A flexible and robust rate-distortion optimization algorithm is presented to select macroblock coding mode for H.264/AVC transmission over wireless channels subject to burst errors. A two-state Markov model is used to describe the burst errors on the packet level. With the feedback information from the receiver and the estimation of the channel errors, the algorithm analyzes the distortion of the reconstructed macroblock at the decoder due to the channel errors and spatial and temporal error propagation. The optimal coding mode is chosen for each macroblock in rate-distortion (R-D)-based framework. Experimental results using the H.264/AVC test model show a significant performance of resilience to the burst errors.

The cost-effective provision of IP services requires multi-layered traffic engineering to obtain dynamic cooperation between IP and photonic layers. The effective control and management of generalized multi-protocol label-switching (GMPLS) networks is an essential part of this. Huge photonic capacities and the number of IP and photonic networks make it likely that enormous amounts of GMPLS network-related data will have to be managed in the near future. At the same time, routing burdens on individual GMPLS routers are critical because of the strong need for per-path quality of service (QoS). To solve these problems, we propose a hierarchically distributed network-management system (NMS) in which we flexibly allocate a GMPLS subnetwork to each sub-NMS and at the same time conduct QoS routing. The distributed nature of our architecture reduces the burden on the NMS as a whole and also lets us remove the routing-burden from GMPLS routers with minimum effect on management processes.

In this paper, two efficient VLSI architectures for biorthogonal wavelet transform are proposed. One is constructed by the filter bank implementation and another is constructed by the lifting scheme. In the filter bank implementation, due to the symmetric property of biorthogonal wavelet transform, the proposed architecture uses fewer multipliers than the orthogonal wavelet transform. Besides, the polyphase decomposition is adopted to speed up the processing by a factor of 2. In the lifting scheme implementation, the pipeline-scheduling technique is employed to optimize the architecture. Both two architectures are with advantages of lower implementation complexity and higher throughput rate. Moreover, they can also be applied to realize the inverse DWT efficiently. Based on the above properties, the two architectures can be applied to time-critical image compressions, such as JPEG2000. Finally, the architecture constructed by the lifting scheme is implemented into a single chip on 0.35 µm 1P4M CMOS technology, and its area and working performance are 5.005 5.005 mm2 and 50 MHz, respectively.

We have developed Multi-modal Neural Networks (MNN) to improve the accuracy of symbolic sequence pattern classification. The basic structure of the MNN is composed of several sub-classifiers using neural networks and a decision unit. Two types of the MNN are proposed: a primary MNN and a twofold MNN. In the primary MNN, the sub-classifier is composed of a conventional three-layer neural network. The decision unit uses the majority decision to produce the final decisions from the outputs of the sub-classifiers. In the twofold MNN, the sub-classifier is composed of the primary MNN for partial classification. The decision unit uses a three-layer neural network to produce the final decisions. In the latter type of the MNN, since the structure of the primary MNN is folded into the sub-classifier, the basic structure of the MNN is used twice, which is the reason why we call the method twofold MNN. The MNN is validated with two benchmark tests: EPR (English Pronunciation Reasoning) and prediction of protein secondary structure. The reasoning accuracy of EPR is improved from 85.4% by using a three-layer neural network to 87.7% by using the primary MNN. In the prediction of protein secondary structure, the average accuracy is improved from 69.1% of a three-layer neural network to 74.6% by the primary MNN and 75.6% by the twofold MNN. The prediction test is based on a database of 126 non-homologous protein sequences.

Motion capture technology is widely used to make a realistic motion in these days. Different motion capture devices use different motion capture data formats. Because of the lack of compatibility of motion capture data animators can't reuse the already captured motion sequence. In addition, it is difficult for integrating, storing and retrieving motion capture data with different formats in the storage. In this paper, we propose a standard format for integrating a different motion capture data formats. In addition, we propose a framework of a system that manages motion capture data using our standard format. Our standard format is called MCML (Motion Capture Markup Language). It is a markup language for motion capture data and is based on XML (extensible Markup Language). Our system designed to manage motion capture data consists of a several components -- Mocap Syntax Analyzer, MCML Converter, MCML Editor, Motion Viewer, MCML Storage Wrapper.

Cluster computation has been used in the applications that demand performance, reliability, and availability, such as cluster server groups, large-scale scientific computations, distributed databases, distributed media-on-demand servers and search engines etc. In those applications, multicast can play the vital roles for the information dissemination among groups of servers and users. This paper proposes a set of novel efficient fault-tolerant multicast routing algorithms on hypercube interconnection of cluster computers using multicast shared tree approach. We present some new algorithms for selecting an optimal core (root) and constructing the shared tree so as to minimize the average delay for multicast messages. Simulation results indicate that our algorithms are efficient in the senses of short end-to-end average delay, load balance and less resource utilizations over hypercube cluster interconnection networks.

Performance of selective Rake (SRake) receiver is evaluated for direct sequence ultra wideband (DS-UWB) communications considering an independent Rayleigh channel having exponentially decaying power delay profile (PDP). BEP performances are shown. The results obtained are compared with similar results in a channel having flat PDP. Assumption of a flat PDP is found to predict the optimum spreading bandwidth to be lower and sub-optimum operating performance beyond optimum spreading bandwidth to be severely worse than that is achievable in a channel having exponentially decaying PDP by employing an SRake receiver having fixed number of combined paths. Optimum spreading bandwidth for SRake in a channel having exponentially decaying PDP is shown to be much larger than the one in a channel having flat PDP; that is specifically a good-news for UWB communications. Effects of partial band interference are also investigated. Interference is found to be less effective in exponentially decaying PDP.

In this paper, based on maximum neural network, we propose a new parallel algorithm that can escape from local minima and has powerful ability of searching the globally optimal or near-optimum solution for the maximum clique problem (MCP). In graph theory a clique is a completely connected subgraph and the MCP is to find a clique of maximum size of a graph. The MCP is a classic optimization problem in computer science and in graph theory with many real-world applications, and is also known to be NP-complete. Lee and Takefuji have presented a very powerful neural approach called maximum neural network for this NP-complete problem. The maximum neural model always guarantees a valid solution and greatly reduces the search space without a burden on the parameter-tuning. However, the model has a tendency to converge to the local minimum easily because it is based on the steepest descent method. By adding a negative self-feedback to the maximum neural network, we proposed a parallel algorithm that introduces richer and more flexible chaotic dynamics and can prevent the network from getting stuck at local minima. After the chaotic dynamics vanishes, the proposed algorithm is then fundamentally reined by the gradient descent dynamics and usually converges to a stable equilibrium point. The proposed algorithm has the advantages of both the maximum neural network and the chaotic neurodynamics. A large number of instances have been simulated to verify the proposed algorithm.

Many techniques have been proposed for segmenting feature point trajectories tracked through a video sequence into independent motions, but objects in the scene are usually assumed to undergo general 3-D motions. As a result, the segmentation accuracy considerably deteriorates in realistic video sequences in which object motions are nearly degenerate. In this paper, we propose a multi-stage unsupervised learning scheme first assuming degenerate motions and then assuming general 3-D motions and show by simulated and real video experiments that the segmentation accuracy significantly improves without compromising the accuracy for general 3-D motions.

This letter proposes a low-power tournament branch predictor, in which the number of accesses to the branch predictors (local predictor or global predictor) is reduced. Analysis results with Samsung Memory Compiler show that the proposed branch predictor reduces the power consumption by 24-45%, compared to the conventional tournament branch predictor, not requiring any additional storage arrays, not incurring any additional delay and never harming accuracy.

To address the extreme dynamism and the rapidly changing user's requirements in current information systems, an Autonomous Community Information System (ACIS) has been developed. ACIS is a decentralized architecture that forms a community of individual end-users (community members) having the same interests and demands at specified time and location. It admits them to mutually cooperate and share information without loading up any single node excessively. ACIS does not embrace the advantage of heterogeneity of the community nodes-nodes latencies. In this paper, ACIS-Hierarchy, decentralized hierarchical community architecture is proposed. It considers latency between community nodes as an important criterion that need to be optimized. It takes advantage of heterogeneity of the community nodes-nodes latencies to improve the communication delay among the community. In this paper, an efficient autonomous decentralized community construction technology is proposed to reduce: the communication delays among members taking into consideration the latency among them and the required time to join/leave. This paper illustrates the step-step construction technology and the membership management operations for ACIS-Hierarchy. Experimental results show that ACIS-Hierarchy improves the community communication delay.

In this report we present all-optical switches and modulators based on the intersubband transition in semiconductor quantum wells. The use of InGaAs/AlAsSb coupled double quantum well structures is proposed to facilitate intersubband transitions in the optical-communication band, and to reduce the intersubband absorption recovery time from several picoseconds to a few hundred femtoseconds by utilizing enhanced electron-phonon scattering. Subpicosecond all-optical gating and modulation in coupled double quantum wells are observed using pump-probe experiments at optical-communication wavelengths. The results indicate that the intersubband transition in this structure is very useful for ultrafast all-optical switching devices.

In CC-NUMA multiprocessor systems, it is important to reduce the remote memory access time. Based upon the fact that increasing the size of the LRU second-level (L2) cache more than a certain value does not reduce the cache miss rate significantly, in this paper, we propose two split L2 caches to utilize the surplus of the L2 cache. The split L2 caches are composed of a traditional LRU cache and another cache to reduce the remote memory access time. Both work together to reduce total L2 cache miss time by keeping remote (or long-distance) blocks as well as recently used blocks. For another cache, we propose two alternatives: an L2-RVC (Level 2 - Remote Victim Cache) and an L2-DAVC (Level 2 - Distance-Aware Victim Cache). The proposed split L2 caches reduce total execution time by up to 27%. It is also found that the proposed split L2 caches outperform the traditional single LRU cache of double size.

We have proposed a speculative selection function for adaptive routing, which uses idle cycles of the network physical links to exchange network information between nodes, thus helps to decide the best selection. Previous study on the mesh network showed that SSR gives message selection flexibility that improves network performance by balancing the network traffic in both global and local scopes. This paper evaluates the speculative selection function on 2D torus network with simulation. The simulation compares the network throughput and latency with various traffic patterns. The visualization graphs show how the speculative selection eliminates hotspots and disperses traffic in the global scope. The simulation results demonstrate that by using speculative selection, the network performance is increased by around 7%. Compared to the mesh network, the torus's version has smaller gain due to the high performance nature of the torus network.

Multiple-input multiple-output (MIMO) systems can improve the spectral efficiency of a wireless link, by transmitting several data streams simultaneously from different transmit antennas. However, at the receiver, multi-stream detection is needed for extracting the transmitted data streams from the received signals. This letter considers ordered successive detection (OSD) for multi-stream detection. OSD consists of several stages, and at each stage only one data stream is chosen to be detected among the remaining streams according to a specified ordering metric. OSD has been formulated using both the zero forcing (ZF) and minimum mean square error (MMSE) criteria. This letter clarifies the reason behind the superiority of OSD using the MMSE criterion to OSD using the ZF criterion through the investigation of the relation between their ordering metrics. For uncorrelated MIMO channels, we show that both ordering metrics yield the same performance for OSD using either ZF or MMSE criterion. Accordingly, the superiority of OSD using the MMSE criterion to OSD using the ZF criterion is clarified to be a direct result of the inherent superiority of MMSE nulling to ZF nulling, and to be independent of the ordering operation. Performance comparisons of OSD and maximum likelihood detection are also given for modulation schemes of different sizes.

This paper proposes an algorithm for detecting 3-way interactions. As far as the authors know, this is the first proposal ever made for a detection algorithm of 3-way interactions. In this paper, by analyzing examples, the mechanism of 3-way interactions is clarified and a detection algorithm of 3-way interactions is proposed. Namely the proposed detection algorithm is heuristic. To evaluate the algorithm, we implemented a detection system based on the proposed algorithm and applied it to 12 services, and 82 3-way interactions were detected. This shows the proposed algorithm is effective.

In this paper we present a component approach for configurable network processing for active documents. The approach has two key ideas. The first is to enable documents to process themselves on networks. That is, documents can define their own itineraries, like the notion of active packets in active network technology. The second is to enable documents to transmit other documents to their destinations as first-class objects, such as the notion of active nodes in active netwwork technology. The approach also enables buidling and managing active documents as compound documents. The dynamic deployment of network processing for exchanging documents can be defined and achieved by means of GUI-based manipulation of compound documents. Therefore, the approach allows a user to easily and rapidly develop and customize network processing in the same way as if that user had edited the documents. A prototype implementation of the approach and its applications were constructed on a Java-based mobile agent system to evaluate the effectiveness of the approach.

The codes by the u/u+v construction have the extended error capacity for additional Hamming weight. It is a simple way to construct a large size block code using smaller ones. In this letter, low density parity check (LDPC) codes on u/u+v construction are investigated. Consequently, the study can enrich the family of LDPC codes and provide some special LDPC implementations.

Effective counteraction to Distributed Denial-of-Services (DDoS) attacks is a pressing problem over the Internet. For this counteraction, it is considered important to locate the router interfaces closest to the attackers in order to effectively filter a great number of identification jammed packets with spoofed source addresses from widely distributed area. Edge sample (ES) based Probabilistic Packet Marking (PPM) is an encouraging method to cope with source IP spoofing, which usually accompanies DDoS attacks. But its fragmentation of path information leads to inefficiency in terms of necessary number of packets, path calculation time and identification accuracy. We propose Branch Label (BL) based PPM to solve the above inefficiency problem. In BL, a whole single path information is marked in a packet without fragmentation in contrast to ES based PPM. The whole path information in packets by the BL approach is expressed with branch information of each router interfaces. This brings the following three key advantages in the process of detecting the interfaces: quick increase in true-positives detected (efficiency), quick decrease in false-negatives detected (accuracy) and fast convergence (quickness).

Recently with the advent of the IT and the wide spread use of the Internet, new user oriented production and logistic systems, such as the Supply Chain Management System, have been required in order to cope with the drastic and continuous changes on the markets and users' preferences. Therefore, heterogeneous database systems need to be integrated in a common environment which can cope with the heterogeneous requirements of each company under an ever-evolving changing environment. That is assurance. Autonomous Decentralized Database System (ADDS) is proposed as a system architecture in order to realize assurance in distributed database systems. In this system architecture, a loosely-consistency management technology is proposed in order to maintain the consistency of the system, each database can update autonomously, and confer the real time property. A background coordination technology, performed by an autonomous mobile agent, is devised to adapt the system to evolving situations. The system can achieve real time by allocating the information in advance among the sites that has different time constraints for updating. Moreover, an assurance information allocation technology is proposed when considering that a failure in the background coordination mechanism may lead to loss of data and unavailability of the system. This mechanism, in which the mobile agent autonomously regulate its own capacity for allocating the information, is proposed based on the real-time property and system's availability considerations. The effectiveness of the proposed architecture and technologies are evaluated by simulation.