Fourier analysis of sinusoidal and/or quasi-periodic signals in additive noise has been used in various fields. So far, many analysis algorithms including the well-known DFT have been developed. In particular, many adaptive algorithms have been proposed to handle non-stationary signals whose discrete Fourier coefficients (DFCs) are time-varying. Notch Fourier Transform (NFT) and Constrained Notch Fourier Transform(CNFT) proposed by Tadokoro et al. and Kilani et al., respectively, are two of them, which are implemented by filter banks and estimate the DFCs via simple sliding algorithms of their own. This paper presents, for the first time, statistical performance analyses of the NFT and the CNFT. Estimation biases and mean square errors (MSEs) of their sliding algorithms will be derived in closed form. As a result, it is revealed that both algorithms are unbiased, and their estimation MSEs are related to the signal frequencies, the additive noise variance and orders of comb filters used in their filter banks. Extensive simulations are performed to confirm the analytical findings.

In this paper, as a practical application, we focus on the genetic algorithm (GA) for multi-head surface mounting machines which are used to populate printed circuit boards (PCBs). Although there have been numerous studies on the surface mounting machine, studies on the multi-head case are rare because of its complexity. The multi-head surface mounting machine can pick multiple components simultaneously in one pickup operation and this operation can reduce much portion of the assembly time. Hence we try to minimize the assembly time by maximizing the number of simultaneous pickups, resulting in reduction of PCB production cost. This research introduces a partial-link GA method for the single-head case. Then, we apply this method to the multi-head case by regarding a reel-group as one reel and a component-cluster as one component. The results of computer simulation show that our genetic algorithm is greatly superior to the heuristic algorithm that is currently used in industry.

Magnetoencephalography (MEG) is a powerful and non-invasive technique for measuring human brain activity with a high temporal resolution. The motivation for studying MEG data analysis is to extract the essential features from measured data and represent them corresponding to the human brain functions. In this paper, a novel MEG data analysis method based on independent component analysis (ICA) approach with pre-processing and post-processing multistage procedures is proposed. Moreover, several kinds of ICA algorithms are investigated for analyzing MEG single-trial data which is recorded in the experiment of phantom. The analyzed results are presented to illustrate the effectiveness and high performance both in source decomposition by ICA approaches and source localization by equivalent current dipoles fitting method.

This paper presents an efficient graph-based evolutionary optimization technique called Evolutionary Graph Generation (EGG), and its application to the design of fast constant-coefficient multipliers using parallel counter-tree architecture. An important feature of EGG is its capability to handle the general graph structures directly in evolution process instead of encoding the graph structures into indirect representations, such as bit strings and trees. This paper also addresses the major problem of EGG regarding the significant computation time required for verifying the function of generated circuits. To solve this problem, a new functional verification technique for arithmetic circuits is proposed. It is demonstrated that the EGG system can create efficient multiplier structures which are comparable or superior to the known conventional designs.

This paper describes how to generate, analyze and design a novel current-mode filter model using tunable multiple-output operational transconductance amplifiers and grounded capacitors (MO-OTA-Cs) for synthesizing both transmission poles and zeros. Transfer functions of low-order, high-order, general type, and special type are realized based on the filter model. The theory focuses mainly on establishing a relationship between the cascaded MO-OTA-Cs and the multiple-loop feedback matrix, which makes the structural generation and design formulas. Adopting the theory allows us to systematically generate many interesting new configurations along with some known structures. All the filter architectures contain only grounded capacitors, which can absorb parasitic capacitances and require smaller chip areas than floating ones. The paper also presents numerical design examples and simulation results to confirm the theoretical analysis.

Reinforcement Learning (RL) is an efficient method for solving Markov Decision Processes (MDPs) without a priori knowledge about an environment, and can be classified into the exploitation oriented method and the exploration oriented method. Q-learning is a representative RL and is classified as an exploration oriented method. It is guaranteed to obtain an optimal policy, however, Q-learning needs numerous trials to learn it because there is not action-selecting mechanism in Q-learning. For accelerating the learning rate of the Q-learning and realizing exploitation and exploration at a learning process, the Q-ee learning system has been proposed, which uses pre-action-selector, action-selector and back propagation of Q values to improve the performance of Q-learning. But the Q-ee learning is merely suitable for deterministic MDPs, and its convergent guarantee to derive an optimal policy has not been proved. In this paper, based on discussing different exploration methods, replacing the pre-action-selector in the Q-ee learning, we introduce a method that can be used to implement an active exploration to an environment, the Active Exploration Planning (AEP), into the learning system, which we call the Q-ae learning. With this replacement, the Q-ae learning not only maintains advantages of the Q-ee learning but also is adapted to a stochastic environment. Moreover, under deterministic MDPs, this paper presents the convergent condition and its proof for an agent to obtain the optimal policy by the method of the Q-ae learning. Further, by discussions and experiments, it is shown that by adjusting the relation between the learning factor and the discounted rate, the exploration process to an environment can be controlled on a stochastic environment. And, experimental results about the exploration rate to an environment and the correct rate of learned policies also illustrate the efficiency of the Q-ae learning on the stochastic environment.

The main contribution of this work is to propose energy-efficient randomized initialization protocols for ad-hoc radio networks (ARN, for short). First, we show that if the number n of stations is known beforehand, the single-channel ARN can be initialized by a protocol that terminates, with high probability, in O(n) time slots with no station being awake for more than O(log n) time slots. We then go on to address the case where the number n of stations in the ARN is not known beforehand. We begin by discussing, an elegant protocol that provides a tight approximation of n. Interestingly, this protocol terminates, with high probability, in O((log n)²) time slots and no station has to be awake for more than O(log n) time slots. We use this protocol to design an energy-efficient initialization protocol that terminates, with high probability, in O(n) time slots with no station being awake for more than O(log n) time slots. Finally, we design an energy-efficient initialization protocol for the k-channel ARN that terminates, with high probability, in O(n/k+log n) time slots, with no station being awake for more than O(log n) time slots.

We consider the problem of embedding complete binary trees into 2-dimensional tori with minimum (edge) congestion. It is known that for a positive integer n, a 2ⁿ-1-vertex complete binary tree can be embedded in a (2^n/2+1)(2^n/2+1)-grid and a 2^n/2 2^n/2-grid with congestion 1 and 2, respectively. However, it is not known if 2ⁿ-1-vertex complete binary tree is embeddable in a 2^n/2 2^n/2-grid with unit congestion. In this paper, we show that a positive answer can be obtained by adding wrap-around edges to grids, i.e., a 2ⁿ-1-vertex complete binary tree can be embedded with unit congestion in a 2^n/2 2^n/2-torus. The embedding proposed here achieves the minimum congestion and an almost minimum size of a torus (up to the constant term of 1). In particular, the embedding is optimal for the problem of embedding a 2ⁿ-1-vertex complete binary tree with an even integer n into a square torus with unit congestion.

Gaudry has described a new algorithm (Gaudry's variant) for the discrete logarithm problem (DLP) in hyperelliptic curves. For a hyperelliptic curve of a small genus on a finite field GF(q), Gaudry's variant solves for the DLP in time O(q^2+ε). This paper shows that C_ab curves can be attacked with a modified form of Gaudry's variant and presents the timing results of such attack. However, Gaudry's variant cannot be effective in all of the C_ab curve cryptosystems. This paper also provides an example of a C_ab curve that is unassailable by Gaudry's variant.

A constraint resolution scheme in the Hopfield-type neural network named "Neuron Filter" is presented for efficiently solving combinatorial optimization problems. The neuron filter produces an output that satisfies the constraints of the problem as best as possible according to both neuron inputs and outputs. This paper defines the neuron filter and shows its introduction into existing neural networks for N-queens problems and FPGA board-level routing problems. The performance is evaluated through simulations where the results show that our neuron filter improves the searching capability of the neural network with the shorter computation time.

This paper presents an application of Complex-Valued Associative Memory Model(CAMM) for image processing. An image association system applying CAMM, combined with a 2-dimensional discrete Fourier transform (2-D DFT) process is proposed. Discussed are how a gray level image can be expressed using CAMM, and the image association that can be performed by CAMM. In the proposed system, input images are transformed to phase matrices and the image association can be performed by making use of the phase information. Practical examples are also presented.

The interconnect analysis of on- and off-chips is very important in the design of high-speed signal processing, digital communication, and microwave electronic systems. When the interconnects are characterized by sampled data via electromagnetic analysis, the circuit-level simulation of the network requires rational approximation of the sampled data. Since the frequency band of the sampled data is more than 10 GHz, the rational function must fit into it at many frequency points. The rational function is approximated using the orthogonal least-squares method. With an increase in the number of the fitting data, the least-squares method suffers from a singularity problem. To avoid this, the sampled data are hierarchically approximated in this paper. Moreover, to reduce the computational cost of the circuit-level simulation, the parameter matrix of the interconnects is approximated by a rational matrix with one common denominator polynomial, and the selective orthogonalization procedure is presented.

This paper concentrates on the model useful for analyzing the error performance of M-estimators of a single unknown signal parameter: that is the error intensity model. We develop the point process representation for the estimation error, the conditional distribution of the estimator, and the distribution of error candidate point process. Then the error intensity function is defined as the probability density of the estimate and the general form of the error intensity function is derived. We compute the explicit form of the intensity functions based on the local maxima model of the error generating point process. While the methods described in this paper are applicable to any estimation problem with continuous parameters, our main application will be time delay estimation. Specifically, we will consider the case where coherent impulsive interference is involved in addition to the Gaussian noise. Based on numerical simulation results, we compare each of the error intensity model in terms of the accuracy of both error probability and mean squared error (MSE) predictions, and the issue of extendibility to multiple parameter estimation is also discussed.

Recently, cerebral disease is being a serious problem in an aging society. But, rank evaluation of cerebral disease is not developed and therefore rehabilitation is hard. In this study, we try to assess slight cerebral disease by taking notice of recognition mechanism of face and realizing face image synthesis using computer technology. If we can find a slight cerebral disease and rank evaluation, we can apply to rehabilitation, and a load of medical doctor and patient decreases. We have obtained a result by the experiment, so we report it.

This paper describes a DLL (Delay Locked Loop) circuit with the mixed-mode phase tuning method. The circuit accomplishes unlimited phase shift and accurate phase alignment through the coarse and fine phase tuning technique. It is based on a dual delay locked loop structure. The main loop is for generating coarsely spaced clocks and the second loop is for fast and accurate phase tuning with digital and analog phase detection. Simulations show that this circuit has 360 degree phase shift capability and can resolve 10 ps phase error using 0.6 µm CMOS technology.

A low bit-rate encoding method which yields a good performance in edge reconstruction while achieving a high compression is proposed through MTF function and the spatial anisotropy of human vision. Human visual weighting factors applied to sub-blocks within each subband in wavelet domain are produced by the spatial anisotropic-filter, then a good perceptual performance can be obtained.