15301928
New techniques on deformed image motion estimation and compensation
In this paper, new techniques for deformed image motion estimation and compensation using variable-size block-matching are proposed, which can be applied to an image sequence compression system or a moving object recognition system. The motion estimation and compensation techniques have been successfully applied in the area of image sequence coding. Many research papers on improving the performance of these techniques have been published; many directions are proposed, which can all lead to better performance than the conventional techniques. Among them, both generalized block-matching and variable-size block-matching are successfully applied in reducing the data rate of compensation error and motion information, respectively. These two algorithms have their merits, but suffer from their drawbacks. Moreover, reducing the data rate in compensation error is sometimes increasing the data rate in motion information, or vice versa. Based on these two algorithms, we propose and examine several algorithms which are effective in reducing the data rate. We then incorporate these algorithms into a system, in which they work together to overcome the disadvantages to individual and keep their merits at the same time. The proposed system can optimally balance the amount of data rate in two aspects (i.e., compensation error and motion information). Experimental results show that the proposed system outweighs the conventional techniques. Since we propose a recovery operation which tries to recover the incorrect motion vectors from the global motion, this proposed system can also be applied to the moving object recognition in image sequences.
4468971
Tracking a maneuvering target using neural fuzzy network
A fast target maneuver detection and highly accurate tracking technique using a neural fuzzy network based on a Kalman filter is proposed in this paper. In the automatic target tracking system, there exists an important and difficult problem: how to detect the target maneuvers and fast response to avoid miss-tracking? The traditional maneuver detection algorithms, such as variable dimension filter (VDF) and input estimation (IE) etc., are computation intensive and difficult to implement in real time. To solve this problem, neural network algorithms have been issued recently. However, normal neural networks such as backpropagation networks usually produce the extra problems of low convergence speed and/or large network size. Furthermore, the way to decide the network structure is heuristic. To overcome these defects and to make use of neural learning ability, a developed standard Kalman filter with a self-constructing neural fuzzy inference network (KF-SONFIN) algorithm for target tracking is presented in this paper. By generating possible target trajectories including maneuver information to train the SONFIN, the trained SONFIN can detect when the maneuver occurred, the magnitude of maneuver values and when the maneuver disappeared. Without having to change the structure of the Kalman filter or modeling the maneuvering target, this new algorithm, SONFIN, can always find an economic network size with a fast learning process. Simulation results show that KF-SONFIN is superior to traditional IE and VDF methods in estimation accuracy.
44754516
Noisy Speech Segmentation/Enhancement with Multiband Analysis and Neural Fuzzy Networks
This paper addresses the problem of speech segmentation and enhancement in the presence of noise. We first propose a new word boundary detection algorithm by using a neural fuzzy network (called ATF-based SONFIN algorithm) for identifying islands of word signals in fixed noise-level environment. We further propose a new RTF-based RSONFIN algorithm where the background noise level varies during the procedure of recording. The adaptive time-frequency (ATF) and refined time-frequency (RTF) parameters extend the TF parameter from single band to multiband spectrum analysis, and help to make the distinction of speech and noise signals clear. The ATF and RTF parameters can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. Due to the self-learning ability of SONFIN and RSONFIN, the proposed algorithms avoid the need of empirically determining thresholds and ambiguous rules. The RTF-based RSONFIN algorithm can also find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level by processing the temporal relations. Our experimental results show that both in the fixed and variable noise-level environment, the algorithms that we proposed achieved higher recognition rate than several commonly used word boundary detection algorithms and reduced the recognition error rate due to endpoint detection.
16836522
Real-time gang schedulings with workload models for parallel computers
Gang scheduling has been shown to be an effective job scheduling policy for parallel computers that combines elements of space sharing and time sharing. We propose new policies to enable gang scheduling to adapt to environments with real-time constraints. Our work, to our best knowledge, is the first work to attempt to address the real-time aspects of gang scheduling. Our system guided by a metric, called "task utilization workload", can schedule both real-time and non-real-time tasks at the same time. We report simulation results with a family of scheduling algorithms based on our proposed metric. Our scheme is designed to be a practical scheme to be used for large scale industrial and commercial parallel systems. Preliminary simulation results also show that our proposed policy is an effective scheme to perform real-time scheduling, while scheduling non-real-time jobs with fairness and good throughput.
5012485
Stochastic High-Level Petri Nets and Applications
A class of stochastic Petri nets called stochastic high-level Petri nets (SHLPNs) is proposed. SHLPNs are high-level Petri nets augmented with exponentially distributed firing times. SHLPNs generally lead to models with a smaller state space. A computer marking concept is introduced that allows a considerable reduction of the number of states and induces a correct grouping of states in the Markov-domain SHLPN models of multiprocessor systems. The main advantage of modeling homogeneous systems using SHLPNs is that the resulting models are simpler and more intuitive and have a smaller number of states, as shown by examples. >
11669518
Error checking with client-driven pointer analysis
This paper presents a new client-driven pointer analysis algorithm that automatically adjusts its precision in response to the needs of client analyses. Using five significant error detection problems as clients, we evaluate our algorithm on 18 real C programs. We compare the accuracy and performance of our algorithm against several commonly used fixed-precision algorithms. We find that the client-driven approach effectively balances cost and precision, often producing results as accurate as fixed-precision algorithms that are many times more costly. Our algorithm works because many client problems only need a small amount of extra precision applied to selected portions of each input program.
11473366
Class-Based Latency Assurances for Web Servers
This paper presents a fuzzy control approach for process assignment in order to guarantee absolute percentile delay in web servers. Comparing with mean delay used in previous works, percentile delay introduces strong nonlinearities to the plant; it is therefore difficult to achieve satisfactory performance by a linear controller, which is commonly used to guarantee mean delay performance. In this paper we propose a fuzzy controller. Its key feature is independence of the plant and robust to model uncertainty, which is very suitable to the strong nonlinear web servers. The experiments have demonstrated the efficiency of the proposed fuzzy controller in handing the variety of requests.
40583624
Portable Parallel Programming: Cross Machine Comparisons for SIMPLE