Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

On robust estimation of the location parameter

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Stable non-Gaussian random processes , by G. Samorodnitsky and M. S. Taqqu. Pp. 632. £49.50. 1994. ISBN 0-412-05171-0 (Chapman and Hall).

One of the tasks for which empirical mode decomposition (EMD) is potentially useful is nonparametric signal denoising, an area for which wavelet thresholding has been the dominant technique for many years. In this paper, the wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal. We show that although a direct application of this principle is not feasible in the EMD case, it can be appropriately adapted by exploiting the special characteristics of the EMD decomposition modes. In the same manner, inspired by the translation invariant wavelet thresholding, a similar technique adapted to EMD is developed, leading to enhanced denoising performance.

Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding

This paper deals with relay selection in cooperative networks with secrecy constraints. The proposed scheme enables an opportunistic selection of two relay nodes to increase security against eavesdroppers. The first relay operates as a conventional mode and assists a source to deliver its data to a destination via a decode-and-forward strategy. The second relay is used in order to create intentional interference at the eavesdropper nodes. The proposed selection technique jointly protects the primary destination against interference and eavesdropping and jams the reception of the eavesdropper. The new approach is analyzed for different complexity requirements based on instantaneous and average knowledge of the eavesdropper channels. In addition an investigation of an hybrid security scheme which switches between jamming and non-jamming protection is discussed in the paper. It is proven that an appropriate application of these two modes further improves security. The enhancements of the proposed selection techniques are demonstrated analytically and with simulation results.

Relay selection for secure cooperative networks with jamming

This article provides a general overview of time-frequency (T-F) reassignment and synchrosqueezing techniques applied to multicomponent signals, covering the theoretical background and applications. We explain how synchrosqueezing can be viewed as a special case of reassignment enabling mode reconstruction and place emphasis on the interest of using such T-F distributions throughout with illustrative examples.

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Time-Frequency Reassignment and Synchrosqueezing: An Overview

Thrombosis of coronary arteries is a condition responsible for many acute coronary syndromes. The ability to categorise thrombus belonging to distinct pathological groups, would contribute to the understanding of the pathophysiologic structure of individual lesions, as well as making a significant contribution to treatment choice. Here, the authors investigate the use of statistical texture analysis techniques to assess the ability of 30 MHz intravascular ultrasound (IVUS) data, in raw and scan-converted form, to characterise intracoronary thrombus. Three clot types were assessed in the study, these were, red (R), white (W) and plasma (P). Histopathological analysis, the de facto standard in identifying tissue composition, was used to form a Gold Standard based upon clot composition, from which the results were verified. The results show the ability of the texture analysis techniques used to discriminate clot lesions, and highlights the advantage of using the raw data over the scan-converted data in assessing thrombus composition in vitro.

Comparative study of textural analysis techniques to characterise tissue from intravascular ultrasound

This letter offers a statistical analysis of the basic two-hop Amplify-and-Forward link, where the relay node is selected based on instantaneous and partial knowledge of the channel. In contrast with previously reported work, where relay selection requires global knowledge (2 hops) of the relaying link, the problem considered is interesting in practical ad-hoc systems, where only neighboring (1 hop) channel information is available to the nodes. The probability density function of the received signal-to-noise ratio for the considered relaying link is approximated in closed form, and an asymptotic exponential expression is proposed to simplify performance estimation.

Stephen McLaughlin

Papers

Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding

Relay selection for secure cooperative networks with jamming

Time-Frequency Reassignment and Synchrosqueezing: An Overview

Comparative study of textural analysis techniques to characterise tissue from intravascular ultrasound

Amplify-and-forward with partial relay selection