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.

Analyzing fracture patterns in theran wall paintings

Abstract: In this paper, we analyze the fracture patterns observed in wall paintings excavated from Akrotiri, a Bronze Age Aegean city destroyed by earthquakes preceding a volcanic eruption on Thera (modern Santorini) 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 could be useful for predicting fracture patterns of other wall paintings and/or for guiding future computer-assisted reconstruction algorithms.

Soliton-guided phase shifter and beam splitter

Abstract: We propose, analyze, and study numerically a phase shifter for light wave packets trapped by Kerr solitons in a nonlinear medium. We also study numerically a previously proposed soliton-guided nonpolarizing beam splitter.

Optimization of pre- and post-filters in the presence of near and far-end crosstalk

Abstract: Full-duplex data communications are considered over a linear, time-invariant, multi-input/multi-output channel. For both the continuous- and discrete-time cases, optimal multi-input/multi-output transmitter and receiver filters are derived using the minimum mean-square error (MSE) criterion, with a power constraint on the transmitted signal, in the presence of both near- and far-end crosstalk. The discrete-time problem is solved for two different filter models: arbitrary linear (IIR) (infinite-complexity) and fixed-order (FIR) filters. In addition, the optimal transmitter and receiver filters are derived for the case in which the transmitted signal is a pulse-amplitude-modulated data signal. For a particular two-input/two output channel model in the FIR case, the behavior of the MSE as a function of the allocation of matrix taps between transmitter and receiver filters and of timing phase is studied. In this case, the jointly optimal transmitter and receiver filters are obtained numerically using an iterative technique. For the channel model considered, the MSE is a very sensitive function of timing phase but is nearly independent of how taps are allocated between the transmitter and receiver filters. >

FAST: a functional algorithm simulation testbed

Abstract: We extend the practical range of simulations of parallel executions by "functional algorithm simulation," that is, simulation without actually performing most of the numerical computations involved. We achieve this by introducing a new approach for generating and collecting communication and computation characteristics for a class of parallel scientific algorithms. We describe FAST (Fast Algorithm Simulation Testbed), a prototype system that we developed to implement and test our approach. FAST overcomes some of the difficulties imposed by the very high complexity of interesting scientific algorithms, collects profile information representative of the algorithms rather than the underlying mapping strategies and hardware design choices, and allows a performance assessment of parallel machines with various sites and different interconnection schemes. >

Optimization of one-bit full adders embedded in regular structures

Abstract: We study the problem of optimizing the transistor sizes in the one-bit nMOS full adder either isolated or embedded in a regular array. A local optimization method that we call the critical-path optimization method is developed. In this method, two parameters at a time are changed along the critical path until a locally optimal choice of transistor sizes is found. The critical-path optimization method uses the Berkeley VLSI tools and the hierarchical layout language ALLENDE developed at Princeton. First, we optimize the isolated one-bit full adder implemented in three ways: as a PLA, data selector, and with random logic. The details of the critical-path optimization method and power-time tradeoff curves are illustrated here. Second, we optimize the one-bit full adder embedded in a simple array multiplier. The entire 3 × 3, 4 × 4, 8 × 8, and 10 × 10 multipliers are optimized and their local optima are compared. Because the optimization of the entire circuit becomes less practical when the circuit becomes larger, we develop a method that makes use of circuit regularity. We prove that some small array of one-bit full adders, called the canonical configuration, has the same local optima as the n × n multiplier for large n, with the criterion of minimizing the delay time T. Hence, we can greatly reduce the computation load by optimizing this canonical configuration instead of optimizing the entire circuit. Experimental results confirm the effectiveness of this approach.

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.