An adaptive service framework is expected to support real-time multimedia services in wirless/mobile cellular networks with various classes of traffic and diverse bandwidth requirements. Quality of service (QoS) provisioning in an adaptive framework is another challenging consideration, such as quantifying the level of bandwidth degradation of an ongoing calls and guaranteeing stable QoS levels. Considering both the period and the depth of degradation, the degradation area ratio (DAR) represents the average ratio of a call's degradation and is one of the meaningful measures for adaptive service in call level analysis. In this paper, analytical models for estimating the DAR and finding the optimal control parameters are presented in multi-class traffic call management situations. In complete partitioning capacity based threshold-type call admission control (CAC), a one-dimensional Markov chain with an absorbing state is proposed for estimating the DAR in each traffic class. We formulate a two-leveled optimization problem minimizing the total blocking probabilities subject to QoS requirements and present the procedures required in finding the optimal capacities and threshold values by using modified dynamic programming. In complete sharing capacity based threshold-type CAC, the multidimensional Markov model is approximately reduced to a one-dimensional model in order to reduce complexity and hence calculation time. The reduced model is compared with multidimensional Markov model in numerical examples. The optimization problem is formulated minimizing the total blocking probabilities subject to QoS requirements and the optimal threshold parameters are found by using a genetic algorithm. Performance of two adopted admission policies in adaptive framework situations is illustrated by numerical results.

In this paper, we propose a parametric model checking algorithm for a subclass of Timed Automata called Parametric Time-Interval Automata (PTIA). In a PTIA, we can specify upper- and lower-bounds of the execution time (time-interval) of each transition using parameter variables. The proposed algorithm takes two inputs, a model described in a PTIA and a property described in a PTIA accepting all invalid infinite/finite runs (called a never claim), or valid finite runs of the model. In the proposed algorithm, firstly we determinize and complement the given property PTIA if it accepts valid finite runs. Secondly, we accelerate the given model, that is, we regard all the actions that are not appeared in the given property PTIA as invisible actions and eliminate them from the model while preserving the set of visible traces and their timings. Thirdly, we construct a parallel composition of the model and the property PTIAs which is accepting all invalid runs that are accepted by the model. Finally, we perform the extension of Double Depth First Search (DDFS), which is used in the automata-theoretic approach to Linear-time Temporal Logic (LTL) model checking, to derive the weakest parameter condition in order that the given model never executes the invalid runs specified by the given property.

CR (Computed Radiography) is characterized by high sensitivity and wide dynamic range. Moreover, it has the advantage of being able to transfer exposed images directly to a computer-aided detection (CAD) system which is not possible using conventional film digitizer systems. This paper proposes a high-performance clustered microcalcification detection system for CR mammography. Before detecting and classifying candidate regions, the system preprocesses images with a normalization step to take into account various imaging conditions and to enhance microcalcifications with weak contrast. Large-scale experiments using images taken under various imaging conditions at seven hospitals were performed. According to analysis of the experimental results, the proposed system displays high performance. In particular, at a true positive detection rate of 97.1%, the false positive clusters average is only 0.4 per image. The introduction of geometrical features of each microcalcification for identifying true microcalcifications contributed to the performance improvement. One of the aims of this study was to develop a system for practical use. The results indicate that the proposed system is promising.

Horn functions are Boolean functions where each of the prime implicants contains at most one negative literal. A class of Boolean functions is considered in this letter where a single term containing two negative literals is added by logical-or operation to a Horn function. We show that the function does not have any prime implicant containing three negative literals. We also show that if two terms containing two negative literals are added to a Horn function, then it may have many prime implicants all of which contain three negative literals. We show that it is P-complete to determine whether a given Boolean function in disjunctive normal form of the considered class is a tautology.

To take intercarrier interference (ICI) attributed to time variations of the channel into consideration, the time- and frequency-selective (doubly-selective) channel is parameterized by a finite parameter model. By capitalizing on the finite parameter model to approximate the doubly-selective channel, a Kalman filter is developed for channel estimation. The ICI suppressing, reduced-complexity Viterbi-type Maximum Likelihood (RML) equalizer is incorporated into the Kalman filter for recursive channel tracking and equalization to improve the system performance. An enhancement in the channel tracking ability is validated by theoretical analysis, and a significant improvement in BER performance using the channel estimates obtained by the recursive channel estimation method is verified by Monte-Carlo simulations.

The authors propose a system for searching the shape of human hands and fingers in real time and with high accuracy, without using any special peripheral equipment such as range sensor, PC cluster, etc., by a method of retrieving similar image quickly with high accuracy from a large volume of image database containing the complicated shapes and self-occlusions. In designing the system, we constructed a database in a way to be adaptable even to differences among individuals, and searched CG images of hand similar to unknown hand image, through extraction of characteristics using high-order local autocorrelational patterns, reduction of the amount of characteristics centering on principal component analysis, and prior rearrangement of data corresponding to the amount of characteristics. As a result of experiments, our system performed high-accuracy estimation of human hand shape where mean error was 7 degrees in finger joint angles, with the processing speed of 30 fps or over.

An efficient hybrid image vector quantization (VQ) technique based on a classification in the DCT domain is presented in this letter. This algorithm combines two kinds of VQ, predictive VQ (PVQ) and discrete cosine transform domain VQ (DCTVQ), and adopts a simple classifier which employs only three DCT coefficients in the 88 block. For each image block, the classifier switches to the PVQ coder if the block is relatively complex, and otherwise switches to the DCTVQ coder. Experimental results show that the proposed algorithm can achieve higher PSNR values than ordinary VQ, PVQ, JPEG, and JPEG2000 at the same bit-rate.

This paper describes a pattern classifier for detecting frontal-view faces via learning a decision boundary. The proposed classifier consists of two major parts for improving classification accuracy: the implicit modeling of both the face and the near-face classes resulting in an extended discriminative feature set, and the subsequent composite Support Vector Machines (SVMs) for speeding up the classification. For the extended discriminative feature set, Principal Component Analysis (PCA) or Independent Component Analysis (ICA) is performed for the face and near-face classes separately. The projections and distances to the two different subspaces are complementary, which significantly enhances classification accuracy of SVM. Multiple nonlinear SVMs are trained for the local facial feature spaces considering the general multi-modal characteristic of the face space. Each component SVM has a simpler boundary than that of a single SVM for the whole face space. The most appropriate component SVM is selected by a gating mechanism based on clustering. The classification by utilizing one of the multiple SVMs guarantees good generalization performance and speeds up face detection. The proposed classifier is finally implemented to work in real-time by cascading a boosting based face detector.

Grid computing is a state-of-the-art parallel computing technology which enables worldwide computers to dynamically share their computing powers and resource to each other. The grid takes advantage of Internet as a universal communication platform to carry messages. Basically, Internet doesn't guarantee loss-free and ordered transmission, hence, the grid should keep the cause and effect of events by itself to ensure the correct ordering of command invocations at the remote hosts. The ordering issue arises when the messages travel across the networks with unpredictable delay. Recent research has studied the security and resource control issues, but failed to address the requirements of transport layer on the grid communication platform. In this paper, we propose the Causal Ordered Grid (COG) architecture and implement it to study the transport performance issues when the grid is built over worldwide networks. The COG provides a novel service model to the applications with time-sensitive and causal-ordered transportation. From our experiments, the design of the grid middleware should use a causal-ordered, time-sensitive transportation rather than TCP. Our research will be beneficial to the improvement of the grid computing and can provide wealthy empirical results for the designer.

The optical network is a promising approach for realizing a scalable backbone network. In backbone networks, survivability is very important because great volumes of traffic incur damage from faulty equipment. To address this issue, various recovery schemes have been proposed for optical backbone networks. Among those schemes, shared mesh restoration utilizes link bandwidth efficiently because the backup lightpaths share link bandwidth if they protect against different failures and are never utilized simultaneously. However, a route computation method for the backup lightpaths that promotes such bandwidth sharing is necessary to achieve efficient bandwidth utilization. This paper proposes a distributed route computation method for the backup lightpaths in shared mesh restoration. In this method, the link weight is estimated to be smaller if a backup lightpath newly established can share the link bandwidth with the backup lightpaths already accommodated in that link. The link weight can be calculated using the Markov Decision Theory. The bandwidth sharing between the backup lightpaths can be promoted by selecting the shortest route based on such modified link weights. The proposed method effectively realizes efficient utilization of the link bandwidth and achieves low loss rate of reliable lightpath establishment requests under the same traffic load. The proposed method restricts the amount of link state information advertised by the routing protocol and achieves a sufficiently small amount of route calculation.

In this paper, we propose a broadband 3-dB rat-race ring coupler that uses tightly coupled lines. An aperture compensation technique that can simplify the fabrication of tightly coupled lines, is also discussed here. The effective bandwidth of the proposed rat-race coupler with a return loss better than -20 dB can be increased by 14.3%, in comparison with that of March's. Its isolation is always below -20 dB and the phase shift errors less than 6.

In this paper, we shall construct mathematical theory based on the concept of set-valued mappings, suitable for available operation of extraordinarily complicated large-scale network systems by introducing some connected-block structures. A fine estimation technique for availability of system behaviors of such network systems are obtained finally in the form of fixed point theorem for a special system of fuzzy-set-valued mappings.

We studied 10 Gbit/s-based time-spreading and wave-length-hopping (TS-WH) optical code division multiplexing (OCDM) using fiber Bragg gratings (FBGs). To apply it to such the high bit rate system more than ten gigabit, two techniques are adopted. One is encoding with the maximum spreading time of 400 ps, which is four times as data bit duration, to encode without shortening chip duration. Another is encoder design. The apodized refractive index profile to the unit-gratings composing the encoder is designed to encode the pulses with 10-20 ps width at 10 Gbit/s rate. Using these techniques, 210 Gbit/s OCDM is demonstrated successfully. In this scheme, transmission distance is limited due to dispersion effect because the signal has wide bandwidth to assign a wavelength-hopping pattern. We use no additional devices to compensate the dispersion, in order to construct simple and cost-effective system. Novel FBG encoder is designed to incorporate both encoding and compensating of group delay among chip pulses within one device. We confirm the extension of transmission distance in the TS-WH OCDM from the demonstration over 40 km-long single mode fiber.

The linear complexity and its stability of periodic sequences are of fundamental importance as measure indexes on the security of stream ciphers and the k-error linear complexity reveals the stability of the linear complexity properly. The k-error linear complexity of periodic sequences is defined to be the smallest linear complexity that can be obtained by changing k or fewer bits of the sequence per period. For 2pn-periodic binary sequences, where p is an odd prime and 2 is a primitive root modulo p2, we present and prove the unique expression of the linear complexity. Moreover we show a relationship between the linear complexity and the minimum value k for which the k-error linear complexity is strictly less than the linear complexity.

Although data compression is popularly used, compressed data have a problem that they are very sensitive to errors. This paper proposes a single burst error recovery method for Huffman coding by using the bidirectionally decodable Huffman coding. Computer simulation shows that the proposed method can recover 2.5lburst bits burst error with high probability, where lburst is the maximum length of burst errors which the proposed method is expected to be able to recover.

This paper proposes the MultiPath streaming scheme with Media Synchronization control (MPMS) for audio-video transmission in wireless ad hoc networks. In many audio-video streaming applications, media compensate each other from a perceptual point of view. On the basis of this property, we treat the two streams as separate transport streams, and then the source transmits them into two different routes if multiple routes to the destination are available. The multipath transmission disturbs the temporal structure of the streams; in MPMS, the disturbance is remedied by media synchronization control. In order to implement MPMS in this paper, we enhance the existing Dynamic Source Routing (DSR) protocol. We compare the application-level QoS of MPMS and three other schemes for audio-video transmission by simulation with ns-2. In the simulation, we also assess the influence of the multipath transmission on other traffic. The simulation result shows that MPMS is effective in achieving high QoS at the application-level.

This paper present three characteristic functions which can express the luminance distribute characteristic much better. Based on these functions a region classification algorithm is presented. The algorithm can offer more information on regions' similarity and greatly improve the efficiency and performance of match seeking in fractal coding. It can be widely applied to many kinds of fractal coding algorithms. Analysis and experimental results proved that it can offer more information on luminance distribute characteristics among regions and greatly improve the decoding quality and compression ratio with holding the running speed.

In this paper, we propose an agent architecture for a combination of speculative computation and abduction. Speculative computation is a tentative computation when complete information for performing computation is not obtained. We use a default value to complement such incomplete information. Unlike usual default reasoning, the real value for the information can be obtained during the computation and the computation can be revised on the fly. In the previous work, we applied this technique to handling distributed problem solving under incomplete communication environments in the context of multi-agent systems and proposed correct procedures in abductive logic programming in terms of perfect model semantics. In the previous work, however, we regarded assumptions as defaults and used these assumptions for speculative computation. Thus, we could not perform hypothetical reasoning, that is, the original usage of abduction. In this paper, we extend our framework so that speculative computation and abduction can be both performed. As a result, our procedure becomes an extension of the abductive procedure developed by Kakas and Mancarella augmented by dynamic belief revision mechanism about outside world.

We study the complexity of the reachability problem for a new subclass of Petri nets called simple-circuit Petri nets, which properly contains several well known subclasses such as conflict-free, BPP, normal Petri nets and more. A new decomposition approach is applied to developing an integer linear programming formulation for characterizing the reachability sets of such Petri nets. Consequently, the reachability problem is shown to be NP-complete. The model checking problem for some temporal logics is also investigated for simple-circuit Petri nets.

We propose a new two-stage blind separation and deconvolution strategy for multiple-input multiple-output (MIMO)-FIR systems driven by colored sound sources, in which single-input multiple-output (SIMO)-model-based ICA (SIMO-ICA) and blind multichannel inverse filtering are combined. SIMO-ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. After the separation by the SIMO-ICA, a blind deconvolution technique for the SIMO model can be applied even when each source signal is temporally correlated and the mixing system has a nonminimum phase property. The simulation results reveal that the proposed algorithm can successfully achieve separation and deconvolution of a convolutive mixture of speech, and outperforms a number of conventional ICA-based BSD methods.