Fun Ye

Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression

Abstract This article develops an evolutional fuzzy particle swarm optimization (FPSO) learning algorithm to self extract the near optimum codebook of vector quantization (VQ) for carrying on image compression. The fuzzy particle swarm optimization vector quantization (FPSOVQ) learning schemes, combined advantages of the adaptive fuzzy inference method (FIM), the simple VQ concept and the efficient particle swarm optimization (PSO), are considered at the same time to automatically create near optimum codebook to achieve the application of image compression. The FIM is known as a soft decision to measure the relational grade for a given sequence. In our research, the FIM is applied to determine the similar grade between the codebook and the original image patterns. In spite of popular usage of Linde–Buzo–Grey (LBG) algorithm, the powerful evolutional PSO learning algorithm is taken to optimize the fuzzy inference system, which is used to extract appropriate codebooks for compressing several input testing grey-level images. The proposed FPSOVQ learning scheme compared with LBG based VQ learning method is presented to demonstrate its great result in several real image compression examples.

The impact of RTS threshold on IEEE 802.11 MAC protocol

Wireless technologies and applications received great attention in recent years. The medium access control (MAC) protocol is the main element that determines the efficiency in sharing the limited communication bandwidth of the wireless channel in wireless local area networks (WLANs). The request-to-send/clear-to-send (RTSICTS) mechanism is an optional handshaking procedure used by the IEEE 802.11 wireless network to reduce the possibility of collision. The RTS-Threshold (RT) value which determines when the RTS/CTS handshaking mechanism should be used is an important parameter to investigate; since different RT values will produce different performance characteristics in data transmission. This paper presents an analysis of the influence of the RT parameter on the IEEE 802.11 wireless network, and gives a guideline to dynamically adjust the RT value. Simulation results of this paper show that the RTSICTS mechanism should be always turned on (RT = 0) to achieve an excellent performance while saving complex work designing a dynamic RT mechanism which will not have notable effect.

An improved data flushing MAC protocol for IEEE 802.11 wireless ad hoc network

We propose a data flushing data transfer (DFDT) protocol. The distributed coordinate function (DCF) of IEEE 802.11 supports data transmissions using the data-ACK method and the request-to-send/clear-to-send (RTS/CTS) method. The data-ACK method has a low protocol overhead, however, the transmissions are prone to collision. Although the RTS/CTS mechanism reduces the probability of collisions of data packets, the handshaking generates extensive overhead. Another issue with the IEEE 802.11 DCF is the contention for channel access; much bandwidth is wasted with the contention, especially when the mean data length is short. DFDT is capable of sending out multiple data packets from the upper layer, after acquiring channel access by a successful contention, within one frame which we call compiled MPDU (cMPDU). Right after the transmission of the data frame, the destination nodes will reply an positive/negative acknowledgement in a consecutive manner. By using this method, the protocol overhead is relatively lowered while retaining service quality and the waste of bandwidth for contention is also reduced. Simulation results show that DFDT can handle higher traffic load and has better throughput then the IEEE 802.11 MAC protocol.

ADAPTIVE HYPER-FUZZY PARTITION PARTICLE SWARM OPTIMIZATION CLUSTERING ALGORITHM

This article presents an adaptive hyper-fuzzy partition particle swarm optimization clustering algorithm to optimally classify different geometrical structure data sets into correct groups. In this architecture, we use a novel hyper-fuzzy partition metric to improve the traditional common-used Euclidean norm metric clustering method. Since one fuzzy rule describes one pattern feature and implies the detection of one cluster center, it is encouraged to decrease the number of fuzzy rules with the hyper-fuzzy partition metric. According to the adaptive particle swarm optimization, it is very suitable to manage the clustering task for a complex, irregular, and high dimensional data set. To demonstrate the robustness of the proposed adaptive hyper-fuzzy partition particle swarm optimization clustering algorithms, various clustering simulations are experimentally compared with K-means and fuzzy c-means learning methods.

Efficient neural network approach of self-localization for humanoid robot

Robot soccer game is one of the significant and interesting areas among most of the autonomous robotic researches. Following the humanoid soccer robot basic movement and strategy actions, the robot is operated in a dynamic and unpredictable contest environment and must recognize the position of itself in the field all the time. Therefore, the localization system of the soccer robot becomes the key technology to improve the performance. This work proposes efficient approaches for humanoid robot and uses one landmark to accomplish the self-localization. This localization mechanism integrates the information from the pan/tilt motors and a single camera on the robot head together with the artificial neural network technique to adaptively adjust the humanoid robot position. The neural network approach can improve the precision of the localization. The experimental results indicate that the average accuracy ratio is 88.5% under frame rate of 15 frames per second (fps), and the average error for the distance between the actual position and the measured position of the object is 6.68cm.

An efficient object recognition system for humanoid robot vision

The research of autonomous robots is one of the most important issues in recent years. In the numerous robot researches, the humanoid robot soccer competition is very popular. The robot soccer players rely on their vision systems very heavily when they are in the unpredictable and dynamic environments. This paper proposes a simple and fast real-time object recognition system for the RoboCup soccer humanoid league rules of the 2009 competition. This vision system can help the robot to collect various environment information as the terminal data to finish the functions of robot localization, robot tactic, barrier avoiding,…, etc. It can decrease the computing efforts by using our proposed approach, Adaptive Resolution Method (ARM), to recognize the critical objects in the contest field by object features which can be obtained easily. The experimental results indicate that the proposed approach can increase the real time and accurate recognition efficiency.

Fuzzy Power Quality Indicator

This paper proposes a novel fuzzy power quality indicator for representing the level of power quality status. In this work, we develop the fuzzy rules, degree membership function and inference rules for identifying the power quality level on six encountered types of power quality events. The traditional power quality indices are not performed well for assessing the status of the power quality because of their hard limits. Hence, this study develops the soft (human thinking like) indices for presenting the serious degree of power quality. Simulation results show the proposed fuzzy power quality indicator is suitable for representing the level of power quality events.

HEURISTIC PARTICLE SWARM OPTIMIZATION LEARNING-BASED IMAGE COMPRESSION SYSTEM

Traditional LBG algorithm is a pure iterative optimization procedure to achieve the vector quantization (VQ) codebook, where an initial codebook is continually refined at every iteration to reduce the distortion between code-vectors and a given training data set. However, such interactive type learning algorithms will easily direct final results converging toward the local optimization while the high quality of the initial codebook is not available. In this article, an efficient heuristic-based learning method, called novel particle swarm optimization (NPSO), is proposed to design the proper codebook of VQ scheme that can develop the image compression system. To improve the performance of the basic PSO, the centroid updating machine applies the one step-size gradient descent learning step in the heuristic learning procedure. Additionally, the presented NPSO with advantages of the centroid updating machine is proposed to quickly achieve the near-optimal reconstructive image. For demonstrating the proposed NPSO learning scheme, the image with several horizontal grey bars is first applied to present the efficiency of the NPSO learning mechanism. LBG and NPSO learning methods are also applied to test the reconstructing performance in several type images “Lena,” “Airplane,” “Cameraman”, and “peppers.” In our experiments, the NPSO learning algorithm provides the higher performance than conventional LBG methods in the application of building image compression system.

A multimedia software for the analysis and design of circuits and electronics

PC-based material is created for the analysis and design of circuits and electronics. A Windows version of PSpice, DesignLab 8.0, which is a circuit simulation primer from MicroSim, was used to develop the materials which cover DC circuits, AC circuits and Monte Carlo analysis. Examples are given to illustrate how to set up and run the different analyses. Exercises are given with simulation results. The multimedia software is on an auto-run CD, and can be browsed with Internet Explorer. The software is also stored on a Web server, and can be accessed via the World Wide Web.