The design and performance evaluation is presented of a partially adaptive array that suppresses clutter from low elevation angles in atmospheric radar observations. The norm-constrained and directionally constrained minimization of power (NC-DCMP) algorithm has been widely used to suppress clutter in atmospheric radars, because it can limit the signal-to-noise ratio (SNR) loss to a designated amount, which is the most important design factor for atmospheric radars. To suppress clutter from low elevation angles, adding supplemental antennas that have high response to the incoming directions of clutter has been considered to be more efficient than to divide uniformly the high-gain main array. However, the proper handling of the gain differences of main and sub-arrays has not been well studied. We performed numerical simulations to show that using the proper gain weighting, the sub-array configuration has better clutter suppression capability per unit SNR loss than the uniformly divided arrays of the same size. The method developed is also applied to an actual observation dataset from the MU radar at Shigaraki, Japan. The properly gain-weighted NC-DCMP algorithm suppresses the ground clutter sufficiently with an average SNR loss of about 1 dB less than that of the uniform-gain configuration.

A continuous flow reactor equipped with a low-loss flow channel and a microwave cavity was developed for synthesizing nanophosphors. A continuous solution synthesis of YVO4:Eu,Bi nanophosphor was succeeded through the rapid hydrothermal method using this equipment. Internal quantum efficiency of YVO4:Eu,Bi nanophosphor obtained by 20 minutes microwave heating is about 30% at 320 nm as high as that obtained by 6 hours hydrothermal treatment in autoclave.

For the nonlinear acoustic echo cancellation, we present an algorithm to estimate the threshold of the clipping effect and the room impulse response vector by suppressing their time-varying cost function. A common way to suppress the time-varying cost function of a pair of parameters is to alternatingly minimize the function with respect to each parameter while keeping the other fixed, which we refer to as adaptive alternating minimization. However, since the cost function for the threshold is nonconvex, the conventional methods approximate the exact minimizations by gradient descent updates, which causes serious degradation of the estimation accuracy in some occasions. In this paper, by exploring the fact that the cost function for the threshold becomes piecewise quadratic, we propose to exactly minimize the cost function for the threshold in a closed form while suppressing the cost function for the impulse response vector in an online manner, which we call exact-online adaptive alternating minimization. The proposed method is expected to approximate more efficiently the adaptive alternating minimization strategy than the conventional methods. Numerical experiments demonstrate the efficacy of the proposed method.

In this paper, a novel scheme of the adaptive sampling of block compressive sensing is proposed for natural images. In view of the contents of images, the edge proportion in a block can be used to represent its sparsity. Furthermore, according to the edge proportion, the adaptive sampling rate can be adaptively allocated for better compressive sensing recovery. Given that there are too many blocks in an image, it may lead to a overhead cost for recording the ratio of measurement of each block. Therefore, K-means method is applied to classify the blocks into clusters and for each cluster a kind of ratio of measurement can be allocated. In addition, we design an iterative termination condition to reduce time-consuming in the iteration of compressive sensing recovery. The experimental results show that compared with the corresponding methods, the proposed scheme can acquire a better reconstructed image at the same sampling rate.

In this paper, we propose and analyze the opportunistic amplify-and-forward (AF) relaying scheme using antenna selection in conjunction with different adaptive transmission techniques over Rayleigh fading channels. In this scheme, the best antenna of a source and the best relay are selected for communication between the source and destination. Closed-form expressions for the outage probability and average symbol error rate (SER) are derived to confirm that increasing the number of antennas is the best option as compared with increasing the number of relays. We also obtain closed-form expressions for the average channel capacity under three different adaptive transmission techniques: 1) optimal power and rate adaptation; 2) constant power with optimal rate adaptation; and 3) channel inversion with a fixed rate. The channel capacity performance of the considered adaptive transmission techniques is evaluated and compared with a different number of relays and various antennas configurations for each adaptive technique. Our derived analytical results are verified through extensive Monte Carlo simulations.

As one of the techniques enabling individual singers to produce the varieties of voice timbre beyond their own physical constraints, a statistical voice timbre control technique based on the perceived age has been developed. In this technique, the perceived age of a singing voice, which is the age of the singer as perceived by the listener, is used as one of the intuitively understandable measures to describe voice characteristics of the singing voice. The use of statistical voice conversion (SVC) with a singer-dependent multiple-regression Gaussian mixture model (MR-GMM), which effectively models the voice timbre variations caused by a change of the perceived age, makes it possible for individual singers to manipulate the perceived ages of their own singing voices while retaining their own singer identities. However, there still remain several issues; e.g., 1) a controllable range of the perceived age is limited; 2) quality of the converted singing voice is significantly degraded compared to that of a natural singing voice; and 3) each singer needs to sing the same phrase set as sung by a reference singer to develop the singer-dependent MR-GMM. To address these issues, we propose the following three methods; 1) a method using gender-dependent modeling to expand the controllable range of the perceived age; 2) a method using direct waveform modification based on spectrum differential to improve quality of the converted singing voice; and 3) a rapid unsupervised adaptation method based on maximum a posteriori (MAP) estimation to easily develop the singer-dependent MR-GMM. The experimental results show that the proposed methods achieve a wider controllable range of the perceived age, a significant quality improvement of the converted singing voice, and the development of the singer-dependnet MR-GMM using only a few arbitrary phrases as adaptation data.

Rain attenuation can drastically impact the service availability of satellite communication, especially in the higher frequency bands above 20 GHz, such as the Ka-band. Several countermeasures, including site and time diversity, have been proposed to maintain satellite link service. In this paper, we evaluate the performance of a power boost beam method, which is an adaptive satellite power control technology based on using rain radar data obtained throughout Japan to forecast the power margin. Boost beam analysis is considered for different beam sizes (50, 100, 150, and 200km) and beam numbers (1-4 beams) for a total of 16 cases. Moreover, we used a constant boost power corresponding to the rainfall rate of 20mm/h. The obtained results show that in comparison to the case with no boost, the effective rain intensity in each boost case was reduced.

Among various training concepts for speaker adaptation, Speaker Adaptive Training (SAT) has been successfully applied to a standard Hidden Markov Model (HMM) speech recognizer, whose state is associated with Gaussian Mixture Models (GMMs). On the other hand, focusing on the high discriminative power of Deep Neural Networks (DNNs), a new type of speech recognizer structure, which combines DNNs and HMMs, has been vigorously investigated in the speaker adaptation research field. Along these two lines, it is natural to conceive of further improvement to a DNN-HMM recognizer by employing the training concept of SAT. In this paper, we propose a novel speaker adaptation scheme that applies SAT to a DNN-HMM recognizer. Our SAT scheme allocates a Speaker Dependent (SD) module to one of the intermediate layers of DNN, treats its remaining layers as a Speaker Independent (SI) module, and jointly trains the SD and SI modules while switching the SD module in a speaker-by-speaker manner. We implement the scheme using a DNN-HMM recognizer, whose DNN has seven layers, and elaborate its utility over TED Talks corpus data. Our experimental results show that in the supervised adaptation scenario, our Speaker-Adapted (SA) SAT-based recognizer reduces the word error rate of the baseline SI recognizer and the lowest word error rate of the SA SI recognizer by 8.4% and 0.7%, respectively, and by 6.4% and 0.6% in the unsupervised adaptation scenario. The error reductions gained by our SA-SAT-based recognizers proved to be significant by statistical testing. The results also show that our SAT-based adaptation outperforms, regardless of the SD module layer selection, its counterpart SI-based adaptation, and that the inner layers of DNN seem more suitable for SD module allocation than the outer layers.

This paper is a sequel to [4] in which the system is generalized by including unknown time-varying delays in both states and input. Regarding the controller, the design of adaptive gain is simplified by including only x1 and u whereas full states are used in [4]. Moreover, it is shown that the proposed controller is also applicable to a class of upper triangular nonlinear systems. An example is given for illustration.

The introduction of deep neural networks (DNNs) leads to a significant improvement of the automatic speech recognition (ASR) performance. However, the whole ASR system remains sophisticated due to the dependent on the hidden Markov model (HMM). Recently, a new end-to-end ASR framework, which utilizes recurrent neural networks (RNNs) to directly model context-independent targets with connectionist temporal classification (CTC) objective function, is proposed and achieves comparable results with the hybrid HMM/DNN system. In this paper, we investigate per-dimensional learning rate methods, ADAGRAD and ADADELTA included, to improve the recognition of the end-to-end system, based on the fact that the blank symbol used in CTC technique dominates the output and these methods give frequent features small learning rates. Experiment results show that more than 4% relative reduction of word error rate (WER) as well as 5% absolute improvement of label accuracy on the training set are achieved when using ADADELTA, and fewer epochs of training are needed.

This paper aims to investigate the performance improvements made possible by combining various major language model (LM) technologies together and to reveal the interactions between LM technologies in spontaneous automatic speech recognition tasks. While it is clear that recent practical LMs have several problems, isolated use of major LM technologies does not appear to offer sufficient performance. In consideration of this fact, combining various LM technologies has been also examined. However, previous works only focused on modeling technologies with limited text resources, and did not consider other important technologies in practical language modeling, i.e., use of external text resources and unsupervised adaptation. This paper, therefore, employs not only manual transcriptions of target speech recognition tasks but also external text resources. In addition, unsupervised LM adaptation based on multi-pass decoding is also added to the combination. We divide LM technologies into three categories and employ key ones including recurrent neural network LMs or discriminative LMs. Our experiments show the effectiveness of combining various LM technologies in not only in-domain tasks, the subject of our previous work, but also out-of-domain tasks. Furthermore, we also reveal the relationships between the technologies in both tasks.

Antenna radiation patterns have side-lobes that add to ambiguity in the form of ghosting and object repetition in SAR images. An L-band 1.27GHz, 2×5 element proximity-coupled corner-truncated patch array antenna synthesized using the Dolph-Chebyshev method to reduce side-lobe levels is proposed. The designed antenna was sim-ulated, optimized, and fabricated for antenna performance parameter measurements. Antenna performance characteristics show good agree-ment with simulated results. A set of antennas were fabricated and then used together with a custom synthetic aperture radar system and SAR imaging performed on a point target in an anechoic chamber. Imaging results are also discussed in this paper showing improvement in image output. The antenna and its connected SAR systems developed in this work are different from most previous work in that this work is utilizing circular polarization as opposed to linear polarization.

In this paper, we address the problem of projective template matching which aims to estimate parameters of projective transformation. Although homography can be estimated by combining key-point-based local features and RANSAC, it can hardly be solved with feature-less images or high outlier rate images. Estimating the projective transformation remains a difficult problem due to high-dimensionality and strong non-convexity. Our approach is to quantize the parameters of projective transformation with binary finite field and search for an appropriate solution as the final result over the discrete sampling set. The benefit is that we can avoid searching among a huge amount of potential candidates. Furthermore, in order to approximate the global optimum more efficiently, we develop a level-wise adaptive sampling (LAS) method under genetic algorithm framework. With LAS, the individuals are uniformly selected from each fitness level and the elite solution finally converges to the global optimum. In the experiment, we compare our method against the popular projective solution and systematically analyse our method. The result shows that our method can provide convincing performance and holds wider application scope.

Self-luminous light sources in the real world often have nonnegligible sizes and radiate light inhomogeneously. Acquiring the model of such a light source is highly important for accurate image synthesis and understanding. In this paper, we propose an approach to measuring 4D light fields of self-luminous extended light sources by using a liquid crystal (LC) panel, i.e. a programmable optical filter and a diffuse-reflection board. The proposed approach recovers the 4D light field from the images of the board illuminated by the light radiated from a light source and passing through the LC panel. We make use of the feature that the transmittance of the LC panel can be controlled both spatially and temporally. The approach enables multiplexed sensing and adaptive sensing, and therefore is able to acquire 4D light fields more efficiently and densely than the straightforward method. We implemented the prototype setup, and confirmed through a number of experiments that our approach is effective for modeling self-luminous extended light sources in the real world.

In this paper, a new non-uniform weight-updating scheme for adaptive digital beamforming (DBF) is proposed. The unique feature of the letter is that the effective working range of the beamformer is extended and the computational complexity is reduced by introducing the robust DBF based on worst-case performance optimization. The robust parameter for each weight updating is chosen by analyzing the changing rate of the Direction of Arrival (DOA) of desired signal in LEO satellite communication. Simulation results demonstrate the improved performance of the new Non-Uniform Weight-Updating Beamformer (NUWUB).

Recently, Doppler radars have been used in various applications from the detection and the classification of indoor human activities to the detection of airplanes. To improve both the degrees of freedom (DOF) and the estimation accuracy of the direction-of-arrival (DOA) of targets, multiple-input multiple-output (MIMO) radar has received much attention in recent years. The temporal spatial virtual array based on Doppler shift of a moving target has been one of methods to improve DOA estimation accuracy. However, the DOA estimation accuracy based on the method depends on the velocity and the direction of the target on which we focus. Also, the temporal spatial virtual array should be generated based on the information of the single target. Thus, it is difficult to implement the method if there are multiple targets. In this paper, we propose a new method that provides high accuracy of DOA estimation by using the temporal spatial virtual array without dependence on the velocity, the direction and the number of existing targets. We demonstrate the DOA estimation accuracy and the effectiveness of the proposed method via simulations.

In densely deployed wireless local area network (WLAN) environments, the arbitrary deployment of WLAN access points (APs) can cause serious cell overlaps among APs. In such situations, the ability to realize adaptable coverage using transmission power control (TPC) is effective for improving the area spectral efficiency. Meanwhile, it should be guaranteed that no coverage holes occur and that connectivity between APs and wireless stations (STAs) is maintained. In this paper, the self-organization of coverage domains of APs using TPC is proposed. The proposed technique reduces the incidence of coverage overlaps without generating area coverage holes. To detect coverage holes, STAs and/or APs are used as sensors that inform each AP of whether or not the points at which they exist are covered by the APs. However, there is a problem with this approach in that when the density of STAs is not sufficiently large, the occurrence of area coverage holes is inevitable because the points at which the sensors do not exist are not guaranteed to be covered by APs. This paper overcomes the problem by focusing APs that belong to network's outer boundary (boundary APs) and prohibiting the APs from operating at low transmission power levels, the idea being that the coverage domains of such APs always include the region covered by only those APs. The boundary APs are determined by performing Delaunay triangulation of the set of points at which all APs exist. Simulation results confirm the effectiveness of the proposed TPC scheme in terms of its ability to reduce the total overlap area while avoiding the occurrence of area coverage holes.

A self-adaptive scaled min-sum algorithm for LDPC decoding based on the difference between the first two minima of the check node messages (Δmin) is proposed. Δmin is utilized for adjusting the scaling factor of the check node messages, and simulation results show that the proposed algorithm improves the error correcting performance compared to existing algorithms.

Bistatic multi-input multi-output (MIMO) radar has the capability of measuring the transmit angle from the receiving array, which means the existence of information redundancy and benefits data association. In this paper, a data association decision for bistatic MIMO radar is proposed and the performance advantages of bistatic MIMO radar in data association is analyzed and evaluated. First, the parameters obtained by receiving array are sent to the association center via coordinate conversion. Second, referencing the nearest neighbor association (NN) algorithm, an improved association decision is proposed with the transmit angle and target range as association statistics. This method can evade the adverse effects of the angle system errors to data association. Finally, data association probability in the presence of array directional error is derived and the correctness of derivation result is testified via Monte Carlo simulation experiments. Besides that performance comparison with the conventional phased array radar verifies the excellent performance of bistatic MIMO Radar in data association.

Estimation of the time delay of arrival (TDOA) problem is important to acoustic source localization. The TDOA estimation problem is defined as finding the relative delay between several microphone signals for the direct sound. To estimate TDOA, the generalized cross-correlation (GCC) method is the most frequently used, but it has a disadvantage in terms of reverberant environments. In order to overcome this problem, the adaptive eigenvalue decomposition (AED) method has been developed, which estimates the room transfer function and finds the direct-path delay. However, the algorithm does not take into account the fact that the room transfer function is a sparse channel, and so sometimes the estimated transfer function is too dense, resulting in failure to exact direct-path and delay. In this paper, an enhanced AED algorithm that makes use of a proportionate step-size control and a direct-path constraint is proposed instead of a constant step size and the L2-norm constraint. The simulation results show that the proposed algorithm has enhanced performance as compared to both the conventional AED method and the phase-transform (PHAT) algorithm.