Kenneth Steiglitz

Bio: Kenneth Steiglitz is an academic researcher from Princeton University. The author has contributed to research in topics: Signal processing & Very-large-scale integration. The author has an hindex of 46, co-authored 202 publications receiving 14495 citations. Previous affiliations of Kenneth Steiglitz include Telcordia Technologies & Northwestern University.

A pattern classification algorithm for the voiced/Unvoiced decision

Abstract: An algorithm for making the voiced/unvoiced decision in speech analysis is presented. Three features (LPC normalized minimum error, ratio of energy content at high to low frequencies, and input RMS) define a three-dimensional space in which the decision making process is viewed as a pattern classification problem. This is formulated as a linear program which runs on a training set to find a hyperplane dividing the V/UV regions if they are separable, or minimizing the distance by which misclassification occurs if they are not. A procedure is given for selecting the features and constructing the training set.

A probabilistic model for clock skew

Abstract: A probabilistic model for the accumulation of clock skew in synchronous systems is presented. The model is used to derive upper bounds for expected skew and its variance, in tree distribution systems with N synchronously clocked processing elements. The results are applied to two specific models for clock distribution. In the first, which is called metric-free, the skew in a buffer stage is Gaussian with a variance independent of wire length. The second, metric, model, is intended to reflect VLSI constraints: the clock skew in a stage is Gaussian with a variance proportional to wire length, and the distribution tree is an H-tree embedded in the plane. Upper bounds on skew are obtained for both models. Estimates of the constants of proportionality as well as the asymptotic behavior have been obtained and verified by simulation. >

Transmission of an analog signal over a fixed bit-rate channel

Abstract: The transmission of a nonbandlimited analog signal over a digital channel with a fixed bit-rate is considered. The trade-off between the mean-square error due to quantizing and the mean-square error due to the process of sampling and reconstructing the signal is investigated. Simple approximations to these errors, which are valid in most practical situations, are derived, and simple expressions are obtained from which the optimum sampling interval and number of bits per sample can be calculated. Results for first-, second-, and third-order Butterworth and fiat bandlimited spectra, together with the zero-order hold and the linear point connector, are included. The resulting mean-square error goes to zero with large channel bit-rates in a slower manner than the Shannon limit, which assumes a strictly bandlimited signal and perfect reconstruction.

Implementation of parallel arithmetic in a cellular automaton

Abstract: We describe an approach to parallel computation using particle propagation and collisions in a one-dimensional cellular automaton using a Particle model-a Particle Machine (PM). Such a machine has the parallelism, structural regularity, and local connectivity of systolic arrays, but is general and programmable. It contains no explicit multipliers, adders, or other fixed arithmetic operations; these are implemented using fine-grain interactions of logical particles which are injected into the medium of the cellular automaton, and which represent both data and processors. We give parallel, linear-time implementations of addition, subtraction, multiplication and division.

On system identification from noise-obscured input and output measurements†

Abstract: This paper deals with maximum-likelihood system identification when both the input and the output signals are corrupted by Gaussian observation noise. A derivation of exact maximum-likelihood estimation for this problem is included, but the difficulty of implementing it numerically precludes its practical evaluation at this time. A new approximate method is introduced, called the ‘output reference’ method, in which the input noise is referred to the output, and an iterative gradient search method used. This technique requires no a priori knowledge of the noise covariance matrix. The method of Koopmans—Levin, which does require knowledge of the noise covariance matrix, is then reviewed in detail, and experimental results are presented for the white noise case which indicate that the output reference method is more accurate.

Numerical Optimization

Abstract: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side.

Machine Learning : A Probabilistic Perspective

Abstract: Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer

Abstract: A digital computer is generally believed to be an efficient universal computing device; that is, it is believed able to simulate any physical computing device with an increase in computation time by at most a polynomial factor. This may not be true when quantum mechanics is taken into consideration. This paper considers factoring integers and finding discrete logarithms, two problems which are generally thought to be hard on a classical computer and which have been used as the basis of several proposed cryptosystems. Efficient randomized algorithms are given for these two problems on a hypothetical quantum computer. These algorithms take a number of steps polynomial in the input size, e.g., the number of digits of the integer to be factored.

Shape matching and object recognition using shape contexts

Abstract: We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on the two shapes; (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set.

Planning Algorithms: Introductory Material

Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.