Photonic crystals have optical properties characterized by photonic bandgap, large anisotropy and high dispersion, which can be applied to various optical devices. We have proposed an autocloning method for fabricating 2D or 3D photonic crystals and are developing novel structures and functions in photonic crystals. The autocloning is an easy process based on the combination of sputter deposition and sputter etching and is suitable for industry. We have already demonstrated devices or functions such as polarization splitters and surface-normal waveguides. In this paper, we describe our latest work on photonic crystals utilizing the autocloning technology. Phase plates and polarization selective gratings for optical pick-ups are demonstrated utilizing TiO2/SiO2 photonic crystals. The technology to introduce CdS into 3D photonic crystals is also developed and photoluminescence from the introduced CdS is observed, which is the first step to realize luminescent devices with 3D confinement or high polarization controllability.

Simultaneous wavelength conversion of multi-WDM channels is expected to be a key technique in near-future networks. In this paper, 4-channel wavelength conversion using four-wave mixing (FWM) in a hybrid wavelength selector is successfully demonstrated. The wavelength selector consists of two four-channel spot-size-converter-integrated semiconductor optical amplifier (SS-SOA) gate arrays on a planar-lightwave-circuit (PLC) platform and two PLC-arrayed-waveguide-gratings (AWGs). As the wavelength selector has an individual SS-SOA for the wavelength conversion of each channel, there is negligible interference between channels. Four WDM channels with an 2.5 Gb/s modulation were converted from 1555 to 1575 nm. Clear eye openings and only a small power penalty of less than 0.5 dB were observed. The receiver sensitivity was -31 dBm at a bit error rate (BER) of 10-9.

Tracking many targets simultaneously using a search radar has been one of the major research areas in radar signal processing. The primary difficulty in this problem arises from the noise characteristics of the incoming data. Hence it is crucial to obtain an accurate association between targets and noisy measurements in multi-target tracking. We introduce a new scheme for optimal data association, based on a MAP approach, and thereby derive an efficient energy function. Unlike the previous approaches, the new constraints between targets and measurements can manage the cases of target missing and false alarm. Presently, most algorithms need heuristic adjustments of the parameters. Instead, this paper suggests a mechanism that determines the parameters in an automated manner. Experimental results, including PDA and NNF, show that the proposed method reduces position errors in crossing trajectories by 32.8% on the average compared to NNF.

We demonstrate a large nonlinear optical response of GaAs thin films using degenerate four-wave mixing (DFWM) with picosecond pulses. The obtained DFWM signal is thickness-dependent and peaks at around 110 nm. The nonlocal theory fully explains these results.

A rigorous modal approach based on the transmission-line description has developed to explore effectively the filtering characteristics of planar optical DFB guiding structures. Using the modal transmission-line theory, the leakage and filtering characteristics of metal-strip gratings and dielectric gratings with gain or loss are first evaluated in details at the first- and third-order Bragg regimes. It can thus serve as a powerful template for computational algorithms to determine systematically and rigorously the optical effects of multilayered periodic guiding structures, which are not readily obtained by other methods.

In this paper, we propose the majority algorithm to choose the connection weights for the neural networks with quantized connection weights of 1 and 0. We also obtained the layered network to solve the parity problem with the input of arbitrary number N through an application of this algorithm. The network can be expected to have the same ability of generalization as the network trained with learning rules. This is because it is possible to decide the connection weights, regardless of the size of the training set. One can decide connection weights without learning according to our case study. Thus, we expect that the proposed algorithm may be applied for a real-time processing.

Taking traveling salesman problems (TSPs) as examples of combinatorial optimization problems, an "optimal" Hopfield network for ("optimal" neural representation of) TSPs is presented, where a vertex of state hypercube of the network is asymptotically stable if and only if it is an optimal solution. Of all the Hopfield networks for TSPs, this network most sharply distinguishes an optimal solution from other nonoptimal solutions and infeasible solutions. In this sense, we call this network "optimal" for TSPs. Whenever the network converges to a vertex, we can always obtain an optimal solution. However, we can not design such network without knowing an optimal solution to the problem. So, its approximate realization, which can be designed without a-priori knowledge of an optimal solution, is proposed. Simulations show that the "optimal" network and its approximate realization obtain optimal or good feasible solutions more frequently than familiar Hopfield networks. We can also design such "optimal" Hopfield networks for many combinatorial optimization problems as well as for TSPs.

In this paper, a new network structure called generalized Hierarchical Completely-Connected networks (HCCs) is proposed, and its properties and features are evaluated. Simple routing strategies for HCCs are also developed for shortest-paths routing algorithms. A set of HCCs constructed by the proposed method includes some conventional hierarchical networks, then it is called generalized one. The construction of an HCC starts from a basic block (a level-1 block) which consists of n nodes of constant degree. Then a level-h block for h 2 is constructed recursively by interconnecting any pair of macro nodes (n level-(h-1) blocks) completely. An HCC has a constant node-degree regardless of an increase in its size (the number of nodes). Furthermore, since an HCC has a hierarchically structured topology and the feature of uniformity, a wide variety of inter-cluster connections is possible. Evaluation results show that an HCC is suitable for very large computer systems.

This paper proposes a survivor-correction Viterbi algorithm (SCVA) and presents its application to an iterative sequence estimation in order to improve bit error rate performance of decision-feedback sequence estimation (DFSE) in the presence of intersymbol interference. The SCVA can mitigate erroneous survivor selections due to DFSE, because it modifies the add-compare-select operation to an add-correct-compare-select operation. Finally, it is confirmed by computer simulation that complexity of the proposed scheme is independent of delay of the main delayed ray and its performance is superior to that of DFSE at the same number of states.

Today's market share of Intelligent Network (IN) service is growing rapidly in wireless networks due to the rapid advances in wireless telecommunication and IN technology. To guarantee network independent IN services, mobility of IN service subscribers has to be taken into account. This paper proposes new designs of Global Service Logic for the IN service enhancement, which increase call completion rates in wired and wireless intelligent networks. In order to apply this logic to wireless service subscribers as well as wired service subscribers, we implement a Queue Manager applied to the call queuing service feature in the Service Control Point (SCP). In the case of wireless service subscribers, the Home Location Register (HLR) handles the service registration flags to notify the Queue Manager of the corresponding service subscribers' mobility. In addition, we present a dynamic queue management mechanism, which dynamically manages the queue size based on the parallel server queuing model as the wireless subscribers roam the service groups due to their mobility characteristics. In order to determine the queue size allocated by the dynamic queue manager, we simulate the relationship between the number of the subscriber's terminals and the drop rate by considering the service increment rate. Moreover, the appropriate waiting time in the queue as required is simulated according to the above relationship. We evaluate call completion rates of the proposed mechanism in the paper by comparing to that of the existing mechanism.

In this paper, we study efficient scheduling algorithms that are suitable for ATM networks. In ATM networks, all packets have a fixed small length of 53 bytes and they are transmitted at very high rate. Thus time complexity of a scheduling algorithm is quite important. Most scheduling algorithms proposed so far have a complexity of O(log N) per packet, where N denotes the number of connections sharing the link. In contrast, weighted round robin (WRR) has the advantage of having O(1) complexity; however, it is known that its delay property gets worse as N increases. To solve this problem, in this paper we propose two new variants of WRR, uniform round robin (URR) and idling uniform round robin (I-URR). Both disciplines provide end-to-end delay and fairness bounds which are independent of N. Complexity of URR, however, slightly increases as N increases, while I-URR has complexity of O(1) per packet. I-URR also works as a traffic shaper, so that it can significantly alleviate congestion on the network. We also introduce a hierarchical WRR discipline (H-WRR) which consists of different WRR servers using I-URR as the root server. H-WRR efficiently accommodates both guaranteed and best-effort connections, while maintaining O(1) complexity per packet. If several connections are reserving the same bandwidth, H-WRR provides them with delay bounds that are close to those of weighted fair queueing.

A software antenna, which will be a key device realizing flexible and highly reliable wireless communications systems, is inherently matched with software defined radios (SDR). In this paper, first, key technologies on the software antenna are introduced. The technologies contain i) how to recognize the radio environment, ii) how to determine the optimum adaptive signal processing algorithm, and iii) how to reconfigure the digital beamforming circuit. Then, an image of a software antenna with reconfigurable eigenvector-beamspace configuration is presented. Finally, by assuming various propagation conditions, performance of the software antenna in terms of algorithm diversity is demonstrated.

In this paper, a new configuration method of multimode software radio system by parameter controlled and telecommunication component block embedded digital signal processing hardware (DSPH) is proposed for the future flexible multimedia communications. In this method, in advance, basic telecommunication component blocks are implemented in the DSPH like DSP and FPGA. And, external parameters, which are simple but important information, change the specification of each block. This proposed method has the following features: i) People need to have only one mobile handset and select communication services as they like. ii) The volume of download software is reduced drastically in comparison with conventional full-download-type software radio system. iii) Since important component blocks have already been implemented into the DSPH except for some external parameters in advance, the know-how related to the implementation of DSPH never leak out. In this paper, we evaluate the effectiveness of the proposed configuration method by using computer simulation and developed experimental prototype and comparing with full-download-type software radio system from the viewpoint of the volume of download software. Finally, we introduce several new software radio systems by using the proposed configuration method.

An architecture for a digital-to-RF converter for a software defined radio (SDR) transmitter is proposed. The ideal hardware architecture for an SDR is a digital-signal to RF-signal direct conversion transmitter. However no conventional digital-to-analog converter (DAC) has converted over 1-GHz RF signal with enough resolution, in the present condition. In this paper, a digital-to-RF direct converter architecture using a ΔΣ modulation technique is proposed for the amplitude-phase modulated signal. The experimental results show that the proposed direct converter outputs a sufficiently accurate signal.

In this paper, a new frequency transformation for complex analog filter design which is suitable for integration is discussed. Arbitrary specified passband and stopband edges are easily transformed into those of the normalized LPF by solving simultaneous equations with four unknowns. Different from previous methods, the proposed transformation provides better performance in active realization of complex filters.

In this paper, a realization of an imaginary resistor using an ideal transformer is proposed. In the same fashion as the conventional method, a signal path is divided into a real signal path and an imaginary path. We name circuits which constitute a real signal path and an imaginary signal path, a real circuit and an imaginary circuit, respectively. An imaginary resistor is converted into an ideal transformer embedded between the imaginary circuit and the real circuit. The imaginary circuit becomes a dual circuit of the real circuit. This filter consists of terminating resistors, inductors, capacitors and ideal transformers. This prototype circuit is simulated by using operational amplifiers. A 3rd-order complex Chebyshev bandpass filter is designed and its frequency response is measured. Finally, the sensitivity property of the proposed filter is evaluated by a computer simulation.

In this paper we propose a new lattice based second-order adaptive infinite impulse response (IIR) notch filter that uses a simplified adaptation algorithm. Steady-state analysis of the proposed structure is then studied based on the mean-squared error analysis of the steady-state variable coefficient fluctuations. The analysis is used to derive simple analytical expressions for steady-state variable coefficient variance and an upper bound for the step size adaptation constant. The results are shown to be useful in designing an FSK demodulator using the proposed structure. Computer simulation results are shown to confirm derived analytical expressions.

The propagation and the gate operation of femtosecond pulses in nonlinear optical waveguides utilizing the saturation of the intersubband absorption at 1.55 µm in nitride multiple quantum wells are simulated for the first time. The calculation was carried out by a one-dimensional finite-difference time-domain (FD-TD) method combined with three-level rate equations describing the intersubband carrier dynamics. The absorption recovers within 1 ps when the pulse width is less than 200 fs, which will allow 1-Tb/s operation. However, the pulse shape may be deformed with the propagation due to the coherent effect and the interference between the signal and the control pulses, and thus, optimization of the pulse widths and the incident timing is required. Since the transparent window (width of the control pulse) becomes shorter according to the propagation, the width of the control pulse should be set broader than that of the signal pulse. As an example, we assume the case where a 1.6-µm, 100-fs signal pulse is gated by a 300-fs control pulse at a wavelength of 1.5 µm in a 500-µm length waveguide. A 140-fs gated signal pulse with a smooth envelope is expected to appear after the band-pass filter. The extinction ratio is expected to be greater than 15 dB.

In many of machine learning problems, it is essential to use not only the training data, but also a priori knowledge about how the world is constrained. In many cases, such knowledge is given in the forms of constraints on differential data or more specifically partial differential equations (PDEs). Neural networks with capabilities to learn differential data can take advantage of such knowledge and easily incorporate such constraints into the learning of training value data. In this paper, we report a structure, an algorithm, and results of experiments on neural networks learing differential data.

We present two estimation methods for camera rotation from two images obtained by the active camera before and after rotation. Based on the representation of the projected rotation group, quasi moment features are constructed. Camera rotation can be estimated by applying the singular value decomposition (SVD) or Newton's method to tensor quasi moment features. In both cases, we can estimate 3D rotation of the active camera from only two projected images. We also give some experiments for the estimation of the actual active camera rotation to show the effectiveness of these methods.