphorylation of the smad1/5/8 pathway. In vitro, BMP signaling promoted immature astrocytes to adopt multiple characteristics of mature astrocytes, including a more process-bearing morphology, aquaporin-4 (AQP4) and S100β immunoreactivity, limited proliferation, and strong downregulation of epidermal growth factor receptor (EGFR). In vivo, activation of the smad1/5/8 pathway in astrocytes was seen during early postnatal development, but inhibition of astrocyte proliferation was not observed. These insights can aid in the further dissection of the mechanisms and pathways controlling astrocyte biology and development. PMID:25330173

Regulation of EGFR signal transduction by analogue-to-digital conversion in endosomes

PubMed Central

Villaseñor, Roberto; Nonaka, Hidenori; Del Conte-Zerial, Perla; Kalaidzidis, Yannis; Zerial, Marino

2015-01-01

An outstanding question is how receptor tyrosine kinases (RTKs) determine different cell-fate decisions despite sharing the same signalling cascades. Here, we uncovered an unexpected mechanism of RTK trafficking in this process. By quantitative high-resolution FRET microscopy, we found that phosphorylated epidermal growth factor receptor (p-EGFR) is not randomly distributed but packaged at constant mean amounts in endosomes. Cells respond to higher EGF concentrations by increasing the number of endosomes but keeping the mean p-EGFR content per endosome almost constant. By mathematical modelling, we found that this mechanism confers both robustness and regulation to signalling output. Different growth factors caused specific changes in endosome number and size in various cell systems and changing the distribution of p-EGFR between endosomes was sufficient to reprogram cell-fate decision upon EGF stimulation. We propose that the packaging of p-RTKs in endosomes is a general mechanism to ensure the fidelity and specificity of the signalling response. DOI: http://dx.doi.org/10.7554/eLife.06156.001 PMID:25650738

Radar modulation classification using time-frequency representation and nonlinear regression

NASA Astrophysics Data System (ADS)

De Luigi, Christophe; Arques, Pierre-Yves; Lopez, Jean-Marc; Moreau, Eric

1999-09-01

In naval electronic environment, pulses emitted by radars are collected by ESM receivers. For most of them the intrapulse signal is modulated by a particular law. To help the classical identification process, a classification and estimation of this modulation law is applied on the intrapulse signal measurements. To estimate with a good accuracy the time-varying frequency of a signal corrupted by an additive noise, one method has been chosen. This method consists on the Wigner distribution calculation, the instantaneous frequency is then estimated by the peak location of the distribution. Bias and variance of the estimator are performed by computed simulations. In a estimated sequence of frequencies, we assume the presence of false and good estimated ones, the hypothesis of Gaussian distribution is made on the errors. A robust non linear regression method, based on the Levenberg-Marquardt algorithm, is thus applied on these estimated frequencies using a Maximum Likelihood Estimator. The performances of the method are tested by using varied modulation laws and different signal to noise ratios.

Method for traceable measurement of LTE signals

NASA Astrophysics Data System (ADS)

Sunder Dash, Soumya; Pythoud, Frederic; Leuchtmann, Pascal; Leuthold, Juerg

2018-04-01

This contribution presents a reference setup to measure the power of the cell-specific resource elements present in downlink long term evolution (LTE) signals in a way that the measurements are traceable to the international system of units. This setup can be used to calibrate the LTE code-selective field probes that are used to measure the radiation of base stations for mobile telephony. It can also be used to calibrate LTE signal generators and receivers. The method is based on traceable scope measurements performed directly at the output of a measuring antenna. It implements offline digital signal processing demodulation algorithms that consider the digital down-conversion, timing synchronization, frequency synchronization, phase synchronization and robust LTE cell identification to produce the downlink time-frequency LTE grid. Experimental results on conducted test scenarios, both single-input-single-output and multiple-input-multiple-output antenna configuration, show promising results confirming measurement uncertainties of the order of 0.05 dB with a coverage factor of 2.

A Fast MEANSHIFT Algorithm-Based Target Tracking System

PubMed Central

Sun, Jian

2012-01-01

Tracking moving targets in complex scenes using an active video camera is a challenging task. Tracking accuracy and efficiency are two key yet generally incompatible aspects of a Target Tracking System (TTS). A compromise scheme will be studied in this paper. A fast mean-shift-based Target Tracking scheme is designed and realized, which is robust to partial occlusion and changes in object appearance. The physical simulation shows that the image signal processing speed is >50 frame/s. PMID:22969397

Low-order auditory Zernike moment: a novel approach for robust music identification in the compressed domain

NASA Astrophysics Data System (ADS)

Li, Wei; Xiao, Chuan; Liu, Yaduo

2013-12-01

Audio identification via fingerprint has been an active research field for years. However, most previously reported methods work on the raw audio format in spite of the fact that nowadays compressed format audio, especially MP3 music, has grown into the dominant way to store music on personal computers and/or transmit it over the Internet. It will be interesting if a compressed unknown audio fragment could be directly recognized from the database without decompressing it into the wave format at first. So far, very few algorithms run directly on the compressed domain for music information retrieval, and most of them take advantage of the modified discrete cosine transform coefficients or derived cepstrum and energy type of features. As a first attempt, we propose in this paper utilizing compressed domain auditory Zernike moment adapted from image processing techniques as the key feature to devise a novel robust audio identification algorithm. Such fingerprint exhibits strong robustness, due to its statistically stable nature, against various audio signal distortions such as recompression, noise contamination, echo adding, equalization, band-pass filtering, pitch shifting, and slight time scale modification. Experimental results show that in a music database which is composed of 21,185 MP3 songs, a 10-s long music segment is able to identify its original near-duplicate recording, with average top-5 hit rate up to 90% or above even under severe audio signal distortions.

Lagged correlations between the NAO and the 11-year solar cycle: forced response or internal variability?

NASA Astrophysics Data System (ADS)

Oehrlein, J.; Chiodo, G.; Polvani, L. M.; Smith, A. K.

2017-12-01

Recently, the North Atlantic Oscillation has been suggested to respond to the 11-year solar cycle with a lag of a few years. The solar/NAO relationship provides a potential pathway for solar activity to modulate surface climate. However, a short observational record paired with the strong internal variability of the NAO raises questions about the robustness of the claimed solar/NAO relationship. For the first time, we investigate the robustness of the solar/NAO signal in four different reanalysis data sets and long integrations from an ocean-coupled chemistry-climate model forced with the 11-year solar cycle. The signal appears to be robust in the different reanalysis datasets. We also show, for the first time, that many features of the observed signal, such as amplitude, spatial pattern, and lag of 2/3 years, can be accurately reproduced in our model simulations. However, in both the reanalysis and model simulations, we find that this signal is non-stationary. A lagged NAO/solar signal can also be reproduced in two sets of model integrations without the 11-year solar cycle. This suggests that the correlation found in observational data could be the result of internal decadal variability in the NAO and not a response to the solar cycle. This has wide implications towards the interpretation of solar signals in observational data.

Assuring SS7 dependability: A robustness characterization of signaling network elements

NASA Astrophysics Data System (ADS)

Karmarkar, Vikram V.

1994-04-01

Current and evolving telecommunication services will rely on signaling network performance and reliability properties to build competitive call and connection control mechanisms under increasing demands on flexibility without compromising on quality. The dimensions of signaling dependability most often evaluated are the Rate of Call Loss and End-to-End Route Unavailability. A third dimension of dependability that captures the concern about large or catastrophic failures can be termed Network Robustness. This paper is concerned with the dependability aspects of the evolving Signaling System No. 7 (SS7) networks and attempts to strike a balance between the probabilistic and deterministic measures that must be evaluated to accomplish a risk-trend assessment to drive architecture decisions. Starting with high-level network dependability objectives and field experience with SS7 in the U.S., potential areas of growing stringency in network element (NE) dependability are identified to improve against current measures of SS7 network quality, as per-call signaling interactions increase. A sensitivity analysis is presented to highlight the impact due to imperfect coverage of duplex network component or element failures (i.e., correlated failures), to assist in the setting of requirements on NE robustness. A benefit analysis, covering several dimensions of dependability, is used to generate the domain of solutions available to the network architect in terms of network and network element fault tolerance that may be specified to meet the desired signaling quality goals.

Differential effect of visual masking in perceptual categorization.

PubMed

Hélie, Sébastien; Cousineau, Denis

2015-06-01

This article explores the visual information used to categorize stimuli drawn from a common stimulus space into verbal and nonverbal categories using 2 experiments. Experiment 1 explores the effect of target duration on verbal and nonverbal categorization using backward masking to interrupt visual processing. With categories equated for difficulty for long and short target durations, intermediate target duration shows an advantage for verbal categorization over nonverbal categorization. Experiment 2 tests whether the results of Experiment 1 can be explained by shorter target duration resulting in a smaller signal-to-noise ratio of the categorization stimulus. To test for this possibility, Experiment 2 used integration masking with the same stimuli, categories, and masks as Experiment 1 with a varying level of mask opacity. As predicted, low mask opacity yielded similar results to long target duration while high mask opacity yielded similar results to short target duration. Importantly, intermediate mask opacity produced an advantage for verbal categorization over nonverbal categorization, similar to intermediate target duration. These results suggest that verbal and nonverbal categorization are affected differently by manipulations affecting the signal-to-noise ratio of the stimulus, consistent with multiple-system theories of categorizations. The results further suggest that verbal categorization may be more digital (and more robust to low signal-to-noise ratio) while the information used in nonverbal categorization may be more analog (and less robust to lower signal-to-noise ratio). This article concludes with a discussion of how these new results affect the use of masking in perceptual categorization and multiple-system theories of perceptual category learning. (c) 2015 APA, all rights reserved).

Acoustic Remote Sensing

NASA Astrophysics Data System (ADS)

Dowling, David R.; Sabra, Karim G.

2015-01-01

Acoustic waves carry information about their source and collect information about their environment as they propagate. This article reviews how these information-carrying and -collecting features of acoustic waves that travel through fluids can be exploited for remote sensing. In nearly all cases, modern acoustic remote sensing involves array-recorded sounds and array signal processing to recover multidimensional results. The application realm for acoustic remote sensing spans an impressive range of signal frequencies (10-2 to 107 Hz) and distances (10-2 to 107 m) and involves biomedical ultrasound imaging, nondestructive evaluation, oil and gas exploration, military systems, and Nuclear Test Ban Treaty monitoring. In the past two decades, approaches have been developed to robustly localize remote sources; remove noise and multipath distortion from recorded signals; and determine the acoustic characteristics of the environment through which the sound waves have traveled, even when the recorded sounds originate from uncooperative sources or are merely ambient noise.

Secure relay selection based on learning with negative externality in wireless networks

NASA Astrophysics Data System (ADS)

Zhao, Caidan; Xiao, Liang; Kang, Shan; Chen, Guiquan; Li, Yunzhou; Huang, Lianfen

2013-12-01

In this paper, we formulate relay selection into a Chinese restaurant game. A secure relay selection strategy is proposed for a wireless network, where multiple source nodes send messages to their destination nodes via several relay nodes, which have different processing and transmission capabilities as well as security properties. The relay selection utilizes a learning-based algorithm for the source nodes to reach their best responses in the Chinese restaurant game. In particular, the relay selection takes into account the negative externality of relay sharing among the source nodes, which learn the capabilities and security properties of relay nodes according to the current signals and the signal history. Simulation results show that this strategy improves the user utility and the overall security performance in wireless networks. In addition, the relay strategy is robust against the signal errors and deviations of some user from the desired actions.

Phase Sensitive Demodulation in Multiphoton Microscopy

NASA Astrophysics Data System (ADS)

Fisher, Walt G.; Piston, David W.; Wachter, Eric A.

2002-06-01

Multiphoton laser scanning microscopy offers advantages in depth of penetration into intact samples over other optical sectioning techniques. To achieve these advantages it is necessary to detect the emitted light without spatial filtering. In this nondescanned (nonconfocal) approach, ambient room light can easily contaminate the signal, forcing experiments to be performed in absolute darkness. For multiphoton microscope systems employing mode-locked lasers, signal processing can be used to reduce such problems by taking advantage of the pulsed characteristics of such lasers. Specifically, by recovering fluorescence generated at the mode-locked frequency, interference from stray light and other ambient noise sources can be significantly reduced. This technology can be adapted to existing microscopes by inserting demodulation circuitry between the detector and data collection system. The improvement in signal-to-noise ratio afforded by this approach yields a more robust microscope system and opens the possibility of moving multiphoton microscopy from the research lab to more demanding settings, such as the clinic.

Automatic detection of slight parameter changes associated to complex biomedical signals using multiresolution q-entropy1.

PubMed

Torres, M E; Añino, M M; Schlotthauer, G

2003-12-01

It is well known that, from a dynamical point of view, sudden variations in physiological parameters which govern certain diseases can cause qualitative changes in the dynamics of the corresponding physiological process. The purpose of this paper is to introduce a technique that allows the automated temporal localization of slight changes in a parameter of the law that governs the nonlinear dynamics of a given signal. This tool takes, from the multiresolution entropies, the ability to show these changes as statistical variations at each scale. These variations are held in the corresponding principal component. Appropriately combining these techniques with a statistical changes detector, a complexity change detection algorithm is obtained. The relevance of the approach, together with its robustness in the presence of moderate noise, is discussed in numerical simulations and the automatic detector is applied to real and simulated biological signals.

RAPID COMMUNICATION Time-resolved measurements with a vortex flowmeter in a pulsating turbulent flow using wavelet analysis

NASA Astrophysics Data System (ADS)

Laurantzon, F.; Örlü, R.; Segalini, A.; Alfredsson, P. H.

2010-12-01

Vortex flowmeters are commonly employed in technical applications and are obtainable in a variety of commercially available types. However their robustness and accuracy can easily be impaired by environmental conditions, such as inflow disturbances and/or pulsating conditions. Various post-processing techniques of the vortex signal have been used, but all of these methods are so far targeted on obtaining an improved estimate of the time-averaged bulk velocity. Here, on the other hand, we propose, based on wavelet analysis, a straightforward way to utilize the signal from a vortex shedder to extract the time-resolved and thereby the phase-averaged velocity under pulsatile flow conditions. The method was verified with hot-wire and laser Doppler velocimetry measurements.

Scaling up digital circuit computation with DNA strand displacement cascades.

PubMed

Qian, Lulu; Winfree, Erik

2011-06-03

To construct sophisticated biochemical circuits from scratch, one needs to understand how simple the building blocks can be and how robustly such circuits can scale up. Using a simple DNA reaction mechanism based on a reversible strand displacement process, we experimentally demonstrated several digital logic circuits, culminating in a four-bit square-root circuit that comprises 130 DNA strands. These multilayer circuits include thresholding and catalysis within every logical operation to perform digital signal restoration, which enables fast and reliable function in large circuits with roughly constant switching time and linear signal propagation delays. The design naturally incorporates other crucial elements for large-scale circuitry, such as general debugging tools, parallel circuit preparation, and an abstraction hierarchy supported by an automated circuit compiler.

Brain Performance versus Phase Transitions

NASA Astrophysics Data System (ADS)

Torres, Joaquín J.; Marro, J.

2015-07-01

We here illustrate how a well-founded study of the brain may originate in assuming analogies with phase-transition phenomena. Analyzing to what extent a weak signal endures in noisy environments, we identify the underlying mechanisms, and it results a description of how the excitability associated to (non-equilibrium) phase changes and criticality optimizes the processing of the signal. Our setting is a network of integrate-and-fire nodes in which connections are heterogeneous with rapid time-varying intensities mimicking fatigue and potentiation. Emergence then becomes quite robust against wiring topology modification—in fact, we considered from a fully connected network to the Homo sapiens connectome—showing the essential role of synaptic flickering on computations. We also suggest how to experimentally disclose significant changes during actual brain operation.

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.

Ephrin-A/EphA specific co-adaptation as a novel mechanism in topographic axon guidance

PubMed Central

Fiederling, Felix; Weschenfelder, Markus; Fritz, Martin; von Philipsborn, Anne; Bastmeyer, Martin; Weth, Franco

2017-01-01

Genetic hardwiring during brain development provides computational architectures for innate neuronal processing. Thus, the paradigmatic chick retinotectal projection, due to its neighborhood preserving, topographic organization, establishes millions of parallel channels for incremental visual field analysis. Retinal axons receive targeting information from quantitative guidance cue gradients. Surprisingly, novel adaptation assays demonstrate that retinal growth cones robustly adapt towards ephrin-A/EphA forward and reverse signals, which provide the major mapping cues. Computational modeling suggests that topographic accuracy and adaptability, though seemingly incompatible, could be reconciled by a novel mechanism of coupled adaptation of signaling channels. Experimentally, we find such ‘co-adaptation’ in retinal growth cones specifically for ephrin-A/EphA signaling. Co-adaptation involves trafficking of unliganded sensors between the surface membrane and recycling endosomes, and is presumably triggered by changes in the lipid composition of membrane microdomains. We propose that co-adaptative desensitization eventually relies on guidance sensor translocation into cis-signaling endosomes to outbalance repulsive trans-signaling. DOI: http://dx.doi.org/10.7554/eLife.25533.001 PMID:28722651

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.

A flexible continuous-variable QKD system using off-the-shelf components

NASA Astrophysics Data System (ADS)

Comandar, Lucian C.; Brunner, Hans H.; Bettelli, Stefano; Fung, Fred; Karinou, Fotini; Hillerkuss, David; Mikroulis, Spiros; Wang, Dawei; Kuschnerov, Maxim; Xie, Changsong; Poppe, Andreas; Peev, Momtchil

2017-10-01

We present the development of a robust and versatile CV-QKD architecture based on commercially available optical and electronic components. The system uses a pilot tone for phase synchronization with a local oscillator, as well as local feedback loops to mitigate frequency and polarization drifts. Transmit and receive-side digital signal processing is performed fully in software, allowing for rapid protocol reconfiguration. The quantum link is complemented with a software stack for secure-key processing, key storage and encrypted communication. All these features allow for the system to be at the same time a prototype for a future commercial product and a research platform.

What does voice-processing technology support today?

PubMed Central

Nakatsu, R; Suzuki, Y

1995-01-01

This paper describes the state of the art in applications of voice-processing technologies. In the first part, technologies concerning the implementation of speech recognition and synthesis algorithms are described. Hardware technologies such as microprocessors and DSPs (digital signal processors) are discussed. Software development environment, which is a key technology in developing applications software, ranging from DSP software to support software also is described. In the second part, the state of the art of algorithms from the standpoint of applications is discussed. Several issues concerning evaluation of speech recognition/synthesis algorithms are covered, as well as issues concerning the robustness of algorithms in adverse conditions. Images Fig. 3 PMID:7479720

Performance Evaluation of Localization Accuracy for a Log-Normal Shadow Fading Wireless Sensor Network under Physical Barrier Attacks

PubMed Central

Abdulqader Hussein, Ahmed; Rahman, Tharek A.; Leow, Chee Yen

2015-01-01

Localization is an apparent aspect of a wireless sensor network, which is the focus of much interesting research. One of the severe conditions that needs to be taken into consideration is localizing a mobile target through a dispersed sensor network in the presence of physical barrier attacks. These attacks confuse the localization process and cause location estimation errors. Range-based methods, like the received signal strength indication (RSSI), face the major influence of this kind of attack. This paper proposes a solution based on a combination of multi-frequency multi-power localization (C-MFMPL) and step function multi-frequency multi-power localization (SF-MFMPL), including the fingerprint matching technique and lateration, to provide a robust and accurate localization technique. In addition, this paper proposes a grid coloring algorithm to detect the signal hole map in the network, which refers to the attack-prone regions, in order to carry out corrective actions. The simulation results show the enhancement and robustness of RSS localization performance in the face of log normal shadow fading effects, besides the presence of physical barrier attacks, through detecting, filtering and eliminating the effect of these attacks. PMID:26690159

Systems analysis of transcriptome data provides new hypotheses about Arabidopsis root response to nitrate treatments

PubMed Central

Canales, Javier; Moyano, Tomás C.; Villarroel, Eva; Gutiérrez, Rodrigo A.

2014-01-01

Nitrogen (N) is an essential macronutrient for plant growth and development. Plants adapt to changes in N availability partly by changes in global gene expression. We integrated publicly available root microarray data under contrasting nitrate conditions to identify new genes and functions important for adaptive nitrate responses in Arabidopsis thaliana roots. Overall, more than 2000 genes exhibited changes in expression in response to nitrate treatments in Arabidopsis thaliana root organs. Global regulation of gene expression by nitrate depends largely on the experimental context. However, despite significant differences from experiment to experiment in the identity of regulated genes, there is a robust nitrate response of specific biological functions. Integrative gene network analysis uncovered relationships between nitrate-responsive genes and 11 highly co-expressed gene clusters (modules). Four of these gene network modules have robust nitrate responsive functions such as transport, signaling, and metabolism. Network analysis hypothesized G2-like transcription factors are key regulatory factors controlling transport and signaling functions. Our meta-analysis highlights the role of biological processes not studied before in the context of the nitrate response such as root hair development and provides testable hypothesis to advance our understanding of nitrate responses in plants. PMID:24570678

Performance Evaluation of Localization Accuracy for a Log-Normal Shadow Fading Wireless Sensor Network under Physical Barrier Attacks.

PubMed

Hussein, Ahmed Abdulqader; Rahman, Tharek A; Leow, Chee Yen

2015-12-04

Localization is an apparent aspect of a wireless sensor network, which is the focus of much interesting research. One of the severe conditions that needs to be taken into consideration is localizing a mobile target through a dispersed sensor network in the presence of physical barrier attacks. These attacks confuse the localization process and cause location estimation errors. Range-based methods, like the received signal strength indication (RSSI), face the major influence of this kind of attack. This paper proposes a solution based on a combination of multi-frequency multi-power localization (C-MFMPL) and step function multi-frequency multi-power localization (SF-MFMPL), including the fingerprint matching technique and lateration, to provide a robust and accurate localization technique. In addition, this paper proposes a grid coloring algorithm to detect the signal hole map in the network, which refers to the attack-prone regions, in order to carry out corrective actions. The simulation results show the enhancement and robustness of RSS localization performance in the face of log normal shadow fading effects, besides the presence of physical barrier attacks, through detecting, filtering and eliminating the effect of these attacks.

Female mating preferences determine system-level evolution in a gene network model.

PubMed

Fierst, Janna L

2013-06-01

Environmental patterns of directional, stabilizing and fluctuating selection can influence the evolution of system-level properties like evolvability and mutational robustness. Intersexual selection produces strong phenotypic selection and these dynamics may also affect the response to mutation and the potential for future adaptation. In order to to assess the influence of mating preferences on these evolutionary properties, I modeled a male trait and female preference determined by separate gene regulatory networks. I studied three sexual selection scenarios: sexual conflict, a Gaussian model of the Fisher process described in Lande (in Proc Natl Acad Sci 78(6):3721-3725, 1981) and a good genes model in which the male trait signalled his mutational condition. I measured the effects these mating preferences had on the potential for traits and preferences to evolve towards new states, and mutational robustness of both the phenotype and the individual's overall viability. All types of sexual selection increased male phenotypic robustness relative to a randomly mating population. The Fisher model also reduced male evolvability and mutational robustness for viability. Under good genes sexual selection, males evolved an increased mutational robustness for viability. Females choosing their mates is a scenario that is sufficient to create selective forces that impact genetic evolution and shape the evolutionary response to mutation and environmental selection. These dynamics will inevitably develop in any population where sexual selection is operating, and affect the potential for future adaptation.

On the inequivalence of the CH and CHSH inequalities due to finite statistics

NASA Astrophysics Data System (ADS)

Renou, M. O.; Rosset, D.; Martin, A.; Gisin, N.

2017-06-01

Different variants of a Bell inequality, such as CHSH and CH, are known to be equivalent when evaluated on nonsignaling outcome probability distributions. However, in experimental setups, the outcome probability distributions are estimated using a finite number of samples. Therefore the nonsignaling conditions are only approximately satisfied and the robustness of the violation depends on the chosen inequality variant. We explain that phenomenon using the decomposition of the space of outcome probability distributions under the action of the symmetry group of the scenario, and propose a method to optimize the statistical robustness of a Bell inequality. In the process, we describe the finite group composed of relabeling of parties, measurement settings and outcomes, and identify correspondences between the irreducible representations of this group and properties of outcome probability distributions such as normalization, signaling or having uniform marginals.

Velocity interferometer signal de-noising using modified Wiener filter

NASA Astrophysics Data System (ADS)

Rav, Amit; Joshi, K. D.; Roy, Kallol; Kaushik, T. C.

2017-05-01

The accuracy and precision of the non-contact velocity interferometer system for any reflector (VISAR) depends not only on the good optical design and linear optical-to- electrical conversion system, but also on accurate and robust post-processing techniques. The performance of these techniques, such as the phase unwrapping algorithm, depends on the signal-to-noise ratio (SNR) of the recorded signal. In the present work, a novel method of improving the SNR of the recorded VISAR signal, based on the knowledge of the noise characteristic of the signal conversion and recording system, is presented. The proposed method uses a modified Wiener filter, for which the signal power spectrum estimation is obtained using a spectral subtraction method (SSM), and the noise power spectrum estimation is obtained by taking the average of the recorded signal during the period when no target movement is expected. Since the noise power spectrum estimate is dynamic in nature, and obtained for each experimental record individually, the improved signal quality is high. The proposed method is applied to the simulated standard signals, and is not only found to be better than the SSM, but is also less sensitive to the selection of the noise floor during signal power spectrum estimation. Finally, the proposed method is applied to the recorded experimental signal and an improvement in the SNR is reported.

Penetrating transmission zeros in the design of robust servomechanism systems

NASA Technical Reports Server (NTRS)

Wang, S. H.; Davison, E. J.

1981-01-01

In the design of a robust servomechanism system, it is well known that the system cannot track a reference signal whose frequency coincides with the transmission zeros of the system. This paper proposes a new design method for overcoming this difficulty. The controller to be used employs a sampler and holding device with exponential decay. It is shown that the transmission zeros of the discretized system can be shifted by changing the rate of the exponential decay of the holding device. Thus, it is possible to design a robust controller for the discretized system to track any reference signal of given frequency, even if the given frequency coincides with the transmission zeros of the original continuous-time system.

Waveform Design for Multimedia Airborne Networks: Robust Multimedia Data Transmission in Cognitive Radio Networks

DTIC Science & Technology

2011-03-01

at the sensor. According to Candes, Tao and Romberg [1], a small number of random projections of a signal that is compressible is all the...Projection of Signal Transform i. DWT ii. FFT iii. DCT Solve the Minimization problem Reconstruct Signal Channel (AWGN ) De -noise Signal Original...Signal (Noisy) Random Projection of Signal Transform i. DWT ii. FFT iii. DCT Solve the Minimization problem Reconstruct Signal Channel (Noiseless) De

Symmetric Phase Only Filtering for Improved DPIV Data Processing

NASA Technical Reports Server (NTRS)

Wernet, Mark P.

2006-01-01

The standard approach in Digital Particle Image Velocimetry (DPIV) data processing is to use Fast Fourier Transforms to obtain the cross-correlation of two single exposure subregions, where the location of the cross-correlation peak is representative of the most probable particle displacement across the subregion. This standard DPIV processing technique is analogous to Matched Spatial Filtering, a technique commonly used in optical correlators to perform the crosscorrelation operation. Phase only filtering is a well known variation of Matched Spatial Filtering, which when used to process DPIV image data yields correlation peaks which are narrower and up to an order of magnitude larger than those obtained using traditional DPIV processing. In addition to possessing desirable correlation plane features, phase only filters also provide superior performance in the presence of DC noise in the correlation subregion. When DPIV image subregions contaminated with surface flare light or high background noise levels are processed using phase only filters, the correlation peak pertaining only to the particle displacement is readily detected above any signal stemming from the DC objects. Tedious image masking or background image subtraction are not required. Both theoretical and experimental analyses of the signal-to-noise ratio performance of the filter functions are presented. In addition, a new Symmetric Phase Only Filtering (SPOF) technique, which is a variation on the traditional phase only filtering technique, is described and demonstrated. The SPOF technique exceeds the performance of the traditionally accepted phase only filtering techniques and is easily implemented in standard DPIV FFT based correlation processing with no significant computational performance penalty. An "Automatic" SPOF algorithm is presented which determines when the SPOF is able to provide better signal to noise results than traditional PIV processing. The SPOF based optical correlation processing approach is presented as a new paradigm for more robust cross-correlation processing of low signal-to-noise ratio DPIV image data."

The DISC Quotient

NASA Astrophysics Data System (ADS)

Elliott, John R.; Baxter, Stephen

2012-09-01

D.I.S.C: Decipherment Impact of a Signal's Content. The authors present a numerical method to characterise the significance of the receipt of a complex and potentially decipherable signal from extraterrestrial intelligence (ETI). The purpose of the scale is to facilitate the public communication of work on any such claimed signal, as such work proceeds, and to assist in its discussion and interpretation. Building on a "position" paper rationale, this paper looks at the DISC quotient proposed and develops the algorithmic steps and comprising measures that form this post detection strategy for information dissemination, based on prior work on message detection, decipherment. As argued, we require a robust and incremental strategy, to disseminate timely, accurate and meaningful information, to the scientific community and the general public, in the event we receive an "alien" signal that displays decipherable information. This post-detection strategy is to serve as a stepwise algorithm for a logical approach to information extraction and a vehicle for sequential information dissemination, to manage societal impact. The "DISC Quotient", which is based on signal analysis processing stages, includes factors based on the signal's data quantity, structure, affinity to known human languages, and likely decipherment times. Comparisons with human and other phenomena are included as a guide to assessing likely societal impact. It is submitted that the development, refinement and implementation of DISC as an integral strategy, during the complex processes involved in post detection and decipherment, is essential if we wish to minimize disruption and optimize dissemination.

Receptor density balances signal stimulation and attenuation in membrane-assembled complexes of bacterial chemotaxis signaling proteins

PubMed Central

Besschetnova, Tatiana Y.; Montefusco, David J.; Asinas, Abdalin E.; Shrout, Anthony L.; Antommattei, Frances M.; Weis, Robert M.

2008-01-01

All cells possess transmembrane signaling systems that function in the environment of the lipid bilayer. In the Escherichia coli chemotaxis pathway, the binding of attractants to a two-dimensional array of receptors and signaling proteins simultaneously inhibits an associated kinase and stimulates receptor methylation—a slower process that restores kinase activity. These two opposing effects lead to robust adaptation toward stimuli through a physical mechanism that is not understood. Here, we provide evidence of a counterbalancing influence exerted by receptor density on kinase stimulation and receptor methylation. Receptor signaling complexes were reconstituted over a range of defined surface concentrations by using a template-directed assembly method, and the kinase and receptor methylation activities were measured. Kinase activity and methylation rates were both found to vary significantly with surface concentration—yet in opposite ways: samples prepared at high surface densities stimulated kinase activity more effectively than low-density samples, whereas lower surface densities produced greater methylation rates than higher densities. FRET experiments demonstrated that the cooperative change in kinase activity coincided with a change in the arrangement of the membrane-associated receptor domains. The counterbalancing influence of density on receptor methylation and kinase stimulation leads naturally to a model for signal regulation that is compatible with the known logic of the E. coli pathway. Density-dependent mechanisms are likely to be general and may operate when two or more membrane-related processes are influenced differently by the two-dimensional concentration of pathway elements. PMID:18711126

Cell-type-specific modelling of intracellular calcium signalling: a urothelial cell model.

PubMed

Appleby, Peter A; Shabir, Saqib; Southgate, Jennifer; Walker, Dawn

2013-09-06

Calcium signalling plays a central role in regulating a wide variety of cell processes. A number of calcium signalling models exist in the literature that are capable of reproducing a variety of experimentally observed calcium transients. These models have been used to examine in more detail the mechanisms underlying calcium transients, but very rarely has a model been directly linked to a particular cell type and experimentally verified. It is important to show that this can be achieved within the general theoretical framework adopted by these models. Here, we develop a framework designed specifically for modelling cytosolic calcium transients in urothelial cells. Where possible, we draw upon existing calcium signalling models, integrating descriptions of components known to be important in this cell type from a number of studies in the literature. We then add descriptions of several additional pathways that play a specific role in urothelial cell signalling, including an explicit ionic influx term and an active pumping mechanism that drives the cytosolic calcium concentration to a target equilibrium. The resulting one-pool model of endoplasmic reticulum (ER)-dependent calcium signalling relates the cytosolic, extracellular and ER calcium concentrations and can generate a wide range of calcium transients, including spikes, bursts, oscillations and sustained elevations in the cytosolic calcium concentration. Using single-variate robustness and multivariate sensitivity analyses, we quantify how varying each of the parameters of the model leads to changes in key features of the calcium transient, such as initial peak amplitude and the frequency of bursting or spiking, and in the transitions between bursting- and plateau-dominated modes. We also show that, novel to our urothelial cell model, the ionic and purinergic P2Y pathways make distinct contributions to the calcium transient. We then validate the model using human bladder epithelial cells grown in monolayer cell culture and show that the model robustly captures the key features of the experimental data in a way that is not possible using more generic calcium models from the literature.

Accurate derivation of heart rate variability signal for detection of sleep disordered breathing in children.

PubMed

Chatlapalli, S; Nazeran, H; Melarkod, V; Krishnam, R; Estrada, E; Pamula, Y; Cabrera, S

2004-01-01

The electrocardiogram (ECG) signal is used extensively as a low cost diagnostic tool to provide information concerning the heart's state of health. Accurate determination of the QRS complex, in particular, reliable detection of the R wave peak, is essential in computer based ECG analysis. ECG data from Physionet's Sleep-Apnea database were used to develop, test, and validate a robust heart rate variability (HRV) signal derivation algorithm. The HRV signal was derived from pre-processed ECG signals by developing an enhanced Hilbert transform (EHT) algorithm with built-in missing beat detection capability for reliable QRS detection. The performance of the EHT algorithm was then compared against that of a popular Hilbert transform-based (HT) QRS detection algorithm. Autoregressive (AR) modeling of the HRV power spectrum for both EHT- and HT-derived HRV signals was achieved and different parameters from their power spectra as well as approximate entropy were derived for comparison. Poincare plots were then used as a visualization tool to highlight the detection of the missing beats in the EHT method After validation of the EHT algorithm on ECG data from the Physionet, the algorithm was further tested and validated on a dataset obtained from children undergoing polysomnography for detection of sleep disordered breathing (SDB). Sensitive measures of accurate HRV signals were then derived to be used in detecting and diagnosing sleep disordered breathing in children. All signal processing algorithms were implemented in MATLAB. We present a description of the EHT algorithm and analyze pilot data for eight children undergoing nocturnal polysomnography. The pilot data demonstrated that the EHT method provides an accurate way of deriving the HRV signal and plays an important role in extraction of reliable measures to distinguish between periods of normal and sleep disordered breathing (SDB) in children.

Optimization of Tape Winding Process Parameters to Enhance the Performance of Solid Rocket Nozzle Throat Back Up Liners using Taguchi's Robust Design Methodology

NASA Astrophysics Data System (ADS)

Nath, Nayani Kishore

2017-08-01

The throat back up liners is used to protect the nozzle structural members from the severe thermal environment in solid rocket nozzles. The throat back up liners is made with E-glass phenolic prepregs by tape winding process. The objective of this work is to demonstrate the optimization of process parameters of tape winding process to achieve better insulative resistance using Taguchi's robust design methodology. In this method four control factors machine speed, roller pressure, tape tension, tape temperature that were investigated for the tape winding process. The presented work was to study the cogency and acceptability of Taguchi's methodology in manufacturing of throat back up liners. The quality characteristic identified was Back wall temperature. Experiments carried out using L 9 ' (34) orthogonal array with three levels of four different control factors. The test results were analyzed using smaller the better criteria for Signal to Noise ratio in order to optimize the process. The experimental results were analyzed conformed and successfully used to achieve the minimum back wall temperature of the throat back up liners. The enhancement in performance of the throat back up liners was observed by carrying out the oxy-acetylene tests. The influence of back wall temperature on the performance of throat back up liners was verified by ground firing test.

Stochastic phase of ventral furrow formation in the Drosophila embryo: cellular constriction chains, mechanical feedback, and robustness

NASA Astrophysics Data System (ADS)

Blawzdziewicz, Jerzy; Gao, Guo-Jie J.; Holcomb, Michael C.; Thomas, Jeffrey H.

The key process giving rise to ventral furrow formation (VFF) in Drosophila embryo is apical constriction of cells in the ventral region. The constriction produces negative spontaneous curvature of the cell layer. During the initial slower phase of VFF approximately 40% of cells constrict in a seemingly random order. We show that this initial phase of VFF does not depend on random uncorrelated events. Instead, constricted cell apices form well-defined correlated structures, i.e., cellular constriction chains (CCCs), indicative of strong spatial and directional correlations between the constriction events. We argue that this chain formation is a signature of mechanical signaling that coordinates apical constrictions through tensile stress. To gain insights into the mechanisms involved in this correlated constriction process, we propose an active granular fluid (AGF) model which considers a tissue as a collection of mechanically active, stress-responsive objects. Our AGF molecular dynamics simulations show that cell constriction sensitivity to tensile stress results in formation of CCCs whereas compressive-stress sensitivity leads to compact constricted cell clusters; the CCCs, which can penetrate less-active regions, increase the robustness of the VFF process.

Robust power detector for wideband signals among many single tone signals

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

Musgrove, Cameron H.; Thompson, Douglas

Various technologies for isolating a signal of interest from signals received contemporaneously by an antenna are described herein. A time period for which a signal of interest is present in a second signal can be identified based upon ratios of values of the second signal to the mean value of the second signal. When the ratio of the value of the second signal at a particular time to the mean of the second signal exceeds a threshold value, the signal of interest is considered to be present in the second signal.

Mechanical design in embryos: mechanical signalling, robustness and developmental defects.

PubMed

Davidson, Lance A

2017-05-19

Embryos are shaped by the precise application of force against the resistant structures of multicellular tissues. Forces may be generated, guided and resisted by cells, extracellular matrix, interstitial fluids, and how they are organized and bound within the tissue's architecture. In this review, we summarize our current thoughts on the multiple roles of mechanics in direct shaping, mechanical signalling and robustness of development. Genetic programmes of development interact with environmental cues to direct the composition of the early embryo and endow cells with active force production. Biophysical advances now provide experimental tools to measure mechanical resistance and collective forces during morphogenesis and are allowing integration of this field with studies of signalling and patterning during development. We focus this review on concepts that highlight this integration, and how the unique contributions of mechanical cues and gradients might be tested side by side with conventional signalling systems. We conclude with speculation on the integration of large-scale programmes of development, and how mechanical responses may ensure robust development and serve as constraints on programmes of tissue self-assembly.This article is part of the themed issue 'Systems morphodynamics: understanding the development of tissue hardware'. © 2017 The Author(s).

Selection vector filter framework

NASA Astrophysics Data System (ADS)

Lukac, Rastislav; Plataniotis, Konstantinos N.; Smolka, Bogdan; Venetsanopoulos, Anastasios N.

2003-10-01

We provide a unified framework of nonlinear vector techniques outputting the lowest ranked vector. The proposed framework constitutes a generalized filter class for multichannel signal processing. A new class of nonlinear selection filters are based on the robust order-statistic theory and the minimization of the weighted distance function to other input samples. The proposed method can be designed to perform a variety of filtering operations including previously developed filtering techniques such as vector median, basic vector directional filter, directional distance filter, weighted vector median filters and weighted directional filters. A wide range of filtering operations is guaranteed by the filter structure with two independent weight vectors for angular and distance domains of the vector space. In order to adapt the filter parameters to varying signal and noise statistics, we provide also the generalized optimization algorithms taking the advantage of the weighted median filters and the relationship between standard median filter and vector median filter. Thus, we can deal with both statistical and deterministic aspects of the filter design process. It will be shown that the proposed method holds the required properties such as the capability of modelling the underlying system in the application at hand, the robustness with respect to errors in the model of underlying system, the availability of the training procedure and finally, the simplicity of filter representation, analysis, design and implementation. Simulation studies also indicate that the new filters are computationally attractive and have excellent performance in environments corrupted by bit errors and impulsive noise.

Multisensory integration in early vestibular processing in mice: the encoding of passive vs. active motion

PubMed Central

Medrea, Ioana

2013-01-01

The mouse has become an important model system for studying the cellular basis of learning and coding of heading by the vestibular system. Here we recorded from single neurons in the vestibular nuclei to understand how vestibular pathways encode self-motion under natural conditions, during which proprioceptive and motor-related signals as well as vestibular inputs provide feedback about an animal's movement through the world. We recorded neuronal responses in alert behaving mice focusing on a group of neurons, termed vestibular-only cells, that are known to control posture and project to higher-order centers. We found that the majority (70%, n = 21/30) of neurons were bimodal, in that they responded robustly to passive stimulation of proprioceptors as well as passive stimulation of the vestibular system. Additionally, the linear summation of a given neuron's vestibular and neck sensitivities predicted well its responses when both stimuli were applied simultaneously. In contrast, neuronal responses were suppressed when the same motion was actively generated, with the one striking exception that the activity of bimodal neurons similarly and robustly encoded head on body position in all conditions. Our results show that proprioceptive and motor-related signals are combined with vestibular information at the first central stage of vestibular processing in mice. We suggest that these results have important implications for understanding the multisensory integration underlying accurate postural control and the neural representation of directional heading in the head direction cell network of mice. PMID:24089394

Dynamics of intracellular information decoding.

PubMed

Kobayashi, Tetsuya J; Kamimura, Atsushi

2011-10-01

A variety of cellular functions are robust even to substantial intrinsic and extrinsic noise in intracellular reactions and the environment that could be strong enough to impair or limit them. In particular, of substantial importance is cellular decision-making in which a cell chooses a fate or behavior on the basis of information conveyed in noisy external signals. For robust decoding, the crucial step is filtering out the noise inevitably added during information transmission. As a minimal and optimal implementation of such an information decoding process, the autocatalytic phosphorylation and autocatalytic dephosphorylation (aPadP) cycle was recently proposed. Here, we analyze the dynamical properties of the aPadP cycle in detail. We describe the dynamical roles of the stationary and short-term responses in determining the efficiency of information decoding and clarify the optimality of the threshold value of the stationary response and its information-theoretical meaning. Furthermore, we investigate the robustness of the aPadP cycle against the receptor inactivation time and intrinsic noise. Finally, we discuss the relationship among information decoding with information-dependent actions, bet-hedging and network modularity.

Realisation and robustness evaluation of a blind spatial domain watermarking technique

NASA Astrophysics Data System (ADS)

Parah, Shabir A.; Sheikh, Javaid A.; Assad, Umer I.; Bhat, Ghulam M.

2017-04-01

A blind digital image watermarking scheme based on spatial domain is presented and investigated in this paper. The watermark has been embedded in intermediate significant bit planes besides the least significant bit plane at the address locations determined by pseudorandom address vector (PAV). The watermark embedding using PAV makes it difficult for an adversary to locate the watermark and hence adds to security of the system. The scheme has been evaluated to ascertain the spatial locations that are robust to various image processing and geometric attacks JPEG compression, additive white Gaussian noise, salt and pepper noise, filtering and rotation. The experimental results obtained, reveal an interesting fact, that, for all the above mentioned attacks, other than rotation, higher the bit plane in which watermark is embedded more robust the system. Further, the perceptual quality of the watermarked images obtained in the proposed system has been compared with some state-of-art watermarking techniques. The proposed technique outperforms the techniques under comparison, even if compared with the worst case peak signal-to-noise ratio obtained in our scheme.

Robust Real-Time Music Transcription with a Compositional Hierarchical Model.

PubMed

Pesek, Matevž; Leonardis, Aleš; Marolt, Matija

2017-01-01

The paper presents a new compositional hierarchical model for robust music transcription. Its main features are unsupervised learning of a hierarchical representation of input data, transparency, which enables insights into the learned representation, as well as robustness and speed which make it suitable for real-world and real-time use. The model consists of multiple layers, each composed of a number of parts. The hierarchical nature of the model corresponds well to hierarchical structures in music. The parts in lower layers correspond to low-level concepts (e.g. tone partials), while the parts in higher layers combine lower-level representations into more complex concepts (tones, chords). The layers are learned in an unsupervised manner from music signals. Parts in each layer are compositions of parts from previous layers based on statistical co-occurrences as the driving force of the learning process. In the paper, we present the model's structure and compare it to other hierarchical approaches in the field of music information retrieval. We evaluate the model's performance for the multiple fundamental frequency estimation. Finally, we elaborate on extensions of the model towards other music information retrieval tasks.

Internal respiratory surrogate in multislice 4D CT using a combination of Fourier transform and anatomical features

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

Hui, Cheukkai; Suh, Yelin; Robertson, Daniel

Purpose: The purpose of this study was to develop a novel algorithm to create a robust internal respiratory signal (IRS) for retrospective sorting of four-dimensional (4D) computed tomography (CT) images. Methods: The proposed algorithm combines information from the Fourier transform of the CT images and from internal anatomical features to form the IRS. The algorithm first extracts potential respiratory signals from low-frequency components in the Fourier space and selected anatomical features in the image space. A clustering algorithm then constructs groups of potential respiratory signals with similar temporal oscillation patterns. The clustered group with the largest number of similar signalsmore » is chosen to form the final IRS. To evaluate the performance of the proposed algorithm, the IRS was computed and compared with the external respiratory signal from the real-time position management (RPM) system on 80 patients. Results: In 72 (90%) of the 4D CT data sets tested, the IRS computed by the authors’ proposed algorithm matched with the RPM signal based on their normalized cross correlation. For these data sets with matching respiratory signals, the average difference between the end inspiration times (Δt{sub ins}) in the IRS and RPM signal was 0.11 s, and only 2.1% of Δt{sub ins} were more than 0.5 s apart. In the eight (10%) 4D CT data sets in which the IRS and the RPM signal did not match, the average Δt{sub ins} was 0.73 s in the nonmatching couch positions, and 35.4% of them had a Δt{sub ins} greater than 0.5 s. At couch positions in which IRS did not match the RPM signal, a correlation-based metric indicated poorer matching of neighboring couch positions in the RPM-sorted images. This implied that, when IRS did not match the RPM signal, the images sorted using the IRS showed fewer artifacts than the clinical images sorted using the RPM signal. Conclusions: The authors’ proposed algorithm can generate robust IRSs that can be used for retrospective sorting of 4D CT data. The algorithm is completely automatic and requires very little processing time. The algorithm is cost efficient and can be easily adopted for everyday clinical use.« less

Analysis of signals under compositional noise with applications to SONAR data

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

Tucker, J. Derek; Wu, Wei; Srivastava, Anuj

2013-07-09

In this paper, we consider the problem of denoising and classification of SONAR signals observed under compositional noise, i.e., they have been warped randomly along the x-axis. The traditional techniques do not account for such noise and, consequently, cannot provide a robust classification of signals. We apply a recent framework that: 1) uses a distance-based objective function for data alignment and noise reduction; and 2) leads to warping-invariant distances between signals for robust clustering and classification. We use this framework to introduce two distances that can be used for signal classification: a) a y-distance, which is the distance between themore » aligned signals; and b) an x-distance that measures the amount of warping needed to align the signals. We focus on the task of clustering and classifying objects, using acoustic spectrum (acoustic color), which is complicated by the uncertainties in aspect angles at data collections. Small changes in the aspect angles corrupt signals in a way that amounts to compositional noise. As a result, we demonstrate the use of the developed metrics in classification of acoustic color data and highlight improvements in signal classification over current methods.« less

The PREP pipeline: standardized preprocessing for large-scale EEG analysis.

PubMed

Bigdely-Shamlo, Nima; Mullen, Tim; Kothe, Christian; Su, Kyung-Min; Robbins, Kay A

2015-01-01

The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.

A Robust Design Methodology for Optimal Microscale Secondary Flow Control in Compact Inlet Diffusers

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Keller, Dennis J.

2001-01-01

It is the purpose of this study to develop an economical Robust design methodology for microscale secondary flow control in compact inlet diffusers. To illustrate the potential of economical Robust Design methodology, two different mission strategies were considered for the subject inlet, namely Maximum Performance and Maximum HCF Life Expectancy. The Maximum Performance mission maximized total pressure recovery while the Maximum HCF Life Expectancy mission minimized the mean of the first five Fourier harmonic amplitudes, i.e., 'collectively' reduced all the harmonic 1/2 amplitudes of engine face distortion. Each of the mission strategies was subject to a low engine face distortion constraint, i.e., DC60<0.10, which is a level acceptable for commercial engines. For each of these missions strategies, an 'Optimal Robust' (open loop control) and an 'Optimal Adaptive' (closed loop control) installation was designed over a twenty degree angle-of-incidence range. The Optimal Robust installation used economical Robust Design methodology to arrive at a single design which operated over the entire angle-of-incident range (open loop control). The Optimal Adaptive installation optimized all the design parameters at each angle-of-incidence. Thus, the Optimal Adaptive installation would require a closed loop control system to sense a proper signal for each effector and modify that effector device, whether mechanical or fluidic, for optimal inlet performance. In general, the performance differences between the Optimal Adaptive and Optimal Robust installation designs were found to be marginal. This suggests, however, that Optimal Robust open loop installation designs can be very competitive with Optimal Adaptive close loop designs. Secondary flow control in inlets is inherently robust, provided it is optimally designed. Therefore, the new methodology presented in this paper, combined array 'Lower Order' approach to Robust DOE, offers the aerodynamicist a very viable and economical way of exploring the concept of Robust inlet design, where the mission variables are brought directly into the inlet design process and insensitivity or robustness to the mission variables becomes a design objective.

Robust Sliding Mode Control of PMSM Based on Rapid Nonlinear Tracking Differentiator and Disturbance Observer

PubMed Central

Zhou, Zhanmin; Zhang, Bao; Mao, Dapeng

2018-01-01

Torque ripples caused by cogging torque, flux harmonics, and current measurement error seriously restrict the application of a permanent magnet synchronous motor (PMSM), which has been paid more and more attention for the use in inertial stabilized platforms. Sliding mode control (SMC), in parallel with the classical proportional integral (PI) controller, has a high advantage to suppress the torque ripples as its invariance to disturbances. However, since the high switching gain tends to cause chattering and it requires derivative of signals which is not readily obtainable without an acceleration signal sensor. Therefore, this paper proposes a robust SMC scheme based on a rapid nonlinear tracking differentiator (NTD) and a disturbance observer (DOB) to further improve the performance of the SMC. The NTD is employed to providing the derivative of the signal, and the DOB is utilized to estimate the system lumped disturbances, including parameter variations and external disturbances. On the one hand, DOB can compensate the robust SMC speed controller, it can reduce the chattering of SMC on the other hand. Experiments were carried out on an ARM and DSP-based platform. The obtained experimental results demonstrate that the robust SMC scheme has an improved performance with inertia stability and it exhibits a satisfactory anti-disturbance performance compared to the traditional methods. PMID:29596387

Robust backstepping control of an interlink converter in a hybrid AC/DC microgrid based on feedback linearisation method

NASA Astrophysics Data System (ADS)

Dehkordi, N. Mahdian; Sadati, N.; Hamzeh, M.

2017-09-01

This paper presents a robust dc-link voltage as well as a current control strategy for a bidirectional interlink converter (BIC) in a hybrid ac/dc microgrid. To enhance the dc-bus voltage control, conventional methods strive to measure and feedforward the load or source power in the dc-bus control scheme. However, the conventional feedforward-based approaches require remote measurement with communications. Moreover, conventional methods suffer from stability and performance issues, mainly due to the use of the small-signal-based control design method. To overcome these issues, in this paper, the power from DG units of the dc subgrid imposed on the BIC is considered an unmeasurable disturbance signal. In the proposed method, in contrast to existing methods, using the nonlinear model of BIC, a robust controller that does not need the remote measurement with communications effectively rejects the impact of the disturbance signal imposed on the BIC's dc-link voltage. To avoid communication links, the robust controller has a plug-and-play feature that makes it possible to add a DG/load to or remove it from the dc subgrid without distorting the hybrid microgrid stability. Finally, Monte Carlo simulations are conducted to confirm the effectiveness of the proposed control strategy in MATLAB/SimPowerSystems software environment.

Robust Sliding Mode Control of PMSM Based on a Rapid Nonlinear Tracking Differentiator and Disturbance Observer.

PubMed

Zhou, Zhanmin; Zhang, Bao; Mao, Dapeng

2018-03-29

Torque ripples caused by cogging torque, flux harmonics, and current measurement error seriously restrict the application of a permanent magnet synchronous motor (PMSM), which has been paid more and more attention for the use in inertial stabilized platforms. Sliding mode control (SMC), in parallel with the classical proportional integral (PI) controller, has a high advantage to suppress the torque ripples as its invariance to disturbances. However, since the high switching gain tends to cause chattering and it requires derivative of signals which is not readily obtainable without an acceleration signal sensor. Therefore, this paper proposes a robust SMC scheme based on a rapid nonlinear tracking differentiator (NTD) and a disturbance observer (DOB) to further improve the performance of the SMC. The NTD is employed to providing the derivative of the signal, and the DOB is utilized to estimate the system lumped disturbances, including parameter variations and external disturbances. On the one hand, DOB can compensate the robust SMC speed controller, it can reduce the chattering of SMC on the other hand. Experiments were carried out on an ARM and DSP-based platform. The obtained experimental results demonstrate that the robust SMC scheme has an improved performance with inertia stability and it exhibits a satisfactory anti-disturbance performance compared to the traditional methods.

Scaling of Optogenetically Evoked Signaling in a Higher-Order Corticocortical Pathway in the Anesthetized Mouse.

PubMed

Li, Xiaojian; Yamawaki, Naoki; Barrett, John M; Körding, Konrad P; Shepherd, Gordon M G

2018-01-01

Quantitative analysis of corticocortical signaling is needed to understand and model information processing in cerebral networks. However, higher-order pathways, hodologically remote from sensory input, are not amenable to spatiotemporally precise activation by sensory stimuli. Here, we combined parametric channelrhodopsin-2 (ChR2) photostimulation with multi-unit electrophysiology to study corticocortical driving in a parietofrontal pathway from retrosplenial cortex (RSC) to posterior secondary motor cortex (M2) in mice in vivo . Ketamine anesthesia was used both to eliminate complex activity associated with the awake state and to enable stable recordings of responses over a wide range of stimulus parameters. Photostimulation of ChR2-expressing neurons in RSC, the upstream area, produced local activity that decayed quickly. This activity in turn drove downstream activity in M2 that arrived rapidly (5-10 ms latencies), and scaled in amplitude across a wide range of stimulus parameters as an approximately constant fraction (~0.1) of the upstream activity. A model-based analysis could explain the corticocortically driven activity with exponentially decaying kernels (~20 ms time constant) and small delay. Reverse (antidromic) driving was similarly robust. The results show that corticocortical signaling in this pathway drives downstream activity rapidly and scalably, in a mostly linear manner. These properties, identified in anesthetized mice and represented in a simple model, suggest a robust basis for supporting complex non-linear dynamic activity in corticocortical circuits in the awake state.

Robust Satellite Techniques for monitoring earth emitted radiation in the Japanese seismic area by using MTSAT observations in the TIR spectral range

NASA Astrophysics Data System (ADS)

Genzano, Nicola; Filizzola, Carolina; Hattori, Katsumi; Lisi, Mariano; Paciello, Rossana; Pergola, Nicola; Tramutoli, Valerio

2016-04-01

Since eighties, the fluctuations of Earth's thermally emitted radiation, measured by satellite sensors operating in the thermal infrared (TIR) spectral range, have been associated with the complex process of preparation for major earthquakes. But, like other claimed earthquake precursors (seismological, physical, chemical, biological, etc.) they have been for long-time considered with some caution by scientific community. The lack of a rigorous definition of anomalous TIR signal fluctuations and the scarce attention paid to the possibility that other causes (e.g. meteorological) different from seismic activity could be responsible for the observed TIR variations were the main causes of such skepticism. Compared with previously proposed approaches the general change detection approach, named Robust Satellite Techniques (RST), showed good ability to discriminate anomalous TIR signals possibly associated to seismic activity, from the normal variability of TIR signal due to other causes. Thanks to its full exportability on different satellite packages, since 2001 RST has been implemented on TIR images acquired by polar (e.g. NOAA-AVHRR, EOS -MODIS) and geostationary (e.g. MSG-SEVIRI, NOAA-GOES/W, GMS-5/VISSR) satellite sensors, in order to verify the presence (or absence) of TIR anomalies in presence (absence) of earthquakes (with M>4) in different seismogenic areas around the world (e.g. Italy, Greece, Turkey, India, Taiwan, etc.). In this paper, the RST data analysis approach has been implemented on TIR satellite records collected over Japan by the geostationary satellite sensor MTSAT (Multifunctional Transport SATellites) and RETIRA (Robust Estimator of TIR Anomalies) index was used to identify Significant Sequences of TIR Anomalies (SSTAs) in a possible space-time relations with seismic events. Achieved results will be discussed in the perspective of a multi-parametric approach for a time-Dependent Assessment of Seismic Hazard (t-DASH).

Methodology for qualitative uncertainty assessment of climate impact indicators

NASA Astrophysics Data System (ADS)

Otto, Juliane; Keup-Thiel, Elke; Rechid, Diana; Hänsler, Andreas; Pfeifer, Susanne; Roth, Ellinor; Jacob, Daniela

2016-04-01

The FP7 project "Climate Information Portal for Copernicus" (CLIPC) is developing an integrated platform of climate data services to provide a single point of access for authoritative scientific information on climate change and climate change impacts. In this project, the Climate Service Center Germany (GERICS) has been in charge of the development of a methodology on how to assess the uncertainties related to climate impact indicators. Existing climate data portals mainly treat the uncertainties in two ways: Either they provide generic guidance and/or express with statistical measures the quantifiable fraction of the uncertainty. However, none of the climate data portals give the users a qualitative guidance how confident they can be in the validity of the displayed data. The need for such guidance was identified in CLIPC user consultations. Therefore, we aim to provide an uncertainty assessment that provides the users with climate impact indicator-specific guidance on the degree to which they can trust the outcome. We will present an approach that provides information on the importance of different sources of uncertainties associated with a specific climate impact indicator and how these sources affect the overall 'degree of confidence' of this respective indicator. To meet users requirements in the effective communication of uncertainties, their feedback has been involved during the development process of the methodology. Assessing and visualising the quantitative component of uncertainty is part of the qualitative guidance. As visual analysis method, we apply the Climate Signal Maps (Pfeifer et al. 2015), which highlight only those areas with robust climate change signals. Here, robustness is defined as a combination of model agreement and the significance of the individual model projections. Reference Pfeifer, S., Bülow, K., Gobiet, A., Hänsler, A., Mudelsee, M., Otto, J., Rechid, D., Teichmann, C. and Jacob, D.: Robustness of Ensemble Climate Projections Analyzed with Climate Signal Maps: Seasonal and Extreme Precipitation for Germany, Atmosphere (Basel)., 6(5), 677-698, doi:10.3390/atmos6050677, 2015.

Completed Ensemble Empirical Mode Decomposition: a Robust Signal Processing Tool to Identify Sequence Strata

NASA Astrophysics Data System (ADS)

Purba, H.; Musu, J. T.; Diria, S. A.; Permono, W.; Sadjati, O.; Sopandi, I.; Ruzi, F.

2018-03-01

Well logging data provide many geological information and its trends resemble nonlinear or non-stationary signals. As long well log data recorded, there will be external factors can interfere or influence its signal resolution. A sensitive signal analysis is required to improve the accuracy of logging interpretation which it becomes an important thing to determine sequence stratigraphy. Complete Ensemble Empirical Mode Decomposition (CEEMD) is one of nonlinear and non-stationary signal analysis method which decomposes complex signal into a series of intrinsic mode function (IMF). Gamma Ray and Spontaneous Potential well log parameters decomposed into IMF-1 up to IMF-10 and each of its combination and correlation makes physical meaning identification. It identifies the stratigraphy and cycle sequence and provides an effective signal treatment method for sequence interface. This method was applied to BRK- 30 and BRK-13 well logging data. The result shows that the combination of IMF-5, IMF-6, and IMF-7 pattern represent short-term and middle-term while IMF-9 and IMF-10 represent the long-term sedimentation which describe distal front and delta front facies, and inter-distributary mouth bar facies, respectively. Thus, CEEMD clearly can determine the different sedimentary layer interface and better identification of the cycle of stratigraphic base level.

Quantifying the signals contained in heterogeneous neural responses and determining their relationships with task performance

PubMed Central

Pagan, Marino

2014-01-01

The responses of high-level neurons tend to be mixtures of many different types of signals. While this diversity is thought to allow for flexible neural processing, it presents a challenge for understanding how neural responses relate to task performance and to neural computation. To address these challenges, we have developed a new method to parse the responses of individual neurons into weighted sums of intuitive signal components. Our method computes the weights by projecting a neuron's responses onto a predefined orthonormal basis. Once determined, these weights can be combined into measures of signal modulation; however, in their raw form these signal modulation measures are biased by noise. Here we introduce and evaluate two methods for correcting this bias, and we report that an analytically derived approach produces performance that is robust and superior to a bootstrap procedure. Using neural data recorded from inferotemporal cortex and perirhinal cortex as monkeys performed a delayed-match-to-sample target search task, we demonstrate how the method can be used to quantify the amounts of task-relevant signals in heterogeneous neural populations. We also demonstrate how these intuitive quantifications of signal modulation can be related to single-neuron measures of task performance (d′). PMID:24920017

An efficient robust sound classification algorithm for hearing aids.

PubMed

Nordqvist, Peter; Leijon, Arne

2004-06-01

An efficient robust sound classification algorithm based on hidden Markov models is presented. The system would enable a hearing aid to automatically change its behavior for differing listening environments according to the user's preferences. This work attempts to distinguish between three listening environment categories: speech in traffic noise, speech in babble, and clean speech, regardless of the signal-to-noise ratio. The classifier uses only the modulation characteristics of the signal. The classifier ignores the absolute sound pressure level and the absolute spectrum shape, resulting in an algorithm that is robust against irrelevant acoustic variations. The measured classification hit rate was 96.7%-99.5% when the classifier was tested with sounds representing one of the three environment categories included in the classifier. False-alarm rates were 0.2%-1.7% in these tests. The algorithm is robust and efficient and consumes a small amount of instructions and memory. It is fully possible to implement the classifier in a DSP-based hearing instrument.

Automation of extrusion of porous cable products based on a digital controller

NASA Astrophysics Data System (ADS)

Chostkovskii, B. K.; Mitroshin, V. N.

2017-07-01

This paper presents a new approach to designing an automated system for monitoring and controlling the process of applying porous insulation material on a conductive cable core, which is based on using structurally and parametrically optimized digital controllers of an arbitrary order instead of calculating typical PID controllers using known methods. The digital controller is clocked by signals from the clock length sensor of a measuring wheel, instead of a timer signal, and this provides the robust properties of the system with respect to the changing insulation speed. Digital controller parameters are tuned to provide the operating parameters of the manufactured cable using a simulation model of stochastic extrusion and are minimized by moving a regular simplex in the parameter space of the tuned controller.

Ionization Electron Signal Processing in Single Phase LArTPCs I. Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation

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

Adams, C.; et al.

We describe the concept and procedure of drifted-charge extraction developed in the MicroBooNE experiment, a single-phase liquid argon time projection chamber (LArTPC). This technique converts the raw digitized TPC waveform to the number of ionization electrons passing through a wire plane at a given time. A robust recovery of the number of ionization electrons from both induction and collection anode wire planes will augment the 3D reconstruction, and is particularly important for tomographic reconstruction algorithms. A number of building blocks of the overall procedure are described. The performance of the signal processing is quantitatively evaluated by comparing extracted charge withmore » the true charge through a detailed TPC detector simulation taking into account position-dependent induced current inside a single wire region and across multiple wires. Some areas for further improvement of the performance of the charge extraction procedure are also discussed.« less

Balancing Between Aging and Cancer: Molecular Genetics Meets Traditional Chinese Medicine.

PubMed

Liu, Jing; Peng, Lei; Huang, Wenhui; Li, Zhiming; Pan, Jun; Sang, Lei; Lu, Siqian; Zhang, Jihong; Li, Wanyi; Luo, Ying

2017-09-01

The biological consequences of cellular senescence and immortalization in aging and cancer are in conflict. Organisms have developed common cellular signaling pathways and surveillance mechanisms to control the processing of aging against tumorigenesis. The imbalance of any signals involved in this process may result in either premature aging or tumorigenesis and reduce the life span of the organism. In contrast, the balance between aging and tumorigenesis at a higher level (homeostatic-balance) may benefit the organism with tumor-free longevity. The focus of this perspective is to review the literature on the balance between "Yin" and "Yang" in traditional Chinese medicine. Modern cellular and molecular techniques now permit a more robust system to screen herbs in traditional Chinese medicine for their activity in balancing aging and tumorigenesis. The understanding of the crosstalk between aging and tumorigenesis and new perspectives on the application of Chinese medicine might shed light on anti-aging and tumor-free strategies. J. Cell. Biochem. 118: 2581-2586, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Extreme temperature robust optical sensor designs and fault-tolerant signal processing

DOEpatents

Riza, Nabeel Agha [Oviedo, FL; Perez, Frank [Tujunga, CA

2012-01-17

Silicon Carbide (SiC) probe designs for extreme temperature and pressure sensing uses a single crystal SiC optical chip encased in a sintered SiC material probe. The SiC chip may be protected for high temperature only use or exposed for both temperature and pressure sensing. Hybrid signal processing techniques allow fault-tolerant extreme temperature sensing. Wavelength peak-to-peak (or null-to-null) collective spectrum spread measurement to detect wavelength peak/null shift measurement forms a coarse-fine temperature measurement using broadband spectrum monitoring. The SiC probe frontend acts as a stable emissivity Black-body radiator and monitoring the shift in radiation spectrum enables a pyrometer. This application combines all-SiC pyrometry with thick SiC etalon laser interferometry within a free-spectral range to form a coarse-fine temperature measurement sensor. RF notch filtering techniques improve the sensitivity of the temperature measurement where fine spectral shift or spectrum measurements are needed to deduce temperature.

A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications

PubMed Central

Vitola, Jaime; Pozo, Francesc; Tibaduiza, Diego A.; Anaya, Maribel

2017-01-01

Civil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational and maintenance costs. In this sense, the use of sensors permanently attached to the structures has demonstrated a great versatility and benefit since the inspection system can be automated. This automation is carried out with signal processing tasks with the aim of a pattern recognition analysis. This work presents the detailed description of a structural health monitoring (SHM) system based on the use of a piezoelectric (PZT) active system. The SHM system includes: (i) the use of a piezoelectric sensor network to excite the structure and collect the measured dynamic response, in several actuation phases; (ii) data organization; (iii) advanced signal processing techniques to define the feature vectors; and finally; (iv) the nearest neighbor algorithm as a machine learning approach to classify different kinds of damage. A description of the experimental setup, the experimental validation and a discussion of the results from two different structures are included and analyzed. PMID:28230796

A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications.

PubMed

Vitola, Jaime; Pozo, Francesc; Tibaduiza, Diego A; Anaya, Maribel

2017-02-21

Civil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational and maintenance costs. In this sense, the use of sensors permanently attached to the structures has demonstrated a great versatility and benefit since the inspection system can be automated. This automation is carried out with signal processing tasks with the aim of a pattern recognition analysis. This work presents the detailed description of a structural health monitoring (SHM) system based on the use of a piezoelectric (PZT) active system. The SHM system includes: (i) the use of a piezoelectric sensor network to excite the structure and collect the measured dynamic response, in several actuation phases; (ii) data organization; (iii) advanced signal processing techniques to define the feature vectors; and finally; (iv) the nearest neighbor algorithm as a machine learning approach to classify different kinds of damage. A description of the experimental setup, the experimental validation and a discussion of the results from two different structures are included and analyzed.

Rapid Genetic Analysis of Epithelial-Mesenchymal Signaling During Hair Regeneration

PubMed Central

Zhen, Hanson H.; Oro, Anthony E.

2013-01-01

Hair follicle morphogenesis, a complex process requiring interaction between epithelia-derived keratinocytes and the underlying mesenchyme, is an attractive model system to study organ development and tissue-specific signaling. Although hair follicle development is genetically tractable, fast and reproducible analysis of factors essential for this process remains a challenge. Here we describe a procedure to generate targeted overexpression or shRNA-mediated knockdown of factors using lentivirus in a tissue-specific manner. Using a modified version of a hair regeneration model 5, 6, 11, we can achieve robust gain- or loss-of-function analysis in primary mouse keratinocytes or dermal cells to facilitate study of epithelial-mesenchymal signaling pathways that lead to hair follicle morphogenesis. We describe how to isolate fresh primary mouse keratinocytes and dermal cells, which contain dermal papilla cells and their precursors, deliver lentivirus containing either shRNA or cDNA to one of the cell populations, and combine the cells to generate fully formed hair follicles on the backs of nude mice. This approach allows analysis of tissue-specific factors required to generate hair follicles within three weeks and provides a fast and convenient companion to existing genetic models. PMID:23486463

Characterization of electroencephalography signals for estimating saliency features in videos.

PubMed

Liang, Zhen; Hamada, Yasuyuki; Oba, Shigeyuki; Ishii, Shin

2018-05-12

Understanding the functions of the visual system has been one of the major targets in neuroscience formany years. However, the relation between spontaneous brain activities and visual saliency in natural stimuli has yet to be elucidated. In this study, we developed an optimized machine learning-based decoding model to explore the possible relationships between the electroencephalography (EEG) characteristics and visual saliency. The optimal features were extracted from the EEG signals and saliency map which was computed according to an unsupervised saliency model ( Tavakoli and Laaksonen, 2017). Subsequently, various unsupervised feature selection/extraction techniques were examined using different supervised regression models. The robustness of the presented model was fully verified by means of ten-fold or nested cross validation procedure, and promising results were achieved in the reconstruction of saliency features based on the selected EEG characteristics. Through the successful demonstration of using EEG characteristics to predict the real-time saliency distribution in natural videos, we suggest the feasibility of quantifying visual content through measuring brain activities (EEG signals) in real environments, which would facilitate the understanding of cortical involvement in the processing of natural visual stimuli and application developments motivated by human visual processing. Copyright © 2018 Elsevier Ltd. All rights reserved.

Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters: Part II

PubMed Central

Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg Y.; Fernández, Eduardo

2010-01-01

In this paper, the fast least-mean-squares (LMS) algorithm was used to both eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications, and improve the convergence rate of the filtering process based on the conventional LMS algorithm. The response of the accelerometer under test was corrupted by process and measurement noise, and the signal processing stage was carried out by using both conventional filtering, which was already shown in a previous paper, and optimal adaptive filtering. The adaptive filtering process relied on the LMS adaptive filtering family, which has shown to have very good convergence and robustness properties, and here a comparative analysis between the results of the application of the conventional LMS algorithm and the fast LMS algorithm to solve a real-life filtering problem was carried out. In short, in this paper the piezoresistive accelerometer was tested for a multi-frequency acceleration excitation. Due to the kind of test conducted in this paper, the use of conventional filtering was discarded and the choice of one adaptive filter over the other was based on the signal-to-noise ratio improvement and the convergence rate. PMID:22315579

Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS).

PubMed

Latifoğlu, Fatma; Polat, Kemal; Kara, Sadik; Güneş, Salih

2008-02-01

In this study, we proposed a new medical diagnosis system based on principal component analysis (PCA), k-NN based weighting pre-processing, and Artificial Immune Recognition System (AIRS) for diagnosis of atherosclerosis from Carotid Artery Doppler Signals. The suggested system consists of four stages. First, in the feature extraction stage, we have obtained the features related with atherosclerosis disease using Fast Fourier Transformation (FFT) modeling and by calculating of maximum frequency envelope of sonograms. Second, in the dimensionality reduction stage, the 61 features of atherosclerosis disease have been reduced to 4 features using PCA. Third, in the pre-processing stage, we have weighted these 4 features using different values of k in a new weighting scheme based on k-NN based weighting pre-processing. Finally, in the classification stage, AIRS classifier has been used to classify subjects as healthy or having atherosclerosis. Hundred percent of classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in diagnosis of atherosclerosis disease.

A Universal and Robust Integrated Platform for the Scalable Production of Human Cardiomyocytes From Pluripotent Stem Cells.

PubMed

Fonoudi, Hananeh; Ansari, Hassan; Abbasalizadeh, Saeed; Larijani, Mehran Rezaei; Kiani, Sahar; Hashemizadeh, Shiva; Zarchi, Ali Sharifi; Bosman, Alexis; Blue, Gillian M; Pahlavan, Sara; Perry, Matthew; Orr, Yishay; Mayorchak, Yaroslav; Vandenberg, Jamie; Talkhabi, Mahmood; Winlaw, David S; Harvey, Richard P; Aghdami, Nasser; Baharvand, Hossein

2015-12-01

Recent advances in the generation of cardiomyocytes (CMs) from human pluripotent stem cells (hPSCs), in conjunction with the promising outcomes from preclinical and clinical studies, have raised new hopes for cardiac cell therapy. We report the development of a scalable, robust, and integrated differentiation platform for large-scale production of hPSC-CM aggregates in a stirred suspension bioreactor as a single-unit operation. Precise modulation of the differentiation process by small molecule activation of WNT signaling, followed by inactivation of transforming growth factor-β and WNT signaling and activation of sonic hedgehog signaling in hPSCs as size-controlled aggregates led to the generation of approximately 100% beating CM spheroids containing virtually pure (∼90%) CMs in 10 days. Moreover, the developed differentiation strategy was universal, as demonstrated by testing multiple hPSC lines (5 human embryonic stem cell and 4 human inducible PSC lines) without cell sorting or selection. The produced hPSC-CMs successfully expressed canonical lineage-specific markers and showed high functionality, as demonstrated by microelectrode array and electrophysiology tests. This robust and universal platform could become a valuable tool for the mass production of functional hPSC-CMs as a prerequisite for realizing their promising potential for therapeutic and industrial applications, including drug discovery and toxicity assays. Recent advances in the generation of cardiomyocytes (CMs) from human pluripotent stem cells (hPSCs) and the development of novel cell therapy strategies using hPSC-CMs (e.g., cardiac patches) in conjunction with promising preclinical and clinical studies, have raised new hopes for patients with end-stage cardiovascular disease, which remains the leading cause of morbidity and mortality globally. In this study, a simplified, scalable, robust, and integrated differentiation platform was developed to generate clinical grade hPSC-CMs as cell aggregates under chemically defined culture conditions. This approach resulted in approximately 100% beating CM spheroids with virtually pure (∼90%) functional cardiomyocytes in 10 days from multiple hPSC lines. This universal and robust bioprocessing platform can provide sufficient numbers of hPSC-CMs for companies developing regenerative medicine technologies to rescue, replace, and help repair damaged heart tissues and for pharmaceutical companies developing advanced biologics and drugs for regeneration of lost heart tissue using high-throughput technologies. It is believed that this technology can expedite clinical progress in these areas to achieve a meaningful impact on improving clinical outcomes, cost of care, and quality of life for those patients disabled and experiencing heart disease. ©AlphaMed Press.

A Universal and Robust Integrated Platform for the Scalable Production of Human Cardiomyocytes From Pluripotent Stem Cells

PubMed Central

Fonoudi, Hananeh; Ansari, Hassan; Abbasalizadeh, Saeed; Larijani, Mehran Rezaei; Kiani, Sahar; Hashemizadeh, Shiva; Zarchi, Ali Sharifi; Bosman, Alexis; Blue, Gillian M.; Pahlavan, Sara; Perry, Matthew; Orr, Yishay; Mayorchak, Yaroslav; Vandenberg, Jamie; Talkhabi, Mahmood; Winlaw, David S.; Harvey, Richard P.; Aghdami, Nasser

2015-01-01

Recent advances in the generation of cardiomyocytes (CMs) from human pluripotent stem cells (hPSCs), in conjunction with the promising outcomes from preclinical and clinical studies, have raised new hopes for cardiac cell therapy. We report the development of a scalable, robust, and integrated differentiation platform for large-scale production of hPSC-CM aggregates in a stirred suspension bioreactor as a single-unit operation. Precise modulation of the differentiation process by small molecule activation of WNT signaling, followed by inactivation of transforming growth factor-β and WNT signaling and activation of sonic hedgehog signaling in hPSCs as size-controlled aggregates led to the generation of approximately 100% beating CM spheroids containing virtually pure (∼90%) CMs in 10 days. Moreover, the developed differentiation strategy was universal, as demonstrated by testing multiple hPSC lines (5 human embryonic stem cell and 4 human inducible PSC lines) without cell sorting or selection. The produced hPSC-CMs successfully expressed canonical lineage-specific markers and showed high functionality, as demonstrated by microelectrode array and electrophysiology tests. This robust and universal platform could become a valuable tool for the mass production of functional hPSC-CMs as a prerequisite for realizing their promising potential for therapeutic and industrial applications, including drug discovery and toxicity assays. Significance Recent advances in the generation of cardiomyocytes (CMs) from human pluripotent stem cells (hPSCs) and the development of novel cell therapy strategies using hPSC-CMs (e.g., cardiac patches) in conjunction with promising preclinical and clinical studies, have raised new hopes for patients with end-stage cardiovascular disease, which remains the leading cause of morbidity and mortality globally. In this study, a simplified, scalable, robust, and integrated differentiation platform was developed to generate clinical grade hPSC-CMs as cell aggregates under chemically defined culture conditions. This approach resulted in approximately 100% beating CM spheroids with virtually pure (∼90%) functional cardiomyocytes in 10 days from multiple hPSC lines. This universal and robust bioprocessing platform can provide sufficient numbers of hPSC-CMs for companies developing regenerative medicine technologies to rescue, replace, and help repair damaged heart tissues and for pharmaceutical companies developing advanced biologics and drugs for regeneration of lost heart tissue using high-throughput technologies. It is believed that this technology can expedite clinical progress in these areas to achieve a meaningful impact on improving clinical outcomes, cost of care, and quality of life for those patients disabled and experiencing heart disease. PMID:26511653

Robust Smoothing: Smoothing Parameter Selection and Applications to Fluorescence Spectroscopy∂

PubMed Central

Lee, Jong Soo; Cox, Dennis D.

2009-01-01

Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. Various robust smoothing methods for estimating fluorescence emission spectra are compared and data driven methods for the selection of smoothing parameter are suggested. The methods currently implemented in R for smoothing parameter selection proved to be unsatisfactory, and a computationally efficient procedure that approximates robust leave-one-out cross validation is presented. PMID:20729976

A fast, robust pattern recognition asystem for low light level image registration and its application to retinal imaging

NASA Astrophysics Data System (ADS)

Wade, Alex Robert; Fitzke, Frederick W.

1998-08-01

We describe an image processing system which we have developed to align autofluorescence and high-magnification images taken with a laser scanning ophthalmoscope. The low signal to noise ratio of these images makes pattern recognition a non-trivial task. However, once n images are aligned and averaged, the noise levels drop by a factor of n and the image quality is improved. We include examples of autofluorescence images and images of the cone photoreceptor mosaic obtained using this system.

Robust optical signal-to-noise ratio monitoring scheme using a phase-modulator-embedded fiber loop mirror.

PubMed

Ku, Yuen-Ching; Chan, Chun-Kit; Chen, Lian-Kuan

2007-06-15

We propose and experimentally demonstrate a novel in-band optical signal-to-noise ratio (OSNR) monitoring technique using a phase-modulator-embedded fiber loop mirror. This technique measures the in-band OSNR accurately by observing the output power of a fiber loop mirror filter, where the transmittance is adjusted by an embedded phase modulator driven by a low-frequency periodic signal. The measurement errors are less than 0.5 dB for an OSNR between 0 and 40 dB in a 10 Gbit/s non-return-to-zero system. This technique was also shown experimentally to have high robustness against various system impairments and high feasibility to be deployed in practical implementation.

Robust distant-entanglement generation using coherent multiphoton scattering

NASA Astrophysics Data System (ADS)

Chan, Ching-Kit; Sham, L. J.

2013-03-01

The generation and controllability of entanglement between distant quantum states have been the heart of quantum computation and quantum information processing. Existing schemes for solid state qubit entanglement are based on the single-photon spectroscopy that has the merit of a high fidelity entanglement creation, but with a very limited efficiency. This severely restricts the scalability for a qubit network system. Here, we describe a new distant entanglement protocol using coherent multiphoton scattering. The scheme makes use of the postselection of large and distinguishable photon signals, and has both a high success probability and a high entanglement fidelity. Our result shows that the entanglement generation is robust against photon fluctuations, and has an average entanglement duration within the decoherence time in various qubit systems, based on existing experimental parameters. This research was supported by the U.S. Army Research Office MURI award W911NF0910406 and by NSF grant PHY-1104446.

Validating Coherence Measurements Using Aligned and Unaligned Coherence Functions

NASA Technical Reports Server (NTRS)

Miles, Jeffrey Hilton

2006-01-01

This paper describes a novel approach based on the use of coherence functions and statistical theory for sensor validation in a harsh environment. By the use of aligned and unaligned coherence functions and statistical theory one can test for sensor degradation, total sensor failure or changes in the signal. This advanced diagnostic approach and the novel data processing methodology discussed provides a single number that conveys this information. This number as calculated with standard statistical procedures for comparing the means of two distributions is compared with results obtained using Yuen's robust statistical method to create confidence intervals. Examination of experimental data from Kulite pressure transducers mounted in a Pratt & Whitney PW4098 combustor using spectrum analysis methods on aligned and unaligned time histories has verified the effectiveness of the proposed method. All the procedures produce good results which demonstrates how robust the technique is.

An automatic rat brain extraction method based on a deformable surface model.

PubMed

Li, Jiehua; Liu, Xiaofeng; Zhuo, Jiachen; Gullapalli, Rao P; Zara, Jason M

2013-08-15

The extraction of the brain from the skull in medical images is a necessary first step before image registration or segmentation. While pre-clinical MR imaging studies on small animals, such as rats, are increasing, fully automatic imaging processing techniques specific to small animal studies remain lacking. In this paper, we present an automatic rat brain extraction method, the Rat Brain Deformable model method (RBD), which adapts the popular human brain extraction tool (BET) through the incorporation of information on the brain geometry and MR image characteristics of the rat brain. The robustness of the method was demonstrated on T2-weighted MR images of 64 rats and compared with other brain extraction methods (BET, PCNN, PCNN-3D). The results demonstrate that RBD reliably extracts the rat brain with high accuracy (>92% volume overlap) and is robust against signal inhomogeneity in the images. Copyright © 2013 Elsevier B.V. All rights reserved.

A novel speech watermarking algorithm by line spectrum pair modification

NASA Astrophysics Data System (ADS)

Zhang, Qian; Yang, Senbin; Chen, Guang; Zhou, Jun

2011-10-01

To explore digital watermarking specifically suitable for the speech domain, this paper experimentally investigates the properties of line spectrum pair (LSP) parameters firstly. The results show that the differences between contiguous LSPs are robust against common signal processing operations and small modifications of LSPs are imperceptible to the human auditory system (HAS). According to these conclusions, three contiguous LSPs of a speech frame are selected to embed a watermark bit. The middle LSP is slightly altered to modify the differences of these LSPs when embedding watermark. Correspondingly, the watermark is extracted by comparing these differences. The proposed algorithm's transparency is adjustable to meet the needs of different applications. The algorithm has good robustness against additive noise, quantization, amplitude scale and MP3 compression attacks, for the bit error rate (BER) is less than 5%. In addition, the algorithm allows a relatively low capacity, which approximates to 50 bps.

Robust Adaptive Thresholder For Document Scanning Applications

NASA Astrophysics Data System (ADS)

Hsing, To R.

1982-12-01

In document scanning applications, thresholding is used to obtain binary data from a scanner. However, due to: (1) a wide range of different color backgrounds; (2) density variations of printed text information; and (3) the shading effect caused by the optical systems, the use of adaptive thresholding to enhance the useful information is highly desired. This paper describes a new robust adaptive thresholder for obtaining valid binary images. It is basically a memory type algorithm which can dynamically update the black and white reference level to optimize a local adaptive threshold function. The results of high image quality from different types of simulate test patterns can be obtained by this algorithm. The software algorithm is described and experiment results are present to describe the procedures. Results also show that the techniques described here can be used for real-time signal processing in the varied applications.

Dual-pump Kerr Micro-cavity Optical Frequency Comb with varying FSR spacing

PubMed Central

Wang, Weiqiang; Chu, Sai T.; Little, Brent E.; Pasquazi, Alessia; Wang, Yishan; Wang, Leiran; Zhang, Wenfu; Wang, Lei; Hu, Xiaohong; Wang, Guoxi; Hu, Hui; Su, Yulong; Li, Feitao; Liu, Yuanshan; Zhao, Wei

2016-01-01

In this paper, we demonstrate a novel dual-pump approach to generate robust optical frequency comb with varying free spectral range (FSR) spacing in a CMOS-compatible high-Q micro-ring resonator (MRR). The frequency spacing of the comb can be tuned by an integer number FSR of the MRR freely in our dual-pump scheme. The dual pumps are self-oscillated in the laser cavity loop and their wavelengths can be tuned flexibly by programming the tunable filter embedded in the cavity. By tuning the pump wavelength, broadband OFC with the bandwidth of >180 nm and the frequency-spacing varying from 6 to 46-fold FSRs is realized at a low pump power. This approach could find potential and practical applications in many areas, such as optical metrology, optical communication, and signal processing systems, for its excellent flexibility and robustness. PMID:27338250

Introduction to the special section on communication and wartime deployment.

PubMed

Maguire, Katheryn C; Wilson, Steven R

2013-01-01

Over the past decade, the wars in Iraq and Afghanistan have taken a heavy toll on the physical, psychological, and relational health of military service members and their families. The articles included in this special section of Health Communication add to the robust, interdisciplinary body of research on the health consequences of wartime deployment by examining how communication enables the recovery process of service members and their families. Because communication processes can signal health problems, construct and promote family resiliency, and shape the content and delivery of health interventions, our discipline's theory and research can help inform ongoing efforts to support military families as the wars in Iraq and Afghanistan wind down.

Simulation-based robust optimization for signal timing and setting.

DOT National Transportation Integrated Search

2009-12-30

The performance of signal timing plans obtained from traditional approaches for : pre-timed (fixed-time or actuated) control systems is often unstable under fluctuating traffic : conditions. This report develops a general approach for optimizing the ...

A Low-Power CMOS Front-End for Photoplethysmographic Signal Acquisition With Robust DC Photocurrent Rejection.

PubMed

Wong, A K Y; Kong-Pang Pun; Yuan-Ting Zhang; Ka Nang Leung

2008-12-01

A micro-power CMOS front-end, consisting of a transimpedance amplifier (TIA) and an ultralow cutoff frequency lowpass filter for the acquisition of photoplethysmographic signal (PPG) is presented. Robust DC photocurrent rejection for the pulsed signal source is achieved through a sample-and-hold stage in the feed-forward signal path and an error amplifier in the feedback path. Ultra-low cutoff frequency of the filter is achieved with a proposed technique that incorporates a pair of current-steering transistors that increases the effective filter capacitance. The design was realized in a 0.35-mum CMOS technology. It consumes 600 muW at 2.5 V, rejects DC photocurrent ranged from 100 nA to 53.6 muA, and achieves lower-band and upper-band - 3-dB cutoff frequencies of 0.46 and 2.8 Hz, respectively.

A new strategy for in vivo spectral editing. Application to GABA editing using selective homonuclear polarization transfer spectroscopy

NASA Astrophysics Data System (ADS)

Shen, Jun; Yang, Jehoon; Choi, In-Young; Li, Shizhe Steve; Chen, Zhengguang

2004-10-01

A novel single-shot in vivo spectral editing method is proposed in which the signal to be detected, is regenerated anew from the thermal equilibrium magnetization of a source to which it is J-coupled. The thermal equilibrium magnetization of the signal to be detected together with those of overlapping signals are suppressed by single-shot gradient dephasing prior to the signal regeneration process. Application of this new strategy to in vivo GABA editing using selective homonuclear polarization transfer allows complete suppression of overlapping creatine and glutathione while detecting the GABA-4 methylene resonance at 3.02 ppm with an editing yield similar to that of conventional editing methods. The NAA methyl group at 2.02 ppm was simultaneously detected and can be used as an internal navigator echo for correcting the zero order phase and frequency shifts and as an internal reference for concentration. This new method has been demonstrated for robust in vivo GABA editing in the rat brain and for study of GABA synthesis after acute vigabatrin administration.

Detecting and Quantifying Paleoseasonality in Stalagmites using Geochemical and Modelling Approaches

NASA Astrophysics Data System (ADS)

Baldini, J. U. L.

2017-12-01

Stalagmites are now well established sources of terrestrial paleoclimate information, providing insights into climate change on a variety of timescales. One of the most exciting aspects of stalagmites as climate archives is their ability to provide information regarding seasonality, a notoriously difficult component of climate change to characterise. However, stalagmite geochemistry may reflect not only the most apparent seasonal signal in external climate parameters, but also cave-specific signals such as seasonal changes in cave air carbon dioxide concentrations, sudden shifts in ventilation, and stochastic hydrological processes. Additionally, analytical bias may dampen or completely obfuscate any paleoseasonality, highlighting the need for appropriate quantification of this issue using simple models. Evidence from stalagmites now suggests that a seasonal signal is extractable from many samples, and that this signal can provide an important extra dimension to paleoclimate interpretations. Additionally, lower resolution annual- to decadal-scale isotope ratio records may also reflect shifts in seasonality, but identifying these is often challenging. Integrating geochemical datasets with models and cave monitoring data can greatly increase the accuracy of climate reconstructions, and yield the most robust records.

The bantam microRNA acts through Numb to exert cell growth control and feedback regulation of Notch in tumor-forming stem cells in the Drosophila brain.

PubMed

Wu, Yen-Chi; Lee, Kyu-Sun; Song, Yan; Gehrke, Stephan; Lu, Bingwei

2017-05-01

Notch (N) signaling is central to the self-renewal of neural stem cells (NSCs) and other tissue stem cells. Its deregulation compromises tissue homeostasis and contributes to tumorigenesis and other diseases. How N regulates stem cell behavior in health and disease is not well understood. Here we show that N regulates bantam (ban) microRNA to impact cell growth, a process key to NSC maintenance and particularly relied upon by tumor-forming cancer stem cells. Notch signaling directly regulates ban expression at the transcriptional level, and ban in turn feedback regulates N activity through negative regulation of the Notch inhibitor Numb. This feedback regulatory mechanism helps maintain the robustness of N signaling activity and NSC fate. Moreover, we show that a Numb-Myc axis mediates the effects of ban on nucleolar and cellular growth independently or downstream of N. Our results highlight intricate transcriptional as well as translational control mechanisms and feedback regulation in the N signaling network, with important implications for NSC biology and cancer biology.

Emergence of tissue polarization from synergy of intracellular and extracellular auxin signaling

PubMed Central

Wabnik, Krzysztof; Kleine-Vehn, Jürgen; Balla, Jozef; Sauer, Michael; Naramoto, Satoshi; Reinöhl, Vilém; Merks, Roeland M H; Govaerts, Willy; Friml, Jiří

2010-01-01

Plant development is exceptionally flexible as manifested by its potential for organogenesis and regeneration, which are processes involving rearrangements of tissue polarities. Fundamental questions concern how individual cells can polarize in a coordinated manner to integrate into the multicellular context. In canalization models, the signaling molecule auxin acts as a polarizing cue, and feedback on the intercellular auxin flow is key for synchronized polarity rearrangements. We provide a novel mechanistic framework for canalization, based on up-to-date experimental data and minimal, biologically plausible assumptions. Our model combines the intracellular auxin signaling for expression of PINFORMED (PIN) auxin transporters and the theoretical postulation of extracellular auxin signaling for modulation of PIN subcellular dynamics. Computer simulations faithfully and robustly recapitulated the experimentally observed patterns of tissue polarity and asymmetric auxin distribution during formation and regeneration of vascular systems and during the competitive regulation of shoot branching by apical dominance. Additionally, our model generated new predictions that could be experimentally validated, highlighting a mechanistically conceivable explanation for the PIN polarization and canalization of the auxin flow in plants. PMID:21179019

Feedback regulation of TGF-β signaling.

PubMed

Yan, Xiaohua; Xiong, Xiangyang; Chen, Ye-Guang

2018-01-01

Transforming growth factor beta (TGF-β) is a multi-functional polypeptide that plays a critical role in regulating a broad range of cellular functions and physiological processes. Signaling is initiated when TGF-β ligands bind to two types of cell membrane receptors with intrinsic Ser/Thr kinase activity and transmitted by the intracellular Smad proteins, which act as transcription factors to regulate gene expression in the nucleus. Although it is relatively simple and straight-forward, this TGF-β/Smad pathway is regulated by various feedback loops at different levels, including the ligand, the receptor, Smads and transcription, and is thus fine-tuned in terms of signaling robustness, duration, specificity, and plasticity. The precise control gives rise to versatile and context-dependent pathophysiological functions. In this review, we firstly give an overview of TGF-β signaling, and then discuss how each step of TGF-β signaling is finely controlled by distinct modes of feedback mechanisms, involving both protein regulators and miRNAs. © The Author 2017. Published by Oxford University Press on behalf of the Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations From Extreme Learning.

PubMed

Betthauser, Joseph L; Hunt, Christopher L; Osborn, Luke E; Masters, Matthew R; Levay, Gyorgy; Kaliki, Rahul R; Thakor, Nitish V

2018-04-01

Myoelectric signals can be used to predict the intended movements of an amputee for prosthesis control. However, untrained effects like limb position changes influence myoelectric signal characteristics, hindering the ability of pattern recognition algorithms to discriminate among motion classes. Despite frequent and long training sessions, these deleterious conditional influences may result in poor performance and device abandonment. We present a robust sparsity-based adaptive classification method that is significantly less sensitive to signal deviations resulting from untrained conditions. We compare this approach in the offline and online contexts of untrained upper-limb positions for amputee and able-bodied subjects to demonstrate its robustness compared against other myoelectric classification methods. We report significant performance improvements () in untrained limb positions across all subject groups. The robustness of our suggested approach helps to ensure better untrained condition performance from fewer training conditions. This method of prosthesis control has the potential to deliver real-world clinical benefits to amputees: better condition-tolerant performance, reduced training burden in terms of frequency and duration, and increased adoption of myoelectric prostheses.

Cytokinin signalling inhibitory fields provide robustness to phyllotaxis

NASA Astrophysics Data System (ADS)

Besnard, Fabrice; Refahi, Yassin; Morin, Valérie; Marteaux, Benjamin; Brunoud, Géraldine; Chambrier, Pierre; Rozier, Frédérique; Mirabet, Vincent; Legrand, Jonathan; Lainé, Stéphanie; Thévenon, Emmanuel; Farcot, Etienne; Cellier, Coralie; Das, Pradeep; Bishopp, Anthony; Dumas, Renaud; Parcy, François; Helariutta, Ykä; Boudaoud, Arezki; Godin, Christophe; Traas, Jan; Guédon, Yann; Vernoux, Teva

2014-01-01

How biological systems generate reproducible patterns with high precision is a central question in science. The shoot apical meristem (SAM), a specialized tissue producing plant aerial organs, is a developmental system of choice to address this question. Organs are periodically initiated at the SAM at specific spatial positions and this spatiotemporal pattern defines phyllotaxis. Accumulation of the plant hormone auxin triggers organ initiation, whereas auxin depletion around organs generates inhibitory fields that are thought to be sufficient to maintain these patterns and their dynamics. Here we show that another type of hormone-based inhibitory fields, generated directly downstream of auxin by intercellular movement of the cytokinin signalling inhibitor ARABIDOPSIS HISTIDINE PHOSPHOTRANSFER PROTEIN 6 (AHP6), is involved in regulating phyllotactic patterns. We demonstrate that AHP6-based fields establish patterns of cytokinin signalling in the meristem that contribute to the robustness of phyllotaxis by imposing a temporal sequence on organ initiation. Our findings indicate that not one but two distinct hormone-based fields may be required for achieving temporal precision during formation of reiterative structures at the SAM, thus indicating an original mechanism for providing robustness to a dynamic developmental system.

Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters.

PubMed

Feng, Jianyuan; Turksoy, Kamuran; Samadi, Sediqeh; Hajizadeh, Iman; Littlejohn, Elizabeth; Cinar, Ali

2017-12-01

Supervision and control systems rely on signals from sensors to receive information to monitor the operation of a system and adjust manipulated variables to achieve the control objective. However, sensor performance is often limited by their working conditions and sensors may also be subjected to interference by other devices. Many different types of sensor errors such as outliers, missing values, drifts and corruption with noise may occur during process operation. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. The system includes a nominal angle analysis (NAA) method to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in process operation. The performance of the system is illustrated with clinical data continuous glucose monitoring (CGM) sensors from people with type 1 diabetes. More than 50,000 CGM sensor errors were added to original CGM signals from 25 clinical experiments, then the performance of error detection and functional redundancy algorithms were analyzed. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable estimated values computed by functional redundancy system.

Nonlinear robust control of hypersonic aircrafts with interactions between flight dynamics and propulsion systems.

PubMed

Li, Zhaoying; Zhou, Wenjie; Liu, Hao

2016-09-01

This paper addresses the nonlinear robust tracking controller design problem for hypersonic vehicles. This problem is challenging due to strong coupling between the aerodynamics and the propulsion system, and the uncertainties involved in the vehicle dynamics including parametric uncertainties, unmodeled model uncertainties, and external disturbances. By utilizing the feedback linearization technique, a linear tracking error system is established with prescribed references. For the linear model, a robust controller is proposed based on the signal compensation theory to guarantee that the tracking error dynamics is robustly stable. Numerical simulation results are given to show the advantages of the proposed nonlinear robust control method, compared to the robust loop-shaping control approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Development of Advanced Signal Processing and Source Imaging Methods for Superparamagnetic Relaxometry

PubMed Central

Huang, Ming-Xiong; Anderson, Bill; Huang, Charles W.; Kunde, Gerd J.; Vreeland, Erika C.; Huang, Jeffrey W.; Matlashov, Andrei N.; Karaulanov, Todor; Nettles, Christopher P.; Gomez, Andrew; Minser, Kayla; Weldon, Caroline; Paciotti, Giulio; Harsh, Michael; Lee, Roland R.; Flynn, Edward R.

2017-01-01

Superparamagnetic Relaxometry (SPMR) is a highly sensitive technique for the in vivo detection of tumor cells and may improve early stage detection of cancers. SPMR employs superparamagnetic iron oxide nanoparticles (SPION). After a brief magnetizing pulse is used to align the SPION, SPMR measures the time decay of SPION using Super-conducting Quantum Interference Device (SQUID) sensors. Substantial research has been carried out in developing the SQUID hardware and in improving the properties of the SPION. However, little research has been done in the pre-processing of sensor signals and post-processing source modeling in SPMR. In the present study, we illustrate new pre-processing tools that were developed to: 1) remove trials contaminated with artifacts, 2) evaluate and ensure that a single decay process associated with bounded SPION exists in the data, 3) automatically detect and correct flux jumps, and 4) accurately fit the sensor signals with different decay models. Furthermore, we developed an automated approach based on multi-start dipole imaging technique to obtain the locations and magnitudes of multiple magnetic sources, without initial guesses from the users. A regularization process was implemented to solve the ambiguity issue related to the SPMR source variables. A procedure based on reduced chi-square cost-function was introduced to objectively obtain the adequate number of dipoles that describe the data. The new pre-processing tools and multi-start source imaging approach have been successfully evaluated using phantom data. In conclusion, these tools and multi-start source modeling approach substantially enhance the accuracy and sensitivity in detecting and localizing sources from the SPMR signals. Furthermore, multi-start approach with regularization provided robust and accurate solutions for a poor SNR condition similar to the SPMR detection sensitivity in the order of 1000 cells. We believe such algorithms will help establishing the industrial standards for SPMR when applying the technique in pre-clinical and clinical settings. PMID:28072579

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

Wiebenga, J. H.; Atzema, E. H.; Boogaard, A. H. van den

Robust design of forming processes using numerical simulations is gaining attention throughout the industry. In this work, it is demonstrated how robust optimization can assist in further stretching the limits of metal forming processes. A deterministic and a robust optimization study are performed, considering a stretch-drawing process of a hemispherical cup product. For the robust optimization study, both the effect of material and process scatter are taken into account. For quantifying the material scatter, samples of 41 coils of a drawing quality forming steel have been collected. The stochastic material behavior is obtained by a hybrid approach, combining mechanical testingmore » and texture analysis, and efficiently implemented in a metamodel based optimization strategy. The deterministic and robust optimization results are subsequently presented and compared, demonstrating an increased process robustness and decreased number of product rejects by application of the robust optimization approach.« less

Examining the robustness of automated aural classification of active sonar echoes.

PubMed

Murphy, Stefan M; Hines, Paul C

2014-02-01

Active sonar systems are used to detect underwater man-made objects of interest (targets) that are too quiet to be reliably detected with passive sonar. Performance of active sonar can be degraded by false alarms caused by echoes returned from geological seabed structures (clutter) in shallow regions. To reduce false alarms, a method of distinguishing target echoes from clutter echoes is required. Research has demonstrated that perceptual-based signal features similar to those employed in the human auditory system can be used to automatically discriminate between target and clutter echoes, thereby reducing the number of false alarms and improving sonar performance. An active sonar experiment on the Malta Plateau in the Mediterranean Sea was conducted during the Clutter07 sea trial and repeated during the Clutter09 sea trial. The dataset consists of more than 95,000 pulse-compressed echoes returned from two targets and many geological clutter objects. These echoes were processed using an automatic classifier that quantifies the timbre of each echo using a number of perceptual signal features. Using echoes from 2007, the aural classifier was trained to establish a boundary between targets and clutter in the feature space. Temporal robustness was then investigated by testing the classifier on echoes from the 2009 experiment.

High-Throughput RNA Interference Screening: Tricks of the Trade

PubMed Central

Nebane, N. Miranda; Coric, Tatjana; Whig, Kanupriya; McKellip, Sara; Woods, LaKeisha; Sosa, Melinda; Sheppard, Russell; Rasmussen, Lynn; Bjornsti, Mary-Ann; White, E. Lucile

2016-01-01

The process of validating an assay for high-throughput screening (HTS) involves identifying sources of variability and developing procedures that minimize the variability at each step in the protocol. The goal is to produce a robust and reproducible assay with good metrics. In all good cell-based assays, this means coefficient of variation (CV) values of less than 10% and a signal window of fivefold or greater. HTS assays are usually evaluated using Z′ factor, which incorporates both standard deviation and signal window. A Z′ factor value of 0.5 or higher is acceptable for HTS. We used a standard HTS validation procedure in developing small interfering RNA (siRNA) screening technology at the HTS center at Southern Research. Initially, our assay performance was similar to published screens, with CV values greater than 10% and Z′ factor values of 0.51 ± 0.16 (average ± standard deviation). After optimizing the siRNA assay, we got CV values averaging 7.2% and a robust Z′ factor value of 0.78 ± 0.06 (average ± standard deviation). We present an overview of the problems encountered in developing this whole-genome siRNA screening program at Southern Research and how equipment optimization led to improved data quality. PMID:23616418

A Robust Analysis Method For Δ13c Signal Of Bulk Organic Matter In Speleothems

NASA Astrophysics Data System (ADS)

Bian, F.; Blyth, A. J.; Smith, C.; Baker, A.

2017-12-01

Speleothems preserve organic matter that is derived from both the surface soil and cave environments. This organic matter can be used to understand paleoclimate and paleoenvironments. However, a stable and quick micro-analysis method to measure the δ13C signals from speleothem organic matter separate from the total δ13C remains absent. And speleothem organic geochemistry is still relatively unexplored compared to inorganic geochemistry. In this research, for the organic matter analysis, bulk homogeneous power samples were obtained from one large stalagmite. These were dissolved by phosphoric acid to produce the aqueous solution. Then, the processed solution was degassed through a rotational vacuum concentrator. A liquid chromatograph was coupled to IRMS to control the oxidization and the measurement of analytes. This method is demonstrated to be robust for the analysis of speleothem d13C organic matter analysis under different preparation and instrumental settings, with the low standard deviation ( 0.2‰), and low sample consumption (<25 mg). Considering the complexity of cave environments, this method will be useful in further investigations the δ13C of entrapped organic matter and environmental controls in other climatic and ecological contexts, including the determination of whether vegetation or soil microbial activity is the dominant control on speleothem d13C of organic matter.

A low cost mobile phone dark-field microscope for nanoparticle-based quantitative studies.

PubMed

Sun, Dali; Hu, Tony Y

2018-01-15

Dark-field microscope (DFM) analysis of nanoparticle binding signal is highly useful for a variety of research and biomedical applications, but current applications for nanoparticle quantification rely on expensive DFM systems. The cost, size, limited robustness of these DFMs limits their utility for non-laboratory settings. Most nanoparticle analyses use high-magnification DFM images, which are labor intensive to acquire and subject to operator bias. Low-magnification DFM image capture is faster, but is subject to background from surface artifacts and debris, although image processing can partially compensate for background signal. We thus mated an LED light source, a dark-field condenser and a 20× objective lens with a mobile phone camera to create an inexpensive, portable and robust DFM system suitable for use in non-laboratory conditions. This proof-of-concept mobile DFM device weighs less than 400g and costs less than $2000, but analysis of images captured with this device reveal similar nanoparticle quantitation results to those acquired with a much larger and more expensive desktop DFMM system. Our results suggest that similar devices may be useful for quantification of stable, nanoparticle-based activity and quantitation assays in resource-limited areas where conventional assay approaches are not practical. Copyright © 2017 Elsevier B.V. All rights reserved.

Improving Robot Motor Learning with Negatively Valenced Reinforcement Signals

PubMed Central

Navarro-Guerrero, Nicolás; Lowe, Robert J.; Wermter, Stefan

2017-01-01

Both nociception and punishment signals have been used in robotics. However, the potential for using these negatively valenced types of reinforcement learning signals for robot learning has not been exploited in detail yet. Nociceptive signals are primarily used as triggers of preprogrammed action sequences. Punishment signals are typically disembodied, i.e., with no or little relation to the agent-intrinsic limitations, and they are often used to impose behavioral constraints. Here, we provide an alternative approach for nociceptive signals as drivers of learning rather than simple triggers of preprogrammed behavior. Explicitly, we use nociception to expand the state space while we use punishment as a negative reinforcement learning signal. We compare the performance—in terms of task error, the amount of perceived nociception, and length of learned action sequences—of different neural networks imbued with punishment-based reinforcement signals for inverse kinematic learning. We contrast the performance of a version of the neural network that receives nociceptive inputs to that without such a process. Furthermore, we provide evidence that nociception can improve learning—making the algorithm more robust against network initializations—as well as behavioral performance by reducing the task error, perceived nociception, and length of learned action sequences. Moreover, we provide evidence that punishment, at least as typically used within reinforcement learning applications, may be detrimental in all relevant metrics. PMID:28420976

Decomposing decision components in the Stop-signal task: A model-based approach to individual differences in inhibitory control

PubMed Central

White, Corey N.; Congdon, Eliza; Mumford, Jeanette A.; Karlsgodt, Katherine H.; Sabb, Fred W.; Freimer, Nelson B.; London, Edythe D.; Cannon, Tyrone D.; Bilder, Robert M.; Poldrack, Russell A.

2014-01-01

The Stop-signal task (SST), in which participants must inhibit prepotent responses, has been used to identify neural systems that vary with individual differences in inhibitory control. To explore how these differences relate to other aspects of decision-making, a drift diffusion model of simple decisions was fitted to SST data from Go trials to extract measures of caution, motor execution time, and stimulus processing speed for each of 123 participants. These values were used to probe fMRI data to explore individual differences in neural activation. Faster processing of the Go stimulus correlated with greater activation in the right frontal pole for both Go and Stop trials. On Stop trials stimulus processing speed also correlated with regions implicated in inhibitory control, including the right inferior frontal gyrus, medial frontal gyrus, and basal ganglia. Individual differences in motor execution time correlated with activation of the right parietal cortex. These findings suggest a robust relationship between the speed of stimulus processing and inhibitory processing at the neural level. This model-based approach provides novel insight into the interrelationships among decision components involved in inhibitory control, and raises interesting questions about strategic adjustments in performance and inhibitory deficits associated with psychopathology. PMID:24405185

Time-Warp–Invariant Neuronal Processing

PubMed Central

Gütig, Robert; Sompolinsky, Haim

2009-01-01

Fluctuations in the temporal durations of sensory signals constitute a major source of variability within natural stimulus ensembles. The neuronal mechanisms through which sensory systems can stabilize perception against such fluctuations are largely unknown. An intriguing instantiation of such robustness occurs in human speech perception, which relies critically on temporal acoustic cues that are embedded in signals with highly variable duration. Across different instances of natural speech, auditory cues can undergo temporal warping that ranges from 2-fold compression to 2-fold dilation without significant perceptual impairment. Here, we report that time-warp–invariant neuronal processing can be subserved by the shunting action of synaptic conductances that automatically rescales the effective integration time of postsynaptic neurons. We propose a novel spike-based learning rule for synaptic conductances that adjusts the degree of synaptic shunting to the temporal processing requirements of a given task. Applying this general biophysical mechanism to the example of speech processing, we propose a neuronal network model for time-warp–invariant word discrimination and demonstrate its excellent performance on a standard benchmark speech-recognition task. Our results demonstrate the important functional role of synaptic conductances in spike-based neuronal information processing and learning. The biophysics of temporal integration at neuronal membranes can endow sensory pathways with powerful time-warp–invariant computational capabilities. PMID:19582146

A robust algorithm for optimisation and customisation of fractal dimensions of time series modified by nonlinearly scaling their time derivatives: mathematical theory and practical applications.

PubMed

Fuss, Franz Konstantin

2013-01-01

Standard methods for computing the fractal dimensions of time series are usually tested with continuous nowhere differentiable functions, but not benchmarked with actual signals. Therefore they can produce opposite results in extreme signals. These methods also use different scaling methods, that is, different amplitude multipliers, which makes it difficult to compare fractal dimensions obtained from different methods. The purpose of this research was to develop an optimisation method that computes the fractal dimension of a normalised (dimensionless) and modified time series signal with a robust algorithm and a running average method, and that maximises the difference between two fractal dimensions, for example, a minimum and a maximum one. The signal is modified by transforming its amplitude by a multiplier, which has a non-linear effect on the signal's time derivative. The optimisation method identifies the optimal multiplier of the normalised amplitude for targeted decision making based on fractal dimensions. The optimisation method provides an additional filter effect and makes the fractal dimensions less noisy. The method is exemplified by, and explained with, different signals, such as human movement, EEG, and acoustic signals.

A Robust Algorithm for Optimisation and Customisation of Fractal Dimensions of Time Series Modified by Nonlinearly Scaling Their Time Derivatives: Mathematical Theory and Practical Applications

PubMed Central

2013-01-01

Standard methods for computing the fractal dimensions of time series are usually tested with continuous nowhere differentiable functions, but not benchmarked with actual signals. Therefore they can produce opposite results in extreme signals. These methods also use different scaling methods, that is, different amplitude multipliers, which makes it difficult to compare fractal dimensions obtained from different methods. The purpose of this research was to develop an optimisation method that computes the fractal dimension of a normalised (dimensionless) and modified time series signal with a robust algorithm and a running average method, and that maximises the difference between two fractal dimensions, for example, a minimum and a maximum one. The signal is modified by transforming its amplitude by a multiplier, which has a non-linear effect on the signal's time derivative. The optimisation method identifies the optimal multiplier of the normalised amplitude for targeted decision making based on fractal dimensions. The optimisation method provides an additional filter effect and makes the fractal dimensions less noisy. The method is exemplified by, and explained with, different signals, such as human movement, EEG, and acoustic signals. PMID:24151522

Directed Field Ionization

NASA Astrophysics Data System (ADS)

Gregoric, Vincent C.; Kang, Xinyue; Liu, Zhimin Cheryl; Rowley, Zoe A.; Carroll, Thomas J.; Noel, Michael W.

2017-04-01

Selective field ionization is an important experimental technique used to study the state distribution of Rydberg atoms. This is achieved by applying a steadily increasing electric field, which successively ionizes more tightly bound states. An atom prepared in an energy eigenstate encounters many avoided Stark level crossings on the way to ionization. As it traverses these avoided crossings, its amplitude is split among multiple different states, spreading out the time resolved electron ionization signal. By perturbing the electric field ramp, we can change how the atoms traverse the avoided crossings, and thus alter the shape of the ionization signal. We have used a genetic algorithm to evolve these perturbations in real time in order to arrive at a target ionization signal shape. This process is robust to large fluctuations in experimental conditions. This work was supported by the National Science Foundation under Grants No. 1607335 and No. 1607377 and used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant Number OCI-1053575.

Diagnosing Breast Cancer with Microwave Technology: remaining challenges and potential solutions with machine learning.

PubMed

Oliveira, Bárbara L; Godinho, Daniela; O'Halloran, Martin; Glavin, Martin; Jones, Edward; Conceição, Raquel C

2018-05-19

Currently, breast cancer often requires invasive biopsies for diagnosis, motivating researchers to design and develop non-invasive and automated diagnosis systems. Recent microwave breast imaging studies have shown how backscattered signals carry relevant information about the shape of a tumour, and tumour shape is often used with current imaging modalities to assess malignancy. This paper presents a comprehensive analysis of microwave breast diagnosis systems which use machine learning to learn characteristics of benign and malignant tumours. The state-of-the-art, the main challenges still to overcome and potential solutions are outlined. Specifically, this work investigates the benefit of signal pre-processing on diagnostic performance, and proposes a new set of extracted features that capture the tumour shape information embedded in a signal. This work also investigates if a relationship exists between the antenna topology in a microwave system and diagnostic performance. Finally, a careful machine learning validation methodology is implemented to guarantee the robustness of the results and the accuracy of performance evaluation.

Adult Stem Cells and Diseases of Aging

PubMed Central

Boyette, Lisa B.; Tuan, Rocky S.

2014-01-01

Preservation of adult stem cells pools is critical for maintaining tissue homeostasis into old age. Exhaustion of adult stem cell pools as a result of deranged metabolic signaling, premature senescence as a response to oncogenic insults to the somatic genome, and other causes contribute to tissue degeneration with age. Both progeria, an extreme example of early-onset aging, and heritable longevity have provided avenues to study regulation of the aging program and its impact on adult stem cell compartments. In this review, we discuss recent findings concerning the effects of aging on stem cells, contributions of stem cells to age-related pathologies, examples of signaling pathways at work in these processes, and lessons about cellular aging gleaned from the development and refinement of cellular reprogramming technologies. We highlight emerging therapeutic approaches to manipulation of key signaling pathways corrupting or exhausting adult stem cells, as well as other approaches targeted at maintaining robust stem cell pools to extend not only lifespan but healthspan. PMID:24757526

IQM: An Extensible and Portable Open Source Application for Image and Signal Analysis in Java

PubMed Central

Kainz, Philipp; Mayrhofer-Reinhartshuber, Michael; Ahammer, Helmut

2015-01-01

Image and signal analysis applications are substantial in scientific research. Both open source and commercial packages provide a wide range of functions for image and signal analysis, which are sometimes supported very well by the communities in the corresponding fields. Commercial software packages have the major drawback of being expensive and having undisclosed source code, which hampers extending the functionality if there is no plugin interface or similar option available. However, both variants cannot cover all possible use cases and sometimes custom developments are unavoidable, requiring open source applications. In this paper we describe IQM, a completely free, portable and open source (GNU GPLv3) image and signal analysis application written in pure Java. IQM does not depend on any natively installed libraries and is therefore runnable out-of-the-box. Currently, a continuously growing repertoire of 50 image and 16 signal analysis algorithms is provided. The modular functional architecture based on the three-tier model is described along the most important functionality. Extensibility is achieved using operator plugins, and the development of more complex workflows is provided by a Groovy script interface to the JVM. We demonstrate IQM’s image and signal processing capabilities in a proof-of-principle analysis and provide example implementations to illustrate the plugin framework and the scripting interface. IQM integrates with the popular ImageJ image processing software and is aiming at complementing functionality rather than competing with existing open source software. Machine learning can be integrated into more complex algorithms via the WEKA software package as well, enabling the development of transparent and robust methods for image and signal analysis. PMID:25612319

Modeling borehole microseismic and strain signals measured by a distributed fiber optic sensor

NASA Astrophysics Data System (ADS)

Mellors, R. J.; Sherman, C. S.; Ryerson, F. J.; Morris, J.; Allen, G. S.; Messerly, M. J.; Carr, T.; Kavousi, P.

2017-12-01

The advent of distributed fiber optic sensors installed in boreholes provides a new and data-rich perspective on the subsurface environment. This includes the long-term capability for vertical seismic profiles, monitoring of active borehole processes such as well stimulation, and measuring of microseismic signals. The distributed fiber sensor, which measures strain (or strain-rate), is an active sensor with highest sensitivity parallel to the fiber and subject to varying types of noise, both external and internal. We take a systems approach and include the response of the electronics, fiber/cable, and subsurface to improve interpretation of the signals. This aids in understanding noise sources, assessing error bounds on amplitudes, and developing appropriate algorithms for improving the image. Ultimately, a robust understanding will allow identification of areas for future improvement and possible optimization in fiber and cable design. The subsurface signals are simulated in two ways: 1) a massively parallel multi-physics code that is capable of modeling hydraulic stimulation of heterogeneous reservoir with a pre-existing discrete fracture network, and 2) a parallelized 3D finite difference code for high-frequency seismic signals. Geometry and parameters for the simulations are derived from fiber deployments, including the Marcellus Shale Energy and Environment Laboratory (MSEEL) project in West Virginia. The combination mimics both the low-frequency strain signals generated during the fracture process and high-frequency signals from microseismic and perforation shots. Results are compared with available fiber data and demonstrate that quantitative interpretation of the fiber data provides valuable constraints on the fracture geometry and microseismic activity. These constraints appear difficult, if not impossible, to obtain otherwise.

IQM: an extensible and portable open source application for image and signal analysis in Java.

PubMed

Kainz, Philipp; Mayrhofer-Reinhartshuber, Michael; Ahammer, Helmut

2015-01-01

Image and signal analysis applications are substantial in scientific research. Both open source and commercial packages provide a wide range of functions for image and signal analysis, which are sometimes supported very well by the communities in the corresponding fields. Commercial software packages have the major drawback of being expensive and having undisclosed source code, which hampers extending the functionality if there is no plugin interface or similar option available. However, both variants cannot cover all possible use cases and sometimes custom developments are unavoidable, requiring open source applications. In this paper we describe IQM, a completely free, portable and open source (GNU GPLv3) image and signal analysis application written in pure Java. IQM does not depend on any natively installed libraries and is therefore runnable out-of-the-box. Currently, a continuously growing repertoire of 50 image and 16 signal analysis algorithms is provided. The modular functional architecture based on the three-tier model is described along the most important functionality. Extensibility is achieved using operator plugins, and the development of more complex workflows is provided by a Groovy script interface to the JVM. We demonstrate IQM's image and signal processing capabilities in a proof-of-principle analysis and provide example implementations to illustrate the plugin framework and the scripting interface. IQM integrates with the popular ImageJ image processing software and is aiming at complementing functionality rather than competing with existing open source software. Machine learning can be integrated into more complex algorithms via the WEKA software package as well, enabling the development of transparent and robust methods for image and signal analysis.

Emotional stress recognition using a new fusion link between electroencephalogram and peripheral signals

PubMed Central

Hosseini, Seyyed Abed; Khalilzadeh, Mohammad Ali; Naghibi-Sistani, Mohammad Bagher; Homam, Seyyed Mehran

2015-01-01

Background: This paper proposes a new emotional stress assessment system using multi-modal bio-signals. Electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. Methods: We design an efficient acquisition protocol to acquire the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) and peripheral signals such as blood volume pulse, skin conductance (SC) and respiration, under images induction (calm-neutral and negatively excited) for the participants. The visual stimuli images are selected from the subset International Affective Picture System database. The qualitative and quantitative evaluation of peripheral signals are used to select suitable segments of EEG signals for improving the accuracy of signal labeling according to emotional stress states. After pre-processing, wavelet coefficients, fractal dimension, and Lempel-Ziv complexity are used to extract the features of the EEG signals. The vast number of features leads to the problem of dimensionality, which is solved using the genetic algorithm as a feature selection method. Results: The results show that the average classification accuracy is 89.6% for two categories of emotional stress states using the support vector machine (SVM). Conclusion: This is a great improvement in results compared to other similar researches. We achieve a noticeable improvement of 11.3% in accuracy using SVM classifier, in compared to previous studies. Therefore, a new fusion between EEG and peripheral signals are more robust in comparison to the separate signals. PMID:26622979

Emotional stress recognition using a new fusion link between electroencephalogram and peripheral signals.

PubMed

Hosseini, Seyyed Abed; Khalilzadeh, Mohammad Ali; Naghibi-Sistani, Mohammad Bagher; Homam, Seyyed Mehran

2015-07-06

This paper proposes a new emotional stress assessment system using multi-modal bio-signals. Electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. We design an efficient acquisition protocol to acquire the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) and peripheral signals such as blood volume pulse, skin conductance (SC) and respiration, under images induction (calm-neutral and negatively excited) for the participants. The visual stimuli images are selected from the subset International Affective Picture System database. The qualitative and quantitative evaluation of peripheral signals are used to select suitable segments of EEG signals for improving the accuracy of signal labeling according to emotional stress states. After pre-processing, wavelet coefficients, fractal dimension, and Lempel-Ziv complexity are used to extract the features of the EEG signals. The vast number of features leads to the problem of dimensionality, which is solved using the genetic algorithm as a feature selection method. The results show that the average classification accuracy is 89.6% for two categories of emotional stress states using the support vector machine (SVM). This is a great improvement in results compared to other similar researches. We achieve a noticeable improvement of 11.3% in accuracy using SVM classifier, in compared to previous studies. Therefore, a new fusion between EEG and peripheral signals are more robust in comparison to the separate signals.

Robust Distant Entanglement Generation Using Coherent Multiphoton Scattering

NASA Astrophysics Data System (ADS)

Chan, Ching-Kit; Sham, L. J.

2013-02-01

We describe a protocol to entangle two qubits at a distance by using resonance fluorescence. The scheme makes use of the postselection of large and distinguishable fluorescence signals corresponding to entangled and unentangled qubit states and has the merits of both high success probability and high entanglement fidelity owing to the multiphoton nature. Our result shows that the entanglement generation is robust against photon fluctuations in the fluorescence signals for a wide range of driving fields. We also demonstrate that this new protocol has an average entanglement duration within the decoherence time of corresponding qubit systems, based on current experimental photon efficiency.

Robust distant entanglement generation using coherent multiphoton scattering.

PubMed

Chan, Ching-Kit; Sham, L J

2013-02-15

We describe a protocol to entangle two qubits at a distance by using resonance fluorescence. The scheme makes use of the postselection of large and distinguishable fluorescence signals corresponding to entangled and unentangled qubit states and has the merits of both high success probability and high entanglement fidelity owing to the multiphoton nature. Our result shows that the entanglement generation is robust against photon fluctuations in the fluorescence signals for a wide range of driving fields. We also demonstrate that this new protocol has an average entanglement duration within the decoherence time of corresponding qubit systems, based on current experimental photon efficiency.

Sonic hedgehog-Dependent Induction of MicroRNA 31 and MicroRNA 150 Regulates Mycobacterium bovis BCG-Driven Toll-Like Receptor 2 Signaling

PubMed Central

Ghorpade, Devram Sampat; Holla, Sahana; Kaveri, Srini V.; Bayry, Jagadeesh; Patil, Shripad A.

2013-01-01

Hedgehog (HH) signaling is a significant regulator of cell fate decisions during embryogenesis, development, and perpetuation of various disease conditions. Testing whether pathogen-specific HH signaling promotes unique innate recognition of intracellular bacteria, we demonstrate that among diverse Gram-positive or Gram-negative microbes, Mycobacterium bovis BCG, a vaccine strain, elicits a robust activation of Sonic HH (SHH) signaling in macrophages. Interestingly, sustained tumor necrosis factor alpha (TNF-α) secretion by macrophages was essential for robust SHH activation, as TNF-α−/− macrophages exhibited compromised ability to activate SHH signaling. Neutralization of TNF-α or blockade of TNF-α receptor signaling significantly reduced the infection-induced SHH signaling activation both in vitro and in vivo. Intriguingly, activated SHH signaling downregulated M. bovis BCG-mediated Toll-like receptor 2 (TLR2) signaling events to regulate a battery of genes associated with divergent functions of M1/M2 macrophages. Genome-wide expression profiling as well as conventional gain-of-function or loss-of-function analysis showed that SHH signaling-responsive microRNA 31 (miR-31) and miR-150 target MyD88, an adaptor protein of TLR2 signaling, thus leading to suppression of TLR2 responses. SHH signaling signatures could be detected in vivo in tuberculosis patients and M. bovis BCG-challenged mice. Collectively, these investigations identify SHH signaling to be what we believe is one of the significant regulators of host-pathogen interactions. PMID:23166298

Concurrent-scene/alternate-pattern analysis for robust video-based docking systems

NASA Technical Reports Server (NTRS)

Udomkesmalee, Suraphol

1991-01-01

A typical docking target employs a three-point design of retroreflective tape, one at each endpoint of the center-line, and one on the tip of the central post. Scenes, sensed via laser diode illumination, produce pictures with spots corresponding to desired reflection from the retroreflectors and other reflections. Control corrections for each axis of the vehicle can then be properly applied if the desired spots are accurately tracked. However, initial acquisition of these three spots (detection and identification problem) are non-trivial under a severe noise environment. Signal-to-noise enhancement, accomplished by subtracting the non-illuminated scene from the target scene illuminated by laser diodes, can not eliminate every false spot. Hence, minimization of docking failures due to target mistracking would suggest needed inclusion of added processing features pertaining to target locations. In this paper, we present a concurrent processing scheme for a modified docking target scene which could lead to a perfect docking system. Since the non-illuminated target scene is already available, adding another feature to the three-point design by marking two non-reflective lines, one between the two end-points and one from the tip of the central post to the center-line, would allow this line feature to be picked-up only when capturing the background scene (sensor data without laser illumination). Therefore, instead of performing the image subtraction to generate a picture with a high signal-to-noise ratio, a processed line-image based on the robust line detection technique (Hough transform) can be used to fuse with the actively sensed three-point target image to deduce the true locations of the docking target. This dual-channel confirmation scheme is necessary if a fail-safe system is to be realized from both the sensing and processing point-of-views. Detailed algorithms and preliminary results are presented.

Automatic characteristic frequency association and all-sideband demodulation for the detection of a bearing fault

NASA Astrophysics Data System (ADS)

Firla, Marcin; Li, Zhong-Yang; Martin, Nadine; Pachaud, Christian; Barszcz, Tomasz

2016-12-01

This paper proposes advanced signal-processing techniques to improve condition monitoring of operating machines. The proposed methods use the results of a blind spectrum interpretation that includes harmonic and sideband series detection. The first contribution of this study is an algorithm for automatic association of harmonic and sideband series to characteristic fault frequencies according to a kinematic configuration. The approach proposed has the advantage of taking into account a possible slip of the rolling-element bearings. In the second part, we propose a full-band demodulation process from all sidebands that are relevant to the spectral estimation. To do so, a multi-rate filtering process in an iterative schema provides satisfying precision and stability over the targeted demodulation band, even for unsymmetrical and extremely narrow bands. After synchronous averaging, the filtered signal is demodulated for calculation of the amplitude and frequency modulation functions, and then any features that indicate faults. Finally, the proposed algorithms are validated on vibration signals measured on a test rig that was designed as part of the European Innovation Project 'KAStrion'. This rig simulates a wind turbine drive train at a smaller scale. The data show the robustness of the method for localizing and extracting a fault on the main bearing. The evolution of the proposed features is a good indicator of the fault severity.

How MAP kinase modules function as robust, yet adaptable, circuits.

PubMed

Tian, Tianhai; Harding, Angus

2014-01-01

Genetic and biochemical studies have revealed that the diversity of cell types and developmental patterns evident within the animal kingdom is generated by a handful of conserved, core modules. Core biological modules must be robust, able to maintain functionality despite perturbations, and yet sufficiently adaptable for random mutations to generate phenotypic variation during evolution. Understanding how robust, adaptable modules have influenced the evolution of eukaryotes will inform both evolutionary and synthetic biology. One such system is the MAP kinase module, which consists of a 3-tiered kinase circuit configuration that has been evolutionarily conserved from yeast to man. MAP kinase signal transduction pathways are used across eukaryotic phyla to drive biological functions that are crucial for life. Here we ask the fundamental question, why do MAPK modules follow a conserved 3-tiered topology rather than some other number? Using computational simulations, we identify a fundamental 2-tiered circuit topology that can be readily reconfigured by feedback loops and scaffolds to generate diverse signal outputs. When this 2-kinase circuit is connected to proximal input kinases, a 3-tiered modular configuration is created that is both robust and adaptable, providing a biological circuit that can regulate multiple phenotypes and maintain functionality in an uncertain world. We propose that the 3-tiered signal transduction module has been conserved through positive selection, because it facilitated the generation of phenotypic variation during eukaryotic evolution.

How MAP kinase modules function as robust, yet adaptable, circuits

PubMed Central

Tian, Tianhai; Harding, Angus

2014-01-01

Genetic and biochemical studies have revealed that the diversity of cell types and developmental patterns evident within the animal kingdom is generated by a handful of conserved, core modules. Core biological modules must be robust, able to maintain functionality despite perturbations, and yet sufficiently adaptable for random mutations to generate phenotypic variation during evolution. Understanding how robust, adaptable modules have influenced the evolution of eukaryotes will inform both evolutionary and synthetic biology. One such system is the MAP kinase module, which consists of a 3-tiered kinase circuit configuration that has been evolutionarily conserved from yeast to man. MAP kinase signal transduction pathways are used across eukaryotic phyla to drive biological functions that are crucial for life. Here we ask the fundamental question, why do MAPK modules follow a conserved 3-tiered topology rather than some other number? Using computational simulations, we identify a fundamental 2-tiered circuit topology that can be readily reconfigured by feedback loops and scaffolds to generate diverse signal outputs. When this 2-kinase circuit is connected to proximal input kinases, a 3-tiered modular configuration is created that is both robust and adaptable, providing a biological circuit that can regulate multiple phenotypes and maintain functionality in an uncertain world. We propose that the 3-tiered signal transduction module has been conserved through positive selection, because it facilitated the generation of phenotypic variation during eukaryotic evolution. PMID:25483189

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

Enhancing coronary Wave Intensity Analysis robustness by high order central finite differences.

PubMed

Rivolo, Simone; Asrress, Kaleab N; Chiribiri, Amedeo; Sammut, Eva; Wesolowski, Roman; Bloch, Lars Ø; Grøndal, Anne K; Hønge, Jesper L; Kim, Won Y; Marber, Michael; Redwood, Simon; Nagel, Eike; Smith, Nicolas P; Lee, Jack

2014-09-01

Coronary Wave Intensity Analysis (cWIA) is a technique capable of separating the effects of proximal arterial haemodynamics from cardiac mechanics. Studies have identified WIA-derived indices that are closely correlated with several disease processes and predictive of functional recovery following myocardial infarction. The cWIA clinical application has, however, been limited by technical challenges including a lack of standardization across different studies and the derived indices' sensitivity to the processing parameters. Specifically, a critical step in WIA is the noise removal for evaluation of derivatives of the acquired signals, typically performed by applying a Savitzky-Golay filter, to reduce the high frequency acquisition noise. The impact of the filter parameter selection on cWIA output, and on the derived clinical metrics (integral areas and peaks of the major waves), is first analysed. The sensitivity analysis is performed either by using the filter as a differentiator to calculate the signals' time derivative or by applying the filter to smooth the ensemble-averaged waveforms. Furthermore, the power-spectrum of the ensemble-averaged waveforms contains little high-frequency components, which motivated us to propose an alternative approach to compute the time derivatives of the acquired waveforms using a central finite difference scheme. The cWIA output and consequently the derived clinical metrics are significantly affected by the filter parameters, irrespective of its use as a smoothing filter or a differentiator. The proposed approach is parameter-free and, when applied to the 10 in-vivo human datasets and the 50 in-vivo animal datasets, enhances the cWIA robustness by significantly reducing the outcome variability (by 60%).

Cell-based quantification of biomarkers from an ultra-fast microfluidic immunofluorescent staining: application to human breast cancer cell lines

NASA Astrophysics Data System (ADS)

Migliozzi, D.; Nguyen, H. T.; Gijs, M. A. M.

2018-02-01

Immunohistochemistry (IHC) is one of the main techniques currently used in the clinics for biomarker characterization. It consists in colorimetric labeling with specific antibodies followed by microscopy analysis. The results are then used for diagnosis and therapeutic targeting. Well-known drawbacks of such protocols are their limited accuracy and precision, which prevent the clinicians from having quantitative and robust IHC results. With our work, we combined rapid microfluidic immunofluorescent staining with efficient image-based cell segmentation and signal quantification to increase the robustness of both experimental and analytical protocols. The experimental protocol is very simple and based on fast-fluidic-exchange in a microfluidic chamber created on top of the formalin-fixed-paraffin-embedded (FFPE) slide by clamping it a silicon chip with a polydimethyl siloxane (PDMS) sealing ring. The image-processing protocol is based on enhancement and subsequent thresholding of the local contrast of the obtained fluorescence image. As a case study, given that the human epidermal growth factor receptor 2 (HER2) protein is often used as a biomarker for breast cancer, we applied our method to HER2+ and HER2- cell lines. We report very fast (5 minutes) immunofluorescence staining of both HER2 and cytokeratin (a marker used to define the tumor region) on FFPE slides. The image-processing program can segment cells correctly and give a cell-based quantitative immunofluorescent signal. With this method, we found a reproducible well-defined separation for the HER2-to-cytokeratin ratio for positive and negative control samples.

Mean apparent propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure.

PubMed

Özarslan, Evren; Koay, Cheng Guan; Shepherd, Timothy M; Komlosh, Michal E; İrfanoğlu, M Okan; Pierpaoli, Carlo; Basser, Peter J

2013-09-01

Diffusion-weighted magnetic resonance (MR) signals reflect information about underlying tissue microstructure and cytoarchitecture. We propose a quantitative, efficient, and robust mathematical and physical framework for representing diffusion-weighted MR imaging (MRI) data obtained in "q-space," and the corresponding "mean apparent propagator (MAP)" describing molecular displacements in "r-space." We also define and map novel quantitative descriptors of diffusion that can be computed robustly using this MAP-MRI framework. We describe efficient analytical representation of the three-dimensional q-space MR signal in a series expansion of basis functions that accurately describes diffusion in many complex geometries. The lowest order term in this expansion contains a diffusion tensor that characterizes the Gaussian displacement distribution, equivalent to diffusion tensor MRI (DTI). Inclusion of higher order terms enables the reconstruction of the true average propagator whose projection onto the unit "displacement" sphere provides an orientational distribution function (ODF) that contains only the orientational dependence of the diffusion process. The representation characterizes novel features of diffusion anisotropy and the non-Gaussian character of the three-dimensional diffusion process. Other important measures this representation provides include the return-to-the-origin probability (RTOP), and its variants for diffusion in one- and two-dimensions-the return-to-the-plane probability (RTPP), and the return-to-the-axis probability (RTAP), respectively. These zero net displacement probabilities measure the mean compartment (pore) volume and cross-sectional area in distributions of isolated pores irrespective of the pore shape. MAP-MRI represents a new comprehensive framework to model the three-dimensional q-space signal and transform it into diffusion propagators. Experiments on an excised marmoset brain specimen demonstrate that MAP-MRI provides several novel, quantifiable parameters that capture previously obscured intrinsic features of nervous tissue microstructure. This should prove helpful for investigating the functional organization of normal and pathologic nervous tissue. Copyright © 2013 Elsevier Inc. All rights reserved.

Transcriptional Regulation of Pattern-Triggered Immunity in Plants.

PubMed

Li, Bo; Meng, Xiangzong; Shan, Libo; He, Ping

2016-05-11

Perception of microbe-associated molecular patterns (MAMPs) by cell-surface-resident pattern recognition receptors (PRRs) induces rapid, robust, and selective transcriptional reprogramming, which is central for launching effective pattern-triggered immunity (PTI) in plants. Signal relay from PRR complexes to the nuclear transcriptional machinery via intracellular kinase cascades rapidly activates primary immune response genes. The coordinated action of gene-specific transcription factors and the general transcriptional machinery contribute to the selectivity of immune gene activation. In addition, PRR complexes and signaling components are often transcriptionally upregulated upon MAMP perception to ensure the robustness and sustainability of PTI outputs. In this review, we discuss recent advances in deciphering the signaling pathways and regulatory mechanisms that coordinately lead to timely and accurate MAMP-induced gene expression in plants. Copyright © 2016 Elsevier Inc. All rights reserved.

Robust Multimodal Cognitive Load Measurement

DTIC Science & Technology

2014-03-26

dimension, Hurst exponent ) of electroencephalogram (EEG) signals to evaluate changes in working memory load during the performance of a cognitive task...dimension, Hurst exponent ) of electroencephalogram (EEG) signals to evaluate changes in working memory load during the performance of a cognitive task with...approximate entropies, wavelet-based complexity measures, correlation dimension, Hurst exponent ) of electroencephalogram (EEG) signals to evaluate changes

Pan-Antarctic analysis aggregating spatial estimates of Adélie penguin abundance reveals robust dynamics despite stochastic noise.

PubMed

Che-Castaldo, Christian; Jenouvrier, Stephanie; Youngflesh, Casey; Shoemaker, Kevin T; Humphries, Grant; McDowall, Philip; Landrum, Laura; Holland, Marika M; Li, Yun; Ji, Rubao; Lynch, Heather J

2017-10-10

Colonially-breeding seabirds have long served as indicator species for the health of the oceans on which they depend. Abundance and breeding data are repeatedly collected at fixed study sites in the hopes that changes in abundance and productivity may be useful for adaptive management of marine resources, but their suitability for this purpose is often unknown. To address this, we fit a Bayesian population dynamics model that includes process and observation error to all known Adélie penguin abundance data (1982-2015) in the Antarctic, covering >95% of their population globally. We find that process error exceeds observation error in this system, and that continent-wide "year effects" strongly influence population growth rates. Our findings have important implications for the use of Adélie penguins in Southern Ocean feedback management, and suggest that aggregating abundance across space provides the fastest reliable signal of true population change for species whose dynamics are driven by stochastic processes.Adélie penguins are a key Antarctic indicator species, but data patchiness has challenged efforts to link population dynamics to key drivers. Che-Castaldo et al. resolve this issue using a pan-Antarctic Bayesian model to infer missing data, and show that spatial aggregation leads to more robust inference regarding dynamics.

System and method for detection of dispersed broadband signals

DOEpatents

Qian, S.; Dunham, M.E.

1999-06-08

A system and method for detecting the presence of dispersed broadband signals in real time are disclosed. The present invention utilizes a bank of matched filters for detecting the received dispersed broadband signals. Each matched filter uses a respective robust time template that has been designed to approximate the dispersed broadband signals of interest, and each time template varies across a spectrum of possible dispersed broadband signal time templates. The received dispersed broadband signal x(t) is received by each of the matched filters, and if one or more matches occurs, then the received data is determined to have signal data of interest. This signal data can then be analyzed and/or transmitted to Earth for analysis, as desired. The system and method of the present invention will prove extremely useful in many fields, including satellite communications, plasma physics, and interstellar research. The varying time templates used in the bank of matched filters are determined as follows. The robust time domain template is assumed to take the form w(t)=A(t)cos[l brace]2[phi](t)[r brace]. Since the instantaneous frequency f(t) is known to be equal to the derivative of the phase [phi](t), the trajectory of a joint time-frequency representation of x(t) is used as an approximation of [phi][prime](t). 10 figs.

System and method for detection of dispersed broadband signals

DOEpatents

Qian, Shie; Dunham, Mark E.

1999-06-08

A system and method for detecting the presence of dispersed broadband signals in real time. The present invention utilizes a bank of matched filters for detecting the received dispersed broadband signals. Each matched filter uses a respective robust time template that has been designed to approximate the dispersed broadband signals of interest, and each time template varies across a spectrum of possible dispersed broadband signal time templates. The received dispersed broadband signal x(t) is received by each of the matched filters, and if one or more matches occurs, then the received data is determined to have signal data of interest. This signal data can then be analyzed and/or transmitted to Earth for analysis, as desired. The system and method of the present invention will prove extremely useful in many fields, including satellite communications, plasma physics, and interstellar research. The varying time templates used in the bank of matched filters are determined as follows. The robust time domain template is assumed to take the form w(t)=A(t)cos{2.phi.(t)}. Since the instantaneous frequency f(t) is known to be equal to the derivative of the phase .phi.(t), the trajectory of a joint time-frequency representation of x(t) is used as an approximation of .phi.'(t).

Measuring spatial and temporal Ca2+ signals in Arabidopsis plants.

PubMed

Zhu, Xiaohong; Taylor, Aaron; Zhang, Shenyu; Zhang, Dayong; Feng, Ying; Liang, Gaimei; Zhu, Jian-Kang

2014-09-02

Developmental and environmental cues induce Ca(2+) fluctuations in plant cells. Stimulus-specific spatial-temporal Ca(2+) patterns are sensed by cellular Ca(2+) binding proteins that initiate Ca(2+) signaling cascades. However, we still know little about how stimulus specific Ca(2+) signals are generated. The specificity of a Ca(2+) signal may be attributed to the sophisticated regulation of the activities of Ca(2+) channels and/or transporters in response to a given stimulus. To identify these cellular components and understand their functions, it is crucial to use systems that allow a sensitive and robust recording of Ca(2+) signals at both the tissue and cellular levels. Genetically encoded Ca(2+) indicators that are targeted to different cellular compartments have provided a platform for live cell confocal imaging of cellular Ca(2+) signals. Here we describe instructions for the use of two Ca(2+) detection systems: aequorin based FAS (film adhesive seedlings) luminescence Ca(2+) imaging and case12 based live cell confocal fluorescence Ca(2+) imaging. Luminescence imaging using the FAS system provides a simple, robust and sensitive detection of spatial and temporal Ca(2+) signals at the tissue level, while live cell confocal imaging using Case12 provides simultaneous detection of cytosolic and nuclear Ca(2+) signals at a high resolution.

A Robust Sound Source Localization Approach for Microphone Array with Model Errors

NASA Astrophysics Data System (ADS)

Xiao, Hua; Shao, Huai-Zong; Peng, Qi-Cong

In this paper, a robust sound source localization approach is proposed. The approach retains good performance even when model errors exist. Compared with previous work in this field, the contributions of this paper are as follows. First, an improved broad-band and near-field array model is proposed. It takes array gain, phase perturbations into account and is based on the actual positions of the elements. It can be used in arbitrary planar geometry arrays. Second, a subspace model errors estimation algorithm and a Weighted 2-Dimension Multiple Signal Classification (W2D-MUSIC) algorithm are proposed. The subspace model errors estimation algorithm estimates unknown parameters of the array model, i. e., gain, phase perturbations, and positions of the elements, with high accuracy. The performance of this algorithm is improved with the increasing of SNR or number of snapshots. The W2D-MUSIC algorithm based on the improved array model is implemented to locate sound sources. These two algorithms compose the robust sound source approach. The more accurate steering vectors can be provided for further processing such as adaptive beamforming algorithm. Numerical examples confirm effectiveness of this proposed approach.

Architecture and inherent robustness of a bacterial cell-cycle control system.

PubMed

Shen, Xiling; Collier, Justine; Dill, David; Shapiro, Lucy; Horowitz, Mark; McAdams, Harley H

2008-08-12

A closed-loop control system drives progression of the coupled stalked and swarmer cell cycles of the bacterium Caulobacter crescentus in a near-mechanical step-like fashion. The cell-cycle control has a cyclical genetic circuit composed of four regulatory proteins with tight coupling to processive chromosome replication and cell division subsystems. We report a hybrid simulation of the coupled cell-cycle control system, including asymmetric cell division and responses to external starvation signals, that replicates mRNA and protein concentration patterns and is consistent with observed mutant phenotypes. An asynchronous sequential digital circuit model equivalent to the validated simulation model was created. Formal model-checking analysis of the digital circuit showed that the cell-cycle control is robust to intrinsic stochastic variations in reaction rates and nutrient supply, and that it reliably stops and restarts to accommodate nutrient starvation. Model checking also showed that mechanisms involving methylation-state changes in regulatory promoter regions during DNA replication increase the robustness of the cell-cycle control. The hybrid cell-cycle simulation implementation is inherently extensible and provides a promising approach for development of whole-cell behavioral models that can replicate the observed functionality of the cell and its responses to changing environmental conditions.

Robust synergetic control design under inputs and states constraints

NASA Astrophysics Data System (ADS)

Rastegar, Saeid; Araújo, Rui; Sadati, Jalil

2018-03-01

In this paper, a novel robust-constrained control methodology for discrete-time linear parameter-varying (DT-LPV) systems is proposed based on a synergetic control theory (SCT) approach. It is shown that in DT-LPV systems without uncertainty, and for any unmeasured bounded additive disturbance, the proposed controller accomplishes the goal of stabilising the system by asymptotically driving the error of the controlled variable to a bounded set containing the origin and then maintaining it there. Moreover, given an uncertain DT-LPV system jointly subject to unmeasured and constrained additive disturbances, and constraints in states, input commands and reference signals (set points), then invariant set theory is used to find an appropriate polyhedral robust invariant region in which the proposed control framework is guaranteed to robustly stabilise the closed-loop system. Furthermore, this is achieved even for the case of varying non-zero control set points in such uncertain DT-LPV systems. The controller is characterised to have a simple structure leading to an easy implementation, and a non-complex design process. The effectiveness of the proposed method and the implications of the controller design on feasibility and closed-loop performance are demonstrated through application examples on the temperature control on a continuous-stirred tank reactor plant, on the control of a real-coupled DC motor plant, and on an open-loop unstable system example.

Development of a robust and cost-effective 3D respiratory motion monitoring system using the kinect device: Accuracy comparison with the conventional stereovision navigation system.

PubMed

Bae, Myungsoo; Lee, Sangmin; Kim, Namkug

2018-07-01

To develop and validate a robust and cost-effective 3D respiratory monitoring system based on a Kinect device with a custom-made simple marker. A 3D respiratory monitoring system comprising the simple marker and the Microsoft Kinect v2 device was developed. The marker was designed for simple and robust detection, and the tracking algorithm was developed using the depth, RGB, and infra-red images acquired from the Kinect sensor. A Kalman filter was used to suppress movement noises. The major movements of the marker attached to the four different locations of body surface were determined from the initially collected tracking points of the marker while breathing. The signal level of respiratory motion with the tracking point was estimated along the major direction vector. The accuracy of the results was evaluated through a comparison with those of the conventional stereovision navigation system (NDI Polaris Spectra). Sixteen normal volunteers were enrolled to evaluate the accuracy of this system. The correlation coefficients between the respiratory motion signal from the Kinect device and conventional navigation system ranged from 0.970 to 0.999 and from 0.837 to 0.995 at the abdominal and thoracic surfaces, respectively. The respiratory motion signal from this system was obtained at 27-30 frames/s. This system with the Kinect v2 device and simple marker could be used for cost-effective, robust and accurate 3D respiratory motion monitoring. In addition, this system is as reliable for respiratory motion signal generation and as practically useful as the conventional stereovision navigation system and is less sensitive to patient posture. Copyright © 2018 Elsevier B.V. All rights reserved.

Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma E.; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart J. J. M.

2018-06-01

High-resolution climate information provided by e.g. regional climate models (RCMs) is valuable for exploring the changing weather under global warming, and assessing the local impact of climate change. While there is generally more confidence in the representativeness of simulated processes at higher resolutions, internal variability of the climate system—`noise', intrinsic to the chaotic nature of atmospheric and oceanic processes—is larger at smaller spatial scales as well, limiting the predictability of the climate signal. To quantify the internal variability and robustly estimate the climate signal, large initial-condition ensembles of climate simulations conducted with a single model provide essential information. We analyze a regional downscaling of a 16-member initial-condition ensemble over western Europe and the Alps at 0.11° resolution, similar to the highest resolution EURO-CORDEX simulations. We examine the strength of the forced climate response (signal) in mean and extreme daily precipitation with respect to noise due to internal variability, and find robust small-scale geographical features in the forced response, indicating regional differences in changes in the probability of events. However, individual ensemble members provide only limited information on the forced climate response, even for high levels of global warming. Although the results are based on a single RCM-GCM chain, we believe that they have general value in providing insight in the fraction of the uncertainty in high-resolution climate information that is irreducible, and can assist in the correct interpretation of fine-scale information in multi-model ensembles in terms of a forced response and noise due to internal variability.

Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma E.; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart J. J. M.

2017-09-01

High-resolution climate information provided by e.g. regional climate models (RCMs) is valuable for exploring the changing weather under global warming, and assessing the local impact of climate change. While there is generally more confidence in the representativeness of simulated processes at higher resolutions, internal variability of the climate system—`noise', intrinsic to the chaotic nature of atmospheric and oceanic processes—is larger at smaller spatial scales as well, limiting the predictability of the climate signal. To quantify the internal variability and robustly estimate the climate signal, large initial-condition ensembles of climate simulations conducted with a single model provide essential information. We analyze a regional downscaling of a 16-member initial-condition ensemble over western Europe and the Alps at 0.11° resolution, similar to the highest resolution EURO-CORDEX simulations. We examine the strength of the forced climate response (signal) in mean and extreme daily precipitation with respect to noise due to internal variability, and find robust small-scale geographical features in the forced response, indicating regional differences in changes in the probability of events. However, individual ensemble members provide only limited information on the forced climate response, even for high levels of global warming. Although the results are based on a single RCM-GCM chain, we believe that they have general value in providing insight in the fraction of the uncertainty in high-resolution climate information that is irreducible, and can assist in the correct interpretation of fine-scale information in multi-model ensembles in terms of a forced response and noise due to internal variability.

Role of semantic paradigms for optimization of language mapping in clinical FMRI studies.

PubMed

Zacà, D; Jarso, S; Pillai, J J

2013-10-01

The optimal paradigm choice for language mapping in clinical fMRI studies is challenging due to the variability in activation among different paradigms, the contribution to activation of cognitive processes other than language, and the difficulties in monitoring patient performance. In this study, we compared language localization and lateralization between 2 commonly used clinical language paradigms and 3 newly designed dual-choice semantic paradigms to define a streamlined and adequate language-mapping protocol. Twelve healthy volunteers performed 5 language paradigms: Silent Word Generation, Sentence Completion, Visual Antonym Pair, Auditory Antonym Pair, and Noun-Verb Association. Group analysis was performed to assess statistically significant differences in fMRI percentage signal change and lateralization index among these paradigms in 5 ROIs: inferior frontal gyrus, superior frontal gyrus, middle frontal gyrus for expressive language activation, middle temporal gyrus, and superior temporal gyrus for receptive language activation. In the expressive ROIs, Silent Word Generation was the most robust and best lateralizing paradigm (greater percentage signal change and lateralization index than semantic paradigms at P < .01 and P < .05 levels, respectively). In the receptive region of interest, Sentence Completion and Noun-Verb Association were the most robust activators (greater percentage signal change than other paradigms, P < .01). All except Auditory Antonym Pair were good lateralizing tasks (the lateralization index was significantly lower than other paradigms, P < .05). The combination of Silent Word Generation and ≥1 visual semantic paradigm, such as Sentence Completion and Noun-Verb Association, is adequate to determine language localization and lateralization; Noun-Verb Association has the additional advantage of objective monitoring of patient performance.

Preserving privacy of online digital physiological signals using blind and reversible steganography.

PubMed

Shiu, Hung-Jr; Lin, Bor-Sing; Huang, Chien-Hung; Chiang, Pei-Ying; Lei, Chin-Laung

2017-11-01

Physiological signals such as electrocardiograms (ECG) and electromyograms (EMG) are widely used to diagnose diseases. Presently, the Internet offers numerous cloud storage services which enable digital physiological signals to be uploaded for convenient access and use. Numerous online databases of medical signals have been built. The data in them must be processed in a manner that preserves patients' confidentiality. A reversible error-correcting-coding strategy will be adopted to transform digital physiological signals into a new bit-stream that uses a matrix in which is embedded the Hamming code to pass secret messages or private information. The shared keys are the matrix and the version of the Hamming code. An online open database, the MIT-BIH arrhythmia database, was used to test the proposed algorithms. The time-complexity, capacity and robustness are evaluated. Comparisons of several evaluations subject to related work are also proposed. This work proposes a reversible, low-payload steganographic scheme for preserving the privacy of physiological signals. An (n,  m)-hamming code is used to insert (n - m) secret bits into n bits of a cover signal. The number of embedded bits per modification is higher than in comparable methods, and the computational power is efficient and the scheme is secure. Unlike other Hamming-code based schemes, the proposed scheme is both reversible and blind. Copyright © 2017 Elsevier B.V. All rights reserved.

Individuals with increased inflammatory response to ozone demonstrate muted signaling of immune cell trafficking pathways

EPA Science Inventory

Background Exposure to ozone activates innate immune function and causes neutrophilic (PMN) airway inflammation that in some individuals is robustly elevated. The interplay between immunoinflammatory function and genomic signaling in those with heightened inflammatory responsive...

Robustness of plasmon phased array nanoantennas to disorder

PubMed Central

Arango, Felipe Bernal; Thijssen, Rutger; Brenny, Benjamin; Coenen, Toon; Koenderink, A. Femius

2015-01-01

We present cathodoluminescence experiments that quantify the response of plasmonic Yagi-Uda antennas fabricated on one-dimensional silicon nitride waveguides as function of electron beam excitation position and emission wavelength. At the near-infrared antenna design wavelength cathodoluminescence signal robustly is strongest when exciting the antenna at the reflector element. Yet at just slightly shorter wavelengths the signal is highly variable from antenna to antenna and wavelength to wavelength. Hypothesizing that fabrication randomness is at play, we analyze the resilience of plasmon Yagi-Uda antennas to varations in element size of just 5 nm. While in our calculations the appearance of directivity is robust, both the obtained highest directivity and the wavelength at which it occurs vary markedly between realizations. The calculated local density of states is invariably high at the reflector for the design wavelength, but varies dramatically in spatial distribution for shorter wavelengths, consistent with the cathodoluminescence experiments. PMID:26038871

Homeostatic enhancement of active mechanotransduction

NASA Astrophysics Data System (ADS)

Milewski, Andrew; O'Maoiléidigh, Dáibhid; Hudspeth, A. J.

2018-05-01

Our sense of hearing boasts exquisite sensitivity to periodic signals. Experiments and modeling imply, however, that the auditory system achieves this performance for only a narrow range of parameter values. As a result, small changes in these values could compromise the ability of the mechanosensory hair cells to detect stimuli. We propose that, rather than exerting tight control over parameters, the auditory system employs a homeostatic mechanism that ensures the robustness of its operation to variation in parameter values. Through analytical techniques and computer simulations we investigate whether a homeostatic mechanism renders the hair bundle's signal-detection ability more robust to alterations in experimentally accessible parameters. When homeostasis is enforced, the range of values for which the bundle's sensitivity exceeds a threshold can increase by more than an order of magnitude. The robustness of cochlear function based on somatic motility or hair bundle motility may be achieved by employing the approach we describe here.

Reward reduces conflict by enhancing attentional control and biasing visual cortical processing.

PubMed

Padmala, Srikanth; Pessoa, Luiz

2011-11-01

How does motivation interact with cognitive control during challenging behavioral conditions? Here, we investigated the interactions between motivation and cognition during a response conflict task and tested a specific model of the effect of reward on cognitive processing. Behaviorally, participants exhibited reduced conflict during the reward versus no-reward condition. Brain imaging results revealed that a group of subcortical and fronto-parietal regions was robustly influenced by reward at cue processing and, importantly, that cue-related responses in fronto-parietal attentional regions were predictive of reduced conflict-related signals in the medial pFC (MPFC)/ACC during the upcoming target phase. Path analysis revealed that the relationship between cue responses in the right intraparietal sulcus (IPS) and interference-related responses in the MPFC during the subsequent target phase was mediated via signals in the left fusiform gyrus, which we linked to distractor-related processing. Finally, reward increased functional connectivity between the right IPS and both bilateral putamen and bilateral nucleus accumbens during the cue phase, a relationship that covaried with across-individual sensitivity to reward in the case of the right nucleus accumbens. Taken together, our findings are consistent with a model in which motivationally salient cues are employed to upregulate top-down control processes that bias the selection of visual information, thereby leading to more efficient stimulus processing during conflict conditions.

The PREP pipeline: standardized preprocessing for large-scale EEG analysis

PubMed Central

Bigdely-Shamlo, Nima; Mullen, Tim; Kothe, Christian; Su, Kyung-Min; Robbins, Kay A.

2015-01-01

The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode. PMID:26150785

Robust adaptive 3-D segmentation of vessel laminae from fluorescence confocal microscope images and parallel GPU implementation.

PubMed

Narayanaswamy, Arunachalam; Dwarakapuram, Saritha; Bjornsson, Christopher S; Cutler, Barbara M; Shain, William; Roysam, Badrinath

2010-03-01

This paper presents robust 3-D algorithms to segment vasculature that is imaged by labeling laminae, rather than the lumenal volume. The signal is weak, sparse, noisy, nonuniform, low-contrast, and exhibits gaps and spectral artifacts, so adaptive thresholding and Hessian filtering based methods are not effective. The structure deviates from a tubular geometry, so tracing algorithms are not effective. We propose a four step approach. The first step detects candidate voxels using a robust hypothesis test based on a model that assumes Poisson noise and locally planar geometry. The second step performs an adaptive region growth to extract weakly labeled and fine vessels while rejecting spectral artifacts. To enable interactive visualization and estimation of features such as statistical confidence, local curvature, local thickness, and local normal, we perform the third step. In the third step, we construct an accurate mesh representation using marching tetrahedra, volume-preserving smoothing, and adaptive decimation algorithms. To enable topological analysis and efficient validation, we describe a method to estimate vessel centerlines using a ray casting and vote accumulation algorithm which forms the final step of our algorithm. Our algorithm lends itself to parallel processing, and yielded an 8 x speedup on a graphics processor (GPU). On synthetic data, our meshes had average error per face (EPF) values of (0.1-1.6) voxels per mesh face for peak signal-to-noise ratios from (110-28 dB). Separately, the error from decimating the mesh to less than 1% of its original size, the EPF was less than 1 voxel/face. When validated on real datasets, the average recall and precision values were found to be 94.66% and 94.84%, respectively.

A multi-sensor RSS spatial sensing-based robust stochastic optimization algorithm for enhanced wireless tethering.

PubMed

Parasuraman, Ramviyas; Fabry, Thomas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel

2014-12-12

The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the "server-relay-client" framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions.

A Multi-Sensor RSS Spatial Sensing-Based Robust Stochastic Optimization Algorithm for Enhanced Wireless Tethering

PubMed Central

Parasuraman, Ramviyas; Fabry, Thomas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel

2014-01-01

The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions. PMID:25615734

Principles of Temporal Processing Across the Cortical Hierarchy.

PubMed

Himberger, Kevin D; Chien, Hsiang-Yun; Honey, Christopher J

2018-05-02

The world is richly structured on multiple spatiotemporal scales. In order to represent spatial structure, many machine-learning models repeat a set of basic operations at each layer of a hierarchical architecture. These iterated spatial operations - including pooling, normalization and pattern completion - enable these systems to recognize and predict spatial structure, while robust to changes in the spatial scale, contrast and noisiness of the input signal. Because our brains also process temporal information that is rich and occurs across multiple time scales, might the brain employ an analogous set of operations for temporal information processing? Here we define a candidate set of temporal operations, and we review evidence that they are implemented in the mammalian cerebral cortex in a hierarchical manner. We conclude that multiple consecutive stages of cortical processing can be understood to perform temporal pooling, temporal normalization and temporal pattern completion. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Robust stochastic resonance: Signal detection and adaptation in impulsive noise

NASA Astrophysics Data System (ADS)

Kosko, Bart; Mitaim, Sanya

2001-11-01

Stochastic resonance (SR) occurs when noise improves a system performance measure such as a spectral signal-to-noise ratio or a cross-correlation measure. All SR studies have assumed that the forcing noise has finite variance. Most have further assumed that the noise is Gaussian. We show that SR still occurs for the more general case of impulsive or infinite-variance noise. The SR effect fades as the noise grows more impulsive. We study this fading effect on the family of symmetric α-stable bell curves that includes the Gaussian bell curve as a special case. These bell curves have thicker tails as the parameter α falls from 2 (the Gaussian case) to 1 (the Cauchy case) to even lower values. Thicker tails create more frequent and more violent noise impulses. The main feedback and feedforward models in the SR literature show this fading SR effect for periodic forcing signals when we plot either the signal-to-noise ratio or a signal correlation measure against the dispersion of the α-stable noise. Linear regression shows that an exponential law γopt(α)=cAα describes this relation between the impulsive index α and the SR-optimal noise dispersion γopt. The results show that SR is robust against noise ``outliers.'' So SR may be more widespread in nature than previously believed. Such robustness also favors the use of SR in engineering systems. We further show that an adaptive system can learn the optimal noise dispersion for two standard SR models (the quartic bistable model and the FitzHugh-Nagumo neuron model) for the signal-to-noise ratio performance measure. This also favors practical applications of SR and suggests that evolution may have tuned the noise-sensitive parameters of biological systems.

A robust and versatile signal-on fluorescence sensing strategy based on SYBR Green I dye and graphene oxide

PubMed Central

Qiu, Huazhang; Wu, Namei; Zheng, Yanjie; Chen, Min; Weng, Shaohuang; Chen, Yuanzhong; Lin, Xinhua

2015-01-01

A robust and versatile signal-on fluorescence sensing strategy was developed to provide label-free detection of various target analytes. The strategy used SYBR Green I dye and graphene oxide as signal reporter and signal-to-background ratio enhancer, respectively. Multidrug resistance protein 1 (MDR1) gene and mercury ion (Hg2+) were selected as target analytes to investigate the generality of the method. The linear relationship and specificity of the detections showed that the sensitive and selective analyses of target analytes could be achieved by the proposed strategy with low detection limits of 0.5 and 2.2 nM for MDR1 gene and Hg2+, respectively. Moreover, the strategy was used to detect real samples. Analytical results of MDR1 gene in the serum indicated that the developed method is a promising alternative approach for real applications in complex systems. Furthermore, the recovery of the proposed method for Hg2+ detection was acceptable. Thus, the developed label-free signal-on fluorescence sensing strategy exhibited excellent universality, sensitivity, and handling convenience. PMID:25565810

Critical bounds on noise and SNR for robust estimation of real-time brain activity from functional near infra-red spectroscopy.

PubMed

Aqil, Muhammad; Jeong, Myung Yung

2018-04-24

The robust characterization of real-time brain activity carries potential for many applications. However, the contamination of measured signals by various instrumental, environmental, and physiological sources of noise introduces a substantial amount of signal variance and, consequently, challenges real-time estimation of contributions from underlying neuronal sources. Functional near infra-red spectroscopy (fNIRS) is an emerging imaging modality whose real-time potential is yet to be fully explored. The objectives of the current study are to (i) validate a time-dependent linear model of hemodynamic responses in fNIRS, and (ii) test the robustness of this approach against measurement noise (instrumental and physiological) and mis-specification of the hemodynamic response basis functions (amplitude, latency, and duration). We propose a linear hemodynamic model with time-varying parameters, which are estimated (adapted and tracked) using a dynamic recursive least square algorithm. Owing to the linear nature of the activation model, the problem of achieving robust convergence to an accurate estimation of the model parameters is recast as a problem of parameter error stability around the origin. We show that robust convergence of the proposed method is guaranteed in the presence of an acceptable degree of model misspecification and we derive an upper bound on noise under which reliable parameters can still be inferred. We also derived a lower bound on signal-to-noise-ratio over which the reliable parameters can still be inferred from a channel/voxel. Whilst here applied to fNIRS, the proposed methodology is applicable to other hemodynamic-based imaging technologies such as functional magnetic resonance imaging. Copyright © 2018 Elsevier Inc. All rights reserved.

The Electrooculogram and a New Blink Detection Algorithm

DTIC Science & Technology

2015-10-30

applications, and physiological monitoring has proven quite helpful with this assessment. One such physiological signal , the electrooculogram ( EOG ...significantly improve performance. One such physiological signal , the electrooculogram ( EOG ), can provide blink rate and blink duration measures. Blink...that such variability substantiates the need for blink detection algorithms, using the EOG signal , that are robust to noise, artifacts, and intra- and

New digital capacitive measurement system for blade clearances

NASA Astrophysics Data System (ADS)

Moenich, Marcel; Bailleul, Gilles

This paper presents a totally new concept for tip blade clearance evaluation in turbine engines. This system is able to detect exact 'measurands' even under high temperature and severe conditions like ionization. The system is based on a heavy duty probe head, a miniaturized thick-film hybrid electronic circuit and a signal processing unit for real time computing. The high frequency individual measurement values are digitally filtered and linearized in real time. The electronic is built in hybrid technology and therefore can be kept extremely small and robust, so that the system can be used on actual flights.

Encryption key distribution via chaos synchronization

NASA Astrophysics Data System (ADS)

Keuninckx, Lars; Soriano, Miguel C.; Fischer, Ingo; Mirasso, Claudio R.; Nguimdo, Romain M.; van der Sande, Guy

2017-02-01

We present a novel encryption scheme, wherein an encryption key is generated by two distant complex nonlinear units, forced into synchronization by a chaotic driver. The concept is sufficiently generic to be implemented on either photonic, optoelectronic or electronic platforms. The method for generating the key bitstream from the chaotic signals is reconfigurable. Although derived from a deterministic process, the obtained bit series fulfill the randomness conditions as defined by the National Institute of Standards test suite. We demonstrate the feasibility of our concept on an electronic delay oscillator circuit and test the robustness against attacks using a state-of-the-art system identification method.

Noise-Driven Manifestation of Learning in Mature Neural Networks

NASA Astrophysics Data System (ADS)

Monterola, Christopher; Saloma, Caesar

2002-10-01

We show that the generalization capability of a mature thresholding neural network to process above-threshold disturbances in a noise-free environment is extended to subthreshold disturbances by ambient noise without retraining. The ability to benefit from noise is intrinsic and does not have to be learned separately. Nonlinear dependence of sensitivity with noise strength is significantly narrower than in individual threshold systems. Noise has a minimal effect on network performance for above-threshold signals. We resolve two seemingly contradictory responses of trained networks to noise-their ability to benefit from its presence and their robustness against noisy strong disturbances.

Coherent optical OFDM: theory and design.

PubMed

Shieh, W; Bao, H; Tang, Y

2008-01-21

Coherent optical OFDM (CO-OFDM) has recently been proposed and the proof-of-concept transmission experiments have shown its extreme robustness against chromatic dispersion and polarization mode dispersion. In this paper, we first review the theoretical fundamentals for CO-OFDM and its channel model in a 2x2 MIMO-OFDM representation. We then present various design choices for CO-OFDM systems and perform the nonlinearity analysis for RF-to-optical up-converter. We also show the receiver-based digital signal processing to mitigate self-phase-modulation (SPM) and Gordon-Mollenauer phase noise, which is equivalent to the midspan phase conjugation.

Multimodal properties and dynamics of gradient echo quantum memory.

PubMed

Hétet, G; Longdell, J J; Sellars, M J; Lam, P K; Buchler, B C

2008-11-14

We investigate the properties of a recently proposed gradient echo memory (GEM) scheme for information mapping between optical and atomic systems. We show that GEM can be described by the dynamic formation of polaritons in k space. This picture highlights the flexibility and robustness with regards to the external control of the storage process. Our results also show that, as GEM is a frequency-encoding memory, it can accurately preserve the shape of signals that have large time-bandwidth products, even at moderate optical depths. At higher optical depths, we show that GEM is a high fidelity multimode quantum memory.

Wavelet Applications for Flight Flutter Testing

NASA Technical Reports Server (NTRS)

Lind, Rick; Brenner, Marty; Freudinger, Lawrence C.

1999-01-01

Wavelets present a method for signal processing that may be useful for analyzing responses of dynamical systems. This paper describes several wavelet-based tools that have been developed to improve the efficiency of flight flutter testing. One of the tools uses correlation filtering to identify properties of several modes throughout a flight test for envelope expansion. Another tool uses features in time-frequency representations of responses to characterize nonlinearities in the system dynamics. A third tool uses modulus and phase information from a wavelet transform to estimate modal parameters that can be used to update a linear model and reduce conservatism in robust stability margins.

On the robustness of EC-PC spike detection method for online neural recording.

PubMed

Zhou, Yin; Wu, Tong; Rastegarnia, Amir; Guan, Cuntai; Keefer, Edward; Yang, Zhi

2014-09-30

Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13 μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

Relevance of phenotypic noise to adaptation and evolution.

PubMed

Kaneko, K; Furusawa, C

2008-09-01

Biological processes are inherently noisy, as highlighted in recent measurements of stochasticity in gene expression. Here, the authors show that such phenotypic noise is essential to the adaptation of organisms to a variety of environments and also to the evolution of robustness against mutations. First, the authors show that for any growing cell showing stochastic gene expression, the adaptive cellular state is inevitably selected by noise, without the use of a specific signal transduction network. In general, changes in any protein concentration in a cell are products of its synthesis minus dilution and degradation, both of which are proportional to the rate of cell growth. In an adaptive state, both the synthesis and dilution terms of proteins are large, and so the adaptive state is less affected by stochasticity in gene expression, whereas for a non-adaptive state, both terms are smaller, and so cells are easily knocked out of their original state by noise. This leads to a novel, generic mechanism for the selection of adaptive states. The authors have confirmed this selection by model simulations. Secondly, the authors consider the evolution of gene networks to acquire robustness of the phenotype against noise and mutation. Through simulations using a simple stochastic gene expression network that undergoes mutation and selection, the authors show that a threshold level of noise in gene expression is required for the network to acquire both types of robustness. The results reveal how the noise that cells encounter during growth and development shapes any network's robustness, not only to noise but also to mutations. The authors also establish a relationship between developmental and mutational robustness.

Robust motion artefact resistant circuit for calculation of Mean Arterial Pressure from pulse transit time.

PubMed

Bhattacharya, Tinish; Gupta, Ankesh; Singh, Salam ThoiThoi; Roy, Sitikantha; Prasad, Anamika

2017-07-01

Cuff-less and non-invasive methods of Blood Pressure (BP) monitoring have faced a lot of challenges like stability, noise, motion artefact and requirement for calibration. These factors are the major reasons why such devices do not get approval from the medical community easily. One such method is calculating Blood Pressure indirectly from pulse transit time (PTT) obtained from electrocardiogram (ECG) and Photoplethysmogram (PPG). In this paper we have proposed two novel analog signal conditioning circuits for ECG and PPG that increase stability, remove motion artefacts, remove the sinusoidal wavering of the ECG baseline due to respiration and provide consistent digital pulses corresponding to blood pulses/heart-beat. We have combined these two systems to obtain the PTT and then correlated it with the Mean Arterial Pressure (MAP). The aim was to perform major part of the processing in analog domain to decrease processing load over microcontroller so as to reduce cost and make it simple and robust. We have found from our experiments that the proposed circuits can calculate the Heart Rate (HR) with a maximum error of ~3.0% and MAP with a maximum error of ~2.4% at rest and ~4.6% in motion.

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

PubMed Central

Shlizerman, Eli; Riffell, Jeffrey A.; Kutz, J. Nathan

2014-01-01

The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns. PMID:25165442

Beyond histones - the expanding roles of protein lysine methylation.

PubMed

Wu, Zhouran; Connolly, Justin; Biggar, Kyle K

2017-09-01

A robust signaling network is essential for cell survival. At the molecular level, this is often mediated by as many as 200 different types of post-translational modifications (PTMs) that are made to proteins. These include well-documented examples such as phosphorylation, ubiquitination, acetylation and methylation. Of these modifications, non-histone protein lysine methylation has only recently emerged as a prevalent modification occurring on numerous proteins, thus extending its role well beyond the histone code. To date, this modification has been found to regulate protein activity, protein-protein interactions and interplay with other PTMs. As a result, lysine methylation is now known to be a coordinator of protein function and is a key driver in several cellular signaling events. Recent advances in mass spectrometry have also allowed the characterization of a growing number of lysine methylation events on an increasing number of proteins. As a result, we are now beginning to recognize lysine methylation as a dynamic event that is involved in a number of biological processes, including DNA damage repair, cell growth, metabolism and signal transduction among others. In light of current research advances, the stage is now set to study the extent of lysine methylation that exists within the entire proteome, its dynamics, and its association with physiological and pathological processes. © 2017 Federation of European Biochemical Societies.

On Limitations of the Ultrasonic Characterization of Pieces Manufactured with Highly Attenuating Materials

NASA Astrophysics Data System (ADS)

Ramos, A.; Moreno, E.; Rubio, B.; Calas, H.; Galarza, N.; Rubio, J.; Diez, L.; Castellanos, L.; Gómez, T.

Some technical aspects of two Spanish cooperation projects, funded by DPI and Innpacto Programs of the R&D National Plan, are discussed. The objective is to analyze the common belief about than the ultrasonic testing in MHz range is not a tool utilizable to detect internal flaws in highly attenuating pieces made of coarse-grained steel. In fact high-strength steels, used in some safe industrial infrastructures of energy & transport sectors, are difficult to be inspected using the conventional "state of the art" in ultrasonic technology, due to their internal microstructures are very attenuating and coarse-grained. It is studied if this inspection difficulty could be overcome by finding intense interrogating pulses and advanced signal processing of the acquired echoes. A possible solution would depend on drastically improving signal-to-noise-ratios, by applying new advances on: ultrasonic transduction, HV electronics for intense pulsed driving of the testing probes, and an "ad-hoc" digital processing or focusing of the received noisy signals, in function of each material to be inspected. To attain this challenging aim on robust steel pieces would open the possibility of obtaining improvements in inspecting critical industrial components made of highly attenuating & dispersive materials, as new composites in aeronautic and motorway bridges, or new metallic alloys in nuclear area, where additional testing limitations often appear.

Automated seismic detection of landslides at regional scales: a Random Forest based detection algorithm

NASA Astrophysics Data System (ADS)

Hibert, C.; Michéa, D.; Provost, F.; Malet, J. P.; Geertsema, M.

2017-12-01

Detection of landslide occurrences and measurement of their dynamics properties during run-out is a high research priority but a logistical and technical challenge. Seismology has started to help in several important ways. Taking advantage of the densification of global, regional and local networks of broadband seismic stations, recent advances now permit the seismic detection and location of landslides in near-real-time. This seismic detection could potentially greatly increase the spatio-temporal resolution at which we study landslides triggering, which is critical to better understand the influence of external forcings such as rainfalls and earthquakes. However, detecting automatically seismic signals generated by landslides still represents a challenge, especially for events with small mass. The low signal-to-noise ratio classically observed for landslide-generated seismic signals and the difficulty to discriminate these signals from those generated by regional earthquakes or anthropogenic and natural noises are some of the obstacles that have to be circumvented. We present a new method for automatically constructing instrumental landslide catalogues from continuous seismic data. We developed a robust and versatile solution, which can be implemented in any context where a seismic detection of landslides or other mass movements is relevant. The method is based on a spectral detection of the seismic signals and the identification of the sources with a Random Forest machine learning algorithm. The spectral detection allows detecting signals with low signal-to-noise ratio, while the Random Forest algorithm achieve a high rate of positive identification of the seismic signals generated by landslides and other seismic sources. The processing chain is implemented to work in a High Performance Computers centre which permits to explore years of continuous seismic data rapidly. We present here the preliminary results of the application of this processing chain for years of continuous seismic record by the Alaskan permanent seismic network and Hi-Climb trans-Himalayan seismic network. The processing chain we developed also opens the possibility for a near-real time seismic detection of landslides, in association with remote-sensing automated detection from Sentinel 2 images for example.

Real-time processing of ASL signs: Delayed first language acquisition affects organization of the mental lexicon

PubMed Central

Lieberman, Amy M.; Borovsky, Arielle; Hatrak, Marla; Mayberry, Rachel I.

2014-01-01

Sign language comprehension requires visual attention to the linguistic signal and visual attention to referents in the surrounding world, whereas these processes are divided between the auditory and visual modalities for spoken language comprehension. Additionally, the age-onset of first language acquisition and the quality and quantity of linguistic input and for deaf individuals is highly heterogeneous, which is rarely the case for hearing learners of spoken languages. Little is known about how these modality and developmental factors affect real-time lexical processing. In this study, we ask how these factors impact real-time recognition of American Sign Language (ASL) signs using a novel adaptation of the visual world paradigm in deaf adults who learned sign from birth (Experiment 1), and in deaf individuals who were late-learners of ASL (Experiment 2). Results revealed that although both groups of signers demonstrated rapid, incremental processing of ASL signs, only native-signers demonstrated early and robust activation of sub-lexical features of signs during real-time recognition. Our findings suggest that the organization of the mental lexicon into units of both form and meaning is a product of infant language learning and not the sensory and motor modality through which the linguistic signal is sent and received. PMID:25528091

Surface-Enhanced Raman Scattering Using Silica Whispering-Gallery Mode Resonators

NASA Technical Reports Server (NTRS)

Anderson, Mark S.

2013-01-01

The motivation of this work was to have robust spectroscopic sensors for sensitive detection and chemical analysis of organic and molecular compounds. The solution is to use silica sphere optical resonators to provide surface-enhanced spectroscopic signal. Whispering-gallery mode (WGM) resonators made from silica microspheres were used for surface-enhanced Raman scattering (SERS) without coupling to a plasmonic mechanism. Large Raman signal enhancement is observed by exclusively using 5.08-micron silica spheres with 785-nm laser excitation. The advantage of this non-plasmonic approach is that the active substrate is chemically inert silica, thermally stable, and relatively simple to fabricate. The Raman signal enhancement is broadly applicable to a wide range of molecular functional groups including aliphatic hydrocarbons, siloxanes, and esters. Applications include trace organic analysis, particularly for in situ planetary instruments that require robust sensors with consistent response.

Robust Indoor Human Activity Recognition Using Wireless Signals.

PubMed

Wang, Yi; Jiang, Xinli; Cao, Rongyu; Wang, Xiyang

2015-07-15

Wireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

Simulated performance of an order statistic threshold strategy for detection of narrowband signals

NASA Technical Reports Server (NTRS)

Satorius, E.; Brady, R.; Deich, W.; Gulkis, S.; Olsen, E.

1988-01-01

The application of order statistics to signal detection is becoming an increasingly active area of research. This is due to the inherent robustness of rank estimators in the presence of large outliers that would significantly degrade more conventional mean-level-based detection systems. A detection strategy is presented in which the threshold estimate is obtained using order statistics. The performance of this algorithm in the presence of simulated interference and broadband noise is evaluated. In this way, the robustness of the proposed strategy in the presence of the interference can be fully assessed as a function of the interference, noise, and detector parameters.

Uncertainty Quantification for Robust Control of Wind Turbines using Sliding Mode Observer

NASA Astrophysics Data System (ADS)

Schulte, Horst

2016-09-01

A new quantification method of uncertain models for robust wind turbine control using sliding-mode techniques is presented with the objective to improve active load mitigation. This approach is based on the so-called equivalent output injection signal, which corresponds to the average behavior of the discontinuous switching term, establishing and maintaining a motion on a so-called sliding surface. The injection signal is directly evaluated to obtain estimates of the uncertainty bounds of external disturbances and parameter uncertainties. The applicability of the proposed method is illustrated by the quantification of a four degree-of-freedom model of the NREL 5MW reference turbine containing uncertainties.

Efficient Robust Optimization of Metal Forming Processes using a Sequential Metamodel Based Strategy

NASA Astrophysics Data System (ADS)

Wiebenga, J. H.; Klaseboer, G.; van den Boogaard, A. H.

2011-08-01

The coupling of Finite Element (FE) simulations to mathematical optimization techniques has contributed significantly to product improvements and cost reductions in the metal forming industries. The next challenge is to bridge the gap between deterministic optimization techniques and the industrial need for robustness. This paper introduces a new and generally applicable structured methodology for modeling and solving robust optimization problems. Stochastic design variables or noise variables are taken into account explicitly in the optimization procedure. The metamodel-based strategy is combined with a sequential improvement algorithm to efficiently increase the accuracy of the objective function prediction. This is only done at regions of interest containing the optimal robust design. Application of the methodology to an industrial V-bending process resulted in valuable process insights and an improved robust process design. Moreover, a significant improvement of the robustness (>2σ) was obtained by minimizing the deteriorating effects of several noise variables. The robust optimization results demonstrate the general applicability of the robust optimization strategy and underline the importance of including uncertainty and robustness explicitly in the numerical optimization procedure.

Design and experimental evaluation of robust controllers for a two-wheeled robot

NASA Astrophysics Data System (ADS)

Kralev, J.; Slavov, Ts.; Petkov, P.

2016-11-01

The paper presents the design and experimental evaluation of two alternative μ-controllers for robust vertical stabilisation of a two-wheeled self-balancing robot. The controllers design is based on models derived by identification from closed-loop experimental data. In the first design, a signal-based uncertainty representation obtained directly from the identification procedure is used, which leads to a controller of order 29. In the second design the signal uncertainty is approximated by an input multiplicative uncertainty, which leads to a controller of order 50, subsequently reduced to 30. The performance of the two μ-controllers is compared with the performance of a conventional linear quadratic controller with 17th-order Kalman filter. A proportional-integral controller of the rotational motion around the vertical axis is implemented as well. The control code is generated using Simulink® controller models and is embedded in a digital signal processor. Results from the simulation of the closed-loop system as well as experimental results obtained during the real-time implementation of the designed controllers are given. The theoretical investigation and experimental results confirm that the closed-loop system achieves robust performance in respect to the uncertainties related to the identified robot model.

Robust energy harvesting from walking vibrations by means of nonlinear cantilever beams

NASA Astrophysics Data System (ADS)

Kluger, Jocelyn M.; Sapsis, Themistoklis P.; Slocum, Alexander H.

2015-04-01

In the present work we examine how mechanical nonlinearity can be appropriately utilized to achieve strong robustness of performance in an energy harvesting setting. More specifically, for energy harvesting applications, a great challenge is the uncertain character of the excitation. The combination of this uncertainty with the narrow range of good performance for linear oscillators creates the need for more robust designs that adapt to a wider range of excitation signals. A typical application of this kind is energy harvesting from walking vibrations. Depending on the particular characteristics of the person that walks as well as on the pace of walking, the excitation signal obtains completely different forms. In the present work we study a nonlinear spring mechanism that is composed of a cantilever wrapping around a curved surface as it deflects. While for the free cantilever, the force acting on the free tip depends linearly on the tip displacement, the utilization of a contact surface with the appropriate distribution of curvature leads to essentially nonlinear dependence between the tip displacement and the acting force. The studied nonlinear mechanism has favorable mechanical properties such as low frictional losses, minimal moving parts, and a rugged design that can withstand excessive loads. Through numerical simulations we illustrate that by utilizing this essentially nonlinear element in a 2 degrees-of-freedom (DOF) system, we obtain strongly nonlinear energy transfers between the modes of the system. We illustrate that this nonlinear behavior is associated with strong robustness over three radically different excitation signals that correspond to different walking paces. To validate the strong robustness properties of the 2DOF nonlinear system, we perform a direct parameter optimization for 1DOF and 2DOF linear systems as well as for a class of 1DOF and 2DOF systems with nonlinear springs similar to that of the cubic spring that are physically realized by the cantilever-surface mechanism. The optimization results show that the 2DOF nonlinear system presents the best average performance when the excitation signals have three possible forms. Moreover, we observe that while for the linear systems the optimal performance is obtained for small values of the electromagnetic damping, for the 2DOF nonlinear system optimal performance is achieved for large values of damping. This feature is of particular importance for the system's robustness to parasitic damping.

Physiological roles of STIM1 and Orai1 homologs and CRAC channels in the genetic model organism Caenorhabditis elegans

PubMed Central

Strange, Kevin; Yan, Xiaohui; Lorin-Nebel, Catherine; Xing, Juan

2007-01-01

Summary The nematode Caenorhabditis elegans provides numerous experimental advantages for developing an integrative molecular understanding of physiological processes and has proven to be a valuable model for characterizing Ca2+ signaling mechanisms. This review will focus on the role of Ca2+ release activated Ca2+ (CRAC) channel activity in function of the worm gonad and intestine. Inositol 1,4,5-trisphosphate (IP3)-dependent oscillatory Ca2+ signaling regulates contractile activity of the gonad and rhythmic posterior body wall muscle contraction (pBoc) required for ovulation and defecation, respectively. The C. elegans genome contains a single homolog of both STIM1 and Orai1, proteins required for CRAC channel function in mammalian and Drosophila cells. C. elegans STIM-1 and ORAI-1 are coexpressed in the worm gonad and intestine and give rise to robust CRAC channel activity when coexpressed in HEK293 cells. STIM-1 or ORAI-1 knockdown causes complete sterility demonstrating that the genes are essential components of gonad Ca2+ signaling. Knockdown of either protein dramatically inhibits intestinal cell CRAC channel activity, but surprisingly has no effect on pBoc, intestinal Ca2+ oscillations or intestinal ER Ca2+ store homeostasis. CRAC channels thus do not play obligate roles in all IP3-dependent signaling processes in C. elegans. Instead, we suggest that CRAC channels carry out highly specialized and cell specific signaling roles and that they may function as a failsafe mechanism to prevent Ca2+ store depletion under pathophysiological and stress conditions. PMID:17376526

Robust high-resolution quantification of time signals encoded by in vivo magnetic resonance spectroscopy

NASA Astrophysics Data System (ADS)

Belkić, Dževad; Belkić, Karen

2018-01-01

This paper on molecular imaging emphasizes improving specificity of magnetic resonance spectroscopy (MRS) for early cancer diagnostics by high-resolution data analysis. Sensitivity of magnetic resonance imaging (MRI) is excellent, but specificity is insufficient. Specificity is improved with MRS by going beyond morphology to assess the biochemical content of tissue. This is contingent upon accurate data quantification of diagnostically relevant biomolecules. Quantification is spectral analysis which reconstructs chemical shifts, amplitudes and relaxation times of metabolites. Chemical shifts inform on electronic shielding of resonating nuclei bound to different molecular compounds. Oscillation amplitudes in time signals retrieve the abundance of MR sensitive nuclei whose number is proportional to metabolite concentrations. Transverse relaxation times, the reciprocal of decay probabilities of resonances, arise from spin-spin coupling and reflect local field inhomogeneities. In MRS single voxels are used. For volumetric coverage, multi-voxels are employed within a hybrid of MRS and MRI called magnetic resonance spectroscopic imaging (MRSI). Common to MRS and MRSI is encoding of time signals and subsequent spectral analysis. Encoded data do not provide direct clinical information. Spectral analysis of time signals can yield the quantitative information, of which metabolite concentrations are the most clinically important. This information is equivocal with standard data analysis through the non-parametric, low-resolution fast Fourier transform and post-processing via fitting. By applying the fast Padé transform (FPT) with high-resolution, noise suppression and exact quantification via quantum mechanical signal processing, advances are made, presented herein, focusing on four areas of critical public health importance: brain, prostate, breast and ovarian cancers.

In-situ sensing using mass spectrometry and its use for run-to-run control on a W-CVD cluster tool

NASA Astrophysics Data System (ADS)

Gougousi, T.; Sreenivasan, R.; Xu, Y.; Henn-Lecordier, L.; Rubloff, G. W.; Kidder, , J. N.; Zafiriou, E.

2001-01-01

A 300 amu closed-ion-source RGA (Leybold-Inficon Transpector 2) sampling gases directly from the reactor of an ULVAC ERA-1000 cluster tool has been used for real time process monitoring of a W CVD process. The process involves H2 reduction of WF6 at a total pressure of 67 Pa (0.5 torr) to produce W films on Si wafers heated at temperatures around 350 °C. The normalized RGA signals for the H2 reagent depletion and the HF product generation were correlated with the W film weight as measured post-process with an electronic microbalance for the establishment of thin-film weight (thickness) metrology. The metrology uncertainty (about 7% for the HF product) was limited primarily by the very low conversion efficiency of the W CVD process (around 2-3%). The HF metrology was then used to drive a robust run-to-run control algorithm, with the deposition time selected as the manipulated (or controlled) variable. For that purpose, during a 10 wafer run, a systematic process drift was introduced as a -5 °C processing temperature change for each successive wafer, in an otherwise unchanged process recipe. Without adjustment of the deposition time the W film weight (thickness) would have declined by about 50% by the 10th wafer. With the aid of the process control algorithm, an adjusted deposition time was computed so as to maintain constant HF sensing signal, resulting in weight (thickness) control comparable to the accuracy of the thickness metrology. These results suggest that in-situ chemical sensing, and particularly mass spectrometry, provide the basis for wafer state metrology as needed to achieve run-to-run control. Furthermore, since the control accuracy was consistent with the metrology accuracy, we anticipate significant improvements for processes as used in manufacturing, where conversion rates are much higher (40-50%) and corresponding signals for metrology will be much larger.

Statistical optimisation techniques in fatigue signal editing problem

NASA Astrophysics Data System (ADS)

Nopiah, Z. M.; Osman, M. H.; Baharin, N.; Abdullah, S.

2015-02-01

Success in fatigue signal editing is determined by the level of length reduction without compromising statistical constraints. A great reduction rate can be achieved by removing small amplitude cycles from the recorded signal. The long recorded signal sometimes renders the cycle-to-cycle editing process daunting. This has encouraged researchers to focus on the segment-based approach. This paper discusses joint application of the Running Damage Extraction (RDE) technique and single constrained Genetic Algorithm (GA) in fatigue signal editing optimisation.. In the first section, the RDE technique is used to restructure and summarise the fatigue strain. This technique combines the overlapping window and fatigue strain-life models. It is designed to identify and isolate the fatigue events that exist in the variable amplitude strain data into different segments whereby the retention of statistical parameters and the vibration energy are considered. In the second section, the fatigue data editing problem is formulated as a constrained single optimisation problem that can be solved using GA method. The GA produces the shortest edited fatigue signal by selecting appropriate segments from a pool of labelling segments. Challenges arise due to constraints on the segment selection by deviation level over three signal properties, namely cumulative fatigue damage, root mean square and kurtosis values. Experimental results over several case studies show that the idea of solving fatigue signal editing within a framework of optimisation is effective and automatic, and that the GA is robust for constrained segment selection.

Statistical optimisation techniques in fatigue signal editing problem

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

Nopiah, Z. M.; Osman, M. H.; Baharin, N.

Success in fatigue signal editing is determined by the level of length reduction without compromising statistical constraints. A great reduction rate can be achieved by removing small amplitude cycles from the recorded signal. The long recorded signal sometimes renders the cycle-to-cycle editing process daunting. This has encouraged researchers to focus on the segment-based approach. This paper discusses joint application of the Running Damage Extraction (RDE) technique and single constrained Genetic Algorithm (GA) in fatigue signal editing optimisation.. In the first section, the RDE technique is used to restructure and summarise the fatigue strain. This technique combines the overlapping window andmore » fatigue strain-life models. It is designed to identify and isolate the fatigue events that exist in the variable amplitude strain data into different segments whereby the retention of statistical parameters and the vibration energy are considered. In the second section, the fatigue data editing problem is formulated as a constrained single optimisation problem that can be solved using GA method. The GA produces the shortest edited fatigue signal by selecting appropriate segments from a pool of labelling segments. Challenges arise due to constraints on the segment selection by deviation level over three signal properties, namely cumulative fatigue damage, root mean square and kurtosis values. Experimental results over several case studies show that the idea of solving fatigue signal editing within a framework of optimisation is effective and automatic, and that the GA is robust for constrained segment selection.« less

Magnetoencephalographic Signals Identify Stages in Real-Life Decision Processes

PubMed Central

Braeutigam, Sven; Stins, John F.; Rose, Steven P. R.; Swithenby, Stephen J.; Ambler, Tim

2001-01-01

We used magnetoencephalography (MEG) to study the dynamics of neural responses in eight subjects engaged in shopping for day-to-day items from supermarket shelves. This behavior not only has personal and economic importance but also provides an example of an experience that is both personal and shared between individuals. The shopping experience enables the exploration of neural mechanisms underlying choice based on complex memories. Choosing among different brands of closely related products activated a robust sequence of signals within the first second after the presentation of the choice images. This sequence engaged first the visual cortex (80-100 ms), then as the images were analyzed, predominantly the left temporal regions (310-340 ms). At longer latency, characteristic neural activetion was found in motor speech areas (500-520 ms) for images requiring low salience choices with respect to previous (brand) memory, and in right parietal cortex for high salience choices (850-920 ms). We argue that the neural processes associated with the particular brand-choice stimulus can be separated into identifiable stages through observation of MEG responses and knowledge of functional anatomy. PMID:12018772

Biomimetic photo-actuation: sensing, control and actuation in sun-tracking plants.

PubMed

Dicker, M P M; Rossiter, J M; Bond, I P; Weaver, P M

2014-09-01

Although the actuation mechanisms that drive plant movement have been investigated from a biomimetic perspective, few studies have looked at the wider sensing and control systems that regulate this motion. This paper examines photo-actuation-actuation induced by, and controlled with light-through a review of the sun-tracking functions of the Cornish Mallow. The sun-tracking movement of the Cornish Mallow leaf results from an extraordinarily complex-yet extremely elegant-process of signal perception, generation, filtering and control. Inspired by this process, a concept for a simplified biomimetic analogue of this leaf is proposed: a multifunctional structure employing chemical sensing, signal transmission, and control of composite hydrogel actuators. We present this multifunctional structure, and show that the success of the concept will require improved selection of materials and structural design. This device has application in the solar-tracking of photovoltaic panels for increased energy yield. More broadly it is envisaged that the concept of chemical sensing and control can be expanded beyond photo-actuation to many other stimuli, resulting in new classes of robust solid-state devices.

Synthetic in vitro transcriptional oscillators

PubMed Central

Kim, Jongmin; Winfree, Erik

2011-01-01

The construction of synthetic biochemical circuits from simple components illuminates how complex behaviors can arise in chemistry and builds a foundation for future biological technologies. A simplified analog of genetic regulatory networks, in vitro transcriptional circuits, provides a modular platform for the systematic construction of arbitrary circuits and requires only two essential enzymes, bacteriophage T7 RNA polymerase and Escherichia coli ribonuclease H, to produce and degrade RNA signals. In this study, we design and experimentally demonstrate three transcriptional oscillators in vitro. First, a negative feedback oscillator comprising two switches, regulated by excitatory and inhibitory RNA signals, showed up to five complete cycles. To demonstrate modularity and to explore the design space further, a positive-feedback loop was added that modulates and extends the oscillatory regime. Finally, a three-switch ring oscillator was constructed and analyzed. Mathematical modeling guided the design process, identified experimental conditions likely to yield oscillations, and explained the system's robust response to interference by short degradation products. Synthetic transcriptional oscillators could prove valuable for systematic exploration of biochemical circuit design principles and for controlling nanoscale devices and orchestrating processes within artificial cells. PMID:21283141

A decentralized mechanism for improving the functional robustness of distribution networks.

PubMed

Shi, Benyun; Liu, Jiming

2012-10-01

Most real-world distribution systems can be modeled as distribution networks, where a commodity can flow from source nodes to sink nodes through junction nodes. One of the fundamental characteristics of distribution networks is the functional robustness, which reflects the ability of maintaining its function in the face of internal or external disruptions. In view of the fact that most distribution networks do not have any centralized control mechanisms, we consider the problem of how to improve the functional robustness in a decentralized way. To achieve this goal, we study two important problems: 1) how to formally measure the functional robustness, and 2) how to improve the functional robustness of a network based on the local interaction of its nodes. First, we derive a utility function in terms of network entropy to characterize the functional robustness of a distribution network. Second, we propose a decentralized network pricing mechanism, where each node need only communicate with its distribution neighbors by sending a "price" signal to its upstream neighbors and receiving "price" signals from its downstream neighbors. By doing so, each node can determine its outflows by maximizing its own payoff function. Our mathematical analysis shows that the decentralized pricing mechanism can produce results equivalent to those of an ideal centralized maximization with complete information. Finally, to demonstrate the properties of our mechanism, we carry out a case study on the U.S. natural gas distribution network. The results validate the convergence and effectiveness of our mechanism when comparing it with an existing algorithm.

Audiovisual Asynchrony Detection in Human Speech

ERIC Educational Resources Information Center

Maier, Joost X.; Di Luca, Massimiliano; Noppeney, Uta

2011-01-01

Combining information from the visual and auditory senses can greatly enhance intelligibility of natural speech. Integration of audiovisual speech signals is robust even when temporal offsets are present between the component signals. In the present study, we characterized the temporal integration window for speech and nonspeech stimuli with…

Evaluation of the Contribution of Signals Originating from Large Blood Vessels to Signals of Functionally Specific Brain Areas

PubMed Central

Chung, Jun-Young; Ogawa, Seiji

2015-01-01

The fusiform face area (FFA) is known to play a pivotal role in face processing. The FFA is located in the ventral region, at the base of the brain, through which large blood vessels run. The location of the FFA via functional MRI (fMRI) may be influenced by these large blood vessels. Responses of large blood vessels may not exactly correspond to neuronal activity in a target area, because they may be diluted and influenced by inflow effects. In this study, we investigated the effects of large blood vessels in the FFA, that is, whether the FFA includes large blood vessels and/or whether inflow signals contribute to fMRI signals of the FFA. For this purpose, we used susceptibility-weighted imaging (SWI) sequences to visualize large blood vessels and dual-echo gradient-echo echo-planar imaging (GE-EPI) to measure inflow effects. These results showed that the location and response signals of the FFA were not influenced by large blood vessels or inflow effects, although large blood vessels were located near the FFA. Therefore, the data from the FFA obtained by individual analysis were robust to large blood vessels but leaving a warning that the data obtained by group analysis may be prone to large blood vessels. PMID:26413511

An orthosteric inhibitor of the RAS-SOS interaction.

PubMed

Nickerson, Seth; Joy, Stephen T; Arora, Paramjit S; Bar-Sagi, Dafna

2013-01-01

Rat sarcoma (RAS) proteins are signaling nodes that transduce extracellular cues into precise alterations in cellular physiology by engaging effector pathways. RAS signaling thus regulates diverse cell processes including proliferation, migration, differentiation, and survival. Owing to this central role in governing mitogenic signals, RAS pathway components are often dysregulated in human diseases. Targeted therapy of RAS pathways has generally not been successful, largely because of the robust biochemistry of the targets and their multifaceted network of molecular regulators. The rate-limiting step of RAS activation is Son of Sevenless (SOS)-mediated nucleotide exchange involving a single evolutionarily conserved catalytic helix from SOS. Structure function data of this mechanism provided a strong platform to design an SOS-derived, helically constrained peptide mimic as an inhibitor of the RAS-SOS interaction. In this chapter, we review RAS-SOS signaling dynamics and present evidence supporting the novel paradigm of inhibiting their interaction as a therapeutic strategy. We then describe a method of generating helically constrained peptide mimics of protein surfaces, which we have employed to inhibit the RAS-SOS active site interaction. The biochemical and functional properties of this SOS mimic support the premise that inhibition of RAS-nucleotide exchange can effectively block RAS activation and downstream signaling. © 2013 Elsevier Inc. All rights reserved.

Intensity invariance properties of auditory neurons compared to the statistics of relevant natural signals in grasshoppers.

PubMed

Clemens, Jan; Weschke, Gerroth; Vogel, Astrid; Ronacher, Bernhard

2010-04-01

The temporal pattern of amplitude modulations (AM) is often used to recognize acoustic objects. To identify objects reliably, intensity invariant representations have to be formed. We approached this problem within the auditory pathway of grasshoppers. We presented AM patterns modulated at different time scales and intensities. Metric space analysis of neuronal responses allowed us to determine how well, how invariantly, and at which time scales AM frequency is encoded. We find that in some neurons spike-count cues contribute substantially (20-60%) to the decoding of AM frequency at a single intensity. However, such cues are not robust when intensity varies. The general intensity invariance of the system is poor. However, there exists a range of AM frequencies around 83 Hz where intensity invariance of local interneurons is relatively high. In this range, natural communication signals exhibit much variation between species, suggesting an important behavioral role for this frequency band. We hypothesize, just as has been proposed for human speech, that the communication signals might have evolved to match the processing properties of the receivers. This contrasts with optimal coding theory, which postulates that neuronal systems are adapted to the statistics of the relevant signals.

Targeting disease through novel pathways of apoptosis and autophagy.

PubMed

Maiese, Kenneth; Chong, Zhao Zhong; Shang, Yan Chen; Wang, Shaohui

2012-12-01

Apoptosis and autophagy impact cell death in multiple systems of the body. Development of new therapeutic strategies that target these processes must address their complex role during developmental cell growth as well as during the modulation of toxic cellular environments. Novel signaling pathways involving Wnt1-inducible signaling pathway protein 1 (WISP1), phosphoinositide 3-kinase (PI3K), protein kinase B (Akt), β-catenin and mammalian target of rapamycin (mTOR) govern apoptotic and autophagic pathways during oxidant stress that affect the course of a broad spectrum of disease entities including Alzheimer's disease, Parkinson's disease, myocardial injury, skeletal system trauma, immune system dysfunction and cancer progression. Implications of potential biological and clinical outcome for these signaling pathways are presented. The CCN family member WISP1 and its intimate relationship with canonical and non-canonical wingless signaling pathways of PI3K, Akt1, β-catenin and mTOR offer an exciting approach for governing the pathways of apoptosis and autophagy especially in clinical disorders that are currently without effective treatments. Future studies that can elucidate the intricate role of these cytoprotective pathways during apoptosis and autophagy can further the successful translation and development of these cellular targets into robust and safe clinical therapeutic strategies.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

PubMed Central

Peng, Yuan; Qiu, Mengran; Shi, Jianfei; Liu, Liangliang

2018-01-01

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved. PMID:29780407

A near-infrared SETI experiment: A multi-time resolution data analysis

NASA Astrophysics Data System (ADS)

Tallis, Melisa; Maire, Jerome; Wright, Shelley; Drake, Frank D.; Duenas, Andres; Marcy, Geoffrey W.; Stone, Remington P. S.; Treffers, Richard R.; Werthimer, Dan; NIROSETI

2016-06-01

We present new post-processing routines which are used to detect very fast optical and near-infrared pulsed signals using the latest NIROSETI (Near-Infrared Optical Search for Extraterrestrial Intelligence) instrument. NIROSETI was commissioned in 2015 at Lick Observatory and searches for near-infrared (0.95 to 1.65μ) nanosecond pulsed laser signals transmitted by distant civilizations. Traditional optical SETI searches rely on analysis of coincidences that occur between multiple detectors at a fixed time resolution. We present a multi-time resolution data analysis that extends our search from the 1ns to 1ms range. This new feature greatly improves the versatility of the instrument and its search parameters for near-infrared SETI. We aim to use these algorithms to assist us in our search for signals that have varying duty cycles and pulse widths. We tested the fidelity and robustness of our algorithms using both synthetic embedded pulsed signals, as well as data from a near-infrared pulsed laser installed on the instrument. Applications of NIROSETI are widespread in time domain astrophysics, especially for high time resolution transients, and astronomical objects that emit short-duration high-energy pulses such as pulsars.

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

Nabeel Riza

This final report contains the main results from a 3-year program to further investigate the merits of SiC-based hybrid sensor designs for extreme environment measurements in gas turbines. The study is divided in three parts. Part 1 studies the material properties of SiC such as temporal response, refractive index change with temperature, and material thermal response reversibility. Sensor data from a combustion rig-test using this SiC sensor technology is analyzed and a robust distributed sensor network design is proposed. Part 2 of the study focuses on introducing redundancy in the sensor signal processing to provide improved temperature measurement robustness. Inmore » this regard, two distinct measurement methods emerge. A first method uses laser wavelength sensitivity of the SiC refractive index behavior and a second method that engages the Black-Body (BB) radiation of the SiC package. Part 3 of the program investigates a new way to measure pressure via a distance measurement technique that applies to hot objects including corrosive fluids.« less

A system for real-time measurement of the brachial artery diameter in B-mode ultrasound images.

PubMed

Gemignani, Vincenzo; Faita, Francesco; Ghiadoni, Lorenzo; Poggianti, Elisa; Demi, Marcello

2007-03-01

The measurement of the brachial artery diameter is frequently used in clinical studies for evaluating the flow-mediated dilation and, in conjunction with the blood pressure value, for assessing arterial stiffness. This paper presents a system for computing the brachial artery diameter in real-time by analyzing B-mode ultrasound images. The method is based on a robust edge detection algorithm which is used to automatically locate the two walls of the vessel. The measure of the diameter is obtained with subpixel precision and with a temporal resolution of 25 samples/s, so that the small dilations induced by the cardiac cycle can also be retrieved. The algorithm is implemented on a standalone video processing board which acquires the analog video signal from the ultrasound equipment. Results are shown in real-time on a graphical user interface. The system was tested both on synthetic ultrasound images and in clinical studies of flow-mediated dilation. Accuracy, robustness, and intra/inter observer variability of the method were evaluated.

Feature Matching of Historical Images Based on Geometry of Quadrilaterals

NASA Astrophysics Data System (ADS)

Maiwald, F.; Schneider, D.; Henze, F.; Münster, S.; Niebling, F.

2018-05-01

This contribution shows an approach to match historical images from the photo library of the Saxon State and University Library Dresden (SLUB) in the context of a historical three-dimensional city model of Dresden. In comparison to recent images, historical photography provides diverse factors which make an automatical image analysis (feature detection, feature matching and relative orientation of images) difficult. Due to e.g. film grain, dust particles or the digitalization process, historical images are often covered by noise interfering with the image signal needed for a robust feature matching. The presented approach uses quadrilaterals in image space as these are commonly available in man-made structures and façade images (windows, stones, claddings). It is explained how to generally detect quadrilaterals in images. Consequently, the properties of the quadrilaterals as well as the relationship to neighbouring quadrilaterals are used for the description and matching of feature points. The results show that most of the matches are robust and correct but still small in numbers.

An Efficient Audio Watermarking Algorithm in Frequency Domain for Copyright Protection

NASA Astrophysics Data System (ADS)

Dhar, Pranab Kumar; Khan, Mohammad Ibrahim; Kim, Cheol-Hong; Kim, Jong-Myon

Digital Watermarking plays an important role for copyright protection of multimedia data. This paper proposes a new watermarking system in frequency domain for copyright protection of digital audio. In our proposed watermarking system, the original audio is segmented into non-overlapping frames. Watermarks are then embedded into the selected prominent peaks in the magnitude spectrum of each frame. Watermarks are extracted by performing the inverse operation of watermark embedding process. Simulation results indicate that the proposed watermarking system is highly robust against various kinds of attacks such as noise addition, cropping, re-sampling, re-quantization, MP3 compression, and low-pass filtering. Our proposed watermarking system outperforms Cox's method in terms of imperceptibility, while keeping comparable robustness with the Cox's method. Our proposed system achieves SNR (signal-to-noise ratio) values ranging from 20 dB to 28 dB, in contrast to Cox's method which achieves SNR values ranging from only 14 dB to 23 dB.

Design and realization of a new agorithm of calculating the absolute positon angle based on the incremental encoder

NASA Astrophysics Data System (ADS)

Liu, Peng; Yang, Yong-qing; Li, Zhi-guo; Han, Jun-feng; Wei, Yu; Jing, Feng

2018-02-01

Aiming at the shortage of the incremental encoder with simple process to change along the count "in the presence of repeatability and anti disturbance ability, combined with its application in a large project in the country, designed an electromechanical switch for generating zero, zero crossing signal. A mechanical zero electric and zero coordinate transformation model is given to meet the path optimality, single, fast and accurate requirements of adaptive fast change algorithm, the proposed algorithm can effectively solve the contradiction between the accuracy and the change of the time change. A test platform is built to verify the effectiveness and robustness of the proposed algorithm. The experimental data show that the effect of the algorithm accuracy is not influenced by the change of the speed of change, change the error of only 0.0013. Meet too fast, the change of system accuracy, and repeated experiments show that this algorithm has high robustness.

New approach to wireless data communication in a propagation environment

NASA Astrophysics Data System (ADS)

Hunek, Wojciech P.; Majewski, Paweł

2017-10-01

This paper presents a new idea of perfect signal reconstruction in multivariable wireless communications systems including a different number of transmitting and receiving antennas. The proposed approach is based on the polynomial matrix S-inverse associated with Smith factorization. Crucially, the above mentioned inverse implements the so-called degrees of freedom. It has been confirmed by simulation study that the degrees of freedom allow to minimalize the negative impact of the propagation environment in terms of increasing the robustness of whole signal reconstruction process. Now, the parasitic drawbacks in form of dynamic ISI and ICI effects can be eliminated in framework described by polynomial calculus. Therefore, the new method guarantees not only reducing the financial impact but, more importantly, provides potentially the lower consumption energy systems than other classical ones. In order to show the potential of new approach, the simulation studies were performed by author's simulator based on well-known OFDM technique.

Relative Navigation for Formation Flying of Spacecraft

NASA Technical Reports Server (NTRS)

Alonso, Roberto; Du, Ju-Young; Hughes, Declan; Junkins, John L.; Crassidis, John L.

2001-01-01

This paper presents a robust and efficient approach for relative navigation and attitude estimation of spacecraft flying in formation. This approach uses measurements from a new optical sensor that provides a line of sight vector from the master spacecraft to the secondary satellite. The overall system provides a novel, reliable, and autonomous relative navigation and attitude determination system, employing relatively simple electronic circuits with modest digital signal processing requirements and is fully independent of any external systems. Experimental calibration results are presented, which are used to achieve accurate line of sight measurements. State estimation for formation flying is achieved through an optimal observer design. Also, because the rotational and translational motions are coupled through the observation vectors, three approaches are suggested to separate both signals just for stability analysis. Simulation and experimental results indicate that the combined sensor/estimator approach provides accurate relative position and attitude estimates.

Practical considerations of image analysis and quantification of signal transduction IHC staining.

PubMed

Grunkin, Michael; Raundahl, Jakob; Foged, Niels T

2011-01-01

The dramatic increase in computer processing power in combination with the availability of high-quality digital cameras during the last 10 years has fertilized the grounds for quantitative microscopy based on digital image analysis. With the present introduction of robust scanners for whole slide imaging in both research and routine, the benefits of automation and objectivity in the analysis of tissue sections will be even more obvious. For in situ studies of signal transduction, the combination of tissue microarrays, immunohistochemistry, digital imaging, and quantitative image analysis will be central operations. However, immunohistochemistry is a multistep procedure including a lot of technical pitfalls leading to intra- and interlaboratory variability of its outcome. The resulting variations in staining intensity and disruption of original morphology are an extra challenge for the image analysis software, which therefore preferably should be dedicated to the detection and quantification of histomorphometrical end points.

Functional Optical Coherence Tomography Enables In Vivo Physiological Assessment of Retinal Rod and Cone Photoreceptors

PubMed Central

Zhang, Qiuxiang; Lu, Rongwen; Wang, Benquan; Messinger, Jeffrey D.; Curcio, Christine A.; Yao, Xincheng

2015-01-01

Transient intrinsic optical signal (IOS) changes have been observed in retinal photoreceptors, suggesting a unique biomarker for eye disease detection. However, clinical deployment of IOS imaging is challenging due to unclear IOS sources and limited signal-to-noise ratios (SNRs). Here, by developing high spatiotemporal resolution optical coherence tomography (OCT) and applying an adaptive algorithm for IOS processing, we were able to record robust IOSs from single-pass measurements. Transient IOSs, which might reflect an early stage of light phototransduction, are consistently observed in the photoreceptor outer segment almost immediately (<4 ms) after retinal stimulation. Comparative studies of dark- and light-adapted retinas have demonstrated the feasibility of functional OCT mapping of rod and cone photoreceptors, promising a new method for early disease detection and improved treatment of diseases such as age-related macular degeneration (AMD) and other eye diseases that can cause photoreceptor damage. PMID:25901915

Robust Foregrounds Removal for 21-cm Experiments

NASA Astrophysics Data System (ADS)

Mertens, F.; Ghosh, A.; Koopmans, L. V. E.

2018-05-01

Direct detection of the Epoch of Reionization via the redshifted 21-cm line will have unprecedented implications on the study of structure formation in the early Universe. To fulfill this promise current and future 21-cm experiments will need to detect the weak 21-cm signal over foregrounds several order of magnitude greater. This requires accurate modeling of the galactic and extragalactic emission and of its contaminants due to instrument chromaticity, ionosphere and imperfect calibration. To solve for this complex modeling, we propose a new method based on Gaussian Process Regression (GPR) which is able to cleanly separate the cosmological signal from most of the foregrounds contaminants. We also propose a new imaging method based on a maximum likelihood framework which solves for the interferometric equation directly on the sphere. Using this method, chromatic effects causing the so-called ``wedge'' are effectively eliminated (i.e. deconvolved) in the cylindrical (k⊥, k∥) power spectrum.

Hybrid Circuits with Nanofluidic Diodes and Load Capacitors

NASA Astrophysics Data System (ADS)

Ramirez, P.; Garcia-Morales, V.; Gomez, V.; Ali, M.; Nasir, S.; Ensinger, W.; Mafe, S.

2017-06-01

The chemical and physical input signals characteristic of micro- and nanofluidic devices operating in ionic solutions should eventually be translated into output electric currents and potentials that are monitored with solid-state components. This crucial step requires the design of hybrid circuits showing robust electrical coupling between ionic solutions and electronic elements. We study experimentally and theoretically the connectivity of the nanofluidic diodes in single-pore and multipore membranes with conventional capacitor systems for the cases of constant, periodic, and white-noise input potentials. The experiments demonstrate the reliable operation of these hybrid circuits over a wide range of membrane resistances, electrical capacitances, and solution p H values. The model simulations are based on empirical equations that have a solid physical basis and provide a convenient description of the electrical circuit operation. The results should contribute to advance signal transduction and processing using nanopore-based biosensors and bioelectronic interfaces.

Hopf bifurcation and chaos in a third-order phase-locked loop

NASA Astrophysics Data System (ADS)

Piqueira, José Roberto C.

2017-01-01

Phase-locked loops (PLLs) are devices able to recover time signals in several engineering applications. The literature regarding their dynamical behavior is vast, specifically considering that the process of synchronization between the input signal, coming from a remote source, and the PLL local oscillation is robust. For high-frequency applications it is usual to increase the PLL order by increasing the order of the internal filter, for guarantying good transient responses; however local parameter variations imply structural instability, thus provoking a Hopf bifurcation and a route to chaos for the phase error. Here, one usual architecture for a third-order PLL is studied and a range of permitted parameters is derived, providing a rule of thumb for designers. Out of this range, a Hopf bifurcation appears and, by increasing parameters, the periodic solution originated by the Hopf bifurcation degenerates into a chaotic attractor, therefore, preventing synchronization.

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.

Functional Optical Coherence Tomography Enables In Vivo Physiological Assessment of Retinal Rod and Cone Photoreceptors

NASA Astrophysics Data System (ADS)

Zhang, Qiuxiang; Lu, Rongwen; Wang, Benquan; Messinger, Jeffrey D.; Curcio, Christine A.; Yao, Xincheng

2015-04-01

Transient intrinsic optical signal (IOS) changes have been observed in retinal photoreceptors, suggesting a unique biomarker for eye disease detection. However, clinical deployment of IOS imaging is challenging due to unclear IOS sources and limited signal-to-noise ratios (SNRs). Here, by developing high spatiotemporal resolution optical coherence tomography (OCT) and applying an adaptive algorithm for IOS processing, we were able to record robust IOSs from single-pass measurements. Transient IOSs, which might reflect an early stage of light phototransduction, are consistently observed in the photoreceptor outer segment almost immediately (<4 ms) after retinal stimulation. Comparative studies of dark- and light-adapted retinas have demonstrated the feasibility of functional OCT mapping of rod and cone photoreceptors, promising a new method for early disease detection and improved treatment of diseases such as age-related macular degeneration (AMD) and other eye diseases that can cause photoreceptor damage.

SNR-adaptive stream weighting for audio-MES ASR.

PubMed

Lee, Ki-Seung

2008-08-01

Myoelectric signals (MESs) from the speaker's mouth region have been successfully shown to improve the noise robustness of automatic speech recognizers (ASRs), thus promising to extend their usability in implementing noise-robust ASR. In the recognition system presented herein, extracted audio and facial MES features were integrated by a decision fusion method, where the likelihood score of the audio-MES observation vector was given by a linear combination of class-conditional observation log-likelihoods of two classifiers, using appropriate weights. We developed a weighting process adaptive to SNRs. The main objective of the paper involves determining the optimal SNR classification boundaries and constructing a set of optimum stream weights for each SNR class. These two parameters were determined by a method based on a maximum mutual information criterion. Acoustic and facial MES data were collected from five subjects, using a 60-word vocabulary. Four types of acoustic noise including babble, car, aircraft, and white noise were acoustically added to clean speech signals with SNR ranging from -14 to 31 dB. The classification accuracy of the audio ASR was as low as 25.5%. Whereas, the classification accuracy of the MES ASR was 85.2%. The classification accuracy could be further improved by employing the proposed audio-MES weighting method, which was as high as 89.4% in the case of babble noise. A similar result was also found for the other types of noise.

A reverberation-time-aware DNN approach leveraging spatial information for microphone array dereverberation

NASA Astrophysics Data System (ADS)

Wu, Bo; Yang, Minglei; Li, Kehuang; Huang, Zhen; Siniscalchi, Sabato Marco; Wang, Tong; Lee, Chin-Hui

2017-12-01

A reverberation-time-aware deep-neural-network (DNN)-based multi-channel speech dereverberation framework is proposed to handle a wide range of reverberation times (RT60s). There are three key steps in designing a robust system. First, to accomplish simultaneous speech dereverberation and beamforming, we propose a framework, namely DNNSpatial, by selectively concatenating log-power spectral (LPS) input features of reverberant speech from multiple microphones in an array and map them into the expected output LPS features of anechoic reference speech based on a single deep neural network (DNN). Next, the temporal auto-correlation function of received signals at different RT60s is investigated to show that RT60-dependent temporal-spatial contexts in feature selection are needed in the DNNSpatial training stage in order to optimize the system performance in diverse reverberant environments. Finally, the RT60 is estimated to select the proper temporal and spatial contexts before feeding the log-power spectrum features to the trained DNNs for speech dereverberation. The experimental evidence gathered in this study indicates that the proposed framework outperforms the state-of-the-art signal processing dereverberation algorithm weighted prediction error (WPE) and conventional DNNSpatial systems without taking the reverberation time into account, even for extremely weak and severe reverberant conditions. The proposed technique generalizes well to unseen room size, array geometry and loudspeaker position, and is robust to reverberation time estimation error.

CSF generation by pineal gland results in a robust melatonin circadian rhythm in the third ventricle as an unique light/dark signal.

PubMed

Tan, Dun-Xian; Manchester, Lucien C; Reiter, Russel J

2016-01-01

Pineal gland is an important organ for the regulation of the bio-clock in all vertebrate species. Its major secretory product is melatonin which is considered as the chemical expression of darkness due to its circadian peak exclusively at night. Pineal melatonin can be either released into the blood stream or directly enter into the CSF of the third ventricle via the pineal recess. We have hypothesized that rather than the peripheral circulatory melatonin circadian rhythm serving as the light/dark signal, it is the melatonin rhythm in CSF of the third ventricle that serves this purpose. This is due to the fact that melatonin circadian rhythm in the CSF is more robust in terms of its extremely high concentration and its precise on/off peaks. Thus, extrapineal-generated melatonin or diet-derived melatonin which enters blood would not interfere with the bio-clock function of vertebrates. In addition, based on the relationship of the pineal gland to the CSF and the vascular structure of this gland, we also hypothesize that pineal gland is an essential player for CSF production. We feel it participates in both the formation and reabsorption of CSF. The mechanisms associated with these processes are reviewed and discussed in this brief review. Copyright © 2015 Elsevier Ltd. All rights reserved.

Efficient Transmission of Subthreshold Signals in Complex Networks of Spiking Neurons

PubMed Central

Torres, Joaquin J.; Elices, Irene; Marro, J.

2015-01-01

We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances—that naturally balances the network with excitatory and inhibitory synapses—and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest. PMID:25799449

Proinflammatory Cytokine Tumor Necrosis Factor (TNF)-like Weak Inducer of Apoptosis (TWEAK) Suppresses Satellite Cell Self-renewal through Inversely Modulating Notch and NF-κB Signaling Pathways*

PubMed Central

Ogura, Yuji; Mishra, Vivek; Hindi, Sajedah M.; Kuang, Shihuan; Kumar, Ashok

2013-01-01

Satellite cell self-renewal is an essential process to maintaining the robustness of skeletal muscle regenerative capacity. However, extrinsic factors that regulate self-renewal of satellite cells are not well understood. Here, we demonstrate that TWEAK cytokine reduces the proportion of Pax7+/MyoD− cells (an index of self-renewal) on myofiber explants and represses multiple components of Notch signaling in satellite cell cultures. The number of Pax7+ cells is significantly increased in skeletal muscle of TWEAK knock-out (KO) mice compared with wild-type in response to injury. Furthermore, Notch signaling is significantly elevated in cultured satellite cells and in regenerating myofibers of TWEAK-KO mice. Forced activation of Notch signaling through overexpression of the Notch1 intracellular domain (N1ICD) rescued the TWEAK-mediated inhibition of satellite cell self-renewal. TWEAK also activates the NF-κB transcription factor in satellite cells and inhibition of NF-κB significantly improved the number of Pax7+ cells in TWEAK-treated cultures. Furthermore, our results demonstrate that a reciprocal interaction between NF-κB and Notch signaling governs the inhibitory effect of TWEAK on satellite cell self-renewal. Collectively, our study demonstrates that TWEAK suppresses satellite cell self-renewal through activating NF-κB and repressing Notch signaling. PMID:24151074

Proinflammatory cytokine tumor necrosis factor (TNF)-like weak inducer of apoptosis (TWEAK) suppresses satellite cell self-renewal through inversely modulating Notch and NF-κB signaling pathways.

PubMed

Ogura, Yuji; Mishra, Vivek; Hindi, Sajedah M; Kuang, Shihuan; Kumar, Ashok

2013-12-06

Satellite cell self-renewal is an essential process to maintaining the robustness of skeletal muscle regenerative capacity. However, extrinsic factors that regulate self-renewal of satellite cells are not well understood. Here, we demonstrate that TWEAK cytokine reduces the proportion of Pax7(+)/MyoD(-) cells (an index of self-renewal) on myofiber explants and represses multiple components of Notch signaling in satellite cell cultures. The number of Pax7(+) cells is significantly increased in skeletal muscle of TWEAK knock-out (KO) mice compared with wild-type in response to injury. Furthermore, Notch signaling is significantly elevated in cultured satellite cells and in regenerating myofibers of TWEAK-KO mice. Forced activation of Notch signaling through overexpression of the Notch1 intracellular domain (N1ICD) rescued the TWEAK-mediated inhibition of satellite cell self-renewal. TWEAK also activates the NF-κB transcription factor in satellite cells and inhibition of NF-κB significantly improved the number of Pax7(+) cells in TWEAK-treated cultures. Furthermore, our results demonstrate that a reciprocal interaction between NF-κB and Notch signaling governs the inhibitory effect of TWEAK on satellite cell self-renewal. Collectively, our study demonstrates that TWEAK suppresses satellite cell self-renewal through activating NF-κB and repressing Notch signaling.

Digital algorithms for parallel pipelined single-detector homodyne fringe counting in laser interferometry

NASA Astrophysics Data System (ADS)

Rerucha, Simon; Sarbort, Martin; Hola, Miroslava; Cizek, Martin; Hucl, Vaclav; Cip, Ondrej; Lazar, Josef

2016-12-01

The homodyne detection with only a single detector represents a promising approach in the interferometric application which enables a significant reduction of the optical system complexity while preserving the fundamental resolution and dynamic range of the single frequency laser interferometers. We present the design, implementation and analysis of algorithmic methods for computational processing of the single-detector interference signal based on parallel pipelined processing suitable for real time implementation on a programmable hardware platform (e.g. the FPGA - Field Programmable Gate Arrays or the SoC - System on Chip). The algorithmic methods incorporate (a) the single detector signal (sine) scaling, filtering, demodulations and mixing necessary for the second (cosine) quadrature signal reconstruction followed by a conic section projection in Cartesian plane as well as (a) the phase unwrapping together with the goniometric and linear transformations needed for the scale linearization and periodic error correction. The digital computing scheme was designed for bandwidths up to tens of megahertz which would allow to measure the displacements at the velocities around half metre per second. The algorithmic methods were tested in real-time operation with a PC-based reference implementation that employed the advantage pipelined processing by balancing the computational load among multiple processor cores. The results indicate that the algorithmic methods are suitable for a wide range of applications [3] and that they are bringing the fringe counting interferometry closer to the industrial applications due to their optical setup simplicity and robustness, computational stability, scalability and also a cost-effectiveness.

Composite adaptive control of belt polishing force for aero-engine blade

NASA Astrophysics Data System (ADS)

Zhsao, Pengbing; Shi, Yaoyao

2013-09-01

The existing methods for blade polishing mainly focus on robot polishing and manual grinding. Due to the difficulty in high-precision control of the polishing force, the blade surface precision is very low in robot polishing, in particular, quality of the inlet and exhaust edges can not satisfy the processing requirements. Manual grinding has low efficiency, high labor intensity and unstable processing quality, moreover, the polished surface is vulnerable to burn, and the surface precision and integrity are difficult to ensure. In order to further improve the profile accuracy and surface quality, a pneumatic flexible polishing force-exerting mechanism is designed and a dual-mode switching composite adaptive control(DSCAC) strategy is proposed, which combines Bang-Bang control and model reference adaptive control based on fuzzy neural network(MRACFNN) together. By the mode decision-making mechanism, Bang-Bang control is used to track the control command signal quickly when the actual polishing force is far away from the target value, and MRACFNN is utilized in smaller error ranges to improve the system robustness and control precision. Based on the mathematical model of the force-exerting mechanism, simulation analysis is implemented on DSCAC. Simulation results show that the output polishing force can better track the given signal. Finally, the blade polishing experiments are carried out on the designed polishing equipment. Experimental results show that DSCAC can effectively mitigate the influence of gas compressibility, valve dead-time effect, valve nonlinear flow, cylinder friction, measurement noise and other interference on the control precision of polishing force, which has high control precision, strong robustness, strong anti-interference ability and other advantages compared with MRACFNN. The proposed research achieves high-precision control of the polishing force, effectively improves the blade machining precision and surface consistency, and significantly reduces the surface roughness.

r -process nucleosynthesis from matter ejected in binary neutron star mergers [On r -process nucleosynthesis from matter ejected in binary neutron star mergers

DOE PAGES

Bovard, Luke; Martin, Dirk; Guercilena, Federico; ...

2017-12-05

Here, when binary systems of neutron stars merge, a very small fraction of their rest mass is ejected, either dynamically or secularly. This material is neutron-rich and its nucleosynthesis provides the astrophysical site for the production of heavy elements in the Universe, together with a kilonova signal confirming neutron-star mergers as the origin of short gamma-ray bursts. We perform full general-relativistic simulations of binary neutron-star mergers employing three different nuclear-physics equations of state (EOSs), considering both equal- and unequal-mass configurations, and adopting a leakage scheme to account for neutrino radiative losses. Using a combination of techniques, we carry out anmore » extensive and systematic study of the hydrodynamical, thermodynamical, and geometrical properties of the matter ejected dynamically, employing the WinNet nuclear-reaction network to recover the relative abundances of heavy elements produced by each configurations. Among the results obtained, three are particularly important. First, we find that, within the sample considered here, both the properties of the dynamical ejecta and the nucleosynthesis yields are robust against variations of the EOS and masses. Second, using a conservative but robust criterion for unbound matter, we find that the amount of ejected mass is ≲10 –3 M⊙, hence at least one order of magnitude smaller than what normally assumed in modelling kilonova signals. Finally, using a simplified and gray-opacity model we assess the observability of the infrared kilonova emission finding, that for all binaries the luminosity peaks around ~1/2 day in the H-band, reaching a maximum magnitude of –13, and decreasing rapidly after one day.« less

InFlo: a novel systems biology framework identifies cAMP-CREB1 axis as a key modulator of platinum resistance in ovarian cancer.

PubMed

Dimitrova, N; Nagaraj, A B; Razi, A; Singh, S; Kamalakaran, S; Banerjee, N; Joseph, P; Mankovich, A; Mittal, P; DiFeo, A; Varadan, V

2017-04-27

Characterizing the complex interplay of cellular processes in cancer would enable the discovery of key mechanisms underlying its development and progression. Published approaches to decipher driver mechanisms do not explicitly model tissue-specific changes in pathway networks and the regulatory disruptions related to genomic aberrations in cancers. We therefore developed InFlo, a novel systems biology approach for characterizing complex biological processes using a unique multidimensional framework integrating transcriptomic, genomic and/or epigenomic profiles for any given cancer sample. We show that InFlo robustly characterizes tissue-specific differences in activities of signalling networks on a genome scale using unique probabilistic models of molecular interactions on a per-sample basis. Using large-scale multi-omics cancer datasets, we show that InFlo exhibits higher sensitivity and specificity in detecting pathway networks associated with specific disease states when compared to published pathway network modelling approaches. Furthermore, InFlo's ability to infer the activity of unmeasured signalling network components was also validated using orthogonal gene expression signatures. We then evaluated multi-omics profiles of primary high-grade serous ovarian cancer tumours (N=357) to delineate mechanisms underlying resistance to frontline platinum-based chemotherapy. InFlo was the only algorithm to identify hyperactivation of the cAMP-CREB1 axis as a key mechanism associated with resistance to platinum-based therapy, a finding that we subsequently experimentally validated. We confirmed that inhibition of CREB1 phosphorylation potently sensitized resistant cells to platinum therapy and was effective in killing ovarian cancer stem cells that contribute to both platinum-resistance and tumour recurrence. Thus, we propose InFlo to be a scalable and widely applicable and robust integrative network modelling framework for the discovery of evidence-based biomarkers and therapeutic targets.

Improved Uncertainty Quantification in Groundwater Flux Estimation Using GRACE

NASA Astrophysics Data System (ADS)

Reager, J. T., II; Rao, P.; Famiglietti, J. S.; Turmon, M.

2015-12-01

Groundwater change is difficult to monitor over large scales. One of the most successful approaches is in the remote sensing of time-variable gravity using NASA Gravity Recovery and Climate Experiment (GRACE) mission data, and successful case studies have created the opportunity to move towards a global groundwater monitoring framework for the world's largest aquifers. To achieve these estimates, several approximations are applied, including those in GRACE processing corrections, the formulation of the formal GRACE errors, destriping and signal recovery, and the numerical model estimation of snow water, surface water and soil moisture storage states used to isolate a groundwater component. A major weakness in these approaches is inconsistency: different studies have used different sources of primary and ancillary data, and may achieve different results based on alternative choices in these approximations. In this study, we present two cases of groundwater change estimation in California and the Colorado River basin, selected for their good data availability and varied climates. We achieve a robust numerical estimate of post-processing uncertainties resulting from land-surface model structural shortcomings and model resolution errors. Groundwater variations should demonstrate less variability than the overlying soil moisture state does, as groundwater has a longer memory of past events due to buffering by infiltration and drainage rate limits. We apply a model ensemble approach in a Bayesian framework constrained by the assumption of decreasing signal variability with depth in the soil column. We also discuss time variable errors vs. time constant errors, across-scale errors v. across-model errors, and error spectral content (across scales and across model). More robust uncertainty quantification for GRACE-based groundwater estimates would take all of these issues into account, allowing for more fair use in management applications and for better integration of GRACE-based measurements with observations from other sources.

Enhancing coronary Wave Intensity Analysis robustness by high order central finite differences

PubMed Central

Rivolo, Simone; Asrress, Kaleab N.; Chiribiri, Amedeo; Sammut, Eva; Wesolowski, Roman; Bloch, Lars Ø.; Grøndal, Anne K.; Hønge, Jesper L.; Kim, Won Y.; Marber, Michael; Redwood, Simon; Nagel, Eike; Smith, Nicolas P.; Lee, Jack

2014-01-01

Background Coronary Wave Intensity Analysis (cWIA) is a technique capable of separating the effects of proximal arterial haemodynamics from cardiac mechanics. Studies have identified WIA-derived indices that are closely correlated with several disease processes and predictive of functional recovery following myocardial infarction. The cWIA clinical application has, however, been limited by technical challenges including a lack of standardization across different studies and the derived indices' sensitivity to the processing parameters. Specifically, a critical step in WIA is the noise removal for evaluation of derivatives of the acquired signals, typically performed by applying a Savitzky–Golay filter, to reduce the high frequency acquisition noise. Methods The impact of the filter parameter selection on cWIA output, and on the derived clinical metrics (integral areas and peaks of the major waves), is first analysed. The sensitivity analysis is performed either by using the filter as a differentiator to calculate the signals' time derivative or by applying the filter to smooth the ensemble-averaged waveforms. Furthermore, the power-spectrum of the ensemble-averaged waveforms contains little high-frequency components, which motivated us to propose an alternative approach to compute the time derivatives of the acquired waveforms using a central finite difference scheme. Results and Conclusion The cWIA output and consequently the derived clinical metrics are significantly affected by the filter parameters, irrespective of its use as a smoothing filter or a differentiator. The proposed approach is parameter-free and, when applied to the 10 in-vivo human datasets and the 50 in-vivo animal datasets, enhances the cWIA robustness by significantly reducing the outcome variability (by 60%). PMID:25187852

r -process nucleosynthesis from matter ejected in binary neutron star mergers [On r -process nucleosynthesis from matter ejected in binary neutron star mergers

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

Bovard, Luke; Martin, Dirk; Guercilena, Federico

Here, when binary systems of neutron stars merge, a very small fraction of their rest mass is ejected, either dynamically or secularly. This material is neutron-rich and its nucleosynthesis provides the astrophysical site for the production of heavy elements in the Universe, together with a kilonova signal confirming neutron-star mergers as the origin of short gamma-ray bursts. We perform full general-relativistic simulations of binary neutron-star mergers employing three different nuclear-physics equations of state (EOSs), considering both equal- and unequal-mass configurations, and adopting a leakage scheme to account for neutrino radiative losses. Using a combination of techniques, we carry out anmore » extensive and systematic study of the hydrodynamical, thermodynamical, and geometrical properties of the matter ejected dynamically, employing the WinNet nuclear-reaction network to recover the relative abundances of heavy elements produced by each configurations. Among the results obtained, three are particularly important. First, we find that, within the sample considered here, both the properties of the dynamical ejecta and the nucleosynthesis yields are robust against variations of the EOS and masses. Second, using a conservative but robust criterion for unbound matter, we find that the amount of ejected mass is ≲10 –3 M⊙, hence at least one order of magnitude smaller than what normally assumed in modelling kilonova signals. Finally, using a simplified and gray-opacity model we assess the observability of the infrared kilonova emission finding, that for all binaries the luminosity peaks around ~1/2 day in the H-band, reaching a maximum magnitude of –13, and decreasing rapidly after one day.« less

Quantitative Analysis of Rat Dorsal Root Ganglion Neurons Cultured on Microelectrode Arrays Based on Fluorescence Microscopy Image Processing.

PubMed

Mari, João Fernando; Saito, José Hiroki; Neves, Amanda Ferreira; Lotufo, Celina Monteiro da Cruz; Destro-Filho, João-Batista; Nicoletti, Maria do Carmo

2015-12-01

Microelectrode Arrays (MEA) are devices for long term electrophysiological recording of extracellular spontaneous or evocated activities on in vitro neuron culture. This work proposes and develops a framework for quantitative and morphological analysis of neuron cultures on MEAs, by processing their corresponding images, acquired by fluorescence microscopy. The neurons are segmented from the fluorescence channel images using a combination of segmentation by thresholding, watershed transform, and object classification. The positioning of microelectrodes is obtained from the transmitted light channel images using the circular Hough transform. The proposed method was applied to images of dissociated culture of rat dorsal root ganglion (DRG) neuronal cells. The morphological and topological quantitative analysis carried out produced information regarding the state of culture, such as population count, neuron-to-neuron and neuron-to-microelectrode distances, soma morphologies, neuron sizes, neuron and microelectrode spatial distributions. Most of the analysis of microscopy images taken from neuronal cultures on MEA only consider simple qualitative analysis. Also, the proposed framework aims to standardize the image processing and to compute quantitative useful measures for integrated image-signal studies and further computational simulations. As results show, the implemented microelectrode identification method is robust and so are the implemented neuron segmentation and classification one (with a correct segmentation rate up to 84%). The quantitative information retrieved by the method is highly relevant to assist the integrated signal-image study of recorded electrophysiological signals as well as the physical aspects of the neuron culture on MEA. Although the experiments deal with DRG cell images, cortical and hippocampal cell images could also be processed with small adjustments in the image processing parameter estimation.

The Cross-Correlation and Reshuffling Tests in Discerning Induced Seismicity

NASA Astrophysics Data System (ADS)

Schultz, Ryan; Telesca, Luciano

2018-05-01

In recent years, cases of newly emergent induced clusters have increased seismic hazard and risk in locations with social, environmental, and economic consequence. Thus, the need for a quantitative and robust means to discern induced seismicity has become a critical concern. This paper reviews a Matlab-based algorithm designed to quantify the statistical confidence between two time-series datasets. Similar to prior approaches, our method utilizes the cross-correlation to delineate the strength and lag of correlated signals. In addition, use of surrogate reshuffling tests allows for the dynamic testing against statistical confidence intervals of anticipated spurious correlations. We demonstrate the robust nature of our algorithm in a suite of synthetic tests to determine the limits of accurate signal detection in the presence of noise and sub-sampling. Overall, this routine has considerable merit in terms of delineating the strength of correlated signals, one of which includes the discernment of induced seismicity from natural.

A Robust Real Time Direction-of-Arrival Estimation Method for Sequential Movement Events of Vehicles.

PubMed

Liu, Huawei; Li, Baoqing; Yuan, Xiaobing; Zhou, Qianwei; Huang, Jingchang

2018-03-27

Parameters estimation of sequential movement events of vehicles is facing the challenges of noise interferences and the demands of portable implementation. In this paper, we propose a robust direction-of-arrival (DOA) estimation method for the sequential movement events of vehicles based on a small Micro-Electro-Mechanical System (MEMS) microphone array system. Inspired by the incoherent signal-subspace method (ISM), the method that is proposed in this work employs multiple sub-bands, which are selected from the wideband signals with high magnitude-squared coherence to track moving vehicles in the presence of wind noise. The field test results demonstrate that the proposed method has a better performance in emulating the DOA of a moving vehicle even in the case of severe wind interference than the narrowband multiple signal classification (MUSIC) method, the sub-band DOA estimation method, and the classical two-sided correlation transformation (TCT) method.

Robustness and flexibility in nematode vulva development.

PubMed

Félix, Marie-Anne; Barkoulas, Michalis

2012-04-01

The Caenorhabditis elegans vulva has served as a paradigm for how conserved developmental pathways, such as EGF-Ras-MAPK, Notch and Wnt signaling, participate in networks driving animal organogenesis. Here, we discuss an emerging direction in the field, which places vulva research in a quantitative and microevolutionary framework. The final vulval cell fate pattern is known to be robust to change, but only recently has the variation of vulval traits been measured under stochastic, environmental or genetic variation. Whereas the resulting cell fate pattern is invariant among rhabditid nematodes, recent studies indicate that the developmental system has accumulated cryptic variation, even among wild C. elegans isolates. Quantitative differences in the signaling network have emerged through experiments and modeling as the driving force behind cryptic variation in Caenorhabditis species. On a wider evolutionary scale, the establishment of new model species has informed about the presence of qualitative variation in vulval signaling pathways. Copyright © 2012 Elsevier Ltd. All rights reserved.

PSGMiner: A modular software for polysomnographic analysis.

PubMed

Umut, İlhan

2016-06-01

Sleep disorders affect a great percentage of the population. The diagnosis of these disorders is usually made by polysomnography. This paper details the development of new software to carry out feature extraction in order to perform robust analysis and classification of sleep events using polysomnographic data. The software, called PSGMiner, is a tool, which visualizes, processes and classifies bioelectrical data. The purpose of this program is to provide researchers with a platform with which to test new hypotheses by creating tests to check for correlations that are not available in commercially available software. The software is freely available under the GPL3 License. PSGMiner is composed of a number of diverse modules such as feature extraction, annotation, and machine learning modules, all of which are accessible from the main module. Using the software, it is possible to extract features of polysomnography using digital signal processing and statistical methods and to perform different analyses. The features can be classified through the use of five classification algorithms. PSGMiner offers an architecture designed for integrating new methods. Automatic scoring, which is available in almost all commercial PSG software, is not inherently available in this program, though it can be implemented by two different methodologies (machine learning and algorithms). While similar software focuses on a certain signal or event composed of a small number of modules with no expansion possibility, the software introduced here can handle all polysomnographic signals and events. The software simplifies the processing of polysomnographic signals for researchers and physicians that are not experts in computer programming. It can find correlations between different events which could help predict an oncoming event such as sleep apnea. The software could also be used for educational purposes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Signal processing and neural network toolbox and its application to failure diagnosis and prognosis

NASA Astrophysics Data System (ADS)

Tu, Fang; Wen, Fang; Willett, Peter K.; Pattipati, Krishna R.; Jordan, Eric H.

2001-07-01

Many systems are comprised of components equipped with self-testing capability; however, if the system is complex involving feedback and the self-testing itself may occasionally be faulty, tracing faults to a single or multiple causes is difficult. Moreover, many sensors are incapable of reliable decision-making on their own. In such cases, a signal processing front-end that can match inference needs will be very helpful. The work is concerned with providing an object-oriented simulation environment for signal processing and neural network-based fault diagnosis and prognosis. In the toolbox, we implemented a wide range of spectral and statistical manipulation methods such as filters, harmonic analyzers, transient detectors, and multi-resolution decomposition to extract features for failure events from data collected by data sensors. Then we evaluated multiple learning paradigms for general classification, diagnosis and prognosis. The network models evaluated include Restricted Coulomb Energy (RCE) Neural Network, Learning Vector Quantization (LVQ), Decision Trees (C4.5), Fuzzy Adaptive Resonance Theory (FuzzyArtmap), Linear Discriminant Rule (LDR), Quadratic Discriminant Rule (QDR), Radial Basis Functions (RBF), Multiple Layer Perceptrons (MLP) and Single Layer Perceptrons (SLP). Validation techniques, such as N-fold cross-validation and bootstrap techniques, are employed for evaluating the robustness of network models. The trained networks are evaluated for their performance using test data on the basis of percent error rates obtained via cross-validation, time efficiency, generalization ability to unseen faults. Finally, the usage of neural networks for the prediction of residual life of turbine blades with thermal barrier coatings is described and the results are shown. The neural network toolbox has also been applied to fault diagnosis in mixed-signal circuits.

Tuning and Robustness Analysis for the Orion Absolute Navigation System

NASA Technical Reports Server (NTRS)

Holt, Greg N.; Zanetti, Renato; D'Souza, Christopher

2013-01-01

The Orion Multi-Purpose Crew Vehicle (MPCV) is currently under development as NASA's next-generation spacecraft for exploration missions beyond Low Earth Orbit. The MPCV is set to perform an orbital test flight, termed Exploration Flight Test 1 (EFT-1), some time in late 2014. The navigation system for the Orion spacecraft is being designed in a Multi-Organizational Design Environment (MODE) team including contractor and NASA personnel. The system uses an Extended Kalman Filter to process measurements and determine the state. The design of the navigation system has undergone several iterations and modifications since its inception, and continues as a work-in-progress. This paper seeks to show the efforts made to-date in tuning the filter for the EFT-1 mission and instilling appropriate robustness into the system to meet the requirements of manned space ight. Filter performance is affected by many factors: data rates, sensor measurement errors, tuning, and others. This paper focuses mainly on the error characterization and tuning portion. Traditional efforts at tuning a navigation filter have centered around the observation/measurement noise and Gaussian process noise of the Extended Kalman Filter. While the Orion MODE team must certainly address those factors, the team is also looking at residual edit thresholds and measurement underweighting as tuning tools. Tuning analysis is presented with open loop Monte-Carlo simulation results showing statistical errors bounded by the 3-sigma filter uncertainty covariance. The Orion filter design uses 24 Exponentially Correlated Random Variable (ECRV) parameters to estimate the accel/gyro misalignment and nonorthogonality. By design, the time constant and noise terms of these ECRV parameters were set to manufacturer specifications and not used as tuning parameters. They are included in the filter as a more analytically correct method of modeling uncertainties than ad-hoc tuning of the process noise. Tuning is explored for the powered-flight ascent phase, where measurements are scarce and unmodelled vehicle accelerations dominate. On orbit, there are important trade-off cases between process and measurement noise. On entry, there are considerations about trading performance accuracy for robustness. Process Noise is divided into powered flight and coasting ight and can be adjusted for each phase and mode of the Orion EFT-1 mission. Measurement noise is used for the integrated velocity measurements during pad alignment. It is also used for Global Positioning System (GPS) pseudorange and delta- range measurements during the rest of the flight. The robustness effort has been focused on maintaining filter convergence and performance in the presence of unmodeled error sources. These include unmodeled forces on the vehicle and uncorrected errors on the sensor measurements. Orion uses a single-frequency, non-keyed GPS receiver, so the effects due to signal distortion in Earth's ionosphere and troposphere are present in the raw measurements. Results are presented showing the efforts to compensate for these errors as well as characterize the residual effect for measurement noise tuning. Another robustness tool in use is tuning the residual edit thresholds. The trade-off between noise tuning and edit thresholds is explored in the context of robustness to errors in dynamics models and sensor measurements. Measurement underweighting is also presented as a method of additional robustness when processing highly accurate measurements in the presence of large filter uncertainties.

Robust estimation for class averaging in cryo-EM Single Particle Reconstruction.

PubMed

Huang, Chenxi; Tagare, Hemant D

2014-01-01

Single Particle Reconstruction (SPR) for Cryogenic Electron Microscopy (cryo-EM) aligns and averages the images extracted from micrographs to improve the Signal-to-Noise ratio (SNR). Outliers compromise the fidelity of the averaging. We propose a robust cross-correlation-like w-estimator for combating the effect of outliers on the average images in cryo-EM. The estimator accounts for the natural variation of signal contrast among the images and eliminates the need for a threshold for outlier rejection. We show that the influence function of our estimator is asymptotically bounded. Evaluations of the estimator on simulated and real cryo-EM images show good performance in the presence of outliers.

Light field reconstruction robust to signal dependent noise

NASA Astrophysics Data System (ADS)

Ren, Kun; Bian, Liheng; Suo, Jinli; Dai, Qionghai

2014-11-01

Capturing four dimensional light field data sequentially using a coded aperture camera is an effective approach but suffers from low signal noise ratio. Although multiplexing can help raise the acquisition quality, noise is still a big issue especially for fast acquisition. To address this problem, this paper proposes a noise robust light field reconstruction method. Firstly, scene dependent noise model is studied and incorporated into the light field reconstruction framework. Then, we derive an optimization algorithm for the final reconstruction. We build a prototype by hacking an off-the-shelf camera for data capturing and prove the concept. The effectiveness of this method is validated with experiments on the real captured data.

The 32nd CDC: System identification using interval dynamic models

NASA Technical Reports Server (NTRS)

Keel, L. H.; Lew, J. S.; Bhattacharyya, S. P.

1992-01-01

Motivated by the recent explosive development of results in the area of parametric robust control, a new technique to identify a family of uncertain systems is identified. The new technique takes the frequency domain input and output data obtained from experimental test signals and produces an 'interval transfer function' that contains the complete frequency domain behavior with respect to the test signals. This interval transfer function is one of the key concepts in the parametric robust control approach and identification with such an interval model allows one to predict the worst case performance and stability margins using recent results on interval systems. The algorithm is illustrated by applying it to an 18 bay Mini-Mast truss structure.

Implementation of Context Aware e-Health Environments Based on Social Sensor Networks

PubMed Central

Aguirre, Erik; Led, Santiago; Lopez-Iturri, Peio; Azpilicueta, Leyre; Serrano, Luís; Falcone, Francisco

2016-01-01

In this work, context aware scenarios applied to e-Health and m-Health in the framework of typical households (urban and rural) by means of deploying Social Sensors will be described. Interaction with end-users and social/medical staff is achieved using a multi-signal input/output device, capable of sensing and transmitting environmental, biomedical or activity signals and information with the aid of a combined Bluetooth and Mobile system platform. The devices, which play the role of Social Sensors, are implemented and tested in order to guarantee adequate service levels in terms of multiple signal processing tasks as well as robustness in relation with the use wireless transceivers and channel variability. Initial tests within a Living Lab environment have been performed in order to validate overall system operation. The results obtained show good acceptance of the proposed system both by end users as well as by medical and social staff, increasing interaction, reducing overall response time and social inclusion levels, with a compact and moderate cost solution that can readily be largely deployed. PMID:26938539

Real-Time Algebraic Derivative Estimations Using a Novel Low-Cost Architecture Based on Reconfigurable Logic

PubMed Central

Morales, Rafael; Rincón, Fernando; Gazzano, Julio Dondo; López, Juan Carlos

2014-01-01

Time derivative estimation of signals plays a very important role in several fields, such as signal processing and control engineering, just to name a few of them. For that purpose, a non-asymptotic algebraic procedure for the approximate estimation of the system states is used in this work. The method is based on results from differential algebra and furnishes some general formulae for the time derivatives of a measurable signal in which two algebraic derivative estimators run simultaneously, but in an overlapping fashion. The algebraic derivative algorithm presented in this paper is computed online and in real-time, offering high robustness properties with regard to corrupting noises, versatility and ease of implementation. Besides, in this work, we introduce a novel architecture to accelerate this algebraic derivative estimator using reconfigurable logic. The core of the algorithm is implemented in an FPGA, improving the speed of the system and achieving real-time performance. Finally, this work proposes a low-cost platform for the integration of hardware in the loop in MATLAB. PMID:24859033

Perceptually controlled doping for audio source separation

NASA Astrophysics Data System (ADS)

Mahé, Gaël; Nadalin, Everton Z.; Suyama, Ricardo; Romano, João MT

2014-12-01

The separation of an underdetermined audio mixture can be performed through sparse component analysis (SCA) that relies however on the strong hypothesis that source signals are sparse in some domain. To overcome this difficulty in the case where the original sources are available before the mixing process, the informed source separation (ISS) embeds in the mixture a watermark, which information can help a further separation. Though powerful, this technique is generally specific to a particular mixing setup and may be compromised by an additional bitrate compression stage. Thus, instead of watermarking, we propose a `doping' method that makes the time-frequency representation of each source more sparse, while preserving its audio quality. This method is based on an iterative decrease of the distance between the distribution of the signal and a target sparse distribution, under a perceptual constraint. We aim to show that the proposed approach is robust to audio coding and that the use of the sparsified signals improves the source separation, in comparison with the original sources. In this work, the analysis is made only in instantaneous mixtures and focused on voice sources.

Plants under Stress: Involvement of Auxin and Cytokinin

PubMed Central

Bielach, Agnieszka; Hrtyan, Monika; Tognetti, Vanesa B.

2017-01-01

Plant growth and development are critically influenced by unpredictable abiotic factors. To survive fluctuating changes in their environments, plants have had to develop robust adaptive mechanisms. The dynamic and complementary actions of the auxin and cytokinin pathways regulate a plethora of developmental processes, and their ability to crosstalk makes them ideal candidates for mediating stress-adaptation responses. Other crucial signaling molecules responsible for the tremendous plasticity observed in plant morphology and in response to abiotic stress are reactive oxygen species (ROS). Proper temporal and spatial distribution of ROS and hormone gradients is crucial for plant survival in response to unfavorable environments. In this regard, the convergence of ROS with phytohormone pathways acts as an integrator of external and developmental signals into systemic responses organized to adapt plants to their environments. Auxin and cytokinin signaling pathways have been studied extensively. Nevertheless, we do not yet understand the impact on plant stress tolerance of the sophisticated crosstalk between the two hormones. Here, we review current knowledge on the function of auxin and cytokinin in redirecting growth induced by abiotic stress in order to deduce their potential points of crosstalk. PMID:28677656

Using self-organizing maps to classify humpback whale song units and quantify their similarity.

PubMed

Allen, Jenny A; Murray, Anita; Noad, Michael J; Dunlop, Rebecca A; Garland, Ellen C

2017-10-01

Classification of vocal signals can be undertaken using a wide variety of qualitative and quantitative techniques. Using east Australian humpback whale song from 2002 to 2014, a subset of vocal signals was acoustically measured and then classified using a Self-Organizing Map (SOM). The SOM created (1) an acoustic dictionary of units representing the song's repertoire, and (2) Cartesian distance measurements among all unit types (SOM nodes). Utilizing the SOM dictionary as a guide, additional song recordings from east Australia were rapidly (manually) transcribed. To assess the similarity in song sequences, the Cartesian distance output from the SOM was applied in Levenshtein distance similarity analyses as a weighting factor to better incorporate unit similarity in the calculation (previously a qualitative process). SOMs provide a more robust and repeatable means of categorizing acoustic signals along with a clear quantitative measurement of sound type similarity based on acoustic features. This method can be utilized for a wide variety of acoustic databases especially those containing very large datasets and can be applied across the vocalization research community to help address concerns surrounding inconsistency in manual classification.

Plants under Stress: Involvement of Auxin and Cytokinin.

PubMed

Bielach, Agnieszka; Hrtyan, Monika; Tognetti, Vanesa B

2017-07-04

Plant growth and development are critically influenced by unpredictable abiotic factors. To survive fluctuating changes in their environments, plants have had to develop robust adaptive mechanisms. The dynamic and complementary actions of the auxin and cytokinin pathways regulate a plethora of developmental processes, and their ability to crosstalk makes them ideal candidates for mediating stress-adaptation responses. Other crucial signaling molecules responsible for the tremendous plasticity observed in plant morphology and in response to abiotic stress are reactive oxygen species (ROS). Proper temporal and spatial distribution of ROS and hormone gradients is crucial for plant survival in response to unfavorable environments. In this regard, the convergence of ROS with phytohormone pathways acts as an integrator of external and developmental signals into systemic responses organized to adapt plants to their environments. Auxin and cytokinin signaling pathways have been studied extensively. Nevertheless, we do not yet understand the impact on plant stress tolerance of the sophisticated crosstalk between the two hormones. Here, we review current knowledge on the function of auxin and cytokinin in redirecting growth induced by abiotic stress in order to deduce their potential points of crosstalk.

The interdependence of the Rho GTPases and apicobasal cell polarity.

PubMed

Mack, Natalie Ann; Georgiou, Marios

2014-01-01

Signaling via the Rho GTPases provides crucial regulation of numerous cell polarization events, including apicobasal (AB) polarity, polarized cell migration, polarized cell division and neuronal polarity. Here we review the relationships between the Rho family GTPases and epithelial AB polarization events, focusing on the 3 best-characterized members: Rho, Rac and Cdc42. We discuss a multitude of processes that are important for AB polarization, including lumen formation, apical membrane specification, cell-cell junction assembly and maintenance, as well as tissue polarity. Our discussions aim to highlight the immensely complex regulatory mechanisms that encompass Rho GTPase signaling during AB polarization. More specifically, in this review we discuss several emerging common themes, that include: 1) the need for Rho GTPase activities to be carefully balanced in both a spatial and temporal manner through a multitude of mechanisms; 2) the existence of signaling feedback loops and crosstalk to create robust cellular responses; and 3) the frequent multifunctionality that exists among AB polarity regulators. Regarding this latter theme, we provide further discussion of the potential plasticity of the cell polarity machinery and as a result the possible implications for human disease.

Finding Optimal Apertures in Kepler Data

NASA Astrophysics Data System (ADS)

Smith, Jeffrey C.; Morris, Robert L.; Jenkins, Jon M.; Bryson, Stephen T.; Caldwell, Douglas A.; Girouard, Forrest R.

2016-12-01

With the loss of two spacecraft reaction wheels precluding further data collection for the Kepler primary mission, even greater pressure is placed on the processing pipeline to eke out every last transit signal in the data. To that end, we have developed a new method to optimize the Kepler Simple Aperture Photometry (SAP) photometric apertures for both planet detection and minimization of systematic effects. The approach uses a per cadence modeling of the raw pixel data and then performs an aperture optimization based on signal-to-noise ratio and the Kepler Combined Differential Photometric Precision (CDPP), which is a measure of the noise over the duration of a reference transit signal. We have found the new apertures to be superior to the previous Kepler apertures. We can now also find a per cadence flux fraction in aperture and crowding metric. The new approach has also been proven to be robust at finding apertures in K2 data that help mitigate the larger motion-induced systematics in the photometry. The method further allows us to identify errors in the Kepler and K2 input catalogs.

Implementation of Context Aware e-Health Environments Based on Social Sensor Networks.

PubMed

Aguirre, Erik; Led, Santiago; Lopez-Iturri, Peio; Azpilicueta, Leyre; Serrano, Luís; Falcone, Francisco

2016-03-01

In this work, context aware scenarios applied to e-Health and m-Health in the framework of typical households (urban and rural) by means of deploying Social Sensors will be described. Interaction with end-users and social/medical staff is achieved using a multi-signal input/output device, capable of sensing and transmitting environmental, biomedical or activity signals and information with the aid of a combined Bluetooth and Mobile system platform. The devices, which play the role of Social Sensors, are implemented and tested in order to guarantee adequate service levels in terms of multiple signal processing tasks as well as robustness in relation with the use wireless transceivers and channel variability. Initial tests within a Living Lab environment have been performed in order to validate overall system operation. The results obtained show good acceptance of the proposed system both by end users as well as by medical and social staff, increasing interaction, reducing overall response time and social inclusion levels, with a compact and moderate cost solution that can readily be largely deployed.

Peptidoglycan-Sensing Receptors Trigger the Formation of Functional Amyloids of the Adaptor Protein Imd to Initiate Drosophila NF-κB Signaling.

PubMed

Kleino, Anni; Ramia, Nancy F; Bozkurt, Gunes; Shen, Yanfang; Nailwal, Himani; Huang, Jing; Napetschnig, Johanna; Gangloff, Monique; Chan, Francis Ka-Ming; Wu, Hao; Li, Jixi; Silverman, Neal

2017-10-17

In the Drosophila immune response, bacterial derived diaminopimelic acid-type peptidoglycan binds the receptors PGRP-LC and PGRP-LE, which through interaction with the adaptor protein Imd leads to activation of the NF-κB homolog Relish and robust antimicrobial peptide gene expression. PGRP-LC, PGRP-LE, and Imd each contain a motif with some resemblance to the RIP Homotypic Interaction Motif (RHIM), a domain found in mammalian RIPK proteins forming functional amyloids during necroptosis. Here we found that despite sequence divergence, these Drosophila cryptic RHIMs formed amyloid fibrils in vitro and in cells. Amyloid formation was required for signaling downstream of Imd, and in contrast to the mammalian RHIMs, was not associated with cell death. Furthermore, amyloid formation constituted a regulatable step and could be inhibited by Pirk, an endogenous feedback regulator of this pathway. Thus, diverse sequence motifs are capable of forming amyloidal signaling platforms, and the formation of these platforms may present a regulatory point in multiple biological processes. Copyright © 2017 Elsevier Inc. All rights reserved.

Visual attention mitigates information loss in small- and large-scale neural codes

PubMed Central

Sprague, Thomas C; Saproo, Sameer; Serences, John T

2015-01-01

Summary The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires processing sensory signals in a manner that protects information about relevant stimuli from degradation. Such selective processing – or selective attention – is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding. PMID:25769502