Multi-source broadcasting is one of the information dissemination problems on interconnection networks such that some (but not all) units disseminate distinct information to all other units. In this paper, we discuss multi-source broadcasting on the Kautz digraph which is one of the models of interconnection networks. We decompose the Kautz digraph K(d,n) into isomorphic cycle-rooted trees whose root-cycle has length 2, then we present an algorithm for multi-source broadcasting using these cycle-rooted trees. This algorithm is able to treat d(d+1) messages simultaneously and takes the same order for required times as lower bound.

In a direct-sequence spread spectrum communication system, its multiple access interference, security and user number are mainly decided by correlation, linear span and family size of spreading sequences employed by such a system, respectively. In this letter, based on several families of the known sequences, a method for improving their linear span and family sizes is presented. It is worthy of mentioning that although the number of the proposed sequences with linear span not less than that of the known sequences is enormously increased, the former's correlation distribution is the same as the latter's one. In addition, the proposed sequences include No sequences and the known sequences mentioned above as special cases.

In this paper, we propose a genetic algorithm (GA) based equalization approach for direct sequence ultra-wideband (DS-UWB) wireless communication systems, where the GA is combined with a RAKE receiver to combat the inter-symbol interference (ISI) due to the frequency selective nature of UWB channels for high data rate transmission. The proposed GA based equalizer outperforms significantly the RAKE and the RAKE-minimum mean square error (MMSE) receivers according to results obtained from intensive simulation work. The RAKE-GA receiver also provides bit-error-rate (BER) performance very close to that of the optimal RAKE-maximum likelihood detection (MLD) approach, while offering a much lower computational complexity.

Various contrast enhancement methods such as histogram equalization (HE) and local contrast enhancement (LCE) have been developed to increase the visibility and details of a degraded image. We propose an image contrast enhancement method based on the global and local adjustment of gray levels by combining HE with LCE methods. For the optimal combination of both, we introduce a discrete entropy. Evaluation of our experimental results shows that the proposed method outperforms both the HE and LCE methods.

Estimating the ratio of two probability density functions (a.k.a. the importance) has recently gathered a great deal of attention since importance estimators can be used for solving various machine learning and data mining problems. In this paper, we propose a new importance estimation method using a mixture of probabilistic principal component analyzers. The proposed method is more flexible than existing approaches, and is expected to work well when the target importance function is correlated and rank-deficient. Through experiments, we illustrate the validity of the proposed approach.

This paper examines the structural relationships between Information Technology (IT) governance and Enterprise Architecture (EA), with the objective of enhancing business value in the enterprise society. Structural models consisting of four related hypotheses reveal the relationship between IT governance and EA in the improvement of business values. We statistically examined the hypotheses by analyzing validated questionnaire items from respondents within firms listed on the Japanese stock exchange who were qualified to answer them. We concluded that firms which have organizational ability controlled by IT governance are more likely to deliver business value based on IT portfolio management.

Time synchronization is an essential service for wireless sensor networks (WSNs). However, fixed-period time synchronization can not serve multiple users efficiently in terms of energy consumption. This paper proposes a lightweight precision-adaptive protocol for cluster-based multi-user networks. It consists of a basic average time synchronization algorithm and an adaptive control loop. The basic average time synchronization algorithm achieves 1 µs instantaneous synchronization error performance. It also prolongs re-synchronization period by taking the average of two specified nodes' local time to be cluster global time. The adaptive control loop realizes diverse levels of synchronization precision based on the proportional relationship between sync error and re-synchronization period. Experimental results show that the proposed precision-adaptive protocol can respond to the sync error bound change within 2 steps. It is faster than the exponential convergence of the adaptive protocols based on multiplicative iterations.

In the conventional multi-input multi-output (MIMO) communication systems, most of the antenna selection methods considered are suitable only for spatially separated uni-polarized system under Rayleigh fading channel in non-line of sight (NLOS) condition. There have a few antenna selection schemes for the cross-polarized system in LOS condition and Ricean fading channel, and no antenna selection scheme for the MIMO channel with both LOS and NLOS. In the practical MIMO channel case, influence of LOS and NLOS conditions in the channel can vary from time to time according to the channel parameters and user movement in the system. Based on these influences and channel condition, uni-polarized system may outperform a cross-polarized. Thus, we should consider this kind of practical MIMO channel environment when developing the antenna selection scheme. Moreover, no research work has been done on reducing the complexity of antenna selection for this kind of practical MIMO channel environment. In this paper, reduced complexity in antenna selection is proposed to give the higher throughput in the practical MIMO channel environment. In the proposed scheme, suitable polarized antennas are selected based on the calculation of singular value decomposition (SVD) of channel matrix and then adaptive bit loading is applied. Simulation results show that throughput of the system can be improved under the constraint of target BER and total transmit power of the MIMO system.

The code division multiplexing (CDM)-based MIMO channel sounder architecture is efficient at measuring fast fading MIMO channels. This paper examines loosely synchronous (LS), CAZAC, Kasami, and Chaotic sequences as probing signals in the CDM architecture. After comparing the performance of the channel measurement among the sequences, it is concluded that the LS sequences are the most appropriate codes for the probing signals. However, because LS sequences have a significant drawback in that the number of transmit antennas is limited to less than 4, we propose using a hybrid architecture combining CDM with TDM for supporting a greater number of transmit antennas. The simulation results show that the proposed scheme can improve the measurement performance when more than 4 transmit antennas are used.

Interrupt service routines are a key technology for embedded systems. In this paper, we introduce the standard approach for using Generalized Stochastic Petri Nets (GSPNs) as a high-level model for generating CTMC Continuous-Time Markov Chains (CTMCs) and then use Markov Reward Models (MRMs) to compute the performance for embedded systems. This framework is employed to analyze two embedded controllers with low cost and high performance, ARM7 and Cortex-M3. Cortex-M3 is designed with a tail-chaining mechanism to improve the performance of ARM7 when a nested interrupt occurs on an embedded controller. The Platform Independent Petri net Editor 2 (PIPE2) tool is used to model and evaluate the controllers in terms of power consumption and interrupt overhead performance. Using numerical results, in spite of the power consumption or interrupt overhead, Cortex-M3 performs better than ARM7.

This paper focuses on the problem of performance degradation in mismatched speech recognition. The F-Ratio analysis method is utilized to analyze the significance of different frequency bands for speech unit classification, and we find that frequencies around 1 kHz and 3 kHz, which are the upper bounds of the first and the second formants for most of the vowels, should be emphasized in comparison to the Mel-frequency cepstral coefficients (MFCC). The analysis result is further observed to be stable in several typical mismatched situations. Similar to the Mel-Frequency scale, another frequency scale called the F-Ratio-scale is thus proposed to optimize the filter bank design for the MFCC features, and make each subband contains equal significance for speech unit classification. Under comparable conditions, with the modified features we get a relative 43.20% decrease compared with the MFCC in sentence error rate for the emotion affected speech recognition, 35.54%, 23.03% for the noisy speech recognition at 15 dB and 0 dB SNR (signal to noise ratio) respectively, and 64.50% for the three years' 863 test data. The application of the F-Ratio analysis on the clean training set of the Aurora2 database demonstrates its robustness over languages, texts and sampling rates.

We propose a segment-based voice conversion technique using hidden Markov model (HMM)-based speech synthesis with nonparallel training data. In the proposed technique, the phoneme information with durations and a quantized F0 contour are extracted from the input speech of a source speaker, and are transmitted to a synthesis part. In the synthesis part, the quantized F0 symbols are used as prosodic context. A phonetically and prosodically context-dependent label sequence is generated from the transmitted phoneme and the F0 symbols. Then, converted speech is generated from the label sequence with durations using the target speaker's pre-trained context-dependent HMMs. In the model training, the models of the source and target speakers can be trained separately, hence there is no need to prepare parallel speech data of the source and target speakers. Objective and subjective experimental results show that the segment-based voice conversion with phonetic and prosodic contexts works effectively even if the parallel speech data is not available.

A novel Position-Invariant Robust Feature, designated as PIRF, is presented to address the problem of highly dynamic scene recognition. The PIRF is obtained by identifying existing local features (i.e. SIFT) that have a wide baseline visibility within a place (one place contains more than one sequential images). These wide-baseline visible features are then represented as a single PIRF, which is computed as an average of all descriptors associated with the PIRF. Particularly, PIRFs are robust against highly dynamical changes in scene: a single PIRF can be matched correctly against many features from many dynamical images. This paper also describes an approach to using these features for scene recognition. Recognition proceeds by matching an individual PIRF to a set of features from test images, with subsequent majority voting to identify a place with the highest matched PIRF. The PIRF system is trained and tested on 2000+ outdoor omnidirectional images and on COLD datasets. Despite its simplicity, PIRF offers a markedly better rate of recognition for dynamic outdoor scenes (ca. 90%) than the use of other features. Additionally, a robot navigation system based on PIRF (PIRF-Nav) can outperform other incremental topological mapping methods in terms of time (70% less) and memory. The number of PIRFs can be reduced further to reduce the time while retaining high accuracy, which makes it suitable for long-term recognition and localization.

We propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multi-objective evolutionary algorithm (MOEA), i.e., NSGA-II. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity as well as convergence rate. Experimental results indicate that the proposed MOGP-based FEP construction system outperforms the two conventional MOEAs (i.e., NSGA-II and SPEA2) for a test problem. Moreover, we compared the programs constructed by the proposed MOGP with four human-designed object recognition programs. The results show that the constructed programs are better than two human-designed methods and are comparable with the other two human-designed methods for the test problem.

We propose a new channel adaptive distributed video coding algorithm, which is adaptive to time-varying available bitrate and packet loss ratio. The proposed method controls the quantization parameter according to channel condition of especially error-prone mobile channel. Simulation shows that the proposed algorithm outperforms the conventional rate-control-only algorithm.

This paper studies the performance of a coded convolutional spreading CDMA system with cyclic prefix (CS-CDMA/CP) combined with the zero correlation zone code generated from the M-sequence (M-ZCZ code) for downlink transmission over a multipath fast fading channel. In particular, we propose a new pilot-aided channel estimation scheme based on the shift property of the M-ZCZ code and show the robustness of the scheme against fast fading through comparison with the W-CDMA system empolying time-multiplexed pilot signals.

The aim of this research is to study the power energy cost reduction of the mobile telecom industry through the supervisor control and data acquisition (SCADA) system application during globalization and liberalization competition. Yet this management system can be proposed functions: operating monitors, the analysis on load characteristics and dropping the cost of management.

The IEEE 802.11 MAC standard for wireless ad hoc networks adopts Binary Exponential Back-off (BEB) mechanism to resolve bandwidth contention between stations. BEB mechanism controls the bandwidth allocation for each station by choosing a back-off value from one to CW according to the uniform random distribution, where CW is the contention window size. However, in asymmetric multi-hop networks, some stations are disadvantaged in opportunity of access to the shared channel and may suffer severe throughput degradation when the traffic load is large. Then, the network performance is degraded in terms of throughput and fairness. In this paper, we propose a new cross-layer scheme aiming to solve the per-flow unfairness problem and achieve good throughput performance in IEEE 802.11 multi-hop ad hoc networks. Our cross-layer scheme collects useful information from the physical, MAC and link layers of own station. This information is used to determine the optimal Contention Window (CW) size for per-station fairness. We also use this information to adjust CW size for each flow in the station in order to achieve per-flow fairness. Performance of our cross-layer scheme is examined on various asymmetric multi-hop network topologies by using Network Simulator (NS-2).

We describe a user scheduling scheme suitable for zero-forcing beamforming (ZFBF) downlink multiuser multiple-input multiple-output (MU-MIMO) orthogonal frequency-division multiplexing (OFDM) transmissions in time-division-duplex distributed antenna systems. This user scheduling scheme consists of inter-cell-interference mitigation scheduling by using fractional frequency reuse, proportional fair scheduling in the OFDM frequency domain, and high-capacity ZFBF-MU-MIMO scheduling by using zero-forcing with selection (ZFS). Simulation results demonstrate in a severe user-distribution condition that includes cell-edge users that the proposed user scheduling scheme achieves high average cell throughputs close to that provided by only ZFS and that it also achieves almost the same degree of user fairness as round-robin user scheduling.

The performance of the least-mean-square (LMS) beamformer is heavily dependent on the choice of the step-size, for it governs the convergence rate and steady-state excess mean squared error. To meet the conflicting requirement of low misadjustment, especially for the beamformer being modified in response to the multipath environmental changes, it needs to be controlled in a proper way. In this letter, we present an efficient adaptive step-size subarray LMS to achieve good performance. Simulation results are provided for illustrating the effectiveness of the proposed scheme.