Xem 1-20 trên 25 kết quả Signal classification
  • The concept and theory of signals and systems are needed in almost all electrical engineering fields and in many other engineering and scientific disciplines as well. In this chapter we introduce the mathematical description and representation of signals and systems and their classifications. We also define several important basic signals essential to our studies.

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  • The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering. Scores of worked examples illustrate fine points, compare techniques and algorithms and facilitate comprehension of fundamental concepts. Also features an abundance of interesting and challenging problems at the end of every chapter.

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  • Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform. Signal detection algorithms decide whether the waveform consists of “noise alone” or “signal masked by noise.” Signal classification algorithms decide whether a detected signal belongs to one or another of prespecified classes of signals. The objective of signal detection and classification theory is to specify systematic strategies for designing algorithms which minimize the average number of decision errors.

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  • Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image processing, telecommunications, medical signal processing and several data mining issues.

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  • This paper presents a method that assists in maintaining a rule-based named-entity recognition and classification system. The underlying idea is to use a separate system, constructed with the use of machine learning, to monitor the performance of the rule-based system. The training data for the second system is generated with the use of the rule-based system, thus avoiding the need for manual tagging. The disagreement of the two systems acts as a signal for updating the rule-based system.

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  • .HWEI P. HSU is Professor of Electrical Engineering at Fairleigh Dickinson University. He received his B.S. from National Taiwan University and M.S. and Ph.D. from Case InsThe concept and theory of signals and systems are needed in almost all electrical engineering fields and in many other engineering and scientific disciplines as well. In this chapter we introduce the mathematical description and representation of signals and systems and their classifications. We also define several important basic signals essential to our studies.titute of Technology.

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  • This book presents a collection of recent and extended academic works in selected topics of biomedical technology, biomedical instrumentations, biomedical signal processing and bio-imaging. This wide range of topics provide a valuable update to researchers in the multidisciplinary area of biomedical engineering and an interesting introduction for engineers new to the area. The techniques covered include modelling, experimentation and discussion with the application areas ranging from bio-sensors development to neurophysiology, telemedicine and biomedical signal classification....

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  • Topics in Digital Communications Digital communication system advantages and disadvantages Digital communication system classification Digitization of analog signals Digital transmission systems Data communication systems Integrated Services Digital Network and other advanced digital communication systems.

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  • Discrete wavelet transform (DWT) algorithms have become standard tools for discrete-time signal and image processing in several areas in research and industry. As DWT provides both frequency and location information of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Theory and Applications describes the latest progress in DWT analysis in non-stationary signal processing, multi-scale image enhancement as well as in biomedical and industrial applications....

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  • Chapter 1 presents IVUS. Intravascular ultrasound images represent a unique tool to guide interventional coronary procedures; this technique allows to supervise the cross-sectional locations of the vessel morphology and to provide quantitative and qualitative information about the causes and severity of coronary diseases. At the moment, the automatic extraction of this kind of information is performed without taking into account the basic signal principles that guide the process of image generation....

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  • The book is an edited collection of research articles covering the current state of sonar systems, the signal processing methods and their applications prepared by experts in the field. The first section is dedicated to the theory and applications of innovative synthetic aperture, interferometric, multistatic sonars and modeling and simulation.

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  • Important features of digital communication systems Some basic concepts and definitions such as as signal classification, spectral density, random process, linear systems and signal bandwidth.The first important step in any DCS: Transforming the information source to a form compatible with a digital syst

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  • Mportant features of digital communication systems - Some basic concepts and definitions as signal classification, spectral density, random process, linear systems and signal bandwidth.

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  • Much of signal processing involves extracting signals of interest from noise. Without noise to combat, a radar receiver could detect an echo by simple energy thresholding. In a noiseless world an infinite amount of information could be transmitted through a communications channel every second. Were it not for noise, signal classification would be reduced to dictionary lookup. Yet signals in the real world are always noisy. Radar echoes are buried under noise, making their detection impossible without sophisticated processing. ...

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  • Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform.

    pdf15p nguyen4 17-11-2009 26 3   Download

  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Single-trial classification of NIRS signals during emotional induction tasks: towards a corporeal machine interface

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài:Single-trial classification of NIRS signals during emotional induction tasks: towards a corporeal machine interface

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Classification of rhythmic locomotor patterns in electromyographic signals using fuzzy sets

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  • Classification a. Means of transmission b. Service c. Circuit switched network d. Method of signalling e. Electronic system f. Light sources g. Remote part of an exchange Characteristics /Function A. It provides a means of asynchronous data transmission. B. It uses one pair for performing all signalling functions of one route. C. It involves the sampling and coding of analogue signals. D. They enable PCM transmission to be used further out in the local network.

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  • (BQ) Ebook Microelectronic Circuit Design writers integrates the authors’ extensive indus- trial backgrounds in precision analog and digital design with their many years of experience in the classroom. A broad spectrum of topics is included, and material can easily be selected to satisfy either a two-semester or three-quarter sequence in electronics.

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