9644406
Moving vehicles segmentation based on Bayesian framework for Gaussian motion model
Accurate moving vehicles segmentation is an interesting yet difficult problem in intelligent transportation system. Robust segmentation is obstructed by some problems: the interferential moving objects in dynamic scenes, the vibration of cameras and the change of lighting conditions are all examples. In this paper, we propose Gaussian motion model for moving vehicles segmentation in the dynamic scenes. By investigating the distinction between the motion vectors of the dynamic background and those of the moving vehicles, we find the fact that the motion vectors of the moving vehicles cluster in a small region while those of the dynamic background are dispersive. Gaussian motion model is then proposed to model the motion of the moving vehicles and that of the dynamic background. Bayesian framework is utilized to classify the motion in the scenes to improve the robustness of the model and EM algorithm is used to estimate the parameters of the model. Experimental results on typical scenes, such as waving trees, camera vibration, snowfall and fog, show that the proposed model can segment the moving vehicles correctly in the complex scenes. Quantitative evaluations demonstrate that the proposed method outperform the existing methods.
14422830
Compiler-guided register reliability improvement against soft errors
With the scaling of technology, transient errors caused by external particle strikes have become a critical challenge for microprocessor design. As embedded processors are widely used in reliability-sensitive environments, it becomes increasingly important to develop cost-effective techniques to improve the processor reliability against soft errors. This paper focuses on studying the register file immunity against soft errors since modern processors typically employ a large number of registers, which are accessed very frequently. As a result, soft errors occurred in registers can easily propagate to functional units or the memory system, leading to silent data error (SDC) or system crash.To develop cost-effective techniques to fight soft errors for embedded processors, the first step is to understand the register file susceptibility to soft errors and its impact on the system reliability accurately. Toward this goal, this paper proposes the concept of register vulnerability factor (RVF) to characterize the probability that register transient errors can escape the register file and thus potentially impact the system reliability. Built upon the RVF concept, we then propose two cost-effective compiler-guided techniques to improve the register file reliability by lowering the RVF value. Our experiments indicate that on average, the RVF can be reduced to 9.1% and 9.5% by the hyperblock-based instruction re-scheduling and the reliability-oriented register assignment respectively, which can potentially lower the reliability cost significantly while protecting register files against transient errors.
9354212
High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network
Job-shop scheduling is an important task for manufacturing industries. We are interested in the particular task of scheduling payload processing for NASA's space shuttle program. This paper summarizes our previous work on formulating this task for solution by the reinforcement learning algorithm T D(). A shortcoming of this previous work was its reliance on hand-engineered input features. This paper shows how to extend the time-delay neural network (TDNN) architecture to apply it to irregular-length schedules. Experimental tests show that this TDNN-TD() network can match the performance of our previous hand-engineered system. The tests also show that both neural network approaches signiicantly out-perform the best previous (non-learning) solution to this problem in terms of the quality of the resulting schedules and the number of search steps required to construct them.
12895493
Improvement of HITS-based algorithms on web documents
In this paper, we present two ways to improve the precision of HITS-based algorithms on Web documents. First, by analyzing the limitations of current HITS-based algorithms, we propose a new weighted HITS-based method that assigns appropriate weights to in-links of root documents. Then, we combine content analysis with HITS-based algorithms and study the effects of four representative relevance scoring methods, VSM, Okapi, TLS, and CDR, using a set of broad topic queries. Our experimental results show that our weighted HITS-based method performs significantly better than Bharat's improved HITS algorithm. When we combine our weighted HITS-based method or Bharat's HITS algorithm with any of the four relevance scoring methods, the combined methods are only marginally better than our weighted HITS-based method. Between the four relevance-scoring methods, there is no significant quality difference when they are combined with a HITS-based algorithm.
43025995
Processing Probabilistic Range Query over Imprecise Data Based on Quality of Result
Sensors are employed in many applications to monitor entities such as environment temperature or illumination. Because of limited resources (e.g., battery power and wireless bandwidth), it is impractical for the database to track continuous changing readings from sensors at all times. Consequently, database may produce incorrect query results based on recorded data. However, if the error between the recorded value and the actual value is bounded, probabilistic approach can be applied to process queries over the imprecise data in database. The paper proposes a quality of result based probabilistic range query processing method. Two measures are defined to evaluate the quality of result. A probability model is proposed to estimate the quality of returned results. Based on the model, an algorithm is developed to choose the probability threshold according to user’s requirement on the quality of result.
17633305
The Impact of Cache Organization in Optimizing Microprocessor Power Consumption
In the recent years, power consumption has become increasingly an important design concern as silicon area and performance in modern computer systems design. Several factors have contributed to this trend. Perhaps the most visible have been the remarkable success and growth of Personal Digital Assistants (PDA’s), Cellular phones and pagers, etc .This work is an attempt to explore the impact of feature size shrinkage and cache configuration on optimizing power consumption in modern processors. The work studies two commercially known RISC microprocessors. The StrongARM and the Alpha-21064 microprocessors. The latter is the ancestor of the former and is therefore used here as the baseline in our analysis. Our analysis has illustrated quantitatively great power saving when technology is downsized and cache is well organized
14026338
The Asymmetric Traveling Salesman Problem: Algorithms, Instance Generators, and Tests
The purpose of this paper is to provide a preliminary report on the first broad-based experimental comparison of modern heuristics for the asymmetric traveling salesmen problem (ATSP). There are currently three general classes of such heuristics: classical tour construction heuristics such as Nearest Neighbor and the Greedy algorithm, local search algorithms based on re-arranging segments of the tour, as exemplified by the Kanellakis-Papadimitriou algorithm [KP80], and algorithms based on patching together the cycles in a minimum cycle cover, the best of which are variants on an algorithm proposed by Zhang [Zha93]. We test implementations of the main contenders from each class on a variety of instance types, introducing a variety of new random instance generators modeled on real-world applications of the ATSP. Among the many tentative conclusions we reach is that no single algorithm is dominant over all instance classes, although for each class the best tours are found either by Zhang's algorithm or an iterated variant on Kanellakis-Papadimitriou.
27074889
On pilot pattern design for PSAM-OFDM system
Pilot symbol assisted modulation (PSAM) of subcarriers is an attractive scheme for high data-rate OFDM transmission systems and has been widely used in broadcasting systems like DVB-T. In this paper, a new and simple pilot pattern for PSAM-OFDM system operated in the mobile communication environment is proposed and analyzed in terms of its bit error rate. Analytical and simulation results show that the proposed clustered pilot pattern for OFDM channel estimation gives a better performance than the conventional equally spaced pilot pattern in fading channel, yet without sacrificing the bandwidth efficiency.
12967582
An Improved Integer Local Search for Complex Scheduling Problems
We consider complex scheduling problems that can be captured as optimization under hard and soft constraints. The objective of such an optimization problem is to satisfy as many hard constraints as possible and meanwhile to minimize a penalty function determined by the unsatisfied soft constraints. We present an efficient local search algorithm for these problems which improves upon Wsat(oip), a Walksat-based local search algorithm for overconstrained problems represented in integer programs. We introduce three techniques to the Wsat(oip) algorithm to extend its capability and improve its performance: backbone guided biased moves to drive the search to the regions in search space where high-quality and optimal solutions reside; sampling-based aspiration search to reduce search cost and make anytime solutions available over the course of the search; and dynamic parameter tuning to dynamically adjust the key parameters of the algorithm to make it robust and flexible for various applications. Our experimental results on large-scale crew scheduling, basketball tournament scheduling and progressive party scheduling show that the new improved algorithm can find better solutions with less computation than Wsat(oip).
2426741
An analysis and application of distributed constraint satisfaction and optimization algorithms in sensor networks
This research is motivated by a distributed scheduling problem in distributed sensor networks, in which computational resources are scarce. To cope with limited computational resources and restricted real-time requirement, it is imperative to apply distributed algorithms that have low overhead on resource requirement and high anytime performance. In this paper, We study distributed stochastic algorithm (DSA) and distributed breakout algorithm (DBA), two distributed algorithms developed earlier for distributed constraint satisfaction problems. We experimentally investigate their properties and compare their performance using our distributed scheduling problem as a benchmark. We first formulate the scheduling problem as a distributed multi-coloring problem. We then experimentally show that the solution quality and communication cost of DSA exhibit phase-transition or threshold behavior, in that the performance degenerates abruptly and dramatically when the degree of parallel executions of distributed agents increases beyond some critical value. The results show that when controlled properly, DSA is superior to DBA, having better or competitive solution quality and significantly smaller communication cost than DBA. Therefore, DSA is the algorithm of choice for our distributed scan scheduling problem.
40080812
Efficient Strategies for (Weighted) Maximum Satisfiability
It is well known that the Davis-Putnam-Logemann-Loveland (DPLL) algorithm for satisfiability (SAT) can be extended to an algorithm for maximum SAT (max-SAT). In this paper, we propose a number of strategies to significantly improve this max-SAT method. The first strategy is a set of unit propagation rules; the second is an effective lookahead heuristic based on linear programming; and the third strategy is a dynamic variable ordering that exploits problem constrainedness during search. We integrate these strategies in an efficient complete solver for both max-SAT and weighted max-SAT. Our experimental results on random problem instances and many instances from SATLIB demonstrate the efficacy of these strategies and show that the new solver is able to significantly outperform most of the existing complete max-SAT solvers, with a few orders of magnitude of improvement in running time in many cases. 
 
This research was supported in part by NSF grants IIS-0196057 and ITR/EIA-0113618, and in part by DARPA Cooperative Agreement F30602-00-2-0531. We thank Zhongsheng Guo for an early implementation of the DPLL algorithm and Fadi Aloul, Javier Larrosa and Jordi Plane for making their programs available to us for this research.