The NMRPipe system is a UNIX software environment of processing, graphics, and analysis tools designed to meet current routine and research-oriented multidimensional processing requirements, and to anticipate and accommodate future demands and developments. The system is based on UNIX pipes, which allow programs running simultaneously to exchange streams of data under user control. In an NMRPipe processing scheme, a stream of spectral data flows through a pipeline of processing programs, each of which performs one component of the overall scheme, such as Fourier transformation or linear prediction. Complete multidimensional processing schemes are constructed as simple UNIX shell scripts. The processing modules themselves maintain and exploit accurate records of data sizes, detection modes, and calibration information in all dimensions, so that schemes can be constructed without the need to explicitly define or anticipate data sizes or storage details of real and imaginary channels during processing. The asynchronous pipeline scheme provides other substantial advantages, including high flexibility, favorable processing speeds, choice of both all-in-memory and disk-bound processing, easy adaptation to different data formats, simpler software development and maintenance, and the ability to distribute processing tasks on multi-CPU computers and computer networks.

NMRPipe: a multidimensional spectral processing system based on UNIX pipes

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

The quintessential goal of sensor array signal processing is the estimation of parameters by fusing temporal and spatial information, captured via sampling a wavefield with a set of judiciously placed antenna sensors. The wavefield is assumed to be generated by a finite number of emitters, and contains information about signal parameters characterizing the emitters. A review of the area of array processing is given. The focus is on parameter estimation methods, and many relevant problems are only briefly mentioned. We emphasize the relatively more recent subspace-based methods in relation to beamforming. The article consists of background material and of the basic problem formulation. Then we introduce spectral-based algorithmic solutions to the signal parameter estimation problem. We contrast these suboptimal solutions to parametric methods. Techniques derived from maximum likelihood principles as well as geometric arguments are covered. Later, a number of more specialized research topics are briefly reviewed. Then, we look at a number of real-world problems for which sensor array processing methods have been applied. We also include an example with real experimental data involving closely spaced emitters and highly correlated signals, as well as a manufacturing application example.

Two decades of array signal processing research: the parametric approach

In the choice of an estimator for the spectrum of a stationary time series from a finite sample of the process, the problems of bias control and consistency, or "smoothing," are dominant. In this paper we present a new method based on a "local" eigenexpansion to estimate the spectrum in terms of the solution of an integral equation. Computationally this method is equivalent to using the weishted average of a series of direct-spectrum estimates based on orthogonal data windows (discrete prolate spheroidal sequences) to treat both the bias and smoothing problems. Some of the attractive features of this estimate are: there are no arbitrary windows; it is a small sample theory; it is consistent; it provides an analysis-of-variance test for line components; and it has high resolution. We also show relations of this estimate to maximum-likelihood estimates, show that the estimation capacity of the estimate is high, and show applications to coherence and polyspectrum estimates.

Spectrum estimation and harmonic analysis

There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world

Robust Model-Based Fault Diagnosis for Dynamic Systems

The odd-order real zeros of a real-valued signal, s(t), correspond to its conventional or overt zero-crossings. They can be easily determined. But the complex zeros of s(t) are not easy to determine. This suggests the possibility of converting s(t) by using some invertible transformation, into a different signal, whose real zero-crossings determine s(t) completely. This process is known as real-zero conversion. The real zero-crossings of the converted signal are termed covert zero-crossings (cozecs), since they only indirectly determine s(t). In this paper we present a new adaptive algorithm for converting a band-pass signal into a pair of signals whose real zero-crossings essentially determine its envelope and phase. Our algorithm is based on modeling s(t) using pole-zero models in the complex-time (C) plane. Drawing parallels to well known ideas in the area of speech/spectral analysis, we decompose s(t) into its minimum-phase and all-phase components. These components are then represented by their associated zero-crossings.

Covert zero-crossings represent envelope and phase of band-pass signals

Memory intensive architectures are highly suited to VLSI technology. In this paper we present efficient architectures for implementing the discrete Fourier transform (DFT) and the discrete Hartley transform (DHART) based on prime factor decomposition. We use the multiplier-less distributed arithmetic in our architecture. But the look-up table sizes needed for implementing the short DFT/DHART modules are minimized by judiciously decomposing the short DFT/DHART matrices.

Efficient architectures for implementing the prime-factor Fourier transform modules

We have developed a novel approach to speech feature extraction based on a modulation model of a band-pass signal. Speech is processed by a bank of band-pass filters. At the output of the band-pass filters the signal is subjected to a log-derivative operation which naturally decomposes the band-pass signal into analytic (called /spl alpha//spl dot/(t)+j/spl alpha//spl dot//spl circ/(t)) and antianalytic (called /spl beta//spl dot/(t)-j/spl beta//spl dot//spl circ/(t)) components. The average instantaneous frequency (AIF) and average log-envelope (ALE) are then extracted as coarse features at the output of each filter. We indicate how further refined features may also be extracted from the analytic and antianalytic components. We then evaluated the feature extraction procedure on the Aurora 2 task where noise corruption is synthetic. For clean training, (compared to the mel-cepstrum front end, with 5 mixture HMM back-end) our AIF/ALE front end achieves an average improvement of 13.97% with set A and 17.92% improvement with set B and -31.72% (negative) 'improvement' with set C. The overall improvement in accuracy rates for clean training is 7.97%. Although the improvements are modest, the novelty of the front-end and its potential for future enhancements are our strengths.

Decomposition of a bandpass signal and its applications to speech processing

Using the human auditory system as a guide we propose a signal processing strategy for decomposing composite signals into bandpass signal components and extracting their features. We use two parallel filterbanks, one composed of a set of wideband overlapping filters and another consisting of a set of narrowband filters. The filterbanks cooperate to isolate regions of persistent and transient signal activity. The narrowband filters help identify spectral regions containing significant signal energy and groups of wideband filters in those regions are then optimally combined to isolate and track each of the bandpass signal components. Each group of wideband filters are combined to track one bandpass component while suppressing all other neighboring components. Narrowband filters in cascade with nonlinearities are used to characterize the transient components.

Adaptive filterbanks inspired by the auditory system for speech feature extraction

The authors have discovered the presence of weak sinusoidal components located at formant frequencies in voiced speech segments. They refer to them as formant tones and propose a method to estimate and track the frequencies of these formant tones. These sinusoidal components are often located close to the strongest harmonic component under the formant peak and closely track the respective formant peaks with time. The formant tones seem to provide crucial incremental information required precisely to identify and track the formant location. These tones are not easily discernible using standard short-time Fourier transform or linear prediction methods. However, when the speech signal is decomposed into its harmonic components, tell-tale evidence of these formant tones is visible in their envelopes. Then the strong harmonic components are removed by high-pass filtering the envelopes. The high-pass filtered envelopes are subsequently used to identify the frequency tracks of these tones. Since the formant tones are non-harmonic, they must originate in the vocal tract as a reaction to the impinging glottal pulses. These tones appear to be angle-modulated by the glottal pulses. >

Ramdas Kumaresan

Papers

Covert zero-crossings represent envelope and phase of band-pass signals

Efficient architectures for implementing the prime-factor Fourier transform modules

Decomposition of a bandpass signal and its applications to speech processing

Adaptive filterbanks inspired by the auditory system for speech feature extraction

Formant tones: weak components at formant frequencies actively generated in the vocal tract