A novel video retrieval method based on Web community extraction using audio and visual features and textual features of video materials is proposed in this paper. In this proposed method, canonical correlation analysis is applied to these three features calculated from video materials and their Web pages, and transformation of each feature into the same variate space is possible. The transformed variates are based on the relationships between visual, audio and textual features of video materials, and the similarity between video materials in the same feature space for each feature can be calculated. Next, the proposed method introduces the obtained similarities of video materials into the link relationship between their Web pages. Furthermore, by performing link analysis of the obtained weighted link relationship, this approach extracts Web communities including similar topics and provides the degree of attribution of video materials in each Web community for each feature. Therefore, by calculating similarities of the degrees of attribution between the Web communities extracted from the three kinds of features, the desired ones are automatically selected. Consequently, by monitoring the degrees of attribution of the obtained Web communities, the proposed method can perform effective video retrieval. Some experimental results obtained by applying the proposed method to video materials obtained from actual Web pages are shown to verify the effectiveness of the proposed method.

In this letter, we propose an efficient method to improve the performance of voiced/unvoiced (V/UV) sounds decision for the selectable mode vocoder (SMV) of 3GPP2 using the Gaussian mixture model (GMM). We first present an effective analysis of the features and the classification method adopted in the SMV. And feature vectors which are applied to the GMM are then selected from relevant parameters of the SMV for the efficient V/UV classification. The performance of the proposed algorithm are evaluated under various conditions and yield better results compared to the conventional method of the SMV.

This paper presents a fuzzy-based path selection method for improving the security level, in which each cluster chooses paths based on the detection power of false data and energy efficiency.

In this paper, a new approach to channel order selection of single-input multiple-output (SIMO), finite impulse response (FIR) channels is proposed for blind channel estimation. The approach utilizes cross spectral density (CSD) of the channel outputs, and minimizes the distance between two CSD's, one calculated non-parametrically from the observed output data, and the other calculated from the blindly estimated channel parameters. The CSD criterion is numerically tested on randomly generated SIMO-FIR channels, and shown to be very effective compared to existing channel order selection methods especially under low SNR settings. Blind estimates of the channels with the selected channel order also show superiority of the CSD criterion.

This paper proposes a scheme to select an appropriate gateway based on multiple metrics such as remaining energy, mobility or speed, and number of hops in Mobile Ad Hoc Network (MANET) and the infrastructure network integration. The Multiple Criteria Decision Making (MCDM) method called Simple Additive Weighting (SAW) is used to rank and to select the gateway node. SAW method calculates the weights of gateway node candidates by considering these three metrics. The node with the highest weight will be selected as the gateway. Simulation results show that our scheme can reduce the average energy consumption of MANET nodes, and improve throughput performance, gateway lifetime, Packet Delivery Ratio (PDR) of the MANET and the infrastructure network.

In this study we introduce AdjScales, a method for scaling similar adjectives by their strength. It combines existing Web-based computational linguistic techniques in order to automatically differentiate between similar adjectives that describe the same property by strength. Though this kind of information is rarely present in most of the lexical resources and dictionaries, it may be useful for language learners that try to distinguish between similar words. Additionally, learners might gain from a simple visualization of these differences using unidimensional scales. The method is evaluated by comparison with annotation on a subset of adjectives from WordNet by four native English speakers. It is also compared against two non-native speakers of English. The collected annotation is an interesting resource in its own right. This work is a first step toward automatic differentiation of meaning between similar words for language learners. AdjScales can be useful for lexical resource enhancement.

In this paper, we propose a new approximate algorithm for the model predictive control (MPC) problem with a time-varying reference of hybrid systems. The proposed algorithm consists of an offline computation and an online computation. In the offline computation, candidates of mode sequences are derived. In the online computation, after the mode sequence is uniquely decided among candidates, the finite-time optimal control problem, i.e., the quadratic programming problem, is solved. So by applying the proposed algorithm, the computational amount of the online computation is decreased. First, the MPC problem with a time-varying reference is formulated. Next, the proposed algorithm is explained, and the accuracy of the obtained approximate solution is discussed. Finally, the effectiveness of the proposed method is shown by a numerical example.

Blind beamforming plays an important role in multiple-input multiple-output (MIMO) Systems, radar, cognitive radio, and system identification. In this paper, we propose a new algorithm for multiple blind beamforming algorithm based on the least square constant modulus algorithm (LSCMA). The new method consists of the following three parts: (a) beamforming of one signal with LSCMA. (b) direction-of-arrival (DOA) estimation of the remaining signals by rooting the weight vector polynomial. (c) beamforming of the remaining signals with linear constraints minimum variance (LCMV) method. After the convergence of LSCMA, one signal is captured and the arrival angles of the remaining signals can be obtained by rooting the weight vector polynomial. Therefore, beamforming can be quickly established for the remaining signals using LCMV method. Simultaneously the DOA of the signals can also be obtained. Simulation results show the performance of the presented method.

In this letter, we present an automatic approach of objective singing performance evaluation for untrained singers by relating acoustic measurements to perceptual ratings of singing voice quality. Several acoustic parameters and their combination features are investigated to find objective correspondences of the perceptual evaluation criteria. Experimental results show relative strong correlation between perceptual ratings and the combined features and the reliability of the proposed evaluation system is tested to be comparable to human judges.

In this letter, we present the impact of carrier frequency offset (CFO) in dual-hop orthogonal frequency division multiplexing (OFDM) systems with a fixed relay for frequency-selective fading channels. Approximate expressions of the average signal-to-noise ratios (SNRs) for both downlink and uplink are obtained and validated by simulations. It is shown that dual-hop systems have slightly worse average SNR degradation than single-hop systems. We also show that the average SNR degradation due to the CFO varies according to the gap between average received SNRs for the first and the second hop.

In this letter, we propose a novel frequency-domain equalizer (FDE) for single-carrier systems characterized by severe inter-symbol interference (ISI) channels; it consists of a linear FDE and an iterative block noise-predictor (IBNP). Unlike the FDE with time-domain noise predictor (FDE-NP), the proposed scheme allows the feedback equalizer being an uncausal filter, and performs the noise prediction in an iterative manner. For this reason, FDE-IBNP can remove both precursor and postcursor ISI, and alleviate the impact of error-propagation. Besides, our scheme has lower computational complexity than the present iterative block equalizers.

In this paper, we focus on the adaptive resource allocation issue for uplink OFDMA systems. The resources are allocated according to a proportional fairness criterion, which can strike an alterable balance between fairness and efficiency. Optimization theory is used to analyze the multi-constraint resource allocation problem and some heuristic characteristics about the optimal solution are obtained. To deal with the cohesiveness of the necessary conditions, we resort to bargaining theory that has been deeply investigated in game theory. Firstly, we summarize some assumptions about bargaining theory and show their similarities with the resource allocation process. Then we propose a priority-ranked bargaining model, whose primary contribution is applying the economic thought to the resource allocation process. A priority-ranked bargaining algorithm (PRBA) is subsequently proposed to permit the base station to auction the subcarriers one by one according to the users' current priority. By adjusting the predefined rate ratio flexibly, PRBA can achieve different degrees of fairness among the users' capacity. Simulation results show that PRBA can achieve similar performance of the max-min scheme and the NBS scheme in the case of appropriate predefined rate ratio.

In the latest video coding frameworks, efficiency of motion vector (MV) coding is becoming increasingly important because of the growing bit rate portion of motion information. However, neither the conventional median predictor, nor the newer schemes such as the minimum bit rate prediction scheme and the hybrid scheme, can effectively eliminate the local redundancy of motion vectors. In this paper, we present the prioritized reference decision scheme for efficient motion vector coding, based on the H.264/AVC framework. This scheme makes use of a boolean indicator to specify whether the median predictor is to be used for the current MV or not. If not, the median prediction is considered not suitable for the current MV, and this information is used for refining the possible space of a group of reference MVs including 4 neighboring MVs and the zero MV. This group of MVs is organized to be a prioritized list so that the reference MV with highest priority is to be selected as the prediction value. Furthermore, the boolean indicators are coded into the modified code words of mb_type and sub_mb_type, so as to reduce the overhead. By applying the proposed scheme, the structure and the applicability problems with the state-of-the-art MBP scheme have been overcome. Experimental result shows that the proposed scheme achieves a considerable reduction of bits for MVDs, compared with the conventional median prediction algorithm. It also achieves a better and much stabler performance than MBP-based MV coding.

The cooperative characteristics of electromagnetic relay's attraction torque and reaction torque are the key property to ensure its reliability, and it is important to attain better cooperative characteristics by analyzing and optimizing relay's electromagnetic system and mechanical system. From the standpoint of changing reaction torque of mechanical system, in this paper, adjusted parameters (armature's maximum angular displacement αarm_max, initial return spring's force Finiti_return_spring, normally closed (NC) contacts' force FNC_contacts, contacts' gap δgap, and normally opened (NO) contacts' over travel δNO_contacts) were adopted as design variables, and objective function was provided for with the purpose of increasing breaking velocities of both NC contacts and NO contacts. Finally, genetic algorithm (GA) was used to attain optimization of the objective function. Accuracy of calculation for the relay's dynamic characteristics was verified by experiment.

Multicarrier code division multiple access (MC-CDMA) systems are well suited for high data rate wireless multimedia services, due to their ability to convert frequency-selective fading channels to distinct flat fading channels with low complexity fast Fourier transform (FFT) devices. However, when multiple users are present, the performance of MC-CDMA systems is degraded by the multiuser interference (MUI) when the channel is frequency-selective. In order to mitigate MUI, we present a joint algorithm that combines transmit power control, antenna array processing and multiuser detection at the receiver. Interestingly, the frequency-selectivity that entails the MUI also provides multipath diversity which can help suppress the MUI. Performance of the algorithm in a number of MC-CDMA system models is evaluated in terms of the average transmit power to achieve the target signal to interference plus noise ratio (SINR). Simulations confirm the outstanding performance of this algorithm compared with the existing ones in MC-CDMA systems.

The purpose of this paper is to study sender authenticated key agreements by a third party, which uses the received parameters to verify the fact that a sender of a message knows his long-term private key. In particular, we propose a standard model for the protocol among three entities for the first time. The security of this protocol depends on the difficulty of solving two new problems related to one-way isomorphisms and the decision co-bilinear Diffie-Hellman problem on multiplicative cyclic groups. It is the first time that the security of a key agreement has been formally proven by using negligible probability. We believe that our contribution gives many applications in the cryptographic community.

In multiuser MIMO-BC (Multiple-Input Multiple-Output Broadcasting) systems, user selection is important to achieve multiuser diversity. The optimal user selection algorithm is to try all the combinations of users to find the user group that can achieve the multiuser diversity. Unfortunately, the high calculation cost of the optimal algorithm prevents its implementation. Thus, instead of the optimal algorithm, some suboptimal user selection algorithms were proposed based on semiorthogonality of user channel vectors. The purpose of this paper is to achieve multiuser diversity with a small amount of calculation. For this purpose, we propose a user selection algorithm that can improve the orthogonality of a selected user group. We also apply a channel prediction technique to a MIMO-BC system to get more accurate channel information at the transmitter. Simulation results show that the channel prediction can improve the accuracy of channel information for user selections, and the proposed user selection algorithm achieves higher sum rate capacity than the SUS (Semiorthogonal User Selection) algorithm. Also we discuss the setting of the algorithm threshold. As the result of a discussion on the calculation complexity, which uses the number of complex multiplications as the parameter, the proposed algorithm is shown to have a calculation complexity almost equal to that of the SUS algorithm, and they are much lower than that of the optimal user selection algorithm.

In this paper, we propose a new feature selection algorithm for multi-class classification. The proposed algorithm is based on Gaussian mixture models (GMMs) of the features, and it uses the distance between the two least separable classes as a metric for feature selection. The proposed system was tested with a support vector machine (SVM) for multi-class classification of music. Results show that the proposed feature selection scheme is superior to conventional schemes.

This work focuses on a vulnerability of hash functions due to sloppy usages or implementations in the real world. If our cryptographic research community succeeded in the development of a perfectly secure random function as the random oracle, it might be broken in some sense by invalid uses. In this paper, we propose a new variant of the random oracle model in order to analyze the security of cryptographic protocols under the situation of an invalid use of hash functions. Our model allows adversaries to obtain contents of the hash list of input and output pairs arbitrarily. Also, we analyze the security of several prevailing protocols (FDH, OAEP, Cramer-Shoup cryptosystem, Kurosawa-Desmedt cryptosystem, NAXOS) in our model. As the result of analyses, we clarify that FDH and Cramer-Shoup cryptosystem are still secure but others are insecure in our model. This result shows the separation between our model and the standard model.

This paper introduces a multilayer traffic network model and traffic network clustering method for solving the route selection problem (RSP) in car navigation system (CNS). The purpose of the proposed method is to reduce the computation time of route selection substantially with acceptable loss of accuracy by preprocessing the large size traffic network into new network form. The proposed approach further preprocesses the traffic network than the traditional hierarchical network method by clustering method. The traffic network clustering considers two criteria. We specify a genetic clustering algorithm for traffic network clustering and use NSGA-II for calculating the multiple objective Pareto optimal set. The proposed method can overcome the size limitations when solving route selection in CNS. Solutions provided by the proposed algorithm are compared with the optimal solutions to analyze and quantify the loss of accuracy.