Neural networks in signal processing

International Nuclear Information System (INIS)

Govil, R.

2000-01-01

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.)

Machine Learning Techniques for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

DEFF Research Database (Denmark)

Thrane, Jakob; Wass, Jesper; Piels, Molly

2017-01-01

Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, while the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal...... detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical...

Digital signal processing for NDT

International Nuclear Information System (INIS)

Georgel, B.

1994-01-01

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

Neural Correlates of Success and Failure Signals During Neurofeedback Learning.

Science.gov (United States)

Radua, Joaquim; Stoica, Teodora; Scheinost, Dustin; Pittenger, Christopher; Hampson, Michelle

2018-05-15

Feedback-driven learning, observed across phylogeny and of clear adaptive value, is frequently operationalized in simple operant conditioning paradigms, but it can be much more complex, driven by abstract representations of success and failure. This study investigates the neural processes involved in processing success and failure during feedback learning, which are not well understood. Data analyzed were acquired during a multisession neurofeedback experiment in which ten participants were presented with, and instructed to modulate, the activity of their orbitofrontal cortex with the aim of decreasing their anxiety. We assessed the regional blood-oxygenation-level-dependent response to the individualized neurofeedback signals of success and failure across twelve functional runs acquired in two different magnetic resonance sessions in each of ten individuals. Neurofeedback signals of failure correlated early during learning with deactivation in the precuneus/posterior cingulate and neurofeedback signals of success correlated later during learning with deactivation in the medial prefrontal/anterior cingulate cortex. The intensity of the latter deactivations predicted the efficacy of the neurofeedback intervention in the reduction of anxiety. These findings indicate a role for regulation of the default mode network during feedback learning, and suggest a higher sensitivity to signals of failure during the early feedback learning and to signals of success subsequently. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Python for signal processing featuring IPython notebooks

CERN Document Server

Unpingco, José

2013-01-01

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

Machinery vibration signal denoising based on learned dictionary and sparse representation

International Nuclear Information System (INIS)

Guo, Liang; Gao, Hongli; Li, Jun; Huang, Haifeng; Zhang, Xiaochen

2015-01-01

Mechanical vibration signal denoising has been an import problem for machine damage assessment and health monitoring. Wavelet transfer and sparse reconstruction are the powerful and practical methods. However, those methods are based on the fixed basis functions or atoms. In this paper, a novel method is presented. The atoms used to represent signals are learned from the raw signal. And in order to satisfy the requirements of real-time signal processing, an online dictionary learning algorithm is adopted. Orthogonal matching pursuit is applied to extract the most pursuit column in the dictionary. At last, denoised signal is calculated with the sparse vector and learned dictionary. A simulation signal and real bearing fault signal are utilized to evaluate the improved performance of the proposed method through the comparison with kinds of denoising algorithms. Then Its computing efficiency is demonstrated by an illustrative runtime example. The results show that the proposed method outperforms current algorithms with efficiency calculation. (paper)

CERN Technical Training 2003: Learning for the LHC ! DISP-2003 - Digital Signal Processing

CERN Multimedia

2003-01-01

DISP-2003 - Digital Signal Processing DISP-2003 is a two-term course given by CERN and University of Lausanne (UNIL) experts within the framework of the Technical Training Programme. The course will review the current techniques dealing with Digital Signal Processing, and it is intended for an audience who work or will work on digital signal processing aspects, and who need an introductory or refresher/update course. The course will be in English, with question and answers also in French. Spring 2 Term: DISP-2003: Advanced Digital Signal Processing 30 April 2003 - 21 May 2003, 4 lectures, Wednesdays afternoon (attendance cost: 40.- CHF, registration required) Lecturers: Léonard Studer, UNIL; Laurent Deniau, AT-MTM; Elena Wildner, AT-MAS Programme: Intelligent signal processing (ISP). Non-linear time series analysis. Image processing. Wavelets. (Basic concepts and definitions have been introduced during the previous Spring 1 Term: DISP-2003: Introduction to Digital Signal Processing). DISP-2003 is open...

CERN Technical Training 2003: Learning for the LHC! DISP-2003 - Digital Signal Processing

CERN Multimedia

2003-01-01

DISP-2003 is a two-term course given by CERN and University of Lausanne (UNIL) experts within the framework of the Technical Training Programme. The course will review the current techniques dealing with Digital Signal Processing, and it is intended for an audience who work or will work on digital signal processing aspects, and who need an introductory or refresher/update course. The course will be in English, with question and answers also in French. Spring 2 Term: DISP-2003: Advanced Digital Signal Processing 30 April 2003 - 21 May 2003, 4 lectures, Wednesdays afternoon. Attendance cost: 40.- CHF, registration required. Lecturers: Léonard Studer, UNIL; Laurent Deniau, AT-MTM; Elena Wildner, AT-MAS. Programme: Intelligent signal processing (ISP). Non-linear time series analysis. Image processing. Wavelets. Basic concepts and definitions have been introduced during the previous Spring 1 Term: DISP-2003: Introduction to Digital Signal Processing. DISP-2003 is open to all people interested, but registrat...

2nd International Symposium on Signal Processing and Intelligent Recognition Systems

CERN Document Server

Bandyopadhyay, Sanghamitra; Krishnan, Sri; Li, Kuan-Ching; Mosin, Sergey; Ma, Maode

2016-01-01

This Edited Volume contains a selection of refereed and revised papers originally presented at the second International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS-2015), December 16-19, 2015, Trivandrum, India. The program committee received 175 submissions. Each paper was peer reviewed by at least three or more independent referees of the program committee and the 59 papers were finally selected. The papers offer stimulating insights into biometrics, digital watermarking, recognition systems, image and video processing, signal and speech processing, pattern recognition, machine learning and knowledge-based systems. The book is directed to the researchers and scientists engaged in various field of signal processing and related areas. .

Trends in Machine Learning for Signal Processing

DEFF Research Database (Denmark)

Adali, Tulay; Miller, David J.; Diamantaras, Konstantinos I.

2011-01-01

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....

Learning sparse generative models of audiovisual signals

OpenAIRE

Monaci, Gianluca; Sommer, Friedrich T.; Vandergheynst, Pierre

2008-01-01

This paper presents a novel framework to learn sparse represen- tations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that can be positioned independently and arbitrarily in space and time. We design an algorithm capable of learning sets of such audiovi- sual, synchronous, shift-invariant functions by alternatingly solving a coding and a learning pr...

Mixed signal learning by spike correlation propagation in feedback inhibitory circuits.

Directory of Open Access Journals (Sweden)

Naoki Hiratani

2015-04-01

Full Text Available The brain can learn and detect mixed input signals masked by various types of noise, and spike-timing-dependent plasticity (STDP is the candidate synaptic level mechanism. Because sensory inputs typically have spike correlation, and local circuits have dense feedback connections, input spikes cause the propagation of spike correlation in lateral circuits; however, it is largely unknown how this secondary correlation generated by lateral circuits influences learning processes through STDP, or whether it is beneficial to achieve efficient spike-based learning from uncertain stimuli. To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve. We derive analytical conditions at which neurons detect minor signals with STDP, and show that depending on the origin of the noise, different correlation timescales are useful for learning. In particular, we show that non-precise spike correlation is beneficial for learning in the presence of cross-talk noise. We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. In addition, we demonstrate that the model performs blind source separation in a manner similar to the sequential sampling approximation of the Bayesian independent component analysis algorithm. Our results provide a basic understanding of STDP learning in feedback circuits by integrating analyses from both dynamical systems and information theory.

Denoising of gravitational wave signals via dictionary learning algorithms

Science.gov (United States)

Torres-Forné, Alejandro; Marquina, Antonio; Font, José A.; Ibáñez, José M.

2016-12-01

Gravitational wave astronomy has become a reality after the historical detections accomplished during the first observing run of the two advanced LIGO detectors. In the following years, the number of detections is expected to increase significantly with the full commissioning of the advanced LIGO, advanced Virgo and KAGRA detectors. The development of sophisticated data analysis techniques to improve the opportunities of detection for low signal-to-noise-ratio events is, hence, a most crucial effort. In this paper, we present one such technique, dictionary-learning algorithms, which have been extensively developed in the last few years and successfully applied mostly in the context of image processing. However, to the best of our knowledge, such algorithms have not yet been employed to denoise gravitational wave signals. By building dictionaries from numerical relativity templates of both binary black holes mergers and bursts of rotational core collapse, we show how machine-learning algorithms based on dictionaries can also be successfully applied for gravitational wave denoising. We use a subset of signals from both catalogs, embedded in nonwhite Gaussian noise, to assess our techniques with a large sample of tests and to find the best model parameters. The application of our method to the actual signal GW150914 shows promising results. Dictionary-learning algorithms could be a complementary addition to the gravitational wave data analysis toolkit. They may be used to extract signals from noise and to infer physical parameters if the data are in good enough agreement with the morphology of the dictionary atoms.

Basic digital signal processing

CERN Document Server

Lockhart, Gordon B

1985-01-01

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.

Larger error signals in major depression are associated with better avoidance learning

Directory of Open Access Journals (Sweden)

James F eCavanagh

2011-11-01

Full Text Available The medial prefrontal cortex (mPFC is particularly reactive to signals of error, punishment, and conflict in the service of behavioral adaptation and it is consistently implicated in the etiology of Major Depressive Disorder (MDD. This association makes conceptual sense, given that MDD has been associated with hyper-reactivity in neural systems associated with punishment processing. Yet in practice, depression-related variance in measures of mPFC functioning often fails to relate to performance. For example, neuroelectric reflections of mediofrontal error signals are often found to be larger in MDD, but a deficit in post-error performance suggests that these error signals are not being used to rapidly adapt behavior. Thus, it remains unknown if depression-related variance in error signals reflects a meaningful alteration in the use of error or punishment information. However, larger mediofrontal error signals have also been related to another behavioral tendency: increased accuracy in avoidance learning. The integrity of this error-avoidance system remains untested in MDD. In this study, EEG was recorded as 21 symptomatic, drug-free participants with current or past MDD and 24 control participants performed a probabilistic reinforcement learning task. Depressed participants had larger mPFC EEG responses to error feedback than controls. The direct relationship between error signal amplitudes and avoidance learning accuracy was replicated. Crucially, this relationship was stronger in depressed participants for high conflict lose-lose situations, demonstrating a selective alteration of avoidance learning. This investigation provided evidence that larger error signal amplitudes in depression are associated with increased avoidance learning, identifying a candidate mechanistic model for hypersensitivity to negative outcomes in depression.

The newest digital signal processing

International Nuclear Information System (INIS)

Lee, Chae Uk

2002-08-01

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.

Explaining neural signals in human visual cortex with an associative learning model.

Science.gov (United States)

Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

2012-08-01

"Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

Fetal Cardiac Doppler Signal Processing Techniques: Challenges and Future Research Directions

Directory of Open Access Journals (Sweden)

Saeed Abdulrahman Alnuaimi

2017-12-01

Full Text Available The fetal Doppler Ultrasound (DUS is commonly used for monitoring fetal heart rate and can also be used for identifying the event timings of fetal cardiac valve motions. In early-stage fetuses, the detected Doppler signal suffers from noise and signal loss due to the fetal movements and changing fetal location during the measurement procedure. The fetal cardiac intervals, which can be estimated by measuring the fetal cardiac event timings, are the most important markers of fetal development and well-being. To advance DUS-based fetal monitoring methods, several powerful and well-advanced signal processing and machine learning methods have recently been developed. This review provides an overview of the existing techniques used in fetal cardiac activity monitoring and a comprehensive survey on fetal cardiac Doppler signal processing frameworks. The review is structured with a focus on their shortcomings and advantages, which helps in understanding fetal Doppler cardiogram signal processing methods and the related Doppler signal analysis procedures by providing valuable clinical information. Finally, a set of recommendations are suggested for future research directions and the use of fetal cardiac Doppler signal analysis, processing, and modeling to address the underlying challenges.

CERN Technical Training 2003: Learning for the LHC ! DISP-2003  -  Digital Signal Processing

CERN Multimedia

2003-01-01

DISP-2003 is a two-term course given by CERN and University of Lausanne (UNIL) experts within the framework of the Technical Training Programme. The course will review the current techniques dealing with Digital Signal Processing. The DISP-2003 lecture series is composed of two Terms, and it is intended for an audience who work or will work on digital signal processing aspects, and who need an introductory or refresher/update course. The course will be in English, with questions and answers also in French. Spring 1 Term: DISP-2003: Introduction to Digital Signal Processing 20 February 2003 - 3 April 2003, 7 lectures, Thursdays (attendance cost: 70.- CHF, registration required) Lecturers: Maria Elena Angoletta, AB-BDI; Guy Baribaud, AB-BDI; Philippe Baudrenghien, AB-RF; Laurent Deniau, AT-MTM Programme: 'Classical' digital signal processing. Fourier analysis. The Laplace transform. The z-transform. Digital filters. Statistics for Signal Processing. Signal Estimation and Spectral Analysis. Spring 2 T...

pySPACE-a signal processing and classification environment in Python.

Science.gov (United States)

Krell, Mario M; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Teiwes, Johannes; Metzen, Jan H; Kirchner, Elsa A; Kirchner, Frank

2013-01-01

In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries.

Topological signal processing

CERN Document Server

Robinson, Michael

2014-01-01

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.

A probablistic neural network classification system for signal and image processing

Energy Technology Data Exchange (ETDEWEB)

Bowman, B. [Lawrence Livermore National Lab., CA (United States)

1994-11-15

The Acoustical Heart Valve Analysis Package is a system for signal and image processing and classification. It is being developed in both Matlab and C, to provide an interactive, interpreted environment, and has been optimized for large scale matrix operations. It has been used successfully to classify acoustic signals from implanted prosthetic heart valves in human patients, and will be integrated into a commercial Heart Valve Screening Center. The system uses several standard signal processing algorithms, as well as supervised learning techniques using the probabilistic neural network (PNN). Although currently used for the acoustic heart valve application, the algorithms and modular design allow it to be used for other applications, as well. We will describe the signal classification system, and show results from a set of test valves.

pySPACE - A Signal Processing and Classification Environment in Python

Directory of Open Access Journals (Sweden)

Mario Michael Krell

2013-12-01

Full Text Available In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace, signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG. The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries.

Signals structural analysis and processing: application to acoustic signals recorded during sodium boiling in a nuclear reactor

International Nuclear Information System (INIS)

Rodriguez, J.

1986-02-01

An acoustic system that uses examples to learn the structure of specific signals linked to a corresponding class of physical phenomena, and classify an unknown signal (possibly with noise present) into one of the learned classes is presented. The first stage consists of smoothing the data. The signal is represented as a trace according to background and event. To learn the structures in each class, smoothed, segmented signals are used. For classification, three operations to modify the signal so that it perfectly verifies the model description are available [fr

VLSI signal processing technology

CERN Document Server

Swartzlander, Earl

1994-01-01

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...

Opponent appetitive-aversive neural processes underlie predictive learning of pain relief.

Science.gov (United States)

Seymour, Ben; O'Doherty, John P; Koltzenburg, Martin; Wiech, Katja; Frackowiak, Richard; Friston, Karl; Dolan, Raymond

2005-09-01

Termination of a painful or unpleasant event can be rewarding. However, whether the brain treats relief in a similar way as it treats natural reward is unclear, and the neural processes that underlie its representation as a motivational goal remain poorly understood. We used fMRI (functional magnetic resonance imaging) to investigate how humans learn to generate expectations of pain relief. Using a pavlovian conditioning procedure, we show that subjects experiencing prolonged experimentally induced pain can be conditioned to predict pain relief. This proceeds in a manner consistent with contemporary reward-learning theory (average reward/loss reinforcement learning), reflected by neural activity in the amygdala and midbrain. Furthermore, these reward-like learning signals are mirrored by opposite aversion-like signals in lateral orbitofrontal cortex and anterior cingulate cortex. This dual coding has parallels to 'opponent process' theories in psychology and promotes a formal account of prediction and expectation during pain.

Biomedical signal and image processing

CERN Document Server

Najarian, Kayvan

2012-01-01

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

The role of learning-related dopamine signals in addiction vulnerability.

Science.gov (United States)

Huys, Quentin J M; Tobler, Philippe N; Hasler, Gregor; Flagel, Shelly B

2014-01-01

Dopaminergic signals play a mathematically precise role in reward-related learning, and variations in dopaminergic signaling have been implicated in vulnerability to addiction. Here, we provide a detailed overview of the relationship between theoretical, mathematical, and experimental accounts of phasic dopamine signaling, with implications for the role of learning-related dopamine signaling in addiction and related disorders. We describe the theoretical and behavioral characteristics of model-free learning based on errors in the prediction of reward, including step-by-step explanations of the underlying equations. We then use recent insights from an animal model that highlights individual variation in learning during a Pavlovian conditioning paradigm to describe overlapping aspects of incentive salience attribution and model-free learning. We argue that this provides a computationally coherent account of some features of addiction. © 2014 Elsevier B.V. All rights reserved.

Continuous residual reinforcement learning for traffic signal control optimization

NARCIS (Netherlands)

Aslani, Mohammad; Seipel, Stefan; Wiering, Marco

2018-01-01

Traffic signal control can be naturally regarded as a reinforcement learning problem. Unfortunately, it is one of the most difficult classes of reinforcement learning problems owing to its large state space. A straightforward approach to address this challenge is to control traffic signals based on

Ultrahigh bandwidth signal processing

DEFF Research Database (Denmark)

Oxenløwe, Leif Katsuo

2016-01-01

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....

Digital signal processing using MATLAB

CERN Document Server

Schilling, Robert L

2016-01-01

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.

Fundamentals of adaptive signal processing

CERN Document Server

Uncini, Aurelio

2015-01-01

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.

Signal processing for radiation detectors

CERN Document Server

Nakhostin, Mohammad

2018-01-01

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...

Interference in Ballistic Motor Learning: Specificity and Role of Sensory Error Signals

Science.gov (United States)

Lundbye-Jensen, Jesper; Petersen, Tue Hvass; Rothwell, John C.; Nielsen, Jens Bo

2011-01-01

Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity in overlapping circuits and predicted specificity. To test this, subjects learned a ballistic motor task. Interference was observed following subsequent learning of an accuracy-tracking task, but only if the competing task involved the same muscles and movement direction. Interference was not observed from a non-learning task suggesting that interference requires competing learning. Subsequent learning of the competing task 4 h after initial learning did not cause interference suggesting disruption of early motor memory consolidation as one possible mechanism underlying interference. Repeated transcranial magnetic stimulation (rTMS) of corticospinal motor output at intensities below movement threshold did not cause interference, whereas suprathreshold rTMS evoking motor responses and (re)afferent activation did. Finally, the experiments revealed that suprathreshold repetitive electrical stimulation of the agonist (but not antagonist) peripheral nerve caused interference. The present study is, to our knowledge, the first to demonstrate that peripheral nerve stimulation may cause interference. The finding underscores the importance of sensory feedback as error signals in motor learning. We conclude that interference requires competing plasticity in overlapping circuits. Interference is remarkably specific for circuits involved in a specific movement and it may relate to sensory error signals. PMID:21408054

Digital signal processing the Tevatron BPM signals

International Nuclear Information System (INIS)

Cancelo, G.; James, E.; Wolbers, S.

2005-01-01

The Beam Position Monitor (TeV BPM) readout system at Fermilab's Tevatron has been updated and is currently being commissioned. The new BPMs use new analog and digital hardware to achieve better beam position measurement resolution. The new system reads signals from both ends of the existing directional stripline pickups to provide simultaneous proton and antiproton measurements. The signals provided by the two ends of the BPM pickups are processed by analog band-pass filters and sampled by 14-bit ADCs at 74.3MHz. A crucial part of this work has been the design of digital filters that process the signal. This paper describes the digital processing and estimation techniques used to optimize the beam position measurement. The BPM electronics must operate in narrow-band and wide-band modes to enable measurements of closed-orbit and turn-by-turn positions. The filtering and timing conditions of the signals are tuned accordingly for the operational modes. The analysis and the optimized result for each mode are presented

Experiment and practice on signal processing

International Nuclear Information System (INIS)

2002-11-01

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.

Experiment and practice on signal processing

Energy Technology Data Exchange (ETDEWEB)

NONE

2002-11-15

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.

Motor-related signals in the auditory system for listening and learning.

Science.gov (United States)

Schneider, David M; Mooney, Richard

2015-08-01

In the auditory system, corollary discharge signals are theorized to facilitate normal hearing and the learning of acoustic behaviors, including speech and music. Despite clear evidence of corollary discharge signals in the auditory cortex and their presumed importance for hearing and auditory-guided motor learning, the circuitry and function of corollary discharge signals in the auditory cortex are not well described. In this review, we focus on recent developments in the mouse and songbird that provide insights into the circuitry that transmits corollary discharge signals to the auditory system and the function of these signals in the context of hearing and vocal learning. Copyright © 2015 Elsevier Ltd. All rights reserved.

Transnational Learning Processes

DEFF Research Database (Denmark)

Nedergaard, Peter

This paper analyses and compares the transnational learning processes in the employment field in the European Union and among the Nordic countries. Based theoretically on a social constructivist model of learning and methodologically on a questionnaire distributed to the relevant participants......, a number of hypotheses concerning transnational learning processes are tested. The paper closes with a number of suggestions regarding an optimal institutional setting for facilitating transnational learning processes.Key words: Transnational learning, Open Method of Coordination, Learning, Employment......, European Employment Strategy, European Union, Nordic countries....

Dissociable Learning Processes Underlie Human Pain Conditioning.

Science.gov (United States)

Zhang, Suyi; Mano, Hiroaki; Ganesh, Gowrishankar; Robbins, Trevor; Seymour, Ben

2016-01-11

Pavlovian conditioning underlies many aspects of pain behavior, including fear and threat detection [1], escape and avoidance learning [2], and endogenous analgesia [3]. Although a central role for the amygdala is well established [4], both human and animal studies implicate other brain regions in learning, notably ventral striatum and cerebellum [5]. It remains unclear whether these regions make different contributions to a single aversive learning process or represent independent learning mechanisms that interact to generate the expression of pain-related behavior. We designed a human parallel aversive conditioning paradigm in which different Pavlovian visual cues probabilistically predicted thermal pain primarily to either the left or right arm and studied the acquisition of conditioned Pavlovian responses using combined physiological recordings and fMRI. Using computational modeling based on reinforcement learning theory, we found that conditioning involves two distinct types of learning process. First, a non-specific "preparatory" system learns aversive facial expressions and autonomic responses such as skin conductance. The associated learning signals-the learned associability and prediction error-were correlated with fMRI brain responses in amygdala-striatal regions, corresponding to the classic aversive (fear) learning circuit. Second, a specific lateralized system learns "consummatory" limb-withdrawal responses, detectable with electromyography of the arm to which pain is predicted. Its related learned associability was correlated with responses in ipsilateral cerebellar cortex, suggesting a novel computational role for the cerebellum in pain. In conclusion, our results show that the overall phenotype of conditioned pain behavior depends on two dissociable reinforcement learning circuits. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Digital signal processing

CERN Document Server

O'Shea, Peter; Hussain, Zahir M

2011-01-01

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

Fetal QRS extraction from abdominal recordings via model-based signal processing and intelligent signal merging

International Nuclear Information System (INIS)

Haghpanahi, Masoumeh; Borkholder, David A

2014-01-01

Noninvasive fetal ECG (fECG) monitoring has potential applications in diagnosing congenital heart diseases in a timely manner and assisting clinicians to make more appropriate decisions during labor. However, despite advances in signal processing and machine learning techniques, the analysis of fECG signals has still remained in its preliminary stages. In this work, we describe an algorithm to automatically locate QRS complexes in noninvasive fECG signals obtained from a set of four electrodes placed on the mother’s abdomen. The algorithm is based on an iterative decomposition of the maternal and fetal subspaces and filtering of the maternal ECG (mECG) components from the fECG recordings. Once the maternal components are removed, a novel merging technique is applied to merge the signals and detect the fetal QRS (fQRS) complexes. The algorithm was trained and tested on the fECG datasets provided by the PhysioNet/CinC challenge 2013. The final results indicate that the algorithm is able to detect fetal peaks for a variety of signals with different morphologies and strength levels encountered in clinical practice. (paper)

Signal Processing and Neural Network Simulator

Science.gov (United States)

Tebbe, Dennis L.; Billhartz, Thomas J.; Doner, John R.; Kraft, Timothy T.

1995-04-01

The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAIC's Industrial Strength Neural Networks (ISNN) Software into SPW.

Genomic signal processing

CERN Document Server

Shmulevich, Ilya

2007-01-01

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

Agent-specific learning signals for self-other distinction during mentalising.

Directory of Open Access Journals (Sweden)

Sam Ereira

2018-04-01

Full Text Available Humans have a remarkable ability to simulate the minds of others. How the brain distinguishes between mental states attributed to self and mental states attributed to someone else is unknown. Here, we investigated how fundamental neural learning signals are selectively attributed to different agents. Specifically, we asked whether learning signals are encoded in agent-specific neural patterns or whether a self-other distinction depends on encoding agent identity separately from this learning signal. To examine this, we tasked subjects to learn continuously 2 models of the same environment, such that one was selectively attributed to self and the other was selectively attributed to another agent. Combining computational modelling with magnetoencephalography (MEG enabled us to track neural representations of prediction errors (PEs and beliefs attributed to self, and of simulated PEs and beliefs attributed to another agent. We found that the representational pattern of a PE reliably predicts the identity of the agent to whom the signal is attributed, consistent with a neural self-other distinction implemented via agent-specific learning signals. Strikingly, subjects exhibiting a weaker neural self-other distinction also had a reduced behavioural capacity for self-other distinction and displayed more marked subclinical psychopathological traits. The neural self-other distinction was also modulated by social context, evidenced in a significantly reduced decoding of agent identity in a nonsocial control task. Thus, we show that self-other distinction is realised through an encoding of agent identity intrinsic to fundamental learning signals. The observation that the fidelity of this encoding predicts psychopathological traits is of interest as a potential neurocomputational psychiatric biomarker.

An imperfect dopaminergic error signal can drive temporal-difference learning.

Directory of Open Access Journals (Sweden)

Wiebke Potjans

2011-05-01

Full Text Available An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards.

Modulation of learning and memory by cytokines: signaling mechanisms and long term consequences.

Science.gov (United States)

Donzis, Elissa J; Tronson, Natalie C

2014-11-01

This review describes the role of cytokines and their downstream signaling cascades on the modulation of learning and memory. Immune proteins are required for many key neural processes and dysregulation of these functions by systemic inflammation can result in impairments of memory that persist long after the resolution of inflammation. Recent research has demonstrated that manipulations of individual cytokines can modulate learning, memory, and synaptic plasticity. The many conflicting findings, however, have prevented a clear understanding of the precise role of cytokines in memory. Given the complexity of inflammatory signaling, understanding its modulatory role requires a shift in focus from single cytokines to a network of cytokine interactions and elucidation of the cytokine-dependent intracellular signaling cascades. Finally, we propose that whereas signal transduction and transcription may mediate short-term modulation of memory, long-lasting cellular and molecular mechanisms such as epigenetic modifications and altered neurogenesis may be required for the long lasting impact of inflammation on memory and cognition. Copyright © 2014 Elsevier Inc. All rights reserved.

Advanced digital signal processing and noise reduction

CERN Document Server

Vaseghi, Saeed V

2008-01-01

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

Fast digitizing and digital signal processing of detector signals

International Nuclear Information System (INIS)

Hannaske, Roland

2008-01-01

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.)

An adaptive signal-processing approach to online adaptive tutoring.

Science.gov (United States)

Bergeron, Bryan; Cline, Andrew

2011-01-01

Conventional intelligent or adaptive tutoring online systems rely on domain-specific models of learner behavior based on rules, deep domain knowledge, and other resource-intensive methods. We have developed and studied a domain-independent methodology of adaptive tutoring based on domain-independent signal-processing approaches that obviate the need for the construction of explicit expert and student models. A key advantage of our method over conventional approaches is a lower barrier to entry for educators who want to develop adaptive online learning materials.

SignalPlant: an open signal processing software platform.

Science.gov (United States)

Plesinger, F; Jurco, J; Halamek, J; Jurak, P

2016-07-01

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.

Advanced Methods of Biomedical Signal Processing

CERN Document Server

Cerutti, Sergio

2011-01-01

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

Handbook of Signal Processing in Acoustics

CERN Document Server

Havelock, David; Vorländer, Michael

2009-01-01

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.

Signalling chains with probe and adjust learning

Science.gov (United States)

Gosti, Giorgio

2018-04-01

Many models explain the evolution of signalling in repeated stage games on social networks, differently in this study each signalling game evolves a communication strategy to transmit information across the network. Specifically, I formalise signalling chain games as a generalisation of Lewis' signalling games, where a number of players are placed on a chain network and play a signalling game in which they have to propagate information across the network. I show that probe and adjust learning allows the system to develop communication conventions, but it may temporarily perturb the system out of conventions. Through simulations, I evaluate how long the system takes to evolve a signalling convention and the amount of time it stays in it. This discussion presents a mechanism in which simple players can evolve signalling across a social network without necessarily understanding the entire system.

Statistical mechanics of learning orthogonal signals for general covariance models

International Nuclear Information System (INIS)

Hoyle, David C

2010-01-01

Statistical mechanics techniques have proved to be useful tools in quantifying the accuracy with which signal vectors are extracted from experimental data. However, analysis has previously been limited to specific model forms for the population covariance C, which may be inappropriate for real world data sets. In this paper we obtain new statistical mechanical results for a general population covariance matrix C. For data sets consisting of p sample points in R N we use the replica method to study the accuracy of orthogonal signal vectors estimated from the sample data. In the asymptotic limit of N,p→∞ at fixed α = p/N, we derive analytical results for the signal direction learning curves. In the asymptotic limit the learning curves follow a single universal form, each displaying a retarded learning transition. An explicit formula for the location of the retarded learning transition is obtained and we find marked variation in the location of the retarded learning transition dependent on the distribution of population covariance eigenvalues. The results of the replica analysis are confirmed against simulation

Signal Processing

International Nuclear Information System (INIS)

Anon.

1992-01-01

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

Fractional Processes and Fractional-Order Signal Processing Techniques and Applications

CERN Document Server

Sheng, Hu; Qiu, TianShuang

2012-01-01

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...

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal.

Science.gov (United States)

Satija, Udit; Ramkumar, Barathram; Sabarimalai Manikandan, M

2017-02-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.

Effect of signal noise on the learning capability of an artificial neural network

International Nuclear Information System (INIS)

Vega, J.J.; Reynoso, R.; Calvet, H. Carrillo

2009-01-01

Digital Pulse Shape Analysis (DPSA) by artificial neural networks (ANN) is becoming an important tool to extract relevant information from digitized signals in different areas. In this paper, we present a systematic evidence of how the concomitant noise that distorts the signals or patterns to be identified by an ANN set limits to its learning capability. Also, we present evidence that explains overtraining as a competition between the relevant pattern features, on the one side, against the signal noise, on the other side, as the main cause defining the shape of the error surface in weight space and, consequently, determining the steepest descent path that controls the ANN adaptation process.

Application of adaptive digital signal processing to speech enhancement for the hearing impaired.

Science.gov (United States)

Chabries, D M; Christiansen, R W; Brey, R H; Robinette, M S; Harris, R W

1987-01-01

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.

Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

Directory of Open Access Journals (Sweden)

Xin Tian

2017-06-01

Full Text Available We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster’s sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.

Signal processing: opportunities for superconductive circuits

International Nuclear Information System (INIS)

Ralston, R.W.

1985-01-01

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

Multiresolution, Geometric, and Learning Methods in Statistical Image Processing, Object Recognition, and Sensor Fusion

National Research Council Canada - National Science Library

Willsky, Alan

2004-01-01

.... Our research blends methods from several fields-statistics and probability, signal and image processing, mathematical physics, scientific computing, statistical learning theory, and differential...

Reward processing in the value-driven attention network: reward signals tracking cue identity and location.

Science.gov (United States)

Anderson, Brian A

2017-03-01

Through associative reward learning, arbitrary cues acquire the ability to automatically capture visual attention. Previous studies have examined the neural correlates of value-driven attentional orienting, revealing elevated activity within a network of brain regions encompassing the visual corticostriatal loop [caudate tail, lateral occipital complex (LOC) and early visual cortex] and intraparietal sulcus (IPS). Such attentional priority signals raise a broader question concerning how visual signals are combined with reward signals during learning to create a representation that is sensitive to the confluence of the two. This study examines reward signals during the cued reward training phase commonly used to generate value-driven attentional biases. High, compared with low, reward feedback preferentially activated the value-driven attention network, in addition to regions typically implicated in reward processing. Further examination of these reward signals within the visual system revealed information about the identity of the preceding cue in the caudate tail and LOC, and information about the location of the preceding cue in IPS, while early visual cortex represented both location and identity. The results reveal teaching signals within the value-driven attention network during associative reward learning, and further suggest functional specialization within different regions of this network during the acquisition of an integrated representation of stimulus value. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Fundamentals of statistical signal processing

CERN Document Server

Kay, Steven M

1993-01-01

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.

Radar signal processing and its applications

CERN Document Server

Hummel, Robert; Stoica, Petre; Zelnio, Edmund

2003-01-01

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.

Silicon Photonics for Signal Processing of Tbit/s Serial Data Signals

DEFF Research Database (Denmark)

Oxenløwe, Leif Katsuo; Ji, Hua; Galili, Michael

2012-01-01

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/...

Digital signal processing an experimental approach

CERN Document Server

Engelberg, Shlomo

2008-01-01

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

Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System.

Science.gov (United States)

Jiang, Yizhang; Wu, Dongrui; Deng, Zhaohong; Qian, Pengjiang; Wang, Jun; Wang, Guanjin; Chung, Fu-Lai; Choi, Kup-Sze; Wang, Shitong

2017-12-01

Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.

Biomedical signal and image processing.

Science.gov (United States)

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

2011-01-01

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 [1], [2]. 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.

Signal processing in microdosimetry

International Nuclear Information System (INIS)

Arbel, A.

1984-01-01

Signals occurring in microdosimetric measurements cover a dynamic range of 100 dB at a counting rate which normally stays below 10 4 but could increase significantly in case of an accident. The need for high resolution at low energies, non-linear signal processing to accommodate the specified dynamic range, easy calibration and thermal stability are conflicting requirements which pose formidable design problems. These problems are reviewed, and a practical approach to their solution is given employing a single processing channel. (author)

Impaired implicit learning and feedback processing after stroke.

Science.gov (United States)

Lam, J M; Globas, C; Hosp, J A; Karnath, H-O; Wächter, T; Luft, A R

2016-02-09

The ability to learn is assumed to support successful recovery and rehabilitation therapy after stroke. Hence, learning impairments may reduce the recovery potential. Here, the hypothesis is tested that stroke survivors have deficits in feedback-driven implicit learning. Stroke survivors (n=30) and healthy age-matched control subjects (n=21) learned a probabilistic classification task with brain activation measured using functional magnetic resonance imaging in a subset of these individuals (17 stroke and 10 controls). Stroke subjects learned slower than controls to classify cues. After being rewarded with a smiley face, they were less likely to give the same response when the cue was repeated. Stroke subjects showed reduced brain activation in putamen, pallidum, thalamus, frontal and prefrontal cortices and cerebellum when compared with controls. Lesion analysis identified those stroke survivors as learning-impaired who had lesions in frontal areas, putamen, thalamus, caudate and insula. Lesion laterality had no effect on learning efficacy or brain activation. These findings suggest that stroke survivors have deficits in reinforcement learning that may be related to dysfunctional processing of feedback-based decision-making, reward signals and working memory. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

Radiation signal processing system

International Nuclear Information System (INIS)

Bennett, M.; Knoll, G.; Strange, D.

1980-01-01

An improved signal processing system for radiation imaging apparatus comprises: a radiation transducer producing transducer signals proportional to apparent spatial coordinates of detected radiation events; means for storing true spatial coordinates corresponding to a plurality of predetermined apparent spatial coordinates relative to selected detected radiation events said means for storing responsive to said transducer signal and producing an output signal representative of said true spatial coordinates; and means for interpolating the true spatial coordinates of the detected radiation events located intermediate the stored true spatial coordinates, said means for interpolating communicating with said means for storing

Interactive Teaching of Adaptive Signal Processing

OpenAIRE

Stewart, R W; Harteneck, M; Weiss, S

2000-01-01

Over the last 30 years adaptive digital signal processing has progressed from being a strictly graduate level advanced class in signal processing theory to a topic that is part of the core curriculum for many undergraduate signal processing classes. The key reason is the continued advance of communications technology, with its need for echo control and equalisation, and the widespread use of adaptive filters in audio, biomedical, and control applications. In this paper we will review the basi...

Social signals and aversive learning in honey bee drones and workers

Science.gov (United States)

Pérez, Eddie; Vallejo, Lianna; Pérez, María E.; Abramson, Charles I.; Giray, Tugrul

2017-01-01

ABSTRACT The dissemination of information is a basic element of group cohesion. In honey bees (Apis mellifera Linnaeus 1758), like in other social insects, the principal method for colony-wide information exchange is communication via pheromones. This medium of communication allows multiple individuals to conduct tasks critical to colony survival. Social signaling also establishes conflict at the level of the individual who must trade-off between attending to the immediate environment or the social demand. In this study we examined this conflict by challenging highly social worker honey bees, and less social male drone honey bees undergoing aversive training by presenting them with a social stress signal (isopentyl acetate, IPA). We utilized IPA exposure methods that caused lower learning performance in appetitive learning in workers. Exposure to isopentyl acetate (IPA) did not affect performance of drones and had a dose-specific effect on worker response, with positive effects diminishing at higher IPA doses. The IPA effects are specific because non-social cues, such as the odor cineole, improve learning performance in drones, and social homing signals (geraniol) did not have a discernible effect on drone or worker performance. We conclude that social signals do generate conflict and that response to them is dependent on signal relevance to the individual as well as the context. We discuss the effect of social signal on learning both related to its social role and potential evolutionary history. PMID:27895050

Social signals and aversive learning in honey bee drones and workers

Directory of Open Access Journals (Sweden)

Arian Avalos

2017-01-01

Full Text Available The dissemination of information is a basic element of group cohesion. In honey bees (Apis mellifera Linnaeus 1758, like in other social insects, the principal method for colony-wide information exchange is communication via pheromones. This medium of communication allows multiple individuals to conduct tasks critical to colony survival. Social signaling also establishes conflict at the level of the individual who must trade-off between attending to the immediate environment or the social demand. In this study we examined this conflict by challenging highly social worker honey bees, and less social male drone honey bees undergoing aversive training by presenting them with a social stress signal (isopentyl acetate, IPA. We utilized IPA exposure methods that caused lower learning performance in appetitive learning in workers. Exposure to isopentyl acetate (IPA did not affect performance of drones and had a dose-specific effect on worker response, with positive effects diminishing at higher IPA doses. The IPA effects are specific because non-social cues, such as the odor cineole, improve learning performance in drones, and social homing signals (geraniol did not have a discernible effect on drone or worker performance. We conclude that social signals do generate conflict and that response to them is dependent on signal relevance to the individual as well as the context. We discuss the effect of social signal on learning both related to its social role and potential evolutionary history.

Social signals and aversive learning in honey bee drones and workers.

Science.gov (United States)

Avalos, Arian; Pérez, Eddie; Vallejo, Lianna; Pérez, María E; Abramson, Charles I; Giray, Tugrul

2017-01-15

The dissemination of information is a basic element of group cohesion. In honey bees (Apis mellifera Linnaeus 1758), like in other social insects, the principal method for colony-wide information exchange is communication via pheromones. This medium of communication allows multiple individuals to conduct tasks critical to colony survival. Social signaling also establishes conflict at the level of the individual who must trade-off between attending to the immediate environment or the social demand. In this study we examined this conflict by challenging highly social worker honey bees, and less social male drone honey bees undergoing aversive training by presenting them with a social stress signal (isopentyl acetate, IPA). We utilized IPA exposure methods that caused lower learning performance in appetitive learning in workers. Exposure to isopentyl acetate (IPA) did not affect performance of drones and had a dose-specific effect on worker response, with positive effects diminishing at higher IPA doses. The IPA effects are specific because non-social cues, such as the odor cineole, improve learning performance in drones, and social homing signals (geraniol) did not have a discernible effect on drone or worker performance. We conclude that social signals do generate conflict and that response to them is dependent on signal relevance to the individual as well as the context. We discuss the effect of social signal on learning both related to its social role and potential evolutionary history. © 2017. Published by The Company of Biologists Ltd.

A signal theoretic introduction to random processes

CERN Document Server

Howard, Roy M

2015-01-01

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

Knee joint vibroarthrographic signal processing and analysis

CERN Document Server

Wu, Yunfeng

2015-01-01

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.

Advanced optical signal processing of broadband parallel data signals

DEFF Research Database (Denmark)

Oxenløwe, Leif Katsuo; Hu, Hao; Kjøller, Niels-Kristian

2016-01-01

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....

Pedagogical reforms of digital signal processing education

Science.gov (United States)

Christensen, Michael

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

Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning

KAUST Repository

Teng, Haotian; Cao, Minh Duc; Hall, Michael B; Duarte, Tania; Wang, Sheng; Coin, Lachlan J M

2018-01-01

Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using desktop computer graphics processing units.

Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning

KAUST Repository

Teng, Haotian

2018-04-10

Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using desktop computer graphics processing units.

Digital Signal Processing applied to Physical Signals

CERN Document Server

Alberto, Diego; Musa, L

2011-01-01

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...

Electronic devices for analog signal processing

CERN Document Server

Rybin, Yu K

2012-01-01

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...

Dynamic time warping and machine learning for signal quality assessment of pulsatile signals

International Nuclear Information System (INIS)

Li, Q; Clifford, G D

2012-01-01

In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal. (paper)

Dynamic time warping and machine learning for signal quality assessment of pulsatile signals.

Science.gov (United States)

Li, Q; Clifford, G D

2012-09-01

In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal.

Machine learning approaches for the prediction of signal peptides and otherprotein sorting signals

DEFF Research Database (Denmark)

Nielsen, Henrik; Brunak, Søren; von Heijne, Gunnar

1999-01-01

Prediction of protein sorting signals from the sequence of amino acids has great importance in the field of proteomics today. Recently,the growth of protein databases, combined with machine learning approaches, such as neural networks and hidden Markov models, havemade it possible to achieve...

Handbook of signal processing systems

CERN Document Server

Deprettere, Ed; Leupers, Rainer; Takala, Jarmo

2013-01-01

Handbook of Signal Processing Systems is organized in three 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, describes models of computation and their associated design tools and methodologies. This handbook is an essential tool for professionals in many fields and researchers of all levels.

Development of an Ontology-Directed Signal Processing Toolbox

Energy Technology Data Exchange (ETDEWEB)

Stephen W. Lang

2011-05-27

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

Dynamic mesolimbic dopamine signaling during action sequence learning and expectation violation

Science.gov (United States)

Collins, Anne L.; Greenfield, Venuz Y.; Bye, Jeffrey K.; Linker, Kay E.; Wang, Alice S.; Wassum, Kate M.

2016-01-01

Prolonged mesolimbic dopamine concentration changes have been detected during spatial navigation, but little is known about the conditions that engender this signaling profile or how it develops with learning. To address this, we monitored dopamine concentration changes in the nucleus accumbens core of rats throughout acquisition and performance of an instrumental action sequence task. Prolonged dopamine concentration changes were detected that ramped up as rats executed each action sequence and declined after earned reward collection. With learning, dopamine concentration began to rise increasingly earlier in the execution of the sequence and ultimately backpropagated away from stereotyped sequence actions, becoming only transiently elevated by the most distal and unexpected reward predictor. Action sequence-related dopamine signaling was reactivated in well-trained rats if they became disengaged in the task and in response to an unexpected change in the value, but not identity of the earned reward. Throughout training and test, dopamine signaling correlated with sequence performance. These results suggest that action sequences can engender a prolonged mode of dopamine signaling in the nucleus accumbens core and that such signaling relates to elements of the motivation underlying sequence execution and is dynamic with learning, overtraining and violations in reward expectation. PMID:26869075

Advances in heuristic signal processing and applications

CERN Document Server

Chatterjee, Amitava; Siarry, Patrick

2013-01-01

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

Spared internal but impaired external reward prediction error signals in major depressive disorder during reinforcement learning.

Science.gov (United States)

Bakic, Jasmina; Pourtois, Gilles; Jepma, Marieke; Duprat, Romain; De Raedt, Rudi; Baeken, Chris

2017-01-01

Major depressive disorder (MDD) creates debilitating effects on a wide range of cognitive functions, including reinforcement learning (RL). In this study, we sought to assess whether reward processing as such, or alternatively the complex interplay between motivation and reward might potentially account for the abnormal reward-based learning in MDD. A total of 35 treatment resistant MDD patients and 44 age matched healthy controls (HCs) performed a standard probabilistic learning task. RL was titrated using behavioral, computational modeling and event-related brain potentials (ERPs) data. MDD patients showed comparable learning rate compared to HCs. However, they showed decreased lose-shift responses as well as blunted subjective evaluations of the reinforcers used during the task, relative to HCs. Moreover, MDD patients showed normal internal (at the level of error-related negativity, ERN) but abnormal external (at the level of feedback-related negativity, FRN) reward prediction error (RPE) signals during RL, selectively when additional efforts had to be made to establish learning. Collectively, these results lend support to the assumption that MDD does not impair reward processing per se during RL. Instead, it seems to alter the processing of the emotional value of (external) reinforcers during RL, when additional intrinsic motivational processes have to be engaged. © 2016 Wiley Periodicals, Inc.

Poisson pre-processing of nonstationary photonic signals: Signals with equality between mean and variance.

Science.gov (United States)

Poplová, Michaela; Sovka, Pavel; Cifra, Michal

2017-01-01

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.

Nonlinear filtering for LIDAR signal processing

Directory of Open Access Journals (Sweden)

D. G. Lainiotis

1996-01-01

Full Text Available LIDAR (Laser Integrated Radar is an engineering problem of great practical importance in environmental monitoring sciences. Signal processing for LIDAR applications involves highly nonlinear models and consequently nonlinear filtering. Optimal nonlinear filters, however, are practically unrealizable. In this paper, the Lainiotis's multi-model partitioning methodology and the related approximate but effective nonlinear filtering algorithms are reviewed and applied to LIDAR signal processing. Extensive simulation and performance evaluation of the multi-model partitioning approach and its application to LIDAR signal processing shows that the nonlinear partitioning methods are very effective and significantly superior to the nonlinear extended Kalman filter (EKF, which has been the standard nonlinear filter in past engineering applications.

Application of wavelet transform in seismic signal processing

International Nuclear Information System (INIS)

Ghasemi, M. R.; Mohammadzadeh, A.; Salajeghe, E.

2005-01-01

Wavelet transform is a new tool for signal analysis which can perform a simultaneous signal time and frequency representations. Under Multi Resolution Analysis, one can quickly determine details for signals and their properties using Fast Wavelet Transform algorithms. In this paper, for a better physical understanding of a signal and its basic algorithms, Multi Resolution Analysis together with wavelet transforms in a form of Digital Signal Processing will be discussed. For a Seismic Signal Processing, sets of Orthonormal Daubechies Wavelets are suggested. when dealing with the application of wavelets in SSP, one may discuss about denoising from the signal and data compression existed in the signal, which is important in seismic signal data processing. Using this techniques, EL-Centro and Nagan signals were remodeled with a 25% of total points, resulted in a satisfactory results with an acceptable error drift. Thus a total of 1559 and 2500 points for EL-centro and Nagan seismic curves each, were reduced to 389 and 625 points respectively, with a very reasonable error drift, details of which are recorded in the paper. Finally, the future progress in signal processing, based on wavelet theory will be appointed

Signal processing for cognitive radios

CERN Document Server

Jayaweera, Sudharman K

2014-01-01

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  

With you or against you: social orientation dependent learning signals guide actions made for others.

Science.gov (United States)

Christopoulos, George I; King-Casas, Brooks

2015-01-01

In social environments, it is crucial that decision-makers take account of the impact of their actions not only for oneself, but also on other social agents. Previous work has identified neural signals in the striatum encoding value-based prediction errors for outcomes to oneself; also, recent work suggests that neural activity in prefrontal cortex may similarly encode value-based prediction errors related to outcomes to others. However, prior work also indicates that social valuations are not isomorphic, with social value orientations of decision-makers ranging on a cooperative to competitive continuum; this variation has not been examined within social learning environments. Here, we combine a computational model of learning with functional neuroimaging to examine how individual differences in orientation impact neural mechanisms underlying 'other-value' learning. Across four experimental conditions, reinforcement learning signals for other-value were identified in medial prefrontal cortex, and were distinct from self-value learning signals identified in striatum. Critically, the magnitude and direction of the other-value learning signal depended strongly on an individual's cooperative or competitive orientation toward others. These data indicate that social decisions are guided by a social orientation-dependent learning system that is computationally similar but anatomically distinct from self-value learning. The sensitivity of the medial prefrontal learning signal to social preferences suggests a mechanism linking such preferences to biases in social actions and highlights the importance of incorporating heterogeneous social predispositions in neurocomputational models of social behavior. Published by Elsevier Inc.

Signal processing in noise waveform radar

CERN Document Server

Kulpa, Krzysztof

2013-01-01

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

Processing of acoustic signal in rock desintegration

Directory of Open Access Journals (Sweden)

Futó Jozef

2002-12-01

Full Text Available For the determination of an effective rock disintegration for a given tool and rock type it is needed to define an optimal disintegration regime. Optimisation of the disintegration process by drilling denotes the finding out an appropriate couple of input parameters of disintegration, i.e. the thrust and revolutions for a quasi-equal rock environment. The disintegration process can be optimised to reach the maximum immediate drilling rate, to reach the minimum specific disintegration energy or to reach the maximum ratio of immediate drilling rate and specific disintegration energy. For the determination of the optimal thrust and revolutions it is needed to monitor the disintegration process. Monitoring of the disintegration process in real conditions is complicated by unfavourable factors, such as the presence of water, dust, vibrations etc. Following our present experience in the monitoring of drilling or full-profile driving, we try to replace the monitoring of input values by monitoring of the scanned acoustic signal. This method of monitoring can extend the optimisation of disintegration process in the technical practice. Its advantage consists in the registration of one acoustic signal by an appropriate microphone. Monitoring of acoustic signal is used also in monitoring of metal machining by milling and turning jobs. The research results of scanning of the acoustic signal in machining of metals are encouraging. Acoustic signal can be processed by different statistical parameters. The paper decribes some results of monitoring of the acoustic signal in rock disintegration on the drilling stand of the Institute of Geotechnics SAS in Košice. The acoustic signal has been registered and processed in no-load run of electric motor, in no-load run of electric motor with a drilling fluid, and in the Ruskov andesite drilling. Registration and processing of the acoustic signal is solved as a part of the research grant task within the basic research

Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning

Science.gov (United States)

Kastner, Lucas; Kube, Jana; Villringer, Arno; Neumann, Jane

2017-01-01

Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1) Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2) Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3) Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning. PMID:29163004

Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning

Directory of Open Access Journals (Sweden)

Lucas Kastner

2017-10-01

Full Text Available Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1 Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2 Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3 Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning.

Ultrafast Nonlinear Signal Processing in Silicon Waveguides

DEFF Research Database (Denmark)

Oxenløwe, Leif Katsuo; Mulvad, Hans Christian Hansen; Hu, Hao

2012-01-01

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.......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....

Radar signal analysis and processing using Matlab

CERN Document Server

Mahafza, Bassem R

2008-01-01

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.

Invariance algorithms for processing NDE signals

Science.gov (United States)

Mandayam, Shreekanth; Udpa, Lalita; Udpa, Satish S.; Lord, William

1996-11-01

Signals that are obtained in a variety of nondestructive evaluation (NDE) processes capture information not only about the characteristics of the flaw, but also reflect variations in the specimen's material properties. Such signal changes may be viewed as anomalies that could obscure defect related information. An example of this situation occurs during in-line inspection of gas transmission pipelines. The magnetic flux leakage (MFL) method is used to conduct noninvasive measurements of the integrity of the pipe-wall. The MFL signals contain information both about the permeability of the pipe-wall and the dimensions of the flaw. Similar operational effects can be found in other NDE processes. This paper presents algorithms to render NDE signals invariant to selected test parameters, while retaining defect related information. Wavelet transform based neural network techniques are employed to develop the invariance algorithms. The invariance transformation is shown to be a necessary pre-processing step for subsequent defect characterization and visualization schemes. Results demonstrating the successful application of the method are presented.

Nonlinear signal processing using neural networks: Prediction and system modelling

Energy Technology Data Exchange (ETDEWEB)

Lapedes, A.; Farber, R.

1987-06-01

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.

Method to predict process signals to learn for SVM

International Nuclear Information System (INIS)

Minowa, Hirotsugu; Gofuku, Akio

2013-01-01

Study of diagnostic system using machine learning to reduce the incidents of the plant is in advance because an accident causes large damage about human, economic and social loss. There is a problem that 2 performances between a classification performance and generalization performance on the machine diagnostic machine is exclusive. However, multi agent diagnostic system makes it possible to use a diagnostic machine specialized either performance by multi diagnostic machines can be used. We propose method to select optimized variables to improve classification performance. The method can also be used for other supervised learning machine but Support Vector Machine. This paper reports that our method and result of evaluation experiment applied our method to output 40% of Monju. (author)

Signal processing for boiling noise detection

International Nuclear Information System (INIS)

Ledwidge, T.J.; Black, J.L.

1989-01-01

The present paper deals with investigations of acoustic signals from a boiling experiment performed on the KNS I loop at KfK Karlsruhe. Signals have been analysed in frequency as well as in time domain. Signal characteristics successfully used to detect the boiling process have been found in time domain. (author). 6 refs, figs

E-learning educational process

Directory of Open Access Journals (Sweden)

Leszek Rudak

2012-06-01

Full Text Available The e-learning educational process differs fundamentally from the teaching-learning process in the face-to-face teaching. A reason of differences is the nature of the distance education: the teacher cannot observe the student at work. Thus, the natural process of teaching, based on performing particular actions by teacher and students in relays, is disturbed. So, one has to consider the e-learning educational process as two separate sets of actions. The first, strongly regular, consists of teachers operations. The second, unorganized, contains the student activities. In the article some relations between the both structures are investigated. Moreover, some methods of arranging the set of students’ activities to better fit in with the educational goals are provided.

Acoustic MIMO signal processing

CERN Document Server

Huang, Yiteng; Chen, Jingdong

2006-01-01

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.

Process Dissociation and Mixture Signal Detection Theory

Science.gov (United States)

DeCarlo, Lawrence T.

2008-01-01

The process dissociation procedure was developed in an attempt to separate different processes involved in memory tasks. The procedure naturally lends itself to a formulation within a class of mixture signal detection models. The dual process model is shown to be a special case. The mixture signal detection model is applied to data from a widely…

Which is the best intrinsic motivation signal for learning multiple skills?

Directory of Open Access Journals (Sweden)

Vieri Giuliano Santucci

2013-11-01

Full Text Available Humans and other biological agents are able to autonomously learn and cache different skills in the absence of any biological pressure or any assigned task. In this respect, Intrinsic Motivations (i.e. motivations not connected to reward-related stimuli play a cardinal role in animal learning, and can be considered as a fundamental tool for developing more autonomous and more adaptive artificial agents. In this work, we provide an exhaustive analysis of a scarcely investigated problem: which kind of IM reinforcement signal is the most suitable for driving the acquisition of multiple skills in the shortest time? To this purpose we implemented an artificial agent with a hierarchical architecture that allows to learn and cache different skills. We tested the system in a setup with continuous states and actions, in particular, with a cinematic robotic arm that has to learn different reaching tasks. We compare the results of different versions of the system driven by several different intrinsic motivation signals. The results show a that intrinsic reinforcements purely based on the knowledge of the system are not appropriate to guide the acquisition of multiple skills, and b that the stronger the link between the IM signal and the competence of the system, the better the performance.

Digital signal processing with Matlab examples

CERN Document Server

Giron-Sierra, Jose Maria

2017-01-01

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.

PEANO, a toolbox for real-time process signal validation and estimation

International Nuclear Information System (INIS)

Fantoni, Paolo F.; Figedy, Stefan; Racz, Attila

1998-02-01

PEANO (Process Evaluation and Analysis by Neural Operators), a toolbox for real time process signal validation and condition monitoring has been developed. This system analyses the signals, which are e.g. the readings of process monitoring sensors, computes their expected values and alerts if real values are deviated from the expected ones more than limits allow. The reliability level of the current analysis is also produced. The system is based on neuro-fuzzy techniques. Artificial Neural Networks and Fuzzy Logic models can be combined to exploit learning and generalisation capability of the first technique with the approximate reasoning embedded in the second approach. Real-time process signal validation is an application field where the use of this technique can improve the diagnosis of faulty sensors and the identification of outliers in a robust and reliable way. This study implements a fuzzy and possibilistic clustering algorithm to classify the operating region where the validation process has to be performed. The possibilistic approach (rather than probabilistic) allows a ''don't know'' classification that results in a fast detection of unforeseen plant conditions or outliers. Specialised Artificial Neural Networks are used for the validation process, one for each fuzzy cluster in which the operating map has been divided. There are two main advantages in using this technique: the accuracy and generalisation capability is increased compared to the case of a single network working in the entire operating region, and the ability to identify abnormal conditions, where the system is not capable to operate with a satisfactory accuracy, is improved. This model has been tested in a simulated environment on a French PWR, to monitor safety-related reactor variables over the entire power-flow operating map. (author)

PEANO, a toolbox for real-time process signal validation and estimation

Energy Technology Data Exchange (ETDEWEB)

Fantoni, Paolo F.; Figedy, Stefan; Racz, Attila

1998-02-01

PEANO (Process Evaluation and Analysis by Neural Operators), a toolbox for real time process signal validation and condition monitoring has been developed. This system analyses the signals, which are e.g. the readings of process monitoring sensors, computes their expected values and alerts if real values are deviated from the expected ones more than limits allow. The reliability level of the current analysis is also produced. The system is based on neuro-fuzzy techniques. Artificial Neural Networks and Fuzzy Logic models can be combined to exploit learning and generalisation capability of the first technique with the approximate reasoning embedded in the second approach. Real-time process signal validation is an application field where the use of this technique can improve the diagnosis of faulty sensors and the identification of outliers in a robust and reliable way. This study implements a fuzzy and possibilistic clustering algorithm to classify the operating region where the validation process has to be performed. The possibilistic approach (rather than probabilistic) allows a ''don't know'' classification that results in a fast detection of unforeseen plant conditions or outliers. Specialised Artificial Neural Networks are used for the validation process, one for each fuzzy cluster in which the operating map has been divided. There are two main advantages in using this technique: the accuracy and generalisation capability is increased compared to the case of a single network working in the entire operating region, and the ability to identify abnormal conditions, where the system is not capable to operate with a satisfactory accuracy, is improved. This model has been tested in a simulated environment on a French PWR, to monitor safety-related reactor variables over the entire power-flow operating map. (author)

Signal processing methods for MFE plasma diagnostics

International Nuclear Information System (INIS)

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

1985-02-01

The application of various signal processing methods to extract energy storage information from plasma diamagnetism sensors occurring during physics experiments on the Tandom Mirror Experiment-Upgrade (TMX-U) is discussed. We show how these processing techniques can be used to decrease the uncertainty in the corresponding sensor measurements. The algorithms suggested are implemented using SIG, an interactive signal processing package developed at LLNL

A novel deep learning approach for classification of EEG motor imagery signals.

Science.gov (United States)

Tabar, Yousef Rezaei; Halici, Ugur

2017-02-01

Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.

All-optical signal processing of OTDM and OFDM signals based on time-domain Optical Fourier Transformation

DEFF Research Database (Denmark)

Clausen, Anders; Guan, Pengyu; Mulvad, Hans Christian Hansen

2014-01-01

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....

Signals and Systems in Biomedical Engineering Signal Processing and Physiological Systems Modeling

CERN Document Server

Devasahayam, Suresh R

2013-01-01

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...

Television picture signal processing

NARCIS (Netherlands)

1998-01-01

Field or frame memories are often used in television receivers for video signal processing functions, such as noise reduction and/or flicker reduction. Television receivers also have graphic features such as teletext, menu-driven control systems, multilingual subtitling, an electronic TV-Guide, etc.

Wavelets and multiscale signal processing

CERN Document Server

Cohen, Albert

1995-01-01

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...

Non-commutative tomography and signal processing

International Nuclear Information System (INIS)

Mendes, R Vilela

2015-01-01

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)

The Signal Validation method of Digital Process Instrumentation System on signal conditioner for SMART

International Nuclear Information System (INIS)

Moon, Hee Gun; Park, Sang Min; Kim, Jung Seon; Shon, Chang Ho; Park, Heui Youn; Koo, In Soo

2005-01-01

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

Computer-based and screencasts approach in the signal processing basics electrical engineering course : does blended-learning work to motivate students?

NARCIS (Netherlands)

Gomez Puente, S.M.; Sommen, P.C.W.

2016-01-01

This paper presents the first experience of the computer-based and screencast approach used in the Signal Processing Basics course to motivate first year electrical engineering students to assess own knowledge and skills. Signal Processing Basics is a freshman course with up to 240 students. With

Processing Electromyographic Signals to Recognize Words

Science.gov (United States)

Jorgensen, C. C.; Lee, D. D.

2009-01-01

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.

Sensor-based atomic layer deposition for rapid process learning and enhanced manufacturability

Science.gov (United States)

Lei, Wei

In the search for sensor based atomic layer deposition (ALD) process to accelerate process learning and enhance manufacturability, we have explored new reactor designs and applied in-situ process sensing to W and HfO 2 ALD processes. A novel wafer scale ALD reactor, which features fast gas switching, good process sensing compatibility and significant similarity to the real manufacturing environment, is constructed. The reactor has a unique movable reactor cap design that allows two possible operation modes: (1) steady-state flow with alternating gas species; or (2) fill-and-pump-out cycling of each gas, accelerating the pump-out by lifting the cap to employ the large chamber volume as ballast. Downstream quadrupole mass spectrometry (QMS) sampling is applied for in-situ process sensing of tungsten ALD process. The QMS reveals essential surface reaction dynamics through real-time signals associated with byproduct generation as well as precursor introduction and depletion for each ALD half cycle, which are then used for process learning and optimization. More subtle interactions such as imperfect surface saturation and reactant dose interaction are also directly observed by QMS, indicating that ALD process is more complicated than the suggested layer-by-layer growth. By integrating in real-time the byproduct QMS signals over each exposure and plotting it against process cycle number, the deposition kinetics on the wafer is directly measured. For continuous ALD runs, the total integrated byproduct QMS signal in each ALD run is also linear to ALD film thickness, and therefore can be used for ALD film thickness metrology. The in-situ process sensing is also applied to HfO2 ALD process that is carried out in a furnace type ALD reactor. Precursor dose end-point control is applied to precisely control the precursor dose in each half cycle. Multiple process sensors, including quartz crystal microbalance (QCM) and QMS are used to provide real time process information. The

Classification of EEG signals using a genetic-based machine learning classifier.

Science.gov (United States)

Skinner, B T; Nguyen, H T; Liu, D K

2007-01-01

This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices.

Real-Time Digital Signal Processing Based on FPGAs for Electronic Skin Implementation †

Directory of Open Access Journals (Sweden)

Ali Ibrahim

2017-03-01

Full Text Available Enabling touch-sensing capability would help appliances understand interaction behaviors with their surroundings. Many recent studies are focusing on the development of electronic skin because of its necessity in various application domains, namely autonomous artificial intelligence (e.g., robots, biomedical instrumentation, and replacement prosthetic devices. An essential task of the electronic skin system is to locally process the tactile data and send structured information either to mimic human skin or to respond to the application demands. The electronic skin must be fabricated together with an embedded electronic system which has the role of acquiring the tactile data, processing, and extracting structured information. On the other hand, processing tactile data requires efficient methods to extract meaningful information from raw sensor data. Machine learning represents an effective method for data analysis in many domains: it has recently demonstrated its effectiveness in processing tactile sensor data. In this framework, this paper presents the implementation of digital signal processing based on FPGAs for tactile data processing. It provides the implementation of a tensorial kernel function for a machine learning approach. Implementation results are assessed by highlighting the FPGA resource utilization and power consumption. Results demonstrate the feasibility of the proposed implementation when real-time classification of input touch modalities are targeted.

Classifying BCI signals from novice users with extreme learning machine

Directory of Open Access Journals (Sweden)

Rodríguez-Bermúdez Germán

2017-07-01

Full Text Available Brain computer interface (BCI allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.

Digital signal processing application in nuclear spectroscopy

Directory of Open Access Journals (Sweden)

O. V. Zeynalova

2009-06-01

Full Text Available 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 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 to the final signal-to-noise ratio of the spectrometer 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 ionisation 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 investigated in terms of the distance between piled-up pulses. The efficiency of developed algorithms compared with other signal processing schemes published in literature.

Signal processing for smart cards

Science.gov (United States)

Quisquater, Jean-Jacques; Samyde, David

2003-06-01

In 1998, Paul Kocher showed that when a smart card computes cryptographic algorithms, for signatures or encryption, its consumption or its radiations leak information. The keys or the secrets hidden in the card can then be recovered using a differential measurement based on the intercorrelation function. A lot of silicon manufacturers use desynchronization countermeasures to defeat power analysis. In this article we detail a new resynchronization technic. This method can be used to facilitate the use of a neural network to do the code recognition. It becomes possible to reverse engineer a software code automatically. Using data and clock separation methods, we show how to optimize the synchronization using signal processing. Then we compare these methods with watermarking methods for 1D and 2D signal. The very last watermarking detection improvements can be applied to signal processing for smart cards with very few modifications. Bayesian processing is one of the best ways to do Differential Power Analysis, and it is possible to extract a PIN code from a smart card in very few samples. So this article shows the need to continue to set up effective countermeasures for cryptographic processors. Although the idea to use advanced signal processing operators has been commonly known for a long time, no publication explains that results can be obtained. The main idea of differential measurement is to use the cross-correlation of two random variables and to repeat consumption measurements on the processor to be analyzed. We use two processors clocked at the same external frequency and computing the same data. The applications of our design are numerous. Two measurements provide the inputs of a central operator. With the most accurate operator we can improve the signal noise ratio, re-synchronize the acquisition clock with the internal one, or remove jitter. The analysis based on consumption or electromagnetic measurements can be improved using our structure. At first sight

All-optical signal processing data communication and storage applications

CERN Document Server

Eggleton, Benjamin

2015-01-01

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...

Learning during processing Word learning doesn’t wait for word recognition to finish

Science.gov (United States)

Apfelbaum, Keith S.; McMurray, Bob

2017-01-01

Previous research on associative learning has uncovered detailed aspects of the process, including what types of things are learned, how they are learned, and where in the brain such learning occurs. However, perceptual processes, such as stimulus recognition and identification, take time to unfold. Previous studies of learning have not addressed when, during the course of these dynamic recognition processes, learned representations are formed and updated. If learned representations are formed and updated while recognition is ongoing, the result of learning may incorporate spurious, partial information. For example, during word recognition, words take time to be identified, and competing words are often active in parallel. If learning proceeds before this competition resolves, representations may be influenced by the preliminary activations present at the time of learning. In three experiments using word learning as a model domain, we provide evidence that learning reflects the ongoing dynamics of auditory and visual processing during a learning event. These results show that learning can occur before stimulus recognition processes are complete; learning does not wait for ongoing perceptual processing to complete. PMID:27471082

Digital signal processing theory and practice

CERN Document Server

Rao, K Deergha

2018-01-01

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...

E-learning process maturity level: a conceptual framework

Science.gov (United States)

Rahmah, A.; Santoso, H. B.; Hasibuan, Z. A.

2018-03-01

ICT advancement is a sure thing with the impact influencing many domains, including learning in both formal and informal situations. It leads to a new mindset that we should not only utilize the given ICT to support the learning process, but also improve it gradually involving a lot of factors. These phenomenon is called e-learning process evolution. Accordingly, this study attempts to explore maturity level concept to provide the improvement direction gradually and progression monitoring for the individual e-learning process. Extensive literature review, observation, and forming constructs are conducted to develop a conceptual framework for e-learning process maturity level. The conceptual framework consists of learner, e-learning process, continuous improvement, evolution of e-learning process, technology, and learning objectives. Whilst, evolution of e-learning process depicted as current versus expected conditions of e-learning process maturity level. The study concludes that from the e-learning process maturity level conceptual framework, it may guide the evolution roadmap for e-learning process, accelerate the evolution, and decrease the negative impact of ICT. The conceptual framework will be verified and tested in the future study.

Grating geophone signal processing based on wavelet transform

Science.gov (United States)

Li, Shuqing; Zhang, Huan; Tao, Zhifei

2008-12-01

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.

An introduction to digital signal processing

CERN Document Server

Karl, John H

1989-01-01

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

Reinforcement learning signals in the human striatum distinguish learners from nonlearners during reward-based decision making.

Science.gov (United States)

Schönberg, Tom; Daw, Nathaniel D; Joel, Daphna; O'Doherty, John P

2007-11-21

The computational framework of reinforcement learning has been used to forward our understanding of the neural mechanisms underlying reward learning and decision-making behavior. It is known that humans vary widely in their performance in decision-making tasks. Here, we used a simple four-armed bandit task in which subjects are almost evenly split into two groups on the basis of their performance: those who do learn to favor choice of the optimal action and those who do not. Using models of reinforcement learning we sought to determine the neural basis of these intrinsic differences in performance by scanning both groups with functional magnetic resonance imaging. We scanned 29 subjects while they performed the reward-based decision-making task. Our results suggest that these two groups differ markedly in the degree to which reinforcement learning signals in the striatum are engaged during task performance. While the learners showed robust prediction error signals in both the ventral and dorsal striatum during learning, the nonlearner group showed a marked absence of such signals. Moreover, the magnitude of prediction error signals in a region of dorsal striatum correlated significantly with a measure of behavioral performance across all subjects. These findings support a crucial role of prediction error signals, likely originating from dopaminergic midbrain neurons, in enabling learning of action selection preferences on the basis of obtained rewards. Thus, spontaneously observed individual differences in decision making performance demonstrate the suggested dependence of this type of learning on the functional integrity of the dopaminergic striatal system in humans.

Beyond the Learning Process and toward the Knowledge Creation Process: Linking Learning and Knowledge in the Supportive Learning Culture

Science.gov (United States)

Yoon, Seung Won; Song, Ji Hoon; Lim, Doo Hun

2009-01-01

This integrative literature review synthesizes the concepts and process of organizational knowledge creation with theories of individual learning. The knowledge conversion concept (Nonaka & Takeuchi, 1995; Nonaka, Toyama, & Byosiere, 2001) is used as the basis of the organizational knowledge creation process, while major learning theories relevant…

Multiscale Signal Analysis and Modeling

CERN Document Server

Zayed, Ahmed

2013-01-01

Multiscale Signal Analysis and Modeling presents recent advances in multiscale analysis and modeling using wavelets and other systems. This book also presents applications in digital signal processing using sampling theory and techniques from various function spaces, filter design, feature extraction and classification, signal and image representation/transmission, coding, nonparametric statistical signal processing, and statistical learning theory. This book also: Discusses recently developed signal modeling techniques, such as the multiscale method for complex time series modeling, multiscale positive density estimations, Bayesian Shrinkage Strategies, and algorithms for data adaptive statistics Introduces new sampling algorithms for multidimensional signal processing Provides comprehensive coverage of wavelets with presentations on waveform design and modeling, wavelet analysis of ECG signals and wavelet filters Reviews features extraction and classification algorithms for multiscale signal and image proce...

Neural mechanisms of human perceptual learning: electrophysiological evidence for a two-stage process.

Science.gov (United States)

Hamamé, Carlos M; Cosmelli, Diego; Henriquez, Rodrigo; Aboitiz, Francisco

2011-04-26

Humans and other animals change the way they perceive the world due to experience. This process has been labeled as perceptual learning, and implies that adult nervous systems can adaptively modify the way in which they process sensory stimulation. However, the mechanisms by which the brain modifies this capacity have not been sufficiently analyzed. We studied the neural mechanisms of human perceptual learning by combining electroencephalographic (EEG) recordings of brain activity and the assessment of psychophysical performance during training in a visual search task. All participants improved their perceptual performance as reflected by an increase in sensitivity (d') and a decrease in reaction time. The EEG signal was acquired throughout the entire experiment revealing amplitude increments, specific and unspecific to the trained stimulus, in event-related potential (ERP) components N2pc and P3 respectively. P3 unspecific modification can be related to context or task-based learning, while N2pc may be reflecting a more specific attentional-related boosting of target detection. Moreover, bell and U-shaped profiles of oscillatory brain activity in gamma (30-60 Hz) and alpha (8-14 Hz) frequency bands may suggest the existence of two phases for learning acquisition, which can be understood as distinctive optimization mechanisms in stimulus processing. We conclude that there are reorganizations in several neural processes that contribute differently to perceptual learning in a visual search task. We propose an integrative model of neural activity reorganization, whereby perceptual learning takes place as a two-stage phenomenon including perceptual, attentional and contextual processes.

Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning

Directory of Open Access Journals (Sweden)

Shandilya Sharad

2012-10-01

Full Text Available Abstract Background Ventricular Fibrillation (VF is a common presenting dysrhythmia in the setting of cardiac arrest whose main treatment is defibrillation through direct current countershock to achieve return of spontaneous circulation. However, often defibrillation is unsuccessful and may even lead to the transition of VF to more nefarious rhythms such as asystole or pulseless electrical activity. Multiple methods have been proposed for predicting defibrillation success based on examination of the VF waveform. To date, however, no analytical technique has been widely accepted. We developed a unique approach of computational VF waveform analysis, with and without addition of the signal of end-tidal carbon dioxide (PetCO2, using advanced machine learning algorithms. We compare these results with those obtained using the Amplitude Spectral Area (AMSA technique. Methods A total of 90 pre-countershock ECG signals were analyzed form an accessible preshosptial cardiac arrest database. A unified predictive model, based on signal processing and machine learning, was developed with time-series and dual-tree complex wavelet transform features. Upon selection of correlated variables, a parametrically optimized support vector machine (SVM model was trained for predicting outcomes on the test sets. Training and testing was performed with nested 10-fold cross validation and 6–10 features for each test fold. Results The integrative model performs real-time, short-term (7.8 second analysis of the Electrocardiogram (ECG. For a total of 90 signals, 34 successful and 56 unsuccessful defibrillations were classified with an average Accuracy and Receiver Operator Characteristic (ROC Area Under the Curve (AUC of 82.2% and 85%, respectively. Incorporation of the end-tidal carbon dioxide signal boosted Accuracy and ROC AUC to 83.3% and 93.8%, respectively, for a smaller dataset containing 48 signals. VF analysis using AMSA resulted in accuracy and ROC AUC of 64

Signal processing techniques for sodium boiling noise detection

International Nuclear Information System (INIS)

1989-05-01

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

Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems

Science.gov (United States)

Hearn, Tristan A.

2015-01-01

This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.

Learning-based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility

Energy Technology Data Exchange (ETDEWEB)

Aziz, H. M. Abdul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Zhu, Feng [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering; Ukkusuri, Satish V. [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering

2017-10-04

Here, this research applies R-Markov Average Reward Technique based reinforcement learning (RL) algorithm, namely RMART, for vehicular signal control problem leveraging information sharing among signal controllers in connected vehicle environment. We implemented the algorithm in a network of 18 signalized intersections and compare the performance of RMART with fixed, adaptive, and variants of the RL schemes. Results show significant improvement in system performance for RMART algorithm with information sharing over both traditional fixed signal timing plans and real time adaptive control schemes. Additionally, the comparison with reinforcement learning algorithms including Q learning and SARSA indicate that RMART performs better at higher congestion levels. Further, a multi-reward structure is proposed that dynamically adjusts the reward function with varying congestion states at the intersection. Finally, the results from test networks show significant reduction in emissions (CO, CO₂, NO_x, VOC, PM₁₀) when RL algorithms are implemented compared to fixed signal timings and adaptive schemes.

On the organizational learning work process

International Nuclear Information System (INIS)

Weil, Richard; Apostolakis, George

2000-01-01

This paper presents an organizational learning work process for use at nuclear power plants or other high-risk industries. Relying on insights gained from surveying organizational learning activities at nuclear power plants, the proposed work process synthesizes distributed learning activities and improves upon existing organizational learning processes. A root-cause analysis that targets organizational factors is presented. Additionally, a more accurate and objective methodology for prioritizing operating experience is presented. This methodology was applied to a case study during a workshop with utility personnel held at MIT. (author)

Explicit and Implicit Processes Constitute the Fast and Slow Processes of Sensorimotor Learning.

Science.gov (United States)

McDougle, Samuel D; Bond, Krista M; Taylor, Jordan A

2015-07-01

A popular model of human sensorimotor learning suggests that a fast process and a slow process work in parallel to produce the canonical learning curve (Smith et al., 2006). Recent evidence supports the subdivision of sensorimotor learning into explicit and implicit processes that simultaneously subserve task performance (Taylor et al., 2014). We set out to test whether these two accounts of learning processes are homologous. Using a recently developed method to assay explicit and implicit learning directly in a sensorimotor task, along with a computational modeling analysis, we show that the fast process closely resembles explicit learning and the slow process approximates implicit learning. In addition, we provide evidence for a subdivision of the slow/implicit process into distinct manifestations of motor memory. We conclude that the two-state model of motor learning is a close approximation of sensorimotor learning, but it is unable to describe adequately the various implicit learning operations that forge the learning curve. Our results suggest that a wider net be cast in the search for the putative psychological mechanisms and neural substrates underlying the multiplicity of processes involved in motor learning. Copyright © 2015 the authors 0270-6474/15/359568-12$15.00/0.

High-speed optical coherence tomography signal processing on GPU

International Nuclear Information System (INIS)

Li Xiqi; Shi Guohua; Zhang Yudong

2011-01-01

The signal processing speed of spectral domain optical coherence tomography (SD-OCT) has become a bottleneck in many medical applications. Recently, a time-domain interpolation method was proposed. This method not only gets a better signal-to noise ratio (SNR) but also gets a faster signal processing time for the SD-OCT than the widely used zero-padding interpolation method. Furthermore, the re-sampled data is obtained by convoluting the acquired data and the coefficients in time domain. Thus, a lot of interpolations can be performed concurrently. So, this interpolation method is suitable for parallel computing. An ultra-high optical coherence tomography signal processing can be realized by using graphics processing unit (GPU) with computer unified device architecture (CUDA). This paper will introduce the signal processing steps of SD-OCT on GPU. An experiment is performed to acquire a frame SD-OCT data (400A-linesx2048 pixel per A-line) and real-time processed the data on GPU. The results show that it can be finished in 6.208 milliseconds, which is 37 times faster than that on Central Processing Unit (CPU).

Analogue Signal Processing: Collected Papers 1994-95

DEFF Research Database (Denmark)

1996-01-01

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....

Supervised Learning with Complex-valued Neural Networks

CERN Document Server

Suresh, Sundaram; Savitha, Ramasamy

2013-01-01

Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

Nicotinic modulation of hippocampal cell signaling and associated effects on learning and memory.

Science.gov (United States)

Kutlu, Munir Gunes; Gould, Thomas J

2016-03-01

The hippocampus is a key brain structure involved in synaptic plasticity associated with long-term declarative memory formation. Importantly, nicotine and activation of nicotinic acetylcholine receptors (nAChRs) can alter hippocampal plasticity and these changes may occur through modulation of hippocampal kinases and transcription factors. Hippocampal kinases such as cAMP-dependent protein kinase (PKA), calcium/calmodulin-dependent protein kinases (CAMKs), extracellular signal-regulated kinases 1 and 2 (ERK1/2), and c-jun N-terminal kinase 1 (JNK1), and the transcription factor cAMP-response element-binding protein (CREB) that are activated either directly or indirectly by nicotine may modulate hippocampal plasticity and in parallel hippocampus-dependent learning and memory. Evidence suggests that nicotine may alter hippocampus-dependent learning by changing the time and magnitude of activation of kinases and transcription factors normally involved in learning and by recruiting additional cell signaling molecules. Understanding how nicotine alters learning and memory will advance basic understanding of the neural substrates of learning and aid in understanding mental disorders that involve cognitive and learning deficits. Copyright © 2015 Elsevier Inc. All rights reserved.

Hot topics: Signal processing in acoustics

Science.gov (United States)

Gaumond, Charles F.

2005-09-01

Signal processing in acoustics is a multidisciplinary group of people that work in many areas of acoustics. We have chosen two areas that have shown exciting new applications of signal processing to acoustics or have shown exciting and important results from the use of signal processing. In this session, two hot topics are shown: the use of noiselike acoustic fields to determine sound propagation structure and the use of localization to determine animal behaviors. The first topic shows the application of correlation on geo-acoustic fields to determine the Greens function for propagation through the Earth. These results can then be further used to solve geo-acoustic inverse problems. The first topic also shows the application of correlation using oceanic noise fields to determine the Greens function through the ocean. These results also have utility for oceanic inverse problems. The second topic shows exciting results from the detection, localization, and tracking of marine mammals by two different groups. Results from detection and localization of bullfrogs are shown, too. Each of these studies contributed to the knowledge of animal behavior. [Work supported by ONR.

How initial representations shape coupled learning processes

DEFF Research Database (Denmark)

Puranam, Phanish; Swamy, M.

2016-01-01

Coupled learning processes, in which specialists from different domains learn how to make interdependent choices among alternatives, are common in organizations. We explore the role played by initial representations held by the learners in coupled learning processes using a formal agent-based model....... We find that initial representations have important consequences for the success of the coupled learning process, particularly when communication is constrained and individual rates of learning are high. Under these conditions, initial representations that generate incorrect beliefs can outperform...... one that does not discriminate among alternatives, or even a mix of correct and incorrect representations among the learners. We draw implications for the design of coupled learning processes in organizations. © 2016 INFORMS....

'Steps in the learning Process'

International Nuclear Information System (INIS)

Cheung, Kyung Mo; Cheung, Hwan

1984-01-01

The process by which a student learns is extremely complicated. Whether he is simply learning facts, laws or formulae, changing his values or mastering a skill the way in which his brain functions is impossible to describe. The idea of learning domains is put forward not to explain in biological terms what happens in the brain but simply to attempt to break the system down into simpler units so that the learning process can be organized in an easier, more systematic way. In the most commonly used description of this process, the one described by BLOOM, this is BLOOM's Taxonomy. In addition to, I'd like to compare with the work of Lewis (Levels of Knowledge and Understanding). As a result, let us discuss about the most effective method in teaching in order to supply high-quality education

Serial Learning Process: Test of Chaining, Position, and Dual-Process Hypotheses

Science.gov (United States)

Giurintano, S. L.

1973-01-01

The chaining, position, and dual-process hypotheses of serial learning (SL) as well as serial recall, reordering, and relearning of paired-associate learning were examined to establish learning patterns. Results provide evidence for dual-process hypothesis. (DS)

Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning.

Science.gov (United States)

Pant, Jeevan K; Krishnan, Sridhar

2014-04-01

A new algorithm for the reconstruction of electrocardiogram (ECG) signals and a dictionary learning algorithm for the enhancement of its reconstruction performance for a class of signals are proposed. The signal reconstruction algorithm is based on minimizing the lp pseudo-norm of the second-order difference, called as the lp(2d) pseudo-norm, of the signal. The optimization involved is carried out using a sequential conjugate-gradient algorithm. The dictionary learning algorithm uses an iterative procedure wherein a signal reconstruction and a dictionary update steps are repeated until a convergence criterion is satisfied. The signal reconstruction step is implemented by using the proposed signal reconstruction algorithm and the dictionary update step is implemented by using the linear least-squares method. Extensive simulation results demonstrate that the proposed algorithm yields improved reconstruction performance for temporally correlated ECG signals relative to the state-of-the-art lp(1d)-regularized least-squares and Bayesian learning based algorithms. Also for a known class of signals, the reconstruction performance of the proposed algorithm can be improved by applying it in conjunction with a dictionary obtained using the proposed dictionary learning algorithm.

Local learning processes in Malaysian industry

DEFF Research Database (Denmark)

Wangel, Arne

1999-01-01

Local learning processes are a vital part of any dynamic assimilation of transferred technology. The paper raises the question about the interaction between the training paradigms, which transnational corporations introduce in their subsidiaries in Malaysia and the specific basis for learning...... of Malaysian labour. Experiences from Malaysian industry indicate that local learning processes are shaped, among other things, by the concept of knowledge in a particular training programme, labour market structures, and learning cultures....

Signal Processing Methods Monitor Cranial Pressure

Science.gov (United States)

2010-01-01

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.

Altered neural reward and loss processing and prediction error signalling in depression

Science.gov (United States)

Ubl, Bettina; Kuehner, Christine; Kirsch, Peter; Ruttorf, Michaela

2015-01-01

Dysfunctional processing of reward and punishment may play an important role in depression. However, functional magnetic resonance imaging (fMRI) studies have shown heterogeneous results for reward processing in fronto-striatal regions. We examined neural responsivity associated with the processing of reward and loss during anticipation and receipt of incentives and related prediction error (PE) signalling in depressed individuals. Thirty medication-free depressed persons and 28 healthy controls performed an fMRI reward paradigm. Regions of interest analyses focused on neural responses during anticipation and receipt of gains and losses and related PE-signals. Additionally, we assessed the relationship between neural responsivity during gain/loss processing and hedonic capacity. When compared with healthy controls, depressed individuals showed reduced fronto-striatal activity during anticipation of gains and losses. The groups did not significantly differ in response to reward and loss outcomes. In depressed individuals, activity increases in the orbitofrontal cortex and nucleus accumbens during reward anticipation were associated with hedonic capacity. Depressed individuals showed an absence of reward-related PEs but encoded loss-related PEs in the ventral striatum. Depression seems to be linked to blunted responsivity in fronto-striatal regions associated with limited motivational responses for rewards and losses. Alterations in PE encoding might mirror blunted reward- and enhanced loss-related associative learning in depression. PMID:25567763

Software for biomedical engineering signal processing laboratory experiments.

Science.gov (United States)

Tompkins, Willis J; Wilson, J

2009-01-01

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.

Learning and Motivational Processes When Students Design Curriculum-Based Digital Learning Games

DEFF Research Database (Denmark)

Weitze, Charlotte Lærke

2015-01-01

This design-based research (DBR) project has developed an overall gamified learning design (big Game) to facilitate the learning process for adult students by inviting them to be their own learning designers through designing digital learning games (small games) in cross-disciplinary subject...... matters. The DBR project has investigated and experimented with which elements, methods, and processes are important when aiming at creating a cognitive complex (Anderson and Krathwohl, 2001) and motivating learning process within a reusable game-based learning design. This project took place in a co......, or programming provide a rich context for learning, since the construction of artefacts, in this case learning games, enables reflection and new ways of thinking. The students learned from reflection and interaction with the tools alone as well as in collaboration with peers. After analysing the students...

Learning dialog act processing

OpenAIRE

Wermter, Stefan; Löchel, Matthias

1996-01-01

In this paper we describe a new approach for learning dialog act processing. In this approach we integrate a symbolic semantic segmentation parser with a learning dialog act network. In order to support the unforeseeable errors and variations of spoken language we have concentrated on robust data-driven learning. This approach already compares favorably with the statistical average plausibility method, produces a segmentation and dialog act assignment for all utterances in a robust manner,...

Book: Marine Bioacoustic Signal Processing and Analysis

Science.gov (United States)

2011-09-30

physicists , and mathematicians . However, more and more biologists and psychologists are starting to use advanced signal processing techniques and...Book: Marine Bioacoustic Signal Processing and Analysis 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT ...chapters than it should be, since the project must be finished by Dec. 31. I have started setting aside 2 hours of uninterrupted per workday to work

Analogue Signal Processing: Collected Papers 1996-97

DEFF Research Database (Denmark)

1997-01-01

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.......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....

Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation.

Science.gov (United States)

Kato, Ayaka; Morita, Kenji

2016-10-01

It has been suggested that dopamine (DA) represents reward-prediction-error (RPE) defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of 'Go' or 'No-Go' selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. Mathematical analyses revealed that underlying potential mechanisms are twofold: (1) decay-induced sustained RPE creates a gradient of 'Go' values towards a goal, and (2) value-contrasts between 'Go' and 'No-Go' are generated because while chosen values are continually updated, unchosen values simply decay. Our model provides potential explanations for the key experimental findings that suggest DA's roles in motivation: (i) slowdown of behavior by post-training blockade of DA signaling, (ii) observations that DA blockade severely impairs effortful actions to obtain rewards while largely sparing seeking of easily obtainable rewards, and (iii) relationships between the reward amount, the level of motivation reflected in the speed of behavior, and the average level of DA. These results indicate that reinforcement learning with value-decay, or forgetting, provides a parsimonious mechanistic account for the DA's roles in value-learning and motivation. Our results also suggest that when biological systems for value-learning

Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation.

Directory of Open Access Journals (Sweden)

Ayaka Kato

2016-10-01

Full Text Available It has been suggested that dopamine (DA represents reward-prediction-error (RPE defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of 'Go' or 'No-Go' selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. Mathematical analyses revealed that underlying potential mechanisms are twofold: (1 decay-induced sustained RPE creates a gradient of 'Go' values towards a goal, and (2 value-contrasts between 'Go' and 'No-Go' are generated because while chosen values are continually updated, unchosen values simply decay. Our model provides potential explanations for the key experimental findings that suggest DA's roles in motivation: (i slowdown of behavior by post-training blockade of DA signaling, (ii observations that DA blockade severely impairs effortful actions to obtain rewards while largely sparing seeking of easily obtainable rewards, and (iii relationships between the reward amount, the level of motivation reflected in the speed of behavior, and the average level of DA. These results indicate that reinforcement learning with value-decay, or forgetting, provides a parsimonious mechanistic account for the DA's roles in value-learning and motivation. Our results also suggest that when biological systems

Modeling laser velocimeter signals as triply stochastic Poisson processes

Science.gov (United States)

Mayo, W. T., Jr.

1976-01-01

Previous models of laser Doppler velocimeter (LDV) systems have not adequately described dual-scatter signals in a manner useful for analysis and simulation of low-level photon-limited signals. At low photon rates, an LDV signal at the output of a photomultiplier tube is a compound nonhomogeneous filtered Poisson process, whose intensity function is another (slower) Poisson process with the nonstationary rate and frequency parameters controlled by a random flow (slowest) process. In the present paper, generalized Poisson shot noise models are developed for low-level LDV signals. Theoretical results useful in detection error analysis and simulation are presented, along with measurements of burst amplitude statistics. Computer generated simulations illustrate the difference between Gaussian and Poisson models of low-level signals.

Signal Processing in Medical Ultrasound B-mode Imaging

International Nuclear Information System (INIS)

Song, Tai Kyong

2000-01-01

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

Processes of Learning with Regard to Students’ Learning Difficulties in Mathematics

Directory of Open Access Journals (Sweden)

Amalija Zakelj

2014-06-01

Full Text Available In the introduction, we write about the process of learning mathematics: the development of mathematical concepts, numerical and spatial imagery on reading and understanding of texts, etc. The central part of the paper is devoted to the study, in which we find that identifying the learning processes associated with learning difficulties of students in mathematics, is not statistically significantly different between primary school teachers and teachers of mathematics. Both groups expose the development of numerical concepts, logical reasoning, and reading and understanding the text as the ones with which difficulties in learning mathematics appear the most frequently. All the processes of learning that the teachers assessed as the ones that represent the greatest barriers to learning have a fairly uniform average estimates of the degree of complexity, ranging from 2.6 to 2.8, which is very close to the estimate makes learning very difficult.

Striatal and Hippocampal Entropy and Recognition Signals in Category Learning: Simultaneous Processes Revealed by Model-Based fMRI

Science.gov (United States)

Davis, Tyler; Love, Bradley C.; Preston, Alison R.

2012-01-01

Category learning is a complex phenomenon that engages multiple cognitive processes, many of which occur simultaneously and unfold dynamically over time. For example, as people encounter objects in the world, they simultaneously engage processes to determine their fit with current knowledge structures, gather new information about the objects, and…

Learning and Motivational Processes When Students Design Curriculum‐Based Digital Learning Games

DEFF Research Database (Denmark)

Weitze, Charlotte Lærke

2016-01-01

This design‐based research (DBR) project has developed an overall gamified learning design (big Game) to facilitate the learning process for adult students by inviting them to be their own learning designers through designing digital learning games (small games) in cross‐disciplinary subject...... matters. The DBR project has investigated and experimented with which elements, methods, and processes are important when aiming at creating a cognitive complex (Anderson and Krathwohl, 2001) and motivating learning process within a reusable game‐based learning design. This project took place in a co......, or programming provide a rich context for learning, since the construction of artefacts, in this case learning games, enables reflection and new ways of thinking. The students learned from reflection and interaction with the tools alone as well as in collaboration with peers. After analysing the students...

Ultra-Fast Optical Signal Processing in Nonlinear Silicon Waveguides

DEFF Research Database (Denmark)

Oxenløwe, Leif Katsuo; Galili, Michael; Pu, Minhao

2011-01-01

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....

A soft-core processor architecture optimised for radar signal processing applications

CSIR Research Space (South Africa)

Broich, R

2013-12-01

Full Text Available -performance soft-core processing architecture is proposed. To develop such a processing architecture, data and signal-flow characteristics of common radar signal processing algorithms are analysed. Each algorithm is broken down into signal processing...

Decoding Signal Processing at the Single-Cell Level

Energy Technology Data Exchange (ETDEWEB)

Wiley, H. Steven

2017-12-01

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.

SignalPlant: an open signal processing software platform

Czech Academy of Sciences Publication Activity Database

Plešinger, Filip; Jurčo, Juraj; Halámek, Josef; Jurák, Pavel

2016-01-01

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

Digital signal processing for He3 proportional counter

International Nuclear Information System (INIS)

Zeynalov, Sh.S.; Ahmadov, Q.S.

2010-01-01

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.

Chronically Increased G[subscript s][alpha] Signaling Disrupts Associative and Spatial Learning

Science.gov (United States)

Bourtchouladze, Rusiko; Patterson, Susan L.; Kelly, Michele P.; Kreibich, Arati; Kandel, Eric R.; Abel, Ted

2006-01-01

The cAMP/PKA pathway plays a critical role in learning and memory systems in animals ranging from mice to "Drosophila" to "Aplysia." Studies of olfactory learning in "Drosophila" suggest that altered expression of either positive or negative regulators of the cAMP/PKA signaling pathway beyond a certain optimum range may be deleterious. Here we…

Roles of NO signaling in long-term memory formation in visual learning in an insect.

Directory of Open Access Journals (Sweden)

Yukihisa Matsumoto

Full Text Available Many insects exhibit excellent capability of visual learning, but the molecular and neural mechanisms are poorly understood. This is in contrast to accumulation of information on molecular and neural mechanisms of olfactory learning in insects. In olfactory learning in insects, it has been shown that cyclic AMP (cAMP signaling critically participates in the formation of protein synthesis-dependent long-term memory (LTM and, in some insects, nitric oxide (NO-cyclic GMP (cGMP signaling also plays roles in LTM formation. In this study, we examined the possible contribution of NO-cGMP signaling and cAMP signaling to LTM formation in visual pattern learning in crickets. Crickets that had been subjected to 8-trial conditioning to associate a visual pattern with water reward exhibited memory retention 1 day after conditioning, whereas those subjected to 4-trial conditioning exhibited 30-min memory retention but not 1-day retention. Injection of cycloheximide, a protein synthesis inhibitor, into the hemolymph prior to 8-trial conditioning blocked formation of 1-day memory, whereas it had no effect on 30-min memory formation, indicating that 1-day memory can be characterized as protein synthesis-dependent long-term memory (LTM. Injection of an inhibitor of the enzyme producing an NO or cAMP prior to 8-trial visual conditioning blocked LTM formation, whereas it had no effect on 30-min memory formation. Moreover, injection of an NO donor, cGMP analogue or cAMP analogue prior to 4-trial conditioning induced LTM. Induction of LTM by an NO donor was blocked by DDA, an inhibitor of adenylyl cyclase, an enzyme producing cAMP, but LTM induction by a cAMP analogue was not impaired by L-NAME, an inhibitor of NO synthase. The results indicate that cAMP signaling is downstream of NO signaling for visual LTM formation. We conclude that visual learning and olfactory learning share common biochemical cascades for LTM formation.

Phonocardiography Signal Processing

CERN Document Server

Abbas, Abbas K

2009-01-01

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

Digital signal processing algorithms for nuclear particle spectroscopy

International Nuclear Information System (INIS)

Zejnalova, O.; Zejnalov, Sh.; Hambsch, F.J.; Oberstedt, S.

2007-01-01

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

Registration and processing of acoustic signal in rock drilling

Directory of Open Access Journals (Sweden)

Futó Jozef

2002-03-01

Full Text Available For the determination of an effective rock disintegration for a given tool and rock type it is needed to define an optimal disintegration regime. Optimisation of the disintegration process by drilling denotes the finding out an appropriate couple of input parameters of disintegration, i.e. the thrust and revolutions for a quasi-equal rock environment. The disintegration process can be optimised to reach the maximum immediate drilling rate, to reach the minimum specific disintegration energy or to reach the maximum ratio of immediate drilling rate and specific disintegration energy. For the determination of the optimal thrust and revolutions it is needed to monitor the disintegration process. Monitoring of the disintegration process in real conditions is complicated by unfavourable factors, such as the presence of water, dust, vibrations etc. Following our present experience in the monitoring of drilling or full-profile driving, we try to replace the monitoring of input values by monitoring of the scanned acoustic signal. This method of monitoring can extend the optimisation of disintegration process in the technical practice. Its advantage consists in the registration of one acoustic signal by an appropriate microphone. Monitoring of acoustic signal is used also in monitoring of metal machining by milling and turning jobs. The research results of scanning of the acoustic signal in machining of metals are encouraging. Acoustic signal can be processed by different statistical parameters. The paper decribes some results of monitoring of the acoustic signal in rock disintegration on the drilling stand of the Institute of Geotechnics SAS in Košice. The acoustic signal has been registered and processed in no-load run of electric motor, in no-load run of electric motor with a drilling fluid, and in the Ruskov andesite drilling. Registration and processing of the acoustic signal is solved as a part of the research grant task within the basic research

Removing Background Noise with Phased Array Signal Processing

Science.gov (United States)

Podboy, Gary; Stephens, David

2015-01-01

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.

Predicting Process Behaviour using Deep Learning

OpenAIRE

Evermann, Joerg; Rehse, Jana-Rebecca; Fettke, Peter

2016-01-01

Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real da...

Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals

Directory of Open Access Journals (Sweden)

Kai Wang

2016-01-01

Full Text Available Health is vital to every human being. To further improve its already respectable medical technology, the medical community is transitioning towards a proactive approach which anticipates and mitigates risks before getting ill. This approach requires measuring the physiological signals of human and analyzes these data at regular intervals. In this paper, we present a novel approach to apply deep learning in physiological signals analysis that allows doctor to identify latent risks. However, extracting high level information from physiological time-series data is a hard problem faced by the machine learning communities. Therefore, in this approach, we apply model based on convolutional neural network that can automatically learn features from raw physiological signals in an unsupervised manner and then based on the learned features use multivariate Gauss distribution anomaly detection method to detect anomaly data. Our experiment is shown to have a significant performance in physiological signals anomaly detection. So it is a promising tool for doctor to identify early signs of illness even if the criteria are unknown a priori.

Detectors and signal processing for high-energy physics

International Nuclear Information System (INIS)

Rehak, P.

1981-01-01

Basic principles of the particle detection and signal processing for high-energy physics experiments are presented. It is shown that the optimum performance of a properly designed detector system is not limited by incidental imperfections, but solely by more fundamental limitations imposed by the quantum nature and statistical behavior of matter. The noise sources connected with the detection and signal processing are studied. The concepts of optimal filtering and optimal detector/amplifying device matching are introduced. Signal processing for a liquid argon calorimeter is analyzed in some detail. The position detection in gas counters is studied. Resolution in drift chambers for the drift coordinate measurement as well as the second coordinate measurement is discussed

Digital signals processing using non-linear orthogonal transformation in frequency domain

Directory of Open Access Journals (Sweden)

Ivanichenko E.V.

2017-12-01

Full Text Available The rapid progress of computer technology in recent decades led to a wide introduction of methods of digital information processing practically in all fields of scientific research. In this case, among various applications of computing one of the most important places is occupied by digital processing systems signals (DSP that are used in data processing remote solution tasks of navigation of aerospace and marine objects, communications, radiophysics, digital optics and in a number of other applications. Digital Signal Processing (DSP is a dynamically developing an area that covers both technical and software tools. Related areas for digital signal processing are theory information, in particular, the theory of optimal signal reception and theory pattern recognition. In the first case, the main problem is signal extraction against a background of noise and interference of a different physical nature, and in the second - automatic recognition, i.e. classification and signal identification. In the digital processing of signals under a signal, we mean its mathematical description, i.e. a certain real function, containing information on the state or behavior of a physical system under an event that can be defined on a continuous or discrete space of time variation or spatial coordinates. In the broad sense, DSP systems mean a complex algorithmic, hardware and software. As a rule, systems contain specialized technical means of preliminary (or primary signal processing and special technical means for secondary processing of signals. Means of pretreatment are designed to process the original signals observed in general case against a background of random noise and interference of a different physical nature and represented in the form of discrete digital samples, for the purpose of detecting and selection (selection of the useful signal and evaluation characteristics of the detected signal. A new method of digital signal processing in the frequency

Attracting and repelling in homogeneous signal processes

International Nuclear Information System (INIS)

Downarowicz, T; Grzegorek, P; Lacroix, Y

2010-01-01

Attracting and repelling are discussed on two levels: in abstract signal processes and in signal processes arising as returns to a fixed set in an ergodic dynamical system. In the first approach, among other things, we give three examples in which the sum of two Poisson (hence neutral—neither attracting nor repelling) processes comes out either neutral or attracting, or repelling, depending on how the two processes depend on each other. The main new result of the second type concerns so-called 'composite events' in the form of a union of all cylinders over blocks belonging to the δ-ball in the Hamming distance around a fixed block. We prove that in a typical ergodic nonperiodic process the majority of such 'composite events' reveal strong attracting. We discuss the practical interpretation of this result

Multivariate Analysis for the Processing of Signals

Directory of Open Access Journals (Sweden)

Beattie J.R.

2014-01-01

Full Text Available Real-world experiments are becoming increasingly more complex, needing techniques capable of tracking this complexity. Signal based measurements are often used to capture this complexity, where a signal is a record of a sample’s response to a parameter (e.g. time, displacement, voltage, wavelength that is varied over a range of values. In signals the responses at each value of the varied parameter are related to each other, depending on the composition or state sample being measured. Since signals contain multiple information points, they have rich information content but are generally complex to comprehend. Multivariate Analysis (MA has profoundly transformed their analysis by allowing gross simplification of the tangled web of variation. In addition MA has also provided the advantage of being much more robust to the influence of noise than univariate methods of analysis. In recent years, there has been a growing awareness that the nature of the multivariate methods allows exploitation of its benefits for purposes other than data analysis, such as pre-processing of signals with the aim of eliminating irrelevant variations prior to analysis of the signal of interest. It has been shown that exploiting multivariate data reduction in an appropriate way can allow high fidelity denoising (removal of irreproducible non-signals, consistent and reproducible noise-insensitive correction of baseline distortions (removal of reproducible non-signals, accurate elimination of interfering signals (removal of reproducible but unwanted signals and the standardisation of signal amplitude fluctuations. At present, the field is relatively small but the possibilities for much wider application are considerable. Where signal properties are suitable for MA (such as the signal being stationary along the x-axis, these signal based corrections have the potential to be highly reproducible, and highly adaptable and are applicable in situations where the data is noisy or

Processing oscillatory signals by incoherent feedforward loops

Science.gov (United States)

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

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

2012 Proceedings of the International Conference on Communications, Signal Processing, and Systems

CERN Document Server

Wang, Wei; Mu, Jiasong; Liang, Jing; Zhang, Baoju; Pi, Yiming; Zhao, Chenglin

2012-01-01

Communications, Signal Processing, and Systems is a collection of contributions coming out of the International Conference on Communications, Signal Processing, and Systems (CSPS) held October 2012. This book provides the state-of-art developments of Communications, Signal Processing, and Systems, and their interactions in multidisciplinary fields, such as Smart Grid. The book also examines Radar Systems, Sensor Networks, Radar Signal Processing, Design and Implementation of Signal Processing Systems and Applications. Written by experts and students in the fields of Communications, Signal Processing, and Systems.

Discrete random signal processing and filtering primer with Matlab

CERN Document Server

Poularikas, Alexander D

2013-01-01

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

Advanced Signal Processing for Wireless Multimedia Communications

Directory of Open Access Journals (Sweden)

Xiaodong Wang

2000-01-01

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.

Signal and image processing in medical applications

CERN Document Server

Kumar, Amit; Rahim, B Abdul; Kumar, D Sravan

2016-01-01

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.

Streamlining digital signal processing a tricks of the trade guidebook

CERN Document Server

2012-01-01

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.

Learning during Processing: Word Learning Doesn't Wait for Word Recognition to Finish

Science.gov (United States)

Apfelbaum, Keith S.; McMurray, Bob

2017-01-01

Previous research on associative learning has uncovered detailed aspects of the process, including what types of things are learned, how they are learned, and where in the brain such learning occurs. However, perceptual processes, such as stimulus recognition and identification, take time to unfold. Previous studies of learning have not addressed…

Framework for Conducting Empirical Observations of Learning Processes.

Science.gov (United States)

Fischer, Hans Ernst; von Aufschnaiter, Stephan

1993-01-01

Reviews four hypotheses about learning: Comenius's transmission-reception theory, information processing theory, Gestalt theory, and Piagetian theory. Uses the categories preunderstanding, conceptual change, and learning processes to classify and assess investigations on learning processes. (PR)

Digital signal processing for He3 proportional counter

International Nuclear Information System (INIS)

Ahmadov, Q.S.; Institute of Radiation Problems, ANAS, Baku

2011-01-01

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 [1] [2]. 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

Computational Analysis and Simulation of Empathic Behaviors: a Survey of Empathy Modeling with Behavioral Signal Processing Framework.

Science.gov (United States)

Xiao, Bo; Imel, Zac E; Georgiou, Panayiotis; Atkins, David C; Narayanan, Shrikanth S

2016-05-01

Empathy is an important psychological process that facilitates human communication and interaction. Enhancement of empathy has profound significance in a range of applications. In this paper, we review emerging directions of research on computational analysis of empathy expression and perception as well as empathic interactions, including their simulation. We summarize the work on empathic expression analysis by the targeted signal modalities (e.g., text, audio, and facial expressions). We categorize empathy simulation studies into theory-based emotion space modeling or application-driven user and context modeling. We summarize challenges in computational study of empathy including conceptual framing and understanding of empathy, data availability, appropriate use and validation of machine learning techniques, and behavior signal processing. Finally, we propose a unified view of empathy computation and offer a series of open problems for future research.

Biomedical signal acquisition, processing and transmission using smartphone

International Nuclear Information System (INIS)

Roncagliolo, Pablo; Arredondo, Luis; Gonzalez, AgustIn

2007-01-01

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

Biomedical signal acquisition, processing and transmission using smartphone

Energy Technology Data Exchange (ETDEWEB)

Roncagliolo, Pablo [Department of Electronics, Universidad Tecnica Federico Santa Maria, Casilla 110-V, ValparaIso (Chile); Arredondo, Luis [Department of Biomedical Engineering, Universidad de ValparaIso, Casilla 123-V, ValparaIso (Chile); Gonzalez, AgustIn [Department of Electronics, Universidad Tecnica Federico Santa MarIa, Casilla 110-V, ValparaIso (Chile)

2007-11-15

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.

Biomedical signal acquisition, processing and transmission using smartphone

Science.gov (United States)

Roncagliolo, Pablo; Arredondo, Luis; González, Agustín

2007-11-01

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.

Multidimensional Signal Processing for Sensing & Communications

Science.gov (United States)

2015-07-29

Spectrum Sensing,” submitted to IEEE Intl. Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Cancun, Mexico , 13-16 Dec. 2015...Sensing,” submitted to IEEE Intl. Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Cancun, Mexico , 13-16 Dec. 2015...diversity in echolocating mammals ,” IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 65- 75, Jan. 2009. DISTRIBUTION A: Distribution approved for

Application of machine learning and expert systems to Statistical Process Control (SPC) chart interpretation

Science.gov (United States)

Shewhart, Mark

1991-01-01

Statistical Process Control (SPC) charts are one of several tools used in quality control. Other tools include flow charts, histograms, cause and effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division has developed a hybrid machine learning expert system prototype which automates the process of constructing and interpreting control charts.

Quaternion Fourier transforms for signal and image processing

CERN Document Server

Ell, Todd A; Sangwine, Stephen J

2014-01-01

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.

Linear signal processing using silicon micro-ring resonators

DEFF Research Database (Denmark)

Peucheret, Christophe; Ding, Yunhong; Ou, Haiyan

2012-01-01

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.......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....

Learning Process Questionnaire Manual. Student Approaches to Learning and Studying.

Science.gov (United States)

Biggs, John B.

This manual describes the theory behind the Learning Process Questionnaire (LPQ) used in Australia and defines what the subscale and scale scores mean. The LPQ is a 36-item self-report questionnaire that yields scores on three basic motives for learning and three learning strategies, and on the approaches to learning that are formed by these…

THE LET ME LEARN PROFESSIONAL LEARNING PROCESS FOR TEACHER TRANSFORMATION

OpenAIRE

Calleja, Colin

2013-01-01

This research set out to explore how a group of nine educators from a Catholic Church school in Malta, who have attended the Let Me Learn professional Learning process (LMLpLp), experienced personal and professional transformation. This study investigates those factors influencing participants in their transformative learning journey. It also explores the dynamics of transformative learning and whether individual transformation affects the school’s transformative learning experience. More spe...

Methods and systems for the processing of physiological signals

International Nuclear Information System (INIS)

Cosnac, B. de; Gariod, R.; Max, J.; Monge, V.

1975-01-01

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

Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.

Science.gov (United States)

Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk

2018-04-06

Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry-wide problem in BPM, referred to as the "Low-N" problem, wherein a product has a limited production history. The current best industrial practice to address the Low-N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low-N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine-learning methods. To the best of authors' knowledge, this is the first article to provide a solution to the Low-N problem in biopharmaceutical manufacturing using machine-learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low-N scenarios. © 2018 Wiley Periodicals, Inc.

A Signal for the Need to Restructure the Learning Process.

Science.gov (United States)

Breivik, Patricia Senn

1991-01-01

Although the U.S. will not disintegrate tomorrow if information literacy and resource-based learning remain underfunded, today's disadvantaged groups will fall further behind, as a new "information elite" emerges. The American Library Association's 1989 information literacy report is one step toward creating a national agenda for…

Propofol exposure during late stages of pregnancy impairs learning and memory in rat offspring via the BDNF-TrkB signalling pathway.

Science.gov (United States)

Zhong, Liang; Luo, Foquan; Zhao, Weilu; Feng, Yunlin; Wu, Liuqin; Lin, Jiamei; Liu, Tianyin; Wang, Shengqiang; You, Xuexue; Zhang, Wei

2016-10-01

The brain-derived neurotrophic factor (BDNF)-tyrosine kinase B (TrkB) (BDNF-TrkB) signalling pathway plays a crucial role in regulating learning and memory. Synaptophysin provides the structural basis for synaptic plasticity and depends on BDNF processing and subsequent TrkB signalling. Our previous studies demonstrated that maternal exposure to propofol during late stages of pregnancy impaired learning and memory in rat offspring. The purpose of this study is to investigate whether the BDNF-TrkB signalling pathway is involved in propofol-induced learning and memory impairments. Propofol was intravenously infused into pregnant rats for 4 hrs on gestational day 18 (E18). Thirty days after birth, learning and memory of offspring was assessed by the Morris water maze (MWM) test. After the MWM test, BDNF and TrkB transcript and protein levels were measured in rat offspring hippocampus tissues using real-time PCR (RT-PCR) and immunohistochemistry (IHC), respectively. The levels of phosphorylated-TrkB (phospho-TrkB) and synaptophysin were measured by western blot. It was discovered that maternal exposure to propofol on day E18 impaired spatial learning and memory of rat offspring, decreased mRNA and protein levels of BDNF and TrkB, and decreased the levels of both phospho-TrkB and synaptophysin in the hippocampus. Furthermore, the TrkB agonist 7,8-dihydroxyflavone (7,8-DHF) reversed all of the observed changes. Treatment with 7,8-DHF had no significant effects on the offspring that were not exposed to propofol. The results herein indicate that maternal exposure to propofol during the late stages of pregnancy impairs spatial learning and memory of offspring by disturbing the BDNF-TrkB signalling pathway. The TrkB agonist 7,8-DHF might be a potential therapy for learning and memory impairments induced by maternal propofol exposure. © 2016 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular

Pseudo random signal processing theory and application

CERN Document Server

Zepernick, Hans-Jurgen

2013-01-01

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

Machine learning techniques applied to system characterization and equalization

DEFF Research Database (Denmark)

Zibar, Darko; Thrane, Jakob; Wass, Jesper

2016-01-01

Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals.......Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals....

Research on on-line monitoring technology for steel ball's forming process based on load signal analysis method

Science.gov (United States)

Li, Ying-jun; Ai, Chang-sheng; Men, Xiu-hua; Zhang, Cheng-liang; Zhang, Qi

2013-04-01

This paper presents a novel on-line monitoring technology to obtain forming quality in steel ball's forming process based on load signal analysis method, in order to reveal the bottom die's load characteristic in initial cold heading forging process of steel balls. A mechanical model of the cold header producing process is established and analyzed by using finite element method. The maximum cold heading force is calculated. The results prove that the monitoring on the cold heading process with upsetting force is reasonable and feasible. The forming defects are inflected on the three feature points of the bottom die signals, which are the initial point, infection point, and peak point. A novel PVDF piezoelectric force sensor which is simple on construction and convenient on installation is designed. The sensitivity of the PVDF force sensor is calculated. The characteristics of PVDF force sensor are analyzed by FEM. The PVDF piezoelectric force sensor is fabricated to acquire the actual load signals in the cold heading process, and calibrated by a special device. The measuring system of on-line monitoring is built. The characteristics of the actual signals recognized by learning and identification algorithm are in consistence with simulation results. Identification of actual signals shows that the timing difference values of all feature points for qualified products are not exceed ±6 ms, and amplitude difference values are less than ±3%. The calibration and application experiments show that PVDF force sensor has good static and dynamic performances, and is competent at dynamic measuring on upsetting force. It greatly improves automatic level and machining precision. Equipment capacity factor with damages identification method depends on grade of steel has been improved to 90%.

Conceptualizing impact assessment as a learning process

International Nuclear Information System (INIS)

Sánchez, Luis E.; Mitchell, Ross

2017-01-01

This paper explores how project developers and their consultants, government regulators and stakeholders can learn from the impact assessment (IA) process, thus potentially improving its effectiveness and enhancing project sustainability. Despite the benefits that learning can bring to an organization, failure to learn appears commonplace both within the IA process and, once approved, subsequent industrial development. To nurture organizational learning through IA, enabling structures that foster information sharing and interpretation and enhance organizational memory are needed. In this paper learning outcomes are grouped into three categories: acquisition of knowledge and skills, developing new behaviors and developing sustainability-oriented norms and values. Means to achieve such outcomes include education and training, experiential learning, learning through public participation (social learning) and a ‘learning organization approach’. Societal expectations increasingly demand not only projects that ‘pass’ the review criteria of regulators, financiers and the community, but IA processes capable of delivering sustainable outcomes that include learning and sharing of knowledge. It is proposed that learning be treated as a purposeful – not as an accidental – outcome of IA, and facilitated by adopting a ‘learning organization approach’ coupled with best practice such as early stakeholder engagement. - Highlights: • Proponents are challenged to develop projects that deliver sustainable outcomes. • Passing the test of government approval may be insufficient to obtain a social license. • Learning by all stakeholders is vital to meet these challenges. • Learning outcomes have to go beyond instrumental learning to reach new behaviors, norms and values. • A “learning organization approach” can promote mutual learning and improve project design.

Conceptualizing impact assessment as a learning process

Energy Technology Data Exchange (ETDEWEB)

Sánchez, Luis E., E-mail: lsanchez@usp.br [Escola Politécnica, University of São Paulo, Av. Prof. Mello Moraes, 2373, 05508-900 São Paulo (Brazil); Mitchell, Ross, E-mail: ross.mitchell@ualberta.net [Shell International Exploration & Production BV (Netherlands)

2017-01-15

This paper explores how project developers and their consultants, government regulators and stakeholders can learn from the impact assessment (IA) process, thus potentially improving its effectiveness and enhancing project sustainability. Despite the benefits that learning can bring to an organization, failure to learn appears commonplace both within the IA process and, once approved, subsequent industrial development. To nurture organizational learning through IA, enabling structures that foster information sharing and interpretation and enhance organizational memory are needed. In this paper learning outcomes are grouped into three categories: acquisition of knowledge and skills, developing new behaviors and developing sustainability-oriented norms and values. Means to achieve such outcomes include education and training, experiential learning, learning through public participation (social learning) and a ‘learning organization approach’. Societal expectations increasingly demand not only projects that ‘pass’ the review criteria of regulators, financiers and the community, but IA processes capable of delivering sustainable outcomes that include learning and sharing of knowledge. It is proposed that learning be treated as a purposeful – not as an accidental – outcome of IA, and facilitated by adopting a ‘learning organization approach’ coupled with best practice such as early stakeholder engagement. - Highlights: • Proponents are challenged to develop projects that deliver sustainable outcomes. • Passing the test of government approval may be insufficient to obtain a social license. • Learning by all stakeholders is vital to meet these challenges. • Learning outcomes have to go beyond instrumental learning to reach new behaviors, norms and values. • A “learning organization approach” can promote mutual learning and improve project design.

Loss of Signal, Aeromedical Lessons Learned from the STS-107 Columbia Space Shuttle Mishap

Science.gov (United States)

Stepaniak, Phillip C.; Patlach, Robert

2014-01-01

Loss of Signal, a NASA publication to be available in May 2014 presents the aeromedical lessons learned from the Columbia accident that will enhance crew safety and survival on human space flight missions. These lessons were presented to limited audiences at three separate Aerospace Medical Association (AsMA) conferences: in 2004 in Anchorage, Alaska, on the causes of the accident; in 2005 in Kansas City, Missouri, on the response, recovery, and identification aspects of the investigation; and in 2011, again in Anchorage, Alaska, on future implications for human space flight. As we embark on the development of new spacefaring vehicles through both government and commercial efforts, the NASA Johnson Space Center Human Health and Performance Directorate is continuing to make this information available to a wider audience engaged in the design and development of future space vehicles. Loss of Signal summarizes and consolidates the aeromedical impacts of the Columbia mishap process-the response, recovery, identification, investigative studies, medical and legal forensic analysis, and future preparation that are needed to respond to spacecraft mishaps. The goal of this book is to provide an account of the aeromedical aspects of the Columbia accident and the investigation that followed, and to encourage aerospace medical specialists to continue to capture information, learn from it, and improve procedures and spacecraft designs for the safety of future crews. This poster presents an outline of Loss of Signal contents and highlights from each of five sections - the mission and mishap, the response, the investigation, the analysis and the future.

Investigation of the Relationship between Learning Process and Learning Outcomes in E-Learning Environments

Science.gov (United States)

Yurdugül, Halil; Menzi Çetin, Nihal

2015-01-01

Problem Statement: Learners can access and participate in online learning environments regardless of time and geographical barriers. This brings up the umbrella concept of learner autonomy that contains self-directed learning, self-regulated learning and the studying process. Motivation and learning strategies are also part of this umbrella…

Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

Science.gov (United States)

Hwang, Bosun; You, Jiwoo; Vaessen, Thomas; Myin-Germeys, Inez; Park, Cheolsoo; Zhang, Byoung-Tak

2018-02-08

Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods. The Deep ECGNet was developed through various experiments and analysis of ECG waveforms. We proposed the optimal recurrent and convolutional neural networks architecture, and also the optimal convolution filter length (related to the P, Q, R, S, and T wave durations of ECG) and pooling length (related to the heart beat period) based on the optimization experiments and analysis on the waveform characteristics of ECG signals. The experiments were also conducted with conventional methods using HRV parameters and frequency features as a benchmark test. The data used in this study were obtained from Kwangwoon University in Korea (13 subjects, Case 1) and KU Leuven University in Belgium (9 subjects, Case 2). Experiments were designed according to various experimental protocols to elicit stressful conditions. The proposed framework to recognize stress conditions, the Deep ECGNet, outperformed the conventional approaches with the highest accuracy of 87.39% for Case 1 and 73.96% for Case 2, respectively, that is, 16.22% and 10.98% improvements compared with those of the conventional HRV method. We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.

Enhancement of the automatic ultrasonic signal processing system using digital technology

International Nuclear Information System (INIS)

Koo, In Soo; Park, H. Y.; Suh, Y. S.; Kim, D. Hoon; Huh, S.; Sung, S. H.; Jang, G. S.; Ryoo, S. G.; Choi, J. H.; Kim, Y. H.; Lee, J. C.; Kim, D. Hyun; Park, H. J.; Kim, Y. C.; Lee, J. P.; Park, C. H.; Kim, M. S.

1999-12-01

The objective of this study is to develop the automatic ultrasonic signal processing system which can be used in the inspection equipment to assess the integrity of the reactor vessel by enhancing the performance of the ultrasonic signal processing system. Main activities of this study divided into three categories such as the development of the circuits for generating ultrasonic signal and receiving the signal from the inspection equipment, the development of signal processing algorithm and H/W of the data processing system, and the development of the specification for application programs and system S/W for the analysis and evaluation computer. The results of main activities are as follows 1) the design of the ultrasonic detector and the automatic ultrasonic signal processing system by using the investigation of the state-of-the-art technology in the inside and outside of the country. 2) the development of H/W and S/W of the data processing system based on the results. Especially, the H/W of the data processing system, which have both advantages of digital and analog controls through the real-time digital signal processing, was developed using the DSP which can process the digital signal in the real-time, and was developed not only firmware of the data processing system in order for the peripherals but also the test algorithm of specimen for the calibration. The application programs and the system S/W of the analysis/evaluation computer were developed. Developed equipment was verified by the performance test. Based on developed prototype for the automatic ultrasonic signal processing system, the localization of the overall ultrasonic inspection equipment for nuclear industries would be expected through the further studies of the H/W establishment of real applications, developing the S/W specification of the analysis computer. (author)

All-optical signal processing for optical packet switching networks

NARCIS (Netherlands)

Liu, Y.; Hill, M.T.; Calabretta, N.; Tangdiongga, E.; Geldenhuys, R.; Zhang, S.; Li, Z.; Waardt, de H.; Khoe, G.D.; Dorren, H.J.S.; Iftekharuddin, K.M.; awwal, A.A.S.

2005-01-01

We discuss how all-optical signal processing might play a role in future all-optical packet switched networks. We introduce a concept of optical packet switches that employ entirely all-optical signal processing technology. The optical packet switch is made out of three functional blocks: the

Time reversal signal processing in acoustic emission testing

Czech Academy of Sciences Publication Activity Database

Převorovský, Zdeněk; Krofta, Josef; Kober, Jan; Dvořáková, Zuzana; Chlada, Milan; Dos Santos, S.

2014-01-01

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

Recent Advancements in Semiconductor-based Optical Signal Processing

DEFF Research Database (Denmark)

Nielsen, M L; Mørk, Jesper

2006-01-01

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....

Liquid Argon TPC Signal Formation, Signal Processing and Hit Reconstruction

Energy Technology Data Exchange (ETDEWEB)

Baller, Bruce [Fermilab

2017-03-11

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.

LEOS 2002: summer electronics and signal processing symposium

International Nuclear Information System (INIS)

Karadzhinov, Ljupcho; Ivanovski, Zoran

2002-01-01

LEOS 2002 was the first Macedonian symposium on electronics and signal processing. It was organized in recognition to a growing need to exchange the research results as well as to raise competent discussions among different research groups from both academic and industrial environment in Macedonia. The topics covered in this meeting were defined by the IEEE experts as follows: Power Electronics, Industrial Electronics, Signal Processing, Image and Video Processing, Instrumentation and Measurements, Engineering in Medicine and Biology, Electron Devices and Automatic Control. Papers were mainly from Macedonia, but there was one invited lecture

Learning processes across knowledge domains

DEFF Research Database (Denmark)

Hall-Andersen, Lene Bjerg; Broberg, Ole

2014-01-01

Purpose - The purpose of this paper is to shed light on the problematics of learning across knowledge boundaries in organizational settings. The paper specifically explores learning processes that emerge, when a new knowledge domain is introduced into an existing organizational practice with the ...

Digital signal processing - growth of a technology

International Nuclear Information System (INIS)

Peek, J.B.H.

1985-01-01

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.)

Applying advanced digital signal processing techniques in industrial radioisotopes applications

International Nuclear Information System (INIS)

Mahmoud, H.K.A.E.

2012-01-01

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

Cellular Signal Mechanisms of Reward-Related Plasticity in the Hippocampus

Directory of Open Access Journals (Sweden)

Masako Isokawa

2012-01-01

Full Text Available The hippocampus has the extraordinary capacity to process and store information. Consequently, there is an intense interest in the mechanisms that underline learning and memory. Synaptic plasticity has been hypothesized to be the neuronal substrate for learning. Ca2+ and Ca2+-activated kinases control cellular processes of most forms of hippocampal synapse plasticity. In this paper, I aim to integrate our current understanding of Ca2+-mediated synaptic plasticity and metaplasticity in motivational and reward-related learning in the hippocampus. I will introduce two representative neuromodulators that are widely studied in reward-related learning (e.g., ghrelin and endocannabinoids and show how they might contribute to hippocampal neuron activities and Ca2+-mediated signaling processes in synaptic plasticity. Additionally, I will discuss functional significance of these two systems and their signaling pathways for its relevance to maladaptive reward learning leading to addiction.

Real-time Color Codes for Assessing Learning Process

OpenAIRE

Dzelzkalēja, L; Kapenieks, J

2016-01-01

Effective assessment is an important way for improving the learning process. There are existing guidelines for assessing the learning process, but they lack holistic digital knowledge society considerations. In this paper the authors propose a method for real-time evaluation of students’ learning process and, consequently, for quality evaluation of teaching materials both in the classroom and in the distance learning environment. The main idea of the proposed Color code method (CCM) is to use...

High-speed optical signal processing using time lenses

DEFF Research Database (Denmark)

Galili, Michael; Hu, Hao; Guan, Pengyu

2015-01-01

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....

Students' learning processes during school-based learning and workplace learning in vocational education : a review

NARCIS (Netherlands)

Dr. Harmen Schaap; Dr. Liesbeth Baartman; Prof.Dr. Elly de Bruijn

2012-01-01

This article reviews 24 articles in order to get a structured view on student's learning processes when dealing with a combination of school-based learning and workplace learning in vocational education. It focuses on six main themes: students' expertise development, students' learning styles,

Holistic processing from learned attention to parts.

Science.gov (United States)

Chua, Kao-Wei; Richler, Jennifer J; Gauthier, Isabel

2015-08-01

Attention helps us focus on what is most relevant to our goals, and prior work has shown that aspects of attention can be learned. Learned inattention to parts can abolish holistic processing of faces, but it is unknown whether learned attention to parts is sufficient to cause a change from part-based to holistic processing with objects. We trained subjects to individuate nonface objects (Greebles) from 2 categories: Ploks and Glips. Diagnostic information was in complementary halves for the 2 categories. Holistic processing was then tested with Plok-Glip composites that combined the kind of part that was diagnostic or nondiagnostic during training. Exposure to Greeble parts resulted in general failures of selective attention for nondiagnostic composites, but face-like holistic processing was only observed for diagnostic composites. These results demonstrated a novel link between learned attentional control and the acquisition of holistic processing. (c) 2015 APA, all rights reserved).

Multibeam swath bathymetry signal processing techniques

Digital Repository Service at National Institute of Oceanography (India)

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...

All-Optical Signal Processing using Silicon Devices

DEFF Research Database (Denmark)

Oxenløwe, Leif Katsuo; Pu, Minhao; Ding, Yunhong

2014-01-01

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...

Digital signal processing in power system protection and control

CERN Document Server

Rebizant, Waldemar; Wiszniewski, Andrzej

2011-01-01

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

Surface light scattering: integrated technology and signal processing

DEFF Research Database (Denmark)

Lading, L.; Dam-Hansen, C.; Rasmussen, E.

1997-01-01

systems representing increasing levels of integration are considered. It is demonstrated that efficient signal and data processing can be achieved by evaluation of the statistics of the derivative of the instantaneous phase of the detector signal. (C) 1997 Optical Society of America....

A FPGA-based signal processing unit for a GEM array detector

International Nuclear Information System (INIS)

Yen, W.W.; Chou, H.P.

2013-06-01

in the present study, a signal processing unit for a GEM one-dimensional array detector is presented to measure the trajectory of photoelectrons produced by cosmic X-rays. The present GEM array detector system has 16 signal channels. The front-end unit provides timing signals from trigger units and energy signals from charge sensitive amplifies. The prototype of the processing unit is implemented using commercial field programmable gate array circuit boards. The FPGA based system is linked to a personal computer for testing and data analysis. Tests using simulated signals indicated that the FPGA-based signal processing unit has a good linearity and is flexible for parameter adjustment for various experimental conditions (authors)

Mathematical principles of signal processing Fourier and wavelet analysis

CERN Document Server

Brémaud, Pierre

2002-01-01

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...

All-optical microwave signal processing based on optical phase modulation

Science.gov (United States)

Zeng, Fei

This thesis presents a theoretical and experimental study of optical phase modulation and its applications in all-optical microwave signal processing, which include all-optical microwave filtering, all-optical microwave mixing, optical code-division multiple-access (CDMA) coding, and ultrawideband (UWB) signal generation. All-optical microwave signal processing can be considered as the use of opto-electronic devices and systems to process microwave signals in the optical domain, which provides several significant advantages such as low loss, low dispersion, light weight, high time bandwidth products, and immunity to electromagnetic interference. In conventional approaches, the intensity of an optical carrier is modulated by a microwave signal based on direct modulation or external modulation. The intensity-modulated optical signal is then fed to a photonic circuit or system to achieve specific signal processing functionalities. The microwave signal being processed is usually obtained based on direct detection, i.e., an opto-electronic conversion by use of a photodiode. In this thesis, the research efforts are focused on the optical phase modulation and its applications in all-optical microwave signal processing. To avoid using coherent detection which is complicated and costly, simple and effective phase modulation to intensity modulation (PM-IM) conversion schemes are pursued. Based on a theoretical study of optical phase modulation, two approaches to achieving PM-IM conversions are proposed. In the first approach, the use of chromatic dispersion induced by a dispersive device to alter the phase relationships among the sidebands and the optical carrier of a phase-modulated optical signal to realize PM-IM conversion is investigated. In the second approach, instead of using a dispersive device, the PM-IM conversion is realized based on optical frequency discrimination implemented using an optical filter. We show that the proposed PM-IM conversion schemes can be

Considerations for implementing an organizational lessons learned process.

Energy Technology Data Exchange (ETDEWEB)

Fosshage, Erik D

2013-05-01

This report examines the lessons learned process by a review of the literature in a variety of disciplines, and is intended as a guidepost for organizations that are considering the implementation of their own closed-loop learning process. Lessons learned definitions are provided within the broader context of knowledge management and the framework of a learning organization. Shortcomings of existing practices are summarized in an attempt to identify common pitfalls that can be avoided by organizations with fledgling experiences of their own. Lessons learned are then examined through a dual construct of both process and mechanism, with emphasis on integrating into organizational processes and promoting lesson reuse through data attributes that contribute toward changed behaviors. The report concludes with recommended steps for follow-on efforts.

K-mean clustering algorithm for processing signals from compound semiconductor detectors

International Nuclear Information System (INIS)

Tada, Tsutomu; Hitomi, Keitaro; Wu, Yan; Kim, Seong-Yun; Yamazaki, Hiromichi; Ishii, Keizo

2011-01-01

The K-mean clustering algorithm was employed for processing signal waveforms from TlBr detectors. The signal waveforms were classified based on its shape reflecting the charge collection process in the detector. The classified signal waveforms were processed individually to suppress the pulse height variation of signals due to the charge collection loss. The obtained energy resolution of a 137 Cs spectrum measured with a 0.5 mm thick TlBr detector was 1.3% FWHM by employing 500 clusters.

Machine Learning for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

DEFF Research Database (Denmark)

Wass, J.; Thrane, Jakob; Piels, Molly

2016-01-01

Supervised machine learning methods are applied and demonstrated experimentally for inband OSNR estimation and modulation format classification in optical communication systems. The proposed methods accurately evaluate coherent signals up to 64QAM using only intensity information....

Haptic teleoperation systems signal processing perspective

CERN Document Server

Lee, Jae-young

2015-01-01

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.

Signal and image processing for monitoring and testing at EDF

International Nuclear Information System (INIS)

Georgel, B.; Garreau, D.

1992-04-01

The quality of monitoring and non destructive testing devices in plants and utilities today greatly depends on the efficient processing of signal and image data. In this context, signal or image processing techniques, such as adaptive filtering or detection or 3D reconstruction, are required whenever manufacturing nonconformances or faulty operation have to be recognized and identified. This paper reviews the issues of industrial image and signal processing, by briefly considering the relevant studies and projects under way at EDF. (authors). 1 fig., 11 refs

Social multimedia signals a signal processing approach to social network phenomena

CERN Document Server

Roy, Suman Deb

2014-01-01

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

Homework in the Learning Process

Directory of Open Access Journals (Sweden)

Gómez Sandra M.

2000-08-01

Full Text Available A problem has been observed that creates difficulties in the normal and productive development of the English courses. Without any doubt, doing homework is very important in the learning process of a new language. Doubtless it affects the student’s active participation in the classroom and his relationship to partners and teachers. Because of this, a research project was done with the aim to finding out strategies to ensure students do homework and make it part of the learning process, erasing the image of homework as a punishment.

Blind I/Q Signal Separation-Based Solutions for Receiver Signal Processing

Directory of Open Access Journals (Sweden)

Visa Koivunen

2005-09-01

Full Text Available This paper introduces some novel digital signal processing (DSP-based approaches to some of the most fundamental tasks of radio receivers, namely, channel equalization, carrier synchronization, and I/Q mismatch compensation. The leading principle is to show that all these problems can be solved blindly (i.e., without training signals by forcing the I and Q components of the observed data as independent as possible. Blind signal separation (BSS is then introduced as an efficient tool to carry out these tasks, and simulation examples are used to illustrate the performance of the proposed approaches. The main application area of the presented carrier synchronization and I/Q mismatch compensation techniques is in direct-conversion type receivers, while the proposed channel equalization principles basically apply to any radio architecture.

Digital signal processing for wireless communication using Matlab

CERN Document Server

Gopi, E S

2016-01-01

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.

Learning multimodal dictionaries.

Science.gov (United States)

Monaci, Gianluca; Jost, Philippe; Vandergheynst, Pierre; Mailhé, Boris; Lesage, Sylvain; Gribonval, Rémi

2007-09-01

Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multimodal data can, in fact, reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multimodal patterns could offer deep insight into the structure of such signals. In this paper, we present a novel model of multimodal signals based on their sparse decomposition over a dictionary of multimodal structures. An algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal is proposed, as well. The learning is defined in such a way that it can be accomplished by iteratively solving a generalized eigenvector problem, which makes the algorithm fast, flexible, and free of user-defined parameters. The proposed algorithm is applied to audiovisual sequences and it is able to discover underlying structures in the data. The detection of such audio-video patterns in audiovisual clips allows to effectively localize the sound source on the video in presence of substantial acoustic and visual distractors, outperforming state-of-the-art audiovisual localization algorithms.

An implementation of signal processing algorithms for ultrasonic NDE

International Nuclear Information System (INIS)

Ericsson, L.; Stepinski, T.

1994-01-01

Probability of detection flaws during ultrasonic pulse-echo inspection is often limited by the presence of backscattered echoes from the material structure. A digital signal processing technique for removal of this material noise, referred to as split spectrum processing (SSP), has been developed and verified using laboratory experiments during the last decade. The authors have performed recently a limited scale evaluation of various SSP techniques for ultrasonic signals acquired during the inspection of welds in austenitic steel. They have obtained very encouraging results that indicate promising capabilities of the SSP for inspection of nuclear power plants. Thus, a more extensive investigation of the technique using large amounts of ultrasonic data is motivated. This analysis should employ different combinations of materials, flaws and transducers. Due to the considerable number of ultrasonic signals required to verify the technique for future practical use, a custom-made computer software is necessary. At the request of the Swedish nuclear power industry the authors have developed such a program package. The program provides a user-friendly graphical interface and is intended for processing of B-scan data in a flexible way. Assembled in the program are a number of signal processing algorithms including traditional Split Spectrum Processing and the more recent Cut Spectrum Processing algorithm developed by them. The program and some results obtained using the various algorithms are presented in the paper

ECG signal processing

NARCIS (Netherlands)

2009-01-01

A system extracts an ECG signal from a composite signal (308) representing an electric measurement of a living subject. Identification means (304) identify a plurality of temporal segments (309) of the composite signal corresponding to a plurality of predetermined segments (202,204,206) of an ECG

Single photon laser altimeter simulator and statistical signal processing

Science.gov (United States)

Vacek, Michael; Prochazka, Ivan

2013-05-01

Spaceborne altimeters are common instruments onboard the deep space rendezvous spacecrafts. They provide range and topographic measurements critical in spacecraft navigation. Simultaneously, the receiver part may be utilized for Earth-to-satellite link, one way time transfer, and precise optical radiometry. The main advantage of single photon counting approach is the ability of processing signals with very low signal-to-noise ratio eliminating the need of large telescopes and high power laser source. Extremely small, rugged and compact microchip lasers can be employed. The major limiting factor, on the other hand, is the acquisition time needed to gather sufficient volume of data in repetitive measurements in order to process and evaluate the data appropriately. Statistical signal processing is adopted to detect signals with average strength much lower than one photon per measurement. A comprehensive simulator design and range signal processing algorithm are presented to identify a mission specific altimeter configuration. Typical mission scenarios (celestial body surface landing and topographical mapping) are simulated and evaluated. The high interest and promising single photon altimeter applications are low-orbit (˜10 km) and low-radial velocity (several m/s) topographical mapping (asteroids, Phobos and Deimos) and landing altimetry (˜10 km) where range evaluation repetition rates of ˜100 Hz and 0.1 m precision may be achieved. Moon landing and asteroid Itokawa topographical mapping scenario simulations are discussed in more detail.

Design of signal reception and processing system of embedded ultrasonic endoscope

Science.gov (United States)

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

2009-11-01

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

Creative Problem Solving as a Learning Process

Directory of Open Access Journals (Sweden)

Andreas Ninck

2013-12-01

Full Text Available The Business School at the Bern University of Applied Sciences is offering a new MScBA degree program in business development. The paper presents a practical report about the action learning approach in the course 'Business Analysis and Design'. Our problem-based approach is more than simply 'learning by doing'. In a world of increasing complexity, taking action alone will not result in a learning effect per se. What is imperative is to structure and facilitate the learning process on different levels: individual construction of mental models; understanding needs and developing adequate solutions; critical reflection of methods and processes. Reflective practice, where individuals are learning from their own professional experiences rather than from formal teaching or knowledge transfer, may be the most important source for lifelong learning.

Extinction of avoidance behavior by safety learning depends on endocannabinoid signaling in the hippocampus.

Science.gov (United States)

Micale, Vincenzo; Stepan, Jens; Jurik, Angela; Pamplona, Fabricio A; Marsch, Rudolph; Drago, Filippo; Eder, Matthias; Wotjak, Carsten T

2017-07-01

The development of exaggerated avoidance behavior is largely responsible for the decreased quality of life in patients suffering from anxiety disorders. Studies using animal models have contributed to the understanding of the neural mechanisms underlying the acquisition of avoidance responses. However, much less is known about its extinction. Here we provide evidence in mice that learning about the safety of an environment (i.e., safety learning) rather than repeated execution of the avoided response in absence of negative consequences (i.e., response extinction) allowed the animals to overcome their avoidance behavior in a step-down avoidance task. This process was context-dependent and could be blocked by pharmacological (3 mg/kg, s.c.; SR141716) or genetic (lack of cannabinoid CB1 receptors in neurons expressing dopamine D1 receptors) inactivation of CB1 receptors. In turn, the endocannabinoid reuptake inhibitor AM404 (3 mg/kg, i.p.) facilitated safety learning in a CB1-dependent manner and attenuated the relapse of avoidance behavior 28 days after conditioning. Safety learning crucially depended on endocannabinoid signaling at level of the hippocampus, since intrahippocampal SR141716 treatment impaired, whereas AM404 facilitated safety learning. Other than AM404, treatment with diazepam (1 mg/kg, i.p.) impaired safety learning. Drug effects on behavior were directly mirrored by drug effects on evoked activity propagation through the hippocampal trisynaptic circuit in brain slices: As revealed by voltage-sensitive dye imaging, diazepam impaired whereas AM404 facilitated activity propagation to CA1 in a CB1-dependent manner. In line with this, systemic AM404 enhanced safety learning-induced expression of Egr1 at level of CA1. Together, our data render it likely that AM404 promotes safety learning by enhancing information flow through the trisynaptic circuit to CA1. Copyright © 2017 Elsevier Ltd. All rights reserved.

Problem-Based Learning: An Overview of its Process and Impact on Learning

Directory of Open Access Journals (Sweden)

Elaine H.J. Yew

2016-12-01

Full Text Available In this review, we provide an overview of the process of problem-based learning (PBL and the studies examining the effectiveness of PBL. We also discuss a number of naturalistic and empirical studies that have examined the process of PBL and how its various components impact students’ learning. We conclude that the studies comparing the relative effectiveness of PBL are generally consistent in demonstrating its superior efficacy for longer-term knowledge retention and in the application of knowledge. Studies on the process of PBL, however, are still inconclusive as to which component(s of PBL most significantly impact students’ learning, although causal studies have demonstrated that all the phases of PBL are necessary in influencing students’ learning outcomes.

RF applications in digital signal processing

CERN Document Server

Schilcher, T

2008-01-01

Ever higher demands for stability, accuracy, reproducibility, and monitoring capability are being placed on Low-Level Radio Frequency (LLRF) systems of particle accelerators. Meanwhile, continuing rapid advances in digital signal processing technology are being exploited to meet these demands, thus leading to development of digital LLRF systems. The rst part of this course will begin by focusing on some of the important building-blocks of RF signal processing including mixer theory and down-conversion, I/Q (amplitude and phase) detection, digital down-conversion (DDC) and decimation, concluding with a survey of I/Q modulators. The second part of the course will introduce basic concepts of feedback systems, including examples of digital cavity eld and phase control, followed by radial loop architectures. Adaptive feed-forward systems used for the suppression of repetitive beam disturbances will be examined. Finally, applications and principles of system identi cation approaches will be summarized.

Self-regulated learning processes of medical students during an academic learning task.

Science.gov (United States)

Gandomkar, Roghayeh; Mirzazadeh, Azim; Jalili, Mohammad; Yazdani, Kamran; Fata, Ladan; Sandars, John

2016-10-01

This study was designed to identify the self-regulated learning (SRL) processes of medical students during a biomedical science learning task and to examine the associations of the SRL processes with previous performance in biomedical science examinations and subsequent performance on a learning task. A sample of 76 Year 1 medical students were recruited based on their performance in biomedical science examinations and stratified into previous high and low performers. Participants were asked to complete a biomedical science learning task. Participants' SRL processes were assessed before (self-efficacy, goal setting and strategic planning), during (metacognitive monitoring) and after (causal attributions and adaptive inferences) their completion of the task using an SRL microanalytic interview. Descriptive statistics were used to analyse the means and frequencies of SRL processes. Univariate and multiple logistic regression analyses were conducted to examine the associations of SRL processes with previous examination performance and the learning task performance. Most participants (from 88.2% to 43.4%) reported task-specific processes for SRL measures. Students who exhibited higher self-efficacy (odds ratio [OR] 1.44, 95% confidence interval [CI] 1.09-1.90) and reported task-specific processes for metacognitive monitoring (OR 6.61, 95% CI 1.68-25.93) and causal attributions (OR 6.75, 95% CI 2.05-22.25) measures were more likely to be high previous performers. Multiple analysis revealed that similar SRL measures were associated with previous performance. The use of task-specific processes for causal attributions (OR 23.00, 95% CI 4.57-115.76) and adaptive inferences (OR 27.00, 95% CI 3.39-214.95) measures were associated with being a high learning task performer. In multiple analysis, only the causal attributions measure was associated with high learning task performance. Self-efficacy, metacognitive monitoring and causal attributions measures were associated

Silicon nanowires for ultra-fast and ultrabroadband optical signal processing

DEFF Research Database (Denmark)

Ji, Hua; Hu, Hao; Pu, Minhao

2015-01-01

In this paper, we present recent research on silicon nanowires for ultra-fast and ultra-broadband optical signal processing at DTU Fotonik. The advantages and limitations of using silicon nanowires for optical signal processing are revealed through experimental demonstrations of various optical...

Signal processing method for Johnson noise thermometry

International Nuclear Information System (INIS)

Hwang, I. G.; Moon, B. S.; Kinser, Rpger

2003-01-01

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

USING PCU-CAMEL, A WEB-BASED LEARNING ENVIRONMENT, IN EVALUATING TEACHING-LEARNING PROCESS

Directory of Open Access Journals (Sweden)

Arlinah Imam Rahardjo

2008-01-01

Full Text Available PCU-CAMEL (Petra Christian University-Computer Aided Mechanical Engineering Department Learning Environment has been developed to integrate the use of this web-based learning environment into the traditional, face-to-face setting of class activities. This integrated learning method is designed as an effort to enrich and improve the teaching-learning process at Petra Christian University. A study was conducted to introduce the use of PCU-CAMEL as a tool in evaluating teaching learning process. The study on this method of evaluation was conducted by using a case analysis on the integration of PCU-CAMEL to the traditional face-to-face meetings of LIS (Library Information System class at the Informatics Engineering Department of Petra Christian University. Students’ responses documented in some features of PCU-CAMEL were measured and analyzed to evaluate the effectiveness of this integrated system in developing intrinsic motivation of the LIS students of the first and second semester of 2004/2005 to learn. It is believed that intrinsic motivation can drive students to learn more. From the study conducted, it is concluded that besides its capability in developing intrinsic motivation, PCU-CAMEL as a web-based learning environment, can also serve as an effective tool for both students and instructors to evaluate the teaching-learning process. However, some weaknesses did exist in using this method of evaluating teaching-learning process. The free style and unstructured form of the documentation features of this web-based learning environment can lead to ineffective evaluation results

Fast, multi-channel real-time processing of signals with microsecond latency using graphics processing units

Energy Technology Data Exchange (ETDEWEB)

Rath, N., E-mail: Nikolaus@rath.org; Levesque, J. P.; Mauel, M. E.; Navratil, G. A.; Peng, Q. [Department of Applied Physics and Applied Mathematics, Columbia University, 500 W 120th St, New York, New York 10027 (United States); Kato, S. [Department of Information Engineering, Nagoya University, Nagoya (Japan)

2014-04-15

Fast, digital signal processing (DSP) has many applications. Typical hardware options for performing DSP are field-programmable gate arrays (FPGAs), application-specific integrated DSP chips, or general purpose personal computer systems. This paper presents a novel DSP platform that has been developed for feedback control on the HBT-EP tokamak device. The system runs all signal processing exclusively on a Graphics Processing Unit (GPU) to achieve real-time performance with latencies below 8 μs. Signals are transferred into and out of the GPU using PCI Express peer-to-peer direct-memory-access transfers without involvement of the central processing unit or host memory. Tests were performed on the feedback control system of the HBT-EP tokamak using forty 16-bit floating point inputs and outputs each and a sampling rate of up to 250 kHz. Signals were digitized by a D-TACQ ACQ196 module, processing done on an NVIDIA GTX 580 GPU programmed in CUDA, and analog output was generated by D-TACQ AO32CPCI modules.

Fast, multi-channel real-time processing of signals with microsecond latency using graphics processing units

International Nuclear Information System (INIS)

Rath, N.; Levesque, J. P.; Mauel, M. E.; Navratil, G. A.; Peng, Q.; Kato, S.

2014-01-01

Fast, digital signal processing (DSP) has many applications. Typical hardware options for performing DSP are field-programmable gate arrays (FPGAs), application-specific integrated DSP chips, or general purpose personal computer systems. This paper presents a novel DSP platform that has been developed for feedback control on the HBT-EP tokamak device. The system runs all signal processing exclusively on a Graphics Processing Unit (GPU) to achieve real-time performance with latencies below 8 μs. Signals are transferred into and out of the GPU using PCI Express peer-to-peer direct-memory-access transfers without involvement of the central processing unit or host memory. Tests were performed on the feedback control system of the HBT-EP tokamak using forty 16-bit floating point inputs and outputs each and a sampling rate of up to 250 kHz. Signals were digitized by a D-TACQ ACQ196 module, processing done on an NVIDIA GTX 580 GPU programmed in CUDA, and analog output was generated by D-TACQ AO32CPCI modules

Photonic Ultra-Wideband 781.25-Mb/s Signal Generation and Transmission Incorporating Digital Signal Processing Detection

DEFF Research Database (Denmark)

Gibbon, Timothy Braidwood; Yu, Xianbin; Tafur Monroy, Idelfonso

2009-01-01

The generation of photonic ultra-wideband (UWB) impulse signals using an uncooled distributed-feedback laser is proposed. For the first time, we experimentally demonstrate bit-for-bit digital signal processing (DSP) bit-error-rate measurements for transmission of a 781.25-Mb/s photonic UWB signal...

Fixed-point signal processing

CERN Document Server

Padgett, Wayne T

2009-01-01

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

All-optical signal processing of OTDM and OFDM signals based on time-domain optical fourier transformation

DEFF Research Database (Denmark)

Galili, Michael; Guan, Pengyu; Lillieholm, Mads

2017-01-01

In the talk, we will review recent work on optical signal processing based on time lenses. Various applications of optical Fourier transformation for optical communications will be discussed.......In the talk, we will review recent work on optical signal processing based on time lenses. Various applications of optical Fourier transformation for optical communications will be discussed....

Ultrafast optical signal processing using semiconductor quantum dot amplifiers

DEFF Research Database (Denmark)

Berg, Tommy Winther; Mørk, Jesper

2002-01-01

The linear and nonlinear properties of quantum dot amplifiers are discussed on the basis of an extensive theoretical model. These devices show great potential for linear amplification as well as ultrafast signal processing.......The linear and nonlinear properties of quantum dot amplifiers are discussed on the basis of an extensive theoretical model. These devices show great potential for linear amplification as well as ultrafast signal processing....

Utilization of Smartphone Literacy In Learning Process

Directory of Open Access Journals (Sweden)

Yenni Yuniati

2017-01-01

Full Text Available The utilization of smartphones is increasingly developing among the students. It causes various modifications of attitude and behavior, that media literacy nowadays becomes highly important. Therefore, media literacy shall become the priority for related parties specifically parents and teachers. In addition to helping to find information and to conduct fast communication, smartphone is also functions in formal learning process among the students.The aim of this research is to acknowledge the utilization of smartphones in formal learning process. This study uses qualitative descriptive method which makes serious efforts in describing and depicting utilization of smartphones in learning process among Junior High School students in Bandung. The research result shows that smartphones may function as a device to channel messages and to stimulate the mind, feeling and desire of the students which may encourage learning process in them and to give positive values and to bridge media literacy among the students.

Structural health monitoring an advanced signal processing perspective

CERN Document Server

Chen, Xuefeng; Mukhopadhyay, Subhas

2017-01-01

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.

Supporting Multimedia Learning with Visual Signalling and Animated Pedagogical Agent: Moderating Effects of Prior Knowledge

Science.gov (United States)

Johnson, A. M.; Ozogul, G.; Reisslein, M.

2015-01-01

An experiment examined the effects of visual signalling to relevant information in multiple external representations and the visual presence of an animated pedagogical agent (APA). Students learned electric circuit analysis using a computer-based learning environment that included Cartesian graphs, equations and electric circuit diagrams. The…

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

Science.gov (United States)

Tam, Wing-Kin; Yang, Zhi

2018-05-01

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

General programmed system for physiological signal processing

Energy Technology Data Exchange (ETDEWEB)

Tournier, E; Monge, J; Magnet, C; Sonrel, C

1975-01-01

Improvements made to the general programmed signal acquisition and processing system, Plurimat S, are described, the aim being to obtain a less specialized system adapted to the biological and medical field. In this modified system the acquisition will be simplified. The standard processings offered will be integrated to a real advanced language which will enable the user to create his own processings, the loss of speed being compensated by a greater flexibility and universality. The observation screen will be large and the quality of the recording very good so that a large signal fraction may be displayed. The data will be easily indexed and filed for subsequent display and processing. This system will be used for two kinds of task: it can either be specialized, as an integral part of measurement and diagnostic preparation equipment used routinely in clinical work (e.g. vectocardiographic examination), or its versatility can be used for studies of limited duration to gain information in a given field or to study new diagnosis or treatment methods.

Genomic signal processing for DNA sequence clustering.

Science.gov (United States)

Mendizabal-Ruiz, Gerardo; Román-Godínez, Israel; Torres-Ramos, Sulema; Salido-Ruiz, Ricardo A; Vélez-Pérez, Hugo; Morales, J Alejandro

2018-01-01

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.

Pulse shaping for all-optical signal processing of ultra-high bit rate serial data signals

DEFF Research Database (Denmark)

Palushani, Evarist

The following thesis concerns pulse shaping and optical waveform manipulation for all-optical signal processing of ultra-high bit rate serial data signals, including generation of optical pulses in the femtosecond regime, serial-to-parallel conversion and terabaud coherent optical time division...

Distributed learning process: principles of design and implementation

Directory of Open Access Journals (Sweden)

G. N. Boychenko

2016-01-01

Full Text Available At the present stage, broad information and communication technologies (ICT usage in educational practices is one of the leading trends of global education system development. This trend has led to the instructional interaction models transformation. Scientists have developed the theory of distributed cognition (Salomon, G., Hutchins, E., and distributed education and training (Fiore, S. M., Salas, E., Oblinger, D. G., Barone, C. A., Hawkins, B. L.. Educational process is based on two separated in time and space sub-processes of learning and teaching which are aimed at the organization of fl exible interactions between learners, teachers and educational content located in different non-centralized places.The purpose of this design research is to fi nd a solution for the problem of formalizing distributed learning process design and realization that is signifi cant in instructional design. The solution to this problem should take into account specifi cs of distributed interactions between team members, which becomes collective subject of distributed cognition in distributed learning process. This makes it necessary to design roles and functions of the individual team members performing distributed educational activities. Personal educational objectives should be determined by decomposition of team objectives into functional roles of its members with considering personal and learning needs and interests of students.Theoretical and empirical methods used in the study: theoretical analysis of philosophical, psychological, and pedagogical literature on the issue, analysis of international standards in the e-learning domain; exploration on practical usage of distributed learning in academic and corporate sectors; generalization, abstraction, cognitive modelling, ontology engineering methods.Result of the research is methodology for design and implementation of distributed learning process based on the competency approach. Methodology proposed by

Interference in ballistic motor learning: specificity and role of sensory error signals

DEFF Research Database (Denmark)

Lundbye-Jensen, Jesper; Petersen, Tue Hvass; Rothwell, John C

2011-01-01

Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity in overlap......Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity...... in overlapping circuits and predicted specificity. To test this, subjects learned a ballistic motor task. Interference was observed following subsequent learning of an accuracy-tracking task, but only if the competing task involved the same muscles and movement direction. Interference was not observed from a non......-learning task suggesting that interference requires competing learning. Subsequent learning of the competing task 4 h after initial learning did not cause interference suggesting disruption of early motor memory consolidation as one possible mechanism underlying interference. Repeated transcranial magnetic...

Functions of the learning portfolio in student teachers' learning process

NARCIS (Netherlands)

Mansvelder-Longayroux, D.D.; Beijaard, D.; Verloop, N.; Vermunt, J.D.

2007-01-01

In this study, we aimed to develop a framework that could be used to describe the value of the learning portfolio for the learning process of individual student teachers. Retrospective interviews with 21 student teachers were used, as were their portfolio-evaluation reports on their experiences of

Functions of the learning portfolio in student teachers' learning process

NARCIS (Netherlands)

Mansvelder-Longayroux, Desiree D.; Beijaard, Douwe; Verloop, Nico; Vermunt, Jan D.

In this study, we aimed to develop a framework that could be used to describe the value of the learning portfolio for the learning process of individual student teachers. Retrospective interviews with 21 student teachers were used, as were their portfolio-evaluation reports on their experiences Of

Smart signal processing for an evolving electric grid

Science.gov (United States)

Silva, Leandro Rodrigues Manso; Duque, Calos Augusto; Ribeiro, Paulo F.

2015-12-01

Electric grids are interconnected complex systems consisting of generation, transmission, distribution, and active loads, recently called prosumers as they produce and consume electric energy. Additionally, these encompass a vast array of equipment such as machines, power transformers, capacitor banks, power electronic devices, motors, etc. that are continuously evolving in their demand characteristics. Given these conditions, signal processing is becoming an essential assessment tool to enable the engineer and researcher to understand, plan, design, and operate the complex and smart electronic grid of the future. This paper focuses on recent developments associated with signal processing applied to power system analysis in terms of characterization and diagnostics. The following techniques are reviewed and their characteristics and applications discussed: active power system monitoring, sparse representation of power system signal, real-time resampling, and time-frequency (i.e., wavelets) applied to power fluctuations.

An integrated domain specific language for post-processing and visualizing electrophysiological signals in Java.

Science.gov (United States)

Strasser, T; Peters, T; Jagle, H; Zrenner, E; Wilke, R

2010-01-01

Electrophysiology of vision - especially the electroretinogram (ERG) - is used as a non-invasive way for functional testing of the visual system. The ERG is a combined electrical response generated by neural and non-neuronal cells in the retina in response to light stimulation. This response can be recorded and used for diagnosis of numerous disorders. For both clinical practice and clinical trials it is important to process those signals in an accurate and fast way and to provide the results as structured, consistent reports. Therefore, we developed a freely available and open-source framework in Java (http://www.eye.uni-tuebingen.de/project/idsI4sigproc). The framework is focused on an easy integration with existing applications. By leveraging well-established software patterns like pipes-and-filters and fluent interfaces as well as by designing the application programming interfaces (API) as an integrated domain specific language (DSL) the overall framework provides a smooth learning curve. Additionally, it already contains several processing methods and visualization features and can be extended easily by implementing the provided interfaces. In this way, not only can new processing methods be added but the framework can also be adopted for other areas of signal processing. This article describes in detail the structure and implementation of the framework and demonstrate its application through the software package used in clinical practice and clinical trials at the University Eye Hospital Tuebingen one of the largest departments in the field of visual electrophysiology in Europe.

Loss of Signal, Aeromedical Lessons Learned for the STS-I07 Columbia Space Shuttle Mishap

Science.gov (United States)

Patlach, Robert; Stepaniak, Philip C.; Lane, Helen W.

2014-01-01

Loss of Signal, a NASA publication to be available in May 2014, presents the aeromedical lessons learned from the Columbia accident that will enhance crew safety and survival on human space flight missions. These lessons were presented to limited audiences at three separate Aerospace Medical Association (AsMA) conferences: in 2004 in Anchorage, Alaska, on the causes of the accident; in 2005 in Kansas City, Missouri, on the response, recovery, and identification aspects of the investigation; and in 2011, again in Anchorage, Alaska, on future implications for human space flight. As we embark on the development of new spacefaring vehicles through both government and commercial efforts, the NASA Johnson Space Center Human Health and Performance Directorate is continuing to make this information available to a wider audience engaged in the design and development of future space vehicles. Loss of Signal summarizes and consolidates the aeromedical impacts of the Columbia mishap process-the response, recovery, identification, investigative studies, medical and legal forensic analysis, and future preparation that are needed to respond to spacecraft mishaps. The goals of this book are to provide an account of the aeromedical aspects of the Columbia accident and the investigation that followed, and to encourage aerospace medical specialists to continue to capture information, learn from it, and improve procedures and spacecraft designs for the safety of future crews.

Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods

Science.gov (United States)

Soroush, Masoud; Weinberger, Charles B.

2010-01-01

This manuscript presents a successful application of inductive learning in process modeling. It describes two process modeling courses that use inductive learning methods such as inquiry learning and problem-based learning, among others. The courses include a novel collection of multi-disciplinary complementary process modeling examples. They were…

Cyclic LTI Systems in Digital Signal Processing

National Research Council Canada - National Science Library

Vaidyanathan, P

1998-01-01

.... While circular convolution has been the centerpiece of many algorithms in signal processing for decades, such freedom, especially from the viewpoint of linear system theory, has not been studied in the past...

Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

Science.gov (United States)

Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

2015-01-01

Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

Acoustic Emission Signal Processing Technique to Characterize Reactor In-Pile Phenomena

Energy Technology Data Exchange (ETDEWEB)

Vivek Agarwal; Magdy Samy Tawfik; James A Smith

2014-07-01

Existing and developing advanced sensor technologies and instrumentation will allow non-intrusive in-pile measurement of temperature, extension, and fission gases when coupled with advanced signal processing algorithms. The transmitted measured sensor signals from inside to the outside of containment structure are corrupted by noise and are attenuated, thereby reducing the signal strength and signal-to-noise ratio. Identification and extraction of actual signal (representative of an in-pile phenomenon) is a challenging and complicated process. In this paper, empirical mode decomposition technique is proposed to reconstruct actual sensor signal by partially combining intrinsic mode functions. Reconstructed signal corresponds to phenomena and/or failure modes occurring inside the reactor. In addition, it allows accurate non-intrusive monitoring and trending of in-pile phenomena.

The Promise of Process. Learning through Enterprise in Higher Education

DEFF Research Database (Denmark)

Frederiksen, Signe Hedeboe

Entrepreneurial process increasingly attracts attention as an opportunity to learn in higher education. Students learn “through” enterprise, when they actively engage in an entrepreneurial process while reflecting on their actions and experiences. In this qualitative field study, I investigate how...... postgraduate students pursued opportunities to learn in a process-driven entrepreneurship module. Drawing on situated learning theory, I find that students tried to access learning opportunities through a constant dynamic of participation which involved contradictory participatory stances. The learning through...... paradigm in enterprise education imposes conditions on the learning environment and involves images of a particular learner, who is able to take advantage of this learning opportunity. The findings indicate a contradictory process of becoming a legitimate entrepreneurial learner which is more uncertain...

Proceedings of IEEE Workshop on Machine Learning for Signal Processing XIV

DEFF Research Database (Denmark)

Larsen, Jan

of machine learning. We would like to express our appreciation and gratitude to UFMA, EMAP, ELETROBRÁS, ELETRONORTE, ALUMAR and BASA, who contributed to the workshop by providing technical and financial support in various forms. Our warmest, special thanks go to our plenary speakers: Prof. Petar M. Djuric...

Gaussian processes for machine learning.

Science.gov (United States)

Seeger, Matthias

2004-04-01

Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical analyses of various properties are available. This paper gives an introduction to Gaussian processes on a fairly elementary level with special emphasis on characteristics relevant in machine learning. It draws explicit connections to branches such as spline smoothing models and support vector machines in which similar ideas have been investigated. Gaussian process models are routinely used to solve hard machine learning problems. They are attractive because of their flexible non-parametric nature and computational simplicity. Treated within a Bayesian framework, very powerful statistical methods can be implemented which offer valid estimates of uncertainties in our predictions and generic model selection procedures cast as nonlinear optimization problems. Their main drawback of heavy computational scaling has recently been alleviated by the introduction of generic sparse approximations.13,78,31 The mathematical literature on GPs is large and often uses deep concepts which are not required to fully understand most machine learning applications. In this tutorial paper, we aim to present characteristics of GPs relevant to machine learning and to show up precise connections to other "kernel machines" popular in the community. Our focus is on a simple presentation, but references to more detailed sources are provided.

Ultrasonic signal processing for sizing under-clad flaws

International Nuclear Information System (INIS)

Shankar, R.; Paradiso, T.J.; Lane, S.S.; Quinn, J.R.

1985-01-01

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

Maglev Train Signal Processing Architecture Based on Nonlinear Discrete Tracking Differentiator.

Science.gov (United States)

Wang, Zhiqiang; Li, Xiaolong; Xie, Yunde; Long, Zhiqiang

2018-05-24

In a maglev train levitation system, signal processing plays an important role for the reason that some sensor signals are prone to be corrupted by noise due to the harsh installation and operation environment of sensors and some signals cannot be acquired directly via sensors. Based on these concerns, an architecture based on a new type of nonlinear second-order discrete tracking differentiator is proposed. The function of this signal processing architecture includes filtering signal noise and acquiring needed signals for levitation purposes. The proposed tracking differentiator possesses the advantages of quick convergence, no fluttering, and simple calculation. Tracking differentiator's frequency characteristics at different parameter values are studied in this paper. The performance of this new type of tracking differentiator is tested in a MATLAB simulation and this tracking-differentiator is implemented in Very-High-Speed Integrated Circuit Hardware Description Language (VHDL). In the end, experiments are conducted separately on a test board and a maglev train model. Simulation and experiment results show that the performance of this novel signal processing architecture can fulfill the real system requirement.

Maglev Train Signal Processing Architecture Based on Nonlinear Discrete Tracking Differentiator

Directory of Open Access Journals (Sweden)

Zhiqiang Wang

2018-05-01

Full Text Available In a maglev train levitation system, signal processing plays an important role for the reason that some sensor signals are prone to be corrupted by noise due to the harsh installation and operation environment of sensors and some signals cannot be acquired directly via sensors. Based on these concerns, an architecture based on a new type of nonlinear second-order discrete tracking differentiator is proposed. The function of this signal processing architecture includes filtering signal noise and acquiring needed signals for levitation purposes. The proposed tracking differentiator possesses the advantages of quick convergence, no fluttering, and simple calculation. Tracking differentiator’s frequency characteristics at different parameter values are studied in this paper. The performance of this new type of tracking differentiator is tested in a MATLAB simulation and this tracking-differentiator is implemented in Very-High-Speed Integrated Circuit Hardware Description Language (VHDL. In the end, experiments are conducted separately on a test board and a maglev train model. Simulation and experiment results show that the performance of this novel signal processing architecture can fulfill the real system requirement.

Down-Regulation of Neuregulin1/ErbB4 Signaling in the Hippocampus Is Critical for Learning and Memory.

Science.gov (United States)

Tian, Jia; Geng, Fei; Gao, Feng; Chen, Yi-Hua; Liu, Ji-Hong; Wu, Jian-Lin; Lan, Yu-Jie; Zeng, Yuan-Ning; Li, Xiao-Wen; Yang, Jian-Ming; Gao, Tian-Ming

2017-08-01

Hippocampal function is important for learning and memory, and dysfunction of the hippocampus has been linked to the pathophysiology of neuropsychiatric diseases such as schizophrenia. Neuregulin1 (NRG1) and ErbB4, two susceptibility genes for schizophrenia, reportedly modulate long-term potentiation (LTP) at hippocampal Schaffer collateral (SC)-CA1 synapses. However, little is known regarding the contribution of hippocampal NRG1/ErbB4 signaling to learning and memory function. Here, quantitative real-time PCR and Western blotting were used to assess the mRNA and protein levels of NRG1 and ErbB4. Pharmacological and genetic approaches were used to manipulate NRG1/ErbB4 signaling, following which learning and memory behaviors were evaluated using the Morris water maze, Y-maze test, and the novel object recognition test. Spatial learning was found to reduce hippocampal NRG1 and ErbB4 expression. The blockade of NRG1/ErbB4 signaling in hippocampal CA1, either by neutralizing endogenous NRG1 or inhibiting/ablating ErbB4 receptor activity, enhanced hippocampus-dependent spatial learning, spatial working memory, and novel object recognition memory. Accordingly, administration of exogenous NRG1 impaired those functions. More importantly, the specific ablation of ErbB4 in parvalbumin interneurons also improved learning and memory performance. The manipulation of NRG1/ErbB4 signaling in the present study revealed that NRG1/ErbB4 activity in the hippocampus is critical for learning and memory. These findings might provide novel insights on the pathophysiological mechanisms of schizophrenia and a new target for the treatment of Alzheimer's disease, which is characterized by a progressive decline in cognitive function.

Flexible Processes in Project-Centred Learning

NARCIS (Netherlands)

Ceri, Stefano; Matera, Maristella; Raffio, Alessandro; Spoelstra, Howard

2007-01-01

Ceri, S., Matera, M., Raffio, A. & Spoelstra, H. (2007). Flexible Processes in Project-Centred Learning. In E. Duval, R. Klamma, and M. Wolpers (Eds.), European Conference on Technology Enhanced Learning, Lecture Notes in Computer Science, Vol. 4753, pp. 463-468. Berlin Heidelberg: Springer-Verlag

Digital signal processing for velocity measurements in dynamical material's behaviour studies

International Nuclear Information System (INIS)

Devlaminck, Julien; Luc, Jerome; Chanal, Pierre-Yves

2014-01-01

In this work, we describe different configurations of optical fiber interferometers (types Michelson and Mach-Zehnder) used to measure velocities during dynamical material's behaviour studies. We detail the algorithms of processing developed and optimized to improve the performance of these interferometers especially in terms of time and frequency resolutions. Three methods of analysis of interferometric signals were studied. For Michelson interferometers, the time-frequency analysis of signals by Short-Time Fourier Transform (STFT) is compared to a time-frequency analysis by Continuous Wavelet Transform (CWT). The results have shown that the CWT was more suitable than the STFT for signals with low signal-to-noise, and low velocity and high acceleration areas. For Mach- Zehnder interferometers, the measurement is carried out by analyzing the phase shift between three interferometric signals (Triature processing). These three methods of digital signal processing were evaluated, their measurement uncertainties estimated, and their restrictions or operational limitations specified from experimental results performed on a pulsed power machine. (authors)

Generation of a command language for nuclear signal and image processing on the basis of a general interactive system

International Nuclear Information System (INIS)

Pretschner, D.P.; Pfeiffer, G.; Deutsches Elektronen-Sychnchrotron

1981-01-01

In the field of nuclear medicine, BASIC and FORTRAN are currently being favoured as higher-level programming languages for computer-aided signal processing, and most operating systems of so-called ''freely programmable analyzers'' in nuclear wards have compilers for this purpose. However, FORTRAN is not an interactive language and thus not suited for conversational computing as a man-machine interface. BASIC, on the other hand, although a useful starting language for beginners, is not sufficiently sophisticated for complex nuclear medicine problems involving detailed calculations. Integration of new methods of signal acquisition, processing and presentation into an existing system or generation of new systems is difficult in FORTRAN, BASIC or ASSEMBLER and can only be done by system specialists, not by nuclear physicians. This problem may be solved by suitable interactive systems that are easy to learn, flexible, transparent and user-friendly. An interactive system of this type, XDS, was developed in the course of a project on evaluation of radiological image sequences. An XDS-generated command processing system for signal and image processing in nuclear medicine is described. The system is characterized by interactive program development and execution, problem-relevant data types, a flexible procedure concept and an integrated system implementation language for modern image processing algorithms. The advantages of the interactive system are illustrated by an example of diagnosis by nuclear methods. (orig.) [de

Separation of pulsar signals from noise using supervised machine learning algorithms

Science.gov (United States)

Bethapudi, S.; Desai, S.

2018-04-01

We evaluate the performance of four different machine learning (ML) algorithms: an Artificial Neural Network Multi-Layer Perceptron (ANN MLP), Adaboost, Gradient Boosting Classifier (GBC), and XGBoost, for the separation of pulsars from radio frequency interference (RFI) and other sources of noise, using a dataset obtained from the post-processing of a pulsar search pipeline. This dataset was previously used for the cross-validation of the SPINN-based machine learning engine, obtained from the reprocessing of the HTRU-S survey data (Morello et al., 2014). We have used the Synthetic Minority Over-sampling Technique (SMOTE) to deal with high-class imbalance in the dataset. We report a variety of quality scores from all four of these algorithms on both the non-SMOTE and SMOTE datasets. For all the above ML methods, we report high accuracy and G-mean for both the non-SMOTE and SMOTE cases. We study the feature importances using Adaboost, GBC, and XGBoost and also from the minimum Redundancy Maximum Relevance approach to report algorithm-agnostic feature ranking. From these methods, we find that the signal to noise of the folded profile to be the best feature. We find that all the ML algorithms report FPRs about an order of magnitude lower than the corresponding FPRs obtained in Morello et al. (2014), for the same recall value.

Advanced Signal Processing for MIMO-OFDM Receivers

DEFF Research Database (Denmark)

Manchón, Carles Navarro

This thesis deals with a wide range of topics within the research area of advanced baseband receiver design for wireless communication systems. In particular, the work focuses on signal processing algorithms for receivers in multiple-input multiple-output (MIMO) orthogonal frequency-division mult......This thesis deals with a wide range of topics within the research area of advanced baseband receiver design for wireless communication systems. In particular, the work focuses on signal processing algorithms for receivers in multiple-input multiple-output (MIMO) orthogonal frequency...... the structure of the receiver with the hope that the resulting heuristic architecture will exhibit the desired behavior and performance. On the other hand, one can employ analytical frameworks to pose the problem as the optimization of a global objective function subject to certain constraints. This work...

Impaired insulin signaling and spatial learning in middle-aged rats: The role of PTP1B.

Science.gov (United States)

Kuga, Gabriel Keine; Muñoz, Vitor Rosetto; Gaspar, Rafael Calais; Nakandakari, Susana Castelo Branco Ramos; da Silva, Adelino Sanchez Ramos; Botezelli, José Diego; Leme, José Alexandre Curiacos de Almeida; Gomes, Ricardo José; de Moura, Leandro Pereira; Cintra, Dennys Esper; Ropelle, Eduardo Rochete; Pauli, José Rodrigo

2018-04-01

The insulin and Brain-Derived Neurotrophic Factor (BDNF) signaling in the hippocampus promotes synaptic plasticity and memory formation. On the other hand, aging is related to the cognitive decline and is the main risk factor for Alzheimer's Disease (AD). The Protein-Tyrosine Phosphatase 1B (PTP1B) is related to several deleterious processes in neurons and emerges as a promising target for new therapies. In this context, our study aims to investigate the age-related changes in PTP1B content, insulin signaling, β-amyloid content, and Tau phosphorylation in the hippocampus of middle-aged rats. Young (3 months) and middle-aged (17 months) Wistar rats were submitted to Morris-water maze (MWM) test, insulin tolerance test, and molecular analysis in the hippocampus. Aging resulted in increased body weight, and insulin resistance and decreases learning process in MWM. Interestingly, the middle-aged rats have higher levels of PTP-1B, lower phosphorylation of IRS-1, Akt, GSK3β, mTOR, and TrkB. Also, the aging process increased Tau phosphorylation and β-amyloid content in the hippocampus region. In summary, this study provides new evidence that aging-related PTP1B increasing, contributing to insulin resistance and the onset of the AD. Copyright © 2018 Elsevier Inc. All rights reserved.

Real-time digital signal processing fundamentals, implementations and applications

CERN Document Server

Kuo, Sen M; Tian, Wenshun

2013-01-01

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

Smart Educational Process Based on Personal Learning Capabilities

OpenAIRE

Gavriushenko, Mariia; Lindberg, Renny S. N.; Khriyenko, Oleksiy

2017-01-01

Personalized learning is increasingly gaining popularity, especially with the development of information technology and modern educational resources for learning. Each person is individual and has different knowledge background, different kind of memory, different learning speed. Teacher can adapt learning course, learning instructions or learning material according to the majority of learners in class, but that means that learning process is not adapted to the personality of each...

High signal to noise ratio THz spectroscopy with ASOPS and signal processing schemes for mapping and controlling molecular and bulk relaxation processes

International Nuclear Information System (INIS)

Hadjiloucas, S; Walker, G C; Bowen, J W; Becerra, V M; Zafiropoulos, A; Galvao, R K H

2009-01-01

Asynchronous Optical Sampling has the potential to improve signal to noise ratio in THz transient sperctrometry. The design of an inexpensive control scheme for synchronising two femtosecond pulse frequency comb generators at an offset frequency of 20 kHz is discussed. The suitability of a range of signal processing schemes adopted from the Systems Identification and Control Theory community for further processing recorded THz transients in the time and frequency domain are outlined. Finally, possibilities for femtosecond pulse shaping using genetic algorithms are mentioned.

High signal to noise ratio THz spectroscopy with ASOPS and signal processing schemes for mapping and controlling molecular and bulk relaxation processes

Energy Technology Data Exchange (ETDEWEB)

Hadjiloucas, S; Walker, G C; Bowen, J W; Becerra, V M [Cybernetics, School of Systems Engineering, University of Reading, RG6 6AY (United Kingdom); Zafiropoulos, A [Biosystems Engineering Department, School of Agricultural Technology, Technological Educational Institute of Larissa, 411 10, Larissa (Greece); Galvao, R K H, E-mail: s.hadjiloucas@reading.ac.u [Divisao de Engenharia Eletronica, Instituto Tecnologico de Aeronautica, Sao Jose dos Campos, SP, 12228-900 Brazil (Brazil)

2009-08-01

Asynchronous Optical Sampling has the potential to improve signal to noise ratio in THz transient sperctrometry. The design of an inexpensive control scheme for synchronising two femtosecond pulse frequency comb generators at an offset frequency of 20 kHz is discussed. The suitability of a range of signal processing schemes adopted from the Systems Identification and Control Theory community for further processing recorded THz transients in the time and frequency domain are outlined. Finally, possibilities for femtosecond pulse shaping using genetic algorithms are mentioned.

Frames and operator theory in analysis and signal processing

CERN Document Server

Larson, David R; Nashed, Zuhair; Nguyen, Minh Chuong; Papadakis, Manos

2008-01-01

This volume contains articles based on talks presented at the Special Session Frames and Operator Theory in Analysis and Signal Processing, held in San Antonio, Texas, in January of 2006. Recently, the field of frames has undergone tremendous advancement. Most of the work in this field is focused on the design and construction of more versatile frames and frames tailored towards specific applications, e.g., finite dimensional uniform frames for cellular communication. In addition, frames are now becoming a hot topic in mathematical research as a part of many engineering applications, e.g., matching pursuits and greedy algorithms for image and signal processing. Topics covered in this book include: Application of several branches of analysis (e.g., PDEs; Fourier, wavelet, and harmonic analysis; transform techniques; data representations) to industrial and engineering problems, specifically image and signal processing. Theoretical and applied aspects of frames and wavelets. Pure aspects of operator theory empha...

Introduction to section 1 - Learning as a process

DEFF Research Database (Denmark)

Sriskandarajah, Nadarajah; Cerf, Marianne; Noe, Egon

2006-01-01

As an introduction to the workshop where 18 papers and posters were presented on the theme of ‘Learning as a Process’, the linked nature of the learning – knowing – acting field in rural development in Europe is emphasised. The workshop took up the issues of human interactions in foster learning...... processes, capacity building and development of collective action as a bottom-up process....

Wavelet-Based Signal Processing of Electromagnetic Pulse Generated Waveforms

National Research Council Canada - National Science Library

Ardolino, Richard S

2007-01-01

This thesis investigated and compared alternative signal processing techniques that used wavelet-based methods instead of traditional frequency domain methods for processing measured electromagnetic pulse (EMP) waveforms...

Integration of e-learning outcomes into work processes

Directory of Open Access Journals (Sweden)

Kerstin Grundén

2011-07-01

Full Text Available Three case studies of in-house developed e-learning education in public organizations with different pedagogical approaches are used as a starting point for discussion regarding the implementation challenges of e-learning at work. The aim of this article is to contribute to the understanding of integrating mechanisms of e-learning outcomes into work processes in large, public organizations. The case studies were analyzed from a socio-cultural perspective using the MOA-model as a frame of reference. Although the pedagogical approaches for all of the cases seemed to be relevant and most of the learners showed overall positive attitudes towards the courses, there were problems with integration of the e-learning outcomes into work processes. There were deficiencies in the adaption of the course contents to the local educational needs. There was also a lack of adjusting the local work organization and work routines in order to facilitate the integration of the e-learning outcomes into the work processes. A lack of local management engagement affected the learners’ motivation negatively. Group discussions in local work groups facilitated the integration of the e-learning outcomes. Much of the difficulties of integrating e-learning outcomes into work processes in big organizations are related to the problems with adjusting centrally developed e-learning courses to local needs and a lack of co-operation among among the developers (often IT-professionals and the Human Resources Department of the organizations.

Application of wavelet analysis to signal processing methods for eddy-current test

International Nuclear Information System (INIS)

Chen, G.; Yoneyama, H.; Yamaguchi, A.; Uesugi, N.

1998-01-01

This study deals with the application of wavelet analysis to detection and characterization of defects from eddy-current and ultrasonic testing signals of a low signal-to-noise ratio. Presented in this paper are the methods for processing eddy-current testing signals of heat exchanger tubes of a steam generator in a nuclear power plant. The results of processing eddy-current testing signals of tube testpieces with artificial flaws show that the flaw signals corrupted by noise and/or non-defect signals can be effectively detected and characterized by using the wavelet methods. (author)

On diffusion processes with variable drift rates as models for decision making during learning

International Nuclear Information System (INIS)

Eckhoff, P; Holmes, P; Law, C; Connolly, P M; Gold, J I

2008-01-01

We investigate Ornstein-Uhlenbeck and diffusion processes with variable drift rates as models of evidence accumulation in a visual discrimination task. We derive power-law and exponential drift-rate models and characterize how parameters of these models affect the psychometric function describing performance accuracy as a function of stimulus strength and viewing time. We fit the models to psychophysical data from monkeys learning the task to identify parameters that best capture performance as it improves with training. The most informative parameter was the overall drift rate describing the signal-to-noise ratio of the sensory evidence used to form the decision, which increased steadily with training. In contrast, secondary parameters describing the time course of the drift during motion viewing did not exhibit steady trends. The results indicate that relatively simple versions of the diffusion model can fit behavior over the course of training, thereby giving a quantitative account of learning effects on the underlying decision process

Dual mechanisms governing reward-driven perceptual learning [version 1; referees: 2 approved

Directory of Open Access Journals (Sweden)

Dongho Kim

2015-09-01

Full Text Available In this review, we explore how reward signals shape perceptual learning in animals and humans. Perceptual learning is the well-established phenomenon by which extensive practice elicits selective improvement in one’s perceptual discrimination of basic visual features, such as oriented lines or moving stimuli. While perceptual learning has long been thought to rely on ‘top-down’ processes, such as attention and decision-making, a wave of recent findings suggests that these higher-level processes are, in fact, not necessary.  Rather, these recent findings indicate that reward signals alone, in the absence of the contribution of higher-level cognitive processes, are sufficient to drive the benefits of perceptual learning. Here, we will review the literature tying reward signals to perceptual learning. Based on these findings, we propose dual underlying mechanisms that give rise to perceptual learning: one mechanism that operates ‘automatically’ and is tied directly to reward signals, and another mechanism that involves more ‘top-down’, goal-directed computations.

Optimality of Poisson Processes Intensity Learning with Gaussian Processes

NARCIS (Netherlands)

Kirichenko, A.; van Zanten, H.

2015-01-01

In this paper we provide theoretical support for the so-called "Sigmoidal Gaussian Cox Process" approach to learning the intensity of an inhomogeneous Poisson process on a d-dimensional domain. This method was proposed by Adams, Murray and MacKay (ICML, 2009), who developed a tractable computational

Living and learning food processing

Science.gov (United States)

This year’s annual event promises to be both exciting and educational for those who wish to learn more about food processing. This column will provide a brief overview of the multitude of scientific sessions that reveal new research related to food processing. In addition to the symposia previewed h...

Processing of seismic signals from a seismometer network

International Nuclear Information System (INIS)

Key, F.A.; Warburton, P.J.

1983-08-01

A description is given of the Seismometer Network Analysis Computer (SNAC) which processes short period data from a network of seismometers (UKNET). The nine stations of the network are distributed throughout the UK and their outputs are transmitted to a control laboratory (Blacknest) where SNAC monitors the data for seismic signals. The computer gives an estimate of the source location of the detected signals and stores the waveforms. The detection logic is designed to maintain high sensitivity without excessive ''false alarms''. It is demonstrated that the system is able to detect seismic signals at an amplitude level consistent with a network of single stations and, within the limitations of signal onset time measurements made by machine, can locate the source of the seismic disturbance. (author)

IDP++: signal and image processing algorithms in C++ version 4.1

International Nuclear Information System (INIS)

Lehman, S.K.

1996-11-01

IDP++ (Image and Data Processing in C++) is a collection of signal and image processing algorithms written in C++. It is a compiled signal processing environment which supports four data types of up to four dimensions. It is developed within Lawrence Livermore National Laboratory's Image and Data Processing group as a partial replacement for View. IDP ++ takes advantage of the latest, implemented and actually working, object-oriented compiler technology to provide 'information hiding.' Users need only know C, not C++. Signals are treated like any other variable with a defined set of operators and functions in an intuitive manner. IDP++ is designed for real-time environment where interpreted processing packages are less efficient. IDP++ exists for both SUNs and Silicon Graphics using their most current compilers

Digital signal and image processing using Matlab

CERN Document Server

Blanchet , Gérard

2015-01-01

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

Digital signal and image processing using MATLAB

CERN Document Server

Blanchet , Gérard

2014-01-01

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

DBPM signal processing with field programmable gate arrays

International Nuclear Information System (INIS)

Lai Longwei; Yi Xing; Zhang Ning; Yang Guisen; Wang Baopeng; Xiong Yun; Leng Yongbin; Yan Yingbing

2011-01-01

DBPM system performance is determined by the design and implementation of beam position signal processing algorithm. In order to develop the system, a beam position signal processing algorithm is implemented on FPGA. The hardware is a PMC board ICS-1554A-002 (GE Corp.) with FPGA chip XC5VSX95T. This paper adopts quadrature frequency mixing to down convert high frequency signal to base. Different from conventional method, the mixing is implemented by CORDIC algorithm. The algorithm theory and implementation details are discussed in this paper. As the board contains no front end gain controller, this paper introduces a published patent-pending technique that has been adopted to realize the function in digital logic. The whole design is implemented with VHDL language. An on-line evaluation has been carried on SSRF (Shanghai Synchrotron Radiation Facility)storage ring. Results indicate that the system turn-by-turn data can measure the real beam movement accurately,and system resolution is 1.1μm. (authors)

Awake, Offline Processing during Associative Learning.

Science.gov (United States)

Bursley, James K; Nestor, Adrian; Tarr, Michael J; Creswell, J David

2016-01-01

Offline processing has been shown to strengthen memory traces and enhance learning in the absence of conscious rehearsal or awareness. Here we evaluate whether a brief, two-minute offline processing period can boost associative learning and test a memory reactivation account for these offline processing effects. After encoding paired associates, subjects either completed a distractor task for two minutes or were immediately tested for memory of the pairs in a counterbalanced, within-subjects functional magnetic resonance imaging study. Results showed that brief, awake, offline processing improves memory for associate pairs. Moreover, multi-voxel pattern analysis of the neuroimaging data suggested reactivation of encoded memory representations in dorsolateral prefrontal cortex during offline processing. These results signify the first demonstration of awake, active, offline enhancement of associative memory and suggest that such enhancement is accompanied by the offline reactivation of encoded memory representations.

Optimisation in signal and image processing

CERN Document Server

Siarry, Patrick

2010-01-01

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).

Liquid argon TPC signal formation, signal processing and reconstruction techniques

Science.gov (United States)

Baller, B.

2017-07-01

This document describes a 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 benefits from the 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. A unique clustering algorithm reconstructs line-like trajectories and vertices in two dimensions which are then matched to create of 3D objects. These techniques and algorithms are available to all experiments that use the LArSoft suite of software.

Techware: www.sspnet.eu: A Web Portal for Social Signal Processing

NARCIS (Netherlands)

Vinciarelli, Alessandro; Ortega, A.; Pantic, Maja

In this issue, “Best of the Web‿ focuses on introducing the social signal processing network (SSPNet), a large European collaboration aimed at establishing a research community in social signal processing (SSP), the new, emerging domain aimed at bringing social intelligence in computers.

Nonlinear Silicon Photonic Signal Processing Devices for Future Optical Networks

Directory of Open Access Journals (Sweden)

Cosimo Lacava

2017-01-01

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.

Towards a Web-Based Handbook of Generic, Process-Oriented Learning Designs

Science.gov (United States)

Marjanovic, Olivera

2005-01-01

Process-oriented learning designs are innovative learning activities that include a set of inter-related learning tasks and are generic (could be used across disciplines). An example includes a problem-solving process widely used in problem-based learning today. Most of the existing process-oriented learning designs are not documented, let alone…

Digital signal processing at GEND's data center

International Nuclear Information System (INIS)

Jackson, J.E.

1977-01-01

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

Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brain.

Science.gov (United States)

Niv, Yael; Edlund, Jeffrey A; Dayan, Peter; O'Doherty, John P

2012-01-11

Humans and animals are exquisitely, though idiosyncratically, sensitive to risk or variance in the outcomes of their actions. Economic, psychological, and neural aspects of this are well studied when information about risk is provided explicitly. However, we must normally learn about outcomes from experience, through trial and error. Traditional models of such reinforcement learning focus on learning about the mean reward value of cues and ignore higher order moments such as variance. We used fMRI to test whether the neural correlates of human reinforcement learning are sensitive to experienced risk. Our analysis focused on anatomically delineated regions of a priori interest in the nucleus accumbens, where blood oxygenation level-dependent (BOLD) signals have been suggested as correlating with quantities derived from reinforcement learning. We first provide unbiased evidence that the raw BOLD signal in these regions corresponds closely to a reward prediction error. We then derive from this signal the learned values of cues that predict rewards of equal mean but different variance and show that these values are indeed modulated by experienced risk. Moreover, a close neurometric-psychometric coupling exists between the fluctuations of the experience-based evaluations of risky options that we measured neurally and the fluctuations in behavioral risk aversion. This suggests that risk sensitivity is integral to human learning, illuminating economic models of choice, neuroscientific models of affective learning, and the workings of the underlying neural mechanisms.

PC add on card for processing of LSC signals

International Nuclear Information System (INIS)

Jadhav, S.R.; Nikhare, D.M.; Gurna, R.K.; Paulson, Molly; Kulkarni, C.P.; Vaidya, P.P.

2001-01-01

This paper describes PC- add on card developed at Electronics Division for processing of LSC signals. This card uses highly integrated digital and analog circuits, for entire processing of signals available from preamplifiers to get complete beta energy spectrum corresponding to coincident events in Liquid Scintillation Counting. LSC card along with High Voltage PC-add on card gives complete electronics required for LSC system. This card is also used in automatic LSC system along with interface circuits, which are used to control mechanical movements. (author)

Ultrafast signal processing in quantum dot amplifiers through effective spectral holeburning

DEFF Research Database (Denmark)

Berg, Tommy Winther; Mørk, Jesper; Uskov, A. V.

2002-01-01

suitable for ultrafast signal processing. The basis of this property is that the process of spectral hole burning (SHB) can become very effective. We consider a traveling wave optical amplifier consisting of the dot states, which interact with the optical signal (no inhomogeneous broadening included...

Neuron class-specific requirements for Fragile X Mental Retardation Protein in critical period development of calcium signaling in learning and memory circuitry.

Science.gov (United States)

Doll, Caleb A; Broadie, Kendal

2016-05-01

Neural circuit optimization occurs through sensory activity-dependent mechanisms that refine synaptic connectivity and information processing during early-use developmental critical periods. Fragile X Mental Retardation Protein (FMRP), the gene product lost in Fragile X syndrome (FXS), acts as an activity sensor during critical period development, both as an RNA-binding translation regulator and channel-binding excitability regulator. Here, we employ a Drosophila FXS disease model to assay calcium signaling dynamics with a targeted transgenic GCaMP reporter during critical period development of the mushroom body (MB) learning/memory circuit. We find FMRP regulates depolarization-induced calcium signaling in a neuron-specific manner within this circuit, suppressing activity-dependent calcium transients in excitatory cholinergic MB input projection neurons and enhancing calcium signals in inhibitory GABAergic MB output neurons. Both changes are restricted to the developmental critical period and rectified at maturity. Importantly, conditional genetic (dfmr1) rescue of null mutants during the critical period corrects calcium signaling defects in both neuron classes, indicating a temporally restricted FMRP requirement. Likewise, conditional dfmr1 knockdown (RNAi) during the critical period replicates constitutive null mutant defects in both neuron classes, confirming cell-autonomous requirements for FMRP in developmental regulation of calcium signaling dynamics. Optogenetic stimulation during the critical period enhances depolarization-induced calcium signaling in both neuron classes, but this developmental change is eliminated in dfmr1 null mutants, indicating the activity-dependent regulation requires FMRP. These results show FMRP shapes neuron class-specific calcium signaling in excitatory vs. inhibitory neurons in developing learning/memory circuitry, and that FMRP mediates activity-dependent regulation of calcium signaling specifically during the early

Tutorial: Signal Processing in Brain-Computer Interfaces

NARCIS (Netherlands)

Garcia Molina, G.

2010-01-01

Research in Electroencephalogram (EEG) based Brain-Computer Interfaces (BCIs) has been considerably expanding during the last few years. Such an expansion owes to a large extent to the multidisciplinary and challenging nature of BCI research. Signal processing undoubtedly constitutes an essential

Machine learning concepts in coherent optical communication systems

DEFF Research Database (Denmark)

Zibar, Darko; Schäffer, Christian G.

2014-01-01

Powerful statistical signal processing methods, used by the machine learning community, are addressed and linked to current problems in coherent optical communication. Bayesian filtering methods are presented and applied for nonlinear dynamic state tracking. © 2014 OSA.......Powerful statistical signal processing methods, used by the machine learning community, are addressed and linked to current problems in coherent optical communication. Bayesian filtering methods are presented and applied for nonlinear dynamic state tracking. © 2014 OSA....

The Adoption of E-Learning in Teaching and Learning Processes; an Option for Life-Long Education

OpenAIRE

Simaibang, Baginda

2013-01-01

The objective of this study is to investigate the benefits of the adoption of electronic learning (E-Learning)in teaching and learning processes. E-Learning is an educational approach that utilizes computer technology, particularly digital technologies that are internet-based, to provide instruction and learning experiences. The definition of e-learning refers to a wide range of applications and processes designed to deliver instruction through electronic means. This means is normally employe...

New signal processing methods for the evaluation of eddy current NDT data

International Nuclear Information System (INIS)

Anon.

1987-01-01

Signal processing and pattern recognition methods play a crucial role in a number of areas associated with nondestructive evaluation. Defect characterization schemes often involve mapping the signal onto an appropriate feature domain and using pattern recognition techniques for classification. In addition, signal processing methods are also used to acquire, enhance, restore, and compress data. EPRI Project RP 2673-4 is concerned with developing new signal processing and pattern recognition techniques for evaluating eddy current signals. Efforts under this project have focused on three closely related areas. The thrust has been to: (1) develop a scheme to compress eddy current signals for the purposes of storing them in a compact form, (2) develop a robust clustering algorithm capable of discarding feature vectors that fall in the gray areas between clusters, and (3) investigate the feasibility of designing and developing a digital eddyscope

The windmill of learning processes

DEFF Research Database (Denmark)

Kragelund, Linda

2011-01-01

that part of the nursing education has been reduced in some countries as e.g. Denmark.  The approach is presented through a model termed the 'Windmill of Learning Processes', which draws on empirical data from a qualitative investigation with an explorative and descriptive design, and on the theoretical......This article presents a new approach to student nurses' learning from their interaction with psychiatric patients. Using the approach can enable students and mentors to exploit students' learning opportunities, and help students to get the most out of their clinical placement in a time, where...... concepts of 'disjuncture', and 'everyday life activities'. 'Disjuncture' is defined as a situation in which there is disharmony between a person's experiences and the current situation. In such a situation there is potential for learning. My analysis of the empirical data led to the identification of a new...

Refining the learning process in Newfoundland : E-learning innovation at North Atlantic

Energy Technology Data Exchange (ETDEWEB)

Murray, D.

2001-11-01

The Learning Technologies in the Workplace Awards were launched by the Conference Board of Canada in April 2001 with funding from Human Resources Development Canada's Office of Learning Technologies. This paper described the innovative and outstanding efforts made by the winner, North Atlantic. The North Atlantic refinery is located on an inlet on the Avalon Peninsula approximately 135 kilometres west of St. John's, Newfoundland. Each day, 105,000 barrels of oil are processed for export to 25 countries. In 1998, the company recognized that better training was required in the areas of improved safety, performance, and employee innovation and capacity. The isolation faced by the employees was a key driver behind the decision to implement the TRAQS training program in 1999 for e-learning developed by Illuminatus. This on-line training program also features testing through CHALLENGE, a software package compatible with TRAQS learning management system. Process emergency simulation exercises were developed by North Atlantic which are now being used externally. Job-specific technical information is delivered through the local area network (LAN). The keys to success were identified as being: innovative organizational culture; vision and action; executive management support, commitment to learning and employee development; positive work life balance; union cooperation; technology intensive workplace; linking learning with work process and performance management; and, tracking and certification.

Rapid e-Learning Tools Selection Process for Cognitive and Psychomotor Learning Objectives

Science.gov (United States)

Ku, David Tawei; Huang, Yung-Hsin

2012-01-01

This study developed a decision making process for the selection of rapid e-learning tools that could match different learning domains. With the development of the Internet, the speed of information updates has become faster than ever. E-learning has rapidly become the mainstream for corporate training and academic instruction. In order to reduce…

Hybrid digital signal processing and neural networks applications in PWRs

International Nuclear Information System (INIS)

Eryurek, E.; Upadhyaya, B.R.; Kavaklioglu, K.

1991-01-01

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

Physics-based signal processing algorithms for micromachined cantilever arrays

Science.gov (United States)

Candy, James V; Clague, David S; Lee, Christopher L; Rudd, Robert E; Burnham, Alan K; Tringe, Joseph W

2013-11-19

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.

Understanding the cognitive processes involved in writing to learn.

Science.gov (United States)

Arnold, Kathleen M; Umanath, Sharda; Thio, Kara; Reilly, Walter B; McDaniel, Mark A; Marsh, Elizabeth J

2017-06-01

Writing is often used as a tool for learning. However, empirical support for the benefits of writing-to-learn is mixed, likely because the literature conflates diverse activities (e.g., summaries, term papers) under the single umbrella of writing-to-learn. Following recent trends in the writing-to-learn literature, the authors focus on the underlying cognitive processes. They draw on the largely independent writing-to-learn and cognitive psychology learning literatures to identify important cognitive processes. The current experiment examines learning from 3 writing tasks (and 1 nonwriting control), with an emphasis on whether or not the tasks engaged retrieval. Tasks that engaged retrieval (essay writing and free recall) led to better final test performance than those that did not (note taking and highlighting). Individual differences in structure building (the ability to construct mental representations of narratives; Gernsbacher, Varner, & Faust, 1990) modified this effect; skilled structure builders benefited more from essay writing and free recall than did less skilled structure builders. Further, more essay-like responses led to better performance, implicating the importance of additional cognitive processes such as reorganization and elaboration. The results highlight how both task instructions and individual differences affect the cognitive processes involved when writing-to-learn, with consequences for the effectiveness of the learning strategy. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

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

Science.gov (United States)

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

2015-10-14

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

Explaining discontinuity in organizational learning : a process analysis

NARCIS (Netherlands)

Berends, J.J.; Lammers, I.S.

2010-01-01

This paper offers a process analysis of organizational learning as it unfolds in a social and temporal context. Building upon the 4I framework (Crossan et al. 1999), we examine organizational learning processes in a longitudinal case study of an implementation of knowledge management in an

Design and measurement of signal processing system for cavity beam position monitor

International Nuclear Information System (INIS)

Wang Baopeng; Leng Yongbin; Yu Luyang; Zhou Weimin; Yuan Renxian; Chen Zhichu

2013-01-01

In this paper, in order to achieve the output signal processing of cavity beam position monitor (CBPM), we develop a digital intermediate frequency receiver architecture based signal processing system, which consists of radio frequency (RF) front end and high speed data acquisition board. The beam position resolution in the CBPM signal processing system is superior to 1 μm. Two signal processing algorithms, fast Fourier transform (FFT) and digital down converter (DDC), are evaluated offline using MATLAB platform, and both can be used to achieve, the CW input signal, position resolutions of 0.31 μm and 0.10 μm at -16 dBm. The DDC algorithm for its good compatibility is downloaded into the FPGA to realize online measurement, reaching the position resolution of 0.49 μm due to truncation error. The whole system works well and the performance meets design target. (authors)

Awake, Offline Processing during Associative Learning.

Directory of Open Access Journals (Sweden)

James K Bursley

Full Text Available Offline processing has been shown to strengthen memory traces and enhance learning in the absence of conscious rehearsal or awareness. Here we evaluate whether a brief, two-minute offline processing period can boost associative learning and test a memory reactivation account for these offline processing effects. After encoding paired associates, subjects either completed a distractor task for two minutes or were immediately tested for memory of the pairs in a counterbalanced, within-subjects functional magnetic resonance imaging study. Results showed that brief, awake, offline processing improves memory for associate pairs. Moreover, multi-voxel pattern analysis of the neuroimaging data suggested reactivation of encoded memory representations in dorsolateral prefrontal cortex during offline processing. These results signify the first demonstration of awake, active, offline enhancement of associative memory and suggest that such enhancement is accompanied by the offline reactivation of encoded memory representations.

Problem-centric Process for Research-based Learning

Directory of Open Access Journals (Sweden)

Khaled Shaban

2015-05-01

Full Text Available Research-based Learning (RbL extends Inquiry and Project-based Learning by facilitating an early stage exposure and training for future scientists through authentic research activities. In this paper, an iterative problem-centric RbL process is introduced, and its activities and management aspects are described. The process helps implement course-integrated research systematically and practically. Furthermore, the novel process follows constructivist methods in incorporating inquiry, scaffolding, open-ended projects, as well as a goal oriented learning approach. The RbL process is adopted in two advanced computing courses, at two different universities: a leading comprehensive Western university and a new university in a developing country. The paper summarizes new lessons learned in these rewarding experiences. In particular, the instructor should help students start their projects, by providing them with previous work or data and pre-approving the papers to review by students. He should also maintain a continuous feedback to and from students to keep the students motivated and help the instructor refine and adapt the RBL process. We note that research collaborators can help students in identifying the research topics early. The paper also shows how to alleviate difficulties that may be encountered by students who find the novel approach demanding, and consequently it also helps the instructors better manage the course contents.

Filtering and spectral processing of 1-D signals using cellular neural networks

NARCIS (Netherlands)

Moreira-Tamayo, O.; Pineda de Gyvez, J.

1996-01-01

This paper presents cellular neural networks (CNN) for one-dimensional discrete signal processing. Although CNN has been extensively used in image processing applications, little has been done for 1-dimensional signal processing. We propose a novel CNN architecture to carry out these tasks. This

Active Learning of Markov Decision Processes for System Verification

DEFF Research Database (Denmark)

Chen, Yingke; Nielsen, Thomas Dyhre

2012-01-01

deterministic Markov decision processes from data by actively guiding the selection of input actions. The algorithm is empirically analyzed by learning system models of slot machines, and it is demonstrated that the proposed active learning procedure can significantly reduce the amount of data required...... demanding process, and this shortcoming has motivated the development of algorithms for automatically learning system models from observed system behaviors. Recently, algorithms have been proposed for learning Markov decision process representations of reactive systems based on alternating sequences...... of input/output observations. While alleviating the problem of manually constructing a system model, the collection/generation of observed system behaviors can also prove demanding. Consequently we seek to minimize the amount of data required. In this paper we propose an algorithm for learning...

Perceptual learning increases the strength of the earliest signals in visual cortex.

Science.gov (United States)

Bao, Min; Yang, Lin; Rios, Cristina; He, Bin; Engel, Stephen A

2010-11-10

Training improves performance on most visual tasks. Such perceptual learning can modify how information is read out from, and represented in, later visual areas, but effects on early visual cortex are controversial. In particular, it remains unknown whether learning can reshape neural response properties in early visual areas independent from feedback arising in later cortical areas. Here, we tested whether learning can modify feedforward signals in early visual cortex as measured by the human electroencephalogram. Fourteen subjects were trained for >24 d to detect a diagonal grating pattern in one quadrant of the visual field. Training improved performance, reducing the contrast needed for reliable detection, and also reliably increased the amplitude of the earliest component of the visual evoked potential, the C1. Control orientations and locations showed smaller effects of training. Because the C1 arises rapidly and has a source in early visual cortex, our results suggest that learning can increase early visual area response through local receptive field changes without feedback from later areas.

Tunable signal processing in synthetic MAP kinase cascades.

Science.gov (United States)

O'Shaughnessy, Ellen C; Palani, Santhosh; Collins, James J; Sarkar, Casim A

2011-01-07

The flexibility of MAPK cascade responses enables regulation of a vast array of cell fate decisions, but elucidating the mechanisms underlying this plasticity is difficult in endogenous signaling networks. We constructed insulated mammalian MAPK cascades in yeast to explore how intrinsic and extrinsic perturbations affect the flexibility of these synthetic signaling modules. Contrary to biphasic dependence on scaffold concentration, we observe monotonic decreases in signal strength as scaffold concentration increases. We find that augmenting the concentration of sequential kinases can enhance ultrasensitivity and lower the activation threshold. Further, integrating negative regulation and concentration variation can decouple ultrasensitivity and threshold from the strength of the response. Computational analyses show that cascading can generate ultrasensitivity and that natural cascades with different kinase concentrations are innately biased toward their distinct activation profiles. This work demonstrates that tunable signal processing is inherent to minimal MAPK modules and elucidates principles for rational design of synthetic signaling systems. Copyright © 2011 Elsevier Inc. All rights reserved.

Visible light communications modulation and signal processing

CERN Document Server

Wang, Zhaocheng; Huang, Wei; Xu, Zhengyuan

2018-01-01

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...

Feasibility of Johnson Noise Thermometry based on Digital Signal Processing Techniques

International Nuclear Information System (INIS)

Hwang, In Koo; Kim, Yang Mo

2014-01-01

This paper presents an implementation strategy of noise thermometry based on a digital signal processing technique and demonstrates its feasibilities. A key factor in its development is how to extract the small thermal noise signal from other noises, for example, random noise from amplifiers and continuous electromagnetic interference from the environment. The proposed system consists of two identical amplifiers and uses a cross correlation function to cancel the random noise of the amplifiers. Then, the external interference noises are eliminated by discriminating the difference in the peaks between the thermal signal and external noise. The gain of the amplifiers is estimated by injecting an already known pilot signal. The experimental simulation results of signal processing methods have demonstrated that the proposed approach is an effective method in eliminating an external noise signal and performing gain correction for development of the thermometry

Learning and Motivational Processes When Students Design Curriculum-Based Digital Learning Games

DEFF Research Database (Denmark)

Weitze, Charlotte Lærke

2015-01-01

, or programming provide a rich context for learning, since the construction of artefacts, in this case learning games, enables reflection and new ways of thinking. The students learned from reflection and interaction with the tools alone as well as in collaboration with peers. After analysing the students...... another. The study found that the students benefitted from this way of learning as a valid variation to more conventional teaching approaches, and teachers found that the students learned at least the same amount or more compared to traditional teaching processes. The students were able to think outside...

Monitoring of drilling process with the application of acoustic signal

Directory of Open Access Journals (Sweden)

Labaš Milan

2000-09-01

Full Text Available Monitoring of rock disintegration process at drilling, scanning of input quantities: thrust F, revolution n and the course of some output quantities: the drilling rate v and the power input P are needed for the control of this process. We can calculate the specific volume work of rock disintegration w and ϕ - quotient of drilling rate v and the specific volume work of disintegration w from the presented quantities.Works on an expertimental stand showed that the correlation relationships between the input and output quantities can be found by scanning the accompanying sound of the drilling proces.Research of the rock disintegration with small-diameter diamond drill tools and different rock types is done at the Institute of Geotechnics. The aim of this research is the possibility of monitoring and controlling the rock disintegration process with the application of acoustic signal. The acoustic vibrations accompanying the drilling process are recorded by a microphone placed in a defined position in the acoustic space. The drilling device (drilling stand, the drilling tool and the rock are the source of sound. Two basic sound states exist in the drilling stand research : the noise at no-load running and the noise at the rotary drilling of rock. Suitable quantities for optimizing the rock disintegration process are searched by the study of the acoustic signal. The dominant frequencies that characterize the disintegration process for the given rock and tool are searched by the analysis of the acoustic signal. The analysis of dominant frequencies indicates the possibility of determining an optimal regime for the maximal drilling rate. Extreme of the specific disintegration energy is determinated by the dispersion of the dominant frequency.The scanned acoustic signal is processed by the Fourier transformation. The Fourier transformation facilitates the distribution of the general non-harmonic periodic process into harmonic components. The harmonic

Missile signal processing common computer architecture for rapid technology upgrade

Science.gov (United States)

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

2004-10-01

Interceptor missiles process IR images to locate an intended target and guide the interceptor towards it. Signal processing requirements have increased as the sensor bandwidth increases and interceptors operate against more sophisticated targets. A typical interceptor signal processing chain is comprised of two parts. Front-end video processing operates on all pixels of the image and performs such operations as non-uniformity correction (NUC), image stabilization, frame integration and detection. Back-end target processing, which tracks and classifies targets detected in the image, performs such algorithms as Kalman tracking, spectral feature extraction and target discrimination. In the past, video processing was implemented using ASIC components or FPGAs because computation requirements exceeded the throughput of general-purpose processors. Target processing was performed using hybrid architectures that included ASICs, DSPs and general-purpose processors. The resulting systems tended to be function-specific, and required custom software development. They were developed using non-integrated toolsets and test equipment was developed along with the processor platform. The lifespan of a system utilizing the signal processing platform often spans decades, while the specialized nature of processor hardware and software makes it difficult and costly to upgrade. As a result, the signal processing systems often run on outdated technology, algorithms are difficult to update, and system effectiveness is impaired by the inability to rapidly respond to new threats. A new design approach is made possible three developments; Moore's Law - driven improvement in computational throughput; a newly introduced vector computing capability in general purpose processors; and a modern set of open interface software standards. Today's multiprocessor commercial-off-the-shelf (COTS) platforms have sufficient throughput to support interceptor signal processing requirements. This application

Nuclear spectrometry signal acquisition and processing system based on LabVIEW and C

International Nuclear Information System (INIS)

Chen Xiaojun; Fang Fang; Chen Mingchi; Jiang Zancheng; Wang Min

2008-01-01

The process of designing nuclear spectrometry signal acquisition and processing system based on virtual instrument technology is showed in this article. For the deficiency of LabVIEW in big data analyzing and processing, a method is presented in which C programmer is inserted and applied in signal smoothing, peak searching and area of the peak calculating. A complete nuclear spectrometry signal acquisition, processing and document management system is implemented. (authors)

Low power digital signal processing

DEFF Research Database (Denmark)

Paker, Ozgun

2003-01-01

hardwired ASICs and more than 6 21 times lower than current state of the art low-power DSP processors. An orthogonal but practical contribution of this thesis is the test bench implementation. A PCI-based FPGA board has been used to equip a standard desktop PC with tester facilities. The test bench proved...... to be a viable alternative to conventional expensive test equipment. Finally, the work presented in this thesis has been published at several IEEE workshops and conferences, and in the Journal of VLSI Signal Processing....

Validity and Reliability of Revised Inventory of Learning Processes.

Science.gov (United States)

Gadzella, B. M.; And Others

The Inventory of Learning Processes (ILP) was developed by Schmeck, Ribich, and Ramanaiah in 1977 as a self-report inventory to assess learning style through a behavioral-oriented approach. The ILP was revised by Schmeck in 1983. The Revised ILP contains six scales: (1) Deep Processing; (2) Elaborative Processing; (3) Shallow Processing; (4)…

Signal processing for the profoundly deaf.

Science.gov (United States)

Boothyroyd, A

1990-01-01

Profound deafness, defined here as a hearing loss in excess of 90 dB, is characterized by high thresholds, reduced hearing range in the intensity and frequency domains, and poor resolution in the frequency and time domains. The high thresholds call for hearing aids with unusually high gains or remote microphones that can be placed close to the signal source. The former option creates acoustic feedback problems for which digital signal processing may yet offer solutions. The latter option calls for carrier wave technology that is already available. The reduced frequency and intensity ranges would appear to call for frequency and/or amplitude compression. It might also be argued, however, that any attempts to compress the acoustic signal into the limited hearing range of the profoundly deaf will be counterproductive because of poor frequency and time resolution, especially when the signal is present in noise. In experiments with a 2-channel compression system, only 1 of 9 subjects showed an improvement of perception with the introduction of fast-release (20 ms) compression. The other 8 experienced no benefit or a slight deterioration of performance. These results support the concept of providing the profoundly deaf with simpler, rather than more complex, patterns, perhaps through the use of feature extraction hearing aids. Data from users of cochlear implants already employing feature extraction techniques also support this concept.

Optimal and adaptive methods of processing hydroacoustic signals (review)

Science.gov (United States)

Malyshkin, G. S.; Sidel'nikov, G. B.

2014-09-01

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.

Digital Signal Processing and Control for the Study of Gene Networks

Science.gov (United States)

Shin, Yong-Jun

2016-04-01

Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

Signal processing for 5G algorithms and implementations

CERN Document Server

Luo, Fa-Long

2016-01-01

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...

Power systems signal processing for smart grids

NARCIS (Netherlands)

Ribeiro, P.F.; Duque, C.A.; Da Silveira, P.M.; Cerqueira, A.S.

2013-01-01

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

Broadband Nonlinear Signal Processing in Silicon Nanowires