Harn and Lin proposed an algorithm to detect and identify cheaters in Shamir's secret sharing scheme in the journal Designs, Codes and Cryptography, 2009. In particular, their algorithm for cheater identification is inefficient. We point out that some of their conditions for cheater detection and identification essentially follow from those on error detection/correction of Reed-Solomon codes, which have efficient decoding algorithms, while some other presented conditions turn out to be incorrect. The extended and improved version of the above mentioned scheme was recently presented at the conference International Computer Symposium 2012 (and the journal version appeared in the journal IET Information Security). The new scheme, which is ideal (i.e. the share size is equal to that of the secret), attempts to identify cheaters from minimal number of shares (i.e. the threshold of them). We show that the proposed cheater identification is impossible using the arguments from coding theory.

The paper addresses a scheme of lightly supervised training of an acoustic model, which exploits a large amount of data with closed caption texts but not faithful transcripts. In the proposed scheme, a sequence of the closed caption text and that of the ASR hypothesis by the baseline system are aligned. Then, a set of dedicated classifiers is designed and trained to select the correct one among them or reject both. It is demonstrated that the classifiers can effectively filter the usable data for acoustic model training. The scheme realizes automatic training of the acoustic model with an increased amount of data. A significant improvement in the ASR accuracy is achieved from the baseline system and also in comparison with the conventional method of lightly supervised training based on simple matching.

We consider a chain of integrators system that has an uncertain delay in the input. Also, there is a measurement noise in the feedback channel that only noisy output is available. We develop a new output feedback control scheme along with amplification such that the ultimate bounds of all states and output of the controlled system can be made arbitrarily small. We note that the condition imposed on the sensor noise is quite general over the existing results such that the sensor noise is uncertain and is only required to be bounded by a known bound. The benefit of our control method is shown via an example.

In Worldwide interoperability for Microwave Access (WiMAX) network, QoS(quality of service) is provided for service flows. For this, five classes of services are defined in IEEE 802.16. They are Unsolicited Grant Service (UGS), Extended Real-Time Polling Service (ertPS), Real-Time Polling Service (rtPS), Non Real-Time Polling Service (nrtPS) and Best Effort (BE). For real-time classes, the sent packet has a deadline. As the transmission delay is over the limitation of deadline, the packet becomes useless and will be discarded. Thus, they will be served earlier and have higher probability. Nevertheless, non-real-time packets need also to be served from time to time. The scheduler should assign proper bandwidth for non-real-time flows and send the real-time packets before they are discarded. To deicide the right allocated bandwidth, the arrival rate of each flow is a good parameter for assignment. The average µ and standard deviation σ of arrival rate correspond to the long term need and variation of load for one flow. Thus, we proposed a scheduling algorithm named BAcSOA in which µ+kσ is used as a reference to allocate bandwidth with weighted round robin for one flow [5]. Different classes of flows will be given different values of k which corresponds to the priorities of classes. In this algorithm, flow with higher priority should have larger value of k. The value of k will decide the performance of this class. In this paper, we revise the algorithm to EBAcSOA and propose a mathematical way to decide the value of k for a required performance. Then, a simulation platform is proposed to decide k such that a required performance can be obtained for an operating system. This approach may be different from other researches in which there is no required performance and the performance results are obtained only for several operating points. However, the approach proposed is more practical from the view of an operator and may become an attractive point for other researchers.

This paper investigates active period selection for cluster-based WSNs employing traffic adaptive IEEE 802.15.4 beacon enabled medium access control (MAC) under spatial non-uniform traffic and cluster mobility environments, and proposes a simple autonomous distributed superframe duration (SD) selection scheme to enhance network performance under such environments. The proposed scheme autonomously selects an active SD at each cluster head (CH) by using beacon reception power monitoring (conducted in distributed control manner) and also introduces a beacon status notice from sensor nodes (SNs) to their parent CHs in order to prevent unnecessary SD selection at CHs. Moreover, SD reuse mechanism and joint operation with previously proposed distributed backoff mechanism are proposed for the proposed SD selection scheme to further enhance the network performance. The results of computer simulation show that the proposed scheme can improve both the transmission and power efficiency performance of cluster-based WSNs under spatial non-uniform traffic and cluster mobility environments.

Irreversible k-conversion set is introduced in connection with the mathematical modeling of the spread of diseases or opinions. We show that the problem to find a minimum irreversible 2-conversion set can be solved in O(n2log 6n) time for graphs with maximum degree at most 3 (subcubic graphs) by reducing it to the graphic matroid parity problem, where n is the number of vertices in a graph. This affirmatively settles an open question posed by Kyncl et al. (2014).

We propose a one-step error detection and correction interface for a voice word processor. This correction interface performs analysis region detection, user intention understanding and error correction utterance recognition, all from a single user utterance input. We evaluate the performance of each component first, and then compare the effectiveness of our interface to two previous interfaces. Our evaluation demonstrates that each component is technically superior to the baselines and that our one-step error detection and correction method yields an error correction interface that is more convenient and natural than the two previous interfaces.

Wireless Sensor Networks (WSNs) have been proposed for monitoring vital signs of patients at home. This scenario requires inter-cell mobility; however, WSNs are not designed to support this characteristic. In this paper we propose a cross-layer protocol to manage the handoff, called WSN-HaDaS (Handoff aware of Data Sending), which operates in the transport layer and medium access control (MAC) sub-layer based on an interaction between the layers (transport and MAC). This protocol interacts with a sending data mechanism (like TCP protocol) to notify the beginning or ending of the handoff process; therefore, the mechanism can stop or resume data sending, respectively. Therefore, WSN-HaDaS prevents packet loss during the handoff process. WSN-HaDaS comprises two main processes to manage mobility: Monitoring Handoff Trigger (MHT) and Handoff Execution Process (HEP); they are responsible for generating the handoff warning messages and executing the handoff process, respectively. Therefore, MHT delay and HEP delay are used as the key performance metrics. To evaluate the proposal, we use a physical test-bed in an indoor environment with the intention of obtaining practical results. The results demonstrate that the proposed protocol performs the handoff process with less delay than the selected reference work. They also show that WSN-HaDaS is an appropriate solution to provide inter-cell mobility in WSNs. Furthermore, we demonstrate the possibility of embedding the WSN-HaDaS in devices with limited resources using the IEEE 802.1.5.4 standard.

This paper describes an evaluation of a temporally stable spectral envelope estimator proposed in our past research. The past research demonstrated that the proposed algorithm can synthesize speech that is as natural as the input speech. This paper focuses on an objective comparison, in which the proposed algorithm is compared with two modern estimation algorithms in terms of estimation performance and temporal stability. The results show that the proposed algorithm is superior to the others in both aspects.

String-topology multi-hop network is often selected as an analysis object because it is one of the fundamental network topologies. The purpose of this paper is to establish expression for end-to-end delay for IEEE 802.11 string-topology multi-hop networks. For obtaining the analytical expression, the effects of frame collisions and carrier-sensing effect from other nodes under the non-saturated condition are obtained for each node in the network. For expressing the properties in non-saturated condition, a new parameter, which is frame-existence probability, is defined. The end-to-end delay of a string-topology multi-hop network can be derived as the sum of the transmission delays in the network flow. The analytical predictions agree with simulation results well, which show validity of the obtained analytical expressions.

In traditional speech emotion recognition systems, when the training and testing utterances are obtained from different corpora, the recognition rates will decrease dramatically. To tackle this problem, in this letter, inspired from the recent developments of sparse coding and transfer learning, a novel sparse transfer learning method is presented for speech emotion recognition. Firstly, a sparse coding algorithm is employed to learn a robust sparse representation of emotional features. Then, a novel sparse transfer learning approach is presented, where the distance between the feature distributions of source and target datasets is considered and used to regularize the objective function of sparse coding. The experimental results demonstrate that, compared with the automatic recognition approach, the proposed method achieves promising improvements on recognition rates and significantly outperforms the classic dimension reduction based transfer learning approach.

We address a robust algorithm for the interference-plus-noise covariance matrix reconstruction (RA-INCMR) against random arbitrary steering vector mismatches (RASVMs) of the interferences, which lead to substantial degradation of the original INCMR beamformer performance. Firstly, using the worst-case performance optimization (WCPO) criteria, we model these RASVMs as uncertainty sets and then propose the RA-INCMR to obtain the robust INCM (RINCM) based on the Robust Capon Beamforming (RCB) algorithm. Finally, we substitute the RINCM back into the original WCPO beamformer problem for the sample covariance matrix to formulate the new RA-INCM-WCPO beamformer problem. Simulation results demonstrate that the performance of the proposed beamformer is much better than the original INCMR beamformer when there exist RASVMs, especially at low signal-to-noise ratio (SNR).

Using traditional single-layer dictionary learning methods, it is difficult to reveal the complex structures hidden in the hyperspectral images. Motivated by deep learning technique, a deep dictionary learning approach is proposed for hyperspectral image denoising, which consists of hierarchical dictionary learning, feature denoising and fine-tuning. Hierarchical dictionary learning is helpful for uncovering the hidden factors in the spectral dimension, and fine-tuning is beneficial for preserving the spectral structure. Experiments demonstrate the effectiveness of the proposed approach.

It is unclear whether Hidden Markov Model (HMM) or Dynamic Time Warping (DTW) mapping is more appropriate for visual speech recognition when only small data samples are available. In this letter, the two approaches are compared in terms of sensitivity to the amount of training samples and computing time with the objective of determining the tipping point. The limited training data problem is addressed by exploiting a straightforward template matching via weighted-DTW. The proposed framework is a refined DTW by adjusting the warping paths with judicially injected weights to ensure a smooth diagonal path for accurate alignment without added computational load. The proposed WDTW is evaluated on three databases (two in the public domain and one developed in-house) for visual recognition performance. Subsequent experiments indicate that the proposed WDTW significantly enhances the recognition rate compared to the DTW and HMM based algorithms, especially under limited data samples.

This paper proposes reflection and transmission control panels using artificially designed materials. As the artificially designed material, finite- and infinite-length metal wire array sheets are used here. Laminated structures consisting of the metal wire array sheets and dielectric material are proposed. Reflection and transmission characteristics of these structures can be controlled by changing the metal wire parameters such as wire length, spacing gaps between the wires, and the dielectric material's thickness and relative permittivity. The reflection and transmission characteristics of the laminated structures are evaluated by measurements in free space and by transmission line theory.

This paper proposes an updating state dependent disturbance observer (USDDOB) to reject position dependent disturbances when parameters vary slowly, and input and output are time-delayed. To reject the effects of resultant slowly-varying position dependent disturbances, the USDDOB uses the control method of the state dependent disturbance observer (SDDOB) and time-invariance approximation. The USDDOB and a main proportional integral (PI) controller constitute a robust controller. Simulations and experiments using a 1-degree-of-freedom (1-DOF) tilted planar robot show the effectiveness of the proposed method.

Multiple-input multiple-output (MIMO) transmission is attracting interest for increasing the transmission rates of wireless systems. This paper surveys MIMO transmission technology from the viewpoints of transmission methods, access control schemes, and total transmission efficiency. We consider wireless local area networks (WLAN) systems that use MIMO technology; moreover, we focus on multiuser MIMO (MU-MIMO) technology, which will be introduced in next-generation WLAN systems such as IEEE802.11ac. This paper explains the differences in the detailed access control procedures for MIMO and MU-MIMO transmission, including channel state information (CSI) acquisition. Furthermore, the issues related to CSI feedback and solutions are also discussed. Related works on the medium access control (MAC) protocol in MIMO/MU-MIMO transmission are introduced. In addition, the throughput performance using MIMO/MU-MIMO transmission is evaluated considering an IEEE802.11ac-based WLAN system. From the numerical evaluation, it is shown that the overhead due to CSI feedback from the user terminals to the base station causes a decrease in the throughput. We verified that implicit beamforming, which eliminates CSI feedback, is effective for solving this issue.

A term tree pattern is a rooted ordered tree pattern which consists of ordered tree structures with edge labels and structured variables with labels. All variables with the same label in a term tree pattern can be simultaneously replaced with ordered trees isomorphic to the same rooted ordered tree. Then, a term tree pattern is suitable for representing structural features common to tree structured data such as XML documents on the web, the secondary structures of RNA in biology and parse trees describing grammatical structures of natural languages. Let $ott$ be the set of all term tree patterns which have one or more variables with the same label. Let $lott$ be the set of all term tree patterns t such that all variables in t have distinct labels. We remark that $lottsubsetneq ott$ holds. In this paper, we consider a problem, called Matching problem for term tree patterns, of deciding whether or not a given rooted ordered tree T is obtained from a given term tree pattern t by replacing variables in t with rooted ordered trees. We show that Matching problem for term tree patterns in $ott$ is NP-complete, by giving a reduction from the string pattern matching problem, which is NP-complete. Next, by giving operations on an interval, which is a set containing all integers between two given integers representing vertex identifiers, we propose an efficient algorithm for solving Matching problem for term tree patterns in $lottsubsetneq ott$. Then, we show that, when an ordered tree having N vertices and a term tree pattern $t in lott$ having n vertices are given, the proposed matching algorithm correctly solves this problem in O(nN) time.

Suppose one of the edges is attacked in a graph G, where some number of guards are placed on some of its vertices. If a guard is placed on one of the end-vertices of the attacked edge, she can defend such an attack to protect G by passing over the edge. For each of such attacks, every guard is allowed either to move to a neighboring vertex, or to stay at where she is. The eternal vertex cover number τ∞(G) is the minimum number of guards sufficient to protect G from any length of any sequence of edge attacks. This paper derives the eternal vertex cover number τ∞(G) of such graphs constructed by replacing each edge of a tree by an arbitrary elementary bipartite graph (or by an arbitrary clique), in terms of easily computable graph invariants only, thereby showing that τ∞(G) can be computed in polynomial time for such graphs G.

A binary tree is regarded as a prefix-free binary code, in which the weighted sum of the lengths of root-leaf paths is equal to the expected codeword length. Huffman's algorithm computes an optimal tree in O(n log n) time, where n is the number of leaves. The problem was later generalized by allowing each leaf to have its own function of its depth and setting the sum of the function values as the objective function. The generalized problem was proved to be NP-hard. In this paper we study the case where every function is a unit step function, that is, a function that takes a lower constant value if the depth does not exceed a threshold, and a higher constant value otherwise. We show that for this case, the problem can be solved in O(n log n) time, by reducing it to the Coin Collector's problem.