Binary symbol detection based on a sequence of finite observation signals is formulated in the multidimensional signal space. A systematic space partitioning method is proposed to divide the entire space into two decision regions using a set of hyperplanes. The resulting detector structure consists of K parallel linear classifiers followed by a K-to-1 Boolean mapper, and is well suited to high-speed implementation. Compared to direct implementation of the fixed-delay tree search (FDTS) detection rule, the proposed signal-space formulation results in a considerable saving in digital hardware. Examples taken from binary-input intersymbol interference (ISI) channels are used to demonstrate the proposed technique. Block processing strategies suitable for high-speed applications are also discussed.

This paper presents several techniques for the VLSI implementation of the MAP algorithm. In general, knowledge about the implementation of the Viterbi algorithm can be applied to the MAP algorithm. Bounds are derived for the dynamic range of the state metrics which enable the designer to optimize the word length. The computational kernel of the algorithm is the Add-MAX operation, which is the Add-Compare-Select (ACS) operation of the Viterbi algorithm with an added Offset (ACSO). We show that the critical path of the algorithm can be reduced if the Add-MAX operation is reordered into an Offset-Add-Compare-Select (OACS) operation by adjusting the location of registers. A general scheduling for the MAP algorithm is presented which gives the trade-offs between computational complexity, latency and memory size. Some of these architectures eliminate the need for RAM blocks with unusual form-factors or can replace the RAM with registers. These architectures are suited to VLSI implementation of turbo decoders.

Model-based diagnosis has largely operated on hardware systems. However, in most complex systems today, hardware is augmented with software functions that influence the system’s behavior. In this paper, hardware models are extended to include the behavior of associated embedded software, resulting in more comprehensive diagnoses. Prior work introduced probabilistic, hierarchical, constraint-based automata (PHCA) to allow the uniform and compact encoding of both hardware and software behavior. This paper focuses on PHCA-based monitoring and diagnosis to ensure the robustness of complex systems. We introduce a novel approach that frames diagnosis over a finite time horizon as a soft constraint optimization problem (COP), allowing us to leverage an extensive body of efficient solution methods for COPs. The solutions to the COP correspond to the most likely evolutions of the complex system. We demonstrate our approach on a vision-based rover navigation system, and models of the SPHERES and Earth Observing One spacecraft.

Abstract — An interesting practical consideration for decoding of serial or parallel concatenated codes with more than two components is the determination of the lowest complexity component decoder schedule which results in convergence. This paper presents an algorithm that finds such optimal decoder schedule. A technique is also given for combining and projecting a series of three-dimensional extrinsic information transfer (EXIT) functions onto a single two-dimensional EXIT chart. This is a useful technique for visualizing the convergence threshold for multiple concatenated codes and provides a design tool for concatenated codes with more than two components. Index Terms — EXIT chart, iterative decoding, multiple concatenated codes, optimal scheduling. I.

This report is concerned with the further development, tuning and real-data testing of a tracking technique for over-the-horizon radar. The tracker is based on a technique called Viterbi data association #VDA# originally developed for sonar in #1# and adapted to OTHR in #2#. The VDA tracker described here incorporates both measurement gating, leading to a signi#cant reduction in computational complexity relative to Quach and Farooq's algorithm, and target con#dence modelling for

A multi-level multifont character recognition is presented. The system proceeds by first delimiting the context of the characters. As a way or enhancing system performance, typographical information is extracted and used for font identification before actual character recognition is performed. This has the advantage of sure character identification as well as text reproduction in original form. The font identification is based on decision trees where the characters are automatically arranged differently in confusion classes according to the physical characteristics of fonts. The character recognizers are built around the first and second order hidden Markov models (HMM) as well as Euclidean distance measures. The HMMs use the Viterbi and the Extended Viterbi algorithms to which enhancements were made. Also present is a majority-vote system that polls the other systems for "advice" before deciding on the identity of a character. Among other things, this last system is shown to give bett...

We propose a new Maximum A Posteriori (MAP) detector, without the need for explicit channel coding, to lessen the impact of communication channel errors on compressed image sources. The MAP detector exploits the spatial correlation in the compressed bit-stream as well as the temporal memory in the channel to correct channel errors. We first present a technique for computing the residual redundancy inherent in a compressed greyscale image (compressed using VQ). The performance of the proposed MAP detector is compared to that of a memoryless MAP detector. We also investigate the dependence of the performance on memory characteristics of the Gilbert-Elliott channel as well as average channel error rate. Finally, we study the robustness of the proposed MAP detector's performance to estimation errors. Keywords: Vector Quantization, MAP detection, Viterbi algorithm, channels with memory, residual redundancy, non-causal source modeling. Work supported by a NSF Young Investigator Award, NSF ...

Abstract—Differential signaling is often used for digital chip-to-chip interconnects because it provides common-mode noise rejection. Unfortunately, differential signals generally require 2 signal paths to communicate signals. In this paper, a method for differential signaling is described that requires as few as C1 signal paths for signals. Using this method, the signal values appear incrementally between neighboring matched signal paths. The technique, called incremental signaling, is similar to dicode (1) partial response signaling except that the sequence is transmitted in parallel over a bus of wires rather than sequentially in time. Theoretical and simulated bit error rates are presented for several possible implementations of an encoder/transmitter and receiver/decoder for a digital data bus including peak detection and maximum likelihood sequence detection (MLSD). Peak detection uses C1 signal paths and results in a 3-dB performance degradation with respect to independent noise compared with fully differential signaling. The Viterbi algorithm for MLSD uses C2 signal paths but provides only a 1.25 dB improvement over peak detection due to correlated noise on the (1)-coded sequence. Modified Viterbi algorithms that use C2 signal paths are introduced to cancel the correlated noise sources, resulting in a bit error rate performance comparable with fully differential signaling. Index Terms—Chip-to-chip interface, differential signaling, maximum likelihood sequence detection. I.

A signal space partitioning technique is presented for detecting symbols transmitted through intersymbol interference channels. The decision boundary is piecewise linear and is made up of several hyperplanes. The goal here is to minimize the number of hyperplanes for a given performance measure, namely, the minimum distance between any signal and the decision boundary. Unlike in Voronoi partitioning, individual hyperplanes are chosen to separate signal clusters rather than signal pairs. The convex regions associated with individual signals, which together form the overall decision region, generally overlap or coincide among in-class signals. The technique leads to an asymptotically optimum detector when the target distance is set at half the minimum distance associated with the maximum-likelihood sequence detector. Complexity and performance can be easily traded as the target distance is a flexible design parameter.

. This chapter introduces evolutionary computation (EC) and genetic algorithms (GAs) for face recognition tasks. We first address eye detection as a visual routine and show how to implement it using a hybrid approach integrating learning and evolution. The goals of the novel architecture for eye detection are twofold: (i) derivation of the saliency attention map using consensus between navigation routines encoded as finite state automata (FSA) evolved using GAs and (ii) selection of optimal features using GAs and induction of DT (decision trees) for possibly classifying as eyes the most salient locations identified earlier. Experimental results, using 30 face images from the FERET data base show the feasibility of our hybrid approach. We then introduce the Optimal Projection Axes (OPA) method for face recognition. OPA works by searching through all the rotations defined over whitened Principal Component Analysis (PCA) subspaces. Whitening, which does not preserve norms, plays a dual ro...