Parallel programs for distributed memory machines are not easy to create and maintain, especially when they involve sparse matrix computations. In this paper, we propose a program translation system for generating parallel sparse matrix computation codes utilizing PSBLAS. The purpose of the development of the system is to offer the user a convenient way to construct parallel sparse code based on PSBLAS. The system is build up on the idea of bridging the gap between the easy-to-read program representations and highly-tuned parallel executables based on existing parallel sparse matrix computation libraries. The system accepts a MATLAB program with annotations and generates subroutines for an SPMD-style parallel program which runs on distributed-memory machines. Experimental results on parallel machines show that the prototype of our system can generate fairly efficient PSBLAS codes for simple applications such as CG and Bi-CGSTAB programs.

Conditional random fields (CRFs) have been successfully applied to various applications of predicting and labeling structured data, such as natural language tagging & parsing, image segmentation & object recognition, and protein secondary structure prediction. The key advantages of CRFs are the ability to encode a variety of overlapping, non-independent features from empirical data as well as the capability of reaching the global normalization and optimization. However, estimating parameters for CRFs is very time-consuming due to an intensive forward-backward computation needed to estimate the likelihood function and its gradient during training. This paper presents a high-performance training of CRFs on massively parallel processing systems that allows us to handle huge datasets with hundreds of thousand data sequences and millions of features. We performed the experiments on an important natural language processing task (text chunking) on large-scale corpora and achieved significant results in terms of both the reduction of computational time and the improvement of prediction accuracy.

Providing data availability in a high performance computing environment is very important, especially in this data-intensive world. Most clusters either equip with RAID (Redundant Array of Independent Disks) devices or use redundant nodes to protect data from loss. However, neither of these can really solve the reliability problem incurred in a striped file system. Striping provides an efficient way to increase I/O throughput both in the distributed and parallel paradigms. But it also reduces the overall reliability of a disk system by N fold, where N is the number of independent disks in the system. Parallel Virtual File System (PVFS) is an open source parallel file system which has been widely used in the Linux environment. Its striping structure is good for performance but provides no fault tolerance. We implement Reliable Parallel File System (RPFS) based on PVFS but with reliability support. Our quantitative analysis shows that MTTF (Mean Time To Failure) of our RPFS is better than that of PVFS. Besides, we propose a parity cache table (PCT) to alleviate the penalty of parity updating. The evaluation of our RPFS shows that its read performance is almost the same as that of PVFS (2% to 13% degradation). As to the write performance, 28% to 45% improvement can be achieved depending on the behavior of the operations.

This paper presents the design and implementation of a hip range of motion (ROM) estimation method that is capable of fine-grained estimation during total hip replacement (THR) surgery. Our method is based on two acceleration strategies: (1) adaptive mesh refinement (AMR) for complexity reduction and (2) parallelization for further acceleration. On the assumption that the hip ROM is a single closed region, the AMR strategy reduces the complexity for N N N stance configurations from O(N³) to O(N^D), where 2≤D≤3 and D is a data-dependent value that can be approximated by 2 in most cases. The parallelization strategy employs the master-worker paradigm with multiple task queues, reducing synchronization between processors with load balancing. The experimental results indicate that the implementation on a cluster of 64 PCs completes estimation of 360360180 stance configurations in 20 seconds, playing a key role in selecting and aligning the optimal combination of artificial joint components during THR surgery.

To make the best use of the resources in a shared grid environment, an application scheduler must make a prediction of available performance on each resource. In this paper, we examine the problem of predicting available CPU performance in time-shared grid system. We present and evaluate a new and innovative method to predict the one-step-ahead CPU load in a grid. Our prediction strategy forecasts the future CPU load based on the variety tendency in several past steps and in previous similar patterns, and uses a polynomial fitting method. Our experimental results on large load traces collected from four different kinds of machines demonstrate that this new prediction strategy achieves average prediction errors which are between 22% and 86% less than those incurred by four previous methods.

While web proxy caching is a widely deployed technique, the performance of a proxy cache is limited by the local storage. Some studies have addressed this limitation by using the residual resources of clients via a p2p method and have achieved a very high hit rate. However, these approaches treat web objects as homogeneous objects and there is no consideration of various web characteristics. Consequently, the byte hit rate of the system is limited, external bandwidth is wasted, and perceived user latency is increased. The present paper suggests an efficient p2p based web caching technique that manages objects with different policies so as to exploit the characteristics of web objects, such as size and temporal locality. Small objects are stored alone whereas large objects are stored by dividing them into numerous small blocks, which are distributed in clients. On a proxy cache, header blocks of large objects take the place of objects themselves and smaller objects are cached. This technique increases the hit rate. Unlike a web cache, which evicts large objects as soon as possible in the case where clients fulfill the role of backup storage, large objects are given higher priority than small objects in the proposed approach. This maximizes the effect of hits for large objects and thereby increases the byte hit rate. Furthermore, we construct simple latency models for various p2p based web caching systems and analyze the effects of the proposed policies on these systems. We then examine the performances of the efficient policies via a trace driven simulation. The results demonstrate that the proposed techniques effectively enhance web cache performance, including hit rate, byte hit rate, and response time.

Wireless connections between computers and devices introduce a new problem called device migration, which is the dynamic switching of a connection between a computer and a device. This paper describes a new system called the Network Extended Device Management System (NextD), which enables users to continue using a device even if device migration occurs. NextD's functions are device tracking, remote device access, seamless switching, and providing services over network boundaries. The system supports many device types, including a human interface device (HID) and audio and voice devices over both wireless and wired connections. We have implemented NextD on Linux. Experimental results showed that NextD is capable of supporting HIDs, and audio and voice devices, and that its processing time for a device migration is sufficient enough for actual use.

The cascade of two baseline networks in tandem is a rearrangeable network. The cascade of two omega networks appended with a certain interconnection pattern is also rearrangeable. These belong to the general problem: for what banyan-type network (i.e., bit-permuting unique-routing network) is the tandem cascade a rearrangeable network? We relate the problem to the trace and guide of banyan-type networks. Let τ denote the trace permutation of a 2ⁿ2ⁿ banyan-type network and γ the guide permutation of it. This paper proves that rearrangeability of the tandem cascade of the network is solely determined by the transposition τγ^-1. Such a permutation is said to be tandem rearrangeable when the tandem cascade is indeed rearrangeable. We identify a few tandem rearrangeable permutations, each implying the rearrangeability of the tandem cascade of a wide class of banyan-type networks.

Under the broadband-ubiquitous environment, digital content creation/distribution will be the key factor to activating new industries. This paper first describes the impact of a broadband-ubiquitous environment on digital content creation/distribution; then it proposes new models for digital content creation/distribution businesses. In a broadband-ubiquitous environment, the key is creation of moving picture content; thus the paper describes a system that allows non-CG experts to make CG movies easily.

It is well-known that one of the best wavelet-based image compression techniques, called Wavelet Difference Reduction-WDR, has both simple algorithm and comparative rate-distortion results comparing to other coders in the literature. In this paper, we propose an algorithm to enhance the performance of WDR coder in a very efficient way. The methodology is highly based on the context adaptive model. High-order statistical context modeling is used for significant coefficients prediction by scanning order adaptation of WDR. In binary mode, the coder is clearly better than Set Partitioning in Hierarchical Trees (SPIHT). A new conditional Arithmetic Coding (AC) using array of contexts is specifically designed for WDR-based coder. Based on our simulation results of all images in the test set which covers various types of images, it is apparent that our proposed coder in AC mode generates the rate-distortion results that are superior to those of all other WDR-based coders in the literature. Furthermore, it is very competitive to SPIHT-AC and JPEG2000, the best coders, at all bit rates.

The coexistence of MPEG-2 and its powerful successor H.264/AVC has created a huge need for MPEG-2/H.264 video transcoding. However, a traditional transcoder where an MPEG-2 decoder is simply cascaded to an H.264 encoder requires huge computational power due to the adoption of a complicated rate-distortion based mode decision process in H.264. This paper proposes a 2-D Sobel filter based motion vector domain method and a DCT domain method to measure macroblock complexity and realize content-based H.264 candidate mode decision. A new local edge based fast INTRA prediction mode decision method is also adopted to boost the encoding efficiency. Simulation results confirm that with the proposed methods the computational burden of a traditional transcoder can be reduced by 20%30% with only a negligible bit-rate increase for a wide range of video sequences.

In this paper, we study and analyze the computational complexity of deblocking filter in H.264/AVC baseline decoder based on SimpleScalar/ARM simulator. The simulation result shows that the memory reference, content activity check operations, and filter operations are known to be very time consuming in the decoder of this new video coding standard. In order to improve overall system performance, we propose a novel processing order with efficient VLSI architecture which simultaneously processes the horizontal filtering of vertical edge and vertical filtering of horizontal edge. As a result, the memory performance of the proposed architecture is improved by four times when compared to the software implementation. Moreover, the system performance of our design significantly outperforms the previous proposals.

This paper presents two hardware-friendly low-power oriented fast motion estimation (ME) algorithms and their VLSI implementations. The basic idea of the proposed partial distortion sorting (PDS) algorithm is to disable the search points which have larger partial distortions during the ME process, and only keep those search points with smaller ones. To further reduce the computation overhead, a simplified local PDS (LPDS) algorithm is also presented. Experiments show that the PDS and LPDS algorithms can provide almost the same image quality as full search only with 36.7% computation complexity. The proposed two algorithms can be integrated into different FSBMA architectures to save power consumption. In this paper, the 1-D inter ME architecture [12] is used as an detailed example. Under the worst working conditions (1.62 V, 125) and 166 MHz clock frequency, the PDS algorithm can reduce 33.3% power consumption with 4.05 K gates extra hardware cost, and the LPDS can reduce 37.8% power consumption with 1.73 K gates overhead.

In this paper, we proposed an efficient coding method for digital hologram (fringe pattern) acquired with a CCD camera or by computer generation using multi-view prediction and MPEG video compression standard techniques. It processes each R, G, or B color component separately. The basic processing unit is a partial image segmented as the size of MN. Each partial image retains the information of the whole object. This method generates an assembled image for a column of the segmented and frequency-transformed partial images, which is the basis of the coding process. That is, a motion estimation and compensation technique of MPEG is applied between the reconstructed images from the assembled images with the disparities found during generation of assembled image and the original partial images. Therefore the compressed results are the disparity of each partial image to form the assembled image for the corresponding column, assembled image, and the motion vectors and the compensated image for each partial image. The experimental results with the implemented algorithm showed that the proposed method has NC (Normalized Correlation) values about 4% higher than the previous method at the same compression ratios, which convinced us that ours has better compression efficiency. Consequently, the proposed method is expected to be used effectively in the application areas to transmit or store in digital format the digital hologram data.

This paper presents a secure and simple content-based digital signature method for verifying the image authentication under JPEG, JPEG2000 compression and scaling based on a novel concept named lowest authenticable difference (LAD). The whole method, which is extended from the crypto-based digital signature scheme, mainly consists of statistical analysis and signature generation/verification. The invariant features, which are generated from the relationship among image blocks in the spatial domain, are coded and signed by the sender's private key to create a digital signature for each image, regardless of the image size. The main contributions of the proposed scheme are: (1) only the spatial domain is adopted during feature generation and verification, making domain transformation process unnecessary; (2) more non-malicious manipulated images (JPEG, JPEG2000 compressed and scaled images) than related studies can be authenticated by LAD, achieving a good trade-off of image authentication between fragile and robust under practical image processing; (3) non-malicious manipulation is clearly defined to meet closely the requirements of storing images or sending them over the Internet. The related analysis and discussion are presented. The experimental results indicate that the proposed method is feasible and effective.

Intelligent scissors is an interactive image segmentation algorithm which allows a user to select piece-wise globally optimal contour segment corresponding to a desired object boundary. However, the intelligent scissors is too sensitive to a noise and texture patterns in an image since it utilizes the gradient information concerning the pixel intensities. This paper describes a new intelligent scissors based on the concept of the separability in order to improve the object boundary extraction performance. The effectiveness of the proposed method has been confirmed by some experiments for actual images acquired by an ordinary digital camera.

In vector analysis, it is important to classify three flow primitives as translation, rotation and divergence. These three primitives can be detected utilizing line integral and surface integral according to the knowledge of vector analysis. In this paper, we introduce a method for extracting these three primitives utilizing edges in an image based on vector analysis, namely edge field analysis. The edge has the information of inclination. However, the edge has no information of the direction unlike vector. Hence, line integral and surface integral can not be directly applied to detect these three primitives utilizing edges. We firstly formulate the problem and describe the algorithm for detecting the three primitives in vector analysis. We then propose an algorithm for estimating three primitives regarding edge image as pseudo-vector field. For illustration, we apply edge field analysis to quasi-motion extraction and feature extraction. We also show the experimental results in terms of estimating the center of the flowers, the cell body of neuron, the eye of the storm, the center of the explosion and so on.

A method for evaluating image segmentation methods is proposed in this paper. The method is based on a perception model where the drawing act is used to represent visual mental percepts. Each segmented image is represented by a minimal set of features and the segmentation method is tested against a set of sketches that represent a subset of the original image database, using the Mahalanobis distance function. The covariance matrix is set using a collection of sketches drawn by different users. The different drawings are demonstrated to be consistent across users. This evaluation method can be used to solve the problem of parameter selection in image segmentation, as well as to show the goodness or limitations of the different segmentation algorithms. Different well-known color segmentation algorithms are analyzed with the proposed method and the nature of each one is discussed. This evaluation method is also compared with heuristic functions that serve for the same purpose, showing the importance of using users' pictorial knowledge.

We present the use of a Modified Generalized Hough Transform (MGHT) and deformable contours for image data retrieval where a given contour, gray-scale, or color template image can be detected in the target image, irrespective of its position, size, rotation, and smooth deformation transformations. Potential template positions are found in the target image using our novel modified Generalized Hough Transform method that takes measurements from the template features by extending a line from each edge contour point in its gradient direction to the other end of the object. The gradient difference is used to create a relationship with the orientation and length of this line segment. Potential matching positions in the target image are then searched by also extending a line from each target edge point to another end along the normal, then looking up the measurements data from the template image. Positions with high votes become candidate positions. Each candidate position is used to find a match by allowing the template to undergo a contour transformation. The deformed template contour is matched with the target by measuring the similarity in contour tangent direction and the smoothness of the matching vector. The deformation parameters are then updated via a Bayesian algorithm to find the best match. To avoid getting stuck in a local minimum solution, a novel coarse-and-fine model for contour matching is included. Results are presented for real images of several kinds including bin picking and fingerprint identification.

Adaptation process of retina helps human visual system to see a high dynamic range scene in real world. This paper presents a simple static local adaptation method for high dynamic range image compression based on a retinal model. The proposed simple model aims at recreating the same sensations between the real scene and the range compressed image on display device when viewed after reaching steady state local adaptation respectively. Our new model takes the display adaptation into account in relation to the scene adaptation based on the retinal model. In computing local adaptation, the use of nonlinear edge preserving bilateral filter presents a better tonal rendition in preserving the local contrast and details while avoiding banding artifacts normally seen in local methods. Finally, we demonstrate the effectiveness of the proposed model by estimating the color difference between the recreated image and the target visual image obtained by trial and error method.

The estimation of the point-spread function (PSF) is one of very important and indispensable tasks for the practical image restoration. Especially, for the motion blur, various PSF estimation algorithms have been developed so far. However, a majority of them becomes useless in the low blurred signal-to-noise ratio (BSNR) environment. This paper describes a new robust PSF estimation algorithm based on Hough transform concerning gradient vectors, which can accurately and robustly estimate the motion blur PSF even in low BSNR case. The effectiveness and validity of the proposed algorithm are verified by applying it to the PSF estimation and the image restoration for noisy and motion blurred images.

This paper describes a method of free iris and focus image generation based on transformation integrating multiple differently focused images. First, we assume that objects are defocused by a geometrical blurring model. And we combine acquired images on certain imaging planes and spatial information of objects by using a convolution of a three-dimensional blur. Then, based on spatial frequency analysis of the blur, we design three-dimensional filters that generate free iris and focus images from the acquired images. The method enables us to generate not only an all-in-focus image corresponding to an ideal pin-hole iris but also various images, which would be acquired with virtual irises whose sizes are different from the original one. In order to generate a certain image by using multiple differently focused images, especially very many images, conventional methods usually analyze focused regions of each acquired image independently and construct a depth map. Then, based on the map, the regions are merged into a desired image with some effects. However, generally, it is so difficult to conduct such depth estimation robustly in all regions that these methods cannot prevent merged results from including visible artifacts, which decrease the quality of generated images awfully. In this paper, we propose a method of generating desired images directly and robustly from very many differently focused images without depth estimation. Simulations of image generation are performed utilizing synthetic images to study how certain parameters of the blur and the filter affect the quality of generated images. We also introduce pre-processing that corrects the size of acquired images and a simple method for estimating the parameter of the three-dimensional blur. Finally, we show experimental results of free iris and focus image generation from real images.

In this paper, we propose a flexible gaze detection method using single PTZ (Pan-Tilt-Zoom) camera. In this method, a PTZ camera traces user's face and measures position of their viewing point, so they do not need to fix their head in front of camera. Furthermore, to realize accurate gaze detection, we employ elliptical iris template matching. To reduce calculation amount of iris template matching, we get rough gaze direction by simple method on ahead to decide ellipse shape. In this paper, we also adapt to variation of facial orientations, which will affect to detect viewing position and gaze direction. After several experiments, we examine accuracy of gaze detection and head tracking ability of this system.

Traffic sign detection is a valuable part of future driver support system. In this paper, we present a novel framework to accurately detect traffic signs from a single color image by analyzing geometrical, physical and text/symbol features of traffic signs. First, we utilize an elaborate edge detection algorithm to extract edge map and accurate edge pixel gradient information. Then, we extract 2-D geometric primitives (circles, ellipses, rectangles and triangles) efficiently from image edge map. Third, the candidate traffic sign regions are selected by analyzing the intrinsic color features, which are invariant to different illumination conditions, of each region circumvented by geometric primitives. Finally, a text and symbol detection algorithm is introduced to classify true traffic signs. Experimental results demonstrated the capabilities of our algorithm to detect traffic signs with respect to different size, shape, color and illumination conditions.

Recently, cars are equipped with a lot of sensors for safety driving. We have been trying to store the driving-scene video with such sensor data and to detect the change of scenery of streets. Detection results can be used for building historical database of town scenery, automatic landmark updating of maps, and so forth. In order to compare images to detect changes, image retrieval taken at nearly identical locations is required as the first step. Since Global Positioning System (GPS) data essentially contain some noises, we cannot rely only on GPS data for our image retrieval. Therefore, we have developed an image retrieval algorithm employing edge-histogram-based image features in conjunction with hierarchical search. By using edge histograms projected onto the vertical and horizontal axes, the retrieval has been made robust to image variation due to weather change, clouds, obstacles, and so on. In addition, matching cost has been made small by limiting the matching candidates employing the hierarchical search. Experimental results have demonstrated that the mean retrieval accuracy has been improved from 65% to 76% for the front-view images and from 34% to 53% for the side-view images.

We describe an online method for selecting and annotating highlight scenes in soccer matches being televised. The stadium crowd noise and the play-by-play announcer's voice are used as input signals. Candidate scenes for highlights are extracted from the crowd noise by dynamic thresholding and spectral envelope analysis. Using a dynamic threshold solves the problem in conventional methods of how to determine an appropriate threshold. Semantic-meaning information about the kind of play and the related team and player is extracted from the announcer's commentary by using domain-based rules. The information extracted from the two types of audio input is integrated to generate segment-metadata of highlight scenes. Application of the method to six professional soccer games has confirmed its effectiveness.

This paper presents a fast elemental image generation algorithm, called the Viewpoint Vector Rendering (VVR), for the computer-generated integral imaging system. VVR produces a set of elemental images in real-time by assembling the segmented area of the directional scenes taken from a range of viewpoints. This algorithm is less affected by system factors such as the number of elemental lens and the number of polygons. It also supports all display modes of the integral imaging system, real, virtual and focused mode. This paper first describes the characteristics of integral imaging system. It then discusses the design, implementation, and performance evaluation of the VVR algorithm, which can be easily adapted to render the integral images of complex 3D objects.

In this paper, we propose a new region extraction method using chroma key with a two-tone checker pattern background. The method solves the problem in conventional chroma key techniques that foreground objects become transparent if their colors are the same as the background color. The method utilizes the adjacency condition between two-tone regions of the background and the geometrical information of the background grid line. The procedure of the proposed method consists of four steps: 1) background color extraction, 2) background grid line extraction, 3) foreground extraction, and 4) image composition. As to background color extraction, a color space approach is used. As to background grid line extraction, it is difficult to extract background grid line by a color space approach because the color of this region may be a composite of two background colors and different from them. Therefore, the background grid line is extracted from adjacency conditions between two background colors. As to foreground extraction, the boundary between the foreground and the background is detected to recheck the foreground region whose color is same as the background, and the background region whose color is same as the foreground. To detect regions whose colors are same as the background, the adjacency conditions with the background grid line are utilized. As to image composition, the process that smoothes the color of the foreground's boundary against the new background is carried out to create natural images. Experimental results show that the foreground objects can be segmented exactly from the background regardless of the colors of the foreground objects.

In this paper we propose a novel method to inpaint highlights and to remove the specularity in the image with specular objects by the color line projection. Color line projection is the method that a color with a surface reflection component is projected near the diffuse color line by following the direction of the specular color line. We use two captured images using different exposure time so that the clue of the original color in a highlight area is searched from two images since the color at the highlight region is distorted and saturated to the illumination color. In the first step of the proposed procedure, the region corresponding to the highlight is generated and the clue of the original highlight color is acquired. In the next step, the color line is generated by the restricted region growing method around the highlight region, and the color line is divided into the diffuse color line and the specular color line. In the final step, pixels near the specular color line are projected onto near the diffuse color line by the color line projection, in which the modified random function is applied to realistically inpaint the highlight. One of advantages in our method is to find the highlight region and the clue of the original color of the highlight with ease. It also efficiently estimates the surface reflection component which is utilized to remove specularity and to inpaint the highlight. The proposed method performs the highlight inpainting and the specular removal simultaneously once the color line is generated. In addition, color line projection with the modified random function can make the result more realistic. We show experimental results from the real images and make a synthesis of the real image and the image modified by the proposed method.

Automatic detection of normal mammograms, as a "first look" for breast cancer, is a new approach to computer-aided diagnosis. This approach may be limited, however, by two main causes. The first problem is the presence of poorly separable "crossed-distributions" in which the correct classification depends upon the value of each feature. The second problem is overlap of the feature distributions that are extracted from digitized mammograms of normal and abnormal patients. Here we introduce a new Support Vector Machine (SVM) based method utilizing with the proposed uncrossing mapping and Local Probability Difference (LPD). Crossed-distribution feature pairs are identified and mapped into a new features that can be separated by a zero-hyperplane of the new axis. The probability density functions of the features of normal and abnormal mammograms are then sampled and the local probability difference functions are estimated to enhance the features. From 1,000 ground-truth-known mammograms, 250 normal and 250 abnormal cases, including spiculated lesions, circumscribed masses or microcalcifications, are used for training a support vector machine. The classification results tested with another 250 normal and 250 abnormal sets show improved testing performances with 90% sensitivity and 89% specificity.

Accurate thickness measurement of sheet-like structure such as articular cartilage in CT images is required in clinical diagnosis as well as in fundamental research. Using a conventional measurement method based on the zero-crossing edge detection (zero-crossings method), several studies have already analyzed the accuracy limitation on thickness measurement of the single sheet structure that is not influenced by peripheral structures. However, no studies, as of yet, have assessed measurement accuracy of two adjacent sheet structures such as femoral and acetabular cartilages in the hip joint. In this paper, we present a model of the CT scanning process of two parallel sheet structures separated by a small distance, and use the model to predict the shape of the gray-level profiles along the sheet normal orientation. The difference between the predicted and the actual gray-level profiles observed in the CT data is minimized by refining the model parameters. Both a one-by-one search (exhaustive combination search) technique and a nonlinear optimization technique based on the Levenberg-Marquardt algorithm are used to minimize the difference. Using CT images of phantoms, we present results showing that when applying the one-by-one search method to obtain the initial values of the model parameters, Levenberg-Marquardt method is more accurate than zero-crossings and one-by-one search methods for estimating the thickness of two adjacent sheet structures, as well as the thickness of a single sheet structure.

A new phase retrieval method using an active contour model (snake) for image reconstruction is proposed. The proposed method reconstructs a target image by retrieving the phase from the magnitude of its Fourier transform and the measured area of the image. In general, the measured area is different from the true area where the target image exists. Thus a snake, which can extract the shape of the target image, is utilized to renew the measured area. By processing this renewal iteratively, the area obtained by the snake converges to the true area and as a result the proposed method can accurately reconstruct a target image even when the measured area is different from the true area. Experimental results show the effectiveness of the proposed method.

A tri-template-based codes (TTBC) method is proposed to reduce test cost of intellectual property (IP) cores. In order to reduce test data volume (TDV), the approach utilizes three templates, i.e., all 0, all 1, and the previously applied test data, for generating the subsequent test data by flipping the inconsistent bits. The approach employs a small number of test channels I to supply a large number of internal scan chains 2^I-3 such that it can achieve significant reduction in test application time (TAT). Furthermore, as a non-intrusive and automatic test pattern generation (ATPG) independent solution, the approach is suitable for IP core testing because it requires neither redesign of the core under test (CUT) nor running any additional ATPG for the encoding procedure. In addition, the decoder has low hardware overhead, and its design is independent of the CUT and the given test set. Theoretical analysis and experimental results for ISCAS 89 benchmark circuits have proven the efficiency of the proposed approach.

In this paper, we propose a method of accelerating test generation for sequential circuits by using the knowledge about the availability of state justification sequences, the bound on the length of state distinguishing sequences, differentiation between valid and invalid states, and the existence of a reset state. We also propose a method of synthesis for testability (SfT) which takes the features of our test generation method into consideration to synthesize sequential circuits from given FSM descriptions. The SfT method guarantees that the test generator will be able to find a state distinguishing sequence. The proposed method extracts the state justification sequence from the FSM produced by the synthesizer to improve the performance of its test generation process. Experimental results show that the proposed method can achieve 100% fault efficiency in relatively short test generation time.

In this paper, we propose an algorithm that solves the node-to-node disjoint paths problem in n-burnt pancake graphs in polynomial-order time of n. We also give a proof of its correctness as well as the estimates of time complexity O(n³) and the maximum path length 3n+4. We conducted a computer experiment for n=2 to 100 to measure the average performance of our algorithm. The results show that the average time complexity is O(n^3.0) and the maximum path length is 3n+4.

Mobile-agent-based distributed computing is one of the most promising paradigms to support autonomic computing in a large-scale of distributed system with dynamics and diversity: mobile agents traverse the distributed system and carry out a sophisticated task at each node adaptively. In mobile-agent-based systems, a larger number of agents generally require shorter time to complete the whole task but consume more resources (e.g., processing power and network bandwidth). Therefore, it is indispensable to keep an appropriate number of agents for the application on the mobile-agent-based system. This paper considers the mobile agent population control problem in dynamic networks: it requires adjusting the number of agents to a constant fraction of the current network size. This paper proposes algorithms inspired by the single species population model, which is a well-known population ecology model. These two algorithms are different in knowledge of networks each node requires. The first algorithm requires global information at each node, while the second algorithm requires only the local information. This paper shows by simulations that the both algorithms realize self-adaptation of mobile agent population in dynamic networks, but the second algorithm attains slightly lower accuracy than the first one.

In January 2005, an open evaluation of corpus-based text-to-speech synthesis systems using common speech datasets, named Blizzard Challenge 2005, was conducted. Nitech group participated in this challenge, entering an HMM-based speech synthesis system called Nitech-HTS 2005. This paper describes the technical details, building processes, and performance of our system. We first give an overview of the basic HMM-based speech synthesis system, and then describe new features integrated into Nitech-HTS 2005 such as STRAIGHT-based vocoding, HSMM-based acoustic modeling, and a speech parameter generation algorithm considering GV. Constructed Nitech-HTS 2005 voices can generate speech waveforms at 0.3RT (real-time ratio) on a 1.6 GHz Pentium 4 machine, and footprints of these voices are less than 2 Mbytes. Subjective listening tests showed that the naturalness and intelligibility of the Nitech-HTS 2005 voices were much better than expected.

This paper presents a novel optimized real-time Huffman encoder using a pipelined data path based on CAM technology and a parallel code-word-table optimizer. The exploitation of CAM technology enables fast parallel search of the code word table. At the same time, the code word table is optimized according to the frequency of received input symbols and is up-dated in real-time. Since these two functions work in parallel, the proposed architecture realizes fast parallel encoding and keeps a constantly high compression ratio. Evaluation results for the JPEG application show that the proposed architecture can achieve up to 28% smaller encoded picture sizes than the conventional architectures. The obtained encoding time can be reduced by 95% in comparison to a conventional SRAM-based architecture, which is suitable even for the latest end-user-devices requiring fast frame-rates. Furthermore, the proposed architecture provides the only encoder that can simultaneously realize small compressed data size and fast processing speed.

This paper presents a scalable FPGA/ASIC implementation architecture for high-speed parallel table-lookup-coding using multi-ported content addressable memory, aiming at facilitating effective table-lookup-coding solutions. The multi-ported CAM adopts a Flexible Multi-ported Content Addressable Memory (FMCAM) technology, which represents an effective parallel processing architecture and was previously reported in [1]. To achieve a high-speed parallel table-lookup-coding solution, FMCAM is improved by additional schemes for a single search mode and counting value setting mode, so that it permits fast parallel table-lookup-coding operations. Evaluation results for Huffman encoding within the JPEG application show that a synthesized semi-custom ASIC implementation of the proposed architecture can already reduce the required clock-cycle number by 93% in comparison to a conventional DSP. Furthermore, the performance per area unit, measured in MOPS/mm², can be improved by a factor of 3.8 in comparison to parallel operated DSPs. Consequently, the proposed architecture is very suitable for FPGA/ASIC implementation, and is a promising solution for small area integrated realization of real-time table-lookup-coding applications.

This paper presents a motion-based boundary tracking method for a moving deformable object in an image sequence using a parametric active contour model. Deciding the local converging directions of the contour points is essential for correctly extracting the boundary of a moving deformable object. Thus, a new energy function for a parametric active contour model is proposed based on the addition of a directional energy term using a frame difference map to the greedy snake. The frame difference map is used to obtain motion information on an object with fast and non-rigid motion. Plus, updating rules for the frame difference map are also developed to encourage the stable convergence of the contour points. Experiments on a set of synthetic and real image sequences show that the proposed method could fully track a speedy deformable object while exactly extracting the boundary of the object in every frame.

We propose the extended Bezier spiral in this paper. The spiral is useful for both design purposes and improved aesthetics. This is because the spiral is one of the Bezier curves, which play an important role in interactive curve design, and because the assessment of the curve is based on the human reception of the curve. For the latter purpose we utilize the logarithmic distribution graph that quantifies the designers' preferences. This paper contributes the unification of the two different curve design objectives (the interactive operation and so called "eye pleasing" result generation); which have been independently investigated so far.

We focus on the overall representation of network user behavior and observe that the number of destinations accessed by a network user is limited, which means users have certain preferential haunts in networks. And the distribution of users closely matches heavy tail distribution instead of Possion Distribution.

A semi-supervised classification method is presented. A robust unsupervised spectral mapping method is extended to a semi-supervised situation. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis for the iris and wine data and is also more accurate than a semi-supervised algorithm of the logistic GRF for the ionosphere dataset.

This letter describes a two-band excitation model for HMM-based speech synthesis. The HMM-based speech synthesis system generates speech from the HMM training data of the spectral and excitation parameters. Synthesized speech has a typical quality of "vocoded sound" mostly because of the simple excitation model with the voiced/unvoiced selection. In this letter, two-band excitation based on the harmonic plus noise speech model is proposed for generating the mixed excitation source. With this model, we can generate the mixed excitation more accurately and reduce the memory for the trained excitation data as well.

In this paper, we propose a modified reproduction strategy of a Genetic Algorithm (GA) utilizing a Self-Organizing Map (SOM) with a novel updating rule of binary weight vectors based on a significance of elements of inputs. In this rule, an updating order of elements is decided by considering fitness values of individuals in a population. The SOM with the proposed updating rule can realize an effective reproduction.