Machine Learning For Signal Processing Research Articles

Filter-free color pixel sensor using gated PIN photodiodes and machine learning techniques

This work addresses the possibility of light wavelength discrimination, using a specially designed Lateral PIN Gated Photodiode and Machine Learning signal processing techniques. The devices were designed at FEI University using IBM BiCMOS 8HP 0.13 μm technology. The terminal gate of the lateral PIN photodiode controls the vertical depletion layer, in order to modulate the capture of the electron-hole pairs generated by different wavelengths along the intrinsic region. The collected data were organized in optical power classes with the corresponding cathode currents. The experimental characterization shows that the terminal gate voltage can be used to discriminate different incident colors. The simulation results allowed the acquisition of a complete dataset to apply machine learning techniques and classify the photodetector captured color. This technique can be extended to photosensors arrays and eliminate the organic color filters. The best result was obtained using the “Linear and Quadratic Discriminant” algorithms, with the average accuracy of 92.46% and 96.82%, respectively.

Wavelet Power Spectral Domain Functional Principal Component Analysis for Feature Extraction of Epileptic EEGs

Feature extraction plays an important role in machine learning for signal processing, particularly for low-dimensional data visualization and predictive analytics. Data from real-world complex systems are often high-dimensional, multi-scale, and non-stationary. Extracting key features of this type of data is challenging. This work proposes a novel approach to analyze Epileptic EEG signals using both wavelet power spectra and functional principal component analysis. We focus on how the feature extraction method can help improve the separation of signals in a low-dimensional feature subspace. By transforming EEG signals into wavelet power spectra, the functionality of signals is significantly enhanced. Furthermore, the power spectra transformation makes functional principal component analysis suitable for extracting key signal features. Therefore, we refer to this approach as a double feature extraction method since both wavelet transform and functional PCA are feature extractors. To demonstrate the applicability of the proposed method, we have tested it using a set of publicly available epileptic EEGs and patient-specific, multi-channel EEG signals, for both ictal signals and pre-ictal signals. The obtained results demonstrate that combining wavelet power spectra and functional principal component analysis is promising for feature extraction of epileptic EEGs. Therefore, they can be useful in computer-based medical systems for epilepsy diagnosis and epileptic seizure detection problems.

Machine monitoring system: a decade in review

In recent years, significant advancements have been achieved in the domain of machine monitoring as witnessed from the beginning of the new industrial revolution (IR) known as IR 4.0. This new revolution is characterized by complete automation, and an increase in the technological deployments and advanced devices used by various systems. As a result, considerable advancement has been reported by researchers and academia around the world by adopting and adapting the new technology related to the machine condition monitoring problem. For these reasons, it is important to highlight new findings and approaches in machine condition monitoring based on the wireless sensor solution and machine learning signal processing methodology that are relevant to assist in advancing this new revolution. This article presents a comprehensive review on tool condition monitoring (TCM), tool wear, and chatter based on vibration, cutting force, temperature, surface image, and smart label monitoring parameters from signal acquisition, signal processing methodology, and decision-making, particularly for the milling process. The paper also provides a brief introduction to the manufacturing industries and computer numerical control (CNC) machine tool demand and a review of machine monitoring and countries involvement in machine monitoring, as well as the approaches and a survey of machine monitoring. The aim is to contribute to this rapidly growing field of machine condition monitoring research by exploring the latest research findings on the solution approach for milling machine process monitoring, to help expedite future research, and to give some future direction that needs to be considered as a path to produce a standardized CNC machine platform.

Guest Editorial: Machine Learning for Signal Processing

One of the fundamental goals of signal processing is to model observed signals or their underlying processes accurately. This is a very challenging problem due to issues such as observation noise and the finite nature of observations. To overcome these difficulties, signal processing has proactively employed the relatively new and powerful discipline of machine learning. The type of signal processing that incorporates machine learning as its booster, i.e., Machine Learning for Signal Processing (MSLP), is now working as a promising technological paradigm that opens the way to discovering salient features from noisy signals, predicting unseen signals, and achieving accurate modeling of the process that generates signals. A large number of papers related to MLSP have been published in different places. However, to advance research in the area effectively, one comprehensive forum would clearly be advantageous. Motivated by this need, we organized the publication project of this special issue onMSLP. Twenty-one papers were submitted to the issue, and the eleven papers presented here were selected through a rigorous review process. The selected papers cover most of the key factors of signal processing, in particular its important sub-field of pattern classification. Pattern classification basically consists of a featureextraction (selection) stage, which converts an input pattern to features, and a classification stage, which labels a set of converted features as one of various preset class indexes. Research on pattern classification sometimes focuses on either of these component stages. Each of the selected papers more or less refers to both of the stages, but they can be divided into three groups to give readers a clear understanding of the issue and the relative positions of the included papers. The first four papers focus on theoretical or algorithmic research topics related to the classification stage by studying ways of designing (training) class models. The next three papers study the extraction of salient features, or those robust to unseen patterns, from a limited amount of input data. Finally, the remaining four papers study applications of signal modeling and/or pattern classification to particular types of input data, such as face images and speech signals.

Cross-categorization of legal concepts across boundaries of legal systems: in consideration of inferential links

This work contrasts Giovanni Sartor's view of inferential semantics of legal concepts (Sartor in Artif Intell Law 17:217---251, 2009) with a probabilistic model of theory formation (Kemp et al. in Cognition 114:165---196, 2010). The work further explores possibilities of implementing Kemp's probabilistic model of theory formation in the context of mapping legal concepts between two individual legal systems. For implementing the legal concept mapping, we propose a cross-categorization approach that combines three mathematical models: the Bayesian Model of Generalization (BMG; Tenenbaum and Griffiths in Behav Brain Sci 4:629---640, 2001), the probabilistic model of theory formation, i.e., the Infinite Relational Model (IRM) first introduced by Kemp et al. (The twenty-first national conference on artificial intelligence, 2006, Cognition 114:165---196, 2010) and its extended model, i.e., the normal-IRM (n-IRM) proposed by Herlau et al. (IEEE International Workshop on Machine Learning for Signal Processing, 2012). We apply our cross-categorization approach to datasets where legal concepts related to educational systems are respectively defined by the Japanese- and the Danish authorities according to the International Standard Classification of Education. The main contribution of this work is the proposal of a conceptual framework of the cross-categorization approach that, inspired by Sartor (Artif Intell Law 17:217---251, 2009), attempts to explain reasoner's inferential mechanisms.

Entropy-Relevant Dimensions in the Kernel Feature Space: Cluster-Capturing Dimensionality Reduction

The nonlinear mapping to the feature space is a very important concept in kernel-based machine learning for signal processing, within the framework of positive semidefinite (psd) kernels. Given labeled data, algorithms such as support vector machines (SVMs) or projection methods such as Fisher discriminant analysis may be executed in the feature space. For unsupervised dimensionality reduction in the feature space, the most common approach is to perform principal component analysis (PCA) in that space, thus maximally capturing the variability of the feature space data, however, doing so without necessarily capturing any cluster structure in the data. In this tutorial, we review the theory behind feature space mapping and review recent advances that broaden the understanding and interpretability of the mapping in terms of a key input space quantity, particularly the quadratic Renyi entropy of the data via the eigenvalues and eigenfunctions of a psd convolution operator. Focusing on the unsupervised case, we describe the identification of entropy-relevant dimensions in the feature space. We review recent results showing that these dimensions capture structure in the data in the form of clusters, and that they are, in general, different from the kernel PCA (KPCA) dimensions. Differences between these approaches to dimensionality reduction for visualization and clustering are illustrated.

Trends in Machine Learning for Signal Processing [In the Spotlight

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.

Special Issue on Machine Learning for Signal Processing

Machine learning is devoted to the design of methods and algorithms able to learn from empirical data. This approach is especially important in signal and image processing, where sets of sensors, usually large and heterogeneous, provide large amounts of data, usually noisy and corrupted with various sources of interference. From a methodological point of view, machine learning is concerned with multi-dimensional and statistical signal processing, especially with problems such as detection, estimation, and optimization. In addition to classical supervised or unsupervised learning, reinforcement learning and semisupervised learning, machine learning methods include Bayesian modeling, Markov models, support vector machines, and kernel methods. It spans a broad area of applications, such as adaptive filtering, pattern recognition, scene analysis in computer vision, data mining, robot control, data fusion, blind and semi-blind source separation, sparse component analysis, brain-computer interfaces, hyperspectral images, and cognitive radio. This special issue has been designed following the IEEE international workshop Machine Learning for Signal Processing which was held in Grenoble (France) in September 2009. The papers have been extended and rereviewed. In addition to theoretical contributions in signal detection, pattern recognition and classification, blind source separation, learning theory, Bayesian learning and modeling, and to applied contributions in speech and audio processing, biomedical application and communications, we have featured three Special Sessions, one on Braincomputer Interfaces, the second on Machine Learning in Remote Sensing Data Processing, and the third one on Learning in Markov Models. The 19 papers of this special issue reflect the current trends in Machine Learning.

Guest Editorial: Special Issue on Machine Learning for Signal Processing

Machine Learning (ML) is a generic term referring to methods and algorithms that learn based on empirical observations. In recent years, the field has matured considerably in both methodology and real-world application domains. ML methods belong not only to the classical supervised, unsupervised, or reinforcement learning paradigms (often associated with neural networks) but also to an increasingly wide range of methodologies including kernel methods, support vector machines (SVMs), Bayesian learning, etc. Also ML has become particularly important for the solution of problems in signal processing. Machine learning for signal processing combines many ideas from adaptive signal/image processing, optimization theory, learning theory and models, and statistics in order to solve complex real-world signal processing applications. The range of applications is also growing, including pattern recognition, adaptive filtering, computer vision, content based image and video retrieval, data mining, cognitive radio, robot control, data fusion, blind signal processing, sparse component analysis, brain-computer interfaces, etc. This special issue has been put together from extended versions of papers presented in the 2007 IEEE International Workshop on Machine Learning for Signal Processing (MLSP-2007), held in Thessaloniki, Greece, between August 27 and 29, 2007. The guest editor committee invited the authors of the top ranking papers—based on the review scores they received at MLSP-2007—to submit their extended papers to the special issue. The committee also decided to invite the keynote and plenary speakers to submit their contributions in a full paper format. All papers went through a regular reviewing process and were duly revised, if necessary, prior to acceptance. Since the workshop covered the overall area of machine learning the papers represent a wide range of topics including feature-extraction and classification, nonlinear learning methods, speech separation, image and video processing, inference and programming methods. We would like to take this opportunity to thank the rest of the organizing committee of the workshop for their help and support: Tulay Adali, University of Maryland, Baltimore County, USA, Jan Larsen, Technical University of Denmark, Theophilos Papadimitriou, University of Thrace, Greece, Marc Van Hulle, Katholieke Universiteit Leuven, Belgium, Scott Douglas, Southern Methodist University, TX, USA, and Deniz Erdogmus, Oregon Health & Science University, OR, USA. I. Pitas (*) Aristotle University of Thessaloniki, Thessaloniki 54124, Greece e-mail: pitas@aiia.csd.auth.gr

Complex ICA using generalized uncorrelating transform

An extension of the whitening transformation for complex random vectors, called the generalized uncorrelating transformation (GUT), is introduced. GUT is a generalization of the strong-uncorrelating transform [J. Eriksson, V. Koivunen, Complex-valued ICA using 2nd-order statistics, in: Proceedings of the IEEE Workshop on Machine Learning for Signal Processing (MLSP’04), Sao Luis, Brazil, 2004] based upon generalized estimators of the covariance and pseudo-covariance matrix, called the scatter matrix and spatial pseudo-scatter matrix, respectively. Depending on the selected scatter and spatial pseudo-scatter matrix, GUT estimators can have largely different statistical properties. Special emphasis is put on robust GUT estimators. We show that GUT is a separating matrix estimator for complex-valued independent component analysis (ICA) when at most one source random variable possess circularly symmetric distribution and sources do not have identical distribution. In the context of ICA, our approach is computationally attractive as it is based on straightforward matrix computations. Simulations and examples are used to confirm reliable performance of our method.

2007 IEEE Workshop on Machine Learning for Signal Processing

2007 IEEE Workshop on Machine Learning for Signal Processing

Guest Editorial for Special Issue on the 2005 IEEE Workshop on Machine Learning for Signal Processing

Guest Editorial for Special Issue on the 2005 IEEE Workshop on Machine Learning for Signal Processing

2007 IEEE Workshop on Machine Learning for Signal Processing [Call for Papers

2007 IEEE Workshop on Machine Learning for Signal Processing [Call for Papers

2006 IEEE Workshop on Machine Learning for Signal Processing

2006 IEEE Workshop on Machine Learning for Signal Processing

2006 IEEE Workshop on Machine Learning for Signal Processing

2006 IEEE Workshop on Machine Learning for Signal Processing

2006 IEEE Workshop on Machine Learning for Signal Processing

2006 IEEE Workshop on Machine Learning for Signal Processing

2006 IEEE Workshop on Machine Learning for Signal Processing

2006 IEEE Workshop on Machine Learning for Signal Processing

2006 IEEE Workshop on Machine Learning for Signal Processing - Call for Papers

2006 IEEE Workshop on Machine Learning for Signal Processing - Call for Papers

2005 IEEE Workshop on Machine Learning for Signal Processing

2005 IEEE Workshop on Machine Learning for Signal Processing

2005 IEEE Workshop on Machine Learning for Signal Processing - Call for Papers

2005 IEEE Workshop on Machine Learning for Signal Processing - Call for Papers