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 authors propose a model for signal representation at the output of the cochlea based on minimum phase (MP) signals. A minimum phase signal has the property that its phase and the logarithm of its envelope are related by Hilbert transformation. Hence by retaining either the phase or the envelope the entire signal can be represented to within a scale factor. First the authors show how to convert a given signal into one that is minimum phase by suitably adding a sinewave at the band-edge of the signal. Subsequently the authors show how to recover the original signal from the minimum phase signal's phase or envelope. With this procedure signals can be automatically scaled (automatic gain control) to lie within a given dynamic range; the authors conjecture that such a procedure may indeed be used by the cochlea to represent signals that vary in intensity over several orders of magnitude. >

A model for signal representation at the output of the cochlea based on minimum phase signals

Techniques based on linear prediction (LP) and the singular value decomposition (SVD) for the robust estimation of the parameters of closely spaced exponentially damped sinusoidal signals in additive noise are extended and improved. An iterative method of fitting lower-rank least squares approximations subject to a general choice of weights is used. The method is applied to data sequences consisting of one and two signals with impulsive noise or with missing data samples. >

A low rank weighted matrix approximation method for robust estimation of sinusoid parameters

In this paper, we extend our previous sparse linear prediction (SLP) method for two-dimensional (2-D) applications. The temporal and spatial frequencies of multiple sinusoids are simultaneously estimated in pairs, rather than separately, from the outputs of a sparse linear array. Possible ambiguities due to the large interelement spacings can be resolved by properly choosing the interelement spacings and by incorporating different SLP polynomials, the coefficients of which can be calculated in parallel. The temporal and spatial frequencies are simultaneously estimated from the minima of a 2-D objective function, which can be calculated using a 2-D DFT of the properly mapped 2-D SLP coefficient matrix. >

Estimation of the spatial and temporal frequencies of multiple sinusoids using a sparse linear array

An adaptive synchrony capture filterbank (SCFB) signal processing architecture for tracking signal frequency components was proposed in a previous paper. Detailed analysis and further improvements are presented here. The original SCFB signal processing architecture consisted of a fixed array of traditional, passive linear, gammatone filters in cascade with a bank of three adaptively tunable bandpass filters that form part of a frequency discriminator loop (FDL). The SCFB exhibits many desirable properties for processing speech, music, and other complex sounds. In this article, an improved SCFB architecture is presented along with detailed specification of parameters that yield better frequency tracking. This architecture combines a FDL with a phase-locked loop, a concept borrowed from communication systems, to acquire and track frequency components in a time-varying signal. This modified algorithm estimates frequencies with higher accuracy even in the presence of closely spaced tones in the input. The improved performance of the algorithm is demonstrated by comparing the mean square error in the frequency estimates of synthetic time-varying signals.

https://link.springer.com/content/pdf/10.1007/s42979-019-0060-6.pdf

Improved Auditory-Inspired Signal Processing Algorithm Design for Tracking Multiple Frequency Components

The aim of this paper is to apply a new mul- tidirectional resolution analysis to obtain boundaries be- tween areas with different grades of "roughness". To this end, the image texture is parameterized with respect to di- rection, based on the model of isotropic two dimensional fractional Brownian Motion (2-d-FBM). This model un- derlies many different investigations in the literature even though some theoretical aspects are not clarified yet. How- ever, in the first part of this paper, we provide some of the crucial missing links in the theory of 2-d-FBM and provide a concrete realisation of this stochastic process. In addi- tion, we derive a formula for its spectral behaviour. In the second part we describe in detail the segmentation procedure. At first, we present a suboptimal, but fast esti- mation algorithm to obtain angle-dependent fractal parame- ter-subimages, and compare its performance to the Cramer- Rao bound. Afterwards, we apply matched edge-detection procedures to each subimage separately, and finally combine the angle-dependent boundary pixels to obtain the texture boundaries of the entire scene.

Ramdas Kumaresan

Papers

A model for signal representation at the output of the cochlea based on minimum phase signals

A low rank weighted matrix approximation method for robust estimation of sinusoid parameters

Estimation of the spatial and temporal frequencies of multiple sinusoids using a sparse linear array

Improved Auditory-Inspired Signal Processing Algorithm Design for Tracking Multiple Frequency Components

Segmention of textures with different roughness fractional brownian motion using the model of isotropic two-dimensional