scispace - formally typeset
Search or ask a question
Author

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.


Papers
More filters
Proceedings ArticleDOI
01 Oct 1965
TL;DR: In this paper, an adaptive filter which reconstructs a continuous signal from its samples is described based on the minimum mean square error reconstruction filter, assuming an all-pole model for the sampled spectral density of the input signal.
Abstract: An adaptive filter which reconstructs a continuous signal from its samples is described This filter is based on the minimum mean-square-error reconstruction filter, assuming an all-pole model for the sampled spectral density of the input signal The use of this model leads to two important simplifications First, simple linear regression can be used to identify the unknown parameters of the signal spectral density Second, the resulting filter has an impulse response which is of finite duration These simplifications lead to an adaptive filter which is at the same time both generally applicable and easily implemented on a digital or hybrid computer Experiments with both deterministic and random inputs are described which show that the adaptive filter yields significant improvement over a linear point connector or other commonly used reconstructors with relatively low order models and with relatively short identification times

1 citations

Journal ArticleDOI
01 Aug 1993-Networks
TL;DR: The algorithm is deadlock-free and, with k failures, maintains at least M – k matching pairs during the reconfiguration process, where M is the size of the original maximum matching.
Abstract: We present an on-line distributed reconfiguration algorithm for finding a new maximum matching incrementally after some nodes have failed. Our algorithm is deadlock-free and, with k failures, maintains at least M – k matching pairs during the reconfiguration process, where M is the size of the original maximum matching. The algorithm tolerates failures that occur during reconfiguration. The worst-case reconfiguration time is O(k min(|A|, |B|)) after k failures, where A and B are the node sets, but simulations show that the average-case reconfiguration time is much better. The algorithm is also simple enough to be implemented in hardware. © 1993 by John Wiley & Sons, Inc.

1 citations

Posted Content
TL;DR: In this paper, the authors study independent private value auctions where bidders have preferences over relative payoffs and propose a method to solve the problem of preference-based private value auction.
Abstract: We study independent private value auctions where bidders have preferences over relative payoffs.

1 citations

Proceedings ArticleDOI
01 Jan 1984
TL;DR: The design, layout, and simulation of a recursively defined VLSI chip is described, using a constraint-based, procedural layout language, and the results verify the expected asymptotic behavior of the implementation as a function of B.
Abstract: We describe the design, layout, and simulation of a recursively defined VLSI chip, using a constraint-based, procedural layout language. We use as an example the problem of counting the number of 1's in a set of (B - 1) input bits, where B is a power of 2. A regular, recursive structure, called a unary-to-binary converter (UBC(B)), tally circuit, or parallel counter, is described, based on the original design of Swartzlander. Area from the CIF plots and worst-case delay from simulations are given for 5 instantiations of the circuit, for B = 4, 8, 16, 32, and 64. The results verify the expected asymptotic behavior of the implementation as a function of B. The high-level, procedural approach leads to a succinct and parameterized description of the circuit. Verification and simulation of different versions of the circuit is much easier than with the conventional, hand-layout approach.

1 citations

Book ChapterDOI
01 Jan 2012

1 citations


Cited by
More filters
Book
01 Nov 2008
TL;DR: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization, responding to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.
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.

17,420 citations

Book
24 Aug 2012
TL;DR: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach, and is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
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.

8,059 citations

Journal ArticleDOI
TL;DR: In this paper, the authors considered factoring integers and finding discrete logarithms on a quantum computer and gave an efficient randomized algorithm for these two problems, which takes a number of steps polynomial in the input size of the integer to be factored.
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.

7,427 citations

Journal ArticleDOI
TL;DR: This paper presents work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation, and proposes shape detection using a feature called shape context, which is descriptive of the shape of the object.
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.

6,693 citations

MonographDOI
01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
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.

6,340 citations