Constructing ideal (t,n) threshold secret sharing schemes leads to some limitations on the maximum number of users, that are able to join the secret sharing scheme. We aim to remove these limitations by reducing the information rate of the constructed threshold secret sharing schemes. In this paper we propose recursive construction algorithms of (t,n) threshold secret sharing schemes, based on the generalized vector space construction. Using these algorithms we are able to construct a (t,n) threshold secret sharing scheme for any arbitrary n.

This paper considers numerical methods for stability analyses of periodic solutions of ordinary differential equations. Stability of a periodic solution can be determined by the corresponding monodromy matrix and its eigenvalues. Some commonly used numerical methods can produce inaccurate results of them in some cases, for example, near bifurcation points or when one of the eigenvalues is very large or very small. This work proposes a numerical method using a periodic boundary condition for vector fields, which preserves a critical property of the monodromy matrix. Numerical examples demonstrate effectiveness and a drawback of this method.

A branch-and-bound algorithm (BB for short) is the most general technique to deal with various combinatorial optimization problems. Even if it is used, computation time is likely to increase exponentially. So we consider its parallelization to reduce it. It has been reported that the computation time of a parallel BB heavily depends upon node-variable selection strategies. And, in case of a parallel BB, it is also necessary to prevent increase in communication time. So, it is important to pay attention to how many and what kind of nodes are to be transferred (called sending-node selection strategy). In this paper, for the graph coloring problem, we propose some sending-node selection strategies for a parallel BB algorithm by adopting MPI for parallelization and experimentally evaluate how these strategies affect computation time of a parallel BB on a PC cluster network.

We investigate the MIMO broadcast channels with imperfect channel knowledge due to estimation error and much more users than transmit antennas to exploit multiuser diversity. The channel estimation error causes the interference among users, resulting in the sum-rate loss. A tight upper bound of this sum-rate loss based on zeroforcing beamforming is derived theoretically. This bound only depends on the channel estimation quality and transmit antenna number, but not on the user number. Based on this upper bound, we show this system maintains full multiuser diversity, and always benefits from the increasing transmit power.

This letter presents new delayed perturbation bounds (DPBs) for stabilizing receding horizon H∞ control (RHHC). The linear matrix inequality (LMI) approach to determination of DPBs for the RHHC is proposed. We show through a numerical example that the RHHC can guarantee an H∞ norm bound for a larger class of systems with delayed perturbations than conventional infinite horizon H∞ control (IHHC).

Burn-in is a widely used method to improve the quality of products or systems after they have been produced. In this paper, optimal burn-in procedures for a system with two types of failures (i.e., minor and catastrophic failures) are investigated. A new system surviving burn-in time b is put into field operation and the system is used under a warranty policy under which the manufacturer agrees to provide a replacement system for any system that fails to achieve a lifetime of at least w. Upper bounds for optimal burn-in time minimizing the total expected warranty cost are obtained under a more general assumption on the shape of the failure rate function which includes the bathtub shaped failure rate function as a special case.

Based on the concept of sliding mode control, this paper investigates the upper bound covariance assignment with H∞ norm and variance constrained problem for bilinear stochastic systems. We find that the invariance property of sliding mode control ensures nullity of the matched bilinear term in the system on the sliding mode. Moreover, using the upper bound covariance control approach and combining the sliding phase and hitting phase of the system design, we will derive the control feedback gain matrix G, which is essential to the controller u(t) design, to achieve the performance requirements. Finally, a numerical example is given to illustrate the control effect of the proposed method.

The problem of estimating the timing of ultra-wide band signal is considered in the letter. We develop a maximum likelihood timing estimation algorithm for binary PAM DS-UWB systems. The derivation of the proposed algorithm is based on the known training sequence and AWGN channel. The Cramer-Rao Bound (CRB) for the ML timing estimator is presented as a performance benchmark. It is found via numerical results that the ML timing estimator on AWGN channels achieves the CRB when the values of Eb/N0 for the observation bits Nb=50 are sufficiently high. Finally, the performance of the proposed ML estimator is evaluated on actual channels with intersymbol interference such as an IEEE UWB indoor multipath channel model.

This paper addresses a new surface reconstruction scheme for approximating the isosurface from a set of tomographic cross sectional images. Differently from the novel Marching Cubes (MC) algorithm, our method does not extract the iso-density surface (isosurface) directly from the voxel data but calculates the iso-density point (isopoint) first. After building a coarse initial mesh approximating the ideal isosurface by the cell-boundary representation, it metamorphoses the mesh into the final isosurface by a relaxation scheme, called shrink-wrapping process. Compared with the MC algorithm, our method is robust and does not make any cracks on surface. Furthermore, since it is possible to utilize lots of additional isopoints during the surface reconstruction process by extending the adjacency definition, theoretically the resulting surface can be better in quality than the MC algorithm. According to experiments, it is proved to be very robust and efficient for isosurface reconstruction from cross sectional images.

Coded modulation systems based on low density parity check (LDPC) codes of finite lengths are considered. The union bounds on the bit error probabilities of the maximum likelihood (ML) decoding are presented for both additive white Gaussian noise (AWGN) and flat Rayleigh fading channels. The tightness of the derived bound is verified by simulating the ML decoding of a very short LDPC code. For medium-length codes, performance of the sum-product decoding can asymptotically approach the bounds.

Automatic prosody labeling is the task of automatically annotating prosodic labels such as syllable stresses or break indices into speech corpora. Prosody-labeled corpora are important for speech synthesis and automatic speech understanding. However, the subtleness of physical features makes accurate labeling difficult. Since errors in the prosodic labels can lead to incorrect prosody estimation and unnatural synthetic sound, the accuracy of the labels is a key factor for text-to-speech (TTS) systems. In particular, mora accent labels relevant to pitch are very important for Japanese, since Japanese is a pitch-accent language and Japanese people have a particularly keen sense of pitch accents. However, the determination of the mora accents of Japanese is a more difficult task than English stress detection in a way. This is because the context of words changes the mora accents within the word, which is different from English stress where the stress is normally put at the lexical primary stress of a word. In this paper, we propose a method that can accurately determine the prosodic labels of Japanese using both acoustic and linguistic models. A speaker-independent linguistic model provides mora-level knowledge about the possible correct accentuations in Japanese, and contributes to reduction of the required size of the speaker-dependent speech corpus for training the other stochastic models. Our experiments show the effectiveness of the combination of models.

The support vector machine has received wide acceptance for its high generalization ability in real world classification applications. But a drawback is that it uniquely classifies each pattern to one class or none. This is not appropriate to be applied in classification problem involves overlapping patterns. In this paper, a novel multi-model classifier (DR-SVM) which combines SVM classifier with kNN algorithm under rough set technique is proposed. Instead of classifying the patterns directly, patterns lying in the overlapped region are extracted firstly. Then, upper and lower approximations of each class are defined on the basis of rough set technique. The classification operation is carried out on these new sets. Simulation results on synthetic data set and benchmark data sets indicate that, compared with conventional classifiers, more reasonable and accurate information about the pattern's category could be obtained by use of DR-SVM.

IEEE 802.11e Medium Access Control (MAC) is a supplement to the IEEE 802.11 Wireless Network (WLAN) standard to support Quality of Service (QoS). The 802.11e MAC defines a new coordination function, namely Hybrid Coordination Function (HCF), which takes the QoS requirements of flows into account and allocates Transmission Opportunity (TXOP) to stations. On the basis of mean sending rate, delay of Variable Bit Rate (VBR) traffic cannot be bounded with the reference HCF scheduling algorithm proposed in this supplement. In this paper, we propose a new Connection Admission Control (CAC) and a scheduling algorithm that utilize the token bucket and a modified Latency-Rate (LR) scheduling algorithm to guarantee a bounded delay for HCF Controlled Channel Access (HCCA). The new Service Interval (SI) is calculated to optimize the number of stations accommodated and takes into account delay bound and token bucket parameters. We show that it is possible to obtain worst-case performance guarantees on delay. First, we analyze the behavior of the new scheduler with a loss free wireless channel model and after this, with a burst loss model and we explain how it is possible to extend this scheduler for a multi-rate scheme. Properties of the proposal are investigated both theoretically and using ns-2 simulations. We present a set of simulations with both Constant Bit Rate (CBR) and VBR flows and performance comparisons with HCF scheduling algorithm. The results show that the delay upper bound can be achieved for a large range of networks load with bandwidth optimization.

This paper discusses the behavior of the second-order modes (Hankel singular values) of linear discrete-time systems under bounded-real transformations, where the transformations are given by arbitrary transfer functions with magnitude bounded by unity. Our main result reveals that the values of the second-order modes are decreased under any of the above-mentioned transformations. This result is the generalization of the theory of Mullis and Roberts, who proved that the second-order modes are invariant under any allpass transformation, i.e. any lossless bounded-real transformation. We derive our main result by describing the controllability/observability Gramians of transformed systems with the help of the discrete-time bounded-real lemma.

This paper presents an ultra wideband (UWB) channel sounding scheme with a technique for estimating time of arrival (TOA) and angle of arrival (AOA) using measurement signals. Since the power spectrum over the UWB bandwidth can be measured in advance, we propose a signal model using the measurement power spectrum to design the proper UWB signals model. This signal model is more similar to measurement signals than the flat spectrum model which is an ideal model. If more than three waves impinge on a receiver, we must determine the proper grouping of the elements of TOA vector and AOA vector. It is difficult to determine the grouping using only measurement signals because of many degradation factors. We also propose pairing the elements of TOA vector and that of AOA vector using correlation method based on measurement signals and the proposed signal model. This technique is available for more than the case of three paths if pairing the estimated TOAs and AOAs of measurement signals is not accurately determined. We evaluated the proposed techniques for a vector network analyzer (VNA) with a three-dimensional virtual antenna array.

Target location estimation is one of many promising applications of wireless sensor networks. However, until now only few studies have examined location estimation performances in real environments. In this paper, we analyze the effect of walking people on target location estimation performance in three experimental locations. The location estimation is based on received signal strength indicator (RSSI) and maximum likelihood (ML) estimation, and the experimental locations are a corridor of a shopping center, a foyer of a conference center and a laboratory room. The results show that walking people have a positive effect on the location estimation performance if the number of RSSI measurements used in the ML estimation is equal or greater than 3, 2 and 2 in the case of the experiments conducted in the corridor, foyer and laboratory room, respectively. The target location estimation accuracy ranged between 2.8 and 2.3 meters, 2.5 and 2.1 meters, and 1.5 and 1.4 meters in the case of the corridor, foyer and laboratory room, respectively.

This paper presents a new analysis of power complementary filters using the state-space representation. Our analysis is based on the bounded-real Riccati equations that were developed in the field of control theory. Through this new state-space analysis of power complementary filters, we prove that the sum of the controllability/observability Gramians of a pair of power complementary filters is represented by a constant matrix, which is given as a solution to the bounded-real Riccati equations. This result shows that power complementary filters possess complementary properties with respect to the Gramians, as well as the magnitude responses of systems. Furthermore, we derive new theorems on a specific family of power complementary filters that are generated by a pair of invertible solutions to the bounded-real Riccati equations. These theorems show some interesting relationships of this family with respect to the Gramians, zeros, and coefficients of systems. Finally, we give a numerical example to demonstrate our results.

This research aims to develop automatic knowledge mining of causality from texts for supporting an automatic question answering system (QA) in answering 'why' question, which is among the most crucial forms of questions. The out come of this research will assist people in diagnosing problems, such as in plant diseases, health, industrial and etc. While the previous works have extracted causality knowledge within only one or two adjacent EDUs (Elementary Discourse Units), this research focuses to mine causality knowledge existing within multiple EDUs which takes multiple causes and multiple effects in to consideration, where the adjacency between cause and effect is unnecessary. There are two main problems: how to identify the interesting causality events from documents, and how to identify the boundaries of the causative unit and the effective unit in term of the multiple EDUs. In addition, there are at least three main problems involved in boundaries identification: the implicit boundary delimiter, the nonadjacent cause-consequence, and the effect surrounded by causes. This research proposes using verb-pair rules learnt by comparing the Naïve Bayes classifier (NB) and Support Vector Machine (SVM) to identify causality EDUs in Thai agricultural and health news domains. The boundary identification problems are solved by utilizing verb-pair rules, Centering Theory and cue phrase set. The reason for emphasizing on using verbs to extract causality is that they explicitly make, in a certain way, the consequent events of cause-effect, e.g. 'Aphids suck the sap from rice leaves. Then leaves will shrink. Later, they will become yellow and dry.'. The outcome of the proposed methodology shown that the verb-pair rules extracted from NB outperform those extracted from SVM when the corpus contains high occurence of each verb, while the results from SVM is better than NB when the corpus contains less occurence of each verb. The verb-pair rules extracted from NB for causality extraction has the highest precision (0.88) with the recall of 0.75 from the plant disease corpus whereas from SVM has the highest precision (0.89) with the recall of 0.76 from bird flu news. For boundary determination, our methodology can handle very well with approximate 96% accuracy. In addition, the extracted causality results from this research can be generalized as laws in the Inductive-Statistical theory of Hempel's explanation theory, which will be useful for QA and reasoning.

We give a centralized deterministic algorithm for constructing linear network error-correcting codes that attain the Singleton bound of network error-correcting codes. The proposed algorithm is based on the algorithm by Jaggi et al. We give estimates on the time complexity and the required symbol size of the proposed algorithm. We also estimate the probability of a random choice of local encoding vectors by all intermediate nodes giving a network error-correcting codes attaining the Singleton bound. We also clarify the relationship between the robust network coding and the network error-correcting codes with known locations of errors.

This study examines treatment of a boundary between media to simulate an acoustic field using the CIP method. The handling of spatial derivatives of fields is extremely important for CIP acoustic field analysis. We demonstrate a method of handling this boundary and report results of CIP acoustic field analysis using the present treatment.