9437835
Computing Requirements of Modern Medical Diagnostic Ultrasound Machines
Abstract Ultrasound is one of the leading medical imaging modalities because it is safe, noninvasive, portable, easy to use, relatively inexpensive and displays images in real-time. Due to its real-time nature, an ultrasound machine must be able to process its incoming data quickly. High computational and throughput requirements in modern ultrasound machines have restricted their internal design to algorithm-specific hardware with limited programmability. Adding new ultrasound imaging applications or improving a machine's internal algorithms can require costly hardware redesigns and replacements of boards or of the entire machine. In an effort to address these problems, we have reviewed each of the essential functions in modern ultrasound machines and analyzed their computational requirements in programmable systems. These functions include dynamic downconversion, tissue signal processing, color flow processing, scan conversion, and tissue/flow decision. Our estimate of the total computing requirement to current ultrasound machines is 54.71 billion operations per second (BOPS) when transcendental functions are implemented in software and 31.26 BOPS when transcendental functions are implemented in lookup tables taking 160.26 Mbytes of memory. To investigate the feasibility of programmable generalized ultrasound systems, we have designed a flexible and parallel programmable ultrasound processing subsystem, called the Programmable Ultrasound Image Processor (PUIP). As the need for programmable ultrasound machines increases in the future due to various advantages (e.g., lower system cost, faster clinical use and lower research/development expenses), it will be crucial to develop not only a high-performance, scalable, reconfigurable parallel computer architecture meeting the computing requirement, but also efficient ultrasound algorithms that can be optimally mapped into the parallel architecture.
40331728
Efficient 2-D Convolution Algorithm with the Single-Data Multiple Kernel Approach
Abstract A fast implementation of convolution operation is described. The implementation takes advantage of various properties of commonly used kernels. First, the kernel elements are analyzed and rearranged, grouping the kernel elements of the same absolute value together and arranging trivial multiplication cases separately. Then the sequence of computing convolution sums is modified so that all the kernel elements could be multiplied by the same data element at each step. This new algorithm significantly reduces the number of multiplication operations for most widely used kernels. Our experiment shows that the performance improvement of two to seven over the direct implementation is achievable for most kernels. The required overhead (extra memory and preprocessing) is proportional to the kernel size and is negligible.
17681470
New Usage of Sammon's Mapping for Genetic Visualization
It is a hard problem to understand the fitness landscape of a problem as well as the evolution of genetic algorithms. For the purpose, we adopt Sammon's mapping for the investigation. We demonstrate its usefulness by applying it to the graph partitioning problem which is a well-known NP-hard problem. Also, through the investigation of schema traces, we explain the genetic process and the reordering effect in the genetic algorithm.
26568006
Defense Mechanism for Information and Communication Infrastructure
35466192
Scheduling on parallel identical machines to minimize total tardiness
This paper focuses on the problem of scheduling n independent jobs on m identical parallel machines for the objective of minimizing total tardiness of the jobs. We develop dominance properties and lower bounds, and develop a branch and bound algorithm using these properties and lower bounds as well as upper bounds obtained from a heuristic algorithm. Computational experiments are performed on randomly generated test problems and results show that the algorithm solves problems with moderate sizes in a reasonable amount of computation time. � 2005 Elsevier B.V. All rights reserved.
3094791
Multicampaign assignment problem
It is crucial to maximize targeting efficiency and customer satisfaction in personalized marketing. State-of-the-art techniques for targeting focus on the optimization of individual campaigns. Our motivation is the belief that the effectiveness of a campaign with respect to a customer is affected by how many precedent campaigns have been recently delivered to the customer. We raise the multiple recommendation problem, which occurs when performing several personalized campaigns simultaneously. We formulate the multicampaign assignment problem to solve this issue and propose algorithms for the problem. The algorithms include dynamic programming and efficient heuristic methods. We verify by experiments the effectiveness of the problem formulation and the proposed algorithms.
2304269
Lightweight logging and recovery for distributed shared memory over virtual interface architecture
As software Distributed Shared Memory(DSM) systems become attractive on larger clusters, the focus of attention moves toward improving the reliability of systems. In this paper, we propose a lightweight logging scheme, called remote logging, and a recovery protocol for home-based DSM. Remote logging stores coherence-related data to the volatile memory of a remote node. The logging overhead can be moderated with high-speed system area network and user-level DMA operations supported by modern communication protocols. Remote logging tolerates multiple failures if the backup nodes of failed nodes are alive. It makes the reliability of DSM grow much higher. Experimental results show that our fault-tolerant DSM has low overhead compared to conventional stable logging and it can be effectively recovered from some concurrent failures.