12929901
Completeness of Neighbourhood Logic
This paper presents a completeness result for a first-order interval temporal logic, called Neighbourhood Logic (NL) which has two neighbourhood modalities. NL can support the specification of liveness and fairness properties of computing systems as well as formalisation of many concepts of real analysis. These two modalities are also adequate in the sense that they can derive other important unary and binary modalities of interval temporal logic. We prove the completeness result for NL by giving a Kripke model semantics and then mapping the Kripke models to the interval models for NL.
37032385
Completeness of neighbourhood logic
35912465
An Algorithm for Trading Off Quantization Error with Hardware Resources for MATLAB-Based FPGA Design
Most practical FPGA designs of digital signal processing (DSP) applications are limited to fixed-point arithmetic owing to the cost and complexity of floating-point hardware. While mapping DSP applications onto FPGAs, a DSP algorithm designer must determine the dynamic range and desired precision of input, intermediate, and output signals in a design implementation. The first step in a MATLAB-based hardware design flow is the conversion of the floating-point MATLAB code into a fixed-point version using "quantizers" from the filter design and analysis (FDA) toolbox for MATLAB. This paper describes an approach to automate the conversion of floating-point MATLAB programs into fixed-point MATLAB programs, for mapping to FPGAs by profiling the expected inputs to estimate errors. Our algorithm attempts to minimize the hardware resources while constraining the quantization error within a specified limit. Experimental results on five MATLAB benchmarks are reported for Xilinx Virtex II FPGAs.
5422021
Fast and Robust Projective Matching for Fingerprints Using Geometric Hashing
Fingerprint matching is the most important module in automatic person recognition using fingerprints. We model the nonlinear distortions and noise obtained during the fingerprint capture process as transformations in the projective domain. Matching of the fingerprints involves computing the homography matrix for the projective transformation, mapping of the minutia points by this homography and finally computing the points that match each other within a pre-determined threshold. We perform a fast match using a Geometric Hashing-based algorithm which matches the points in polynomial time. Preliminary experiments with a sample representative database show promising results.
10991562
Strings: a high-performance distributed shared memory for symmetrical multiprocessor clusters
Introduces Strings, a high-performance distributed shared memory system designed for clusters of symmetrical multiprocessors (SMPs). The distinguishing feature of this system is the use of a fully multithreaded runtime system, written using POSIX threads. Strings also allows multiple application threads to be run on each node in a cluster. Since most modern UNIX systems can multiplex these threads on kernel-level lightweight processes, applications written using Strings can use all the processors in a SMP machine. This paper describes some of the architectural details of the system and analyzes the performance improvements with two example programs and a few benchmark programs from the SPLASH-2 suite.
9930427
An algorithm for converting floating-point computations to fixed-point in MATLAB based FPGA design
Most practical FPGA designs of digital signal processing applications are limited to fixed-point arithmetic owing to the cost and complexiry of floating-point hardware. While mapping DSP applications onto FPGAs, a DSP algorithm designer, who often develops his applications in MATLAB, must determine the dynamic range and desired precision of input, intermediate and output signals in a design implementation to ensure that the algorithm fidelity criteria are met. The first step in a flow to map MATLAB applications into hardware is the conversion of the floating-point MATLAB algorithm into a fixed-point version. This paper describes an approach to automate this conversion, for mapping to FPGAs by profiling the expected inputs to estimate errors. Our algorithm attempts to minimize the hardware resources while constraining the quantization error within a specified limit
13954615
An algorithm for trading off quantization error with hardware resources for MATLAB-based FPGA design
Most practical FPGA designs of digital signal processing (DSP) applications are limited to fixed-point arithmetic owing to the cost and complexity of floating-point hardware. While mapping DSP applications onto FPGAs, a DSP algorithm designer must determine the dynamic range and desired precision of input, intermediate, and output signals in a design implementation. The first step in a MATLAB-based hardware design flow is the conversion of the floating-point MATLAB code into a fixed-point version using "quantizers" from the filter design and analysis (FDA) toolbox for MATLAB. This paper describes an approach to automate the conversion of floating-point MATLAB programs into fixed-point MATLAB programs, for mapping to FPGAs by profiling the expected inputs to estimate errors. Our algorithm attempts to minimize the hardware resources while constraining the quantization error within a specified limit. Experimental results on five MATLAB benchmarks are reported for Xilinx Virtex II FPGAs.