This book is an accessible guide to adaptive signal processing methods that equips the reader with advanced theoretical and practical tools for the study and development of circuit structures and provides robust algorithms relevant to a wide variety of application scenarios. Examples include multimodal and multimedia communications, the biological and biomedical fields, economic models, environmental sciences, acoustics, telecommunications, remote sensing, monitoring, and, in general, the modeling and prediction of complex physical phenomena. The reader will learn not only how to design and implement the algorithms but also how to evaluate their performance for specific applications utilizing the tools provided. While using a simple mathematical language, the employed approach is very rigorous. The text will be of value both for research purposes and for courses of study.
Hall, Gregory A.; Youngquist, Robert; Mikhael, Wasfy
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.
Chen, Ya-Chin; Juang, Jer-Nan
Adaptive linear predictors have been used extensively in practice in a wide variety of forms. In the main, their theoretical development is based upon the assumption of stationarity of the signals involved, particularly with respect to the second order statistics. On this basis, the well-known normal equations can be formulated. If high- order statistical stationarity is assumed, then the equivalent normal equations involve high-order signal moments. In either case, the cross moments (second or higher) are needed. This renders the adaptive prediction procedure non-blind. A novel procedure for blind adaptive prediction has been proposed and considerable implementation has been made in our contributions in the past year. The approach is based upon a suitable interpretation of blind equalization methods that satisfy the constant modulus property and offers significant deviations from the standard prediction methods. These blind adaptive algorithms are derived by formulating Lagrange equivalents from mechanisms of constrained optimization. In this report, other new update algorithms are derived from the fundamental concepts of advanced system identification to carry out the proposed blind adaptive prediction. The results of the work can be extended to a number of control-related problems, such as disturbance identification. The basic principles are outlined in this report and differences from other existing methods are discussed. The applications implemented are speech processing, such as coding and synthesis. Simulations are included to verify the novel modelling method.
Muhammad Ali Raza Anjum
Full Text Available A unified linear algebraic approach to adaptive signal processing (ASP is presented. Starting from just Ax=b, key ASP algorithms are derived in a simple, systematic, and integrated manner without requiring any background knowledge to the field. Algorithms covered are Steepest Descent, LMS, Normalized LMS, Kaczmarz, Affine Projection, RLS, Kalman filter, and MMSE/Least Square Wiener filters. By following this approach, readers will discover a synthesis; they will learn that one and only one equation is involved in all these algorithms. They will also learn that this one equation forms the basis of more advanced algorithms like reduced rank adaptive filters, extended Kalman filter, particle filters, multigrid methods, preconditioning methods, Krylov subspace methods and conjugate gradients. This will enable them to enter many sophisticated realms of modern research and development. Eventually, this one equation will not only become their passport to ASP but also to many highly specialized areas of computational science and engineering.
Kozel, David; Nelson, Richard
A signal to noise ratio dependent adaptive spectral subtraction algorithm is developed to eliminate noise from noise corrupted speech signals. The algorithm determines the signal to noise ratio and adjusts the spectral subtraction proportion appropriately. After spectra subtraction low amplitude signals are squelched. A single microphone is used to obtain both eh 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 unvoice frames an estimate of the noise is obtained. A running average of the noise is used to approximate the expected value of the noise. Applications include the emergency egress vehicle and the crawler transporter.
Malyshkin, G. S.; Sidel'nikov, G. B.
Different methods of optimal and adaptive processing of hydroacoustic signals for multipath propagation and scattering are considered. Advantages and drawbacks of the classical adaptive (Capon, MUSIC, and Johnson) algorithms and "fast" projection algorithms are analyzed for the case of multipath propagation and scattering of strong signals. The classical optimal approaches to detecting multipath signals are presented. A mechanism of controlled normalization of strong signals is proposed to automatically detect weak signals. The results of simulating the operation of different detection algorithms for a linear equidistant array under multipath propagation and scattering are presented. An automatic detector is analyzed, which is based on classical or fast projection algorithms, which estimates the background proceeding from median filtering or the method of bilateral spatial contrast.
Sala i Álvarez, Josep; Vázquez Grau, Gregorio
A general criterion for the design of adaptive systems in digital communications called the statistical reference criterion is proposed. The criterion is based on imposition of the probability density function of the signal of interest at the output of the adaptive system, with its application to the scenario of highly powerful interferers being the main focus of this paper. The knowledge of the pdf of the wanted signal is used as a discriminator between signals so that i...
Xuan Huang; Wenhua Zeng
As with the development of computer technology and informatization, network technique, sensor technique and communication technology become three necessary components of information industry. As the core technique of sensor application, signal processing mainly determines the sensor performances. For this reason, study on signal processing mode is very important to sensors and the application of sensor network. In this paper, we introduce a new sensor coarse signal processing mode based on ad...
Prance, R J; Beardsmore-Rust, S; Prance, H; Harland, C J; Stiffell, P B
Current applications of the Electric Potential Sensor operate in a strongly (capacitively) coupled limit, with the sensor physically close to or touching the source. This mode of operation screens the sensor effectively from the majority of external noise. To date however the full capability of these sensors operating in a remote mode has not been realised outside of a screened environment (Faraday cage). This paper describes the results of preliminary work in tailoring the response of the sensors to particular signals and so reject background noise, thereby enhancing both the dynamic range and signal to noise ratio significantly
Chabries, D M; Christiansen, R W; Brey, R H; Robinette, M S; Harris, R W
A major complaint of individuals with normal hearing and hearing impairments is a reduced ability to understand speech in a noisy environment. This paper describes the concept of adaptive noise cancelling for removing noise from corrupted speech signals. Application of adaptive digital signal processing has long been known and is described from a historical as well as technical perspective. The Widrow-Hoff LMS (least mean square) algorithm developed in 1959 forms the introduction to modern adaptive signal processing. This method uses a "primary" input which consists of the desired speech signal corrupted with noise and a second "reference" signal which is used to estimate the primary noise signal. By subtracting the adaptively filtered estimate of the noise, the desired speech signal is obtained. Recent developments in the field as they relate to noise cancellation are described. These developments include more computationally efficient algorithms as well as algorithms that exhibit improved learning performance. A second method for removing noise from speech, for use when no independent reference for the noise exists, is referred to as single channel noise suppression. Both adaptive and spectral subtraction techniques have been applied to this problem--often with the result of decreased speech intelligibility. Current techniques applied to this problem are described, including signal processing techniques that offer promise in the noise suppression application.
Ahmad, Muneer; Jung, Low Tan; Bhuiyan, Al-Amin
Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals. This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise. Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms. This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary
Gauthier, Philippe-Aubert; Berry, Alain
Sound field reproduction is a physical approach to the reproduction of the natural spatial character of hearing. It is also useful in experimental acoustics and psychoacoustics. Wave field synthesis (WFS) is a known open-loop technology which assumes that the reproduction environment is anechoic. A real reflective reproduction space thus reduces the objective accuracy of WFS. Recently, adaptive wave field synthesis (AWFS) was defined as a combination of WFS and active compensation. AWFS is based on the minimization of reproduction errors and on the penalization of departure from the WFS solution. This paper focuses on signal processing for AWFS. A classical adaptive algorithm is modified for AWFS: filtered-reference least-mean-square. This modified algorithm and the classical equivalent leaky algorithm have similar convergence properties except that the WFS solution influences the adaptation rule of the modified algorithm. The paper also introduces signal processing for independent radiation mode control of AWFS on the basis of plant decoupling. Simulation results for AWFS are introduced for free-field and reflective spaces. The two algorithms effectively reproduce the sound field and compensate for the reproduction errors at the error sensors. The independent radiation mode control allows a more flexible tuning of the algorithm.
Ibey, Bennett; Subramanian, Hariharan; Ericson, Nance; Xu, Weijian; Wilson, Mark; Cote, Gerard L.
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.
Žigić Aleksandar D.
Full Text Available Experimental verifications of two optimized adaptive digital signal processing algorithms implemented in two pre set time count rate meters were per formed ac cording to appropriate standards. The random pulse generator realized using a personal computer, was used as an artificial radiation source for preliminary system tests and performance evaluations of the pro posed algorithms. Then measurement results for background radiation levels were obtained. Finally, measurements with a natural radiation source radioisotope 90Sr-90Y, were carried out. Measurement results, con ducted without and with radio isotopes for the specified errors of 10% and 5% showed to agree well with theoretical predictions.
An experimental, general purpose adaptive signal processor system has been developed, utilizing a quantized (clipped) version of the Widrow-Hoff least-mean-square adaptive algorithm developed by Moschner. The system accommodates 64 adaptive weight channels with 8-bit resolution for each weight. Internal weight update arithmetic is performed with 16-bit resolution, and the system error signal is measured with 12-bit resolution. An adapt cycle of adjusting all 64 weight channels is accomplished in 8 ..mu..sec. Hardware of the signal processor utilizes primarily Schottky-TTL type integrated circuits. A prototype system with 24 weight channels has been constructed and tested. This report presents details of the system design and describes basic experiments performed with the prototype signal processor. Finally some system configurations and applications for this adaptive signal processor are discussed.
An experimental, general purpose adaptive signal processor system has been developed, utilizing a quantized (clipped) version of the Widrow-Hoff least-mean-square adaptive algorithm developed by Moschner. The system accommodates 64 adaptive weight channels with 8-bit resolution for each weight. Internal weight update arithmetic is performed with 16-bit resolution, and the system error signal is measured with 12-bit resolution. An adapt cycle of adjusting all 64 weight channels is accomplished in 8 μsec. Hardware of the signal processor utilizes primarily Schottky-TTL type integrated circuits. A prototype system with 24 weight channels has been constructed and tested. This report presents details of the system design and describes basic experiments performed with the prototype signal processor. Finally some system configurations and applications for this adaptive signal processor are discussed
Chai, Tianyou; Jia, Yao; Wang, Hong; Su, Chun-Yi
The industrial heat supply process (HSP) is a highly nonlinear cascaded process which uses a steam valve opening as its control input, the steam flow-rate as its inner loop output and the supply water temperature as its outer loop output. The relationship between the heat exchange rate and the model parameters, such as steam density, entropy, and fouling correction factor and heat exchange efficiency are unknown and nonlinear. Moreover, these model parameters vary in line with steam pressure, ambient temperature and the residuals caused by the quality variations of the circulation water. When the steam pressure and the ambient temperature are of high values and are subjected to frequent external random disturbances, the supply water temperature and the steam flow-rate would interact with each other and fluctuate a lot. This is also true when the process exhibits unknown characteristic variations of the process dynamics caused by the unexpected changes of the heat exchange residuals. As a result, it is difficult to control the supply water temperature and the rates of changes of steam flow-rate well inside their targeted ranges. In this paper, a novel compensation signal based dual rate adaptive controller is developed by representing the unknown variations of dynamics as unmodeled dynamics. In the proposed controller design, such a compensation signal is constructed and added onto the control signal obtained from the linear deterministic model based feedback control design. Such a compensation signal aims at eliminating the unmodeled dynamics and the rate of changes of the currently sample unmodeled dynamics. A successful industrial application is carried out, where it has been shown that both the supply water temperature and the rate of the changes of the steam flow-rate can be controlled well inside their targeted ranges when the process is subjected to unknown variations of its dynamics.
A contaminant detection technique and its optimization algorithms have two principal functions. One is the adaptive signal treatment that suppresses background noise and enhances contaminant signals, leading to a promising detection of water quality changes at a false rate as low...
Moody, D. I.; Smith, D. A.; Light, T. E.; Suszcynsky, D. M.; Heavner, M.
Ongoing research at Los Alamos National Laboratory studies the Earth's radio frequency (RF) transient background utilizing satellite-based RF observations of terrestrial lightning. Such impulsive signals are dispersed as they travel through the ionosphere and appear as nonlinear chirps at a receiver on-orbit. Signals of interest are typically observed in the presence of additive noise and structured clutter, including gated and continuous-wave (CW) sources. Detection and classification of such non-stationary signals against a complex, non-stationary background can present challenges for standard physics-based approaches. The FORTE satellite provided a rich satellite lightning database that has been previously used for some event classification. We now develop and implement new event classification capability on the FORTE database using state-of-the-art adaptive signal processing combined with compressive sensing and machine learning techniques. The focus of our work is improved feature extraction using representations in overcomplete analytical dictionaries. We choose a dictionary based on Gabor chirplets, which is designed to represent both pulses (chirping or non-chirping) and CW signals in very few representative elements from the dictionary. One feature extraction approach is based on obtaining sparse representations of our data using a matching pursuit search of the dictionary. A second approach is based on using a frame operator on the dictionary to obtain a dense representation of our data. We explore robustness of extracted features to changes in background clutter and noise levels. Both feature extraction algorithms will be used in conjunction with statistical classifiers to explore classification performance of major lightning types. Performance will be evaluated both qualitatively, as well as quantitatively using a small validated test set. We present preliminary results of our work and discuss future areas of development.
Signal processing techniques, extensively used nowadays to maximize the performance of audio and video equipment, have been a key part in the design of hardware and software for high energy physics detectors since pioneering applications in the UA1 experiment at CERN in 1979
Wolfe, Jace; Neumann, Sara; Marsh, Megan; Schafer, Erin; Lianos, Leslie; Gilden, Jan; O'Neill, Lori; Arkis, Pete; Menapace, Christine; Nel, Esti; Jones, Marian
Cochlear implant recipients often experience difficulty understanding speech in noise. The primary objective of this study was to evaluate the potential improvement in speech recognition in noise provided by an adaptive, commercially available sound processor that performs acoustic scene classification and automatically adjusts input signal processing to maximize performance in noise. Within-subjects, repeated-measures design. This multicenter study was conducted across five sites in the U.S.A. and Australia. Ninety-three adults and children with Nucleus Freedom, CI422, and CI512 cochlear implants. Subjects (previous users of the Nucleus 5 sound processor) were fitted with the Nucleus 6 sound processor. Performance was assessed while these subjects used each sound processor in the manufacturer's recommended default program (standard directionality, ASC + ADRO for the Nucleus 5 processor and ASC + ADRO and SNR-NR with SCAN for the Nucleus 6 sound processor). The subjects were also evaluated with the Nucleus 6 with standard directionality, ASC + ADRO and SNR-NR enabled but SCAN disabled. Speech recognition in noise was assessed with AzBio sentences. Sentence recognition in noise was significantly better with the Nucleus 6 sound processor when used with the default input processing (ASC + ADRO, SNR-NR, and SCAN) compared to performance with the Nucleus 5 sound processor and default input processing (standard directionality, ASC + ADRO). Specifically, use of the Nucleus 6 at default settings resulted in a mean improvement in sentence recognition in noise of 27 percentage points relative to performance with the Nucleus 5 sound processor. Use of the Nucleus 6 sound processor using standard directionality, ASC + ADRO and SNR-NR (SCAN disabled) resulted in a mean improvement of 9 percentage points in sentence recognition in noise compared to performance with the Nucleus 5. The results of this study suggest that the Nucleus 6 sound processor with acoustic scene
Oliveira Filho, J.A. de; Papp, Z.; Djapic, R.; Oostveen, J.C.
The paper describes a model based approach for architecture design of runtime reconfigurable, large-scale, networked signal processing applications. A graph based modeling formalism is introduced to describe all relevant aspects of the design (functional, concurrency, hardware, communication,
Moniem, T. A.
This article presents a methodology for an integrated Bragg grating using an alloy of GaAs, AlGaAs, and InGaAs with a controllable refractive index to obtain an adaptive Bragg grating suitable for many applications on optical processing and adaptive control systems, such as limitation and filtering. The refractive index of a Bragg grating is controlled by using an external electric field for controlling periodic modulation of the refractive index of the active waveguide region. The designed Bragg grating has refractive indices programmed by using that external electric field. This article presents two approaches for designing the controllable refractive indices active region of a Bragg grating. The first approach is based on the modification of a planar micro-strip structure of the iGaAs traveling wave as the active region, and the second is based on the modification of self-assembled InAs/GaAs quantum dots of an alloy from GaAs and InGaAs with a GaP traveling wave. The overall design and results are discussed through numerical simulation by using the finite-difference time-domain, plane wave expansion, and opto-wave simulation methods to confirm its operation and feasibility.
Vetterli, Martin; Goyal, Vivek K
This comprehensive and engaging textbook introduces the basic principles and techniques of signal processing, from the fundamental ideas of signals and systems theory to real-world applications. Students are introduced to the powerful foundations of modern signal processing, including the basic geometry of Hilbert space, the mathematics of Fourier transforms, and essentials of sampling, interpolation, approximation and compression. The authors discuss real-world issues and hurdles to using these tools, and ways of adapting them to overcome problems of finiteness and localisation, the limitations of uncertainty and computational costs. Standard engineering notation is used throughout, making mathematical examples easy for students to follow, understand and apply. It includes over 150 homework problems and over 180 worked examples, specifically designed to test and expand students' understanding of the fundamentals of signal processing, and is accompanied by extensive online materials designed to aid learning, ...
Lidický, L.; Hoogeboom, P.
This paper presents a multi-channel synthetic aperture radar (SAR) signal model. Its purpose is to study nonstationary raw SAR signals in moving target indication (MTI) systems. This is opposed to the traditional approach in which only stationary (harmonic) signals are considered. The principal
Lee, Chae Uk
This book deal with the newest digital signal processing, which contains introduction on conception of digital signal processing, constitution and purpose, signal and system such as signal, continuos signal, discrete signal and discrete system, I/O expression on impress response, convolution, mutual connection of system and frequency character,z transform of definition, range, application of z transform and relationship with laplace transform, Discrete fourier, Fast fourier transform on IDFT algorithm and FFT application, foundation of digital filter of notion, expression, types, frequency characteristic of digital filter and design order of filter, Design order of filter, Design of FIR digital filter, Design of IIR digital filter, Adaptive signal processing, Audio signal processing, video signal processing and application of digital signal processing.
the signal can be reconstructed - + by forming; under the assumption that proju(s) = IsI costo and A0 - A A1+k] z1 proju(n) = InI cosx with 4 and W...state probability. A-2 2+T+p(-))+ Pk() C2 a. b b c b remaining states P2 -(Lc(b - a) + ac +PHa(b - c)) + L p(02) -, &2 abc remaining sLates -rk P 2 .abe
Choi, Julia T; Jensen, Peter; Nielsen, Jens Bo
anaesthesia (n = 5) instead of repetitive nerve stimulation. Foot anaesthesia reduced ankle adaptation to external force perturbations during walking. Our results suggest that cutaneous input plays a role in force perception, and may contribute to the 'error' signal involved in driving walking adaptation when...
Zhong, Zhun; Chen, Yingwei; Lan, Tse-Hua
Video decoding at reduced resolution with resizing embedded in the decoding loop saves computational resources such as memory, memory bandwidth and CPU cycles. Key to such embedded resizing is proper filtering/scaling of DCT data, and motion compensation at the reduced resolution. Although MPEG-2 video decoding with embedded resizing has been investigated in the past, little work has been reported on solving problems associated with interlaced video undergoing decoding with embedded resizing. In particular, annoying artifacts may occur in moving areas of interlaced video due to improper scaling or motion compensation. In this paper, we introduce the notion of the Local Interlacing Property for interlaced moving areas and propose algorithms to detect and process data with the Local Interlacing Property properly in the context of decoding with embedded resizing. Specifically, we demonstrate that 1) vertical high frequency in interlaced moving areas should be preserved during downscaling, and 2) phase shift must be added for motion compensation in interlaced moving areas under certain circumstances. Experimental results show that our method effectively removes artifacts in interlaced moving areas, making MPEG-2 video decoding with embedded resizing a practical tradeoff for interlaced video.
Lockhart, Gordon B
Basic Digital Signal Processing describes the principles of digital signal processing and experiments with BASIC programs involving the fast Fourier theorem (FFT). The book reviews the fundamentals of the BASIC program, continuous and discrete time signals including analog signals, Fourier analysis, discrete Fourier transform, signal energy, power. The text also explains digital signal processing involving digital filters, linear time-variant systems, discrete time unit impulse, discrete-time convolution, and the alternative structure for second order infinite impulse response (IIR) sections.
Delisle-Rodriguez, Denis; Villa-Parra, Ana Cecilia; Bastos-Filho, Teodiano; López-Delis, Alberto; Frizera-Neto, Anselmo; Krishnan, Sridhar; Rocon, Eduardo
This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly ( p < 0.01 ) improved for most of the subjects ( A C C ≥ 74.79 % ) , when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry.
Full Text Available This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs based on Canonical Correlation Analysis (CCA to recognize 40 targets of steady-state visual evoked potential (SSVEP, providing an accuracy (ACC of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly ( p < 0.01 improved for most of the subjects ( A C C ≥ 74.79 % , when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry.
Biemond, J.; Slump, C.H.; Lagendijk, R.L.; Tolhuizen, L.M.G.M.; de With, P.H.N.
Digital Signal Processing (DSP) concerns the theoretical and practical aspects of representing information-bearing signals in digital form and the use of processors or special purpose hardware to extract that information or to transform the signals in useful ways. Areas where digital signal
NDT begins to adapt and use the most recent developments of digital signal and image processing. We briefly sum up the main characteristics of NDT situations (particularly noise and inverse problem formulation) and comment on techniques already used or just emerging (SAFT, split spectrum, adaptive learning network, noise reference filtering, stochastic models, neural networks). This survey is focused on ultrasonics, eddy currents and X-ray radiography. The final objective of end users (availability of automatic diagnosis systems) cannot be achieved only by signal processing algorithms. A close cooperation with other techniques such as artificial intelligence has therefore to be implemented. (author). 20 refs
Sophisticated techniques for signal processing are now available to the biomedical specialist! Written in an easy-to-read, straightforward style, Biomedical Signal Processing presents techniques to eliminate background noise, enhance signal detection, and analyze computer data, making results easy to comprehend and apply. In addition to examining techniques for electrical signal analysis, filtering, and transforms, the author supplies an extensive appendix with several computer programs that demonstrate techniques presented in the text.
A universal expansion of a CAMAC-subsystem - BORER 3000 - for adapting analysis instruments in biochemistry to a processing computer is described. The possibility of standardizing input interfaces for lab instruments with such circuits is discussed and the advantages achieved by applying the CAMAC-specifications are described
Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)
Santurette, Sébastien; Christensen-Dalsgaard, Jakob; Tranebjærg, Lisbeth
induced by hearing impairment and the compensation provided by hearing devices. These devices themselves are now able to adapt to the listener’s individual environment, attentional state, and behavior. These topics related to auditory adaptation, in the broad sense of the term, were central to the 6th......Our auditory environment is constantly changing and evolving over time, requiring us to rapidly adapt to a complex dynamic sensory input. This adaptive ability of our auditory system can be observed at different levels, from individual cell responses to complex neural mechanisms and behavior...... International Symposium on Auditory and Audiological Research held in Nyborg, Denmark, in August 2017. The symposium addressed adaptive processes in hearing from different angles, together with a wide variety of other auditory and audiological topics. The papers in this special issue result from some...
O'Shea, Peter; Hussain, Zahir M
In three parts, this book contributes to the advancement of engineering education and that serves as a general reference on digital signal processing. Part I presents the basics of analog and digital signals and systems in the time and frequency domain. It covers the core topics: convolution, transforms, filters, and random signal analysis. It also treats important applications including signal detection in noise, radar range estimation for airborne targets, binary communication systems, channel estimation, banking and financial applications, and audio effects production. Part II considers sel
Oxenløwe, Leif Katsuo
Optical time lenses have proven to be very versatile for advanced optical signal processing. Based on a controlled interplay between dispersion and phase-modulation by e.g. four-wave mixing, the processing is phase-preserving, an hence useful for all types of data signals including coherent multi......-level modulation founats. This has enabled processing of phase-modulated spectrally efficient data signals, such as orthogonal frequency division multiplexed (OFDM) signa In that case, a spectral telescope system was used, using two time lenses with different focal lengths (chirp rates), yielding a spectral...... regeneratio These operations require a broad bandwidth nonlinear platform, and novel photonic integrated nonlinear platform like aluminum gallium arsenide nano-waveguides used for 1.28 Tbaud optical signal processing will be described....
Huang, Yiteng; Chen, Jingdong
A timely and important book addressing a variety of acoustic signal processing problems under multiple-input multiple-output (MIMO) scenarios. It uniquely investigates these problems within a unified framework offering a novel and penetrating analysis.
Signal processing is the discipline of extracting information from collections of measurements. To be effective, the measurements must be organized and then filtered, detected, or transformed to expose the desired information. Distortions caused by uncertainty, noise, and clutter degrade the performance of practical signal processing systems. In aggressively uncertain situations, the full truth about an underlying signal cannot be known. This book develops the theory and practice of signal processing systems for these situations that extract useful, qualitative information using the mathematics of topology -- the study of spaces under continuous transformations. Since the collection of continuous transformations is large and varied, tools which are topologically-motivated are automatically insensitive to substantial distortion. The target audience comprises practitioners as well as researchers, but the book may also be beneficial for graduate students.
This book is the first in a set of forthcoming books focussed on state-of-the-art development in the VLSI Signal Processing area. It is a response to the tremendous research activities taking place in that field. These activities have been driven by two factors: the dramatic increase in demand for high speed signal processing, especially in consumer elec tronics, and the evolving microelectronic technologies. The available technology has always been one of the main factors in determining al gorithms, architectures, and design strategies to be followed. With every new technology, signal processing systems go through many changes in concepts, design methods, and implementation. The goal of this book is to introduce the reader to the main features of VLSI Signal Processing and the ongoing developments in this area. The focus of this book is on: • Current developments in Digital Signal Processing (DSP) pro cessors and architectures - several examples and case studies of existing DSP chips are discussed in...
Genomic signal processing (GSP) can be defined as the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Situated at the crossroads of engineering, biology, mathematics, statistics, and computer science, GSP requires the development of both nonlinear dynamical models that adequately represent genomic regulation, and diagnostic and therapeutic tools based on these models. This book facilitates these developments by providing rigorous mathema
National Aeronautics and Space Administration — Micro Analysis and Design (MA&D) is pleased to submit this proposal to design an Intelligent Multimodal Signal Adaptation System. This system will dynamically...
Taking a novel, less classical approach to the subject, the authors have written this book with the conviction that signal processing should be fun. Their treatment is less focused on the mathematics and more on the conceptual aspects, allowing students to think about the subject at a higher conceptual level, thus building the foundations for more advanced topics and helping students solve real-world problems. The last chapter pulls together the individual topics into an in-depth look at the development of an end-to-end communication system. Richly illustrated with examples and exercises in ea
Abbas, Abbas K
The auscultation method is an important diagnostic indicator for hemodynamic anomalies. Heart sound classification and analysis play an important role in the auscultative diagnosis. The term phonocardiography refers to the tracing technique of heart sounds and the recording of cardiac acoustics vibration by means of a microphone-transducer. Therefore, understanding the nature and source of this signal is important to give us a tendency for developing a competent tool for further analysis and processing, in order to enhance and optimize cardiac clinical diagnostic approach. This book gives the
Sahr, John D.; Mir, Hasan; Morabito, Andrew; Grossman, Matthew
Our role in this project was to participate in the design of the signal processing suite to analyze plasma density measurements on board a small constellation (3 or 4) satellites in Low Earth Orbit. As we are new to space craft experiments, one of the challenges was to simply gain understanding of the quantity of data which would flow from the satellites, and possibly to interact with the design teams in generating optimal sampling patterns. For example, as the fleet of satellites were intended to fly through the same volume of space (displaced slightly in time and space), the bulk plasma structure should be common among the spacecraft. Therefore, an optimal, limited bandwidth data downlink would take advantage of this commonality. Also, motivated by techniques in ionospheric radar, we hoped to investigate the possibility of employing aperiodic sampling in order to gain access to a wider spatial spectrum without suffering aliasing in k-space.
INTRODUCTION TO DIGITAL SIGNAL AND IMAGE PROCESSINGSignals and Biomedical Signal ProcessingIntroduction and OverviewWhat is a ""Signal""?Analog, Discrete, and Digital SignalsProcessing and Transformation of SignalsSignal Processing for Feature ExtractionSome Characteristics of Digital ImagesSummaryProblemsFourier TransformIntroduction and OverviewOne-Dimensional Continuous Fourier TransformSampling and NYQUIST RateOne-Dimensional Discrete Fourier TransformTwo-Dimensional Discrete Fourier TransformFilter DesignSummaryProblemsImage Filtering, Enhancement, and RestorationIntroduction and Overview
Vatsa, Mayank; Majumdar, Angshul; Kumar, Ajay
This book comprises chapters on key problems in machine learning and signal processing arenas. The contents of the book are a result of a 2014 Workshop on Machine Intelligence and Signal Processing held at the Indraprastha Institute of Information Technology. Traditionally, signal processing and machine learning were considered to be separate areas of research. However in recent times the two communities are getting closer. In a very abstract fashion, signal processing is the study of operator design. The contributions of signal processing had been to device operators for restoration, compression, etc. Applied Mathematicians were more interested in operator analysis. Nowadays signal processing research is gravitating towards operator learning – instead of designing operators based on heuristics (for example wavelets), the trend is to learn these operators (for example dictionary learning). And thus, the gap between signal processing and machine learning is fast converging. The 2014 Workshop on Machine Intel...
This book provides a clear understanding of the principles of signal processing of radiation detectors. It puts great emphasis on the characteristics of pulses from various types of detectors and offers a full overview on the basic concepts required to understand detector signal processing systems and pulse processing techniques. Signal Processing for Radiation Detectors covers all of the important aspects of signal processing, including energy spectroscopy, timing measurements, position-sensing, pulse-shape discrimination, and radiation intensity measurement. The book encompasses a wide range of applications so that readers from different disciplines can benefit from all of the information. In addition, this resource: * Describes both analog and digital techniques of signal processing * Presents a complete compilation of digital pulse processing algorithms * Extrapolates content from more than 700 references covering classic papers as well as those of today * Demonstrates concepts with more than 340 origin...
Crocker, Matthew W
This book investigates the adaptation of cognitive processes to limited resources. The central topics of this book are heuristics considered as results of the adaptation to resource limitations, through natural evolution in the case of humans, or through artificial construction in the case of computational systems; the construction and analysis of resource control in cognitive processes; and an analysis of resource-adaptivity within the paradigm of concurrent computation. The editors integrated the results of a collaborative 5-year research project that involved over 50 scientists. After a mot
Kay, Steven M
A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. Covers important approaches to obtaining an optimal estimator and analyzing its performance; and includes numerous examples as well as applications to real- world problems. MARKETS: For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals — radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc.
Full Text Available Motivated by the benefits of array signal processing in quaternion domain, we investigate the problem of adaptive beamforming based on complex quaternion processes in this paper. First, a complex quaternion least-mean squares (CQLMS algorithm is proposed and its performance is analyzed. The CQLMS algorithm is suitable for adaptive beamforming of vector-sensor array. The weight vector update of CQLMS algorithm is derived based on the complex gradient, leading to lower computational complexity. Because the complex quaternion can exhibit the orthogonal structure of an electromagnetic vector-sensor in a natural way, a complex quaternion model in time domain is provided for a 3-component vector-sensor array. And the normalized adaptive beamformer using CQLMS is presented. Finally, simulation results are given to validate the performance of the proposed adaptive beamformer.
Rechester, A.B.; White, R.B.
Complex dynamic processes exhibit many complicated patterns of evolution. How can all these patterns be recognized using only output (observational, experimental) data without prior knowledge of the equations of motion? The powerful method for doing this is based on symbolic dynamics: (1) Present output data in symbolic form (trial language). (2) Topological and metric entropies are constructed. (3) Develop algorithms for computer optimization of entropies. (4) By maximizing entropies, find the most appropriate symbolic language for the purpose of pattern recognition. (5) Test this method using a variety of dynamical models from nonlinear science. The authors are in the process of applying this method for analysis of MHD fluctuations in tokamaks
Alberto, Diego; Musa, L
It is well known that many of the scientific and technological discoveries of the XXI century will depend on the capability of processing and understanding a huge quantity of data. With the advent of the digital era, a fully digital and automated treatment can be designed and performed. From data mining to data compression, from signal elaboration to noise reduction, a processing is essential to manage and enhance features of interest after every data acquisition (DAQ) session. In the near future, science will go towards interdisciplinary research. In this work there will be given an example of the application of signal processing to different fields of Physics from nuclear particle detectors to biomedical examinations. In Chapter 1 a brief description of the collaborations that allowed this thesis is given, together with a list of the publications co-produced by the author in these three years. The most important notations, definitions and acronyms used in the work are also provided. In Chapter 2, the last r...
Naidu, Prabhakar S
Chapter One: An Overview of Wavefields 1.1 Types of Wavefields and the Governing Equations 1.2 Wavefield in open space 1.3 Wavefield in bounded space 1.4 Stochastic wavefield 1.5 Multipath propagation 1.6 Propagation through random medium 1.7 ExercisesChapter Two: Sensor Array Systems 2.1 Uniform linear array (ULA) 2.2 Planar array 2.3 Distributed sensor array 2.4 Broadband sensor array 2.5 Source and sensor arrays 2.6 Multi-component sensor array2.7 ExercisesChapter Three: Frequency Wavenumber Processing 3.1 Digital filters in the w-k domain 3.2 Mapping of 1D into 2D filters 3.3 Multichannel Wiener filters 3.4 Wiener filters for ULA and UCA 3.5 Predictive noise cancellation 3.6 Exercises Chapter Four: Source Localization: Frequency Wavenumber Spectrum4.1 Frequency wavenumber spectrum 4.2 Beamformation 4.3 Capon's w-k spectrum 4.4 Maximum entropy w-k spectrum 4.5 Doppler-Azimuth Processing4.6 ExercisesChapter Five: Source Localization: Subspace Methods 5.1 Subspace methods (Narrowband) 5.2 Subspace methods (B...
Plesinger, F; Jurco, J; Halamek, J; Jurak, P
The growing technical standard of acquisition systems allows the acquisition of large records, often reaching gigabytes or more in size as is the case with whole-day electroencephalograph (EEG) recordings, for example. Although current 64-bit software for signal processing is able to process (e.g. filter, analyze, etc) such data, visual inspection and labeling will probably suffer from rather long latency during the rendering of large portions of recorded signals. For this reason, we have developed SignalPlant-a stand-alone application for signal inspection, labeling and processing. The main motivation was to supply investigators with a tool allowing fast and interactive work with large multichannel records produced by EEG, electrocardiograph and similar devices. The rendering latency was compared with EEGLAB and proves significantly faster when displaying an image from a large number of samples (e.g. 163-times faster for 75 × 10(6) samples). The presented SignalPlant software is available free and does not depend on any other computation software. Furthermore, it can be extended with plugins by third parties ensuring its adaptability to future research tasks and new data formats.
Rao, K Deergha
The book provides a comprehensive exposition of all major topics in digital signal processing (DSP). With numerous illustrative examples for easy understanding of the topics, it also includes MATLAB-based examples with codes in order to encourage the readers to become more confident of the fundamentals and to gain insights into DSP. Further, it presents real-world signal processing design problems using MATLAB and programmable DSP processors. In addition to problems that require analytical solutions, it discusses problems that require solutions using MATLAB at the end of each chapter. Divided into 13 chapters, it addresses many emerging topics, which are not typically found in advanced texts on DSP. It includes a chapter on adaptive digital filters used in the signal processing problems for faster acceptable results in the presence of changing environments and changing system requirements. Moreover, it offers an overview of wavelets, enabling readers to easily understand the basics and applications of this po...
Frankenstein, B.; Froehlich, K.-J.; Hentschel, D.; Reppe, G.
Acoustic monitoring of technological processes requires methods that eliminate noise as much as possible. Sensor-near signal evaluation can contribute substantially. Frequently, a further necessity exists to integrate the measuring technique in the monitored structure. The solution described contains components for analog preprocessing of acoustic signals, their digitization, algorithms for data reduction, and digital communication. The core component is a digital signal processor (DSP). Digital signal processors perform the algorithms necessary for filtering, down sampling, FFT computation and correlation of spectral components particularly effective. A compact, sensor-near signal processing structure was realized. It meets the Match-X standard, which as specified by the German Association for Mechanical and Plant Engineering (VDMA) for development of micro-technical modules, which can be combined to applicaiton specific systems. The solution is based on AL2O3 ceramic components including different signal processing modules as ADC, as well as memory and power supply. An arbitrary waveform generator has been developed and combined with a power amplifier for piezoelectric transducers in a special module. A further module interfaces to these transducers. It contains a multi-channel preamplifier, some high-pass filters for analog signal processing and an ADC-driver. A Bluetooth communication chip for wireless data transmission and a DiscOnChip module are under construction. As a first application, the combustion behavior of safety-relevant contacts is monitored. A special waveform up to 5MHz is produced and sent to the monitored object. The resulting signal form is evaluated with special algorithms, which extract significant parameters of the signal, and transmitted via CAN-bus.
Ubiquitin can form eight different linkage types of chains using the intrinsic Met 1 residue or one of the seven intrinsic Lys residues. Each linkage type of ubiquitin chain has a distinct three-dimensional topology, functioning as a tag to attract specific signaling molecules, which are so-called ubiquitin readers, and regulates various biological functions. Ubiquitin chains linked via Met 1 in a head-to-tail manner are called linear ubiquitin chains. Linear ubiquitination plays an important role in the regulation of cellular signaling, including the best-characterized tumor necrosis factor (TNF)-induced canonical nuclear factor-κB (NF-κB) pathway. Linear ubiquitin chains are specifically generated by an E3 ligase complex called the linear ubiquitin chain assembly complex (LUBAC) and hydrolyzed by a deubiquitinase (DUB) called ovarian tumor (OTU) DUB with linear linkage specificity (OTULIN). LUBAC linearly ubiquitinates critical molecules in the TNF pathway, such as NEMO and RIPK1. The linear ubiquitin chains are then recognized by the ubiquitin readers, including NEMO, which control the TNF pathway. Accumulating evidence indicates an importance of the LUBAC complex in the regulation of apoptosis, development, and inflammation in mice. In this article, I focus on the role of linear ubiquitin chains in adaptive immune responses with an emphasis on the TNF-induced signaling pathways. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Cerutti, Sergio; Baselli, Giuseppe; Bianchi, Anna; Caiani, Enrico; Contini, Davide; Cubeddu, Rinaldo; Dercole, Fabio; Rienzo, Luca; Liberati, Diego; Mainardi, Luca; Ravazzani, Paolo; Rinaldi, Sergio; Signorini, Maria; Torricelli, Alessandro
Generally, physiological modeling and biomedical signal processing constitute two important paradigms of biomedical engineering (BME): their fundamental concepts are taught starting from undergraduate studies and are more completely dealt with in the last years of graduate curricula, as well as in Ph.D. courses. Traditionally, these two cultural aspects were separated, with the first one more oriented to physiological issues and how to model them and the second one more dedicated to the development of processing tools or algorithms to enhance useful information from clinical data. A practical consequence was that those who did models did not do signal processing and vice versa. However, in recent years,the need for closer integration between signal processing and modeling of the relevant biological systems emerged very clearly , . This is not only true for training purposes(i.e., to properly prepare the new professional members of BME) but also for the development of newly conceived research projects in which the integration between biomedical signal and image processing (BSIP) and modeling plays a crucial role. Just to give simple examples, topics such as brain–computer machine or interfaces,neuroengineering, nonlinear dynamical analysis of the cardiovascular (CV) system,integration of sensory-motor characteristics aimed at the building of advanced prostheses and rehabilitation tools, and wearable devices for vital sign monitoring and others do require an intelligent fusion of modeling and signal processing competences that are certainly peculiar of our discipline of BME.
Fu, Changjun; Ji, Xiangyang; Dai, Qionghai
Perfect compressed sensing (CS) recovery can be achieved when a certain basis space is found to sparsely represent the original signal. However, due to the diversity of the signals, there does not exist a universal predetermined basis space that can sparsely represent all kinds of signals, which results in an unsatisfying performance. To improve the accuracy of recovered signal, this paper proposes an adaptive basis CS reconstruction algorithm by minimizing the rank of an accumulated matrix (MRAM), whose eigenvectors approximate the optimal basis sparsely representing the original signal. The accumulated matrix is constructed to efficiently exploit the second-order statistical property of the signal's autocorrelations. Based on the theory of matrix completion, MRAM reconstructs the original signal from its random projections under the observation that the constructed accumulated matrix is of low rank for most natural signals such as periodic signals and those coming from an autoregressive stationary process. Experimental results show that the proposed MRAM efficiently improves the reconstruction quality compared with the existing algorithms.
The seventeenth of a series of workshops sponsored by the IEEE Signal Processing Society and organized by the Machine Learning for Signal Processing Technical Committee (MLSP-TC). The field of machine learning has matured considerably in both methodology and real-world application domains and has...... become particularly important for solution of problems in signal processing. As reflected in this collection, machine learning for signal processing combines many ideas from adaptive signal/image processing, learning theory and models, and statistics in order to solve complex real-world signal processing......, and two papers from the winners of the Data Analysis Competition. The program included papers in the following areas: genomic signal processing, pattern recognition and classification, image and video processing, blind signal processing, models, learning algorithms, and applications of machine learning...
Jayaweera, Sudharman K
This book covers power electronics, in depth, by presenting the basic principles and application details, and it can be used both as a textbook and reference book. Introduces the specific type of CR that has gained the most research attention in recent years: the CR for Dynamic Spectrum Access (DSA). Provides signal processing solutions to each task by relating the tasks to materials covered in Part II. Specialized chapters then discuss specific signal processing algorithms required for DSA and DSS cognitive radios
PSpice for Digital Signal Processing is the last in a series of five books using Cadence Orcad PSpice version 10.5 and introduces a very novel approach to learning digital signal processing (DSP). DSP is traditionally taught using Matlab/Simulink software but has some inherent weaknesses for students particularly at the introductory level. The 'plug in variables and play' nature of these software packages can lure the student into thinking they possess an understanding they don't actually have because these systems produce results quicklywithout revealing what is going on. However, it must be
Written by leaders in the field, Signal Processing for Remote Sensing explores the data acquisitions segment of remote sensing. Each chapter presents a major research result or the most up to date development of a topic. The book includes a chapter by Dr. Norden Huang, inventor of the Huang-Hilbert transform who, along with and Dr. Steven Long discusses the application of the transform to remote sensing problems. It also contains a chapter by Dr. Enders A. Robinson, who has made major contributions to seismic signal processing for over half a century, on the basic problem of constructing seism
Since their appearance in mid-1980s, wavelets and, more generally, multiscale methods have become powerful tools in mathematical analysis and in applications to numerical analysis and signal processing. This book is based on "Ondelettes et Traitement Numerique du Signal" by Albert Cohen. It has been translated from French by Robert D. Ryan and extensively updated by both Cohen and Ryan. It studies the existing relations between filter banks and wavelet decompositions and shows how these relations can be exploited in the context of digital signal processing. Throughout, the book concentrates on the fundamentals. It begins with a chapter on the concept of multiresolution analysis, which contains complete proofs of the basic results. The description of filter banks that are related to wavelet bases is elaborated in both the orthogonal case (Chapter 2), and in the biorthogonal case (Chapter 4). The regularity of wavelets, how this is related to the properties of the filters and the importance of regularity for t...
Alvarez, A.; Premkumar, A. B.
An economical and efficient VLSI implementation of a mixed signal processing system (MSP) is presented in this paper. The MSP concept is investigated and the functional blocks of the proposed MSP are described. The requirements of each of the blocks are discussed in detail. A sample application using active acoustic cancellation technique is described to demonstrate the power of the MSP approach.
Bhattacharyya, Shuvra S; Leupers, Rainer; Takala, Jarmo
The Handbook is organized in four parts. The first part motivates representative applications that drive and apply state-of-the art methods for design and implementation of signal processing systems; the second part discusses architectures for implementing these applications; the third part focuses on compilers and simulation tools; and the fourth part describes models of computation and their associated design tools and methodologies.
Zhang, Carolyn; Tsoi, Ryan; Wu, Feilun; 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 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.
Craven, Darren; McGinley, Brian; Kilmartin, Liam; Glavin, Martin; Jones, Edward
This paper proposes a novel adaptive dictionary (AD) reconstruction scheme to improve the performance of compressed sensing (CS) with electrocardiogram signals (ECG). The method is based on the use of multiple dictionaries, created using dictionary learning (DL) techniques for CS signal reconstruction. The modified reconstruction framework is a two-stage process that leverages information about the signal from an initial signal reconstruction stage. By identifying whether a QRS complex is present and if so, determining a location estimate of the QRS, the most appropriate dictionary is selected and a second stage more refined signal reconstruction can be obtained. The performance of the proposed algorithm is compared with state-of-the-art CS implementations in the literature, as well as the set partitioning in hierarchical trees (SPIHT) wavelet-based lossy compression algorithm. The results indicate that the proposed reconstruction scheme outperforms all existing CS implementations in terms of signal fidelity at each compression ratio tested. The performance of the proposed approach also compares favorably with SPIHT in terms of signal reconstruction quality. Furthermore, an analysis of the overall power consumption of the proposed ECG compression framework as would be used in a body area network (BAN) demonstrates positive results for the proposed CS approach when compared with existing CS techniques and SPIHT.
Huxtable, Barton D.; Jackson, Christopher R.; Skaron, Steve A.
Synthetic aperture radar (SAR) is uniquely suited to help solve the Search and Rescue problem since it can be utilized either day or night and through both dense fog or thick cloud cover. Other papers in this session, and in this session in 1997, describe the various SAR image processing algorithms that are being developed and evaluated within the Search and Rescue Program. All of these approaches to using SAR data require substantial amounts of digital signal processing: for the SAR image formation, and possibly for the subsequent image processing. In recognition of the demanding processing that will be required for an operational Search and Rescue Data Processing System (SARDPS), NASA/Goddard Space Flight Center and NASA/Stennis Space Center are conducting a technology demonstration utilizing SHARC multi-chip modules from Boeing to perform SAR image formation processing.
Zidan, Mohammed A.
Disclosed are various embodiments for adaptive processing for sequence alignment. In one embodiment, among others, a method includes obtaining a query sequence and a plurality of database sequences. A first portion of the plurality of database sequences is distributed to a central processing unit (CPU) and a second portion of the plurality of database sequences is distributed to a graphical processing unit (GPU) based upon a predetermined splitting ratio associated with the plurality of database sequences, where the database sequences of the first portion are shorter than the database sequences of the second portion. A first alignment score for the query sequence is determined with the CPU based upon the first portion of the plurality of database sequences and a second alignment score for the query sequence is determined with the GPU based upon the second portion of the plurality of database sequences.
This thesis introduces a novel approach to programmable and low power platform design for audio signal processing, in particular hearing aids. The proposed programmable platform is a heterogeneous multiprocessor architecture consisting of small and simple instruction set processors called mini......-cores as well as standard DSP/CPU-cores that communicate using message passing. The work has been based on a study of the algorithm suite covering the application domain. The observation of dominant tasks for certain algorithms (FIR, IIR, correlation, etc.) that require custom computational units and special...... data addressing capabilities lead to the design of low power mini-cores. The algorithm suite also consisted of less demanding and/or irregular algorithms (LMS, compression) that required subsample rate signal processing justifying the use of a DSP/CPU-core. The thesis also contributes to the recent...
A transparent signal-processing payload architecture applicable to mobile communication satellites is introduced, and its features and implementation issues are discussed. In its basic form it is characterized by the formation of a large number of narrowband beams directed at the individual users on ground, and is demonstrated to offer improved transmit power efficiency, frequency-reuse capability and traffic-routing flexibility. The processor implementation is envisaged to make extensive use of digital processing functions and ASIC technology combined with advanced SAW techniques. In addition to its inherent attractive features, this architecture provides many of the benefits of full onboard regeneration and processing while preserving most of the flexibility of conventional analog transponders. Simplified derivatives of the basic configuration that offer reduced processing complexity while preserving the essential advantages gained are also presented. Although initially conceived for FDMA/SCPC-type traffic, the concept can also be adapted to other transmission formats.
Padgett, Wayne T
This book is intended to fill the gap between the ""ideal precision"" digital signal processing (DSP) that is widely taught, and the limited precision implementation skills that are commonly required in fixed-point processors and field programmable gate arrays (FPGAs). These skills are often neglected at the university level, particularly for undergraduates. We have attempted to create a resource both for a DSP elective course and for the practicing engineer with a need to understand fixed-point implementation. Although we assume a background in DSP, Chapter 2 contains a review of basic theory
Oxenløwe, Leif Katsuo; Galili, Michael; Mulvad, Hans Christian Hansen
This paper describes the use of optical time lenses for optical signal processing of advanced optical data signals. Examples given include 1.28 Tbaud Nyquist channel serial-to-parallel conversion and spectral magnification of OFDM signals.......This paper describes the use of optical time lenses for optical signal processing of advanced optical data signals. Examples given include 1.28 Tbaud Nyquist channel serial-to-parallel conversion and spectral magnification of OFDM signals....
Vaseghi, Saeed V
Digital signal processing plays a central role in the development of modern communication and information processing systems. The theory and application of signal processing is concerned with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and therefore noise reduction, the removal of channel distortion, and replacement of lost samples are important parts of a signal processing system. The fourth edition of Advanced Digital Signal Processing and Noise Reduction updates an
Shen Kuan; Cai Yufang; Duan Liming
This paper describes principle and character of digital radiography. After analyzing the drawbacks of current processing methods and specialty of collected signals, a new self-adapting method based on the wavelet transform is applied to process radiation image. The method maps the subsection of signal to 0-255 to form several gray images and then fuses these images to form a new enhanced image, then uses nonlinear color assignment scheme increasing the image resolution. The experiment results show that the self-adapting processing method is better than traditional ones. (authors)
The contents of this book contains basic practice of CEM Tool, discrete time signal and experiment and practice of system, experiment and practice of discrete time signal sampling, practice of frequency analysis, experiment of digital filter design, application of digital signal processing, project related voice, basic principle of signal processing, the technique of basic image signal processing, biology astronomy and Robot soccer with apply of image signal processing technique, control video signal and project related image. It also has an introduction of CEM Linker I. O in the end.
Schilling, Robert L
Focus on the development, implementation, and application of modern DSP techniques with DIGITAL SIGNAL PROCESSING USING MATLAB(R), 3E. Written in an engaging, informal style, this edition immediately captures your attention and encourages you to explore each critical topic. Every chapter starts with a motivational section that highlights practical examples and challenges that you can solve using techniques covered in the chapter. Each chapter concludes with a detailed case study example, a chapter summary with learning outcomes, and practical homework problems cross-referenced to specific chapter sections for your convenience. DSP Companion software accompanies each book to enable further investigation. The DSP Companion software operates with MATLAB(R) and provides intriguing demonstrations as well as interactive explorations of analysis and design concepts.
Kalinin, Yu.G.; Martyanov, I.S.; Sadykov, Kh.; Zastrozhnova, N.N.
In this paper we briefly describe the time sampling method, which is adapted to the speed of the signal change. Principally, this method is based on a simple idea--the combination of discrete integration with differentiation of the analog signal. This method can be used in nuclear electronics research into the characteristics of detectors and the shape of the pulse signal, pulse and transitive characteristics of inertial systems of processing of signals, etc
This paper presents an adaptive, heart-model-based electrocardiography (ECG) compression method. After conventional pre-filtering the waves from the signal are localized and the model's parameters are determined...
Meyers, James F.; Clemmons, James I., Jr.
A new scheme for processing signals from laser velocimeter systems is described. The technique utilizes the capabilities of advanced digital electronics to yield a smart instrument that is able to configure itself, based on the characteristics of the input signals, for optimum measurement accuracy. The signal processor is composed of a high-speed 2-bit transient recorder for signal capture and a combination of adaptive digital filters with energy and/or zero crossing detection signal processing. The system is designed to accept signals with frequencies up to 100 MHz with standard deviations up to 20 percent of the average signal frequency. Results from comparative simulation studies indicate measurement accuracies 2.5 times better than with a high-speed burst counter, from signals with as few as 150 photons per burst.
Purpose: To carry out the works signal processing, in a nuclear power plant, automatically. Constitution: Neutron flux signal from reactor instruments is inputted into a reactivity meter, and processes in accordance with reactor characteristic equations and, as the result, outputted as a reactivity signal rho. The signal rho, as well as the neutron flux signal and temperature signal are inputted together to a pen recorder and recorded on a record chart. While on the other hand, these signals are inputted into a signal processor, together with a range-switching signal from the reactivity meter and with a control-rod-position signal n from other instrument. In the signal processor, the differentiation and integration values such as Δrho/(n 1 -n 2 ) and ΣΔrho of the control element are conducted automatically. The results are indicated on a graphic display. (J.P.N.)
Award 1 Title: Acoustic Communications 2011 Experiment: Deployment Support and Post Experiment Data Handling and Analysis. Award 2 Title: Exploiting Structured Dependencies in the Design of Adaptive Algorithms for Underwater Communication Award. 3 Title: Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems
Exploiting Structured Dependencies in the Design of Adaptive Algorithms for Underwater Communication Award #3 Title Coupled Research in Ocean Acoustics ...and Signal Processing for theNext Generation of Underwater Acoustic Communication Systems James Preisig Woods Hole Oceanographic Institution...Dept. of Applied Ocean Physics and Engineering Ocean Acoustics and Signals Laboratory Woods Hole, MA 02540 AND JPAnalytics LLC 638 Brick Kiln
Garcia, Thomas R.
A digital communications signal is a sinusoidal waveform that is modified by a binary (digital) information signal. The sinusoidal waveform is called the carrier. The carrier may be modified in amplitude, frequency, phase, or a combination of these. In this project a binary phase shift keyed (BPSK) signal is the communication signal. In a BPSK signal the phase of the carrier is set to one of two states, 180 degrees apart, by a binary (i.e., 1 or 0) information signal. A digital signal is a sampled version of a "real world" time continuous signal. The digital signal is generated by sampling the continuous signal at discrete points in time. The rate at which the signal is sampled is called the sampling rate (f(s)). The device that performs this operation is called an analog-to-digital (A/D) converter or a digitizer. The digital signal is composed of the sequence of individual values of the sampled BPSK signal. Digital signal processing (DSP) is the modification of the digital signal by mathematical operations. A device that performs this processing is called a digital signal processor. After processing, the digital signal may then be converted back to an analog signal using a digital-to-analog (D/A) converter. The goal of this project is to develop a system that will recover the digital information from a BPSK signal using DSP techniques. The project is broken down into the following steps: (1) Development of the algorithms required to demodulate the BPSK signal; (2) Simulation of the system; and (3) Implementation a BPSK receiver using digital signal processing hardware.
Mahafza, Bassem R
Offering radar-related software for the analysis and design of radar waveform and signal processing, this book provides comprehensive coverage of radar signals and signal processing techniques and algorithms. It contains numerous graphical plots, common radar-related functions, table format outputs, and end-of-chapter problems. The complete set of MATLAB[registered] functions and routines are available for download online.
Marouf, Mohamed; Saranovac, Lazar
In this paper the usage of noise level approximation for adaptive Electromyogram (EMG) noise reduction in the Electrocardiogram (ECG) signals is introduced. To achieve the adequate adaptiveness, a translation-invariant noise level approximation is employed. The approximation is done in the form of a guiding signal extracted as an estimation of the signal quality vs. EMG noise. The noise reduction framework is based on a bank of low pass filters. So, the adaptive noise reduction is achieved by selecting the appropriate filter with respect to the guiding signal aiming to obtain the best trade-off between the signal distortion caused by filtering and the signal readability. For the evaluation purposes; both real EMG and artificial noises are used. The tested ECG signals are from the MIT-BIH Arrhythmia Database Directory, while both real and artificial records of EMG noise are added and used in the evaluation process. Firstly, comparison with state of the art methods is conducted to verify the performance of the proposed approach in terms of noise cancellation while preserving the QRS complex waves. Additionally, the signal to noise ratio improvement after the adaptive noise reduction is computed and presented for the proposed method. Finally, the impact of adaptive noise reduction method on QRS complexes detection was studied. The tested signals are delineated using a state of the art method, and the QRS detection improvement for different SNR is presented.
Timothy R. McJunkin; Milos Manic
Tomography, used to create images of the internal properties and features of an object, from phased array ultasonics is improved through many sophisiticated methonds of post processing of data. One approach used to improve tomographic results is to prescribe the collection of more data, from different points of few so that data fusion might have a richer data set to work from. This approach can lead to rapid increase in the data needed to be stored and processed. It also does not necessarily lead to have the needed data. This article describes a novel approach to utilizing the data aquired as a basis for adapting the sensors focusing parameters to locate more precisely the features in the material: specifically, two evolutionary methods of autofocusing on a returned signal are coupled with the derivations of the forumulas for spatially locating the feature are given. Test results of the two novel methods of evolutionary based focusing (EBF) illustrate the improved signal strength and correction of the position of feature using the optimized focal timing parameters, called Focused Delay Identification (FoDI).
Singh, Avtar; Hines, John; Somps, Chris
This is an attempt to develop a biotelemetry receiver using digital signal processing technology and techniques. The receiver developed in this work is based on recovering signals that have been encoded using either Pulse Position Modulation (PPM) or Pulse Code Modulation (PCM) technique. A prototype has been developed using state-of-the-art digital signal processing technology. A Printed Circuit Board (PCB) is being developed based on the technique and technology described here. This board is intended to be used in the UCSF Fetal Monitoring system developed at NASA. The board is capable of handling a variety of PPM and PCM signals encoding signals such as ECG, temperature, and pressure. A signal processing program has also been developed to analyze the received ECG signal to determine heart rate. This system provides a base for using digital signal processing in biotelemetry receivers and other similar applications.
Gardinier, Joseph D; Mohamed, Fatma; Kohn, David H
Improving the structural integrity of bone reduces fracture risk and development of osteoporosis later in life. Exercise can increase the mechanical properties of bone, and this increase is often attributed to the dynamic loading created during exercise. However, the increase in systemic parathyroid hormone (PTH) levels during exercise gives reason to hypothesize that PTH signaling also regulates bone adaptation in response to exercise. Therefore, the first aim of this study was to establish the impact PTH signaling has on bone adaptation during exercise by inhibiting PTH signaling with PTH(7-34); the second aim was to determine whether increasing PTH levels during exercise with PTH(1-34) can augment bone adaptation. Thirty minutes after a single bout of running on a treadmill, mice exhibited a twofold increase in systemic PTH levels. Under the same exercise regimen, the influence of PTH signaling on bone adaptation during exercise was then evaluated in mice after 21 consecutive days of exercise and treatment with PTH(7-34), PTH(1-34), or vehicle. Exercise alone caused a significant increase in trabecular bone volume with adaptation to a more platelike structure, which was inhibited with PTH(7-34) during exercise. Changes in structural-level and tissue-level mechanical properties during exercise occurred in the absence of significant changes to cortical bone geometry. Inhibition of PTH signaling during exercise attenuated the changes in structural-level mechanical properties, but not tissue-level properties. Enhanced PTH signaling during exercise with PTH(1-34) increased trabecular and cortical bone volume, but had little effect on the structural-level and tissue-level mechanical properties compared to exercise alone. Our study is the first to demonstrate that bone adaptation during exercise is not only a function of dynamic loading, but also PTH release, and that PTH signaling contributes differently at the structural and tissue levels. © 2015 American Society
Howard, Roy M
A fresh introduction to random processes utilizing signal theory By incorporating a signal theory basis, A Signal Theoretic Introduction to Random Processes presents a unique introduction to random processes with an emphasis on the important random phenomena encountered in the electronic and communications engineering field. The strong mathematical and signal theory basis provides clarity and precision in the statement of results. The book also features: A coherent account of the mathematical fundamentals and signal theory that underpin the presented material Unique, in-depth coverage of
Kalfod Nielsen, H.
A circuit for processing signals from a detector and occuring at random time intervals has a pulse-shaper, a delay and a processing circuit. The signal path is divided over part of its extent into parallel part-signal paths, each including an electronic switch and signal modifying circuits, a discriminator to detect a signal in the path and a control circuit for the switches and controlled by the discriminator being connected to the path ahead of the delay. The parallel paths are identical and the switch in each is ahead of the modifying circuits. When the discriminator detects a signal in the path the switch in on part path is made to conduct for at least as long as the duration of the signal as detected by the discriminator. The switches are preferable made to conduct cyclically. Processes increased number of signals, with quality of results not dependent on pulse rate and risk of errors substantially reduced. (au)
Rojo-Alvarez, José Luis; Muñoz-Marí, Jordi; Camps-Valls, Gustavo
A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors. * Presents the necess...
AFRL-RY-WP-TR-2017-0172 SIGNAL PROCESSING UTILIZING RADIO FREQUENCY PHOTONICS Preetpaul S. Devgan RF/EO Subsystems Branch Aerospace Components...4. TITLE AND SUBTITLE SIGNAL PROCESSING UTILIZING RADIO FREQUENCY PHOTONICS 5a. CONTRACT NUMBER In-house 5b. GRANT NUMBER N/A 5c. PROGRAM ELEMENT...can be used for multiple signal processing applications. Down conversion, oscillators analog to digital conversion and waveform generation are
Full Text Available We describe recent progress in integrated microwave photonics in wideband signal processing applications with a focus on the key signal processing building blocks, the realization of monolithic integration, and cascaded photonic signal processing for analog radio frequency (RF photonic links. New developments in integration-based microwave photonic techniques, that have high potentialities to be used in a variety of sensing applications for enhanced resolution and speed are also presented.
This book grew out of the IEEE-EMBS Summer Schools on Biomedical Signal Processing, which have been held annually since 2002 to provide the participants state-of-the-art knowledge on emerging areas in biomedical engineering. Prominent experts in the areas of biomedical signal processing, biomedical data treatment, medicine, signal processing, system biology, and applied physiology introduce novel techniques and algorithms as well as their clinical or physiological applications. The book provides an overview of a compelling group of advanced biomedical signal processing techniques, such as mult
Hummel, Robert; Stoica, Petre; Zelnio, Edmund
Radar Signal Processing and Its Applications brings together in one place important contributions and up-to-date research results in this fast-moving area. In twelve selected chapters, it describes the latest advances in architectures, design methods, and applications of radar signal processing. The contributors to this work were selected from the leading researchers and practitioners in the field. This work, originally published as Volume 14, Numbers 1-3 of the journal, Multidimensional Systems and Signal Processing, will be valuable to anyone working or researching in the field of radar signal processing. It serves as an excellent reference, providing insight into some of the most challenging issues being examined today.
Oxenløwe, Leif Katsuo; Hu, Hao; Kjøller, Niels-Kristian
Optical signal processing may aid in reducing the number of active components in communication systems with many parallel channels, by e.g. using telescopic time lens arrangements to perform format conversion and allow for WDM regeneration.......Optical signal processing may aid in reducing the number of active components in communication systems with many parallel channels, by e.g. using telescopic time lens arrangements to perform format conversion and allow for WDM regeneration....
Van Nee, D.J.R.
Abstract of CA 2109759 (A1) A method and device for processing a signal are described, wherein an estimate of a multipath-induced contribution to a demodulated navigation signal is calculated and subtracted from said demodulated navigation signal to obtain an estimated line of sight contribution to
Wright, A J; Poland, S P; Girkin, J M; Freudiger, C W; Evans, C L; Xie, X S
We report the use of adaptive optics with coherent anti-Stokes Raman scattering (CARS) microscopy for label-free deep tissue imaging based on molecular vibrational spectroscopy. The setup employs a deformable membrane mirror and a random search optimization algorithm to improve signal intensity and image quality at large sample depths. We demonstrate the ability to correct for both system and sample-induced aberrations in test samples as well as in muscle tissue in order to enhance the CARS signal. The combined system and sample-induced aberration correction increased the signal by an average factor of approximately 3x for the test samples at a depth of 700 microm and approximately 6x for muscle tissue at a depth of 260 microm. The enhanced signal and higher penetration depth offered by adaptive optics will augment CARS microscopy as an in vivo and in situ biomedical imaging modality.
Da Ros, Francesco
signal processing, including wavelength conversion, optical phase conjugation (OPC), and signal regeneration. This project focuses precisely on the applications of OPAs for all-optical signal processing with a two-fold focus: on the one hand, processing the advanced modulation formats required......) waveguides, are investigated. The limits of parametric amplification for 16-quadrature amplitude modulation (QAM) signals are first characterized. The acquired knowledge is then applied to the design of a black-box OPC-device used to provide Kerr nonlinearity compensation for a 5-channel polarization......-division multiplexing (PDM) 16-QAM signal at 1.12 Tbps with significant improvements in received signal quality. Furthermore, the first demonstration of phase regeneration for binary phase-shift keying (BPSK) signals using the silicon platform is presented. The silicon-based OPA relies on a novel design where a reverse...
The optimal control and safe operation of a nuclear power plant requires reliable information concerning the state of the process. Signal validation is the detection, isolation, and characterization of faulty signals. Properly validated process signals are beneficial from the standpoint of increased plant availability and reliability of operator actions. This paper reports on a signal validation technique utilizing a process hypercube comparison (PHC) originated during this research. The hypercube is merely a multidimensional joint histogram of the process conditions. The hypercube is created off-line during a learning phase using operational plant data. In the event that a newly observed plant state does not match with those in the learned hypercube, the PHV algorithm performs signal validation by progressively hypothesizing that one or more signals is in error. This assumption is then either substantiated or denied. In the case where many signals are found to be in error, a conclusion that the process conditions are abnormal is reached. The global data base contained within the hypercube provides a best estimate of the process conditions in the event a signal is deemed failed. The hypercube signal validation methodology was tested using operational data from a commercial pressurized water reactor (PWR) and the Experimental Breeder Reactor II (EBR-II). This research was part of a larger project aimed at the development of a comprehensive signal validation software system for application to nuclear power plants
Caffrey, M.; Briles, S.
RF signals are dispersed in frequency as they propagate through the ionosphere. For wide-band signals, this results in nonlinearly- chirped-frequency, transient signals in the VHF portion of the spectrum. This ionospheric dispersion provide a means of discriminating wide-band transients from other signals (e.g., continuous-wave carriers, burst communications, chirped-radar signals, etc.). The transient nature of these dispersed signals makes them candidates for wavelet feature selection. Rather than choosing a wavelet ad hoc, we adaptively compute an optimal mother wavelet via a neural network. Gaussian weighted, linear frequency modulate (GLFM) wavelets are linearly combined by the network to generate our application specific mother wavelet, which is optimized for its capacity to select features that discriminate between the dispersed signals and clutter (e.g., multiple continuous-wave carriers), not for its ability to represent the dispersed signal. The resulting mother wavelet is then used to extract features for a neutral network classifier. The performance of the adaptive wavelet classifier is the compared to an FFT based neural network classifier.
Wahl, Daniel E.; Yocky, David A.
This report describes the significant processing steps that were used to take the raw recorded digitized signals from the bistatic synthetic aperture RADAR (SAR) hardware built for the NCNS Bistatic SAR project to a final bistatic SAR image. In general, the process steps herein are applicable to bistatic SAR signals that include the direct-path signal and the reflected signal. The steps include preprocessing steps, data extraction to for a phase history, and finally, image format. Various plots and values will be shown at most steps to illustrate the processing for a bistatic COSMO SkyMed collection gathered on June 10, 2013 on Kirtland Air Force Base, New Mexico.
Digital Signal Processing is a mathematically rigorous but accessible treatment of digital signal processing that intertwines basic theoretical techniques with hands-on laboratory instruction. Divided into three parts, the book covers various aspects of the digital signal processing (DSP) ""problem."" It begins with the analysis of discrete-time signals and explains sampling and the use of the discrete and fast Fourier transforms. The second part of the book???covering digital to analog and analog to digital conversion???provides a practical interlude in the mathematical content before Part II
Cavallaro, A.; Lagendijk, R. (Inald) L.; Erkin, Zekeriya; Erkin, Zekeriya; Kwasinski, A.; Barni, Mauro
In recent years, signal processing applications that deal with user-related data have aroused privacy concerns. For instance, face recognition and personalized recommendations rely on privacy-sensitive information that can be abused if the signal processing is executed on remote servers or in the
Cartledge, John C.; Guiomar, Fernando P.; Kschischang, Frank R.
This paper reviews digital signal processing techniques that compensate, mitigate, and exploit fiber nonlinearities in coherent optical fiber transmission systems......This paper reviews digital signal processing techniques that compensate, mitigate, and exploit fiber nonlinearities in coherent optical fiber transmission systems...
A fast-digitizer data acquisition system recently installed at the neutron time-of-flight experiment nELBE, which is located at the superconducting electron accelerator ELBE of Forschungszentrum Dresden-Rossendorf, is tested with two different detector types. Preamplifier signals from a high-purity germanium detector are digitized, stored and finally processed. For a precise determination of the energy of the detected radiation, the moving-window deconvolution algorithm is used to compensate the ballistic deficit and different shaping algorithms are applied. The energy resolution is determined in an experiment with γ-rays from a 22 Na source and is compared to the energy resolution achieved with analogously processed signals. On the other hand, signals from the photomultipliers of barium fluoride and plastic scintillation detectors are digitized. These signals have risetimes of a few nanoseconds only. The moment of interaction of the radiation with the detector is determined by methods of digital signal processing. Therefore, different timing algorithms are implemented and tested with data from an experiment at nELBE. The time resolutions achieved with these algorithms are compared to each other as well as to reference values coming from analog signal processing. In addition to these experiments, some properties of the digitizing hardware are measured and a program for the analysis of stored, digitized data is developed. The analysis of the signals shows that the energy resolution achieved with the 10-bit digitizer system used here is not competitive to a 14-bit peak-sensing ADC, although the ballistic deficit can be fully corrected. However, digital methods give better result in sub-ns timing than analog signal processing. (orig.)
Havelock, David; Vorländer, Michael
The Handbook of Signal Processing in Acoustics presents signal processing as it is practiced in the field of acoustics. The Handbook is organized by areas of acoustics, with recognized leaders coordinating the self-contained chapters of each section. It brings together a wide range of perspectives from over 100 authors to reveal the interdisciplinary nature of signal processing in acoustics. Success in acoustic applications often requires juggling both the acoustic and the signal processing parameters of the problem. This handbook brings the key issues from both into perspective and is complementary to other reference material on the two subjects. It is a unique resource for experts and practitioners alike to find new ideas and techniques within the diversity of signal processing in acoustics.
Bakhshi, M; Noroozian, R; Gharehpetian, G B
This paper proposes a novel, passive-based anti-islanding method for both inverter and synchronous machine-based distributed generation (DG) units. Unfortunately, when the active/reactive power mismatches are near to zero, majority of the passive anti-islanding methods cannot detect the islanding situation, correctly. This study introduces a new islanding detection method based on exponentially damped signal estimation method. The proposed method uses adaptive identifier method for estimating of the frequency deviation of the point of common coupling (PCC) link as a target signal that can detect the islanding condition with near-zero active power imbalance. Main advantage of the adaptive identifier method over other signal estimation methods is its small sampling window. In this paper, the adaptive identifier based islanding detection method introduces a new detection index entitled decision signal by estimating of oscillation frequency of the PCC frequency and can detect islanding conditions, properly. In islanding conditions, oscillations frequency of PCC frequency reach to zero, thus threshold setting for decision signal is not a tedious job. The non-islanding transient events, which can cause a significant deviation in the PCC frequency are considered in simulations. These events include different types of faults, load changes, capacitor bank switching, and motor starting. Further, for islanding events, the capability of the proposed islanding detection method is verified by near-to-zero active power mismatches. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
This report describes the software that was developed to process test waveforms that were recorded by crash test data acquisition systems. The test waveforms are generated by an electronic waveform generator developed by MGA Research Corporation unde...
Chatterjee, Amitava; Siarry, Patrick
There have been significant developments in the design and application of algorithms for both one-dimensional signal processing and multidimensional signal processing, namely image and video processing, with the recent focus changing from a step-by-step procedure of designing the algorithm first and following up with in-depth analysis and performance improvement to instead applying heuristic-based methods to solve signal-processing problems. In this book the contributing authors demonstrate both general-purpose algorithms and those aimed at solving specialized application problems, with a spec
Computing is rapidly becoming ubiquitous as users expect devices that can augment and interact naturally with the world around them. In these systems it is necessary to have an acoustic front-end that is able to capture and reproduce natural human communication. Whether the end point is a speech recognizer or another human listener, the reduction of noise, reverberation, and acoustic echoes are all necessary and complex challenges. The focus of this dissertation is to provide a general method for approaching these problems using spherical microphone and loudspeaker arrays.. In this work, a theory of capturing and reproducing three-dimensional acoustic fields is introduced from a signal processing perspective. In particular, the decomposition of the spatial part of the acoustic field into an orthogonal basis of spherical harmonics provides not only a general framework for analysis, but also many processing advantages. The spatial sampling error limits the upper frequency range with which a sound field can be accurately captured or reproduced. In broadband arrays, the cost and complexity of using multiple transducers is an issue. This work provides a flexible optimization method for determining the location of array elements to minimize the spatial aliasing error. The low frequency array processing ability is also limited by the SNR, mismatch, and placement error of transducers. To address this, a robust processing method is introduced and used to design a reproduction system for rendering over arbitrary loudspeaker arrays or binaurally over headphones. In addition to the beamforming problem, the multichannel acoustic echo cancellation (MCAEC) issue is also addressed. A MCAEC must adaptively estimate and track the constantly changing loudspeaker-room-microphone response to remove the sound field presented over the loudspeakers from that captured by the microphones. In the multichannel case, the system is overdetermined and many adaptive schemes fail to converge to
Communicating with a machine in a natural mode such as speech brings out not only several technological challenges, but also limitations in our understanding of how people communicate so effortlessly. The key is to understand the distinction between speech processing (as is done in human communication) and speech ...
Lewis, Daniel D; Chavez, Michael; Chiu, Kwan Lun; Tan, Cheemeng
Living cells are known for their capacity for versatile signal processing, particularly the ability to respond differently to the same stimuli using biochemical networks that integrate environmental signals and reconfigure their dynamic responses. However, the complexity of natural biological networks confounds the discovery of fundamental mechanisms behind versatile signaling. Here, we study one specific aspect of reconfigurable signal processing in which a minimal biological network integrates two signals, using one to reconfigure the network's transfer function with respect to the other, producing an emergent switch between induction and repression. In contrast to known mechanisms, the new mechanism reconfigures transfer functions through genetic networks without extensive protein-protein interactions. These results provide a novel explanation for the versatility of genetic programs, and suggest a new mechanism of signal integration that may govern flexibility and plasticity of gene expression.
K. A. Zimenko
Full Text Available A method of electromyogram signals processing and identification for implementation in rehabilitation devices control is given. The method is based on the high-frequency components filtration which improves the signal/noise ratio; also it is based on the wavelet analysis for signal preprocessing and motion type classification by taught artificial neural network. Obtained accuracy of motion type classification is 94%.
In recent years, pseudo random signal processing has proven to be a critical enabler of modern communication, information, security and measurement systems. The signal's pseudo random, noise-like properties make it vitally important as a tool for protecting against interference, alleviating multipath propagation and allowing the potential of sharing bandwidth with other users. Taking a practical approach to the topic, this text provides a comprehensive and systematic guide to understanding and using pseudo random signals. Covering theoretical principles, design methodologies and applications
Kumar, Amit; Rahim, B Abdul; Kumar, D Sravan
This book highlights recent findings on and analyses conducted on signals and images in the area of medicine. The experimental investigations involve a variety of signals and images and their methodologies range from very basic to sophisticated methods. The book explains how signal and image processing methods can be used to detect and forecast abnormalities in an easy-to-follow manner, offering a valuable resource for researchers, engineers, physicians and bioinformatics researchers alike.
Chowdhury, Rubana H.; Reaz, Mamun B. I.; Ali, Mohd Alauddin Bin Mohd; Bakar, Ashrif A. A.; Chellappan, Kalaivani; Chang, Tae. G.
Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above. PMID:24048337
Rybin, Yu K
Electronic Devices for Analog Signal Processing is intended for engineers and post graduates and considers electronic devices applied to process analog signals in instrument making, automation, measurements, and other branches of technology. They perform various transformations of electrical signals: scaling, integration, logarithming, etc. The need in their deeper study is caused, on the one hand, by the extension of the forms of the input signal and increasing accuracy and performance of such devices, and on the other hand, new devices constantly emerge and are already widely used in practice, but no information about them are written in books on electronics. The basic approach of presenting the material in Electronic Devices for Analog Signal Processing can be formulated as follows: the study with help from self-education. While divided into seven chapters, each chapter contains theoretical material, examples of practical problems, questions and tests. The most difficult questions are marked by a diamon...
Oxenløwe, Leif Katsuo; Mulvad, Hans Christian Hansen; Hu, Hao
We describe recent demonstrations of exploiting highly nonlinear silicon waveguides for ultrafast optical signal processing. We describe wavelength conversion and serial-to-parallel conversion of 640 Gbit/s data signals and 1.28 Tbit/s demultiplexing and all-optical sampling....
Ranade, G.; Sudhakar, T.
Mathematical advances and the advances in the real time signal processing techniques in the recent times, have considerably improved the state of art in the bathymetry systems. These improvements have helped in developing high resolution swath...
Mendes, R Vilela
Non-commutative tomography is a technique originally developed and extensively used by Professors M A Man’ko and V I Man’ko in quantum mechanics. Because signal processing deals with operators that, in general, do not commute with time, the same technique has a natural extension to this domain. Here, a review is presented of the theory and some applications of non-commutative tomography for time series as well as some new results on signal processing on graphs. (paper)
Prime motivators in the evolution of increasingly sophisticated communication and detection systems are the needs for handling ever wider signal bandwidths and higher data processing speeds. These same needs drive the development of electronic device technology. Until recently the superconductive community has been tightly focused on digital devices for high speed computers. The purpose of this paper is to describe opportunities and challenges which exist for both analog and digital devices in a less familiar area, that of wideband signal processing. The function and purpose of analog signal-processing components, including matched filters, correlators and Fourier transformers, will be described and examples of superconductive implementations given. A canonic signal-processing system is then configured using these components in combination with analog/digital converters and digital output circuits to highlight the important issues of dynamic range, accuracy and equivalent computation rate. Superconductive circuits hold promise for processing signals of 10-GHz bandwidth. Signal processing systems, however, can be properly designed and implemented only through a synergistic combination of the talents of device physicists, circuit designers, algorithm architects and system engineers. An immediate challenge to the applied superconductivity community is to begin sharing ideas with these other researchers
The future of the engineering discipline is arguably predicated heavily upon appealing to the future generation, in all its sensibilities. The greatest burden in doing so, one might rightly believe, lies on the shoulders of the educators. In examining the causal means by which the profession arrived at such a state, one finds that the technical revolution, precipitated by global war, had, as its catalyst, institutions as expansive as the government itself to satisfy the demand for engineers, who, as a result of such an existential crisis, were taught predominantly theoretical underpinnings to address a finite purpose. By contrast, the modern engineer, having expanded upon this vision and adapted to an evolving society, is increasingly placed in the proverbial role of the worker who must don many hats: not solely a scientist, yet often an artist; not a businessperson alone, but neither financially naive; not always a representative, though frequently a collaborator. Inasmuch as change then serves as the only constancy in a global climate, therefore, the educational system - if it is to mimic the demands of the industry - is left with an inherent need for perpetual revitalization to remain relevant. This work aims to serve that end. Motivated by existing research in engineering education, an epistemological challenge is molded into the framework of the electrical engineer with emphasis on digital signal processing. In particular, it is investigated whether students are better served by a learning paradigm that tolerates and, when feasible, encourages error via a medium free of traditional adjudication. Through the creation of learning modules using the Adobe Captivate environment, a wide range of fundamental knowledge in signal processing is challenged within the confines of existing undergraduate courses. It is found that such an approach not only conforms to the research agenda outlined for the engineering educator, but also reflects an often neglected reality
Roč. 11, - (2002), s. 13 - 18 ISSN 0862-9846. [Datastat'01. Brno, 27.08.2001-30.08.2001] R&D Projects: GA ČR GA102/96/1136; GA AV ČR IAA2065201 Institutional research plan: CEZ:AV0Z2065902 Keywords : Wavelet filtration * adaptive filtration * magnetic resonance signal Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering
This book is devoted to the emerging technology of noise waveform radar and its signal processing aspects. It is a new kind of radar, which use noise-like waveform to illuminate the target. The book includes an introduction to basic radar theory, starting from classical pulse radar, signal compression, and wave radar. The book then discusses the properties, difficulties and potential of noise radar systems, primarily for low-power and short-range civil applications. The contribution of modern signal processing techniques to making noise radar practical are emphasized, and application examples
Baller, Bruce [Fermilab
This document describes the early stage of the reconstruction chain that was developed for the ArgoNeuT and MicroBooNE experiments at Fermilab. These experiments study accelerator neutrino interactions that occur in a Liquid Argon Time Projection Chamber. Reconstructing the properties of particles produced in these interactions requires knowledge of the micro-physics processes that affect the creation and transport of ionization electrons to the readout system. A wire signal deconvolution technique was developed to convert wire signals to a standard form for hit reconstruction, to remove artifacts in the electronics chain and to remove coherent noise.
Full Text Available We consider the problem of adaptive signal detection in the presence of Gaussian noise with unknown covariance matrix. We propose a parametric radar detector by introducing a design parameter to trade off the target sensitivity with sidelobes energy rejection. The resulting detector merges the statistics of Kelly's GLRT and of the Rao test and so covers Kelly's GLRT and the Rao test as special cases. Both invariance properties and constant false alarm rate (CFAR behavior for this detector are studied. At the analysis stage, the performance of the new receiver is assessed and compared with several traditional adaptive detectors. The results highlight better rejection capabilities of this proposed detector for mismatched signals. Further, we develop two two-stage detectors, one of which consists of an adaptive matched filter (AMF followed by the aforementioned detector, and the other is obtained by cascading a GLRT-based Subspace Detector (SD and the proposed adaptive detector. We show that the former two-stage detector outperforms traditional two-stage detectors in terms of selectivity, and the latter yields more robustness.
Pause, Bettina M
Brain development in mammals has been proposed to be promoted by successful adaptations to the social complexity as well as to the social and non-social chemical environment. Therefore, the communication via chemosensory signals might have been and might still be a phylogenetically ancient communication channel transmitting evolutionary significant information. In humans, the neuronal underpinnings of the processing of social chemosignals have been investigated in relation to kin recognition, mate choice, the reproductive state and emotional contagion. These studies reveal that human chemosignals are probably not processed within olfactory brain areas but through neuronal relays responsible for the processing of social information. It is concluded that the processing of human social chemosignals resembles the processing of social signals originating from other modalities, except that human social chemosignals are usually communicated without the allocation of attentional resources, that is below the threshold of consciousness. Deviances in the processing of human social chemosignals might be related to the development and maintenance of mental disorders.
A self-contained approach to DSP techniques and applications in radar imagingThe processing of radar images, in general, consists of three major fields: Digital Signal Processing (DSP); antenna and radar operation; and algorithms used to process the radar images. This book brings together material from these different areas to allow readers to gain a thorough understanding of how radar images are processed.The book is divided into three main parts and covers:* DSP principles and signal characteristics in both analog and digital domains, advanced signal sampling, and
Dorband, John E.; Aburdene, Maurice F.
Recently, networked and cluster computation have become very popular for both signal processing and system simulation. A new language is ideally suited for parallel signal processing applications and system simulation since it allows the programmer to explicitly express the computations that can be performed concurrently. In addition, the new C based parallel language (ace C) for architecture-adaptive programming allows programmers to implement algorithms and system simulation applications on parallel architectures by providing them with the assurance that future parallel architectures will be able to run their applications with a minimum of modification. In this paper, we will focus on some fundamental features of ace C and present a signal processing application (FFT).
Wu, Hau-Tieng; Talmon, Ronen; Lo, Yu-Lun
In this paper, two modern adaptive signal processing techniques, empirical intrinsic geometry and synchrosqueezing transform, are applied to quantify different dynamical features of the respiratory and electroencephalographic signals. We show that the proposed features are theoretically rigorously supported, as well as capture the sleep information hidden inside the signals. The features are used as input to multiclass support vector machines with the radial basis function to automatically classify sleep stages. The effectiveness of the classification based on the proposed features is shown to be comparable to human expert classification-the proposed classification of awake, REM, N1, N2, and N3 sleeping stages based on the respiratory signal (resp. respiratory and EEG signals) has the overall accuracy 81.7% (resp. 89.3%) in the relatively normal subject group. In addition, by examining the combination of the respiratory signal with the electroencephalographic signal, we conclude that the respiratory signal consists of ample sleep information, which supplements to the information stored in the electroencephalographic signal.
Full Text Available The potential application of a quantum-inspired adaptive wavelet shrinkage (QAWS technique to mechanical vibration signals with a focus on noise reduction is studied in this paper. This quantum-inspired shrinkage algorithm combines three elements: an adaptive non-Gaussian statistical model of dual-tree complex wavelet transform (DTCWT coefficients proposed to improve practicability of prior information, the quantum superposition introduced to describe the interscale dependencies of DTCWT coefficients, and the quantum-inspired probability of noise defined to shrink wavelet coefficients in a Bayesian framework. By combining all these elements, this signal processing scheme incorporating the DTCWT with quantum theory can both reduce noise and preserve signal details. A practical vibration signal measured from a power-shift steering transmission is utilized to evaluate the denoising ability of QAWS. Application results demonstrate the effectiveness of the proposed method. Moreover, it achieves better performance than hard and soft thresholding.
Pazos Garcia, Antonio A.
This work have two clearly differentiated parts. The first one refers to the instrumental development of a digital short period seismic station, while in second one some aspects of the digital signal processing are studied. In the first part, it is shown as a simple capacitor in series with the load resistance increase the band width of the response in more than 30%, without any reduction of the effective gain and the internal noise is not increased regarding the classical load. Also, the system of acquisition has been developed, based on the analogical-digital converter CS5323/CS5322 that it provides a dynamic range of 130 decibels to 125 mps. The data acquisition for the parallel port of an embedded PC, working with LINUX operating system, is an innovation in this instrumental field. The programs and the necessary drivers were developed. The synchronization system was developed by a PLL software that permit a precision better than one millisecond. Finally, the calibration methods proposed by means of the measure of the equivalent impedance of the sensor (parametric method), as well as the modifications of the empiric calibration method by comparison the response of two sensors have been decisive, suggesting that the usually accepted models suffer of some parasite capacities that would justify the observed differences between both methods. In second part, a detailed analysis on the design of digital filters is showed, as much FIR as IIR filters. A non-linear filter that applies the coherent structures for levels, based on the Wavelet transform, is proposed. It includes the detection and reduction of "spikes" and a method for filtering periodic noises, based on the time Fourier series. Finally, an exhaustive comparison of several detection algorithms, working on a single component, is made, analyzing the detection percentages and their "picking" capabilities. Their results show that none of them is able to adapt to all the circumstances, highlighting those based on
Pedersen, David Bue; Zhang, Yang; Nielsen, Jakob Skov
This research aim to show how manufacturing speeds during vat polymerisation can be vastly increased through an adaptive layer height strategy that takes the geometry into account through analysis of the relationship between layer height, cross-section variability and surface structure. This allows...... for considerable process speedup during the Additive Manufacture of components that contain areas of low cross-section variability, at no loss of surface quality. The adaptive slicing strategy was tested with a purpose built vat polymerisation system and numerical engine designed and constructed to serve as a Next...
Full Text Available Continuation and development of the research in Adaptable Programming Initialization [Tod-05.1,2,3] is presented. As continuation of [Tod-05.2,3] in this paper metalinguistic tools used in the process of introduction of new constructions (data, operations, instructions and controls are developed. The generalization schemes of evaluation of adaptable languages and systems are discussed. These results analogically with [Tod-05.2,3] are obtained by the team, composed from the researchers D. Todoroi [Tod-05.4], Z. Todoroi [ZTod-05], and D. Micusa [Mic-03]. Presented results will be included in the book [Tod-06].
Abbaspour, S; Fallah, A
Background: The electrocardiogram artifact is a major contamination in the electromyogram signals when electromyogram signal is recorded from upper trunk muscles and because of that the contaminated electromyogram is not useful. Objective: Removing electrocardiogram contamination from electromyogram signals. Methods: In this paper, the clean electromyogram signal, electrocardiogram artifact and electrocardiogram signal were recorded from leg muscles, the pectoralis major muscle of the left side and V4, respectively. After the pre-processing, contaminated electromyogram signal is simulated with a combination of clean electromyogram and electrocardiogram artifact. Then, contaminated electromyogram is cleaned using adaptive subtraction method. This method contains some steps; (1) QRS detection, (2) formation of electrocardiogram template by averaging the electrocardiogram complexes, (3) using low pass filter to remove undesirable artifacts, (4) subtraction. Results: Performance of our method is evaluated using qualitative criteria, power spectrum density and coherence and quantitative criteria signal to noise ratio, relative error and cross correlation. The result of signal to noise ratio, relative error and cross correlation is equal to 10.493, 0.04 and %97 respectively. Finally, there is a comparison between proposed method and some existing methods. Conclusion: The result indicates that adaptive subtraction method is somewhat effective to remove electrocardiogram artifact from contaminated electromyogram signal and has an acceptable result. PMID:25505766
Plešinger, Filip; Jurčo, Juraj; Halámek, Josef; Jurák, Pavel
Roč. 37, č. 7 (2016), N38-N48 ISSN 0967-3334 R&D Projects: GA ČR GAP103/11/0933; GA MŠk(CZ) LO1212; GA ČR GAP102/12/2034 Institutional support: RVO:68081731 Keywords : data visualization * software * signal processing * ECG * EEG Subject RIV: FS - Medical Facilities ; Equipment Impact factor: 2.058, year: 2016
Sen, Satyabrata [ORNL; Glover, Charles Wayne [ORNL
We propose an adaptive waveform design technique for an orthogonal frequency division multiplexing (OFDM) radar signal employing a space-time adaptive processing (STAP) technique. We observe that there are inherent variabilities of the target and interference responses in the frequency domain. Therefore, the use of an OFDM signal can not only increase the frequency diversity of our system, but also improve the target detectability by adaptively modifying the OFDM coefficients in order to exploit the frequency-variabilities of the scenario. First, we formulate a realistic OFDM-STAP measurement model considering the sparse nature of the target and interference spectra in the spatio-temporal domain. Then, we show that the optimal STAP-filter weight-vector is equal to the generalized eigenvector corresponding to the minimum generalized eigenvalue of the interference and target covariance matrices. With numerical examples we demonstrate that the resultant OFDM-STAP filter-weights are adaptable to the frequency-variabilities of the target and interference responses, in addition to the spatio-temporal variabilities. Hence, by better utilizing the frequency variabilities, we propose an adaptive OFDM-waveform design technique, and consequently gain a significant amount of STAP-performance improvement.
This title provides the most important theoretical aspects of Image and Signal Processing (ISP) for both deterministic and random signals. The theory is supported by exercises and computer simulations relating to real applications.More than 200 programs and functions are provided in the MATLAB® language, with useful comments and guidance, to enable numerical experiments to be carried out, thus allowing readers to develop a deeper understanding of both the theoretical and practical aspects of this subject.
Veselin N. Ivanović
Full Text Available This paper outlines the development of a multiple-clock-cycle implementation (MCI of a signal adaptive two-dimensional (2D system for space/spatial-frequency (S/SF signal analysis. The design is based on a method for improved S/SF representation of the analyzed 2D signals, also proposed here. The proposed MCI design optimizes critical design performances related to hardware complexity, making it a suitable system for real time implementation on an integrated chip. Additionally, the design allows the implemented system to take a variable number of clock cycles (CLKs (the only necessary ones regarding desirable—2D Wigner distribution-presentation of autoterms in different frequency-frequency points during the execution. This ability represents a major advantage of the proposed design which helps to optimize the time required for execution and produce an improved, cross-terms-free S/SF signal representation. The design has been verified by a field-programmable gate array (FPGA circuit design, capable of performing S/SF analysis of 2D signals in real time.
In recent years, sonar performance has mainly improved via a significant increase in array ap-erture, signal bandwidth and computational power. This thesis aims at improving sonar array processing techniques based on these three steps forward. In applications such as anti-submarine warfare and mine
Kulkarni,Sanjeev R; Dmitry M. Malioutov
The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analy...
Bradley, Robert W; Wang, Baojun
Microorganisms are able to respond effectively to diverse signals from their environment and internal metabolism owing to their inherent sophisticated information processing capacity. A central aim of synthetic biology is to control and reprogramme the signal processing pathways within living cells so as to realise repurposed, beneficial applications ranging from disease diagnosis and environmental sensing to chemical bioproduction. To date most examples of synthetic biological signal processing have been built based on digital information flow, though analogue computing is being developed to cope with more complex operations and larger sets of variables. Great progress has been made in expanding the categories of characterised biological components that can be used for cellular signal manipulation, thereby allowing synthetic biologists to more rationally programme increasingly complex behaviours into living cells. Here we present a current overview of the components and strategies that exist for designer cell signal processing and decision making, discuss how these have been implemented in prototype systems for therapeutic, environmental, and industrial biotechnological applications, and examine emerging challenges in this promising field. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.
Susan M Bertram
Full Text Available Phenotypic plasticity can be adaptive when phenotypes are closely matched to changes in the environment. In crickets, rhythmic fluctuations in the biotic and abiotic environment regularly result in diel rhythms in density of sexually active individuals. Given that density strongly influences the intensity of sexual selection, we asked whether crickets exhibit plasticity in signaling behavior that aligns with these rhythmic fluctuations in the socio-sexual environment. We quantified the acoustic mate signaling behavior of wild-caught males of two cricket species, Gryllus veletis and G. pennsylvanicus. Crickets exhibited phenotypically plastic mate signaling behavior, with most males signaling more often and more attractively during the times of day when mating activity is highest in the wild. Most male G. pennsylvanicus chirped more often and louder, with shorter interpulse durations, pulse periods, chirp durations, and interchirp durations, and at slightly higher carrier frequencies during the time of the day that mating activity is highest in the wild. Similarly, most male G. veletis chirped more often, with more pulses per chirp, longer interpulse durations, pulse periods, and chirp durations, shorter interchirp durations, and at lower carrier frequencies during the time of peak mating activity in the wild. Among-male variation in signaling plasticity was high, with some males signaling in an apparently maladaptive manner. Body size explained some of the among-male variation in G. pennsylvanicus plasticity but not G. veletis plasticity. Overall, our findings suggest that crickets exhibit phenotypically plastic mate attraction signals that closely match the fluctuating socio-sexual context they experience.
Kim, Dai Il; Shin, Won Ky; Oh, Sung Hun; Yun, Won Young
Loose Part Monitoring System (LPMS) is one of the fundamental diagnostic tools installed in the nuclear power plants. In this paper, recovery process algorithm and model for the corrupted impact signal generated by loose parts is presented. The characteristics of this algorithm can obtain a proper burst signal even though background noise is considerably high level comparing with actual impact signal. To verify performance of the proposed algorithm, we evaluate mathematically signal-to-noise ratio of primary output and noise. The performance of this recovery process algorithm is shown through computer simulation
Dr. Norden Huang, of Goddard Space Flight Center, invented a set of algorithms (called the Hilbert-Huang Transform, or HHT) for analyzing nonlinear and nonstationary signals that developed into a user-friendly signal processing technology for analyzing time-varying processes. At an auction managed by Ocean Tomo Federal Services LLC, licenses of 10 U.S. patents and 1 domestic patent application related to HHT were sold to DynaDx Corporation, of Mountain View, California. DynaDx is now using the licensed NASA technology for medical diagnosis and prediction of brain blood flow-related problems, such as stroke, dementia, and traumatic brain injury.
Karl, John H
An Introduction to Digital Signal Processing is written for those who need to understand and use digital signal processing and yet do not wish to wade through a multi-semester course sequence. Using only calculus-level mathematics, this book progresses rapidly through the fundamentals to advanced topics such as iterative least squares design of IIR filters, inverse filters, power spectral estimation, and multidimensional applications--all in one concise volume.This book emphasizes both the fundamental principles and their modern computer implementation. It presents and demonstrates how si
The rapid development of microelectronics has led to an increasing extent in circuits and systems for digital signal processing. This happened first in professional applications, e.g. geophysics, astronomy and space flight, and now, with the Compact Disc player, these techniques have entered the consumer field. In the near future digital TV applications will undoubtedly follow. This article outlines a number of the developments behind the advancing 'digitization' of modern technology. The article also considers the main advantages and disadvantages of digital signal processing the main modules now used and some common applications. Particular attention is paid to medical applications. (Auth.)
This book covers the fundamental concepts in signal processing illustrated with Python code and made available via IPython Notebooks, which are live, interactive, browser-based documents that allow one to change parameters, redraw plots, and tinker with the ideas presented in the text. Everything in the text is computable in this format and thereby invites readers to ""experiment and learn"" as they read. The book focuses on the core, fundamental principles of signal processing. The code corresponding to this book uses the core functionality of the scientific Python toolchain that should remai
Xiong, X.; Šmídl, Václav; Filippone, M.
Roč. 87, č. 8 (2017), s. 1644-1665 ISSN 0094-9655 R&D Projects: GA MŠk(CZ) 7F14287 Institutional support: RVO:67985556 Keywords : Gaussian Process * Bayesian estimation * Adaptive importance sampling Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 0.757, year: 2016 http://library.utia.cas.cz/separaty/2017/AS/smidl-0469804.pdf
Meyers, James F.; Lee, Joseph W.; Cavone, Angelo A.
Two schemes for processing signals obtained from the Doppler global velocimeter are described. The analog approach is a simple, real time method for obtaining an RS-170 video signal containing the normalized intensity image. Pseudo colors are added using a monochromatic frame grabber producing a standard NTSC video signal that can be monitored and/or recorded. The digital approach is more complicated, but maintains the full resolution of the acquisition cameras with the capabilities to correct the signal image for pixel sensitivity variations and to remove of background light. Prototype circuits for each scheme are described and example results from the investigation of the vortical flow field above a 75-degree delta wing presented.
Hoppe, Elizabeth; Roan, Michael
A method is introduced where blind source separation of acoustical sources is combined with spatial processing to remove non-Gaussian, broadband interferers from space-time displays such as bearing track recorder displays. This differs from most standard techniques such as generalized sidelobe cancellers in that the separation of signals is not done spatially. The algorithm performance is compared to adaptive beamforming techniques such as minimum variance distortionless response beamforming. Simulations and experiments using two acoustic sources were used to verify the performance of the algorithm. Simulations were also used to determine the effectiveness of the algorithm under various signal to interference, signal to noise, and array geometry conditions. A voice activity detection algorithm was used to benchmark the performance of the source isolation.
Full Text Available Speech segmentation is an essential stage in designing automatic speech recognition systems and one can ﬁnd several algorithms proposed in the literature. It is a difﬁcult problem, as speech is immensely variable. The aim of the authors’ studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi et al., (2006, and it is a modiﬁed version of Brandt’s algorithm. The article presents a new algorithm for unsupervised automatic speech signal segmentation. It performs segmentation without access to information about the phonetic content of the utterances, relying exclusively on second-order statistics of a speech signal. The starting point for the proposed method is time-varying Schur coefﬁcients of an innovation adaptive ﬁlter. The Schur algorithm is known to be fast, precise, stable and capable of rapidly tracking changes in second order signal statistics. A transfer from one phoneme to another in the speech signal always indicates a change in signal statistics caused by vocal track changes. In order to allow for the properties of human hearing, detection of inter-phoneme boundaries is performed based on statistics deﬁned on the mel spectrum determined from the reﬂection coefﬁcients. The paper presents the structure of the algorithm, deﬁnes its properties, lists parameter values, describes detection efﬁciency results, and compares them with those for another algorithm. The obtained segmentation results, are satisfactory.
Ospina Romero, Angélica María; Muñoz de Rodríguez, Lucy; Ruiz de Cárdenas, Carmen Helena
The puerperium is a stage that produces changes and adaptations in women, couples and family. Effective coping, during this stage, depends on the relationship between the demands of stressful or difficult situations and the recourses that the puerperal individual has. Roy (2004), in her Middle Range Theory about the Coping and Adaptation Processing, defines Coping as the ''behavioral and cognitive efforts that a person makes to meet the environment demands''. For the puerperal individual, the correct coping is necessary to maintain her physical and mental well being, especially against situations that can be stressful like breastfeeding and return to work. According to Lazarus and Folkman (1986), a resource for coping is to have someone who receives emotional support, informative and / or tangible. To review the issue of women coping and adaptation during the puerperium stage and the strategies that enhance this adaptation. SEARCH AND SELECTION OF DATABASE ARTICLES: Cochrane, Medline, Ovid, ProQuest, Scielo, and Blackwell Synergy. Other sources: unpublished documents by Roy, published books on Roy´s Model, Websites from of international health organizations. the need to recognize the puerperium as a stage that requires comprehensive care is evident, where nurses must be protagonist with the care offered to women and their families, considering the specific demands of this situation and recourses that promote effective coping and the family, education and health services.
Aalfs, David D.; Baxa, Ernest G., Jr.; Bracalente, Emedio M.
Low-altitude windshear (LAWS) has been identified as a major hazard to aircraft, particularly during takeoff and landing. The Federal Aviation Administration (FAA) has been involved with developing technology to detect LAWS. A key element in this technology is high resolution pulse Doppler weather radar equipped with signal and data processing to provide timely information about possible hazardous conditions.
Yvind, Kresten; Pu, Minhao; Hvam, Jørn Märcher
The fast non-linearity of silicon allows Tbit/s optical signal processing. By choosing suitable dimensions of silicon nanowires their dispersion can be tailored to ensure a high nonlinearity at power levels low enough to avoid significant two-photon abso We have fabricated low insertion...
Dow, Chyi-Ren; Li, Yi-Hsung; Bai, Jin-Yu
This work designs and implements a virtual digital signal processing laboratory, VDSPL. VDSPL consists of four parts: mobile agent execution environments, mobile agents, DSP development software, and DSP experimental platforms. The network capability of VDSPL is created by using mobile agent and wrapper techniques without modifying the source code…
This book describes the optimization methods most commonly encountered in signal and image processing: artificial evolution and Parisian approach; wavelets and fractals; information criteria; training and quadratic programming; Bayesian formalism; probabilistic modeling; Markovian approach; hidden Markov models; and metaheuristics (genetic algorithms, ant colony algorithms, cross-entropy, particle swarm optimization, estimation of distribution algorithms, and artificial immune systems).
Giron-Sierra, Jose Maria
This is the first volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs. This book includes MATLAB codes to illustrate each of the main steps of the theory, offering a self-contained guide suitable for independent study. The code is embedded in the text, helping readers to put into practice the ideas and methods discussed. The book is divided into three parts, the first of which introduces readers to periodic and non-periodic signals. The second part is devoted to filtering, which is an important and commonly used application. The third part addresses more advanced topics, including the analysis of real-world non-stationary signals and data, e.g. structural fatigue, earthquakes, electro-encephalograms, birdsong, etc. The book’s last chapter focuses on modulation, an example of the intentional use of non-stationary signals.
Boyraz, Pinar; Takeda, Kazuya; Abut, Hüseyin
Compiled from papers of the 4th Biennial Workshop on DSP (Digital Signal Processing) for In-Vehicle Systems and Safety this edited collection features world-class experts from diverse fields focusing on integrating smart in-vehicle systems with human factors to enhance safety in automobiles. Digital Signal Processing for In-Vehicle Systems and Safety presents new approaches on how to reduce driver inattention and prevent road accidents. The material addresses DSP technologies in adaptive automobiles, in-vehicle dialogue systems, human machine interfaces, video and audio processing, and in-vehicle speech systems. The volume also features: Recent advances in Smart-Car technology – vehicles that take into account and conform to the driver Driver-vehicle interfaces that take into account the driving task and cognitive load of the driver Best practices for In-Vehicle Corpus Development and distribution Information on multi-sensor analysis and fusion techniques for robust driver monitoring and driver recognition ...
Garces Correa, A; Laciar, E; Patino, H D; Valentinuzzi, M E
Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records
Blanchet , Gérard
The most important theoretical aspects of Image and Signal Processing (ISP) for both deterministic and random signals, the theory being supported by exercises and computer simulations relating to real applications. More than 200 programs and functions are provided in the MATLAB® language, with useful comments and guidance, to enable numerical experiments to be carried out, thus allowing readers to develop a deeper understanding of both the theoretical and practical aspects of this subject. Following on from the first volume, this second installation takes a more practical stance, provi
compression signals,” IEEE Trans. Information Theory , vol. 10, no. 1, pp. 61-67, Jan. 1964.  C.E. Cook, “A class of nonlinear FM pulse compression...Trans. Information Theory , vol. 12, no. 3, pp. 305-311, July 1966.  J.A. Johnston and A.C. Fairhead, “Waveform design and Doppler sensitivity...diversity in echolocating mammals,” IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 65- 75, Jan. 2009. DISTRIBUTION A: Distribution approved for
Blanchet , Gérard
This fully revised and updated second edition presents the most important theoretical aspects of Image and Signal Processing (ISP) for both deterministic and random signals. The theory is supported by exercises and computer simulations relating to real applications. More than 200 programs and functions are provided in the MATLABÒ language, with useful comments and guidance, to enable numerical experiments to be carried out, thus allowing readers to develop a deeper understanding of both the theoretical and practical aspects of this subject. This fully revised new edition updates : - the
Full Text Available There is at present a worldwide effort to develop next-generation wireless communication systems. It is envisioned that many of the future wireless systems will incorporate considerable signal-processing intelligence in order to provide advanced services such as multimedia transmission. In general, wireless channels can be very hostile media through which to communicate, due to substantial physical impediments, primarily radio-frequency interference and time-arying nature of the channel. The need of providing universal wireless access at high data-rate (which is the aim of many merging wireless applications presents a major technical challenge, and meeting this challenge necessitates the development of advanced signal processing techniques for multiple-access communications in non-stationary interference-rich environments. In this paper, we present some key advanced signal processing methodologies that have been developed in recent years for interference suppression in wireless networks. We will focus primarily on the problem of jointly suppressing multiple-access interference (MAI and intersymbol interference (ISI, which are the limiting sources of interference for the high data-rate wireless systems being proposed for many emerging application areas, such as wireless multimedia. We first present a signal subspace approach to blind joint suppression of MAI and ISI. We then discuss a powerful iterative technique for joint interference suppression and decoding, so-called Turbo multiuser detection, that is especially useful for wireless multimedia packet communications. We also discuss space-time processing methods that employ multiple antennas for interference rejection and signal enhancement. Finally, we touch briefly on the problems of suppressing narrowband interference and impulsive ambient noise, two other sources of radio-frequency interference present in wireless multimedia networks.
Zhang, Zhaoliang; Liao, Hong; Lucas, William J
As an essential plant macronutrient, the low availability of phosphorus (P) in most soils imposes serious limitation on crop production. Plants have evolved complex responsive and adaptive mechanisms for acquisition, remobilization and recycling of phosphate (Pi) to maintain P homeostasis. Spatio-temporal molecular, physiological, and biochemical Pi deficiency responses developed by plants are the consequence of local and systemic sensing and signaling pathways. Pi deficiency is sensed locally by the root system where hormones serve as important signaling components in terms of developmental reprogramming, leading to changes in root system architecture. Root-to-shoot and shoot-to-root signals, delivered through the xylem and phloem, respectively, involving Pi itself, hormones, miRNAs, mRNAs, and sucrose, serve to coordinate Pi deficiency responses at the whole-plant level. A combination of chromatin remodeling, transcriptional and posttranslational events contribute to globally regulating a wide range of Pi deficiency responses. In this review, recent advances are evaluated in terms of progress toward developing a comprehensive understanding of the molecular events underlying control over P homeostasis. Application of this knowledge, in terms of developing crop plants having enhanced attributes for P use efficiency, is discussed from the perspective of agricultural sustainability in the face of diminishing global P supplies. © 2014 Institute of Botany, Chinese Academy of Sciences.
Susannah J Sample
Full Text Available Sex steroids have direct effects on the skeleton. Estrogen acts on the skeleton via the classical genomic estrogen receptors alpha and beta (ERα and ERβ, a membrane ER, and the non-genomic G-protein coupled estrogen receptor (GPER. GPER is distributed throughout the nervous system, but little is known about its effects on bone. In male rats, adaptation to loading is neuronally regulated, but this has not been studied in females.We used the rat ulna end-loading model to induce an adaptive modeling response in ovariectomized (OVX female Sprague-Dawley rats. Rats were treated with a placebo, estrogen (17β-estradiol, or G-1, a GPER-specific agonist. Fourteen days after OVX, rats underwent unilateral cyclic loading of the right ulna; half of the rats in each group had brachial plexus anesthesia (BPA of the loaded limb before loading. Ten days after loading, serum estrogen concentrations, dorsal root ganglion (DRG gene expression of ERα, ERβ, GPER, CGRPα, TRPV1, TRPV4 and TRPA1, and load-induced skeletal responses were quantified. We hypothesized that estrogen and G-1 treatment would influence skeletal responses to cyclic loading through a neuronal mechanism. We found that estrogen suppresses periosteal bone formation in female rats. This physiological effect is not GPER-mediated. We also found that absolute mechanosensitivity in female rats was decreased, when compared with male rats. Blocking of adaptive bone formation by BPA in Placebo OVX females was reduced.Estrogen acts to decrease periosteal bone formation in female rats in vivo. This effect is not GPER-mediated. Gender differences in absolute bone mechanosensitivity exist in young Sprague-Dawley rats with reduced mechanosensitivity in females, although underlying bone formation rate associated with growth likely influences this observation. In contrast to female and male rats, central neuronal signals had a diminished effect on adaptive bone formation in estrogen-deficient female rats.
Jorgensen, C. C.; Lee, D. D.
A recently invented speech-recognition method applies to words that are articulated by means of the tongue and throat muscles but are otherwise not voiced or, at most, are spoken sotto voce. This method could satisfy a need for speech recognition under circumstances in which normal audible speech is difficult, poses a hazard, is disturbing to listeners, or compromises privacy. The method could also be used to augment traditional speech recognition by providing an additional source of information about articulator activity. The method can be characterized as intermediate between (1) conventional speech recognition through processing of voice sounds and (2) a method, not yet developed, of processing electroencephalographic signals to extract unspoken words directly from thoughts. This method involves computational processing of digitized electromyographic (EMG) signals from muscle innervation acquired by surface electrodes under a subject's chin near the tongue and on the side of the subject s throat near the larynx. After preprocessing, digitization, and feature extraction, EMG signals are processed by a neural-network pattern classifier, implemented in software, that performs the bulk of the recognition task as described.
Maher, M.; Bahhou, B.; Roux, G. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Maher, M. [Faculte des Sciences, Rabat (Morocco). Lab. de Physique
This paper presents a multivariable adaptive control of a continuous-flow fermentation process for the alcohol production. The linear quadratic control strategy is used for the regulation of substrate and ethanol concentrations in the bioreactor. The control inputs are the dilution rate and the influent substrate concentration. A robust identification algorithm is used for the on-line estimation of linear MIMO model`s parameters. Experimental results of a pilot-plant fermenter application are reported and show the control performances. (authors) 8 refs.
This is a very new concept for learning Signal Processing, not only from the physically-based scientific fundamentals, but also from the didactic perspective, based on modern results of brain research. The textbook together with the DVD form a learning system that provides investigative studies and enables the reader to interactively visualize even complex processes. The unique didactic concept is built on visualizing signals and processes on the one hand, and on graphical programming of signal processing systems on the other. The concept has been designed especially for microelectronics, computer technology and communication. The book allows to develop, modify, and optimize useful applications using DasyLab - a professional and globally supported software for metrology and control engineering. With the 3rd edition, the software is also suitable for 64 bit systems running on Windows 7. Real signals can be acquired, processed and played on the sound card of your computer. The book provides more than 200 pre-pr...
Hwang, I. G.; Moon, B. S.; Kinser, Rpger
The development of Johnson Noise Thermometry requires a high sensitive preamplifier circuit to pick up the temperature-related noise on the sensing element. However, the random noise generated in this amplification circuit causes a significant erroneous influence to the measurement. This paper describes signal processing mechanism of the Johnson Noise Thermometry system which is underway of development in collaboration between KAERI and ORNL. It adopts two identical amplifier channels and utilizes a digital signal processing technique to remove the independent noise of each channel. The CPSD(Cross Power Spectral Density) function is used to cancel the independent noise and the differentiation of narrow or single frequency peak from the CPSD data separates the common mode electromagnetic interference noise
Full Text Available Genomic signal processing (GSP methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data.
Mendizabal-Ruiz, Gerardo; Román-Godínez, Israel; Torres-Ramos, Sulema; Salido-Ruiz, Ricardo A; Vélez-Pérez, Hugo; Morales, J Alejandro
Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data.
Wang, Zhaocheng; Huang, Wei; Xu, Zhengyuan
This informative new book on state-of-the-art visible light communication (VLC) provides, for the first time, a systematical and advanced treatment of modulation and signal processing for VLC. Visible Light Communications: Modulation and Signal Processing offers a practical guide to designing VLC, linking academic research with commercial applications. In recent years, VLC has attracted attention from academia and industry since it has many advantages over the traditional radio frequency, including wide unregulated bandwidth, high security, and low cost. It is a promising complementary technique in 5G and beyond wireless communications, especially in indoor applications. However, lighting constraints have not been fully considered in the open literature when considering VLC system design, and its importance has been underestimated. That’s why this book—written by a team of experts with both academic research experience and industrial development experience in the field—is so welcome. To help readers u...
This book examines the signal processing perspective in haptic teleoperation systems. This text covers the topics of prediction, estimation, architecture, data compression, and error correction that can be applied to haptic teleoperation systems. The authors begin with an overview of haptic teleoperation systems, then look at a Bayesian approach to haptic teleoperation systems. They move onto a discussion of haptic data compression, haptic data digitization and forward error correction. · Presents haptic data prediction/estimation methods that compensate for unreliable networks · Discusses haptic data compression that reduces haptic data size over limited network bandwidth and haptic data error correction that compensate for packet loss problem · Provides signal processing techniques used with existing control architectures.
Pretlow, Robert A., III; Stoughton, John W.
Research and development is presented of real time signal processing methodologies for the detection of fetal heart tones within a noise-contaminated signal from a passive acoustic sensor. A linear predictor algorithm is utilized for detection of the heart tone event and additional processing derives heart rate. The linear predictor is adaptively 'trained' in a least mean square error sense on generic fetal heart tones recorded from patients. A real time monitor system is described which outputs to a strip chart recorder for plotting the time history of the fetal heart rate. The system is validated in the context of the fetal nonstress test. Comparisons are made with ultrasonic nonstress tests on a series of patients. Comparative data provides favorable indications of the feasibility of the acoustic monitor for clinical use.
Ogawa, Takashi; Wanielik, Gerd
In recent years, the lidar sensor has been receiving greater attention as being one of the prospective sensors for future intelligent vehicles. In order to enable advanced applications in a variety of road environments, it has become more important to detect various objects at a wider distance. Therefore, in this research we have focused on lidar signal processing to detect low signal-to-noise ratio (SNR) targets and proposed a higher sensitive detector. The detector is based on the constant false alarm rate (CFAR) processing framework in which an additional functionality of adaptive intensity integration is incorporated. Fundamental results through static experiments have shown a significant advantage in the detection performance in comparison to a conventional detector with constant thresholding.
Taylor, S.; Hinton, M.; Turner, R.
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.
Burke, Christopher J; Baddeley, Michelle; Tobler, Philippe N; Schultz, Wolfram
Given that the range of rewarding and punishing outcomes of actions is large but neural coding capacity is limited, efficient processing of outcomes by the brain is necessary. One mechanism to increase efficiency is to rescale neural output to the range of outcomes expected in the current context, and process only experienced deviations from this expectation. However, this mechanism comes at the cost of not being able to discriminate between unexpectedly low losses when times are bad versus unexpectedly high gains when times are good. Thus, too much adaptation would result in disregarding information about the nature and absolute magnitude of outcomes, preventing learning about the longer-term value structure of the environment. Here we investigate the degree of adaptation in outcome coding brain regions in humans, for directly experienced outcomes and observed outcomes. We scanned participants while they performed a social learning task in gain and loss blocks. Multivariate pattern analysis showed two distinct networks of brain regions adapt to the most likely outcomes within a block. Frontostriatal areas adapted to directly experienced outcomes, whereas lateral frontal and temporoparietal regions adapted to observed social outcomes. Critically, in both cases, adaptation was incomplete and information about whether the outcomes arose in a gain block or a loss block was retained. Univariate analysis confirmed incomplete adaptive coding in these regions but also detected nonadapting outcome signals. Thus, although neural areas rescale their responses to outcomes for efficient coding, they adapt incompletely and keep track of the longer-term incentives available in the environment. Optimal value-based choice requires that the brain precisely and efficiently represents positive and negative outcomes. One way to increase efficiency is to adapt responding to the most likely outcomes in a given context. However, too strong adaptation would result in loss of precise
Schobben, Daniel W E
Blind Signal Separation (BSS) deals with recovering (filtered versions of) source signals from an observed mixture thereof. The term `blind' relates to the fact that there are no reference signals for the source signals and also that the mixing system is unknown. This book presents a new method for blind signal separation, which is developed to work on microphone signals. Acoustic Echo Cancellation (AEC) is a well-known technique to suppress the echo that a microphone picks up from a loudspeaker in the same room. Such acoustic feedback occurs for example in hands-free telephony and can lead to a perceived loud tone. For an application such as a voice-controlled television, a stereo AEC is required to suppress the contribution of the stereo loudspeaker setup. A generalized AEC is presented that is suited for multi-channel operation. New algorithms for Blind Signal Separation and multi-channel Acoustic Echo Cancellation are presented. A background is given in array signal processing methods, adaptive filter the...
The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing.Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are addressed.Secondly, estimation approaches based on least squares methods and instrumental variable techniques are presented.Finally, the book deals with optimal filters, i.e. Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and the
Full Text Available 16332394 ITAM-based signaling beyond the adaptive immune response. Fodor S, Jakus Z..., Mocsai A. Immunol Lett. 2006 Apr 15;104(1-2):29-37. Epub 2005 Nov 28. (.png) (.svg) (.html) (.csml) Show ITAM-based sign...aling beyond the adaptive immune response. PubmedID 16332394 Title ITAM-based signaling beyond
Axline, Jr., Robert M.; Sloan, George R.; Spalding, Richard E.
An active, phase-coded, time-grating transponder and a synthetic-aperture radar (SAR) and signal processor means, in combination, allow the recognition and location of the transponder (tag) in the SAR image and allow communication of information messages from the transponder to the SAR. The SAR is an illuminating radar having special processing modifications in an image-formation processor to receive an echo from a remote transponder, after the transponder receives and retransmits the SAR illuminations, and to enhance the transponder's echo relative to surrounding ground clutter by recognizing special transponder modulations from phase-shifted from the transponder retransmissions. The remote radio-frequency tag also transmits information to the SAR through a single antenna that also serves to receive the SAR illuminations. Unique tag-modulation and SAR signal processing techniques, in combination, allow the detection and precise geographical location of the tag through the reduction of interfering signals from ground clutter, and allow communication of environmental and status information from said tag to be communicated to said SAR.
This book presents theory, design methods and novel applications for integrated circuits for analog signal processing. The discussion covers a wide variety of active devices, active elements and amplifiers, working in voltage mode, current mode and mixed mode. This includes voltage operational amplifiers, current operational amplifiers, operational transconductance amplifiers, operational transresistance amplifiers, current conveyors, current differencing transconductance amplifiers, etc. Design methods and challenges posed by nanometer technology are discussed and applications described, including signal amplification, filtering, data acquisition systems such as neural recording, sensor conditioning such as biomedical implants, actuator conditioning, noise generators, oscillators, mixers, etc. Presents analysis and synthesis methods to generate all circuit topologies from which the designer can select the best one for the desired application; Includes design guidelines for active devices/elements...
Nassar, A.M.; Mohamed, A.H.; Mohamed, A.H.
Case Based Reasoning (CBR) Systems is one of the important decision making systems applied in many fields all over the world. The effectiveness of any CBR system based on the quality of the storage cases in the case library. Similar cases can be retrieved and adapted to produce the solution for the new problem. One of the main issues faced the CBR systems is the difficulties of achieving the useful cases. The proposed system introduces a new approach that uses the genetic algorithm (GA) technique to automate constructing the cases into the case library. Also, it can optimize the best one to be stored in the library for the future uses. However, the proposed system can avoid the problems of the uncertain and noisy cases. Besides, it can simply the retrieving and adaptation processes. So, it can improve the performance of the CBR system. The suggested system can be applied for many real-time problems. It has been applied for diagnosis the faults of the wireless network, diagnosis of the cancer diseases, diagnosis of the debugging of a software as cases of study. The proposed system has proved its performance in this field
Full Text Available Climatic variations affect agriculture in a process with no known end means. Adaptations help to reduce the adverse impacts of climate change. Unfortunately, adaptation has never been considered as a process. Current study empirically identified the adaptation process and its different stages. Moreover, little is known about the farm level adaptation strategies and their determinants. The study in hand found farm level adaptation strategies and determinants of these strategies. The study identified three stages of adaptation i.e. perception, intention and adaptation. It was found that 71.4% farmers perceived about climate change, 58.5% intended to adapt while 40.2% actually adapted. The study further explored that farmers do adaptations through changing crop variety (56.3%, changing planting dates (44.6%, tree plantation (37.5%, increase/conserve irrigation (39.3% and crop diversification (49.2%. The adaptation strategies used by farmers were autonomous and mostly determined perception to climate change. It was also noted that the adaptation strategies move in a circular process and once they are adapted they remained adapted for a longer period of time. Some constraints slow the adaptation process so; we recommend farmers should be given price incentives to speed-up this process.
At the first sight the problem to determine the beam position from the ratio of the induced charges of the opposite electrodes of a beam monitor seems trivial, but up to now no unique solution has been found that fits the various demands of all particle accelerators. The purpose of this paper is to help "instrumentalists" to choose the best processing system for their particular application, depending on the machine size, the input dynamic range, the required resolution and the acquisition speed. After a general introduction and an analysis of the electrical signals to be treated (frequency and time domain), the definition of the electronic specifications will be reviewed. The tutorial will present the different families in which the processing systems can be grouped. A general description of the operating principles with relative advantages and disadvantages for the most employed processing systems is presented. Special emphasis will be put on recent technological developments based on telecommunication circ...
Chiu, Leung Kin
As mobile platforms continue to pack on more computational power, electronics manufacturers start to differentiate their products by enhancing the audio features. However, consumers also demand smaller devices that could operate for longer time, hence imposing design constraints. In this research, we investigate two design strategies that would allow us to efficiently process audio signals on embedded systems such as mobile phones and portable electronics. In the first strategy, we exploit properties of the human auditory system to process audio signals. We designed a sound enhancement algorithm to make piezoelectric loudspeakers sound ”richer" and "fuller." Piezoelectric speakers have a small form factor but exhibit poor response in the low-frequency region. In the algorithm, we combine psychoacoustic bass extension and dynamic range compression to improve the perceived bass coming out from the tiny speakers. We also developed an audio energy reduction algorithm for loudspeaker power management. The perceptually transparent algorithm extends the battery life of mobile devices and prevents thermal damage in speakers. This method is similar to audio compression algorithms, which encode audio signals in such a ways that the compression artifacts are not easily perceivable. Instead of reducing the storage space, however, we suppress the audio contents that are below the hearing threshold, therefore reducing the signal energy. In the second strategy, we use low-power analog circuits to process the signal before digitizing it. We designed an analog front-end for sound detection and implemented it on a field programmable analog array (FPAA). The system is an example of an analog-to-information converter. The sound classifier front-end can be used in a wide range of applications because programmable floating-gate transistors are employed to store classifier weights. Moreover, we incorporated a feature selection algorithm to simplify the analog front-end. A machine
Jepsen, Morten L; Ewert, Stephan D; Dau, Torsten
A model of computational auditory signal-processing and perception that accounts for various aspects of simultaneous and nonsimultaneous masking in human listeners is presented. The model is based on the modulation filterbank model described by Dau et al. [J. Acoust. Soc. Am. 102, 2892 (1997)] but includes major changes at the peripheral and more central stages of processing. The model contains outer- and middle-ear transformations, a nonlinear basilar-membrane processing stage, a hair-cell transduction stage, a squaring expansion, an adaptation stage, a 150-Hz lowpass modulation filter, a bandpass modulation filterbank, a constant-variance internal noise, and an optimal detector stage. The model was evaluated in experimental conditions that reflect, to a different degree, effects of compression as well as spectral and temporal resolution in auditory processing. The experiments include intensity discrimination with pure tones and broadband noise, tone-in-noise detection, spectral masking with narrow-band signals and maskers, forward masking with tone signals and tone or noise maskers, and amplitude-modulation detection with narrow- and wideband noise carriers. The model can account for most of the key properties of the data and is more powerful than the original model. The model might be useful as a front end in technical applications.
Gokhberg, Alexey; Ermert, Laura; Fichtner, Andreas
The processing of seismic signals - including the correlation of massive ambient noise data sets - represents an important part of a wide range of seismological applications. It is characterized by large data volumes as well as high computational input/output intensity. Development of efficient approaches towards seismic signal processing on emerging high performance computing systems is therefore essential. Heterogeneous supercomputing systems introduced in the recent years provide numerous computing nodes interconnected via high throughput networks, every node containing a mix of processing elements of different architectures, like several sequential processor cores and one or a few graphical processing units (GPU) serving as accelerators. A typical representative of such computing systems is "Piz Daint", a supercomputer of the Cray XC 30 family operated by the Swiss National Supercomputing Center (CSCS), which we used in this research. Heterogeneous supercomputers provide an opportunity for manifold application performance increase and are more energy-efficient, however they have much higher hardware complexity and are therefore much more difficult to program. The programming effort may be substantially reduced by the introduction of modular libraries of software components that can be reused for a wide class of seismology applications. The ultimate goal of this research is design of a prototype for such library suitable for implementing various seismic signal processing applications on heterogeneous systems. As a representative use case we have chosen an ambient noise correlation application. Ambient noise interferometry has developed into one of the most powerful tools to image and monitor the Earth's interior. Future applications will require the extraction of increasingly small details from noise recordings. To meet this demand, more advanced correlation techniques combined with very large data volumes are needed. This poses new computational problems that
The conversion and recording of analog signals in digital form has been an active element in the manufacturing operations of the General Electric Neutron Devices Department (GEND) since 1966. The first computerized data system for these digitized waveforms was implemented at GEND's data center approximately two years later during 1968. The evolution and integration of these two activities at GEND are addressed in this paper. Beginning with the tester--data center interface, emphasis is placed on previous approaches, current capabilities, near-term trends, and future requirements. The digitizing process has developed into a firmly established set of hardware and associated software techniques which has proven itself as an accurate, reliable procedure for capturing waveform characteristics. The most important aspect of this process is the recent trend toward increased sampling rates and a greater number of digitized parameters per operation. The combined effect is a tremendous increase in output data volumes. Since digital signal processing carries the potential for significant contributions to manufacturing quality and reliability, as well as engineering design and development, increased activity in this area appears extremely desirable. 11 figures
Ferdjallah, M; Barr, R E
This paper investigates adaptive digital notch filters for the elimination of powerline noise from biomedical signals. Since the distribution of the frequency variation of the powerline noise may or may not be centered at 60 Hz, three different adaptive digital notch filters are considered. For the first case, an adaptive FIR second-order digital notch filter is designed to track the center frequency variation. For the second case, the zeroes of an adaptive IIR second-order digital notch filter are fixed on the unit circle and the poles are adapted to find an optimum bandwidth to eliminate the noise to a pre-defined attenuation level. In the third case, both the poles and zeroes of the adaptive IIR second-order filter are adapted to track the center frequency variation within an optimum bandwidth. The adaptive process is considerably simplified by designing the notch filters by pole-zero placement on the unit circle using some suggested rules. A constrained least mean-squared (CLMS) algorithm is used for the adaptive process. To evaluate their performance, the three adaptive notch filters are applied to a powerline noise sample and to a noisy EEG as an illustration of a biomedical signal.
Ribeiro, Paulo Fernando; Ribeiro, Paulo Márcio; Cerqueira, Augusto Santiago
With special relation to smart grids, this book provides clear and comprehensive explanation of how Digital Signal Processing (DSP) and Computational Intelligence (CI) techniques can be applied to solve problems in the power system. Its unique coverage bridges the gap between DSP, electrical power and energy engineering systems, showing many different techniques applied to typical and expected system conditions with practical power system examples. Surveying all recent advances on DSP for power systems, this book enables engineers and researchers to understand the current state of the art a
Full Text Available The phase, amplitude, speed, and polarization, in addition to many other properties of light, can be modulated by photonic Bragg structures. In conjunction with nonlinearity and quantum effects, a variety of ensuing micro- or nano-photonic applications can be realized. This paper reviews various optical phenomena in several exemplary 1D Bragg gratings. Important examples are resonantly absorbing photonic structures, chirped Bragg grating, and cholesteric liquid crystals; their unique operation capabilities and key issues are considered in detail. These Bragg structures are expected to be used in wide-spread applications involving light field modulations, especially in the rapidly advancing field of ultrafast optical signal processing.
Saklani, Vipin; Meenakshi Sundari, A.; Rai, A.K.; Sarma, C.V.R.
Gamma Ray Spectrometry is an essential tool for mapping radio elemental concentrations and has found widespread applications in mineral exploration, geological mapping and environmental mapping. Spectrometry based on analog amplifiers with Gaussian pulse shaping has been used for more than 40 years. Digital electronics and digital signal processing methods have enhanced the spectrometer specifications. This paper presents the design and development of a FPGA based Gamma Ray Spectrometer featuring 1024 channel Multi Channel Analyser (MCA) with NaI(Tl) scintillation detector. The system shows significant improvements in energy resolution, count rate capability and dead time as compared to its analog counterpart. (author)
Płaza, Mirosław; Szcześniak, Zbigniew
The system of signal processing for electrotherapeutic applications is proposed in the paper. The system makes it possible to model the curve of threshold human sensitivity to current (Dalziel's curve) in full medium frequency range (1kHz-100kHz). The tests based on the proposed solution were conducted and their results were compared with those obtained according to the assumptions of High Tone Power Therapy method and referred to optimum values. Proposed system has high dynamics and precision of mapping the curve of threshold human sensitivity to current and can be used in all methods where threshold curves are modelled.
Full Text Available In the past three decades, a lot of various applications of Ground Penetrating Radar (GPR took place in real life. There are important challenges of this radar in civil applications and also in military applications. In this paper, the fundamentals of GPR systems will be covered and three important signal processing methods (Wavelet Transform, Matched Filter and Hilbert Huang will be compared to each other in order to get most accurate information about objects which are in subsurface or behind the wall.
In this thesis, digital signal processing (DSP) algorithms are studied to compensate for physical layer impairments in optical fiber coherent communication systems. The physical layer impairments investigated in this thesis include optical fiber chromatic dispersion, polarization demultiplexing......, light sources frequency and phase offset and phase noise. The studied DSP algorithms are considered as key building blocks in digital coherent receivers for the next generation of optical communication systems such as 112-Gb/s dual polarization (DP) quadrature phase shift keying (QPSK) optical...... spectrum narrowing tolerance 112-Gb/s DP-QPSK optical coherent systems using digital adaptive equalizer. The demonstrated results show that off-line DSP algorithms are able to reduce the bit error rate (BER) penalty induced by signal spectrum narrowing. Third, we also investigate bi...
Anderson, James A.; Korrapati, Raghu; Swain, Nikunja K.
The area of test and measurement is changing rapidly because of the recent developments in software and hardware. The test and measurement systems are increasingly becoming PC based. Most of these PC based systems use graphical programming language to design test and measurement modules called virtual instruments (Vis). These Vis provide visual representation of dat or models, and make understanding of abstract concepts and algorithms easier. This allows users to express their ideas in a concise manner. One such virtual instruments package is LabVIEW from National Instruments Corporation at Austin, Texas. This software package is one of the first graphical programming products and is currently used in number of academic institutions, industries, Department of Defense graphical programming products and is currently sued in number of academic institutions, industries, Department of Defense, Department of Energy, and National Aeronautics and Space Administration for various test, measurement, and control applications. LabVIEW has an extensive built-in VI library that can be used to design and develop solutions for different applications. Besides using the built-in VI library that can be used to design and develop solutions for different applications. Besides using the built-in VI modules in LabVIEW, the user can design new VI modules easily. This paper discusses the use of LabVIEW to design and develop digital signal processing VI modules such as Fourier Analysis and Windowing. Instructors can use these modules to teach some of the signal processing concepts effectively.
Lapedes, A.; Farber, R.
The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.
Full Text Available In this paper, an approach is presented to detect faint signals with strong noises in sensors by stochastic resonance (SR. We adopt the power spectrum as the evaluation tool of SR, which can be obtained by the fast Fourier transform (FFT. Furthermore, we introduce the adaptive filtering scheme to realize signal processing automatically. The key of the scheme is how to adjust the barrier height to satisfy the optimal condition of SR in the presence of any input. For the given input signal, we present an operable procedure to execute the adjustment scheme. An example utilizing one audio sensor to detect the fault information from the power supply is given. Simulation results show that th
Vogt, M. C.
Many industrial and environmental processes, including bioremediation, would benefit from the feedback and control information provided by a local multi-analyte chemical sensor. For most processes, such a sensor would need to be rugged enough to be placed in situ for long-term remote monitoring, and inexpensive enough to be fielded in useful numbers. The multi-analyte capability is difficult to obtain from common passive sensors, but can be provided by an active device that produces a spectrum-type response. Such new active gas microsensor technology has been developed at Argonne National Laboratory. The technology couples an electrocatalytic ceramic-metallic (cermet) microsensor with a voltammetric measurement technique and advanced neural signal processing. It has been demonstrated to be flexible, rugged, and very economical to produce and deploy. Both narrow interest detectors and wide spectrum instruments have been developed around this technology. Much of this technology's strength lies in the active measurement technique employed. The technique involves applying voltammetry to a miniature electrocatalytic cell to produce unique chemical ''signatures'' from the analytes. These signatures are processed with neural pattern recognition algorithms to identify and quantify the components in the analyte. The neural signal processing allows for innovative sampling and analysis strategies to be employed with the microsensor. In most situations, the whole response signature from the voltammogram can be used to identify, classify, and quantify an analyte, without dissecting it into component parts. This allows an instrument to be calibrated once for a specific gas or mixture of gases by simple exposure to a multi-component standard rather than by a series of individual gases. The sampled unknown analytes can vary in composition or in concentration, the calibration, sensing, and processing methods of these active voltammetric microsensors can
Vogt, Michael C.; Skubal, Laura R.
Many industrial and environmental processes, including bioremediation, would benefit from the feedback and control information provided by a local multi-analyte chemical sensor. For most processes, such a sensor would need to be rugged enough to be placed in situ for long-term remote monitoring, and inexpensive enough to be fielded in useful numbers. The multi-analyte capability is difficult to obtain from common passive sensors, but can be provided by an active device that produces a spectrum-type response. Such new active gas microsensor technology has been developed at Argonne National Laboratory. The technology couples an electrocatalytic ceramic-metallic (cermet) microsensor with a voltammetric measurement technique and advanced neural signal processing. It has been demonstrated to be flexible, rugged, and very economical to produce and deploy. Both narrow interest detectors and wide spectrum instruments have been developed around this technology. Much of this technology's strength lies in the active measurement technique employed. The technique involves applying voltammetry to a miniature electrocatalytic cell to produce unique chemical 'signatures' from the analytes. These signatures are processed with neural pattern recognition algorithms to identify and quantify the components in the analyte. The neural signal processing allows for innovative sampling and analysis strategies to be employed with the microsensor. In most situations, the whole response signature from the voltammogram can be used to identify, classify, and quantify an analyte, without dissecting it into component parts. This allows an instrument to be calibrated once for a specific gas or mixture of gases by simple exposure to a multi-component standard rather than by a series of individual gases. The sampled unknown analytes can vary in composition or in concentration; the calibration, sensing, and processing methods of these active voltammetric microsensors can detect, recognize, and
Liu, Yang; Fang, Xiaoyi; Cai, Rong; Wu, Yang; Zhang, Yaofang
This article employs qualitative research methods to explore the urban adaptation and adaptation processes of Chinese migrant children. Through twenty-one in-depth interviews with migrant children, the researchers discovered: The participant migrant children showed a fairly high level of adaptation to the city; their process of urban adaptation…
Swenson, Cory V.
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.
A new stochastic gradient robust filtering method, based on a non-linear amplitude transformation, is proposed. The method requires no a priori knowledge of the characteristics of the input signals and it is insensitive to the signals distribution and to the stationarity of the signals. A simulat...
Oxenløwe, Leif Katsuo; Ji, Hua; Galili, Michael
In this paper, we describe our recent work on signal processing of terabit per second optical serial data signals using pure silicon waveguides. We employ nonlinear optical signal processing in nanoengineered silicon waveguides to perform demultiplexing and optical waveform sampling of 1.28-Tbit....../s data signals as well as wavelength conversion of up to 320-Gbit/s data signals. We demonstrate that the silicon waveguides are equally useful for amplitude and phase-modulated data signals....
Ultrasound imaging has become one of the most widely used diagnostic tools in medicine. While it has advantages, compared with other modalities, in terms of safety, low-cost, accessibility, portability and capability of real-time imaging, it has limitations. One of the major disadvantages of ultrasound imaging is the relatively low image quality, especially the low signal-to-noise ratio (SNR) and the low spatial resolution. Part of this dissertation is dedicated to the development of digital ultrasound signal and image processing methods to improve ultrasound image quality. Conventional B-mode ultrasound systems display the demodulated signals, i.e., the envelopes, in the images. In this dissertation, I introduce the envelope matched quadrature filtering (EMQF) technique, which is a novel demodulation technique generating optimal performance in envelope detection. In ultrasonography, the echo signals are the results of the convolution of the pulses and the medium responses, and the finite pulse length is a major source of the degradation of the image resolution. Based on the more appropriate complex-valued medium response assumption rather than the real-valued assumption used by many researchers, a nonparametric iterative deconvolution method, the Least Squares method with Point Count regularization (LSPC), is proposed. This method was tested using simulated and experimental data, and has produced excellent results showing significant improvements in resolution. During the past two decades, ultrasound tissue characterization (UTC) has emerged as an active research field and shown potentials of applications in a variety of clinical areas. Particularly interesting to me is a group of methods characterizing the scatterer spatial distribution. For resolvable regular structures, a deconvolution based method is proposed to estimate parameters characterizing such structures, including mean scatterer spacing, and has demonstrated superior performance when compared to
Ghica, Daniela; Radulian, Mircea; Popa, Mihaela
Since July 2002, a new seismic monitoring station, the Bucovina Seismic Array (BURAR), has been installed in the northern part of Romania, in a joint effort of the Air Force Technical Applications Center, USA, and the National Institute for Earth Physics (NIEP), Romania. The array consists of 10 seismic sensors (9 short-period and one broad band) located in boreholes and distributed in a 5 x 5 km 2 area. At present, the seismic data are continuously recorded by BURAR and transmitted in real-time to the Romanian National Data Centre (ROM N DC), in Bucharest and to the National Data Center of USA, in Florida. The statistical analysis for the seismic information gathered at ROM N DC by the BURAR in the August 2002 - December 2003 time interval points out a much better efficiency of the BURAR system in detecting teleseismic events and local events occurred in the N-NE part of Romanian territory, in comparison with the actual Romanian Telemetered Network. Furthermore, the BURAR monitoring system has proven to be an important source of reliable data for NIEP efforts in issuing the seismic bulletins. Signal processing capability of the system provides useful information in order to improve the location of the local seismic events, using the array beamforming procedure. This method increases significantly the signal-to-noise ratio by summing up the coherent signals from the array components. In this way, possible source nucleation phases can be detected. At the same time, using the slowness and back azimuth estimations by f-k analysis, locations for the seismic events can be established based only on the information recorded by the BURAR array, acting like a single seismic station recording system. (authors)
This dissertation focuses on signal modeling and processing issues of the following problems in reflection tomography: synthetic aperture radar (SAR) imaging of a runway and surroundings from an aircraft approaching for landing, acoustic imaging of objects buried in soil, and lidar imaging of underwater objects. The highly squinted geometry of runway imaging necessitates the incorporation of wavefront curvature into the signal model. We investigate the feasibility of using the wavenumber-domain (ω - k) SAR inversion algorithm, which models the actual curvature of the wavefront, for runway imaging. We demonstrate the aberrations that the algorithm can produce when the squint angle is close to 90° and show that high-quality reconstruction is still possible provided that the interpolation is performed accurately enough, which can be achieved by increasing the temporal sampling rate. We compare the performance with that of a more general inversion method (GIM) that solves the measurement equation directly. The performances of both methods are comparable in the noise- free case. Being inherently robust to noise, GIM produces superior results in the noisy case. We also present a solution to the left-right ambiguity of runway imaging using interferometric processing. In imaging of objects buried in soil, we pursue an acoustic approach primarily for detection and imaging of cultural artifacts. We have developed a mathematical model and associated computer software in order to simulate the signals acquired by the actual experimental system, and a bistatic SAR-type algorithm for reconstruction. In the reconstructions from simulated data, objects were detectable, but near-field objects suffered from shifts and smears. To account for wavefront curvature, we formulated processing of the simulated data using the 3-D version of the monostatic ω - k algorithm. In lidar imaging of underwater objects, we formulate the problem as a 3-D tomographic reconstruction problem. We have
Lei, Jian; Meng, Xiangtao; Xiang, Zheng
Due to various moving parts inside, when a spacecraft runs in orbits, its structure could get a minor angular vibration, which results in vague image formation of space camera. Thus, image compensation technique is required to eliminate or alleviate the effect of movement on image formation and it is necessary to realize precise measuring of flutter angle. Due to the advantages such as high sensitivity, broad bandwidth, simple structure and no inner mechanical moving parts, FOG (fiber optical gyro) is adopted in this study to measure minor angular vibration. Then, movement leading to image degeneration is achieved by calculation. The idea of the movement information extracting algorithm based on self-adaptive sparse representation is to use arctangent function approximating L0 norm to construct unconstrained noisy-signal-aimed sparse reconstruction model and then solve the model by a method based on steepest descent algorithm and BFGS algorithm to estimate sparse signal. Then taking the advantage of the principle of random noises not able to be represented by linear combination of elements, useful signal and random noised are separated effectively. Because the main interference of minor angular vibration to image formation of space camera is random noises, sparse representation algorithm could extract useful information to a large extent and acts as a fitting pre-process method of image restoration. The self-adaptive sparse representation algorithm presented in this paper is used to process the measured minor-angle-vibration signal of FOG used by some certain spacecraft. By component analysis of the processing results, we can find out that the algorithm could extract micro angular vibration signal of FOG precisely and effectively, and can achieve the precision degree of 0.1".
Matthews, Luke J
Recent research on the evolution of religion has focused on whether religion is an unselected by-product of evolutionary processes or if it is instead an adaptation by natural selection. Adaptive hypotheses for religion include direct fitness benefits from improved health and indirect fitness benefits mediated by costly signals and/or cultural group selection. Herein, I propose that religious denominations achieve indirect fitness gains for members through the use of ecologically arbitrary beliefs, rituals, and moral rules that function as recognition markers of cultural inheritance analogous to kin and species recognition of genetic inheritance in biology. This recognition signal hypotheses could act in concert with either costly signaling or cultural group selection to produce evolutionarily altruistic behaviors within denominations. Using a cultural phylogenetic analysis, I show that a large set of religious behaviors among extant Christian denominations supports the prediction of the recognition signal hypothesis that characters change more frequently near historical schisms. By incorporating demographic data into the model, I show that more-distinctive denominations, as measured through dissimilar characteristics, appear to be protected from intrusion by nonmembers in mixed-denomination households, and that they may be experiencing greater biological growth of their populations even in the present day.
Kurusu, Takamitsu; Kuchitsu, Kazuyuki
Considering the important issues concerning food, environment, and energy that humans are facing in the 21st century, humans mostly depend on plants. Unlike animals which move from an inappropriate environment, plants do not move, but rapidly sense diverse environmental changes or invasion by other organisms such as pathogens and insects in the place they root, and adapt themselves by changing their own bodies, through which they developed adaptability. Whole genetic information corresponding to the blueprints of many biological systems has recently been analyzed, and comparative genomic studies facilitated tracing strategies of each organism in their evolutional processes. Comparison of factors involved in intracellular signal transduction between animals and plants indicated diversification of different gene sets. Reversible binding of Ca2+ to sensor proteins play key roles as a molecular switch both in animals and plants. Molecular mechanisms for signaling network of environmental sensing and adaptation in plants will be discussed with special reference to Ca2+ as a key element in information processing.
Christensen, Lars P.B.
by allowing an increased reuse of network resources. To achieve this goal, one must first understand the nature of the problem and an introduction is therefore provided. In addition, the concept of graph-based models and approximations for wireless communications is introduced along with various Belief......This thesis is concerned with signal processing for improving the performance of wireless communication receivers for well-established cellular networks such as the GSM/EDGE and WCDMA/HSPA systems. The goal of doing so, is to improve the end-user experience and/or provide a higher system capacity...... Propagation (BP) methods for detecting the transmitted information, including the Turbo principle. Having established a framework for the research, various approximate detection schemes are discussed. First, the general form of linear detection is presented and it is argued that this may be preferable...
This thesis describes the physics and applications of quantum dot semiconductor optical ampliers through numerical simulations. As nano-structured materials with zero-dimensional quantum connement, semiconductor quantum dot material provides a number of unique physical properties compared...... with other semiconductor materials. The understanding of such properties is important in order to improve the performance of existing devices and to trigger the development of new semiconductor devices for dierent optical signal processing functionalities in the future. We present a detailed quantum dot...... semiconductor optical amplier model incorporating a carrier dynamics rate equation model for quantum dots with inhomogeneous broadening as well as equations describing propagation. A phenomenological description has been used to model the intradot electron scattering between discrete quantum dot states...
A readable, understandable introduction to DSP for professionals and students alike . . . This practical guide is a welcome alternative to more complicated introductions to DSP. It assumes no prior DSP experience and takes the reader step-by-step through the most basic signal processing concepts to more complex functions and devices, including sampling, filtering, frequency transforms, data compression, and even DSP design decisions. The guide provides clear, concise explanations and examples, while keeping mathematics to a minimum, to help develop a fundamental understanding of DSP. Other features include: * An extensive resource bibliography of more advanced DSP books. * An example of a typical DSP system development cycle, including tool descriptions. * A complete glossary of DSP-related acronyms Whether you're a working engineer looking into DSP for the first time or an undergraduate struggling to comprehend the subject, this engaging introduction provides easy access to the basic knowledge that will l...
This book introduces the Statistical Drake Equation where, from a simple product of seven positive numbers, the Drake Equation is turned into the product of seven positive random variables. The mathematical consequences of this transformation are demonstrated and it is proven that the new random variable N for the number of communicating civilizations in the Galaxy must follow the lognormal probability distribution when the number of factors in the Drake equation is allowed to increase at will. Mathematical SETI also studies the proposed FOCAL (Fast Outgoing Cyclopean Astronomical Lens) space mission to the nearest Sun Focal Sphere at 550 AU and describes its consequences for future interstellar precursor missions and truly interstellar missions. In addition the author shows how SETI signal processing may be dramatically improved by use of the Karhunen-Loève Transform (KLT) rather than Fast Fourier Transform (FFT). Finally, he describes the efforts made to persuade the United Nations to make the central part...
The discovery of adaptation processes in cells (i.e., increased resistance to effects of a challenge dose administered after a lower adapting dose) has fuelled the debate on possible cellular processes relevant for low dose exposures. However, numerous experiments on radioadaptive response do not provide a clear picture of the nature of adaptive response and the conditions under which it occurs. This work proposes a model that succeeds in modelling data obtained from various experiments on radioadaptation. The model assumes impaired DNA integrity as triggering signal for induction of adaptation. Induction of adaptive response is seen as two-phase process. First, ionizing radiation induces radicals by water radiolysis which give rise to specific DNA lesions. On the other hand, these lesions must be perceived and, in a way, processed by the cell, thereby creating the final signal necessary for the comprehensive adaptive response. This processing occurs through some event in S-phase and can be halted by local conformational changes of chromatin induced by ionizing radiation. Thus, the model assumes two counteracting processes that have to be balanced for the triggering signal of adaptation to occur, each of them related to different target volumes. This work comprises mathematical treatment of radical formation, DNA lesion induction and inhibition of local initiation of replication which finally provides functions that quantify the reduction of double strand breaks introduced by challenge doses in adapted cells as compared to non-adapted cells. Non-linear regression analyses based upon data from experiments on radioadaptation yield regression curves which describe existing data satisfactorily. Thus, it corroborates the existence of adaptive response as, in principle, universal feature of cells and specifies conditions which favor development of radioadaptation. (author)
Hao, Nan; Budnik, Bogdan A; Gunawardena, Jeremy; O'Shea, Erin K
Signaling pathways can induce different dynamics of transcription factor (TF) activation. We explored how TFs process signaling inputs to generate diverse dynamic responses. The budding yeast general stress-responsive TF Msn2 acted as a tunable signal processor that could track, filter, or integrate signals in an input-dependent manner. This tunable signal processing appears to originate from dual regulation of both nuclear import and export by phosphorylation, as mutants with one form of regulation sustained only one signal-processing function. Versatile signal processing by Msn2 is crucial for generating distinct dynamic responses to different natural stresses. Our findings reveal how complex signal-processing functions are integrated into a single molecule and provide a guide for the design of TFs with "programmable" signal-processing functions.
Continuous-Time and Discrete-Time Signals and SystemsIntroductionContinuous-Time SignalsPeriodic FunctionsUnit Step FunctionGraphical Representation of FunctionsEven and Odd Parts of a FunctionDirac-Delta ImpulseBasic Properties of the Dirac-Delta ImpulseOther Important Properties of the ImpulseContinuous-Time SystemsCausality, StabilityExamples of Electrical Continuous-Time SystemsMechanical SystemsTransfer Function and Frequency ResponseConvolution and CorrelationA Right-Sided and a Left-Sided FunctionConvolution with an Impulse and Its DerivativesAdditional Convolution PropertiesCorrelation FunctionProperties of the Correlation FunctionGraphical InterpretationCorrelation of Periodic FunctionsAverage, Energy and Power of Continuous-Time SignalsDiscrete-Time SignalsPeriodicityDifference EquationsEven/Odd DecompositionAverage Value, Energy and Power SequencesCausality, StabilityProblemsAnswers to Selected ProblemsFourier Series ExpansionTrigonometric Fourier SeriesExponential Fourier SeriesExponential versus ...
ARL-TR-8276• FEB 2018 US Army Research Laboratory Signal Processing for Time-Series Functions on a Graph by Humberto Muñoz-Barona, Jean Vettel, and...ARL-TR-8276• FEB 2018 US Army Research Laboratory Signal Processing for Time-Series Functions on a Graph by Humberto Muñoz-Barona Southern University...email@example.com>. Previous research introduced signal processing on graphs, an approach to generalize signal processing tools such
Mu, Jiasong; Wang, Wei; Liang, Qilian; Pi, Yiming
The Proceedings of The Second International Conference on Communications, Signal Processing, and Systems provides the state-of-art developments of Communications, Signal Processing, and Systems. The conference covered such topics as wireless communications, networks, systems, signal processing for communications. This book is a collection of contributions coming out of The Second International Conference on Communications, Signal Processing, and Systems (CSPS) held September 2013 in Tianjin, China.
The 21st IEEE International Workshop on Machine Learning for Signal Processing will be held in Beijing, China, on September 18–21, 2011. The workshop series is the major annual technical event of the IEEE Signal Processing Society's Technical Committee on Machine Learning for Signal Processing...
Berg, Tommy Winther; Uskov, A. V.; Bischoff, Svend
The dynamics of quantum dot semiconductor amplifiers are investigated theoretically with respect to the potential for ultrafast signal processing. The high-speed signal processing capacity of these devices is found to be limited by the wetting layer dynamics in case of electrical pumping, while...... optical pumping partly removes this limitation. Also, the possibility of using spectral hole burning for signal processing is discussed....
STAR Performance with SPEAR ( Signal Processing Electronic Attack RFIC) Luciano Boglione, Clayton Davis, Joel Goodman, Matthew McKeon, David...Parrett, Sanghoon Shin and Naomi Walker Naval Research Laboratory Washington, DC, 20375 Figure 1: The Signal Processing Electronic Attack RFIC...SPEAR) system. Abstract: The Signal Processing Electronic Attack RFIC (SPEAR) is a simultaneous transmit and receive (STAR) system capable of
Liang, Qilian; Wang, Wei; Zhang, Baoju; Pi, Yiming
The Proceedings of The Third International Conference on Communications, Signal Processing, and Systems provides the state-of-art developments of communications, signal processing, and systems. This book is a collection of contributions from the conference and covers such topics as wireless communications, networks, systems, and signal processing for communications. The conference was held July 2014 in Hohhot, Inner Mongolia, China.
Multistatic Active Sonar Signal Processing," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada, May 26-31...2015, Genoa, Italy, May 18-21. HONORS/AWARDS/PRIZES Dr. Jian Li gave a plenary talk at the IEEE Sensor Array and Multichannel Signal Processing
Voesenek, Laurentius A C J|info:eu-repo/dai/nl/074850849; Bailey-Serres, Julia
Unanticipated flooding challenges plant growth and fitness in natural and agricultural ecosystems. Here we describe mechanisms of developmental plasticity and metabolic modulation that underpin adaptive traits and acclimation responses to waterlogging of root systems and submergence of aerial
Adaptive management planning projects use multiparty, multidisciplinary workshops and simulation modeling to facilitate dialogue, negotiation, and planning. However, they have been criticized as a poor medium for conflict resolution. Alternative processes from the conflict resolution tradition, e.g., principled negotiation and sequenced negotiation, address uncertainty and biophysical constraints much less skillfully than does adaptive management. When we evaluate adaptive management planning...
van Nort, Douglas
parameters. In this view, desired gestural dynamics and sonic response are achieved through modular construction of mapping layers that are themselves subject to parametric control. Complementing this view of the design process, the work concludes with an approach in which the creation of gestural control/sound dynamics are considered in the low-level of the underlying sound model. The result is an adaptive system that is specialized to noise-based transformations that are particularly relevant in an electroacoustic music context. Taken together, these different approaches to design and evaluation result in a unified framework for creation of an instrumental system. The key point is that this framework addresses the influence that mapping structure and control dynamics have on the perceived feel of the instrument. Each of the results illustrate this using either top-down or bottom-up approaches that consider musical control context, thereby pointing to the greater potential for refined sonic articulation that can be had by combining them in the design process.
Meteor wind radar systems are a powerful tool for study of the horizontal wind field in the mesosphere and lower thermosphere (MLT). While such systems have been operated for many years, virtually no literature has focused on radar system error analysis. The instrumental error may prevent scientists from getting correct conclusions on geophysical variability. The radar system instrumental error comes from different sources, including hardware, software, algorithms and etc. Radar signal processing plays an important role in radar system and advanced signal processing algorithms may dramatically reduce the radar system errors. In this dissertation, radar system error propagation is analyzed and several advanced signal processing algorithms are proposed to optimize the performance of radar system without increasing the instrument costs. The first part of this dissertation is the development of a time-frequency waveform detector, which is invariant to noise level and stable to a wide range of decay rates. This detector is proposed to discriminate the underdense meteor echoes from the background white Gaussian noise. The performance of this detector is examined using Monte Carlo simulations. The resulting probability of detection is shown to outperform the often used power and energy detectors for the same probability of false alarm. Secondly, estimators to determine the Doppler shift, the decay rate and direction of arrival (DOA) of meteors are proposed and evaluated. The performance of these estimators is compared with the analytically derived Cramer-Rao bound (CRB). The results show that the fast maximum likelihood (FML) estimator for determination of the Doppler shift and decay rate and the spatial spectral method for determination of the DOAs perform best among the estimators commonly used on other radar systems. For most cases, the mean square error (MSE) of the estimator meets the CRB above a 10dB SNR. Thus meteor echoes with an estimated SNR below 10dB are
Full Text Available 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.
Kelly, J. J.
A detailed analysis of signal processing concerns for measuring aircraft flyover noise is presented. Development of a de-Dopplerization scheme for both corrected time history and spectral data is discussed along with an analysis of motion effects on measured spectra. A computer code was written to implement the de-Dopplerization scheme. Input to the code is the aircraft position data and the pressure time histories. To facilitate ensemble averaging, a level uniform flyover is considered in the study, but the code can accept more general flight profiles. The effects of spectral smearing and its removal are discussed. Using test data acquired from an XV-15 tilt-rotor flyover, comparisons are made between the measured and corrected spectra. Frequency shifts are accurately accounted for by the de-Dopplerization procedure. It is shown that by correcting for spherical spreading and Doppler amplitude, along with frequency, can give some idea about noise source directivity. The analysis indicated that smearing increases with frequency and is more severe on approach than recession.
Xu Chi; Guo Dongming; Jin Zhuji; Kang Renke
A signal processing method for the friction-based endpoint detection system of a chemical mechanical polishing (CMP) process is presented. The signal process method uses the wavelet threshold denoising method to reduce the noise contained in the measured original signal, extracts the Kalman filter innovation from the denoised signal as the feature signal, and judges the CMP endpoint based on the feature of the Kalman filter innovation sequence during the CMP process. Applying the signal processing method, the endpoint detection experiments of the Cu CMP process were carried out. The results show that the signal processing method can judge the endpoint of the Cu CMP process. (semiconductor technology)
Fu, Chi Yung; Petrich, Loren I.
The method and system described herein use a biologically-based signal processing system for noise removal for signal extraction. A wavelet transform may be used in conjunction with a neural network to imitate a biological system. The neural network may be trained using ideal data derived from physical principles or noiseless signals to determine to remove noise from the signal.
adaptive signal/image processing, machine learning, and statistics in order to solve complex real-world signal processing applications. This year, two topics attracting particular interest were presented at two special sessions; one on bioinformatics and a second one on space and aeronautics. High quality...... methodology and real-world application domains and is widely entering into everyday solutions adopted by research and industry, going far beyond “traditional” neural networks and academic examples. As reflected in this collection, contemporary neural networks for signal processing combine many ideas from...
"With the proliferation of digital audio distribution over digital media, audio content analysis is fast becoming a requirement for designers of intelligent signal-adaptive audio processing systems. Written by a well-known expert in the field, this book provides quick access to different analysis algorithms and allows comparison between different approaches to the same task, making it useful for newcomers to audio signal processing and industry experts alike. A review of relevant fundamentals in audio signal processing, psychoacoustics, and music theory, as well as downloadable MATLAB files are also included"--
The internal bone adaptation of the proximal femur is considered. A three-dimensional finite element model of the proximal femur is used. The bone remodeling in this work is numerically described byan evolutionary remodeling scheme with anisotropic material parameters andtime-dependent loading...
van den Broek, Egon; Janssen, Joris H.; van der Zwaag, Marjolein D.; Healey, Jennifer A.; Fred, A.; Filipe, J.; Gamboa, H.
This is the third part in a series on prerequisites for affective signal processing (ASP). So far, six prerequisites were identified: validation (e.g., mapping of constructs on signals), triangulation, a physiology-driven approach, and contributions of the signal processing community (van den Broek
Full Text Available Wide band digital audio signals have a very high data-rate associated with them due to their complex nature and demand for high-quality reproduction. Although recent technological advancements have significantly reduced the cost of bandwidth and miniaturized storage facilities, the rapid increase in the volume of digital audio content constantly compels the need for better compression algorithms. Over the years various perceptually lossless compression techniques have been introduced, and transform-based compression techniques have made a significant impact in recent years. In this paper, we propose one such transform-based compression technique, where the joint time-frequency (TF properties of the nonstationary nature of the audio signals were exploited in creating a compact energy representation of the signal in fewer coefficients. The decomposition coefficients were processed and perceptually filtered to retain only the relevant coefficients. Perceptual filtering (psychoacoustics was applied in a novel way by analyzing and performing TF specific psychoacoustics experiments. An added advantage of the proposed technique is that, due to its signal adaptive nature, it does not need predetermined segmentation of audio signals for processing. Eight stereo audio signal samples of different varieties were used in the study. Subjective (mean opinion scoreÃ¢Â€Â”MOS listening tests were performed and the subjective difference grades (SDG were used to compare the performance of the proposed coder with MP3, AAC, and HE-AAC encoders. Compression ratios in the range of 8 to 40 were achieved by the proposed technique with subjective difference grades (SDG ranging from Ã¢Â€Â“0.53 to Ã¢Â€Â“2.27.
Michael P. Curran; Douglas G. Maynard; Ronald L. Heninger; Thomas A. Terry; Steven W. Howes; Douglas M. Stone; Thomas Niemann; Richard E. Miller; Robert F. Powers
Soil disturbance guidelines should be based on comparable disturbance categories adapted to specific local soil conditions, validated by monitoring and research. Guidelines, standards, and practices should be continually improved based on an adaptive management process, which is presented in this paper. Core components of this process include: reliable monitoring...
Clausen, Anders; Mulvad, Hans Christian Hansen; Palushani, Evarist
To ensure that ultra high-speed serial data signals can be utilised in future optical communication networks, it is indispensable to have all-optical signal processing elements at our disposal. In this paper, the most recent advances in our use of non-linear materials incorporated in different...... function blocks for high-speed signal processing are reviewed....
This book presents the cutting-edge technologies of knee joint vibroarthrographic signal analysis for the screening and detection of knee joint injuries. It describes a number of effective computer-aided methods for analysis of the nonlinear and nonstationary biomedical signals generated by complex physiological mechanics. This book also introduces several popular machine learning and pattern recognition algorithms for biomedical signal classifications. The book is well-suited for all researchers looking to better understand knee joint biomechanics and the advanced technology for vibration arthrometry. Dr. Yunfeng Wu is an Associate Professor at the School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China.
Cleland, W.E.; Stern, E.G.
We present the results of a study of the effects of thermal and pileup noise in liquid ionization calorimeters operating in a high luminosity calorimeters operating in a high luminosity environment. The method of optimal filtering of multiply-sampled signals which may be used to improve the timing and amplitude resolution of calorimeter signals is described, and its implications for signal shaping functions are examined. The dependence of the time and amplitude resolution on the relative strength of the pileup and thermal noise, which varies with such parameters as luminosity, rapidity and calorimeter cell size, is examined
Covariance Bounds," Proc 07th Asilo - mar Conf on Signals, Systems, and Computers, Pacific Grove, CA (November 1993). [MuS9l] C. T. Mullis and L. L. Scharf...Transforms," Proc Asilo - mar Con. on Signals, Systems, and Computers, Asilomar, CA (November 1991). [SpS94] M. Spurbeck and L. L. Scharf, "Least Squares...McWhorter and L. L. Scharf, "Multiwindow Estimators of Correlation," Proc 28th Annual Asilo - mar Conf on Signals, Systems, and Computers, Asilomar, CA
Fotheringham, Edeline B.
Have you ever found yourself listening to the music playing from the closest stereo rather than to the bromidic (uninspiring) person speaking to you? Your ears receive information from two sources but your brain listens to only one. What if your cell phone could distinguish among signals sharing the same bandwidth too? There would be no "full" channels to stop you from placing or receiving a call. This thesis presents a nonlinear optical circuit capable of distinguishing uncorrelated signals that have overlapping temporal bandwidths. This so called autotuning filter is the size of a U.S. quarter dollar and requires less than 3 mW of optical power to operate. It is basically an oscillator in which the losses are compensated with dynamic holographic gain. The combination of two photorefractive crystals in the resonator governs the filter's winner-take-all dynamics through signal-competition for gain. This physical circuit extracts what is mathematically referred to as the largest principal component of its spatio-temporal input space. The circuit's practicality is demonstrated by its incorporation in an RF-photonic system. An unknown mixture of unknown microwave signals, received by an antenna array, constitutes the input to the system. The output electronically returns one of the original microwave signals. The front-end of the system down converts the 10 GHz microwave signals and amplifies them before the signals phase modulate optical beams. The optical carrier is suppressed from these beams so that it may not be considered as a signal itself to the autotuning filter. The suppression is achieved with two-beam coupling in a single photorefractive crystal. The filter extracts the more intense of the signals present on the carrier-suppressed input beams. The detection of the extracted signal restores the microwave signal to an electronic form. The system, without the receiving antenna array, is packaged in a 13 x 18 x 6″ briefcase. Its power consumption equals that
Artuso, M.; Bezshyiko, I.; Blusk, S.; Bruendler, R.; Bugiel, S.; Dasgupta, R.; Dendek, A.; Dey, B.; Ely, S.; Lionetto, F.; Petruzzo, M.; Polyakov, I.; Rudolph, M.; Schindler, H.; Steinkamp, O.; Stone, S.
We have examined the effects of embedded pitch adapters on signal formation in n-substrate silicon microstrip sensors with data from beam tests and simulation. According to simulation, the presence of the pitch adapter metal layer changes the electric field inside the sensor, resulting in slowed signal formation on the nearby strips and a pick-up effect on the pitch adapter. This can result in an inefficiency to detect particles passing through the pitch adapter region. All these effects have been observed in the beam test data.
V. K. Hohlov
Full Text Available The article forms the rationale for selecting the informative features of the helicopter and aircraft acoustic signals to solve a problem of their recognition and shows that the most informative ones are the counts of extrema in the energy spectra of the input signals, which represent non-centered random variables. An apparatus of the multiple initial regression coefficients was selected as a mathematical tool of research. The application of digital re-circulators with positive and negative feedbacks, which have the comb-like frequency characteristics, solves the problem of selecting informative features. A distinguishing feature of such an approach is easy agility of the comb frequency characteristics just through the agility of a delay value of digital signal in the feedback circuit. Adding an adaptation block to the selection block of the informative features enables us to ensure the invariance of used informative feature and counts of local extrema of the spectral power density to the airspeed of a helicopter. The paper gives reasons for the principle of adaptation and the structure of the adaptation block. To form the discriminator characteristics are used the cross-correlation statistical characteristics of the quadrature components of acoustic signal realizations, obtained by Hilbert transform. The paper proposes to provide signal recognition using a regression algorithm that allows handling the non-centered informative features and using a priori information about coefficients of initial regression of signal and noise.The simulation in Matlab Simulink has shown that selected informative features of signals in regressive processing of signal realizations of 0.5 s duration have good separability, and based on a set of 100 acoustic signal realizations in each class in full-scale conditions, has proved ensuring a correct recognition probability of 0.975, at least. The considered principles of informative features selection and adaptation can
van den Broek, Egon; Janssen, Joris H.; Westerink, Joyce H.D.M.; Healey, Jennifer A.; Encarnacao, P.; Veloso, A.
Although emotions are embraced by science, their recognition has not reached a satisfying level. Through a concise overview of affect, its signals, features, and classification methods, we provide understanding for the problems encountered. Next, we identify the prerequisites for successful
Webster, John G
ELECTROMAGNETIC VARIABLES MEASUREMENTVoltage MeasurementCurrent Measurement Power Measurement Power Factor Measurement Phase Measurement Energy Measurement Electrical Conductivity and Resistivity Charge Measurement Capacitance and Capacitance Measurements Permittivity Measurement Electric Field Strength Magnetic Field Measurement Permeability and Hysteresis MeasurementInductance Measurement Immittance MeasurementQ Factor Measurement Distortion Measurement Noise Measurement.Microwave Measurement SIGNAL PROCESSINGAmplifiers and Signal ConditionersModulation Filters Spectrum Analysis and Correlat
IJsselmuide, S.P. van; Deruaz, L.; Been, R.; Doisy, Y.; Beerens, S.P.
For littoral ASW, reverberation is a big problem and rejection of reverberation is of utmost importance. The influence of the transmitted signal on the signal to reverberation ratio has been presented in three preceding papers. In this paper, the influence of improved signal processing on the
Erskine, David J.
A method of signal processing a high bandwidth signal by coherently subdividing it into many narrow bandwidth channels which are individually processed at lower frequencies in a parallel manner. Autocorrelation and correlations can be performed using reference frequencies which may drift slowly with time, reducing cost of device. Coordinated adjustment of channel phases alters temporal and spectral behavior of net signal process more precisely than a channel used individually. This is a method of implementing precision long coherent delays, interferometers, and filters for high bandwidth optical or microwave signals using low bandwidth electronics. High bandwidth signals can be recorded, mathematically manipulated, and synthesized.
Full Text Available The phenotypic plasticity of skeletal muscle affords a considerable degree of adaptability not seen in other bodily tissues. The mechanical properties of skeletal muscle are highly dependent on loading conditions. The extent of skeletal muscle plasticity is distinctly highlighted by a loss of muscle mass, or atrophy, after a period of reduced weight-bearing activity, for example during periods of extended bed rest, space flight and in spinal cord injury. On the other hand, increased mechanical loading, or resistance training, induces muscle growth, or hypertrophy. Endurance exercise performance is also dependent on the adaptability of skeletal muscle, especially muscles that contribute to posture, locomotion and the mechanics of breathing. However, the molecular pathways governing skeletal muscle adaptations are yet to be satisfactorily delineated and require further investigation. Researchers in the areas of exercise physiology, physiotherapy and sports medicine are endeavoring to translate experimental knowledge into effective, innovative treatments and regimens in order to improve physical performance and health in both elite athletes and the general community. The efficacy of the translation of molecular biological paradigms in experimental exercise physiology has long been underappreciated. Indeed, molecular biology tools can now be used to answer questions regarding skeletal muscle adaptation in response to exercise and provide new frameworks to improve physical performance. Furthermore, transgenic animal models, knockout animal models and in vivo studies provide tools to test questions concerned with how exercise initiates adaptive changes in gene expression. In light of these perceived deficiencies, an attempt is made here to elucidate the molecular mechanisms of skeletal muscle adaptation to exercise. An examination will be made of the functional capacity of skeletal muscle to respond to a variety of exercise conditions, namely
These proceedings contains refereed papers presented at the Fifteenth IEEE Workshop on Machine Learning for Signal Processing (MLSP’2005), held in Mystic, Connecticut, USA, September 28-30, 2005. This is a continuation of the IEEE Workshops on Neural Networks for Signal Processing (NNSP) organized...... by the NNSP Technical Committee of the IEEE Signal Processing Society. The name of the Technical Committee, hence of the Workshop, was changed to Machine Learning for Signal Processing in September 2003 to better reflect the areas represented by the Technical Committee. The conference is organized...... by the Machine Learning for Signal Processing Technical Committee with sponsorship of the IEEE Signal Processing Society. Following the practice started two years ago, the bound volume of the proceedings is going to be published by IEEE following the Workshop, and we are pleased to offer to conference attendees...
Chen, Jian; Chen, Hong; Cai, Xiaoxia; Weng, Pengfei; Nie, Hao
With the development of mathematical morphology theory, the application of mathematical morphology in image processing has been very extensive, in recent years, with in-depth study of mathematical morphology and its applications in signal processing development is receiving more and more attention. As a kind of nonlinear signal processing method, its signal feature extraction is performed in time domain, compared with some other nonlinear and non-stationary signal processing method, which has no phase offset and amplitude attenuation etc. many advantages, so this method is applied to the signal processing in various industries. This paper mainly expounds the basic theory of mathematical morphology, and puts forward the method of mathematical morphology denoising pretreatment. Finally, the paper summarizes the application of mathematical morphology in speech signal processing and the combination of neural network.
Zhang, Enzheng; Chen, Benyong; Yan, Liping; Yang, Tao; Hao, Qun; Dong, Wenjun; Li, Chaorong
A novel phase measurement method composed of the rising-edge locked signal processing and the digital frequency mixing is proposed for laser heterodyne interferometer. The rising-edge locked signal processing, which employs a high frequency clock signal to lock the rising-edges of the reference and measurement signals, not only can improve the steepness of the rising-edge, but also can eliminate the error counting caused by multi-rising-edge phenomenon in fringe counting. The digital frequency mixing is realized by mixing the digital interference signal with a digital base signal that is different from conventional frequency mixing with analogue signals. These signal processing can improve the measurement accuracy and enhance anti-interference and measurement stability. The principle and implementation of the method are described in detail. An experimental setup was constructed and a series of experiments verified the feasibility of the method in large displacement measurement with high speed and nanometer resolution.
Schacter, Daniel L.
Memory serves critical functions in everyday life, but is also prone to error. This article examines adaptive constructive processes, which play a functional role in memory and cognition but can also produce distortions, errors, or illusions. The article describes several types of memory errors that are produced by adaptive constructive processes, and focuses in particular on the process of imagining or simulating events that might occur in one’s personal future. Simulating future events reli...
The 21st IEEE International Workshop on Machine Learning for Signal Processing will be held in Beijing, China, on September 18–21, 2011. The workshop series is the major annual technical event of the IEEE Signal Processing Society's Technical Committee on Machine Learning for Signal Processing. T....... This year the workshop is held in the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences....
The 21st IEEE International Workshop on Machine Learning for Signal Processing will be held in Beijing, China, on September 18–21, 2011. The workshop series is the major annual technical event of the IEEE Signal Processing Society's Technical Committee on Machine Learning for Signal Processing....... This year the workshop is held in the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences....
Prabhu, K M M
Window functions-otherwise known as weighting functions, tapering functions, or apodization functions-are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing.Comprehensively reviewing previous research and re
Pinggera, J.; Zugal, S.; Weber, B.; Wild, W.; Reichert, M.U.
The need for more flexiblity of process-aware information systems (PAIS) has been discussed for several years and different approaches for adaptive process management have emerged. Only few of them provide support for both changes of individual process instances and the propagation of process type
Adali, Tulay; Miller, David J.; Diamantaras, Konstantinos I.
By putting the accent on learning from the data and the environment, the Machine Learning for SP (MLSP) Technical Committee (TC) provides the essential bridge between the machine learning and SP communities. While the emphasis in MLSP is on learning and data-driven approaches, SP defines the main...... applications of interest, and thus the constraints and requirements on solutions, which include computational efficiency, online adaptation, and learning with limited supervision/reference data....
Poplová, Michaela; Sovka, Pavel; Cifra, Michal
Photonic signals are broadly exploited in communication and sensing and they typically exhibit Poisson-like statistics. In a common scenario where the intensity of the photonic signals is low and one needs to remove a nonstationary trend of the signals for any further analysis, one faces an obstacle: due to the dependence between the mean and variance typical for a Poisson-like process, information about the trend remains in the variance even after the trend has been subtracted, possibly yielding artifactual results in further analyses. Commonly available detrending or normalizing methods cannot cope with this issue. To alleviate this issue we developed a suitable pre-processing method for the signals that originate from a Poisson-like process. In this paper, a Poisson pre-processing method for nonstationary time series with Poisson distribution is developed and tested on computer-generated model data and experimental data of chemiluminescence from human neutrophils and mung seeds. The presented method transforms a nonstationary Poisson signal into a stationary signal with a Poisson distribution while preserving the type of photocount distribution and phase-space structure of the signal. The importance of the suggested pre-processing method is shown in Fano factor and Hurst exponent analysis of both computer-generated model signals and experimental photonic signals. It is demonstrated that our pre-processing method is superior to standard detrending-based methods whenever further signal analysis is sensitive to variance of the signal.
Oxenløwe, Leif Katsuo; Galili, Michael; Mulvad, Hans Christian Hansen
. Combining time lenses into telescopic arrangements allows for more advanced signal processing, such as temporal or spectral compression or magnification. A spectral telescope may for instance allow for conversion of OFDM signals to DWDM-like signals, which can be separated passively, i.e. without additional...
van den Broek, Egon; Janssen, Joris H.; Healey, Jennifer A.; van der Zwaag, Marjolein; Fred, A.; Filipe, J.; Gamboa, H.
Last year, in van den Broek et al. (2009a), a start was made with defining prerequisites for affective signal processing (ASP). Four prerequisites were identified: validation (e.g., mapping of constructs on signals), triangulation, a physiology-driven approach, and contributions of the signal
den Dunnen, Jeroen; Gringhuis, Sonja I.; Geijtenbeek, Teunis B. H.
Pathogen recognition by dendritic cells (DCs) is central to the induction of adaptive immunity. Pattern recognition receptors (PRRs) on DCs interact with pathogens, leading to signaling events that dictate adaptive immune responses. It is becoming clear that C-type lectins are important PRRs that
This book provides a comprehensive review of the state-of-the art of optical signal processing technologies and devices. It presents breakthrough solutions for enabling a pervasive use of optics in data communication and signal storage applications. It presents presents optical signal processing as solution to overcome the capacity crunch in communication networks. The book content ranges from the development of innovative materials and devices, such as graphene and slow light structures, to the use of nonlinear optics for secure quantum information processing and overcoming the classical Shannon limit on channel capacity and microwave signal processing. Although it holds the promise for a substantial speed improvement, today’s communication infrastructure optics remains largely confined to the signal transport layer, as it lags behind electronics as far as signal processing is concerned. This situation will change in the near future as the tremendous growth of data traffic requires energy efficient and ful...
Ravishankar, Saiprasad; Moore, Brian E; Nadakuditi, Raj Rao; Fessler, Jeffrey A
Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery fromundersampledmeasurements. In the context of inverse problems in dynamic imaging, recent research has demonstrated the promise of sparsity and low-rank techniques. For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements. In this work, we investigate a data-adaptive extension of the L+S model, dubbed LASSI, where the temporal image sequence is decomposed into a low-rank component and a component whose spatiotemporal (3D) patches are sparse in some adaptive dictionary domain. We investigate various formulations and efficient methods for jointly estimating the underlying dynamic signal components and the spatiotemporal dictionary from limited measurements. We also obtain efficient sparsity penalized dictionary-blind compressed sensing methods as special cases of our LASSI approaches. Our numerical experiments demonstrate the promising performance of LASSI schemes for dynamicmagnetic resonance image reconstruction from limited k-t space data compared to recent methods such as k-t SLR and L+S, and compared to the proposed dictionary-blind compressed sensing method.
Schacter, Daniel L.
Memory serves critical functions in everyday life but is also prone to error. This article examines adaptive constructive processes, which play a functional role in memory and cognition but can also produce distortions, errors, and illusions. The article describes several types of memory errors that are produced by adaptive constructive processes…
"Signal Conditioning” is a comprehensive introduction to electronic signal processing. The book presents the mathematical basics including the implications of various transformed domain representations in signal synthesis and analysis in an understandable and lucid fashion and illustrates the theory through many applications and examples from communication systems. The ease to learn is supported by well-chosen exercises which give readers the flavor of the subject. Supplementary electronic materials available on http://extras.springer.com including MATLAB codes illuminating applications in the domain of one dimensional electrical signal processing, image processing and speech processing. The book is an introduction for students with a basic understanding in engineering or natural sciences.
Stephen W. Lang
This project was focused on the development of tools for the automatic configuration of signal processing systems. The goal is to develop tools that will be useful in a variety of Government and commercial areas and useable by people who are not signal processing experts. In order to get the most benefit from signal processing techniques, deep technical expertise is often required in order to select appropriate algorithms, combine them into a processing chain, and tune algorithm parameters for best performance on a specific problem. Therefore a significant benefit would result from the assembly of a toolbox of processing algorithms that has been selected for their effectiveness in a group of related problem areas, along with the means to allow people who are not signal processing experts to reliably select, combine, and tune these algorithms to solve specific problems. Defining a vocabulary for problem domain experts that is sufficiently expressive to drive the configuration of signal processing functions will allow the expertise of signal processing experts to be captured in rules for automated configuration. In order to test the feasibility of this approach, we addressed a lightning classification problem, which was proposed by DOE as a surrogate for problems encountered in nuclear nonproliferation data processing. We coded a toolbox of low-level signal processing algorithms for extracting features of RF waveforms, and demonstrated a prototype tool for screening data. We showed examples of using the tool for expediting the generation of ground-truth metadata, for training a signal recognizer, and for searching for signals with particular characteristics. The public benefits of this approach, if successful, will accrue to Government and commercial activities that face the same general problem - the development of sensor systems for complex environments. It will enable problem domain experts (e.g. analysts) to construct signal and image processing chains without
Kovacevic, Branko; Veinović, Mladen; Marković, Milan
This book focuses on speech signal phenomena, presenting a robustification of the usual speech generation models with regard to the presumed types of excitation signals, which is equivalent to the introduction of a class of nonlinear models and the corresponding criterion functions for parameter estimation. Compared to the general class of nonlinear models, such as various neural networks, these models possess good properties of controlled complexity, the option of working in “online” mode, as well as a low information volume for efficient speech encoding and transmission. Providing comprehensive insights, the book is based on the authors’ research, which has already been published, supplemented by additional texts discussing general considerations of speech modeling, linear predictive analysis and robust parameter estimation.
and Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 11 Example: ẋ = f(t) 27 12 Example: RC circuit with...deterministic chirp driving force 28 13 Example: RLC circuit with generic deterministic input 29 14 Example: The Exact Solution to the Gliding Tone Problem 30...The Wigner distribution highlights the lowpass behavior of the system, that filters out the input signal as t →∞. 13 Example: RLC circuit with
Smith, Stephen J.; Whitford, Chris H.; Fraser, George W.
We describe optimal filtering algorithms for determining energy and position resolution in position-sensitive Transition Edge Sensor (TES) Distributed Read-Out Imaging Devices (DROIDs). Improved algorithms, developed using a small-signal finite-element model, are based on least-squares minimisation of the total noise power in the correlated dual TES DROID. Through numerical simulations we show that significant improvements in energy and position resolution are theoretically possible over existing methods
The book tries to briefly introduce the diverse literatures in the field of fractional order signal processing which is becoming an emerging topic among an interdisciplinary community of researchers. This book is aimed at postgraduate and beginning level research scholars who would like to work in the field of Fractional Order Signal processing (FOSP). The readers should have preliminary knowledge about basic signal processing techniques. Prerequisite knowledge of fractional calculus is not essential and is exposited at relevant places in connection to the appropriate signal processing topics. Basic signal processing techniques like filtering, estimation, system identification, etc. in the light of fractional order calculus are presented along with relevant application areas. The readers can easily extend these concepts to varied disciplines like image or speech processing, pattern recognition, time series forecasting, financial data analysis and modeling, traffic modeling in communication channels, optics, b...
Full Text Available Multi-user detection is an effective method to reduce multiple access interference in code division multiple access (CDMA systems. This paper discusses a signal subspace based blind adaptive multiuser detector and a Kalman filtering blind adaptive multiuser detector. Combining them together, a new Kalman filtering blind adaptive multiuser detector based on a tracking algorithm of the signal subspace is proposed. Analysis and simulation show that the proposed blind multiuser detector achieves better suppression of multiple access interference and has a higher convergence rate.
Ding, Jinli; Zhao, Jiayin; Wang, Lanzhou; Li, Qiao
A model of the autoregressive model of weak electric signals in two plants was set up for the first time. The result of the AR model to forecast 10 values of the weak electric signals is well. It will construct a standard set of the AR model coefficient of the plant electric signal and the environmental factor, and can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on agricultural productions.
Many space station processes are highly complex systems subject to sudden, major transients. In any complex process control system, a critical aspect of the human/machine interface is the analysis and display of process information. Human operators can be overwhelmed by large clusters of alarms that inhibit their ability to diagnose and respond to a disturbance. Using artificial intelligence techniques and a knowledge base approach to this problem, the power of the computer can be used to filter and analyze plant sensor data. This will provide operators with a better description of the process state. Once a process state is recognized, automatic action could be initiated and proper system response monitored.
Streamlining Digital Signal Processing, Second Edition, presents recent advances in DSP that simplify or increase the computational speed of common signal processing operations and provides practical, real-world tips and tricks not covered in conventional DSP textbooks. It offers new implementations of digital filter design, spectrum analysis, signal generation, high-speed function approximation, and various other DSP functions. It provides:Great tips, tricks of the trade, secrets, practical shortcuts, and clever engineering solutions from seasoned signal processing professionalsAn assortment.
Li, Zhenzhen; Wu, Xiaoming
Adventitious respiratory sound signal processing has been an important researching topic in the field of computerized respiratory sound analysis system. In recent years, new progress has been achieved in adventitious respiratory sound signal analysis due to the applications of techniques of non-stationary random signal processing. Algorithm progress of adventitious respiratory sound detections is discussed in detail in this paper. Then the state of art of adventitious respiratory sound analysis is reviewed, and development directions of next phase are pointed out.
Joo, Sung Jun; Czuba, Thaddeus B; Cormack, Lawrence K; Huk, Alexander C
Although the visual system uses both velocity- and disparity-based binocular information for computing 3D motion, it is unknown whether (and how) these two signals interact. We found that these two binocular signals are processed distinctly at the levels of both cortical activity in human MT and perception. In human MT, adaptation to both velocity-based and disparity-based 3D motions demonstrated direction-selective neuroimaging responses. However, when adaptation to one cue was probed using the other cue, there was no evidence of interaction between them (i.e., there was no "cross-cue" adaptation). Analogous psychophysical measurements yielded correspondingly weak cross-cue motion aftereffects (MAEs) in the face of very strong within-cue adaptation. In a direct test of perceptual independence, adapting to opposite 3D directions generated by different binocular cues resulted in simultaneous, superimposed, opposite-direction MAEs. These findings suggest that velocity- and disparity-based 3D motion signals may both flow through area MT but constitute distinct signals and pathways. Recent human neuroimaging and monkey electrophysiology have revealed 3D motion selectivity in area MT, which is driven by both velocity-based and disparity-based 3D motion signals. However, to elucidate the neural mechanisms by which the brain extracts 3D motion given these binocular signals, it is essential to understand how-or indeed if-these two binocular cues interact. We show that velocity-based and disparity-based signals are mostly separate at the levels of both fMRI responses in area MT and perception. Our findings suggest that the two binocular cues for 3D motion might be processed by separate specialized mechanisms. Copyright © 2016 the authors 0270-6474/16/3610791-12$15.00/0.
Ell, Todd A; Sangwine, Stephen J
Based on updates to signal and image processing technology made in the last two decades, this text examines the most recent research results pertaining to Quaternion Fourier Transforms. QFT is a central component of processing color images and complex valued signals. The book's attention to mathematical concepts, imaging applications, and Matlab compatibility render it an irreplaceable resource for students, scientists, researchers, and engineers.
Shankar, R.; Lane, S.S.; Paradiso, T.J.; Quinn, J.R.
The techniques developed in this work provide a means of sizing underclad cracks and quality control methods for assessing the accuracy of the data. Data were collected with a minicomputer (LSI 11-02), a transient recorder (Biomaton 8100) and anti-aliasing filter. Three techniques were developed: the calibration curve, phase velocity and epicentral. The phase reversal characteristic in the data is a strong indication of the nature of the signal source. That is, cracks are clearly seperable from two isolated inclusions on the basis of observed phase reversal. These methods have been implemented on a computer and appear to provide an accurate rapid method to discriminate and size underclad cracks
Ezell, N. Dianne Bull [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Britton, Jr, Charles L. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Roberts, Michael [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
This report summarizes the newly developed algorithm that subtracted the Electromagnetic Interference (EMI). The EMI performance is very important to this measurement because any interference in the form on pickup from external signal sources from such as fluorescent lighting ballasts, motors, etc. can skew the measurement. Two methods of removing EMI were developed and tested at various locations. This report also summarizes the testing performed at different facilities outside Oak Ridge National Laboratory using both EMI removal techniques. The first EMI removal technique reviewed in previous milestone reports and therefore this report will detail the second method.
Lane, Alison E.; Young, Robyn L.; Baker, Amy E. Z.; Angley, Manya T.
Children with autism are frequently observed to experience difficulties in sensory processing. This study examined specific patterns of sensory processing in 54 children with autistic disorder and their association with adaptive behavior. Model-based cluster analysis revealed three distinct sensory processing subtypes in autism. These subtypes…
Peucheret, Christophe; Ding, Yunhong; Xu, Jing
Our recent results on the demonstration of on-chip mode-division multiplexing are reviewed, with special emphasis on nonlinear all-optical signal processing. Mode-selective parametric processes are demonstrated in a silicon-on-insulator waveguide.......Our recent results on the demonstration of on-chip mode-division multiplexing are reviewed, with special emphasis on nonlinear all-optical signal processing. Mode-selective parametric processes are demonstrated in a silicon-on-insulator waveguide....
Full Text Available The color filters that are used to attenuate noise are usually optimized to perform extremely well when dealing with certain noise distributions. Unfortunately it is often the case that the noise corrupting the image is not known. It is thus beneficial to know a priori the type of noise corrupting the image in order to select the optimal filter. A method of extracting and characterizing the noise within a digital color image using the generalized Gaussian probability density function (pdf (B.D. Jeffs and W.H. Pun, IEEE Transactions on Image Processing, 4(10, 1451–1456, 1995 and Proceedings of the Int. Conference on Image Processing, 465–468, 1996, is presented. In this paper simulation results are included to demonstrate the effectiveness of the proposed methodology.
Full Text Available The color filters that are used to attenuate noise are usually optimized to perform extremely well when dealing with certain noise distributions. Unfortunately it is often the case that the noise corrupting the image is not known. It is thus beneficial to know a priori the type of noise corrupting the image in order to select the optimal filter. A method of extracting and characterizing the noise within a digital color image using the generalized Gaussian probability density function (pdf (B.D. Jeffs and W.H. Pun, IEEE Transactions on Image Processing, 4(10, 1451–1456, 1995 and Proceedings of the Int. Conference on Image Processing, 465–468, 1996, is presented. In this paper simulation results are included to demonstrate the effectiveness of the proposed methodology.
The electronics of the LUX-ZEPLIN (LZ) experiment, the 10-tonne dark matter detector to be installed at the Sanford Underground Research Facility (SURF), consists of low-noise dual-gain amplifiers and a 100-MHz, 14-bit data acquisition system for the TPC PMTs. Pre-prototypes of the analog amplifiers and the 32-channel digitizers were tested extensively with simulated pulses that are similar to the prompt scintillation light and the electroluminescence signals expected in LZ. These studies are used to characterize the noise and to measure the linearity of the system. By increasing the amplitude of the test signals, the effect of saturating the amplifier and the digitizers was studied. The RMS ADC noise of the digitizer channels was measured to be 1.19± 0.01 ADCC. When a high-energy channel of the amplifier is connected to the digitizer, the measured noise remained virtually unchanged, while the noise added by a low-energy channel was estimated to be 0.38 ± 0.02 ADCC (46 ± 2 μV). A test facility is under construction to study saturation, mitigate noise and measure the performance of the LZ electronics and data acquisition chain
These proceedings contains refereed papers presented at the sixteenth IEEE Workshop on Machine Learning for Signal Processing (MLSP'2006), held in Maynooth, Co. Kildare, Ireland, September 6-8, 2006. This is a continuation of the IEEE Workshops on Neural Networks for Signal Processing (NNSP......). The name of the Technical Committee, hence of the Workshop, was changed to Machine Learning for Signal Processing in September 2003 to better reflect the areas represented by the Technical Committee. The conference is organized by the Machine Learning for Signal Processing Technical Committee...... the same standard as the printed version and facilitates the reading and searching of the papers. The field of machine learning has matured considerably in both methodology and real-world application domains and has become particularly important for solution of problems in signal processing. As reflected...
Foussier, Jerome; Teichmann, Daniel; Jia, Jing; Misgeld, Berno; Leonhardt, Steffen
Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min(-1) (0.3 min(-1)) and -0.7 bpm (1.7 bpm) (compared to -0.2 min(-1) (0.4 min(-1)) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed
Chou, Yongxin; Zhang, Aihua; Ou, Jiqing; Qi, Yusheng
In order to derive dynamic pulse rate variability (DPRV) signal from dynamic pulse signal in real time, a method for extracting DPRV signal was proposed and a portable mobile monitoring system was designed. The system consists of a front end for collecting and wireless sending pulse signal and a mobile terminal. The proposed method is employed to extract DPRV from dynamic pulse signal in mobile terminal, and the DPRV signal is analyzed both in the time domain and the frequency domain and also with non-linear method in real time. The results show that the proposed method can accurately derive DPRV signal in real time, the system can be used for processing and analyzing DPRV signal in real time.
Kompis, Martin; Kurz, Anja; Pfiffner, Flurin; Senn, Pascal; Arnold, Andreas; Caversaccio, Marco
To establish whether complex signal processing is beneficial for users of bone anchored hearing aids. Review and analysis of two studies from our own group, each comparing a speech processor with basic digital signal processing (either Baha Divino or Baha Intenso) and a processor with complex digital signal processing (either Baha BP100 or Baha BP110 power). The main differences between basic and complex signal processing are the number of audiologist accessible frequency channels and the availability and complexity of the directional multi-microphone noise reduction and loudness compression systems. Both studies show a small, statistically non-significant improvement of speech understanding in quiet with the complex digital signal processing. The average improvement for speech in noise is +0.9 dB, if speech and noise are emitted both from the front of the listener. If noise is emitted from the rear and speech from the front of the listener, the advantage of the devices with complex digital signal processing as opposed to those with basic signal processing increases, on average, to +3.2 dB (range +2.3 … +5.1 dB, p ≤ 0.0032). Complex digital signal processing does indeed improve speech understanding, especially in noise coming from the rear. This finding has been supported by another study, which has been published recently by a different research group. When compared to basic digital signal processing, complex digital signal processing can increase speech understanding of users of bone anchored hearing aids. The benefit is most significant for speech understanding in noise.
Young, Derek P.; Jacklin, Neil; Punnoose, Ratish J.; Counsil, David T.
Time-reversal is a wave focusing technique that makes use of the reciprocity of wireless propagation channels. It works particularly well in a cluttered environment with associated multipath reflection. This technique uses the multipath in the environment to increase focusing ability. Time-reversal can also be used to null signals, either to reduce unintentional interference or to prevent eavesdropping. It does not require controlled geometric placement of the transmit antennas. Unlike existing techniques it can work without line-of-sight. We have explored the performance of time-reversal focusing in a variety of simulated environments. We have also developed new algorithms to simultaneously focus at a location while nulling at an eavesdropper location. We have experimentally verified these techniques in a realistic cluttered environment.
Darren S Proppe
Full Text Available Charles Darwin posited that secondary sexual characteristics result from competition to attract mates. In male songbirds, specialized vocalizations represent secondary sexual characteristics of particular importance because females prefer songs at specific frequencies, amplitudes, and duration. For birds living in human-dominated landscapes, historic selection for song characteristics that convey fitness may compete with novel selective pressures from anthropogenic noise. Here we show that black-capped chickadees (Poecile atricapillus use shorter, higher-frequency songs when traffic noise is high, and longer, lower-frequency songs when noise abates. We suggest that chickadees balance opposing selective pressures by use low-frequency songs to preserve vocal characteristics of dominance that repel competitors and attract females, and high frequency songs to increase song transmission when their environment is noisy. The remarkable vocal flexibility exhibited by chickadees may be one reason that they thrive in urban environments, and such flexibility may also support subsequent genetic adaptation to an increasingly urbanized world.
Poyneer, L; Veran, J P
In many scenarios, an Adaptive Optics (AO) control system operates in the presence of temporally non-white noise. We use a Kalman filter with a state space formulation that allows suppression of this colored noise, hence improving residual error over the case where the noise is assumed to be white. We demonstrate the effectiveness of this new filter in the case of the estimated Gemini Planet Imager tip-tilt environment, where there are both common-path and non-common path vibrations. We discuss how this same framework can also be used to suppress spatial aliasing during predictive wavefront control assuming frozen flow in a low-order AO system without a spatially filtered wavefront sensor, and present experimental measurements from Altair that clearly reveal these aliased components.
Krusienski, Dean J; Grosse-Wentrup, Moritz; Galán, Ferran; Coyle, Damien; Miller, Kai J; Forney, Elliott; Anderson, Charles W
This paper reviews several critical issues facing signal processing for brain-computer interfaces (BCIs) and suggests several recent approaches that should be further examined. The topics were selected based on discussions held during the 4th International BCI Meeting at a workshop organized to review and evaluate the current state of, and issues relevant to, feature extraction and translation of field potentials for BCIs. The topics presented in this paper include the relationship between electroencephalography and electrocorticography, novel features for performance prediction, time-embedded signal representations, phase information, signal non-stationarity, and unsupervised adaptation.
Edmonson, William W.; Tucker, Jerry
different adaptive noise cancellation algorithms and provide an operational prototype to understand the behavior of the system under test. DSP software was required to interface the processor with the data converters using interrupt routines. The goal is to build a complete ANC system that can be placed on a flexible circuit with added memory circuitry that also contains the power supply, sensors and actuators. This work on the digital signal processing system for active noise reduction was completed in collaboration with another ASEE Fellow, Dr. Jerry Tucker from Virginia Commonwealth University, Richmond, VA.
Clausen, Anders; Guan, Pengyu; Mulvad, Hans Christian Hansen
All-optical time-domain Optical Fourier Transformation utilised for signal processing of ultra-high-speed OTDM signals and OFDM signals will be presented.......All-optical time-domain Optical Fourier Transformation utilised for signal processing of ultra-high-speed OTDM signals and OFDM signals will be presented....
Oxenløwe, Leif Katsuo; Galili, Michael; Pu, Minhao
We describe recent demonstrations of exploiting highly nonlinear silicon nanowires for processing Tbit/s optical data signals. We perform demultiplexing and optical waveform sampling of 1.28 Tbit/s and wavelength conversion of 640 Gbit/s data signals.......We describe recent demonstrations of exploiting highly nonlinear silicon nanowires for processing Tbit/s optical data signals. We perform demultiplexing and optical waveform sampling of 1.28 Tbit/s and wavelength conversion of 640 Gbit/s data signals....
Zhang Tao; Wang Yonggang; Li Kai; Yan Tianxin
In order to meet the requirement of the fast luminosity monitor system of Beijing electron-positron collider (BEPC II), a high-speed bunch-by-bunch luminosity signal processing and displaying system was designed. The techniques such as fast signal amplification, discrimination, long-distance signal transmission, anti-coincidence event judgment, counting for each bunch and ping-pang storage were involved effectively. The preliminary test result shows that the system can process and display the luminosity signals for bunches with 4 ns separation. (authors)
Clairet, F.; Bottereau, C.; Ricaud, B.; Briolle, F.; Heuraux, S.
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.
Peng, Fulai; Liu, Hongyun; Wang, Weidong
A photoplethysmographic (PPG) signal can provide very useful information about a subject's cardiovascular status. Motion artifacts (MAs), which usually deteriorate the waveform of a PPG signal, severely obstruct its applications in the clinical diagnosis and healthcare area. To reduce the MAs from a PPG signal, in the present study we present a comb filter based signal processing method. Firstly, wavelet de-noising was implemented to preliminarily suppress a part of the MAs. Then, the PPG signal in the time domain was transformed into the frequency domain by a fast Fourier transform (FFT). Thirdly, the PPG signal period was estimated from the frequency domain by tracking the fundamental frequency peak of the PPG signal. Lastly, the MAs were removed by the comb filter which was designed based on the obtained PPG signal period. Experiments with synthetic and real-world datasets were implemented to validate the performance of the method. Results show that the proposed method can effectively restore the PPG signals from the MA corrupted signals. Also, the accuracy of blood oxygen saturation (SpO2), calculated from red and infrared PPG signals, was significantly improved after the MA reduction by the proposed method. Our study demonstrates that the comb filter can effectively reduce the MAs from a PPG signal provided that the PPG signal period is obtained.
Koush, Yury; Zvyagintsev, Mikhail; Dyck, Miriam; Mathiak, Krystyna A; Mathiak, Klaus
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.
Serbes, Gorkem; Gulcur, Halil Ozcan; Aydin, Nizamettin
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.
Sadler, Brian M; Hoyos, Sebastian
The conventional analog-to-digital conversion (ADC) and digital signal processing (DSP) architecture has led to major advances in miniature and micro-systems technology over the past several decades. The outlook for these systems is significantly enhanced by advances in sensing, signal processing, communications and control, and the combination of these technologies enables autonomous robotics on the miniature to micro scales. In this article we look at trends in the combination of analog and digital (mixed-signal) processing, and consider a generalized sampling architecture. Employing a parallel analog basis expansion of the input signal, this scalable approach is adaptable and reconfigurable, and is suitable for a large variety of current and future applications in networking, perception, cognition, and control.
Full Text Available Objectives. To study cognitive test profiles with a focus on processing speed in a representative group of preschool children with autism spectrum disorder (ASD and relate processing speed to adaptive functioning. Methods. Cognitive assessments were performed in 190 3.6–6.6-year-old children (164 boys and 26 girls with ASD, using either Griffiths' developmental scales (n=77 or the Wechsler Preschool and Primary Scale of Intelligence-Third Edition (WPPSI-III (n=113. Cognitive data were related to adaptive functioning as measured by Vineland Adaptive Behavior Scales (VABS. Results. Cognitive profiles were characterized by low verbal skills. Low processing speed quotients (PSQs were found in 66 (78% of the 85 children who were able to participate in the processing speed subtests. Except for Socialization, all VABS domains (Communication, Motor Skills, Daily Living Skills, and Adaptive Behavior Composite scores correlated significantly with PSQ. Multiple regression analysis showed that PSQ predicted 38%, 35%, 34%, and 37% of the variance for Communication, Daily Living Skills, Motor Skills, and total Adaptive Composite scores, respectively. Conclusion. Preschool children with ASD had uneven cognitive profiles with low verbal skills, and, relatively, even lower PSQs. Except for Socialization, adaptive functioning was predicted to a considerable degree by PSQ.
Hedvall, Åsa; Fernell, Elisabeth; Holm, Anette; Åsberg Johnels, Jakob; Gillberg, Christopher; Billstedt, Eva
Objectives. To study cognitive test profiles with a focus on processing speed in a representative group of preschool children with autism spectrum disorder (ASD) and relate processing speed to adaptive functioning. Methods. Cognitive assessments were performed in 190 3.6–6.6-year-old children (164 boys and 26 girls) with ASD, using either Griffiths' developmental scales (n = 77) or the Wechsler Preschool and Primary Scale of Intelligence-Third Edition (WPPSI-III) (n = 113). Cognitive data were related to adaptive functioning as measured by Vineland Adaptive Behavior Scales (VABS). Results. Cognitive profiles were characterized by low verbal skills. Low processing speed quotients (PSQs) were found in 66 (78%) of the 85 children who were able to participate in the processing speed subtests. Except for Socialization, all VABS domains (Communication, Motor Skills, Daily Living Skills, and Adaptive Behavior Composite scores) correlated significantly with PSQ. Multiple regression analysis showed that PSQ predicted 38%, 35%, 34%, and 37% of the variance for Communication, Daily Living Skills, Motor Skills, and total Adaptive Composite scores, respectively. Conclusion. Preschool children with ASD had uneven cognitive profiles with low verbal skills, and, relatively, even lower PSQs. Except for Socialization, adaptive functioning was predicted to a considerable degree by PSQ. PMID:23766675
Hoogeboom, P.; Dekker, R.J.; Otten, M.P.G.
Synthetic Aperture Radar (SAR) is today a valuable source of remote sensing information. SAR is a side-looking imaging radar and operates from airborne and spacebome platforms. Coverage, resolution and image quality are strongly influenced by the platform. SAR processing can be performed on standard
Zortman, William A.
An apparatus for optical modulation is provided. The apparatus includes a modulator structure and a heater structure. The modulator structure comprises a ring or disk optical resonator having a closed curvilinear periphery and a pair of oppositely doped semiconductor regions within and/or adjacent to the optical resonator and conformed to modify the optical length of the optical resonator upon application of a bias voltage. The heater structure comprises a relatively resistive annulus of semiconductor material enclosed between an inner disk and an outer annulus of relatively conductive semiconductor material. The inner disk and the outer annulus are adapted as contact regions for a heater activation current. The heater structure is situated within the periphery of the optical resonator such that in operation, at least a portion of the resonator is heated by radial conductive heat flow from the heater structure. The apparatus further includes a substantially annular isolation region of dielectric or relatively resistive semiconductor material interposed between the heater structure and the modulator structure. The isolation region is effective to electrically isolate the bias voltage from the heater activation current.
Antoni, Michael H
A diagnosis of cancer and subsequent treatments place demands on psychological adaptation. Behavioral research suggests the importance of cognitive, behavioral, and social factors in facilitating adaptation during active treatment and throughout cancer survivorship, which forms the rationale for the use of many psychosocial interventions in cancer patients. This cancer experience may also affect physiological adaptation systems (e.g., neuroendocrine) in parallel with psychological adaptation changes (negative affect). Changes in adaptation may alter tumor growth-promoting processes (increased angiogenesis, migration and invasion, and inflammation) and tumor defense processes (decreased cellular immunity) relevant for cancer progression and the quality of life of cancer patients. Some evidence suggests that psychosocial intervention can improve psychological and physiological adaptation indicators in cancer patients. However, less is known about whether these interventions can influence tumor activity and tumor growth-promoting processes and whether changes in these processes could explain the psychosocial intervention effects on recurrence and survival documented to date. Documenting that psychosocial interventions can modulate molecular activities (e.g., transcriptional indicators of cell signaling) that govern tumor promoting and tumor defense processes on the one hand, and clinical disease course on the other is a key challenge for biobehavioral oncology research. This mini-review will summarize current knowledge on psychological and physiological adaptation processes affected throughout the stress of the cancer experience, and the effects of psychosocial interventions on psychological adaptation, cancer disease progression, and changes in stress-related biobehavioral processes that may mediate intervention effects on clinical cancer outcomes. Very recent intervention work in breast cancer will be used to illuminate emerging trends in molecular probes of
Rao, K. R.
In view of the 56 KBPS digital switched network services and the ISDN, low bit rate codecs for providing real time full motion color video are under various stages of development. Some companies have already brought the codecs into the market. They are being used by industry and some Federal Agencies for video teleconferencing. In general, these codecs have various features such as multiplexing audio and data, high resolution graphics, encryption, error detection and correction, self diagnostics, freezeframe, split video, text overlay etc. To transmit the original color video on a 56 KBPS network requires bit rate reduction of the order of 1400:1. Such a large scale bandwidth compression can be realized only by implementing a number of sophisticated,digital signal processing techniques. This paper provides an overview of such techniques and outlines the newer concepts that are being investigated. Before resorting to the data compression techniques, various preprocessing operations such as noise filtering, composite-component transformation and horizontal and vertical blanking interval removal are to be implemented. Invariably spatio-temporal subsampling is achieved by appropriate filtering. Transform and/or prediction coupled with motion estimation and strengthened by adaptive features are some of the tools in the arsenal of the data reduction methods. Other essential blocks in the system are quantizer, bit allocation, buffer, multiplexer, channel coding etc.
Full Text Available Adaptive management planning projects use multiparty, multidisciplinary workshops and simulation modeling to facilitate dialogue, negotiation, and planning. However, they have been criticized as a poor medium for conflict resolution. Alternative processes from the conflict resolution tradition, e.g., principled negotiation and sequenced negotiation, address uncertainty and biophysical constraints much less skillfully than does adaptive management. When we evaluate adaptive management planning using conflict resolution practice as a benchmark, we can design better planning procedures. Adaptive management planning procedures emerge that explore system structure, dynamics, and uncertainty, and that also provide a strong negotiation process, grounded in principled exploration of stakeholders' interests and needs. "Crossing" procedures in this manner is a fertile way of developing new forms of professional practice.
Li, Shuqing; Zhang, Huan; Tao, Zhifei
Grating digital geophone is designed based on grating measurement technique benefiting averaging-error effect and wide dynamic range to improve weak signal detected precision. This paper introduced the principle of grating digital geophone and its post signal processing system. The signal acquisition circuit use Atmega 32 chip as core part and display the waveform on the Labwindows through the RS232 data link. Wavelet transform is adopted this paper to filter the grating digital geophone' output signal since the signal is unstable. This data processing method is compared with the FIR filter that widespread use in current domestic. The result indicates that the wavelet algorithm has more advantages and the SNR of seismic signal improve obviously.
The theories, hardware, applications, limitations, and research proceeding in real time signal processing are explored. Parallel processing algorithms and architectures are discussed, including parallel, systolic array, and asynchronous processing. Electro-optical, acousto-optical, and optical scanning methods are described. FFT processing, correlation cancellation, spread spectrum code processing, CCD two-dimensional correlator, and adaptive filtering methods and hardware are investigated. Attention is devoted to phased array radar processing with an optical matrix-vector processor, a two-dimensional magneto-optic spatial light modulator for signal processing, spatial filters for background suppression in IR mosaic sensor systems, and charge injection and IR focal plane arrays. The usage of VHSIC for real time processing is studied, and consideration is given to multiple processor systems for war games, distributed processors, and multiple data stream architectures. No individual items are abstracted in this volume
Karev Dmitry Vladimirovich
Full Text Available On the basis of the analysis of systems of adaptive management board business proposed the original version of the real system of adaptive management, the basis of which used dynamic recursive model cash flow forecast and real data. Proposed definitions and the simulation of scales and intervals of model time in the control system, as well as the thresholds observations and conditions of changing (correction of the administrative decisions. The process of adaptive management is illustrated on the basis proposed by the author of the script of development of business.
INTRODUCTIONSignal Processing for Future Mobile Communications Systems: Challenges and Perspectives; Quazi Mehbubar Rahman and Mohamed IbnkahlaCHANNEL MODELING AND ESTIMATIONMultipath Propagation Models for Broadband Wireless Systems; Andreas F. Molisch and Fredrik TufvessonModeling and Estimation of Mobile Channels; Jitendra K. TugnaitMobile Satellite Channels: Statistical Models and Performance Analysis; Giovanni E. Corazza, Alessandro Vanelli-Coralli, Raffaella Pedone, and Massimo NeriMobile Velocity Estimation for Wireless Communications; Bouchra Senadji, Ghazem Azemi, and Boualem Boashash
Aanestad, Margun; Jensen, Tina Blegind
The organizational consequences of implementing information systems (IS) in organizations have primarily been studied during the implementation or early post-implementation phase. We argue for the need to study the continuous organizational adaptation of evolving IS because of the challenges...... identify the way in which the organizational capability we call "collective mindfulness" was achieved. Being aware of how to practically achieve collective mindfulness, managers may be able to better facilitate mindful handling of post-implementation IS adaptation processes....
Candy, James V; Clague, David S; Lee, Christopher L; Rudd, Robert E; Burnham, Alan K; Tringe, Joseph W
A method of using physics-based signal processing algorithms for micromachined cantilever arrays. The methods utilize deflection of a micromachined cantilever that represents the chemical, biological, or physical element being detected. One embodiment of the method comprises the steps of modeling the deflection of the micromachined cantilever producing a deflection model, sensing the deflection of the micromachined cantilever and producing a signal representing the deflection, and comparing the signal representing the deflection with the deflection model.
Iwayama, N.; Ishigaki, K.
We propose a new approach to context processing in on-line handwritten character recognition (OLCR). Based on the observation that writers often repeat the strings that they input, we take the approach of adaptive context processing. (ACP). In ACP, the strings input by a writer are automatically
Peucheret, Christophe; Oxenløwe, Leif Katsuo; Mulvad, Hans Christian Hansen
We review recent progress in all-optical signal processing techniques making use of conventional silica-based highly nonlinear fibres. In particular, we focus on recent demonstrations of ultra-fast processing at 640 Gbit/s and above, as well as on signal processing of novel modulation formats...... relying on the phase of the optical field. Topics covered include all-optical switching of 640 Gbit/s and 1.28 Tbit/s serial data, wavelength conversion at 640 Gbit/s, optical amplitude regeneration of differential phase shift keying (DPSK) signals, as well as midspan spectral inversion for differential 8...
Poularikas, Alexander D
Engineers in all fields will appreciate a practical guide that combines several new effective MATLAB® problem-solving approaches and the very latest in discrete random signal processing and filtering.Numerous Useful Examples, Problems, and Solutions - An Extensive and Powerful ReviewWritten for practicing engineers seeking to strengthen their practical grasp of random signal processing, Discrete Random Signal Processing and Filtering Primer with MATLAB provides the opportunity to doubly enhance their skills. The author, a leading expert in the field of electrical and computer engineering, offe
Song, Tai Kyong
Ultrasonic imaging is the most widely used modality among modern imaging device for medical diagnosis and the system performance has been improved dramatically since early 90's due to the rapid advances in DSP performance and VLSI technology that made it possible to employ more sophisticated algorithms. This paper describes 'main stream' digital signal processing functions along with the associated implementation considerations in modern medical ultrasound imaging systems. Topics covered include signal processing methods for resolution improvement, ultrasound imaging system architectures, roles and necessity of the applications of DSP and VLSI technology in the development of the medical ultrasound imaging systems, and array signal processing techniques for ultrasound focusing
Kumar, S; ICSIP 2012
The proceedings includes cutting-edge research articles from the Fourth International Conference on Signal and Image Processing (ICSIP), which is organised by Dr. N.G.P. Institute of Technology, Kalapatti, Coimbatore. The Conference provides academia and industry to discuss and present the latest technological advances and research results in the fields of theoretical, experimental, and application of signal, image and video processing. The book provides latest and most informative content from engineers and scientists in signal, image and video processing from around the world, which will benefit the future research community to work in a more cohesive and collaborative way.
Holland, S. Douglas
The purpose of this paper is to describe systems and components of systems developed by personnel in the Signal Processing Section of the Tracking and Communications Division. The scope of this includes past developments which are in current use in NASA flight operations and future developments which are targeted for upcoming NASA applications. These projects specifically are: (1) NASA High Definition Television (HDTV) Project, (2) Video Codecs, (3) NASA Electronic Still Camera (ESC) Project, (4) Hercules Payload, (5) Ku-band Communications Adapter (KCA), (6) Windows Drivers for Satellite Interfacing to Commercial Equipment, and (7) Advanced Statistical Multiplexers. The methods used to determine what projects should be done in-house as opposed to which should not is based in NASA applications versus commercially available systems to meet those applications. If a commercial-off-the-shelf (COTS) component or system is available which meets the need, the first choice is to use COTS equipment. If it is not, and there is a NASA requirement, it is developed in-house. This results in technology which is being developed which otherwise was not available. Personnel involved in these projects have been contacted by many commercial companies interested in licensing or obtaining the NASA design.
Szcześniak, Adam; Szcześniak, Zbigniew
This article is a presentation of designed methods which interpolate signals from the optoelectronic transducer. This enables a way to distinguish the motion direction of the optoelectronic transducer and also to increase its accuracy. In this article methods based on logic functions, logic functions and RC circuits, phase processing were analyzed. In methods which are based on processing logic functions of transducer's signals there is a possibility of two times and four times increase in the transducer glass scale. The presented method of generating and processing sine signals with 18 degrees of the shift enables the reception of square signals with five times higher frequency compared to the basic signals. This method is universal and it can be used to the different scale of frequency multiplication of the optoelectronic transducer. The simulations of the methods were performed by using the MATLAB-SIMULINK software.
Schlums, Heinrich; Cichocki, Frank; Tesi, Bianca; Theorell, Jakob; Beziat, Vivien; Holmes, Tim D.; Han, Hongya; Chiang, Samuel C.C.; Foley, Bree; Mattsson, Kristin; Larsson, Stella; Schaffer, Marie; Malmberg, Karl-Johan; Ljunggren, Hans-Gustaf; Miller, Jeffrey S.; Bryceson, Yenan T.
SUMMARY The mechanisms underlying human natural killer (NK) cell phenotypic and functional heterogeneity are unknown. Here, we describe the emergence of diverse subsets of human NK cells selectively lacking expression of signaling proteins after human cytomegalovirus (HCMV) infection. The absence of B and myeloid cell-related signaling protein expression in these NK cell subsets correlated with promoter DNA hyperme-thylation. Genome-wide DNA methylation patterns were strikingly similar between HCMV-associated adaptive NK cells and cytotoxic effector T cells but differed from those of canonical NK cells. Functional interrogation demonstrated altered cytokine responsiveness in adaptive NK cells that was linked to reduced expression of the transcription factor PLZF. Furthermore, subsets of adaptive NK cells demonstrated significantly reduced functional responses to activated autologous T cells. The present results uncover a spectrum of epigenetically unique adaptive NK cell subsets that diversify in response to viral infection and have distinct functional capabilities compared to canonical NK cell subsets. PMID:25786176
Mulvad, Hans Christian Hansen; Palushani, Evarist; Hu, Hao
We review recent advances in the optical signal processing of ultra-high-speed serial data signals up to 1.28 Tbit/s, with focus on applications of time-domain optical Fourier transformation. Experimental methods for the generation of symbol rates up to 1.28 Tbaud are also described....
Abstract. Binary and ternary sequences with peaky autocorrelation, measured in terms of high discrimination and merit factor have been searched earlier, using optimization techniques. It is shown that the use of neural network processing of the return signal is much more advantageous. It opens up a new signal design ...
Software-defined radios and test equipment use a variety of digital signal processing techniques to improve system performance. Interpolation is one technique that can be used to increase the sample rate of digital signals. In this work, we illustrated interpolation in the time domain by writing appropriate codes using ...
Complete analytical expressions for distortions caused by signal processing in analog AM modulators are developed. The salient features in these expressions are shown to be consistent with displays of actual spectra of AM signals. Finally suggestions are given on how the distortions may be practically minimized. (author). 6 refs, 3 figs
Rao, Yong; Zipursky, S. Lawrence
Tyrosine phosphorylation has been implicated in growth-cone guidance through genetic, biochemical, and pharmacological studies. Adapter proteins containing src homology 2 (SH2) domains and src homology 3 (SH3) domains provide a means of linking guidance signaling through phosphotyrosine to downstream effectors regulating growth-cone motility. The Drosophila adapter, Dreadlocks (Dock), the homolog of mammalian Nck containing three N-terminal SH3 domains and a single SH2 domain, is highly speci...
M. R. HAIDER
Full Text Available A low-power signal processing and telemetry circuit for any generic biosensor applications has been presented. The complete system manifests a potentiostat, a signal processing block and a modulator block. The on-chip potentiostat biases the sensor electrodes for proper extraction of the sensor signals. The signal processing block integrates and buffers the sensor signal to make it a data signal and finally a simple modulator block converts this data signal to an on-off-keying (OOK signal with a high frequency carrier. Package pin of the fabricated circuit is used as an antenna and measurement results demonstrate the successful signal transmission from the chip within a few cm ranges. The entire system has been realized using 0.35 μm CMOS technology that consumes only 400 μW of power and occupies an area of 0.66 mm2. Test results show that this scheme is an effective candidate for low-power sensor applications.
Ludwig, David E.
The objective of the Pushbroom Spectral Imaging Program was to develop on-focal plane electronics which compensate for detector array non-uniformities. The approach taken was to implement a simple two point calibration algorithm on focal plane which allows for offset and linear gain correction. The key on focal plane features which made this technique feasible was the use of a high quality transimpedance amplifier (TIA) and an analog-to-digital converter for each detector channel. Gain compensation is accomplished by varying the feedback capacitance of the integrate and dump TIA. Offset correction is performed by storing offsets in a special on focal plane offset register and digitally subtracting the offsets from the readout data during the multiplexing operation. A custom integrated circuit was designed, fabricated, and tested on this program which proved that nonuniformity compensated, analog-to-digital converting circuits may be used to read out infrared detectors. Irvine Sensors Corporation (ISC) successfully demonstrated the following innovative on-focal-plane functions that allow for correction of detector non-uniformities. Most of the circuit functions demonstrated on this program are finding their way onto future IC's because of their impact on reduced downstream processing, increased focal plane performance, simplified focal plane control, reduced number of dewar connections, as well as the noise immunity of a digital interface dewar. The potential commercial applications for this integrated circuit are primarily in imaging systems. These imaging systems may be used for: security monitoring systems, manufacturing process monitoring, robotics, and for spectral imaging when used in analytical instrumentation.
Sant'Anna, Leandro Machado
Full Text Available Introduction: Professionals that work in this area know how important the orientation to the use of auditory device is, which involves the care required to handle and use the equipment and aspects aiming at the adaptation. Objective: To compare the knowledge of both long-term and new users about the adaptation process to the use of auditory device, so as to provide speech and language pathologists with a greater knowledge about the aspects that most influence the adaptation process. Method: This research is an observational, descriptive, cross-sectional, contemporary, retrospective study. The new and old users answered a questionnaire with information on the auditory device conservation and the adaptation process. The results were compared quantitatively through statistical review and qualitatively. Results: The age of the individuals in this study varied between 28 and 90 years. In some aspects, old and new users presented the same knowledge level. Conclusion: New users of auditory devices have been looking for hearing (re habilitation increasingly later. The action of phonoaudiology in the process of selection and adaptation to auditory devices among experienced and new users is extremely important to an effective acclimatization.
Fyhn, Karsten; Arildsen, Thomas; Larsen, Torben
We show that to lower the sampling rate in a spread spectrum communication system using Direct Sequence Spread Spectrum (DSSS), compressive signal processing can be applied to demodulate the received signal. This may lead to a decrease in the power consumption or the manufacturing price of wireless...
Full Text Available In the field of adaptive radar detection, an effective strategy to improve the detection performance is to exploit the structural information of the covariance matrix, especially in the case of insufficient reference cells. Thus, in this study, the problem of detecting multidimensional subspace signals is discussed by considering the persymmetric structure of the clutter covariance matrix, which implies that the covariance matrix is persymmetric about its cross diagonal. Persymmetric adaptive detectors are derived on the basis of the one-step principle as well as the two-step Generalized Likelihood Ratio Test (GLRT in homogeneous and partially homogeneous clutter. The proposed detectors consider the structural information of the covariance matrix at the design stage. Simulation results suggest performance improvement compared with existing detectors when reference cells are insufficient. Moreover, the detection performance is assessed with respect to the effects of the covariance matrix, signal subspace dimension, and mismatched performance of signal subspace as well as signal fluctuations.
Wei, Yanbo; Lu, Zhizhong; Yuan, Gannan; Fang, Zhao; Huang, Yu
In this paper, the application of the emerging compressed sensing (CS) theory and the geometric characteristics of the targets in radar images are investigated. Currently, the signal detection algorithms based on the CS theory require knowing the prior knowledge of the sparsity of target signals. However, in practice, it is often impossible to know the sparsity in advance. To solve this problem, a novel sparsity adaptive matching pursuit (SAMP) detection algorithm is proposed. This algorithm executes the detection task by updating the support set and gradually increasing the sparsity to approximate the original signal. To verify the effectiveness of the proposed algorithm, the data collected in 2010 at Pingtan, which located on the coast of the East China Sea, were applied. Experiment results illustrate that the proposed method adaptively completes the detection task without knowing the signal sparsity, and the similar detection performance is close to the matching pursuit (MP) and orthogonal matching pursuit (OMP) detection algorithms.
Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang
An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal.
Istepanian, Robert S H; Sungoor, Ala; Nebel, Jean-Christophe
Genomic signal processing is a new area of research that combines advanced digital signal processing methodologies for enhanced genetic data analysis. It has many promising applications in bioinformatics and next generation of healthcare systems, in particular, in the field of microarray data clustering. In this paper we present a comparative performance analysis of enhanced digital spectral analysis methods for robust clustering of gene expression across multiple microarray data samples. Three digital signal processing methods: linear predictive coding, wavelet decomposition, and fractal dimension are studied to provide a comparative evaluation of the clustering performance of these methods on several microarray datasets. The results of this study show that the fractal approach provides the best clustering accuracy compared to other digital signal processing and well known statistical methods.
Pham, Timothy T.; Jongeling, Andre P.
In this paper, we will describe the benefits of arraying and past as well as expected future use of this application. The signal processing aspects of array system are described. Field measurements via actual tracking spacecraft are also presented.
Mørk, Jesper; Romstad, Francis Pascal; Højfeldt, Sune
Reverse-biased semiconductor waveguides are efficient saturable absorbers and have a number of promising all-optical signal processing applications. Results on ultrafast modulator dynamics as well as demonstrations and investigations of wavelength conversion and regeneration are presented....
Some measuring methods and signal processing systems based on analogue and digital technics, which have been applied in magnetic field research using magnetometers with ferromagnetic transducers, are presented. (author)
The physical principles and signal processing techniques underlying bat echolocation are investigated. It is shown, by calculation and simulation, how the measured echolocation performance of bats can be achieved.
Tompkins, Willis J; Wilson, J
In the early 1990's we developed a special computer program called UW DigiScope to provide a mechanism for anyone interested in biomedical digital signal processing to study the field without requiring any other instrument except a personal computer. There are many digital filtering and pattern recognition algorithms used in processing biomedical signals. In general, students have very limited opportunity to have hands-on access to the mechanisms of digital signal processing. In a typical course, the filters are designed non-interactively, which does not provide the student with significant understanding of the design constraints of such filters nor their actual performance characteristics. UW DigiScope 3.0 is the first major update since version 2.0 was released in 1994. This paper provides details on how the new version based on MATLAB! works with signals, including the filter design tool that is the programming interface between UW DigiScope and processing algorithms.
This document is a collection of the papers presented at international conferences and in international journals by the analogue signal processing group of the Department of Information Technology, Technical University of Denmark, in 1996 and 1997....
Peucheret, Christophe; Ding, Yunhong; Ou, Haiyan
We review our recent achievements on the use of silicon micro-ring resonators for linear optical signal processing applications, including modulation format conversion, phase-to-intensity modulation conversion and waveform shaping....
Gopi, E S
Mathematical Summary for Digital Signal Processing Applications with Matlab consists of Mathematics which is not usually dealt with in the DSP core subject, but used in DSP applications. It gives Matlab programs with illustrations.
Thomas, J. B.
Over the past year, two Rogue GPS prototype receivers have been assembled and successfully subjected to a variety of laboratory and field tests. A functional description is presented of signal processing in the Rogue receiver, tracing the signal from RF input to the output values of group delay, phase, and data bits. The receiver can track up to eight satellites, without time multiplexing among satellites or channels, simultaneously measuring both group delay and phase for each of three channels (L1-C/A, L1-P, L2-P). The Rogue signal processing described requires generation of the code for all three channels. Receiver functional design, which emphasized accuracy, reliability, flexibility, and dynamic capability, is summarized. A detailed functional description of signal processing is presented, including C/A-channel and P-channel processing, carrier-aided averaging of group delays, checks for cycle slips, acquistion, and distinctive features.
Mu, Jiasong; Wang, Wei; Zhang, Baoju
This book brings together papers presented at the 4th International Conference on Communications, Signal Processing, and Systems, which provides a venue to disseminate the latest developments and to discuss the interactions and links between these multidisciplinary fields. Spanning topics ranging from Communications, Signal Processing and Systems, this book is aimed at undergraduate and graduate students in Electrical Engineering, Computer Science and Mathematics, researchers and engineers from academia and industry as well as government employees (such as NSF, DOD, DOE, etc).
Nielsen, M L; Mørk, Jesper
Significant advancements in technology and basic understanding of device physics are bringing optical signal processing closer to a commercial breakthrough. In this paper we describe the main challenges in high-speed SOA-based switching.......Significant advancements in technology and basic understanding of device physics are bringing optical signal processing closer to a commercial breakthrough. In this paper we describe the main challenges in high-speed SOA-based switching....
Galili, Michael; Hu, Hao; Guan, Pengyu
This paper will discuss time lenses and their broad range of applications. A number of recent demonstrations of complex high-speed optical signal processing using time lenses will be outlined with focus on the operating principle.......This paper will discuss time lenses and their broad range of applications. A number of recent demonstrations of complex high-speed optical signal processing using time lenses will be outlined with focus on the operating principle....
This document is a collection of the papers presented at international conferences and in international journals by the analogue signal processing group of Electronics Institute, Technical University of Denmark, in 1994 and 1995.......This document is a collection of the papers presented at international conferences and in international journals by the analogue signal processing group of Electronics Institute, Technical University of Denmark, in 1994 and 1995....
Převorovský, Zdeněk; Krofta, Josef; Kober, Jan; Dvořáková, Zuzana; Chlada, Milan; Dos Santos, S.
Roč. 19, č. 12 (2014) ISSN 1435-4934. [European Conference on Non-Destructive Testing (ECNDT 2014) /11./. Praha, 06.10.2014-10.10.2014] Institutional support: RVO:61388998 Keywords : acoustic emission (AE) * ultrasonic testing (UT) * signal processing * source location * time reversal acoustic s * acoustic emission * signal processing and transfer Subject RIV: BI - Acoustic s http://www.ndt.net/events/ECNDT2014/app/content/Slides/637_Prevorovsky.pdf
Eryurek, E.; Upadhyaya, B.R.; Kavaklioglu, K.
Signal validation and plant subsystem tracking in power and process industries require the prediction of one or more state variables. Both heteroassociative and auotassociative neural networks were applied for characterizing relationships among sets of signals. A multi-layer neural network paradigm was applied for sensor and process monitoring in a Pressurized Water Reactor (PWR). This nonlinear interpolation technique was found to be very effective for these applications
Zeynalov, Sh.S.; Ahmadov, Q.S.
Full text : Data acquisition systems for nuclear spectroscopy have traditionally been based on systems with analog shaping amplifiers followed by analog-to-digital converters. Recently, however, new systems based on digital signal processing make possible to replace the analog shaping and timing circuitry the numerical algorithms to derive properties of the pulse such as its amplitude. DSP is a fully numerical analysis of the detector pulse signals and this technique demonstrates significant advantages over analog systems in some circumstances. From a mathematical point of view, one can consider the signal evolution from the detector to the ADC as a sequence of transformations that can be described by precisely defined mathematical expressions. Digital signal processing with ADCs has the possibility to utilize further information on the signal pulses from radiation detectors. In the experiment each step of the signal generation in the 3He filled proportional counter was described using digital signal processing techniques (DSP). The electronic system has consisted of a detector, a preamplifier and a digital oscilloscope. The pulses from the detector were digitized using a digital storage oscilloscope. This oscilloscope allowed signal digitization with accuracy of 8 bit (256 levels) and with frequency of up to 5 * 10 8 samples/s. As a neutron source was used Cf-252. To obtain detector output current pulse I(t) created by the motions of the ions/electrons pairs was written an algorithm which can easily be programmed using modern computer programming languages.
Radioisotopes can be used to obtain signals or images in order to recognize the information inside the industrial systems. The main problems of using these techniques are the difficulty of identification of the obtained signals or images and the requirement of skilled experts for the interpretation process of the output data of these applications. Now, the interpretation of the output data from these applications is performed mainly manually, depending heavily on the skills and the experience of trained operators. This process is time consuming and the results typically suffer from inconsistency and errors. The objective of the thesis is to apply the advanced digital signal processing techniques for improving the treatment and the interpretation of the output data from the different Industrial Radioisotopes Applications (IRA). This thesis focuses on two IRA; the Residence Time Distribution (RTD) measurement and the defect inspection of welded pipes using a gamma source (gamma radiography). In RTD measurement application, this thesis presents methods for signal pre-processing and modeling of the RTD signals. Simulation results have been presented for two case studies. The first case study is a laboratory experiment for measuring the RTD in a water flow rig. The second case study is an experiment for measuring the RTD in a phosphate production unit. The thesis proposes an approach for RTD signal identification in the presence of noise. In this approach, after signal processing, the Mel Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients are extracted from the processed signal or from one of its transforms. The Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) have been tested and compared for efficient feature extraction. Neural networks have been used for matching of the extracted features. Furthermore, the Power Density Spectrum (PDS) of the RTD signal has been also used instead of the discrete
Ashour, M.A.; Abo Shosha, A.M.
In this paper, a Digital Signal Processing (DSP) Computer-based system for the nuclear scintillation signals with exponential decay is presented. The main objective of this work is to identify the characteristics of the acquired signals smoothly, this can be done by transferring the signal environment from random signal domain to deterministic domain using digital manipulation techniques. The proposed system consists of two major parts. The first part is the high performance data acquisition system (DAQ) that depends on a multi-channel Logic Scope. Which is interfaced with the host computer through the General Purpose Interface Board (GPIB) Ver. IEEE 488.2. Also, a Graphical User Interface (GUI) has been designed for this purpose using the graphical programming facilities. The second of the system is the DSP software Algorithm which analyses, demonstrates, monitoring these data to obtain the main characteristics of the acquired signals; the amplitude, the pulse count, the pulse width, decay factor, and the arrival time
Comoretto, Gianni; Chiello, Riccardo; Roberts, Matt; Halsall, Rob; Adami, Kristian Zarb; Alderighi, Monica; Aminaei, Amin; Baker, Jeremy; Belli, Carolina; Chiarucci, Simone; D'Angelo, Sergio; De Marco, Andrea; Mura, Gabriele Dalle; Magro, Alessio; Mattana, Andrea; Monari, Jader; Naldi, Giovanni; Pastore, Sandro; Perini, Federico; Poloni, Marco; Pupillo, Giuseppe; Rusticelli, Simone; Schiaffino, Marco; Schillirò, Francesco; Zaccaro, Emanuele
The signal processing firmware that has been developed for the Low Frequency Aperture Array component of the Square Kilometre Array (SKA) is described. The firmware is implemented on a dual FPGA board, that is capable of processing the streams from 16 dual polarization antennas. Data processing includes channelization of the sampled data for each antenna, correction for instrumental response and for geometric delays and formation of one or more beams by combining the aligned streams. The channelizer uses an oversampling polyphase filterbank architecture, allowing a frequency continuous processing of the input signal without discontinuities between spectral channels. Each board processes the streams from 16 antennas, as part of larger beamforming system, linked by standard Ethernet interconnections. These are envisaged to be 8192 of these signal processing platforms in the first phase of the SKA so particular attention has been devoted to ensure the design is low cost and low power.
Wiley, H. Steven
The ability of cells to detect and decode information about their extracellular environment is critical to generating an appropriate response. In multicellular organisms, cells must decode dozens of signals from their neighbors and extracellular matrix to maintain tissue homeostasis while still responding to environmental stressors. How cells detect and process information from their surroundings through a surprisingly limited number of signal transduction pathways is one of the most important question in biology. Despite many decades of research, many of the fundamental principles that underlie cell signal processing remain obscure. However, in this issue of Cell Systems, Gillies et al present compelling evidence that the early response gene circuit can act as a linear signal integrator, thus providing significant insight into how cells handle fluctuating signals and noise in their environment.
Thomas, J. B.
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.
Rebizant, Waldemar; Wiszniewski, Andrzej
Digital Signal Processing in Power System Protection and Control bridges the gap between the theory of protection and control and the practical applications of protection equipment. Understanding how protection functions is crucial not only for equipment developers and manufacturers, but also for their users who need to install, set and operate the protection devices in an appropriate manner. After introductory chapters related to protection technology and functions, Digital Signal Processing in Power System Protection and Control presents the digital algorithms for signal filtering, followed
Turley, Jordan A; Nilsson, Michael; Walker, Frederick Rohan; Johnson, Sarah J
Intrinsic Optical Signal imaging is a technique which allows the visualisation and mapping of activity related changes within the brain with excellent spatial and temporal resolution. We analysed a variety of signal and image processing techniques applied to real mouse imaging data. The results were compared in an attempt to overcome the unique issues faced when performing the technique on mice and improve the understanding of post processing options available.
Oxenløwe, Leif Katsuo; Galili, Michael; Hu, Hao
This paper reviews our recent advances in ultra-high speed serial optical communications. It describes Tbit/s optical signal processing and various materials allowing for this, as well as network scenarios embracing this technology......This paper reviews our recent advances in ultra-high speed serial optical communications. It describes Tbit/s optical signal processing and various materials allowing for this, as well as network scenarios embracing this technology...
Fu, Chi Yung; Petrich, Loren
A signal processing method and system combining smooth level wavelet pre-processing together with artificial neural networks all in the wavelet domain for signal denoising and extraction. Upon receiving a signal corrupted with noise, an n-level decomposition of the signal is performed using a discrete wavelet transform to produce a smooth component and a rough component for each decomposition level. The n.sup.th level smooth component is then inputted into a corresponding neural network pre-trained to filter out noise in that component by pattern recognition in the wavelet domain. Additional rough components, beginning at the highest level, may also be retained and inputted into corresponding neural networks pre-trained to filter out noise in those components also by pattern recognition in the wavelet domain. In any case, an inverse discrete wavelet transform is performed on the combined output from all the neural networks to recover a clean signal back in the time domain.
Podboy, Gary; Stephens, David
Preliminary results are presented from a test conducted to determine how well microphone phased array processing software could pull an acoustic signal out of background noise. The array consisted of 24 microphones in an aerodynamic fairing designed to be mounted in-flow. The processing was conducted using Functional Beam forming software developed by Optinav combined with cross spectral matrix subtraction. The test was conducted in the free-jet of the Nozzle Acoustic Test Rig at NASA GRC. The background noise was produced by the interaction of the free-jet flow with the solid surfaces in the flow. The acoustic signals were produced by acoustic drivers. The results show that the phased array processing was able to pull the acoustic signal out of the background noise provided the signal was no more than 20 dB below the background noise level measured using a conventional single microphone equipped with an aerodynamic forebody.
Roncagliolo, Pablo; Arredondo, Luis; Gonzalez, AgustIn
This article describes technical aspects involved in the programming of a system of acquisition, processing and transmission of biomedical signals by using mobile devices. This task is aligned with the permanent development of new technologies for the diagnosis and sickness treatment, based on the feasibility of measuring continuously different variables as electrocardiographic signals, blood pressure, oxygen concentration, pulse or simply temperature. The contribution of this technology is settled on its portability and low cost, which allows its massive use. Specifically this work analyzes the feasibility of acquisition and the processing of signals from a standard smartphone. Work results allow to state that nowadays these equipments have enough processing capacity to execute signals acquisition systems. These systems along with external servers make it possible to imagine a near future where the possibility of making continuous measures of biomedical variables will not be restricted only to hospitals but will also begin to be more frequently used in the daily life and at home
The purpose of this study is to develop methods in array signal processing which achieve accurate signal reconstruction from limited observations resulting in high-resolution imaging. The focus is on underwater acoustic applications and sonar signal processing both in active (transmit and receive...... in active sonar signal processing for detection and imaging of submerged oil contamination in sea water from a deep-water oil leak. The submerged oil _eld is modeled as a uid medium exhibiting spatial perturbations in the acoustic parameters from their mean ambient values which cause weak scattering......) and passive (only receive) mode. The study addresses the limitations of existing methods and shows that, in many cases, the proposed methods overcome these limitations and outperform traditional methods for acoustic imaging. The project comprises two parts; The first part deals with computational methods...
Cosnac, B. de; Gariod, R.; Max, J.; Monge, V.
This note is a general survey of the processing of physiological signals. After an introduction about electrodes and their limitations, the physiological nature of the main signals are shortly recalled. Different methods (signal averaging, spectral analysis, shape morphological analysis) are described as applications to the fields of magnetocardiography, electro-encephalography, cardiography, electronystagmography. As for processing means (single portable instruments and programmable), they are described through the example of application to rheography and to the Plurimat'S general system. As a conclusion the methods of signal processing are dominated by the morphological analysis of curves and by the necessity of a more important introduction of the statistical classification. As for the instruments, microprocessors will appear but specific operators linked to computer will certainly grow [fr
Fourier analysis is one of the most useful tools in many applied sciences. The recent developments of wavelet analysis indicates that in spite of its long history and well-established applications, the field is still one of active research. This text bridges the gap between engineering and mathematics, providing a rigorously mathematical introduction of Fourier analysis, wavelet analysis and related mathematical methods, while emphasizing their uses in signal processing and other applications in communications engineering. The interplay between Fourier series and Fourier transforms is at the heart of signal processing, which is couched most naturally in terms of the Dirac delta function and Lebesgue integrals. The exposition is organized into four parts. The first is a discussion of one-dimensional Fourier theory, including the classical results on convergence and the Poisson sum formula. The second part is devoted to the mathematical foundations of signal processing - sampling, filtering, digital signal proc...
Moon, Hee Gun; Park, Sang Min; Kim, Jung Seon; Shon, Chang Ho; Park, Heui Youn; Koo, In Soo
The function of PIS(Process Instrumentation System) for SMART is to acquire the process data from sensor or transmitter. The PIS consists of signal conditioner, A/D converter, DSP(Digital Signal Process) and NIC(Network Interface Card). So, It is fully digital system after A/D converter. The PI cabinet and PDAS(Plant Data Acquisition System) in commercial plant is responsible for data acquisition of the sensor or transmitter include RTD, TC, level, flow, pressure and so on. The PDAS has the software that processes each sensor data and PI cabinet has the signal conditioner, which is need for maintenance and test. The signal conditioner has the potentiometer to adjust the span and zero for test and maintenance. The PIS of SMART also has the signal conditioner which has the span and zero adjust same as the commercial plant because the signal conditioner perform the signal condition for AD converter such as 0∼10Vdc. But, To adjust span and zero is manual test and calibration. So, This paper presents the method of signal validation and calibration, which is used by digital feature in SMART. There are I/E(current to voltage), R/E(resistor to voltage), F/E(frequency to voltage), V/V(voltage to voltage). Etc. In this paper show only the signal validation and calibration about I/E converter that convert level, pressure, flow such as 4∼20mA into signal for AD conversion such as 0∼10Vdc
Fan Wanchun; Shi Ren
Based on the analysis of auto-correlation function, the notion of the distance between auto-correlation function was quoted, and the characterization of the noise and the signal with noise were discussed by using the distance. Then, the method of auto- adaptable endpoint detection of seismic signal based on auto-correlated similarity was summed up. The steps of implementation and determining of the thresholds were presented in detail. The experimental results that were compared with the methods based on artificial detecting show that this method has higher sensitivity even in a low signal with noise ratio circumstance
properties that are appropriate to model networks of spiking neurons (e.g., Hodgkin & Huxley , 1952) or mean-field descriptions of neural populations...for the forced Kuramoto model. Chaos, 18(4), 043128. doi:papers2://publication/doi/10.1063/1.3049136 Eguìluz, V. M., Ospeck, M., Choe, Y ., Hudspeth
Rangaswamy, Muralidhar; Lin, Freeman C; Gerlach, Karl R
.... Specifically, we present the performance of the normalized matched filter test in a background of disturbance consisting of clutter having a covariance matrix with known structure and unknown scaling...
Schaller, T.; Raggenbass, A.; Reissner, J.
The increasing level of competition in the sheet-metal working industry requires measures to reduce costs. There is considerable potential in the simplification of process procedures. Automated handling and the further processing of bent semifinished products require a high level of manufacturing precision in bending processes. However deviations in angles of several degrees from the desired values can arise as a result of material variations within one batch. An adaptive control system is developed in order to avoid expensive further manufacturing steps. This calculates the parameters required for the correction of the process control from variations in process values measured online. These adjustment values must be communicated to the bending machine and set during the ongoing forming process. For larger series of parts the coefficients of the correction matrix are also continuously improved, so that an optimum adaptive correction can be achieved for the current scatter in the material properties. The adaptive correction procedure is particularly effective in combination with the three-point bending technology. The highest levels of angular precision can already be achieved after a short independent optimization phase. However, the current spectrum of parts for a certain manufacturing task and a defined quality requirement should provide the basis for decisions concerning the economy of this process arrangement.
Devasahayam, Suresh R
The use of digital signal processing is ubiquitous in the field of physiology and biomedical engineering. The application of such mathematical and computational tools requires a formal or explicit understanding of physiology. Formal models and analytical techniques are interlinked in physiology as in any other field. This book takes a unitary approach to physiological systems, beginning with signal measurement and acquisition, followed by signal processing, linear systems modelling, and computer simulations. The signal processing techniques range across filtering, spectral analysis and wavelet analysis. Emphasis is placed on fundamental understanding of the concepts as well as solving numerical problems. Graphs and analogies are used extensively to supplement the mathematics. Detailed models of nerve and muscle at the cellular and systemic levels provide examples for the mathematical methods and computer simulations. Several of the models are sufficiently sophisticated to be of value in understanding real wor...
Amanda C. Graham; Linda E. Kruger
This report analyzes how a small group of Forest Service scientists participating in efforts to implement adaptive management approach working relations, and how they understand and apply the research process. Nine scientists completed a questionnaire to assess their preferred mode of thinking (the Herrmann Brain Dominance Instrument), engaged in a facilitated...
Lee, James S. J.; Nguyen, Dziem D.; Lin, C.
A real-time adaptive scheme is introduced to detect and track moving objects under noisy, dynamic conditions including moving sensors. This approach integrates the adaptiveness and incremental learning characteristics of neural networks with intelligent reasoning and process control. Spatiotemporal filtering is used to detect and analyze motion, exploiting the speed and accuracy of multiresolution processing. A neural network algorithm constitutes the basic computational structure for classification. A recognition and learning controller guides the on-line training of the network, and invokes pattern recognition to determine processing parameters dynamically and to verify detection results. A tracking controller acts as the central control unit, so that tracking goals direct the over-all system. Performance is benchmarked against the Widrow-Hoff algorithm, for target detection scenarios presented in diverse FLIR image sequences. Efficient algorithm design ensures that this recognition and control scheme, implemented in software and commercially available image processing hardware, meets the real-time requirements of tracking applications.
Full Text Available Face identity and facial expression are processed in two distinct neural pathways. However, most of the existing face adaptation literature studies them separately, despite the fact that they are two aspects from the same face. The current study conducted a systematic comparison between these two aspects by face adaptation, investigating how top- and bottom-half face parts contribute to the processing of face identity and facial expression. A real face (sad, “Adam” and its two size-equivalent face parts (top- and bottom-half were used as the adaptor in separate conditions. For face identity adaptation, the test stimuli were generated by morphing Adam's sad face with another person's sad face (“Sam”. For facial expression adaptation, the test stimuli were created by morphing Adam's sad face with his neutral face and morphing the neutral face with his happy face. In each trial, after exposure to the adaptor, observers indicated the perceived face identity or facial expression of the following test face via a key press. They were also tested in a baseline condition without adaptation. Results show that the top- and bottom-half face each generated a significant face identity aftereffect. However, the aftereffect by top-half face adaptation is much larger than that by the bottom-half face. On the contrary, only the bottom-half face generated a significant facial expression aftereffect. This dissociation of top- and bottom-half face adaptation suggests that face parts play different roles in face identity and facial expression. It thus provides further evidence for the distributed systems of face perception.
At the Specialists' Meeting on Sodium Boiling Detection organized by the International Working Group on Fast Reactors (IWGFR) of the International Atomic Energy Agency at Chester in the United Kingdom in 1981 various methods of detecting sodium boiling were reported. But, it was not possible to make a comparative assessment of these methods because the signal condition in each experiment was different from others. That is why participants of this meeting recommended that a benchmark test should be carried out in order to evaluate and compare signal processing methods for boiling detection. Organization of the Co-ordinated Research Programme (CRP) on signal processing techniques for sodium boiling noise detection was also recommended at the 16th meeting of the IWGFR. The CRP on Signal Processing Techniques for Sodium Boiling Noise Detection was set up in 1984. Eight laboratories from six countries have agreed to participate in this CRP. The overall objective of the programme was the development of reliable on-line signal processing techniques which could be used for the detection of sodium boiling in an LMFBR core. During the first stage of the programme a number of existing processing techniques used by different countries have been compared and evaluated. In the course of further work, an algorithm for implementation of this sodium boiling detection system in the nuclear reactor will be developed. It was also considered that the acoustic signal processing techniques developed for boiling detection could well make a useful contribution to other acoustic applications in the reactor. This publication consists of two parts. Part I is the final report of the co-ordinated research programme on signal processing techniques for sodium boiling noise detection. Part II contains two introductory papers and 20 papers presented at four research co-ordination meetings since 1985. A separate abstract was prepared for each of these 22 papers. Refs, figs and tabs
Full Text Available Abstract Background Heart signals represent an important way to evaluate cardiovascular function and often what is desired is to quantify the level of some signal of interest against the louder backdrop of the beating of the heart itself. An example of this type of application is the quantification of cavitation in mechanical heart valve patients. Methods An algorithm is presented for the quantification of high-frequency, non-deterministic events such as cavitation from recorded signals. A closed-form mathematical analysis of the algorithm investigates its capabilities. The algorithm is implemented on real heart signals to investigate usability and implementation issues. Improvements are suggested to the base algorithm including aligning heart sounds, and the implementation of the Short-Time Fourier Transform to study the time evolution of the energy in the signal. Results The improvements result in better heart beat alignment and better detection and measurement of the random events in the heart signals, so that they may provide a method to quantify nondeterministic events in heart signals. The use of the Short-Time Fourier Transform allows the examination of the random events in both time and frequency allowing for further investigation and interpretation of the signal. Conclusions The presented algorithm does allow for the quantification of nondeterministic events but proper care in signal acquisition and processing must be taken to obtain meaningful results.
Millette, Véronique; Baddour, Natalie
Heart signals represent an important way to evaluate cardiovascular function and often what is desired is to quantify the level of some signal of interest against the louder backdrop of the beating of the heart itself. An example of this type of application is the quantification of cavitation in mechanical heart valve patients. An algorithm is presented for the quantification of high-frequency, non-deterministic events such as cavitation from recorded signals. A closed-form mathematical analysis of the algorithm investigates its capabilities. The algorithm is implemented on real heart signals to investigate usability and implementation issues. Improvements are suggested to the base algorithm including aligning heart sounds, and the implementation of the Short-Time Fourier Transform to study the time evolution of the energy in the signal. The improvements result in better heart beat alignment and better detection and measurement of the random events in the heart signals, so that they may provide a method to quantify nondeterministic events in heart signals. The use of the Short-Time Fourier Transform allows the examination of the random events in both time and frequency allowing for further investigation and interpretation of the signal. The presented algorithm does allow for the quantification of nondeterministic events but proper care in signal acquisition and processing must be taken to obtain meaningful results.
Kuo, Sen M; Tian, Wenshun
Combines both the DSP principles and real-time implementations and applications, and now updated with the new eZdsp USB Stick, which is very low cost, portable and widely employed at many DSP labs. Real-Time Digital Signal Processing introduces fundamental digital signal processing (DSP) principles and will be updated to include the latest DSP applications, introduce new software development tools and adjust the software design process to reflect the latest advances in the field. In the 3rd edition of the book, the key aspect of hands-on experiments will be enhanced to make the DSP principle
Full Text Available Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features from input data. Nowadays, researchers have intensively investigated deep learning algorithms for solving challenging problems in many areas such as image classification, speech recognition, signal processing, and natural language processing. In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply deep learning to specific areas such as road crack detection, fault diagnosis, and human activity detection. Besides, this study also discusses the challenges of designing and training deep neural networks.
Oxenløwe, Leif Katsuo; Pu, Minhao; Ding, Yunhong
This paper presents an overview of recent wo rk on the use of silicon waveguides for processing optical data signals. We will describe ultra-fast, ultra-broadband, polarisation-insensitive and phase-sensitive applications including processing of spectrally-efficient data formats and optical phase...
The author analyzes the basic characteristic of parallel DSP (digital signal processor) TMS320C80 and proposes related optimized image algorithm and the parallel processing method based on parallel DSP. The realtime for many image processing can be achieved in this way
Emmanuel, Babatunde S
This paper presents an overview of approaches to analysis of heart sound signals. The paper reviews the milestones in the development of phonocardiogram (PCG) signal analysis. It describes the various stages involved in the analysis of heart sounds and discrete wavelet transform as a preferred method for bio-signal processing. In addition, the gaps that still exist between contemporary methods of signal analysis of heart sounds and their applications for clinical diagnosis is reviewed. A lot of progress has been made but crucial gaps still exist. The findings of this review paper are as follows: there is a lack of consensus in research outputs; inter-patient adaptability of signal processing algorithm is still problematic; the process of clinical validation of analysis techniques was not sufficiently rigorous in most of the reviewed literature; and as such data integrity and measurement are still in doubt, which most of the time led to inaccurate interpretation of results. In addition, the existing diagnostic systems are too complex and expensive. The paper concluded that the ability to correctly acquire, analyse and interpret heart sound signals for improved clinical diagnostic processes has become a priority.
Millecamps, Alexandre; Lowry, Kristin A; Brach, Jennifer S; Perera, Subashan; Redfern, Mark S; Sejdić, Ervin
Gait accelerometry is an important approach for gait assessment. Previous contributions have adopted various pre-processing approaches for gait accelerometry signals, but none have thoroughly investigated the effects of such pre-processing operations on the obtained results. Therefore, this paper investigated the influence of pre-processing operations on signal features extracted from gait accelerometry signals. These signals were collected from 35 participants aged over 65years: 14 of them were healthy controls (HC), 10 had Parkinson׳s disease (PD) and 11 had peripheral neuropathy (PN). The participants walked on a treadmill at preferred speed. Signal features in time, frequency and time-frequency domains were computed for both raw and pre-processed signals. The pre-processing stage consisted of applying tilt correction and denoising operations to acquired signals. We first examined the effects of these operations separately, followed by the investigation of their joint effects. Several important observations were made based on the obtained results. First, the denoising operation alone had almost no effects in comparison to the trends observed in the raw data. Second, the tilt correction affected the reported results to a certain degree, which could lead to a better discrimination between groups. Third, the combination of the two pre-processing operations yielded similar trends as the tilt correction alone. These results indicated that while gait accelerometry is a valuable approach for the gait assessment, one has to carefully adopt any pre-processing steps as they alter the observed findings. Copyright © 2015 Elsevier Ltd. All rights reserved.
Jain, Aakanksha; Pasare, Chandrashekhar
Activation of cells in the adaptive immune system is a highly orchestrated process dictated by multiples cues from the innate immune system. Although the fundamental principles of innate control of adaptive immunity are well established, it is not fully understood how innate cells integrate qualitative pathogenic information to generate tailored protective adaptive immune responses. In this review, we discuss complexities involved in the innate control of adaptive immunity that extend beyond TCR engagement, costimulation, and priming cytokine production but are critical for the generation of protective T cell immunity. Copyright © 2017 by The American Association of Immunologists, Inc.
Full Text Available In this paper, we present a review on silicon-based nonlinear devices for all optical nonlinear processing of complex telecommunication signals. We discuss some recent developments achieved by our research group, through extensive collaborations with academic partners across Europe, on optical signal processing using silicon-germanium and amorphous silicon based waveguides as well as novel materials such as silicon rich silicon nitride and tantalum pentoxide. We review the performance of four wave mixing wavelength conversion applied on complex signals such as Differential Phase Shift Keying (DPSK, Quadrature Phase Shift Keying (QPSK, 16-Quadrature Amplitude Modulation (QAM and 64-QAM that dramatically enhance the telecom signal spectral efficiency, paving the way to next generation terabit all-optical networks.
Aksenov, Daniil P; Li, Limin; Miller, Michael J; Wyrwicz, Alice M
The adaptation of neuronal responses to stimulation, in which a peak transient response is followed by a sustained plateau, has been well-studied. The blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) signal has also been shown to exhibit adaptation on a longer time scale. However, some regions such as the visual and auditory cortices exhibit significant BOLD adaptation, whereas other such as the whisker barrel cortex may not adapt. In the sensory cortex a combination of thalamic inputs and intracortical activity drives hemodynamic changes, although the relative contributions of these components are not entirely understood. The aim of this study is to assess the role of thalamic inputs vs. intracortical processing in shaping BOLD adaptation during stimulation in the somatosensory cortex. Using simultaneous fMRI and electrophysiology in awake rabbits, we measured BOLD, local field potentials (LFPs), single- and multi-unit activity in the cortex during whisker and optogenetic stimulation. This design allowed us to compare BOLD and haemodynamic responses during activation of the normal thalamocortical sensory pathway (i.e., both inputs and intracortical activity) vs. the direct optical activation of intracortical circuitry alone. Our findings show that whereas LFP and multi-unit (MUA) responses adapted, neither optogenetic nor sensory stimulation produced significant BOLD adaptation. We observed for both paradigms a variety of excitatory and inhibitory single unit responses. We conclude that sensory feed-forward thalamic inputs are not primarily responsible for shaping BOLD adaptation to stimuli; but the single-unit results point to a role in this behaviour for specific excitatory and inhibitory neuronal sub-populations, which may not correlate with aggregate neuronal activity. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
Tu, Bing; Li, De Sheng; Lin, En Huai; Ji, Miao Miao
Wireless measure while drilling (MWD) transmits data by using mud pulse signal ; the ground decoding system collects the mud pulse signal and then decodes and displays the parameters under the down-hole according to the designed encoding rules and the correct detection and recognition of the ground decoding system towards the received mud pulse signal is one kind of the key technology of MWD. This paper introduces digit of Manchester encoding that transmits data and the format of the wireless transmission of data under the down-hole and develops a set of ground decoding systems. The ground decoding algorithm uses FIR (Finite impulse response) digital filtering to make de-noising on the mud pulse signal, then adopts the related base value modulating algorithm to eliminate the pump pulse base value of the denoised mud pulse signal, finally analyzes the mud pulse signal waveform shape of the selected Manchester encoding in three bits cycles, and applies the pattern similarity recognition algorithm to the mud pulse signal recognition. The field experiment results show that the developed device can make correctly extraction and recognition for the mud pulse signal with simple and practical decoding process and meet the requirements of engineering application.
Sheng, Hu; Qiu, TianShuang
Fractional processes are widely found in science, technology and engineering systems. In Fractional Processes and Fractional-order Signal Processing, some complex random signals, characterized by the presence of a heavy-tailed distribution or non-negligible dependence between distant observations (local and long memory), are introduced and examined from the ‘fractional’ perspective using simulation, fractional-order modeling and filtering and realization of fractional-order systems. These fractional-order signal processing (FOSP) techniques are based on fractional calculus, the fractional Fourier transform and fractional lower-order moments. Fractional Processes and Fractional-order Signal Processing: • presents fractional processes of fixed, variable and distributed order studied as the output of fractional-order differential systems; • introduces FOSP techniques and the fractional signals and fractional systems point of view; • details real-world-application examples of FOSP techniques to demonstr...
Ghodsi, Zara; Silva, Emmanuel Sirimal; Hassani, Hossein
The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraction in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average. Copyright © 2015 The Authors. Production and hosting by Elsevier Ltd.. All rights reserved.
Karr, C. L.
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Karr, C. L.
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Full Text Available Electroencephalogram (EEG signals are often used to diagnose diseases such as seizure, alzheimer, and schizophrenia. One main problem with the recorded EEG samples is that they are not equally reliable due to the artifacts at the time of recording. EEG signal classification algorithms should have a mechanism to handle this issue. It seems that using adaptive classifiers can be useful for the biological signals such as EEG. In this paper, a general adaptive method named weighted distance nearest neighbor (WDNN is applied for EEG signal classification to tackle this problem. This classification algorithm assigns a weight to each training sample to control its influence in classifying test samples. The weights of training samples are used to find the nearest neighbor of an input query pattern. To assess the performance of this scheme, EEG signals of thirteen schizophrenic patients and eighteen normal subjects are analyzed for the classification of these two groups. Several features including, fractal dimension, band power and autoregressive (AR model are extracted from EEG signals. The classification results are evaluated using Leave one (subject out cross validation for reliable estimation. The results indicate that combination of WDNN and selected features can significantly outperform the basic nearest-neighbor and the other methods proposed in the past for the classification of these two groups. Therefore, this method can be a complementary tool for specialists to distinguish schizophrenia disorder.
Jack B. Dennis
Full Text Available Complex signal-processing problems are naturally described by compositions of program modules that process streams of data. In this article we discuss how such compositions may be analyzed and mapped onto multiprocessor computers to effectively exploit the massive parallelism of these applications. The methods are illustrated with an example of signal processing for an optical surveillance problem. Program transformation and analysis are used to construct a program description tree that represents the given computation as an acyclic interconnection of stream-processing modules. Each module may be mapped to a set of threads run on a group of processing elements of a target multiprocessor. Performance is considered for two forms of multiprocessor architecture, one based on conventional DSP technology and the other on a multithreaded-processing element design.
The present work reports some experimental results from the adaptive walls wind tunnel in Naples and must be considered a logical step forward in validating the wall adaptation process of this tunnel. Two sets of new tests were made for evaluating the effects of wall adaptation: one on the location of laminar-turbulent transition and turbulent separation points, the other one on the measurement of aerodynamic forces and moments, taken by a strain gage balance. Up to now, aerodynamic forces and moments were evaluated by the integration of pressure distributions on the model surface. All results agree with the theory, and measurement of aerodynamic forces has also been partially validated. Using a balance proved to be a technical shortcoming in the testing device.
Y. G. Tabakov
Full Text Available The problem of control signals generating for intellectual trainer intended for the human musculoskeletal system recovery is considered. A method for low-frequency signal processing (frequency 50 Hz readout from the surface of cerebral cortex has been developed. These signals are connected to the activity of the human brain and, directly, with α- and β-rhythms responsible for limb movements. The proposed method is based on the application of differential functions and Daubechies and Morlaix algorithms for wavelet transforms. To avoid errors occurring during low-frequency signal readout from the surface of cerebral cortex, a modular signal processing is suggested. Research was carried out on 10 male volunteers, who performed hand movement in the course of the experiment staying in a relaxed wakefulness. The findings showed that the proposed method gives the possibility for detecting the amplitude of the control signals from 5 to 15 mV in a frequency range from 10 Hz to 50 Hz. This level of signals makes it possible to adapt them for intellectual trainer control. The results are applicable in medical rehabilitation facilities, as well as in the training of athletes for competitive events.
Processing Techniques for Landmine Detection Using GPR The views, opinions and/or findings contained in this report are those of the author(s) and should not...310 Jesse Hall Columbia, MO 65211 -1230 654808 633606 ABSTRACT Advanced Statistical Signal Processing Techniques for Landmine Detection Using GPR Report...Aggregation Operator For Humanitarian Demining Using Hand- Held GPR , , (01 2008): . doi: D. Ho, P. Gader, J. Wilson, H. Frigui. Subspace Processing
Jepsen, Morten Løve; Ewert, Stephan D.; Dau, Torsten
A model of computational auditory signal-processing and perception that accounts for various aspects of simultaneous and nonsimultaneous masking in human listeners is presented. The model is based on the modulation filterbank model described by Dau et al. [J. Acoust. Soc. Am. 102, 2892 (1997)] but includes major changes at the peripheral and more central stages of processing. The model contains outer- and middle-ear transformations, a nonlinear basilar-membrane processing stage, a hair-cell t...
Full Text Available The global navigation satellite system (GNSS has been widely used in both military and civil fields. This study focuses on enhancing the carrier tracking ability of the phase-locked loop (PLL in GNSS receivers for high-dynamic application. The PLL is a very popular and practical approach for tracking the GNSS carrier signal which propagates in the form of electromagnetic wave. However, a PLL with constant coefficient would be suboptimal. Adaptive loop noise bandwidth techniques proposed by previous researches can improve PLL tracking behavior to some extent. This paper presents a novel PLL with an adaptive loop gain control filter (AGCF-PLL that can provide an alternative. The mathematical model based on second- and third-order PLL was derived. The error characteristics of the AGCF-PLL were also derived and analyzed under different signal conditions, which mainly refers to the different combinations of carrier phase dynamic and signal strength. Based on error characteristic curves, the optimal loop gain control method has been achieved to minimize tracking error. Finally, the completely adaptive loop gain control algorithm was designed. Comparable test results and analysis using the new method, conventional PLL, FLL-assisted PLL, and FAB-LL demonstrate that the AGCF-PLL has stronger adaptability to high target movement dynamic.
Gopi, E S
This book examines signal processing techniques used in wireless communication illustrated by using the Matlab program. The author discusses these techniques as they relate to Doppler spread; delay spread; Rayleigh and Rician channel modeling; rake receiver; diversity techniques; MIMO and OFDM -based transmission techniques; and array signal processing. Related topics such as detection theory, link budget, multiple access techniques, and spread spectrum are also covered. · Illustrates signal processing techniques involved in wireless communication using Matlab · Discusses multiple access techniques such as Frequency division multiple access, Time division multiple access, and Code division multiple access · Covers band pass modulation techniques such as Binary phase shift keying, Differential phase shift keying, Quadrature phase shift keying, Binary frequency shift keying, Minimum shift keying, and Gaussian minimum shift keying.
Woo, Wai; Sulaiman, Hamzah; Othman, Mohd; Saat, Mohd
This book presents important research findings and recent innovations in the field of machine learning and signal processing. A wide range of topics relating to machine learning and signal processing techniques and their applications are addressed in order to provide both researchers and practitioners with a valuable resource documenting the latest advances and trends. The book comprises a careful selection of the papers submitted to the 2015 International Conference on Machine Learning and Signal Processing (MALSIP 2015), which was held on 15–17 December 2015 in Ho Chi Minh City, Vietnam with the aim of offering researchers, academicians, and practitioners an ideal opportunity to disseminate their findings and achievements. All of the included contributions were chosen by expert peer reviewers from across the world on the basis of their interest to the community. In addition to presenting the latest in design, development, and research, the book provides access to numerous new algorithms for machine learni...
Andrés Julio Demski
Full Text Available The electrocardiogram kit ('ecg-kit' for Matlab is an application-programming interface (API developed to provide users a common interface to access and process cardiovascular signals. In the current version, the toolbox supports several ECG recording formats, most of them used by the most popular databases, which allows access to more than 7 TB of information, stored in public databases such as those included in Physionet or the THEW project. The toolbox includes several algorithms frequently used in cardiovascular signal processing, such as heartbeat detectors and classifiers, pulse detectors for pulsatile signals and an ECG delineator. In addition, it provides a tool for manually reviewing and correcting the results produced by the automatic algorithms. The results obtained can be stored in a Matlab (.MAT file for backup or subsequent processing, or used to create a PDF report.
Zejnalova, O.; Zejnalov, Sh.; Hambsch, F.J.; Oberstedt, S.
Digital signal processing algorithms for nuclear particle spectroscopy are described along with a digital pile-up elimination method applicable to equidistantly sampled detector signals pre-processed by a charge-sensitive preamplifier. The signal processing algorithms are provided as recursive one- or multi-step procedures which can be easily programmed using modern computer programming languages. The influence of the number of bits of the sampling analogue-to-digital converter on the final signal-to-noise ratio of the spectrometer is considered. Algorithms for a digital shaping-filter amplifier, for a digital pile-up elimination scheme and for ballistic deficit correction were investigated using a high purity germanium detector. The pile-up elimination method was originally developed for fission fragment spectroscopy using a Frisch-grid back-to-back double ionization chamber and was mainly intended for pile-up elimination in case of high alpha-radioactivity of the fissile target. The developed pile-up elimination method affects only the electronic noise generated by the preamplifier. Therefore the influence of the pile-up elimination scheme on the final resolution of the spectrometer is investigated in terms of the distance between pile-up pulses. The efficiency of the developed algorithms is compared with other signal processing schemes published in literature
Inspired by ongoing evolutions in the field of wireless body area networks (WBANs), this tutorial paper presents a conceptual and exploratory study of wireless electroencephalography (EEG) sensor networks (WESNs), with an emphasis on distributed signal processing aspects. A WESN is conceived as a modular neuromonitoring platform for high-density EEG recordings, in which each node is equipped with an electrode array, a signal processing unit, and facilities for wireless communication. We first address the advantages of such a modular approach, and we explain how distributed signal processing algorithms make WESNs more power-efficient, in particular by avoiding data centralization. We provide an overview of distributed signal processing algorithms that are potentially applicable in WESNs, and for illustration purposes, we also provide a more detailed case study of a distributed eye blink artifact removal algorithm. Finally, we study the power efficiency of these distributed algorithms in comparison to their centralized counterparts in which all the raw sensor signals are centralized in a near-end or far-end fusion center.
Ahmadov, Q.S.; Institute of Radiation Problems, ANAS, Baku
Full text: Data acquisition systems for nuclear spectroscopy have traditionally been based on systems with analog shaping amplifiers followed by analog-to-digital converters. Recently, however, new systems based on digital signal processing allow us to replace the analog shaping and timing circuitry the numerical algorithms to derive properties of the pulse such as its amplitude. DSP is a fully numerical analysis of the detector pulse signals and this technique demonstrates significant advantages over analog systems in some circumstances. From a mathematical point of view, one can consider the signal evolution from the detector to the ADC as a sequence of transformations that can be described by precisely defined mathematical expressions.Digital signal processing with ADCs has the possibility to utilize further information on the signal pulses from radiation detectors  . In the experiment each step of the signal generation in the 3He filled proportional counter was described using digital signal processing techniques (DSP). The electronic system has consisted of a detector, a preamplifier and a digital oscilloscope. The pulses from the detector were digitized using a OTSZS-02 (250USB)-4 digital storage oscilloscope from ZAO R UDNEV-SHILYAYEV . This oscilloscope allowed signal digitization with accuracy of 8 bit(256 levels) and with frequency of up to 5.10''8 samples/s. As a neutron source was used Cf-252.To obtain detector output current pulse I(t) created by the motions of the ions/electrons pairs was written an algorithm which can easily be programmed using modern computer programming languages
Carroll, C. W.; Vijaya Kumar, B. V. K.
The results of the investigation of the applicability of optical processing to Adaptive Phased Array Radar (APAR) data processing will be summarized. Subjects that are covered include: (1) new iterative Fourier transform based technique to determine the array antenna weight vector such that the resulting antenna pattern has nulls at desired locations; (2) obtaining the solution of the optimal Wiener weight vector by both iterative and direct methods on two laboratory Optical Linear Algebra Processing (OLAP) systems; and (3) an investigation of the effects of errors present in OLAP systems on the solution vectors.
Broom, Donald M
The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and
Matthews, N. D.; Kaupp, V. H.; Waite, W. P.; Macdonald, H. C.
Seventeen interpreters ranked sets of computer-generated radar imagery to assess the value of post-correlation processing on the interpretability of SAR (synthetic aperture radar) imagery. The post-correlation processing evaluated amounts to a nonlinear mapping of the signal exiting a digital correlator and allows full use of signal bandwidth for improving the spatial resolution or for noise reduction. The results indicate that it is reasonable to hypothesize an optimal SAR presentation format for specific applications even though this study was too limited to be specific.
Oxenløwe, Leif Katsuo
We have recently seen that nanowires can provide unparalleled optical signal processing (OSP) bandwidth and provide ultra-fast operation as well. Utilising the ultra-broad OSP bandwidth for e.g. optical time lenses allows for energy-efficient optical switching. © 2015 OSA.......We have recently seen that nanowires can provide unparalleled optical signal processing (OSP) bandwidth and provide ultra-fast operation as well. Utilising the ultra-broad OSP bandwidth for e.g. optical time lenses allows for energy-efficient optical switching. © 2015 OSA....
""a useful addition to the fields of both magnetic resonance (MR) as well as signal processing. … immensely useful as a practical resource handbook to dip into from time to time and to find specific advice on issues faced during the course of work in MR techniques for cancer research. … the best feature of this book is how it positions the very practical area of digital signal processing in the contextual framework of a much more esoteric and fundamental field-that of quantum mechanics. The direct link between quantum-mechanical spectral analysis and rational response functions and the gene
Shankar, R.; Paradiso, T.J.; Lane, S.S.; Quinn, J.R.
Ultrasonic digital data were collected from underclad cracks in sample pressure vessel specimen blocks. These blocks were weld cladded under different processes to simulate actual conditions in US Pressure Water Reactors. Each crack was represented by a flaw-echo dynamic curve which is a plot of the transducer motion on the surface as a function of the ultrasonic response into the material. Crack depth sizing was performed by identifying in the dynamic curve the crack tip diffraction signals from the upper and lower tips. This paper describes the experimental procedure, digital signal processing methods used and algorithms developed for crack depth sizing
Chen, Xuefeng; Mukhopadhyay, Subhas
This book highlights the latest advances and trends in advanced signal processing (such as wavelet theory, time-frequency analysis, empirical mode decomposition, compressive sensing and sparse representation, and stochastic resonance) for structural health monitoring (SHM). Its primary focus is on the utilization of advanced signal processing techniques to help monitor the health status of critical structures and machines encountered in our daily lives: wind turbines, gas turbines, machine tools, etc. As such, it offers a key reference guide for researchers, graduate students, and industry professionals who work in the field of SHM.
Jackson, G.P.; Elliott, A.
Beam detectors such as striplines and wall current monitors rely on matched electrical networks to transmit and process beam information. Frequency bandwidth, noise immunity, reflections, and signal to noise ratio are considerations that require compromises limiting the quality of the measurement. Recent advances in fiber optics related technologies have made it possible to acquire and process beam signals in the optical domain. This paper describes recent developments in the application of these technologies to accelerator beam diagnostics. The design and construction of an optical notch filter used for a stochastic cooling system is used as an example. Conceptual ideas for future beam detectors are also presented
El-Shennawy, Khamies Mohammed Ali
This book is tailored to fulfil the requirements in the area of the signal processing in communication systems. The book contains numerous examples, solved problems and exercises to explain the methodology of Fourier Series, Fourier Analysis, Fourier Transform and properties, Fast Fourier Transform FFT, Discrete Fourier Transform DFT and properties, Discrete Cosine Transform DCT, Discrete Wavelet Transform DWT and Contourlet Transform CT. The book is characterized by three directions, the communication theory and signal processing point of view, the mathematical point of view and utility compu
Zhu, Yong; Jiang, Wan-lu; Kong, Xiang-dong [Yanshan University, Hebei (China)
In mechanical fault diagnosis and condition monitoring, extracting and eliminating the trend term of machinery signal are necessary. In this paper, an adaptive extraction method for trend term of machinery signal based on Extreme-point symmetric mode decomposition (ESMD) was proposed. This method fully utilized ESMD, including the self-adaptive decomposition feature and optimal fitting strategy. The effectiveness and practicability of this method are tested through simulation analysis and measured data validation. Results indicate that this method can adaptively extract various trend terms hidden in machinery signal, and has commendable self-adaptability. Moreover, the extraction results are better than those of empirical mode decomposition.
Wagner, Anita; Pals, Carina; de Blecourt, Charlotte M; Sarampalis, Anastasios; Başkent, Deniz
Speech perception is formed based on both the acoustic signal and listeners' knowledge of the world and semantic context. Access to semantic information can facilitate interpretation of degraded speech, such as speech in background noise or the speech signal transmitted via cochlear implants (CIs). This paper focuses on the latter, and investigates the time course of understanding words, and how sentential context reduces listeners' dependency on the acoustic signal for natural and degraded speech via an acoustic CI simulation.In an eye-tracking experiment we combined recordings of listeners' gaze fixations with pupillometry, to capture effects of semantic information on both the time course and effort of speech processing. Normal-hearing listeners were presented with sentences with or without a semantically constraining verb (e.g., crawl) preceding the target (baby), and their ocular responses were recorded to four pictures, including the target, a phonological (bay) competitor and a semantic (worm) and an unrelated distractor.The results show that in natural speech, listeners' gazes reflect their uptake of acoustic information, and integration of preceding semantic context. Degradation of the signal leads to a later disambiguation of phonologically similar words, and to a delay in integration of semantic information. Complementary to this, the pupil dilation data show that early semantic integration reduces the effort in disambiguating phonologically similar words. Processing degraded speech comes with increased effort due to the impoverished nature of the signal. Delayed integration of semantic information further constrains listeners' ability to compensate for inaudible signals.
Full Text Available 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
Grotevant, Harold D; McDermott, Jennifer M
Children join adoptive families through domestic adoption from the public child welfare system, infant adoption through private agencies, and international adoption. Each pathway presents distinctive developmental opportunities and challenges. Adopted children are at higher risk than the general population for problems with adaptation, especially externalizing, internalizing, and attention problems. This review moves beyond the field's emphasis on adoptee-nonadoptee differences to highlight biological and social processes that affect adaptation of adoptees across time. The experience of stress, whether prenatal, postnatal/preadoption, or during the adoption transition, can have significant impacts on the developing neuroendocrine system. These effects can contribute to problems with physical growth, brain development, and sleep, activating cascading effects on social, emotional, and cognitive development. Family processes involving contact between adoptive and birth family members, co-parenting in gay and lesbian adoptive families, and racial socialization in transracially adoptive families affect social development of adopted children into adulthood.
Sing, O.P.; Srinivasan, G.S.; Vyjayanthi, R.K.; Prabhakar, R.
International Atomic Energy Agency (IAEA) has organized the Co-ordinated Research Programme (CRP) on Acoustic Signal Processing for the Detection of Boiling or Sodium/Water Reaction in LMFR over the period 1990-1995. This was an extension of an earlier CRP on Signal Processing Techniques for Sodium Boiling Noise Detection which ended in 1988. The objectives of the extended CRP were to evaluate the suitability of signal processing techniques for the detection of boiling noise under very poor signal to noise ratio conditions and then to develop the techniques further to cover leak detection in the Steam Generator Units of Liquid Metal Fast Reactors. The Indian analysis showed that Determinant and Trace features and Adaptive Learning Network techniques were able to detect with very good discrimination boiling signals down to a signal to noise ratio (S/N) of -12 dB. Determinant and Trace features were very sensitive to boiling noise and could detect signals even down to -21 dB S/N, but with a lower reliability of detection. Among the various analysis techniques evaluated by India on the acoustic leak noise data during the period 1991-1994, Determinant and Trace of covariance matrix constructed from auto power spectral density proved to be the most sensitive features for leak noise detection. These features have detected leak signals even down to -24 dB S/N, but with reduced reliability of detection below -16 dB. The data for the final year of the CRP (1995) were generated from the end-of-life acoustic leak experiments on the evaporator unit of the Prototype Fast Reactor at Dounreay, United Kingdom. The Indian analysis on this data pertained to the detection of onset and duration of leak signal, to the identification of injection medium, to the determination of leak noise transmission characteristics and to the detection of leak location. (author). 13 refs, 14 figs, 5 tabs
Among organ systems, skeletal muscle is perhaps the most structurally specialized. The remarkable subcellular architecture of this tissue allows it to empower movement with instructions from motor neurons. Despite this high degree of specialization, skeletal muscle also has intrinsic signaling mechanisms that allow adaptation to long-term changes in demand and regeneration after acute damage. The second messenger adenosine 3′,5′-monophosphate (cAMP) not only elicits acute changes within myofibers during exercise but also contributes to myofiber size and metabolic phenotype in the long term. Strikingly, sustained activation of cAMP signaling leads to pronounced hypertrophic responses in skeletal myofibers through largely elusive molecular mechanisms. These pathways can promote hypertrophy and combat atrophy in animal models of disorders including muscular dystrophy, age-related atrophy, denervation injury, disuse atrophy, cancer cachexia, and sepsis. cAMP also participates in muscle development and regeneration mediated by muscle precursor cells; thus, downstream signaling pathways may potentially be harnessed to promote muscle regeneration in patients with acute damage or muscular dystrophy. In this review, we summarize studies implicating cAMP signaling in skeletal muscle adaptation. We also highlight ligands that induce cAMP signaling and downstream effectors that are promising pharmacological targets. PMID:22354781
Berdeaux, Rebecca; Stewart, Randi
Among organ systems, skeletal muscle is perhaps the most structurally specialized. The remarkable subcellular architecture of this tissue allows it to empower movement with instructions from motor neurons. Despite this high degree of specialization, skeletal muscle also has intrinsic signaling mechanisms that allow adaptation to long-term changes in demand and regeneration after acute damage. The second messenger adenosine 3',5'-monophosphate (cAMP) not only elicits acute changes within myofibers during exercise but also contributes to myofiber size and metabolic phenotype in the long term. Strikingly, sustained activation of cAMP signaling leads to pronounced hypertrophic responses in skeletal myofibers through largely elusive molecular mechanisms. These pathways can promote hypertrophy and combat atrophy in animal models of disorders including muscular dystrophy, age-related atrophy, denervation injury, disuse atrophy, cancer cachexia, and sepsis. cAMP also participates in muscle development and regeneration mediated by muscle precursor cells; thus, downstream signaling pathways may potentially be harnessed to promote muscle regeneration in patients with acute damage or muscular dystrophy. In this review, we summarize studies implicating cAMP signaling in skeletal muscle adaptation. We also highlight ligands that induce cAMP signaling and downstream effectors that are promising pharmacological targets.
Kilby, J; Prasad, K; Mawston, G
This paper covers the design aspects of a new multi-channel electrode for the acquisition of surface electromyography signals from a selected muscle. The new multi-channel electrode has 11 pins where the monopolar signals produced will be configured in a software either as Linear array or Laplacian configuration. The design specification of the pre-amplifier ideally was to have a voltage gain of 500 with bandpass filtering of 5 Hz-1 kHz. The final design of the pre-amplifier circuit using an INA 118 instrumentation amplifier was built and tested to give values for voltage gain of 484 with bandpass filtering of 6.8 Hz-1.02 kHz. The software configuration that gives clearer and more defined signals in terms of motor unit action potentials for future signal processing is the Laplacian rather than Linear array.
Luis E. Mendoza
Full Text Available This paper presents the results obtained from the recording, processing and classification of words in the Spanish language by means of the analysis of subvocal speech signals. The processed database has six words (forward, backward, right, left, start and stop. In this work, the signals were sensed with surface electrodes placed on the surface of the throat and acquired with a sampling frequency of 50 kHz. The signal conditioning consisted in: the location of area of interest using energy analysis, and filtering using Discrete Wavelet Transform. Finally, the feature extraction was made in the time-frequency domain using Wavelet Packet and statistical techniques for windowing. The classification was carried out with a backpropagation neural network whose training was performed with 70% of the database obtained. The correct classification rate was 75%±2.
Dharmapurikar, A.; Bhattacharya, S.; Mukhopadhyay, P.K.; Sawhney, A.; Patil, R.K.
A networked radiation and parameter monitoring system with three tier architecture is being developed. Signal Processing Device (SPD) is a second level sub-system node in the network. SPD is an embedded system which has multiple input channels and output communication interfaces. It acquires and processes data from first level parametric sensor devices, and sends to third level devices in response to request commands received from host. It also performs scheduled diagnostic operations and passes on the information to host. It supports inputs in the form of differential digital signals and analog voltage signals. SPD communicates with higher level devices over RS232/RS422/USB channels. The system has been designed with main requirements of minimal power consumption and harsh environment in radioactive plants. This paper discusses the hardware and software design details of SPD. (author)
Comorett, G.; Chiarucc, S.; Belli, C.
Modern radio telescopes require the processing of wideband signals, with sample rates from tens of MHz to tens of GHz, and are composed from hundreds up to a million of individual antennas. Digital signal processing of these signals include digital receivers (the digital equivalent of the heterodyne receiver), beamformers, channelizers, spectrometers. FPGAs present the advantage of providing a relatively low power consumption, relative to GPUs or dedicated computers, a wide signal data path, and high interconnectivity. Efficient algorithms have been developed for these applications. Here we will review some of the signal processing cores developed for the SKA telescope. The LFAA beamformer/channelizer architecture is based on an oversampling channelizer, where the channelizer output sampling rate and channel spacing can be set independently. This is useful where an overlap between adjacent channels is required to provide an uniform spectral coverage. The architecture allows for an efficient and distributed channelization scheme, with a final resolution corresponding to a million of spectral channels, minimum leakage and high out-of-band rejection. An optimized filter design procedure is used to provide an equiripple response with a very large number of spectral channels. A wideband digital receiver has been designed in order to select the processed bandwidth of the SKA Mid receiver. The receiver extracts a 2.5 MHz bandwidth form a 14 GHz input bandwidth. The design allows for non-integer ratios between the input and output sampling rates, with a resource usage comparable to that of a conventional decimating digital receiver. Finally, some considerations on quantization of radioastronomic signals are presented. Due to the stochastic nature of the signal, quantization using few data bits is possible. Good accuracies and dynamic range are possible even with 2-3 bits, but the nonlinearity in the correlation process must be corrected in post-processing. With at least 6
Lane, Alison E; Young, Robyn L; Baker, Amy E Z; Angley, Manya T
Children with autism are frequently observed to experience difficulties in sensory processing. This study examined specific patterns of sensory processing in 54 children with autistic disorder and their association with adaptive behavior. Model-based cluster analysis revealed three distinct sensory processing subtypes in autism. These subtypes were differentiated by taste and smell sensitivity and movement-related sensory behavior. Further, sensory processing subtypes predicted communication competence and maladaptive behavior. The findings of this study lay the foundation for the generation of more specific hypotheses regarding the mechanisms of sensory processing dysfunction in autism, and support the continued use of sensory-based interventions in the remediation of communication and behavioral difficulties in autism.
Piersanti, M.; Materassi, M.; Cicone, A.; Spogli, L.; Zhou, H.; Ezquer, R. G.
Many real-life signals and, in particular, in the space physics domain, exhibit variations across different temporal scales. Hence, their statistical momenta may depend on the time scale at which the signal is studied. To identify and quantify such variations, a time-frequency analysis has to be performed on these signals. The dependence of the statistical properties of a signal fluctuation on the space and time scales is the distinctive character of systems with nonlinear couplings among different modes. Hence, assessing how the statistics of signal fluctuations vary with scale will be of help in understanding the corresponding multiscale statistics of such dynamics. This paper presents a new multiscale data analysis technique, the adaptive local iterative filtering (ALIF), which allows to describe the multiscale nature of the geophysical signal studied better than via Fourier transform, and improves scale resolution with respect to discrete wavelet transform. The example of geophysical signal, to which ALIF has been applied, is ionospheric radio power scintillation on L band. ALIF appears to be a promising technique to study the small-scale structures of radio scintillation due to ionospheric turbulence.
Rao, Y; Zipursky, S L
Tyrosine phosphorylation has been implicated in growth-cone guidance through genetic, biochemical, and pharmacological studies. Adapter proteins containing src homology 2 (SH2) domains and src homology 3 (SH3) domains provide a means of linking guidance signaling through phosphotyrosine to downstream effectors regulating growth-cone motility. The Drosophila adapter, Dreadlocks (Dock), the homolog of mammalian Nck containing three N-terminal SH3 domains and a single SH2 domain, is highly specialized for growth-cone guidance. In this paper, we demonstrate that Dock can couple signals in either an SH2-dependent or an SH2-independent fashion in photoreceptor (R cell) growth cones, and that Dock displays different domain requirements in different neurons.
Gray, Andrew; Kang, Edward; Lay, Norman; Vilnrotter, Victor; Srinivasan, Meera; Lee, Clement
A parallel-processing algorithm and a hardware architecture to implement the algorithm have been devised for timeslot synchronization in the reception of pulse-position-modulated (PPM) optical or radio signals. As in the cases of some prior algorithms and architectures for parallel, discrete-time, digital processing of signals other than PPM, an incoming broadband signal is divided into multiple parallel narrower-band signals by means of sub-sampling and filtering. The number of parallel streams is chosen so that the frequency content of the narrower-band signals is low enough to enable processing by relatively-low speed complementary metal oxide semiconductor (CMOS) electronic circuitry. The algorithm and architecture are intended to satisfy requirements for time-varying time-slot synchronization and post-detection filtering, with correction of timing errors independent of estimation of timing errors. They are also intended to afford flexibility for dynamic reconfiguration and upgrading. The architecture is implemented in a reconfigurable CMOS processor in the form of a field-programmable gate array. The algorithm and its hardware implementation incorporate three separate time-varying filter banks for three distinct functions: correction of sub-sample timing errors, post-detection filtering, and post-detection estimation of timing errors. The design of the filter bank for correction of timing errors, the method of estimating timing errors, and the design of a feedback-loop filter are governed by a host of parameters, the most critical one, with regard to processing very broadband signals with CMOS hardware, being the number of parallel streams (equivalently, the rate-reduction parameter).
Yli-Harja, Olli; Ylipää, Antti; Nykter, Matti; Zhang, Wei
In this editorial we introduce the research paradigms of signal processing in the era of systems biology. Signal processing is a field of science traditionally focused on modeling electronic and communications systems, but recently it has turned to biological applications with astounding results. The essence of signal processing is to describe the natural world by mathematical models and then, based on these models, develop efficient computational tools for solving engineering problems. Here, we underline, with examples, the endless possibilities which arise when the battle-hardened tools of engineering are applied to solve the problems that have tormented cancer researchers. Based on this approach, a new field has emerged, called cancer systems biology. Despite its short history, cancer systems biology has already produced several success stories tackling previously impracticable problems. Perhaps most importantly, it has been accepted as an integral part of the major endeavors of cancer research, such as analyzing the genomic and epigenomic data produced by The Cancer Genome Atlas (TCGA) project. Finally, we show that signal processing and cancer research, two fields that are seemingly distant from each other, have merged into a field that is indeed more than the sum of its parts. PMID:21439242
An excellent introductory text, this book covers the basic theoretical, algorithmic and real-time aspects of digital signal processing (DSP). Detailed information is provided on off-line, real-time and DSP programming and the reader is effortlessly guided through advanced topics such as DSP hardware design, FIR and IIR filter design and difference equation manipulation.
Abstract. This paper is concerned with a review of some recent work on derivation and synthesis of lattice structures for digital signal processing (DSP). In particular, synthesis of canonical structures for both finite impulse response (FIR) and infinite impulse response (IIR) transfer functions is presented in detail. This has ...
Poulsen, Henrik Nørskov; Jepsen, Kim Stokholm; Clausen, Anders
As all-optical signal processing is maturing, optical time division multiplexing (OTDM) has also gained interest for simple networking in high capacity backbone networks. As an example of a network scenario we show an OTDM bus interconnecting another OTDM bus, a single high capacity user represen...
Popovskiy, V. V.
The universal method of processing of signals with the help of downturn of their dimensions is considered. This work has methodical, generalizing nature, and is aimed at drawing the attention of specialist and scientist to the unity of the definitions and solutions of the problems, connected with N-dimensional presentation and confluent presentations.
The application of multiplexing and signal processing techniques used for reactor operation and utilisation of data from the in-core instrumentation system is described. After a brief recall about in-core instrumentation, the aims, the advantages of multiplexing are presented. One of the aims of this realization is to show the compatibity between the technologies used with a PWR environment [fr
Dos Santos, S.; Vejvodová, Šárka; Převorovský, Zdeněk
Roč. 59, č. 2 (2010), s. 108-117 ISSN 1736-6046 Institutional research plan: CEZ:AV0Z20760514 Keywords : nonlinear signal processing * TR-NEWS * symmetry analysis * DORT Subject RIV: BI - Acoustics Impact factor: 0.464, year: 2010 www.eap.ee/proceedings
Manchón, Carles Navarro
-division multiplexing (OFDM) systems, with a particular emphasis on the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) standard as a study case. Signal processing in wireless receivers can be designed following different strategies. On the one hand, one can use intuitive argumentation to define...
the capa- Lility of the AFI’ signal processing laboratory and to make it mor , user-oriented. Three areas--digitizing operations, siqll p)rocess un...ilurrird (Thlcd. Tl’lery aind A ’pplic,- ii ,1 ij;ita 1Siqnal Procossing. Englow wod Cliffs: Pren- -Ii’ .t I c. , 1975. ,). igal T ’ic ,ii ,qy, InC. I
Sun, W. Z.; Jiang, M. Y.; Ren, L.; Dang, J.; You, T.; Yin, F.-F.
To improve the prediction accuracy of respiratory signals using adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for gated treatment of moving target in radiation therapy. The respiratory signals acquired using a real-time position management (RPM) device from 138 previous 4DCT scans were retrospectively used in this study. The ADMLP-NN was composed of several artificial neural networks (ANNs) which were used as weaker predictors to compose a stronger predictor. The respiratory signal was initially smoothed using a Savitzky-Golay finite impulse response smoothing filter (S-G filter). Then, several similar multi-layer perceptron neural networks (MLP-NNs) were configured to estimate future respiratory signal position from its previous positions. Finally, an adaptive boosting (Adaboost) decision algorithm was used to set weights for each MLP-NN based on the sample prediction error of each MLP-NN. Two prediction methods, MLP-NN and ADMLP-NN (MLP-NN plus adaptive boosting), were evaluated by calculating correlation coefficient and root-mean-square-error between true and predicted signals. For predicting 500 ms ahead of prediction, average correlation coefficients were improved from 0.83 (MLP-NN method) to 0.89 (ADMLP-NN method). The average of root-mean-square-error (relative unit) for 500 ms ahead of prediction using ADMLP-NN were reduced by 27.9%, compared to those using MLP-NN. The preliminary results demonstrate that the ADMLP-NN respiratory prediction method is more accurate than the MLP-NN method and can improve the respiration prediction accuracy.
Lorrain, T; Niazi, I K; Thibergien, O; Jiang, N; Farina, D
Various research fields, such as brain computer interface, requires online acquisition and analysis of biological data to validate assumptions or to help obtaining insights into the physiological processes of the human body. In this paper we introduce the LivBioSig toolbox for online bio-signals processing and experimentation. This open source and modularized MATLAB toolbox allows performing various experiment paradigms involving online signal processing. These currently include synchronous and asynchronous BCI experiments, and event related stimulation experiments. The use of Graphic User Interfaces (GUI) makes the system suitable even for beginner Matlab users, and the experiments easily configurable. The modularized structure allows advanced users to develop the toolbox further to adapt it to the needs of the research fields.
Darolles, Serges; Jay, Emmanuelle
With recent outbreaks of multiple large-scale financial crises, amplified by interconnected risk sources, a new paradigm of fund management has emerged. This new paradigm leverages "embedded" quantitative processes and methods to provide more transparent, adaptive, reliable and easily implemented "risk assessment-based" practices.This book surveys the most widely used factor models employed within the field of financial asset pricing. Through the concrete application of evaluating risks in the hedge fund industry, the authors demonstrate that signal processing techniques are an intere
Bravo, Gustavo A; Remsen, J V; Brumfield, Robb T
Phylogenetic niche conservatism (PNC) and convergence are contrasting evolutionary patterns that describe phenotypic similarity across independent lineages. Assessing whether and how adaptive processes give origin to these patterns represent a fundamental step toward understanding phenotypic evolution. Phylogenetic model-based approaches offer the opportunity not only to distinguish between PNC and convergence, but also to determine the extent that adaptive processes explain phenotypic similarity. The Myrmotherula complex in the Neotropical family Thamnophilidae is a polyphyletic group of sexually dimorphic small insectivorous forest birds that are relatively homogeneous in size and shape. Here, we integrate a comprehensive species-level molecular phylogeny of the Myrmotherula complex with morphometric and ecological data within a comparative framework to test whether phenotypic similarity is described by a pattern of PNC or convergence, and to identify evolutionary mechanisms underlying body size and shape evolution. We show that antwrens in the Myrmotherula complex represent distantly related clades that exhibit adaptive convergent evolution in body size and divergent evolution in body shape. Phenotypic similarity in the group is primarily driven by their tendency to converge toward smaller body sizes. Differences in body size and shape across lineages are associated to ecological and behavioral factors. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.
Zhao, Yuan; Hu, Bingliang; He, Zhen-An; Xie, Wenjia; Gao, Xiaohui
We demonstrate an optical quadrature phase-shift keying (QPSK) signal transmitter and an optical receiver for demodulating optical QPSK signal with homodyne detection and digital signal processing (DSP). DSP on the homodyne detection scheme is employed without locking the phase of the local oscillator (LO). In this paper, we present an extracting one-dimensional array of down-sampling method for reducing unwanted samples of constellation diagram measurement. Such a novel scheme embodies the following major advantages over the other conventional optical QPSK signal detection methods. First, this homodyne detection scheme does not need strict requirement on LO in comparison with linear optical sampling, such as having a flat spectral density and phase over the spectral support of the source under test. Second, the LabVIEW software is directly used for recovering the QPSK signal constellation without employing complex DSP circuit. Third, this scheme is applicable to multilevel modulation formats such as M-ary PSK and quadrature amplitude modulation (QAM) or higher speed signals by making minor changes.
Gogniat, Guy; Morawiec, Adam; Erdogan, Ahmet
Advances in signal and image processing together with increasing computing power are bringing mobile technology closer to applications in a variety of domains like automotive, health, telecommunication, multimedia, entertainment and many others. The development of these leading applications, involving a large diversity of algorithms (e.g. signal, image, video, 3D, communication, cryptography) is classically divided into three consecutive steps: a theoretical study of the algorithms, a study of the target architecture, and finally the implementation. Such a linear design flow is reaching its li
Elliott, Taffeta M; Christensen-Dalsgaard, Jakob; Kelley, Darcy B
Perception of the temporal structure of acoustic signals contributes critically to vocal signaling. In the aquatic clawed frog Xenopus laevis, calls differ primarily in the temporal parameter of click rate, which conveys sexual identity and reproductive state. We show here that an ensemble...... click rates ranged from 4 to 50 Hz, the rate at which the clicks begin to overlap. Frequency selectivity and temporal processing were characterized using response-intensity curves, temporal-discharge patterns, and autocorrelations of reduplicated responses to click trains. Characteristic frequencies...
Jiang, Zhongjin; Qiu, Xiaojun; Lin, Jun; Chen, Zubin
In conventional vibroseis signal processing, algorithms including cross correlation and deconvolution are applied to convert the raw trace data into a seismic section. However, their performance deteriorates when the trace data are corrupted by the ambient noise, so the mathematical tool for time-frequency analysis and wavelet transform is applied in this paper to overcome the difficulty. A time-frequency cross correlation (TFCC) algorithm based on wavelet transform is proposed to extract the reflection from the trace data by detecting the reflected sweeps and estimating their time delay. The source signal and the trace data are transformed into time-frequency domain with respect to a same wavelet basis function; then time-frequency cross correlation is performed between the source signal and the trace data. The reflected sweeps are converted into time-frequency correlation wavelets in the result; meanwhile, the trace data are converted into seismic section. In wavelet decomposition, the high-frequency noise can be suppressed automatically. In the time-frequency representation of the trace data, the ambient noise and the reflected sweeps can be separated from each other. So in the TFCC algorithm, the interference of the ambient noise can be decreased considerably, and the weak reflections can be extracted clearly. Real vibroseis trace data were processed with the TFCC algorithm and the conventional cross correlation. The results showed the superiority of the proposed new algorithm in vibroseis signal processing.
A comprehensive and invaluable guide to 5G technology, implementation and practice in one single volume. For all things 5G, this book is a must-read. Signal processing techniques have played the most important role in wireless communications since the second generation of cellular systems. It is anticipated that new techniques employed in 5G wireless networks will not only improve peak service rates significantly, but also enhance capacity, coverage, reliability , low-latency, efficiency, flexibility, compatibility and convergence to meet the increasing demands imposed by applications such as big data, cloud service, machine-to-machine (M2M) and mission-critical communications. This book is a comprehensive and detailed guide to all signal processing techniques employed in 5G wireless networks. Uniquely organized into four categories, New Modulation and &n sp;Coding, New Spatial Processing, New Spectrum Opportunities and New System-level Enabling Technologies, it covers everything from network architecture...
Qian, Kun; Guo, Jian; Xu, Huijie; Zhu, Zhaomeng; Zhang, Gongxuan
Snore related signals (SRS) have been demonstrated to carry important information about the obstruction site and degree in the upper airway of Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) patients in recent years. To make this acoustic signal analysis method more accurate and robust, big SRS data processing is inevitable. As an emerging concept and technology, cloud computing has motivated numerous researchers and engineers to exploit applications both in academic and industry field, which could have an ability to implement a huge blue print in biomedical engineering. Considering the security and transferring requirement of biomedical data, we designed a system based on private cloud computing to process SRS. Then we set the comparable experiments of processing a 5-hour audio recording of an OSAHS patient by a personal computer, a server and a private cloud computing system to demonstrate the efficiency of the infrastructure we proposed.
Faulkner, Andrew; Zarb-Adami, Kristian; De Vaate, Jan Geralt Bij
Signal processing and communications are driving the latest generation of radio telescopes with major developments taking place for use on the Square Kilometre Array, SKA, the next generation low frequency radio telescope. The data rates and processing performance that can be achieved with currently available components means that concepts from the earlier days of radio astronomy, phased arrays, can be used at higher frequencies, larger bandwidths and higher numbers of beams. Indeed it has been argued that the use of dishes as a mechanical beamformer only gained strong acceptance to mitigate the processing load from phased array technology. The balance is changing and benefits in both performance and cost can be realised. In this paper we will mostly consider the signal processing implementation and control for very large phased arrays consisting of hundreds of thousands of antennas or even millions of antennas. They can use current technology for the initial deployments. These systems are very large extending to hundreds of racks with thousands of signal processing modules that link through high-speed, but commercially available data networking devices. There are major challenges to accurately calibrate the arrays, mitigate power consumption and make the system maintainable
Luo, Jinhong; Moss, Cynthia F
Many species of bat emit acoustic signals and use information carried by echoes reflecting from nearby objects to navigate and forage. It is widely documented that echolocating bats adjust the features of sonar calls in response to echo feedback; however, it remains unknown whether audiovocal feedback contributes to sonar call design. Audiovocal feedback refers to the monitoring of one's own vocalizations during call production and has been intensively studied in nonecholocating animals. Audiovocal feedback not only is a necessary component of vocal learning but also guides the control of the spectro-temporal structure of vocalizations. Here, we show that audiovocal feedback is directly involved in the echolocating bat's control of sonar call features. As big brown bats tracked targets from a stationary position, we played acoustic jamming signals, simulating calls of another bat, timed to selectively perturb audiovocal feedback or echo feedback. We found that the bats exhibited the largest call-frequency adjustments when the jamming signals occurred during vocal production. By contrast, bats did not show sonar call-frequency adjustments when the jamming signals coincided with the arrival of target echoes. Furthermore, bats rapidly adapted sonar call design in the first vocalization following the jamming signal, revealing a response latency in the range of 66 to 94 ms. Thus, bats, like songbirds and humans, rely on audiovocal feedback to structure sonar signal design.
Roy, Suman Deb
This book provides a comprehensive coverage of the state-of-the-art in understanding media popularity and trends in online social networks through social multimedia signals. With insights from the study of popularity and sharing patterns of online media, trend spread in social media, social network analysis for multimedia and visualizing diffusion of media in online social networks. In particular, the book will address the following important issues: Understanding social network phenomena from a signal processing point of view; The existence and popularity of multimedia as shared and social me
Nisii, V.; Saccorotti, G.
Wavelets transforms allow for precise time-frequency localization in the analysis of non-stationary signals. In wavelet analysis the trade-off between frequency bandwidth and time duration, also known as Heisenberg inequality, is by-passed using a fully scalable modulated window which solves the signal-cutting problem of Windowed Fourier Transform. We propose a new seismic array data processing procedure capable of displaying the localized spatial coherence of the signal in both the time- and frequency-domain, in turn deriving the propagation parameters of the most coherent signals crossing the array. The procedure consists in: a) Wavelet coherence analysis for each station pair of the instruments array, aimed at retrieving the frequency- and time-localisation of coherent signals. To this purpose, we use the normalised wavelet cross- power spectrum, smoothed along the time and scale domains. We calculate different coherence spectra adopting smoothing windows of increasing lengths; a final, robust estimate of the time-frequency localisation of spatially-coherent signals is eventually retrieved from the stack of the individual coherence distribution. This step allows for a quick and reliable signal discrimination: wave groups propagating across the network will manifest as high-coherence patches spanning the corresponding time-scale region. b) Once the signals have been localised in the time and frequency domain,their propagation parameters are estimated using a modified MUSIC (MUltiple SIgnal Characterization) algorithm. We select the MUSIC approach as it demonstrated superior performances in the case of low SNR signals, more plane waves contemporaneously impinging at the array and closely separated sources. The narrow-band Coherent Signal Subspace technique is applied to the complex Continuous Wavelet Transform of multichannel data for improving the singularity of the estimated cross-covariance matrix and the accuracy of the estimated signal eigenvectors. Using
Jepsen, Morten Løve; Ewert, Stephan D.; Dau, Torsten
A model of computational auditory signal-processing and perception that accounts for various aspects of simultaneous and nonsimultaneous masking in human listeners is presented. The model is based on the modulation filterbank model described by Dau et al. [J. Acoust. Soc. Am. 102, 2892 (1997...... discrimination with pure tones and broadband noise, tone-in-noise detection, spectral masking with narrow-band signals and maskers, forward masking with tone signals and tone or noise maskers, and amplitude-modulation detection with narrow- and wideband noise carriers. The model can account for most of the key...... properties of the data and is more powerful than the original model. The model might be useful as a front end in technical applications....
Petersson, Karl Magnus
Cognitive neuroscience approaches the brain as a cognitive system: a system that functionally is conceptualized in terms of information processing. We outline some aspects of this concept and consider a physical system to be an information processing device when a subclass of its physical states can be viewed as representational/cognitive and transitions between these can be conceptualized as a process operating on these states by implementing operations on the corresponding representational structures. We identify a generic and fundamental problem in cognition: sequentially organized structured processing. Structured sequence processing provides the brain, in an essential sense, with its processing logic. In an approach addressing this problem, we illustrate how to integrate levels of analysis within a framework of adaptive dynamical systems. We note that the dynamical system framework lends itself to a description of asynchronous event-driven devices, which is likely to be important in cognition because the brain appears to be an asynchronous processing system. We use the human language faculty and natural language processing as a concrete example through out.
Kim, Ji Chul; Large, Edward W
Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing.
Sugimoto, Yohei; Ozawa, Satoru; Inaba, Noriyasu
Synthetic Aperture Radar (SAR) imagery requires image reproduction through successive signal processing of received data before browsing images and extracting information. The received signal data records of the ALOS-2/PALSAR-2 are stored in the onboard mission data storage and transmitted to the ground. In order to compensate the storage usage and the capacity of transmission data through the mission date communication networks, the operation duty of the PALSAR-2 is limited. This balance strongly relies on the network availability. The observation operations of the present spaceborne SAR systems are rigorously planned by simulating the mission data balance, given conflicting user demands. This problem should be solved such that we do not have to compromise the operations and the potential of the next-generation spaceborne SAR systems. One of the solutions is to compress the SAR data through onboard image reproduction and information extraction from the reproduced images. This is also beneficial for fast delivery of information products and event-driven observations by constellation. The Emergence Studio (Sōhatsu kōbō in Japanese) with Japan Aerospace Exploration Agency is developing evaluation models of FPGA-based signal processing system for onboard SAR image reproduction. The model, namely, "Fast L1 Processor (FLIP)" developed in 2016 can reproduce a 10m-resolution single look complex image (Level 1.1) from ALOS/PALSAR raw signal data (Level 1.0). The processing speed of the FLIP at 200 MHz results in twice faster than CPU-based computing at 3.7 GHz. The image processed by the FLIP is no way inferior to the image processed with 32-bit computing in MATLAB.
Guhaniyogi, Rajarshi; Finley, Andrew O.; Banerjee, Sudipto; Gelfand, Alan E.
Large point referenced datasets occur frequently in the environmental and natural sciences. Use of Bayesian hierarchical spatial models for analyzing these datasets is undermined by onerous computational burdens associated with parameter estimation. Low-rank spatial process models attempt to resolve this problem by projecting spatial effects to a lower-dimensional subspace. This subspace is determined by a judicious choice of “knots” or locations that are fixed a priori. One such representation yields a class of predictive process models (e.g., Banerjee et al., 2008) for spatial and spatial-temporal data. Our contribution here expands upon predictive process models with fixed knots to models that accommodate stochastic modeling of the knots. We view the knots as emerging from a point pattern and investigate how such adaptive specifications can yield more flexible hierarchical frameworks that lead to automated knot selection and substantial computational benefits. PMID:22298952
Hu, Zhikun; Chen, Zhiwen; Gui, Weihua; Jiang, Bin
In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Bieberle, A., E-mail: A.Bieberle@hzdr.de [Helmholtz-Zentrum Dresden-Rossendorf, Institute of Safety Research, Bautzner Landstrasse 400, 01328 Dresden (Germany); Berger, R.; Yadav, R.; Schleicher, E.; Hampel, U. [Helmholtz-Zentrum Dresden-Rossendorf, Institute of Safety Research, Bautzner Landstrasse 400, 01328 Dresden (Germany)
In this paper a new modular signal processing board (MSPB) for high-resolution gamma-ray computed tomography (GCT) is presented. The MSPB is optimised for parallel signal processing of eight detector channels operating in pulse counting mode. Signal processing stages comprise of variable gain amplifiers, pulse height discrimination stages, 13-bit counters with corresponding latches as well as logic circuitry for coordinated data transfer with a multitude of MSPBs. The digital signal processing units are realised in commercially available complex programmable logic devices (CPLD). Each MSPB is addressable by an 8-bit DIP-switch, which allows the use of up to 256 modules or 2048 detector pixels within one detector system. The geometry of the MSPB allows a multiple and seamless detector module arrangement, which eases the adaptation of a given gamma-ray detector system to specific industrial and laboratory applications. The choice of the electronic devices and the thermal design was optimised for low power consumption in order to minimise internal heat production, which would affect the characteristics of the detector's intrinsic gain strongly. Thermal measurements have been executed to prove the functionality of the thermal design.
Bieberle, A.; Berger, R.; Yadav, R.; Schleicher, E.; Hampel, U.
In this paper a new modular signal processing board (MSPB) for high-resolution gamma-ray computed tomography (GCT) is presented. The MSPB is optimised for parallel signal processing of eight detector channels operating in pulse counting mode. Signal processing stages comprise of variable gain amplifiers, pulse height discrimination stages, 13-bit counters with corresponding latches as well as logic circuitry for coordinated data transfer with a multitude of MSPBs. The digital signal processing units are realised in commercially available complex programmable logic devices (CPLD). Each MSPB is addressable by an 8-bit DIP-switch, which allows the use of up to 256 modules or 2048 detector pixels within one detector system. The geometry of the MSPB allows a multiple and seamless detector module arrangement, which eases the adaptation of a given gamma-ray detector system to specific industrial and laboratory applications. The choice of the electronic devices and the thermal design was optimised for low power consumption in order to minimise internal heat production, which would affect the characteristics of the detector's intrinsic gain strongly. Thermal measurements have been executed to prove the functionality of the thermal design.
Full Text Available Discontinuous spectrum signal which has separate subbands distributed over a wide spectrum band is a solution to synthesize a wideband waveform in a highly congested spectrum environment. In this paper, we present a general range-velocity processing scheme for the discontinuous spectrum-frequency modulated continuous wave (DS-FMCW signal specifically. In range domain, we propose a simple time rearrangement operation which converts the range transform problem of the DS-FMCW signal to a general spectral estimation problem of nonuniformly sampled data. Conventional periodogram results in a dirty range spectrum with high sidelobes which cannot be suppressed by traditional spectral weighting. In this paper, we introduce the iterative adaptive approach (IAA in the estimation of the range spectrum. IAA is shown to have the ability to provide a clean range spectrum. On the other hand, the discontinuity of the signal spectrum has little impact on the velocity processing. However, with the range resolution improved, the influence of the target motion becomes nonnegligible. We present a velocity compensation strategy which includes the intersweep compensation and in-sweep compensation. Our processing scheme with the velocity compensation is shown to provide an accurate and clean range-velocity image which benefits the following detection process.
Adithya, Prashanth Chetlur; Sankar, Ravi; Moreno, Wilfrido A; Hart, Stuart
In this study, a novel acoustic stethoscope based on an intravenous catheter was introduced to measure vascular pressures from a Yorkshire pig. Our hypothesis is that by means of this single device (measurement system) and by applying signal analysis and processing framework, multiple vital bio signals can be extracted. In contrast, current conventional state-of-the-art technologies use multiple devices to provide the same information. The framework used to extract these bio signals consisted of a noise cancellation technique and wavelet based source separation. The preliminary results obtained from the acquired pressure data revealed the presence of acoustic heart and respiratory pulses. Finally, the computed heart and respiratory rates were benchmarked with actual values measured using conventional devices to validate our hypothesis.
Srividhya, Jeyaraman; Li, Yongfeng; Pomerening, Joseph R
Cell signaling is achieved predominantly by reversible phosphorylation–dephosphorylation reaction cascades. Up until now, circuits conferring adaptation have all required the presence of a cascade with some type of closed topology: negative-feedback loop with a buffering node, or incoherent feed-forward loop with a proportioner node. In this paper—using Goldbeter and Koshland-type expressions—we propose a differential equation model to describe a generic, open signaling cascade that elicits an adaptation response. This is accomplished by coupling N phosphorylation–dephosphorylation cycles unidirectionally, without any explicit feedback loops. Using this model, we show that as the length of the cascade grows, the steady states of the downstream cycles reach a limiting value. In other words, our model indicates that there are a minimum number of cycles required to achieve a maximum in sensitivity and amplitude in the response of a signaling cascade. We also describe for the first time that the phenomenon of ultrasensitivity can be further subdivided into three sub-regimes, separated by sharp stimulus threshold values: OFF, OFF-ON-OFF, and ON. In the OFF-ON-OFF regime, an interesting property emerges. In the presence of a basal amount of activity, the temporal evolution of early cycles yields damped peak responses. On the other hand, the downstream cycles switch rapidly to a higher activity state for an extended period of time, prior to settling to an OFF state (OFF-ON-OFF). This response arises from the changing dynamics between a feed-forward activation module and dephosphorylation reactions. In conclusion, our model gives the new perspective that open signaling cascades embedded in complex biochemical circuits may possess the ability to show a switch-like adaptation response, without the need for any explicit feedback circuitry
Srividhya, Jeyaraman; Li, Yongfeng; Pomerening, Joseph R.
Cell signaling is achieved predominantly by reversible phosphorylation-dephosphorylation reaction cascades. Up until now, circuits conferring adaptation have all required the presence of a cascade with some type of closed topology: negative-feedback loop with a buffering node, or incoherent feed-forward loop with a proportioner node. In this paper—using Goldbeter and Koshland-type expressions—we propose a differential equation model to describe a generic, open signaling cascade that elicits an adaptation response. This is accomplished by coupling N phosphorylation-dephosphorylation cycles unidirectionally, without any explicit feedback loops. Using this model, we show that as the length of the cascade grows, the steady states of the downstream cycles reach a limiting value. In other words, our model indicates that there are a minimum number of cycles required to achieve a maximum in sensitivity and amplitude in the response of a signaling cascade. We also describe for the first time that the phenomenon of ultrasensitivity can be further subdivided into three sub-regimes, separated by sharp stimulus threshold values: OFF, OFF-ON-OFF, and ON. In the OFF-ON-OFF regime, an interesting property emerges. In the presence of a basal amount of activity, the temporal evolution of early cycles yields damped peak responses. On the other hand, the downstream cycles switch rapidly to a higher activity state for an extended period of time, prior to settling to an OFF state (OFF-ON-OFF). This response arises from the changing dynamics between a feed-forward activation module and dephosphorylation reactions. In conclusion, our model gives the new perspective that open signaling cascades embedded in complex biochemical circuits may possess the ability to show a switch-like adaptation response, without the need for any explicit feedback circuitry.
Full Text Available Intelligent sensing is drastically changing our everyday life including healthcare by biomedical signal monitoring, collection, and analytics. However, long-term healthcare monitoring generates tremendous data volume and demands significant wireless transmission power, which imposes a big challenge for wearable healthcare sensors usually powered by batteries. Efficient compression engine design to reduce wireless transmission data rate with ultra-low power consumption is essential for wearable miniaturized healthcare sensor systems. This paper presents an ultra-low power biomedical signal compression engine for healthcare data sensing and analytics in the era of big data and sensor intelligence. It extracts the feature points of the biomedical signal by window-based turning angle detection. The proposed approach has low complexity and thus low power consumption while achieving a large compression ratio (CR and good quality of reconstructed signal. Near-threshold design technique is adopted to further reduce the power consumption on the circuit level. Besides, the angle threshold for compression can be adaptively tuned according to the error between the original signal and reconstructed signal to address the variation of signal characteristics from person to person or from channel to channel to meet the required signal quality with optimal CR. For demonstration, the proposed biomedical compression engine has been used and evaluated for ECG compression. It achieves an average (CR of 71.08% and percentage root-mean-square difference (PRD of 5.87% while consuming only 39 nW. Compared to several state-of-the-art ECG compression engines, the proposed design has significantly lower power consumption while achieving similar CRD and PRD, making it suitable for long-term wearable miniaturized sensor systems to sense and collect healthcare data for remote data analytics.
Teke, Oguzhan; Vaidyanathan, Palghat P.
A variety of different areas consider signals that are defined over graphs. Motivated by the advancements in graph signal processing, this study first reviews some of the recent results on the extension of classical multirate signal processing to graphs. In these results, graphs are allowed to have directed edges. The possibly non-symmetric adjacency matrix A is treated as the graph operator. These results investigate the fundamental concepts for multirate processing of graph signals such as noble identities, aliasing, and perfect reconstruction (PR). It is shown that unless the graph satisfies some conditions, these concepts cannot be extended to graph signals in a simple manner. A structure called M-Block cyclic structure is shown to be sufficient to generalize the results for bipartite graphs on two-channels to M-channel filter banks. Many classical multirate ideas can be extended to graphs due to the unique eigenstructure of M-Block cyclic graphs. For example, the PR condition for filter banks on these graphs is identical to PR in classical theory, which allows the use of well-known filter bank design techniques. In order to utilize these results, the adjacency matrix of an M-Block cyclic graph should be given in the correct permutation. In the final part, this study proposes a spectral technique to identify the hidden M-Block cyclic structure from a graph with noisy edges whose adjacency matrix is given under a random permutation. Numerical simulation results show that the technique can recover the underlying M-Block structure in the presence of random addition and deletion of the edges.
Full Text Available Complex gene regulation requires responses that depend not only on the current levels of input signals but also on signals received in the past. In digital electronics, logic circuits with this property are referred to as sequential logic, in contrast to the simpler combinatorial logic without such internal memory. In molecular biology, memory is implemented in various forms such as biochemical modification of proteins or multistable gene circuits, but the design of the regulatory interface, which processes the input signals and the memory content, is often not well understood. Here, we explore design constraints for such regulatory interfaces using coarse-grained nonlinear models and stochastic simulations of detailed biochemical reaction networks. We test different designs for biological analogs of the most versatile memory element in digital electronics, the JK-latch. Our analysis shows that simple protein-protein interactions and protein-DNA binding are sufficient, in principle, to implement genetic circuits with the capabilities of a JK-latch. However, it also exposes fundamental limitations to its reliability, due to the fact that biological signal processing is asynchronous, in contrast to most digital electronics systems that feature a central clock to orchestrate the timing of all operations. We describe a seemingly natural way to improve the reliability by invoking the master-slave concept from digital electronics design. This concept could be useful to interpret the design of natural regulatory circuits, and for the design of synthetic biological systems.
Hillenbrand, Patrick; Fritz, Georg; Gerland, Ulrich
Complex gene regulation requires responses that depend not only on the current levels of input signals but also on signals received in the past. In digital electronics, logic circuits with this property are referred to as sequential logic, in contrast to the simpler combinatorial logic without such internal memory. In molecular biology, memory is implemented in various forms such as biochemical modification of proteins or multistable gene circuits, but the design of the regulatory interface, which processes the input signals and the memory content, is often not well understood. Here, we explore design constraints for such regulatory interfaces using coarse-grained nonlinear models and stochastic simulations of detailed biochemical reaction networks. We test different designs for biological analogs of the most versatile memory element in digital electronics, the JK-latch. Our analysis shows that simple protein-protein interactions and protein-DNA binding are sufficient, in principle, to implement genetic circuits with the capabilities of a JK-latch. However, it also exposes fundamental limitations to its reliability, due to the fact that biological signal processing is asynchronous, in contrast to most digital electronics systems that feature a central clock to orchestrate the timing of all operations. We describe a seemingly natural way to improve the reliability by invoking the master-slave concept from digital electronics design. This concept could be useful to interpret the design of natural regulatory circuits, and for the design of synthetic biological systems.
Chen, Kun; Liu, Quan; Ai, Qingsong; Zhou, Zude; Xie, Sheng Quan; Meng, Wei
The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.
Kamprath, Kornelia; Romo-Parra, Hector; Häring, Martin; Gaburro, Stefano; Doengi, Michael; Lutz, Beat; Pape, Hans-Christian
The cannabinoid receptor type 1 (CB1) and the central nucleus of the amygdala (CeA) are both known to have crucial roles in the processing of fear and anxiety, whereby they appear to be especially involved in the control of fear states. However, in contrast to many other brain regions including the cortical subregions of the amygdala, the existence of CB1 in the CeA remains enigmatic. In this study we show that CB1 is expressed in the CeA of mice and that CB1 in the CeA mediates short-term synaptic plasticity, namely depolarization-induced suppression of excitation (DSE) and inhibition (DSI). Moreover, the CB1 antagonist AM251 increased both excitatory and inhibitory postsynaptic responses in CeA neurons. Local application of AM251 in the CeA in vivo resulted in an acutely increased fear response in an auditory fear conditioning paradigm. Upon application of AM251 in the basolateral nucleus of the amygdala (BLA) in an otherwise identical protocol, no such acute behavioral effects were detected, but CB1 blockade resulted in increased fear responses during tone exposures on the subsequent days. Moreover, we observed that the efficacy of DSE and DSI in the CeA was increased on the day following fear conditioning, indicating that a single tone-shock pairing resulted in changes in endocannabinoid signaling in the CeA. Taken together, our data show the existence of CB1 proteins in the CeA, and their critical role for ensuring short-term adaptation of responses to fearful events, thereby suggesting a potential therapeutic target to accompany habituation-based therapies of post-traumatic symptoms. PMID:20980994
Alfonzo, Braunnier; Camacho, Candelario; Ortiz de Bertorelli, Ligia; De Venanzi, Frank
With the purpose of evaluating adaptability to the freezing process of super sweet corn sh2 hybrids Krispy King, Victor and 324, 100 cobs of each type were frozen at -18 degrees C. After 120 days of storage, their chemical, microbiological and sensorial characteristics were compared with a sweet corn su. Industrial quality of the process of freezing and length and number of rows in cobs were also determined. Results revealed yields above 60% in frozen corns. Length and number of rows in cobs were acceptable. Most of the chemical characteristics of super sweet hybrids were not different from the sweet corn assayed at the 5% significance level. Moisture content and soluble solids of hybrid Victor, as well as total sugars of hybrid 324 were statistically different. All sh2 corns had higher pH values. During freezing, soluble solids concentration, sugars and acids decreased whereas pH increased. Frozen cobs exhibited acceptable microbiological rank, with low activities of mesophiles and total coliforms, absence of psychrophiles and fecal coliforms, and an appreciable amount of molds. In conclusion, sh2 hybrids adapted with no problems to the freezing process, they had lower contents of soluble solids and higher contents of total sugars, which almost doubled the amount of su corn; flavor, texture, sweetness and appearance of kernels were also better. Hybrid Victor was preferred by the evaluating panel and had an outstanding performance due to its yield and sensorial characteristics.
Serrano, Antonio; Contreras, Carmen; Ruiz-Filippi, Gonzalo; Borja, Rafael; Fermoso, Fernando G
The main objective of this study was to evaluate the suitability of Nannochloropsis gaditana to grow by sequential adaptation to TOPW (Table olive processing water) at increased substrate concentrations (10-80%). Sequential adaptation allows growing Nannochloropsis gaditana up to 80% TOPW, although the maximum microalgae biomass productions were achieved for percentages of 20-40%, i.e. 0.308 ± 0.005 g VSS (Volatile Suspended Solids)/L. In all growth experiments, proteins were the majority compound in the grown microalgae biomass (0.44 ± 0.05 g/g VSS), whereas phenols were retained up to a mean concentration of 12.1 ± 1.9 mg total phenols/g VSS. The highest microalgae biomass production rate at rate of 80% TOPW took place in the first two days when most nutrients were also removed. Average removal efficiencies at this percentage of TOPW were 69.1%, 50.9%, 54.3% and 71.8% for total organic carbon, total soluble nitrogen, phosphate and total phenols, respectively. Sequential adaptation can ensure the obtaining of a sustainable microalgae culture as a treatment method for TOPW.
Rabinkin, Daniel V.; Rutledge, Edward; Monticciolo, Paul
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
Harrison, Ben J; Fullana, Miquel Angel; Via, Esther; Soriano-Mas, Carles; Vervliet, Bram; Martínez-Zalacaín, Ignacio; Pujol, Jesus; Davey, Christopher G; Kircher, Tilo; Straube, Benjamin; Cardoner, Narcís
Human functional magnetic resonance imaging (fMRI) studies suggest that the ventromedial prefrontal cortex (vmPFC) contributes to the learned discrimination of threat and safety signals, although its precise contribution to these processes remains unclear. One hypothesis is that the vmPFC supports the positive affective processing of safety signals linked to their implicit stress-relieving properties. We set out to test this hypothesis and to examine the specificity of vmPFC responses to safety signal processing versus its high level of 'default mode' activity. Sixty participants completed an fMRI conditioning task that involved the generation of a conditioned threat (CS+) and safety (CS-) signal following the completion of a pre-conditioning baseline. Confirming past findings, activation of the vmPFC and other midline cortical and parietal areas - broadly resembling the default mode network - robustly discriminated between the CS- and CS+. However, when adjusting for this network's characteristic 'baseline' activity, only a subset of regions, including the vmPFC, was activated by the CS-. Regional selectivity for safety signal processing was confirmed by demonstrating a significant correlation between the magnitude of vmPFC responses and self-rated positive affect evoked by the CS-. Taken together, our current findings confirm a link between human vmPFC activity and the positive affective processing of safety signals. We discuss these findings with regards a broader model of human vmPFC function and its suggested higher-order contribution to emotionally adaptive behavior. Copyright © 2017 Elsevier Inc. All rights reserved.
Vilamala, Albert; Madsen, Kristoffer Hougaard; Hansen, Lars Kai
Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothing. These pipelines are commonly suboptimal, given the local optimisation strategy they use, treating each step...... by defining a smoothing step as a layer in these networks able to adaptively modulate the degree of smoothing required by each brain volume to better accomplish a given data analysis task. The viability is evaluated on real fMRI data where subjects did alternate between left and right finger tapping tasks....
V. I. Shynkarenko
Full Text Available Purpose.The second part of the paper completes presentation of constructive and the productive structures (CPS, modeling adaptation of data structures in memory (RAM. The purpose of the second part in the research is to develop a model of process of adaptation data in a RAM functioning in different hardware and software environments and scenarios of data processing. Methodology. The methodology of mathematical and algorithmic constructionism was applied. In this part of the paper, changes were developed the constructors of scenarios and adaptation processes based on a generalized CPS through its transformational conversions. Constructors are interpreted, specialized CPS. Were highlighted the terminal alphabets of the constructor scenarios in the form of data processing algorithms and the constructor of adaptation – in the form of algorithmic components of the adaptation process. The methodology involves the development of substitution rules that determine the output process of the relevant structures. Findings. In the second part of the paper, system is represented by CPS modeling adaptation data placement in the RAM, namely, constructors of scenarios and of adaptation processes. The result of the implementation of constructor of scenarios is a set of data processing operations in the form of text in the language of programming C#, constructor of the adaptation processes – a process of adaptation, and the result the process of adaptation – the adapted binary code of processing data structures. Originality. For the first time proposed the constructive model of data processing – the scenario that takes into account the order and number of calls to the various elements of data structures and adaptation of data structures to the different hardware and software environments. At the same the placement of data in RAM and processing algorithms are adapted. Constructionism application in modeling allows to link data models and algorithms for
Pablo A. Alvarado-Durán
Full Text Available In this paper we present a new methodology for detecting sound events in music signals using Gaussian Processes. Our method firstly takes a time-frequency representation, i.e. the spectrogram, of the input audio signal. Secondly the spectrogram dimension is reduced translating the linear Hertz frequency scale into the logarithmic Mel frequency scale using a triangular filter bank. Finally every short-time spectrum, i.e. every Mel spectrogram column, is classified as “Event” or “Not Event” by a Gaussian Processes Classifier. We compare our method with other event detection techniques widely used. To do so, we use MATLAB® to program each technique and test them using two datasets of music with different levels of complexity. Results show that the new methodology outperforms the standard approaches, getting an improvement by about 1.66 % on the dataset one and 0.45 % on the dataset two in terms of F-measure.
Despite the commercial success, video streaming remains a black art owing to its roots in proprietary commercial development. As such, many challenging technological issues that need to be addressed are not even well understood. The purpose of this paper is to review several important signal processing issues related to video streaming, and put them in the context of a client-server based media streaming architecture on the Internet. Such a context is critical, as we shall see that a number of solutions proposed by signal processing researchers are simply unrealistic for real-world video streaming on the Internet. We identify a family of viable solutions and evaluate their pros and cons. We further identify areas of research that have received less attention and point to the problems to which a better solution is eagerly sought by the industry.
Kulkarni, A.V.; Yen, D.W.L.
Many signal and image processing applications impose a severe demand on the I/O bandwidth and computation power of general-purpose computers. The systolic concept offers guidelines in building cost-effective systems that balance I/O with computation. The resulting simplicity and regularity of such systems leads to modular designs suitable for VLSI implementation. The authors describe a linear systolic array capable of evaluating a large class of inner-product functions used in signal and image processing. These include matrix multiplications, multidimensional convolutions using fixed or time-varying kernels, as well as various nonlinear functions of vectors. The system organization of a working prototype is also described. 11 references.
Order statistics and morphological filters belong to a class of nonlinear filters that have recently found many applications in signal analysis and image processing. In this paper, order statistics and morphological filters have been applied to enhance the features of the ultrasonic signal when it has been contaminated by multiple interfering microstructure echoes with random amplitudes and phases. These interfering echoes (i.e., speckles or grain scattering noise) often become significant to the point where detection of flaw echoes becomes very difficult. We have examined order statistic, and morphological filters for improved ultrasonic flaw detection. In particular, the performance of these filters has been evaluated using different ranks of order statistics (minimum, median, maximum), and different shapes of structuring elements in the application of morphological filters. The processed experimental results in testing steel samples demonstrate that these filters are capable of improving flaw detection in ultrasonic systems
Menzel, Claudia; Hayn-Leichsenring, Gregor U; Redies, Christoph; Németh, Kornél; Kovács, Gyula
The presence of noise usually impairs the processing of a stimulus. Here, we studied the effects of noise on face processing and show, for the first time, that adaptation to noise patterns has beneficial effects on face perception. We used noiseless faces that were either surrounded by random noise or presented on a uniform background as stimuli. In addition, the faces were either preceded by noise adaptors or not. Moreover, we varied the statistics of the noise so that its spectral slope either matched that of the faces or it was steeper or shallower. Results of parallel ERP recordings showed that the background noise reduces the amplitude of the face-evoked N170, indicating less intensive face processing. Adaptation to a noise pattern, however, led to reduced P1 and enhanced N170 amplitudes as well as to a better behavioral performance in two of the three noise conditions. This effect was also augmented by the presence of background noise around the target stimuli. Additionally, the spectral slope of the noise pattern affected the size of the P1, N170 and P2 amplitudes. We reason that the observed effects are due to the selective adaptation of noise-sensitive neurons present in the face-processing cortical areas, which may enhance the signal-to-noise-ratio. Copyright © 2017 Elsevier Inc. All rights reserved.
Steam generator tubes in nuclear power plants are periodically checked by means of eddy current probes. The output of a probe is composed of three types of signals: known events (rolling zone, support plates, U-bend part), noise (mainly metallurgical noise) and possible flaws. The latter are random transients, both in arrival time and in shape: they have to be detected and then estimated, before to be fed to the high level stages of a diagnostic system. The objective of the study presented is to develop a semi-automatic system, which could manage and process more than 1 M-bytes of data per tube and provide an operator with reliable diagnostics proposals within a few minutes. This can be achieved only by cooperation of several digital signal processing techniques: detection, segmentation, estimation, noise subtraction, adaptive filtering, modelization, pattern recognition. The paper describes some of these items
Marcello M DiStasio
Full Text Available Decision-making ability in the frontal lobe (among other brain structures relies on the assignment of value to states of the animal and its environment. Then higher valued states can be pursued and lower (or negative valued states avoided. The same principle forms the basis for computational reinforcement learning controllers, which have been fruitfully applied both as models of value estimation in the brain, and as artificial controllers in their own right. This work shows how state desirability signals decoded from frontal lobe hemodynamics, as measured with near-infrared spectroscopy (NIRS, can be applied as reinforcers to an adaptable artificial learning agent in order to guide its acquisition of skills. A set of experiments carried out on an alert macaque demonstrate that both oxy- and deoxyhemoglobin concentrations in the frontal lobe show differences in response to both primarily and secondarily desirable (versus undesirable stimuli. This difference allows a NIRS signal classifier to serve successfully as a reinforcer for an adaptive controller performing a virtual tool-retrieval task. The agent's adaptability allows its performance to exceed the limits of the NIRS classifier decoding accuracy. We also show that decoding state desirabilities is more accurate when using relative concentrations of both oxyhemoglobin and deoxyhemoglobin, rather than either species alone.
exercises in the summer of 1994, and demonstrated its ability to provide real-time collection of acoustic data over 32 channels, to perform onsite data... corrupted by having a support ship in the area of testing. Therefore, the challenge was to develop an acoustic data collection system that allowed full... exercise . The solution proposed by NRL was to develop a system that incorporated on-site signal processing and storage along with a satellite data
Full Text Available In this paper, we apply wavelet thresholding for removing automatically ground and intermittent clutter (airplane echoes from wind profiler radar data. Using the concept of discrete multi-resolution analysis and non-parametric estimation theory, we develop wavelet domain thresholding rules, which allow us to identify the coefficients relevant for clutter and to suppress them in order to obtain filtered reconstructions.Key words. Meteorology and atmospheric dynamics (instruments and techniques – Radio science (remote sensing; signal processing
Popa, Cosmin Radu
Presents the most important classes of computational structures for analog signal processing, including differential or multiplier structures, squaring or square-rooting circuits, exponential or Euclidean distance structures and active resistor circuitsIntroduces the original concept of the multifunctional circuit, an active structure that is able to implement, starting from the same circuit core, a multitude of continuous mathematical functionsCovers mathematical analysis, design and implementation of a multitude of function generator structures
Jeonggon Harrison Kim
Full Text Available A new signal processing algorithm for absolute temperature measurement using white light interferometry has been proposed and investigated theoretically. The proposed algorithm determines the phase delay of an interferometer with very high precision (<< one fringe by identifying the zero order fringe peak of cross-correlation of two fringe scans of white light interferometer. The algorithm features cross-correlation of interferometer fringe scans, hypothesis testing and fine tuning. The hypothesis test looks for a zero order fringe peak candidate about which the cross-correlation is symmetric minimizing the uncertainty of mis-identification. Fine tuning provides the proposed algorithm with high precision subsample resolution phase delay estimation capability. The shot noise limited performance of the proposed algorithm has been analyzed using computer simulations. Root-mean-square (RMS phase error of the estimated zero order fringe peak has been calculated for the changes of three different parameters (SNR, fringe scan sample rate, coherence length of light source. Computer simulations showed that the proposed signal processing algorithm identified the zero order fringe peak with a miss rate of 3 x 10-4 at 31 dB SNR and the extrapolated miss rate at 35 dB was 3 x 10-8. Also, at 35 dB SNR, RMS phase error less than 10-3 fringe was obtained. The proposed signal processing algorithm uses a software approach that is potentially inexpensive, simple and fast.
Roth, Michael; Hendeby, Gustaf; Fritsche, Carsten; Gustafsson, Fredrik
The ensemble Kalman filter (EnKF) is a Monte Carlo-based implementation of the Kalman filter (KF) for extremely high-dimensional, possibly nonlinear, and non-Gaussian state estimation problems. Its ability to handle state dimensions in the order of millions has made the EnKF a popular algorithm in different geoscientific disciplines. Despite a similarly vital need for scalable algorithms in signal processing, e.g., to make sense of the ever increasing amount of sensor data, the EnKF is hardly discussed in our field. This self-contained review is aimed at signal processing researchers and provides all the knowledge to get started with the EnKF. The algorithm is derived in a KF framework, without the often encountered geoscientific terminology. Algorithmic challenges and required extensions of the EnKF are provided, as well as relations to sigma point KF and particle filters. The relevant EnKF literature is summarized in an extensive survey and unique simulation examples, including popular benchmark problems, complement the theory with practical insights. The signal processing perspective highlights new directions of research and facilitates the exchange of potentially beneficial ideas, both for the EnKF and high-dimensional nonlinear and non-Gaussian filtering in general.
Thomas, Jr., Jess B. (Inventor)
A digital signal processor and processing method therefor for use in receivers of the NAVSTAR/GLOBAL POSITIONING SYSTEM (GPS) employs a digital carrier down-converter, digital code correlator and digital tracking processor. The digital carrier down-converter and code correlator consists of an all-digital, minimum bit implementation that utilizes digital chip and phase advancers, providing exceptional control and accuracy in feedback phase and in feedback delay. Roundoff and commensurability errors can be reduced to extremely small values (e.g., less than 100 nanochips and 100 nanocycles roundoff errors and 0.1 millichip and 1 millicycle commensurability errors). The digital tracking processor bases the fast feedback for phase and for group delay in the C/A, P.sub.1, and P.sub.2 channels on the L.sub.1 C/A carrier phase thereby maintaining lock at lower signal-to-noise ratios, reducing errors in feedback delays, reducing the frequency of cycle slips and in some cases obviating the need for quadrature processing in the P channels. Simple and reliable methods are employed for data bit synchronization, data bit removal and cycle counting. Improved precision in averaged output delay values is provided by carrier-aided data-compression techniques. The signal processor employs purely digital operations in the sense that exactly the same carrier phase and group delay measurements are obtained, to the last decimal place, every time the same sampled data (i.e., exactly the same bits) are processed.
Slipko, Valeriy A.; Pershin, Yuriy V.
Traditional studies of memristive devices have mainly focused on their applications in nonvolatile information storage and information processing. Here, we demonstrate that the third fundamental component of information technologies—the transfer of information—can also be employed with memristive devices. For this purpose, we introduce a metastable memristive circuit. Combining metastable memristive circuits into a line, one obtains an architecture capable of transferring a signal edge from one space location to another. We emphasize that the suggested metastable memristive lines employ only resistive circuit components. Moreover, their networks (for example, Y-connected lines) have an information processing capability.
van Straten, W.; Bailes, M.
dspsr is a high-performance, open-source, object-oriented, digital signal processing software library and application suite for use in radio pulsar astronomy. Written primarily in C++, the library implements an extensive range of modular algorithms that can optionally exploit both multiple-core processors and general-purpose graphics processing units. After over a decade of research and development, dspsr is now stable and in widespread use in the community. This paper presents a detailed description of its functionality, justification of major design decisions, analysis of phase-coherent dispersion removal algorithms, and demonstration of performance on some contemporary microprocessor architectures.