Emerging Machine Learning Techniques in Signal Processing

2020

Machine Learning has been getting a charge out of a remarkable many applications that take care of issues and empower computerization in different areas. Essentially, this is because of the blast in the accessibility of information, huge enhancements in ML methods, and headway in registering abilities. Without a doubt, ML has been applied to different unremarkable and complex issues emerging in signal processing activity and the executives. There are different overviews on ML for explicit zones in signal processing or for explicit advances. This overview is unique, since it together presents the use of assorted ML procedures in different key territories of signal handling across various system advances. Right now, will profit by a thorough conversation on the distinctive learning ideal models and ML methods applied to crucial issues in signal processing, including estimation of Bit Error rate, Signal to Noise apportion just as productivity. Besides, this review depicts the confinements, give bits of knowledge, and examine difficulties and future chances to propel ML in signal processing. In this way, this is an opportune commitment of the ramifications of ML for signal processing, that is pushing the boundaries of various system and activity.

The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology, 2000

This special issue, with invited papers solicited from authors who contributed to the 2001 IEEE Workshop on Neural Networks for Signal Processing, gathers papers from three areas where machine learning methods have found fruitful applications. These are: (1) data mining, (2) unsupervised learning, and (3) communications. Data mining and knowledge discovery aims at automating the search for patterns in large data sets, with the goals of either explicitly conveying discovered structure to a user, or of encoding this structure, via learning, into a model that can be used to make inferences or predictions. The emergence of data mining as a distinct research area represents the confluence of several different factors: (i) large database repositories, now available for many important domains, as well as the Internet for their access; (ii) application goals on these domains, such as prediction and data visualization, and (iii) mature developments and continuing advances in statistical analysis and model learning techniques, contributed from the statistics, neural networks, machine learning/pattern recognition, and information theory communities. Data mining tasks include classification, prediction, clustering, data compaction/compression, data visualization, and rule discovery from databases. Data mining application areas include bio-informatics and medical diagnosis, marketing and business applications, information retrieval from the Internet and other data repositories, and scientific discovery (e.g. biological taxonomy). The diversity of applications and goals demands the use of various methodologies, with more than one sometimes brought to bear on the same problem. For example, classical statistical methods can be used to identify relevant features and to discern feature interactions. This in turn may drive the input selection and architecture of a neural network classifier or predictor. Likewise, informationtheoretic criteria may be used as alternatives to more conventional criteria when building classifier, clustering, or

Journal of Autonomous Intelligence

This research paper presents a brief introduction to the key point of Machine Learning (ML) with the application to communication systems. Due to the exceptional accessibility of software and data abilities, there is a great deal of interest in using digital information machine learning thinking to solve issues in a variety of fields. Regarding the phenomenal amount of information and computer facilities, there is a lot more interest in using content-supervised learning methods to resolve obstacles where engineering course techniques are restricted by theoretical or methodological problems. This study starts by clarifying when and why comparable strategies may well be effective. It then goes into the fundamentals of supervised and unsupervised at a high level. Where traditional engineering solutions are being developed Modelling or algorithmic flaws are posing a problem. This paper begins by answering the why and when of these questions. Such methods can be beneficial to resolve rea...

Computational intelligence and neuroscience, 2017

2010

Any brain–computer interface (BCI) system must translate signals from the users brain into messages or commands (see Fig. 1). Many signal processing and machine learning techniques have been developed for this signal translation, and this chapter reviews the most common ones. Although these techniques are often illustrated using electroencephalography (EEG) signals in this chapter, they are also suitable for other brain signals.

1996

Since many signal processing problems can be posed as sample-based decision and estimation tasks, we discuss how studies from other fields such as neural networks might suggest improved architectures (models) and algorithms for these types of problems. We then concentrate on PAM equalization, showing that a reordering of the equivalent classification problem generates a 'staircase' which, while retaining the simplicity of the classical equalizer, allows modifications to made in the outputs and in the training objectives which provide advantages even in the least complex cases.We go on to demonstrate that these advantages increase when one considers, for example, nonlinear channels with memory.

IEEE Transactions on Signal Processing, 2004

2013

1995

Center for Biological and Computational Learning at MIT). The Workshop is designed to serve as a regular forum for researchers from universities and industry who are interested in interdisciplinary research on neural networks for signal processing applications. NNSP'95 offers a showcase for current research results in key areas, including learning algorithms, network architectures, speech processing, image processing, computer vision, adaptive signal processing, medical signal processing, digital communications and other applications. Our deep appreciation is extended to Prof. Abu-Mostafa of Caltech, Prof. John Moody of Oregon Graduate Institute, Prof. S.Y. Kung, of Princeton U., Prof. Michael I. Jordan of MIT and Dr. Vladimir Vapnik of AT&T Bell Labs, for their insightful plenary talks. Thanks to Dr. Gary Kuhn of Siemens Corporate Research for organizing a wonderful evening panel discussion on "Why Neural Networks are not Dead". Our sincere thanks go to all the authors for their timely contributions and to all the members of the Program Committee for the outstanding and high-quality program. We would like to thank the other members of the Organizing Committee: Finance Chair Dr.

Lecture Notes in Electrical Engineering, 2016

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