We study the impact of incorporating handoff prediction information in the session admission control process in mobile cellular networks. We evaluate the performance of optimal policies obtained with and without the predictive information, while taking into account possible prediction errors. Two different approaches to compute the optimal admission policy were studied: dynamic programming and reinforcement learning. Numerical results show significant performance gains when the predictive information is used in the admission process.

In this paper, we briefly describe situations that may cause HFN de-synchronization for ciphering applications in UMTS. Detection methods of HFN de-synchronization are discussed and the lower bound of the HFN de-synchronization perceptibility is derived. A supporting simulation result of the perceptibility is given. Then, an Automatic Recovery of HFN Synchronization (ARHS) algorithm is presented. The average lost PDU number of the ARHS algorithm is derived and supported by simulation results. The average lost SDU number is used as the figure of merit for HFN synchronization recovery procedures. Simulation results of the average lost SDU number show that the ARHS algorithm is quite effective to recover HFN synchronization after HFN de-synchronization situations happen.

The millimeter-wave (MMW) broadband mixers that are useful for measurement instruments to analyze MMW high data rate signals have been investigated. At first, we propose the specialized RF front-end for analyses of MMW high data rate signals. Next, the required specifications for the 1st mixers of the front-end are estimated, and the design, fabrication, and testing results of Q, V, and W-band monolithic broadband resistive mixers are described. The testing results are compared with performances of the diode mixer designed for V-band. It was found that the resistive mixers have very attractive performances of low conversion loss, good frequency flatness and high third order intercept point (IP3) with low Local (LO) oscillators power. The developed resistive mixers are suitable for the proposed MMW band measurement instruments.

In this paper, we present a learning approach, positive correlation learning (PCL), that creates a multilayer neural network with good generalization ability. A correlation function is added to the standard error function of back propagation learning, and the error function is minimized by a steepest-descent method. During training, all the unnecessary units in the hidden layer are correlated with necessary ones in a positive sense. PCL can therefore create positively correlated activities of hidden units in response to input patterns. We show that PCL can reduce the information on the input patterns and decay the weights, which lead to improved generalization ability. Here, the information is defined with respect to hidden unit activity since the hidden unit plays a crucial role in storing the information on the input patterns. That is, as previously proposed, the information is defined by the difference between the uncertainty of the hidden unit at the initial stage of learning and the uncertainty of the hidden unit at the final stage of learning. After deriving new weight update rules for the PCL, we applied this method to several standard benchmark classification problems such as breast cancer, diabetes and glass identification problems. Experimental results confirmed that the PCL produces positively correlated hidden units and reduces significantly the amount of information, resulting improved generalization ability.

An efficient hybrid image vector quantization (VQ) technique based on a classification in the DCT domain is presented in this letter. This algorithm combines two kinds of VQ, predictive VQ (PVQ) and discrete cosine transform domain VQ (DCTVQ), and adopts a simple classifier which employs only three DCT coefficients in the 88 block. For each image block, the classifier switches to the PVQ coder if the block is relatively complex, and otherwise switches to the DCTVQ coder. Experimental results show that the proposed algorithm can achieve higher PSNR values than ordinary VQ, PVQ, JPEG, and JPEG2000 at the same bit-rate.

In this paper, we shall construct mathematical theory based on the concept of set-valued mappings, suitable for available operation of extraordinarily complicated large-scale network systems by introducing some connected-block structures. A fine estimation technique for availability of system behaviors of such network systems are obtained finally in the form of fixed point theorem for a special system of fuzzy-set-valued mappings.

We propose a learning method combining query learning and a "genetic translator" we previously developed. Query learning is a useful technique for high-accuracy, high-speed learning and reduction of training sample size. However, it has not been applied to practical optical character readers (OCRs) because human beings cannot recognize queries as character images in the feature space used in practical OCR devices. We previously proposed a character image reconstruction method using a genetic algorithm. This method is applied as a "translator" from feature space for query learning of character recognition. The results of an experiment with hand-written numeral recognition show the possibility of training sample size reduction.

Prediction of actual symbol probability is crucial for statistical data compression that uses arithmetic coder. Krichevsky-Trofimov (KT) estimator has been a standard predictor and applied in CTW or FWCTW methods. However, KT-estimator performs poorly when non occurring symbols appear. To rectify this we proposed a zero-redundancy estimator, especially with a finite window(Rashid and Kawabata, ISIT2003) for non stationary source. In this paper, we analyze the zero-redundancy estimators in the case of Markovian source and give an asymptotic evaluation of the redundancy. We show that one of the estimators has the per symbol redundancy given by one half of the dimension of positive parameters divided by the window size when the window size is large.

This paper presents Dynamic Attention Map by Ising model for face detection. In general, a face detector can not know where faces there are and how many faces there are in advance. Therefore, the face detector must search the whole regions on the image and requires much computational time. To speed up the search, the information obtained at previous search points should be used effectively. In order to use the likelihood of face obtained at previous search points effectively, Ising model is adopted to face detection. Ising model has the two-state spins; "up" and "down". The state of a spin is updated by depending on the neighboring spins and an external magnetic field. Ising spins are assigned to "face" and "non-face" states of face detection. In addition, the measured likelihood of face is integrated into the energy function of Ising model as the external magnetic field. It is confirmed that face candidates would be reduced effectively by spin flip dynamics. To improve the search performance further, the single level Ising search method is extended to the multilevel Ising search. The interactions between two layers which are characterized by the renormalization group method is used to reduce the face candidates. The effectiveness of the multilevel Ising search method is also confirmed by the comparison with the single level Ising search method.

Multilayered filters with a dielectric distribution along their thickness forming a one-dimensional quasi-fractal structure are theoretically analyzed, focusing on exposing their resonant properties in order to understand a dielectric Menger's sponge resonator [4],[5]. "Quasi-fractal" refers to the triadic Cantor set with finite generation. First, a novel calculation method that has the ability to deal with filters with fine fractal structures is derived. This method takes advantage of Clifford algebra based on the theory of thin-film optics. The method is then applied to classify resonant modes and, especially, to investigate quality factors for them in terms of the following design parameters: a dielectric constant, a loss tangent, and a stage number. The latter determines fractal structure. Finally, behavior of the filters with perfect fractal structure is considered. A crucial finding is that the high quality factor of the modes is not due to the complete self-similarity, but rather to the breaking of such a fractal symmetry.

In 1996, Sipser and Spielman [12] constructed a family of linear-time decodable asymptotically good codes called expander codes. Recently, Barg and Zemor [2] gave a modified construction of expander codes, which greatly improves the code parameters. In this paper we present a new simple algebraic decoding algorithm for the modified expander codes of Barg and Zemor, and give a Justesen-type construction of linear-time decodable asymptotically good binary linear codes that meet the Zyablov bound.

The purpose of this paper is to present a case study of the development, implementation and performance analysis of an autonomous flight control strategy for a 1-meter small-sized unmanned aerial vehicle. Firstly, a learning algorithm based open-loop control is proposed by simulating a skilled human operator's manipulation of the aircraft. This is aimed to generate a set of command data inputs and investigate the multi-channel control characteristics with the open-loop control. Secondly, a feedforward plus a proportional and derivative (PD) feedback control is employed to control the vehicle in following the command data to complete the loitering flight. The PD control gains are tuned automatically according to the attitude of the vehicle using the fuzzy logic theory. Thirdly, autonomous flight experiments conducted on a 1-meter small-sized aerial vehicle demonstrated the effectiveness of the proposed method.

The state-oriented design of IP multicast may lead to the scalability problem, especially when there is a very large number of concurrent multicast groups in the network. Motivated by this problem, explicit multicast offers a stateless design using header space of multicast data packets. In this paper, we propose a novel stateless scheme called Linkcast that efficiently eliminates processing overhead of explicit multicast protocols. In Linkcast, the multicast sender encodes the tree listing its links in a proper way. The tree code is sent with every multicast data packet. Simulation results and experiments with real-trees show that Linkcast completely eliminates processing overhead of other explicit multicast protocols such as Xcast with comparable header size overhead.

We define discretized Markov transformations and find an algorithm to give the number of maximal-period sequences based on discretized Markov transformations. In this report, we focus on the discretized dyadic transformations and the discretized golden mean transformations. Then we find an algorithm to give the number of maximal-period sequences based on these discretized transformations. Moreover, we define a number-theoretic function related to the numbers of maximal-period sequences based on these discretized transformations. We also introduce the entropy of the maximal-period sequences based on these discretized transformations.

This paper deals with new fuzzy controller for handling systems having large dead time and nonlinearities, via approximations of large rule fuzzy logic controller by simplest fuzzy controller (4 rules). The error between large rule fuzzy controller and simplest fuzzy controller are compensated by proposed compensating factors. These compensating factors are modified to handle large dead time and nonlinear systems. Features of proposed approximations are discussed. The concept of variation of nonlinearity factor of fuzzy controller is also discussed. Various processes from different literatures are utilized to demonstrate the proposed methodology. After doing many simulations it has been found that with proper tuning, overall system handles large dead time and nonlinearity which may be difficult by conventional methods. The processes are also simulated for load disturbances and change of operating point (set point) and it has been found that proposed scheme is robust for long dead times.

Though there are intensive researches on off-line electronic cash (e-cash), the current computer network infrastructure sufficiently accepts on-line e-cash. The on-line means that the payment protocol involves with the bank, and the off-line means no involvement. For customers' privacy, the e-cash system should satisfy unlinkability, i.e., any pair of payments is unlinkable w.r.t. the sameness of the payer. In addition, for the convenience, exact payments, i.e., the payments with arbitrary amounts, should be also able to performed. In an existing off-line system with unlinkable exact payments, the customers need massive computations. On the other hand, an existing on-line system does not satisfy the efficiency and the perfect unlinkability simultaneously. This paper proposes an on-line system, where the efficiency and the perfect unlinkability are achieved simultaneously.

We analyze the noise sensitivity of the focal length computation, the principal point estimation, and the orthogonality enforcement for single-view 3-D reconstruction based on vanishing points and orthogonality. We point out that due to the nonlinearity of the problem the standard statistical optimization is not very effective. We present a practical compromise for avoiding the computational failure and preserving high accuracy, allowing a consistent 3-D shape in the presence of however large noise.

In reconstructing 3-D from images based on feature points, one usually defines a triangular mesh that has these feature points as vertices and displays the scene as a polyhedron. If the scene itself is a polyhedron, however, some of the displayed edges may be inconsistent with the true shape. This paper presents a new technique for automatically eliminating such inconsistencies by using a special template. We also present a technique for removing spurious occluding edges. All the procedures do not require any thresholds to be adjusted. Using real images, we demonstrate that our method has high capability to correct inconsistencies.

The most obvious architectural solution for high-speed fuzzy inference is to exploit temporal parallelism and spatial parallelism inherited in a fuzzy inference execution. However, in fact, the active rules in each fuzzy inference execution are often only a small part of the total rules. In this paper, we present a new architecture that uses less hardware resources by discarding non-active rules in the earlier pipeline stage. Compared with previous work, implementation data show that the proposed architecture achieves very good results in terms of the inference speed and the chip area.

This paper proposes a novel PN (Pseudo Noise) synchronization system using Cycle-and-Add property of M-sequence featuring fast acquisition in DS-CDMA (direct sequence-code division multiple access). Fast acquisition is carried out by generating a PN sequence in a simple multiplicative action of a received signal with its delayed one. This multiplicative action is similar to differentially coherent detection and realizes an anti-fading property. Easy implementation is materialized by the fact that the system is mostly composed of baseband devices. The principle, performance evaluation and the detection probability of synchronization for the proposed method are shown. Furthermore, detection probability of synchronization in a fast Rayleigh fading channel is shown for the performance evaluation.