This paper proposes a self-test method of coarse grain dynamically reconfigurable processors (DRPs) without hardware overhead. In the method, processor elements (PEs) compose a test frame, which consists of test pattern generators (TPGs), processor elements under test (PEUTs) and response analyzers (RAs), while testing themselves one another by changing test frames appropriately. We design several test frames with different structures, and discuss the relationship of the structures to the numbers of contexts and test frames for testing all the functions of PEs. A case study shows that there exists an optimal test frame which minimizes the test application time under a constraint.

The quality of the Side-information frame (S frame) influences significantly the rate-distortion performance in the Distributed Video Coding (DVC). In this letter, we propose an efficient Side-Information Frame Generator (SIFG). It considers smoothness constraints of both the motion vector field and spatial adjacent pixels. Simulation results show that the proposed techniques provide potential rate-distortion performance advantages. Besides, the fine visual quality of the S frame is obtained.

In this paper we propose a discrete program-size dependent software reliability growth model flexibly describing the software failure-occurrence phenomenon based on a discrete Weibull distribution. We also conduct model comparisons of our discrete SRGM with existing discrete SRGMs by using actual data sets. The program size is one of the important metrics of software complexity. It is known that flexible discrete software reliability growth modeling is difficult due to the mathematical manipulation under a conventional modeling-framework in which the time-dependent behavior of the cumulative number of detected faults is formulated by a difference equation. Our discrete SRGM is developed under an existing unified modeling-framework based on the concept of general order-statistics, and can incorporate the effect of the program size into software reliability assessment. Further, we discuss the method of parameter estimation, and derive software reliability assessment measures of our discrete SRGM. Finally, we show numerical examples of discrete software reliability analysis based on our discrete SRGM by using actual data.

3D video, which consists of a sequence of mesh models, can reproduce dynamic scenes containing 3D information. To summarize 3D video, a key frame extraction method is developed using rate-distortion (R-D) trade-off. For this purpose, an effective feature vector is extracted for each frame. Shot detection is performed using the feature vectors as a preprocessing followed by key frame extraction. Simple but reasonable definitions of rate and distortion are presented. Based on an assumption of linearity, an R-D curve is generated in each shot, where the locations of the key frames are optimized. Finally, R-D trade-off can be achieved by optimizing a cost function using a Lagrange multiplier, where the number of key frames is optimized in each shot. Therefore, our system will automatically determine the best locations and the number of key frames in the sense of R-D trade-off. Our experimental results show the extracted key frames are compact and faithful to the original 3D video.

Under the assumption that clock can be inputted to each register at an arbitrary timing, the minimum feasible clock period can be determined if delays between registers are given. This minimum feasible clock period might be reduced by register relocation maintaining the circuit behavior and topology. In this paper, we propose a gate-level register relocation method to reduce the minimum feasible clock period. The proposed method is a greedy local circuit modification method. We prove that the proposed method achieves the clock period achieved by retiming with delay decomposition, if the delay of each element in the circuit is unique. Experiments show that the computation time of the proposed method and the number of registers of a circuit obtained by the proposed method are smaller than those obtained by the retiming method in the conventional synchronous framework.

Object caching is a common feature in the scalable distributed object systems. Fine-grained replication optimizes the performance and resource utilization in object caching by enabling a remote object-oriented application to be partially and incrementally on-demand replicated in units of cluster. Despite these benefits, the lack of common and simple implementation framework makes the fine-grained replication scheme not extensively used. This paper proposes the novel frameworks for dynamic, transparent, partial and automatically incremental replication of distributed Java objects based on three techniques that are lazy-object creation, proxy and hook. One framework enables the fine-grained replication of server-side stateful in-memory application, and the other framework enables the fine-grained replication of server-side stateless in-memory application, client-side program, or standalone application. The experimental evaluation demonstrates that the efficiency in terms of response time of both frameworks are relatively practical to the extent of a local method invocation.

This paper introduces a common database, an evaluation framework, and its baseline recognition results for in-car speech recognition, CENSREC-3, as an outcome of the IPSJ-SIG SLP Noisy Speech Recognition Evaluation Working Group. CENSREC-3, which is a sequel to AURORA-2J, has been designed as the evaluation framework of isolated word recognition in real car-driving environments. Speech data were collected using two microphones, a close-talking microphone and a hands-free microphone, under 16 carefully controlled driving conditions, i.e., combinations of three car speeds and six car conditions. CENSREC-3 provides six evaluation environments designed using speech data collected in these conditions.

There are several methods for mining communities on the Web using hyperlinks. One of the well-known ones is a max-flow based method proposed by Flake et al. The method adopts a page-oriented framework, that is, it uses a page on the Web as a unit of information, like other methods including HITS and trawling. Recently, Asano et al. built a site-oriented framework which uses a site as a unit of information, and they experimentally showed that trawling on the site-oriented framework often outputs significantly better communities than trawling on the page-oriented framework. However, it has not been known whether the site-oriented framework is effective in mining communities through the max-flow based method. In this paper, we first point out several problems of the max-flow based method, mainly owing to the page-oriented framework, and then propose solutions to the problems by utilizing several advantages of the site-oriented framework. Computational experiments reveal that our max-flow based method on the site-oriented framework is very effective in mining communities, related to the topics of given pages, in comparison with the original max-flow based method on the page-oriented framework.

Several research fields have to deal with very large classification problems, e.g. handwritten character recognition and speech recognition. Many works have proposed methods to address problems with large number of samples, but few works have been done concerning problems with large numbers of classes. CombNET-II was one of the first methods proposed for such a kind of task. It consists of a sequential clustering VQ based gating network (stem network) and several Multilayer Perceptron (MLP) based expert classifiers (branch networks). With the objectives of increasing the classification accuracy and providing a more flexible model, this paper proposes a new model based on the CombNET-II structure, the CombNET-III. The new model, intended for, but not limited to, problems with large number of classes, replaces the branch networks MLP with multiclass Support Vector Machines (SVM). It also introduces a new probabilistic framework that outputs posterior class probabilities, enabling the model to be applied in different scenarios (e.g. together with Hidden Markov Models). These changes permit the use of a larger number of smaller clusters, which reduce the complexity of the final classifiers. Moreover, the use of binary SVM with probabilistic outputs and a probabilistic decoding scheme permit the use of a pairwise output encoding on the branch networks, which reduces the computational complexity of the training stage. The experimental results show that the proposed model outperforms both the previous model CombNET-II and a single multiclass SVM, while presenting considerably smaller complexity than the latter. It is also confirmed that CombNET-III classification accuracy scales better with the increasing number of clusters, in comparison with CombNET-II.

Combined input-crosspoint buffered (CICB) switches relax arbitration timing and provide high-performance switching for packet switches with high-speed ports. It has been shown that these switches, with one-cell crosspoint buffer and round-robin arbitration at input and output ports, provide 100% throughput under uniform traffic. However, under admissible traffic patterns with nonuniform distributions, only weight-based selection schemes are reported to provide high throughput. This paper proposes a round-robin based arbitration scheme for a CICB packet switch that provides 100% throughput for several admissible traffic patterns, including those with uniform and nonuniform distributions, using one-cell crosspoint buffers and no speedup. The presented scheme uses adaptable-size frames, where the frame size is determined by the traffic load.

Framework technology is one of the promising approaches to reuse effectively software and its key issues are 1) to select the suitable frameworks for the software requirements specification, and 2) to fill the suitable hot spots with application-specific codes (customization). This paper proposes a new technique for automated support of the above issues by using labeled transition systems (LTSs) together with metrics technique. We model the behavior of the frameworks and the system behavior specified in the requirements specification by using two LTSs respectively. By establishing bisimilar relationship between the two LTSs, we check whether the behavior of the framework can match to the requirements and explore how to fill its hot spots. This process is done by means of constructing a graph to extract the bisimilar relationships, and each path of the graph denotes one of the implementations of the requirements by the framework. We attach some measures to the LTS of the framework, such as the number of the hot spots to be filled and the number of the parameters to be set up when filling the corresponding hot spot. These measures are used to estimate developer's efforts in filling the hot spots for each implementation, i.e. for each path of the graph. The result of estimating the efforts guides the developers to select the implementation, and the structure of the application-specific codes to be filled in can be automatically generated from the selected implementation. Furthermore we discuss case studies in the area of Web application, where Struts and Turbine can be used.

H.264 video coding standard has a significant performance better than the other standards are the adoption of variable block sizes, multiple reference frames, and the consideration of rate distortion optimization within the codec. However, these features incur a considerable complexity in the encoder for motion estimation. As for the multiple reference frames motion estimation, the increased computation is in proportion to the number of searched reference frames. In this paper, a fast multiple frame reference frames selection method is proposed for H.264 video coding. The proposed algorithm can efficiently determine the best reference frame from the allowed five reference frames. As determine the number of reference frames to search the motion using the correlation of the different block between the block of current frame and that of previous frame, this scheme can efficiently reduce the computational cost while keeping the similar quality and bit-rate. Simulation results show that the speed of the proposed method is faster than that of the original scheme adapted in JVT reference software JM95 while keeping the similar video quality and bit-rate.

Over the last decade, the Bayesian approach has increased in popularity in many application areas. It uses a probabilistic framework which encodes our beliefs or actions in situations of uncertainty. Information from several models can also be combined based on the Bayesian framework to achieve better inference and to better account for modeling uncertainty. The approach we adopted here is to utilize the benefits of the Bayesian framework to improve acoustic model precision in speech recognition systems, which modeling a wider-than-triphone context by approximating it using several less context-dependent models. Such a composition was developed in order to avoid the crucial problem of limited training data and to reduce the model complexity. To enhance the model reliability due to unseen contexts and limited training data, flooring and smoothing techniques are applied. Experimental results show that the proposed Bayesian pentaphone model improves word accuracy in comparison with the standard triphone model.

We introduce a robust classification method based on the Bayesian predictive distribution (Bayesian Predictive Classification, referred to as BPC) for speech recognition. We and others have recently proposed a total Bayesian framework named Variational Bayesian Estimation and Clustering for speech recognition (VBEC). VBEC includes the practical computation of approximate posterior distributions that are essential for BPC, based on variational Bayes (VB). BPC using VB posterior distributions (VB-BPC) provides an analytical solution for the predictive distribution as the Student's t-distribution, which can mitigate the over-training effects by marginalizing the model parameters of an output distribution. We address the sparse data problem in speech recognition, and show experimentally that VB-BPC is robust against data sparseness.

Modern digital systems design requires us to explore a large and complex design space to find a best configuration which satisfies design requirements. Such exploration requires a sound representation of design space from which design candidates are efficiently generated, each of which then is evaluated. This paper proposes a plan-generation-evaluation framework which supports a complete process of such design space exploration. The plan phase constitutes a design space of all possible design alternatives by means of a formally defined representation scheme of attributed AND-OR graph. The generation phase generates a set of candidates by algorithmic pruning of the design space in an attributed AND-OR graph with respect to design requirements as well as architectural constraints. Finally, the evaluation phase measures performance of design candidates in a pruned graph to select a best one. A complete process of cache design is exemplified to show the effectiveness of the proposed framework.

New service concepts involving mobile devices with a diverse range of embedded sensors are emerging that share contexts supporting communication on a wireless network infrastructure. To promote these services in mobile devices, we propose a method that can efficiently detect a context provider by partitioning the location, time, speed, and discovery sensitivities.

Management of applications in the new world of pervasive computing requires new mechanisms to be developed for admission control, QoS negotiation, allocation and scheduling. To solve such resource-allocation and QoS provisioning problems within pervasive and ubiquitous computational environments, distribution and decomposition of the computation are important. In this paper we present a QoS-based welfare economic resource management model that models the actual price-formation process of an economy. We compare our economy-based approach with a mathematical approach we previously proposed. We use the constructs of application benefit functions and resource demand functions to represent the system configuration and to solve the resource allocation problems. Finally empirical studies are conducted to evaluate the performance of our proposed pricing model and to compare it with other approaches such as priority-based scheme and greedy method.

In this paper, we propose a fast multi-resolution block matching algorithm with three resolution levels (upper, middle, and lower levels) for multiple-frame motion estimation (MFME). The main concept of the algorithm is to perform a fast search while maintaining a PSNR performance similar to a full search block matching algorithm (FSBMA). The algorithm combines motion vector prediction using the spatial correlation of motion vectors and a multiple candidate search based on a multi-resolution search. To further reduce the computational complexity, we propose two temporal reduction schemes. To reduce the number of previous reference frames to be processed, the first scheme is applied to the upper level by using the information obtained from the search results of the spatio-temporally adjacent macroblocks (MBs) and the result from the current MB in the middle level of the first reference frame. The other scheme is applied to the lower level by using statistical information. Experimental results show that the proposed algorithm guarantees an average PSNR loss of less than 0.23 dB with dramatically reduced computational complexity as compared to the FSBMA. In particular, for sequences with fast motion or frame skipping, the proposed method provides a more prominent PSNR performance than those of existing fast schemes with a comparable computational complexity.

This paper proposes a block-based video encoder employing variable frame skipping (VFS) to improve the video quality in low bit rate channel. The basic idea of VFS mechanism is to decide and skip a suitable, non-fixed number of frames in temporal domain to reduce bit usage. The saved bits can be allocated to enhance the spatial quality of video. In literature, several methods of frame skipping decision have been proposed, but most of them only consider the similarities between neighboring coded frames as the decision criteria. Our proposed method takes into account the reconstruction of the skipped frames using motion-compensated frame interpolation at decoder. The proposed VFS models the reconstructed objective quality of the skipped frame and, therefore, can provide a fast estimate to the frame skipping at encoder. The proposed VFS can determine the suitable frame skipping in real time and provide the encoded video with better spatial-temporal bit allocation.

In this paper, we briefly describe situations that may cause HFN de-synchronization for ciphering applications in UMTS. Detection methods of HFN de-synchronization are discussed and the lower bound of the HFN de-synchronization perceptibility is derived. A supporting simulation result of the perceptibility is given. Then, an Automatic Recovery of HFN Synchronization (ARHS) algorithm is presented. The average lost PDU number of the ARHS algorithm is derived and supported by simulation results. The average lost SDU number is used as the figure of merit for HFN synchronization recovery procedures. Simulation results of the average lost SDU number show that the ARHS algorithm is quite effective to recover HFN synchronization after HFN de-synchronization situations happen.