A novel cooperative spectrum sensing scheme suitable for wireless cognitive radio system with imperfect reporting channels is proposed. In the proposed scheme, binary local decision bits are transmitted to the fusion center and combined to form a soft-valued decision statistics in the fusion center. To form a decision statistics, a majority-decision-aided weighting rule is proposed. The proposed scheme provides a reliable sensing capability even with poor reporting channels.

In this paper, a block-constrained trellis coded vector quantization (BC-TCVQ) algorithm is combined with an algebraic codebook to produce an algebraic trellis vector code (ATVC) to be used in ACELP coding. ATVC expands the set of allowed algebraic codebook pulse position, and the trellis branches are labeled with these subsets. The Viterbi algorithm is used to select the excitation codevector. A fast codebook search method using an efficient non-exhaustive search technique is also proposed to reduce the complexity of the ATVC search procedure while maintaining the quality of the reconstructed speech. The ATVC block code is used as the fixed codebook of AMR-NB (12.2 kbps), which reduces the computational complexity compared to the conventional algebraic codebook.

Image warping is usually used to perform real-time geometric transformation of the images captured by the CMOS image sensor of video camera. Several existing look-up table (LUT)-based algorithms achieve real-time performance; however, the size of the LUT is still large, and it has to be stored in off-chip memory. To reduce latency and bandwidth due to the use of off-chip memory, this paper proposes an improved LUT (ILUT) scheme that compresses the LUT to the point that it can be stored in on-chip memory. First, a one-step transformation is adopted instead of using several on-line calculation stages. The memory size of the LUT is then reduced by utilizing the similarity of neighbor coordinates, as well as the symmetric characteristic of video camera images. Moreover, an elaborate pipeline hardware structure, cooperating with a novel 25-point interpolation algorithm, is proposed to accelerate the system and reduce further memory usage. The proposed system is implemented by a field-programmable gate array (FPGA)-based platform. Two different examples show that the proposed ILUT achieves real-time performance with small memory usage and low system requirements.

A novel speech feature generation-based acoustic model training method for robust speaker-independent speech recognition is proposed. For decades, speaker adaptation methods have been widely used. All of these adaptation methods need adaptation data. However, our proposed method aims to create speaker-independent acoustic models that cover not only known but also unknown speakers. We achieve this by adopting inverse maximum likelihood linear regression (MLLR) transformation-based feature generation, and then we train our models using these features. First we obtain MLLR transformation matrices from a limited number of existing speakers. Then we extract the bases of the MLLR transformation matrices using PCA. The distribution of the weight parameters to express the transformation matrices for the existing speakers are estimated. Next, we construct pseudo-speaker transformations by sampling the weight parameters from the distribution, and apply the transformation to the normalized features of the existing speaker to generate the features of the pseudo-speakers. Finally, using these features, we train the acoustic models. Evaluation results show that the acoustic models trained using our proposed method are robust for unknown speakers.

In this letter we propose a practical sensing-based opportunistic spectrum sharing scheme for cognitive radio (CR) downlink MIMO systems. Multi-antennas are exploited at the secondary transmitter to opportunistically access the primary spectrum and effectively achieve a balance between secondary throughput maximization and mitigation of interference probably caused to primary radio link. We first introduce a brief secondary frame structure, in which a sensing phase is exploited to estimate the effective interference channel. According to the sensing result and taking the interference caused by the primary link into account, we propose an enhanced signal-to-leakage-and-noise ratio (SLNR)-based precoding scheme for the secondary transmitter. Compared to conventional schemes where perfect knowledge of the channels over which the CR transmitter interferes with the primary receiver (PR) is assumed, our proposed scheme shows its superiority and simulation results validate this.

In this letter, we focus on detecting a random primary user (PU) network for cognitive radio systems in a cooperative manner by using maximum likelihood (ML) detection. Different from traditional PU network models, the random PU network model in this letter considers the randomness in the PU network topology, and so is better suited for describing the infrastructure-less PU network such as an ad hoc network. Since the joint pdf required for the ML detection is hard to obtain in a closed form, we derive approximate ones from the Gaussian approximation. The performance of the proposed algorithm is comparable to the optimal one.

In this paper, a Spectrum-Aware Routing (SAR) protocol for cognitive radio ad hoc networks, (CRAHN), is proposed which is robust to primary user activity and node failures. The protocol allows nodes to collect spectrum information during a spectrum management interval followed by a transmission period. Cognitive users discover routes by joint channel and next hop selection (synchronization) in the transmission intervals. A restricted geographical routing approach is adopted to avoid performance degradation specially due to routing overhead. We also add spectrum mobility capabilities to routes in our proposed method to provide robustness to primary user activity. SAR protocol performance is investigated through simulations of different scenarios and is compared with the most similar work, CAODV protocol. The results indicate that SAR can achieve significant reduction in control overhead as well as improved throughput.

In this paper, we propose a novel voice activity detection (VAD) algorithm using global speech absence probability (GSAP) based on Teager energy (TE) for speech enhancement. The proposed method provides a better representation of GSAP, resulting in improved decision performance for speech and noise segments by the use of a TE operator which is employed to suppress the influence of noise signals. The performance of our approach is evaluated by objective tests under various environments, and it is found that the suggested method yields better results than conventional schemes.

In this letter, we adopt a new factor analysis of neighborhood-preserving embedding (NPE) for speaker verification. NPE aims at preserving the local neighborhood structure on the data and defines a low-dimensional speaker space called neighborhood-preserving embedding space. We compare the proposed method with the state-of-the-art total variability approach on the telephone-telephone core condition of the NIST 2008 Speaker Recognition Evaluation (SRE) dataset. The experimental results indicate that the proposed NPE method outperforms the total variability approach, providing up to 24% relative improvement.

This paper investigates the use of cooperative spectrum sensing (CSS) to detect primary user (PU) signals during spectrum sharing between the PU and the secondary user (SU). In particular, we employ a variant of CSS (which achieves space diversity), called weighted gain combining CSS (W-CSS), which has the potential to achieve increased diversity gain and enhance detection performance. In a typical W-CSS system, the SU needs to obtain the PU signal power information in order to set the proper weight value. However, as it is hard for the SU to ascertain whether the PU is present or absent, this is difficult to obtain. To address this problem, a PU signal power estimation algorithm is introduced. In addition, we also analyze the statistics of the estimator and derive the detection probability of the W-CSS when the PU signal power estimation algorithm is applied. The analysis and related simulation results reveal that the detection probability of the proposed W-CSS under time-variant Rayleigh fading asymptotically approaches the detection probability in an additive white Gaussian noise channel as the number of antennas is increased. This also follows results from our Monte Carlo simulations, showing that multiple antenna elements could suppress the effect of Rayleigh fading. In short, the accuracy of the estimation algorithm is affected by channel variation (especially in the case of fast Rayleigh fading). Hence, to address this problem, we employ multiple antenna elements with a square-law combining energy detector in the W-CSS.

We propose an active learning framework for speech recognition that reduces the amount of data required for acoustic modeling. This framework consists of two steps. We first obtain a phone-error distribution using an acoustic model estimated from transcribed speech data. Then, from a text corpus we select a sentence whose phone-occurrence distribution is close to the phone-error distribution and collect its speech data. We repeat this process to increase the amount of transcribed speech data. We applied this framework to speaker adaptation and acoustic model training. Our evaluation results showed that it significantly reduced the amount of transcribed data while maintaining the same level of accuracy.

Out-of-vocabulary (OOV) words create serious problems for automatic speech recognition (ASR) systems. Not only are they miss-recognized as in-vocabulary (IV) words with similar phonetics, but the error also causes further errors in nearby words. Language models (LMs) for most open vocabulary ASR systems treat OOV words as a single entity, ignoring the linguistic information. In this paper we present a class-based n-gram LM that is able to deal with OOV words by treating each of them individually without retraining all the LM parameters. OOV words are assigned to IV classes consisting of similar semantic meanings for IV words. The World Wide Web is used to acquire additional data for finding the relation between the OOV and IV words. An evaluation based on adjusted perplexity and word-error-rate was carried out on the Wall Street Journal corpus. The result suggests the preference of the use of multiple classes for OOV words, instead of one unknown class.

The present paper proposes a new method for constructing polyphase asymmetric zero-correlation zone (A-ZCZ) sequence sets. The proposed method can generate A-ZCZ sequence sets that cannot be obtained from methods proposed by other researchers and is a generalized version of our previously proposed method. An A-ZCZ sequence set can be regarded as a ZCZ sequence set. The newly obtained A-ZCZ sequence sets include quasi-optimal ZCZ sequence sets of which the zero-cross-correlation zone (ZCCZ) length between different sequence subsets is larger than the mathematical upper bound of conventional ZCZ sequence sets. A new method for extending the A-ZCZ sequence sets is also presented in the present paper.

A price-based spectrum investment and pricing scheme in cooperative cognitive radio networks is presented to use wireless resource more efficiently in technical and economic aspects. We analyze the impact of cooperative communications and the relationship between spectrum hole cost and leasing cost in the optimal decision of SAP.

Break arcs are rotated by the radial magnetic field formed by a magnet embedded in the pipe-shaped cathode. The arcs are generated in switching a DC42 V resistive circuit. The closed contact current varies from 5 A to 21 A. The curvature of the anode surface is varied to study the dependence of the arc length and the positions of the break arcs in the contact gap. The following results are obtained: (i) as current decreases, there is more difference in arc duration among different curvatures; (ii) as current decreases, the arc duration decreases with decrease of the radius of curvature; (iii) in each contact curvature, the anode spots region is located nearer to the center axis than the cathode spots region; (iv) the arc length just before arc extinction is independent of the curvature of the contacts.

In this letter, we propose a dissimilarity metric (DM) to measure the deviation of a cognitive radio from the network in terms of local sensing reports. Utilizing the probability mass function of the DM, we present a dissimilarity-based attacker detection algorithm to distinguish Byzantine attackers from honest users. The proposed algorithm is able to identify the attackers without a priori information of the attacking styles and is robust against both independent and dependent attacks.

At the stage of software debugging, the effective interaction between software debugging engineers and fault localization techniques can greatly improve fault localization performance. However, most fault localization approaches usually ignore this interaction and merely utilize the information from testing. Due to different goals of testing and fault localization, the lack of interaction may lead to the issue of information inadequacy, which can substantially degrade fault localization performance. In addition, human work is costly and error-prone. It is vital to study and simulate the pattern of debugging engineers as they apply their knowledge and experience to this interaction to promote fault localization effectiveness and reduce their workload. Thus this paper proposes an effective fault localization approach to simulate this interaction via feedback. Based on results obtained from fault localization techniques, this approach utilizes test data generation techniques to automatically produce feedback for interacting with these fault localization techniques, and then iterate this process to improve fault localization performance until a specific stopping condition is satisfied. Experiments on two standard benchmarks demonstrate the significant improvement of our approach over a promising fault localization technique, namely the spectrum-based fault localization technique.

Decision tree-based clustering and parameter estimation are essential steps in the training part of an HMM-based speech synthesis system. These two steps are usually performed based on the maximum likelihood (ML) criterion. However, one of the drawbacks of the ML criterion is that it is sensitive to outliers which usually result in quality degradation of the synthesized speech. In this letter, we propose an approach to detect and remove outliers for HMM-based speech synthesis. Experimental results show that the proposed approach can improve the synthetic speech, particularly when the available training speech database is insufficient.

The circuit switching device by the electrical contact needs the high reliability and long lifetime. The very important factor for the high reliability, long lifetime and electromagnetic noise of the electrical contact is to suppress the duration and electromagnetic noise of arc discharge. Usually, the suppression of arc duration method is applying the external magnetic field. But, this method was not able to suppress the metallic arc duration and increased the voltage fluctuation at arc duration. Therefore, the new method for suppressing the duration and noise for electrical contact is expected. In this paper, a new method for suppressing duration and EM noise of arc discharge by applying housing pressure is proposed. To investigate the availability of proposed method, the measurement and some considerations on arc duration, voltage-fluctuation and current noise up to GHz frequency band generated by breaking contact in the applied pressure relay housing are reported. Firstly, voltage waveform and duration of the arc are measured. The effects of the pressure in the relay housing on the duration of the metallic and gaseous phase arcs are discussed. Secondary, voltage fluctuation, the spectrogram of contact voltage and current noise up to GHz frequency band are discussed. In the results, the proposed method with applying pressure makes shorter both durations of metallic and gaseous phases. The shorter duration of metallic phase is an advantage of the proposed method beyond the applying external magnetic field. As the housing pressure is increase, the voltage fluctuation and current noise becomes smalls. The proposed method can suppress the voltage fluctuation as well as arc duration. Consequently, the proposed method is on of the good solution to suppress the duration and electromagnetic noise of the arc discharge from electrical contact and result of this study indicates the basic considerations necessary to ensure good lifetime and EMC designs for electrical contacts.

Silver electrical contacts are separated at constant speed and break arcs are generated in a DC100 V–450 V/10 A resistive circuit. The transverse magnetic field of a permanent magnet is applied to the break arcs. Dependences of the arc duration, arc dwell time and arc lengthening time on the strength of the magnetic field and supply voltage are investigated. The characteristics of the re-ignition of the break arc are also discussed. Following results are shown. The arc duration D is increased due to the increase of the arc lengthening time tm when the supply voltage E is increased for each magnetic flux density Bx, because the arc dwell time ts is almost constant. The arc duration D is increased due to the increase of both of the arc lengthening time tm and the arc dwell time ts when the magnetic flux density Bx is decreased. The arc lengthening time tended to become long when the re-ignition of the break arc is occurred. The lengthening time tends to become longer when the duration tm1 from the start of the arc lengthening to the start of the re-ignition is increased. Re-ignitions occurred frequently when the magnetic flux density of the transverse magnetic field is increased and the supply voltage is increased.