This paper is intended to provide an alternative approach for the design of FIR filters by using a Hopfield Neural Network (HNN). The proposed approach establishes the error function between the amplitude response of the desired FIR filter and the designed one as a Lyapunov energy function to find the HNN parameters. Using the framework of HNN, the optimal filter coefficients can be obtained from the output state of the network. With the advantages of local connectivity, regularity and modularity, the architecture of the proposed approach can be applied to the design of differentiators and Hilbert transformer with significantly reduction of computational complexity and hardware cost. As the simulation results illustrate, the proposed neural-based method is capable of achieving an excellent performance for filter design.

A microwave photonic mixer utilizing an InGaAs photoconductor for radio over fiber applications is proposed and fabricated. Static and dynamic characteristics of the fabricated microwave photonic mixer were investigated. The microwave photonic mixer showed an optical bandwidth of approximately 300 MHz and a uniform conversion loss characteristic for the electrical input frequency up to 20 GHz.

This paper proposes a Dynamic-Configurable NAT (DCNAT) approach to the inbound session problem for private networks behind NAT routers. DCNAT is a port-based NAT scheme that adopts a registration procedure for a host to register a session with a DCNAT router before originating a communication session to a host behind the DCNAT router. With the registration procedure, the DCNAT router can create NAT binding entries dynamically for address: port translation before the inbound session starts. Furthermore the dynamic creation of NAT binding entries makes DCNAT very flexible in supporting inbound accesses to a large number of services opened dynamically by the private nodes behind an NAT router. Although DCNAT requires minor modification to the originating host and the NAT router, it is highly suitable for proxy-based applications, such as web browsing, or instant message delivery.

A feature for classification of shallowly buried landmine-like objects using a ground penetrating radar (GPR) measurement system is proposed and its performance is evaluated. The feature for classification employed here is a time interval between two pulses reflected from top and bottom sides of landmine-like objects. First, we estimate a time resolution required to detect object thickness from GPR data, and check the actual time resolution through laboratory experiment. Next, we evaluate the classification performance using Monte Carlo simulations from dataset generated by a two-dimensional finite difference time domain (FDTD) method. The results show that good classification performance is achieved even for landmine-like objects buried at shallow depths under rough ground surfaces. Furthermore, we also estimate the effects of ground surface roughness, soil inhomogeneity, and target inclination on the classification performance.

The aim of the paper is to design efficient bit-parallel algorithms for translating regular expressions into nondeterministic finite automata (NFAs). Let r be a regular expression over an alphabet Σ, and let n be the length of r and let m be the number of symbols of Σ occurring in r. Then we present bit-parallel algorithms for translating regular expressions into Glushkov automata (position automata) and follow automata using Thompson automata. For Glushkov automata, we will give an algorithm which runs in O(n+mm/W) time and space. For follow automata, we will give a randomized algorithm which runs in O(n+mm/W) expected time and O(n+mm/W) space. We rely on a W-bit uniform RAM for estimating the complexities of algorithms. Since the best known algorithms for these automata runs in O(n+m2) time and space, our algorithms achieve an almost W-fold speed-up.

Simultaneous all-optical frequency up/downconversion technique utilizing a single semiconductor optical amplifier Mach-Zehnder interferometer (SOA-MZI) for full-duplex WDM radio over fiber (RoF) applications is presented. Using this technique, error-free simultaneous upconversion and downconversion of RoF signals with a finite-length single mode fiber were experimentally demonstrated. The results show the potential of the proposed scheme for use in a cost-effective full-duplex WDM RoF link.

Recently, Wu-Chieu proposed an improvement to their original scheme, in order to make the scheme withstand impersonation attacks. However, the improved scheme is susceptible to an off-line password guessing attack and is inefficiently designed. Accordingly, the current letter demonstrates the vulnerability of Wu-Chieu's modified scheme to an off-line password guessing attack and evaluates the efficiency of their schemes and related schemes.

This paper presents the Physical Optics field calculation in terms of only line integrations by using the Modified Edge Representation technique (MER), the alternative way of the surface integration. Not only the diffracted fields as in the conventional method of equivalent edge currents (EEC) but also the scattering geometrical optics fields are expressed in terms of the MER line integrals. The far field patterns of parabolic reflector antennas with the defocused dipole feed are discussed and the satisfactory agreement with those obtained by the Physical Optics surface integration is demonstrated.

This paper proposes a two-dimensional self-matching receiver for Free Space Optics (FSO) communication system using chaotic spatial synchronization. This system is able to obtain the information of two-dimensional code from received pattern. This paper considers that proposed system is applied to two applications. The first application is image transmission. This paper shows that applying proposed system to image transmission enables to restore the desired image, which doesn't require strict alignment of receiver, and evaluates transmission optical power. The second application is Code Division Multiplexing (CDM). This paper shows that applying proposed system to CDM system enables to demodulate desired digital signals regardless of the uncertainty of received position. Moreover, the required transmission optical power and bit error rate performance are obtained by computer simulation.

Distortion analysis of nonlinear circuits is very important for designing analog integrated circuits and communication systems. In this letter, we propose an efficient frequency-domain approach for calculating frequency response curves, which is based on HB (harmonic balance) method combining with ABMs (Analog Behavior Models) of Spice. Firstly, nonlinear devices such as bipolar transistors and MOSFETs are transformed into the HB device modules executing the Fourier transformations. Using these modules, the determining equation of the HB method is formed by the equivalent sine-cosine circuit in the schematic form or net-list. It consists of the coupled resistive circuits, so that it can be efficiently solved by the DC analysis of Spice. In our algorithm, we need not to derive any troublesome circuit equations, and any kinds of the transformations.

This paper deals with a design problem of an observer-based robust preview control system for uncertain discrete-time systems. In this approach, we adopt 2-stage design scheme and we derive an observer-based robust controller with integral and preview actions such that a disturbance attenuation level is satisfactorily small for allowable uncertainties.

In D.O.A. estimation, identification of the signal and the noise subspaces plays an essential role. This identification process was traditionally achieved by the eigenvalue decomposition (EVD) of the spatial correlation matrix of observations or the generalized eigenvalue decomposition (GEVD) of the spatial correlation matrix of observations with respect to that of an observation noise. The framework based on the GEVD is not always an extension of that based on the EVD, since the GEVD is not applicable to the noise-free case which can be resolved by the framework based on the EVD. Moreover, they are not applicable to the case in which the spatial correlation matrix of the noise is singular. Recently, a quotient-singular-value-decomposition-based framework, that can be applied to problems with singular noise correlation matrices, is introduced for noise reduction. However, this framework also can not treat the noise-free case. Thus, we do not have a unified framework of the identification of these subspaces. In this paper, we show that a unified framework of the identification of these subspaces is realized by the concept of proper and improper eigenspaces of the spatial correlation matrix of the noise with respect to that of observations.

After the popularization of sensor networks, the size of the monitoring region and the number of sensor nodes will grow to an enormous scale. In such large-scale sensor networks, multi-hop communications between sensor nodes will be necessary to cover the whole monitoring region. Moreover, sensor nodes should be grouped into clusters to enhance scalability and robustness. Therefore, we believe that multi-hop communication between clusters is preferable for large-scale sensor networks. To clarify the fundamental characteristics of this form of communication, we analytically derive the network's power consumption and compare it with other routing methods using TDMA communication and an interference-free transmission schedule. The results show multi-hop communication between clusters is preferable in large-scale sensor networks because it can alleviate heavy relaying loads near the sink node and it has a shorter data collection time compared with simple multi-hop communications without clusters. Knowing how much performance degradation arises when interference is unavoidable is essential for multi-hop communications in clustered sensor networks. Therefore, we compare interference-free TDMA communication with CSMA/CA communication which can cause interference in clustered sensor networks. Consequently, we show that although the data collection time is about 3.7 times longer when using CSMA/CA, the power consumption can be suppressed to 12%.

This paper presents a fast transient simulation method for power distribution networks (PDNs) of the PCB/Package. Because these PDNs are modeled as large-scale linear circuits consisting of a large number of RLC elements, it takes large costs to solve by conventional circuit simulators, such as SPICE. Our simulation method is based on the leapfrog algorithm, and can solve RLC circuits of PDNs faster than SPICE. Actual PDNs have frequency-dependent dispersions such as the skin-effect of conductors and the dielectric loss. To model these dispersions, more number of RLC elements are required, and circuit structures of these dispersion models are hard to solve by using the leapfrog algorithm. This paper shows that the circuit structures of dispersion models can be converted to suitable structures for the leapfrog algorithm. Further, in order to reduce the simulation time, our proposed method exploits parallel computation techniques. Numerical results show that our proposed method using single processing element (PE) enables a speedup of 20-100 times and 10 times compared to HSPICE and INDUCTWISE with the same level of accuracy, respectively. In a large-scale example with frequency-dependent dispersions, our method achieves over 94% parallel efficiency with 5PEs.

OBS is a realistic solution to the mismatch of the capacity of optical fiber and electrical switching in backbone photonic networks. One of the critical issues in OBS networks is to avoid burst contention at transit nodes. This problem induces the rapid growth of burst-transmission delay time under heavy traffic loads. In this paper, we propose a low-delay burst transmission scheme using burst segmentation at source node to suppress the growth in burst-transmission delay. In our scheme, a burst is divided and burst-transfer time is determined by the multiple information about reservation of other bursts at all transit nodes. We analyzed capabilities of the proposed scheme and found that it more efficiently suppresses the growth of the burst-transmission delay time in heavy traffic loads compared with some conventional signaling schemes.

We introduce an adaptive subchannel, bit, and power allocation (ASBPA) algorithm to maximize the bandwidth efficiency of the mobile communication system that use orthogonal frequency division multiplexing (OFDM). We propose a suboptimal rate adaptive ASBPA algorithm that guarantees fairness in resource allocation and overcomes inherent co-channel interference (CCI) in the cellular system. Furthermore, we evaluate the maximum possible bandwidth efficiency of the cellular OFDM system achieved by the ASBPA algorithm which is practical to implement. Our simulation results show that the proposed algorithm outperforms the existing ones and achieves the cellular bandwidth efficiency of up to 5 b/s/Hz/cell. We also investigate some of the conditions that govern the bandwidth efficiency of the cellular OFDM system using the proposed ASBPA algorithm.

In this paper, we propose a method of accelerating test generation for sequential circuits by using the knowledge about the availability of state justification sequences, the bound on the length of state distinguishing sequences, differentiation between valid and invalid states, and the existence of a reset state. We also propose a method of synthesis for testability (SfT) which takes the features of our test generation method into consideration to synthesize sequential circuits from given FSM descriptions. The SfT method guarantees that the test generator will be able to find a state distinguishing sequence. The proposed method extracts the state justification sequence from the FSM produced by the synthesizer to improve the performance of its test generation process. Experimental results show that the proposed method can achieve 100% fault efficiency in relatively short test generation time.

Accurate thickness measurement of sheet-like structure such as articular cartilage in CT images is required in clinical diagnosis as well as in fundamental research. Using a conventional measurement method based on the zero-crossing edge detection (zero-crossings method), several studies have already analyzed the accuracy limitation on thickness measurement of the single sheet structure that is not influenced by peripheral structures. However, no studies, as of yet, have assessed measurement accuracy of two adjacent sheet structures such as femoral and acetabular cartilages in the hip joint. In this paper, we present a model of the CT scanning process of two parallel sheet structures separated by a small distance, and use the model to predict the shape of the gray-level profiles along the sheet normal orientation. The difference between the predicted and the actual gray-level profiles observed in the CT data is minimized by refining the model parameters. Both a one-by-one search (exhaustive combination search) technique and a nonlinear optimization technique based on the Levenberg-Marquardt algorithm are used to minimize the difference. Using CT images of phantoms, we present results showing that when applying the one-by-one search method to obtain the initial values of the model parameters, Levenberg-Marquardt method is more accurate than zero-crossings and one-by-one search methods for estimating the thickness of two adjacent sheet structures, as well as the thickness of a single sheet structure.

Automatic detection of normal mammograms, as a "first look" for breast cancer, is a new approach to computer-aided diagnosis. This approach may be limited, however, by two main causes. The first problem is the presence of poorly separable "crossed-distributions" in which the correct classification depends upon the value of each feature. The second problem is overlap of the feature distributions that are extracted from digitized mammograms of normal and abnormal patients. Here we introduce a new Support Vector Machine (SVM) based method utilizing with the proposed uncrossing mapping and Local Probability Difference (LPD). Crossed-distribution feature pairs are identified and mapped into a new features that can be separated by a zero-hyperplane of the new axis. The probability density functions of the features of normal and abnormal mammograms are then sampled and the local probability difference functions are estimated to enhance the features. From 1,000 ground-truth-known mammograms, 250 normal and 250 abnormal cases, including spiculated lesions, circumscribed masses or microcalcifications, are used for training a support vector machine. The classification results tested with another 250 normal and 250 abnormal sets show improved testing performances with 90% sensitivity and 89% specificity.

Traffic sign detection is a valuable part of future driver support system. In this paper, we present a novel framework to accurately detect traffic signs from a single color image by analyzing geometrical, physical and text/symbol features of traffic signs. First, we utilize an elaborate edge detection algorithm to extract edge map and accurate edge pixel gradient information. Then, we extract 2-D geometric primitives (circles, ellipses, rectangles and triangles) efficiently from image edge map. Third, the candidate traffic sign regions are selected by analyzing the intrinsic color features, which are invariant to different illumination conditions, of each region circumvented by geometric primitives. Finally, a text and symbol detection algorithm is introduced to classify true traffic signs. Experimental results demonstrated the capabilities of our algorithm to detect traffic signs with respect to different size, shape, color and illumination conditions.