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.

A transceiver employing hierarchical constellation encodes two hierarchies with different levels of protection and selectively decodes one or both of them, resulting in constellation inconsistency of encoding and decoding. Therefore, a conventional ordered successive interference cancellation (OSIC) receiver, which restores the signals as they are transmitted, can not be compatible with the constellation inconsistency. To mitigate this problem, an OSIC detector with the individual received bit rate per data stream is first designed. To further improve the error performance, the proposed detector is modified, for which distinct criteria are used for demodulation and cancellation. It is shown that the proposed detector achieves spectrally efficient detection while guaranteeing reliable communication.

Single event transient (SET) effects on original static cascade voltage switch logic (CVSL) exclusive-OR (EX-OR) circuits have been investigated using SPICE. SET simulation results have confirmed that the static CVSL EX-OR circuits have increased tolerance to SET. The static CVSL EX-OR circuit is more than 200 times harder than the conventional CMOS circuit.

Least-squares policy iteration is a useful reinforcement learning method in robotics due to its computational efficiency. However, it tends to be sensitive to outliers in observed rewards. In this paper, we propose an alternative method that employs the absolute loss for enhancing robustness and reliability. The proposed method is formulated as a linear programming problem which can be solved efficiently by standard optimization software, so the computational advantage is not sacrificed for gaining robustness and reliability. We demonstrate the usefulness of the proposed approach through a simulated robot-control task.

In this work, spatial information consisting of the position and orientation angle of an acoustic source is estimated by an artificial neural network (ANN). The estimated position of a speaker in an enclosed space is used to refine the estimated time delays for a delay-and-sum beamformer, thus enhancing the output signal. On the other hand, the orientation angle is used to restrict the lexicon used in the recognition phase, assuming that the speaker faces a particular direction while speaking. To compensate the effect of the transmission channel inside a short frame analysis window, a new cepstral mean normalization (CMN) method based on a Gaussian mixture model (GMM) is investigated and shows better performance than the conventional CMN for short utterances. The performance of the proposed method is evaluated through Japanese digit/command recognition experiments.

In this paper we introduce a new framework of audio processing, which is essential to achieve a trigger-free speech interface for home appliances. If the speech interface works continually in real environments, it must extract occasional voice commands and reject everything else. It is extremely important to reduce the number of false alarms because the number of irrelevant inputs is much larger than the number of voice commands even for heavy users of appliances. The framework, called Intentional Voice Command Detection, is based on voice activity detection, but enhanced by various speech/audio processing techniques such as emotion recognition. The effectiveness of the proposed framework is evaluated using a newly-collected large-scale corpus. The advantages of combining various features were tested and confirmed, and the simple LDA-based classifier demonstrated acceptable performance. The effectiveness of various methods of user adaptation is also discussed.

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.

In this paper, a novel approach to speech reinforcement in a low-bit-rate speech coder under ambient noise environments is proposed. The excitation vector of ambient noise is efficiently obtained at the near-end and then combined with the excitation signal of the far-end for a suitable reinforcement gain within the G.729 CS-ACELP Annex. B framework. For this reason, this can be clearly different from previous approaches in that the present approach does not require an additional arithmetic step such as the discrete Fourier transform (DFT). Experimental results indicate that the proposed method shows better performance than or at least comparable to conventional approaches with a lower computational burden.

This paper presents a comprehensive explanation of the formation of the electric arc between opening contacts in a current carrying electric circuit. As the contacts begin to open a molten metal bridge forms between them. The rupture of this bridge and the initial formation of the electric arc are studied in both atmospheric air and vacuum using experiments to determine the direction of metal transfer between the contacts as a function of time after the rupture of the molten metal bridge. High speed streak photography is also used to show the rupture of the molten metal bridge and the initial formation of the electric arc. Analysis of these data show that a very high-pressure, high-temperature metal vapor zone exists between the contacts after the rupture of the molten metal bridge. Under this condition a pseudo-arc forms where current is carried by metal ions and an anomalous, high net transfer of metal to the cathodic contact occurs. The pressure in this region decreases rapidly and there is a transition to the usual electric arc, which still operates in the metal vapor. In this arc the current is now mostly carried by electrons. The data shows that there is still a net transfer of metal to the cathode, but now its volume is a function of the arcing time.

This paper presents a commercial shot classification scheme combining well-designed visual and textual features to automatically detect TV commercials. To identify the inherent difference between commercials and general programs, a special mid-level textual descriptor is proposed, aiming to capture the spatio-temporal properties of the video texts typical of commercials. In addition, we introduce an ensemble-learning based combination method, named Co-AdaBoost, to interactively exploit the intrinsic relations between the visual and textual features employed.

A novel method is proposed that can estimate the tag population in Radio Frequency Identification (RFID) systems by using a Hadamard code for the tag response. We formulate the maximum likelihood estimator for the tag population using the number of observed footprints. The lookup table of the estimation algorithm has low complexity. Simulation results show that the proposed estimator performs considerably better than the conventional schemes.

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.

Statistical speech recognition using continuous-density hidden Markov models (CDHMMs) has yielded many practical applications. However, in general, mismatches between the training data and input data significantly degrade recognition accuracy. Various acoustic model adaptation techniques using a few input utterances have been employed to overcome this problem. In this article, we survey these adaptation techniques, including maximum a posteriori (MAP) estimation, maximum likelihood linear regression (MLLR), and eigenvoice. We also present a schematic view called the adaptation pyramid to illustrate how these methods relate to each other.

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.

The importance of respiratory monitoring systems during sleep have increased due to early diagnosis of sleep apnea syndrome (SAS) in the home. This paper presents a simple respiratory monitoring system suitable for home use having 3D ranging of targets. The range resolution and azimuth resolution are obtained by a stepped frequency transmitting signal and MIMO arrays with preferred pair M-sequence codes doubly modulating in transmission and reception, respectively. Due to the use of these codes, Gold sequence codes corresponding to all the antenna combinations are equivalently modulated in receiver. The signal to interchannel interference ratio of the reconstructed image is evaluated by numerical simulations. The results of experiments on a developed prototype 3D-MIMO radar system show that this system can extract only the motion of respiration of a human subject 2 m apart from a metallic rotatable reflector. Moreover, it is found that this system can successfully measure the respiration information of sleeping human subjects for 96.6 percent of the whole measurement time except for instances of large posture change.

In recent years, the usage of IPTV (Internet Protocol Television) has been increased. The reason is a technological convergence of broadcasting and telecommunication delivering interactive applications and multimedia content through high speed Internet connections. The main critical point of IPTV security requirements is subscriber authentication. That is, IPTV service should have the capability to identify the subscribers to prohibit illegal access. Currently, IPTV service does not provide a sound authentication mechanism to verify the identity of its wireless users (or devices). This paper focuses on a lightweight authentication and key establishment protocol based on the use of hash functions. The proposed approach provides effective authentication for a mobile user with a RFID tag whose authentication information is communicated back and forth with the IPTV authentication server via IPTV set-top box (STB). That is, the proposed protocol generates user's authentication information that is a bundle of two public keys derived from hashing user's private keys and RFID tag's session identifier, and adds 1 bit to this bundled information for subscriber's information confidentiality before passing it to the authentication server.

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).

This paper presents an adaptive modulation and power allocation method for uplink multiuser multiple-input multiple-output systems under the assumption that there is perfect channel information at the receiver but not at the transmitter. The receiver jointly optimizes the power level and modulation order for all users under constraints of transmit power and error requirements, and returns these information to each user via a low-rate feedback channel. Power and modulation optimization maximizes the total throughput under a tolerable bit error ratio for each user. The ellipsoid method is used to design efficient algorithms for optimal power and modulation level.

In this paper, the robustness of the posterior-based confidence measures is improved by utilizing entropy information, which is calculated for speech-unit-level posteriors using only the best recognition result, without requiring a larger computational load than conventional methods. Using different normalization methods, two posterior-based entropy confidence measures are proposed. Practical details are discussed for two typical levels of hidden Markov model (HMM)-based posterior confidence measures, and both levels are compared in terms of their performances. Experiments show that the entropy information results in significant improvements in the posterior-based confidence measures. The absolute improvements of the out-of-vocabulary (OOV) rejection rate are more than 20% for both the phoneme-level confidence measures and the state-level confidence measures for our embedded test sets, without a significant decline of the in-vocabulary accuracy.

In this paper, we propose a new speaker-class modeling and its adaptation method for the LVCSR system and evaluate the method on the Corpus of Spontaneous Japanese (CSJ). In this method, closer speakers are selected from training speakers and the acoustic models are trained by using their utterances for each evaluation speaker. One of the major issues of the speaker-class model is determining the selection range of speakers. In order to solve the problem, several models which have a variety of speaker range are prepared for each evaluation speaker in advance, and the most proper model is selected on a likelihood basis in the recognition step. In addition, we improved the recognition performance using unsupervised speaker adaptation with the speaker-class models. In the recognition experiments, a significant improvement could be obtained by using the proposed speaker adaptation based on speaker-class models compared with the conventional adaptation method.