Ultra-wideband pulse radars are promising candidates for 3-dimensional environment measurements by autonomous robots. Estimating 3-dimensional target shapes by scanning with an omni-directional antenna is an ill-posed inverse problem. Conventional algorithms such as the synthetic aperture method or parametric algorithms have a problem in terms of their calculation times. We have clarified the existence of a reversible transform between received data and target shapes for 3-dimensional systems. Calculation times are remarkably reduced by applying this transform because it directly estimates target shapes without iterations. We propose a new algorithm based on the transform and present an application example using numerical simulations. We confirm that the proposed algorithm has sufficient accuracy and a short calculation time.

In speaker adaptation for speech synthesis, it is desirable to convert both voice characteristics and prosodic features such as F0 and phone duration. For simultaneous adaptation of spectrum, F0 and phone duration within the HMM framework, we need to transform not only the state output distributions corresponding to spectrum and F0 but also the duration distributions corresponding to phone duration. However, it is not straightforward to adapt the state duration because the original HMM does not have explicit duration distributions. Therefore, we utilize the framework of the hidden semi-Markov model (HSMM), which is an HMM having explicit state duration distributions, and we apply an HSMM-based model adaptation algorithm to simultaneously transform both the state output and state duration distributions. Furthermore, we propose an HSMM-based adaptive training algorithm to simultaneously normalize the state output and state duration distributions of the average voice model. We incorporate these techniques into our HSMM-based speech synthesis system, and show their effectiveness from the results of subjective and objective evaluation tests.

In real-time speech recognition applications, there is a need to implement a fast and reliable adaptation algorithm. We propose a method to reduce adaptation time of the rapid unsupervised speaker adaptation based on HMM-Sufficient Statistics. We use only a single arbitrary utterance without transcriptions in selecting the N-best speakers' Sufficient Statistics created offline to provide data for adaptation to a target speaker. Further reduction of N-best implies a reduction in adaptation time. However, it degrades recognition performance due to insufficiency of data needed to robustly adapt the model. Linear interpolation of the global HMM-Sufficient Statistics offsets this negative effect and achieves a 50% reduction in adaptation time without compromising the recognition performance. Furthermore, we compared our method with Vocal Tract Length Normalization (VTLN), Maximum A Posteriori (MAP) and Maximum Likelihood Linear Regression (MLLR). Moreover, we tested in office, car, crowd and booth noise environments in 10 dB, 15 dB, 20 dB and 25 dB SNRs.

We introduce an adaptive subchannel, bit, and power allocation (ASBPA) algorithm to maximize the bandwidth efficiency of the mobile communication system that use orthogonal frequency division multiplexing (OFDM). We propose a suboptimal rate adaptive ASBPA algorithm that guarantees fairness in resource allocation and overcomes inherent co-channel interference (CCI) in the cellular system. Furthermore, we evaluate the maximum possible bandwidth efficiency of the cellular OFDM system achieved by the ASBPA algorithm which is practical to implement. Our simulation results show that the proposed algorithm outperforms the existing ones and achieves the cellular bandwidth efficiency of up to 5 b/s/Hz/cell. We also investigate some of the conditions that govern the bandwidth efficiency of the cellular OFDM system using the proposed ASBPA algorithm.

We present a novel algorithm that can suppress impulsive noise in video colour sequences. It uses order statistics, and directional and adaptive processing techniques.

When Simulated Annealing (SA) is applied to continuous optimization problems, the design of the neighborhood used in SA becomes important. Many experiments are necessary to determine an appropriate neighborhood range in each problem, because the neighborhood range corresponds to distance in Euclidean space and is decided arbitrarily. We propose Multi-point Simulated Annealing with Adaptive Neighborhood (MSA/AN) for continuous optimization problems, which determine the appropriate neighborhood range automatically. The proposed method provides a neighborhood range from the distance and the design variables of two search points, and generates candidate solutions using a probability distribution based on this distance in the neighborhood, and selects the next solutions from them based on the energy. In addition, a new acceptance judgment is proposed for multi-point SA based on the Metropolis criterion. The proposed method shows good performance in solving typical test problems.

A feature for classification of shallowly buried landmine-like objects using a ground penetrating radar (GPR) measurement system is proposed and its performance is evaluated. The feature for classification employed here is a time interval between two pulses reflected from top and bottom sides of landmine-like objects. First, we estimate a time resolution required to detect object thickness from GPR data, and check the actual time resolution through laboratory experiment. Next, we evaluate the classification performance using Monte Carlo simulations from dataset generated by a two-dimensional finite difference time domain (FDTD) method. The results show that good classification performance is achieved even for landmine-like objects buried at shallow depths under rough ground surfaces. Furthermore, we also estimate the effects of ground surface roughness, soil inhomogeneity, and target inclination on the classification performance.

Adaptive modulation is an efficient method to increase the spectral efficiency of OFDM based high-speed wireless data transmission systems in multipath channel. Blind modulation classification schemes play an important role in adaptive modulation systems, eliminating the need for transmitting modulation information, thereby increasing spectral efficiency. In this paper, a novel blind modulation classification algorithm is derived from the finite alphabet property of information symbols and the equivalent parallel model of OFDM systems. The performances of the proposed algorithm and M2M4P algorithm [1] are tested and compared using Monte-Carlo simulations. It is found that, the novel algorithm yields performance better than that of M2M4P algorithm and with much less complexity.

This paper presents the design and implementation of a hip range of motion (ROM) estimation method that is capable of fine-grained estimation during total hip replacement (THR) surgery. Our method is based on two acceleration strategies: (1) adaptive mesh refinement (AMR) for complexity reduction and (2) parallelization for further acceleration. On the assumption that the hip ROM is a single closed region, the AMR strategy reduces the complexity for N N N stance configurations from O(N3) to O(ND), where 2≤D≤3 and D is a data-dependent value that can be approximated by 2 in most cases. The parallelization strategy employs the master-worker paradigm with multiple task queues, reducing synchronization between processors with load balancing. The experimental results indicate that the implementation on a cluster of 64 PCs completes estimation of 360360180 stance configurations in 20 seconds, playing a key role in selecting and aligning the optimal combination of artificial joint components during THR surgery.

In this Letter, we propose a new adaptive step-size widely linear constant modulus algorithm (CMA) in DS-CDMA systems especially for time-varying interference environments. The widely linear estimation enables CMA to produce better output signal to interference plus noise ratio (SINR) and the adaptive step-size tackles the time-varying interference environment effectively. The simulations confirm that the proposed algorithm shows better performance in a DS-CDMA system employing a BPSK modulation than other algorithms without use of widely linear processing.

It is well-known that one of the best wavelet-based image compression techniques, called Wavelet Difference Reduction-WDR, has both simple algorithm and comparative rate-distortion results comparing to other coders in the literature. In this paper, we propose an algorithm to enhance the performance of WDR coder in a very efficient way. The methodology is highly based on the context adaptive model. High-order statistical context modeling is used for significant coefficients prediction by scanning order adaptation of WDR. In binary mode, the coder is clearly better than Set Partitioning in Hierarchical Trees (SPIHT). A new conditional Arithmetic Coding (AC) using array of contexts is specifically designed for WDR-based coder. Based on our simulation results of all images in the test set which covers various types of images, it is apparent that our proposed coder in AC mode generates the rate-distortion results that are superior to those of all other WDR-based coders in the literature. Furthermore, it is very competitive to SPIHT-AC and JPEG2000, the best coders, at all bit rates.

We describe an online method for selecting and annotating highlight scenes in soccer matches being televised. The stadium crowd noise and the play-by-play announcer's voice are used as input signals. Candidate scenes for highlights are extracted from the crowd noise by dynamic thresholding and spectral envelope analysis. Using a dynamic threshold solves the problem in conventional methods of how to determine an appropriate threshold. Semantic-meaning information about the kind of play and the related team and player is extracted from the announcer's commentary by using domain-based rules. The information extracted from the two types of audio input is integrated to generate segment-metadata of highlight scenes. Application of the method to six professional soccer games has confirmed its effectiveness.

Adaptation process of retina helps human visual system to see a high dynamic range scene in real world. This paper presents a simple static local adaptation method for high dynamic range image compression based on a retinal model. The proposed simple model aims at recreating the same sensations between the real scene and the range compressed image on display device when viewed after reaching steady state local adaptation respectively. Our new model takes the display adaptation into account in relation to the scene adaptation based on the retinal model. In computing local adaptation, the use of nonlinear edge preserving bilateral filter presents a better tonal rendition in preserving the local contrast and details while avoiding banding artifacts normally seen in local methods. Finally, we demonstrate the effectiveness of the proposed model by estimating the color difference between the recreated image and the target visual image obtained by trial and error method.

Mobile-agent-based distributed computing is one of the most promising paradigms to support autonomic computing in a large-scale of distributed system with dynamics and diversity: mobile agents traverse the distributed system and carry out a sophisticated task at each node adaptively. In mobile-agent-based systems, a larger number of agents generally require shorter time to complete the whole task but consume more resources (e.g., processing power and network bandwidth). Therefore, it is indispensable to keep an appropriate number of agents for the application on the mobile-agent-based system. This paper considers the mobile agent population control problem in dynamic networks: it requires adjusting the number of agents to a constant fraction of the current network size. This paper proposes algorithms inspired by the single species population model, which is a well-known population ecology model. These two algorithms are different in knowledge of networks each node requires. The first algorithm requires global information at each node, while the second algorithm requires only the local information. This paper shows by simulations that the both algorithms realize self-adaptation of mobile agent population in dynamic networks, but the second algorithm attains slightly lower accuracy than the first one.

The UWB (ultra-wideband) pulse radar is a promising candidate as an environment measurement method for rescue robots. Radar imaging to locate a nearby target is known as an ill-posed inverse problem, on which various studies have been done. However, conventional algorithms require long computational time, which makes it difficult to apply them to real-time operations of robots. We have proposed a fast radar imaging algorithm, the SEABED algorithm, for UWB pulse radars. This algorithm is based on a reversible transform, BST (Boundary Scattering Transform), between the target shape and the observed data. This transform enables us to estimate target shapes quickly and accurately in a noiseless environment. However, in a noisy environment the image estimated by the SEABED algorithm is degraded because BST utilizes differential operations. We have also proposed an image stabilization method, which utilizes the upper bound of the smoothness of received data. This method can be applied only to convex objects, not to concave ones. In this paper, we propose a fractional BST, which is obtained by expanding the conventional BST, and an image stabilization method by using the fractional BST. We show that the estimated image can be stabilized regardless of the shape of target.

In H.264, the context-based adaptive variable length coding (CAVLC) is used for lossless compression. Direct table-lookup implementation requires higher cost because it employs a larger memory to produce the encoded results. In this letter, we present a more efficient technique for CAVLC implementation. Compared with those previous CAVLC chips, our design requires the lowest hardware cost.

This paper analyzes the steady-state properties of a CORDIC-based adaptive ARMA lattice filter. In our previous study, the convergence properties of the filter in the non-steady state were clarified; however, its behavior in the steady state was not discussed. Therefore, we develop a distinct analysis technique based on a Markov chain in order to investigate the steady-state properties of the filter. By using the proposed technique, the relationship between step size and coefficient estimation error is revealed.

We succeeded in isolating two formaldehyde-degrading fungi, the strains BDF001 and 002, from the rhizospheric soil of formaldehyde-exposed potted golden pothos (Epipremnum aureum), and from the formaldehyde-exposed cultivation soil without plants, respectively. Sequence analysis of the ITS-5.8S rDNA regions confirmed that both fungi were of the same species, Trichoderma virens. These two strains, however, obviously differed from each other in formaldehyde resistance and formaldehyde-degrading ability. The formaldehyde concentration allowing a growth during cultivation for 10 days for the strain BDF001 was up to 0.6%, and that for the strain BDF002 was up to 0.35%. The strain BDF001 showed a formaldehyde-degrading activity 2.3 times higher than that of the strain BDF002. Ranges of the possible growth pH and temperature in the presence of 0.21% formaldehyde were between 4 and 9, and around 25, respectively.

In this letter, we define the generalized doubly stochastic processing via Jacket matrices of order-2n and 2n with the integer, n≥2. Different from the Hadamard factorization scheme, we propose a more general case to obtain a set of doubly stochastic matrices according to decomposition of the fundaments of Jacket matrices. From order-2n and order-2n Jacket matrices, we always have the orthostochastoc case, which is the same as that of the Hadamard matrices, if the eigenvalue λ1 = 1, the other ones are zeros. In the case of doubly stochastic, the eigenvalues should lead to nonnegative elements in the probability matrix. The results can be applied to stochastic signal processing, pattern analysis and orthogonal designs.

This paper discusses the on-line frequency identification problem for a measured sinusoidal signal by using the adaptive method and filter theory. The proposed method is based on an identity between the sinusoidal signal and its second order derivative. For a set of chosen parameters, the proposed method is robust to the initial phase, the amplitude, and the frequency in a wide range. The convergence rate can be adjusted by the chosen parameters. The estimation error mainly depends on the frequency of the sinusoid, the measurement noise and a key design parameter.