This Letter proposes an optimal gain filter for the perceptual acoustic echo suppressor. We designed an optimally-modified log-spectral amplitude estimation algorithm for the gain filter in order to achieve robust suppression of echo and noise. A new parameter including information about interferences (echo and noise) of single-talk duration is statistically analyzed, and then the speech absence probability and the a posteriori SNR are judiciously estimated to determine the optimal solution. The experiments show that the proposed gain filter attains a significantly improved reduction of echo and noise with less speech distortion.

To mitigate the asynchronous ICI (Inter-Cell Interference), SCM (Spatial Covariance Matrix) of the asynchronous ICI plus background noise should be accurately estimated for MIMO-OFDMA (Multiple-input Multiple-output-Orthogonal Frequency Division Multiple Access) system. Generally, it is assumed that the SCM of the asynchronous ICI plus background noise is estimated by using training symbols. However, it is difficult to measure the interference statistics for a long time and considering that training symbols are not appropriate for OFDMA system such as LTE (3GPP Long Term Evolution). Therefore, noise reduction method is required to improve the estimation accuracy. Although the conventional time-domain low-pass type weighting method can be effective for noise reduction, it causes significant estimation error due to the spectral leakage in practical OFDM system. Therefore, we propose a time-domain sinc type weighing method which can not only reduce noise effectively minimizing estimation error caused by the spectral leakage but also can be implemented using frequency-domain weighted moving average filter easily. We also consider the iterative CFR (Channel Frequency Response) and SCM estimation method which can effectively reduce the estimation error of both CFR and SCM, and improve the performance for LTE system. By using computer simulation, we show that the proposed method can provide up to 2.5 dB SIR (Signal to Interference Ratio) gain compared with the conventional method, and verify that the proposed method is attractive and suitable for implementation with stable operation.

The key issue in cognitive radio is to design a reliable spectrum sensing method that is able to detect the signal in the target channel as well as to recognize its type. In this paper, focusing on classifying different orthogonal frequency-division multiplexing (OFDM) signals, we propose a two-step detection and identification approach based on the analysis of the cyclic autocorrelation function. The key parameters to separate different OFDM signals are the subcarrier spacing and symbol duration. A symmetric peak detection method is adopted in the first step, while a pulse detection method is used to determine the symbol duration. Simulations validate the proposed method.

Two adaptive energy detectors are proposed for cognitive radio systems to detect the primary users. Unlike the conventional energy detector (CED) where a decision is made after receiving all samples, our detectors make a decision with the sequential arrival of samples. Hence, the sample size of the proposed detectors is adaptive. Simulation results show that for a desired performance, the average sample size of the proposed detectors is much less than that of the CED. Therefore, they are more agile than the CED.

Microarray technology has been applied to various biological and medical research fields. A preliminary step to extract any information from a microarray data set is to identify differentially expressed genes between microarray data. The identification of the differentially expressed genes and their commonly associated GO terms allows us to find stimulation-dependent or disease-related genes and biological events, etc. However, the identification of these deregulated GO terms by general approaches including gene set enrichment analysis (GSEA) does not necessarily provide us with overrepresented GO terms in specific data among a microarray data set (i.e., data-specific GO terms). In this paper, we propose a statistical method to correctly identify the data-specific GO terms, and estimate its availability by simulation using an actual microarray data set.

In spectrum-sharing systems where the secondary user (SU) opportunistically accesses the primary user (PU)'s licensed channel, the SU should satisfy both the transmit power constraint of the SU transmitter and the received power constraint at the PU receiver. This letter studies the ergodic capacity of spectrum-sharing systems in fading channels. The ergodic capacity expression along with the optimal power allocation scheme is derived considering both the average transmit and received power constraints. The capacity function in terms of the two power constraints is found to be divided into transmit power limited region, received power limited region and dual limited region. Numerical results in Rayleigh fading channels are presented to verify our analysis.

In the OTS/CafeOBJ method, software specifications are described in CafeOBJ executable formal specification language, and verification is done by giving scripts to the CafeOBJ system. The script is called a proof score. In this study, we propose a test case generator from an OTS/CafeOBJ specification together with a proof score. Our test case generator gives test cases by analyzing the proof score. The test cases are used to test whether an implementation satisfies the specification and the property verified by the proof score. Since a proof score involves important information for verifying a property, the generated test cases are also expected to be suitable to test the property.

Biologically-inspired approaches are one of the most promising approaches to realize highly-adaptive distributed systems. Biological systems inherently have self-* properties, such as self-stabilization, self-adaptation, self-configuration, self-optimization and self-healing. Thus, the application of biological systems into distributed systems has attracted a lot of attention recently. In this paper, we present one successful result of bio-inspired approach: we propose distributed algorithms for resource replication inspired by the single species population model. Resource replication is a crucial technique for improving system performance of distributed applications with shared resources. In systems using resource replication, generally, a larger number of replicas lead to shorter time to reach a replica of a requested resource but consume more storage of the hosts. Therefore, it is indispensable to adjust the number of replicas appropriately for the resource sharing application. This paper considers the problem for controlling the densities of replicas adaptively in dynamic networks and proposes two bio-inspired distributed algorithms for the problem. In the first algorithm, we try to control the replica density for a single resource. However, in a system where multiple resources coexist, the algorithm needs high network cost and the exact knowledge at each node about all resources in the network. In the second algorithm, the densities of all resources are controlled by the single algorithm without high network cost and the exact knowledge about all resources. This paper shows by simulations that these two algorithms realize self-adaptation of the replica density in dynamic networks.

In this paper, we propose a three-phase text error correction model consisting of a word spacing error correction phase, a syllable-based spelling error correction phase, and a word-based spelling error correction phase. In order to reduce the text error correction complexity, the proposed model corrects text errors step by step. With the aim of correcting word spacing errors, spelling errors, and mixed errors in SMS messages, the proposed model tries to separately manage the word spacing error correction phase and the spelling error correction phase. For the purpose of utilizing both the syllable-based approach covering various errors and the word-based approach correcting some specific errors accurately, the proposed model subdivides the spelling error correction phase into the syllable-based phase and the word-based phase. Experimental results show that the proposed model can improve the performance by solving the text error correction problem based on the divide-and-conquer strategy.

We have proposed a terahertz (THz) emitter utilizing two-dimensional plasmons (2DPs) in a super-grating dual-gate (SGG) high electron mobility transistor (HEMT). The plasmon under each grating gate has a unique feature that its resonant frequency is determined by the plasma-wave velocity over the gate length. Since the drain bias voltage causes a linear potential slope from the source to drain area, the sheet electron densities in periodically distributed 2DP cavities are dispersed. As a result, all the resonant frequencies are dispersed and undesirable spectral broadening occurs. A SGG structure can compensate for the sheet electron density distribution by modulating the grating dimension. The finite difference time domain simulation confirms its spectral narrowing effect. Within a wide detuning range for the gate and drain bias voltages giving a frequency shifting of 0.5 THz from an optimum condition, the SGG structure can preserve the spectral narrowing effect.

How to reduce noise with less speech distortion is a challenging issue for speech enhancement. We propose a novel approach for reducing noise with the cost of less speech distortion. A noise signal can generally be considered to consist of two components, a "white-like" component with a uniform energy distribution and a "color" component with a concentrated energy distribution in some frequency bands. An approach based on noise eigenspace projections is proposed to pack the color component into a subspace, named "noise subspace". This subspace is then removed from the eigenspace to reduce the color component. For the white-like component, a conventional enhancement algorithm is adopted as a complementary processor. We tested our algorithm on a speech enhancement task using speech data from the Texas Instruments and Massachusetts Institute of Technology (TIMIT) dataset and noise data from NOISEX-92. The experimental results show that the proposed algorithm efficiently reduces noise with little speech distortion. Objective and subjective evaluations confirmed that the proposed algorithm outperformed conventional enhancement algorithms.

This paper compares the expressive power of five language-based access control models. We show that the expressive powers are incomparable between any pair of history-based access control, regular stack inspection and shallow history automata. Based on these results, we introduce an extension of HBAC, of which expressive power exceeds that of regular stack inspection.

This paper proposes spread-spectrum clock modulation algorithms for EMI reduction in digitally-controlled DC-DC converters. In switching regulators using PWM, switching noise and harmonic noise concentrated in a narrow spectrum around the switching frequency can cause severe EMI. Spread-spectrum clock modulation can be used to minimize EMI. In conventional switching regulators using analog control it is very difficult to realize complex spread-spectrum clocking, however this paper shows that it is relatively easy to implement spread-spectrum EMI-reduction using digital control. The proposed algorithm was verified using a power converter simulator (SCAT).

In this paper, a novel model of Gaussian pulse propagation in optical fiber is proposed to comprehensively analyze the impact of Group Velocity Dispersion (GVD) on the performance of two-dimensional wavelength hopping/time spreading optical code division multiple access (2-D WH/TS OCDMA) systems. In addition, many noise and interferences, including multiple access interference (MAI), optical beating interference (OBI), and receiver's noise are included in the analysis. Besides, we propose to use the heterodyne detection receiver so that the receiver's sensitivity can be improved. Analytical results show that, under the impact of GVD, the number of supportable users is extremely decreased and the maximum transmission length (i.e. the length at which BER 10-9 can be maintained) is remarkably shortened in the case of normal single mode fiber (ITU-T G.652) is used. The main factor that limits the system performance is time skewing. In addition, we show how the impact of GVD is relieved by dispersion-shifted fiber (ITU-T G.653). For example, a system with 321 Gbit/s users can achieve a maximum transmission length of 111 km when transmitted optical power per bit is -5 dBm.

A prototype of a three-axis electro-optic (EO) probe is developed that has the linearity of approximately 0.5 dB in the specific absorption rate (SAR) range of 0.01 to 100 W/kg and the directivities are eight-shaped with cross-axis sensitivity isolation of greater than 30 dB. It is confirmed that electric fields and SAR distributions can be measured using a three-axis EO probe.

In this letter, we propose a new approach to SVM based speaker recognition, which utilizes a kind of novel phonotactic information as the feature for SVM modeling. Gaussian mixture models (GMMs) have been proven extremely successful for text-independent speaker recognition. The GMM universal background model (UBM) is a speaker-independent model, each component of which can be considered as modeling some underlying phonetic sound classes. We assume that the utterances from different speakers should get different average posterior probabilities on the same Gaussian component of the UBM, and the supervector composed of the average posterior probabilities on all components of the UBM for each utterance should be discriminative. We use these supervectors as the features for SVM based speaker recognition. Experiment results on a NIST SRE 2006 task show that the proposed approach demonstrates comparable performance with the commonly used systems. Fusion results are also presented.

In this paper we describe a new framework of feature combination in the cepstral domain for multi-input robust speech recognition. The general framework of working in the cepstral domain has various advantages over working in the time or hypothesis domain. It is stable, easy to maintain, and less expensive because it does not require precise calibration. It is also easy to configure in a complex speech recognition system. However, it is not straightforward to improve the recognition performance by increasing the number of inputs, and we introduce the concept of variance re-scaling to compensate the negative effect of averaging several input features. Finally, we propose to take another advantage of working in the cepstral domain. The speech can be modeled using hidden Markov models, and the model can be used as prior knowledge. This approach is formulated as a new algorithm, referred to as Hypothesis-Based Feature Combination. The effectiveness of various algorithms are evaluated using two sets of speech databases. We also refer to automatic optimization of some parameters in the proposed algorithms.

In this paper, we propose a distance-based howling canceller with high speech quality. We have developed a distance-based howling canceller that uses only distance information by noticing the property that howling occurs according to the distance between a loudspeaker and a microphone. This method estimates the distance by transmitting a pilot signal from the loudspeaker to the microphone. Multiple frequency candidates for each howling are computed from the estimated distance and eliminated by cascading notch filters that have nulls at them. However degradation of speech quality occurs at the howling canceller output. The first cause is a shot noise occurrence at the beginning and end of the pilot signal transmission due to the discontinuous change of the amplitude. We thus develop a new pilot signal that is robust against ambient noises. We can then reduce the shot noise effect by taking the amplitude small. The second one is a speech degradation caused from overlapped stopbands of the notch filters. We thus derive a condition on the bandwidths so that stopbands do not overlap, and propose an adaptive bandwidth scheme which changes the bandwidth according to the distance.

The Transition Edge Sensor (TES)-Energy Dispersive Spectrometer (EDS) is an X-ray detector with high-energy resolution (12.8 eV). The TES can be mounted to a scanning electron microscope (SEM). The TES-EDS is based on a cryogen-free dilution refrigerator. The high-energy resolution enables analysis of the distribution of various elements in samples under low acceleration voltage (typically under 5 keV) by using K-lines of light elements and M lines of heavy elements. For example, the energy of the arsenic L line differs from the magnesium K line by 28 eV. When used to analyze the spore of the Pteris vittata L plant, the TES-EDS clearly reveals a different distribution of As and Mg in the micro region of the plant. The TES-EDS with SEM yields detailed information about the distribution of multi-elements in a sample.

For statistical language model training, target domain matched corpora are required. However, training corpora sometimes include both target domain matched and unmatched sentences. In such a case, training set selection is effective for both reducing model size and improving model performance. In this paper, training set selection method for statistical language model training is described. The method provides two advantages for training a language model. One is its capacity to improve the language model performance, and the other is its capacity to reduce computational loads for the language model. The method has four steps. 1) Sentence clustering is applied to all available corpora. 2) Language models are trained on each cluster. 3) Perplexity on the development set is calculated using the language models. 4) For the final language model training, we use the clusters whose language models yield low perplexities. The experimental results indicate that the language model trained on the data selected by our method gives lower perplexity on an open test set than a language model trained on all available corpora.