Time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) has higher spectral efficiency than the standard cyclic prefix OFDM (CP-OFDM) OFDM by replacing the random CP with the known training sequence (TS), which could be also used for synchronization and channel estimation. However, TDS-OFDM requires suffers from performance loss over fading channels due to the iterative interference cancellation has to be used to remove the mutual interferences between the TS and the useful data. To solve this problem, the novel TS based OFDM transmission scheme, referred to as the unified time-frequency OFDM (UTF-OFDM), is proposed in which the time-domain TS and the frequency-domain pilots are carefully designed to naturally avoid the interference from the TS to the data without any reconstruction. The proposed UTF-OFDM based flexible frame structure supports effective channel estimation and reliable channel equalization, while imposing a significantly lower complexity than the TDS-OFDM system at the cost of a slightly reduced spectral efficiency. Simulation results demonstrate that the proposed UTF-OFDM substantially outperforms the existing TDS-OFDM, in terms of the system's achievable bit error rate.

We report a trial of 100-GS/s optical quantization with 5-bit resolution using soliton self-frequency shift (SSFS) and spectral compression. We confirm that 100-GS/s 5-bit optical quantization is realized to quantize a 5.0-GHz sinusoid electrical signal in simulation. In order to experimentally verify the possibility of 100-GS/s 5-bit optical quantization, we execute 5-bit optical quantization by using two sampled signals with 10-ps intervals.

We have investigated on a random-texturing process for multi-crystalline Si solar cells by plasmaless dry etching, with chlorine trifluoride (ClF3) gas treatments. The reflectance of textured surfaces was reduced to below 20% at a wavelength of 600 nm. In this study, we tried to improve the electrical characteristics by modifying the fabrication process. The substrate surfaces were dry etched by chlorine trifluoride gas and subsequently etched with an acid solution to form appropriate textured structures. The improved electrical characteristics were demonstrated.

We propose a new analytical method for a composite dielectric grating embedded with conducting strips using scattering factors in the shadow theory. The scattering factor in the shadow theory plays an important role instead of the conventional diffraction amplitude. By specifying the relation between scattering factors and spectral-domain Green's functions, we derive expressions of the Green's functions directly for unit surface electric and magnetic current densities, and apply the spectral Galerkin method to our formulation. From some numerical results, we show that the expressions of the Green's functions are valid, and analyze scattering characteristics by composite gratings.

Conventional entropy measure is derived from full-band (range from 0 Hz to 4 kHz); however, it can not clearly describe the spectrum variability during voice-activity. Here we propose a novel concept of adaptive long-term sub-band entropy ( ALT-SubEnpy ) measure and combine it with a multi-thresholding scheme for voice activity detection. In detail, the ALT-SubEnpy measure developed with four part parameters of sub-entropy which uses different long-term spectral window length at each part. Consequently, the proposed ALT-SubEnpy -based algorithm recursively updates the four adaptive thresholds on each part. The proposed ALT-SubEnpy-based VAD method is shown to be an effective method while working at variable noise-level condition.

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.

A novel long-term sub-band entropy (LT-SubEntropy) measure, which uses improved long-term spectral analysis and sub-band entropy, is proposed for voice activity detection (VAD). Based on the measure, we can accurately exploit the inherent nature of the formant structure on speech spectrogram (the well-known as voiceprint). Results show that the proposed VAD is superior to existing standard VAD methods at low SNR levels, especially at variable-level noise.

With the wide usage of multispectral images, a fast efficient multidimensional clustering method becomes not only meaningful but also necessary. In general, to speed up the multidimensional images' analysis, a multidimensional feature vector should be transformed into a lower dimensional space. The Hilbert curve is a continuous one-to-one mapping from N-dimensional space to one-dimensional space, and can preserves neighborhood as much as possible. However, because the Hilbert curve is generated by a recurve division process, 'Boundary Effects' will happen, which means data that are close in N-dimensional space may not be close in one-dimensional Hilbert order. In this paper, a new efficient approach based on the space-filling curves is proposed for classifying multispectral satellite images. In order to remove 'Boundary Effects' of the Hilbert curve, multiple Hilbert curves, z curves, and the Pseudo-Hilbert curve are used jointly. The proposed method extracts category clusters from one-dimensional data without computing any distance in N-dimensional space. Furthermore, multispectral images can be analyzed hierarchically from coarse data distribution to fine data distribution in accordance with different application. The experimental results performed on LANDSAT data have demonstrated that the proposed method is efficient to manage the multispectral images and can be applied easily.

Traditional two-microphone noise reduction algorithms to deal with highly nonstationary directional noises generally use the direction of arrival or phase difference information. The performance of these algorithms deteriorate when diffuse noises coexist with nonstationary directional noises in realistic adverse environments. In this paper, we present a two-channel noise reduction algorithm using a spatial information-based speech estimator and a spatial-information-controlled soft-decision noise estimator to improve the noise reduction performance in realistic non-stationary noisy environments. A target presence probability estimator based on Bayes rules using both phase difference and magnitude squared coherence is proposed for soft-decision of noise estimation, so that they can share complementary advantages when both directional noises and diffuse noises are present. Performances of the proposed two-microphone noise reduction algorithm are evaluated by noise reduction, log-spectral distance (LSD) and word recognition rate (WRR) of a distant-talking ASR system in a real room's noisy environment. Experimental results show that the proposed algorithm achieves better noises suppression without further distorting the desired signal components over the comparative dual-channel noise reduction algorithms.

Nowadays, clustering is a popular tool for exploratory data analysis, with one technique being K-means clustering. Determining the appropriate number of clusters is a significant problem in K-means clustering because the results of the k-means technique depend on different numbers of clusters. Automatic determination of the appropriate number of clusters in a K-means clustering application is often needed in advance as an input parameter to the K-means algorithm. We propose a new method for automatic determination of the appropriate number of clusters using an extended co-occurrence matrix technique called a tri-co-occurrence matrix technique for multispectral imagery in the pre-clustering steps. The proposed method was tested using a dataset from a known number of clusters. The experimental results were compared with ground truth images and evaluated in terms of accuracy, with the numerical result of the tri-co-occurrence providing an accuracy of 84.86%. The results from the tests confirmed the effectiveness of the proposed method in finding the appropriate number of clusters and were compared with the original co-occurrence matrix technique and other algorithms.

Based on our previous work, this work presents a complete method for time-domain processing of frequency-domain data with evenly-spaced frequency indices, together with its application. The proposed method can be used to calculate the cross spectral and power spectral densities for the frequency indices of interest. A promising application for the time-domain processing of frequency-domain data, particularly for calculating the summation of frequency-domain cross- and auto-correlations in orthogonal frequency-division multiplexing (OFDM) systems, is studied. The advantages of the time-domain processing of frequency-domain data are 1) the ability to rapidly acquire the properties that are readily available in the frequency domain and 2) the reduced complexity. The proposed fast algorithm directly employs time-domain samples, and hence, does not need the fast Fourier transform (FFT) operation. The proposed algorithm has a lower complexity (required complex multiplications ∼ O(N)) than conventional techniques.

of late, many researchers have been interested in sparse representation of signals and its applications such as Compressive Sensing in Cognitive Radio (CR) networks as a way of overcoming the issue of limited bandwidth. Compressive sensing based wideband spectrum sensing is a novel approach in cognitive radio systems. Also in these systems, using spatial-frequency opportunistic reuse is emerged interestingly by constructing and deploying spatial-frequency Power Spectral Density (PSD) maps. Since the CR sensors are distributed in the region of support, the sensed PSD by each sensor should be transmitted to a master node (base-station) in order to construct the PSD maps in space and frequency domains. When the number of sensors is large, this data transmission which is required for construction of PSD map can be challenging. In this paper, in order to transmit the CR sensors' data to the master node, the compressive sensing based scheme is used. Therefore, the measurements are sampled in a lower sampling rate than of the Nyquist rate. By using the proposed method, an acceptable PSD map for cognitive radio purposes can be achieved by only 30% of full data transmission. Also, simulation results show the robustness of the proposed method against the channel variations in comparison with classical methods. Different solution schemes such as Basis Pursuit, Lasso, Lars and Orthogonal Matching Pursuit are used and the quality performance of them is evaluated by several simulation results over a Rician channel with respect to several different compression and Signal to Noise Ratios. It is also illustrated that the performance of Basis Pursuit and Lasso methods outperform the other compression methods particularly in higher compression rates.

This work studies the benefits of heterogeneous cellular networks with overlapping picocells in a large macrocell. We consider three different strategies for resource allocation and cell association. The first model employs a spectrum overlapping strategy with an SINR-based cell association. The second model avoids the interference between macrocell and picocell through a spectrum splitting strategy. Furthermore, picocell range expansion is also considered in this strategy to enable a load balancing between the macrocell and picocells. The last model is a hybrid one, called as fractional spectrum splitting strategy, where spectrum splitting strategy is only applied at the picocell-edge, while the picocell-inner reuses the spectrum of the macrocell. We constructs resource allocation optimization problem for these strategies to maximize the system rate. Our results show that in terms of system rate, all the three strategies outperform the performance of macrocell-only case, which shows the benefit of heterogeneous networks. Moreover, fractional spectrum splitting strategy provides highest system rate at the expense of outage user rate degradation due to inter-macro-pico interference. Spectrum overlapping model provides the second highest system rate gain and also improves outage user rate owing to full spectrum reuse and the benefit of macro diversity, while spectrum splitting model achieves a moderate system rate gain.

In this paper, we propose a speech enhancement algorithm by using MAP estimation with variable speech spectral amplitude probability density function (speech PDF). The variable speech PDF has two adaptive shape parameters which affect the quality of enhanced speech. Noise can be efficiently suppressed when these parameters are properly applied so that the variable speech PDF shape fits to the real-speech PDF one. We derive adaptive shape parameters from real-speech PDF in various narrow SNR intervals. The proposed speech enhancement algorithm with adaptive shape parameters is examined and compared to conventional algorithms. The simulation results show that the proposed method improved SegSNR around 6 and 9 dB when the input speech signal was corrupted by white and tunnel noises at 0 dB, respectively.

Phase structure of nonlinear dynamical system is governed by the vector field and decides the trajectories. Accordingly, the power spectra of trajectories include the structural field effect on the phase space. In this paper, we develop a method for analyzing phase structure using power spectra of trajectories and reconstitute a potential function in the system.

This paper suggests an outphasing scheme to reduce adjacent channel spectral regrowth triggered by the gain and phase mismatch between two signal paths in linear amplification with nonlinear component (LINC) systems. The error vector magnitude and power spectral density of the output signal considering path mismatch are described analytically using path mismatch factor. An outphasing scheme is proposed to reduce the spectral regrowth. The proposed outphasing scheme reshapes the phases of the separated signals in LINC systems to reduce the changes of the phases. Its performance is verified by performing simulations with multi-tone signals. The result shows that the scheme can reduce the spectral regrowth of the multi-tone signals significantly compared to the conventional outphasing scheme for LINC systems with path imbalance.

We propose a novel technique to grow the single-walled carbon nanotubes (SWNTs) with specific chirality at the desired position using free electron laser (FEL) irradiation during growth and surface treatment. As a result, only the semiconducting SWNTs grew at the area between triangle electrodes, where the ozone treatment was done to be hydrophilic when an alcohol chemical vapor deposition (ACCVD) process was carried out with the 800 nm FEL irradiation. Although the number of possible chiral index is 22 in the SWNTs grown without the FEL irradiation, the number is much reduced to be 8 by the FEL.

In this paper, we propose a spectral difference approach for noise power estimation in speech enhancement. The noise power estimate is given by recursively averaging past spectral power values using a smoothing parameter based on the current observation. The smoothing parameter in time and frequency is adjusted by the spectral difference between consecutive frames that can efficiently characterize noise variation. Specifically, we propose an effective technique based on a sigmoid-type function in order to adaptively determine the smoothing parameter based on the spectral difference. Compared to a conventional method, the proposed noise estimate is computationally efficient and able to effectively follow noise changes under various noise conditions.

We propose a blind dereverberation method based on spectral subtraction using a multi-channel least mean squares (MCLMS) algorithm for distant-talking speech recognition. In a distant-talking environment, the channel impulse response is longer than the short-term spectral analysis window. By treating the late reverberation as additive noise, a noise reduction technique based on spectral subtraction was proposed to estimate the power spectrum of the clean speech using power spectra of the distorted speech and the unknown impulse responses. To estimate the power spectra of the impulse responses, a variable step-size unconstrained MCLMS (VSS-UMCLMS) algorithm for identifying the impulse responses in a time domain is extended to a frequency domain. To reduce the effect of the estimation error of the channel impulse response, we normalize the early reverberation by cepstral mean normalization (CMN) instead of spectral subtraction using the estimated impulse response. Furthermore, our proposed method is combined with conventional delay-and-sum beamforming. We conducted recognition experiments on a distorted speech signal simulated by convolving multi-channel impulse responses with clean speech. The proposed method achieved a relative error reduction rate of 22.4% in relation to conventional CMN. By combining the proposed method with beamforming, a relative error reduction rate of 24.5% in relation to the conventional CMN with beamforming was achieved using only an isolated word (with duration of about 0.6 s) to estimate the spectrum of the impulse response.

A new type of modulation called continuous-phase parallel-combinatory high-compaction multicarrier modulation (CPPC/HC-MCM) is proposed. CPPC/HC-MCM employs the technique of continuous-phase modulation (CPM) and avoids the formation of amplitude gaps between two successive signals to enhance the spectral efficiency of conventional PC/HC-MCM. Results of simulations show that CPPC/HC-MCM is spectrally efficient and achieves a smaller bit error rate than conventional (unmodulated) PC/HC-MCM at a common spectral efficiency even if the peak-to-average power ratio is considered.