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Low-Power CMOS Digital Design

by Anantha P. Chandrakasan, Samuel Sheng, Robert W. Brodersen - JOURNAL OF SOLID-STATE CIRCUITS. VOL 27, NO 4. APRIL 1992 413 , 1992
"... Motivated by emerging battery-operated applications that demand intensive computation in portable environments, techniques are investigated which reduce power consumption in CMOS digital circuits while maintaining computational throughput. Techniques for low-power operation are shown which use the ..."
Abstract - Cited by 580 (20 self) - Add to MetaCart
the lowest possible supply voltage coupled with architectural, logic style, circuit, and technology optimizations. An architectural-based scaling strategy is presented which indicates that the optimum voltage is much lower than that determined by other scaling considerations. This optimum is achieved

Coda: A Highly Available File System for a Distributed Workstation Environment

by Mahadev Satyanarayanan, James J. Kistler, Puneet Kumar, Maria E. Okasaki, Ellen H. Siegel, David C. Steere - IN IEEE TRANSACTIONS ON COMPUTERS , 1990
"... Coda is a file system for a large-scale distributed computing environment composed of Unix workstations. It provides resiliency to server and network failures through the use of two distinct but complementary mechanisms. One mechanism, server replication,stores copies of a file at multiple servers ..."
Abstract - Cited by 530 (45 self) - Add to MetaCart
Coda is a file system for a large-scale distributed computing environment composed of Unix workstations. It provides resiliency to server and network failures through the use of two distinct but complementary mechanisms. One mechanism, server replication,stores copies of a file at multiple

A computational approach to edge detection

by John Canny - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1986
"... This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumpti ..."
Abstract - Cited by 4675 (0 self) - Add to MetaCart
. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope

Graphical models, exponential families, and variational inference

by Martin J. Wainwright, Michael I. Jordan , 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
Abstract - Cited by 819 (28 self) - Add to MetaCart
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical

Attention, similarity, and the identification-Categorization Relationship

by Robert M. Nosofsky , 1986
"... A unified quantitative approach to modeling subjects ' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification dat ..."
Abstract - Cited by 690 (28 self) - Add to MetaCart
data were modeled using Sbepard's (1957) multidimensional scaling-choice framework. This framework was then extended to model the subjects ' categorization performance. The categorization model, which generalizes the context theory of classification developed by Medin and Schaffer (1978

Adapting to unknown smoothness via wavelet shrinkage

by David L. Donoho, Iain M. Johnstone - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION , 1995
"... We attempt to recover a function of unknown smoothness from noisy, sampled data. We introduce a procedure, SureShrink, which suppresses noise by thresholding the empirical wavelet coefficients. The thresholding is adaptive: a threshold level is assigned to each dyadic resolution level by the princip ..."
Abstract - Cited by 1006 (18 self) - Add to MetaCart
also; if the unknown function has a smooth piece, the reconstruction is (essentially) as smooth as the mother wavelet will allow. The procedure is in a sense optimally smoothness-adaptive: it is near-minimax simultaneously over a whole interval of the Besov scale; the size of this interval depends

Kernel-Based Object Tracking

by Dorin Comaniciu, Visvanathan Ramesh, Peter Meer , 2003
"... A new approach toward target representation and localization, the central component in visual tracking of non-rigid objects, is proposed. The feature histogram based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity fu ..."
Abstract - Cited by 900 (4 self) - Add to MetaCart
functions suitable for gradient-based optimization, hence, the target localization problem can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as similarity measure, and use the mean shift procedure to perform the optimization

A New Extension of the Kalman Filter to Nonlinear Systems

by Simon J. Julier, Jeffrey K. Uhlmann , 1997
"... The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF) which ..."
Abstract - Cited by 778 (6 self) - Add to MetaCart
The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF

Inducing Features of Random Fields

by Stephen Della Pietra, Vincent Della Pietra, John Lafferty - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
Abstract - Cited by 670 (10 self) - Add to MetaCart
the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random field models and techniques

Capacity of Ad Hoc Wireless Networks

by Jinyang Li, Charles Blake, Douglas S. J. De Couto, Hu Imm Lee, Robert Morris
"... Early simulation experience with wireless ad hoc networks suggests that their capacity can be surprisingly low, due to the requirement that nodes forward each others’ packets. The achievable capacity depends on network size, traffic patterns, and detailed local radio interactions. This paper examine ..."
Abstract - Cited by 636 (14 self) - Add to MetaCart
examines these factors alone and in combination, using simulation and analysis from first principles. Our results include both specific constants and general scaling relationships helpful in understanding the limitations of wireless ad hoc networks. We examine interactions of the 802.11 MAC and ad hoc
Results 11 - 20 of 24,431