Computer implemented empirical mode decomposition method, apparatus, and article of manufacture for two-dimensional signals

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2001-01-01

A computer implemented method of processing two-dimensional physical signals includes five basic components and the associated presentation techniques of the results. The first component decomposes the two-dimensional signal into one-dimensional profiles. The second component is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF's) from each profile based on local extrema and/or curvature extrema. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the profiles. In the third component, the IMF's of each profile are then subjected to a Hilbert Transform. The fourth component collates the Hilbert transformed IMF's of the profiles to form a two-dimensional Hilbert Spectrum. A fifth component manipulates the IMF's by, for example, filtering the two-dimensional signal by reconstructing the two-dimensional signal from selected IMF(s).

Understanding Female Receiver Psychology in Reproductive Contexts.

PubMed

Lynch, Kathleen S

2017-10-01

Mate choice decision-making requires four components: sensory, cognitive, motivation, and salience. During the breeding season, the neural mechanisms underlying these components act in concert to radically transform the way a female perceives the social cues around her as well as the way in which cognitive and motivational processes influence her decision to respond to courting males. The role of each of these four components in mate choice responses will be discussed here as well as the brain regions involved in regulating each component. These components are not independent, modular systems. Instead, they are dependent on one another. This review will discuss the many ways in which these components interact and affect one another. The interaction of these components, however, ultimately leads back to a few key neuromodulators that thread motivation, sensory, salience, and cognitive components into a set of inter-dependent processes. These neuromodulators are estrogens and catecholamines. This review will highlight the need to understand estrogens in reproductive contexts not just as simply a 'sexual motivation modulator' or catecholamines as 'cognitive regulators' but as neuromodulators that work together to fully transform a non-breeding female into a completely reproductive female displaying: heightened sexual interest in courting males, greater arousal and selective attention toward courtship signals, improved signal detection and discrimination abilities, enhanced contextual signal memory, and increased motivation to respond to signals assigned incentive salience. The aim of this review is to build a foundation in which to understand the brain regions associated with cognitive, sensory, motivational, and signal salience not as independently acting systems but as a set of interacting processes that function together in a context-appropriate manner. © The Author 2017. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

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

PubMed

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

2011-01-01

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

Time-frequency analysis of time-varying modulated signals based on improved energy separation by iterative generalized demodulation

NASA Astrophysics Data System (ADS)

Feng, Zhipeng; Chu, Fulei; Zuo, Ming J.

2011-03-01

Energy separation algorithm is good at tracking instantaneous changes in frequency and amplitude of modulated signals, but it is subject to the constraints of mono-component and narrow band. In most cases, time-varying modulated vibration signals of machinery consist of multiple components, and have so complicated instantaneous frequency trajectories on time-frequency plane that they overlap in frequency domain. For such signals, conventional filters fail to obtain mono-components of narrow band, and their rectangular decomposition of time-frequency plane may split instantaneous frequency trajectories thus resulting in information loss. Regarding the advantage of generalized demodulation method in decomposing multi-component signals into mono-components, an iterative generalized demodulation method is used as a preprocessing tool to separate signals into mono-components, so as to satisfy the requirements by energy separation algorithm. By this improvement, energy separation algorithm can be generalized to a broad range of signals, as long as the instantaneous frequency trajectories of signal components do not intersect on time-frequency plane. Due to the good adaptability of energy separation algorithm to instantaneous changes in signals and the mono-component decomposition nature of generalized demodulation, the derived time-frequency energy distribution has fine resolution and is free from cross term interferences. The good performance of the proposed time-frequency analysis is illustrated by analyses of a simulated signal and the on-site recorded nonstationary vibration signal of a hydroturbine rotor during a shut-down transient process, showing that it has potential to analyze time-varying modulated signals of multi-components.

Method of detecting system function by measuring frequency response

DOEpatents

Morrison, John L.; Morrison, William H.

2008-07-01

Real time battery impedance spectrum is acquired using one time record, Compensated Synchronous Detection (CSD). This parallel method enables battery diagnostics. The excitation current to a test battery is a sum of equal amplitude sin waves of a few frequencies spread over range of interest. The time profile of this signal has duration that is a few periods of the lowest frequency. The voltage response of the battery, average deleted, is the impedance of the battery in the time domain. Since the excitation frequencies are known, synchronous detection processes the time record and each component, both magnitude and phase, is obtained. For compensation, the components, except the one of interest, are reassembled in the time domain. The resulting signal is subtracted from the original signal and the component of interest is synchronously detected. This process is repeated for each component.

Method of Detecting System Function by Measuring Frequency Response

NASA Technical Reports Server (NTRS)

Morrison, John L. (Inventor); Morrison, William H. (Inventor)

2008-01-01

Real time battery impedance spectrum is acquired using one time record, Compensated Synchronous Detection (CSD). This parallel method enables battery diagnostics. The excitation current to a test battery is a sum of equal amplitude sin waves of a few frequencies spread over range of interest. The time profile of this signal has duration that is a few periods of the lowest frequency. The voltage response of the battery, average deleted, is the impedance of the battery in the time domain. Since the excitation frequencies are known, synchronous detection processes the time record and each component, both magnitude and phase, is obtained. For compensation, the components, except the one of interest, are reassembled in the time domain. The resulting signal is subtracted from the original signal and the component of interest is synchronously detected. This process is repeated for each component.

Apparatus and method for temperature correction and expanded count rate of inorganic scintillation detectors

DOEpatents

Ianakiev, Kiril D [Los Alamos, NM; Hsue, Sin Tao [Santa Fe, NM; Browne, Michael C [Los Alamos, NM; Audia, Jeffrey M [Abiquiu, NM

2006-07-25

The present invention includes an apparatus and corresponding method for temperature correction and count rate expansion of inorganic scintillation detectors. A temperature sensor is attached to an inorganic scintillation detector. The inorganic scintillation detector, due to interaction with incident radiation, creates light pulse signals. A photoreceiver processes the light pulse signals to current signals. Temperature correction circuitry that uses a fast light component signal, a slow light component signal, and the temperature signal from the temperature sensor to corrected an inorganic scintillation detector signal output and expanded the count rate.

Cortical neurons sensitive to combinations of information-bearing elements of biosonar signals in the mustache bat.

PubMed

Suga, N; O'Neill, W E; Manabe, T

1978-05-19

The auditory cortex of the mustache bat, Pteronotus parnellii rubiginosus, is composed of functional divisions which are differently organized to be suited for processing the elements of its biosonar signal according to their biological significance. Unlike the Doppler-shifted-CF (constant frequency) processing area, the area processing the frequency-modulated components does not show clear tonotopic and amplitopic representations, but consists of several clusters of neurons, each of which is sensitive to a particular combination (or combinations) of information-bearing elements of the biosonar signal and echoes. The response properties of neurons in the major clusters indicate that processing of information carried by the frequency-modulated components of echoes is facilitated by the first harmonic of the emitted biosonar signal. The properties of some of these neurons suggest that they are tuned to a target which has a particular cross-sectional area and which is located at a particular distance.

Mover Position Detection for PMTLM Based on Linear Hall Sensors through EKF Processing

PubMed Central

Yan, Leyang; Zhang, Hui; Ye, Peiqing

2017-01-01

Accurate mover position is vital for a permanent magnet tubular linear motor (PMTLM) control system. In this paper, two linear Hall sensors are utilized to detect the mover position. However, Hall sensor signals contain third-order harmonics, creating errors in mover position detection. To filter out the third-order harmonics, a signal processing method based on the extended Kalman filter (EKF) is presented. The limitation of conventional processing method is first analyzed, and then EKF is adopted to detect the mover position. In the EKF model, the amplitude of the fundamental component and the percentage of the harmonic component are taken as state variables, and they can be estimated based solely on the measured sensor signals. Then, the harmonic component can be calculated and eliminated. The proposed method has the advantages of faster convergence, better stability and higher accuracy. Finally, experimental results validate the effectiveness and superiority of the proposed method. PMID:28383505

[Cell signaling pathways interaction in cellular proliferation: Potential target for therapeutic interventionism].

PubMed

Valdespino-Gómez, Víctor Manuel; Valdespino-Castillo, Patricia Margarita; Valdespino-Castillo, Víctor Edmundo

2015-01-01

Nowadays, cellular physiology is best understood by analysing their interacting molecular components. Proteins are the major components of the cells. Different proteins are organised in the form of functional clusters, pathways or networks. These molecules are ordered in clusters of receptor molecules of extracellular signals, transducers, sensors and biological response effectors. The identification of these intracellular signaling pathways in different cellular types has required a long journey of experimental work. More than 300 intracellular signaling pathways have been identified in human cells. They participate in cell homeostasis processes for structural and functional maintenance. Some of them participate simultaneously or in a nearly-consecutive progression to generate a cellular phenotypic change. In this review, an analysis is performed on the main intracellular signaling pathways that take part in the cellular proliferation process, and the potential use of some components of these pathways as target for therapeutic interventionism are also underlined. Copyright © 2015 Academia Mexicana de Cirugía A.C. Published by Masson Doyma México S.A. All rights reserved.

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

DOEpatents

Jiles, D.C.

1991-04-16

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

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

DOEpatents

Jiles, David C.

1991-04-16

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

Properties of an improved Gabor wavelet transform and its applications to seismic signal processing and interpretation

NASA Astrophysics Data System (ADS)

Ji, Zhan-Huai; Yan, Sheng-Gang

2017-12-01

This paper presents an analytical study of the complete transform of improved Gabor wavelets (IGWs), and discusses its application to the processing and interpretation of seismic signals. The complete Gabor wavelet transform has the following properties. First, unlike the conventional transform, the improved Gabor wavelet transform (IGWT) maps time domain signals to the time-frequency domain instead of the time-scale domain. Second, the IGW's dominant frequency is fixed, so the transform can perform signal frequency division, where the dominant frequency components of the extracted sub-band signal carry essentially the same information as the corresponding components of the original signal, and the subband signal bandwidth can be regulated effectively by the transform's resolution factor. Third, a time-frequency filter consisting of an IGWT and its inverse transform can accurately locate target areas in the time-frequency field and perform filtering in a given time-frequency range. The complete IGW transform's properties are investigated using simulation experiments and test cases, showing positive results for seismic signal processing and interpretation, such as enhancing seismic signal resolution, permitting signal frequency division, and allowing small faults to be identified.

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Method and apparatus for monitoring characteristics of a flow path having solid components flowing therethrough

DOEpatents

Hoskinson, Reed L [Rigby, ID; Svoboda, John M [Idaho Falls, ID; Bauer, William F [Idaho Falls, ID; Elias, Gracy [Idaho Falls, ID

2008-05-06

A method and apparatus is provided for monitoring a flow path having plurality of different solid components flowing therethrough. For example, in the harvesting of a plant material, many factors surrounding the threshing, separating or cleaning of the plant material and may lead to the inadvertent inclusion of the component being selectively harvested with residual plant materials being discharged or otherwise processed. In accordance with the present invention the detection of the selectively harvested component within residual materials may include the monitoring of a flow path of such residual materials by, for example, directing an excitation signal toward of flow path of material and then detecting a signal initiated by the presence of the selectively harvested component responsive to the excitation signal. The detected signal may be used to determine the presence or absence of a selected plant component within the flow path of residual materials.

Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

PubMed

Tam, Wing-Kin; Yang, Zhi

2018-05-01

Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals.

PubMed

Bashashati, Ali; Fatourechi, Mehrdad; Ward, Rabab K; Birch, Gary E

2007-06-01

Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention?

TOPICAL REVIEW: A survey of signal processing algorithms in brain computer interfaces based on electrical brain signals

NASA Astrophysics Data System (ADS)

Bashashati, Ali; Fatourechi, Mehrdad; Ward, Rabab K.; Birch, Gary E.

2007-06-01

Brain computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention?

``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

Tool Condition Monitoring in Micro-End Milling using wavelets

NASA Astrophysics Data System (ADS)

Dubey, N. K.; Roushan, A.; Rao, U. S.; Sandeep, K.; Patra, K.

2018-04-01

In this work, Tool Condition Monitoring (TCM) strategy is developed for micro-end milling of titanium alloy and mild steel work-pieces. Full immersion slot milling experiments are conducted using a solid tungsten carbide end mill for more than 1900 s to have reasonable amount of tool wear. During the micro-end milling process, cutting force and vibration signals are acquired using Kistler piezo-electric 3-component force dynamometer (9256C2) and accelerometer (NI cDAQ-9188) respectively. The force components and the vibration signals are processed using Discrete Wavelet Transformation (DWT) in both time and frequency window. 5-level wavelet packet decomposition using Db-8 wavelet is carried out and the detailed coefficients D1 to D5 for each of the signals are obtained. The results of the wavelet transformation are correlated with the tool wear. In case of vibration signals, de-noising is done for higher frequency components (D1) and force signals were de-noised for lower frequency components (D5). Increasing value of MAD (Mean Absolute Deviation) of the detail coefficients for successive channels depicted tool wear. The predictions of the tool wear are confirmed from the actual wear observed in the SEM of the worn tool.

Analysis of 3D Subharmonic Ultrasound Signals from Patients with Known Breast Masses for Lesion Differentiation

DTIC Science & Technology

2012-10-01

separate image processing course were attended and this programming language will be used for the research component of this project. Subharmonic...4 5 BODY ...lesions. 5 BODY 5.1 Training Component The training component of this research has been split into breast imaging and image processing arms

Analysis of radiofrequency discharges in plasma

DOEpatents

Kumar, Devendra; McGlynn, Sean P.

1992-01-01

Separation of laser optogalvanic signals in plasma into two components: (1) an ionization rate change component, and (2) a photoacoustic mediated component. This separation of components may be performed even when the two components overlap in time, by measuring time-resolved laser optogalvanic signals in an rf discharge plasma as the rf frequency is varied near the electrical resonance peak of the plasma and associated driving/detecting circuits. A novel spectrometer may be constructed to make these measurements. Such a spectrometer would be useful in better understanding and controlling such processes as plasma etching and plasma deposition.

Perceived Synchrony of Frog Multimodal Signal Components Is Influenced by Content and Order.

PubMed

Taylor, Ryan C; Page, Rachel A; Klein, Barrett A; Ryan, Michael J; Hunter, Kimberly L

2017-10-01

Multimodal signaling is common in communication systems. Depending on the species, individual signal components may be produced synchronously as a result of physiological constraint (fixed) or each component may be produced independently (fluid) in time. For animals that rely on fixed signals, a basic prediction is that asynchrony between the components should degrade the perception of signal salience, reducing receiver response. Male túngara frogs, Physalaemus pustulosus, produce a fixed multisensory courtship signal by vocalizing with two call components (whines and chucks) and inflating a vocal sac (visual component). Using a robotic frog, we tested female responses to variation in the temporal arrangement between acoustic and visual components. When the visual component lagged a complex call (whine + chuck), females largely rejected this asynchronous multisensory signal in favor of the complex call absent the visual cue. When the chuck component was removed from one call, but the robofrog inflation lagged the complex call, females responded strongly to the asynchronous multimodal signal. When the chuck component was removed from both calls, females reversed preference and responded positively to the asynchronous multisensory signal. When the visual component preceded the call, females responded as often to the multimodal signal as to the call alone. These data show that asynchrony of a normally fixed signal does reduce receiver responsiveness. The magnitude and overall response, however, depend on specific temporal interactions between the acoustic and visual components. The sensitivity of túngara frogs to lagging visual cues, but not leading ones, and the influence of acoustic signal content on the perception of visual asynchrony is similar to those reported in human psychophysics literature. Virtually all acoustically communicating animals must conduct auditory scene analyses and identify the source of signals. Our data suggest that some basic audiovisual neural integration processes may be at work in the vertebrate brain. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology 2017. This work is written by US Government employees and is in the public domain in the US.

Psychoacoustic processing of test signals

NASA Astrophysics Data System (ADS)

Kadlec, Frantisek

2003-10-01

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

Elementary signaling modes predict the essentiality of signal transduction network components

PubMed Central

2011-01-01

Background Understanding how signals propagate through signaling pathways and networks is a central goal in systems biology. Quantitative dynamic models help to achieve this understanding, but are difficult to construct and validate because of the scarcity of known mechanistic details and kinetic parameters. Structural and qualitative analysis is emerging as a feasible and useful alternative for interpreting signal transduction. Results In this work, we present an integrative computational method for evaluating the essentiality of components in signaling networks. This approach expands an existing signaling network to a richer representation that incorporates the positive or negative nature of interactions and the synergistic behaviors among multiple components. Our method simulates both knockout and constitutive activation of components as node disruptions, and takes into account the possible cascading effects of a node's disruption. We introduce the concept of elementary signaling mode (ESM), as the minimal set of nodes that can perform signal transduction independently. Our method ranks the importance of signaling components by the effects of their perturbation on the ESMs of the network. Validation on several signaling networks describing the immune response of mammals to bacteria, guard cell abscisic acid signaling in plants, and T cell receptor signaling shows that this method can effectively uncover the essentiality of components mediating a signal transduction process and results in strong agreement with the results of Boolean (logical) dynamic models and experimental observations. Conclusions This integrative method is an efficient procedure for exploratory analysis of large signaling and regulatory networks where dynamic modeling or experimental tests are impractical. Its results serve as testable predictions, provide insights into signal transduction and regulatory mechanisms and can guide targeted computational or experimental follow-up studies. The source codes for the algorithms developed in this study can be found at http://www.phys.psu.edu/~ralbert/ESM. PMID:21426566

Bypassing the Limits of Ll Regularization: Convex Sparse Signal Processing Using Non-Convex Regularization

NASA Astrophysics Data System (ADS)

Parekh, Ankit

Sparsity has become the basis of some important signal processing methods over the last ten years. Many signal processing problems (e.g., denoising, deconvolution, non-linear component analysis) can be expressed as inverse problems. Sparsity is invoked through the formulation of an inverse problem with suitably designed regularization terms. The regularization terms alone encode sparsity into the problem formulation. Often, the ℓ1 norm is used to induce sparsity, so much so that ℓ1 regularization is considered to be `modern least-squares'. The use of ℓ1 norm, as a sparsity-inducing regularizer, leads to a convex optimization problem, which has several benefits: the absence of extraneous local minima, well developed theory of globally convergent algorithms, even for large-scale problems. Convex regularization via the ℓ1 norm, however, tends to under-estimate the non-zero values of sparse signals. In order to estimate the non-zero values more accurately, non-convex regularization is often favored over convex regularization. However, non-convex regularization generally leads to non-convex optimization, which suffers from numerous issues: convergence may be guaranteed to only a stationary point, problem specific parameters may be difficult to set, and the solution is sensitive to the initialization of the algorithm. The first part of this thesis is aimed toward combining the benefits of non-convex regularization and convex optimization to estimate sparse signals more effectively. To this end, we propose to use parameterized non-convex regularizers with designated non-convexity and provide a range for the non-convex parameter so as to ensure that the objective function is strictly convex. By ensuring convexity of the objective function (sum of data-fidelity and non-convex regularizer), we can make use of a wide variety of convex optimization algorithms to obtain the unique global minimum reliably. The second part of this thesis proposes a non-linear signal decomposition technique for an important biomedical signal processing problem: the detection of sleep spindles and K-complexes in human sleep electroencephalography (EEG). We propose a non-linear model for the EEG consisting of three components: (1) a transient (sparse piecewise constant) component, (2) a low-frequency component, and (3) an oscillatory component. The oscillatory component admits a sparse time-frequency representation. Using a convex objective function, we propose a fast non-linear optimization algorithm to estimate the three components in the proposed signal model. The low-frequency and oscillatory components are then used to estimate the K-complexes and sleep spindles respectively. The proposed detection method is shown to outperform several state-of-the-art automated sleep spindles detection methods.

Defense Applications of Signal Processing

DTIC Science & Technology

1999-08-27

class of multiscale autoregressive moving average (MARMA) processes. These are generalisations of ARMA models in time series analysis , and they contain...including the two theoretical sinusoidal components. Analysis of the amplitude and frequency time series provided some novel insight into the real...communication channels, underwater acoustic signals, radar systems , economic time series and biomedical signals [7]. The alpha stable (aS) distribution has

A fully reconfigurable photonic integrated signal processor

NASA Astrophysics Data System (ADS)

Liu, Weilin; Li, Ming; Guzzon, Robert S.; Norberg, Erik J.; Parker, John S.; Lu, Mingzhi; Coldren, Larry A.; Yao, Jianping

2016-03-01

Photonic signal processing has been considered a solution to overcome the inherent electronic speed limitations. Over the past few years, an impressive range of photonic integrated signal processors have been proposed, but they usually offer limited reconfigurability, a feature highly needed for the implementation of large-scale general-purpose photonic signal processors. Here, we report and experimentally demonstrate a fully reconfigurable photonic integrated signal processor based on an InP-InGaAsP material system. The proposed photonic signal processor is capable of performing reconfigurable signal processing functions including temporal integration, temporal differentiation and Hilbert transformation. The reconfigurability is achieved by controlling the injection currents to the active components of the signal processor. Our demonstration suggests great potential for chip-scale fully programmable all-optical signal processing.

Optical Profilometers Using Adaptive Signal Processing

NASA Technical Reports Server (NTRS)

Hall, Gregory A.; Youngquist, Robert; Mikhael, Wasfy

2006-01-01

A method of adaptive signal processing has been proposed as the basis of a new generation of interferometric optical profilometers for measuring surfaces. The proposed profilometers would be portable, hand-held units. Sizes could be thus reduced because the adaptive-signal-processing method would make it possible to substitute lower-power coherent light sources (e.g., laser diodes) for white light sources and would eliminate the need for most of the optical components of current white-light profilometers. The adaptive-signal-processing method would make it possible to attain scanning ranges of the order of decimeters in the proposed profilometers.

Time-frequency representation of a highly nonstationary signal via the modified Wigner distribution

NASA Technical Reports Server (NTRS)

Zoladz, T. F.; Jones, J. H.; Jong, J.

1992-01-01

A new signal analysis technique called the modified Wigner distribution (MWD) is presented. The new signal processing tool has been very successful in determining time frequency representations of highly non-stationary multicomponent signals in both simulations and trials involving actual Space Shuttle Main Engine (SSME) high frequency data. The MWD departs from the classic Wigner distribution (WD) in that it effectively eliminates the cross coupling among positive frequency components in a multiple component signal. This attribute of the MWD, which prevents the generation of 'phantom' spectral peaks, will undoubtedly increase the utility of the WD for real world signal analysis applications which more often than not involve multicomponent signals.

Method for enhancing signals transmitted over optical fibers

DOEpatents

Ogle, James W.; Lyons, Peter B.

1983-01-01

A method for spectral equalization of high frequency spectrally broadband signals transmitted through an optical fiber. The broadband signal input is first dispersed by a grating. Narrow spectral components are collected into an array of equalizing fibers. The fibers serve as optical delay lines compensating for material dispersion of each spectral component during transmission. The relative lengths of the individual equalizing fibers are selected to compensate for such prior dispersion. The output of the equalizing fibers couple the spectrally equalized light onto a suitable detector for subsequent electronic processing of the enhanced broadband signal.

Seismic random noise attenuation method based on empirical mode decomposition of Hausdorff dimension

NASA Astrophysics Data System (ADS)

Yan, Z.; Luan, X.

2017-12-01

Introduction Empirical mode decomposition (EMD) is a noise suppression algorithm by using wave field separation, which is based on the scale differences between effective signal and noise. However, since the complexity of the real seismic wave field results in serious aliasing modes, it is not ideal and effective to denoise with this method alone. Based on the multi-scale decomposition characteristics of the signal EMD algorithm, combining with Hausdorff dimension constraints, we propose a new method for seismic random noise attenuation. First of all, We apply EMD algorithm adaptive decomposition of seismic data and obtain a series of intrinsic mode function (IMF)with different scales. Based on the difference of Hausdorff dimension between effectively signals and random noise, we identify IMF component mixed with random noise. Then we use threshold correlation filtering process to separate the valid signal and random noise effectively. Compared with traditional EMD method, the results show that the new method of seismic random noise attenuation has a better suppression effect. The implementation process The EMD algorithm is used to decompose seismic signals into IMF sets and analyze its spectrum. Since most of the random noise is high frequency noise, the IMF sets can be divided into three categories: the first category is the effective wave composition of the larger scale; the second category is the noise part of the smaller scale; the third category is the IMF component containing random noise. Then, the third kind of IMF component is processed by the Hausdorff dimension algorithm, and the appropriate time window size, initial step and increment amount are selected to calculate the Hausdorff instantaneous dimension of each component. The dimension of the random noise is between 1.0 and 1.05, while the dimension of the effective wave is between 1.05 and 2.0. On the basis of the previous steps, according to the dimension difference between the random noise and effective signal, we extracted the sample points, whose fractal dimension value is less than or equal to 1.05 for the each IMF components, to separate the residual noise. Using the IMF components after dimension filtering processing and the effective wave IMF components after the first selection for reconstruction, we can obtained the results of de-noising.

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

NASA Astrophysics Data System (ADS)

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

2015-05-01

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

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

PubMed

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

2014-01-01

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

Analysis of radiofrequency discharges in plasma

DOEpatents

Kumar, D.; McGlynn, S.P.

1992-08-04

Separation of laser optogalvanic signals in plasma into two components: (1) an ionization rate change component, and (2) a photoacoustic mediated component. This separation of components may be performed even when the two components overlap in time, by measuring time-resolved laser optogalvanic signals in an rf discharge plasma as the rf frequency is varied near the electrical resonance peak of the plasma and associated driving/detecting circuits. A novel spectrometer may be constructed to make these measurements. Such a spectrometer would be useful in better understanding and controlling such processes as plasma etching and plasma deposition. 15 figs.

Modified ADALINE algorithm for harmonic estimation and selective harmonic elimination in inverters

NASA Astrophysics Data System (ADS)

Vasumathi, B.; Moorthi, S.

2011-11-01

In digital signal processing, algorithms are very well developed for the estimation of harmonic components. In power electronic applications, an objective like fast response of a system is of primary importance. An effective method for the estimation of instantaneous harmonic components, along with conventional harmonic elimination technique, is presented in this article. The primary function is to eliminate undesirable higher harmonic components from the selected signal (current or voltage) and it requires only the knowledge of the frequency of the component to be eliminated. A signal processing technique using modified ADALINE algorithm has been proposed for harmonic estimation. The proposed method stays effective as it converges to a minimum error and brings out a finer estimation. A conventional control based on pulse width modulation for selective harmonic elimination is used to eliminate harmonic components after its estimation. This method can be applied to a wide range of equipment. The validity of the proposed method to estimate and eliminate voltage harmonics is proved with a dc/ac inverter as a simulation example. Then, the results are compared with existing ADALINE algorithm for illustrating its effectiveness.

Investigation of digital encoding techniques for television transmission

NASA Technical Reports Server (NTRS)

Schilling, D. L.

1983-01-01

Composite color television signals are sampled at four times the color subcarrier and transformed using intraframe two dimensional Walsh functions. It is shown that by properly sampling a composite color signal and employing a Walsh transform the YIQ time signals which sum to produce the composite color signal can be represented, in the transform domain, by three component signals in space. By suitably zonal quantizing the transform coefficients, the YIQ signals can be processed independently to achieve data compression and obtain the same results as component coding. Computer simulations of three bandwidth compressors operating at 1.09, 1.53 and 1.8 bits/ sample are presented. The above results can also be applied to the PAL color system.

P-code enhanced method for processing encrypted GPS signals without knowledge of the encryption code

NASA Technical Reports Server (NTRS)

Young, Lawrence E. (Inventor); Meehan, Thomas K. (Inventor); Thomas, Jr., Jess Brooks (Inventor)

2000-01-01

In the preferred embodiment, an encrypted GPS signal is down-converted from RF to baseband to generate two quadrature components for each RF signal (L1 and L2). Separately and independently for each RF signal and each quadrature component, the four down-converted signals are counter-rotated with a respective model phase, correlated with a respective model P code, and then successively summed and dumped over presum intervals substantially coincident with chips of the respective encryption code. Without knowledge of the encryption-code signs, the effect of encryption-code sign flips is then substantially reduced by selected combinations of the resulting presums between associated quadrature components for each RF signal, separately and independently for the L1 and L2 signals. The resulting combined presums are then summed and dumped over longer intervals and further processed to extract amplitude, phase and delay for each RF signal. Precision of the resulting phase and delay values is approximately four times better than that obtained from straight cross-correlation of L1 and L2. This improved method provides the following options: separate and independent tracking of the L1-Y and L2-Y channels; separate and independent measurement of amplitude, phase and delay L1-Y channel; and removal of the half-cycle ambiguity in L1-Y and L2-Y carrier phase.

Plasmonic integrated circuits comprising metal waveguides, multiplexer/demultiplexer, detectors, and logic circuits on a silicon substrate

NASA Astrophysics Data System (ADS)

Fukuda, M.; Ota, M.; Sumimura, A.; Okahisa, S.; Ito, M.; Ishii, Y.; Ishiyama, T.

2017-05-01

A plasmonic integrated circuit configuration comprising plasmonic and electronic components is presented and the feasibility for high-speed signal processing applications is discussed. In integrated circuits, plasmonic signals transmit data at high transfer rates with light velocity. Plasmonic and electronic components such as wavelength-divisionmultiplexing (WDM) networks comprising metal wires, plasmonic multiplexers/demultiplexers, and crossing metal wires are connected via plasmonic waveguides on the nanometer or micrometer scales. To merge plasmonic and electronic components, several types of plasmonic components were developed. To ensure that the plasmonic components could be easily fabricated and monolithically integrated onto a silicon substrate using silicon complementary metal-oxide-semiconductor (CMOS)-compatible processes, the components were fabricated on a Si substrate and made from silicon, silicon oxides, and metal; no other materials were used in the fabrication. The plasmonic components operated in the 1300- and 1550-nm-wavelength bands, which are typically employed in optical fiber communication systems. The plasmonic logic circuits were formed by patterning a silicon oxide film on a metal film, and the operation as a half adder was confirmed. The computed plasmonic signals can propagate through the plasmonic WDM networks and be connected to electronic integrated circuits at high data-transfer rates.

A microcomputer based frequency-domain processor for laser Doppler anemometry

NASA Technical Reports Server (NTRS)

Horne, W. Clifton; Adair, Desmond

1988-01-01

A prototype multi-channel laser Doppler anemometry (LDA) processor was assembled using a wideband transient recorder and a microcomputer with an array processor for fast Fourier transform (FFT) computations. The prototype instrument was used to acquire, process, and record signals from a three-component wind tunnel LDA system subject to various conditions of noise and flow turbulence. The recorded data was used to evaluate the effectiveness of burst acceptance criteria, processing algorithms, and selection of processing parameters such as record length. The recorded signals were also used to obtain comparative estimates of signal-to-noise ratio between time-domain and frequency-domain signal detection schemes. These comparisons show that the FFT processing scheme allows accurate processing of signals for which the signal-to-noise ratio is 10 to 15 dB less than is practical using counter processors.

Method for enhancing signals transmitted over optical fibers

DOEpatents

Ogle, J.W.; Lyons, P.B.

1981-02-11

A method for spectral equalization of high frequency spectrally broadband signals transmitted through an optical fiber is disclosed. The broadband signal input is first dispersed by a grating. Narrow spectral components are collected into an array of equalizing fibers. The fibers serve as optical delay lines compensating for material dispersion of each spectral component during transmission. The relative lengths of the individual equalizing fibers are selected to compensate for such prior dispersion. The output of the equalizing fibers couple the spectrally equalized light onto a suitable detector for subsequent electronic processing of the enhanced broadband signal.

Low-pass parabolic FFT filter for airborne and satellite lidar signal processing.

PubMed

Jiao, Zhongke; Liu, Bo; Liu, Enhai; Yue, Yongjian

2015-10-14

In order to reduce random errors of the lidar signal inversion, a low-pass parabolic fast Fourier transform filter (PFFTF) was introduced for noise elimination. A compact airborne Raman lidar system was studied, which applied PFFTF to process lidar signals. Mathematics and simulations of PFFTF along with low pass filters, sliding mean filter (SMF), median filter (MF), empirical mode decomposition (EMD) and wavelet transform (WT) were studied, and the practical engineering value of PFFTF for lidar signal processing has been verified. The method has been tested on real lidar signal from Wyoming Cloud Lidar (WCL). Results show that PFFTF has advantages over the other methods. It keeps the high frequency components well and reduces much of the random noise simultaneously for lidar signal processing.

The time course of individual face recognition: A pattern analysis of ERP signals.

PubMed

Nemrodov, Dan; Niemeier, Matthias; Mok, Jenkin Ngo Yin; Nestor, Adrian

2016-05-15

An extensive body of work documents the time course of neural face processing in the human visual cortex. However, the majority of this work has focused on specific temporal landmarks, such as N170 and N250 components, derived through univariate analyses of EEG data. Here, we take on a broader evaluation of ERP signals related to individual face recognition as we attempt to move beyond the leading theoretical and methodological framework through the application of pattern analysis to ERP data. Specifically, we investigate the spatiotemporal profile of identity recognition across variation in emotional expression. To this end, we apply pattern classification to ERP signals both in time, for any single electrode, and in space, across multiple electrodes. Our results confirm the significance of traditional ERP components in face processing. At the same time though, they support the idea that the temporal profile of face recognition is incompletely described by such components. First, we show that signals associated with different facial identities can be discriminated from each other outside the scope of these components, as early as 70ms following stimulus presentation. Next, electrodes associated with traditional ERP components as well as, critically, those not associated with such components are shown to contribute information to stimulus discriminability. And last, the levels of ERP-based pattern discrimination are found to correlate with recognition accuracy across subjects confirming the relevance of these methods for bridging brain and behavior data. Altogether, the current results shed new light on the fine-grained time course of neural face processing and showcase the value of novel methods for pattern analysis to investigating fundamental aspects of visual recognition. Copyright © 2016 Elsevier Inc. All rights reserved.

Signal-processing theory for the TurboRogue receiver

NASA Technical Reports Server (NTRS)

Thomas, J. B.

1995-01-01

Signal-processing theory for the TurboRogue receiver is presented. The signal form is traced from its formation at the GPS satellite, to the receiver antenna, and then through the various stages of the receiver, including extraction of phase and delay. The analysis treats the effects of ionosphere, troposphere, signal quantization, receiver components, and system noise, covering processing in both the 'code mode' when the P code is not encrypted and in the 'P-codeless mode' when the P code is encrypted. As a possible future improvement to the current analog front end, an example of a highly digital front end is analyzed.

Timing Actions to Avoid Refractoriness: A Simple Solution for Streaming Sensory Signals

PubMed Central

Nogueira, Javier; Caputi, Ángel Ariel

2011-01-01

Segmenting self- from allo-generated signals is crucial for active sensory processing. We report a dynamic filter used by South American pulse electric fish to distinguish active electro-sensory signals carried by their own electric discharges from other concomitant electrical stimuli (i.e. communication signals). The filter has a sensory component, consisting of an onset type central electro-sensory neuron, and a motor component, consisting of a change in the fish's discharge rate when allo-generated electrical events occur in temporal proximity to the fish's own discharge. We investigated the sensory component of the filter by in vitro mimicking synaptic inputs occurring during behavioral responses to allo-generated interfering signals. We found that active control of the discharge enhances self-generated over allo-generated responses by forcing allo-generated signals into a central refractory period. This hypothesis was confirmed by field potential recordings in freely discharging fish. Similar sensory-motor mechanisms may also contribute to signal segmentation in other sensory systems. PMID:21789228

In situ X-ray diffraction analysis of (CF x) n batteries: signal extraction by multivariate analysis

DOE PAGES

Rodriguez, Mark A.; Keenan, Michael R.; Nagasubramanian, Ganesan

2007-11-10

In this study, (CF x) n cathode reaction during discharge has been investigated using in situ X-ray diffraction (XRD). Mathematical treatment of the in situ XRD data set was performed using multivariate curve resolution with alternating least squares (MCR–ALS), a technique of multivariate analysis. MCR–ALS analysis successfully separated the relatively weak XRD signal intensity due to the chemical reaction from the other inert cell component signals. The resulting dynamic reaction component revealed the loss of (CF x) n cathode signal together with the simultaneous appearance of LiF by-product intensity. Careful examination of the XRD data set revealed an additional dynamicmore » component which may be associated with the formation of an intermediate compound during the discharge process.« less

Detection of buried mines with seismic sonar

NASA Astrophysics Data System (ADS)

Muir, Thomas G.; Baker, Steven R.; Gaghan, Frederick E.; Fitzpatrick, Sean M.; Hall, Patrick W.; Sheetz, Kraig E.; Guy, Jeremie

2003-10-01

Prior research on seismo-acoustic sonar for detection of buried targets [J. Acoust. Soc. Am. 103, 2333-2343 (1998)] has continued with examination of the target strengths of buried test targets as well as targets of interest, and has also examined detection and confirmatory classification of these, all using arrays of seismic sources and receivers as well as signal processing techniques to enhance target recognition. The target strengths of two test targets (one a steel gas bottle, the other an aluminum powder keg), buried in a sand beach, were examined as a function of internal mass load, to evaluate theory developed for seismic sonar target strength [J. Acoust. Soc. Am. 103, 2344-2353 (1998)]. The detection of buried naval and military targets of interest was achieved with an array of 7 shaker sources and 5, three-axis seismometers, at a range of 5 m. Vector polarization filtering was the main signal processing technique for detection. It capitalizes on the fact that the vertical and horizontal components in Rayleigh wave echoes are 90 deg out of phase, enabling complex variable processing to obtain the imaginary component of the signal power versus time, which is unique to Rayleigh waves. Gabor matrix processing of this signal component was the main technique used to determine whether the target was man-made or just a natural target in the environment. [Work sponsored by ONR.

A data processing method based on tracking light spot for the laser differential confocal component parameters measurement system

NASA Astrophysics Data System (ADS)

Shao, Rongjun; Qiu, Lirong; Yang, Jiamiao; Zhao, Weiqian; Zhang, Xin

2013-12-01

We have proposed the component parameters measuring method based on the differential confocal focusing theory. In order to improve the positioning precision of the laser differential confocal component parameters measurement system (LDDCPMS), the paper provides a data processing method based on tracking light spot. To reduce the error caused by the light point moving in collecting the axial intensity signal, the image centroiding algorithm is used to find and track the center of Airy disk of the images collected by the laser differential confocal system. For weakening the influence of higher harmonic noises during the measurement, Gaussian filter is used to process the axial intensity signal. Ultimately the zero point corresponding to the focus of the objective in a differential confocal system is achieved by linear fitting for the differential confocal axial intensity data. Preliminary experiments indicate that the method based on tracking light spot can accurately collect the axial intensity response signal of the virtual pinhole, and improve the anti-interference ability of system. Thus it improves the system positioning accuracy.

Advanced methods in NDE using machine learning approaches

NASA Astrophysics Data System (ADS)

Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

2018-04-01

Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability prediction based on big data becomes possible, even if components are used in different versions or configurations. This is the promise behind German Industry 4.0.

On Certain New Methodology for Reducing Sensor and Readout Electronics Circuitry Noise in Digital Domain

NASA Technical Reports Server (NTRS)

Kizhner, Semion; Miko, Joseph; Bradley, Damon; Heinzen, Katherine

2008-01-01

NASA Hubble Space Telescope (HST) and upcoming cosmology science missions carry instruments with multiple focal planes populated with many large sensor detector arrays. These sensors are passively cooled to low temperatures for low-level light (L3) and near-infrared (NIR) signal detection, and the sensor readout electronics circuitry must perform at extremely low noise levels to enable new required science measurements. Because we are at the technological edge of enhanced performance for sensors and readout electronics circuitry, as determined by thermal noise level at given temperature in analog domain, we must find new ways of further compensating for the noise in the signal digital domain. To facilitate this new approach, state-of-the-art sensors are augmented at their array hardware boundaries by non-illuminated reference pixels, which can be used to reduce noise attributed to sensors. There are a few proposed methodologies of processing in the digital domain the information carried by reference pixels, as employed by the Hubble Space Telescope and the James Webb Space Telescope Projects. These methods involve using spatial and temporal statistical parameters derived from boundary reference pixel information to enhance the active (non-reference) pixel signals. To make a step beyond this heritage methodology, we apply the NASA-developed technology known as the Hilbert- Huang Transform Data Processing System (HHT-DPS) for reference pixel information processing and its utilization in reconfigurable hardware on-board a spaceflight instrument or post-processing on the ground. The methodology examines signal processing for a 2-D domain, in which high-variance components of the thermal noise are carried by both active and reference pixels, similar to that in processing of low-voltage differential signals and subtraction of a single analog reference pixel from all active pixels on the sensor. Heritage methods using the aforementioned statistical parameters in the digital domain (such as statistical averaging of the reference pixels themselves) zeroes out the high-variance components, and the counterpart components in the active pixels remain uncorrected. This paper describes how the new methodology was demonstrated through analysis of fast-varying noise components using the Hilbert-Huang Transform Data Processing System tool (HHT-DPS) developed at NASA and the high-level programming language MATLAB (Trademark of MathWorks Inc.), as well as alternative methods for correcting for the high-variance noise component, using an HgCdTe sensor data. The NASA Hubble Space Telescope data post-processing, as well as future deep-space cosmology projects on-board instrument data processing from all the sensor channels, would benefit from this effort.

New signal processing technique for density profile reconstruction using reflectometry.

PubMed

Clairet, F; Ricaud, B; Briolle, F; Heuraux, S; Bottereau, C

2011-08-01

Reflectometry profile measurement requires an accurate determination of the plasma reflected signal. Along with a good resolution and a high signal to noise ratio of the phase measurement, adequate data analysis is required. A new data processing based on time-frequency tomographic representation is used. It provides a clearer separation between multiple components and improves isolation of the relevant signals. In this paper, this data processing technique is applied to two sets of signals coming from two different reflectometer devices used on the Tore Supra tokamak. For the standard density profile reflectometry, it improves the initialization process and its reliability, providing a more accurate profile determination in the far scrape-off layer with density measurements as low as 10(16) m(-1). For a second reflectometer, which provides measurements in front of a lower hybrid launcher, this method improves the separation of the relevant plasma signal from multi-reflection processes due to the proximity of the plasma.

Simultaneous estimation of deterministic and fractal stochastic components in non-stationary time series

NASA Astrophysics Data System (ADS)

García, Constantino A.; Otero, Abraham; Félix, Paulo; Presedo, Jesús; Márquez, David G.

2018-07-01

In the past few decades, it has been recognized that 1 / f fluctuations are ubiquitous in nature. The most widely used mathematical models to capture the long-term memory properties of 1 / f fluctuations have been stochastic fractal models. However, physical systems do not usually consist of just stochastic fractal dynamics, but they often also show some degree of deterministic behavior. The present paper proposes a model based on fractal stochastic and deterministic components that can provide a valuable basis for the study of complex systems with long-term correlations. The fractal stochastic component is assumed to be a fractional Brownian motion process and the deterministic component is assumed to be a band-limited signal. We also provide a method that, under the assumptions of this model, is able to characterize the fractal stochastic component and to provide an estimate of the deterministic components present in a given time series. The method is based on a Bayesian wavelet shrinkage procedure that exploits the self-similar properties of the fractal processes in the wavelet domain. This method has been validated over simulated signals and over real signals with economical and biological origin. Real examples illustrate how our model may be useful for exploring the deterministic-stochastic duality of complex systems, and uncovering interesting patterns present in time series.

A portable system for acquiring and removing motion artefact from ECG signals

NASA Astrophysics Data System (ADS)

Griffiths, A.; Das, A.; Fernandes, B.; Gaydecki, P.

2007-07-01

A novel electrocardiograph (ECG) signal acquisition and display system is under development. It is designed for patients ranging from the elderly to athletes. The signals are obtained from electrodes integrated into a vest, amplified, digitally processed and transmitted via Bluetooth to a PC with a Labview ® interface. Digital signal processing is performed to remove movement artefact and electromyographic (EMG) noise, which severely distorts signal morphology and complicates clinical diagnosis. Independent component analysis (ICA) is also used to improve the signal quality. The complete system will integrate the electronics into a single module which will be embedded in the vest.

DigiSeis—A software component for digitizing seismic signals using the PC sound card

NASA Astrophysics Data System (ADS)

Amin Khan, Khalid; Akhter, Gulraiz; Ahmad, Zulfiqar

2012-06-01

An innovative software-based approach to develop an inexpensive experimental seismic recorder is presented. This approach requires no hardware as the built-in PC sound card is used for digitization of seismic signals. DigiSeis, an ActiveX component is developed to capture the digitized seismic signals from the sound card and deliver them to applications for processing and display. A seismic recorder application software SeisWave is developed over this component, which provides real-time monitoring and display of seismic events picked by a pair of external geophones. This recorder can be used as an educational aid for conducting seismic experiments. It can also be connected with suitable seismic sensors to record earthquakes. The software application and the ActiveX component are available for download. This component can be used to develop seismic recording applications according to user specific requirements.

WRKY Transcription Factors: Key Components in Abscisic Acid Signaling

DTIC Science & Technology

2011-01-01

Review article WRKY transcription factors : key components in abscisic acid signalling Deena L. Rushton1, Prateek Tripathi1, Roel C. Rabara1, Jun Lin1...May 2011. *Correspondence (Tel +605 688 5749; fax +605 688 5624; email paul.rushton@sdstate.edu) Keywords: abscisic acid, WRKY transcription factor ...seed germination, drought, abiotic stress. Summary WRKY transcription factors (TFs) are key regulators of many plant processes, including the responses

Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal.

PubMed

Xu, Shanzhi; Hu, Hai; Ji, Linhong; Wang, Peng

2018-02-26

The recorded electroencephalography (EEG) signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA) is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC) and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA) EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.

Transplantation of prokaryotic two-component signaling pathways into mammalian cells.

PubMed

Hansen, Jonathan; Mailand, Erik; Swaminathan, Krishna Kumar; Schreiber, Joerg; Angelici, Bartolomeo; Benenson, Yaakov

2014-11-04

Signaling pathway engineering is a promising route toward synthetic biological circuits. Histidine-aspartate phosphorelays are thought to have evolved in prokaryotes where they form the basis for two-component signaling. Tyrosine-serine-threonine phosphorelays, exemplified by MAP kinase cascades, are predominant in eukaryotes. Recently, a prokaryotic two-component pathway was implemented in a plant species to sense environmental trinitrotoluene. We reasoned that "transplantation" of two-component pathways into mammalian host could provide an orthogonal and diverse toolkit for a variety of signal processing tasks. Here we report that two-component pathways could be partially reconstituted in mammalian cell culture and used for programmable control of gene expression. To enable this reconstitution, coding sequences of histidine kinase (HK) and response regulator (RR) components were codon-optimized for human cells, whereas the RRs were fused with a transactivation domain. Responsive promoters were furnished by fusing DNA binding sites in front of a minimal promoter. We found that coexpression of HKs and their cognate RRs in cultured mammalian cells is necessary and sufficient to strongly induce gene expression even in the absence of pathways' chemical triggers in the medium. Both loss-of-function and constitutive mutants behaved as expected. We further used the two-component signaling pathways to implement two-input logical AND, NOR, and OR gene regulation. Thus, two-component systems can be applied in different capacities in mammalian cells and their components can be used for large-scale synthetic gene circuits.

Integration of Proteomic, Transcriptional, and Interactome Data Reveals Hidden Signaling Components

PubMed Central

Huang, Shao-shan Carol; Fraenkel, Ernest

2009-01-01

Cellular signaling and regulatory networks underlie fundamental biological processes such as growth, differentiation, and response to the environment. Although there are now various high-throughput methods for studying these processes, knowledge of them remains fragmentary. Typically, the vast majority of hits identified by transcriptional, proteomic, and genetic assays lie outside of the expected pathways. These unexpected components of the cellular response are often the most interesting, because they can provide new insights into biological processes and potentially reveal new therapeutic approaches. However, they are also the most difficult to interpret. We present a technique, based on the Steiner tree problem, that uses previously reported protein-protein and protein-DNA interactions to determine how these hits are organized into functionally coherent pathways, revealing many components of the cellular response that are not readily apparent in the original data. Applied simultaneously to phosphoproteomic and transcriptional data for the yeast pheromone response, it identifies changes in diverse cellular processes that extend far beyond the expected pathways. PMID:19638617

Microscopy and bioinformatic analyses of lipid metabolism implicate a sporophytic signaling network supporting pollen development in Arabidopsis.

PubMed

Wang, Yixing; Wu, Hong; Yang, Ming

2008-07-01

The Arabidopsis sporophytic tapetum undergoes a programmed degeneration process to secrete lipid and other materials to support pollen development. However, the molecular mechanism regulating the degeneration process is unknown. To gain insight into this molecular mechanism, we first determined that the most critical period for tapetal secretion to support pollen development is from the vacuolate microspore stage to the early binucleate pollen stage. We then analyzed the expression of enzymes responsible for lipid biosynthesis and degradation with available in-silico data. The genes for these enzymes that are expressed in the stamen but not in the concurrent uninucleate microspore and binucleate pollen are of particular interest, as they presumably hold the clues to unique molecular processes in the sporophytic tissues compared to the gametophytic tissue. No gene for lipid biosynthesis but a single gene encoding a patatin-like protein likely for lipid mobilization was identified based on the selection criterion. A search for genes co-expressed with this gene identified additional genes encoding typical signal transduction components such as a leucine-rich repeat receptor kinase, an extra-large G-protein, other protein kinases, and transcription factors. In addition, proteases, cell wall degradation enzymes, and other proteins were also identified. These proteins thus may be components of a signaling network leading to degradation of a broad range of cellular components. Since a broad range of degradation activities is expected to occur only in the tapetal degeneration process at this stage in the stamen, it is further hypothesized that the signaling network acts in the tapetal degeneration process.

Development of performance criteria for advanced Viking seismic experiments

NASA Technical Reports Server (NTRS)

1972-01-01

The characteristics and requirements of the seismic instrument for mapping the internal structure of the planet Mars are briefly described. The types of signals expected to exist are microseismic background generated by wind and pressure variations and thermal effects, disturbances of or in the landed vehicle, signals caused by faulting and volcanic activity, and signals due to meteoritic impacts. The advanced instrument package should include a short-period vertical component system, a long-period or wide-band 3-component system, a high frequency vertical component system, and a system for detection and rejection of lander noises. The Viking '75, Surveyor, and Apollo systems are briefly described as potential instruments to be considered for modification. Data processing and control systems are also summarized.

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.

CNK1: A New Component in the Control of Insulin Signaling | Center for Cancer Research

Cancer.gov

The control of insulin release after a meal to mediate blood-glucose levels is an essential step in energy regulation. An external signal activates molecular pathways within the cell to control this process.

Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

PubMed Central

Huang, Chih-Sheng; Yang, Wen-Yu; Chuang, Chun-Hsiang; Wang, Yu-Kai

2018-01-01

Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research. PMID:29599950

Complex Signal Kurtosis and Independent Component Analysis for Wideband Radio Frequency Interference Detection

NASA Technical Reports Server (NTRS)

Schoenwald, Adam; Mohammed, Priscilla; Bradley, Damon; Piepmeier, Jeffrey; Wong, Englin; Gholian, Armen

2016-01-01

Radio-frequency interference (RFI) has negatively implicated scientific measurements across a wide variation passive remote sensing satellites. This has been observed in the L-band radiometers SMOS, Aquarius and more recently, SMAP [1, 2]. RFI has also been observed at higher frequencies such as K band [3]. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements [4]. This work explores the use of ICA (Independent Component Analysis) as a blind source separation technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

Instrument to synchronize Thomson scattering diagnostic measurements with MHD acitivity in a tokamak

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

Wintenberg, A.L.

1985-04-01

An instrument to synchronize the firing of a ruby laser for a Thomson scattering diagnostic with plasma oscillations was designed, developed, and evaluated. The instrument will fire the laser at a user-selected phase of an input sine or sawtooth wave with an accuracy of +-15/sup 0/. Allowable frequencies range from 20 to 500 Hz for a sawtooth and from 1 to 30 kHz for a sine wave. The instrument also allows synchronization with a sine wave to be enabled by a preselected sawtooth phase. The instrument uses analog signal processing circuits to separate the signal components, remove unwanted components, andmore » produce zero-phase synchronization pulses. The instrument measures the period between zero-phase pulses in order to produce phase synchronization pulses delayed a fraction of the period from the zero-phase pulses. The laser is fired by the phase synchronization pulse. Unwanted signal components are attenuated by bandpass filters. A digitally controlled self-adjusting bandpass filter for sine processing. The instrument was used to investigate the variation of the electron temperature profile with the phase of the x-ray signal from an Impurity Studies Experiment (ISX-B) plasma exhibiting magnetohydrodynamic (MHD) activity.« less

Gear fault diagnosis based on the structured sparsity time-frequency analysis

NASA Astrophysics Data System (ADS)

Sun, Ruobin; Yang, Zhibo; Chen, Xuefeng; Tian, Shaohua; Xie, Yong

2018-03-01

Over the last decade, sparse representation has become a powerful paradigm in mechanical fault diagnosis due to its excellent capability and the high flexibility for complex signal description. The structured sparsity time-frequency analysis (SSTFA) is a novel signal processing method, which utilizes mixed-norm priors on time-frequency coefficients to obtain a fine match for the structure of signals. In order to extract the transient feature from gear vibration signals, a gear fault diagnosis method based on SSTFA is proposed in this work. The steady modulation components and impulsive components of the defective gear vibration signals can be extracted simultaneously by choosing different time-frequency neighborhood and generalized thresholding operators. Besides, the time-frequency distribution with high resolution is obtained by piling different components in the same diagram. The diagnostic conclusion can be made according to the envelope spectrum of the impulsive components or by the periodicity of impulses. The effectiveness of the method is verified by numerical simulations, and the vibration signals registered from a gearbox fault simulator and a wind turbine. To validate the efficiency of the presented methodology, comparisons are made among some state-of-the-art vibration separation methods and the traditional time-frequency analysis methods. The comparisons show that the proposed method possesses advantages in separating feature signals under strong noise and accounting for the inner time-frequency structure of the gear vibration signals.

Integration of multivariate empirical mode decomposition and independent component analysis for fetal ECG separation from abdominal signals.

PubMed

Thanaraj, Palani; Roshini, Mable; Balasubramanian, Parvathavarthini

2016-11-14

The fetal electrocardiogram (FECG) signals are essential to monitor the health condition of the baby. Fetal heart rate (FHR) is commonly used for diagnosing certain abnormalities in the formation of the heart. Usually, non-invasive abdominal electrocardiogram (AbECG) signals are obtained by placing surface electrodes in the abdomen region of the pregnant woman. AbECG signals are often not suitable for the direct analysis of fetal heart activity. Moreover, the strength and magnitude of the FECG signals are low compared to the maternal electrocardiogram (MECG) signals. The MECG signals are often superimposed with the FECG signals that make the monitoring of FECG signals a difficult task. Primary goal of the paper is to separate the fetal electrocardiogram (FECG) signals from the unwanted maternal electrocardiogram (MECG) signals. A multivariate signal processing procedure is proposed here that combines the Multivariate Empirical Mode Decomposition (MEMD) and Independent Component Analysis (ICA). The proposed method is evaluated with clinical abdominal signals taken from three pregnant women (N= 3) recorded during the 38-41 weeks of the gestation period. The number of fetal R-wave detected (NEFQRS), the number of unwanted maternal peaks (NMQRS), the number of undetected fetal R-wave (NUFQRS) and the FHR detection accuracy quantifies the performance of our method. Clinical investigation with three test subjects shows an overall detection accuracy of 92.8%. Comparative analysis with benchmark signal processing method such as ICA suggests the noteworthy performance of our method.

Global regulation by the seven-component Pi signaling system.

PubMed

Hsieh, Yi-Ju; Wanner, Barry L

2010-04-01

This review concerns how Escherichia coli detects environmental inorganic orthophosphate (P(i)) to regulate genes of the phosphate (Pho) regulon by the PhoR/PhoB two-component system (TCS). P(i) control by the PhoR/PhoB TCS is a paradigm of a bacterial signal transduction pathway in which occupancy of a cell surface receptor(s) controls gene expression in the cytoplasm. The P(i) signaling pathway requires seven proteins, all of which probably interact in a membrane-associated signaling complex. Our latest studies show that P(i) signaling involves three distinct processes, which appear to correspond to different states of the sensory histidine kinase PhoR: an inhibition state, an activation state, and a deactivation state. We describe a revised model for P(i) signal transduction of the E. coli Pho regulon. Copyright 2010 Elsevier Ltd. All rights reserved.

Remote diagnosis server

NASA Technical Reports Server (NTRS)

Deb, Somnath (Inventor); Ghoshal, Sudipto (Inventor); Malepati, Venkata N. (Inventor); Kleinman, David L. (Inventor); Cavanaugh, Kevin F. (Inventor)

2004-01-01

A network-based diagnosis server for monitoring and diagnosing a system, the server being remote from the system it is observing, comprises a sensor for generating signals indicative of a characteristic of a component of the system, a network-interfaced sensor agent coupled to the sensor for receiving signals therefrom, a broker module coupled to the network for sending signals to and receiving signals from the sensor agent, a handler application connected to the broker module for transmitting signals to and receiving signals therefrom, a reasoner application in communication with the handler application for processing, and responding to signals received from the handler application, wherein the sensor agent, broker module, handler application, and reasoner applications operate simultaneously relative to each other, such that the present invention diagnosis server performs continuous monitoring and diagnosing of said components of the system in real time. The diagnosis server is readily adaptable to various different systems.

SLAP, a regulator of immunoreceptor ubiquitination, signaling, and trafficking.

PubMed

Dragone, Leonard L; Shaw, Laura A; Myers, Margaret D; Weiss, Arthur

2009-11-01

Src-like adapter proteins (SLAP and SLAP-2) constitute a family of proteins that are expressed in a variety of cell types but are studied most extensively in lymphocytes. They have been shown to associate with proximal components of the T-cell receptor (TCR) and B-cell receptor (BCR) signaling complexes. An interaction of SLAP with c-Cbl leads to the ubiquitination and degradation of phosphorylated components of the TCR- and BCR-signaling complexes. The absence of this process in immature SLAP-deficient T and B cells leads to increased immunoreceptor levels due to decreased intracellular retention and degradation. We propose a model in which SLAP-dependent regulation of immunoreceptor levels allows for finer control of immunoreceptor signaling. Thus, SLAP functions to dampen immunoreceptor signaling, thereby influencing lymphocyte development and repertoire selection.

A simple iterative independent component analysis algorithm for vibration source signal identification of complex structures

NASA Astrophysics Data System (ADS)

Lee, Dong-Sup; Cho, Dae-Seung; Kim, Kookhyun; Jeon, Jae-Jin; Jung, Woo-Jin; Kang, Myeng-Hwan; Kim, Jae-Ho

2015-01-01

Independent Component Analysis (ICA), one of the blind source separation methods, can be applied for extracting unknown source signals only from received signals. This is accomplished by finding statistical independence of signal mixtures and has been successfully applied to myriad fields such as medical science, image processing, and numerous others. Nevertheless, there are inherent problems that have been reported when using this technique: instability and invalid ordering of separated signals, particularly when using a conventional ICA technique in vibratory source signal identification of complex structures. In this study, a simple iterative algorithm of the conventional ICA has been proposed to mitigate these problems. The proposed method to extract more stable source signals having valid order includes an iterative and reordering process of extracted mixing matrix to reconstruct finally converged source signals, referring to the magnitudes of correlation coefficients between the intermediately separated signals and the signals measured on or nearby sources. In order to review the problems of the conventional ICA technique and to validate the proposed method, numerical analyses have been carried out for a virtual response model and a 30 m class submarine model. Moreover, in order to investigate applicability of the proposed method to real problem of complex structure, an experiment has been carried out for a scaled submarine mockup. The results show that the proposed method could resolve the inherent problems of a conventional ICA technique.

Conformation-based signal transfer and processing at the single-molecule level

NASA Astrophysics Data System (ADS)

Li, Chao; Wang, Zhongping; Lu, Yan; Liu, Xiaoqing; Wang, Li

2017-11-01

Building electronic components made of individual molecules is a promising strategy for the miniaturization and integration of electronic devices. However, the practical realization of molecular devices and circuits for signal transmission and processing at room temperature has proven challenging. Here, we present room-temperature intermolecular signal transfer and processing using SnCl2Pc molecules on a Cu(100) surface. The in-plane orientations of the molecules are effectively coupled via intermolecular interaction and serve as the information carrier. In the coupled molecular arrays, the signal can be transferred from one molecule to another in the in-plane direction along predesigned routes and processed to realize logical operations. These phenomena enable the use of molecules displaying intrinsic bistable states as complex molecular devices and circuits with novel functions.

The modeling of MMI structures for signal processing applications

NASA Astrophysics Data System (ADS)

Le, Thanh Trung; Cahill, Laurence W.

2008-02-01

Microring resonators are promising candidates for photonic signal processing applications. However, almost all resonators that have been reported so far use directional couplers or 2×2 multimode interference (MMI) couplers as the coupling element between the ring and the bus waveguides. In this paper, instead of using 2×2 couplers, novel structures for microring resonators based on 3×3 MMI couplers are proposed. The characteristics of the device are derived using the modal propagation method. The device parameters are optimized by using numerical methods. Optical switches and filters using Silicon on Insulator (SOI) then have been designed and analyzed. This device can become a new basic component for further applications in optical signal processing. The paper concludes with some further examples of photonic signal processing circuits based on MMI couplers.

Orbital component extraction by time-variant sinusoidal modeling.

NASA Astrophysics Data System (ADS)

Sinnesael, Matthias; Zivanovic, Miroslav; De Vleeschouwer, David; Claeys, Philippe; Schoukens, Johan

2016-04-01

Accurately deciphering periodic variations in paleoclimate proxy signals is essential for cyclostratigraphy. Classical spectral analysis often relies on methods based on the (Fast) Fourier Transformation. This technique has no unique solution separating variations in amplitude and frequency. This characteristic makes it difficult to correctly interpret a proxy's power spectrum or to accurately evaluate simultaneous changes in amplitude and frequency in evolutionary analyses. Here, we circumvent this drawback by using a polynomial approach to estimate instantaneous amplitude and frequency in orbital components. This approach has been proven useful to characterize audio signals (music and speech), which are non-stationary in nature (Zivanovic and Schoukens, 2010, 2012). Paleoclimate proxy signals and audio signals have in nature similar dynamics; the only difference is the frequency relationship between the different components. A harmonic frequency relationship exists in audio signals, whereas this relation is non-harmonic in paleoclimate signals. However, the latter difference is irrelevant for the problem at hand. Using a sliding window approach, the model captures time variations of an orbital component by modulating a stationary sinusoid centered at its mean frequency, with a single polynomial. Hence, the parameters that determine the model are the mean frequency of the orbital component and the polynomial coefficients. The first parameter depends on geologic interpretation, whereas the latter are estimated by means of linear least-squares. As an output, the model provides the orbital component waveform, either in the depth or time domain. Furthermore, it allows for a unique decomposition of the signal into its instantaneous amplitude and frequency. Frequency modulation patterns can be used to reconstruct changes in accumulation rate, whereas amplitude modulation can be used to reconstruct e.g. eccentricity-modulated precession. The time-variant sinusoidal model is applied to well-established Pleistocene benthic isotope records to evaluate its performance. Zivanovic M. and Schoukens J. (2010) On The Polynomial Approximation for Time-Variant Harmonic Signal Modeling. IEEE Transactions On Audio, Speech, and Language Processing vol. 19, no. 3, pp. 458-467. Doi: 10.1109/TASL.2010.2049673. Zivanovic M. and Schoukens J. (2012) Single and Piecewise Polynomials for Modeling of Pitched Sounds. IEEE Transactions On Audio, Speech, and Language Processing vol. 20, no. 4, pp. 1270-1281. Doi: 10.1109/TASL.2011.2174228.

Non-stationary least-squares complex decomposition for microseismic noise attenuation

NASA Astrophysics Data System (ADS)

Chen, Yangkang

2018-06-01

Microseismic data processing and imaging are crucial for subsurface real-time monitoring during hydraulic fracturing process. Unlike the active-source seismic events or large-scale earthquake events, the microseismic event is usually of very small magnitude, which makes its detection challenging. The biggest trouble of microseismic data is the low signal-to-noise ratio issue. Because of the small energy difference between effective microseismic signal and ambient noise, the effective signals are usually buried in strong random noise. I propose a useful microseismic denoising algorithm that is based on decomposing a microseismic trace into an ensemble of components using least-squares inversion. Based on the predictive property of useful microseismic event along the time direction, the random noise can be filtered out via least-squares fitting of multiple damping exponential components. The method is flexible and almost automated since the only parameter needed to be defined is a decomposition number. I use some synthetic and real data examples to demonstrate the potential of the algorithm in processing complicated microseismic data sets.

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.

Analogy of transistor function with modulating photonic band gap in electromagnetically induced grating

PubMed Central

Wang, Zhiguo; Ullah, Zakir; Gao, Mengqin; Zhang, Dan; Zhang, Yiqi; Gao, Hong; Zhang, Yanpeng

2015-01-01

Optical transistor is a device used to amplify and switch optical signals. Many researchers focus on replacing current computer components with optical equivalents, resulting in an optical digital computer system processing binary data. Electronic transistor is the fundamental building block of modern electronic devices. To replace electronic components with optical ones, an equivalent optical transistor is required. Here we compare the behavior of an optical transistor with the reflection from a photonic band gap structure in an electromagnetically induced transparency medium. A control signal is used to modulate the photonic band gap structure. Power variation of the control signal is used to provide an analogy between the reflection behavior caused by modulating the photonic band gap structure and the shifting of Q-point (Operation point) as well as amplification function of optical transistor. By means of the control signal, the switching function of optical transistor has also been realized. Such experimental schemes could have potential applications in making optical diode and optical transistor used in quantum information processing. PMID:26349444

Analogy of transistor function with modulating photonic band gap in electromagnetically induced grating

NASA Astrophysics Data System (ADS)

Wang, Zhiguo; Ullah, Zakir; Gao, Mengqin; Zhang, Dan; Zhang, Yiqi; Gao, Hong; Zhang, Yanpeng

2015-09-01

Optical transistor is a device used to amplify and switch optical signals. Many researchers focus on replacing current computer components with optical equivalents, resulting in an optical digital computer system processing binary data. Electronic transistor is the fundamental building block of modern electronic devices. To replace electronic components with optical ones, an equivalent optical transistor is required. Here we compare the behavior of an optical transistor with the reflection from a photonic band gap structure in an electromagnetically induced transparency medium. A control signal is used to modulate the photonic band gap structure. Power variation of the control signal is used to provide an analogy between the reflection behavior caused by modulating the photonic band gap structure and the shifting of Q-point (Operation point) as well as amplification function of optical transistor. By means of the control signal, the switching function of optical transistor has also been realized. Such experimental schemes could have potential applications in making optical diode and optical transistor used in quantum information processing.

Prokaryotic 2-component systems and the OmpR/PhoB superfamily.

PubMed

Nguyen, Minh-Phuong; Yoon, Joo-Mi; Cho, Man-Ho; Lee, Sang-Won

2015-11-01

In bacteria, 2-component regulatory systems (TCSs) are the critical information-processing pathways that link stimuli to specific adaptive responses. Signals perceived by membrane sensors, which are generally histidine kinases, are transmitted by response regulators (RRs) to allow cells to cope rapidly and effectively with environmental challenges. Over the past few decades, genes encoding components of TCSs and their responsive proteins have been identified, crystal structures have been described, and signaling mechanisms have been elucidated. Here, we review recent findings and interesting breakthroughs in bacterial TCS research. Furthermore, we discuss structural features, mechanisms of activation and regulation, and cross-regulation of RRs, with a focus on the largest RR family, OmpR/PhoB, to provide a comprehensive overview of these critically important signaling molecules.

Traversing the Links between Heavy Metal Stress and Plant Signaling

PubMed Central

Jalmi, Siddhi K.; Bhagat, Prakash K.; Verma, Deepanjali; Noryang, Stanzin; Tayyeba, Sumaira; Singh, Kirti; Sharma, Deepika; Sinha, Alok K.

2018-01-01

Plants confront multifarious environmental stresses widely divided into abiotic and biotic stresses, of which heavy metal stress represents one of the most damaging abiotic stresses. Heavy metals cause toxicity by targeting crucial molecules and vital processes in the plant cell. One of the approaches by which heavy metals act in plants is by over production of reactive oxygen species (ROS) either directly or indirectly. Plants act against such overdose of metal in the environment by boosting the defense responses like metal chelation, sequestration into vacuole, regulation of metal intake by transporters, and intensification of antioxidative mechanisms. This response shown by plants is the result of intricate signaling networks functioning in the cell in order to transmit the extracellular stimuli into an intracellular response. The crucial signaling components involved are calcium signaling, hormone signaling, and mitogen activated protein kinase (MAPK) signaling that are discussed in this review. Apart from signaling components other regulators like microRNAs and transcription factors also have a major contribution in regulating heavy metal stress. This review demonstrates the key role of MAPKs in synchronously controlling the other signaling components and regulators in metal stress. Further, attempts have been made to focus on metal transporters and chelators that are regulated by MAPK signaling. PMID:29459874

Analog Signal Pre-Processing For The Fermilab Main Injector BPM Upgrade

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

Saewert, A. L.; Rapisarda, S. M.; Wendt, M.

2006-11-20

An analog signal pre-processing scheme was developed, in the framework of the Fermilab Main Injector Beam Position Monitor (BPM) Upgrade, to interface BPM pickup signals to the new digital receiver based read-out system. A key component is the 8-channel electronics module, which uses separate frequency-selective gain stages to acquire 53 MHz bunched proton and 2.5 MHz antiproton signals. Related hardware includes a filter and combiner box to sum pickup electrode signals in the tunnel. A controller module allows local/remote control of gain settings and activation of gain stages and supplies test signals. Theory of operation, system overview, and some designmore » details are presented, as well as first beam measurements of the prototype hardware.« less

Characterizing near-surface firn from the scattered signal component of glacier surface reflections detected in airborne radio-echo sounding measurements

NASA Astrophysics Data System (ADS)

Rutishauser, A.; Grima, C.; Sharp, M. J.; Blankenship, D. D.; Young, D. A.; Cawkwell, F.; Dowdeswell, J. A.

2016-12-01

With recent summer warming, surface melt on Canadian Arctic ice caps has intensified and extended to higher elevations in ice cap accumulation areas. Consequently, more meltwater percolates into the near-surface firn, and refreezes as ice layers where firn temperatures are below freezing. This process can increase firn densification rates, causing a lowering of the glacier surface height even in the absence of mass changes. Thus, knowledge of spatio-temporal variations in the near-surface firn stratigraphy is important for interpreting altimetrically-derived estimates of ice cap mass balance. We investigate the use of the scattering signal component of glacier surface reflections in airborne radio-echo sounding (RES) measurements to characterize the near-surface firn stratigraphy. The scattering signal distribution over Devon Ice Cap is compared to firn stratigraphy derived from ground-based radar data. We identify three distinct firn facies zones at different elevation ranges. The scattered signal component changes significantly between the different firn facies zones: low scattering correlates to laterally homogeneous firn containing thin, flat and continuous ice layers at elevations above 1800 m and below 1200 m, where firn consists mainly of ice. Higher scattering values are found from 1200-1800 m where the firn contains discrete, undulating ice layers. No correlation was found between the scattering component and surface roughness. Modelled scattering values for the measured roughness were significantly less than the observed values, and did not reproduce their observed spatial distribution. This indicates that the scattering component is determined mainly by the structure of near-surface firn. Our results suggest that the scattering component of surface reflections from airborne RES measurements has potential for characterizing heterogeneity in the spatial structure of firn that is affected by melting and refreezing processes.

A motion-tolerant approach for monitoring SpO2 and heart rate using photoplethysmography signal with dual frame length processing and multi-classifier fusion.

PubMed

Fan, Feiyi; Yan, Yuepeng; Tang, Yongzhong; Zhang, Hao

2017-12-01

Monitoring pulse oxygen saturation (SpO 2 ) and heart rate (HR) using photoplethysmography (PPG) signal contaminated by a motion artifact (MA) remains a difficult problem, especially when the oximeter is not equipped with a 3-axis accelerometer for adaptive noise cancellation. In this paper, we report a pioneering investigation on the impact of altering the frame length of Molgedey and Schuster independent component analysis (ICAMS) on performance, design a multi-classifier fusion strategy for selecting the PPG correlated signal component, and propose a novel approach to extract SpO 2 and HR readings from PPG signal contaminated by strong MA interference. The algorithm comprises multiple stages, including dual frame length ICAMS, a multi-classifier-based PPG correlated component selector, line spectral analysis, tree-based HR monitoring, and post-processing. Our approach is evaluated by multi-subject tests. The root mean square error (RMSE) is calculated for each trial. Three statistical metrics are selected as performance evaluation criteria: mean RMSE, median RMSE and the standard deviation (SD) of RMSE. The experimental results demonstrate that a shorter ICAMS analysis window probably results in better performance in SpO 2 estimation. Notably, the designed multi-classifier signal component selector achieved satisfactory performance. The subject tests indicate that our algorithm outperforms other baseline methods regarding accuracy under most criteria. The proposed work can contribute to improving the performance of current pulse oximetry and personal wearable monitoring devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

Integrated Advanced Microwave Sounding Unit-A (AMSU-A). Performance Verification Report: EOS AMSU-A1 and AMSU-A2 Receiver Assemblies

NASA Technical Reports Server (NTRS)

Ma, Y.

1995-01-01

The AMSU-A receiver subsystem comprises two separated receiver assemblies; AMSU-A1 and AMSU-A2 (P/N 1356441-1). The AMSU-A1 receiver contains 13 channels and the AMSU-A2 receiver 2 channels. The AMSU-A1 receiver assembly is further divided into two parts; AMSU-A1-1 (P/N 1356429-1) and AMSU-A1-2 (P/N 1356409-1), which contain 9 and 4 channels, respectively. The receiver assemblies are highlighted and illustrate the functional block diagrams of the AMSU-A1 and AMSU-A2 systems. The AMSU-A receiver subsystem stands in between the antenna and signal processing subsystems of the AMSU-A instrument and comprises the RF and IF components from isolators to attenuators. It receives the RF signals from the antenna subsystem, down-converts the RF signals to IF signals, amplifies and defines the IF signals to proper power level and frequency bandwidth as specified for each channel, and inputs the IF signals to the signal processing subsystem. This test report presents the test data of the EOS AMSU-A Flight Model No. 1 (FM-1) receiver subsystem. The tests are performed per the Acceptance Test Procedure for the AMSU-A Receiver Subsystem, AE-26002/6A. The functional performance tests are conducted either at the component or subsystem level. While the component-level tests are performed over the entire operating temperature range predicted by thermal analysis, the subsystem-level tests are conducted at ambient temperature only.

Dopamine reward prediction-error signalling: a two-component response

PubMed Central

Schultz, Wolfram

2017-01-01

Environmental stimuli and objects, including rewards, are often processed sequentially in the brain. Recent work suggests that the phasic dopamine reward prediction-error response follows a similar sequential pattern. An initial brief, unselective and highly sensitive increase in activity unspecifically detects a wide range of environmental stimuli, then quickly evolves into the main response component, which reflects subjective reward value and utility. This temporal evolution allows the dopamine reward prediction-error signal to optimally combine speed and accuracy. PMID:26865020

Plant TOR signaling components

PubMed Central

John, Florian; Roffler, Stefan; Wicker, Thomas; Ringli, Christoph

2011-01-01

Cell growth is a process that needs to be tightly regulated. Cells must be able to sense environmental factors like nutrient abundance, the energy level or stress signals and coordinate growth accordingly. The Target Of Rapamycin (TOR) pathway is a major controller of growth-related processes in all eukaryotes. If environmental conditions are favorable, the TOR pathway promotes cell and organ growth and restrains catabolic processes like autophagy. Rapamycin is a specific inhibitor of the TOR kinase and acts as a potent inhibitor of TOR signaling. As a consequence, interfering with TOR signaling has a strong impact on plant development. This review summarizes the progress in the understanding of the biological significance and the functional analysis of the TOR pathway in plants. PMID:22057328

A Stimulated Raman Scattering CMOS Pixel Using a High-Speed Charge Modulator and Lock-in Amplifier.

PubMed

Lioe, De Xing; Mars, Kamel; Kawahito, Shoji; Yasutomi, Keita; Kagawa, Keiichiro; Yamada, Takahiro; Hashimoto, Mamoru

2016-04-13

A complementary metal-oxide semiconductor (CMOS) lock-in pixel to observe stimulated Raman scattering (SRS) using a high speed lateral electric field modulator (LEFM) for photo-generated charges and in-pixel readout circuits is presented. An effective SRS signal generated after the SRS process is very small and needs to be extracted from an extremely large offset due to a probing laser signal. In order to suppress the offset components while amplifying high-frequency modulated small SRS signal components, the lock-in pixel uses a high-speed LEFM for demodulating the SRS signal, resistor-capacitor low-pass filter (RC-LPF) and switched-capacitor (SC) integrator with a fully CMOS differential amplifier. AC (modulated) components remained in the RC-LPF outputs are eliminated by the phase-adjusted sampling with the SC integrator and the demodulated DC (unmodulated) components due to the SRS signal are integrated over many samples in the SC integrator. In order to suppress further the residual offset and the low frequency noise (1/f noise) components, a double modulation technique is introduced in the SRS signal measurements, where the phase of high-frequency modulated laser beam before irradiation of a specimen is modulated at an intermediate frequency and the demodulation is done at the lock-in pixel output. A prototype chip for characterizing the SRS lock-in pixel is implemented and a successful operation is demonstrated. The reduction effects of residual offset and 1/f noise components are confirmed by the measurements. A ratio of the detected small SRS to offset a signal of less than 10(-)⁵ is experimentally demonstrated, and the SRS spectrum of a Benzonitrile sample is successfully observed.

Applications of digital processing for noise removal from plasma diagnostics

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

Kane, R.J.; Candy, J.V.; Casper, T.A.

1985-11-11

The use of digital signal techniques for removal of noise components present in plasma diagnostic signals is discussed, particularly with reference to diamagnetic loop signals. These signals contain noise due to power supply ripple in addition to plasma characteristics. The application of noise canceling techniques, such as adaptive noise canceling and model-based estimation, will be discussed. The use of computer codes such as SIG is described. 19 refs., 5 figs.

Parallel Processing with Digital Signal Processing Hardware and Software

NASA Technical Reports Server (NTRS)

Swenson, Cory V.

1995-01-01

The assembling and testing of a parallel processing system is described which will allow a user to move a Digital Signal Processing (DSP) application from the design stage to the execution/analysis stage through the use of several software tools and hardware devices. The system will be used to demonstrate the feasibility of the Algorithm To Architecture Mapping Model (ATAMM) dataflow paradigm for static multiprocessor solutions of DSP applications. The individual components comprising the system are described followed by the installation procedure, research topics, and initial program development.

Orphan receptor GPR179 forms macromolecular complexes with components of metabotropic signaling cascade in retina ON-bipolar neurons.

PubMed

Orlandi, Cesare; Cao, Yan; Martemyanov, Kirill A

2013-10-29

In the mammalian retina, synaptic transmission between light-excited rod photoreceptors and downstream ON-bipolar neurons is indispensable for dim vision, and disruption of this process leads to congenital stationary night blindness in human patients. The ON-bipolar neurons use the metabotropic signaling cascade, initiated by the mGluR6 receptor, to generate depolarizing responses to light-induced changes in neurotransmitter glutamate release from the photoreceptor axonal terminals. Evidence for the identity of the components involved in transducing these signals is growing rapidly. Recently, the orphan receptor, GPR179, a member of the G protein-coupled receptor (GPCR) superfamily, has been shown to be indispensable for the synaptic responses of ON-bipolar cells. In our study, we investigated the interaction of GPR179 with principle components of the signal transduction cascade. We used immunoprecipitation and proximity ligation assays in transfected cells and native retinas to characterize the protein-protein interactions involving GPR179. The influence of cascade components on GPR179 localization was examined through immunohistochemical staining of the retinas from genetic mouse models. We demonstrated that, in mouse retinas, GPR179 forms physical complexes with the main components of the metabotropic cascade, recruiting mGluR6, TRPM1, and the RGS proteins. Elimination of mGluR6 or RGS proteins, but not TRPM1, detrimentally affects postsynaptic targeting or GPR179 expression. These observations suggest that the mGluR6 signaling cascade is scaffolded as a macromolecular complex in which the interactions between the components ensure the optimal spatiotemporal characteristics of signal transduction.

Overcoming low-alignment signal contrast induced alignment failure by alignment signal enhancement

NASA Astrophysics Data System (ADS)

Lee, Byeong Soo; Kim, Young Ha; Hwang, Hyunwoo; Lee, Jeongjin; Kong, Jeong Heung; Kang, Young Seog; Paarhuis, Bart; Kok, Haico; de Graaf, Roelof; Weichselbaum, Stefan; Droste, Richard; Mason, Christopher; Aarts, Igor; de Boeij, Wim P.

2016-03-01

Overlay is one of the key factors which enables optical lithography extension to 1X node DRAM manufacturing. It is natural that accurate wafer alignment is a prerequisite for good device overlay. However, alignment failures or misalignments are commonly observed in a fab. There are many factors which could induce alignment problems. Low alignment signal contrast is one of the main issues. Alignment signal contrast can be degraded by opaque stack materials or by alignment mark degradation due to processes like CMP. This issue can be compounded by mark sub-segmentation from design rules in combination with double or quadruple spacer process. Alignment signal contrast can be improved by applying new material or process optimization, which sometimes lead to the addition of another process-step with higher costs. If we can amplify the signal components containing the position information and reduce other unwanted signal and background contributions then we can improve alignment performance without process change. In this paper we use ASML's new alignment sensor (as was introduced and released on the NXT:1980Di) and sample wafers with special stacks which can induce poor alignment signal to demonstrate alignment and overlay improvement.

Sensing and Responding to UV-A in Cyanobacteria

PubMed Central

Moon, Yoon-Jung; Kim, Seung Il; Chung, Young-Ho

2012-01-01

Ultraviolet (UV) radiation can cause stresses or act as a photoregulatory signal depending on its wavelengths and fluence rates. Although the most harmful effects of UV on living cells are generally attributed to UV-B radiation, UV-A radiation can also affect many aspects of cellular processes. In cyanobacteria, most studies have concentrated on the damaging effect of UV and defense mechanisms to withstand UV stress. However, little is known about the activation mechanism of signaling components or their pathways which are implicated in the process following UV irradiation. Motile cyanobacteria use a very precise negative phototaxis signaling system to move away from high levels of solar radiation, which is an effective escape mechanism to avoid the detrimental effects of UV radiation. Recently, two different UV-A-induced signaling systems for regulating cyanobacterial phototaxis were characterized at the photophysiological and molecular levels. Here, we review the current understanding of the UV-A mediated signaling pathways in the context of the UV-A perception mechanism, early signaling components, and negative phototactic responses. In addition, increasing evidences supporting a role of pterins in response to UV radiation are discussed. We outline the effect of UV-induced cell damage, associated signaling molecules, and programmed cell death under UV-mediated oxidative stress. PMID:23208372

Processing of Antenna-Array Signals on the Basis of the Interference Model Including a Rank-Deficient Correlation Matrix

NASA Astrophysics Data System (ADS)

Rodionov, A. A.; Turchin, V. I.

2017-06-01

We propose a new method of signal processing in antenna arrays, which is called the Maximum-Likelihood Signal Classification. The proposed method is based on the model in which interference includes a component with a rank-deficient correlation matrix. Using numerical simulation, we show that the proposed method allows one to ensure variance of the estimated arrival angle of the plane wave, which is close to the Cramer-Rao lower boundary and more efficient than the best-known MUSIC method. It is also shown that the proposed technique can be efficiently used for estimating the time dependence of the useful signal.

A Charge Sensitive Pre-Amplifier for Smart Point-of-Care Devices Employing Polymer Based Lab-on-a-Chip

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

Wang, Hanfeng; Britton, Charles; Quaiyum, Farhan

With increasing emphasis on implantable and portable medical devices, low-power, small-chip-area sensor readout system realized in lab-on-a-chip (LOC) platform is gaining more and more importance these days. The main building blocks of the LOC system include a front-end transducer that generates an electrical signal in response to the presence of an analyte of interest, signal processing electronics to process the signal to comply with a specific transmission protocol and a low-power transmitter, all realized in a single integrated circuit platform. Low power consumption and compactness of the components are essential requirements of the LOC system. This paper presents a novelmore » charge sensitive pre-amplifier developed in a standard 180-nm CMOS process suitable for implementing in an LOC platform. The pre-amplifier converts the charge generated by a pyroelectric transducer into a voltage signal, which provides a measurement of the temperature variation in biological fluids. The proposed design is capable of providing 0.8-mV/pC gain while consuming only 2.1 μW of power. Finally, the pre-amplifier composed of integrated components occupies an area of 0.038 mm 2.« less

A Charge Sensitive Pre-Amplifier for Smart Point-of-Care Devices Employing Polymer Based Lab-on-a-Chip

DOE PAGES

Wang, Hanfeng; Britton, Charles; Quaiyum, Farhan; ...

2018-01-01

With increasing emphasis on implantable and portable medical devices, low-power, small-chip-area sensor readout system realized in lab-on-a-chip (LOC) platform is gaining more and more importance these days. The main building blocks of the LOC system include a front-end transducer that generates an electrical signal in response to the presence of an analyte of interest, signal processing electronics to process the signal to comply with a specific transmission protocol and a low-power transmitter, all realized in a single integrated circuit platform. Low power consumption and compactness of the components are essential requirements of the LOC system. This paper presents a novelmore » charge sensitive pre-amplifier developed in a standard 180-nm CMOS process suitable for implementing in an LOC platform. The pre-amplifier converts the charge generated by a pyroelectric transducer into a voltage signal, which provides a measurement of the temperature variation in biological fluids. The proposed design is capable of providing 0.8-mV/pC gain while consuming only 2.1 μW of power. Finally, the pre-amplifier composed of integrated components occupies an area of 0.038 mm 2.« less

Low power sensor network for wireless condition monitoring

NASA Astrophysics Data System (ADS)

Richter, Ch.; Frankenstein, B.; Schubert, L.; Weihnacht, B.; Friedmann, H.; Ebert, C.

2009-03-01

For comprehensive fatigue tests and surveillance of large scale structures, a vibration monitoring system working in the Hz and sub Hz frequency range was realized and tested. The system is based on a wireless sensor network and focuses especially on the realization of a low power measurement, signal processing and communication. Regarding the development, we met the challenge of synchronizing the wireless connected sensor nodes with sufficient accuracy. The sensor nodes ware realized by compact, sensor near signal processing structures containing components for analog preprocessing of acoustic signals, their digitization, algorithms for data reduction and network communication. The core component is a digital micro controller which performs the basic algorithms necessary for the data acquisition synchronization and the filtering. As a first application, the system was installed in a rotor blade of a wind power turbine in order to monitor the Eigen modes over a longer period of time. Currently the sensor nodes are battery powered.

A new method of hybrid frequency hopping signals selection and blind parameter estimation

NASA Astrophysics Data System (ADS)

Zeng, Xiaoyu; Jiao, Wencheng; Sun, Huixian

2018-04-01

Frequency hopping communication is widely used in military communications at home and abroad. In the case of single-channel reception, it is scarce to process multiple frequency hopping signals both effectively and simultaneously. A method of hybrid FH signals selection and blind parameter estimation is proposed. The method makes use of spectral transformation, spectral entropy calculation and PRI transformation basic theory to realize the sorting and parameter estimation of the components in the hybrid frequency hopping signal. The simulation results show that this method can correctly classify the frequency hopping component signal, and the estimated error of the frequency hopping period is about 5% and the estimated error of the frequency hopping frequency is less than 1% when the SNR is 10dB. However, the performance of this method deteriorates seriously at low SNR.

A molecular systems approach to modelling human skin pigmentation: identifying underlying pathways and critical components.

PubMed

Raghunath, Arathi; Sambarey, Awanti; Sharma, Neha; Mahadevan, Usha; Chandra, Nagasuma

2015-04-29

Ultraviolet radiations (UV) serve as an environmental stress for human skin, and result in melanogenesis, with the pigment melanin having protective effects against UV induced damage. This involves a dynamic and complex regulation of various biological processes that results in the expression of melanin in the outer most layers of the epidermis, where it can exert its protective effect. A comprehensive understanding of the underlying cross talk among different signalling molecules and cell types is only possible through a systems perspective. Increasing incidences of both melanoma and non-melanoma skin cancers necessitate the need to better comprehend UV mediated effects on skin pigmentation at a systems level, so as to ultimately evolve knowledge-based strategies for efficient protection and prevention of skin diseases. A network model for UV-mediated skin pigmentation in the epidermis was constructed and subjected to shortest path analysis. Virtual knock-outs were carried out to identify essential signalling components. We describe a network model for UV-mediated skin pigmentation in the epidermis. The model consists of 265 components (nodes) and 429 directed interactions among them, capturing the manner in which one component influences the other and channels information. Through shortest path analysis, we identify novel signalling pathways relevant to pigmentation. Virtual knock-outs or perturbations of specific nodes in the network have led to the identification of alternate modes of signalling as well as enabled determining essential nodes in the process. The model presented provides a comprehensive picture of UV mediated signalling manifesting in human skin pigmentation. A systems perspective helps provide a holistic purview of interconnections and complexity in the processes leading to pigmentation. The model described here is extensive yet amenable to expansion as new data is gathered. Through this study, we provide a list of important proteins essential for pigmentation which can be further explored to better understand normal pigmentation as well as its pathologies including vitiligo and melanoma, and enable therapeutic intervention.

Extracting Independent Local Oscillatory Geophysical Signals by Geodetic Tropospheric Delay

NASA Technical Reports Server (NTRS)

Botai, O. J.; Combrinck, L.; Sivakumar, V.; Schuh, H.; Bohm, J.

2010-01-01

Zenith Tropospheric Delay (ZTD) due to water vapor derived from space geodetic techniques and numerical weather prediction simulated-reanalysis data exhibits non-linear and non-stationary properties akin to those in the crucial geophysical signals of interest to the research community. These time series, once decomposed into additive (and stochastic) components, have information about the long term global change (the trend) and other interpretable (quasi-) periodic components such as seasonal cycles and noise. Such stochastic component(s) could be a function that exhibits at most one extremum within a data span or a monotonic function within a certain temporal span. In this contribution, we examine the use of the combined Ensemble Empirical Mode Decomposition (EEMD) and Independent Component Analysis (ICA): the EEMD-ICA algorithm to extract the independent local oscillatory stochastic components in the tropospheric delay derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) over six geodetic sites (HartRAO, Hobart26, Wettzell, Gilcreek, Westford, and Tsukub32). The proposed methodology allows independent geophysical processes to be extracted and assessed. Analysis of the quality index of the Independent Components (ICs) derived for each cluster of local oscillatory components (also called the Intrinsic Mode Functions (IMFs)) for all the geodetic stations considered in the study demonstrate that they are strongly site dependent. Such strong dependency seems to suggest that the localized geophysical signals embedded in the ZTD over the geodetic sites are not correlated. Further, from the viewpoint of non-linear dynamical systems, four geophysical signals the Quasi-Biennial Oscillation (QBO) index derived from the NCEP/NCAR reanalysis, the Southern Oscillation Index (SOI) anomaly from NCEP, the SIDC monthly Sun Spot Number (SSN), and the Length of Day (LoD) are linked to the extracted signal components from ZTD. Results from the synchronization analysis show that ZTD and the geophysical signals exhibit (albeit subtle) site dependent phase synchronization index.

Nonlinear processing of a multicomponent communication signal by combination-sensitive neurons in the anuran inferior colliculus.

PubMed

Lee, Norman; Schrode, Katrina M; Bee, Mark A

2017-09-01

Diverse animals communicate using multicomponent signals. How a receiver's central nervous system integrates multiple signal components remains largely unknown. We investigated how female green treefrogs (Hyla cinerea) integrate the multiple spectral components present in male advertisement calls. Typical calls have a bimodal spectrum consisting of formant-like low-frequency (~0.9 kHz) and high-frequency (~2.7 kHz) components that are transduced by different sensory organs in the inner ear. In behavioral experiments, only bimodal calls reliably elicited phonotaxis in no-choice tests, and they were selectively chosen over unimodal calls in two-alternative choice tests. Single neurons in the inferior colliculus of awake, passively listening subjects were classified as combination-insensitive units (27.9%) or combination-sensitive units (72.1%) based on patterns of relative responses to the same bimodal and unimodal calls. Combination-insensitive units responded similarly to the bimodal call and one or both unimodal calls. In contrast, combination-sensitive units exhibited both linear responses (i.e., linear summation) and, more commonly, nonlinear responses (e.g., facilitation, compressive summation, or suppression) to the spectral combination in the bimodal call. These results are consistent with the hypothesis that nonlinearities play potentially critical roles in spectral integration and in the neural processing of multicomponent communication signals.

The Seismic Tool-Kit (STK): an open source software for seismology and signal processing.

NASA Astrophysics Data System (ADS)

Reymond, Dominique

2016-04-01

We present an open source software project (GNU public license), named STK: Seismic ToolKit, that is dedicated mainly for seismology and signal processing. The STK project that started in 2007, is hosted by SourceForge.net, and count more than 19 500 downloads at the date of writing. The STK project is composed of two main branches: First, a graphical interface dedicated to signal processing (in the SAC format (SAC_ASCII and SAC_BIN): where the signal can be plotted, zoomed, filtered, integrated, derivated, ... etc. (a large variety of IFR and FIR filter is proposed). The estimation of spectral density of the signal are performed via the Fourier transform, with visualization of the Power Spectral Density (PSD) in linear or log scale, and also the evolutive time-frequency representation (or sonagram). The 3-components signals can be also processed for estimating their polarization properties, either for a given window, or either for evolutive windows along the time. This polarization analysis is useful for extracting the polarized noises, differentiating P waves, Rayleigh waves, Love waves, ... etc. Secondly, a panel of Utilities-Program are proposed for working in a terminal mode, with basic programs for computing azimuth and distance in spherical geometry, inter/auto-correlation, spectral density, time-frequency for an entire directory of signals, focal planes, and main components axis, radiation pattern of P waves, Polarization analysis of different waves (including noize), under/over-sampling the signals, cubic-spline smoothing, and linear/non linear regression analysis of data set. A MINimum library of Linear AlGebra (MIN-LINAG) is also provided for computing the main matrix process like: QR/QL decomposition, Cholesky solve of linear system, finding eigen value/eigen vectors, QR-solve/Eigen-solve of linear equations systems ... etc. STK is developed in C/C++, mainly under Linux OS, and it has been also partially implemented under MS-Windows. Usefull links: http://sourceforge.net/projects/seismic-toolkit/ http://sourceforge.net/p/seismic-toolkit/wiki/browse_pages/

: Signal Decomposition of High Resolution Time Series River data to Separate Local and Regional Components of Conductivity

EPA Science Inventory

Signal processing techniques were applied to high-resolution time series data obtained from conductivity loggers placed upstream and downstream of a wastewater treatment facility along a river. Data was collected over 14-60 days, and several seasons. The power spectral densit...

Design of signal reception and processing system of embedded ultrasonic endoscope

NASA Astrophysics Data System (ADS)

Li, Ming; Yu, Feng; Zhang, Ruiqiang; Li, Yan; Chen, Xiaodong; Yu, Daoyin

2009-11-01

Embedded Ultrasonic Endoscope, based on embedded microprocessor and embedded real-time operating system, sends a micro ultrasonic probe into coelom through the biopsy channel of the Electronic Endoscope to get the fault histology features of digestive organs by rotary scanning, and acquires the pictures of the alimentary canal mucosal surface. At the same time, ultrasonic signals are processed by signal reception and processing system, forming images of the full histology of the digestive organs. Signal Reception and Processing System is an important component of Embedded Ultrasonic Endoscope. However, the traditional design, using multi-level amplifiers and special digital processing circuits to implement signal reception and processing, is no longer satisfying the standards of high-performance, miniaturization and low power requirements that embedded system requires, and as a result of the high noise that multi-level amplifier brought, the extraction of small signal becomes hard. Therefore, this paper presents a method of signal reception and processing based on double variable gain amplifier and FPGA, increasing the flexibility and dynamic range of the Signal Reception and Processing System, improving system noise level, and reducing power consumption. Finally, we set up the embedded experiment system, using a transducer with the center frequency of 8MHz to scan membrane samples, and display the image of ultrasonic echo reflected by each layer of membrane, with a frame rate of 5Hz, verifying the correctness of the system.

Posterior Beta and Anterior Gamma Oscillations Predict Cognitive Insight

ERIC Educational Resources Information Center

Sheth, Bhavin R.; Sandkuhler, Simone; Bhattacharya, Joydeep

2009-01-01

Pioneering neuroimaging studies on insight have revealed neural correlates of the emotional "Aha!" component of the insight process, but neural substrates of the cognitive component, such as problem restructuring (a key to transformative reasoning), remain a mystery. Here, multivariate electroencephalogram signals were recorded from human…

Spectral broadening of VLF transmitter signals observed on DE 1 - A quasi-electrostatic phenomenon?

NASA Technical Reports Server (NTRS)

Inan, U. S.; Bell, T. F.

1985-01-01

Spectrally broadened VLF transmitter signals are observed on the DE 1 satellite using alternatively both electric and magnetic field sensors. It is found that at times when the electric field component undergoes significant bandwidth expansion (up to about 110 Hz) the magnetic field component has a bandwidth of less than 10 Hz. The results support the theory that the off-carrier components are quasi-electrostatic in nature. Measurement of the absolute E and B field magnitudes of the broadened signals are used to determine the wave Poynting vector. It is found that the observed power levels can be understood without invoking any strong amplification process that operates in conjunction with the spectral broadening. The implications of this finding in distinguishing among the various possible mechanisms for spectral broadening are discussed.

EMG amplifier with wireless data transmission

NASA Astrophysics Data System (ADS)

Kowalski, Grzegorz; Wildner, Krzysztof

2017-08-01

Wireless medical diagnostics is a trend in modern technology used in medicine. This paper presents a concept of realization, architecture of hardware and software implementation of an elecromyography signal (EMG) amplifier with wireless data transmission. This amplifier consists of three components: analogue processing of bioelectric signal module, micro-controller circuit and an application enabling data acquisition via a personal computer. The analogue bioelectric signal processing circuit receives electromyography signals from the skin surface, followed by initial analogue processing and preparation of the signals for further digital processing. The second module is a micro-controller circuit designed to wirelessly transmit the electromyography signals from the analogue signal converter to a personal computer. Its purpose is to eliminate the need for wired connections between the patient and the data logging device. The third block is a computer application designed to display the transmitted electromyography signals, as well as data capture and analysis. Its purpose is to provide a graphical representation of the collected data. The entire device has been thoroughly tested to ensure proper functioning. In use, the device displayed the captured electromyography signal from the arm of the patient. Amplitude- frequency characteristics were set in order to investigate the bandwidth and the overall gain of the device.

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP.

PubMed

Winkler, Irene; Debener, Stefan; Müller, Klaus-Robert; Tangermann, Michael

2015-01-01

Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.

Analog signal pre-processing for the Fermilab Main Injector BPM upgrade

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

Saewert, A.L.; Rapisarda, S.M.; Wendt, M.

2006-05-01

An analog signal pre-processing scheme was developed, in the framework of the Fermilab Main Injector Beam Position Monitor (BPM) Upgrade, to interface BPM pickup signals to the new digital receiver based read-out system. A key component is the 8-channel electronics module, which uses separate frequency selective gain stages to acquire 53 MHz bunched proton, and 2.5 MHz anti-proton signals. Related hardware includes a filter and combiner box to sum pickup electrode signals in the tunnel. A controller module allows local/remote control of gain settings and activation of gain stages, and supplies test signals. Theory of operation, system overview, and somemore » design details are presented, as well as first beam measurements of the prototype hardware.« less

Signal processing in local neuronal circuits based on activity-dependent noise and competition

NASA Astrophysics Data System (ADS)

Volman, Vladislav; Levine, Herbert

2009-09-01

We study the characteristics of weak signal detection by a recurrent neuronal network with plastic synaptic coupling. It is shown that in the presence of an asynchronous component in synaptic transmission, the network acquires selectivity with respect to the frequency of weak periodic stimuli. For nonperiodic frequency-modulated stimuli, the response is quantified by the mutual information between input (signal) and output (network's activity) and is optimized by synaptic depression. Introducing correlations in signal structure resulted in the decrease in input-output mutual information. Our results suggest that in neural systems with plastic connectivity, information is not merely carried passively by the signal; rather, the information content of the signal itself might determine the mode of its processing by a local neuronal circuit.

Effect of TE Mode Power on the PEP II LER BPM System

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

Ng, Cho-K

2011-08-26

The beam chamber of the PEP-II B-Factory Low Energy Ring (LER) arc sections is connected to an antechamber for the absorption of synchrotron radiation on discrete photon stops. The presence of the antechamber substantially reduces the cutoff frequency of the vacuum chamber and, in particular, allows the propagation of higher-order-mode (HOM) TE power generated by beamline components at the BPM signal processing frequency. Calculations of the transmission properties of the TE mode in different sections of the vacuum chamber show that the power is trapped between widely separated bellows in the arc sections. Because of the narrow signal bandwidth andmore » weak coupling of the TE mode to the BPM buttons, the noise contributed by the HOM TE power will not produce a noticeable effect on the BPM position signal voltage. The LER arc vacuum chamber employs an antechamber with a discrete photon stop for absorption of synchrotron radiation and with pumps for maintaining pressure below 10 nTorr [1]. The horizontal dimensions of the antechambers at the pumping chamber section and the magnet chamber section are larger or comparable to that of the beam chamber. Because of the increase in the horizontal dimension, the cutoff frequency of the TE10-like mode (in rectangular coordinates) of the vacuum chamber is considerably reduced and, in particular, is less than the BPM signal processing frequency at 952 MHz. TE power propagating in the vacuum chamber will penetrate through the BPM buttons and will affect the pickup signal if its magnitude is not properly controlled. It is the purpose of this note to clarify various issues pertaining to this problem. TE power is generated when the beam passes a noncylindrically symmetric beamline component such as the RF cavity, the injection region, the IR crotch and the IP region. The beampipes connected to these components have TE cutoff frequencies greater than 952 MHz (for example, the TE cutoff frequency of the RF cavity beampipe is 1.8 GHz), and hence no TE power at this frequency propagates from the component. TE power can also be generated by the scattering of TM power through these beamline components. Since the cutoff frequency of the TM mode is in general higher than that of the TE mode, this mechanism is not pertinent to the problem related to the BPM signal. Consequently, the TE power that needs to be considered is mainly generated by components of the LER arc vacuum chamber, where the TE cutoff frequency is less than the BPM processing frequency.« less

Advances in optical information processing IV; Proceedings of the Meeting, Orlando, FL, Apr. 18-20, 1990

NASA Astrophysics Data System (ADS)

Pape, Dennis R.

1990-09-01

The present conference discusses topics in optical image processing, optical signal processing, acoustooptic spectrum analyzer systems and components, and optical computing. Attention is given to tradeoffs in nonlinearly recorded matched filters, miniature spatial light modulators, detection and classification using higher-order statistics of optical matched filters, rapid traversal of an image data base using binary synthetic discriminant filters, wideband signal processing for emitter location, an acoustooptic processor for autonomous SAR guidance, and sampling of Fresnel transforms. Also discussed are an acoustooptic RF signal-acquisition system, scanning acoustooptic spectrum analyzers, the effects of aberrations on acoustooptic systems, fast optical digital arithmetic processors, information utilization in analog and digital processing, optical processors for smart structures, and a self-organizing neural network for unsupervised learning.

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

PubMed

Taherisadr, Mojtaba; Dehzangi, Omid; Parsaei, Hossein

2017-12-13

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

Context Specificity of Stress-activated Mitogen-activated Protein (MAP) Kinase Signaling: The Story as Told by Caenorhabditis elegans*

PubMed Central

Andrusiak, Matthew G.; Jin, Yishi

2016-01-01

Stress-associated p38 and JNK mitogen-activated protein (MAP) kinase signaling cascades trigger specific cellular responses and are involved in multiple disease states. At the root of MAP kinase signaling complexity is the differential use of common components on a context-specific basis. The roundworm Caenorhabditis elegans was developed as a system to study genes required for development and nervous system function. The powerful genetics of C. elegans in combination with molecular and cellular dissections has led to a greater understanding of how p38 and JNK signaling affects many biological processes under normal and stress conditions. This review focuses on the studies revealing context specificity of different stress-activated MAPK components in C. elegans. PMID:26907690

Fetal ECG extraction from abdominal signals: a review on suppression of fundamental power line interference component and its harmonics.

PubMed

Ţarălungă, Dragoş-Daniel; Ungureanu, Georgeta-Mihaela; Gussi, Ilinca; Strungaru, Rodica; Wolf, Werner

2014-01-01

Interference of power line (PLI) (fundamental frequency and its harmonics) is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG), electroencephalograms (EEG), and electrocardiograms (ECG). When analyzing the fetal ECG (fECG) recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the extraction of any useful information regarding the fetal health state. Many signal processing methods for cancelling the PLI from biopotentials are available in the literature. In this review study, six different principles are analyzed and discussed, and their performance is evaluated on simulated data (three different scenarios), based on five quantitative performance indices.

Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics

PubMed Central

Ţarălungă, Dragoş-Daniel; Ungureanu, Georgeta-Mihaela; Gussi, Ilinca; Strungaru, Rodica; Wolf, Werner

2014-01-01

Interference of power line (PLI) (fundamental frequency and its harmonics) is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG), electroencephalograms (EEG), and electrocardiograms (ECG). When analyzing the fetal ECG (fECG) recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the extraction of any useful information regarding the fetal health state. Many signal processing methods for cancelling the PLI from biopotentials are available in the literature. In this review study, six different principles are analyzed and discussed, and their performance is evaluated on simulated data (three different scenarios), based on five quantitative performance indices. PMID:24660020

Glucose and phytohormone interplay in controlling root directional growth in Arabidopsis.

PubMed

Singh, Manjul; Gupta, Aditi; Laxmi, Ashverya

2014-01-01

Sensing and responding toward gravity vector is a complicated and multistep process. Gravity is a constant factor feeding plants with reliable information for the spatial orientation of their organs. Auxin, cytokinin, ethylene and BRs have been the most explored hormones in relation to gravitropism. We have previously shown that glucose (Glc) could promote brassinosteroid (BR) signaling thereby inducing changes in root directional growth. Auxin signaling and polar transport components are also involved in Glc induced changes in root directional growth. Here, we provide evidence for involvement of cytokinin and ethylene signaling components in regulation of root directional growth downstream to Glc and BR. Altogether, Glc mediated change in root direction is an adaptive feature which is a result of a collaborative effort integrating phytohormonal signaling cues.

The Not-So-Global Blood Oxygen Level-Dependent Signal.

PubMed

Billings, Jacob; Keilholz, Shella

2018-04-01

Global signal regression is a controversial processing step for resting-state functional magnetic resonance imaging, partly because the source of the global blood oxygen level-dependent (BOLD) signal remains unclear. On the one hand, nuisance factors such as motion can readily introduce coherent BOLD changes across the whole brain. On the other hand, the global signal has been linked to neural activity and vigilance levels, suggesting that it contains important neurophysiological information and should not be discarded. Any widespread pattern of coordinated activity is likely to contribute appreciably to the global signal. Such patterns may include large-scale quasiperiodic spatiotemporal patterns, known also to be tied to performance on vigilance tasks. This uncertainty surrounding the separability of the global BOLD signal from concurrent neurological processes motivated an examination of the global BOLD signal's spatial distribution. The results clarify that although the global signal collects information from all tissue classes, a diverse subset of the BOLD signal's independent components contribute the most to the global signal. Further, the timing of each network's contribution to the global signal is not consistent across volunteers, confirming the independence of a constituent process that comprises the global signal.

Regularized non-stationary morphological reconstruction algorithm for weak signal detection in microseismic monitoring: methodology

NASA Astrophysics Data System (ADS)

Huang, Weilin; Wang, Runqiu; Chen, Yangkang

2018-05-01

Microseismic signal is typically weak compared with the strong background noise. In order to effectively detect the weak signal in microseismic data, we propose a mathematical morphology based approach. We decompose the initial data into several morphological multiscale components. For detection of weak signal, a non-stationary weighting operator is proposed and introduced into the process of reconstruction of data by morphological multiscale components. The non-stationary weighting operator can be obtained by solving an inversion problem. The regularized non-stationary method can be understood as a non-stationary matching filtering method, where the matching filter has the same size as the data to be filtered. In this paper, we provide detailed algorithmic descriptions and analysis. The detailed algorithm framework, parameter selection and computational issue for the regularized non-stationary morphological reconstruction (RNMR) method are presented. We validate the presented method through a comprehensive analysis through different data examples. We first test the proposed technique using a synthetic data set. Then the proposed technique is applied to a field project, where the signals induced from hydraulic fracturing are recorded by 12 three-component geophones in a monitoring well. The result demonstrates that the RNMR can improve the detectability of the weak microseismic signals. Using the processed data, the short-term-average over long-term average picking algorithm and Geiger's method are applied to obtain new locations of microseismic events. In addition, we show that the proposed RNMR method can be used not only in microseismic data but also in reflection seismic data to detect the weak signal. We also discussed the extension of RNMR from 1-D to 2-D or a higher dimensional version.

Ras1 interacts with multiple new signaling and cytoskeletal loci in Drosophila eggshell patterning and morphogenesis.

PubMed Central

Schnorr, J D; Holdcraft, R; Chevalier, B; Berg, C A

2001-01-01

Little is known about the genes that interact with Ras signaling pathways to regulate morphogenesis. The synthesis of dorsal eggshell structures in Drosophila melanogaster requires multiple rounds of Ras signaling followed by dramatic epithelial sheet movements. We took advantage of this process to identify genes that link patterning and morphogenesis; we screened lethal mutations on the second chromosome for those that could enhance a weak Ras1 eggshell phenotype. Of 1618 lethal P-element mutations tested, 13 showed significant enhancement, resulting in forked and fused dorsal appendages. Our genetic and molecular analyses together with information from the Berkeley Drosophila Genome Project reveal that 11 of these lines carry mutations in previously characterized genes. Three mutations disrupt the known Ras1 cell signaling components Star, Egfr, and Blistered, while one mutation disrupts Sec61beta, implicated in ligand secretion. Seven lines represent cell signaling and cytoskeletal components that are new to the Ras1 pathway; these are Chickadee (Profilin), Tec29, Dreadlocks, POSH, Peanut, Smt3, and MESK2, a suppressor of dominant-negative Ksr. A twelfth insertion disrupts two genes, Nrk, a "neurospecific" receptor tyrosine kinase, and Tpp, which encodes a neuropeptidase. These results suggest that Ras1 signaling during oogenesis involves novel components that may be intimately associated with additional signaling processes and with the reorganization of the cytoskeleton. To determine whether these Ras1 Enhancers function upstream or downstream of the Egf receptor, four mutations were tested for their ability to suppress an activated Egfr construct (lambdatop) expressed in oogenesis exclusively in the follicle cells. Mutations in Star and l(2)43Bb had no significant effect upon the lambdatop eggshell defect whereas smt3 and dock alleles significantly suppressed the lambdatop phenotype. PMID:11606538

Ras1 interacts with multiple new signaling and cytoskeletal loci in Drosophila eggshell patterning and morphogenesis.

PubMed

Schnorr, J D; Holdcraft, R; Chevalier, B; Berg, C A

2001-10-01

Little is known about the genes that interact with Ras signaling pathways to regulate morphogenesis. The synthesis of dorsal eggshell structures in Drosophila melanogaster requires multiple rounds of Ras signaling followed by dramatic epithelial sheet movements. We took advantage of this process to identify genes that link patterning and morphogenesis; we screened lethal mutations on the second chromosome for those that could enhance a weak Ras1 eggshell phenotype. Of 1618 lethal P-element mutations tested, 13 showed significant enhancement, resulting in forked and fused dorsal appendages. Our genetic and molecular analyses together with information from the Berkeley Drosophila Genome Project reveal that 11 of these lines carry mutations in previously characterized genes. Three mutations disrupt the known Ras1 cell signaling components Star, Egfr, and Blistered, while one mutation disrupts Sec61beta, implicated in ligand secretion. Seven lines represent cell signaling and cytoskeletal components that are new to the Ras1 pathway; these are Chickadee (Profilin), Tec29, Dreadlocks, POSH, Peanut, Smt3, and MESK2, a suppressor of dominant-negative Ksr. A twelfth insertion disrupts two genes, Nrk, a "neurospecific" receptor tyrosine kinase, and Tpp, which encodes a neuropeptidase. These results suggest that Ras1 signaling during oogenesis involves novel components that may be intimately associated with additional signaling processes and with the reorganization of the cytoskeleton. To determine whether these Ras1 Enhancers function upstream or downstream of the Egf receptor, four mutations were tested for their ability to suppress an activated Egfr construct (lambdatop) expressed in oogenesis exclusively in the follicle cells. Mutations in Star and l(2)43Bb had no significant effect upon the lambdatop eggshell defect whereas smt3 and dock alleles significantly suppressed the lambdatop phenotype.

Performance comparison of independent component analysis algorithms for fetal cardiac signal reconstruction: a study on synthetic fMCG data

NASA Astrophysics Data System (ADS)

Mantini, D.; Hild, K. E., II; Alleva, G.; Comani, S.

2006-02-01

Independent component analysis (ICA) algorithms have been successfully used for signal extraction tasks in the field of biomedical signal processing. We studied the performances of six algorithms (FastICA, CubICA, JADE, Infomax, TDSEP and MRMI-SIG) for fetal magnetocardiography (fMCG). Synthetic datasets were used to check the quality of the separated components against the original traces. Real fMCG recordings were simulated with linear combinations of typical fMCG source signals: maternal and fetal cardiac activity, ambient noise, maternal respiration, sensor spikes and thermal noise. Clusters of different dimensions (19, 36 and 55 sensors) were prepared to represent different MCG systems. Two types of signal-to-interference ratios (SIR) were measured. The first involves averaging over all estimated components and the second is based solely on the fetal trace. The computation time to reach a minimum of 20 dB SIR was measured for all six algorithms. No significant dependency on gestational age or cluster dimension was observed. Infomax performed poorly when a sub-Gaussian source was included; TDSEP and MRMI-SIG were sensitive to additive noise, whereas FastICA, CubICA and JADE showed the best performances. Of all six methods considered, FastICA had the best overall performance in terms of both separation quality and computation times.

RFI Detection and Mitigation using Independent Component Analysis as a Pre-Processor

NASA Technical Reports Server (NTRS)

Schoenwald, Adam J.; Gholian, Armen; Bradley, Damon C.; Wong, Mark; Mohammed, Priscilla N.; Piepmeier, Jeffrey R.

2016-01-01

Radio-frequency interference (RFI) has negatively impacted scientific measurements of passive remote sensing satellites. This has been observed in the L-band radiometers Soil Moisture and Ocean Salinity (SMOS), Aquarius and more recently, Soil Moisture Active Passive (SMAP). RFI has also been observed at higher frequencies such as K band. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements. This work explores the use of Independent Component Analysis (ICA) as a blind source separation (BSS) technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

Signal quality and Bayesian signal processing in neurofeedback based on real-time fMRI.

PubMed

Koush, Yury; Zvyagintsev, Mikhail; Dyck, Miriam; Mathiak, Krystyna A; Mathiak, Klaus

2012-01-02

Real-time fMRI allows analysis and visualization of the brain activity online, i.e. within one repetition time. It can be used in neurofeedback applications where subjects attempt to control an activation level in a specified region of interest (ROI) of their brain. The signal derived from the ROI is contaminated with noise and artifacts, namely with physiological noise from breathing and heart beat, scanner drift, motion-related artifacts and measurement noise. We developed a Bayesian approach to reduce noise and to remove artifacts in real-time using a modified Kalman filter. The system performs several signal processing operations: subtraction of constant and low-frequency signal components, spike removal and signal smoothing. Quantitative feedback signal quality analysis was used to estimate the quality of the neurofeedback time series and performance of the applied signal processing on different ROIs. The signal-to-noise ratio (SNR) across the entire time series and the group event-related SNR (eSNR) were significantly higher for the processed time series in comparison to the raw data. Applied signal processing improved the t-statistic increasing the significance of blood oxygen level-dependent (BOLD) signal changes. Accordingly, the contrast-to-noise ratio (CNR) of the feedback time series was improved as well. In addition, the data revealed increase of localized self-control across feedback sessions. The new signal processing approach provided reliable neurofeedback, performed precise artifacts removal, reduced noise, and required minimal manual adjustments of parameters. Advanced and fast online signal processing algorithms considerably increased the quality as well as the information content of the control signal which in turn resulted in higher contingency in the neurofeedback loop. Copyright © 2011 Elsevier Inc. All rights reserved.

NMR signal analysis to attribute the components to the solid/liquid phases present in mixes and ice creams.

PubMed

Mariette, François; Lucas, Tiphaine

2005-03-09

The NMR relaxation signals from complex products such as ice cream are hard to interpret because of the multiexponential behavior of the relaxation signal and the difficulty of attributing the NMR relaxation components to specific molecule fractions. An attribution of the NMR relaxation parameters is proposed, however, based on an approach that combines quantitative analysis of the spin-spin and spin-lattice relaxation times and the signal intensities with characterization of the ice cream components. We have been able to show that NMR can be used to describe the crystallized and liquid phases separately. The first component of the spin-spin and spin-lattice relaxation describes the behavior of the protons of the crystallized fat in the mix. The amount of fat crystals can then be estimated. In the case of ice cream, only the spin-lattice relaxation signal from the crystallized fraction is relevant. However, it enables the ice protons and the protons of the crystallized fat to be distinguished. The spin-lattice relaxation time can be used to describe the mobility of the protons in the different crystallized phases and also to quantify the amount of ice crystals and fat crystals in the ice cream. The NMR relaxation of the liquid phase of the mix has a biexponential behavior. A first component is attributable to the liquid fraction of the fat and to the sugars, while a second component is attributable to the aqueous phase. Overall, the study shows that despite the complexity of the NMR signal from ice cream, a number of relevant parameters can be extracted to study the influence of the formulation and of the process stages on the ice fraction, the crystallized fat fraction, and the liquid aqueous fraction.

Distributed digital signal processors for multi-body structures

NASA Technical Reports Server (NTRS)

Lee, Gordon K.

1990-01-01

Several digital filter designs were investigated which may be used to process sensor data from large space structures and to design digital hardware to implement the distributed signal processing architecture. Several experimental tests articles are available at NASA Langley Research Center to evaluate these designs. A summary of some of the digital filter designs is presented, an evaluation of their characteristics relative to control design is discussed, and candidate hardware microcontroller/microcomputer components are given. Future activities include software evaluation of the digital filter designs and actual hardware inplementation of some of the signal processor algorithms on an experimental testbed at NASA Langley.

Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: a review.

PubMed

Naik, Ganesh R; Arjunan, Sridhar; Kumar, Dinesh

2011-06-01

The surface electromyography (sEMG) signal separation and decphompositions has always been an interesting research topic in the field of rehabilitation and medical research. Subtle myoelectric control is an advanced technique concerned with the detection, processing, classification, and application of myoelectric signals to control human-assisting robots or rehabilitation devices. This paper reviews recent research and development in independent component analysis and Fractal dimensional analysis for sEMG pattern recognition, and presents state-of-the-art achievements in terms of their type, structure, and potential application. Directions for future research are also briefly outlined.

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.

Function of ABA in Stomatal Defense against Biotic and Drought Stresses

PubMed Central

Lim, Chae Woo; Baek, Woonhee; Jung, Jangho; Kim, Jung-Hyun; Lee, Sung Chul

2015-01-01

The plant hormone abscisic acid (ABA) regulates many key processes involved in plant development and adaptation to biotic and abiotic stresses. Under stress conditions, plants synthesize ABA in various organs and initiate defense mechanisms, such as the regulation of stomatal aperture and expression of defense-related genes conferring resistance to environmental stresses. The regulation of stomatal opening and closure is important to pathogen defense and control of transpirational water loss. Recent studies using a combination of approaches, including genetics, physiology, and molecular biology, have contributed considerably to our understanding of ABA signal transduction. A number of proteins associated with ABA signaling and responses—especially ABA receptors—have been identified. ABA signal transduction initiates signal perception by ABA receptors and transfer via downstream proteins, including protein kinases and phosphatases. In the present review, we focus on the function of ABA in stomatal defense against biotic and abiotic stresses, through analysis of each ABA signal component and the relationships of these components in the complex network of interactions. In particular, two ABA signal pathway models in response to biotic and abiotic stress were proposed, from stress signaling to stomatal closure, involving the pyrabactin resistance (PYR)/PYR-like (PYL) or regulatory component of ABA receptor (RCAR) family proteins, 2C-type protein phosphatases, and SnRK2-type protein kinases. PMID:26154766

Advanced Signal Conditioners for Data-Acquisition Systems

NASA Technical Reports Server (NTRS)

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

2004-01-01

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

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.

Signal Processing for Wireless Communication MIMO System with Nano- Scaled CSDG MOSFET based DP4T RF Switch.

PubMed

Srivastava, Viranjay M

2015-01-01

In the present technological expansion, the radio frequency integrated circuits in the wireless communication technologies became useful because of the replacement of increasing number of functions, traditional hardware components by modern digital signal processing. The carrier frequencies used for communication systems, now a day, shifted toward the microwave regime. The signal processing for the multiple inputs multiple output wireless communication system using the Metal- Oxide-Semiconductor Field-Effect-Transistor (MOSFET) has been done a lot. In this research the signal processing with help of nano-scaled Cylindrical Surrounding Double Gate (CSDG) MOSFET by means of Double- Pole Four-Throw Radio-Frequency (DP4T RF) switch, in terms of Insertion loss, Isolation, Reverse isolation and Inter modulation have been analyzed. In addition to this a channel model has been presented. Here, we also discussed some patents relevant to the topic.

Image reconstruction: an overview for clinicians.

PubMed

Hansen, Michael S; Kellman, Peter

2015-03-01

Image reconstruction plays a critical role in the clinical use of magnetic resonance imaging (MRI). The MRI raw data is not acquired in image space and the role of the image reconstruction process is to transform the acquired raw data into images that can be interpreted clinically. This process involves multiple signal processing steps that each have an impact on the image quality. This review explains the basic terminology used for describing and quantifying image quality in terms of signal-to-noise ratio and point spread function. In this context, several commonly used image reconstruction components are discussed. The image reconstruction components covered include noise prewhitening for phased array data acquisition, interpolation needed to reconstruct square pixels, raw data filtering for reducing Gibbs ringing artifacts, Fourier transforms connecting the raw data with image space, and phased array coil combination. The treatment of phased array coils includes a general explanation of parallel imaging as a coil combination technique. The review is aimed at readers with no signal processing experience and should enable them to understand what role basic image reconstruction steps play in the formation of clinical images and how the resulting image quality is described. © 2014 Wiley Periodicals, Inc.

Dysregulation of neural calcium signaling in Alzheimer disease, bipolar disorder and schizophrenia

PubMed Central

Berridge, Michael J.

2013-01-01

Neurons have highly developed Ca2+ signaling systems responsible for regulating a large number of neural functions such as the control of brain rhythms, information processing and the changes in synaptic plasticity that underpin learning and memory. The tonic excitatory drive, which is activated by the ascending arousal system, is particularly important for processes such as sensory perception, cognition and consciousness. The Ca2+ signaling pathway is a key component of this arousal system that regulates the neuronal excitability responsible for controlling the neural brain rhythms required for information processing and cognition. Dysregulation of the Ca2+ signaling pathway responsible for many of these neuronal processes has been implicated in the development of some of the major neural diseases in man such as Alzheimer disease, bipolar disorder and schizophrenia. Various treatments, which are known to act by reducing the activity of Ca2+ signaling, have proved successful in alleviating the symptoms of some of these neural diseases. PMID:22895098

Signal Decomposition of High Resolution Time Series River Data to Separate Local and Regional Components of Conductivity

EPA Science Inventory

Signal processing techniques were applied to high-resolution time series data obtained from conductivity loggers placed upstream and downstream of an oil and gas wastewater treatment facility along a river. Data was collected over 14-60 days. The power spectral density was us...

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

PubMed

Mammone, Nadia; Morabito, Francesco Carlo

2008-09-01

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

Low power signal processing electronics for wearable medical devices.

PubMed

Casson, Alexander J; Rodriguez-Villegas, Esther

2010-01-01

Custom designed microchips, known as Application Specific Integrated Circuits (ASICs), offer the lowest possible power consumption electronics. However, this comes at the cost of a longer, more complex and more costly design process compared to one using generic, off-the-shelf components. Nevertheless, their use is essential in future truly wearable medical devices that must operate for long periods of time from physically small, energy limited batteries. This presentation will demonstrate the state-of-the-art in ASIC technology for providing online signal processing for use in these wearable medical devices.

Laser pulse coded signal frequency measuring device based on DSP and CPLD

NASA Astrophysics Data System (ADS)

Zhang, Hai-bo; Cao, Li-hua; Geng, Ai-hui; Li, Yan; Guo, Ru-hai; Wang, Ting-feng

2011-06-01

Laser pulse code is an anti-jamming measures used in semi-active laser guided weapons. On account of the laser-guided signals adopting pulse coding mode and the weak signal processing, it need complex calculations in the frequency measurement process according to the laser pulse code signal time correlation to meet the request in optoelectronic countermeasures in semi-active laser guided weapons. To ensure accurately completing frequency measurement in a short time, it needed to carry out self-related process with the pulse arrival time series composed of pulse arrival time, calculate the signal repetition period, and then identify the letter type to achieve signal decoding from determining the time value, number and rank number in a signal cycle by Using CPLD and DSP for signal processing chip, designing a laser-guided signal frequency measurement in the pulse frequency measurement device, improving the signal processing capability through the appropriate software algorithms. In this article, we introduced the principle of frequency measurement of the device, described the hardware components of the device, the system works and software, analyzed the impact of some system factors on the accuracy of the measurement. The experimental results indicated that this system improve the accuracy of the measurement under the premise of volume, real-time, anti-interference, low power of the laser pulse frequency measuring device. The practicality of the design, reliability has been demonstrated from the experimental point of view.

Directional dual-tree complex wavelet packet transforms for processing quadrature signals.

PubMed

Serbes, Gorkem; Gulcur, Halil Ozcan; Aydin, Nizamettin

2016-03-01

Quadrature signals containing in-phase and quadrature-phase components are used in many signal processing applications in every field of science and engineering. Specifically, Doppler ultrasound systems used to evaluate cardiovascular disorders noninvasively also result in quadrature format signals. In order to obtain directional blood flow information, the quadrature outputs have to be preprocessed using methods such as asymmetrical and symmetrical phasing filter techniques. These resultant directional signals can be employed in order to detect asymptomatic embolic signals caused by small emboli, which are indicators of a possible future stroke, in the cerebral circulation. Various transform-based methods such as Fourier and wavelet were frequently used in processing embolic signals. However, most of the times, the Fourier and discrete wavelet transforms are not appropriate for the analysis of embolic signals due to their non-stationary time-frequency behavior. Alternatively, discrete wavelet packet transform can perform an adaptive decomposition of the time-frequency axis. In this study, directional discrete wavelet packet transforms, which have the ability to map directional information while processing quadrature signals and have less computational complexity than the existing wavelet packet-based methods, are introduced. The performances of proposed methods are examined in detail by using single-frequency, synthetic narrow-band, and embolic quadrature signals.

Holostrain system: a powerful tool for experimental mechanics

NASA Astrophysics Data System (ADS)

Sciammarella, Cesar A.; Bhat, Gopalakrishna K.

1992-09-01

A portable holographic interferometer that can be used to measure displacements and strains in all kinds of mechanical components and structures is described. The holostrain system captures images on a TV camera that detects interference patterns produced by laser illumination. The video signals are digitized. The digitized interferograms are processed by a fast processing system. The output of the system are the strains or the stresses of the observed mechanical component or structure.

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.

A three-component signalling system fine-tunes expression kinetics of HPPK responsible for folate synthesis by positive feedback loop during stress response of Xanthomonas campestris.

PubMed

Wang, Fang-Fang; Deng, Chao-Ying; Cai, Zhen; Wang, Ting; Wang, Li; Wang, Xiao-Zheng; Chen, Xiao-Ying; Fang, Rong-Xiang; Qian, Wei

2014-07-01

During adaptation to environments, bacteria employ two-component signal transduction systems, which contain histidine kinases and response regulators, to sense and respond to exogenous and cellular stimuli in an accurate spatio-temporal manner. Although the protein phosphorylation process between histidine kinase and response regulator has been well documented, the molecular mechanism fine-tuning phosphorylation levels of response regulators is comparatively less studied. Here we combined genetic and biochemical approaches to reveal that a hybrid histidine kinase, SreS, is involved in the SreK-SreR phosphotransfer process to control salt stress response in the bacterium Xanthomonas campestris. The N-terminal receiver domain of SreS acts as a phosphate sink by competing with the response regulator SreR to accept the phosphoryl group from the latter's cognate histidine kinase SreK. This regulatory process is critical for bacterial survival because the dephosphorylated SreR protein participates in activating one of the tandem promoters (P2) at the 5' end of the sreK-sreR-sreS-hppK operon, and then modulates a transcriptional surge of the stress-responsive gene hppK, which is required for folic acid synthesis. Therefore, our study dissects the biochemical process of a positive feedback loop in which a 'three-component' signalling system fine-tunes expression kinetics of downstream genes. © 2013 Society for Applied Microbiology and John Wiley & Sons Ltd.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines.

PubMed

Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

2016-12-13

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines

PubMed Central

Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

2016-01-01

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods. PMID:27983577

The Seismic Tool-Kit (STK): An Open Source Software For Learning the Basis of Signal Processing and Seismology.

NASA Astrophysics Data System (ADS)

Reymond, D.

2016-12-01

We present an open source software project (GNU public license), named STK: Seismic Tool-Kit, that is dedicated mainly for learning signal processing and seismology. The STK project that started in 2007, is hosted by SourceForge.net, and count more than 20000 downloads at the date of writing.The STK project is composed of two main branches:First, a graphical interface dedicated to signal processing (in the SAC format (SAC_ASCII and SAC_BIN): where the signal can be plotted, zoomed, filtered, integrated, derivated, ... etc. (a large variety of IFR and FIR filter is proposed). The passage in the frequency domain via the Fourier transform is used to introduce the estimation of spectral density of the signal , with visualization of the Power Spectral Density (PSD) in linear or log scale, and also the evolutive time-frequency representation (or sonagram). The 3-components signals can be also processed for estimating their polarization properties, either for a given window, or either for evolutive windows along the time. This polarization analysis is useful for extracting the polarized noises, differentiating P waves, Rayleigh waves, Love waves, ... etc. Secondly, a panel of Utilities-Program are proposed for working in a terminal mode, with basic programs for computing azimuth and distance in spherical geometry, inter/auto-correlation, spectral density, time-frequency for an entire directory of signals, focal planes, and main components axis, radiation pattern of P waves, Polarization analysis of different waves (including noise), under/over-sampling the signals, cubic-spline smoothing, and linear/non linear regression analysis of data set. STK is developed in C/C++, mainly under Linux OS, and it has been also partially implemented under MS-Windows. STK has been used in some schools for viewing and plotting seismic records provided by IRIS, and it has been used as a practical support for teaching the basis of signal processing. Useful links:http://sourceforge.net/projects/seismic-toolkit/http://sourceforge.net/p/seismic-toolkit/wiki/browse_pages/

A Pivotal Role of DELLAs in Regulating Multiple Hormone Signals.

PubMed

Davière, Jean-Michel; Achard, Patrick

2016-01-04

Plant phenotypic plasticity is controlled by diverse hormone pathways, which integrate and convey information from multiple developmental and environmental signals. Moreover, in plants many processes such as growth, development, and defense are regulated in similar ways by multiple hormones. Among them, gibberellins (GAs) are phytohormones with pleiotropic actions, regulating various growth processes throughout the plant life cycle. Previous work has revealed extensive interplay between GAs and other hormones, but the molecular mechanism became apparent only recently. Molecular and physiological studies have demonstrated that DELLA proteins, considered as master negative regulators of GA signaling, integrate multiple hormone signaling pathways through physical interactions with transcription factors or regulatory proteins from different families. In this review, we summarize the latest progress in GA signaling and its direct crosstalk with the main phytohormone signaling, emphasizing the multifaceted role of DELLA proteins with key components of major hormone signaling pathways. Copyright © 2016 The Author. Published by Elsevier Inc. All rights reserved.

Plant hormone signaling lightens up: integrators of light and hormones.

PubMed

Lau, On Sun; Deng, Xing Wang

2010-10-01

Light is an important environmental signal that regulates diverse growth and developmental processes in plants. In these light-regulated processes, multiple hormonal pathways are often modulated by light to mediate the developmental changes. Conversely, hormone levels in plants also serve as endogenous cues in influencing light responsiveness. Although interactions between light and hormone signaling pathways have long been observed, recent studies have advanced our understanding by identifying signaling integrators that connect the pathways. These integrators, namely PHYTOCHROME-INTERACTING FACTOR 3 (PIF3), PIF4, PIF3-LIKE 5 (PIL5)/PIF1 and LONG HYPOCOTYL 5 (HY5), are key light signaling components and they link light signals to the signaling of phytohormones, such as gibberellin (GA), abscisic acid (ABA), auxin and cytokinin, in regulating seedling photomorphogenesis and seed germination. This review focuses on these integrators in illustrating how light and hormone interact. Copyright © 2010 Elsevier Ltd. All rights reserved.

Reduction hybrid artifacts of EMG-EOG in electroencephalography evoked by prefrontal transcranial magnetic stimulation

NASA Astrophysics Data System (ADS)

Bai, Yang; Wan, Xiaohong; Zeng, Ke; Ni, Yinmei; Qiu, Lirong; Li, Xiaoli

2016-12-01

Objective. When prefrontal-transcranial magnetic stimulation (p-TMS) performed, it may evoke hybrid artifact mixed with muscle activity and blink activity in EEG recordings. Reducing this kind of hybrid artifact challenges the traditional preprocessing methods. We aim to explore method for the p-TMS evoked hybrid artifact removal. Approach. We propose a novel method used as independent component analysis (ICA) post processing to reduce the p-TMS evoked hybrid artifact. Ensemble empirical mode decomposition (EEMD) was used to decompose signal into multi-components, then the components were separated with artifact reduced by blind source separation (BSS) method. Three standard BSS methods, ICA, independent vector analysis, and canonical correlation analysis (CCA) were tested. Main results. Synthetic results showed that EEMD-CCA outperformed others as ICA post processing step in hybrid artifacts reduction. Its superiority was clearer when signal to noise ratio (SNR) was lower. In application to real experiment, SNR can be significantly increased and the p-TMS evoked potential could be recovered from hybrid artifact contaminated signal. Our proposed method can effectively reduce the p-TMS evoked hybrid artifacts. Significance. Our proposed method may facilitate future prefrontal TMS-EEG researches.

Energy and Timing Measurement with Time-Based Detector Readout for PET Applications: Principle and Validation with Discrete Circuit Components

PubMed Central

Sun, Xishan; Lan, Allan K.; Bircher, Chad; Deng, Zhi; Liu, Yinong; Shao, Yiping

2011-01-01

A new signal processing method for PET application has been developed, with discrete circuit components to measure energy and timing of a gamma interaction based solely on digital timing processing without using an amplitude-to-digital convertor (ADC) or a constant fraction discriminator (CFD). A single channel discrete component time-based readout (TBR) circuit was implemented in a PC board. Initial circuit functionality and performance evaluations have been conducted. Accuracy and linearity of signal amplitude measurement were excellent, as measured with test pulses. The measured timing accuracy from test pulses reached to less than 300 ps, a value limited mainly by the timing jitter of the prototype electronics circuit. Both suitable energy and coincidence timing resolutions (~18% and ~1.0 ns) have been achieved with 3 × 3 × 20 mm3 LYSO scintillator and photomultiplier tube-based detectors. With its relatively simple circuit and low cost, TBR is expected to be a suitable front-end signal readout electronics for compact PET or other radiation detectors requiring the reading of a large number of detector channels and demanding high performance for energy and timing measurement. PMID:21743761

Working memory component processes: isolating BOLD signal changes.

PubMed

Motes, Michael A; Rypma, Bart

2010-01-15

The chronology of the component processes subserving working memory (WM) and hemodynamic response lags has hindered the use of fMRI for exploring neural substrates of WM. In the present study, however, participants completed full trials that involved encoding two or six letters, maintaining the memory set over a delay, and then deciding whether a probe was in the memory set or not. Additionally, they completed encode-only, encode-and-maintain, and encode-and-decide partial trials intermixed with the full trials. The inclusion of partial trials allowed for the isolation of BOLD signal changes to the different trial periods. The results showed that only lateral and medial prefrontal cortex regions differentially responded to the 2- and 6-letter memory sets over the trial periods, showing greater activation to 6-letter sets during the encode and maintain trial periods. Thus, the data showed the differential involvement of PFC in the encoding and maintenance of supra- and sub-capacity memory sets and show the efficacy of using fMRI partial trial methods to study WM component processes.

Working Memory Component Processes: Isolating BOLD Signal-Changes

PubMed Central

Motes, Michael A.; Rypma, Bart

2009-01-01

The chronology of the component processes subserving working memory (WM) and hemodynamic response lags have hindered the use of fMRI for exploring neural substrates of WM. In the present study, however, participants completed full trials that involved encoding two or six letters, maintaining the memory-set over a delay, and then deciding whether a probe was in the memory-set or not. Additionally, they completed encode only, encode and maintain, and encode and decide partial-trials intermixed with the full-trials. The inclusion of partial-trials allowed for the isolation of BOLD signal-changes to the different trial-periods. The results showed that only lateral and medial prefrontal cortex regions differentially responded to the 2- and 6-letter memory-sets over the trial-periods, showing greater activation to 6-letter sets during the encode and maintain trial-periods. Thus, the data showed the differential involvement of PFC in the encoding and maintenance of supra- and sub-capacity memory-sets and show the efficacy of using fMRI partial-trial methods to study WM component processes. PMID:19732840

Application of laser anemometry in turbine engine research

NASA Technical Reports Server (NTRS)

Seasholtz, R. G.

1983-01-01

The application of laser anemometry to the study of flow fields in turbine engine components is reviewed. Included are discussions of optical configurations, seeding requirements, electronic signal processing, and data processing. Some typical results are presented along with a discussion of ongoing work.

Application of laser anemometry in turbine engine research

NASA Technical Reports Server (NTRS)

Seasholtz, R. G.

1982-01-01

The application of laser anemometry to the study of flow fields in turbine engine components is reviewed. Included are discussions of optical configurations, seeding requirements, electronic signal processing, and data processing. Some typical results are presented along with a discussion of ongoing work.

Modeling Array Stations in SIG-VISA

NASA Astrophysics Data System (ADS)

Ding, N.; Moore, D.; Russell, S.

2013-12-01

We add support for array stations to SIG-VISA, a system for nuclear monitoring using probabilistic inference on seismic signals. Array stations comprise a large portion of the IMS network; they can provide increased sensitivity and more accurate directional information compared to single-component stations. Our existing model assumed that signals were independent at each station, which is false when lots of stations are close together, as in an array. The new model removes that assumption by jointly modeling signals across array elements. This is done by extending our existing Gaussian process (GP) regression models, also known as kriging, from a 3-dimensional single-component space of events to a 6-dimensional space of station-event pairs. For each array and each event attribute (including coda decay, coda height, amplitude transfer and travel time), we model the joint distribution across array elements using a Gaussian process that learns the correlation lengthscale across the array, thereby incorporating information of array stations into the probabilistic inference framework. To evaluate the effectiveness of our model, we perform ';probabilistic beamforming' on new events using our GP model, i.e., we compute the event azimuth having highest posterior probability under the model, conditioned on the signals at array elements. We compare the results from our probabilistic inference model to the beamforming currently performed by IMS station processing.

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.

Ultrasonic Signal Processing for Structural Health Monitoring

NASA Astrophysics Data System (ADS)

Michaels, Jennifer E.; Michaels, Thomas E.

2004-02-01

Permanently mounted ultrasonic sensors are a key component of systems under development for structural health monitoring. Signal processing plays a critical role in the viability of such systems due to the difficulty in interpreting signals received from structures of complex geometry. This paper describes a differential feature-based approach to classifying signal changes as either "environmental" or "structural". Data are presented from piezoelectric discs bonded to an aluminum specimen subjected to both environmental changes and introduction of artificial defects. The classifier developed as part of this study was able to correctly identify artificial defects that were not part of the initial training and evaluation data sets. Central to the success of the classifier was the use of the Short Time Cross Correlation to measure coherency between the signal and reference as a function of time.

WAMS measurements pre-processing for detecting low-frequency oscillations in power systems

NASA Astrophysics Data System (ADS)

Kovalenko, P. Y.

2017-07-01

Processing the data received from measurement systems implies the situation when one or more registered values stand apart from the sample collection. These values are referred to as “outliers”. The processing results may be influenced significantly by the presence of those in the data sample under consideration. In order to ensure the accuracy of low-frequency oscillations detection in power systems the corresponding algorithm has been developed for the outliers detection and elimination. The algorithm is based on the concept of the irregular component of measurement signal. This component comprises measurement errors and is assumed to be Gauss-distributed random. The median filtering is employed to detect the values lying outside the range of the normally distributed measurement error on the basis of a 3σ criterion. The algorithm has been validated involving simulated signals and WAMS data as well.

Advanced High Temperature Polymer Matrix Composites for Gas Turbine Engines Program Expansion

NASA Technical Reports Server (NTRS)

Hanley, David; Carella, John

1999-01-01

This document, submitted by AlliedSignal Engines (AE), a division of AlliedSignal Aerospace Company, presents the program final report for the Advanced High Temperature Polymer Matrix Composites for Gas Turbine Engines Program Expansion in compliance with data requirements in the statement of work, Contract No. NAS3-97003. This document includes: 1 -Technical Summary: a) Component Design, b) Manufacturing Process Selection, c) Vendor Selection, and d) Testing Validation: 2-Program Conclusion and Perspective. Also, see the Appendix at the back of this report. This report covers the program accomplishments from December 1, 1996, to August 24, 1998. The Advanced High Temperature PMC's for Gas Turbine Engines Program Expansion was a one year long, five task technical effort aimed at designing, fabricating and testing a turbine engine component using NASA's high temperature resin system AMB-21. The fiber material chosen was graphite T650-35, 3K, 8HS with UC-309 sizing. The first four tasks included component design and manufacturing, process selection, vendor selection, component fabrication and validation testing. The final task involved monthly financial and technical reports.

Context Specificity of Stress-activated Mitogen-activated Protein (MAP) Kinase Signaling: The Story as Told by Caenorhabditis elegans.

PubMed

Andrusiak, Matthew G; Jin, Yishi

2016-04-08

Stress-associated p38 and JNK mitogen-activated protein (MAP) kinase signaling cascades trigger specific cellular responses and are involved in multiple disease states. At the root of MAP kinase signaling complexity is the differential use of common components on a context-specific basis. The roundwormCaenorhabditis eleganswas developed as a system to study genes required for development and nervous system function. The powerful genetics ofC. elegansin combination with molecular and cellular dissections has led to a greater understanding of how p38 and JNK signaling affects many biological processes under normal and stress conditions. This review focuses on the studies revealing context specificity of different stress-activated MAPK components inC. elegans. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

A feasibility study on age-related factors of wrist pulse using principal component analysis.

PubMed

Jang-Han Bae; Young Ju Jeon; Sanghun Lee; Jaeuk U Kim

2016-08-01

Various analysis methods for examining wrist pulse characteristics are needed for accurate pulse diagnosis. In this feasibility study, principal component analysis (PCA) was performed to observe age-related factors of wrist pulse from various analysis parameters. Forty subjects in the age group of 20s and 40s were participated, and their wrist pulse signal and respiration signal were acquired with the pulse tonometric device. After pre-processing of the signals, twenty analysis parameters which have been regarded as values reflecting pulse characteristics were calculated and PCA was performed. As a results, we could reduce complex parameters to lower dimension and age-related factors of wrist pulse were observed by combining-new analysis parameter derived from PCA. These results demonstrate that PCA can be useful tool for analyzing wrist pulse signal.

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

NASA Astrophysics Data System (ADS)

Mishra, Puneet; Singla, Sunil Kumar

2013-01-01

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.

Signalling Network Construction for Modelling Plant Defence Response

PubMed Central

Miljkovic, Dragana; Stare, Tjaša; Mozetič, Igor; Podpečan, Vid; Petek, Marko; Witek, Kamil; Dermastia, Marina; Lavrač, Nada; Gruden, Kristina

2012-01-01

Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2) triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be utilised for modelling other biological systems, given that an adequate vocabulary is provided. PMID:23272172

Method and apparatus for reconstructing in-cylinder pressure and correcting for signal decay

DOEpatents

Huang, Jian

2013-03-12

A method comprises steps for reconstructing in-cylinder pressure data from a vibration signal collected from a vibration sensor mounted on an engine component where it can generate a signal with a high signal-to-noise ratio, and correcting the vibration signal for errors introduced by vibration signal charge decay and sensor sensitivity. The correction factors are determined as a function of estimated motoring pressure and the measured vibration signal itself with each of these being associated with the same engine cycle. Accordingly, the method corrects for charge decay and changes in sensor sensitivity responsive to different engine conditions to allow greater accuracy in the reconstructed in-cylinder pressure data. An apparatus is also disclosed for practicing the disclosed method, comprising a vibration sensor, a data acquisition unit for receiving the vibration signal, a computer processing unit for processing the acquired signal and a controller for controlling the engine operation based on the reconstructed in-cylinder pressure.

Microwave Photonic Architecture for Direction Finding of LPI Emitters: Front End Analog Circuit Design and Component Characterization

DTIC Science & Technology

2016-09-01

design to control the phase shifters was complex, and the calibration process was time consuming. During the redesign process, we carried out...signals in time domain with a maximum sampling frequency of 20 Giga samples per second. In the previous tests of the design , the performance of...PHOTONIC ARCHITECTURE FOR DIRECTION FINDING OF LPI EMITTERS: FRONT-END ANALOG CIRCUIT DESIGN AND COMPONENT CHARACTERIZATION by Chew K. Tan

Investigation on the coloured noise in GPS-derived position with time-varying seasonal signals

NASA Astrophysics Data System (ADS)

Gruszczynska, Marta; Klos, Anna; Bos, Machiel Simon; Bogusz, Janusz

2016-04-01

The seasonal signals in the GPS-derived time series arise from real geophysical signals related to tidal (residual) or non-tidal (loadings from atmosphere, ocean and continental hydrosphere, thermo elastic strain, etc.) effects and numerical artefacts including aliasing from mismodelling in short periods or repeatability of the GPS satellite constellation with respect to the Sun (draconitics). Singular Spectrum Analysis (SSA) is a method for investigation of nonlinear dynamics, suitable to either stationary or non-stationary data series without prior knowledge about their character. The aim of SSA is to mathematically decompose the original time series into a sum of slowly varying trend, seasonal oscillations and noise. In this presentation we will explore the ability of SSA to subtract the time-varying seasonal signals in GPS-derived North-East-Up topocentric components and show properties of coloured noise from residua. For this purpose we used data from globally distributed IGS (International GNSS Service) permanent stations processed by the JPL (Jet Propulsion Laboratory) in a PPP (Precise Point Positioning) mode. After introducing a threshold of 13 years, 264 stations left with a maximum length reaching 23 years. The data was initially pre-processed for outliers, offsets and gaps. The SSA was applied to pre-processed series to estimate the time-varying seasonal signals. We adopted a 3-years window as the optimal dimension of its size determined with the Akaike's Information Criteria (AIC) values. A Fisher-Snedecor test corrected for the presence of temporal correlation was used to determine the statistical significance of reconstructed components. This procedure showed that first four components describing annual and semi-annual signals, are significant at a 99.7% confidence level, which corresponds to 3-sigma criterion. We compared the non-parametric SSA approach with a commonly chosen parametric Least-Squares Estimation that assumes constant amplitudes and phases over time. We noticed a maximum difference in seasonal oscillation of 3.5 mm and a maximum change in velocity of 0.15 mm/year for Up component (YELL, Yellowknife, Canada), when SSA and LSE are compared. The annual signal has the greatest influence on data variability in time series, while the semi-annual signal in Up component has much smaller contribution in the total variance of data. For some stations more than 35% of the total variance is explained by annual signal. According to the Power Spectral Densities (PSD) we proved that SSA has the ability to properly subtract the seasonals changing in time with almost no influence on power-law character of stochastic part. Then, the modified Maximum Likelihood Estimation (MLE) in Hector software was applied to SSA-filtered time series. We noticed a significant improvement in spectral indices and power-law amplitudes in comparison to classically determined ones with LSE, which will be the main subject of this presentation.

Novel disease susceptibility factors for fungal necrotrophic pathogens in Arabidopsis.

PubMed

Dobón, Albor; Canet, Juan Vicente; García-Andrade, Javier; Angulo, Carlos; Neumetzler, Lutz; Persson, Staffan; Vera, Pablo

2015-04-01

Host cells use an intricate signaling system to respond to invasions by pathogenic microorganisms. Although several signaling components of disease resistance against necrotrophic fungal pathogens have been identified, our understanding for how molecular components and host processes contribute to plant disease susceptibility is rather sparse. Here, we identified four transcription factors (TFs) from Arabidopsis that limit pathogen spread. Arabidopsis mutants defective in any of these TFs displayed increased disease susceptibility to Botrytis cinerea and Plectosphaerella cucumerina, and a general activation of non-immune host processes that contribute to plant disease susceptibility. Transcriptome analyses revealed that the mutants share a common transcriptional signature of 77 up-regulated genes. We characterized several of the up-regulated genes that encode peptides with a secretion signal, which we named PROVIR (for provirulence) factors. Forward and reverse genetic analyses revealed that many of the PROVIRs are important for disease susceptibility of the host to fungal necrotrophs. The TFs and PROVIRs identified in our work thus represent novel genetic determinants for plant disease susceptibility to necrotrophic fungal pathogens.

Importance of phase alignment for interocular suppression.

PubMed

Maehara, Goro; Huang, Pi-Chun; Hess, Robert F

2009-07-01

We measured contrast thresholds for Gabor targets in the presence of maskers which had higher or lower spatial frequencies than the targets. A high-pass fractal masker elevated target contrast thresholds at low and intermediate pedestal contrasts in both monocular and dichoptic modes of presentation, suggesting that the masking occurs after a monocular processing stage. Moreover we found that a high-pass checkerboard masker elevated thresholds at the low and intermediate pedestal contrasts and that most of this threshold elevation disappeared when the phase of the masker's spatial components were scrambled. This masking was effective only in the dichoptic presentation, not in the monocular presentation. These results indicate that phase alignment of the high spatial frequency components plays a crucial role for interocular suppression. We speculate that phase alignments signal the existence of a luminance contour in the monocular image and that this signal suppresses processing of information in the other eye when there is no corresponding signal in that eye.

Long period seismic source characterization at Popocatépetl volcano, Mexico

USGS Publications Warehouse

Arciniega-Ceballos, Alejandra; Dawson, Phillip; Chouet, Bernard A.

2012-01-01

The seismicity of Popocatépetl is dominated by long-period and very-long period signals associated with hydrothermal processes and magmatic degassing. We model the source mechanism of repetitive long-period signals in the 0.4–2 s band from a 15-station broadband network by stacking long-period events with similar waveforms to improve the signal-to-noise ratio. The data are well fitted by a point source located within the summit crater ~250 m below the crater floor and ~200 m from the inferred magma conduit. The inferred source includes a volumetric component that can be modeled as resonance of a horizontal steam-filled crack and a vertical single force component. The long-period events are thought to be related to the interaction between the magmatic system and a perched hydrothermal system. Repetitive injection of fluid into the horizontal fracture and subsequent sudden discharge when a critical pressure threshold is met provides a non-destructive source process.

Acoustooptic Processing of Two Dimensional Signals Using Temporal and Spatial Integration

DTIC Science & Technology

1989-05-12

AND SPATIAL INTEGRATION Demetri Psaltis, John Hong, Scott Hudson, Jeff Yu Fai Mok, Mark Neifeld, and Nabeel Riza, Dave Brady V 13U7101 4 NS7urtn-a...Jeff Yu Fai Mok, Mark Neifeld, and Nabeel Riza, Dave Brady DTIC Grant AFOSR-85-0332 ELECTE Submitted to: S J’ Dr. Lee Giles Air Force Office of...In addition we examine the capacity when the filter is binarized. Vector-matrix multipliers are fundamental components of many signal processing sys

Multichannel temperature controller for hot air solar house

NASA Technical Reports Server (NTRS)

Currie, J. R.

1979-01-01

This paper describes an electronic controller that is optimized to operate a hot air solar system. Thermal information is obtained from copper constantan thermocouples and a wall-type thermostat. The signals from the thermocouples are processed through a single amplifier using a multiplexing scheme. The multiplexing reduces the component count and automatically calibrates the thermocouple amplifier. The processed signals connect to some simple logic that selects one of the four operating modes. This simple, inexpensive, and reliable scheme is well suited to control hot air solar systems.

Spectral responses of gravel beaches to tidal signals

NASA Astrophysics Data System (ADS)

Geng, Xiaolong; Boufadel, Michel C.

2017-01-01

Tides have been recognized as a major driving forcing affecting coastal aquifer system, and deterministic modeling has been very effective in elucidating mechanisms caused by tides. However, such modeling does not lend itself to capture embedded information in the signal, and rather focuses on the primary processes. Here, using yearlong data sets measured at beaches in Alaska Prince William Sound, we performed spectral and correlation analyses to identify temporal behavior of pore-water pressure, temperature and salinity. We found that the response of the beach system was characterized by fluctuations of embedded diurnal, semidiurnal, terdiurnal and quarterdiurnal tidal components. Hydrodynamic dispersion of salinity and temperature, and the thermal conductivity greatly affected pore water signals. Spectral analyses revealed a faster dissipation of the semi-diurnal component with respect to the diurnal components. Correlation functions showed that salinity had a relatively short memory of the tidal signal when inland freshwater recharge was large. In contrast, the signature of the tidal signal on pore-water temperature persisted for longer times, up to a week. We also found that heterogeneity greatly affected beach response. The response varied from a simple linear mapping in the frequency domain to complete modulation and masking of the input frequencies.

Detection and recognition of mechanical, digging and vehicle signals in the optical fiber pre-warning system

NASA Astrophysics Data System (ADS)

Tian, Qing; Yang, Dan; Zhang, Yuan; Qu, Hongquan

2018-04-01

This paper presents detection and recognition method to locate and identify harmful intrusions in the optical fiber pre-warning system (OFPS). Inspired by visual attention architecture (VAA), the process flow is divided into two parts, i.e., data-driven process and task-driven process. At first, data-driven process takes all the measurements collected by the system as input signals, which is handled by detection method to locate the harmful intrusion in both spatial domain and time domain. Then, these detected intrusion signals are taken over by task-driven process. Specifically, we get pitch period (PP) and duty cycle (DC) of the intrusion signals to identify the mechanical and manual digging (MD) intrusions respectively. For the passing vehicle (PV) intrusions, their strong low frequency component can be used as good feature. In generally, since the harmful intrusion signals only account for a small part of whole measurements, the data-driven process reduces the amount of input data for subsequent task-driven process considerably. Furthermore, the task-driven process determines the harmful intrusions orderly according to their severity, which makes a priority mechanism for the system as well as targeted processing for different harmful intrusion. At last, real experiments are performed to validate the effectiveness of this method.

Digital Signal Processing and Generation for a DC Current Transformer for Particle Accelerators

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

Zorzetti, Silvia

2013-01-01

The thesis topic, digital signal processing and generation for a DC current transformer, focuses on the most fundamental beam diagnostics in the field of particle accelerators, the measurement of the beam intensity, or beam current. The technology of a DC current transformer (DCCT) is well known, and used in many areas, including particle accelerator beam instrumentation, as non-invasive (shunt-free) method to monitor the DC current in a conducting wire, or in our case, the current of charged particles travelling inside an evacuated metal pipe. So far, custom and commercial DCCTs are entirely based on analog technologies and signal processing, whichmore » makes them inflexible, sensitive to component aging, and difficult to maintain and calibrate.« less

EEG feature selection method based on decision tree.

PubMed

Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

Application of blind source separation to real-time dissolution dynamic nuclear polarization.

PubMed

Hilty, Christian; Ragavan, Mukundan

2015-01-20

The use of a blind source separation (BSS) algorithm is demonstrated for the analysis of time series of nuclear magnetic resonance (NMR) spectra. This type of data is obtained commonly from experiments, where analytes are hyperpolarized using dissolution dynamic nuclear polarization (D-DNP), both in in vivo and in vitro contexts. High signal gains in D-DNP enable rapid measurement of data sets characterizing the time evolution of chemical or metabolic processes. BSS is based on an algorithm that can be applied to separate the different components contributing to the NMR signal and determine the time dependence of the signals from these components. This algorithm requires minimal prior knowledge of the data, notably, no reference spectra need to be provided, and can therefore be applied rapidly. In a time-resolved measurement of the enzymatic conversion of hyperpolarized oxaloacetate to malate, the two signal components are separated into computed source spectra that closely resemble the spectra of the individual compounds. An improvement in the signal-to-noise ratio of the computed source spectra is found compared to the original spectra, presumably resulting from the presence of each signal more than once in the time series. The reconstruction of the original spectra yields the time evolution of the contributions from the two sources, which also corresponds closely to the time evolution of integrated signal intensities from the original spectra. BSS may therefore be an approach for the efficient identification of components and estimation of kinetics in D-DNP experiments, which can be applied at a high level of automation.

Effect of binding in cyclic phosphorylation-dephosphorylation process and in energy transformation.

PubMed

Sarkar, A; Beard, D A; Franza, B R

2006-07-01

The effects of binding on the phosphorylation-dephosphorylation cycle (PDPC) - one of the key components of the signal transduction processes - is analyzed based on a mathematical model. The model shows that binding of proteins, forming a complex, diminishes the ultrasensitivity of the PDPC to the differences in activity between kinase and phosphatase in the cycle. It is also found that signal amplification depends upon the strength of the binding affinity of the protein (phosphorylated or dephosphorylated) to other proteins . It is also observed that the amplification of signal is not only dependent on phosphorylation potential but also on binding properties and resulting adjustments in binding energies.

Diversity and specificity: auxin perception and signaling through the TIR1/AFB pathway

DOE PAGES

Wang, Renhou; Estelle, Mark

2014-07-15

Auxin is a versatile plant hormone that plays an essential role in most aspects of plant growth and development. Auxin regulates various growth processes by modulating gene transcription through a SCF TIR1/AFB-Aux/IAA-ARF nuclear signaling module. Recent work has generated clues as to how multiple layers of regulation of the auxin signaling components may result in diverse and specific response outputs. Finally, in particular, interaction and structural studies of key auxin signaling proteins have produced novel insights into the molecular basis of auxin-regulated transcription and may lead to a refined auxin signaling model.

Covariance-based direction-of-arrival estimation of wideband coherent chirp signals via sparse representation.

PubMed

Sha, Zhichao; Liu, Zhengmeng; Huang, Zhitao; Zhou, Yiyu

2013-08-29

This paper addresses the problem of direction-of-arrival (DOA) estimation of multiple wideband coherent chirp signals, and a new method is proposed. The new method is based on signal component analysis of the array output covariance, instead of the complicated time-frequency analysis used in previous literatures, and thus is more compact and effectively avoids possible signal energy loss during the hyper-processes. Moreover, the a priori information of signal number is no longer a necessity for DOA estimation in the new method. Simulation results demonstrate the performance superiority of the new method over previous ones.

Adaptation of vestibular signals for self-motion perception

PubMed Central

St George, Rebecca J; Day, Brian L; Fitzpatrick, Richard C

2011-01-01

A fundamental concern of the brain is to establish the spatial relationship between self and the world to allow purposeful action. Response adaptation to unvarying sensory stimuli is a common feature of neural processing, both peripherally and centrally. For the semicircular canals, peripheral adaptation of the canal-cupula system to constant angular-velocity stimuli dominates the picture and masks central adaptation. Here we ask whether galvanic vestibular stimulation circumvents peripheral adaptation and, if so, does it reveal central adaptive processes. Transmastoidal bipolar galvanic stimulation and platform rotation (20 deg s−1) were applied separately and held constant for 2 min while perceived rotation was measured by verbal report. During real rotation, the perception of turn decayed from the onset of constant velocity with a mean time constant of 15.8 s. During galvanic-evoked virtual rotation, the perception of rotation initially rose but then declined towards zero over a period of ∼100 s. For both stimuli, oppositely directed perceptions of similar amplitude were reported when stimulation ceased indicating signal adaptation at some level. From these data the time constants of three independent processes were estimated: (i) the peripheral canal-cupula adaptation with time constant 7.3 s, (ii) the central ‘velocity-storage’ process that extends the afferent signal with time constant 7.7 s, and (iii) a long-term adaptation with time constant 75.9 s. The first two agree with previous data based on constant-velocity stimuli. The third component decayed with the profile of a real constant angular acceleration stimulus, showing that the galvanic stimulus signal bypasses the peripheral transformation so that the brainstem sees the galvanic signal as angular acceleration. An adaptive process involving both peripheral and central processes is indicated. Signals evoked by most natural movements will decay peripherally before adaptation can exert an appreciable effect, making a specific vestibular behavioural role unlikely. This adaptation appears to be a general property of the internal coding of self-motion that receives information from multiple sensory sources and filters out the unvarying components regardless of their origin. In this instance of a pure vestibular sensation, it defines the afferent signal that represents the stationary or zero-rotation state. PMID:20937715

Dissociated roles of the anterior cingulate cortex in reward and conflict processing as revealed by the feedback error-related negativity and N200.

PubMed

Baker, Travis E; Holroyd, Clay B

2011-04-01

The reinforcement learning theory of the error-related negativity (ERN) holds that the impact of reward signals carried by the midbrain dopamine system modulates activity of the anterior cingulate cortex (ACC), alternatively disinhibiting and inhibiting the ACC following unpredicted error and reward events, respectively. According to a recent formulation of the theory, activity that is intrinsic to the ACC produces a component of the event-related brain potential (ERP) called the N200, and following unpredicted rewards, the N200 is suppressed by extrinsically applied positive dopamine reward signals, resulting in an ERP component called the feedback-ERN (fERN). Here we demonstrate that, despite extensive spatial and temporal overlap between the two ERP components, the functional processes indexed by the N200 (conflict) and the fERN (reward) are dissociable. These results point toward avenues for future investigation. Copyright © 2011 Elsevier B.V. All rights reserved.

Communication system with adaptive noise suppression

NASA Technical Reports Server (NTRS)

Kozel, David (Inventor); Devault, James A. (Inventor); Birr, Richard B. (Inventor)

2007-01-01

A signal-to-noise ratio dependent adaptive spectral subtraction process eliminates noise from noise-corrupted speech signals. The process first pre-emphasizes the frequency components of the input sound signal which contain the consonant information in human speech. Next, a signal-to-noise ratio is determined and a spectral subtraction proportion adjusted appropriately. After spectral subtraction, low amplitude signals can be squelched. A single microphone is used to obtain both the noise-corrupted speech and the average noise estimate. This is done by determining if the frame of data being sampled is a voiced or unvoiced frame. During unvoiced frames an estimate of the noise is obtained. A running average of the noise is used to approximate the expected value of the noise. Spectral subtraction may be performed on a composite noise-corrupted signal, or upon individual sub-bands of the noise-corrupted signal. Pre-averaging of the input signal's magnitude spectrum over multiple time frames may be performed to reduce musical noise.

Signal processing for ION mobility spectrometers

NASA Technical Reports Server (NTRS)

Taylor, S.; Hinton, M.; Turner, R.

1995-01-01

Signal processing techniques for systems based upon Ion Mobility Spectrometry will be discussed in the light of 10 years of experience in the design of real-time IMS. Among the topics to be covered are compensation techniques for variations in the number density of the gas - the use of an internal standard (a reference peak) or pressure and temperature sensors. Sources of noise and methods for noise reduction will be discussed together with resolution limitations and the ability of deconvolution techniques to improve resolving power. The use of neural networks (either by themselves or as a component part of a processing system) will be reviewed.

Television animation store: Recording pictures on a parallel transfer magnetic disc

NASA Astrophysics Data System (ADS)

Durey, A. J.

1984-12-01

The recording and replaying of digital video signals using a computer-type magnetic disc-drive as part of an electronic rostrum camera animation system is described. The system was developed to enable picture sequences to be generated directly as television signals, instead of using cine film. The characteristics of the disc-drive are described together with data processing, error protection and signal synchronization systems, which enable digital television YUV component signals, sampled at 12 MHz, 4 MHz and 4 MHz respectively, to be recorded and replayed in real time.

Extraction of pure components from overlapped signals in gas chromatography-mass spectrometry (GC-MS)

PubMed Central

Likić, Vladimir A

2009-01-01

Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique for the identification and quantification of trace chemicals in complex mixtures. When complex samples are analyzed by GC-MS it is common to observe co-elution of two or more components, resulting in an overlap of signal peaks observed in the total ion chromatogram. In such situations manual signal analysis is often the most reliable means for the extraction of pure component signals; however, a systematic manual analysis over a number of samples is both tedious and prone to error. In the past 30 years a number of computational approaches were proposed to assist in the process of the extraction of pure signals from co-eluting GC-MS components. This includes empirical methods, comparison with library spectra, eigenvalue analysis, regression and others. However, to date no approach has been recognized as best, nor accepted as standard. This situation hampers general GC-MS capabilities, and in particular has implications for the development of robust, high-throughput GC-MS analytical protocols required in metabolic profiling and biomarker discovery. Here we first discuss the nature of GC-MS data, and then review some of the approaches proposed for the extraction of pure signals from co-eluting components. We summarize and classify different approaches to this problem, and examine why so many approaches proposed in the past have failed to live up to their full promise. Finally, we give some thoughts on the future developments in this field, and suggest that the progress in general computing capabilities attained in the past two decades has opened new horizons for tackling this important problem. PMID:19818154

Unbiased RNAi screen for hepcidin regulators links hepcidin suppression to proliferative Ras/RAF and nutrient-dependent mTOR signaling

PubMed Central

Mleczko-Sanecka, Katarzyna; Roche, Franziska; Rita da Silva, Ana; Call, Debora; D’Alessio, Flavia; Ragab, Anan; Lapinski, Philip E.; Ummanni, Ramesh; Korf, Ulrike; Oakes, Christopher; Damm, Georg; D’Alessandro, Lorenza A.; Klingmüller, Ursula; King, Philip D.; Boutros, Michael; Hentze, Matthias W.

2014-01-01

The hepatic hormone hepcidin is a key regulator of systemic iron metabolism. Its expression is largely regulated by 2 signaling pathways: the “iron-regulated” bone morphogenetic protein (BMP) and the inflammatory JAK-STAT pathways. To obtain broader insights into cellular processes that modulate hepcidin transcription and to provide a resource to identify novel genetic modifiers of systemic iron homeostasis, we designed an RNA interference (RNAi) screen that monitors hepcidin promoter activity after the knockdown of 19 599 genes in hepatocarcinoma cells. Interestingly, many of the putative hepcidin activators play roles in signal transduction, inflammation, or transcription, and affect hepcidin transcription through BMP-responsive elements. Furthermore, our work sheds light on new components of the transcriptional machinery that maintain steady-state levels of hepcidin expression and its responses to the BMP- and interleukin-6–triggered signals. Notably, we discover hepcidin suppression mediated via components of Ras/RAF MAPK and mTOR signaling, linking hepcidin transcriptional control to the pathways that respond to mitogen stimulation and nutrient status. Thus using a combination of RNAi screening, reverse phase protein arrays, and small molecules testing, we identify links between the control of systemic iron homeostasis and critical liver processes such as regeneration, response to injury, carcinogenesis, and nutrient metabolism. PMID:24385536

Fusing Sensor Paradigms to Acquire Chemical Information: An Integrative Role for Smart Biopolymeric Hydrogels

PubMed Central

Kim, Eunkyoung; Liu, Yi; Ben-Yoav, Hadar; Winkler, Thomas E.; Yan, Kun; Shi, Xiaowen; Shen, Jana; Kelly, Deanna L.; Ghodssi, Reza; Bentley, William E.

2017-01-01

The Information Age transformed our lives but it has had surprisingly little impact on the way chemical information (e.g., from our biological world) is acquired, analyzed and communicated. Sensor systems are poised to change this situation by providing rapid access to chemical information. This access will be enabled by technological advances from various fields: biology enables the synthesis, design and discovery of molecular recognition elements as well as the generation of cell-based signal processors; physics and chemistry are providing nano-components that facilitate the transmission and transduction of signals rich with chemical information; microfabrication is yielding sensors capable of receiving these signals through various modalities; and signal processing analysis enhances the extraction of chemical information. The authors contend that integral to the development of functional sensor systems will be materials that (i) enable the integrative and hierarchical assembly of various sensing components (for chemical recognition and signal transduction) and (ii) facilitate meaningful communication across modalities. It is suggested that stimuli-responsive self-assembling biopolymers can perform such integrative functions, and redox provides modality-spanning communication capabilities. Recent progress toward the development of electrochemical sensors to manage schizophrenia is used to illustrate the opportunities and challenges for enlisting sensors for chemical information processing. PMID:27616350

Analysis of Seasonal Signal in GPS Short-Baseline Time Series

NASA Astrophysics Data System (ADS)

Wang, Kaihua; Jiang, Weiping; Chen, Hua; An, Xiangdong; Zhou, Xiaohui; Yuan, Peng; Chen, Qusen

2018-04-01

Proper modeling of seasonal signals and their quantitative analysis are of interest in geoscience applications, which are based on position time series of permanent GPS stations. Seasonal signals in GPS short-baseline (< 2 km) time series, if they exist, are mainly related to site-specific effects, such as thermal expansion of the monument (TEM). However, only part of the seasonal signal can be explained by known factors due to the limited data span, the GPS processing strategy and/or the adoption of an imperfect TEM model. In this paper, to better understand the seasonal signal in GPS short-baseline time series, we adopted and processed six different short-baselines with data span that varies from 2 to 14 years and baseline length that varies from 6 to 1100 m. To avoid seasonal signals that are overwhelmed by noise, each of the station pairs is chosen with significant differences in their height (> 5 m) or type of the monument. For comparison, we also processed an approximately zero baseline with a distance of < 1 m and identical monuments. The daily solutions show that there are apparent annual signals with annual amplitude of 1 mm (maximum amplitude of 1.86 ± 0.17 mm) on almost all of the components, which are consistent with the results from previous studies. Semi-annual signal with a maximum amplitude of 0.97 ± 0.25 mm is also present. The analysis of time-correlated noise indicates that instead of flicker (FL) or random walk (RW) noise, band-pass-filtered (BP) noise is valid for approximately 40% of the baseline components, and another 20% of the components can be best modeled by a combination of the first-order Gauss-Markov (FOGM) process plus white noise (WN). The TEM displacements are then modeled by considering the monument height of the building structure beneath the GPS antenna. The median contributions of TEM to the annual amplitude in the vertical direction are 84% and 46% with and without additional parts of the monument, respectively. Obvious annual signals with amplitude > 0.4 mm in the horizontal direction are observed in five short-baselines, and the amplitudes exceed 1 mm in four of them. These horizontal seasonal signals are likely related to the propagation of daily/sub-daily TEM displacement or other signals related to the site environment. Mismodeling of the tropospheric delay may also introduce spurious seasonal signals with annual amplitudes of 5 and 2 mm, respectively, for two short-baselines with elevation differences greater than 100 m. The results suggest that the monument height of the additional part of a typical GPS station should be considered when estimating the TEM displacement and that the tropospheric delay should be modeled cautiously, especially with station pairs with apparent elevation differences. The scheme adopted in this paper is expected to explicate more seasonal signals in GPS coordinate time series, particularly in the vertical direction.

A comparison of independent component analysis algorithms and measures to discriminate between EEG and artifact components.

PubMed

Dharmaprani, Dhani; Nguyen, Hoang K; Lewis, Trent W; DeLosAngeles, Dylan; Willoughby, John O; Pope, Kenneth J

2016-08-01

Independent Component Analysis (ICA) is a powerful statistical tool capable of separating multivariate scalp electrical signals into their additive independent or source components, specifically EEG or electroencephalogram and artifacts. Although ICA is a widely accepted EEG signal processing technique, classification of the recovered independent components (ICs) is still flawed, as current practice still requires subjective human decisions. Here we build on the results from Fitzgibbon et al. [1] to compare three measures and three ICA algorithms. Using EEG data acquired during neuromuscular paralysis, we tested the ability of the measures (spectral slope, peripherality and spatial smoothness) and algorithms (FastICA, Infomax and JADE) to identify components containing EMG. Spatial smoothness showed differentiation between paralysis and pre-paralysis ICs comparable to spectral slope, whereas peripherality showed less differentiation. A combination of the measures showed better differentiation than any measure alone. Furthermore, FastICA provided the best discrimination between muscle-free and muscle-contaminated recordings in the shortest time, suggesting it may be the most suited to EEG applications of the considered algorithms. Spatial smoothness results suggest that a significant number of ICs are mixed, i.e. contain signals from more than one biological source, and so the development of an ICA algorithm that is optimised to produce ICs that are easily classifiable is warranted.

Dynamic decomposition of spatiotemporal neural signals

PubMed Central

2017-01-01

Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals. PMID:28558039

Signal extraction and wave field separation in tunnel seismic prediction by independent component analysis

NASA Astrophysics Data System (ADS)

Yue, Y.; Jiang, T.; Zhou, Q.

2017-12-01

In order to ensure the rationality and the safety of tunnel excavation, the advanced geological prediction has been become an indispensable step in tunneling. However, the extraction of signal and the separation of P and S waves directly influence the accuracy of geological prediction. Generally, the raw data collected in TSP system is low quality because of the numerous disturb factors in tunnel projects, such as the power interference and machine vibration interference. It's difficult for traditional method (band-pass filtering) to remove interference effectively as well as bring little loss to signal. The power interference, machine vibration interference and the signal are original variables and x, y, z component as observation signals, each component of the representation is a linear combination of the original variables, which satisfy applicable conditions of independent component analysis (ICA). We perform finite-difference simulations of elastic wave propagation to synthetic a tunnel seismic reflection record. The method of ICA was adopted to process the three-component data, and the results show that extract the estimates of signal and the signals are highly correlated (the coefficient correlation is up to more than 0.93). In addition, the estimates of interference that separated from ICA and the interference signals are also highly correlated, and the coefficient correlation is up to more than 0.99. Thus, simulation results showed that the ICA is an ideal method for extracting high quality data from mixed signals. For the separation of P and S waves, the conventional separation techniques are based on physical characteristics of wave propagation, which require knowledge of the near-surface P and S waves velocities and density. Whereas the ICA approach is entirely based on statistical differences between P and S waves, and the statistical technique does not require a priori information. The concrete results of the wave field separation will be presented in the meeting. In summary, we can safely draw the conclusion that ICA can not only extract high quality data from the mixed signals, but also can separate P and S waves effectively.

A preferential design approach for energy-efficient and robust implantable neural signal processing hardware.

PubMed

Narasimhan, Seetharam; Chiel, Hillel J; Bhunia, Swarup

2009-01-01

For implantable neural interface applications, it is important to compress data and analyze spike patterns across multiple channels in real time. Such a computational task for online neural data processing requires an innovative circuit-architecture level design approach for low-power, robust and area-efficient hardware implementation. Conventional microprocessor or Digital Signal Processing (DSP) chips would dissipate too much power and are too large in size for an implantable system. In this paper, we propose a novel hardware design approach, referred to as "Preferential Design" that exploits the nature of the neural signal processing algorithm to achieve a low-voltage, robust and area-efficient implementation using nanoscale process technology. The basic idea is to isolate the critical components with respect to system performance and design them more conservatively compared to the noncritical ones. This allows aggressive voltage scaling for low power operation while ensuring robustness and area efficiency. We have applied the proposed approach to a neural signal processing algorithm using the Discrete Wavelet Transform (DWT) and observed significant improvement in power and robustness over conventional design.

Digital Signal Processing by Virtual Instrumentation of a MEMS Magnetic Field Sensor for Biomedical Applications

PubMed Central

Juárez-Aguirre, Raúl; Domínguez-Nicolás, Saúl M.; Manjarrez, Elías; Tapia, Jesús A.; Figueras, Eduard; Vázquez-Leal, Héctor; Aguilera-Cortés, Luz A.; Herrera-May, Agustín L.

2013-01-01

We present a signal processing system with virtual instrumentation of a MEMS sensor to detect magnetic flux density for biomedical applications. This system consists of a magnetic field sensor, electronic components implemented on a printed circuit board (PCB), a data acquisition (DAQ) card, and a virtual instrument. It allows the development of a semi-portable prototype with the capacity to filter small electromagnetic interference signals through digital signal processing. The virtual instrument includes an algorithm to implement different configurations of infinite impulse response (IIR) filters. The PCB contains a precision instrumentation amplifier, a demodulator, a low-pass filter (LPF) and a buffer with operational amplifier. The proposed prototype is used for real-time non-invasive monitoring of magnetic flux density in the thoracic cage of rats. The response of the rat respiratory magnetogram displays a similar behavior as the rat electromyogram (EMG). PMID:24196434

Digital signal processing by virtual instrumentation of a MEMS magnetic field sensor for biomedical applications.

PubMed

Juárez-Aguirre, Raúl; Domínguez-Nicolás, Saúl M; Manjarrez, Elías; Tapia, Jesús A; Figueras, Eduard; Vázquez-Leal, Héctor; Aguilera-Cortés, Luz A; Herrera-May, Agustín L

2013-11-05

We present a signal processing system with virtual instrumentation of a MEMS sensor to detect magnetic flux density for biomedical applications. This system consists of a magnetic field sensor, electronic components implemented on a printed circuit board (PCB), a data acquisition (DAQ) card, and a virtual instrument. It allows the development of a semi-portable prototype with the capacity to filter small electromagnetic interference signals through digital signal processing. The virtual instrument includes an algorithm to implement different configurations of infinite impulse response (IIR) filters. The PCB contains a precision instrumentation amplifier, a demodulator, a low-pass filter (LPF) and a buffer with operational amplifier. The proposed prototype is used for real-time non-invasive monitoring of magnetic flux density in the thoracic cage of rats. The response of the rat respiratory magnetogram displays a similar behavior as the rat electromyogram (EMG).

Medical telesensors

NASA Astrophysics Data System (ADS)

Ferrell, Trinidad L.; Crilly, P. B.; Smith, S. F.; Wintenberg, Alan L.; Britton, Charles L., Jr.; Morrison, Gilbert W.; Ericson, M. N.; Hedden, D.; Bouldin, Donald W.; Passian, A.; Downey, Todd R.; Wig, A. G.; Meriaudeau, Fabrice

1998-05-01

Medical telesensors are self-contained integrated circuits for measuring and transmitting vital signs over a distance of approximately 1-2 meters. The circuits are unhoused and contain a sensor, signal processing and modulation electronics, a spread-spectrum transmitter, an antenna and a thin-film battery. We report on a body-temperature telesensor, which is sufficiently small to be placed on a tympanic membrane in a child's ear. We also report on a pulse-oximeter telesensor and a micropack receiver/long- range transmitter unit, which receives form a telesensor array and analyzes and re-transmits the vital signs over a longer range. Signal analytics are presented for the pulse oximeter, which is currently in the form of a finger ring. A multichip module is presented as the basic signal-analysis component. The module contains a microprocessor, a field=programmable gate array, memory elements and other components necessary for determining trauma and reporting signals.

Magnetoencephalographic imaging of deep corticostriatal network activity during a rewards paradigm.

PubMed

Kanal, Eliezer Y; Sun, Mingui; Ozkurt, Tolga E; Jia, Wenyan; Sclabassi, Robert

2009-01-01

The human rewards network is a complex system spanning both cortical and subcortical regions. While much is known about the functions of the various components of the network, research on the behavior of the network as a whole has been stymied due to an inability to detect signals at a high enough temporal resolution from both superficial and deep network components simultaneously. In this paper, we describe the application of magnetoencephalographic imaging (MEG) combined with advanced signal processing techniques to this problem. Using data collected while subjects performed a rewards-related gambling paradigm demonstrated to activate the rewards network, we were able to identify neural signals which correspond to deep network activity. We also show that this signal was not observable prior to filtration. These results suggest that MEG imaging may be a viable tool for the detection of deep neural activity.

Efficient and Robust Signal Approximations

DTIC Science & Technology

2009-05-01

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

Analog Module Architecture for Space-Qualified Field-Programmable Mixed-Signal Arrays

NASA Technical Reports Server (NTRS)

Edwards, R. Timothy; Strohbehn, Kim; Jaskulek, Steven E.; Katz, Richard

1999-01-01

Spacecraft require all manner of both digital and analog circuits. Onboard digital systems are constructed almost exclusively from field-programmable gate array (FPGA) circuits providing numerous advantages over discrete design including high integration density, high reliability, fast turn-around design cycle time, lower mass, volume, and power consumption, and lower parts acquisition and flight qualification costs. Analog and mixed-signal circuits perform tasks ranging from housekeeping to signal conditioning and processing. These circuits are painstakingly designed and built using discrete components due to a lack of options for field-programmability. FPAA (Field-Programmable Analog Array) and FPMA (Field-Programmable Mixed-signal Array) parts exist but not in radiation-tolerant technology and not necessarily in an architecture optimal for the design of analog circuits for spaceflight applications. This paper outlines an architecture proposed for an FPAA fabricated in an existing commercial digital CMOS process used to make radiation-tolerant antifuse-based FPGA devices. The primary concerns are the impact of the technology and the overall array architecture on the flexibility of programming, the bandwidth available for high-speed analog circuits, and the accuracy of the components for high-performance applications.

SPECIAL ISSUE ON OPTICAL PROCESSING OF INFORMATION: Optoelectronic processors with scanning CCD photodetectors

NASA Astrophysics Data System (ADS)

Esepkina, N. A.; Lavrov, A. P.; Anan'ev, M. N.; Blagodarnyi, V. S.; Ivanov, S. I.; Mansyrev, M. I.; Molodyakov, S. A.

1995-10-01

Two new types of optoelectronic radio-signal processors were investigated. Charge-coupled device (CCD) photodetectors are used in these processors under continuous scanning conditions, i.e. in a time delay and storage mode. One of these processors is based on a CCD photodetector array with a reference-signal amplitude transparency and the other is an adaptive acousto-optical signal processor with linear frequency modulation. The processor with the transparency performs multichannel discrete—analogue convolution of an input signal with a corresponding kernel of the transformation determined by the transparency. If a light source is an array of light-emitting diodes of special (stripe) geometry, the optical stages of the processor can be made from optical fibre components and the whole processor then becomes a rigid 'sandwich' (a compact hybrid optoelectronic microcircuit). A report is given also of a study of a prototype processor with optical fibre components for the reception of signals from a system with antenna aperture synthesis, which forms a radio image of the Earth.

Predicting Essential Components of Signal Transduction Networks: A Dynamic Model of Guard Cell Abscisic Acid Signaling

PubMed Central

Li, Song; Assmann, Sarah M; Albert, Réka

2006-01-01

Plants both lose water and take in carbon dioxide through microscopic stomatal pores, each of which is regulated by a surrounding pair of guard cells. During drought, the plant hormone abscisic acid (ABA) inhibits stomatal opening and promotes stomatal closure, thereby promoting water conservation. Dozens of cellular components have been identified to function in ABA regulation of guard cell volume and thus of stomatal aperture, but a dynamic description is still not available for this complex process. Here we synthesize experimental results into a consistent guard cell signal transduction network for ABA-induced stomatal closure, and develop a dynamic model of this process. Our model captures the regulation of more than 40 identified network components, and accords well with previous experimental results at both the pathway and whole-cell physiological level. By simulating gene disruptions and pharmacological interventions we find that the network is robust against a significant fraction of possible perturbations. Our analysis reveals the novel predictions that the disruption of membrane depolarizability, anion efflux, actin cytoskeleton reorganization, cytosolic pH increase, the phosphatidic acid pathway, or K+ efflux through slowly activating K+ channels at the plasma membrane lead to the strongest reduction in ABA responsiveness. Initial experimental analysis assessing ABA-induced stomatal closure in the presence of cytosolic pH clamp imposed by the weak acid butyrate is consistent with model prediction. Simulations of stomatal response as derived from our model provide an efficient tool for the identification of candidate manipulations that have the best chance of conferring increased drought stress tolerance and for the prioritization of future wet bench analyses. Our method can be readily applied to other biological signaling networks to identify key regulatory components in systems where quantitative information is limited. PMID:16968132

An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis

PubMed Central

Ruiz-Gonzalez, Ruben; Gomez-Gil, Jaime; Gomez-Gil, Francisco Javier; Martínez-Martínez, Víctor

2014-01-01

The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels. PMID:25372618

An SVM-based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis.

PubMed

Ruiz-Gonzalez, Ruben; Gomez-Gil, Jaime; Gomez-Gil, Francisco Javier; Martínez-Martínez, Víctor

2014-11-03

The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.

Phosphate Sink Containing Two-Component Signaling Systems as Tunable Threshold Devices

PubMed Central

Amin, Munia; Kothamachu, Varun B.; Feliu, Elisenda; Scharf, Birgit E.; Porter, Steven L.; Soyer, Orkun S.

2014-01-01

Synthetic biology aims to design de novo biological systems and reengineer existing ones. These efforts have mostly focused on transcriptional circuits, with reengineering of signaling circuits hampered by limited understanding of their systems dynamics and experimental challenges. Bacterial two-component signaling systems offer a rich diversity of sensory systems that are built around a core phosphotransfer reaction between histidine kinases and their output response regulator proteins, and thus are a good target for reengineering through synthetic biology. Here, we explore the signal-response relationship arising from a specific motif found in two-component signaling. In this motif, a single histidine kinase (HK) phosphotransfers reversibly to two separate output response regulator (RR) proteins. We show that, under the experimentally observed parameters from bacteria and yeast, this motif not only allows rapid signal termination, whereby one of the RRs acts as a phosphate sink towards the other RR (i.e. the output RR), but also implements a sigmoidal signal-response relationship. We identify two mathematical conditions on system parameters that are necessary for sigmoidal signal-response relationships and define key parameters that control threshold levels and sensitivity of the signal-response curve. We confirm these findings experimentally, by in vitro reconstitution of the one HK-two RR motif found in the Sinorhizobium meliloti chemotaxis pathway and measuring the resulting signal-response curve. We find that the level of sigmoidality in this system can be experimentally controlled by the presence of the sink RR, and also through an auxiliary protein that is shown to bind to the HK (yielding Hill coefficients of above 7). These findings show that the one HK-two RR motif allows bacteria and yeast to implement tunable switch-like signal processing and provides an ideal basis for developing threshold devices for synthetic biology applications. PMID:25357192

Identification of Dynamic Patterns of Speech-Evoked Auditory Brainstem Response Based on Ensemble Empirical Mode Decomposition and Nonlinear Time Series Analysis Methods

NASA Astrophysics Data System (ADS)

Mozaffarilegha, Marjan; Esteki, Ali; Ahadi, Mohsen; Nazeri, Ahmadreza

The speech-evoked auditory brainstem response (sABR) shows how complex sounds such as speech and music are processed in the auditory system. Speech-ABR could be used to evaluate particular impairments and improvements in auditory processing system. Many researchers used linear approaches for characterizing different components of sABR signal, whereas nonlinear techniques are not applied so commonly. The primary aim of the present study is to examine the underlying dynamics of normal sABR signals. The secondary goal is to evaluate whether some chaotic features exist in this signal. We have presented a methodology for determining various components of sABR signals, by performing Ensemble Empirical Mode Decomposition (EEMD) to get the intrinsic mode functions (IMFs). Then, composite multiscale entropy (CMSE), the largest Lyapunov exponent (LLE) and deterministic nonlinear prediction are computed for each extracted IMF. EEMD decomposes sABR signal into five modes and a residue. The CMSE results of sABR signals obtained from 40 healthy people showed that 1st, and 2nd IMFs were similar to the white noise, IMF-3 with synthetic chaotic time series and 4th, and 5th IMFs with sine waveform. LLE analysis showed positive values for 3rd IMFs. Moreover, 1st, and 2nd IMFs showed overlaps with surrogate data and 3rd, 4th and 5th IMFs showed no overlap with corresponding surrogate data. Results showed the presence of noisy, chaotic and deterministic components in the signal which respectively corresponded to 1st, and 2nd IMFs, IMF-3, and 4th and 5th IMFs. While these findings provide supportive evidence of the chaos conjecture for the 3rd IMF, they do not confirm any such claims. However, they provide a first step towards an understanding of nonlinear behavior of auditory system dynamics in brainstem level.

Signals, resistance to change, and conditioned reinforcement in a multiple schedule.

PubMed

Bell, Matthew C; Gomez, Belen E; Kessler, Kira

2008-06-01

The effect of signals on resistance to change was evaluated using pigeons responding on a three-component multiple schedule. Each component contained a variable-interval initial link followed by a fixed-time terminal link. One component was an unsignaled-delay schedule, and two were equivalent signaled-delay schedules. After baseline training, resistance to change was assessed through (a) extinction and (b) adding free food to the intercomponent interval. During these tests, the signal stimulus from one of the signaled-delay components (SIG-T) was replaced with the initial-link stimulus from that component, converting it to an unsignaled-delay schedule. That signal stimulus was added to the delay period of the unsignaled-delay component (UNS), converting it to a signaled-delay schedule. The remaining signaled component remained unchanged (SIG-C). Resistance-to-change tests showed removing the signal had a minimal effect on resistance to change in the SIG-T component compared to the unchanged SIG-C component except for one block during free-food testing. Adding the signal to the UNS component significantly increased response rates suggesting that component had low response strength. Interestingly, the direction of the effect was in the opposite direction from what is typically observed. Results are consistent with the conclusion that the signal functioned as a conditioned reinforcer and inconsistent with a generalization-decrement explanation.

Digital resolver for helicopter model blade motion analysis

NASA Technical Reports Server (NTRS)

Daniels, T. S.; Berry, J. D.; Park, S.

1992-01-01

The paper reports the development and initial testing of a digital resolver to replace existing analog signal processing instrumentation. Radiometers, mounted directly on one of the fully articulated blades, are electrically connected through a slip ring to analog signal processing circuitry. The measured signals are periodic with azimuth angle and are resolved into harmonic components, with 0 deg over the tail. The periodic nature of the helicopter blade motion restricts the frequency content of each flapping and yaw signal to the fundamental and harmonics of the rotor rotational frequency. A minicomputer is employed to collect these data and then plot them graphically in real time. With this and other information generated by the instrumentation, a helicopter test pilot can then adjust the helicopter model's controls to achieve the desired aerodynamic test conditions.

Structural insight into partner specificity and phosphoryl transfer in two-component signal transduction.

PubMed

Casino, Patricia; Rubio, Vicente; Marina, Alberto

2009-10-16

The chief mechanism used by bacteria for sensing their environment is based on two conserved proteins: a sensor histidine kinase (HK) and an effector response regulator (RR). The signal transduction process involves highly conserved domains of both proteins that mediate autokinase, phosphotransfer, and phosphatase activities whose output is a finely tuned RR phosphorylation level. Here, we report the structure of the complex between the entire cytoplasmic portion of Thermotoga maritima class I HK853 and its cognate, RR468, as well as the structure of the isolated RR468, both free and BeF(3)(-) bound. Our results provide insight into partner specificity in two-component systems, recognition of the phosphorylation state of each partner, and the catalytic mechanism of the phosphatase reaction. Biochemical analysis shows that the HK853-catalyzed autokinase reaction proceeds by a cis autophosphorylation mechanism within the HK subunit. The results suggest a model for the signal transduction mechanism in two-component systems.

Identification of Buried Objects in GPR Using Amplitude Modulated Signals Extracted from Multiresolution Monogenic Signal Analysis

PubMed Central

Qiao, Lihong; Qin, Yao; Ren, Xiaozhen; Wang, Qifu

2015-01-01

It is necessary to detect the target reflections in ground penetrating radar (GPR) images, so that surface metal targets can be identified successfully. In order to accurately locate buried metal objects, a novel method called the Multiresolution Monogenic Signal Analysis (MMSA) system is applied in ground penetrating radar (GPR) images. This process includes four steps. First the image is decomposed by the MMSA to extract the amplitude component of the B-scan image. The amplitude component enhances the target reflection and suppresses the direct wave and reflective wave to a large extent. Then we use the region of interest extraction method to locate the genuine target reflections from spurious reflections by calculating the normalized variance of the amplitude component. To find the apexes of the targets, a Hough transform is used in the restricted area. Finally, we estimate the horizontal and vertical position of the target. In terms of buried object detection, the proposed system exhibits promising performance, as shown in the experimental results. PMID:26690146

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Load-induced modulation of signal transduction networks.

PubMed

Jiang, Peng; Ventura, Alejandra C; Sontag, Eduardo D; Merajver, Sofia D; Ninfa, Alexander J; Del Vecchio, Domitilla

2011-10-11

Biological signal transduction networks are commonly viewed as circuits that pass along information--in the process amplifying signals, enhancing sensitivity, or performing other signal-processing tasks--to transcriptional and other components. Here, we report on a "reverse-causality" phenomenon, which we call load-induced modulation. Through a combination of analytical and experimental tools, we discovered that signaling was modulated, in a surprising way, by downstream targets that receive the signal and, in doing so, apply what in physics is called a load. Specifically, we found that non-intuitive changes in response dynamics occurred for a covalent modification cycle when load was present. Loading altered the response time of a system, depending on whether the activity of one of the enzymes was maximal and the other was operating at its minimal rate or whether both enzymes were operating at submaximal rates. These two conditions, which we call "limit regime" and "intermediate regime," were associated with increased or decreased response times, respectively. The bandwidth, the range of frequency in which the system can process information, decreased in the presence of load, suggesting that downstream targets participate in establishing a balance between noise-filtering capabilities and a circuit's ability to process high-frequency stimulation. Nodes in a signaling network are not independent relay devices, but rather are modulated by their downstream targets.

Comparative Analysis on Nonlinear Models for Ron Gasoline Blending Using Neural Networks

NASA Astrophysics Data System (ADS)

Aguilera, R. Carreño; Yu, Wen; Rodríguez, J. C. Tovar; Mosqueda, M. Elena Acevedo; Ortiz, M. Patiño; Juarez, J. J. Medel; Bautista, D. Pacheco

The blending process always being a nonlinear process is difficult to modeling, since it may change significantly depending on the components and the process variables of each refinery. Different components can be blended depending on the existing stock, and the chemical characteristics of each component are changing dynamically, they all are blended until getting the expected specification in different properties required by the customer. One of the most relevant properties is the Octane, which is difficult to control in line (without the component storage). Since each refinery process is quite different, a generic gasoline blending model is not useful when a blending in line wants to be done in a specific process. A mathematical gasoline blending model is presented in this paper for a given process described in state space as a basic gasoline blending process description. The objective is to adjust the parameters allowing the blending gasoline model to describe a signal in its trajectory, representing in neural networks extreme learning machine method and also for nonlinear autoregressive-moving average (NARMA) in neural networks method, such that a comparative work be developed.

Real-Time Detection and Tracking of Vital Signs with an Ambulatory Subject Using Millimeter-Wave Interferometry

NASA Astrophysics Data System (ADS)

Mikhelson, Ilya V.

Finding a subject's heart rate from a distance without any contact is a difficult and very practical problem. This kind of technology would allow more comfortable patient monitoring in hospitals or in home settings. It would also allow another level of security screening, as a person's heart rate increases in stressful situations, such as when lying or hiding malicious intent. In addition, the fact that the heart rate is obtained remotely means that the subject would not have to know he/she is being monitored at all, adding to the efficacy of the measurement. Using millimeter-wave interferometry, a signal can be obtained that contains composite chest wall motion made up of component motions due to cardiac activity, respiration, and interference. To be of use, these components have to be separated from each other by signal processing. To do this, the quadrature and in-phase components of the received signal are analyzed to get a displacement waveform. After that, processing can be done on that waveform in either the time or frequency domains to find the individual heartbeats. The first method is to find the power spectrum of the displacement waveform and to look for peaks corresponding to heartbeats and respiration. Another approach is to examine the signal in the time domain using wavelets for multiresolution analysis. One more method involves studying the statistics of the wavelet-processed signal. The final method uses a heartbeat model along with probabilistic processing to find heartbeats. For any of the above methods to work, the millimeter-wave sensor has to be accurately pointed at the subject's chest. However, even small subject motions can render the rest of the gathered data useless as the antenna may have lost its aim. To combat this, a color and a depth camera are used with a servo-pan/tilt base. My program finds a face in the image and subsequently tracks that face through upcoming frames. The pan/tilt base adjusts the aim of the antenna depending on the subject's position. This makes the entire system self-aiming and also allows the subject to move to a new location and to have data acquisition continue.

Coherent Detection of High-Rate Optical PPM Signals

NASA Technical Reports Server (NTRS)

Vilnrotter, Victor; Fernandez, Michela Munoz

2006-01-01

A method of coherent detection of high-rate pulse-position modulation (PPM) on a received laser beam has been conceived as a means of reducing the deleterious effects of noise and atmospheric turbulence in free-space optical communication using focal-plane detector array technologies. In comparison with a receiver based on direct detection of the intensity modulation of a PPM signal, a receiver based on the present method of coherent detection performs well at much higher background levels. In principle, the coherent-detection receiver can exhibit quantum-limited performance despite atmospheric turbulence. The key components of such a receiver include standard receiver optics, a laser that serves as a local oscillator, a focal-plane array of photodetectors, and a signal-processing and data-acquisition assembly needed to sample the focal-plane fields and reconstruct the pulsed signal prior to detection. The received PPM-modulated laser beam and the local-oscillator beam are focused onto the photodetector array, where they are mixed in the detection process. The two lasers are of the same or nearly the same frequency. If the two lasers are of different frequencies, then the coherent detection process is characterized as heterodyne and, using traditional heterodyne-detection terminology, the difference between the two laser frequencies is denoted the intermediate frequency (IF). If the two laser beams are of the same frequency and remain aligned in phase, then the coherent detection process is characterized as homodyne (essentially, heterodyne detection at zero IF). As a result of the inherent squaring operation of each photodetector, the output current includes an IF component that contains the signal modulation. The amplitude of the IF component is proportional to the product of the local-oscillator signal amplitude and the PPM signal amplitude. Hence, by using a sufficiently strong local-oscillator signal, one can make the PPM-modulated IF signal strong enough to overcome thermal noise in the receiver circuits: this is what makes it possible to achieve near-quantum-limited detection in the presence of strong background. Following quantum-limited coherent detection, the outputs of the individual photodetectors are automatically aligned in phase by use of one or more adaptive array compensation algorithms [e.g., the least-mean-square (LMS) algorithm]. Then the outputs are combined and the resulting signal is processed to extract the high-rate information, as though the PPM signal were received by a single photodetector. In a continuing series of experiments to test this method (see Fig. 1), the local oscillator has a wavelength of 1,064 nm, and another laser is used as a signal transmitter at a slightly different wavelength to establish an IF of about 6 MHz. There are 16 photodetectors in a 4 4 focal-plane array; the detector outputs are digitized at a sampling rate of 25 MHz, and the signals in digital form are combined by use of the LMS algorithm. Convergence of the adaptive combining algorithm in the presence of simulated atmospheric turbulence for optical PPM signals has already been demonstrated in the laboratory; the combined output is shown in Fig. 2(a), and Fig. 2(b) shows the behavior of the phase of the combining weights as a function of time (or samples). We observe that the phase of the weights has a sawtooth shape due to the continuously changing phase in the down-converted output, which is not exactly at zero frequency. Detailed performance analysis of this coherent free-space optical communication system in the presence of simulated atmospheric turbulence is currently under way.

Missile signal processing common computer architecture for rapid technology upgrade

NASA Astrophysics Data System (ADS)

Rabinkin, Daniel V.; Rutledge, Edward; Monticciolo, Paul

2004-10-01

Interceptor missiles process IR images to locate an intended target and guide the interceptor towards it. Signal processing requirements have increased as the sensor bandwidth increases and interceptors operate against more sophisticated targets. A typical interceptor signal processing chain is comprised of two parts. Front-end video processing operates on all pixels of the image and performs such operations as non-uniformity correction (NUC), image stabilization, frame integration and detection. Back-end target processing, which tracks and classifies targets detected in the image, performs such algorithms as Kalman tracking, spectral feature extraction and target discrimination. In the past, video processing was implemented using ASIC components or FPGAs because computation requirements exceeded the throughput of general-purpose processors. Target processing was performed using hybrid architectures that included ASICs, DSPs and general-purpose processors. The resulting systems tended to be function-specific, and required custom software development. They were developed using non-integrated toolsets and test equipment was developed along with the processor platform. The lifespan of a system utilizing the signal processing platform often spans decades, while the specialized nature of processor hardware and software makes it difficult and costly to upgrade. As a result, the signal processing systems often run on outdated technology, algorithms are difficult to update, and system effectiveness is impaired by the inability to rapidly respond to new threats. A new design approach is made possible three developments; Moore's Law - driven improvement in computational throughput; a newly introduced vector computing capability in general purpose processors; and a modern set of open interface software standards. Today's multiprocessor commercial-off-the-shelf (COTS) platforms have sufficient throughput to support interceptor signal processing requirements. This application may be programmed under existing real-time operating systems using parallel processing software libraries, resulting in highly portable code that can be rapidly migrated to new platforms as processor technology evolves. Use of standardized development tools and 3rd party software upgrades are enabled as well as rapid upgrade of processing components as improved algorithms are developed. The resulting weapon system will have a superior processing capability over a custom approach at the time of deployment as a result of a shorter development cycles and use of newer technology. The signal processing computer may be upgraded over the lifecycle of the weapon system, and can migrate between weapon system variants enabled by modification simplicity. This paper presents a reference design using the new approach that utilizes an Altivec PowerPC parallel COTS platform. It uses a VxWorks-based real-time operating system (RTOS), and application code developed using an efficient parallel vector library (PVL). A quantification of computing requirements and demonstration of interceptor algorithm operating on this real-time platform are provided.

Cosmic non-TEM radiation and synthetic feed array sensor system in ASIC mixed signal technology

NASA Astrophysics Data System (ADS)

Centureli, F.; Scotti, G.; Tommasino, P.; Trifiletti, A.; Romano, F.; Cimmino, R.; Saitto, A.

2014-08-01

The paper deals with the opportunity to introduce "Not strictly TEM waves" Synthetic detection Method (NTSM), consisting in a Three Axis Digital Beam Processing (3ADBP), to enhance the performances of radio telescope and sensor systems. Current Radio Telescopes generally use the classic 3D "TEM waves" approximation Detection Method, which consists in a linear tomography process (Single or Dual axis beam forming processing) neglecting the small z component. The Synthetic FEED ARRAY three axis Sensor SYSTEM is an innovative technique using a synthetic detection of the generic "NOT strictly TEM Waves radiation coming from the Cosmo, which processes longitudinal component of Angular Momentum too. Than the simultaneous extraction from radiation of both the linear and quadratic information component, may reduce the complexity to reconstruct the Early Universe in the different requested scales. This next order approximation detection of the observed cosmologic processes, may improve the efficacy of the statistical numerical model used to elaborate the same information acquired. The present work focuses on detection of such waves at carrier frequencies in the bands ranging from LF to MMW. The work shows in further detail the new generation of on line programmable and reconfigurable Mixed Signal ASIC technology that made possible the innovative Synthetic Sensor. Furthermore the paper shows the ability of such technique to increase the Radio Telescope Array Antenna performances.

Photonics for microwave systems and ultra-wideband signal processing

NASA Astrophysics Data System (ADS)

Ng, W.

2016-08-01

The advantages of using the broadband and low-loss distribution attributes of photonics to enhance the signal processing and sensing capabilities of microwave systems are well known. In this paper, we review the progress made in the topical areas of true-time-delay beamsteering, photonic-assisted analog-to-digital conversion, RF-photonic filtering and link performances. We also provide an outlook on the emerging field of integrated microwave photonics (MWP) that promise to reduce the cost of MWP subsystems and components, while providing significantly improved form-factors for system insertion.

Signal to Noise Studies on Thermographic Data with Fabricated Defects for Defense Structures

NASA Technical Reports Server (NTRS)

Zalameda, Joseph N.; Rajic, Nik; Genest, Marc

2006-01-01

There is a growing international interest in thermal inspection systems for asset life assessment and management of defense platforms. The efficacy of flash thermography is generally enhanced by applying image processing algorithms to the observations of raw temperature. Improving the defect signal to noise ratio (SNR) is of primary interest to reduce false calls and allow for easier interpretation of a thermal inspection image. Several factors affecting defect SNR were studied such as data compression and reconstruction using principal component analysis and time window processing.

Neurophysiological correlates of error monitoring and inhibitory processing in juvenile violent offenders.

PubMed

Vilà-Balló, Adrià; Hdez-Lafuente, Prado; Rostan, Carles; Cunillera, Toni; Rodriguez-Fornells, Antoni

2014-10-01

Performance monitoring is crucial for well-adapted behavior. Offenders typically have a pervasive repetition of harmful-impulsive behaviors, despite an awareness of the negative consequences of their actions. However, the link between performance monitoring and aggressive behavior in juvenile offenders has not been closely investigated. Event-related brain potentials (ERPs) were used to investigate performance monitoring in juvenile non-psychopathic violent offenders compared with a well-matched control group. Two ERP components associated with error monitoring, error-related negativity (ERN) and error-positivity (Pe), and two components related to inhibitory processing, the stop-N2 and stop-P3 components, were evaluated using a combined flanker-stop-signal task. The results showed that the amplitudes of the ERN, the stop-N2, the stop-P3, and the standard P3 components were clearly reduced in the offenders group. Remarkably, no differences were observed for the Pe. At the behavioral level, slower stop-signal reaction times were identified for offenders, which indicated diminished inhibitory processing. The present results suggest that the monitoring of one's own behavior is affected in juvenile violent offenders. Specifically, we determined that different aspects of executive function were affected in the studied offenders, including error processing (reduced ERN) and response inhibition (reduced N2 and P3). However, error awareness and compensatory post-error adjustment processes (error correction) were unaffected. The current pattern of results highlights the role of performance monitoring in the acquisition and maintenance of externalizing harmful behavior that is frequently observed in juvenile offenders. Copyright © 2014 Elsevier B.V. All rights reserved.

Estimation of frequency offset in mobile satellite modems

NASA Technical Reports Server (NTRS)

Cowley, W. G.; Rice, M.; Mclean, A. N.

1993-01-01

In mobilesat applications, frequency offset on the received signal must be estimated and removed prior to further modem processing. A straightforward method of estimating the carrier frequency offset is to raise the received MPSK signal to the M-th power, and then estimate the location of the peak spectral component. An analysis of the lower signal to noise threshold of this method is carried out for BPSK signals. Predicted thresholds are compared to simulation results. It is shown how the method can be extended to pi/M MPSK signals. A real-time implementation of frequency offset estimation for the Australian mobile satellite system is described.

Signal transduction by the Wnt family of ligands.

PubMed Central

Dale, T C

1998-01-01

The Wnt genes encode a large family of secreted polypeptides that mediate cell-cell communication in diverse developmental processes. The loss or inappropriate activation of Wnt expression has been shown to alter cell fate, morphogenesis and mitogenesis. Recent progress has identified Wnt receptors and components of an intracellular signalling pathway that mediate Wnt-dependent transcription. This review will highlight this 'core' Wnt signal-transduction pathway, but also aims to reveal the potential diversity of Wnt signalling targets. Particular attention will be paid to the overlap between developmental biology and oncogenesis, since recent progress shows Wnt signalling forms a paradigm for an interdisciplinary approach. PMID:9425102

Characterizing near-surface firn using the scattered signal component of the glacier surface return from airborne radio-echo sounding

NASA Astrophysics Data System (ADS)

Rutishauser, Anja; Grima, Cyril; Sharp, Martin; Blankenship, Donald D.; Young, Duncan A.; Cawkwell, Fiona; Dowdeswell, Julian A.

2016-12-01

We derive the scattered component (hereafter referred to as the incoherent component) of glacier surface echoes from airborne radio-echo sounding measurements over Devon Ice Cap, Arctic Canada, and compare the scattering distribution to firn stratigraphy observations from ground-based radar data. Low scattering correlates to laterally homogeneous firn above 1800 m elevation containing thin, flat, and continuous ice layers and below 1200 m elevation where firn predominantly consists of ice. Increased scattering between elevations of 1200-1800 m corresponds to firn with inhomogeneous, undulating ice layers. No correlation was found to surface roughness and its theoretical incoherent backscattering values. This indicates that the scattering component is mainly influenced by the near-surface firn stratigraphy, whereas surface roughness effects are minor. Our results suggest that analyzing the scattered signal component of glacier surface echoes is a promising approach to characterize the spatial heterogeneity of firn that is affected by melting and refreezing processes.

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.

Multimodal and Multi-tissue Measures of Connectivity Revealed by Joint Independent Component Analysis.

PubMed

Franco, Alexandre R; Ling, Josef; Caprihan, Arvind; Calhoun, Vince D; Jung, Rex E; Heileman, Gregory L; Mayer, Andrew R

2008-12-01

The human brain functions as an efficient system where signals arising from gray matter are transported via white matter tracts to other regions of the brain to facilitate human behavior. However, with a few exceptions, functional and structural neuroimaging data are typically optimized to maximize the quantification of signals arising from a single source. For example, functional magnetic resonance imaging (FMRI) is typically used as an index of gray matter functioning whereas diffusion tensor imaging (DTI) is typically used to determine white matter properties. While it is likely that these signals arising from different tissue sources contain complementary information, the signal processing algorithms necessary for the fusion of neuroimaging data across imaging modalities are still in a nascent stage. In the current paper we present a data-driven method for combining measures of functional connectivity arising from gray matter sources (FMRI resting state data) with different measures of white matter connectivity (DTI). Specifically, a joint independent component analysis (J-ICA) was used to combine these measures of functional connectivity following intensive signal processing and feature extraction within each of the individual modalities. Our results indicate that one of the most predominantly used measures of functional connectivity (activity in the default mode network) is highly dependent on the integrity of white matter connections between the two hemispheres (corpus callosum) and within the cingulate bundles. Importantly, the discovery of this complex relationship of connectivity was entirely facilitated by the signal processing and fusion techniques presented herein and could not have been revealed through separate analyses of both data types as is typically performed in the majority of neuroimaging experiments. We conclude by discussing future applications of this technique to other areas of neuroimaging and examining potential limitations of the methods.

Stratigraphy of the Anthropocene.

PubMed

Zalasiewicz, Jan; Williams, Mark; Fortey, Richard; Smith, Alan; Barry, Tiffany L; Coe, Angela L; Bown, Paul R; Rawson, Peter F; Gale, Andrew; Gibbard, Philip; Gregory, F John; Hounslow, Mark W; Kerr, Andrew C; Pearson, Paul; Knox, Robert; Powell, John; Waters, Colin; Marshall, John; Oates, Michael; Stone, Philip

2011-03-13

The Anthropocene, an informal term used to signal the impact of collective human activity on biological, physical and chemical processes on the Earth system, is assessed using stratigraphic criteria. It is complex in time, space and process, and may be considered in terms of the scale, relative timing, duration and novelty of its various phenomena. The lithostratigraphic signal includes both direct components, such as urban constructions and man-made deposits, and indirect ones, such as sediment flux changes. Already widespread, these are producing a significant 'event layer', locally with considerable long-term preservation potential. Chemostratigraphic signals include new organic compounds, but are likely to be dominated by the effects of CO(2) release, particularly via acidification in the marine realm, and man-made radionuclides. The sequence stratigraphic signal is negligible to date, but may become geologically significant over centennial/millennial time scales. The rapidly growing biostratigraphic signal includes geologically novel aspects (the scale of globally transferred species) and geologically will have permanent effects.

Effects of stop-signal probability in the stop-signal paradigm: the N2/P3 complex further validated.

PubMed

Ramautar, J R; Kok, A; Ridderinkhof, K R

2004-11-01

The aim of this study was to examine the effects of frequency of occurrence of stop signals in the stop-signal paradigm. Presenting stop signals less frequently resulted in faster reaction times to the go stimulus and a lower probability of inhibition. Also, go stimuli elicited larger and somewhat earlier P3 responses when stop signals occurred less frequently. Since the amplitude effect was more pronounced on trials when go signals were followed by fast than slow reactions, it probably reflected a stronger set to produce fast responses. N2 and P3 components to stop signals were observed to be larger and of longer latency when stop signals occurred less frequently. The amplitude enhancement of these N2 and P3 components were more pronounced for unsuccessful than for successful stop-signal trials. Moreover, the successfully inhibited stop trials elicited a frontocentral P3 whereas unsuccessfully inhibited stop trials elicited a more posterior P3 that resembled the classical P3b. P3 amplitude in the unsuccessfully inhibited condition also differed between waveforms synchronized with the stop signal and waveforms synchronized with response onset whereas N2 amplitude did not. Taken together these findings suggest that N2 reflected a greater significance of failed inhibitions after low probability stop signals while P3 reflected continued processing of the erroneous response after response execution.

Calcium Signals: The Lead Currency of Plant Information Processing

PubMed Central

Kudla, Jörg; Batistič, Oliver; Hashimoto, Kenji

2010-01-01

Ca2+ signals are core transducers and regulators in many adaptation and developmental processes of plants. Ca2+ signals are represented by stimulus-specific signatures that result from the concerted action of channels, pumps, and carriers that shape temporally and spatially defined Ca2+ elevations. Cellular Ca2+ signals are decoded and transmitted by a toolkit of Ca2+ binding proteins that relay this information into downstream responses. Major transduction routes of Ca2+ signaling involve Ca2+-regulated kinases mediating phosphorylation events that orchestrate downstream responses or comprise regulation of gene expression via Ca2+-regulated transcription factors and Ca2+-responsive promoter elements. Here, we review some of the remarkable progress that has been made in recent years, especially in identifying critical components functioning in Ca2+ signal transduction, both at the single-cell and multicellular level. Despite impressive progress in our understanding of the processing of Ca2+ signals during the past years, the elucidation of the exact mechanistic principles that underlie the specific recognition and conversion of the cellular Ca2+ currency into defined changes in protein–protein interaction, protein phosphorylation, and gene expression and thereby establish the specificity in stimulus response coupling remain to be explored. PMID:20354197

Processing oscillatory signals by incoherent feedforward loops

NASA Astrophysics Data System (ADS)

Zhang, Carolyn; Wu, Feilun; Tsoi, Ryan; Shats, Igor; You, Lingchong

From the timing of amoeba development to the maintenance of stem cell pluripotency,many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression.While networks underlying this signal decoding are diverse,many are built around a common motif, the incoherent feedforward loop (IFFL),where an input simultaneously activates an output and an inhibitor of the output.With appropriate parameters,this motif can generate temporal adaptation,where the system is desensitized to a sustained input.This property serves as the foundation for distinguishing signals with varying temporal profiles.Here,we use quantitative modeling to examine another property of IFFLs,the ability to process oscillatory signals.Our results indicate that the system's ability to translate pulsatile dynamics is limited by two constraints.The kinetics of IFFL components dictate the input range for which the network can decode pulsatile dynamics.In addition,a match between the network parameters and signal characteristics is required for optimal ``counting''.We elucidate one potential mechanism by which information processing occurs in natural networks with implications in the design of synthetic gene circuits for this purpose. This work was partially supported by the National Science Foundation Graduate Research Fellowship (CZ).

Processing Oscillatory Signals by Incoherent Feedforward Loops

PubMed Central

Zhang, Carolyn; You, Lingchong

2016-01-01

From the timing of amoeba development to the maintenance of stem cell pluripotency, many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression. While the networks underlying this signal decoding are diverse, many are built around a common motif, the incoherent feedforward loop (IFFL), where an input simultaneously activates an output and an inhibitor of the output. With appropriate parameters, this motif can exhibit temporal adaptation, where the system is desensitized to a sustained input. This property serves as the foundation for distinguishing input signals with varying temporal profiles. Here, we use quantitative modeling to examine another property of IFFLs—the ability to process oscillatory signals. Our results indicate that the system’s ability to translate pulsatile dynamics is limited by two constraints. The kinetics of the IFFL components dictate the input range for which the network is able to decode pulsatile dynamics. In addition, a match between the network parameters and input signal characteristics is required for optimal “counting”. We elucidate one potential mechanism by which information processing occurs in natural networks, and our work has implications in the design of synthetic gene circuits for this purpose. PMID:27623175

Does autophagy take a front seat in lifespan extension?

PubMed Central

Petrovski, Goran; Das, Dipak K

2010-01-01

Abstract This review focuses on the interrelationship between ageing and autophagy. There is a striking similarity between the signalling aspects of these two processes. Both ageing and autophagy involve several of the signalling components such as insulin/IGF-1, AMPK, Ras-cAMP-PKA, Sch9 and mTOR. Ageing and ageing-mediated defective autophagy involve accumulation of lipofuscin. Components of anti-ageing and autophagy include SirTs and FoxOs. Nutritional deprivation or calorie restriction as well as several nutriceuticals including resveratrol, spermidine, curcumin and piperine can enhance autophagy and increase lifespan. Such striking similarities indicate that lifespan is strongly dependent on autophagy. PMID:21114762

A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI

PubMed Central

Chang, Hing-Chiu; Bilgin, Ali; Bernstein, Adam; Trouard, Theodore P.

2018-01-01

Over the past several years, significant efforts have been made to improve the spatial resolution of diffusion-weighted imaging (DWI), aiming at better detecting subtle lesions and more reliably resolving white-matter fiber tracts. A major concern with high-resolution DWI is the limited signal-to-noise ratio (SNR), which may significantly offset the advantages of high spatial resolution. Although the SNR of DWI data can be improved by denoising in post-processing, existing denoising procedures may potentially reduce the anatomic resolvability of high-resolution imaging data. Additionally, non-Gaussian noise induced signal bias in low-SNR DWI data may not always be corrected with existing denoising approaches. Here we report an improved denoising procedure, termed diffusion-matched principal component analysis (DM-PCA), which comprises 1) identifying a group of (not necessarily neighboring) voxels that demonstrate very similar magnitude signal variation patterns along the diffusion dimension, 2) correcting low-frequency phase variations in complex-valued DWI data, 3) performing PCA along the diffusion dimension for real- and imaginary-components (in two separate channels) of phase-corrected DWI voxels with matched diffusion properties, 4) suppressing the noisy PCA components in real- and imaginary-components, separately, of phase-corrected DWI data, and 5) combining real- and imaginary-components of denoised DWI data. Our data show that the new two-channel (i.e., for real- and imaginary-components) DM-PCA denoising procedure performs reliably without noticeably compromising anatomic resolvability. Non-Gaussian noise induced signal bias could also be reduced with the new denoising method. The DM-PCA based denoising procedure should prove highly valuable for high-resolution DWI studies in research and clinical uses. PMID:29694400

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

NASA Astrophysics Data System (ADS)

Wen-Bo, Wang; Xiao-Dong, Zhang; Yuchan, Chang; Xiang-Li, Wang; Zhao, Wang; Xi, Chen; Lei, Zheng

2016-01-01

In this paper, a new method to reduce noises within chaotic signals based on ICA (independent component analysis) and EMD (empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions (IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor. Project supported by the National Science and Technology, China (Grant No. 2012BAJ15B04), the National Natural Science Foundation of China (Grant Nos. 41071270 and 61473213), the Natural Science Foundation of Hubei Province, China (Grant No. 2015CFB424), the State Key Laboratory Foundation of Satellite Ocean Environment Dynamics, China (Grant No. SOED1405), the Hubei Provincial Key Laboratory Foundation of Metallurgical Industry Process System Science, China (Grant No. Z201303), and the Hubei Key Laboratory Foundation of Transportation Internet of Things, Wuhan University of Technology, China (Grant No.2015III015-B02).

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

PubMed

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

2011-04-01

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

A modular cell-based biosensor using engineered genetic logic circuits to detect and integrate multiple environmental signals

PubMed Central

Wang, Baojun; Barahona, Mauricio; Buck, Martin

2013-01-01

Cells perceive a wide variety of cellular and environmental signals, which are often processed combinatorially to generate particular phenotypic responses. Here, we employ both single and mixed cell type populations, pre-programmed with engineered modular cell signalling and sensing circuits, as processing units to detect and integrate multiple environmental signals. Based on an engineered modular genetic AND logic gate, we report the construction of a set of scalable synthetic microbe-based biosensors comprising exchangeable sensory, signal processing and actuation modules. These cellular biosensors were engineered using distinct signalling sensory modules to precisely identify various chemical signals, and combinations thereof, with a quantitative fluorescent output. The genetic logic gate used can function as a biological filter and an amplifier to enhance the sensing selectivity and sensitivity of cell-based biosensors. In particular, an Escherichia coli consortium-based biosensor has been constructed that can detect and integrate three environmental signals (arsenic, mercury and copper ion levels) via either its native two-component signal transduction pathways or synthetic signalling sensors derived from other bacteria in combination with a cell-cell communication module. We demonstrate how a modular cell-based biosensor can be engineered predictably using exchangeable synthetic gene circuit modules to sense and integrate multiple-input signals. This study illustrates some of the key practical design principles required for the future application of these biosensors in broad environmental and healthcare areas. PMID:22981411

MyD88-deficient Hydra reveal an ancient function of TLR signaling in sensing bacterial colonizers

PubMed Central

Franzenburg, Sören; Fraune, Sebastian; Künzel, Sven; Baines, John F.; Domazet-Lošo, Tomislav; Bosch, Thomas C. G.

2012-01-01

Toll-like receptor (TLR) signaling is one of the most important signaling cascades of the innate immune system of vertebrates. Studies in invertebrates have focused on the fruit fly Drosophila melanogaster and the nematode Caenorhabditis elegans, and there is little information regarding the evolutionary origin and ancestral function of TLR signaling. In Drosophila, members of the Toll-like receptor family are involved in both embryonic development and innate immunity. In C. elegans, a clear immune function of the TLR homolog TOL-1 is controversial and central components of vertebrate TLR signaling including the key adapter protein myeloid differentiation primary response gene 88 (MyD88) and the transcription factor NF-κB are not present. In basal metazoans such as the cnidarians Hydra magnipapillata and Nematostella vectensis, all components of the vertebrate TLR signaling cascade are present, but their role in immunity is unknown. Here, we use a MyD88 loss-of-function approach in Hydra to demonstrate that recognition of bacteria is an ancestral function of TLR signaling and that this process contributes to both host-mediated recolonization by commensal bacteria as well as to defense against bacterial pathogens. PMID:23112184

MyD88-deficient Hydra reveal an ancient function of TLR signaling in sensing bacterial colonizers.

PubMed

Franzenburg, Sören; Fraune, Sebastian; Künzel, Sven; Baines, John F; Domazet-Loso, Tomislav; Bosch, Thomas C G

2012-11-20

Toll-like receptor (TLR) signaling is one of the most important signaling cascades of the innate immune system of vertebrates. Studies in invertebrates have focused on the fruit fly Drosophila melanogaster and the nematode Caenorhabditis elegans, and there is little information regarding the evolutionary origin and ancestral function of TLR signaling. In Drosophila, members of the Toll-like receptor family are involved in both embryonic development and innate immunity. In C. elegans, a clear immune function of the TLR homolog TOL-1 is controversial and central components of vertebrate TLR signaling including the key adapter protein myeloid differentiation primary response gene 88 (MyD88) and the transcription factor NF-κB are not present. In basal metazoans such as the cnidarians Hydra magnipapillata and Nematostella vectensis, all components of the vertebrate TLR signaling cascade are present, but their role in immunity is unknown. Here, we use a MyD88 loss-of-function approach in Hydra to demonstrate that recognition of bacteria is an ancestral function of TLR signaling and that this process contributes to both host-mediated recolonization by commensal bacteria as well as to defense against bacterial pathogens.

Novel Disease Susceptibility Factors for Fungal Necrotrophic Pathogens in Arabidopsis

PubMed Central

García-Andrade, Javier; Angulo, Carlos; Neumetzler, Lutz; Persson, Staffan; Vera, Pablo

2015-01-01

Host cells use an intricate signaling system to respond to invasions by pathogenic microorganisms. Although several signaling components of disease resistance against necrotrophic fungal pathogens have been identified, our understanding for how molecular components and host processes contribute to plant disease susceptibility is rather sparse. Here, we identified four transcription factors (TFs) from Arabidopsis that limit pathogen spread. Arabidopsis mutants defective in any of these TFs displayed increased disease susceptibility to Botrytis cinerea and Plectosphaerella cucumerina, and a general activation of non-immune host processes that contribute to plant disease susceptibility. Transcriptome analyses revealed that the mutants share a common transcriptional signature of 77 up-regulated genes. We characterized several of the up-regulated genes that encode peptides with a secretion signal, which we named PROVIR (for provirulence) factors. Forward and reverse genetic analyses revealed that many of the PROVIRs are important for disease susceptibility of the host to fungal necrotrophs. The TFs and PROVIRs identified in our work thus represent novel genetic determinants for plant disease susceptibility to necrotrophic fungal pathogens. PMID:25830627

Development of a digital method for neutron/gamma-ray discrimination based on matched filtering

NASA Astrophysics Data System (ADS)

Korolczuk, S.; Linczuk, M.; Romaniuk, R.; Zychor, I.

2016-09-01

Neutron/gamma-ray discrimination is crucial for measurements with detectors sensitive to both neutron and gamma-ray radiation. Different techniques to discriminate between neutrons and gamma-rays based on pulse shape analysis are widely used in many applications, e.g., homeland security, radiation dosimetry, environmental monitoring, fusion experiments, nuclear spectroscopy. A common requirement is to improve a radiation detection level with a high detection reliability. Modern electronic components, such as high speed analog to digital converters and powerful programmable digital circuits for signal processing, allow us to develop a fully digital measurement system. With this solution it is possible to optimize digital signal processing algorithms without changing any electronic components in an acquisition signal path. We report on results obtained with a digital acquisition system DNG@NCBJ designed at the National Centre for Nuclear Research. A 2'' × 2'' EJ309 liquid scintillator was used to register mixed neutron and gamma-ray radiation from PuBe sources. A dedicated algorithm for pulse shape discrimination, based on real-time filtering, was developed and implemented in hardware.

Enhanced performance for differential detection in coherent Brillouin optical time-domain analysis sensors

NASA Astrophysics Data System (ADS)

Shao, Liyang; Zhang, Yunpeng; Li, Zonglei; Zhang, Zhiyong; Zou, Xihua; Luo, Bin; Pan, Wei; Yan, Lianshan

2016-11-01

Logarithmic detectors (LogDs) have been used in coherent Brillouin optical time-domain analysis (BOTDA) sensors to reduce the effect of phase fluctuation, demodulation complexities, and measurement time. However, because of the inherent properties of LogDs, a DC component at the level of hundreds of millivolts that prohibits high-gain signal amplification (SA) could be generated, resulting in unacceptable data acquisition (DAQ) inaccuracies and decoding errors in the process of prototype integration. By generating a reference light at a level similar to the probe light, differential detection can be applied to remove the DC component automatically using a differential amplifier before the DAQ process. Therefore, high-gain SA can be employed to reduce quantization errors. The signal-to-noise ratio of the weak Brillouin gain signal is improved from ˜11.5 to ˜21.8 dB. A BOTDA prototype is implemented based on the proposed scheme. The experimental results show that the measurement accuracy of the Brillouin frequency shift (BFS) is improved from ±1.9 to ±0.8 MHz at the end of a 40-km sensing fiber.

Emerging Insights into Wnt/β-catenin Signaling in Head and Neck Cancer.

PubMed

Alamoud, K A; Kukuruzinska, M A

2018-06-01

Head and neck cancer presents primarily as head and neck squamous cell carcinoma (HNSCC), a debilitating malignancy fraught with high morbidity, poor survival rates, and limited treatment options. Mounting evidence indicates that the Wnt/β-catenin signaling pathway plays important roles in the pathobiology of HNSCC. Wnt/β-catenin signaling affects multiple cellular processes that endow cancer cells with the ability to maintain and expand immature stem-like phenotypes, proliferate, extend survival, and acquire aggressive characteristics by adopting mesenchymal traits. A central component of canonical Wnt signaling is β-catenin, which balances its role as a structural component of E-cadherin junctions with its function as a transcriptional coactivator of numerous target genes. Recent genomic characterization of head and neck cancer revealed that while β-catenin is not frequently mutated in HNSCC, its activity is unchecked by more common mutations in genes encoding upstream regulators of β-catenin, NOTCH1, FAT1, and AJUBA. Wnt/β-catenin signaling affects a wide range epigenetic and transcriptional activities, mediated by the interaction of β-catenin with different transcription factors and transcriptional coactivators and corepressors. Furthermore, Wnt/β-catenin functions in a network with many signaling and metabolic pathways that modulate its activity. In addition to its effects on tumor epithelia, β-catenin activity regulates the tumor microenvironment by regulating extracellular matrix remodeling, fibrotic processes, and immune response. These multifunctional oncogenic effects of β-catenin make it an attractive bona fide target for HNSCC therapy.

Digital Signal Processing Methods for Ultrasonic Echoes.

PubMed

Sinding, Kyle; Drapaca, Corina; Tittmann, Bernhard

2016-04-28

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

Non-stationary signal analysis based on general parameterized time-frequency transform and its application in the feature extraction of a rotary machine

NASA Astrophysics Data System (ADS)

Zhou, Peng; Peng, Zhike; Chen, Shiqian; Yang, Yang; Zhang, Wenming

2018-06-01

With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized time-frequency transform (GPTFT). The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kernel. A multi-component instantaneous frequency (IF) extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI). Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.

Timeseries Signal Processing for Enhancing Mobile Surveys: Learning from Field Studies

NASA Astrophysics Data System (ADS)

Risk, D. A.; Lavoie, M.; Marshall, A. D.; Baillie, J.; Atherton, E. E.; Laybolt, W. D.

2015-12-01

Vehicle-based surveys using laser and other analyzers are now commonplace in research and industry. In many cases when these studies target biologically-relevant gases like methane and carbon dioxide, the minimum detection limits are often coarse (ppm) relative to the analyzer's capabilities (ppb), because of the inherent variability in the ambient background concentrations across the landscape that creates noise and uncertainty. This variation arises from localized biological sinks and sources, but also atmospheric turbulence, air pooling, and other factors. Computational processing routines are widely used in many fields to increase resolution of a target signal in temporally dense data, and offer promise for enhancing mobile surveying techniques. Signal processing routines can both help identify anomalies at very low levels, or can be used inversely to remove localized industrially-emitted anomalies from ecological data. This presentation integrates learnings from various studies in which simple signal processing routines were used successfully to isolate different temporally-varying components of 1 Hz timeseries measured with laser- and UV fluorescence-based analyzers. As illustrative datasets, we present results from industrial fugitive emission studies from across Canada's western provinces and other locations, and also an ecological study that aimed to model near-surface concentration variability across different biomes within eastern Canada. In these cases, signal processing algorithms contributed significantly to the clarity of both industrial, and ecological processes. In some instances, signal processing was too computationally intensive for real-time in-vehicle processing, but we identified workarounds for analyzer-embedded software that contributed to an improvement in real-time resolution of small anomalies. Signal processing is a natural accompaniment to these datasets, and many avenues are open to researchers who wish to enhance existing, and future datasets.

A silicon avalanche photodiode detector circuit for Nd:YAG laser scattering

NASA Astrophysics Data System (ADS)

Hsieh, C.-L.; Haskovec, J.; Carlstrom, T. N.; Deboo, J. C.; Greenfield, C. M.; Snider, R. T.; Trost, P.

1990-06-01

A silicon avalanche photodiode with an internal gain of about 50 to 100 is used in a temperature controlled environment to measure the Nd:YAG laser Thomson scattered spectrum in the wavelength range from 700 to 1150 nm. A charge sensitive preamplifier was developed for minimizing the noise contribution from the detector electronics. Signal levels as low as 20 photoelectrons (S/N = 1) can be detected. Measurements show that both the signal and the variance of the signal vary linearly with the input light level over the range of interest, indicating Poisson statistics. The signal is processed using a 100 ns delay line and a differential amplifier which subtracts the low frequency background light component. The background signal is amplified with a computer controlled variable gain amplifier and is used for an estimate of the measurement error, calibration, and Z sub eff measurements of the plasma. The signal processing was analyzed using a theoretical model to aid the system design and establish the procedure for data error analysis.

Silicon avalanche photodiode detector circuit for Nd:YAG laser scattering

NASA Astrophysics Data System (ADS)

Hsieh, C. L.; Haskovec, J.; Carlstrom, T. N.; DeBoo, J. C.; Greenfield, C. M.; Snider, R. T.; Trost, P.

1990-10-01

A silicon avalanche photodiode with an internal gain of about 50 to 100 is used in a temperature-controlled environment to measure the Nd:YAG laser Thomson scattered spectrum in the wavelength range from 700 to 1150 nm. A charge-sensitive preamplifier has been developed for minimizing the noise contribution from the detector electronics. Signal levels as low as 20 photoelectrons (S/N=1) can be detected. Measurements show that both the signal and the variance of the signal vary linearly with the input light level over the range of interest, indicating Poisson statistics. The signal is processed using a 100 ns delay line and a differential amplifier which subtracts the low-frequency background light component. The background signal is amplified with a computer-controlled variable gain amplifier and is used for an estimate of the measurement error, calibration, and Zeff measurements of the plasma. The signal processing has been analyzed using a theoretical model to aid the system design and establish the procedure for data error analysis.

The Brassinosteroid Signaling Pathway—New Key Players and Interconnections with Other Signaling Networks Crucial for Plant Development and Stress Tolerance

PubMed Central

Gruszka, Damian

2013-01-01

Brassinosteroids (BRs) are a class of steroid hormones regulating a wide range of physiological processes during the plant life cycle from seed development to the modulation of flowering and senescence. The last decades, and recent years in particular, have witnessed a significant advance in the elucidation of the molecular mechanisms of BR signaling from perception by the transmembrane receptor complex to the regulation of transcription factors influencing expression of the target genes. Application of the new approaches shed light on the molecular functions of the key players regulating the BR signaling cascade and allowed identification of new factors. Recent studies clearly indicated that some of the components of BR signaling pathway act as multifunctional proteins involved in other signaling networks regulating diverse physiological processes, such as photomorphogenesis, cell death control, stomatal development, flowering, plant immunity to pathogens and metabolic responses to stress conditions, including salinity. Regulation of some of these processes is mediated through a crosstalk between BR signalosome and the signaling cascades of other hormones, including auxin, abscisic acid, ethylene and salicylic acid. Unravelling the complicated mechanisms of BR signaling and its interconnections with other molecular networks may be of great importance for future practical applications in agriculture. PMID:23615468

Evaluation of dispersive Bragg gratings (BG) structures for the processing of RF signals with large time delays and bandwidths

NASA Astrophysics Data System (ADS)

Kaba, M.; Zhou, F. C.; Lim, A.; Decoster, D.; Huignard, J.-P.; Tonda, S.; Dolfi, D.; Chazelas, J.

2007-11-01

The applications of microwave optoelectronics are extremely large since they extend from the Radio-over-Fibre to the Homeland security and defence systems. Then, the improved maturity of the optoelectronic components operating up to 40GHz permit to consider new optical processing functions (filtering, beamforming, ...) which can operate over very wideband microwave analogue signals. Specific performances are required which imply optical delay lines able to exhibit large Time-Bandwidth product values. It is proposed to evaluate slow light approach through highly dispersive structures based on either uniform or chirped Bragg Gratings. Therefore, we highlight the impact of the major parameters of such structures: index modulation depth, grating length, grating period, chirp coefficient and demonstrate the high potentiality of Bragg Grating for Large RF signals bandwidth processing under slow-light propagation.

Method and apparatus to assess compartment syndrome

NASA Technical Reports Server (NTRS)

Hargens, Alan R. (Inventor); Yost, William T. (Inventor); Ueno, Toshiaki (Inventor)

2008-01-01

A method and apparatus for measuring pressure buildup in a body compartment that encases muscular tissue. The method includes assessing the body compartment configuration and identifying the effect of pulsatile components on at least one compartment dimension. This process is used in preventing tissue necrosis, and in decisions of whether to perform surgery on the body compartment for prevention of Compartment Syndrome. An apparatus is used for measuring excess pressure in the body compartment having components for imparting ultrasonic waves such as a transducer, placing the transducer to impart the ultrasonic waves, capturing the reflected imparted ultrasonic waves, and converting them to electrical signals, a pulsed phase-locked loop device for assessing a body compartment configuration and producing an output signal, and means for mathematically manipulating the output signal to thereby categorize pressure build-up in the body compartment from the mathematical manipulations.

Multi-frequency communication system and method

DOEpatents

Carrender, Curtis Lee; Gilbert, Ronald W.

2004-06-01

A multi-frequency RFID remote communication system is provided that includes a plurality of RFID tags configured to receive a first signal and to return a second signal, the second signal having a first frequency component and a second frequency component, the second frequency component including data unique to each remote RFID tag. The system further includes a reader configured to transmit an interrogation signal and to receive remote signals from the tags. A first signal processor, preferably a mixer, removes an intermediate frequency component from the received signal, and a second processor, preferably a second mixer, analyzes the IF frequency component to output data that is unique to each remote tag.

Coherent-Phase Monitoring Of Cavitation In Turbomachines

NASA Technical Reports Server (NTRS)

Jong, Jen-Yi

1996-01-01

Digital electronic signal-processing system analyzes outputs of accelerometers mounted on turbomachine to detect vibrations characteristic of cavitation. Designed to overcome limitation imposed by interference from discrete components. System digitally implements technique called "coherent-phase wide-band demodulation" (CPWBD), using phase-only (PO) filtering along envelope detection to search for unique coherent-phase relationship associated with cavitation and to minimize influence of large-amplitude discrete components.

Embedding Optical Fibers In Cast Metal Parts

NASA Technical Reports Server (NTRS)

Gibler, William N.; Atkins, Robert A.; Lee, Chung E.; Taylor, Henry F.

1995-01-01

Use of metal strain reliefs eliminates breakage of fibers during casting process. Technique for embedding fused silica optical fibers in cast metal parts devised. Optical fiber embedded in flange, fitting, or wall of vacuum or pressure chamber, to provide hermetically sealed feedthrough for optical transmission of measurement or control signals. Another example, optical-fiber temperature sensor embedded in metal structural component to measure strain or temperature inside component.

Roles of mTOR Signaling in Brain Development.

PubMed

Lee, Da Yong

2015-09-01

mTOR is a serine/threonine kinase composed of multiple protein components. Intracellular signaling of mTOR complexes is involved in many of physiological functions including cell survival, proliferation and differentiation through the regulation of protein synthesis in multiple cell types. During brain development, mTOR-mediated signaling pathway plays a crucial role in the process of neuronal and glial differentiation and the maintenance of the stemness of neural stem cells. The abnormalities in the activity of mTOR and its downstream signaling molecules in neural stem cells result in severe defects of brain developmental processes causing a significant number of brain disorders, such as pediatric brain tumors, autism, seizure, learning disability and mental retardation. Understanding the implication of mTOR activity in neural stem cells would be able to provide an important clue in the development of future brain developmental disorder therapies.

Very-long-period seismic signals - filling the gap between deformation and seismicity

NASA Astrophysics Data System (ADS)

Neuberg, Jurgen; Smith, Paddy

2013-04-01

Good broadband seismic sensors are capable to record seismic transients with dominant wavelengths of several tens or even hundreds of seconds. This allows us to generate a multi-component record of seismic volcanic events that are located in between the conventional high to low-frequency seismic spectrum and deformation signals. With a much higher temporal resolution and accuracy than e.g. GPS records, these signals fill the gap between seismicity and deformation studies. In this contribution we will review the non-trivial processing steps necessary to retrieve ground deformation from the original velocity seismogram and explore which role the resulting displacement signals have in the analysis of volcanic events. We use examples from Soufriere Hills volcano in Montserrat, West Indies, to discuss the benefits and shortcomings of such methods regarding new insights into volcanic processes.

Reducing Artifacts in TMS-Evoked EEG

NASA Astrophysics Data System (ADS)

Fuertes, Juan José; Travieso, Carlos M.; Álvarez, A.; Ferrer, M. A.; Alonso, J. B.

Transcranial magnetic stimulation induces weak currents within the cranium to activate neuronal firing and its response is recorded using electroencephalography in order to study the brain directly. However, different artifacts contaminate the results. The goal of this study is to process these artifacts and reduce them digitally. Electromagnetic, blink and auditory artifacts are considered, and Signal-Space Projection, Independent Component Analysis and Wiener Filtering methods are used to reduce them. These last two produce a successful solution for electromagnetic artifacts. Regarding the other artifacts, processed with Signal-Space Projection, the method reduces the artifact but modifies the signal as well. Nonetheless, they are modified in an exactly known way and the vector used for the projection is conserved to be taken into account when analyzing the resulting signals. A system which combines the proposed methods would improve the quality of the information presented to physicians.

Sensor/amplifier for weak light sources

NASA Technical Reports Server (NTRS)

Desmet, D. J.; Jason, A. J.; Parr, A. C.

1980-01-01

Light sensor/amplifier circuit detects weak light converts it into strong electrical signal in electrically noisy environment. Circuit is relatively simple and uses inexpensive, readily available components. Device is useful in such applications as fire detection and photographic processing.

Apodization of spurs in radar receivers using multi-channel processing

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

Doerry, Armin W.; Bickel, Douglas L.

The various technologies presented herein relate to identification and mitigation of spurious energies or signals (aka "spurs") in radar imaging. Spurious energy in received radar data can be a consequence of non-ideal component and circuit behavior. Such behavior can result from I/Q imbalance, nonlinear component behavior, additive interference (e.g. cross-talk, etc.), etc. The manifestation of the spurious energy in a radar image (e.g., a range-Doppler map) can be influenced by appropriate pulse-to-pulse phase modulation. Comparing multiple images which have been processed using the same data but of different signal paths and modulations enables identification of undesired spurs, with subsequent croppingmore » or apodization of the undesired spurs from a radar image. Spurs can be identified by comparison with a threshold energy. Removal of an undesired spur enables enhanced identification of true targets in a radar image.« less

Iterative dip-steering median filter

NASA Astrophysics Data System (ADS)

Huo, Shoudong; Zhu, Weihong; Shi, Taikun

2017-09-01

Seismic data are always contaminated with high noise components, which present processing challenges especially for signal preservation and its true amplitude response. This paper deals with an extension of the conventional median filter, which is widely used in random noise attenuation. It is known that the standard median filter works well with laterally aligned coherent events but cannot handle steep events, especially events with conflicting dips. In this paper, an iterative dip-steering median filter is proposed for the attenuation of random noise in the presence of multiple dips. The filter first identifies the dominant dips inside an optimized processing window by a Fourier-radial transform in the frequency-wavenumber domain. The optimum size of the processing window depends on the intensity of random noise that needs to be attenuated and the amount of signal to be preserved. It then applies median filter along the dominant dip and retains the signals. Iterations are adopted to process the residual signals along the remaining dominant dips in a descending sequence, until all signals have been retained. The method is tested by both synthetic and field data gathers and also compared with the commonly used f-k least squares de-noising and f-x deconvolution.

Intersecting Roles of Protein Tyrosine Kinase and Calcium Signaling During Fertilization

PubMed Central

Kinsey, William H.

2012-01-01

The oocyte is a highly specialized cell that must respond to fertilization with a preprogrammed series of signal transduction events that establish a block to polyspermy, trigger resumption of the cell cycle and execution of a developmental program. The fertilization-induced calcium transient is a key signal that initiates the process of oocyte activation and studies over the last several years have examined the signaling pathways that act upstream and downstream of this calcium transient. Protein tyrosine kinase signaling was found to be an important component of the upstream pathways that stimulated calcium release at fertilization in oocytes from animals that fertilize externally, but a similar pathway has not been found in mammals which fertilize internally. The following review will examine the diversity of signaling in oocytes from marine invertebrates, amphibians, fish and mammals in an attempt to understand the basis for the observed differences. In addition to the pathways upstream of the fertilization-induced calcium transient, recent studies are beginning to unravel the role of protein tyrosine kinase signaling downstream of the calcium transient. The PYK2 kinase was found to respond to fertilization in the zebrafish system and seems to represent a novel component of the response of the oocyte to fertilization. The potential impact of impaired PTK signaling in oocyte quality will also be discussed. PMID:23201334

A simple predistortion technique for suppression of nonlinear effects in periodic signals generated by nonlinear transducers

NASA Astrophysics Data System (ADS)

Novak, A.; Simon, L.; Lotton, P.

2018-04-01

Mechanical transducers, such as shakers, loudspeakers and compression drivers that are used as excitation devices to excite acoustical or mechanical nonlinear systems under test are imperfect. Due to their nonlinear behaviour, unwanted contributions appear at their output besides the wanted part of the signal. Since these devices are used to study nonlinear systems, it should be required to measure properly the systems under test by overcoming the influence of the nonlinear excitation device. In this paper, a simple method that corrects distorted output signal of the excitation device by means of predistortion of its input signal is presented. A periodic signal is applied to the input of the excitation device and, from analysing the output signal of the device, the input signal is modified in such a way that the undesirable spectral components in the output of the excitation device are cancelled out after few iterations of real-time processing. The experimental results provided on an electrodynamic shaker show that the spectral purity of the generated acceleration output approaches 100 dB after few iterations (1 s). This output signal, applied to the system under test, is thus cleaned from the undesirable components produced by the excitation device; this is an important condition to ensure a correct measurement of the nonlinear system under test.

Signs of Social Class: The Experience of Economic Inequality in Everyday Life

PubMed Central

Kraus, Michael W.; Park, Jun Won; Tan, Jacinth J. X.

2017-01-01

By some accounts, global economic inequality is at its highest point on record. The pernicious effects of this broad societal trend are striking: Rising inequality is linked to poorer health and well-being across countries, continents, and cultures. The economic and psychological forces that perpetuate inequality continue to be studied, and in this theoretical review, we examine the role of daily experiences of economic inequality—the communication of social class signals between interaction partners—in this process. We theorize that social class signals activate social comparison processes that strengthen group boundaries between the haves and have nots in society. In particular, we argue that class signals are a frequent, rapid, and accurate component of person perception, and we provide new data and analyses demonstrating the accuracy of class signaling in 60-s interactions, Facebook photographs, and isolated recordings of brief speech. We suggest that barriers to the reduction of economic inequality in society arise directly from this class signaling process through the augmentation of class boundaries and the elicitation of beliefs and behaviors that favor the economic status quo. PMID:28544871

Signs of Social Class: The Experience of Economic Inequality in Everyday Life.

PubMed

Kraus, Michael W; Park, Jun Won; Tan, Jacinth J X

2017-05-01

By some accounts, global economic inequality is at its highest point on record. The pernicious effects of this broad societal trend are striking: Rising inequality is linked to poorer health and well-being across countries, continents, and cultures. The economic and psychological forces that perpetuate inequality continue to be studied, and in this theoretical review, we examine the role of daily experiences of economic inequality-the communication of social class signals between interaction partners-in this process. We theorize that social class signals activate social comparison processes that strengthen group boundaries between the haves and have nots in society. In particular, we argue that class signals are a frequent, rapid, and accurate component of person perception, and we provide new data and analyses demonstrating the accuracy of class signaling in 60-s interactions, Facebook photographs, and isolated recordings of brief speech. We suggest that barriers to the reduction of economic inequality in society arise directly from this class signaling process through the augmentation of class boundaries and the elicitation of beliefs and behaviors that favor the economic status quo.

Simultaneous multi-component seismic denoising and reconstruction via K-SVD

NASA Astrophysics Data System (ADS)

Hou, Sian; Zhang, Feng; Li, Xiangyang; Zhao, Qiang; Dai, Hengchang

2018-06-01

Data denoising and reconstruction play an increasingly significant role in seismic prospecting for their value in enhancing effective signals, dealing with surface obstacles and reducing acquisition costs. In this paper, we propose a novel method to denoise and reconstruct multicomponent seismic data simultaneously. This method lies within the framework of machine learning and the key points are defining a suitable weight function and a modified inner product operator. The purpose of these two processes are to perform missing data machine learning when the random noise deviation is unknown, and building a mathematical relationship for each component to incorporate all the information of multi-component data. Two examples, using synthetic and real multicomponent data, demonstrate that the new method is a feasible alternative for multi-component seismic data processing.

Optical Vector Receiver Operating Near the Quantum Limit

NASA Astrophysics Data System (ADS)

Vilnrotter, V. A.; Lau, C.-W.

2005-05-01

An optical receiver concept for binary signals with performance approaching the quantum limit at low average-signal energies is developed and analyzed. A conditionally nulling receiver that reaches the quantum limit in the absence of background photons has been devised by Dolinar. However, this receiver requires ideal optical combining and complicated real-time shaping of the local field; hence, it tends to be difficult to implement at high data rates. A simpler nulling receiver that approaches the quantum limit without complex optical processing, suitable for high-rate operation, had been suggested earlier by Kennedy. Here we formulate a vector receiver concept that incorporates the Kennedy receiver with a physical beamsplitter, but it also utilizes the reflected signal component to improve signal detection. It is found that augmenting the Kennedy receiver with classical coherent detection at the auxiliary beamsplitter output, and optimally processing the vector observations, always improves on the performance of the Kennedy receiver alone, significantly so at low average-photon rates. This is precisely the region of operation where modern codes approach channel capacity. It is also shown that the addition of background radiation has little effect on the performance of the coherent receiver component, suggesting a viable approach for near-quantum-limited performance in high background environments.

Optical implementation of polarization-independent, bidirectional, nonblocking Clos network using polarization control technique in free space

NASA Astrophysics Data System (ADS)

Yang, Junbo; Yang, Jiankun; Li, Xiujian; Chang, Shengli; Su, Xianyu; Ping, Xu

2011-04-01

The clos network is one of the earliest multistage interconnection networks. Recently, it has been widely studied in parallel optical information processing systems, and there have been many efforts to develop this network. In this paper, a smart and compact Clos network, including Clos(2,3,2) and Clos(2,4,2), is proposed by using polarizing beam-splitters (PBS), phase spatial light modulators (PSLM), and mirrors. PBS features that are s-component (perpendicular to the incident plane) of the incident light beam is reflected, and the p-component (parallel to the incident plane) passes through it. According to switching logic, under control of external electrical signals, PSLM functions to control routing paths of the signal beams, i.e., the polarization of each optical signal is rotated or not rotated 90° by a programmable PSLM. This new type of configuration grants the features of less optical components, compact in structure, efficient in performance, and insensitive to polarization of signal beam. In addition, the straight, the exchange, and the broadcast functions of the basic switch element are implemented bidirectionally in free-space. Furthermore, the new optical experimental module of 2×3 and 2×4 optical switch is also presented by a cascading polarization-independent bidirectional 2×2 optical switch. Simultaneously, the routing state-table of 2×3 and 2×4 optical switch to perform all permutation output and nonblocking switch for the input signal beam, is achieved. Since the proposed optical setup consists of only optical polarization elements, it is compact in structure, and possesses a low energy loss, a high signal-to-ratio, and an available large number of optical channels. Finally, the discussions and the experimental results show that the Clos network proposed here should be helpful in the design of large-scale network matrix, and may be used in optical communication and optical information processing.

Ultralow-Power Digital Correlator for Microwave Polarimetry

NASA Technical Reports Server (NTRS)

Piepmeier, Jeffrey R.; Hass, K. Joseph

2004-01-01

A recently developed high-speed digital correlator is especially well suited for processing readings of a passive microwave polarimeter. This circuit computes the autocorrelations of, and the cross-correlations among, data in four digital input streams representing samples of in-phase (I) and quadrature (Q) components of two intermediate-frequency (IF) signals, denoted A and B, that are generated in heterodyne reception of two microwave signals. The IF signals arriving at the correlator input terminals have been digitized to three levels (-1,0,1) at a sampling rate up to 500 MHz. Two bits (representing sign and magnitude) are needed to represent the instantaneous datum in each input channel; hence, eight bits are needed to represent the four input signals during any given cycle of the sampling clock. The accumulation (integration) time for the correlation is programmable in increments of 2(exp 8) cycles of the sampling clock, up to a maximum of 2(exp 24) cycles. The basic functionality of the correlator is embodied in 16 correlation slices, each of which contains identical logic circuits and counters (see figure). The first stage of each correlation slice is a logic gate that computes one of the desired correlations (for example, the autocorrelation of the I component of A or the negative of the cross-correlation of the I component of A and the Q component of B). The sampling of the output of the logic gate output is controlled by the sampling-clock signal, and an 8-bit counter increments in every clock cycle when the logic gate generates output. The most significant bit of the 8-bit counter is sampled by a 16-bit counter with a clock signal at 2(exp 8) the frequency of the sampling clock. The 16-bit counter is incremented every time the 8-bit counter rolls over.

Absence of both auditory evoked potentials and auditory percepts dependent on timing cues.

PubMed

Starr, A; McPherson, D; Patterson, J; Don, M; Luxford, W; Shannon, R; Sininger, Y; Tonakawa, L; Waring, M

1991-06-01

An 11-yr-old girl had an absence of sensory components of auditory evoked potentials (brainstem, middle and long-latency) to click and tone burst stimuli that she could clearly hear. Psychoacoustic tests revealed a marked impairment of those auditory perceptions dependent on temporal cues, that is, lateralization of binaural clicks, change of binaural masked threshold with changes in signal phase, binaural beats, detection of paired monaural clicks, monaural detection of a silent gap in a sound, and monaural threshold elevation for short duration tones. In contrast, auditory functions reflecting intensity or frequency discriminations (difference limens) were only minimally impaired. Pure tone audiometry showed a moderate (50 dB) bilateral hearing loss with a disproportionate severe loss of word intelligibility. Those auditory evoked potentials that were preserved included (1) cochlear microphonics reflecting hair cell activity; (2) cortical sustained potentials reflecting processing of slowly changing signals; and (3) long-latency cognitive components (P300, processing negativity) reflecting endogenous auditory cognitive processes. Both the evoked potential and perceptual deficits are attributed to changes in temporal encoding of acoustic signals perhaps occurring at the synapse between hair cell and eighth nerve dendrites. The results from this patient are discussed in relation to previously published cases with absent auditory evoked potentials and preserved hearing.

Noise-assisted data processing with empirical mode decomposition in biomedical signals.

PubMed

Karagiannis, Alexandros; Constantinou, Philip

2011-01-01

In this paper, a methodology is described in order to investigate the performance of empirical mode decomposition (EMD) in biomedical signals, and especially in the case of electrocardiogram (ECG). Synthetic ECG signals corrupted with white Gaussian noise are employed and time series of various lengths are processed with EMD in order to extract the intrinsic mode functions (IMFs). A statistical significance test is implemented for the identification of IMFs with high-level noise components and their exclusion from denoising procedures. Simulation campaign results reveal that a decrease of processing time is accomplished with the introduction of preprocessing stage, prior to the application of EMD in biomedical time series. Furthermore, the variation in the number of IMFs according to the type of the preprocessing stage is studied as a function of SNR and time-series length. The application of the methodology in MIT-BIH ECG records is also presented in order to verify the findings in real ECG signals.

Fault Detection of Bearing Systems through EEMD and Optimization Algorithm

PubMed Central

Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

2017-01-01

This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. PMID:29143772

Imaging of dynamic ion signaling during root gravitropism.

PubMed

Monshausen, Gabriele B

2015-01-01

Gravitropic signaling is a complex process that requires the coordinated action of multiple cell types and tissues. Ca(2+) and pH signaling are key components of gravitropic signaling cascades and can serve as useful markers to dissect the molecular machinery mediating plant gravitropism. To monitor dynamic ion signaling, imaging approaches combining fluorescent ion sensors and confocal fluorescence microscopy are employed, which allow the visualization of pH and Ca(2+) changes at the level of entire tissues, while also providing high spatiotemporal resolution. Here, I describe procedures to prepare Arabidopsis seedlings for live cell imaging and to convert a microscope for vertical stage fluorescence microscopy. With this imaging system, ion signaling can be monitored during all phases of the root gravitropic response.

Functional link between plasma membrane spatiotemporal dynamics, cancer biology, and dietary membrane-altering agents.

PubMed

Erazo-Oliveras, Alfredo; Fuentes, Natividad R; Wright, Rachel C; Chapkin, Robert S

2018-06-02

The cell plasma membrane serves as a nexus integrating extra- and intracellular components, which together enable many of the fundamental cellular signaling processes that sustain life. In order to perform this key function, plasma membrane components assemble into well-defined domains exhibiting distinct biochemical and biophysical properties that modulate various signaling events. Dysregulation of these highly dynamic membrane domains can promote oncogenic signaling. Recently, it has been demonstrated that select membrane-targeted dietary bioactives (MTDBs) have the ability to remodel plasma membrane domains and subsequently reduce cancer risk. In this review, we focus on the importance of plasma membrane domain structural and signaling functionalities as well as how loss of membrane homeostasis can drive aberrant signaling. Additionally, we discuss the intricacies associated with the investigation of these membrane domain features and their associations with cancer biology. Lastly, we describe the current literature focusing on MTDBs, including mechanisms of chemoprevention and therapeutics in order to establish a functional link between these membrane-altering biomolecules, tuning of plasma membrane hierarchal organization, and their implications in cancer prevention.

The design of photoelectric signal processing system for a nuclear magnetic resonance gyroscope based on FPGA

NASA Astrophysics Data System (ADS)

Zhang, Xian; Zhou, Binquan; Li, Hong; Zhao, Xinghua; Mu, Weiwei; Wu, Wenfeng

2017-10-01

Navigation technology is crucial to the national defense and military, which can realize the measurement of orientation, positioning, attitude and speed for moving object. Inertial navigation is not only autonomous, real-time, continuous, hidden, undisturbed but also no time-limited and environment-limited. The gyroscope is the core component of the inertial navigation system, whose precision and size are the bottleneck of the performance. However, nuclear magnetic resonance gyroscope is characteristic of the advantage of high precision and small size. Nuclear magnetic resonance gyroscope can meet the urgent needs of high-tech weapons and equipment development of new generation. This paper mainly designs a set of photoelectric signal processing system for nuclear magnetic resonance gyroscope based on FPGA, which process and control the information of detecting laser .The photoelectric signal with high frequency carrier is demodulated by in-phase and quadrature demodulation method. Finally, the processing system of photoelectric signal can compensate the residual magnetism of the shielding barrel and provide the information of nuclear magnetic resonance gyroscope angular velocity.

[Multi-channel in vivo recording techniques: signal processing of action potentials and local field potentials].

PubMed

Xu, Jia-Min; Wang, Ce-Qun; Lin, Long-Nian

2014-06-25

Multi-channel in vivo recording techniques are used to record ensemble neuronal activity and local field potentials (LFP) simultaneously. One of the key points for the technique is how to process these two sets of recorded neural signals properly so that data accuracy can be assured. We intend to introduce data processing approaches for action potentials and LFP based on the original data collected through multi-channel recording system. Action potential signals are high-frequency signals, hence high sampling rate of 40 kHz is normally chosen for recording. Based on waveforms of extracellularly recorded action potentials, tetrode technology combining principal component analysis can be used to discriminate neuronal spiking signals from differently spatially distributed neurons, in order to obtain accurate single neuron spiking activity. LFPs are low-frequency signals (lower than 300 Hz), hence the sampling rate of 1 kHz is used for LFPs. Digital filtering is required for LFP analysis to isolate different frequency oscillations including theta oscillation (4-12 Hz), which is dominant in active exploration and rapid-eye-movement (REM) sleep, gamma oscillation (30-80 Hz), which is accompanied by theta oscillation during cognitive processing, and high frequency ripple oscillation (100-250 Hz) in awake immobility and slow wave sleep (SWS) state in rodent hippocampus. For the obtained signals, common data post-processing methods include inter-spike interval analysis, spike auto-correlation analysis, spike cross-correlation analysis, power spectral density analysis, and spectrogram analysis.

Host-pathogen interaction in Fusarium oxysporum infections: where do we stand?

PubMed

Husaini, Amjad M; Sakina, Aafreen; Cambay, Souliha R

2018-03-16

Fusarium oxysporum, a ubiquitous soil-borne pathogen causes devastating vascular wilt in more than 100 plant species and ranks fifth among top ten fungal plant pathogens. It has emerged as a human pathogen too, causing infections in immune-compromised patients. It is, therefore, important to gain insight into the molecular processes involved in the pathogenesis of this trans-kingdom pathogen. A complex network comprising of interconnected and over lapping signal pathways; mitogen-activated protein kinase (MAPK) signaling pathways, Ras proteins, G-protein signaling components and their downstream pathways, components of the velvet (LaeA/VeA/VelB) complex and cAMP pathways, is involved in perceiving the host. This network regulates the expression of various pathogenicity genes. Plants have however evolved an elaborate protection system to combat this attack. They too possess intricate mechanisms at molecular level, which once triggered by pathogen attack transduce signals to activate defense response. This review focuses on understanding and presenting a wholistic picture of the molecular mechanisms of F. oxysporum-host interactions in plant immunity.

Advanced Turbine Technology Applications Project (ATTAP)

NASA Technical Reports Server (NTRS)

1994-01-01

Reports technical effort by AlliedSignal Engines in sixth year of DOE/NASA funded project. Topics include: gas turbine engine design modifications of production APU to incorporate ceramic components; fabrication and processing of silicon nitride blades and nozzles; component and engine testing; and refinement and development of critical ceramics technologies, including: hot corrosion testing and environmental life predictive model; advanced NDE methods for internal flaws in ceramic components; and improved carbon pulverization modeling during impact. ATTAP project is oriented toward developing high-risk technology of ceramic structural component design and fabrication to carry forward to commercial production by 'bridging the gap' between structural ceramics in the laboratory and near-term commercial heat engine application. Current ATTAP project goal is to support accelerated commercialization of advanced, high-temperature engines for hybrid vehicles and other applications. Project objectives are to provide essential and substantial early field experience demonstrating ceramic component reliability and durability in modified, available, gas turbine engine applications; and to scale-up and improve manufacturing processes of ceramic turbine engine components and demonstrate application of these processes in the production environment.

Reward positivity is elicited by monetary reward in the absence of response choice.

PubMed

Varona-Moya, Sergio; Morís, Joaquín; Luque, David

2015-02-11

The neural response to positive and negative feedback differs in their event-related potentials. Most often this difference is interpreted as the result of a negative voltage deflection after negative feedback. This deflection has been referred to as the feedback-related negativity component. The reinforcement learning model of the feedback-related negativity establishes that this component reflects an error monitoring process aimed to increase behavior adjustment progressively. However, a recent proposal suggests that the difference observed is actually due to a positivity reflecting the rewarding value of positive feedbacks - that is, the reward positivity component (RewP). From this it follows that RewP could be found even in the absence of any action-monitoring processes. We tested this prediction by means of an experiment in which visual target stimuli were intermixed with nontarget stimuli. Three types of targets signaled money gains, money losses, or the absence of either money gain or money loss, respectively. No motor response was required. Event-related potential analyses showed a central positivity in a 270-370 ms time window that was elicited by target stimuli signaling money gains, as compared with both stimuli signaling losses and no-gain/no-loss neutral stimuli. This is the first evidence to show that RewP is obtained when stimuli with rewarding values are passively perceived.

Processing of simple and complex acoustic signals in a tonotopically organized ear

PubMed Central

Hummel, Jennifer; Wolf, Konstantin; Kössl, Manfred; Nowotny, Manuela

2014-01-01

Processing of complex signals in the hearing organ remains poorly understood. This paper aims to contribute to this topic by presenting investigations on the mechanical and neuronal response of the hearing organ of the tropical bushcricket species Mecopoda elongata to simple pure tone signals as well as to the conspecific song as a complex acoustic signal. The high-frequency hearing organ of bushcrickets, the crista acustica (CA), is tonotopically tuned to frequencies between about 4 and 70 kHz. Laser Doppler vibrometer measurements revealed a strong and dominant low-frequency-induced motion of the CA when stimulated with either pure tone or complex stimuli. Consequently, the high-frequency distal area of the CA is more strongly deflected by low-frequency-induced waves than by high-frequency-induced waves. This low-frequency dominance will have strong effects on the processing of complex signals. Therefore, we additionally studied the neuronal response of the CA to native and frequency-manipulated chirps. Again, we found a dominant influence of low-frequency components within the conspecific song, indicating that the mechanical vibration pattern highly determines the neuronal response of the sensory cells. Thus, we conclude that the encoding of communication signals is modulated by ear mechanics. PMID:25339727

A minireview of E4BP4/NFIL3 in heart failure.

PubMed

Velmurugan, Bharath Kumar; Chang, Ruey-Lin; Marthandam Asokan, Shibu; Chang, Chih-Fen; Day, Cecilia-Hsuan; Lin, Yueh-Min; Lin, Yuan-Chuan; Kuo, Wei-Wen; Huang, Chih-Yang

2018-06-01

Heart failure (HF) remains a major cause of morbidity and mortality worldwide. The primary cause identified for HF is impaired left ventricular myocardial function, and clinical manifestations may lead to severe conditions like pulmonary congestion, splanchnic congestion, and peripheral edema. Development of new therapeutic strategies remains the need of the hour for controlling the problem of HF worldwide. Deeper insights into the molecular mechanisms involved in etiopathology of HF indicate the significant role of calcium signaling, autocrine signaling pathways, and insulin-like growth factor-1 signaling that regulates the physiologic functions of heart growth and development such as contraction, metabolism, hypertrophy, cytokine signaling, and apoptosis. In view of these facts, a transcription factor (TF) regulating the myriad of these signaling pathways may prove as a lead candidate for development of therapeutics. Adenovirus E4 promoter-binding protein (E4BP4), also known as nuclear-factor, interleukin 3 regulated (NFIL3), a type of basic leucine zipper TF, is known to regulate the signaling processes involved in the functioning of heart. The current review discusses about the expression, structure, and functional role of E4BP4 in signaling processes with emphasis on calcium signaling mechanisms, autocrine signaling, and insulin-like growth factor II receptor-mediated processes regulated by E4BP4 that may regulate the pathogenesis of HF. We propose that E4BP4, being the critical component for the regulation of the above signaling processes, may serve as a novel therapeutic target for HF, and scientific investigations are merited in this direction. © 2018 Wiley Periodicals, Inc.

EEG artifact elimination by extraction of ICA-component features using image processing algorithms.

PubMed

Radüntz, T; Scouten, J; Hochmuth, O; Meffert, B

2015-03-30

Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

75 FR 56059 - Patent Examiner Technical Training Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-15

...); statistical methods in validation of microarry data; personalized medicine, manufacture of carbon nanospheres... processing, growing monocrystals, hydrogen production, liquid and gas purification and separation, making... Systems and Components: Mixed signal design and architecture, flexible displays, OLED display technology...

Study in Mice Links Key Signaling Molecule to Underlying Cause of Osteogenesis Imperfecta

MedlinePlus

... by mutations in a gene that codes for collagen, an abundant structural component of bone. This type ... linked to defects in enzymes that help process collagen to its mature form. These types of OI ...

TIME FOR COFFEE Represses Accumulation of the MYC2 Transcription Factor to Provide Time-of-Day Regulation of Jasmonate Signaling in Arabidopsis[C][W][OA

PubMed Central

Shin, Jieun; Heidrich, Katharina; Sanchez-Villarreal, Alfredo; Parker, Jane E.; Davis, Seth J.

2012-01-01

Plants are confronted with predictable daily biotic and abiotic stresses that result from the day–night cycle. The circadian clock provides an anticipation mechanism to respond to these daily stress signals to increase fitness. Jasmonate (JA) is a phytohormone that mediates various growth and stress responses. Here, we found that the circadian-clock component TIME FOR COFFEE (TIC) acts as a negative factor in the JA-signaling pathway. We showed that the tic mutant is hypersensitive to growth-repressive effects of JA and displays altered JA-regulated gene expression. TIC was found to interact with MYC2, a key transcription factor of JA signaling. From this, we discovered that the circadian clock rhythmically regulates JA signaling. TIC is a key determinant in this circadian-gated process, and as a result, the tic mutant is defective in rhythmic JA responses to pathogen infection. TIC acts here by inhibiting MYC2 protein accumulation and by controlling the transcriptional repression of CORONATINE INSENSITIVE1 in an evening-phase–specific manner. Taken together, we propose that TIC acts as an output component of the circadian oscillator to influence JA signaling directly. PMID:22693280

Unveiling the signals from extremely noisy microseismic data for high-resolution hydraulic fracturing monitoring.

PubMed

Huang, Weilin; Wang, Runqiu; Li, Huijian; Chen, Yangkang

2017-09-20

Microseismic method is an essential technique for monitoring the dynamic status of hydraulic fracturing during the development of unconventional reservoirs. However, one of the challenges in microseismic monitoring is that those seismic signals generated from micro seismicity have extremely low amplitude. We develop a methodology to unveil the signals that are smeared in the strong ambient noise and thus facilitate a more accurate arrival-time picking that will ultimately improve the localization accuracy. In the proposed technique, we decompose the recorded data into several morphological multi-scale components. In order to unveil weak signal, we propose an orthogonalization operator which acts as a time-varying weighting in the morphological reconstruction. The orthogonalization operator is obtained using an inversion process. This orthogonalized morphological reconstruction can be interpreted as a projection of the higher-dimensional vector. We first test the proposed technique using a synthetic dataset. Then the proposed technique is applied to a field dataset recorded in a project in China, in which the signals induced from hydraulic fracturing are recorded by twelve three-component (3-C) geophones in a monitoring well. The result demonstrates that the orthogonalized morphological reconstruction can make the extremely weak microseismic signals detectable.

Wind- and Rain-Induced Vibrations Impose Different Selection Pressures on Multimodal Signaling.

PubMed

Halfwerk, Wouter; Ryan, Michael J; Wilson, Preston S

2016-09-01

The world is a noisy place, and animals have evolved a myriad of strategies to communicate in it. Animal communication signals are, however, often multimodal; their components can be processed by multiple sensory systems, and noise can thus affect signal components across different modalities. We studied the effect of environmental noise on multimodal communication in the túngara frog (Physalaemus pustulosus). Males communicate with rivals using airborne sounds combined with call-induced water ripples. We tested males under control as well as noisy conditions in which we mimicked rain- and wind-induced vibrations on the water surface. Males responded more strongly to a multimodal playback in which sound and ripples were combined, compared to a unimodal sound-only playback, but only in the absence of rain and wind. Under windy conditions, males decreased their response to the multimodal playback, suggesting that wind noise interferes with the detection of rival ripples. Under rainy conditions, males increased their response, irrespective of signal playback, suggesting that different noise sources can have different impacts on communication. Our findings show that noise in an additional sensory channel can affect multimodal signal perception and thereby drive signal evolution, but not always in the expected direction.

Controlling a human-computer interface system with a novel classification method that uses electrooculography signals.

PubMed

Wu, Shang-Lin; Liao, Lun-De; Lu, Shao-Wei; Jiang, Wei-Ling; Chen, Shi-An; Lin, Chin-Teng

2013-08-01

Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.

Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA.

PubMed

Zhou, Weidong; Gotman, Jean

2004-01-01

In this study, the methods of wavelet threshold de-noising and independent component analysis (ICA) are introduced. ICA is a novel signal processing technique based on high order statistics, and is used to separate independent components from measurements. The extended ICA algorithm does not need to calculate the higher order statistics, converges fast, and can be used to separate subGaussian and superGaussian sources. A pre-whitening procedure is performed to de-correlate the mixed signals before extracting sources. The experimental results indicate the electromyogram (EMG) and electrocardiograph (ECG) artifacts in electroencephalograph (EEG) can be removed by a combination of wavelet threshold de-noising and ICA.

A 400 MHz Wireless Neural Signal Processing IC With 625 $\\times$ On-Chip Data Reduction and Reconfigurable BFSK/QPSK Transmitter Based on Sequential Injection Locking.

PubMed

Teng, Kok-Hin; Wu, Tong; Liu, Xiayun; Yang, Zhi; Heng, Chun-Huat

2017-06-01

An 8-channel wireless neural signal processing IC, which can perform real-time spike detection, alignment, and feature extraction, and wireless data transmission is proposed. A reconfigurable BFSK/QPSK transmitter (TX) at MICS/MedRadio band is incorporated to support different data rate requirement. By using an Exponential Component-Polynomial Component (EC-PC) spike processing unit with an incremental principal component analysis (IPCA) engine, the detection of neural spikes with poor SNR is possible while achieving 625× data reduction. For the TX, a dual-channel at 401 MHz and 403.8 MHz are supported by applying sequential injection locked techniques while attaining phase noise of -102 dBc/Hz at 100 kHz offset. From the measurement, error vector magnitude (EVM) of 4.60%/9.55% with power amplifier (PA) output power of -15 dBm is achieved for the QPSK at 8 Mbps and the BFSK at 12.5 kbps. Fabricated in 65 nm CMOS with an active area of 1 mm 2 , the design consumes a total current of 5  ∼ 5.6 mA with a maximum energy efficiency of 0.7 nJ/b.

Coherent-subspace array processing based on wavelet covariance: an application to broad-band, seismo-volcanic signals

NASA Astrophysics Data System (ADS)

Saccorotti, G.; Nisii, V.; Del Pezzo, E.

2008-07-01

Long-Period (LP) and Very-Long-Period (VLP) signals are the most characteristic seismic signature of volcano dynamics, and provide important information about the physical processes occurring in magmatic and hydrothermal systems. These events are usually characterized by sharp spectral peaks, which may span several frequency decades, by emergent onsets, and by a lack of clear S-wave arrivals. These two latter features make both signal detection and location a challenging task. In this paper, we propose a processing procedure based on Continuous Wavelet Transform of multichannel, broad-band data to simultaneously solve the signal detection and location problems. Our method consists of two steps. First, we apply a frequency-dependent threshold to the estimates of the array-averaged WCO in order to locate the time-frequency regions spanned by coherent arrivals. For these data, we then use the time-series of the complex wavelet coefficients for deriving the elements of the spatial Cross-Spectral Matrix. From the eigenstructure of this matrix, we eventually estimate the kinematic signals' parameters using the MUltiple SIgnal Characterization (MUSIC) algorithm. The whole procedure greatly facilitates the detection and location of weak, broad-band signals, in turn avoiding the time-frequency resolution trade-off and frequency leakage effects which affect conventional covariance estimates based upon Windowed Fourier Transform. The method is applied to explosion signals recorded at Stromboli volcano by either a short-period, small aperture antenna, or a large-aperture, broad-band network. The LP (0.2 < T < 2s) components of the explosive signals are analysed using data from the small-aperture array and under the plane-wave assumption. In this manner, we obtain a precise time- and frequency-localization of the directional properties for waves impinging at the array. We then extend the wavefield decomposition method using a spherical wave front model, and analyse the VLP components (T > 2s) of the explosion recordings from the broad-band network. Source locations obtained this way are fully compatible with those retrieved from application of more traditional (and computationally expensive) time-domain techniques, such as the Radial Semblance method.

Description, characteristics and testing of the NASA airborne radar

NASA Technical Reports Server (NTRS)

Jones, W. R.; Altiz, O.; Schaffner, P.; Schrader, J. H.; Blume, H. J. C.

1991-01-01

Presented here is a description of a coherent radar scattermeter and its associated signal processing hardware, which have been specifically designed to detect microbursts and record their radar characteristics. Radar parameters, signal processing techniques and detection algorithms, all under computer control, combine to sense and process reflectivity, clutter, and microburst data. Also presented is the system's high density, high data rate recording system. This digital system is capable of recording many minutes of the in-phase and quadrature components and corresponding receiver gains of the scattered returns for selected spatial regions, as well as other aircraft and hardware related parameters of interest for post-flight analysis. Information is given in viewgraph form.

Endocannabinoid signalling and the deteriorating brain

PubMed Central

Di Marzo, Vincenzo; Stella, Nephi; Zimmer, Andreas

2015-01-01

Ageing is characterized by the progressive impairment of physiological functions and increased risk of developing debilitating disorders, including chronic inflammation and neurodegenerative diseases. These disorders have common molecular mechanisms that can be targeted therapeutically. In the wake of the approval of the first cannabinoid-based drug for the symptomatic treatment of multiple sclerosis, we examine how endocannabinoid (eCB) signalling controls — and is affected by — normal ageing and neuroinflammatory and neurodegenerative disorders. We propose a conceptual framework linking eCB signalling to the control of the cellular and molecular hallmarks of these processes, and categorize the key components of endocannabinoid signalling that may serve as targets for novel therapeutics. PMID:25524120

MECHANISTIC PATHWAYS AND BIOLOGICAL ROLES FOR RECEPTOR-INDEPENDENT ACTIVATORS OF G-PROTEIN SIGNALING

PubMed Central

Blumer, Joe B.; Smrcka, Alan V.; Lanier, S.M.

2007-01-01

Signal processing via heterotrimeric G-proteins in response to cell surface receptors is a central and much investigated aspect of how cells integrate cellular stimuli to produce coordinated biological responses. The system is a target of numerous therapeutic agents, plays an important role in adaptive processes of organs, and aberrant processing of signals through these transducing systems is a component of various disease states. In addition to GPCR-mediated activation of G-protein signaling, nature has evolved creative ways to manipulate and utilize the Gαβγ heterotrimer or Gα and Gαβγ subunits independent of the cell surface receptor stimuli. In such situations, the G-protein subunits (Gα and Gαβγ) may actually be complexed with alternative binding partners independent of the typical heterotrimeric Gαβγ. Such regulatory accessory proteins include the family of RGS proteins that accelerate the GTPase activity of Gα and various entities that influence nucleotide binding properties and/or subunit interaction. The latter group of proteins includes receptor independent activators of G-protein signaling or AGS proteins that play surprising roles in signal processing. This review provides an overview of our current knowledge regarding AGS proteins. AGS proteins are indicative of a growing number of accessory proteins that influence signal propagation, facilitate cross talk between various types of signaling pathways and provide a platform for diverse functions of both the heterotrimeric Gαβγ and the individual Gα and Gαβγ subunits. PMID:17240454

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

Balancing Vibrations at Harmonic Frequencies by Injecting Harmonic Balancing Signals into the Armature of a Linear Motor/Alternator Coupled to a Stirling Machine

NASA Technical Reports Server (NTRS)

Holliday, Ezekiel S. (Inventor)

2014-01-01

Vibrations at harmonic frequencies are reduced by injecting harmonic balancing signals into the armature of a linear motor/alternator coupled to a Stirling machine. The vibrations are sensed to provide a signal representing the mechanical vibrations. A harmonic balancing signal is generated for selected harmonics of the operating frequency by processing the sensed vibration signal with adaptive filter algorithms of adaptive filters for each harmonic. Reference inputs for each harmonic are applied to the adaptive filter algorithms at the frequency of the selected harmonic. The harmonic balancing signals for all of the harmonics are summed with a principal control signal. The harmonic balancing signals modify the principal electrical drive voltage and drive the motor/alternator with a drive voltage component in opposition to the vibration at each harmonic.

Two-component signal transduction in Corynebacterium glutamicum and other corynebacteria: on the way towards stimuli and targets.

PubMed

Bott, Michael; Brocker, Melanie

2012-06-01

In bacteria, adaptation to changing environmental conditions is often mediated by two-component signal transduction systems. In the prototypical case, a specific stimulus is sensed by a membrane-bound histidine kinase and triggers autophosphorylation of a histidine residue. Subsequently, the phosphoryl group is transferred to an aspartate residue of the cognate response regulator, which then becomes active and mediates a specific response, usually by activating and/or repressing a set of target genes. In this review, we summarize the current knowledge on two-component signal transduction in Corynebacterium glutamicum. This Gram-positive soil bacterium is used for the large-scale biotechnological production of amino acids and can also be applied for the synthesis of a wide variety of other products, such as organic acids, biofuels, or proteins. Therefore, C. glutamicum has become an important model organism in industrial biotechnology and in systems biology. The type strain ATCC 13032 possesses 13 two-component systems and the role of five has been elucidated in recent years. They are involved in citrate utilization (CitAB), osmoregulation and cell wall homeostasis (MtrAB), adaptation to phosphate starvation (PhoSR), adaptation to copper stress (CopSR), and heme homeostasis (HrrSA). As C. glutamicum does not only face changing conditions in its natural environment, but also during cultivation in industrial bioreactors of up to 500 m(3) volume, adaptability can also be crucial for good performance in biotechnological production processes. Detailed knowledge on two-component signal transduction and regulatory networks therefore will contribute to both the application and the systemic understanding of C. glutamicum and related species.

Membrane guanylate cyclase, a multimodal transduction machine: history, present, and future directions

PubMed Central

Sharma, Rameshwar K.; Duda, Teresa

2014-01-01

A sequel to these authors' earlier comprehensive reviews which covered the field of mammalian membrane guanylate cyclase (MGC) from its origin to the year 2010, this article contains 13 sections. The first is historical and covers MGC from the year 1963–1987, summarizing its colorful developmental stages from its passionate pursuit to its consolidation. The second deals with the establishment of its biochemical identity. MGC becomes the transducer of a hormonal signal and founder of the peptide hormone receptor family, and creates the notion that hormone signal transduction is its sole physiological function. The third defines its expansion. The discovery of ROS-GC subfamily is made and it links ROS-GC with the physiology of phototransduction. Sections ROS-GC, a Ca2+-Modulated Two Component Transduction System to Migration Patterns and Translations of the GCAP Signals Into Production of Cyclic GMP are Different cover its biochemistry and physiology. The noteworthy events are that augmented by GCAPs, ROS-GC proves to be a transducer of the free Ca2+ signals generated within neurons; ROS-GC becomes a two-component transduction system and establishes itself as a source of cyclic GMP, the second messenger of phototransduction. Section ROS-GC1 Gene Linked Retinal Dystrophies demonstrates how this knowledge begins to be translated into the diagnosis and providing the molecular definition of retinal dystrophies. Section Controlled By Low and High Levels of [Ca2+]i, ROS-GC1 is a Bimodal Transduction Switch discusses a striking property of ROS-GC where it becomes a “[Ca2+]i bimodal switch” and transcends its signaling role in other neural processes. In this course, discovery of the first CD-GCAP (Ca2+-dependent guanylate cyclase activator), the S100B protein, is made. It extends the role of the ROS-GC transduction system beyond the phototransduction to the signaling processes in the synapse region between photoreceptor and cone ON-bipolar cells; in section Ca2+-Modulated Neurocalcin δ ROS-GC1 Transduction System Exists in the Inner Plexiform Layer (IPL) of the Retinal Neurons, discovery of another CD-GCAP, NCδ, is made and its linkage with signaling of the inner plexiform layer neurons is established. Section ROS-GC Linkage With Other Than Vision-Linked Neurons discusses linkage of the ROS-GC transduction system with other sensory transduction processes: Pineal gland, Olfaction and Gustation. In the next, section Evolution of a General Ca2+-Interlocked ROS-GC Signal Transduction Concept in Sensory and Sensory-Linked Neurons, a theoretical concept is proposed where “Ca2+-interlocked ROS-GC signal transduction” machinery becomes a common signaling component of the sensory and sensory-linked neurons. Closure to the review is brought by the conclusion and future directions. PMID:25071437

Analysis of protein interactions within the cytokinin-signaling pathway of Arabidopsis thaliana.

PubMed

Dortay, Hakan; Mehnert, Nijuscha; Bürkle, Lukas; Schmülling, Thomas; Heyl, Alexander

2006-10-01

The signal of the plant hormone cytokinin is perceived by membrane-located sensor histidine kinases and transduced by other members of the plant two-component system. In Arabidopsis thaliana, 28 two-component system proteins (phosphotransmitters and response regulators) act downstream of three receptors, transmitting the signal from the membrane to the nucleus and modulating the cellular response. Although the principal signaling mechanism has been elucidated, redundancy in the system has made it difficult to understand which of the many components interact to control the downstream biological processes. Here, we present a large-scale interaction study comprising most members of the Arabidopsis cytokinin signaling pathway. Using the yeast two-hybrid system, we detected 42 new interactions, of which more than 90% were confirmed by in vitro coaffinity purification. There are distinct patterns of interaction between protein families, but only a few interactions between proteins of the same family. An interaction map of this signaling pathway shows the Arabidopsis histidine phosphotransfer proteins as hubs, which interact with members from all other protein families, mostly in a redundant fashion. Domain-mapping experiments revealed the interaction domains of the proteins of this pathway. Analyses of Arabidopsis histidine phosphotransfer protein 5 mutant proteins showed that the presence of the canonical phospho-accepting histidine residue is not required for the interactions. Interaction of A-type response regulators with Arabidopsis histidine phosphotransfer proteins but not with B-type response regulators suggests that their known activity in feedback regulation may be realized by interfering at the level of Arabidopsis histidine phosphotransfer protein-mediated signaling. This study contributes to our understanding of the protein interactions of the cytokinin-signaling system and provides a framework for further functional studies in planta.

Microwave life detector for buried victims using neutrodyning loop based system

NASA Astrophysics Data System (ADS)

Tahar J., Bel Hadj

2009-07-01

This paper describes a new design of an electromagnetic life detector for the detection of buried victims. The principle of the microwave life sensor is based on the detection of the modulated part of a scattered wave which is generated by the breathing activity of the victim. Those movements generate a spectral component located in the low frequency range, which for most of the cases, is located in a spectrum extending from 0.18 Hz to 0.34 Hz. The detection process requires high sensitivity with respect to breathing movements and, simultaneously, a relative insensitivity for other non-modulated or modulated parasitic signals. Developed microwave system, generating a frequency adjustable between 500 MHz and 1 GHz, is based on a neutrodyning loop required to cancel any non-modulated background and reflected signals in order to get better receiver sensitivity without introducing supplementary distortions on the received signal. Life signal is considered practically periodic that facilitates the extraction of this spectral component using several processing techniques, such as adaptive filtering and correlation permitting to ameliorate the detection range to be more than 15 m in low-loss medium. Detection range is a fundamental parameter for a microwave life detector. A range around 1 m doesn't have a large interest for this application. To attain a range more than 15 m, while guaranteeing professional performances, the technology has to optimize the system parameters as well as the involved signal processing for the purpose of overcoming the presence of obstacles, attenuation, and noise perturbation. This constitutes the main contribution of the present work. Experimental measurements have confirmed the potentiality of this microwave technique for life detector with best space covering detection.

Processing of pulse oximeter signals using adaptive filtering and autocorrelation to isolate perfusion and oxygenation components

NASA Astrophysics Data System (ADS)

Ibey, Bennett; Subramanian, Hariharan; Ericson, Nance; Xu, Weijian; Wilson, Mark; Cote, Gerard L.

2005-03-01

A blood perfusion and oxygenation sensor has been developed for in situ monitoring of transplanted organs. In processing in situ data, motion artifacts due to increased perfusion can create invalid oxygenation saturation values. In order to remove the unwanted artifacts from the pulsatile signal, adaptive filtering was employed using a third wavelength source centered at 810nm as a reference signal. The 810 nm source resides approximately at the isosbestic point in the hemoglobin absorption curve where the absorbance of light is nearly equal for oxygenated and deoxygenated hemoglobin. Using an autocorrelation based algorithm oxygenation saturation values can be obtained without the need for large sampling data sets allowing for near real-time processing. This technique has been shown to be more reliable than traditional techniques and proven to adequately improve the measurement of oxygenation values in varying perfusion states.

Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal

PubMed Central

2015-01-01

Several competing aetiologies of developmental dyslexia suggest that the problems with acquiring literacy skills are causally entailed by low-level auditory and/or speech perception processes. The purpose of this study is to evaluate the diverging claims about the specific deficient peceptual processes under conditions of strong inference. Theoretically relevant acoustic features were extracted from a set of artificial speech stimuli that lie on a /bAk/-/dAk/ continuum. The features were tested on their ability to enable a simple classifier (Quadratic Discriminant Analysis) to reproduce the observed classification performance of average and dyslexic readers in a speech perception experiment. The ‘classical’ features examined were based on component process accounts of developmental dyslexia such as the supposed deficit in Envelope Rise Time detection and the deficit in the detection of rapid changes in the distribution of energy in the frequency spectrum (formant transitions). Studies examining these temporal processing deficit hypotheses do not employ measures that quantify the temporal dynamics of stimuli. It is shown that measures based on quantification of the dynamics of complex, interaction-dominant systems (Recurrence Quantification Analysis and the multifractal spectrum) enable QDA to classify the stimuli almost identically as observed in dyslexic and average reading participants. It seems unlikely that participants used any of the features that are traditionally associated with accounts of (impaired) speech perception. The nature of the variables quantifying the temporal dynamics of the speech stimuli imply that the classification of speech stimuli cannot be regarded as a linear aggregate of component processes that each parse the acoustic signal independent of one another, as is assumed by the ‘classical’ aetiologies of developmental dyslexia. It is suggested that the results imply that the differences in speech perception performance between average and dyslexic readers represent a scaled continuum rather than being caused by a specific deficient component. PMID:25834769

A temporal dissociation of subliminal versus supraliminal fear perception: an event-related potential study.

PubMed

Liddell, Belinda J; Williams, Leanne M; Rathjen, Jennifer; Shevrin, Howard; Gordon, Evian

2004-04-01

Current theories of emotion suggest that threat-related stimuli are first processed via an automatically engaged neural mechanism, which occurs outside conscious awareness. This mechanism operates in conjunction with a slower and more comprehensive process that allows a detailed evaluation of the potentially harmful stimulus (LeDoux, 1998). We drew on the Halgren and Marinkovic (1995) model to examine these processes using event-related potentials (ERPs) within a backward masking paradigm. Stimuli used were faces with fear and neutral (as baseline control) expressions, presented above (supraliminal) and below (subliminal) the threshold for conscious detection. ERP data revealed a double dissociation for the supraliminal versus subliminal perception of fear. In the subliminal condition, responses to the perception of fear stimuli were enhanced relative to neutral for the N2 "excitatory" component, which is thought to represent orienting and automatic aspects of face processing. By contrast, supraliminal perception of fear was associated with relatively enhanced responses for the late P3 "inhibitory" component, implicated in the integration of emotional processes. These findings provide evidence in support of Halgren and Marinkovic's temporal model of emotion processing, and indicate that the neural mechanisms for appraising signals of threat may be initiated, not only automatically, but also without the need for conscious detection of these signals.

Integrated Advanced Microwave Sounding Unit-A (AMSU-A). Performance Verification Report, METSAT (S/N:107) AMSU-A1 Receiver Assemblies: P/N 1356429-1, S/N:F04, P/N 1356409-1,S/N F04

NASA Technical Reports Server (NTRS)

Pines, D.

1999-01-01

This is the Performance Verification Report, METSAT (S/N: 107) AMSU-A1 Receiver Assemblies, P/N 1356429-1, SIN: F04, P/N 1356409- 1, S/N: F04, for the Integrated Advanced Microwave Sounding Unit-A (AMSU-A). The AMSU-A receiver subsystem comprises two separated receiver assemblies; AMSU-A1 and AMSU-A2 (P/N 1356441-1). The AMSU-A1 receiver contains 13 channels and the AMSU-A2 receiver 2 channels. The AMSU-A receiver assembly is further divided into two parts; AMSU-A I - I (P/N 13 5 6429- 1) and AMSU-A 1 -2 (P/N 1356409-1), which contain 9 and 4 channels, respectively. The AMSU-A receiver subsystem is located in between the antenna and signal processing subsystems of the AMSU-A instrument and comprises the RF and IF components from isolators to attenuators. It receives the RF signals from the antenna subsystem, down-converts the RF signals to IF signals, amplifies and defines the IF signals to proper power level and frequency bandwidth as specified for each channel, and inputs the IF signals to the signal processing subsystem. The test reports for the METSAT AMSU-A receiver subsystem are prepared separately for Al and A2 receivers so that each receiver stands alone during integration of instruments into the spacecraft. This test report presents the test data of the N4ETSAT AMSU-A1 Flight Model No. 4 (FM-4) receiver subsystem. The tests are performed per the Acceptance Test Procedure (ATP) for the AMSU-A Receiver Subsystem, AE-26002/6A. The functional performance tests are conducted either at the component or subsystem level. While the component-level tests are performed over the entire operating temperature range predicted by thermal analysis, most subsystem-level tests are conducted at ambient temperature only. Key performances (bandpass characteristics and noise figure) of the receiver subsystem are verified over the operating temperature.

Solving difficult problems creatively: a role for energy optimised deterministic/stochastic hybrid computing

PubMed Central

Palmer, Tim N.; O’Shea, Michael

2015-01-01

How is the brain configured for creativity? What is the computational substrate for ‘eureka’ moments of insight? Here we argue that creative thinking arises ultimately from a synergy between low-energy stochastic and energy-intensive deterministic processing, and is a by-product of a nervous system whose signal-processing capability per unit of available energy has become highly energy optimised. We suggest that the stochastic component has its origin in thermal (ultimately quantum decoherent) noise affecting the activity of neurons. Without this component, deterministic computational models of the brain are incomplete. PMID:26528173

Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

DOE PAGES

Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; ...

2014-12-18

Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

A tone analyzer based on a piezoelectric polymer and organic thin film transistors.

PubMed

Hsu, Yu-Jen; Kymissis, Ioannis

2012-12-01

A tone analyzer is demonstrated using a distributed resonator architecture on a tensioned piezoelectric polyvinyledene diuoride (PVDF) sheet. This sheet is used as both the resonator and detection element. Two architectures are demonstrated; one uses distributed, directly addressed elements as a proof of concept, and the other integrates organic thin film transistor-based transimpedance amplifiers directly with the PVDF to convert the piezoelectric charge signal into a current signal. The PVDF sheet material is instrumented along its length, and the amplitude response at 15 sites is recorded and analyzed as a function of the frequency of excitation. The determination of the dominant component of an incoming tone is demonstrated using linear system decomposition of the time-averaged response of the sheet and is performed without any time domain analysis. This design allows for the determination of the spectral composition of a sound using the mechanical signal processing provided by the amplitude response and eliminates the need for time-domain downstream signal processing of the incoming signal.

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

Yan, Ruqiang; Chen, Xuefeng; Li, Weihua

Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

Jasmonate signaling in plant stress responses and development - active and inactive compounds.

PubMed

Wasternack, Claus; Strnad, Miroslav

2016-09-25

Jasmonates (JAs) are lipid-derived signals mediating plant responses to biotic and abiotic stresses and in plant development. Following the elucidation of each step in their biosynthesis and the important components of perception and signaling, several activators, repressors and co-repressors have been identified which contribute to fine-tuning the regulation of JA-induced gene expression. Many of the metabolic reactions in which JA participates, such as conjugation with amino acids, glucosylation, hydroxylation, carboxylation, sulfation and methylation, lead to numerous compounds with different biological activities. These metabolites may be highly active, partially active in specific processes or inactive. Hydroxylation, carboxylation and sulfation inactivate JA signaling. The precursor of JA biosynthesis, 12-oxo-phytodienoic acid (OPDA), has been identified as a JA-independent signaling compound. An increasing number of OPDA-specific processes is being identified. To conclude, the numerous JA compounds and their different modes of action allow plants to respond specifically and flexibly to alterations in the environment. Copyright © 2015 Elsevier B.V. All rights reserved.

Failure detection in high-performance clusters and computers using chaotic map computations

DOEpatents

Rao, Nageswara S.

2015-09-01

A programmable media includes a processing unit capable of independent operation in a machine that is capable of executing 10.sup.18 floating point operations per second. The processing unit is in communication with a memory element and an interconnect that couples computing nodes. The programmable media includes a logical unit configured to execute arithmetic functions, comparative functions, and/or logical functions. The processing unit is configured to detect computing component failures, memory element failures and/or interconnect failures by executing programming threads that generate one or more chaotic map trajectories. The central processing unit or graphical processing unit is configured to detect a computing component failure, memory element failure and/or an interconnect failure through an automated comparison of signal trajectories generated by the chaotic maps.

Multiple resolution chirp reflectometry for fault localization and diagnosis in a high voltage cable in automotive electronics

NASA Astrophysics Data System (ADS)

Chang, Seung Jin; Lee, Chun Ku; Shin, Yong-June; Park, Jin Bae

2016-12-01

A multiple chirp reflectometry system with a fault estimation process is proposed to obtain multiple resolution and to measure the degree of fault in a target cable. A multiple resolution algorithm has the ability to localize faults, regardless of fault location. The time delay information, which is derived from the normalized cross-correlation between the incident signal and bandpass filtered reflected signals, is converted to a fault location and cable length. The in-phase and quadrature components are obtained by lowpass filtering of the mixed signal of the incident signal and the reflected signal. Based on in-phase and quadrature components, the reflection coefficient is estimated by the proposed fault estimation process including the mixing and filtering procedure. Also, the measurement uncertainty for this experiment is analyzed according to the Guide to the Expression of Uncertainty in Measurement. To verify the performance of the proposed method, we conduct comparative experiments to detect and measure faults under different conditions. Considering the installation environment of the high voltage cable used in an actual vehicle, target cable length and fault position are designed. To simulate the degree of fault, the variety of termination impedance (10 Ω , 30 Ω , 50 Ω , and 1 \\text{k} Ω ) are used and estimated by the proposed method in this experiment. The proposed method demonstrates advantages in that it has multiple resolution to overcome the blind spot problem, and can assess the state of the fault.

Wear detection by means of wavelet-based acoustic emission analysis

NASA Astrophysics Data System (ADS)

Baccar, D.; Söffker, D.

2015-08-01

Wear detection and monitoring during operation are complex and difficult tasks especially for materials under sliding conditions. Due to the permanent contact and repetitive motion, the material surface remains during tests non-accessible for optical inspection so that attrition of the contact partners cannot be easily detected. This paper introduces the relevant scientific components of reliable and efficient condition monitoring system for online detection and automated classification of wear phenomena by means of acoustic emission (AE) and advanced signal processing approaches. The related experiments were performed using a tribological system consisting of two martensitic plates, sliding against each other. High sensitive piezoelectric transducer was used to provide the continuous measurement of AE signals. The recorded AE signals were analyzed mainly by time-frequency analysis. A feature extraction module using a novel combination of Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were used for the first time. A detailed correlation analysis between complex signal characteristics and the surface damage resulting from contact fatigue was investigated. Three wear process stages were detected and could be distinguished. To obtain quantitative and detailed information about different wear phases, the AE energy was calculated using STFT and decomposed into a suitable number of frequency levels. The individual energy distribution and the cumulative AE energy of each frequency components were analyzed using CWT. Results show that the behavior of individual frequency component changes when the wear state changes. Here, specific frequency ranges are attributed to the different wear states. The study reveals that the application of the STFT-/CWT-based AE analysis is an appropriate approach to distinguish and to interpret the different damage states occurred during sliding contact. Based on this results a new generation of condition monitoring systems can be build, able to evaluate automatically the surface condition of machine components with sliding surfaces.

Automatic quadrature control and measuring system. [using optical coupling circuitry

NASA Technical Reports Server (NTRS)

Hamlet, J. F. (Inventor)

1974-01-01

A quadrature component cancellation and measuring system comprising a detection system for detecting the quadrature component from a primary signal, including reference circuitry to define the phase of the quadrature component for detection is described. A Raysistor optical coupling control device connects an output from the detection system to a circuit driven by a signal based upon the primary signal. Combining circuitry connects the primary signal and the circuit controlled by the Raysistor device to subtract quadrature components. A known current through the optically sensitive element produces a signal defining the magnitude of the quadrature component.

A new statistical PCA-ICA algorithm for location of R-peaks in ECG.

PubMed

Chawla, M P S; Verma, H K; Kumar, Vinod

2008-09-16

The success of ICA to separate the independent components from the mixture depends on the properties of the electrocardiogram (ECG) recordings. This paper discusses some of the conditions of independent component analysis (ICA) that could affect the reliability of the separation and evaluation of issues related to the properties of the signals and number of sources. Principal component analysis (PCA) scatter plots are plotted to indicate the diagnostic features in the presence and absence of base-line wander in interpreting the ECG signals. In this analysis, a newly developed statistical algorithm by authors, based on the use of combined PCA-ICA for two correlated channels of 12-channel ECG data is proposed. ICA technique has been successfully implemented in identifying and removal of noise and artifacts from ECG signals. Cleaned ECG signals are obtained using statistical measures like kurtosis and variance of variance after ICA processing. This analysis also paper deals with the detection of QRS complexes in electrocardiograms using combined PCA-ICA algorithm. The efficacy of the combined PCA-ICA algorithm lies in the fact that the location of the R-peaks is bounded from above and below by the location of the cross-over points, hence none of the peaks are ignored or missed.

Image informative maps for component-wise estimating parameters of signal-dependent noise

NASA Astrophysics Data System (ADS)

Uss, Mykhail L.; Vozel, Benoit; Lukin, Vladimir V.; Chehdi, Kacem

2013-01-01

We deal with the problem of blind parameter estimation of signal-dependent noise from mono-component image data. Multispectral or color images can be processed in a component-wise manner. The main results obtained rest on the assumption that the image texture and noise parameters estimation problems are interdependent. A two-dimensional fractal Brownian motion (fBm) model is used for locally describing image texture. A polynomial model is assumed for the purpose of describing the signal-dependent noise variance dependence on image intensity. Using the maximum likelihood approach, estimates of both fBm-model and noise parameters are obtained. It is demonstrated that Fisher information (FI) on noise parameters contained in an image is distributed nonuniformly over intensity coordinates (an image intensity range). It is also shown how to find the most informative intensities and the corresponding image areas for a given noisy image. The proposed estimator benefits from these detected areas to improve the estimation accuracy of signal-dependent noise parameters. Finally, the potential estimation accuracy (Cramér-Rao Lower Bound, or CRLB) of noise parameters is derived, providing confidence intervals of these estimates for a given image. In the experiment, the proposed and existing state-of-the-art noise variance estimators are compared for a large image database using CRLB-based statistical efficiency criteria.

A review of demodulation techniques for amplitude-modulation atomic force microscopy

PubMed Central

Harcombe, David M; Ragazzon, Michael R P; Moheimani, S O Reza; Fleming, Andrew J

2017-01-01

In this review paper, traditional and novel demodulation methods applicable to amplitude-modulation atomic force microscopy are implemented on a widely used digital processing system. As a crucial bandwidth-limiting component in the z-axis feedback loop of an atomic force microscope, the purpose of the demodulator is to obtain estimates of amplitude and phase of the cantilever deflection signal in the presence of sensor noise or additional distinct frequency components. Specifically for modern multifrequency techniques, where higher harmonic and/or higher eigenmode contributions are present in the oscillation signal, the fidelity of the estimates obtained from some demodulation techniques is not guaranteed. To enable a rigorous comparison, the performance metrics tracking bandwidth, implementation complexity and sensitivity to other frequency components are experimentally evaluated for each method. Finally, the significance of an adequate demodulator bandwidth is highlighted during high-speed tapping-mode atomic force microscopy experiments in constant-height mode. PMID:28900596

Spatial modeling of the membrane-cytosolic interface in protein kinase signal transduction

PubMed Central

Schröder, Andreas

2018-01-01

The spatial architecture of signaling pathways and the interaction with cell size and morphology are complex, but little understood. With the advances of single cell imaging and single cell biology, it becomes crucial to understand intracellular processes in time and space. Activation of cell surface receptors often triggers a signaling cascade including the activation of membrane-attached and cytosolic signaling components, which eventually transmit the signal to the cell nucleus. Signaling proteins can form steep gradients in the cytosol, which cause strong cell size dependence. We show that the kinetics at the membrane-cytosolic interface and the ratio of cell membrane area to the enclosed cytosolic volume change the behavior of signaling cascades significantly. We suggest an estimate of average concentration for arbitrary cell shapes depending on the cell volume and cell surface area. The normalized variance, known from image analysis, is suggested as an alternative measure to quantify the deviation from the average concentration. A mathematical analysis of signal transduction in time and space is presented, providing analytical solutions for different spatial arrangements of linear signaling cascades. Quantification of signaling time scales reveals that signal propagation is faster at the membrane than at the nucleus, while this time difference decreases with the number of signaling components in the cytosol. Our investigations are complemented by numerical simulations of non-linear cascades with feedback and asymmetric cell shapes. We conclude that intracellular signal propagation is highly dependent on cell geometry and, thereby, conveys information on cell size and shape to the nucleus. PMID:29630597

Analogue step-by-step DC component eliminator for 24-hour PPG signal monitoring.

PubMed

Pilt, Kristjan; Meigas, Kalju; Lass, Jaanus; Rosmann, Mart; Kaik, Jüri

2007-01-01

For applications where PPG signal AC component needs to be measured without disturbances in its shape and recorded digitally with high digitalization accuracy, the step-by-step DC component eliminator is developed. This paper describes step-by-step DC component eliminator, which is utilized with analogue comparator and operational amplifier. It allows to record PPG signal without disturbances in its shape in 24-hours PPG signal monitoring system. The experiments with PPG signal have been carried out.

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.

Signal design study for shuttle/TDRSS Ku-band uplink

NASA Technical Reports Server (NTRS)

1976-01-01

The adequacy of the signal design approach chosen for the TDRSS/orbiter uplink was evaluated. Critical functions and/or components associated with the baseline design were identified, and design alternatives were developed for those areas considered high risk. A detailed set of RF and signal processing performance specifications for the orbiter hardware associated with the TDRSS/orbiter Ku band uplink was analyzed. Performances of a detailed design of the PN despreader, the PSK carrier synchronization loop, and the symbol synchronizer are identified. The performance of the downlink signal by means of computer simulation to obtain a realistic determination of bit error rate degradations was studied. The three channel PM downlink signal was detailed by means of analysis and computer simulation.

Noninvasive Diagnosis of Coronary Artery Disease Using 12-Lead High-Frequency Electrocardiograms

NASA Technical Reports Server (NTRS)

Schlegel, Todd T.; Arenare, Brian

2006-01-01

A noninvasive, sensitive method of diagnosing certain pathological conditions of the human heart involves computational processing of digitized electrocardiographic (ECG) signals acquired from a patient at all 12 conventional ECG electrode positions. In the processing, attention is focused on low-amplitude, high-frequency components of those portions of the ECG signals known in the art as QRS complexes. The unique contribution of this method lies in the utilization of signal features and combinations of signal features from various combinations of electrode positions, not reported previously, that have been found to be helpful in diagnosing coronary artery disease and such related pathological conditions as myocardial ischemia, myocardial infarction, and congestive heart failure. The electronic hardware and software used to acquire the QRS complexes and perform some preliminary analyses of their high-frequency components were summarized in Real-Time, High-Frequency QRS Electrocardiograph (MSC- 23154), NASA Tech Briefs, Vol. 27, No. 7 (July 2003), pp. 26-28. To recapitulate, signals from standard electrocardiograph electrodes are preamplified, then digitized at a sampling rate of 1,000 Hz, then analyzed by the software that detects R waves and QRS complexes and analyzes them from several perspectives. The software includes provisions for averaging signals over multiple beats and for special-purpose nonrecursive digital filters with specific low- and high-frequency cutoffs. These filters, applied to the averaged signal, effect a band-pass operation in the frequency range from 150 to 250 Hz. The output of the bandpass filter is the desired high-frequency QRS signal. Further processing is then performed in real time to obtain the beat-to-beat root mean square (RMS) voltage amplitude of the filtered signal, certain variations of the RMS voltage, and such standard measures as the heart rate and R-R interval at any given time. A key signal feature analyzed in the present method is the presence versus the absence of reduced-amplitude zones (RAZs). In terms that must be simplified for the sake of brevity, an RAZ comprises several cycles of a high-frequency QRS signal during which the amplitude of the high-frequency oscillation in a portion of the signal is abnormally low (see figure). A given signal sample exhibiting an interval of reduced amplitude may or may not be classified as an RAZ, depending on quantitative criteria regarding peaks and troughs within the reduced-amplitude portion of the high-frequency QRS signal. This analysis is performed in all 12 leads in real time.

Polyspectral signal analysis techniques for condition based maintenance of helicopter drive-train system

NASA Astrophysics Data System (ADS)

Hassan Mohammed, Mohammed Ahmed

For an efficient maintenance of a diverse fleet of air- and rotorcraft, effective condition based maintenance (CBM) must be established based on rotating components monitored vibration signals. In this dissertation, we present theory and applications of polyspectral signal processing techniques for condition monitoring of critical components in the AH-64D helicopter tail rotor drive train system. Currently available vibration-monitoring tools are mostly built around auto- and cross-power spectral analysis which have limited performance in detecting frequency correlations higher than second order. Studying higher order correlations and their Fourier transforms, higher order spectra, provides more information about the vibration signals which helps in building more accurate diagnostic models of the mechanical system. Based on higher order spectral analysis, different signal processing techniques are developed to assess health conditions of different critical rotating-components in the AH-64D helicopter drive-train. Based on cross-bispectrum, quadratic nonlinear transfer function is presented to model second order nonlinearity in a drive-shaft running between the two hanger bearings. Then, quadratic-nonlinearity coupling coefficient between frequency harmonics of the rotating shaft is used as condition metric to study different seeded shaft faults compared to baseline case, namely: shaft misalignment, shaft imbalance, and combination of shaft misalignment and imbalance. The proposed quadratic-nonlinearity metric shows better capabilities in distinguishing the four studied shaft settings than the conventional linear coupling based on cross-power spectrum. We also develop a new concept of Quadratic-Nonlinearity Power-Index spectrum, QNLPI(f), that can be used in signal detection and classification, based on bicoherence spectrum. The proposed QNLPI(f) is derived as a projection of the three-dimensional bicoherence spectrum into two-dimensional spectrum that quantitatively describes how much of the mean square power at certain frequency f is generated due to nonlinear quadratic interaction between different frequency components. The proposed index, QNLPI(f), can be used to simplify the study of bispectrum and bicoherence signal spectra. It also inherits useful characteristics from the bicoherence such as high immunity to additive Gaussian noise, high capability of nonlinear-systems identifications, and amplification invariance. The quadratic-nonlinear power spectral density PQNL(f) and percentage of quadratic nonlinear power PQNLP are also introduced based on the QNLPI(f). Concept of the proposed indices and their computational considerations are discussed first using computer generated data, and then applied to real-world vibration data to assess health conditions of different rotating components in the drive train including drive-shaft, gearbox, and hanger bearing faults. The QNLPI(f) spectrum enables us to gain more details about nonlinear harmonic generation patterns that can be used to distinguish between different cases of mechanical faults, which in turn helps to gaining more diagnostic/prognostic capabilities.

Elimination of autofluorescence background from fluorescence tissue images by use of time-gated detection and the AzaDiOxaTriAngulenium (ADOTA) fluorophore.

PubMed

Rich, Ryan M; Stankowska, Dorota L; Maliwal, Badri P; Sørensen, Thomas Just; Laursen, Bo W; Krishnamoorthy, Raghu R; Gryczynski, Zygmunt; Borejdo, Julian; Gryczynski, Ignacy; Fudala, Rafal

2013-02-01

Sample autofluorescence (fluorescence of inherent components of tissue and fixative-induced fluorescence) is a significant problem in direct imaging of molecular processes in biological samples. A large variety of naturally occurring fluorescent components in tissue results in broad emission that overlaps the emission of typical fluorescent dyes used for tissue labeling. In addition, autofluorescence is characterized by complex fluorescence intensity decay composed of multiple components whose lifetimes range from sub-nanoseconds to a few nanoseconds. For these reasons, the real fluorescence signal of the probe is difficult to separate from the unwanted autofluorescence. Here we present a method for reducing the autofluorescence problem by utilizing an azadioxatriangulenium (ADOTA) dye with a fluorescence lifetime of approximately 15 ns, much longer than those of most of the components of autofluorescence. A probe with such a long lifetime enables us to use time-gated intensity imaging to separate the signal of the targeting dye from the autofluorescence. We have shown experimentally that by discarding photons detected within the first 20 ns of the excitation pulse, the signal-to-background ratio is improved fivefold. This time-gating eliminates over 96 % of autofluorescence. Analysis using a variable time-gate may enable quantitative determination of the bound probe without the contributions from the background.

Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework.

PubMed

Yin, X-X; Zhang, Y; Cao, J; Wu, J-L; Hadjiloucas, S

2016-12-01

We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Comparing apples with apples: Using spatially distributed time series of monitoring data for model evaluation

NASA Astrophysics Data System (ADS)

Solazzo, E.; Galmarini, S.

2015-07-01

A more sensible use of monitoring data for the evaluation and development of regional-scale atmospheric models is proposed. The motivation stems from observing current practices in this realm where the quality of monitoring data is seldom questioned and model-to-data deviation is uniquely attributed to model deficiency. Efforts are spent to quantify the uncertainty intrinsic to the measurement process, but aspects connected to model evaluation and development have recently emerged that remain obscure, such as the spatial representativeness and the homogeneity of signals subjects of our investigation. By using time series of hourly records of ozone for a whole year (2006) collected by the European AirBase network the area of representativeness is firstly analysed showing, for similar class of stations (urban, suburban, rural), large heterogeneity and high sensitivity to the density of the network and to the noise of the signal, suggesting the mere station classification to be not a suitable candidate to help select the pool of stations used in model evaluation. Therefore a novel, more robust technique is developed based on the spatial properties of the associativity of the spectral components of the ozone time series, in an attempt to determine the level of homogeneity. The spatial structure of the associativity among stations is informative of the spatial representativeness of that specific component and automatically tells about spatial anisotropy. Time series of ozone data from North American networks have also been analysed to support the methodology. We find that the low energy components (especially the intra-day signal) suffer from a too strong influence of country-level network set-up in Europe, and different networks in North America, showing spatial heterogeneity exactly at the administrative border that separates countries in Europe and at areas separating different networks in North America. For model evaluation purposes these elements should be treated as purely stochastic and discarded, while retaining the portion of the signal useful to the evaluation process. Trans-boundary discontinuity of the intra-day signal along with cross-network grouping has been found to be predominant. Skills of fifteen regional chemical-transport modelling systems have been assessed in light of this result, finding an improved accuracy of up to 5% when the intra-day signal is removed with respect to the case where all components are analysed.

Analysis of the reflection of a micro drop fiber sensor

NASA Astrophysics Data System (ADS)

Sun, Weimin; Liu, Qiang; Zhao, Lei; Li, Yingjuan; Yuan, Libo

2005-01-01

Micro drop fiber sensors are effective tools for measuring characters of liquids. These types of sensors are wildly used in biotechnology, beverage and food markets. For a fiber micro drop sensor, the signal of the output light is wavy with two peaks, normally. Carefully analyzing the wavy process can identify the liquid components. Understanding the reason of forming this wavy signal is important to design a suitable sensing head and to choose a suitable signal-processing method. The dripping process of a type of liquids is relative to the characters of the liquid and the shape of the sensing head. The quasi-Gauss model of the light field from the input-fiber end is used to analyse the distribution of the light field in the liquid drop. In addition, considering the characters of the liquid to be measured, the dripping process of the optical signal from the output-fiber end can be expected. The reflection surface of the micro drop varies as serials of spheres with different radiuses and global centers. The intensity of the reflection light changes with the shape of the surface. The varying process of the intensity relates to the tense, refractive index, transmission et al. To support the analyse above, an experimental system is established. In the system, LED is chosen as the light source and the PIN transform the light signal to the electrical signal, which is collected by a data acquisition card. An on-line testing system is made to check the theory discussed above.

Analysis of cutting force signals by wavelet packet transform for surface roughness monitoring in CNC turning

NASA Astrophysics Data System (ADS)

García Plaza, E.; Núñez López, P. J.

2018-01-01

On-line monitoring of surface finish in machining processes has proven to be a substantial advancement over traditional post-process quality control techniques by reducing inspection times and costs and by avoiding the manufacture of defective products. This study applied techniques for processing cutting force signals based on the wavelet packet transform (WPT) method for the monitoring of surface finish in computer numerical control (CNC) turning operations. The behaviour of 40 mother wavelets was analysed using three techniques: global packet analysis (G-WPT), and the application of two packet reduction criteria: maximum energy (E-WPT) and maximum entropy (SE-WPT). The optimum signal decomposition level (Lj) was determined to eliminate noise and to obtain information correlated to surface finish. The results obtained with the G-WPT method provided an in-depth analysis of cutting force signals, and frequency ranges and signal characteristics were correlated to surface finish with excellent results in the accuracy and reliability of the predictive models. The radial and tangential cutting force components at low frequency provided most of the information for the monitoring of surface finish. The E-WPT and SE-WPT packet reduction criteria substantially reduced signal processing time, but at the expense of discarding packets with relevant information, which impoverished the results. The G-WPT method was observed to be an ideal procedure for processing cutting force signals applied to the real-time monitoring of surface finish, and was estimated to be highly accurate and reliable at a low analytical-computational cost.

[Signaling pathways mTOR and AKT in epilepsy].

PubMed

Romero-Leguizamon, C R; Ramirez-Latorre, J A; Mora-Munoz, L; Guerrero-Naranjo, A

2016-07-01

The signaling pathway AKT/mTOR is a central axis in regulating cellular processes, particularly in neurological diseases. In the case of epilepsy, it has been observed alteration in the pathophysiological process of the same. However, they have not described all the mechanisms of these signaling pathways that could open the opportunity to new research and therapeutic strategies. To review existing partnerships between intracellular signaling pathways AKT and mTOR in the pathophysiology of epilepsy. Epilepsy is a disease with a high epidemiological impact globally, so it is widely investigated regarding the pathophysiological components thereof. In that search they have been involved different intracellular signaling pathways in neurons, as determinants epileptogenic. Advances in this field have even allowed the successful implementation of new therapeutic strategies and to open the way to new research in the field. Improving knowledge about the pathophysiological role of the signaling pathway mTOR/AKT in epilepsy can raise new investigations regarding therapeutic alternatives. The use of mTOR inhibitors, has emerged in recent years as effective in treating this disease entity alternative however is clear the necessity of continue the research for new drug therapies.

Network Modeling Reveals Prevalent Negative Regulatory Relationships between Signaling Sectors in Arabidopsis Immune Signaling

PubMed Central

Sato, Masanao; Tsuda, Kenichi; Wang, Lin; Coller, John; Watanabe, Yuichiro; Glazebrook, Jane; Katagiri, Fumiaki

2010-01-01

Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2). This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i) the components of the network are highly interconnected; and (ii) negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a “sector-switching” network, which effectively balances two apparently conflicting demands, robustness against pathogenic perturbations and moderation of negative impacts of immune responses on plant fitness. PMID:20661428

Independent component analysis based digital signal processing in coherent optical fiber communication systems

NASA Astrophysics Data System (ADS)

Li, Xiang; Luo, Ming; Qiu, Ying; Alphones, Arokiaswami; Zhong, Wen-De; Yu, Changyuan; Yang, Qi

2018-02-01

In this paper, channel equalization techniques for coherent optical fiber transmission systems based on independent component analysis (ICA) are reviewed. The principle of ICA for blind source separation is introduced. The ICA based channel equalization after both single-mode fiber and few-mode fiber transmission for single-carrier and orthogonal frequency division multiplexing (OFDM) modulation formats are investigated, respectively. The performance comparisons with conventional channel equalization techniques are discussed.

A FORTRAN source library for quaternion algebra. Application to multicomponent seismic data

NASA Astrophysics Data System (ADS)

Benaïssa, A.; Benaïssa, Z.; Ouadfeul, S.

2012-04-01

The quaternions, named also hypercomplex numbers, constituted of a real part and three imaginary parts, allow a representation of multi-component physical signals in geophysics. In FORTRAN, the need for programming new applications and extend programs to quaternions requires to enhance capabilities of this language. In this study, we develop, in FORTRAN 95, a source library which provides functions and subroutines making development and maintenance of programs devoted to quaternions, equivalent to those developed for the complex plane. The systematic use of generic functions and generic operators: 1/ allows using FORTRAN statements and operators extended to quaternions without renaming them and 2/ makes use of this statements transparent to the specificity of quaternions. The portability of this library is insured by the standard FORTRAN 95 strict norm which is independent of operating systems (OS). The execution time of quaternion applications, sometimes crucial for huge data sets, depends, generally, of compilers optimizations by the use of in lining and parallelisation. To show the use of the library, Fourier transform of a real one dimensional quaternionic seismic signal is presented. Furthermore, a FORTRAN code, which computes the quaternionic singular values decomposition (QSVD), is developed using the proposed library and applied to wave separation in multicomponent vertical seismic profile (VSP) synthetic and real data. The extracted wavefields have been highly enhanced, compared to those obtained with median filter, due to QSVD which takes into account the correlation between the different components of the seismic signal. Taken in total, these results demonstrate that use of quaternions can bring a significant improvement for some processing on three or four components seismic data. Keywords: Quaternion - FORTRAN - Vectorial processing - Multicomponent signal - VSP - Fourier transform.

[Signaling Systems of Rhizobia (Rhizobiaceae) and Leguminous Plants (Fabaceae) upon the Formation of a Legume-Rhizobium Symbiosis (Review)].

PubMed

Glyan'ko, A K

2015-01-01

Data from the literature and our own data on the participation and interrelation of bacterial signaling Nod-factors and components of the calcium, NADPH-oxidase, and NO-synthase signaling systems of a plant at the preinfection and infectious stages of the formation of a legume-rhizobium symbiosis are summarized in this review. The physiological role of Nod-factors, reactive oxygen species (ROS), calcium (Ca2+), NADPH-oxidase, nitric oxide (NO), and their cross influence on the processes determining the formation of symbiotic structures on the roots of the host plant is discussed.

Driving an Active Vibration Balancer to Minimize Vibrations at the Fundamental and Harmonic Frequencies

NASA Technical Reports Server (NTRS)

Holliday, Ezekiel S. (Inventor)

2014-01-01

Vibrations of a principal machine are reduced at the fundamental and harmonic frequencies by driving the drive motor of an active balancer with balancing signals at the fundamental and selected harmonics. Vibrations are sensed to provide a signal representing the mechanical vibrations. A balancing signal generator for the fundamental and for each selected harmonic processes the sensed vibration signal with adaptive filter algorithms of adaptive filters for each frequency to generate a balancing signal for each frequency. Reference inputs for each frequency are applied to the adaptive filter algorithms of each balancing signal generator at the frequency assigned to the generator. The harmonic balancing signals for all of the frequencies are summed and applied to drive the drive motor. The harmonic balancing signals drive the drive motor with a drive voltage component in opposition to the vibration at each frequency.

Processing of odor mixtures in the zebrafish olfactory bulb.

PubMed

Tabor, Rico; Yaksi, Emre; Weislogel, Jan-Marek; Friedrich, Rainer W

2004-07-21

Components of odor mixtures often are not perceived individually, suggesting that neural representations of mixtures are not simple combinations of the representations of the components. We studied odor responses to binary mixtures of amino acids and food extracts at different processing stages in the olfactory bulb (OB) of zebrafish. Odor-evoked input to the OB was measured by imaging Ca2+ signals in afferents to olfactory glomeruli. Activity patterns evoked by mixtures were predictable within narrow limits from the component patterns, indicating that mixture interactions in the peripheral olfactory system are weak. OB output neurons, the mitral cells (MCs), were recorded extra- and intracellularly and responded to odors with stimulus-dependent temporal firing rate modulations. Responses to mixtures of amino acids often were dominated by one of the component responses. Responses to mixtures of food extracts, in contrast, were more distinct from both component responses. These results show that mixture interactions can result from processing in the OB. Moreover, our data indicate that mixture interactions in the OB become more pronounced with increasing overlap of input activity patterns evoked by the components. Emerging from these results are rules of mixture interactions that may explain behavioral data and provide a basis for understanding the processing of natural odor stimuli in the OB.

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

Shah, Kedar G.; Pannu, Satinderpall S.

An integrated circuit system having an integrated circuit (IC) component which is able to have its functionality destroyed upon receiving a command signal. The system may involve a substrate with the IC component being supported on the substrate. A module may be disposed in proximity to the IC component. The module may have a cavity and a dissolving compound in a solid form disposed in the cavity. A heater component may be configured to heat the dissolving compound to a point of sublimation where the dissolving compound changes from a solid to a gaseous dissolving compound. A triggering mechanism maymore » be used for initiating a dissolution process whereby the gaseous dissolving compound is allowed to attack the IC component and destroy a functionality of the IC component.« less

Characterization of cytokinin signaling and homeostasis gene families in two hardwood tree species: Populus trichocarpa and Prunus persica.

PubMed

Immanen, Juha; Nieminen, Kaisa; Duchens Silva, Héctor; Rodríguez Rojas, Fernanda; Meisel, Lee A; Silva, Herman; Albert, Victor A; Hvidsten, Torgeir R; Helariutta, Ykä

2013-12-16

Through the diversity of cytokinin regulated processes, this phytohormone has a profound impact on plant growth and development. Cytokinin signaling is involved in the control of apical and lateral meristem activity, branching pattern of the shoot, and leaf senescence. These processes influence several traits, including the stem diameter, shoot architecture, and perennial life cycle, which define the development of woody plants. To facilitate research about the role of cytokinin in regulation of woody plant development, we have identified genes associated with cytokinin signaling and homeostasis pathways from two hardwood tree species. Taking advantage of the sequenced black cottonwood (Populus trichocarpa) and peach (Prunus persica) genomes, we have compiled a comprehensive list of genes involved in these pathways. We identified genes belonging to the six families of cytokinin oxidases (CKXs), isopentenyl transferases (IPTs), LONELY GUY genes (LOGs), two-component receptors, histidine containing phosphotransmitters (HPts), and response regulators (RRs). All together 85 Populus and 45 Prunus genes were identified, and compared to their Arabidopsis orthologs through phylogenetic analyses. In general, when compared to Arabidopsis, differences in gene family structure were often seen in only one of the two tree species. However, one class of genes associated with cytokinin signal transduction, the CKI1-like family of two-component histidine kinases, was larger in both Populus and Prunus than in Arabidopsis.

Characterization of cytokinin signaling and homeostasis gene families in two hardwood tree species: Populus trichocarpa and Prunus persica

PubMed Central

2013-01-01

Background Through the diversity of cytokinin regulated processes, this phytohormone has a profound impact on plant growth and development. Cytokinin signaling is involved in the control of apical and lateral meristem activity, branching pattern of the shoot, and leaf senescence. These processes influence several traits, including the stem diameter, shoot architecture, and perennial life cycle, which define the development of woody plants. To facilitate research about the role of cytokinin in regulation of woody plant development, we have identified genes associated with cytokinin signaling and homeostasis pathways from two hardwood tree species. Results Taking advantage of the sequenced black cottonwood (Populus trichocarpa) and peach (Prunus persica) genomes, we have compiled a comprehensive list of genes involved in these pathways. We identified genes belonging to the six families of cytokinin oxidases (CKXs), isopentenyl transferases (IPTs), LONELY GUY genes (LOGs), two-component receptors, histidine containing phosphotransmitters (HPts), and response regulators (RRs). All together 85 Populus and 45 Prunus genes were identified, and compared to their Arabidopsis orthologs through phylogenetic analyses. Conclusions In general, when compared to Arabidopsis, differences in gene family structure were often seen in only one of the two tree species. However, one class of genes associated with cytokinin signal transduction, the CKI1-like family of two-component histidine kinases, was larger in both Populus and Prunus than in Arabidopsis. PMID:24341635

Fluctuations of pol I and fibrillarin contents of the nucleoli.

PubMed

Hornáček, M; Kováčik, L; Mazel, T; Cmarko, D; Bártová, E; Raška, I; Smirnov, E

2017-07-04

Nucleoli are formed on the basis of ribosomal DNA (rDNA) clusters called Nucleolus Organizer Regions (NORs). Each NOR contains multiple genes coding for RNAs of the ribosomal particles. The prominent components of the nucleolar ultrastructure, fibrillar centers (FC) and dense fibrillar components (DFC), together compose FC/DFC units. These units are centers of rDNA transcription by RNA polymerase I (pol I), as well as the early processing events, in which an essential role belongs to fibrillarin. Each FC/DFC unit probably corresponds to a single transcriptionally active gene. In this work, we transfected human-derived cells with GFP-RPA43 (subunit of pol I) and RFP-fibrillarin. Following changes of the fluorescent signals in individual FC/DFC units, we found two kinds of kinetics: 1) the rapid fluctuations with periods of 2-3 min, when the pol I and fibrillarin signals oscillated in anti-phase manner, and the intensities of pol I in the neighboring FC/DFC units did not correlate. 2) fluctuations with periods of 10 to 60 min, in which pol I and fibrillarin signals measured in the same unit did not correlate, but pol I signals in the units belonging to different nucleoli were synchronized. Our data indicate that a complex pulsing activity of transcription as well as early processing is common for ribosomal genes.

Diabetes: Models, Signals and control

NASA Astrophysics Data System (ADS)

Cobelli, C.

2010-07-01

Diabetes and its complications impose significant economic consequences on individuals, families, health systems, and countries. The control of diabetes is an interdisciplinary endeavor, which includes significant components of modeling, signal processing and control. Models: first, I will discuss the minimal (coarse) models which describe the key components of the system functionality and are capable of measuring crucial processes of glucose metabolism and insulin control in health and diabetes; then, the maximal (fine-grain) models which include comprehensively all available knowledge about system functionality and are capable to simulate the glucose-insulin system in diabetes, thus making it possible to create simulation scenarios whereby cost effective experiments can be conducted in silico to assess the efficacy of various treatment strategies - in particular I will focus on the first in silico simulation model accepted by FDA as a substitute to animal trials in the quest for optimal diabetes control. Signals: I will review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the crucial role of models to enhance the interpretation of their time-series signals, and on the opportunities that they present for automation of diabetes control. Control: I will review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, I will discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers.

A CWT-based methodology for piston slap experimental characterization

NASA Astrophysics Data System (ADS)

Buzzoni, M.; Mucchi, E.; Dalpiaz, G.

2017-03-01

Noise and vibration control in mechanical systems has become ever more significant for automotive industry where the comfort of the passenger compartment represents a challenging issue for car manufacturers. The reduction of piston slap noise is pivotal for a good design of IC engines. In this scenario, a methodology has been developed for the vibro-acoustic assessment of IC diesel engines by means of design changes in piston to cylinder bore clearance. Vibration signals have been analysed by means of advanced signal processing techniques taking advantage of cyclostationarity theory. The procedure departs from the analysis of the Continuous Wavelet Transform (CWT) in order to identify a representative frequency band of piston slap phenomenon. Such a frequency band has been exploited as the input data in the further signal processing analysis that involves the envelope analysis of the second order cyclostationary component of the signal. The second order harmonic component has been used as the benchmark parameter of piston slap noise. An experimental procedure of vibrational benchmarking is proposed and verified at different operational conditions in real IC engines actually equipped on cars. This study clearly underlines the crucial role of the transducer positioning when differences among real piston-to-cylinder clearances are considered. In particular, the proposed methodology is effective for the sensors placed on the outer cylinder wall in all the tested conditions.

Advanced Data Acquisition Systems

NASA Technical Reports Server (NTRS)

Perotti, J.

2003-01-01

Current and future requirements of the aerospace sensors and transducers field make it necessary for the design and development of new data acquisition devices and instrumentation systems. New designs are sought to incorporate self-health, self-calibrating, self-repair capabilities, allowing greater measurement reliability and extended calibration cycles. With the addition of power management schemes, state-of-the-art data acquisition systems allow data to be processed and presented to the users with increased efficiency and accuracy. The design architecture presented in this paper displays an innovative approach to data acquisition systems. The design incorporates: electronic health self-check, device/system self-calibration, electronics and function self-repair, failure detection and prediction, and power management (reduced power consumption). These requirements are driven by the aerospace industry need to reduce operations and maintenance costs, to accelerate processing time and to provide reliable hardware with minimum costs. The project's design architecture incorporates some commercially available components identified during the market research investigation like: Field Programmable Gate Arrays (FPGA) Programmable Analog Integrated Circuits (PAC IC) and Field Programmable Analog Arrays (FPAA); Digital Signal Processing (DSP) electronic/system control and investigation of specific characteristics found in technologies like: Electronic Component Mean Time Between Failure (MTBF); and Radiation Hardened Component Availability. There are three main sections discussed in the design architecture presented in this document. They are the following: (a) Analog Signal Module Section, (b) Digital Signal/Control Module Section and (c) Power Management Module Section. These sections are discussed in detail in the following pages. This approach to data acquisition systems has resulted in the assignment of patent rights to Kennedy Space Center under U.S. patent # 6,462,684. Furthermore, NASA KSC commercialization office has issued licensing rights to Circuit Avenue Netrepreneurs, LLC , a minority-owned business founded in 1999 located in Camden, NJ.

Demodulation Processes in Auditory Perception.

DTIC Science & Technology

1992-08-15

not provide a fused image that the listener can process binaurally . 5 A type of dichotic profile has been developed for this study in which the stimulus...the component frequencies between the two ears may allow the i listener to develop a better fused image to be processed i binaurally than in the...listener was seated facing a 3 monitor and computer keyboard (Radio Shack Color Computer II). Signals were presented binaurally via Sennheiser HD414SL

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.

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

Mohamed Abdelrahman; roger Haggard; Wagdy Mahmoud

The final goal of this project was the development of a system that is capable of controlling an industrial process effectively through the integration of information obtained through intelligent sensor fusion and intelligent control technologies. The industry of interest in this project was the metal casting industry as represented by cupola iron-melting furnaces. However, the developed technology is of generic type and hence applicable to several other industries. The system was divided into the following four major interacting components: 1. An object oriented generic architecture to integrate the developed software and hardware components @. Generic algorithms for intelligent signal analysismore » and sensor and model fusion 3. Development of supervisory structure for integration of intelligent sensor fusion data into the controller 4. Hardware implementation of intelligent signal analysis and fusion algorithms« less

A NOVEL TECHNIQUE APPLYING SPECTRAL ESTIMATION TO JOHNSON NOISE THERMOMETRY

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

Ezell, N Dianne Bull; Britton Jr, Charles L; Roberts, Michael

Johnson noise thermometry (JNT) is one of many important measurements used to monitor the safety levels and stability in a nuclear reactor. However, this measurement is very dependent on the electromagnetic environment. Properly removing unwanted electromagnetic interference (EMI) is critical for accurate drift free temperature measurements. The two techniques developed by Oak Ridge National Laboratory (ORNL) to remove transient and periodic EMI are briefly discussed in this document. Spectral estimation is a key component in the signal processing algorithm utilized for EMI removal and temperature calculation. Applying these techniques requires the simple addition of the electronics and signal processing tomore » existing resistive thermometers.« less

Model-Based Fault Diagnosis for Turboshaft Engines

NASA Technical Reports Server (NTRS)

Green, Michael D.; Duyar, Ahmet; Litt, Jonathan S.

1998-01-01

Tests are described which, when used to augment the existing periodic maintenance and pre-flight checks of T700 engines, can greatly improve the chances of uncovering a problem compared to the current practice. These test signals can be used to expose and differentiate between faults in various components by comparing the responses of particular engine variables to the expected. The responses can be processed on-line in a variety of ways which have been shown to reveal and identify faults. The combination of specific test signals and on-line processing methods provides an ad hoc approach to the isolation of faults which might not otherwise be detected during pre-flight checkout.

Agile waveforms for joint SAR-GMTI processing

NASA Astrophysics Data System (ADS)

Jaroszewski, Steven; Corbeil, Allan; McMurray, Stephen; Majumder, Uttam; Bell, Mark R.; Corbeil, Jeffrey; Minardi, Michael

2016-05-01

Wideband radar waveforms that employ spread-spectrum techniques were investigated and experimentally tested. The waveforms combine bi-phase coding with a traditional LFM chirp and are applicable to joint SAR-GMTI processing. After de-spreading, the received signals can be processed to support simultaneous GMTI and high resolution SAR imaging missions by airborne radars. The spread spectrum coding techniques can provide nearly orthogonal waveforms and offer enhanced operations in some environments by distributing the transmitted energy over a large instantaneous bandwidth. The LFM component offers the desired Doppler tolerance. In this paper, the waveforms are formulated and a shift-register approach for de-spreading the received signals is described. Hardware loop-back testing has shown the feasibility of using these waveforms in experimental radar test bed.

Apparatus for Controlling Low Power Voltages in Space Based Processing Systems

NASA Technical Reports Server (NTRS)

Petrick, David J. (Inventor)

2017-01-01

A low power voltage control circuit for use in space missions includes a switching device coupled between an input voltage and an output voltage. The switching device includes a control input coupled to an enable signal, wherein the control input is configured to selectively turn the output voltage on or off based at least in part on the enable signal. A current monitoring circuit is coupled to the output voltage and configured to produce a trip signal, wherein the trip signal is active when a load current flowing through the switching device is determined to exceed a predetermined threshold and is inactive otherwise. The power voltage control circuit is constructed of space qualified components.

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

Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis

NASA Astrophysics Data System (ADS)

Liang, B.; Iwnicki, S. D.; Zhao, Y.

2013-08-01

The power spectrum is defined as the square of the magnitude of the Fourier transform (FT) of a signal. The advantage of FT analysis is that it allows the decomposition of a signal into individual periodic frequency components and establishes the relative intensity of each component. It is the most commonly used signal processing technique today. If the same principle is applied for the detection of periodicity components in a Fourier spectrum, the process is called the cepstrum analysis. Cepstrum analysis is a very useful tool for detection families of harmonics with uniform spacing or the families of sidebands commonly found in gearbox, bearing and engine vibration fault spectra. Higher order spectra (HOS) (also known as polyspectra) consist of higher order moment of spectra which are able to detect non-linear interactions between frequency components. For HOS, the most commonly used is the bispectrum. The bispectrum is the third-order frequency domain measure, which contains information that standard power spectral analysis techniques cannot provide. It is well known that neural networks can represent complex non-linear relationships, and therefore they are extremely useful for fault identification and classification. This paper presents an application of power spectrum, cepstrum, bispectrum and neural network for fault pattern extraction of induction motors. The potential for using the power spectrum, cepstrum, bispectrum and neural network as a means for differentiating between healthy and faulty induction motor operation is examined. A series of experiments is done and the advantages and disadvantages between them are discussed. It has been found that a combination of power spectrum, cepstrum and bispectrum plus neural network analyses could be a very useful tool for condition monitoring and fault diagnosis of induction motors.

Online Continuous Trace Process Analytics Using Multiplexing Gas Chromatography.

PubMed

Wunsch, Marco R; Lehnig, Rudolf; Trapp, Oliver

2017-04-04

The analysis of impurities at a trace level in chemical products, nutrition additives, and drugs is highly important to guarantee safe products suitable for consumption. However, trace analysis in the presence of a dominating component can be a challenging task because of noncompatible linear detection ranges or strong signal overlap that suppresses the signal of interest. Here, we developed a technique for quantitative analysis using multiplexing gas chromatography (mpGC) for continuous and completely automated process trace analytics exemplified for the analysis of a CO 2 stream in a production plant for detection of benzene, toluene, ethylbenzene, and the three structural isomers of xylene (BTEX) in the concentration range of 0-10 ppb. Additional minor components are methane and methanol with concentrations up to 100 ppm. The sample is injected up to 512 times according to a pseudorandom binary sequence (PRBS) with a mean frequency of 0.1 Hz into a gas chromatograph equipped with a flame ionization detector (FID). A superimposed chromatogram is recorded which is deconvoluted into an averaged chromatogram with Hadamard transformation. Novel algorithms to maintain the data acquisition rate of the detector by application of Hadamard transformation and to suppress correlation noise induced by components with much higher concentrations than the target substances are shown. Compared to conventional GC-FID, the signal-to-noise ratio has been increased by a factor of 10 with mpGC-FID. Correspondingly, the detection limits for BTEX in CO 2 have been lowered from 10 to 1 ppb each. This has been achieved despite the presence of detectable components (methane and methanol) with a concentration about 1000 times higher than the target substances. The robustness and reliability of mpGC has been proven in a two-month field test in a chemical production plant.

Nitrogen Assimilation in Escherichia coli: Putting Molecular Data into a Systems Perspective

PubMed Central

van Heeswijk, Wally C.; Westerhoff, Hans V.

2013-01-01

SUMMARY We present a comprehensive overview of the hierarchical network of intracellular processes revolving around central nitrogen metabolism in Escherichia coli. The hierarchy intertwines transport, metabolism, signaling leading to posttranslational modification, and transcription. The protein components of the network include an ammonium transporter (AmtB), a glutamine transporter (GlnHPQ), two ammonium assimilation pathways (glutamine synthetase [GS]-glutamate synthase [glutamine 2-oxoglutarate amidotransferase {GOGAT}] and glutamate dehydrogenase [GDH]), the two bifunctional enzymes adenylyl transferase/adenylyl-removing enzyme (ATase) and uridylyl transferase/uridylyl-removing enzyme (UTase), the two trimeric signal transduction proteins (GlnB and GlnK), the two-component regulatory system composed of the histidine protein kinase nitrogen regulator II (NRII) and the response nitrogen regulator I (NRI), three global transcriptional regulators called nitrogen assimilation control (Nac) protein, leucine-responsive regulatory protein (Lrp), and cyclic AMP (cAMP) receptor protein (Crp), the glutaminases, and the nitrogen-phosphotransferase system. First, the structural and molecular knowledge on these proteins is reviewed. Thereafter, the activities of the components as they engage together in transport, metabolism, signal transduction, and transcription and their regulation are discussed. Next, old and new molecular data and physiological data are put into a common perspective on integral cellular functioning, especially with the aim of resolving counterintuitive or paradoxical processes featured in nitrogen assimilation. Finally, we articulate what still remains to be discovered and what general lessons can be learned from the vast amounts of data that are available now. PMID:24296575

Systems Perturbation Analysis of a Large-Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics

PubMed Central

Puniya, Bhanwar Lal; Allen, Laura; Hochfelder, Colleen; Majumder, Mahbubul; Helikar, Tomáš

2016-01-01

Dysregulation in signal transduction pathways can lead to a variety of complex disorders, including cancer. Computational approaches such as network analysis are important tools to understand system dynamics as well as to identify critical components that could be further explored as therapeutic targets. Here, we performed perturbation analysis of a large-scale signal transduction model in extracellular environments that stimulate cell death, growth, motility, and quiescence. Each of the model’s components was perturbed under both loss-of-function and gain-of-function mutations. Using 1,300 simulations under both types of perturbations across various extracellular conditions, we identified the most and least influential components based on the magnitude of their influence on the rest of the system. Based on the premise that the most influential components might serve as better drug targets, we characterized them for biological functions, housekeeping genes, essential genes, and druggable proteins. The most influential components under all environmental conditions were enriched with several biological processes. The inositol pathway was found as most influential under inactivating perturbations, whereas the kinase and small lung cancer pathways were identified as the most influential under activating perturbations. The most influential components were enriched with essential genes and druggable proteins. Moreover, known cancer drug targets were also classified in influential components based on the affected components in the network. Additionally, the systemic perturbation analysis of the model revealed a network motif of most influential components which affect each other. Furthermore, our analysis predicted novel combinations of cancer drug targets with various effects on other most influential components. We found that the combinatorial perturbation consisting of PI3K inactivation and overactivation of IP3R1 can lead to increased activity levels of apoptosis-related components and tumor-suppressor genes, suggesting that this combinatorial perturbation may lead to a better target for decreasing cell proliferation and inducing apoptosis. Finally, our approach shows a potential to identify and prioritize therapeutic targets through systemic perturbation analysis of large-scale computational models of signal transduction. Although some components of the presented computational results have been validated against independent gene expression data sets, more laboratory experiments are warranted to more comprehensively validate the presented results. PMID:26904540

Comprehensive NMR analysis of compositional changes of black garlic during thermal processing.

PubMed

Liang, Tingfu; Wei, Feifei; Lu, Yi; Kodani, Yoshinori; Nakada, Mitsuhiko; Miyakawa, Takuya; Tanokura, Masaru

2015-01-21

Black garlic is a processed food product obtained by subjecting whole raw garlic to thermal processing that causes chemical reactions, such as the Maillard reaction, which change the composition of the garlic. In this paper, we report a nuclear magnetic resonance (NMR)-based comprehensive analysis of raw garlic and black garlic extracts to determine the compositional changes resulting from thermal processing. (1)H NMR spectra with a detailed signal assignment showed that 38 components were altered by thermal processing of raw garlic. For example, the contents of 11 l-amino acids increased during the first step of thermal processing over 5 days and then decreased. Multivariate data analysis revealed changes in the contents of fructose, glucose, acetic acid, formic acid, pyroglutamic acid, cycloalliin, and 5-(hydroxymethyl)furfural (5-HMF). Our results provide comprehensive information on changes in NMR-detectable components during thermal processing of whole garlic.

Conserved Insulin Signaling in the Regulation of Oocyte Growth, Development, and Maturation

PubMed Central

DAS, DEBABRATA; ARUR, SWATHI

2017-01-01

Insulin signaling regulates various aspects of physiology, such as glucose homeostasis and aging, and is a key determinant of female reproduction in metazoans. That insulin signaling is crucial for female reproductive health is clear from clinical data linking hyperinsulinemic and hypoinsulinemic condition with certain types of ovarian dysfunction, such as altered steroidogenesis, polycystic ovary syndrome, and infertility. Thus, understanding the signaling mechanisms that underlie the control of insulin-mediated ovarian development is important for the accurate diagnosis of and intervention for female infertility. Studies of invertebrate and vertebrate model systems have revealed the molecular determinants that transduce insulin signaling as well as which biological processes are regulated by the insulin-signaling pathway. The molecular determinants of the insulin-signaling pathway, from the insulin receptor to its downstream signaling components, are structurally and functionally conserved across evolution, from worms to mammals – yet, physiological differences in signaling still exist. Insulin signaling acts cooperatively with gonadotropins in mammals and lower vertebrates to mediate various aspects of ovarian development, mainly owing to evolution of the endocrine system in vertebrates. In contrast, insulin signaling in Drosophila and Caenorhabditis elegans directly regulates oocyte growth and maturation. In this review, we compare and contrast insulin-mediated regulation of ovarian functions in mammals, lower vertebrates, C. elegans, and Drosophila, and highlight conserved signaling pathways and regulatory mechanisms in general while illustrating insulin’s unique role in specific reproductive processes. PMID:28379636

Improving MEG source localizations: an automated method for complete artifact removal based on independent component analysis.

PubMed

Mantini, D; Franciotti, R; Romani, G L; Pizzella, V

2008-03-01

The major limitation for the acquisition of high-quality magnetoencephalography (MEG) recordings is the presence of disturbances of physiological and technical origins: eye movements, cardiac signals, muscular contractions, and environmental noise are serious problems for MEG signal analysis. In the last years, multi-channel MEG systems have undergone rapid technological developments in terms of noise reduction, and many processing methods have been proposed for artifact rejection. Independent component analysis (ICA) has already shown to be an effective and generally applicable technique for concurrently removing artifacts and noise from the MEG recordings. However, no standardized automated system based on ICA has become available so far, because of the intrinsic difficulty in the reliable categorization of the source signals obtained with this technique. In this work, approximate entropy (ApEn), a measure of data regularity, is successfully used for the classification of the signals produced by ICA, allowing for an automated artifact rejection. The proposed method has been tested using MEG data sets collected during somatosensory, auditory and visual stimulation. It was demonstrated to be effective in attenuating both biological artifacts and environmental noise, in order to reconstruct clear signals that can be used for improving brain source localizations.

Gaussian process based independent analysis for temporal source separation in fMRI.

PubMed

Hald, Ditte Høvenhoff; Henao, Ricardo; Winther, Ole

2017-05-15

Functional Magnetic Resonance Imaging (fMRI) gives us a unique insight into the processes of the brain, and opens up for analyzing the functional activation patterns of the underlying sources. Task-inferred supervised learning with restrictive assumptions in the regression set-up, restricts the exploratory nature of the analysis. Fully unsupervised independent component analysis (ICA) algorithms, on the other hand, can struggle to detect clear classifiable components on single-subject data. We attribute this shortcoming to inadequate modeling of the fMRI source signals by failing to incorporate its temporal nature. fMRI source signals, biological stimuli and non-stimuli-related artifacts are all smooth over a time-scale compatible with the sampling time (TR). We therefore propose Gaussian process ICA (GPICA), which facilitates temporal dependency by the use of Gaussian process source priors. On two fMRI data sets with different sampling frequency, we show that the GPICA-inferred temporal components and associated spatial maps allow for a more definite interpretation than standard temporal ICA methods. The temporal structures of the sources are controlled by the covariance of the Gaussian process, specified by a kernel function with an interpretable and controllable temporal length scale parameter. We propose a hierarchical model specification, considering both instantaneous and convolutive mixing, and we infer source spatial maps, temporal patterns and temporal length scale parameters by Markov Chain Monte Carlo. A companion implementation made as a plug-in for SPM can be downloaded from https://github.com/dittehald/GPICA. Copyright © 2017 Elsevier Inc. All rights reserved.

Signal Frequency Spectra with Audacity®

ERIC Educational Resources Information Center

Gailey, Alycia

2015-01-01

The primary objective of the activity presented here is to allow students to explore the frequency components of various simple signals, with the ultimate goal of teaching them how to remove unwanted noise from a voice signal. Analysis of the frequency components of a signal allows students to design filters that remove unwanted components of a…

Digital approach to stabilizing optical frequency combs and beat notes of CW lasers

NASA Astrophysics Data System (ADS)

Čížek, Martin; Číp, Ondřej; Å míd, Radek; Hrabina, Jan; Mikel, Břetislav; Lazar, Josef

2013-10-01

In cases when it is necessary to lock optical frequencies generated by an optical frequency comb to a precise radio frequency (RF) standard (GPS-disciplined oscillator, H-maser, etc.) the usual practice is to implement phase and frequency-locked loops. Such system takes the signal generated by the RF standard (usually 10 MHz or 100 MHz) as a reference and stabilizes the repetition and offset frequencies of the comb contained in the RF output of the f-2f interferometer. These control loops are usually built around analog electronic circuits processing the output signals from photo detectors. This results in transferring the stability of the standard from RF to optical frequency domain. The presented work describes a different approach based on digital signal processing and software-defined radio algorithms used for processing the f-2f and beat-note signals. Several applications of digital phase and frequency locks to a RF standard are demonstrated: the repetition (frep) and offset frequency (fceo) of the comb, and the frequency of the beat note between a CW laser source and a single component of the optical frequency comb spectrum.

Seismpol_ a visual-basic computer program for interactive and automatic earthquake waveform analysis

NASA Astrophysics Data System (ADS)

Patanè, Domenico; Ferrari, Ferruccio

1997-11-01

A Microsoft Visual-Basic computer program for waveform analysis of seismic signals is presented. The program combines interactive and automatic processing of digital signals using data recorded by three-component seismic stations. The analysis procedure can be used in either an interactive earthquake analysis or an automatic on-line processing of seismic recordings. The algorithm works in the time domain using the Covariance Matrix Decomposition method (CMD), so that polarization characteristics may be computed continuously in real time and seismic phases can be identified and discriminated. Visual inspection of the particle motion in hortogonal planes of projection (hodograms) reduces the danger of misinterpretation derived from the application of the polarization filter. The choice of time window and frequency intervals improves the quality of the extracted polarization information. In fact, the program uses a band-pass Butterworth filter to process the signals in the frequency domain by analysis of a selected signal window into a series of narrow frequency bands. Significant results supported by well defined polarizations and source azimuth estimates for P and S phases are also obtained for short-period seismic events (local microearthquakes).

Real-time diagnostics for a reusable rocket engine

NASA Technical Reports Server (NTRS)

Guo, T. H.; Merrill, W.; Duyar, A.

1992-01-01

A hierarchical, decentralized diagnostic system is proposed for the Real-Time Diagnostic System component of the Intelligent Control System (ICS) for reusable rocket engines. The proposed diagnostic system has three layers of information processing: condition monitoring, fault mode detection, and expert system diagnostics. The condition monitoring layer is the first level of signal processing. Here, important features of the sensor data are extracted. These processed data are then used by the higher level fault mode detection layer to do preliminary diagnosis on potential faults at the component level. Because of the closely coupled nature of the rocket engine propulsion system components, it is expected that a given engine condition may trigger more than one fault mode detector. Expert knowledge is needed to resolve the conflicting reports from the various failure mode detectors. This is the function of the diagnostic expert layer. Here, the heuristic nature of this decision process makes it desirable to use an expert system approach. Implementation of the real-time diagnostic system described above requires a wide spectrum of information processing capability. Generally, in the condition monitoring layer, fast data processing is often needed for feature extraction and signal conditioning. This is usually followed by some detection logic to determine the selected faults on the component level. Three different techniques are used to attack different fault detection problems in the NASA LeRC ICS testbed simulation. The first technique employed is the neural network application for real-time sensor validation which includes failure detection, isolation, and accommodation. The second approach demonstrated is the model-based fault diagnosis system using on-line parameter identification. Besides these model based diagnostic schemes, there are still many failure modes which need to be diagnosed by the heuristic expert knowledge. The heuristic expert knowledge is implemented using a real-time expert system tool called G2 by Gensym Corp. Finally, the distributed diagnostic system requires another level of intelligence to oversee the fault mode reports generated by component fault detectors. The decision making at this level can best be done using a rule-based expert system. This level of expert knowledge is also implemented using G2.

Signals Involved in Tuber Wound-Healing

USDA-ARS?s Scientific Manuscript database

The induction and regulation of wound-healing (WH) processes in potato tubers and other vegetables are of great nutritional and economic importance. The rapid accumulation of waxes to restrict water vapor loss and formation of suberin barriers to block infection are crucial components of WH. Recen...

Photonics for aerospace sensors

NASA Astrophysics Data System (ADS)

Pellegrino, John; Adler, Eric D.; Filipov, Andree N.; Harrison, Lorna J.; van der Gracht, Joseph; Smith, Dale J.; Tayag, Tristan J.; Viveiros, Edward A.

1992-11-01

The maturation in the state-of-the-art of optical components is enabling increased applications for the technology. Most notable is the ever-expanding market for fiber optic data and communications links, familiar in both commercial and military markets. The inherent properties of optics and photonics, however, have suggested that components and processors may be designed that offer advantages over more commonly considered digital approaches for a variety of airborne sensor and signal processing applications. Various academic, industrial, and governmental research groups have been actively investigating and exploiting these properties of high bandwidth, large degree of parallelism in computation (e.g., processing in parallel over a two-dimensional field), and interconnectivity, and have succeeded in advancing the technology to the stage of systems demonstration. Such advantages as computational throughput and low operating power consumption are highly attractive for many computationally intensive problems. This review covers the key devices necessary for optical signal and image processors, some of the system application demonstration programs currently in progress, and active research directions for the implementation of next-generation architectures.

Military microwaves '84; Proceedings of the Conference, London, England, October 24-26, 1984

NASA Astrophysics Data System (ADS)

The present conference on microwave frequency electronic warfare and military sensor equipment developments consider radar warning receivers, optical frequency spread spectrum systems, mobile digital communications troposcatter effects, wideband bulk encryption, long range air defense radars (such as the AR320, W-2000 and Martello), multistatic radars, and multimode airborne and interceptor radars. IR system and subsystem component topics encompass thermal imaging and active IR countermeasures, class 1 modules, and diamond coatings, while additional radar-related topics include radar clutter in airborne maritime reconnaissance systems, microstrip antennas with dual polarization capability, the synthesis of shaped beam antenna patterns, planar phased arrays, radar signal processing, radar cross section measurement techniques, and radar imaging and pattern analysis. Attention is also given to optical control and signal processing, mm-wave control technology and EW systems, W-band operations, planar mm-wave arrays, mm-wave monolithic solid state components, mm-wave sensor technology, GaAs monolithic ICs, and dielectric resonator and wideband tunable oscillators.

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

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

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

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

Dolphin biosonar signals measured at extreme off-axis angles: insights to sound propagation in the head.

PubMed

Au, Whitlow W L; Branstetter, Brian; Moore, Patrick W; Finneran, James J

2012-08-01

Biosonar signals radiated along the beam axis of an Atlantic bottlenose dolphin resemble short transient oscillations. As the azimuth of the measuring hydrophones in the horizontal plane progressively increases with respect to the beam axis the signals become progressively distorted. At approximately ±45°, the signals begin to divide into two components with the time difference between the components increasing with increasing angles. At ±90° or normal to the longitudinal axis of the animal, the time difference between the two pulses measured by the hydrophone on the right side of the dolphin's head is, on average, ∼11.9 μs larger than the time differences observed by the hydrophone on the left side of the dolphin's head. The center frequency of the first pulse is generally lower, by 33-47 kHz, than the center frequency of the second pulse. When considering the relative locations of the two phonic lips, the data suggest that the signals are being produced by one of the phonic lips and the second pulse resulting from a reflection within the head of the animal. The generation of biosonar signals is a complex process and the propagation pathways through the dolphin's head are not well understood.

Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features.

PubMed

Li, Yang; Cui, Weigang; Luo, Meilin; Li, Ke; Wang, Lina

2018-01-25

The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics. Both Gaussian Mixture Model (GMM) features and Gray Level Co-occurrence Matrix (GLCM) descriptors are then extracted from these sub-band TFI. Additionally, in order to improve classification accuracy, a compact feature selection method by combining the ReliefF and the support vector machine-based recursive feature elimination (RFE-SVM) algorithm is adopted to select the most discriminative feature subset, which is an input to the SVM with the radial basis function (RBF) for classifying epileptic seizure EEG signals. The experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.

Two-Component Elements Mediate Interactions between Cytokinin and Salicylic Acid in Plant Immunity

PubMed Central

Argueso, Cristiana T.; Ferreira, Fernando J.; Epple, Petra; To, Jennifer P. C.; Hutchison, Claire E.; Schaller, G. Eric; Dangl, Jeffery L.; Kieber, Joseph J.

2012-01-01

Recent studies have revealed an important role for hormones in plant immunity. We are now beginning to understand the contribution of crosstalk among different hormone signaling networks to the outcome of plant–pathogen interactions. Cytokinins are plant hormones that regulate development and responses to the environment. Cytokinin signaling involves a phosphorelay circuitry similar to two-component systems used by bacteria and fungi to perceive and react to various environmental stimuli. In this study, we asked whether cytokinin and components of cytokinin signaling contribute to plant immunity. We demonstrate that cytokinin levels in Arabidopsis are important in determining the amplitude of immune responses, ultimately influencing the outcome of plant–pathogen interactions. We show that high concentrations of cytokinin lead to increased defense responses to a virulent oomycete pathogen, through a process that is dependent on salicylic acid (SA) accumulation and activation of defense gene expression. Surprisingly, treatment with lower concentrations of cytokinin results in increased susceptibility. These functions for cytokinin in plant immunity require a host phosphorelay system and are mediated in part by type-A response regulators, which act as negative regulators of basal and pathogen-induced SA–dependent gene expression. Our results support a model in which cytokinin up-regulates plant immunity via an elevation of SA–dependent defense responses and in which SA in turn feedback-inhibits cytokinin signaling. The crosstalk between cytokinin and SA signaling networks may help plants fine-tune defense responses against pathogens. PMID:22291601

Proactive and Reactive Stopping When Distracted: An Attentional Account

PubMed Central

2014-01-01

Performance in response inhibition paradigms is typically attributed to inhibitory control. Here we examined the idea that stopping may largely depend on the outcome of a sensory detection process. Subjects performed a speeded go task, but they were instructed to withhold their response when a visual stop signal was presented. The stop signal could occur in the center of the screen or in the periphery. On half of the trials, perceptual distractors were presented throughout the trial. We found that these perceptual distractors impaired stopping, especially when stop signals could occur in the periphery. Furthermore, the effect of the distractors on going was smallest in the central stop-signal condition, medium in a condition in which no signals could occur, and largest in the condition in which stop signals could occur in the periphery. The results show that an important component of stopping is finding a balance between ignoring irrelevant information in the environment and monitoring for the occurrence of occasional stop signals. These findings highlight the importance of sensory detection processes when stopping and could shed new light on a range of phenomena and findings in the response inhibition literature. PMID:24842070

Independent component analysis for the extraction of reliable protein signal profiles from MALDI-TOF mass spectra.

PubMed

Mantini, Dante; Petrucci, Francesca; Del Boccio, Piero; Pieragostino, Damiana; Di Nicola, Marta; Lugaresi, Alessandra; Federici, Giorgio; Sacchetta, Paolo; Di Ilio, Carmine; Urbani, Andrea

2008-01-01

Independent component analysis (ICA) is a signal processing technique that can be utilized to recover independent signals from a set of their linear mixtures. We propose ICA for the analysis of signals obtained from large proteomics investigations such as clinical multi-subject studies based on MALDI-TOF MS profiling. The method is validated on simulated and experimental data for demonstrating its capability of correctly extracting protein profiles from MALDI-TOF mass spectra. The comparison on peak detection with an open-source and two commercial methods shows its superior reliability in reducing the false discovery rate of protein peak masses. Moreover, the integration of ICA and statistical tests for detecting the differences in peak intensities between experimental groups allows to identify protein peaks that could be indicators of a diseased state. This data-driven approach demonstrates to be a promising tool for biomarker-discovery studies based on MALDI-TOF MS technology. The MATLAB implementation of the method described in the article and both simulated and experimental data are freely available at http://www.unich.it/proteomica/bioinf/.

Oscillator metrology with software defined radio.

PubMed

Sherman, Jeff A; Jördens, Robert

2016-05-01

Analog electrical elements such as mixers, filters, transfer oscillators, isolating buffers, dividers, and even transmission lines contribute technical noise and unwanted environmental coupling in time and frequency measurements. Software defined radio (SDR) techniques replace many of these analog components with digital signal processing (DSP) on rapidly sampled signals. We demonstrate that, generically, commercially available multi-channel SDRs are capable of time and frequency metrology, outperforming purpose-built devices by as much as an order-of-magnitude. For example, for signals at 10 MHz and 6 GHz, we observe SDR time deviation noise floors of about 20 fs and 1 fs, respectively, in under 10 ms of averaging. Examining the other complex signal component, we find a relative amplitude measurement instability of 3 × 10(-7) at 5 MHz. We discuss the scalability of a SDR-based system for simultaneous measurement of many clocks. SDR's frequency agility allows for comparison of oscillators at widely different frequencies. We demonstrate a novel and extreme example with optical clock frequencies differing by many terahertz: using a femtosecond-laser frequency comb and SDR, we show femtosecond-level time comparisons of ultra-stable lasers with zero measurement dead-time.

Noise characteristics of the Escherichia coli rotary motor

PubMed Central

2011-01-01

Background The chemotaxis pathway in the bacterium Escherichia coli allows cells to detect changes in external ligand concentration (e.g. nutrients). The pathway regulates the flagellated rotary motors and hence the cells' swimming behaviour, steering them towards more favourable environments. While the molecular components are well characterised, the motor behaviour measured by tethered cell experiments has been difficult to interpret. Results We study the effects of sensing and signalling noise on the motor behaviour. Specifically, we consider fluctuations stemming from ligand concentration, receptor switching between their signalling states, adaptation, modification of proteins by phosphorylation, and motor switching between its two rotational states. We develop a model which includes all signalling steps in the pathway, and discuss a simplified version, which captures the essential features of the full model. We find that the noise characteristics of the motor contain signatures from all these processes, albeit with varying magnitudes. Conclusions Our analysis allows us to address how cell-to-cell variation affects motor behaviour and the question of optimal pathway design. A similar comprehensive analysis can be applied to other two-component signalling pathways. PMID:21951560

A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings.

PubMed

Corera, Íñigo; Eciolaza, Adrián; Rubio, Oliver; Malanda, Armando; Rodríguez-Falces, Javier; Navallas, Javier

2018-01-11

Scanning-EMG is an electrophysiological technique in which the electrical activity of the motor unit is recorded at multiple points along a corridor crossing the motor unit territory. Correct analysis of the scanning-EMG signal requires prior elimination of interference from nearby motor units. Although the traditional processing based on the median filtering is effective in removing such interference, it distorts the physiological waveform of the scanning-EMG signal. In this study, we describe a new scanning-EMG signal processing algorithm that preserves the physiological signal waveform while effectively removing interference from other motor units. To obtain a cleaned-up version of the scanning signal, the masked least-squares smoothing (MLSS) algorithm recalculates and replaces each sample value of the signal using a least-squares smoothing in the spatial dimension, taking into account the information of only those samples that are not contaminated with activity of other motor units. The performance of the new algorithm with simulated scanning-EMG signals is studied and compared with the performance of the median algorithm and tested with real scanning signals. Results show that the MLSS algorithm distorts the waveform of the scanning-EMG signal much less than the median algorithm (approximately 3.5 dB gain), being at the same time very effective at removing interference components. Graphical Abstract The raw scanning-EMG signal (left figure) is processed by the MLSS algorithm in order to remove the artifact interference. Firstly, artifacts are detected from the raw signal, obtaining a validity mask (central figure) that determines the samples that have been contaminated by artifacts. Secondly, a least-squares smoothing procedure in the spatial dimension is applied to the raw signal using the not contaminated samples according to the validity mask. The resulting MLSS-processed scanning-EMG signal (right figure) is clean of artifact interference.

Position- and polarity-dependent Hippo signaling regulates cell fates in preimplantation mouse embryos.

PubMed

Sasaki, Hiroshi

2015-12-01

During the preimplantation stage, mouse embryos establish two cell lineages by the time of early blastocyst formation: the trophectoderm (TE) and the inner cell mass (ICM). Historical models have proposed that the establishment of these two lineages depends on the cell position within the embryo (e.g., the positional model) or cell polarization along the apicobasal axis (e.g., the polarity model). Recent findings have revealed that the Hippo signaling pathway plays a central role in the cell fate-specification process: active and inactive Hippo signaling in the inner and outer cells promote ICM and TE fates, respectively. Intercellular adhesion activates, while apicobasal polarization suppresses Hippo signaling, and a combination of these processes determines the spatially regulated activation of the Hippo pathway in 32-cell-stage embryos. Therefore, there is experimental evidence in favor of both positional and polarity models. At the molecular level, phosphorylation of the Hippo-pathway component angiomotin at adherens junctions (AJs) in the inner (apolar) cells activates the Lats protein kinase and triggers Hippo signaling. In the outer cells, however, cell polarization sequesters Amot from basolateral AJs and suppresses activation of the Hippo pathway. Other mechanisms, including asymmetric cell division and Notch signaling, also play important roles in the regulation of embryonic development. In this review, I discuss how these mechanisms cooperate with the Hippo signaling pathway during cell fate-specification processes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Empirical mode decomposition for analyzing acoustical signals

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2005-01-01

The present invention discloses a computer implemented signal analysis method through the Hilbert-Huang Transformation (HHT) for analyzing acoustical signals, which are assumed to be nonlinear and nonstationary. The Empirical Decomposition Method (EMD) and the Hilbert Spectral Analysis (HSA) are used to obtain the HHT. Essentially, the acoustical signal will be decomposed into the Intrinsic Mode Function Components (IMFs). Once the invention decomposes the acoustic signal into its constituting components, all operations such as analyzing, identifying, and removing unwanted signals can be performed on these components. Upon transforming the IMFs into Hilbert spectrum, the acoustical signal may be compared with other acoustical signals.

Simultaneous resonant enhanced multiphoton ionization and electron avalanche ionization in gas mixtures

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

Shneider, Mikhail N.; Zhang Zhili; Miles, Richard B.

2008-07-15

Resonant enhanced multiphoton ionization (REMPI) and electron avalanche ionization (EAI) are measured simultaneously in Ar:Xe mixtures at different partial pressures of mixture components. A simple theory for combined REMPI+EAI in gas mixture is developed. It is shown that the REMPI electrons seed the avalanche process, and thus the avalanche process amplifies the REMPI signal. Possible applications are discussed.

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.

Quantifying two-dimensional nonstationary signal with power-law correlations by detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Fan, Qingju; Wu, Yonghong

2015-08-01

In this paper, we develop a new method for the multifractal characterization of two-dimensional nonstationary signal, which is based on the detrended fluctuation analysis (DFA). By applying to two artificially generated signals of two-component ARFIMA process and binomial multifractal model, we show that the new method can reliably determine the multifractal scaling behavior of two-dimensional signal. We also illustrate the applications of this method in finance and physiology. The analyzing results exhibit that the two-dimensional signals under investigation are power-law correlations, and the electricity market consists of electricity price and trading volume is multifractal, while the two-dimensional EEG signal in sleep recorded for a single patient is weak multifractal. The new method based on the detrended fluctuation analysis may add diagnostic power to existing statistical methods.

Endocytosis and Signaling during Development

PubMed Central

Bökel, Christian

2014-01-01

The development of multicellular organisms relies on an intricate choreography of intercellular communication events that pattern the embryo and coordinate the formation of tissues and organs. It is therefore not surprising that developmental biology, especially using genetic model organisms, has contributed significantly to the discovery and functional dissection of the associated signal-transduction cascades. At the same time, biophysical, biochemical, and cell biological approaches have provided us with insights into the underlying cell biological machinery. Here we focus on how endocytic trafficking of signaling components (e.g., ligands or receptors) controls the generation, propagation, modulation, reception, and interpretation of developmental signals. A comprehensive enumeration of the links between endocytosis and signal transduction would exceed the limits of this review. We will instead use examples from different developmental pathways to conceptually illustrate the various functions provided by endocytic processes during key steps of intercellular signaling. PMID:24591521

Ultrasonic Leak Detection System

NASA Technical Reports Server (NTRS)

Youngquist, Robert C. (Inventor); Moerk, J. Steven (Inventor)

1998-01-01

A system for detecting ultrasonic vibrations. such as those generated by a small leak in a pressurized container. vessel. pipe. or the like. comprises an ultrasonic transducer assembly and a processing circuit for converting transducer signals into an audio frequency range signal. The audio frequency range signal can be used to drive a pair of headphones worn by an operator. A diode rectifier based mixing circuit provides a simple, inexpensive way to mix the transducer signal with a square wave signal generated by an oscillator, and thereby generate the audio frequency signal. The sensitivity of the system is greatly increased through proper selection and matching of the system components. and the use of noise rejection filters and elements. In addition, a parabolic collecting horn is preferably employed which is mounted on the transducer assembly housing. The collecting horn increases sensitivity of the system by amplifying the received signals. and provides directionality which facilitates easier location of an ultrasonic vibration source.

Blind source separation of ex-vivo aorta tissue multispectral images

PubMed Central

Galeano, July; Perez, Sandra; Montoya, Yonatan; Botina, Deivid; Garzón, Johnson

2015-01-01

Blind Source Separation methods (BSS) aim for the decomposition of a given signal in its main components or source signals. Those techniques have been widely used in the literature for the analysis of biomedical images, in order to extract the main components of an organ or tissue under study. The analysis of skin images for the extraction of melanin and hemoglobin is an example of the use of BSS. This paper presents a proof of concept of the use of source separation of ex-vivo aorta tissue multispectral Images. The images are acquired with an interference filter-based imaging system. The images are processed by means of two algorithms: Independent Components analysis and Non-negative Matrix Factorization. In both cases, it is possible to obtain maps that quantify the concentration of the main chromophores present in aortic tissue. Also, the algorithms allow for spectral absorbance of the main tissue components. Those spectral signatures were compared against the theoretical ones by using correlation coefficients. Those coefficients report values close to 0.9, which is a good estimator of the method’s performance. Also, correlation coefficients lead to the identification of the concentration maps according to the evaluated chromophore. The results suggest that Multi/hyper-spectral systems together with image processing techniques is a potential tool for the analysis of cardiovascular tissue. PMID:26137366

A long-range laser velocimeter for the National Full-Scale Aerodynamics Complex: New developments and experimental application

NASA Technical Reports Server (NTRS)

Reinath, Michael S.

1989-01-01

A long-range laser velocimeter (LV) developed for remote operation from within the flow fields of the large wind tunnels of the National Full-Scale Aerodynamics Complex is described. Emphasis is placed on recent improvements in optical hardware as well as recent additions to data acquisition and processing techniques. The system has been upgraded from a dual-beam, single-color LV with focal range to 10 m, to a dual-beam, two-color LV with focal range to 20 m. At the new extended measurement range (between 10 and 20 m), signals are photon-resolved, and a photon correlation technique is applied to acquire and process the LV signals. This technique permits recovery of the velocity probability distributions at a particular measurement location from which the mean components of velocity and the corresponding normal stress components of turbulence are obtained. The method used for data reduction is outlined in detail, and a discussion of measurement accuracy is made. To study the performance of the LV and verify the measurement accuracy, laboratory measurements were made in the flow field of a 10 cm-diameter, 30-m/sec axisymmetric jet. A discussion of the requirements and techniques used to seed the flow is made, and boundary-layer surveys of mean velocity and turbulence intensity of the streamwise component and the component normal to the surface are presented.

Cyclostationarity approach for monitoring chatter and tool wear in high speed milling

NASA Astrophysics Data System (ADS)

Lamraoui, M.; Thomas, M.; El Badaoui, M.

2014-02-01

Detection of chatter and tool wear is crucial in the machining process and their monitoring is a key issue, for: (1) insuring better surface quality, (2) increasing productivity and (3) protecting both machines and safe workpiece. This paper presents an investigation of chatter and tool wear using the cyclostationary method to process the vibrations signals acquired from high speed milling. Experimental cutting tests were achieved on slot milling operation of aluminum alloy. The experimental set-up is designed for acquisition of accelerometer signals and encoding information picked up from an encoder. The encoder signal is used for re-sampling accelerometers signals in angular domain using a specific algorithm that was developed in LASPI laboratory. The use of cyclostationary on accelerometer signals has been applied for monitoring chatter and tool wear in high speed milling. The cyclostationarity appears on average properties (first order) of signals, on the energetic properties (second order) and it generates spectral lines at cyclic frequencies in spectral correlation. Angular power and kurtosis are used to analyze chatter phenomena. The formation of chatter is characterized by unstable, chaotic motion of the tool and strong anomalous fluctuations of cutting forces. Results show that stable machining generates only very few cyclostationary components of second order while chatter is strongly correlated to cyclostationary components of second order. By machining in the unstable region, chatter results in flat angular kurtosis and flat angular power, such as a pseudo (white) random signal with flat spectrum. Results reveal that spectral correlation and Wigner Ville spectrum or integrated Wigner Ville issued from second-order cyclostationary are an efficient parameter for the early diagnosis of faults in high speed machining, such as chatter, tool wear and bearings, compared to traditional stationary methods. Wigner Ville representation of the residual signal shows that the energy corresponding to the tooth passing decreases when chatter phenomenon occurs. The effect of the tool wear and the number of broken teeth on the excitation of structure resonances appears in Wigner Ville presentation.

Multisensor fusion for 3-D defect characterization using wavelet basis function neural networks

NASA Astrophysics Data System (ADS)

Lim, Jaein; Udpa, Satish S.; Udpa, Lalita; Afzal, Muhammad

2001-04-01

The primary objective of multi-sensor data fusion, which offers both quantitative and qualitative benefits, has the ability to draw inferences that may not be feasible with data from a single sensor alone. In this paper, data from two sets of sensors are fused to estimate the defect profile from magnetic flux leakage (MFL) inspection data. The two sensors measure the axial and circumferential components of the MFL. Data is fused at the signal level. If the flux is oriented axially, the samples of the axial signal are measured along a direction parallel to the flaw, while the circumferential signal is measured in a direction that is perpendicular to the flaw. The two signals are combined as the real and imaginary components of a complex valued signal. Signals from an array of sensors are arranged in contiguous rows to obtain a complex valued image. A boundary extraction algorithm is used to extract the defect areas in the image. Signals from the defect regions are then processed to minimize noise and the effects of lift-off. Finally, a wavelet basis function (WBF) neural network is employed to map the complex valued image appropriately to obtain the geometrical profile of the defect. The feasibility of the approach was evaluated using the data obtained from the MFL inspection of natural gas transmission pipelines. Results show the effectiveness of the approach.

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.

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

PubMed

Kant, Surya

2018-02-01

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

Unsupervised pattern recognition methods in ciders profiling based on GCE voltammetric signals.

PubMed

Jakubowska, Małgorzata; Sordoń, Wanda; Ciepiela, Filip

2016-07-15

This work presents a complete methodology of distinguishing between different brands of cider and ageing degrees, based on voltammetric signals, utilizing dedicated data preprocessing procedures and unsupervised multivariate analysis. It was demonstrated that voltammograms recorded on glassy carbon electrode in Britton-Robinson buffer at pH 2 are reproducible for each brand. By application of clustering algorithms and principal component analysis visible homogenous clusters were obtained. Advanced signal processing strategy which included automatic baseline correction, interval scaling and continuous wavelet transform with dedicated mother wavelet, was a key step in the correct recognition of the objects. The results show that voltammetry combined with optimized univariate and multivariate data processing is a sufficient tool to distinguish between ciders from various brands and to evaluate their freshness. Copyright © 2016 Elsevier Ltd. All rights reserved.

Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity

PubMed Central

van Hees, Vincent T.; Gorzelniak, Lukas; Dean León, Emmanuel Carlos; Eder, Martin; Pias, Marcelo; Taherian, Salman; Ekelund, Ulf; Renström, Frida; Franks, Paul W.; Horsch, Alexander; Brage, Søren

2013-01-01

Introduction Human body acceleration is often used as an indicator of daily physical activity in epidemiological research. Raw acceleration signals contain three basic components: movement, gravity, and noise. Separation of these becomes increasingly difficult during rotational movements. We aimed to evaluate five different methods (metrics) of processing acceleration signals on their ability to remove the gravitational component of acceleration during standardised mechanical movements and the implications for human daily physical activity assessment. Methods An industrial robot rotated accelerometers in the vertical plane. Radius, frequency, and angular range of motion were systematically varied. Three metrics (Euclidian norm minus one [ENMO], Euclidian norm of the high-pass filtered signals [HFEN], and HFEN plus Euclidean norm of low-pass filtered signals minus 1 g [HFEN+]) were derived for each experimental condition and compared against the reference acceleration (forward kinematics) of the robot arm. We then compared metrics derived from human acceleration signals from the wrist and hip in 97 adults (22–65 yr), and wrist in 63 women (20–35 yr) in whom daily activity-related energy expenditure (PAEE) was available. Results In the robot experiment, HFEN+ had lowest error during (vertical plane) rotations at an oscillating frequency higher than the filter cut-off frequency while for lower frequencies ENMO performed better. In the human experiments, metrics HFEN and ENMO on hip were most discrepant (within- and between-individual explained variance of 0.90 and 0.46, respectively). ENMO, HFEN and HFEN+ explained 34%, 30% and 36% of the variance in daily PAEE, respectively, compared to 26% for a metric which did not attempt to remove the gravitational component (metric EN). Conclusion In conclusion, none of the metrics as evaluated systematically outperformed all other metrics across a wide range of standardised kinematic conditions. However, choice of metric explains different degrees of variance in daily human physical activity. PMID:23626718

Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity.

PubMed

van Hees, Vincent T; Gorzelniak, Lukas; Dean León, Emmanuel Carlos; Eder, Martin; Pias, Marcelo; Taherian, Salman; Ekelund, Ulf; Renström, Frida; Franks, Paul W; Horsch, Alexander; Brage, Søren

2013-01-01

Human body acceleration is often used as an indicator of daily physical activity in epidemiological research. Raw acceleration signals contain three basic components: movement, gravity, and noise. Separation of these becomes increasingly difficult during rotational movements. We aimed to evaluate five different methods (metrics) of processing acceleration signals on their ability to remove the gravitational component of acceleration during standardised mechanical movements and the implications for human daily physical activity assessment. An industrial robot rotated accelerometers in the vertical plane. Radius, frequency, and angular range of motion were systematically varied. Three metrics (Euclidian norm minus one [ENMO], Euclidian norm of the high-pass filtered signals [HFEN], and HFEN plus Euclidean norm of low-pass filtered signals minus 1 g [HFEN+]) were derived for each experimental condition and compared against the reference acceleration (forward kinematics) of the robot arm. We then compared metrics derived from human acceleration signals from the wrist and hip in 97 adults (22-65 yr), and wrist in 63 women (20-35 yr) in whom daily activity-related energy expenditure (PAEE) was available. In the robot experiment, HFEN+ had lowest error during (vertical plane) rotations at an oscillating frequency higher than the filter cut-off frequency while for lower frequencies ENMO performed better. In the human experiments, metrics HFEN and ENMO on hip were most discrepant (within- and between-individual explained variance of 0.90 and 0.46, respectively). ENMO, HFEN and HFEN+ explained 34%, 30% and 36% of the variance in daily PAEE, respectively, compared to 26% for a metric which did not attempt to remove the gravitational component (metric EN). In conclusion, none of the metrics as evaluated systematically outperformed all other metrics across a wide range of standardised kinematic conditions. However, choice of metric explains different degrees of variance in daily human physical activity.

Time-resolved EPR study on the photochemical reactions of benzil

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

Mukai, Masahiro; Yamnauchi, Seigo; Hirota, Noboru

1992-04-16

TREPR and optical studies on the photochemical reactions of benzil in 2-propanol and benzene-TEA conclude that emissive signals are due to the reaction from T{sub n} produced via the S{sub n} pointing right T{sub n} intersystem crossing process. The free-pair radical-pair mechanism can account for the main features of the slow rise component of the chemically induced dynamic electron polarization signal of the ketyl radical in 2-propanol. 27 refs., 10 figs., 2 tabs.

Systematic mapping of two component response regulators to gene targets in a model sulfate reducing bacterium.

PubMed

Rajeev, Lara; Luning, Eric G; Dehal, Paramvir S; Price, Morgan N; Arkin, Adam P; Mukhopadhyay, Aindrila

2011-10-12

Two component regulatory systems are the primary form of signal transduction in bacteria. Although genomic binding sites have been determined for several eukaryotic and bacterial transcription factors, comprehensive identification of gene targets of two component response regulators remains challenging due to the lack of knowledge of the signals required for their activation. We focused our study on Desulfovibrio vulgaris Hildenborough, a sulfate reducing bacterium that encodes unusually diverse and largely uncharacterized two component signal transduction systems. We report the first systematic mapping of the genes regulated by all transcriptionally acting response regulators in a single bacterium. Our results enabled functional predictions for several response regulators and include key processes of carbon, nitrogen and energy metabolism, cell motility and biofilm formation, and responses to stresses such as nitrite, low potassium and phosphate starvation. Our study also led to the prediction of new genes and regulatory networks, which found corroboration in a compendium of transcriptome data available for D. vulgaris. For several regulators we predicted and experimentally verified the binding site motifs, most of which were discovered as part of this study. The gene targets identified for the response regulators allowed strong functional predictions to be made for the corresponding two component systems. By tracking the D. vulgaris regulators and their motifs outside the Desulfovibrio spp. we provide testable hypotheses regarding the functions of orthologous regulators in other organisms. The in vitro array based method optimized here is generally applicable for the study of such systems in all organisms.

GaAs MOEMS Technology

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

SPAHN, OLGA B.; GROSSETETE, GRANT D.; CICH, MICHAEL J.

2003-03-01

Many MEMS-based components require optical monitoring techniques using optoelectronic devices for converting mechanical position information into useful electronic signals. While the constituent piece-parts of such hybrid opto-MEMS components can be separately optimized, the resulting component performance, size, ruggedness and cost are substantially compromised due to assembly and packaging limitations. GaAs MOEMS offers the possibility of monolithically integrating high-performance optoelectronics with simple mechanical structures built in very low-stress epitaxial layers with a resulting component performance determined only by GaAs microfabrication technology limitations. GaAs MOEMS implicitly integrates the capability for radiation-hardened optical communications into the MEMS sensor or actuator component, a vitalmore » step towards rugged integrated autonomous microsystems that sense, act, and communicate. This project establishes a new foundational technology that monolithically combines GaAs optoelectronics with simple mechanics. Critical process issues addressed include selectivity, electrochemical characteristics, and anisotropy of the release chemistry, and post-release drying and coating processes. Several types of devices incorporating this novel technology are demonstrated.« less

Separating higher-order nonlinearities in transient absorption microscopy

NASA Astrophysics Data System (ADS)

Wilson, Jesse W.; Anderson, Miguel; Park, Jong Kang; Fischer, Martin C.; Warren, Warren S.

2015-08-01

The transient absorption response of melanin is a promising optically-accessible biomarker for distinguishing malignant melanoma from benign pigmented lesions, as demonstrated by earlier experiments on thin sections from biopsied tissue. The technique has also been demonstrated in vivo, but the higher optical intensity required for detecting these signals from backscattered light introduces higher-order nonlinearities in the transient response of melanin. These components that are higher than linear with respect to the pump or the probe introduce intensity-dependent changes to the overall response that complicate data analysis. However, our data also suggest these nonlinearities might be advantageous to in vivo imaging, in that different types of melanins have different nonlinear responses. Therefore, methods to separate linear from nonlinear components in transient absorption measurements might provide additional information to aid in the diagnosis of melanoma. We will discuss numerical methods for analyzing the various nonlinear contributions to pump-probe signals, with the ultimate objective of real time analysis using digital signal processing techniques. To that end, we have replaced the lock-in amplifier in our pump-probe microscope with a high-speed data acquisition board, and reprogrammed the coprocessor field-programmable gate array (FPGA) to perform lock-in detection. The FPGA lock-in offers better performance than the commercial instrument, in terms of both signal to noise ratio and speed. In addition, the flexibility of the digital signal processing approach enables demodulation of more complicated waveforms, such as spread-spectrum sequences, which has the potential to accelerate microscopy methods that rely on slow relaxation phenomena, such as photo-thermal and phosphorescence lifetime imaging.

Eddy Current Assessment of Engineered Components Containing Nanofibers

NASA Astrophysics Data System (ADS)

Ko, Ray T.; Hoppe, Wally; Pierce, Jenny

2009-03-01

The eddy current approach has been used to assess engineered components containing nanofibers. Five specimens with different programmed defects were fabricated. A 4-point collinear probe was used to verify the electrical resistivity of each specimen. The liftoff component of the eddy current signal was used to test two extreme cases with different nano contents. Additional eddy current measurements were also used in detecting a missing nano layer simulating a manufacturing process error. The results of this assessment suggest that eddy current liftoff measurement can be a useful tool in evaluating the electrical properties of materials containing nanofibers.

Human Milk Components Modulate Toll-Like Receptor-Mediated Inflammation.

PubMed

He, YingYing; Lawlor, Nathan T; Newburg, David S

2016-01-01

Toll-like receptor (TLR) signaling is central to innate immunity. Aberrant expression of TLRs is found in neonatal inflammatory diseases. Several bioactive components of human milk modulate TLR expression and signaling pathways, including soluble toll-like receptors (sTLRs), soluble cluster of differentiation (sCD) 14, glycoproteins, small peptides, and oligosaccharides. Some milk components, such as sialyl (α2,3) lactose and lacto-N-fucopentaose III, are reported to increase TLR signaling; under some circumstances this might contribute toward immunologic balance. Human milk on the whole is strongly anti-inflammatory, and contains abundant components that depress TLR signaling pathways: sTLR2 and sCD14 inhibit TLR2 signaling; sCD14, lactadherin, lactoferrin, and 2'-fucosyllactose attenuate TLR4 signaling; 3'-galactosyllactose inhibits TLR3 signaling, and β-defensin 2 inhibits TLR7 signaling. Feeding human milk to neonates decreases their risk of sepsis and necrotizing enterocolitis. Thus, the TLR regulatory components found in human milk hold promise as benign oral prophylactic and therapeutic treatments for the many gastrointestinal inflammatory disorders mediated by abnormal TLR signaling. © 2016 American Society for Nutrition.

Human Milk Components Modulate Toll-Like Receptor–Mediated Inflammation12

PubMed Central

He, YingYing; Lawlor, Nathan T

2016-01-01

Toll-like receptor (TLR) signaling is central to innate immunity. Aberrant expression of TLRs is found in neonatal inflammatory diseases. Several bioactive components of human milk modulate TLR expression and signaling pathways, including soluble toll-like receptors (sTLRs), soluble cluster of differentiation (sCD) 14, glycoproteins, small peptides, and oligosaccharides. Some milk components, such as sialyl (α2,3) lactose and lacto-N-fucopentaose III, are reported to increase TLR signaling; under some circumstances this might contribute toward immunologic balance. Human milk on the whole is strongly anti-inflammatory, and contains abundant components that depress TLR signaling pathways: sTLR2 and sCD14 inhibit TLR2 signaling; sCD14, lactadherin, lactoferrin, and 2′-fucosyllactose attenuate TLR4 signaling; 3′-galactosyllactose inhibits TLR3 signaling, and β-defensin 2 inhibits TLR7 signaling. Feeding human milk to neonates decreases their risk of sepsis and necrotizing enterocolitis. Thus, the TLR regulatory components found in human milk hold promise as benign oral prophylactic and therapeutic treatments for the many gastrointestinal inflammatory disorders mediated by abnormal TLR signaling. PMID:26773018

Detection of gear cracks in a complex gearbox of wind turbines using supervised bounded component analysis of vibration signals collected from multi-channel sensors

NASA Astrophysics Data System (ADS)

Li, Zhixiong; Yan, Xinping; Wang, Xuping; Peng, Zhongxiao

2016-06-01

In the complex gear transmission systems, in wind turbines a crack is one of the most common failure modes and can be fatal to the wind turbine power systems. A single sensor may suffer with issues relating to its installation position and direction, resulting in the collection of weak dynamic responses of the cracked gear. A multi-channel sensor system is hence applied in the signal acquisition and the blind source separation (BSS) technologies are employed to optimally process the information collected from multiple sensors. However, literature review finds that most of the BSS based fault detectors did not address the dependence/correlation between different moving components in the gear systems; particularly, the popular used independent component analysis (ICA) assumes mutual independence of different vibration sources. The fault detection performance may be significantly influenced by the dependence/correlation between vibration sources. In order to address this issue, this paper presents a new method based on the supervised order tracking bounded component analysis (SOTBCA) for gear crack detection in wind turbines. The bounded component analysis (BCA) is a state of art technology for dependent source separation and is applied limitedly to communication signals. To make it applicable for vibration analysis, in this work, the order tracking has been appropriately incorporated into the BCA framework to eliminate the noise and disturbance signal components. Then an autoregressive (AR) model built with prior knowledge about the crack fault is employed to supervise the reconstruction of the crack vibration source signature. The SOTBCA only outputs one source signal that has the closest distance with the AR model. Owing to the dependence tolerance ability of the BCA framework, interfering vibration sources that are dependent/correlated with the crack vibration source could be recognized by the SOTBCA, and hence, only useful fault information could be preserved in the reconstructed signal. The crack failure thus could be precisely identified by the cyclic spectral correlation analysis. A series of numerical simulations and experimental tests have been conducted to illustrate the advantages of the proposed SOTBCA method for fatigue crack detection. Comparisons to three representative techniques, i.e. Erdogan's BCA (E-BCA), joint approximate diagonalization of eigen-matrices (JADE), and FastICA, have demonstrated the effectiveness of the SOTBCA. Hence the proposed approach is suitable for accurate gear crack detection in practical applications.

Modeling the effects of Multi-path propagation and scintillation on GPS signals

NASA Astrophysics Data System (ADS)

Habash Krause, L.; Wilson, S. J.

2014-12-01

GPS signals traveling through the earth's ionosphere are affected by charged particles that often disrupt the signal and the information it carries due to "scintillation", which resembles an extra noise source on the signal. These signals are also affected by weather changes, tropospheric scattering, and absorption from objects due to multi-path propagation of the signal. These obstacles cause distortion within information and fading of the signal, which ultimately results in phase locking errors and noise in messages. In this work, we attempted to replicate the distortion that occurs in GPS signals using a signal processing simulation model. We wanted to be able to create and identify scintillated signals so we could better understand the environment that caused it to become scintillated. Then, under controlled conditions, we simulated the receiver's ability to suppress scintillation in a signal. We developed a code in MATLAB that was programmed to: 1. Create a carrier wave and then plant noise (four different frequencies) on the carrier wave, 2. Compute a Fourier transform on the four different frequencies to find the frequency content of a signal, 3. Use a filter and apply it to the Fourier transform of the four frequencies and then compute a Signal-to-noise ratio to evaluate the power (in Decibels) of the filtered signal, and 4.Plot each of these components into graphs. To test the code's validity, we used user input and data from an AM transmitter. We determined that the amplitude modulated signal or AM signal would be the best type of signal to test the accuracy of the MATLAB code due to its simplicity. This code is basic to give students the ability to change and use it to determine the environment and effects of noise on different AM signals and their carrier waves. Overall, we were able to manipulate a scenario of a noisy signal and interpret its behavior and change due to its noisy components: amplitude, frequency, and phase shift.

The bHLH transcription factor Hand is regulated by Alk in the Drosophila embryonic gut

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

Varshney, Gaurav K.; Palmer, Ruth H.

2006-12-29

During embryonic development the midgut visceral muscle is formed by fusion of cells within the visceral mesoderm, a process initiated by the specification of a specialised cell type, the founder cell, within this tissue. Activation of the receptor tyrosine kinase Anaplastic lymphoma kinase (Alk) in the developing visceral muscle of Drosophila melanogaster initiates a signal transduction pathway required for muscle fusion. In this paper, we have investigated downstream components which are regulated by this novel signalling pathway. Here we show that Alk-mediated signal transduction drives the expression of the bHLH transcription factor Hand in vivo. Loss of Alk function resultsmore » in a complete lack of Hand expression in this tissue, whereas Alk gain of function results in an expansion of Hand expression. Finally, we have investigated the process of muscle fusion in the gut of Hand mutant animals and can find no obvious defects in this process, suggesting that Hand is not critical for visceral muscle fusion per se.« less

Thunderstorm Hypothesis Reasoner

NASA Technical Reports Server (NTRS)

Mulvehill, Alice M.

1994-01-01

THOR is a knowledge-based system which incorporates techniques from signal processing, pattern recognition, and artificial intelligence (AI) in order to determine the boundary of small thunderstorms which develop and dissipate over the area encompassed by KSC and the Cape Canaveral Air Force Station. THOR interprets electric field mill data (derived from a network of electric field mills) by using heuristics and algorithms about thunderstorms that have been obtained from several domain specialists. THOR generates two forms of output: contour plots which visually describe the electric field activity over the network and a verbal interpretation of the activity. THOR uses signal processing and pattern recognition to detect signatures associated with noise or thunderstorm behavior in a near real time fashion from over 31 electrical field mills. THOR's AI component generates hypotheses identifying areas which are under a threat from storm activity, such as lightning. THOR runs on a VAX/VMS at the Kennedy Space Center. Its software is a coupling of C and FORTRAN programs, several signal processing packages, and an expert system development shell.

SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults

NASA Astrophysics Data System (ADS)

Golafshan, Reza; Yuce Sanliturk, Kenan

2016-03-01

Ball bearings remain one of the most crucial components in industrial machines and due to their critical role, it is of great importance to monitor their conditions under operation. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. This incapability in identifying the faults makes the de-noising process one of the most essential steps in the field of Condition Monitoring (CM) and fault detection. In the present study, Singular Value Decomposition (SVD) and Hankel matrix based de-noising process is successfully applied to the ball bearing time domain vibration signals as well as to their spectrums for the elimination of the background noise and the improvement the reliability of the fault detection process. The test cases conducted using experimental as well as the simulated vibration signals demonstrate the effectiveness of the proposed de-noising approach for the ball bearing fault detection.

Input-output characterization of an ultrasonic testing system by digital signal analysis

NASA Technical Reports Server (NTRS)

Karaguelle, H.; Lee, S. S.; Williams, J., Jr.

1984-01-01

The input/output characteristics of an ultrasonic testing system used for stress wave factor measurements were studied. The fundamentals of digital signal processing are summarized. The inputs and outputs are digitized and processed in a microcomputer using digital signal processing techniques. The entire ultrasonic test system, including transducers and all electronic components, is modeled as a discrete-time linear shift-invariant system. Then the impulse response and frequency response of the continuous time ultrasonic test system are estimated by interpolating the defining points in the unit sample response and frequency response of the discrete time system. It is found that the ultrasonic test system behaves as a linear phase bandpass filter. Good results were obtained for rectangular pulse inputs of various amplitudes and durations and for tone burst inputs whose center frequencies are within the passband of the test system and for single cycle inputs of various amplitudes. The input/output limits on the linearity of the system are determined.

Guided wave imaging of oblique reflecting interfaces in pipes using common-source synthetic focusing

NASA Astrophysics Data System (ADS)

Sun, Zeqing; Sun, Anyu; Ju, Bing-Feng

2018-04-01

Cross-mode-family mode conversion and secondary reflection of guided waves in pipes complicate the processing of guided waves signals, and can cause false detection. In this paper, filters operating in the spectral domain of wavenumber, circumferential order and frequency are designed to suppress the signal components of unwanted mode-family and unwanted traveling direction. Common-source synthetic focusing is used to reconstruct defect images from the guided wave signals. Simulations of the reflections from linear oblique defects and a semicircle defect are separately implemented. Defect images, which are reconstructed from the simulation results under different excitation conditions, are comparatively studied in terms of axial resolution, reflection amplitude, detectable oblique angle and so on. Further, the proposed method is experimentally validated by detecting linear cracks with various oblique angles (10-40°). The proposed method relies on the guided wave signals that are captured during 2-D scanning of a cylindrical area on the pipe. The redundancy of the signals is analyzed to reduce the time-consumption of the scanning process and to enhance the practicability of the proposed method.

A comprehensive map of the mTOR signaling network

PubMed Central

Caron, Etienne; Ghosh, Samik; Matsuoka, Yukiko; Ashton-Beaucage, Dariel; Therrien, Marc; Lemieux, Sébastien; Perreault, Claude; Roux, Philippe P; Kitano, Hiroaki

2010-01-01

The mammalian target of rapamycin (mTOR) is a central regulator of cell growth and proliferation. mTOR signaling is frequently dysregulated in oncogenic cells, and thus an attractive target for anticancer therapy. Using CellDesigner, a modeling support software for graphical notation, we present herein a comprehensive map of the mTOR signaling network, which includes 964 species connected by 777 reactions. The map complies with both the systems biology markup language (SBML) and graphical notation (SBGN) for computational analysis and graphical representation, respectively. As captured in the mTOR map, we review and discuss our current understanding of the mTOR signaling network and highlight the impact of mTOR feedback and crosstalk regulations on drug-based cancer therapy. This map is available on the Payao platform, a Web 2.0 based community-wide interactive process for creating more accurate and information-rich databases. Thus, this comprehensive map of the mTOR network will serve as a tool to facilitate systems-level study of up-to-date mTOR network components and signaling events toward the discovery of novel regulatory processes and therapeutic strategies for cancer. PMID:21179025

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

Bame, D.

To determine if seismic signals at frequencies up to 50 Hz are useful for detecting events and discriminating between earthquakes and explosions, approximately 180 events from the three-component high-frequency seismic element (HFSE) installed at the center of the Norwegian Regional Seismic Array (NRSA) have been analyzed. The attenuation of high-frequency signals in Scandinavia varies with distance, azimuth, magnitude, and source effects. Most of the events were detected with HFSE, although detections were better on the NRSA where signal processing techniques were used. Based on a preliminary analysis, high-frequency data do not appear to be a useful discriminant in Scandinavia. 21more » refs., 29 figs., 3 tabs.« less

Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-Art and Challenges

PubMed Central

Mahmud, Mufti; Vassanelli, Stefano

2016-01-01

In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data. PMID:27313507

The effect of filtering on the determination of lunar tides

NASA Astrophysics Data System (ADS)

Palumbo, A.; Mazzarella, A.

1980-01-01

The determination of lunar tides obtained by combination of a filtering process and the fixed lunar age technique is proposed. It is shown that such a method allows a reduction of the signal-to-noise ratio without altering the amplitude and the phase angle of the signal. It consequently allows the significant determination of the lunar semidiurnal component M2 from the series of data shorter than those required by other methods and the deduction of other interesting lunisolar components which have not previously been significantly determined in surface pressure and temperature data. The analysis of the data for Gan, Vesuvian Observatory and the Eiffel Tower have provided new determinations of L2(p) and have allowed comparison between the results obtained by the present and other methods.

Radiation effects in reconfigurable FPGAs

NASA Astrophysics Data System (ADS)

Quinn, Heather

2017-04-01

Field-programmable gate arrays (FPGAs) are co-processing hardware used in image and signal processing. FPGA are programmed with custom implementations of an algorithm. These algorithms are highly parallel hardware designs that are faster than software implementations. This flexibility and speed has made FPGAs attractive for many space programs that need in situ, high-speed signal processing for data categorization and data compression. Most commercial FPGAs are affected by the space radiation environment, though. Problems with TID has restricted the use of flash-based FPGAs. Static random access memory based FPGAs must be mitigated to suppress errors from single-event upsets. This paper provides a review of radiation effects issues in reconfigurable FPGAs and discusses methods for mitigating these problems. With careful design it is possible to use these components effectively and resiliently.

Wideband acoustic records of explosive volcanic eruptions at Stromboli: New insights on the explosive process and the acoustic source

NASA Astrophysics Data System (ADS)

Goto, A.; Ripepe, M.; Lacanna, G.

2014-06-01

Wideband acoustic waves, both inaudible infrasound (<20 Hz) and audible component (>20 Hz), generated by strombolian eruptions were recorded at 5 kHz and correlated with video images. The high sample rate revealed that in addition to the known initial infrasound, the acoustic signal includes an energetic high-frequency (typically >100 Hz) coda. This audible signal starts before the positive infrasound onset goes negative. We suggest that the infrasonic onset is due to magma doming at the free surface, whereas the immediate high-frequency signal reflects the following explosive discharge flow. During strong gas-rich eruptions, positively skewed shockwave-like components with sharp compression and gradual depression appeared. We suggest that successive bursting of overpressurized small bubbles and the resultant volcanic jets sustain the highly gas-rich explosions and emit the audible sound. When the jet is supersonic, microexplosions of ambient air entrained in the hot jet emit the skewed waveforms.

On using the Multiple Signal Classification algorithm to study microbaroms

NASA Astrophysics Data System (ADS)

Marcillo, O. E.; Blom, P. S.; Euler, G. G.

2016-12-01

Multiple Signal Classification (MUSIC) (Schmidt, 1986) is a well-known high-resolution algorithm used in array processing for parameter estimation. We report on the application of MUSIC to infrasonic array data in a study of the structure of microbaroms. Microbaroms can be globally observed and display energy centered around 0.2 Hz. Microbaroms are an infrasonic signal generated by the non-linear interaction of ocean surface waves that radiate into the ocean and atmosphere as well as the solid earth in the form of microseisms. Microbaroms sources are dynamic and, in many cases, distributed in space and moving in time. We assume that the microbarom energy detected by an infrasonic array is the result of multiple sources (with different back-azimuths) in the same bandwidth and apply the MUSIC algorithm accordingly to recover the back-azimuth and trace velocity of the individual components. Preliminary results show that the multiple component assumption in MUSIC allows one to resolve the fine structure in the microbarom band that can be related to multiple ocean surface phenomena.

P300 brain computer interface: current challenges and emerging trends

PubMed Central

Fazel-Rezai, Reza; Allison, Brendan Z.; Guger, Christoph; Sellers, Eric W.; Kleih, Sonja C.; Kübler, Andrea

2012-01-01

A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility. PMID:22822397

A Host-Produced Autoinducer-2 Mimic Activates Bacterial Quorum Sensing.

PubMed

Ismail, Anisa S; Valastyan, Julie S; Bassler, Bonnie L

2016-04-13

Host-microbial symbioses are vital to health; nonetheless, little is known about the role crosskingdom signaling plays in these relationships. In a process called quorum sensing, bacteria communicate with one another using extracellular signal molecules called autoinducers. One autoinducer, AI-2, is proposed to promote interspecies bacterial communication, including in the mammalian gut. We show that mammalian epithelia produce an AI-2 mimic activity in response to bacteria or tight-junction disruption. This AI-2 mimic is detected by the bacterial AI-2 receptor, LuxP/LsrB, and can activate quorum-sensing-controlled gene expression, including in the enteric pathogen Salmonella typhimurium. AI-2 mimic activity is induced when epithelia are directly or indirectly exposed to bacteria, suggesting that a secreted bacterial component(s) stimulates its production. Mutagenesis revealed genes required for bacteria to both detect and stimulate production of the AI-2 mimic. These findings uncover a potential role for the mammalian AI-2 mimic in fostering crosskingdom signaling and host-bacterial symbioses. Copyright © 2016 Elsevier Inc. All rights reserved.

The Researches on Damage Detection Method for Truss Structures

NASA Astrophysics Data System (ADS)

Wang, Meng Hong; Cao, Xiao Nan

2018-06-01

This paper presents an effective method to detect damage in truss structures. Numerical simulation and experimental analysis were carried out on a damaged truss structure under instantaneous excitation. The ideal excitation point and appropriate hammering method were determined to extract time domain signals under two working conditions. The frequency response function and principal component analysis were used for data processing, and the angle between the frequency response function vectors was selected as a damage index to ascertain the location of a damaged bar in the truss structure. In the numerical simulation, the time domain signal of all nodes was extracted to determine the location of the damaged bar. In the experimental analysis, the time domain signal of a portion of the nodes was extracted on the basis of an optimal sensor placement method based on the node strain energy coefficient. The results of the numerical simulation and experimental analysis showed that the damage detection method based on the frequency response function and principal component analysis could locate the damaged bar accurately.

Development of a frequency-modulated ultrasonic sensor inspired by bat echolocation

NASA Astrophysics Data System (ADS)

Kepa, Krzysztof; Abaid, Nicole

2015-03-01

Bats have evolved to sense using ultrasonic signals with a variety of different frequency signatures which interact with their environment. Among these signals, those with time-varying frequencies may enable the animals to gather more complex information for obstacle avoidance and target tracking. Taking inspiration from this system, we present the development of a sonar sensor capable of generating frequency-modulated ultrasonic signals. The device is based on a miniature mobile computer, with on board data capture and processing capabilities, which is designed for eventual autonomous operation in a robotic swarm. The hardware and software components of the sensor are detailed, as well their integration. Preliminary results for target detection using both frequency-modulated and constant frequency signals are discussed.

Elimination of autofluorescence background from fluorescence tissue images by use of time-gated detection and the AzaDiOxaTriAngulenium (ADOTA) fluorophore

PubMed Central

Rich, Ryan M.; Stankowska, Dorota L.; Maliwal, Badri P.; Sørensen, Thomas Just; Laursen, Bo W.; Krishnamoorthy, Raghu R.; Gryczynski, Zygmunt; Borejdo, Julian

2013-01-01

Sample autofluorescence (fluorescence of inherent components of tissue and fixative-induced fluorescence) is a significant problem in direct imaging of molecular processes in biological samples. A large variety of naturally occurring fluorescent components in tissue results in broad emission that overlaps the emission of typical fluorescent dyes used for tissue labeling. In addition, autofluorescence is characterized by complex fluorescence intensity decay composed of multiple components whose lifetimes range from sub-nanoseconds to a few nanoseconds. For these reasons, the real fluorescence signal of the probe is difficult to separate from the unwanted autofluorescence. Here we present a method for reducing the autofluorescence problem by utilizing an azadioxatriangulenium (ADOTA) dye with a fluorescence lifetime of approximately 15 ns, much longer than those of most of the components of autofluorescence. A probe with such a long lifetime enables us to use time-gated intensity imaging to separate the signal of the targeting dye from the autofluorescence. We have shown experimentally that by discarding photons detected within the first 20 ns of the excitation pulse, the signal-to-background ratio is improved fivefold. This time-gating eliminates over 96 % of autofluorescence. Analysis using a variable time-gate may enable quantitative determination of the bound probe without the contributions from the background. PMID:23254457

Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex.

PubMed

Kirilina, Evgeniya; Yu, Na; Jelzow, Alexander; Wabnitz, Heidrun; Jacobs, Arthur M; Tachtsidis, Ilias

2013-01-01

Functional Near-Infrared Spectroscopy (fNIRS) is a promising method to study functional organization of the prefrontal cortex. However, in order to realize the high potential of fNIRS, effective discrimination between physiological noise originating from forehead skin haemodynamic and cerebral signals is required. Main sources of physiological noise are global and local blood flow regulation processes on multiple time scales. The goal of the present study was to identify the main physiological noise contributions in fNIRS forehead signals and to develop a method for physiological de-noising of fNIRS data. To achieve this goal we combined concurrent time-domain fNIRS and peripheral physiology recordings with wavelet coherence analysis (WCA). Depth selectivity was achieved by analyzing moments of photon time-of-flight distributions provided by time-domain fNIRS. Simultaneously, mean arterial blood pressure (MAP), heart rate (HR), and skin blood flow (SBF) on the forehead were recorded. WCA was employed to quantify the impact of physiological processes on fNIRS signals separately for different time scales. We identified three main processes contributing to physiological noise in fNIRS signals on the forehead. The first process with the period of about 3 s is induced by respiration. The second process is highly correlated with time lagged MAP and HR fluctuations with a period of about 10 s often referred as Mayer waves. The third process is local regulation of the facial SBF time locked to the task-evoked fNIRS signals. All processes affect oxygenated haemoglobin concentration more strongly than that of deoxygenated haemoglobin. Based on these results we developed a set of physiological regressors, which were used for physiological de-noising of fNIRS signals. Our results demonstrate that proposed de-noising method can significantly improve the sensitivity of fNIRS to cerebral signals.

Nonlinear transient chirp signal modeling of the aortic and pulmonary components of the second heart sound.

PubMed

Xu, J; Durand, L G; Pibarot, P

2000-10-01

This paper describes a new approach based on the time-frequency representation of transient nonlinear chirp signals for modeling the aortic (A2) and the pulmonary (P2) components of the second heart sound (S2). It is demonstrated that each component is a narrow-band signal with decreasing instantaneous frequency defined by its instantaneous amplitude and its instantaneous phase. Each component is also a polynomial phase signal, the instantaneous phase of which can be accurately represented by a polynomial having an order of thirty. A dechirping approach is used to obtain the instantaneous amplitude of each component while reducing the effect of the background noise. The analysis-synthesis procedure is applied to 32 isolated A2 and 32 isolated P2 components recorded in four pigs with pulmonary hypertension. The mean +/- standard deviation of the normalized root-mean-squared error (NRMSE) and the correlation coefficient (rho) between the original and the synthesized signal components were: NRMSE = 2.1 +/- 0.3% and rho = 0.97 +/- 0.02 for A2 and NRMSE = 2.52 +/- 0.5% and rho = 0.96 +/- 0.02 for P2. These results confirm that each component can be modeled as mono-component nonlinear chirp signals of short duration with energy distributions concentrated along its decreasing instantaneous frequency.

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

NASA Astrophysics Data System (ADS)

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

2016-07-01

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

Comparison of DP3 Signals Evoked by Comfortable 3D Images and 2D Images — an Event-Related Potential Study using an Oddball Task

NASA Astrophysics Data System (ADS)

Ye, Peng; Wu, Xiang; Gao, Dingguo; Liang, Haowen; Wang, Jiahui; Deng, Shaozhi; Xu, Ningsheng; She, Juncong; Chen, Jun

2017-02-01

The horizontal binocular disparity is a critical factor for the visual fatigue induced by watching stereoscopic TVs. Stereoscopic images that possess the disparity within the ‘comfort zones’ and remain still in the depth direction are considered comfortable to the viewers as 2D images. However, the difference in brain activities between processing such comfortable stereoscopic images and 2D images is still less studied. The DP3 (differential P3) signal refers to an event-related potential (ERP) component indicating attentional processes, which is typically evoked by odd target stimuli among standard stimuli in an oddball task. The present study found that the DP3 signal elicited by the comfortable 3D images exhibits the delayed peak latency and enhanced peak amplitude over the anterior and central scalp regions compared to the 2D images. The finding suggests that compared to the processing of the 2D images, more attentional resources are involved in the processing of the stereoscopic images even though they are subjectively comfortable.

3-D readout-electronics packaging for high-bandwidth massively paralleled imager

DOEpatents

Kwiatkowski, Kris; Lyke, James

2007-12-18

Dense, massively parallel signal processing electronics are co-packaged behind associated sensor pixels. Microchips containing a linear or bilinear arrangement of photo-sensors, together with associated complex electronics, are integrated into a simple 3-D structure (a "mirror cube"). An array of photo-sensitive cells are disposed on a stacked CMOS chip's surface at a 45.degree. angle from light reflecting mirror surfaces formed on a neighboring CMOS chip surface. Image processing electronics are held within the stacked CMOS chip layers. Electrical connections couple each of said stacked CMOS chip layers and a distribution grid, the connections for distributing power and signals to components associated with each stacked CSMO chip layer.

Role of Silicon on Plant–Pathogen Interactions

PubMed Central

Wang, Min; Gao, Limin; Dong, Suyue; Sun, Yuming; Shen, Qirong; Guo, Shiwei

2017-01-01

Although silicon (Si) is not recognized as an essential element for general higher plants, it has beneficial effects on the growth and production of a wide range of plant species. Si is known to effectively mitigate various environmental stresses and enhance plant resistance against both fungal and bacterial pathogens. In this review, the effects of Si on plant–pathogen interactions are analyzed, mainly on physical, biochemical, and molecular aspects. In most cases, the Si-induced biochemical/molecular resistance during plant–pathogen interactions were dominated as joint resistance, involving activating defense-related enzymes activates, stimulating antimicrobial compound production, regulating the complex network of signal pathways, and activating of the expression of defense-related genes. The most previous studies described an independent process, however, the whole plant resistances were rarely considered, especially the interaction of different process in higher plants. Si can act as a modulator influencing plant defense responses and interacting with key components of plant stress signaling systems leading to induced resistance. Priming of plant defense responses, alterations in phytohormone homeostasis, and networking by defense signaling components are all potential mechanisms involved in Si-triggered resistance responses. This review summarizes the roles of Si in plant–microbe interactions, evaluates the potential for improving plant resistance by modifying Si fertilizer inputs, and highlights future research concerning the role of Si in agriculture. PMID:28529517

Pattern recognition in volcano seismology - Reducing spectral dimensionality

NASA Astrophysics Data System (ADS)

Unglert, K.; Radic, V.; Jellinek, M.

2015-12-01

Variations in the spectral content of volcano seismicity can relate to changes in volcanic activity. Low-frequency seismic signals often precede or accompany volcanic eruptions. However, they are commonly manually identified in spectra or spectrograms, and their definition in spectral space differs from one volcanic setting to the next. Increasingly long time series of monitoring data at volcano observatories require automated tools to facilitate rapid processing and aid with pattern identification related to impending eruptions. Furthermore, knowledge transfer between volcanic settings is difficult if the methods to identify and analyze the characteristics of seismic signals differ. To address these challenges we evaluate whether a machine learning technique called Self-Organizing Maps (SOMs) can be used to characterize the dominant spectral components of volcano seismicity without the need for any a priori knowledge of different signal classes. This could reduce the dimensions of the spectral space typically analyzed by orders of magnitude, and enable rapid processing and visualization. Preliminary results suggest that the temporal evolution of volcano seismicity at Kilauea Volcano, Hawai`i, can be reduced to as few as 2 spectral components by using a combination of SOMs and cluster analysis. We will further refine our methodology with several datasets from Hawai`i and Alaska, among others, and compare it to other techniques.

Development of a Technique for Separating Raman Scattering Signals from Background Emission with Single-Shot Measurement Potential

NASA Technical Reports Server (NTRS)

Hartfield, Roy J., Jr.; Dobson, Chris; Eskridge, Richard; Wehrmeyer, Joseph A.

1997-01-01

A novel technique for extracting Q-branch Raman signals scattered by a diatomic species from the emission spectrum resulting from the irradiation of combustion products using a broadband excimer laser has been developed. This technique is based on the polarization characteristics of vibrational Raman scattering and can be used for both single-shot Raman extraction and time-averaged data collection. The Q-branch Raman signal has a unique set of polarization characteristics which depend on the direction of the scattering while fluorescence signals are unpolarized. For the present work, a calcite crystal is used to separate the horizonal component of a collected signal from the vertical component. The two components are then sent through a UV spectrometer and imaged onto an intensified CCD camera separately. The vertical component contains both the Raman signal and the interfering fluorescence signal. The horizontal component contains the fluorescence signal and a very weak component of the Raman signal; hence, the Raman scatter can be extracted by taking the difference between the two signals. The separation of the Raman scatter from interfering fluorescence signals is critically important to the interpretation of the Raman for cases in which a broadband ultraviolet (UV) laser is used as an excitation source in a hydrogen-oxygen flame and in all hydrocarbon flames. The present work provides a demonstration of the separation of the Raman scatter from the fluorescence background in real time.

Physiological correlates of comodulation masking release in the mammalian ventral cochlear nucleus.

PubMed

Pressnitzer, D; Meddis, R; Delahaye, R; Winter, I M

2001-08-15

Comodulation masking release (CMR) enhances the detection of signals embedded in wideband, amplitude-modulated maskers. At least part of the CMR is attributable to across-frequency processing, however, the relative contribution of different stages in the auditory system to across-frequency processing is unknown. We have measured the responses of single units from one of the earliest stages in the ascending auditory pathway, the ventral cochlear nucleus, where across frequency processing may take place. A sinusoidally amplitude-modulated tone at the best frequency of each unit was used as a masker. A pure tone signal was added in the dips of the masker modulation (reference condition). Flanking components (FCs) were then added at frequencies remote from the unit best frequency. The FCs were pure tones amplitude modulated either in phase (comodulated) or out of phase (codeviant) with the on-frequency component. Psychophysically, this CMR paradigm reduces within-channel cues while producing an advantage of approximately 10 dB for the comodulated condition in comparison with the reference condition. Some of the recorded units showed responses consistent with perceptual CMR. The addition of the comodulated FCs produced a strong reduction in the response to the masker modulation, making the signal more salient in the poststimulus time histograms. A decision statistic based on d' showed that threshold was reached at lower signal levels for the comodulated condition than for reference or codeviant conditions. The neurons that exhibited such a behavior were mainly transient chopper or primary-like units. The results obtained from a subpopulation of transient chopper units are consistent with a possible circuit in the cochlear nucleus consisting of a wideband inhibitor contacting a narrowband cell. A computational model was used to confirm the feasibility of such a circuit.

Use of NMR-Based Metabolomics To Chemically Characterize the Roasting Process of Chicory Root.

PubMed

Wei, Feifei; Furihata, Kazuo; Zhang, Mimin; Miyakawa, Takuya; Tanokura, Masaru

2016-08-16

Roasted chicory root (Cichorium intybus) has been widely accepted as the most important coffee substitute. In this study, a nuclear magnetic resonance (NMR)-based comprehensive analysis was performed to monitor the substantial changes in the composition of chicory root during the roasting process. A detailed signal assignment of dried raw and roasted chicory roots was carried out using 1 H, 13 C, 1 H- 1 H DQF-COSY, 1 H- 13 C edited-HSQC, 1 H- 13 C CT-HMBC, and 1 H- 13 C HSQC-TOCSY NMR spectra. On the basis of the signal assignments, 36 NMR-visible components were monitored simultaneously during roasting. Inulins, sucrose, and most of the amino acids were largely degraded during the roasting process, whereas monosaccharides decreased at the beginning and then increased until the dark roasting stage. Acetamide, 5-hydroxymethylfurfural, di-d-fructose dianhydride, and norfuraneol were newly formed during roasting. Furthermore, a principal component analysis score plot indicated that similar chemical composition profiles could be achieved by roasting the chicory root either at a higher firepower for a shorter time or at a lower firepower for a longer time.

The high frequency characteristics of laser reflection and visible light during solid state disk laser welding

NASA Astrophysics Data System (ADS)

Gao, Xiangdong; You, Deyong; Katayama, Seiji

2015-07-01

Optical properties are related to weld quality during laser welding. Visible light radiation generated from optical-induced plasma and laser reflection is considered a key element reflecting weld quality. An in-depth analysis of the high-frequency component of optical signals is conducted. A combination of a photoelectric sensor and an optical filter helped to obtain visible light reflection and laser reflection in the welding process. Two groups of optical signals were sampled at a high sampling rate (250 kHz) using an oscilloscope. Frequencies in the ranges 1-10 kHz and 10-125 kHz were investigated respectively. Experimental results showed that there was an obvious correlation between the high-frequency signal and the laser power, while the high-frequency signal was not sensitive to changes in welding speed. In particular, when the defocus position was changed, only a high frequency of the visible light signal was observed, while the high frequency of the laser reflection signal remained unchanged. The basic correlation between optical features and welding status during the laser welding process is specified, which helps to provide a new research focus for investigating the stability of welding status.

Multivariate Analysis of Ladle Vibration

NASA Astrophysics Data System (ADS)

Yenus, Jaefer; Brooks, Geoffrey; Dunn, Michelle

2016-08-01

The homogeneity of composition and uniformity of temperature of the steel melt before it is transferred to the tundish are crucial in making high-quality steel product. The homogenization process is performed by stirring the melt using inert gas in ladles. Continuous monitoring of this process is important to make sure the action of stirring is constant throughout the ladle. Currently, the stirring process is monitored by process operators who largely rely on visual and acoustic phenomena from the ladle. However, due to lack of measurable signals, the accuracy and suitability of this manual monitoring are problematic. The actual flow of argon gas to the ladle may not be same as the flow gage reading due to leakage along the gas line components. As a result, the actual degree of stirring may not be correctly known. Various researchers have used one-dimensional vibration, and sound and image signals measured from the ladle to predict the degree of stirring inside. They developed online sensors which are indeed to monitor the online stirring phenomena. In this investigation, triaxial vibration signals have been measured from a cold water model which is a model of an industrial ladle. Three flow rate ranges and varying bath heights were used to collect vibration signals. The Fast Fourier Transform was applied to the dataset before it has been analyzed using principal component analysis (PCA) and partial least squares (PLS). PCA was used to unveil the structure in the experimental data. PLS was mainly applied to predict the stirring from the vibration response. It was found that for each flow rate range considered in this study, the informative signals reside in different frequency ranges. The first latent variables in these frequency ranges explain more than 95 pct of the variation in the stirring process for the entire single layer and the double layer data collected from the cold model. PLS analysis in these identified frequency ranges demonstrated that the latent variables of the response and predictor variables are highly correlated. The predicted variable has shown linear relationship with the stirring energy and bath recirculation speed. This outcome can improve the predictability of the mixing status in ladle metallurgy and make the online control of the process easier. Industrial testing of this input will follow.

Automatic removal of eye-movement and blink artifacts from EEG signals.

PubMed

Gao, Jun Feng; Yang, Yong; Lin, Pan; Wang, Pei; Zheng, Chong Xun

2010-03-01

Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.

Non-stationary component extraction in noisy multicomponent signal using polynomial chirping Fourier transform.

PubMed

Lu, Wenlong; Xie, Junwei; Wang, Heming; Sheng, Chuan

2016-01-01

Inspired by track-before-detection technology in radar, a novel time-frequency transform, namely polynomial chirping Fourier transform (PCFT), is exploited to extract components from noisy multicomponent signal. The PCFT combines advantages of Fourier transform and polynomial chirplet transform to accumulate component energy along a polynomial chirping curve in the time-frequency plane. The particle swarm optimization algorithm is employed to search optimal polynomial parameters with which the PCFT will achieve a most concentrated energy ridge in the time-frequency plane for the target component. The component can be well separated in the polynomial chirping Fourier domain with a narrow-band filter and then reconstructed by inverse PCFT. Furthermore, an iterative procedure, involving parameter estimation, PCFT, filtering and recovery, is introduced to extract components from a noisy multicomponent signal successively. The Simulations and experiments show that the proposed method has better performance in component extraction from noisy multicomponent signal as well as provides more time-frequency details about the analyzed signal than conventional methods.

Discovery of a novel site of opioid action at the innate immune pattern-recognition receptor TLR4 and its role in addiction.

PubMed

Jacobsen, Jonathan Henry W; Watkins, Linda R; Hutchinson, Mark R

2014-01-01

Opioids have historically, and continue to be, an integral component of pain management. However, despite pharmacokinetic and dynamic optimization over the past 100 years, opioids continue to produce many undesirable side effects such as tolerance, reward, and dependence. As such, opioids are liable for addiction. Traditionally, opioid addiction was viewed as a solely neuronal process, and while substantial headway has been made into understanding the molecular and cellular mechanisms mediating this process, research has however, been relatively ambivalent to how the rest of the central nervous system (CNS) responds to opioids. Evidence over the past 20 years has clearly demonstrated the importance of the immunocompetent cells of the CNS (glia) in many aspects of opioid pharmacology. Particular focus has been placed on microglia and astrocytes, who in response to opioids, become activated and release inflammatory mediators. Importantly, the mechanism underlying immune activation is beginning to be elucidated. Evidence suggests an innate immune pattern-recognition receptor (toll-like receptor 4) as an integral component underlying opioid-induced glial activation. The subsequent proinflammatory response may be viewed akin to neurotransmission creating a process termed central immune signaling. Translationally, we are beginning to appreciate the importance of central immune signaling as it contributes to many behavioral actions of addiction including reward, withdrawal, and craving. As such, the aim of this chapter is to review and integrate the neuronal and central immune signaling perspective of addiction. © 2014 Elsevier Inc. All rights reserved.

Aeroelastic Flight Data Analysis with the Hilbert-Huang Algorithm

NASA Technical Reports Server (NTRS)

Brenner, Martin J.; Prazenica, Chad

2006-01-01

This report investigates the utility of the Hilbert Huang transform for the analysis of aeroelastic flight data. It is well known that the classical Hilbert transform can be used for time-frequency analysis of functions or signals. Unfortunately, the Hilbert transform can only be effectively applied to an extremely small class of signals, namely those that are characterized by a single frequency component at any instant in time. The recently-developed Hilbert Huang algorithm addresses the limitations of the classical Hilbert transform through a process known as empirical mode decomposition. Using this approach, the data is filtered into a series of intrinsic mode functions, each of which admits a well-behaved Hilbert transform. In this manner, the Hilbert Huang algorithm affords time-frequency analysis of a large class of signals. This powerful tool has been applied in the analysis of scientific data, structural system identification, mechanical system fault detection, and even image processing. The purpose of this report is to demonstrate the potential applications of the Hilbert Huang algorithm for the analysis of aeroelastic systems, with improvements such as localized online processing. Applications for correlations between system input and output, and amongst output sensors, are discussed to characterize the time-varying amplitude and frequency correlations present in the various components of multiple data channels. Online stability analyses and modal identification are also presented. Examples are given using aeroelastic test data from the F-18 Active Aeroelastic Wing airplane, an Aerostructures Test Wing, and pitch plunge simulation.

Aeroelastic Flight Data Analysis with the Hilbert-Huang Algorithm

NASA Technical Reports Server (NTRS)

Brenner, Marty; Prazenica, Chad

2005-01-01

This paper investigates the utility of the Hilbert-Huang transform for the analysis of aeroelastic flight data. It is well known that the classical Hilbert transform can be used for time-frequency analysis of functions or signals. Unfortunately, the Hilbert transform can only be effectively applied to an extremely small class of signals, namely those that are characterized by a single frequency component at any instant in time. The recently-developed Hilbert-Huang algorithm addresses the limitations of the classical Hilbert transform through a process known as empirical mode decomposition. Using this approach, the data is filtered into a series of intrinsic mode functions, each of which admits a well-behaved Hilbert transform. In this manner, the Hilbert-Huang algorithm affords time-frequency analysis of a large class of signals. This powerful tool has been applied in the analysis of scientific data, structural system identification, mechanical system fault detection, and even image processing. The purpose of this paper is to demonstrate the potential applications of the Hilbert-Huang algorithm for the analysis of aeroelastic systems, with improvements such as localized/online processing. Applications for correlations between system input and output, and amongst output sensors, are discussed to characterize the time-varying amplitude and frequency correlations present in the various components of multiple data channels. Online stability analyses and modal identification are also presented. Examples are given using aeroelastic test data from the F/A-18 Active Aeroelastic Wing aircraft, an Aerostructures Test Wing, and pitch-plunge simulation.

Automatic Identification of Artifact-Related Independent Components for Artifact Removal in EEG Recordings.

PubMed

Zou, Yuan; Nathan, Viswam; Jafari, Roozbeh

2016-01-01

Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from independent component analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event-related potential (ERP)-related independent components. However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g., identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by nonbiological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature-based clustering algorithm used to identify artifacts which have physiological origins; and 2) the electrode-scalp impedance information employed for identifying nonbiological artifacts. The results on EEG data collected from ten subjects show that our algorithm can effectively detect, separate, and remove both physiological and nonbiological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods.

Automatic Identification of Artifact-related Independent Components for Artifact Removal in EEG Recordings

PubMed Central

Zou, Yuan; Nathan, Viswam; Jafari, Roozbeh

2017-01-01

Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from Independent Component Analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event related potential (ERP)-related independent components (ICs). However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g. identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by non-biological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature based clustering algorithm used to identify artifacts which have physiological origins and 2) the electrode-scalp impedance information employed for identifying non-biological artifacts. The results on EEG data collected from 10 subjects show that our algorithm can effectively detect, separate, and remove both physiological and non-biological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods. PMID:25415992

Signal detection in animal psychoacoustics: Analysis and simulation of sensory and decision-related influences

PubMed Central

Alves-Pinto, A.; Sollini, J.; Sumner, C.J.

2012-01-01

Signal detection theory (SDT) provides a framework for interpreting psychophysical experiments, separating the putative internal sensory representation and the decision process. SDT was used to analyse ferret behavioural responses in a (yes–no) tone-in-noise detection task. Instead of measuring the receiver-operating characteristic (ROC), we tested SDT by comparing responses collected using two common psychophysical data collection methods. These (Constant Stimuli, Limits) differ in the set of signal levels presented within and across behavioural sessions. The results support the use of SDT as a method of analysis: SDT sensory component was unchanged between the two methods, even though decisions depended on the stimuli presented within a behavioural session. Decision criterion varied trial-by-trial: a ‘yes’ response was more likely after a correct rejection trial than a hit trial. Simulation using an SDT model with several decision components reproduced the experimental observations accurately, leaving only ∼10% of the variance unaccounted for. The model also showed that trial-by-trial dependencies were unlikely to influence measured psychometric functions or thresholds. An additional model component suggested that inattention did not contribute substantially. Further analysis showed that ferrets were changing their decision criteria, almost optimally, to maximise the reward obtained in a session. The data suggest trial-by-trial reward-driven optimization of the decision process. Understanding the factors determining behavioural responses is important for correlating neural activity and behaviour. SDT provides a good account of animal psychoacoustics, and can be validated using standard psychophysical methods and computer simulations, without recourse to ROC measurements. PMID:22698686

Variability and change of sea level and its components in the Indo-Pacific region during the altimetry era

NASA Astrophysics Data System (ADS)

Wu, Quran; Zhang, Xuebin; Church, John A.; Hu, Jianyu

2017-03-01

Previous studies have shown that regional sea level exhibits interannual and decadal variations associated with the modes of climate variability. A better understanding of those low-frequency sea level variations benefits the detection and attribution of climate change signals. Nonetheless, the contributions of thermosteric, halosteric, and mass sea level components to sea level variability and trend patterns remain unclear. By focusing on signals associated with dominant climate modes in the Indo-Pacific region, we estimate the interannual and decadal fingerprints and trend of each sea level component utilizing a multivariate linear regression of two adjoint-based ocean reanalyses. Sea level interannual, decadal, and trend patterns primarily come from thermosteric sea level (TSSL). Halosteric sea level (HSSL) is of regional importance in the Pacific Ocean on decadal time scale and dominates sea level trends in the northeast subtropical Pacific. The compensation between TSSL and HSSL is identified in their decadal variability and trends. The interannual and decadal variability of temperature generally peak at subsurface around 100 m but that of salinity tend to be surface-intensified. Decadal temperature and salinity signals extend deeper into the ocean in some regions than their interannual equivalents. Mass sea level (MassSL) is critical for the interannual and decadal variability of sea level over shelf seas. Inconsistencies exist in MassSL trend patterns among various estimates. This study highlights regions where multiple processes work together to control sea level variability and change. Further work is required to better understand the interaction of different processes in those regions.

How Genetics Has Helped Piece Together the MAPK Signaling Pathway.

PubMed

Ashton-Beaucage, Dariel; Therrien, Marc

2017-01-01

Cells respond to changes in their environment, to developmental cues, and to pathogen aggression through the action of a complex network of proteins. These networks can be decomposed into a multitude of signaling pathways that relay signals from the microenvironment to the cellular components involved in eliciting a specific response. Perturbations in these signaling processes are at the root of multiple pathologies, the most notable of these being cancer. The study of receptor tyrosine kinase (RTK) signaling led to the first description of a mechanism whereby an extracellular signal is transmitted to the nucleus to induce a transcriptional response. Genetic studies conducted in drosophila and nematodes have provided key elements to this puzzle. Here, we briefly discuss the somewhat lesser known contribution of these multicellular organisms to our understanding of what has come to be known as the prototype of signaling pathways. We also discuss the ostensibly much larger network of regulators that has emerged from recent functional genomic investigations of RTK/RAS/ERK signaling.

EMD self-adaptive selecting relevant modes algorithm for FBG spectrum signal

NASA Astrophysics Data System (ADS)

Chen, Yong; Wu, Chun-ting; Liu, Huan-lin

2017-07-01

Noise may reduce the demodulation accuracy of fiber Bragg grating (FBG) sensing signal so as to affect the quality of sensing detection. Thus, the recovery of a signal from observed noisy data is necessary. In this paper, a precise self-adaptive algorithm of selecting relevant modes is proposed to remove the noise of signal. Empirical mode decomposition (EMD) is first used to decompose a signal into a set of modes. The pseudo modes cancellation is introduced to identify and eliminate false modes, and then the Mutual Information (MI) of partial modes is calculated. MI is used to estimate the critical point of high and low frequency components. Simulation results show that the proposed algorithm estimates the critical point more accurately than the traditional algorithms for FBG spectral signal. While, compared to the similar algorithms, the signal noise ratio of the signal can be improved more than 10 dB after processing by the proposed algorithm, and correlation coefficient can be increased by 0.5, so it demonstrates better de-noising effect.

Correlated components of ongoing EEG point to emotionally laden attention - a possible marker of engagement?

PubMed

Dmochowski, Jacek P; Sajda, Paul; Dias, Joao; Parra, Lucas C

2012-01-01

Recent evidence from functional magnetic resonance imaging suggests that cortical hemodynamic responses coincide in different subjects experiencing a common naturalistic stimulus. Here we utilize neural responses in the electroencephalogram (EEG) evoked by multiple presentations of short film clips to index brain states marked by high levels of correlation within and across subjects. We formulate a novel signal decomposition method which extracts maximally correlated signal components from multiple EEG records. The resulting components capture correlations down to a one-second time resolution, thus revealing that peak correlations of neural activity across viewings can occur in remarkable correspondence with arousing moments of the film. Moreover, a significant reduction in neural correlation occurs upon a second viewing of the film or when the narrative is disrupted by presenting its scenes scrambled in time. We also probe oscillatory brain activity during periods of heightened correlation, and observe during such times a significant increase in the theta band for a frontal component and reductions in the alpha and beta frequency bands for parietal and occipital components. Low-resolution EEG tomography of these components suggests that the correlated neural activity is consistent with sources in the cingulate and orbitofrontal cortices. Put together, these results suggest that the observed synchrony reflects attention- and emotion-modulated cortical processing which may be decoded with high temporal resolution by extracting maximally correlated components of neural activity.

Correlated Components of Ongoing EEG Point to Emotionally Laden Attention – A Possible Marker of Engagement?

PubMed Central

Dmochowski, Jacek P.; Sajda, Paul; Dias, Joao; Parra, Lucas C.

2012-01-01

Recent evidence from functional magnetic resonance imaging suggests that cortical hemodynamic responses coincide in different subjects experiencing a common naturalistic stimulus. Here we utilize neural responses in the electroencephalogram (EEG) evoked by multiple presentations of short film clips to index brain states marked by high levels of correlation within and across subjects. We formulate a novel signal decomposition method which extracts maximally correlated signal components from multiple EEG records. The resulting components capture correlations down to a one-second time resolution, thus revealing that peak correlations of neural activity across viewings can occur in remarkable correspondence with arousing moments of the film. Moreover, a significant reduction in neural correlation occurs upon a second viewing of the film or when the narrative is disrupted by presenting its scenes scrambled in time. We also probe oscillatory brain activity during periods of heightened correlation, and observe during such times a significant increase in the theta band for a frontal component and reductions in the alpha and beta frequency bands for parietal and occipital components. Low-resolution EEG tomography of these components suggests that the correlated neural activity is consistent with sources in the cingulate and orbitofrontal cortices. Put together, these results suggest that the observed synchrony reflects attention- and emotion-modulated cortical processing which may be decoded with high temporal resolution by extracting maximally correlated components of neural activity. PMID:22623915

Temporal processing of speech in a time-feature space

NASA Astrophysics Data System (ADS)

Avendano, Carlos

1997-09-01

The performance of speech communication systems often degrades under realistic environmental conditions. Adverse environmental factors include additive noise sources, room reverberation, and transmission channel distortions. This work studies the processing of speech in the temporal-feature or modulation spectrum domain, aiming for alleviation of the effects of such disturbances. Speech reflects the geometry of the vocal organs, and the linguistically dominant component is in the shape of the vocal tract. At any given point in time, the shape of the vocal tract is reflected in the short-time spectral envelope of the speech signal. The rate of change of the vocal tract shape appears to be important for the identification of linguistic components. This rate of change, or the rate of change of the short-time spectral envelope can be described by the modulation spectrum, i.e. the spectrum of the time trajectories described by the short-time spectral envelope. For a wide range of frequency bands, the modulation spectrum of speech exhibits a maximum at about 4 Hz, the average syllabic rate. Disturbances often have modulation frequency components outside the speech range, and could in principle be attenuated without significantly affecting the range with relevant linguistic information. Early efforts for exploiting the modulation spectrum domain (temporal processing), such as the dynamic cepstrum or the RASTA processing, used ad hoc designed processing and appear to be suboptimal. As a major contribution, in this dissertation we aim for a systematic data-driven design of temporal processing. First we analytically derive and discuss some properties and merits of temporal processing for speech signals. We attempt to formalize the concept and provide a theoretical background which has been lacking in the field. In the experimental part we apply temporal processing to a number of problems including adaptive noise reduction in cellular telephone environments, reduction of reverberation for speech enhancement, and improvements on automatic recognition of speech degraded by linear distortions and reverberation.

Growth plate-derived hedgehog-signal-responsive cells provide skeletal tissue components in growing bone.

PubMed

Haraguchi, Ryuma; Kitazawa, Riko; Imai, Yuuki; Kitazawa, Sohei

2018-04-01

Longitudinal bone growth progresses by continuous bone replacement of epiphyseal cartilaginous tissue, known as "growth plate", produced by columnar proliferated- and differentiated-epiphyseal chondrocytes. The endochondral ossification process at the growth plate is governed by paracrine signals secreted from terminally differentiated chondrocytes (hypertrophic chondrocytes), and hedgehog signaling is one of the best known regulatory signaling pathways in this process. Here, to investigate the developmental relationship between longitudinal endochondral bone formation and osteogenic progenitors under the influence of hedgehog signaling at the growth plate, genetic lineage tracing was carried out with the use of Gli1 CreERT2 mice line to follow the fate of hedgehog-signal-responsive cells during endochondral bone formation. Gli1 CreERT2 genetically labeled cells are detected in hypertrophic chondrocytes and osteo-progenitors at the chondro-osseous junction (COJ); these progeny then commit to the osteogenic lineage in periosteum, trabecular and cortical bone along the developing longitudinal axis. Furthermore, in ageing bone, where longitudinal bone growth ceases, hedgehog-signal responsiveness and its implication in osteogenic lineage commitment is significantly weakened. These results show, for the first time, evidence of the developmental contribution of endochondral progenitors under the influence of epiphyseal chondrocyte-derived secretory signals in longitudinally growing bone. This study provides a precise outline for assessing the skeletal lineage commitment of osteo-progenitors in response to growth-plate-derived regulatory signals during endochondral bone formation.

AIM for Allostery: Using the Ising Model to Understand Information Processing and Transmission in Allosteric Biomolecular Systems.

PubMed

LeVine, Michael V; Weinstein, Harel

2015-05-01

In performing their biological functions, molecular machines must process and transmit information with high fidelity. Information transmission requires dynamic coupling between the conformations of discrete structural components within the protein positioned far from one another on the molecular scale. This type of biomolecular "action at a distance" is termed allostery . Although allostery is ubiquitous in biological regulation and signal transduction, its treatment in theoretical models has mostly eschewed quantitative descriptions involving the system's underlying structural components and their interactions. Here, we show how Ising models can be used to formulate an approach to allostery in a structural context of interactions between the constitutive components by building simple allosteric constructs we termed Allosteric Ising Models (AIMs). We introduce the use of AIMs in analytical and numerical calculations that relate thermodynamic descriptions of allostery to the structural context, and then show that many fundamental properties of allostery, such as the multiplicative property of parallel allosteric channels, are revealed from the analysis of such models. The power of exploring mechanistic structural models of allosteric function in more complex systems by using AIMs is demonstrated by building a model of allosteric signaling for an experimentally well-characterized asymmetric homodimer of the dopamine D2 receptor.

Vibro-acoustic condition monitoring of Internal Combustion Engines: A critical review of existing techniques

NASA Astrophysics Data System (ADS)

Delvecchio, S.; Bonfiglio, P.; Pompoli, F.

2018-01-01

This paper deals with the state-of-the-art strategies and techniques based on vibro-acoustic signals that can monitor and diagnose malfunctions in Internal Combustion Engines (ICEs) under both test bench and vehicle operating conditions. Over recent years, several authors have summarized what is known in critical reviews mainly focused on reciprocating machines in general or on specific signal processing techniques: no attempts to deal with IC engine condition monitoring have been made. This paper first gives a brief summary of the generation of sound and vibration in ICEs in order to place further discussion on fault vibro-acoustic diagnosis in context. An overview of the monitoring and diagnostic techniques described in literature using both vibration and acoustic signals is also provided. Different faulty conditions are described which affect combustion, mechanics and the aerodynamics of ICEs. The importance of measuring acoustic signals, as opposed to vibration signals, is due since the former seem to be more suitable for implementation on on-board monitoring systems in view of their non-intrusive behaviour, capability in simultaneously capturing signatures from several mechanical components and because of the possibility of detecting faults affecting airborne transmission paths. In view of the recent needs of the industry to (-) optimize component structural durability adopting long-life cycles, (-) verify the engine final status at the end of the assembly line and (-) reduce the maintenance costs monitoring the ICE life during vehicle operations, monitoring and diagnosing system requests are continuously growing up. The present review can be considered a useful guideline for test engineers in understanding which types of fault can be diagnosed by using vibro-acoustic signals in sufficient time in both test bench and operating conditions and which transducer and signal processing technique (of which the essential background theory is here reported) could be considered the most reliable and informative to be implemented for the fault in question.

Time delay estimation using new spectral and adaptive filtering methods with applications to underwater target detection

NASA Astrophysics Data System (ADS)

Hasan, Mohammed A.

1997-11-01

In this dissertation, we present several novel approaches for detection and identification of targets of arbitrary shapes from the acoustic backscattered data and using the incident waveform. This problem is formulated as time- delay estimation and sinusoidal frequency estimation problems which both have applications in many other important areas in signal processing. Solving time-delay estimation problem allows the identification of the specular components in the backscattered signal from elastic and non-elastic targets. Thus, accurate estimation of these time delays would help in determining the existence of certain clues for detecting targets. Several new methods for solving these two problems in the time, frequency and wavelet domains are developed. In the time domain, a new block fast transversal filter (BFTF) is proposed for a fast implementation of the least squares (LS) method. This BFTF algorithm is derived by using data-related constrained block-LS cost function to guarantee global optimality. The new soft-constrained algorithm provides an efficient way of transferring weight information between blocks of data and thus it is computationally very efficient compared with other LS- based schemes. Additionally, the tracking ability of the algorithm can be controlled by varying the block length and/or a soft constrained parameter. The effectiveness of this algorithm is tested on several underwater acoustic backscattered data for elastic targets and non-elastic (cement chunk) objects. In the frequency domain, the time-delay estimation problem is converted to a sinusoidal frequency estimation problem by using the discrete Fourier transform. Then, the lagged sample covariance matrices of the resulting signal are computed and studied in terms of their eigen- structure. These matrices are shown to be robust and effective in extracting bases for the signal and noise subspaces. New MUSIC and matrix pencil-based methods are derived these subspaces. The effectiveness of the method is demonstrated on the problem of detection of multiple specular components in the acoustic backscattered data. Finally, a method for the estimation of time delays using wavelet decomposition is derived. The sub-band adaptive filtering uses discrete wavelet transform for multi- resolution or sub-band decomposition. Joint time delay estimation for identifying multi-specular components and subsequent adaptive filtering processes are performed on the signal in each sub-band. This would provide multiple 'look' of the signal at different resolution scale which results in more accurate estimates for delays associated with the specular components. Simulation results on the simulated and real shallow water data are provided which show the promise of this new scheme for target detection in a heavy cluttered environment.

Component isolation for multi-component signal analysis using a non-parametric gaussian latent feature model

NASA Astrophysics Data System (ADS)

Yang, Yang; Peng, Zhike; Dong, Xingjian; Zhang, Wenming; Clifton, David A.

2018-03-01

A challenge in analysing non-stationary multi-component signals is to isolate nonlinearly time-varying signals especially when they are overlapped in time and frequency plane. In this paper, a framework integrating time-frequency analysis-based demodulation and a non-parametric Gaussian latent feature model is proposed to isolate and recover components of such signals. The former aims to remove high-order frequency modulation (FM) such that the latter is able to infer demodulated components while simultaneously discovering the number of the target components. The proposed method is effective in isolating multiple components that have the same FM behavior. In addition, the results show that the proposed method is superior to generalised demodulation with singular-value decomposition-based method, parametric time-frequency analysis with filter-based method and empirical model decomposition base method, in recovering the amplitude and phase of superimposed components.

Radio Frequency Compatibility of an RFID Tag on Glideslope Navigation Receivers

NASA Technical Reports Server (NTRS)

Nguyen, Truong X.; Mielnik, John J.

2008-01-01

A process is demonstrated to show compatibility between a radio frequency identification (RFID) tag and an aircraft glideslope (GS) radio receiver. The particular tag chosen was previously shown to have significant peak spurious emission levels that far exceeded the emission limits in the GS aeronautical band. The spurious emissions are emulated in the study by capturing the RFID fundamental transmission and playing back the signal in the GS band. The signal capturing and playback are achieved with a vector signal generator and a spectrum analyzer that can output the in-phase and quadrature components (IQ). The simulated interference signal is combined with a desired GS signal before being injected into a GS receiver s antenna port for interference threshold determination. Minimum desired propagation loss values to avoid interference are then computed and compared against actual propagation losses for several aircraft.

Adaptive Arrays for Weak Interfering Signals: An Experimental System. M.S. Thesis

NASA Technical Reports Server (NTRS)

Ward, James

1987-01-01

An experimental adaptive antenna system was implemented to study the performance of adaptive arrays in the presence of weak interfering signals. It is a sidelobe canceler with two auxiliary elements. Modified feedback loops, which decorrelate the noise components of the two inputs to the loop correlators, control the array weights. Digital processing is used for algorithm implementation and performance evaluation. The results show that the system can suppress interfering signals which are 0 to 10 dB below the thermal noise level in the main channel by 20 to 30 dB. When the desired signal is strong in the auxiliary elements the amount of interference suppression decreases. The amount of degradation depends on the number of interfering signals incident on the communication system. A modified steering vector which overcomes this problem is proposed.

Redox signaling in the cardiomyocyte: From physiology to failure.

PubMed

Santos, Celio X C; Raza, Sadaf; Shah, Ajay M

2016-05-01

The specific effect of oxygen and reactive oxygen species (ROS) in mediating post-translational modification of protein targets has emerged as a key mechanism regulating signaling components, a process termed redox signaling. ROS act in the post-translational modification of multiple target proteins including receptors, kinases, phosphatases, ion channels and transcription factors. Both O2 and ROS are major source of electrons in redox reactions in aerobic organisms. Because the heart has the highest O2 consumption among body organs, it is not surprising that redox signaling is central to heart function and pathophysiology. In this article, we review some of the main cardiac redox signaling pathways and their roles in the cardiomyocyte and in heart failure, with particular focus on the specific molecular targets of ROS in the heart. Copyright © 2016 Elsevier Ltd. All rights reserved.

The role of cannabinoids in adult neurogenesis

PubMed Central

Prenderville, Jack A; Kelly, Áine M; Downer, Eric J

2015-01-01

The processes underpinning post-developmental neurogenesis in the mammalian brain continue to be defined. Such processes involve the proliferation of neural stem cells and neural progenitor cells (NPCs), neuronal migration, differentiation and integration into a network of functional synapses within the brain. Both intrinsic (cell signalling cascades) and extrinsic (neurotrophins, neurotransmitters, cytokines, hormones) signalling molecules are intimately associated with adult neurogenesis and largely dictate the proliferative activity and differentiation capacity of neural cells. Cannabinoids are a unique class of chemical compounds incorporating plant-derived cannabinoids (the active components of Cannabis sativa), the endogenous cannabinoids and synthetic cannabinoid ligands, and these compounds are becoming increasingly recognized for their roles in neural developmental processes. Indeed, cannabinoids have clear modulatory roles in adult neurogenesis, probably through activation of both CB1 and CB2 receptors. In recent years, a large body of literature has deciphered the signalling networks involved in cannabinoid-mediated regulation of neurogenesis. This timely review summarizes the evidence that the cannabinoid system is intricately associated with neuronal differentiation and maturation of NPCs and highlights intrinsic/extrinsic signalling mechanisms that are cannabinoid targets. Overall, these findings identify the central role of the cannabinoid system in adult neurogenesis in the hippocampus and the lateral ventricles and hence provide insight into the processes underlying post-developmental neurogenesis in the mammalian brain. PMID:25951750

Super-resolution imaging using multi- electrode CMUTs: theoretical design and simulation using point targets.

PubMed

You, Wei; Cretu, Edmond; Rohling, Robert

2013-11-01

This paper investigates a low computational cost, super-resolution ultrasound imaging method that leverages the asymmetric vibration mode of CMUTs. Instead of focusing on the broadband received signal on the entire CMUT membrane, we utilize the differential signal received on the left and right part of the membrane obtained by a multi-electrode CMUT structure. The differential signal reflects the asymmetric vibration mode of the CMUT cell excited by the nonuniform acoustic pressure field impinging on the membrane, and has a resonant component in immersion. To improve the resolution, we propose an imaging method as follows: a set of manifold matrices of CMUT responses for multiple focal directions are constructed off-line with a grid of hypothetical point targets. During the subsequent imaging process, the array sequentially steers to multiple angles, and the amplitudes (weights) of all hypothetical targets at each angle are estimated in a maximum a posteriori (MAP) process with the manifold matrix corresponding to that angle. Then, the weight vector undergoes a directional pruning process to remove the false estimation at other angles caused by the side lobe energy. Ultrasound imaging simulation is performed on ring and linear arrays with a simulation program adapted with a multi-electrode CMUT structure capable of obtaining both average and differential received signals. Because the differential signals from all receiving channels form a more distinctive temporal pattern than the average signals, better MAP estimation results are expected than using the average signals. The imaging simulation shows that using differential signals alone or in combination with the average signals produces better lateral resolution than the traditional phased array or using the average signals alone. This study is an exploration into the potential benefits of asymmetric CMUT responses for super-resolution imaging.

Characterizing the magnetic memory signals on the surface of plasma transferred arc cladding coating under fatigue loads

NASA Astrophysics Data System (ADS)

Huang, Haihong; Han, Gang; Qian, Zhengchun; Liu, Zhifeng

2017-12-01

The metal magnetic memory signals were measured during dynamic tension tests on the surfaces of the cladding coatings by plasma transferred arc (PTA) welding and the 0.45% C steel. Results showed that the slope of the normal component Hp(y) of magnetic signal and the average value of the tangential component Hp(x) reflect the magnetization of the specimens. The signals increased sharply in the few initial cycles; and then fluctuated around a constant value during fatigue process until fracture. For the PTA cladding coating, the slope of Hp(y) was steeper and the average of Hp(x) was smaller, compared with the 0.45% C steel. The hysteresis curves of cladding layer, bonding layer and substrate were measured by vibrating sample magnetometer testing, and then saturation magnetization, initial susceptibility and coercivity were further calculated. The stress-magnetization curves were also plotted based on the J-A model, which showed that the PTA cladding coating has smaller remanence and coercivity compared with the 0.45% C steel. The microstructures of cladding coating confirmed that the dendritic structure and second-phase of alloy hinder the magnetic domain motion, which was the main factor influencing the variation of magnetic signal during the fatigue tests.

A multi-band spectral subtraction-based algorithm for real-time noise cancellation applied to gunshot acoustics

NASA Astrophysics Data System (ADS)

Ramos, António L. L.; Holm, Sverre; Gudvangen, Sigmund; Otterlei, Ragnvald

2013-06-01

Acoustical sniper positioning is based on the detection and direction-of-arrival estimation of the shockwave and the muzzle blast acoustical signals. In real-life situations, the detection and direction-of-arrival estimation processes is usually performed under the influence of background noise sources, e.g., vehicles noise, and might result in non-negligible inaccuracies than can affect the system performance and reliability negatively, specially when detecting the muzzle sound under long range distance and absorbing terrains. This paper introduces a multi-band spectral subtraction based algorithm for real-time noise reduction, applied to gunshot acoustical signals. The ballistic shockwave and the muzzle blast signals exhibit distinct frequency contents that are affected differently by additive noise. In most real situations, the noise component is colored and a multi-band spectral subtraction approach for noise reduction contributes to reducing the presence of artifacts in denoised signals. The proposed algorithm is tested using a dataset generated by combining signals from real gunshots and real vehicle noise. The noise component was generated using a steel tracked military tank running on asphalt and includes, therefore, the sound from the vehicle engine, which varies slightly in frequency over time according to the engine's rpm, and the sound from the steel tracks as the vehicle moves.

Advanced linear and nonlinear compensations for 16QAM SC-400G unrepeatered transmission system

NASA Astrophysics Data System (ADS)

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

2018-02-01

Digital signal processing (DSP) with both linear equalization and nonlinear compensations are studied in this paper for the single-carrier 400G system based on 65-GBaud 16-quadrature amplitude modulation (QAM) signals. The 16-QAM signals are generated and pre-processed with pre-equalization (Pre-EQ) and Look-up-Table (LUT) based pre-distortion (Pre-DT) at the transmitter (Tx)-side. The implementation principle of training-based equalization and pre-distortion are presented here in this paper with experimental studies. At the receiver (Rx)-side, fiber-nonlinearity compensation based on digital backward propagation (DBP) are also utilized to further improve the transmission performances. With joint LUT-based Pre-DT and DBP-based post-compensation to mitigate the opto-electronic components and fiber nonlinearity impairments, we demonstrate the unrepeatered transmission of 1.6Tb/s based on 4-lane 400G single-carrier PDM-16QAM over 205-km SSMF without distributed amplifier.

The human NAD metabolome: Functions, metabolism and compartmentalization

PubMed Central

Nikiforov, Andrey; Kulikova, Veronika; Ziegler, Mathias

2015-01-01

Abstract The metabolism of NAD has emerged as a key regulator of cellular and organismal homeostasis. Being a major component of both bioenergetic and signaling pathways, the molecule is ideally suited to regulate metabolism and major cellular events. In humans, NAD is synthesized from vitamin B3 precursors, most prominently from nicotinamide, which is the degradation product of all NAD-dependent signaling reactions. The scope of NAD-mediated regulatory processes is wide including enzyme regulation, control of gene expression and health span, DNA repair, cell cycle regulation and calcium signaling. In these processes, nicotinamide is cleaved from NAD+ and the remaining ADP-ribosyl moiety used to modify proteins (deacetylation by sirtuins or ADP-ribosylation) or to generate calcium-mobilizing agents such as cyclic ADP-ribose. This review will also emphasize the role of the intermediates in the NAD metabolome, their intra- and extra-cellular conversions and potential contributions to subcellular compartmentalization of NAD pools. PMID:25837229

Nitric oxide signalling via cytoskeleton in plants.

PubMed

Yemets, Alla I; Krasylenko, Yuliya A; Lytvyn, Dmytro I; Sheremet, Yarina A; Blume, Yaroslav B

2011-11-01

Nitric oxide (NO) in plant cell mediates processes of growth and development starting from seed germination to pollination, as well as biotic and abiotic stress tolerance. However, proper understanding of the molecular mechanisms of NO signalling in plants has just begun to emerge. Accumulated evidence suggests that in eukaryotic cells NO regulates functions of proteins by their post-translational modifications, namely tyrosine nitration and S-nitrosylation. Among the candidates for NO-downstream effectors are cytoskeletal proteins because of their involvement in many processes regulated by NO. This review discusses new insights in plant NO signalling focused mainly on the involvement of cytoskeleton components into NO-cascades. Herein, examples of NO-related post-translational modifications of cytoskeletal proteins, and also indirect NO impact, are discussed. Special attention is paid to plant α-tubulin tyrosine nitration as an emerging topic in plant NO research. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

High Frequency Resolution TOA Analysis for ELF/VLFWave Generation Experiments at HAARP

NASA Astrophysics Data System (ADS)

Ruddle, J. D.; Moore, R. C.

2014-12-01

Modulated HF heating of the ionosphere in the presence of natural ionospheric current sources has been used as a method to generate electromagnetic ELF/VLF waves since the 1970's. In the ~1-5 kHz band, the amplitude and phase of the received ELF/VLF signal depends on the amplitude and phase of the conductivity modulation generated throughout the HF-heated ionospheric body, as well as on the signal propagation parameters (i.e., the attenuation and phase constants) between each of the current sources and the receiver. Recent signal processing advances have produced an accurate ELF/VLF time-of-arrival (TOA) analysis technique that differentiates line-of-sight and ionospherically-reflected signal components, determining the amplitude and phase of each component observed at the receiver. This TOA method requires a wide bandwidth (> 2.5 kHz) and therefore is relatively insensitive to the frequency-dependent nature of ELF/VLF wave propagation. In this paper, we present an improved ELF/VLF TOA method that is capable of providing high frequency resolution. The new analysis technique is applied to experimental observations of ELF/VLF signals generated by modulated heating at HAARP. We present measurements of the amplitude and phase of the received ELF/VLF signal as a function of frequency and compare the results with the predictions of an HF heating model.

Inhibitory Network Interactions Shape the Auditory Processing of Natural Communication Signals in the Songbird Auditory Forebrain

PubMed Central

Pinaud, Raphael; Terleph, Thomas A.; Tremere, Liisa A.; Phan, Mimi L.; Dagostin, André A.; Leão, Ricardo M.; Mello, Claudio V.; Vicario, David S.

2008-01-01

The role of GABA in the central processing of complex auditory signals is not fully understood. We have studied the involvement of GABAA-mediated inhibition in the processing of birdsong, a learned vocal communication signal requiring intact hearing for its development and maintenance. We focused on caudomedial nidopallium (NCM), an area analogous to parts of the mammalian auditory cortex with selective responses to birdsong. We present evidence that GABAA-mediated inhibition plays a pronounced role in NCM's auditory processing of birdsong. Using immunocytochemistry, we show that approximately half of NCM's neurons are GABAergic. Whole cell patch-clamp recordings in a slice preparation demonstrate that, at rest, spontaneously active GABAergic synapses inhibit excitatory inputs onto NCM neurons via GABAA receptors. Multi-electrode electrophysiological recordings in awake birds show that local blockade of GABAA-mediated inhibition in NCM markedly affects the temporal pattern of song-evoked responses in NCM without modifications in frequency tuning. Surprisingly, this blockade increases the phasic and largely suppresses the tonic response component, reflecting dynamic relationships of inhibitory networks that could include disinhibition. Thus processing of learned natural communication sounds in songbirds, and possibly other vocal learners, may depend on complex interactions of inhibitory networks. PMID:18480371

Memory and decision making in the frontal cortex during visual motion processing for smooth pursuit eye movements.

PubMed

Shichinohe, Natsuko; Akao, Teppei; Kurkin, Sergei; Fukushima, Junko; Kaneko, Chris R S; Fukushima, Kikuro

2009-06-11

Cortical motor areas are thought to contribute "higher-order processing," but what that processing might include is unknown. Previous studies of the smooth pursuit-related discharge of supplementary eye field (SEF) neurons have not distinguished activity associated with the preparation for pursuit from discharge related to processing or memory of the target motion signals. Using a memory-based task designed to separate these components, we show that the SEF contains signals coding retinal image-slip-velocity, memory, and assessment of visual motion direction, the decision of whether to pursue, and the preparation for pursuit eye movements. Bilateral muscimol injection into SEF resulted in directional errors in smooth pursuit, errors of whether to pursue, and impairment of initial correct eye movements. These results suggest an important role for the SEF in memory and assessment of visual motion direction and the programming of appropriate pursuit eye movements.

The TORC1-Regulated CPA Complex Rewires an RNA Processing Network to Drive Autophagy and Metabolic Reprogramming.

PubMed

Tang, Hong-Wen; Hu, Yanhui; Chen, Chiao-Lin; Xia, Baolong; Zirin, Jonathan; Yuan, Min; Asara, John M; Rabinow, Leonard; Perrimon, Norbert

2018-05-01

Nutrient deprivation induces autophagy through inhibiting TORC1 activity. We describe a novel mechanism in Drosophila by which TORC1 regulates RNA processing of Atg transcripts and alters ATG protein levels and activities via the cleavage and polyadenylation (CPA) complex. We show that TORC1 signaling inhibits CDK8 and DOA kinases, which directly phosphorylate CPSF6, a component of the CPA complex. These phosphorylation events regulate CPSF6 localization, RNA binding, and starvation-induced alternative RNA processing of transcripts involved in autophagy, nutrient, and energy metabolism, thereby controlling autophagosome formation and metabolism. Similarly, we find that mammalian CDK8 and CLK2, a DOA ortholog, phosphorylate CPSF6 to regulate autophagy and metabolic changes upon starvation, revealing an evolutionarily conserved mechanism linking TORC1 signaling with RNA processing, autophagy, and metabolism. Copyright © 2018 Elsevier Inc. All rights reserved.

On the importance of preserving the harmonics and neighboring partials prior to vocoder processing: implications for cochlear implants.

PubMed

Hu, Yi; Loizou, Philipos C

2010-01-01

Pre-processing based noise-reduction algorithms used for cochlear implants (CIs) can sometimes introduce distortions which are carried through the vocoder stages of CI processing. While the background noise may be notably suppressed, the harmonic structure and/or spectral envelope of the signal may be distorted. The present study investigates the potential of preserving the signal's harmonic structure in voiced segments (e.g., vowels) as a means of alleviating the negative effects of pre-processing. The hypothesis tested is that preserving the harmonic structure of the signal is crucial for subsequent vocoder processing. The implications of preserving either the main harmonic components occurring at multiples of F0 or the main harmonics along with adjacent partials are investigated. This is done by first pre-processing noisy speech with a conventional noise-reduction algorithm, regenerating the harmonics, and vocoder processing the stimuli with eight channels of stimulation in steady speech-shaped noise. Results indicated that preserving the main low-frequency harmonics (spanning 1 or 3 kHz) alone was not beneficial. Preserving, however, the harmonic structure of the stimulus, i.e., the main harmonics along with the adjacent partials, was found to be critically important and provided substantial improvements (41 percentage points) in intelligibility.

Time and space integrating acousto-optic folded spectrum processing for SETI

NASA Technical Reports Server (NTRS)

Wagner, K.; Psaltis, D.

1986-01-01

Time and space integrating folded spectrum techniques utilizing acousto-optic devices (AOD) as 1-D input transducers are investigated for a potential application as wideband, high resolution, large processing gain spectrum analyzers in the search for extra-terrestrial intelligence (SETI) program. The space integrating Fourier transform performed by a lens channels the coarse spectral components diffracted from an AOD onto an array of time integrating narrowband fine resolution spectrum analyzers. The pulsing action of a laser diode samples the interferometrically detected output, aliasing the fine resolution components to baseband, as required for the subsequent charge coupled devices (CCD) processing. The raster scan mechanism incorporated into the readout of the CCD detector array is used to unfold the 2-D transform, reproducing the desired high resolution Fourier transform of the input signal.

Calcitonin gene related family peptides: importance in normal placental and fetal development.

PubMed

Yallampalli, Chandra; Chauhan, Madhu; Endsley, Janice; Sathishkumar, Kunju

2014-01-01

Synchronized molecular and cellular events occur between the uterus and the implanting embryo to facilitate successful pregnancy outcome. Nevertheless, the molecular signaling network that coordinates strategies for successful decidualization, placentation and fetal growth are not well understood. The discovery of calcitonin/calcitonin gene-related peptides (CT/CGRP) highlighted new signaling mediators in various physiological processes, including reproduction. It is known that CGRP family peptides including CGRP, adrenomedulin and intermedin play regulatory functions during implantation, trophoblast proliferation and invasion, and fetal organogenesis. In addition, all the CGRP family peptides and their receptor components are found to be expressed in decidual, placental and fetal tissues. Additionally, plasma levels of peptides of the CGRP family were found to fluctuate during normal gestation and to induce placental cellular differentiation, proliferation, and critical hormone signaling. Moreover, aberrant signaling of these CGRP family peptides during gestation has been associated with pregnancy disorders. It indicates the existence of a possible regulatory role for these molecules during decidualization and placentation processes, which are known to be particularly vulnerable. In this review, the influence of the CGRP family peptides in these critical processes is explored and discussed.

Dance band on the Titanic: biomechanical signaling in cardiac hypertrophy.

PubMed

Sussman, Mark A; McCulloch, Andrew; Borg, Thomas K

2002-11-15

Biomechanical signaling is a complex interaction of both intracellular and extracellular components. Both passive and active components are involved in the extracellular environment to signal through specific receptors to multiple signaling pathways. This review provides an overview of extracellular matrix, specific receptors, and signaling pathways for biomechanical stimulation in cardiac hypertrophy.

Multi-Velocity Component LDV

NASA Technical Reports Server (NTRS)

Johnson, Dennis A. (Inventor)

1996-01-01

A laser doppler velocimeter uses frequency shifting of a laser beam to provide signal information for each velocity component. A composite electrical signal generated by a light detector is digitized and a processor produces a discrete Fourier transform based on the digitized electrical signal. The transform includes two peak frequencies corresponding to the two velocity components.

Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis

NASA Astrophysics Data System (ADS)

Wang, Lei; Liu, Zhiwen; Miao, Qiang; Zhang, Xin

2018-03-01

A time-frequency analysis method based on ensemble local mean decomposition (ELMD) and fast kurtogram (FK) is proposed for rotating machinery fault diagnosis. Local mean decomposition (LMD), as an adaptive non-stationary and nonlinear signal processing method, provides the capability to decompose multicomponent modulation signal into a series of demodulated mono-components. However, the occurring mode mixing is a serious drawback. To alleviate this, ELMD based on noise-assisted method was developed. Still, the existing environmental noise in the raw signal remains in corresponding PF with the component of interest. FK has good performance in impulse detection while strong environmental noise exists. But it is susceptible to non-Gaussian noise. The proposed method combines the merits of ELMD and FK to detect the fault for rotating machinery. Primarily, by applying ELMD the raw signal is decomposed into a set of product functions (PFs). Then, the PF which mostly characterizes fault information is selected according to kurtosis index. Finally, the selected PF signal is further filtered by an optimal band-pass filter based on FK to extract impulse signal. Fault identification can be deduced by the appearance of fault characteristic frequencies in the squared envelope spectrum of the filtered signal. The advantages of ELMD over LMD and EEMD are illustrated in the simulation analyses. Furthermore, the efficiency of the proposed method in fault diagnosis for rotating machinery is demonstrated on gearbox case and rolling bearing case analyses.

Vascular endothelial growth factor signaling regulates the segregation of artery and vein via ERK activity during vascular development

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

Kim, Se-Hee; Schmitt, Christopher E.; Woolls, Melissa J.

Highlights: ► VEGF-A signaling regulates the segregation of axial vessels. ► VEGF-A signaling is mediated by PKC and ERK in this process. ► Ectopic activation of ERK is sufficient to rescue defects in vessel segregation. -- Abstract: Segregation of two axial vessels, the dorsal aorta and caudal vein, is one of the earliest patterning events occur during development of vasculature. Despite the importance of this process and recent advances in our understanding on vascular patterning during development, molecular mechanisms that coordinate the segregation of axial vessels remain largely elusive. In this report, we find that vascular endothelial growth factor-A (Vegf-A)more » signaling regulates the segregation of dorsal aorta and axial vein during development. Inhibition of Vegf-A pathway components including ligand Vegf-A and its cognate receptor Kdrl, caused failure in segregation of axial vessels in zebrafish embryos. Similarly, chemical inhibition of Mitogen-activated protein kinase kinase (Map2k1)/Extracellular-signal-regulated kinases (Erk) and phosphatidylinositol 3-kinases (PI3 K), which are downstream effectors of Vegf-A signaling pathway, led to the fusion of two axial vessels. Moreover, we find that restoring Erk activity by over-expression of constitutively active MEK in embryos with a reduced level of Vegf-A signaling can rescue the defects in axial vessel segregation. Taken together, our data show that segregation of axial vessels requires the function of Vegf-A signaling, and Erk may function as the major downstream effector in this process.« less

Genomics and expression profiles of the Hedgehog and Notch signaling pathways in sea urchin development.

PubMed

Walton, Katherine D; Croce, Jenifer C; Glenn, Thomas D; Wu, Shu-Yu; McClay, David R

2006-12-01

The Hedgehog (Hh) and Notch signal transduction pathways control a variety of developmental processes including cell fate choice, differentiation, proliferation, patterning and boundary formation. Because many components of these pathways are conserved, it was predicted and confirmed that pathway components are largely intact in the sea urchin genome. Spatial and temporal location of these pathways in the embryo, and their function in development offer added insight into their mechanistic contributions. Accordingly, all major components of both pathways were identified and annotated in the sea urchin Strongylocentrotus purpuratus genome and the embryonic expression of key components was explored. Relationships of the pathway components, and modifiers predicted from the annotation of S. purpuratus, were compared against cnidarians, arthropods, urochordates, and vertebrates. These analyses support the prediction that the pathways are highly conserved through metazoan evolution. Further, the location of these two pathways appears to be conserved among deuterostomes, and in the case of Notch at least, display similar capacities in endomesoderm gene regulatory networks. RNA expression profiles by quantitative PCR and RNA in situ hybridization reveal that Hedgehog is produced by the endoderm beginning just prior to invagination, and signals to the secondary mesenchyme-derived tissues at least until the pluteus larva stage. RNA in situ hybridization of Notch pathway members confirms that Notch functions sequentially in the vegetal-most secondary mesenchyme cells and later in the endoderm. Functional analyses in future studies will embed these pathways into the growing knowledge of gene regulatory networks that govern early specification and morphogenesis.

Systematic mapping of two component response regulators to gene targets in a model sulfate reducing bacterium

PubMed Central

2011-01-01

Background Two component regulatory systems are the primary form of signal transduction in bacteria. Although genomic binding sites have been determined for several eukaryotic and bacterial transcription factors, comprehensive identification of gene targets of two component response regulators remains challenging due to the lack of knowledge of the signals required for their activation. We focused our study on Desulfovibrio vulgaris Hildenborough, a sulfate reducing bacterium that encodes unusually diverse and largely uncharacterized two component signal transduction systems. Results We report the first systematic mapping of the genes regulated by all transcriptionally acting response regulators in a single bacterium. Our results enabled functional predictions for several response regulators and include key processes of carbon, nitrogen and energy metabolism, cell motility and biofilm formation, and responses to stresses such as nitrite, low potassium and phosphate starvation. Our study also led to the prediction of new genes and regulatory networks, which found corroboration in a compendium of transcriptome data available for D. vulgaris. For several regulators we predicted and experimentally verified the binding site motifs, most of which were discovered as part of this study. Conclusions The gene targets identified for the response regulators allowed strong functional predictions to be made for the corresponding two component systems. By tracking the D. vulgaris regulators and their motifs outside the Desulfovibrio spp. we provide testable hypotheses regarding the functions of orthologous regulators in other organisms. The in vitro array based method optimized here is generally applicable for the study of such systems in all organisms. PMID:21992415

LISA Framework for Enhancing Gravitational Wave Signal Extraction Techniques

NASA Technical Reports Server (NTRS)

Thompson, David E.; Thirumalainambi, Rajkumar

2006-01-01

This paper describes the development of a Framework for benchmarking and comparing signal-extraction and noise-interference-removal methods that are applicable to interferometric Gravitational Wave detector systems. The primary use is towards comparing signal and noise extraction techniques at LISA frequencies from multiple (possibly confused) ,gravitational wave sources. The Framework includes extensive hybrid learning/classification algorithms, as well as post-processing regularization methods, and is based on a unique plug-and-play (component) architecture. Published methods for signal extraction and interference removal at LISA Frequencies are being encoded, as well as multiple source noise models, so that the stiffness of GW Sensitivity Space can be explored under each combination of methods. Furthermore, synthetic datasets and source models can be created and imported into the Framework, and specific degraded numerical experiments can be run to test the flexibility of the analysis methods. The Framework also supports use of full current LISA Testbeds, Synthetic data systems, and Simulators already in existence through plug-ins and wrappers, thus preserving those legacy codes and systems in tact. Because of the component-based architecture, all selected procedures can be registered or de-registered at run-time, and are completely reusable, reconfigurable, and modular.

A Nonlinear Dynamical Systems based Model for Stochastic Simulation of Streamflow

NASA Astrophysics Data System (ADS)

Erkyihun, S. T.; Rajagopalan, B.; Zagona, E. A.

2014-12-01

Traditional time series methods model the evolution of the underlying process as a linear or nonlinear function of the autocorrelation. These methods capture the distributional statistics but are incapable of providing insights into the dynamics of the process, the potential regimes, and predictability. This work develops a nonlinear dynamical model for stochastic simulation of streamflows. In this, first a wavelet spectral analysis is employed on the flow series to isolate dominant orthogonal quasi periodic timeseries components. The periodic bands are added denoting the 'signal' component of the time series and the residual being the 'noise' component. Next, the underlying nonlinear dynamics of this combined band time series is recovered. For this the univariate time series is embedded in a d-dimensional space with an appropriate lag T to recover the state space in which the dynamics unfolds. Predictability is assessed by quantifying the divergence of trajectories in the state space with time, as Lyapunov exponents. The nonlinear dynamics in conjunction with a K-nearest neighbor time resampling is used to simulate the combined band, to which the noise component is added to simulate the timeseries. We demonstrate this method by applying it to the data at Lees Ferry that comprises of both the paleo reconstructed and naturalized historic annual flow spanning 1490-2010. We identify interesting dynamics of the signal in the flow series and epochal behavior of predictability. These will be of immense use for water resources planning and management.

From the ocean to a salt marsh: towards understanding iron reduction processes with FORC-PCA.

NASA Astrophysics Data System (ADS)

Muraszko, J. R.; Lascu, I.; Collins, S. M.; Harrison, R. J.

2017-12-01

Biogenic magnetic minerals are a high fidelity recorder of climate change. Their sensitivity to sedimentary redox conditions and bottom water ventilation have the potential to provide useful insights into past diagenetic conditions. However, the mechanisms controlling preservation and dissolution of magnetosomes are not fully understood, thus undermining the reliability of the paleomagnetic records in marine environments. Recovering information about the diagenetic past of the sediment is a crucial challenge; specifically, the biogenic components need to be identified and unmixed from the bulk magnetic signal. We address the issue in this study by applying Principal Component Analysis on First Order Reversal Curve diagrams (FORC-PCA) in case studies of cores obtained from the Iberian Margin and the sedimentologically active coastal salt marshes of Norfolk. We demonstrate the applicability of FORC-PCA as a new environmental proxy, yielding a high resolution temporal marine record of environmental changes reflected in magnetic composition over the last 194 kyr. The strongest variations are observed in the microbially derived components, the bulk properties of the sediment being controlled by a low coercivity SP-SD component which is generally anticorrelated with the magnetosome signal. Supported by TEM studies, we suggest the prevalence of clusters of nano-particles of magnetite associated with iron reduction. To further investigate the mechanisms controlling these processes, the active sedimentary environment of Norfolk was chosen as a case study of early diagenesis controlled by strong vertical geochemical gradients.

Continued reduction and analysis of data from the Dynamics Explorer Plasma Wave Instrument

NASA Technical Reports Server (NTRS)

Gurnett, Donald A.; Weimer, Daniel R.

1994-01-01

The plasma wave instrument on the Dynamics Explorer 1 spacecraft provided measurements of the electric and magnetic components of plasma waves in the Earth's magnetosphere. Four receiver systems processed signals from five antennas. Sixty-seven theses, scientific papers and reports were prepared from the data generated. Data processing activities and techniques used to analyze the data are described and highlights of discoveries made and research undertaken are tabulated.

Integration of CW / Radionucleotide Detection Systems to the Fido XT Explosives Detector

DTIC Science & Technology

2008-07-31

explosives detected by the Fido XT. Additionally, a platform for centralized storage and processing of Fido XT data files collected in house, targeted...fused silica glass wool (obtained from Restek). The fluorescent signal was easily washed out of the flow cell by a nominal amount of buffer...detector with supporting NRE was processed . The Interceptor components were configured to operate under a Windows CE processor environment, and to

Induction and separation of motion artifacts in EEG data using a mobile phantom head device.

PubMed

Oliveira, Anderson S; Schlink, Bryan R; Hairston, W David; König, Peter; Ferris, Daniel P

2016-06-01

Electroencephalography (EEG) can assess brain activity during whole-body motion in humans but head motion can induce artifacts that obfuscate electrocortical signals. Definitive solutions for removing motion artifact from EEG have yet to be found, so creating methods to assess signal processing routines for removing motion artifact are needed. We present a novel method for investigating the influence of head motion on EEG recordings as well as for assessing the efficacy of signal processing approaches intended to remove motion artifact. We used a phantom head device to mimic electrical properties of the human head with three controlled dipolar sources of electrical activity embedded in the phantom. We induced sinusoidal vertical motions on the phantom head using a custom-built platform and recorded EEG signals with three different acquisition systems while the head was both stationary and in varied motion conditions. Recordings showed up to 80% reductions in signal-to-noise ratio (SNR) and up to 3600% increases in the power spectrum as a function of motion amplitude and frequency. Independent component analysis (ICA) successfully isolated the three dipolar sources across all conditions and systems. There was a high correlation (r > 0.85) and marginal increase in the independent components' (ICs) power spectrum (∼15%) when comparing stationary and motion parameters. The SNR of the IC activation was 400%-700% higher in comparison to the channel data SNR, attenuating the effects of motion on SNR. Our results suggest that the phantom head and motion platform can be used to assess motion artifact removal algorithms and compare different EEG systems for motion artifact sensitivity. In addition, ICA is effective in isolating target electrocortical events and marginally improving SNR in relation to stationary recordings.

Engineering information on an Analog Signal to Discrete Time Interval Converter (ASDT-IC)

NASA Technical Reports Server (NTRS)

Schwarz, F. C.

1974-01-01

An electronic control system for nondissipative dc power converters is presented which improves (1) the routinely attainable static output voltage accuracy to the order of + or - 1% for ambient temperatures from -55 to 100 C and (2) the dynamic stability by utilizing approximately one tenth of the feedback gain needed otherwise. Performance is due to a functional philosophy of deterministic pulse modulation based on pulse area control and to an autocompensated signal processing principle. The system can be implemented with commercially available unselected components.

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 Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.

PubMed

Wang, Gang; Teng, Chaolin; Li, Kuo; Zhang, Zhonglin; Yan, Xiangguo

2016-09-01

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.

Radiation-hardened fast acquisition/weak signal tracking system and method

NASA Technical Reports Server (NTRS)

Winternitz, Luke (Inventor); Boegner, Gregory J. (Inventor); Sirotzky, Steve (Inventor)

2009-01-01

A global positioning system (GPS) receiver and method of acquiring and tracking GPS signals comprises an antenna adapted to receive GPS signals; an analog radio frequency device operatively connected to the antenna and adapted to convert the GPS signals from an analog format to a digital format; a plurality of GPS signal tracking correlators operatively connected to the analog RF device; a GPS signal acquisition component operatively connected to the analog RF device and the plurality of GPS signal tracking correlators, wherein the GPS signal acquisition component is adapted to calculate a maximum vector on a databit correlation grid; and a microprocessor operatively connected to the plurality of GPS signal tracking correlators and the GPS signal acquisition component, wherein the microprocessor is adapted to compare the maximum vector with a predetermined correlation threshold to allow the GPS signal to be fully acquired and tracked.

Bayesian wavelet PCA methodology for turbomachinery damage diagnosis under uncertainty

NASA Astrophysics Data System (ADS)

Xu, Shengli; Jiang, Xiaomo; Huang, Jinzhi; Yang, Shuhua; Wang, Xiaofang

2016-12-01

Centrifugal compressor often suffers various defects such as impeller cracking, resulting in forced outage of the total plant. Damage diagnostics and condition monitoring of such a turbomachinery system has become an increasingly important and powerful tool to prevent potential failure in components and reduce unplanned forced outage and further maintenance costs, while improving reliability, availability and maintainability of a turbomachinery system. This paper presents a probabilistic signal processing methodology for damage diagnostics using multiple time history data collected from different locations of a turbomachine, considering data uncertainty and multivariate correlation. The proposed methodology is based on the integration of three advanced state-of-the-art data mining techniques: discrete wavelet packet transform, Bayesian hypothesis testing, and probabilistic principal component analysis. The multiresolution wavelet analysis approach is employed to decompose a time series signal into different levels of wavelet coefficients. These coefficients represent multiple time-frequency resolutions of a signal. Bayesian hypothesis testing is then applied to each level of wavelet coefficient to remove possible imperfections. The ratio of posterior odds Bayesian approach provides a direct means to assess whether there is imperfection in the decomposed coefficients, thus avoiding over-denoising. Power spectral density estimated by the Welch method is utilized to evaluate the effectiveness of Bayesian wavelet cleansing method. Furthermore, the probabilistic principal component analysis approach is developed to reduce dimensionality of multiple time series and to address multivariate correlation and data uncertainty for damage diagnostics. The proposed methodology and generalized framework is demonstrated with a set of sensor data collected from a real-world centrifugal compressor with impeller cracks, through both time series and contour analyses of vibration signal and principal components.

Experimental facility for the study of acoustic emission registered in the primary circuit components of WWER power units

NASA Astrophysics Data System (ADS)

Petrosyan, V. G.; Hovakimyan, T. H.; Yeghoyan, E. A.; Hovhannisyan, H. T.; Mayilyan, D. G.; Petrosyan, A. P.

2017-01-01

This paper is dedicated to the creation of a facility for the experimental study of a phenomenon of background acoustic emission (AE), which is detected in the main circulation loop (MCL) of WWER power units. The analysis of the operating principle and the design of a primary feed-and-blow down system (FB) deaerator of NPP as the most likely source of continuous acoustic emission is carried out. The experimental facility for the systematic study of a phenomenon of continuous AE is developed. A physical model of a thermal deaerator is designed and constructed. A thermal monitoring system is introduced. An automatic system providing acoustic signal registration in a low frequency (0.03-30 kHz) and high frequency (30-300 kHz) bands and study of its spectral characteristics is designed. Special software for recording and processing of digitized electrical sensor signals is developed. A separate and independent principle of study of the most probable processes responsible for the generation of acoustic emission signals in the deaerator is applied. Trial series of experiments and prechecks of acoustic signals in different modes of the deaerator model are conducted. Compliance of basic technological parameters with operating range of the real deaerator was provided. It is shown that the acoustic signal time-intensity curve has several typical regions. The pilot research showed an impact of various processes that come about during the operation of the deaerator physical model on the intensity of the AE signal. The experimental results suggest that the main sources of generation of the AE signals are the processes of steam condensation, turbulent flow of gas-vapor medium, and water boiling.

Proteases in Fas-mediated apoptosis.

PubMed

Zhivotovsky, B; Burgess, D H; Schlegel, J; Pörn, M I; Vanags, D; Orrenius, S

1997-01-01

Involvement of a unique family of cysteine proteases in the multistep apoptotic process has been documented. Cloning of several mammalian genes identifies some components of this cellular response. However, it is currently unclear which protease plays a role as a signal and/or effector of apoptosis. We summarize contributions to the data concerning proteases in Fas-mediated apoptosis.

Molecular Rotors

DTIC Science & Technology

2006-10-31

microwave signal processing components, and micro-fluidic devices. The projected involved the preparation, surface mounting, and characterization of...Guisinger, R. Basu, and M. C. Hersam, “Atomic-level characterization and control of free radical surface chemistry using scanning tunneling microscopy...Basu, and M. C. Hersam, “Atomic level characterization and control of organosilicon surface chemistry using scanning tunneling microscopy,” presented

Comodulation Masking Release (CMR) in Children and the Influence of Reading Status

ERIC Educational Resources Information Center

Zettler, Cynthia M.; Sevcik, Rose A.; Morris, Robin D.; Clarkson, Marsha G.

2008-01-01

Purpose: Research suggests that children with reading disabilities (RD) have difficulty processing temporal and spectral components of sounds. Comodulation masking release (CMR) measures a listener's ability to use temporal and spectral information in noise to identify a signal. The purpose of this study was to determine whether children with RD…

Extraction of the brachialis muscle activity using HD-sEMG technique and canonical correlation analysis.

PubMed

Al Harrach, M; Afsharipour, B; Boudaoud, S; Carriou, V; Marin, F; Merletti, R

2016-08-01

The Brachialis (BR) is placed under the Biceps Brachii (BB) deep in the upper arm. Therefore, the detection of the corresponding surface Electromyogram (sEMG) is a complex task. The BR is an important elbow flexor, but it is usually not considered in the sEMG based force estimation process. The aim of this study was to attempt to separate the two sEMG activities of the BR and the BB by using a High Density sEMG (HD-sEMG) grid placed at the upper arm and Canonical Component Analysis (CCA) technique. For this purpose, we recorded sEMG signals from seven subjects with two 8 × 4 electrode grids placed over BB and BR. Four isometric voluntary contraction levels were recorded (5, 10, 30 and 50 %MVC) for 90° elbow angle. Then using CCA and image processing tools the sources of each muscle activity were separated. Finally, the corresponding sEMG signals were reconstructed using the remaining canonical components in order to retrieve the activity of the BB and the BR muscles.

Crosstalk between Wnt Signaling and RNA Processing in Colorectal Cancer.

PubMed

Bordonaro, Michael

2013-01-01

RNA processing involves a variety of processes affecting gene expression, including the removal of introns through RNA splicing, as well as 3' end processing (cleavage and polyadenylation). Alternative RNA processing is fundamentally important for gene regulation, and aberrant processing is associated with the initiation and progression of cancer. Deregulated Wnt signaling, which is the initiating event in the development of most cases of human colorectal cancer (CRC), has been linked to modified RNA processing, which may contribute to Wnt-mediated colonic carcinogenesis. Crosstalk between Wnt signaling and alternative RNA splicing with relevance to CRC includes effects on the expression of Rac1b, an alternatively spliced gene associated with tumorigenesis, which exhibits alternative RNA splicing that is influenced by Wnt activity. In addition, Tcf4, a crucial component of Wnt signaling, also exhibits alternative splicing, which is likely involved in colonic tumorigenesis. Modulation of 3' end formation, including of the Wnt target gene COX-2, also can influence the neoplastic process, with implications for CRC. While many human genes are dependent on introns and splicing for normal levels of gene expression, naturally intronless genes exist with a unique metabolism that allows for intron-independent gene expression. Effects of Wnt activity on the RNA metabolism of the intronless Wnt-target gene c-jun is a likely contributor to cancer development. Further, butyrate, a breakdown product of dietary fiber and a histone deacetylase inhibitor, upregulates Wnt activity in CRC cells, and also modulates RNA processing; therefore, the interplay between Wnt activity, the modulation of this activity by butyrate, and differential RNA metabolism in colonic cells can significantly influence tumorigenesis. Determining the role played by altered RNA processing in Wnt-mediated neoplasia may lead to novel interventions aimed at restoring normal RNA metabolism for therapeutic benefit. Therefore, this minireview presents a brief overview of several aspects of RNA processing of relevance to cancer, which potentially influence, or are influenced by, Wnt signaling activity.