Invention and development of the Yagi-Uda antenna, and the self-complementary antenna are described. Analysis methods of large loop antennas and the improved circuit theory (ICT) for design of linear antennas are presented. Recent developments of axial mode helical antennas and spiral antennas for radiating circularly polarized waves are also described.

This paper concerns recognizing 3-dimensional object using proposed multi-layer block model. In particular, we aim to achieve desirable recognition performance while restricting the computational load to a low level using 3-step feature extraction procedure. An input image is first precisely partitioned into hierarchical layers of blocks in the form of base blocks and overlapping blocks. The hierarchical blocks are merged into a matrix, with which abundant local feature information can be obtained. The local features extracted are then employed by the kernel based support vector machines in tournament for enhanced system recognition performance while keeping it to low dimensional feature space. The simulation results show that the proposed feature extraction method reduces the computational load by over 80% and preserves the stable recognition rate from varying illumination and noise conditions.

In many applications of Independent Component Analysis (ICA) and Blind Source Separation (BSS) estimated sources signals and the mixing or separating matrices have some special structure or some constraints are imposed for the matrices such as symmetries, orthogonality, non-negativity, sparseness and specified invariant norm of the separating matrix. In this paper we present several algorithms and overview some known transformations which allows us to preserve several important constraints.

In the last three decades of the 20th Century, research in speech recognition has been intensively carried out worldwide, spurred on by advances in signal processing, algorithms, architectures, and hardware. Recognition systems have been developed for a wide variety of applications, ranging from small vocabulary keyword recognition over dial-up telephone lines, to medium size vocabulary voice interactive command and control systems for business automation, to large vocabulary speech dictation, spontaneous speech understanding, and limited-domain speech translation. Although we have witnessed many new technological promises, we have also encountered a number of practical limitations that hinder a widespread deployment of applications and services. On one hand, fast progress was observed in statistical speech and language modeling. On the other hand only spotty successes have been reported in applying knowledge sources in acoustics, speech and language science to improving speech recognition performance and robustness to adverse conditions. In this paper we review some key advances in several areas of speech recognition. A bottom-up detection framework is also proposed to facilitate worldwide research collaboration for incorporating technology advances in both statistical modeling and knowledge integration into going beyond the current speech recognition limitations and benefiting the society in the 21st century.

A two-qubit system made of electrons running along coupled pairs of quantum wires is described and numerically analyzed. A brief review of the basic gates is given first, based on preliminary investigations, followed by the description of the electron dynamics. A detailed analysis of a conditional phase shifter is carried out by means of a time-dependent Schrodinger solver applied to a two-particle system. A quantum network suitable for creating entanglement is simulated, and results are shown. The physical structure of the proposed network is within the reach of a solid-state implementation. The physical parameters used in the computations have been chosen with reference to silicon quantum wires embedded in silicon dioxide.

This paper describes high-quality and processor-efficient software implementation of an MPEG-2 AAC LC Profile encoder. MDCT and quantization processing are accelerated by 21.3% and 19.0%, respectively, through the use of SIMD instructions. In addition, psycho-acoustic analysis in the MDCT domain makes the use of FFTs unnecessary and reduces the computational cost of the analysis by 56.0%. The results of subjective quality tests show that better sound quality is provided by greater efficiency in quantization processing and Huffman coding. All of this results in high-quality and processor-efficient software implementation of an MPEG-2 AAC encoder. Subjective test results show that the sound quality achieved at 96 kb/s/stereo is equivalent to that of MP3 at 128 kb/s/stereo. The encoder works 13 times faster than realtime for stereo encoding on an 800 MHz Pentium III processor.

The goal of the maximum cut problem is to partition the vertex set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. The maximum cut problem has many important applications including the design of VLSI circuits and communication networks. Moreover, many optimization problems can be formulated in terms of finding the maximum cut in a network or a graph. In this paper, we propose an improved Hopfield neural network algorithm for efficiently solving the maximum cut problem. A large number of instances have been simulated. The simulation results show that the proposed algorithm is much better than previous works for solving the maximum cut problem in terms of the computation time and the solution quality.

We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following two parts: frequency-domain ICA with direction-of-arrival (DOA) estimation, and null beamforming based on the estimated DOA. The alternation of learning between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method.

A high power dissipation density in today's miniature electronic/mechanical systems makes on-chip thermal management very important. In order to achieve quick to evaluate, yet accurate electro-thermal models, needed for the thermal management of microsystems, a model order reduction is necessary. In this paper, we present an automatic, Krylov-subspace-based order reduction of a electro-thermal model, which we illustrate by a novel type of micropropulsion device. Numerical simulation results of the full finite element model and the reduced order model, that describes the transient electro-thermal behavior, are presented. A comparison between Krylov-subspace-based order reduction, order reduction using control theoretical approaches and commercially available reduced order modeling has been performed. A Single-Input-Single-Output setup for the Arnoldi reduction algorithm was proved to be sufficient to accurately represent the complete time-dependent temperature distribution of the device.

This paper discusses a nonlinear function for independent component analysis to process complex-valued signals in frequency-domain blind source separation. Conventionally, nonlinear functions based on the Cartesian coordinates are widely used. However, such functions have a convergence problem. In this paper, we propose a more appropriate nonlinear function that is based on the polar coordinates of a complex number. In addition, we show that the difference between the two types of functions arises from the assumed densities of independent components. Our discussion is supported by several experimental results for separating speech signals, which show that the polar type nonlinear functions behave better than the Cartesian type.

This paper proposes a silence compression algorithm operating at multi-rates (MR) and with dual-bandwidths (DB), a narrowband and a wideband, for the MPEG (Moving Picture Experts Group)-4 CELP (Code Excited Linear Prediction) standard. The MR/DB operations are implemented by a Variable-Frame-size/Dual-Bandwidth Voice Activity Detection (VF/DB-VAD) module with bandwidth conversions of the input signal, and a Variable-Frame-size Comfort Noise Generator (VF-CNG) module. The CNG module adaptively smoothes the Root Mean Square (RMS) value of the input signal to improve the coding quality during transition periods. The algorithm also employs a Dual-Rate Discontinuous Transmission (DR-DTX) module to reduce an average transmission bitrate during silence periods. Subjective test results show that the proposed silence compression algorithm gives no degradation in coding quality for clean and noisy speech signals. These signals include about 20 to 30% non-speech frames and the average transmission bitrates are reduced by 20 to 40%. The proposed algorithm has been adopted as a part of the ISO/IEC MPEG-4 CELP version 2 standard.

This paper defines the distributed arrangement selection problem in a line network in a distributed context and describes the design of a strictly-time-optimal algorithm which solves the problem with a limited local memory space. The problem is regarded as a combined distributed sorting and k-selection problem, namely a problem of sorting elements that are not larger than the kth minimum element in predetermined processes. The algorithm also provides a solution to a resource allocation problem in a line network in a strictly-optimal time.

This paper investigates properties of space-bounded "two-dimensional Turing machines (2-tm's)," whose input tapes are restricted to square ones, with bounded input head reversals in vertical direction. We first investigate a relationship between determinism and nondeterminism for space-bounded and input head reversal-bounded 2-tm's. We then investigate how the number of input head reversals affects the accepting power of sublinearly space-bounded 2-tm's. Finally, we investigate necessary and sufficient spaces for three-way 2-tm's to simulate four-way two-dimensional finite automata with constant input head reversals.

To measure temperature dependent complex permittivity of dielectric materials, a rectangular cavity resonator with a heating system has been developed. In the experiment, microwave power with the frequency of 2.45 GHz is applied to heat the dielectric material. In order to reduce the error of the complex permittivity of dielectric material obtained from the perturbation method, an electromagnetic (EM) field simulator is applied which uses the Transmission Line Modeling (TLM) method. The uniformity of the temperature is also discussed by the use of heat transfer equation which applies the results of TLM simulation. It is found from the results that the accurate temperature dependence of complex permittivity of the material can be obtained by the method presented here.

We propose two simple algorithms based on bounded tickets for the mutual exclusion problem on the asynchronous single-writer/multi-reader shared memory model. These algorithms are modifications of the Bakery algorithm. An unattractive property of the Bakery algorithm is that the size of its shared memory is unbounded. Initially we design a provisional algorithm based on bounded tickets. It guarantees mutual exclusion in the case where a certain condition is satisfied. To remove the condition, we use an additional process that does not correspond to any user. The algorithm with the additional process is a lockout-free mutual exclusion algorithm on the asynchronous single-writer/multi-reader shared memory model. We then modify this algorithm to reduce the shared memory size with the cost of using another additional process. The maximum waiting time using each of the algorithms proposed in this paper is bounded by (n-1)c+O(nl), where n is the number of users, l is an upper bound on the time between two successive atomic steps, and c is an upper bound on the time that any user spends using the resource. The shared memory size needed by the first algorithm and the second algorithm are (n+1)(1+log (4n)) bits and n(1+log (4n-4))+2 bits, respectively.

In this paper, parallelization methods for quantum circuits are studied, where parallelization of quantum circuits means to reconstruct a given quantum circuit to one which realizes the same quantum computation with a smaller depth, and it is based on using additional bits, called ancillae, each of which is initialized to be in a certain state. We propose parallelization methods in terms of the number of available ancillae, for three types of quantum circuits. The proposed parallelization methods are more general than previous one in the sense that the methods are applicable when the number of available ancillae is fixed arbitrarily. As consequences, for the three types of n-bit quantum circuits, we show new upper bounds of the number of ancillae for parallelizing to logarithmic depth, which are 1/log n of previous upper bounds.

By a worst input (for the selection phase of Heapsort) of size n, we mean a heap of size n such that, if it is given to the selection phase of Heapsort, the number of movements of data is maximum among the numbers of movements for all heaps of size n. D. E. Knuth showed a special type of worst inputs which we will call the K-type heaps. His definition of K-type heaps was by the induction on the size n. We give one characterization of K-type worst heaps that is based on their tree structure. This characterization gives more information on the tree structure of K-type heaps than the Knuth's definition.

This paper addresses a maximum likelihood method for source separation in the case of overdetermined mixtures corrupted by additive white Gaussian noise. We consider an approximate likelihood which is based on the Laplace approximation and develop a natural gradient adaptation algorithm to find a local maximum of the corresponding approximate likelihood. We present a detailed mathematical derivation of the algorithm using the Lie group invariance. Useful behavior of the algorithm is verified by numerical experiments.

This letter presents parallel algorithms for matrix multiplication and the fast Fourier transform (FFT) that are significant problems arising in engineering and scientific applications. The proposed algorithms are designed on a 3-dimensional processor array with separable buses (PASb). We show that a PASb consisting of N N h processors can compute matrix multiplication of size N N and the FFT of size N in O(N/h+log N) time, respectively. In order to examine ease of hardware implementation, we also evaluate the VLSI complexity of the algorithms. A result obtained achieves an optimal bound on area-time complexity when h=O(N/log N).

We introduce efficient algorithms for the τ-adic sliding window method, which is a scalar multiplication algorithm on Koblitz curves over F2m. The τ-adic sliding window method is divided into two parts: the precomputation part and the main computation part. Until now, there has been no efficient way to deal with the precomputation part; the required points of the elliptic curves were calculated one by one. We propose two fast algorithms for the precomputation part. One of the proposed methods decreases the cost of the precomputation part by approximately 30%. Since more points are calculated, the total cost of scalar multiplication is decreased by approximately 7.5%.