8697079
Input don't care sequences in FSM networks
We present an approach to compute all input don't care sequences for a component in an FSM network with an arbitrary topology. In a cascade FSM network, Kim and Newborn's (K-N) procedure [13] exactly computes all input don't care sequences for the driven machine. However, for a component in a general FSM network the exact computation of input don't care sequences is unsolved. We demonstrate that this problem can be reduced to one for a cascade circuit. This reduction uses the notion ofan abstract driving machine. In some cases, the exact computation and exploitation of these sequences may be too expensive. We propose methods to compute subsets of input don't care sequences. We discuss the implementation of these algorithms using implicit methods. We also present approximate methods for managing the complexity of large FSM networks. Finally, we give some preliminary results on small networks. •This projectwas supportedby DARPA undercontractnumberJFBI90-073 and NSF undercontractnumber EMC-84-19744.
7539036
Multi-level logic optimization of FSM networks
Current approaches tocompute and exploit the flexibility ofa component in an FSM network are allat the symbolic level [32,42,44,43]. Conventionally, exploitation of this flexibility relies on state minimizers for incompletely specified FSM's (ISFSM's) or pseudo non-deterministic FSM's (PNDFSM's) [44]. However, state-of-the-art state minimizers cannothandle large ISFSM's orPNDFSM's [19,21,45,14, 22]. In addition, these exploitation techniques are at the symbolic level, not directly at the net-list logic level. We present a general approach to exploit exact orapproximate flexibility directly at the net-list logic level, and we demonstrate that many sequential logic optimization techniques can beapplied inexploitation. Moreover, we propose a new procedure for input don't care sequences. As a result, both computation and exploitation of input don't care sequences inlarger FSM networks can be made efficient and effective. Finally, we give preliminary results on some artificially constructed FSM networks. Preliminary results indicate that our approach can beeffective inreducing the size of a componentof an FSM network. •This projectwassupported by NSF undercontract numberMIP-8719546.
15947104
Permissible Observability Relations in FSM Networks
Previous attempts to capture the phenomenon of output don't care sequences for a component in an FSM network have been incomplete. We demonstrate that output don't care sequences for a component can be expressed using a set of observability relations given that its state transition function is kept unchanged. Each observability relation is permissible in the sense that any implementation compatible with one of them is feasible. The representation for a set of permissible observability relations is not unique. We provide a method to find a set with the minimum number of permissible relations. We briefly discuss the exploitation of permissible observability relations in state minimization, circuit implementation and signal encoding. We have implemented these methods and present some preliminary results on a few small artificially constructed examples.
11673267
Object-based video compression scheme with optimal bit allocation among shape, motion and texture
In object-based video, the encoding of the video data is decoupled into the encoding of shape, motion and texture information, which enables certain functionalities like content-based interactivity and scalability. However, the problem of how to jointly encode these separate signals to reach the best coding efficiency has never been solved thoroughly. In this paper, we present an operational rate-distortion optimal bit allocation scheme that provides a solution to this problem. Our approach is based on the Lagrangian relaxation and dynamic programming. Experimental results indicate that the proposed optimal encoding approach has considerable gains over an ad-hoc method without optimization. Furthermore the proposed algorithm is much more efficient than exhaustive search.
7772067
Extractive Chinese Spoken Document Summarization Using Probabilistic Ranking Models
The purpose of extractive summarization is to automatically select indicative sentences, passages, or paragraphs from an original document according to a certain target summarization ratio, and then sequence them to form a concise summary. In this paper, in contrast to conventional approaches, our objective is to deal with the extractive summarization problem under a probabilistic modeling framework. We investigate the use of the hidden Markov model (HMM) for spoken document summarization, in which each sentence of a spoken document is treated as an HMM for generating the document, and the sentences are ranked and selected according to their likelihoods. In addition, the relevance model (RM) of each sentence, estimated from a contemporary text collection, is integrated with the HMM model to improve the representation of the sentence model. The experiments were performed on Chinese broadcast news compiled in Taiwan. The proposed approach achieves noticeable performance gains over conventional summarization approaches.
18971447
Probabilistic Relaxation using the Heat Equation
In this paper a new formulation of probabilistic relaxation labeling is developed using the theory of diffusion processes on graphs. According to this picture, the label probabilities are given by the state-vector of a continuous time random walk on a support graph. The state-vector is the solution of the heat equation on the support-graph. The nodes of the support graph are the Cartesian product of the object-set and label-set of the relaxation process. The compatibility functions are combined in the weight matrix of the support graph. The solution of the heat-equation is found by exponentiating the eigensystem of the Laplacian matrix for the weighted support graph with time. We demonstrate the new relaxation process on a feature correspondence matching problem abstracted in terms of relational graphs
15118493
Anomaly intrusion detection using multi-objective genetic fuzzy system and agent-based evolutionary computation framework
In this paper, we present a multi-objective genetic fuzzy system for anomaly intrusion detection. The proposed system extracts accurate and interpret able fuzzy rule-based knowledge from network data using an agent-based evolutionary computation framework. The experimental results on KDD-Cup99 intrusion detection benchmark data demonstrate that our system can achieve high detection rate for intrusion attacks and low false positive rate for normal network traffic.
2318961
A Novel Robust Statistical Method for Background Initialization and Visual Surveillance
In many visual tracking and surveillance systems, it is important to initialize a background model using a training video sequence which may include foreground objects. In such a case, robust statistical methods are required to handle random occurrences of foreground objects (i.e., outliers), as well as general image noise. The robust statistical method Median has been employed for initializing the background model. However, the Median can tolerate up to only 50% outliers, which cannot satisfy the requirements of some complicated environments. In this paper, we propose a novel robust method for the background initialization. The proposed method can tolerate more than 50% of foreground pixels and noise. We give quantitative evaluations on a number of video sequences and compare our proposed method with five other methods. Experiments show that our method can achieve very promising results in background initialization: including applications in video segmentation, visual tracking and surveillance.