Jeu-Yih JENG Yi-Bing LIN Herman Chung-Hwa RAO
In GSM High Speed Circuit Switched Data (HSCSD), the data rate can be increased by using multiple time slots instead of single time slot. Multiple time-slot assignment results in high blocking rate. To accommodate more users, flexible resource allocation strategies have been proposed. Since GSM follows TDMA/FDMA, the channels (time slots) in a base station are segmented by frequency carriers. The base station must allocate the channels which belong to the same frequency carrier to individual requests. This Flexible Resource Allocation scheme for GSM (FRA-GSM) is contrastive to the scheme proposed in our previous studies where a base station may arbitrarily allocate idle channels in the base station to incoming requests. We define satisfaction indication SI as the measurement to compare the performance of these schemes. Experiment results indicate that FRA-GSM scheme has good performance when the user mobility is high, or when some cost factors are taken into account.
The separation of signals with temporal structure from mixed sources is a challenging problem in signal processing. For this problem, blind source extraction (BSE) is more suitable than blind source separation (BSS) because it has lower computation cost. Nowadays many BSE algorithms can be used to extract signals with temporal structure. However, some of them are not robust because they are too dependent on the estimation precision of time delay; some others need to choose parameters before extracting, which means that arbitrariness can't be avoided. In order to solve the above problems, we propose a robust source extraction algorithm whose performance doesn't rely on the choice of parameters. The algorithm is realized by maximizing the objective function that we develop based on the non-Gaussianity and the temporal structure of source signals. Furthermore, we analyze the stability of the algorithm. Simulation results show that the algorithm can extract the desired signal from large numbers of observed sensor signals and is very robust to error in the estimation of time delay.
The manifold-ranking algorithm has been successfully adopted in content-based image retrieval (CBIR) in recent years. However, while the global low-level features are widely utilized in current systems, region-based features have received little attention. In this paper, a novel attention-driven transductive framework based on a hierarchical graph representation is proposed for region-based image retrieval (RBIR). This approach can be characterized by two key properties: (1) Since the issue about region significance is the key problem in region-based retrieval, a visual attention model is chosen here to measure the regions' significance. (2) A hierarchical graph representation which combines region-level with image-level similarities is utilized for the manifold-ranking method. A novel propagation energy function is defined which takes both low-level visual features and regional significance into consideration. Experimental results demonstrate that the proposed approach shows the satisfactory retrieval performance compared to the global-based and the block-based manifold-ranking methods.
Ichirou OOTA Fumio UENO Takahiro INOUE HUANG Bing Lian
New AC-DC converters using switched-capacitor (SC) transformers are presented. The features of these circuits are as follows. (1) It does not contain any magnetic material. (2) The inrush current of the proposed converter is very small as compared with that of a condenser-input-type rectifier circuit. (3) It is realizable in a hybrid IC form. (4) It excels in size and weight when compared with reactor-type switching regulators of the same output power. As an example, an AC-DC converter using step-up SC transformers was built and tested to confirm the characteristics. The measured characteristics showed good agreement with the calculated ones.
Even correlation and odd correlation of sequences are two kinds of measures for their similarities. Both kinds of correlation have important applications in communication and radar. Compared with vast knowledge on sequences with good even correlation, relatively little is known on sequences with preferable odd correlation. In this paper, a generic construction of sequences with low odd correlation is proposed via interleaving technique. Notably, it can generate new sets of binary sequences with optimal odd correlation asymptotically meeting the Sarwate bound.
Color constancy is the ability to measure colors of objects independent of the light source color. Various methods have been proposed to handle this problem. Most of them depend on the statistical distributions of the pixel values. Recent studies show that incorporation image derivatives are more effective than the direct use of pixel values. Based on this idea, a novel edge-based color constancy algorithm using support vector regression (SVR) is proposed. Contrary to existing SVR color constancy algorithm, which is computed from the zero-order structure of images, our method is based on the higher-order structure of images. The experimental results show that our algorithm is more effective than the zero-order SVR color constancy methods.
Meng YANG Yuehu TAN Erbing LI Cong MA Yechao YOU
The unconditionally stable (US) Laguerre-FDTD method has recently attracted significant attention for its high efficiency and accuracy in modeling fine structures. One of the most attractive characteristics of this method is its marching-on-in-order solution scheme. This paper presents Hermite-Rodriguez functions as another type of orthogonal basis to implement a new 2-D US solution scheme.
Ping-Hung CHIANG Ding-Bing LIN Hsueh-Jyh LI
By applying the differential space-time block code (DSTBC) to wireless multicarrier transmission, Diggavi et al. were the first to propose the two-input-multiple-output (2IMO) differentially space-time-time block coded OFDM (TT-OFDM) system. In this paper, we propose three novel differentially transmit-diversity block coded OFDM (DTDBC-OFDM) systems, namely, the FT-, FF-, and TF-OFDM systems. For instance, the TF-OFDM stands for the differentially space-time-frequency block coded OFDM. Moreover, the noncoherent maximum-likelihood sequence detector (NSD), and its three special cases, namely, the noncoherent one-shot detector, the linearly predictive decision-feedback (DF) detector, and the linearly predictive Viterbi receiver are incorporated to the 2IMO DTDBC-OFDM systems. Furthermore, a simple closed-form BER expression for the systems utilizing the noncoherent one-shot detector in the time-varying multipath Rayleigh fading channels is given. Numerical results have revealed that 2IMO DTDBC-OFDM systems employing the noncoherent one-shot detector can obtain significant performance improvement. However, when few antennas are available, the implementation of the linearly predictive DF detector or the linearly predictive Viterbi receiver is necessary for achieving better performance.
Jinyan LU Quanzhen HUANG Shoubing LIU
For intelligent vision measurement, the geometric image feature extraction is an essential issue. Contour primitive of interest (CPI) means a regular-shaped contour feature lying on a target object, which is widely used for geometric calculation in vision measurement and servoing. To realize that the CPI extraction model can be flexibly applied to different novel objects, the one-shot learning based CPI extraction can be implemented with deep convolutional neural network, by using only one annotated support image to guide the CPI extraction process. In this paper, we propose a multi-stage contour primitives of interest extraction network (MS-CPieNet), which uses the multi-stage strategy to improve the discrimination ability of CPI and complex background. Second, the spatial non-local attention module is utilized to enhance the deep features, by globally fusing the image features with both short and long ranges. Moreover, the dense 4-direction classification is designed to obtain the normal direction of the contour, and the directions can be further used for the contour thinning post-process. The effectiveness of the proposed methods is validated by the experiments with the OCP and ROCM datasets. A 2-D measurement experiments are conducted to demonstrate the convenient application of the proposed MS-CPieNet.
Jiaxin WU Bing LI Li ZHAO Xinzhou XU
The task of Speech Emotion Detection (SED) aims at judging positive class and negetive class when the speaker expresses emotions. The SED performances are heavily dependent on the diversity and prominence of emotional features extracted from the speech. However, most of the existing related research focuses on investigating the effects of single feature source and hand-crafted features. Thus, we propose a SED approach using multi-source low-level information based recurrent branches. The fusion multi-source low-level information obtain variety and discriminative representations from speech emotion signals. In addition, focal-loss function benifit for imbalance classes, resulting in reducing the proportion of well-classified samples and increasing the weights for difficult samples on SED tasks. Experiments on IEMOCAP corpus demonstrate the effectiveness of the proposed method. Compared with the baselines, MSIR achieve the significant performance improvements in terms of Unweighted Average Recall and F1-score.
Jian LI Xiaobing LIANG Dan WEI
Write linear density limit is defined to analyze the magnetic recording process in computer hard disk drives at extremely high recording densities. The digital data with pseudo random sequences are recorded numerically in longitudinal media at different densities by a micromagnetic simulation model. A thin film write head and an ideal GMR read head are utilized in the record and read-back process, respectively. A novel method has been utilized to study the write linear density limit: the simulated read back voltage and the respected linear superposed pulses are compared to find the distortion in the record process. When a severe distortion shows up, the corresponding linear density is considered as the write linear density limit. By the novel method, the write linear density limit is analyzed with different parameters of the recording media.
In this letter, a novel power allocation scheme is proposed to improve the outage performance of an amplify-and-forward (AF) cooperative communication network with multiple potential relays under the assumption that only mean channel gains are available at the transmitters. In this scheme, power allocation is studied jointly with a relay selection algorithm which has low computational complexity. Simulation results demonstrate the performance improvement of the proposed scheme in terms of outage behavior.
Bing LIU Zhengchun ZHOU Udaya PARAMPALLI
Inspired by an idea due to Levenshtein, we apply the low correlation zone constraint in the analysis of the weighted mean square aperiodic correlation. Then we derive a lower bound on the measure for quasi-complementary sequence sets with low correlation zone (LCZ-QCSS). We discuss the conditions of tightness for the proposed bound. It turns out that the proposed bound is tighter than Liu-Guan-Ng-Chen bound for LCZ-QCSS. We also derive a lower bound for QCSS, which improves the Liu-Guan-Mow bound in general.
Hongbing LI Qunfei ZHANG Weike FENG
A novel matrix completion ESPRIT (MC-ESPRIT) algorithm is proposed to estimate the direction of arrival (DOA) with nonuniform linear arrays (NLA). By exploiting the matrix completion theory and the characters of Hankel matrix, the received data matrix of an NLA is tranformed into a two-fold Hankel matrix, which is a treatable for matrix completion. Then the decision variable can be reconstructed by the inexact augmented Lagrange multiplier method. This approach yields a completed data matrix, which is the same as the data matrix of uniform linear array (ULA). Thus the ESPRIT-type algorithm can be used to estimate the DOA. The MC-ESPRIT could resolve more signals than the MUSIC-type algorithms with NLA. Furthermore, the proposed algorithm does not need to divide the field of view of the array compared to the existing virtual interpolated array ESPRIT (VIA-ESPRIT). Simulation results confirm the effectiveness of MC-ESPRIT.
Jun HUANG Yanbing LIU Ruozhou YU Qiang DUAN Yoshiaki TANAKA
Cloud computing is an emerging computing paradigm that may have a significant impact on various aspects of the development of information infrastructure. In a Cloud environment, different types of network resources need to be virtualized as a series of service components by network virtualization, and these service components should be further composed into Cloud services provided to end users. Therefore Quality of Service (QoS) aware service composition plays a crucial role in Cloud service provisioning. This paper addresses the problem on how to compose a sequence of service components for QoS guaranteed service provisioning in a virtualization-based Cloud computing environment. The contributions of this paper include a system model for Cloud service provisioning and two approximation algorithms for QoS-aware service composition. Specifically, a system model is first developed to characterize service provisioning behavior in virtualization-based Cloud computing, then a novel approximation algorithm and a variant of a well-known QoS routing procedure are presented to resolve QoS-aware service composition. Theoretical analysis shows that these two algorithms have the same level of time complexity. Comparison study conducted based on simulation experiments indicates that the proposed novel algorithm achieves better performance in time efficiency and scalability without compromising quality of solution. The modeling technique and algorithms developed in this paper are general and effective; thus are applicable to practical Cloud computing systems.
Yi-Bing LIN Phone LIN Yu-Min CHUANG
Cellular Digital Packet Data (CDPD) provides wireless data communication services to mobile users by sharing unused RF channels with AMPS on a non-interfering basis. To prevent interference on the voice activities, CDPD makes forced hop to a channel stream when a voice request is about to use the RF channel occupied by the channel stream. The number of forced hops is affected by the voice channel selection policy. We propose analytic models to investigate the CDPD channel holding time for the the least-idle and random voice channel selection policies. Under various system parameters and voice channel selection policies, we provide guidelines to reduce the number of forced hops.
Rui LU De XU Xinbin YANG Bing LI
None of the existing color constancy algorithms can be considered universal. Furthermore, they use all the image pixels, although actually not all of the pixels are effective in illumination estimation. Consequently, how to select a proper color constancy algorithm from existing algorithms and how to select effective(or useful) pixels from an image are two most important problems for natural images color constancy. In this paper, a novel Color Constancy method using Effective Regions (CCER) is proposed, which consists of the proper algorithm selection and effective regions selection. For a given image, the most proper algorithm is selected according to its Weilbull distribution while its effective regions are chosen based on image similarity. The experiments show promising results compared with the state-of-the-art methods.
Kiyoshi MORIMOTO Nobuyasu SUZUKI Kazuhiko YAMANAKA Masaaki YURI Janet MILLIEZ Xinbing LIU
This report describes a crystallization method we developed for amorphous (a)-Si film by using 405-nm laser diodes (LDs). The proposed method has been used to fabricate bottom gate (BG) microcrystalline (µc)-Si TFTs for the first time. A µc-Si film with high crystallinity was produced and high-performance BG µc-Si TFTs with a field effect mobility of 3.6 cm2/Vs and a current on/off ratio exceeding 108 were successfully demonstrated. To determine the advantages of a 405-nm wavelength, a heat flow simulation was performed with full consideration of light interference effects. Among commercially available solid-state lasers and LDs with wavelengths having relatively high optical absorption coefficients for a-Si, three (405, 445, and 532 nm) were used in the simulation for comparison. Results demonstrated that wavelength is a crucial factor for the uniformity, efficiency, and process margin in a-Si crystallization for BG µc-Si TFTs. The 405-nm wavelength had the best simulation results. In addition, the maximum temperature profile on the gate electrode through the simulation well explained the actual crystallinity distributions of the µc-Si films.
Jing-Chao LI Yi-Bing LI Shouhei KIDERA Tetsuo KIRIMOTO
As a consequence of recent developments in communications, the parameters of communication signals, such as the modulation parameter values, are becoming unstable because of time-varying SNR under electromagnetic conditions. In general, it is difficult to classify target signals that have time-varying parameters using traditional signal recognition methods. To overcome this problem, this study proposes a novel recognition method that works well even for such time-dependent communication signals. This method is mainly composed of feature extraction and classification processes. In the feature extraction stage, we adopt Shannon entropy and index entropy to obtain the stable features of modulated signals. In the classification stage, the interval gray relation theory is employed as suitable for signals with time-varying parameter spaces. The advantage of our method is that it can deal with time-varying SNR situations, which cannot be handled by existing methods. The results from numerical simulation show that the proposed feature extraction algorithm, based on entropy characteristics in time-varying SNR situations,offers accurate clustering performance, and the classifier, based on interval gray relation theory, can achieve a recognition rate of up to 82.9%, even when the SNR varies from -10 to -6 dB.
Chen ZHANG ShiXiong XIA Bing LIU Lei ZHANG
Maximum margin clustering (MMC) is a newly proposed clustering method that extends the large-margin computation of support vector machine (SVM) to unsupervised learning. Traditionally, MMC is formulated as a nonconvex integer programming problem which makes it difficult to solve. Several methods rely on reformulating and relaxing the nonconvex optimization problem as semidefinite programming (SDP) or second-order cone program (SOCP), which are computationally expensive and have difficulty handling large-scale data sets. In linear cases, by making use of the constrained concave-convex procedure (CCCP) and cutting plane algorithm, several MMC methods take linear time to converge to a local optimum, but in nonlinear cases, time complexity is still high. Since extreme learning machine (ELM) has achieved similar generalization performance at much faster learning speed than traditional SVM and LS-SVM, we propose an extreme maximum margin clustering (EMMC) algorithm based on ELM. It can perform well in nonlinear cases. Moreover, the kernel parameters of EMMC need not be tuned by means of random feature mappings. Experimental results on several real-world data sets show that EMMC performs better than traditional MMC methods, especially in handling large-scale data sets.