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).

The primary goal of this paper is to give examples of the recently developed (finite-field) wavelet coding method by studying the encoder and decoder for some half-rate codes. We propose a decoding methodology based on estimating the polyphase components of the channel error pattern. To demonstrate the striking computational savings of the wavelet coding method over alternatives, we show that bounded-distance decoding of the (24,12,8) Golay code requires only weight computations (or at the worst case, it needs a cyclic lookup table of table size 12). The simplicity and computational savings that finite field wavelets offer for the encoding and decoding of wavelet block codes indicate their powerful capacities for error control coding applications.

Decoding of half-rate wavelet codes; Golay code and more

The UMTS terrestrial radio access (UTRA) is composed of a frequency division duplex (UTRA-FDD) mode and a time division duplex (UTRA-TDD) mode. In UTRA-TDD the uplink and downlink are on the same carrier frequency. This creates additional interference scenarios, to be precise: MS/spl hArr/MS and BS/spl hArr/BS interference. Since power leakage between adjacent carriers cannot be avoided this inherent property of TDD has an impact on adjacent channel interference (ACI) and thus on cell capacity. The power leakage results from transmitter mask imperfections and non-ideal receiver filters. From interference power measurements using a 7th and a 9th order Butterworth receiver filter the relationship between carrier spacing and adjacent channel interference power ratio (ACIR) is established This relationship is used to find the best carrier spacing in UTRA-TDD considering several cell deployment scenarios. It is found that the ACIR must be greater than 30 dB, but the capacity gains from an ACIR greater than 40 dB converge rapidly.

An investigation on capacity versus guard-bands in the TDD mode of UMTS

New Adaptive Multiuser Detection Technique for CDMA Mobile Receivers

Medical ultrasound systems are limited by a tradeoff between axial resolution and the maximum imaging depth which may be achieved. The technique of coded excitation has been used extensively in the field of RADAR and SONAR for some time, but has only relatively recently been exploited in the area of medical ultrasound. This technique is attractive because allows the relationship between the pulse length and the maximum achievable spatial resolution to be changed. The work presented here explores the possibility of using the fractional Fourier transform as an effective means for the processing of signals received after the transmission of linear frequency modulated chirps. Results are presented which demonstrate that this technique is able to offer spatial resolutions similar to those obtained with a single cycle duration signal.

The use of the fractional Fourier transform with coded excitation in ultrasound imaging

Introduction. We aimed to assess the efficacy and safety of digital subtraction angiography (DSA) and super-selective mesenteric artery embolization (SMAE) in managing lower GI bleeding (LGIB). Method. A retrospective case series of patients with LGIB treated with SMAE in our health service. Patients with confirmed active LGIB, on either radionuclide scintigraphy (RS) or contrast-enhanced multidetector CT angiography (CE-MDCT), were referred for DSA

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Stephen McLaughlin

Papers

Decoding of half-rate wavelet codes; Golay code and more

An investigation on capacity versus guard-bands in the TDD mode of UMTS

New Adaptive Multiuser Detection Technique for CDMA Mobile Receivers

The use of the fractional Fourier transform with coded excitation in ultrasound imaging

Super-Selective Mesenteric Embolization Provides Effective Control of Lower GI Bleeding.