RFID (Radio Frequency Identification) technology is expected to be used as a localization tool. By the localization of RFID tags, a mobile robot equipped with an RFID reader can recognize the surrounding environment. In this paper, we propose a novel effective scheme called the communication range recognition (CRR) scheme for localizing RFID tags. In this scheme, an RFID reader determines the boundaries of the communication range when it is appropriately positioned by the robot. We evaluate the estimated position accuracy through numerous experiments. We show that the moving distance of the RFID reader in the proposed scheme is lower than that in conventional schemes.

Cognitive radio has emerged as an efficient approach to reusing the licensed spectrums. How to appropriately set parameters of secondary user (SU) plays a rather important role in constructing cognitive radio networks. In this letter, we have analyzed the theoretical value of SUs' density, which provides a standard for controlling the number of SUs around one primary receiver, in order to guarantee that primary communication links do not experience excessive interference. The simulation result of secondary density well matches with the theoretical result derived from our analysis. Additionally, the achievable rate of secondary user under density control is also analyzed and simulated.

This paper presents a novel method of enhancing esophageal speech using statistical voice conversion. Esophageal speech is one of the alternative speaking methods for laryngectomees. Although it doesn't require any external devices, generated voices usually sound unnatural compared with normal speech. To improve the intelligibility and naturalness of esophageal speech, we propose a voice conversion method from esophageal speech into normal speech. A spectral parameter and excitation parameters of target normal speech are separately estimated from a spectral parameter of the esophageal speech based on Gaussian mixture models. The experimental results demonstrate that the proposed method yields significant improvements in intelligibility and naturalness. We also apply one-to-many eigenvoice conversion to esophageal speech enhancement to make it possible to flexibly control the voice quality of enhanced speech.

We have developed a macro model, which allows us to describe precise LDMOS DC/AC characteristics. Characterization of anomalous gate input capacitance is the key issue in the LDMOS model development. We have newly employed a T-type distributed RC scheme for gate overlapped LDMOS drift region. The bias dependent resistance and capacitance are modeled independently in Verilog-A as R-model and PMOS-capacitance. The dividing factor of the distributed R is introduced to reflect the shield effect of the gate overlap capacitance. Comparison between the new model and measurement results has proven that the developed macro model reproduces accurately not only the gate input capacitance, but also DC characteristics.

This letter proposes extended single parity check product codes and presents their empirical performances on a Gaussian channel by belief propagation (BP) decoding algorithm. The simulation results show that the codes can achieve close-to-capacity performance in high coding rate. The code of length 9603 and of rate 0.96 is only 0.77 dB away from the Shannon limit for a BER of 10-5.

In this paper, we present a novel color independent components based SIFT descriptor (termed CIC-SIFT) for object/scene classification. We first learn an efficient color transformation matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-based color transformation can enhance contrast between the objects and the background in an image. Then we compute CIC-SIFT descriptors over all three transformed color independent components. Since the ICA-based color transformation can boost the objects and suppress the background, the proposed CIC-SIFT can extract more effective and discriminative local features for object/scene classification. The comparison is performed among seven SIFT descriptors, and the experimental classification results show that our proposed CIC-SIFT is superior to other conventional SIFT descriptors.

We have developed a one-to-many eigenvoice conversion (EVC) system that allows us to convert a single source speaker's voice into an arbitrary target speaker's voice using an eigenvoice Gaussian mixture model (EV-GMM). This system is capable of effectively building a conversion model for an arbitrary target speaker by adapting the EV-GMM using only a small amount of speech data uttered by the target speaker in a text-independent manner. However, the conversion performance is still insufficient for the following reasons: 1) the excitation signal is not precisely modeled; 2) the oversmoothing of the converted spectrum causes muffled sounds in converted speech; and 3) the conversion model is affected by redundant acoustic variations among a lot of pre-stored target speakers used for building the EV-GMM. In order to address these problems, we apply the following promising techniques to one-to-many EVC: 1) mixed excitation; 2) a conversion algorithm considering global variance; and 3) adaptive training of the EV-GMM. The experimental results demonstrate that the conversion performance of one-to-many EVC is significantly improved by integrating all of these techniques into the one-to-many EVC system.

This paper proposes methods for accelerating DPA by using the CPU and the GPU in a parallel manner. The overhead of naive DPA evaluation software increases excessively as the number of points in a trace or the number of traces is enlarged due to the rapid increase of file I/O overhead. This paper presents some techniques, with respect to DPA-arithmetic and file handling, which can make the overhead of DPA software become not extreme but gradual as the increase of the amount of trace data to be processed. Through generic experiments, we show that the software, equipped with the proposed methods, using both CPU and GPU can shorten the time for evaluating the DPA resistance of devices by almost half.

We present a session initiation protocol (SIP) network design for a voice-over-IP network to prevent congestion caused by people calling friends and family after a disaster. The design increases the capacity of SIP servers in a network by using all of the SIP servers equally. It takes advantage of the fact that equipment for voice data packets is different from equipment for signaling packets in SIP networks. Furthermore, the design achieves simple routing on the basis of telephone numbers. We evaluated the performance of our design in preventing congestion through simulation. We showed that the proposed design has roughly 20 times more capacity, which is 57 times the normal load, than the conventional design if a disaster were to occur in Niigata Prefecture struck by the Chuetsu earthquake in 2004.

Recognition errors of proper nouns and foreign words significantly decrease the performance of ASR-based speech applications such as voice dialing systems, speech summarization, spoken document retrieval, and spoken query-based information retrieval (IR). The reason is that proper nouns and words that come from other languages are usually the most important key words. The loss of such words due to misrecognition in turn leads to a loss of significant information from the speech source. This paper focuses on how to improve the performance of Indonesian ASR by alleviating the problem of pronunciation variation of proper nouns and foreign words (English words in particular). To improve the proper noun recognition accuracy, proper-noun specific acoustic models are created by supervised adaptation using maximum likelihood linear regression (MLLR). To improve English word recognition, the pronunciation of English words contained in the lexicon is fixed by using rule-based English-to-Indonesian phoneme mapping. The effectiveness of the proposed method was confirmed through spoken query based Indonesian IR. We used Inference Network-based (IN-based) IR and compared its results with those of the classical Vector Space Model (VSM) IR, both using a tf-idf weighting schema. Experimental results show that IN-based IR outperforms VSM IR.

This paper proposes a self-taught classifier of gateways for hybrid SLAM. Gateways are detected and recognized by the self-taught classifier, which is a SVM classifier and self-taught in that its training samples are produced and labeled without user's intervention. Since the detection of gateways at the topological boundaries of an acquired metric map reduces computational complexity in partitioning the metric map into sub-maps as compared with previous hybrid SLAM approaches using spectral clustering methods, from O(2n) to O(n), where n is the number of sub-maps. This makes possible real time hybrid SLAM even for large-scale metric maps. We have confirmed that the self-taught classifier provides satisfactory consistency and computationally efficiency in hybrid SLAM through different experiments.

This paper proposes cooperative coding using cyclic delay diversity (CDD) for OFDM systems. The cooperative diversity is combined with channel coding while CDD is applied to the cooperative transmission of the multiple relays to improve the beneficial effects of the cooperating relays. Analyses of frame error probability (FEP) and the average channel power of the proposed scheme are shown. Simulation results show the frame error rate (FER) of the proposed scheme. The proposed scheme provides not only a simple code design and low system complexity compared to conventional space-time processing, but better FER and diversity gain compared to direct transmission and conventional cooperative coding without CDD.

This paper investigates the effect of several frequency modulation profiles on conducted-noise reduction in dc-dc converters with programmed switching controller. The converter is operated in variable frequency modulation regime. Twelve switching frequency modulation profiles have been studied. Some of the modulation data are prepared using MATLAB software, and others are generated online. Moreover, all the frequency profiles have been designed and implemented using FPGA and experimentally investigated. The experimental results show that the conducted-noise spreading depends on both the modulation sequence profile and the statistical characteristics of the sequence. A substantial part of the manufacturing cost of power converters for telecommunication applications involves designing filters to comply with the EMI limits. Considering this investigation significantly reduces the filter size.

This letter presents a theoretical analysis of the Parallel Iterative Matching (PIM)'s dynamics with multiple iterations used in an input-buffered packet switch. In our approach, by carefully categorizing all unmatched patterns into several representative patterns after each iteration, probabilities of accumulated matched pairs in a recursive manner are successfully obtained. Numerical evaluations of the analytical formulas are performed.

This paper presents an asynchronous FPGA that combines 4-phase dual-rail encoding and LEDR (Level-Encoded Dual-Rail) encoding. 4-phase dual-rail encoding is employed to achieve small area and low power for function units, while LEDR encoding is employed to achieve high throughput and low power for the data transfer using programmable interconnection resources. Area-efficient protocol converters and their control circuits are also proposed in transistor-level implementation. The proposed FPGA is designed using the e-Shuttle 65nm CMOS process. Compared to the 4-phase-dual-rail-based FPGA, the throughput is increased by 69% with almost the same transistor count. Compared to the LEDR-based FPGA, the transistor count is reduced by 47% with almost the same throughput. In terms of power consumption, the proposed FPGA achieves the lowest power compared to the 4-phase-dual-rail-based and the LEDR-based FPGAs. Compared to the synchronous FPGA, the proposed FPGA has lower power consumption when the workload is below 35%.

We report on the first terahertz emission from a novel dual grating gate plasmon-resonant emitter fabricated with InAlAs/InGaAs/InP material systems. The introduction of InP based heterostructure material systems, instead of the GaAs based ones, in order to improve the quality factor, has successfully enhanced the THz emission intensity and realized the spectral narrowing at room temperature.

IEEE 802.15.4a standard enables location-aided routing or topology control in ZigBee networks, since it uses time-of-arrival (TOA)-based ranging technique. However, TOA based techniques may yield location error due to the non-line-of-sight (NLOS) effects, and hence degrade the network performance. In this letter, we demonstrate the impact of NLOS on the localization performance and propose a location error detection and compensation algorithm for IEEE 802.15.4a networks. The proposed algorithm detects NLOS by using the min-max algorithm and compensates the location error by using the Kalman filter. Experimental results show that the proposed algorithm significantly reduces the localization errors in indoor environments.

This paper proposes a high-speed architecture to realize two-variable numeric functions. It represents the given function as an edge-valued multiple-valued decision diagram (EVMDD), and shows a systematic design method based on the EVMDD. To achieve a design, we characterize a numeric function f by the values of l and p for which f is an l-restricted Mp-monotone increasing function. Here, l is a measure of subfunctions of f and p is a measure of the rate at which f increases with an increase in the dependent variable. For the special case of an EVMDD, the EVBDD, we show an upper bound on the number of nodes needed to realize an l-restricted Mp-monotone increasing function. Experimental results show that all of the two-variable numeric functions considered in this paper can be converted into an l-restricted Mp-monotone increasing function with p=1 or 3. Thus, they can be compactly realized by EVBDDs. Since EVMDDs have shorter paths and smaller memory size than EVBDDs, EVMDDs can produce fast and compact NFGs.

Although the probabilistic model checking tool called PRISM has been applied in many communication systems, such as wireless local area network, Bluetooth, and ZigBee, the technique is not used in a controller area network (CAN). In this paper, we use PRISM to model the mechanism of priority messages for CAN because the mechanism has allowed CAN to become the leader in serial communication for automobile and industry control. Through modeling CAN, it is easy to analyze the characteristic of CAN for further improving the security and efficiency of automobiles. The Markov chain model helps us to model the behaviour of priority messages.

This paper first reviews the trends of VLSI design, focusing on the power dissipation and programmability. Then, we show the advantage of Quarternary Decision Diagrams (QDDs) in representing and evaluating logic functions. That is, we show how QDDs are used to implement QDD machines, which yield high-speed implementations. We compare QDD machines with binary decision diagram (BDD) machines, and show a speed improvement of 1.28-2.02 times when QDDs are chosen. We consider 1-and 2-address BDD machines, and 3- and 4-address QDD machines, and we show a method to minimize the number of instructions.