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.

Multi-channel signal processing for data communications in the presence of crosstalk

Abstract: The authors consider transmission of data over multiple coupled channels, such as bundles of twisted-pair cables in the local subscriber loop, and between central offices in the public switched telephone network. Transceiver designs for such channels typically treat the crosstalk between adjacent cables as random noise uncorrelated with the transmitted signal. A transmitter/receiver pair is proposed which compensated for crosstalk by treating an entire bundle of cables as a single multi-input/multioutput channel with a (slowly varying) matrix transfer function. One attribute of the proposed transceiver is the use of a multichannel adaptive FIR (finite-impulse response) filter to cancel near-end crosstalk. Results of numerical simulations, including plots of mean squared error vs. time, and eye diagrams, are presented assuming a standard transmission line mode for the channel. These results indicate that data rates over coupled channels can be significantly increased by exploiting the multidimensional character of the channel. >

Optimal choice of intermediate latching to maximize throughput in VLSI circuits

Abstract: In many computational tasks, especially in signal processing, it is the throughput that is important, rather than the latency, or delay. If a special-purpose VLSI chip is designed for a particular signal processing task, such as FIR filtering, for example, the maximum clock rate, and hence throughput, is determined by the depth of the combinational logic between registers and the time required for the distribution and operation of the clock. If the combinational logic is sufficiently deep (in bit-parallel circuits, for example), the throughput can be increased by inserting intermediate stages of clocked latches. This is at the expense of increased area and delay to operate and clock the intermediate registers. Roughly speaking, the strategy amounts to using more of the chip area to store information useful for pipelining. This paper investigates the optimal tradeoff between the degree of intermediate latching and cost, using the measure AP, where A is the chip area and P is the period (the reciprocal of throughput). We derive expressions for the time and area before and after intermediate latching, using the Mead-Conway model, both for the cases of on-chip and off-chip clock drivers. The results show that significant reductions in AP product (reciprocal of throughput per unit area) can be achieved by intermediate latching in many typical signal processing applications, for a wide range of circuit parameters. The array multiplier is used as an example.

Power Spectrum Identification for Adaptive Systems

Abstract: This paper describes a method for identifying the parameters of a class of power spectra. In contrast to conventional methods of spectral analysis, the method assumes a particular form for the power spectrum and gives direct estimates of unknown parameters. Thus the method is faster than ordinary spectral analysis and can be easily implemented by a digital computer for use in an adaptive loop. The derivation of the estimates assumes that the power spectrum has no zeros, and is based on well-known results in the theory of autoregressive schemes. Some ways of extending the results to the case where zeros are present in the spectrum are suggested. The method can also be used as a prewhitening technique in conjunction with ordinary spectral analysis.

Analyzing and simulating fracture patterns of theran wall paintings

Abstract: In this article, we analyze the fracture patterns observed in wall paintings excavated at Akrotiri, a Bronze Age Aegean settlement destroyed by a volcano on the Greek island of Thera around 1630 BC. We use interactive programs to trace detailed fragment boundaries in images of manually reconstructed wall paintings. Then, we use geometric analysis algorithms to study the shapes and contacts of those fragment boundaries, producing statistical distributions of lengths, angles, areas, and adjacencies found in assembled paintings. The result is a statistical model that suggests a hierarchical fracture pattern where fragments break into two pieces recursively along cracks nearly orthogonal to previous ones. This model is tested by comparing it with simulation results of a hierarchical fracture process. The model could be useful for predicting fracture patterns of other wall paintings and/or for guiding future computer-assisted reconstruction algorithms.

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.