(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 by ICA has received considerable attention be...
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ISBN:
(数字)9781475728514
ISBN:
(纸本)9780792382614;9781441950567
(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 by ICA has received considerable attention because of its potential signal-processingapplications such as speech enhancement systems, telecommunications, medical signal-processing and several data mining issues.;This book presents theories and applications of ICA and includes invaluable examples of several real-world applications. Based on theories in probabilistic models, information theory and artificialneuralnetworks, several unsupervised learning algorithms are presented that can perform ICA. The seemingly different theories such as infomax, maximum likelihood estimation, negentropy maximization, nonlinear PCA, Bussgang algorithm and cumulant-based methods are reviewed and put in an information theoretic framework to unify several lines of ICA research. An algorithm is presented that is able to blindly separate mixed signals with sub- and super-Gaussian source distributions. The learning algorithms can be extended to filter systems, which allows the separation of voices recorded in a real environment (cocktail party problem).;The ICA algorithm has been successfully applied to many biomedical signal-processing problems such as the analysis of electroencephalographic data and functional magnetic resonance imaging data. ICA applied to images results in independent image components that can be used as features in pattern classification problems such as visual lip-reading and face recognition systems. The ICA algorithm can furthermore be embedded in an expectation maximization framework for unsupervised classification.;is the first book to successfully address this fairly new and generally applicable method of blind source separation. It is essential reading for researchers and practitioners with an interest in ICA.
This 5-volume set (CCIS 214-CCIS 218) constitutes the refereed proceedings of the International Conference on Computer Science, Environment, Ecoinformatics, and Education, CSEE 2011, held in Wuhan, China, in July 2011...
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ISBN:
(数字)9783642233210
ISBN:
(纸本)9783642233203
This 5-volume set (CCIS 214-CCIS 218) constitutes the refereed proceedings of the International Conference on Computer Science, Environment, Ecoinformatics, and Education, CSEE 2011, held in Wuhan, China, in July 2011.
The 525 revised full papers presented in the five volumes were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on information security, intelligent information, neuralnetworks, digital library, algorithms, automation, artificial intelligence, bioinformatics, computer networks, computational system, computer vision, computer modelling and simulation, control, databases, data mining, e-learning, e-commerce, e-business, imageprocessing, information systems, knowledge management and knowledge discovering, mulitimedia and its apllication, management and information system, moblie computing, natural computing and computational intelligence, open and innovative education, pattern recognition, parallel and computing, robotics, wireless network, web application, other topics connecting with computer, environment and ecoinformatics, modeling and simulation, environment restoration, environment and energy, information and its influence on environment, computer and ecoinformatics, biotechnology and biofuel, as well as biosensors and bioreactor.
This book reports on innovations and engineering achievements of industrial relevance, with a special emphasis on developments in mechatronics, control engineering and signal processing. It gathers peer-reviewed paper...
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ISBN:
(数字)9783031942235
ISBN:
(纸本)9783031942228
This book reports on innovations and engineering achievements of industrial relevance, with a special emphasis on developments in mechatronics, control engineering and signal processing. It gathers peer-reviewed papers presented at the 4th International Conference “Innovation in Engineering”, ICIE 2025, held on June 18-20, 2025, in Prague, Czech Republic. It covers advances in automated detection and monitoring systems, industrial applications of machine learning and artificialneuralnetworks, and industrial robots and medical devices, among other topics. This second volume of a three-volume set provides engineering researchers and professionals with a timely snapshot of technologies and strategies that should help improve production efficiency, industrial sustainability, and human well-being.
The 111 papers presented in the two volumes were carefully reviewed and selected from numerous submissions. The papers were organized in topical sections named: Learning System, Graph Model, and Adversarial Learning; ...
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ISBN:
(数字)9783030228088
ISBN:
(纸本)9783030228071
The 111 papers presented in the two volumes were carefully reviewed and selected from numerous submissions. The papers were organized in topical sections named: Learning System, Graph Model, and Adversarial Learning; Time Series Analysis, Dynamic Prediction, and Uncertain Estimation; Model Optimization, Bayesian Learning, and Clustering; Game Theory, Stability Analysis, and Control Method; Signal processing, Industrial Application, and Data Generation; image Recognition, Scene Understanding, and Video Analysis; Bio-signal, Biomedical Engineering, and Hardware.
Methods of signal analysis represent a broad research topic with applications in many disciplines, including engineering, technology, biomedicine, seismography, eco nometrics, and many others based upon the proc...
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ISBN:
(数字)9781461217688
ISBN:
(纸本)9780817640422;9781461272731
Methods of signal analysis represent a broad research topic with applications in many disciplines, including engineering, technology, biomedicine, seismography, eco nometrics, and many others based upon the processing of observed variables. Even though these applications are widely different, the mathematical background be hind them is similar and includes the use of the discrete Fourier transform and z-transform for signal analysis, and both linear and non-linear methods for signal identification, modelling, prediction, segmentation, and classification. These meth ods are in many cases closely related to optimization problems, statistical methods, and artificialneuralnetworks. This book incorporates a collection of research papers based upon selected contri butions presented at the First European Conference on Signal Analysis and Predic tion (ECSAP-97) in Prague, Czech Republic, held June 24-27, 1997 at the Strahov Monastery. Even though the Conference was intended as a European Conference, at first initiated by the European Association for Signal processing (EURASIP), it was very gratifying that it also drew significant support from other important scientific societies, including the lEE, Signal processing Society of IEEE, and the Acoustical Society of America. The organizing committee was pleased that the re sponse from the academic community to participate at this Conference was very large; 128 summaries written by 242 authors from 36 countries were received. In addition, the Conference qualified under the Continuing Professional Development Scheme to provide PD units for participants and contributors.
The two volume set LNCS 5263/5264 constitutes the refereed proceedings of the 5th International Symposium on neuralnetworks, ISNN 2008, held in Beijing, China in September 2008. The 192 revised papers presented were ...
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ISBN:
(数字)9783540877349
ISBN:
(纸本)9783540877332
The two volume set LNCS 5263/5264 constitutes the refereed proceedings of the 5th International Symposium on neuralnetworks, ISNN 2008, held in Beijing, China in September 2008. The 192 revised papers presented were carefully reviewed and selected from a total of 522 submissions. The papers are organized in topical sections on computational neuroscience; cognitive science; mathematical modeling of neural systems; stability and nonlinear analysis; feedforward and fuzzy neuralnetworks; probabilistic methods; supervised learning; unsupervised learning; support vector machine and kernel methods; hybrid optimisation algorithms; machine learning and data mining; intelligent control and robotics; pattern recognition; audio image processinc and computer vision; fault diagnosis; applications and implementations; applications of neuralnetworks in electronic engineering; cellular neuralnetworks and advanced control with neuralnetworks; nature inspired methods of high-dimensional discrete data analysis; pattern recognition and information processing using neuralnetworks.
The traditional approaches to quality measurement for milk powder are individually tailored tests which are expensive, laborious, time consuming and somewhat off-line tests. Therefore, a new Advanced Process Control (...
The traditional approaches to quality measurement for milk powder are individually tailored tests which are expensive, laborious, time consuming and somewhat off-line tests. Therefore, a new Advanced Process Control (APC) concept called Real-Time Quality which is focused on managing product quality during processing by using online mathematical tools has aroused people's attention. Dispersibility and bulk density are the vital functional properties of instant whole milk powder (IWMP), and many milk powder properties may affect the dispersibility and bulk density of IWMP. Based on literature and our own experience, particle size and morphology may be the two most important factors. Additionally, surface texture is an important quality characteristic of milk powder, and the possibility of three-dimensional analysis for milk powder texture has aroused more interest. Consequently, the main objective of this project is to investigate the quantitive relationship between the particle size/morphology and the dispersibility/bulk density of IWMP, and to assess milk powder appearance from 3D images. The milk powders were divided into different particle size groups by sieving, and then these were remixed in different proportions. Light microscopy was combined with imageprocessing to measure the shape factors for each particle. The New Zealand Dispersibility Test (NZDB-method) was used to test the dispersibility of each milk powder sample, and the bulk densities of these remixed milk powder samples were measured by tap testing. Then, the photogrammetry equipment was combined with the software RealityCapture to build the 3D models of milk powder samples. Principal component analysis (PCA), partial least squares (PLS), and artificialneuralnetworks (ANN) were used to process the data, and the surface normal and contour slice were used to quantify the surface roughness of milk powder samples. In addition, various partial least squares models constructed from (i) process variables
This book constitutes the refereed proceedings of the Second International Conference, SLAAI-ICAI 2018, held in Moratuwa, Sri Lanka, in December 2018.;The 32 revised full papers presented were carefully reviewed ...
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ISBN:
(数字)9789811391293
ISBN:
(纸本)9789811391286
This book constitutes the refereed proceedings of the Second International Conference, SLAAI-ICAI 2018, held in Moratuwa, Sri Lanka, in December 2018.;The 32 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in the following topical sections: intelligence systems; neuralnetworks; game theory; ontology engineering; natural language processing; agent based system; signal and imageprocessing.
The two-volume set LNAI 9119 and LNAI 9120 constitutes the refereed proceedings of the 14th International Conference on artificial Intelligence and Soft Computing, ICAISC 2015, held in Zakopane, Poland in June 2015. T...
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ISBN:
(数字)9783319193694
ISBN:
(纸本)9783319193687
The two-volume set LNAI 9119 and LNAI 9120 constitutes the refereed proceedings of the 14th International Conference on artificial Intelligence and Soft Computing, ICAISC 2015, held in Zakopane, Poland in June 2015. The 142 revised full papers presented in the volumes, were carefully reviewed and selected from 322 submissions. These proceedings present both traditional artificial intelligence methods and soft computing techniques. The goal is to bring together scientists representing both areas of research. The first volume covers topics as follows neuralnetworks and their applications, fuzzy systems and their applications, evolutionary algorithms and their applications, classification and estimation, computer vision, image and speech analysis and the workshop: large-scale visual recognition and machine learning. The second volume has the focus on the following subjects: data mining, bioinformatics, biometrics and medical applications, concurrent and parallel processing, agent systems, robotics and control, artificial intelligence in modeling and simulation and various problems of artificial intelligence.
Develop neural network applications using the Java environment. After learning the rules involved in neural network processing, this second edition shows you how to manually process your first neural network example. ...
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ISBN:
(数字)9781484273685
ISBN:
(纸本)9781484273678
Develop neural network applications using the Java environment. After learning the rules involved in neural network processing, this second edition shows you how to manually process your first neural network example. The book covers the internals of front and back propagation and helps you understand the main principles of neural network processing. You also will learn how to prepare the data to be used in neural network development and you will be able to suggest various techniques of data preparation for many unconventional tasks.;This book discusses the practical aspects of using Java for neural network processing. You will know how to use the Encog Java framework for processing large-scale neural network applications. Also covered is the use of neuralnetworks for approximation of non-continuous functions. In addition to using neuralnetworks for regression, this second edition shows you how to use neuralnetworks for computer *** focuses on image recognition such as the classification of handwritten digits, input data preparation and conversion, and building the conversion program. And you will learn about topics related to the classification of handwritten digits such as network architecture, program code, programming logic, and execution.;The step-by-step approach taken in the book includes plenty of examples, diagrams, and screenshots to help you grasp the concepts quickly and easily.;Intermediate machine learning and deep learning developers who are interested in switching to Java
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