Alberto Sangiovanni-Vincentelli

Bio: Alberto Sangiovanni-Vincentelli is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Logic synthesis & Finite-state machine. The author has an hindex of 99, co-authored 934 publications receiving 45201 citations. Previous affiliations of Alberto Sangiovanni-Vincentelli include National University of Singapore & Lawrence Berkeley National Laboratory.

Breath: A Self-Adapting Protocol for Wireless Sensor Networks in Control and Automation

Abstract: The novel cross-layer protocol Breath for wireless sensor networks is designed, implemented, and experimentally evaluated. The Breath protocol is based on randomized routing, MAC and duty-cycling, which allow it to minimize the energy consumption of the network while ensuring a desired packet delivery end-to-end reliability and delay. The system model includes a set of source nodes that transmit packets via multi-hop communication to the destination. A constrained optimization problem, for which the objective function is the network energy consumption and the constraints are the packet latency and reliability, is posed and solved. It is shown that the communication layers can be jointly optimized for energy efficiency. The optimal working point of the network is achieved with a simple algorithm, which adapts to traffic variations with negligible overhead. The protocol was implemented on a test-bed with off-the-shelf wireless sensor nodes. It is compared with a standard IEEE 802.15.4 solution. Experimental results show that Breath meets the latency and reliability requirements, and that it exhibits a good distribution of the working load, thus ensuring a long lifetime of the network.

Boolean decomposition in multi-level logic optimization

Abstract: Multiple-valued Boolean minimization is proposed as a technique for identifying and extracting good Boolean factors which can be used as strong divisors to minimize the literal count and the area of a multilevel logic network. Given a two-level logic function, a subset of inputs to the function is selected such that the number of good Boolean factors contained in this subset of inputs is large. If the targeted implementation is a set of interconnected PLAs, the different cube combinations given by the subset of inputs are re-encoded to reduce the number of product terms in the logic function. A novel algorithm for the re-encoding is given that is based on the notion of partial satisfaction of constraints. Algorithms have been developed that identify a set of factors which maximally decrease the literal count of the logic network when they are used as strong divisors. Results obtained on several benchmark examples that illustrate the efficacy of the techniques are presented. >

Behavioral modeling and simulation of data converters

Abstract: The authors present a behavioral representation for the class of Nyquist rate analog to digital (A/D) converters that captures the nominal behavior, as well as all the statistical variations. To describe behavioral effects due to process variations a covariance matrix, Sigma /sub t/ is used. Applications of the model include identification of important A/D error sources, efficient computation of the distributions of integral nonlinearity and differential nonlinearity, signal-to-noise plus distortion ratio and efficient worst case and Monte Carlo system simulations. Parameter extraction results are presented that agree well with actual measurements. >

Gradient-based learning applied to document recognition

Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Abstract: A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.

Phd by thesis

Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Graph-Based Algorithms for Boolean Function Manipulation

Abstract: In this paper we present a new data structure for representing Boolean functions and an associated set of manipulation algorithms. Functions are represented by directed, acyclic graphs in a manner similar to the representations introduced by Lee [1] and Akers [2], but with further restrictions on the ordering of decision variables in the graph. Although a function requires, in the worst case, a graph of size exponential in the number of arguments, many of the functions encountered in typical applications have a more reasonable representation. Our algorithms have time complexity proportional to the sizes of the graphs being operated on, and hence are quite efficient as long as the graphs do not grow too large. We present experimental results from applying these algorithms to problems in logic design verification that demonstrate the practicality of our approach.

Principles of Model Checking

Abstract: Our growing dependence on increasingly complex computer and software systems necessitates the development of formalisms, techniques, and tools for assessing functional properties of these systems. One such technique that has emerged in the last twenty years is model checking, which systematically (and automatically) checks whether a model of a given system satisfies a desired property such as deadlock freedom, invariants, and request-response properties. This automated technique for verification and debugging has developed into a mature and widely used approach with many applications. Principles of Model Checking offers a comprehensive introduction to model checking that is not only a text suitable for classroom use but also a valuable reference for researchers and practitioners in the field. The book begins with the basic principles for modeling concurrent and communicating systems, introduces different classes of properties (including safety and liveness), presents the notion of fairness, and provides automata-based algorithms for these properties. It introduces the temporal logics LTL and CTL, compares them, and covers algorithms for verifying these logics, discussing real-time systems as well as systems subject to random phenomena. Separate chapters treat such efficiency-improving techniques as abstraction and symbolic manipulation. The book includes an extensive set of examples (most of which run through several chapters) and a complete set of basic results accompanied by detailed proofs. Each chapter concludes with a summary, bibliographic notes, and an extensive list of exercises of both practical and theoretical nature.