This paper presents the design consideration of a polarization-transformation transmission filter, which is composed of a multilayered chiral slab. The optimal material parameters and thickness of each layer of the slab can be determined by using a genetic algorithm (GA). Substituting the constitutive relations for a chiral medium into Maxwell's equations, the electromagnetic field in the medium is obtained. A chain-matrix formulation is used to derive the relationship between the components of the incident, the reflected, and the transmitted electric fields. The cross- and co-polarized powers carried by the transmitted and reflected waves are represented in terms of their electric field components. The procedure proposed for the design of a polarization-transformation filter is divided into two stages. An ordinary filter without polarization-transformation and a polarization-transformation filter for the transmitted wave are designed with a multilayered non-chiral slab and a multilayered chiral slab at the first and the second stages, respectively. According to the specifications of the filters, two functionals are defined with the transmitted and reflected powers. Thus the optimal design of a polarization-transformation filter with the multilayered chiral slab is reduced to an optimization problem where the material parameters and thickness of each chiral layer are found by maximizing the functionals. Applying the GA to the maximization of the functionals, one can obtain the optimal material parameters and thicknesses of the multilayered chiral slab. Numerical results are presented to confirm the effectiveness of the two-stage design procedure. For three types of multilayered chiral slabs, optimal values of refractive indices, thicknesses, and chiral admittances are obtained. It is seen from the numerical results that the proposed procedure is very effective in the optimal design of polarization-transformation filters for the transmitted wave.

Weight divided adaptive control method for a microwave FeedForward Power Amplifier (FFPA) is presented. In this adaptive controller, an output signal of a power amplifier is used as reference signal. Additionally, reference signal is divided by the weight of adaptive filter, so that characteristics of the power amplifier, such as temperature dependence, do not have influence on the convergence performances. The proposed adaptive algorithm and the convergence condition are derived analytically and we clarify that the proposed weight divided adaptive algorithm is more stable than the conventional Normalized Least Mean Square (NLMS) algorithm. Then, the convergence condition considering phase calibration error is discussed. The effectiveness of the proposed algorithm are also verified by the nonlinear simulations of the FFPA having AM-AM and AM-PM nonlinearity of GaAsFET.

Multiple access interference and near-far effect cause the performance of the conventional single user detector in DS/CDMA systems to degrade. Due to high complexity of the optimum multiuser detector, suboptimal multiuser detectors with less complexity and reasonable performance have received considerable attention. In this paper we apply the classic and a new modified genetic algorithm for multiuser detection of DS/CDMA signals. It is shown that the classic genetic algorithm (GA) reaches an error floor at high signal to noise ratios (SNR) while the performance of proposed modified GA is much better than the classic one and is comparable to the optimum detector with much less complexity. The results hold true for AWGN and fading channels. We also describe another GA called as meta GA to find the optimum parameters of the modified GA. We compare the performance of proposed method with the other detectors used in CDMA.

We present a method for recognition of two-hand gestures. Two-hand gestures include fine-grain descriptions of hands under a complicated background, and have complex dynamic behaviors. Hence, assuming that two-hand gestures are an interacting process of two hands whose shapes and motions are described by switching linear dynamics, we propose a coupled switching linear dynamic model to capture interactions between both hands. The parameters of the model are learned via EM algorithm using approximate computations. Recognition is performed by selection of the model with maximum likelihood out of a few learned models during tracking. We confirmed the effectiveness of the proposed model in tracking and recognition of two-hand gestures through some experiments.

The high-resolution frequency estimators most commonly used, such as Least Square (LS) method based on AR model, MVSE, MUSIC and ESPRIT, determine estimates of the sinusoidal frequencies from the sample noise-corrupted data. In this paper, a new frequency estimation method named Pole-Placement Least Square (PPLS) is presented, which is a modified LS method with a certain number of model poles restricted to the unit circle. The statistical performance of PPLS is studied numerically, and compared with the Cramer-Rao bound as well as the statistical performance corresponding to the LS methods. PPLS is shown to have higher resolution than the conventional LS method. The relationship between poles location and its resolution is also discussed in detail.

In this paper, we comparatively evaluate two photonic packet switch architectures with WDM-FDL buffers for synchronized variable length packets. The first one is an output buffer type switch, which stores packets in the FDL buffer attached to each output port. Another is a shared buffer type switch, which stores packets in the shared FDL buffer. The performance of a switch is greatly influenced by its architecture and a packet scheduling algorithm. We compare the performances of these two packet switches by applying different packet scheduling algorithms. Through simulation experiments, we show that each architecture has a parameter region for achieving better performance. For the shared buffer type switch, we found that void space introduces unacceptable performance degradation when the traffic load is high. Accordingly, we propose a void space reduction method. Our simulation results show that our proposed method enables to the shared buffer type switch to outperform the output buffer type switch even under high traffic load conditions.

Mobile location can be achieved by using the time-of-arrival (TOA) and angle-of-arrival (AOA) measurements. In this Letter, we analyze the location accuracy of an TOA-AOA hybrid algorithm with a single base station in the line-of-sight scenario. The performance of the algorithm is contrasted with the Cramer-Rao lower bound and Federal Communications Commission Emergency 911 requirements.

This paper proposes a disjoint path selection scheme for Generalized Multi-Protocol Label Switching (GMPLS) networks with Shared Risk Link Group (SRLG) constraints. It is called the weighted-SRLG (WSRLG) scheme. It treats the total number of SRLG members related to a link as part of the link cost when the k-shortest path algorithm is executed. In WSRLG, a link that has many SRLG members is rarely selected as the shortest path. Simulation results show that WSRLG finds more disjoint paths than the conventional k-shortest path algorithm. In addition, since WSRLG searches for the weight of the SRLG factor by using a modified binary search algorithm while satisfying the required number of disjoint paths between source and destination nodes, it can find cost-effective disjoint paths.

This paper defines a new kind of a mixture density model for modeling a quasi-stationary Gaussian process based on mel-cepstral representation. The conventional AR mixture density model can be applied to modeling a quasi-stationary Gaussian AR process. However, it cannot model spectral zeros. In contrast, the proposed model is based on a frequency-warped exponential (EX) model. Accordingly, it can represent spectral poles and zeros with equal weights, and, furthermore, the model spectrum has a high resolution at low frequencies. The parameter estimation algorithm for the proposed model was also derived based on an EM algorithm. Experimental results show that the proposed model has better performance than the AR mixture density model for modeling a frequency-warped EX process.

In this paper, we propose a fast full-search block matching algorithm for motion estimation, based on binary edge information. The binary edge information allows a faster search by reducing the computational complexity. It also reduces error, which is generated by the block located on the boundary of moving objects. After we transform the input image into an edge-based image using Sobel masks, we convert the result into a binary edge image using median-cut quantization. We then perform block matching using the binary edge image. If there exists blocks such that the error of the binary block matching exceeds threshold, we only perform edge intensity-based block matching within those blocks. We improve computational efficiency by eliminating an unnecessary searching process in no-motion regions. Simulation results have shown that the proposed method reduces the computational complexity and provides similar PSNR performance to the Full Search Block Matching Algorithm (FS-BMA)

Many fuzzy traffic controllers adjust the extension time of the green phase with the fuzzy input variables, arrival and queue. However, in our experiments, we found that the two input variables are not sufficient for an intersection where traffic flow rates change and thus, in this paper, traffic volume is used as an additional variable. Traffic volume is defined as the number of vehicles entering an intersection every second. In designing a fuzzy traffic controller, an ad-hoc approach is usually used to find membership functions and fuzzy control rules showing good performance. That is, initial ones are generated by human operators and modified many times based on the results of simulation. To partially overcome the limitations of the ad-hoc approach, we use genetic algorithms to automatically determine the membership functions for terms of each fuzzy variable when fuzzy control rules are given by hand. The experimental results indicate that a fuzzy logic controller with volume variable outperforms conventional ones with no volume variable in terms of the average delay and the average velocity. Also, the controller shows better performance when membership functions generated by a genetic algorithms instead of ones generated by hand are used.

An adaptive blind signal separation filter is proposed using a risk-sensitive criterion framework. This criterion adopts an exponential type function. Hence, the proposed criterion varies the consideration weight of an adaptation quantity depending on errors in the estimates: the adaptation is accelerated when the estimation error is large, and unnecessary acceleration of the adaptation does not occur close to convergence. In addition, since the algorithm derivation process relates to an H filtering, the derived algorithm has robustness to perturbations or estimation errors. Hence, this method converges faster than conventional least squares methods. Such effectiveness of the new algorithm is demonstrated by simulation.

Probabilistic inference by means of a massive probabilistic model usually has exponential-order computational complexity. For such massive probabilistic model, loopy belief propagation was proposed as a scheme to obtain the approximate inference. It is known that the generalized loopy belief propagation is constructed by using a cluster variation method. However, it is difficult to calculate the correlation in every pair of nodes which are not connected directly to each other by means of the generalized loopy belief propagation. In the present paper, we propose a general scheme for calculating an approximate correlation in every pair of nodes in a probabilistic model for probabilistic inference. The general scheme is formulated by combining a cluster variation method with a linear response theory.

Recently, several promising multiobjective evolutionary algorithms such as PESA, NSGA-II, and SPEA2 have been developed. In this paper, we also propose a new multiobjective evolutionary algorithm whose performance is comparable to or better than those promising algorithms. In the new algorithm proposed here, an age concept is introduced and utilized to make the efficiency of the offspring generation high. The performance of the proposed algorithm is superior to those of the promising algorithms mentioned above for several test functions. In this paper, the proposed algorithm will be explained only in two dimensional parameter and objective space to show manifestly the meaning of an age concept.

The conservative mode and the greedy mode scheduling algorithms for OBS switch with shared buffer are presented and discussed. Their performance is evaluated by computer simulations, as well as that of the greedy mode with void-filling algorithm. Simulation results show that the conservative mode and the greedy mode have different characteristics under different input load. The greedy mode and the conservative mode are more applicable in a real system than that with void-filling, owing to their lower computational complexity and FIFO characteristic. Finally, a composite algorithm integrated by the conservative mode and the greedy mode is proposed, which is adapted to the input load with the help of an input load monitor. The simulation results reveal that it has favorable performance under different load.

Many application programs can be modeled as a subgraph isomorphism problem. However, this problem is generally NP-complete and difficult to compute. A custom computing circuit is a prospective solution for such problems. This paper examines various accelerator designs for subgraph isomorphism problems based on Ullmann's algorithm and Konishi's algorithm. These designs are quantitatively evaluated from two points of view: logic scale and execution time. Our study revealed that Ullmann's design is faster but larger in logic scale. Partially sequential versions of Ullmann's algorithm can be more cost-effective than Ullmann's original design. The hardware of Konishi's algorithm is smaller in logic scale, operates at a higher frequency, and is more cost-effective.

Two implementation schemes for a two-step SOVA (Soft Output Viterbi Algorithm) decoder are proposed and verified in a chip. One uses the combination of trace back (TB) logic to find the survivor state and double trace back logic to find the weighting factor of a two-step SOVA. The other is that the reliability values are divided by a scaling factor in order to compensate for the distortion brought by overestimating those values in SOVA. We introduced a fixed scaling factor of 0.25 or 0.33 for a rate 1/3 and designed an 8-state Turbo decoder with a 256-bit frame size to lower the reliability values. The implemented architecture of the two-step SOVA decoder allows important savings in area and high-speed processing compared with the one-step SOVA decoder using register exchange (RE) or trace-back (TB) method. The chip is fabricated using 0.65 µm gate array at Samsung Electronics and it shows higher SNR performance by 2 dB at the BER 10-4 than that of a two-step SOVA decoder without a scaling factor.

The equidistortion principle[1] has recently been proposed as a basic principle for design of an optimal vector quantization (VQ) codebook. The equidistortion principle adjusts all codebook vectors such that they have the same contribution to quantization error. This paper introduces a novel VQ codebook design algorithm based on the equidistortion principle. The proposed algorithm is a variant of the law-of-the-jungle algorithm (LOJ), which duplicates useful codebook vectors and removes useless vectors. Due to the LOJ mechanism, the proposed algorithm can establish the equidistortion condition without wasting learning steps. This is significantly effective in preventing performance degradation caused when initial states of codebook vectors are improper to find an optimal codebook. Therefore, even in the case of improper initialization, the proposed algorithm can achieve minimization of quantization error based on the equidistortion principle. Performance of the proposed algorithm is discussed through experimental results.

This paper presents an improved phoneme-history-dependent (PHD) search algorithm. This method is an optimum algorithm under the assumption that the starting time of a recognized word depends on only a few preceding phonemes (phoneme history). The computational cost and the number of recognition errors can be reduced if the phoneme-history-dependent search uses re-selection of the preceding word and an appropriate length of phoneme histories. These improvements increase the speed of decoding and help to ensure that the resulting word graph has the correct word sequence. In a 65k-word domain-independent Japanese read-speech dictation task and 1000-word spontaneous-speech airline-ticket-reservation task, the improved PHD search was 1.2-1.8 times faster than a traditional word-dependent search under the condition of equal word accuracy. The improved search reduced the number of errors by a maximum of 21% under the condition of equal processing time. The results also show that our search can generate more compact and accurate word graphs than those of the original PHD search method. In addition, we investigated the optimum length of the phoneme history in the search.

Load balancing among multiple mirror servers located at distributed positions in the network is a key technique for content delivery services. For bandwidth allocated services, we consider how to select a suitable server from several candidates containing the same content at the time of a request. We propose limiting the candidates in advance and selecting a server from the limited set of servers in a round-robin fashion. The server sets that minimize the variance of the link load are derived using a greedy method for a given network topology and service demand. Through numerical evaluation, we demonstrate that the proposed method is superior to previous methods.