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.

Optimal design of digital Hilbert transformers with a concavity constraint

Abstract: A linear programming algorithm is described for designing FIR digital filters with the constraint that the magnitude response be concave over prescribed frequency bands. This is applied to odd-length Hilbert Transformers, and computational results are given. The concavity constraint avoids the ripple of the minimax design, and retains the advantage of maintaining half-band symmetry in the case of symmetric transition bands, so that alternate impulse response samples are zero. If N is the length of the impulse response, ΔF the (symmetric) transition width, and δ the maximum error, it is found that N\DeltaF/\log_{10}^{/delta} \approx -1.1 , as opposed to the value of -0.61 in the minimax case (with ripple) reported by Rabiner and Schafer. Thus, the price paid for the absence of ripples is about twice the number of multiplications per sample.

Some intractable problems in digital signal processing

Abstract: Over the past decade a large class of problems, called NP-complete[1], have been shown to be equivalent in the sense that if a fast algorithm can be found for one, fast algorithms can be found for all. At the same time, despite much effort, no fast algorithms have been found for any, and these problems are widely regarded as intractable. This class includes such notoriously difficult problems as the traveling salesman problem, graph coloring, and satisfiability of Boolean expressions. This paper describes some problems in digital signal processing which are NP-complete. These include: (1) Minimize the number of registers required to implement a signal flow graph; (2) Minimize the time to perform the additions (multiplications) of a signal flow graph using P adders (multipliers); (3) Minimize the computational cost for multiplication by a fixed matrix. Large-scale instances of such problems may become important with the use of VLSI technology to implement signal processing.

An efficient method for generating unaliased samples of certain signals

Abstract: In simulating a continuous-time system being driven by a continuous-time signal f(t), we may wish to generate a discrete-time signal whose spectrum agrees with that of f(t) for frequencies up to the Nyquist frequency One important example is provided by the simulation of the vocal tract driven by a triangularly shaped glottal pulse, although a similar problem arises whenever we simulate a known continuous-time input to a system We define classes of signals, called H-synchronous signals, which, after sampling, can be "de-aliased" with a fixed digital filter depending on H The McClellan-Parks-Rabiner algorithm is used to find FIR designs for such de-aliasing filters for the important special cases of piecewise-constant and piecewise-linear signals

A Problem in Single-Machine Sequencing with Nonlinear Delay Costs

Abstract: We examine a class of single-machine sequencing problems which originate from scheduling considerations for a single-server queueing system with nonlinear costs of delay. Associated with each request awaiting service sequencing are a known service time, a known arrival time, and a nondecreasing cost function which is identical for each request. Two sequencing problems are considered; a request incurs cost from its arrival time to the time when it 1 commences service or 2 completes service. Our objective is a sequence which minimizes the total incurred cost. Necessary conditions for the optimal sequencing of requests are given for convex nondecreasing and quadratic cost functions. These conditions and a new lower bound function are used in a branch-and-bound algorithm to obtain computational results.

A class of adaptive matched digital filters

Abstract: An adaptive digital matched filter structure is developed for the case where the input signal is of known form and finite duration and the input noise has a power spectral density which is all-pole The effect of noise spectrum identification errors on system performance is investigated both theoretically and experimentally It is shown that, when the noise is highly correlated, the adapting structure leads to significant improvement in the output signal-to-noise ratio (and hence in the detection characteristics) with relatively short measurement times This suggests the use of switching logic to allow noise adaptation only when measurements indicate a highly correlated noise background

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.