This article describes a large bandwidth and low distortion line driver in a 0.35-µm CMOS process. The line driver drives a 75 Ω resistive load. Its power consumption is 140 mW from a 3.3 V supply. It has a relatively high -3 dB bandwidth (260 MHz) with good phase margin of about 70 degrees. It shows very low THD (-74.5 dB) when drives the load with a 3.3 V peak to peak sine wave at 10 MHz. This architecture reduces the distortion by locating the input differential pair inside the feedback loop and eliminating the distortion of the feedback transistors, which is dominant source of distortion at high frequencies. Thus, it improves the linearity of the output voltage in comparison with previous designs.

This paper proposes an improved type of the Hybrid-Parallel Power-Factor-Correction (HP-PFC) converter. It has the advantage of a higher efficiency and improved input current waveform. This advantage achieved through changing new charging path of the bulk capacitor and balancing the power flow from the two transformers to the output. This new circuit has been analyzed using MATLAB/Simulink and confirmed with experiment. As a conclusion, it is confirmed that this improved HP-PFC converter complies with the severe regulation of IEC61000-3-2 Class D. Moreover, a high efficiency of 90% is achieved for 15 V/6 A output power under the worldwide line voltage conditions.

A reaction-diffusion computer is a large-scale array of elementary processors, micro-volumes of chemical medium, which act, change their states determined by chemical reactions, concurrently and interact locally, via local diffusion of chemical species; it transforms data to results, both represented by concentration profiles of chemical species, by traveling and colliding waves in spatially extended chemical media. We show that reaction-diffusion processors, simulated or experimental, can solve a variety of tasks, including computational geometry, robot navigation, logics and arithmetics.

A large-swing, high-driving, low-power, class-AB buffer amplifier, which consists of a high-gain input stage and a unity-gain class-AB output stage, with low variation of quiescent current is proposed. The low power consumption and low variation of the quiescent output current are achieved by using a weak-driving and a strong-driving pseudo-source followers. The high-driving capability is mainly provided by the strong-driving pseudo-source follower whose output transistors are turned off in the vicinity of the stable state to reduce the power consumption and the variation of output current, while the quiescent state is maintained by the weak-driving pseudo-source follower. The error amplifiers with source-coupled pairs of the same type transistors are merged into a single error amplifier to reduce the area of the buffer and the current consumption. An experimental prototype buffer amplifier implemented in a 0.35-µm CMOS technology demonstrates that the circuit dissipates an average static power consumption of only 388.7 µW with the standard deviation of only 3.4 µW, which is only 0.874% at a power supply of 3.3 V, and exhibits the slew rates of 2.18 V/µs and 2.50 V/µs for the rising and falling edges, respectively, under a 300 Ω /150 pF load. Both of the second and third harmonic distortions (HD2 and HD3) are -69 dB at 20 kHz under the same load.

This paper presents a novel design procedure for class E oscillator. It is the characteristic of the proposed design procedure that a free-running oscillator is considered as a forced oscillator and the feedback waveform is tuned to the timing of the switching. By using the proposed design procedure, it is possible to design class E oscillator that cannot be designed by the conventional one. By carrying out two circuit experiments, we find that the experimental results agree with the calculated ones quantitatively, and show the validity of the proposed design procedure. One experimental measured power conversion efficiency is 90.7% under 6.8 W output power at an operating frequency 2.02 MHz, the other is 89.7% under 2.8 W output power at an operating frequency 1.97 MHz.

This paper proposes dynamic distributed unicast and multicast routing algorithms for multiple classes of QoS guaranteed networks. Each link in such a network is assumed to be able to provide multiple classes of QoS guarantee by reserving various amounts of resource. A distributed unicast routing algorithm, DCSP (Distributed Constrained Shortest Path), for finding a QoS constrained least cost path between each O-D (Originating-Destination) pair, is proposed first. Two class reduction schemes, the linear and logarithmic policies, are develpoed to prevent exponential growth of the number of end-to-end QoS classes. Based on DCSP, two distributed multicast routing algorithms, DCSPT (Distributed Constrained Shortest Path Tree) and DTM (Distributed Takahashi and Mutsuyama), are proposed to find QoS constrained minimum cost trees. Numerical results indicate that DCSP strongly outperforms previously proposed centralized algorithms and it works better with the linear class reduction method. For the multicast routing algorithms, the DCSPT with linear class reduction method yields the best performance of all multicast routing algorithms.

This paper presents the design of an automatic surveillance system to monitor the dangerous non-frontal gazes of the car driver. To track the driver's eyes, we propose a novel filter to locate the "between-eye", which is the middle point between the two eyes, to help the fast locating of eyes. We also propose a specially designed criterion function named mean ratio function to accurately locate the positions of eyes. To analyze the gazes of the driver, a multilayer perceptron neural network is trained to examine whether the driver is losing the proper gaze or not. By incorporating the neural network output with some well-designed alarm-issuing rules, the system performs the monitoring task for single dedicated driver and multiple different drivers with a satisfied performance in our experiments.

A new and fast fingerprint classification method based on direction patterns is presented in this paper. This method is developed to be applicable to today's embedded fingerprint authentication system, in which small area sensors are widely used. Direction patterns are well treated in the direction map at block level, where each block consists of 88 pixels. It is demonstrated that the search of directions pattern in specific area, generally called as pattern area, is able to classify fingerprints clearly and quickly. With our algorithm, the classification accuracy of 89% is achieved over 4000 images in the NIST-4 database, slightly lower than the conventional approaches. However, the classification speed is improved tremendously up to about 10 times as fast as conventional singular point approaches.

In this letter we suggest sets of features to classify genres of web documents. Web documents are different from textual documents in that they contain URL and HTML tags within the pages. We introduce the features specific to web documents, which are extracted from URL and HTML tags. Experimental results enable us to evaluate their characteristics and performances. On the basis of the experimental results, we implement a user interface of a web search engine that presents documents grouped by genres.

We have developed Multi-modal Neural Networks (MNN) to improve the accuracy of symbolic sequence pattern classification. The basic structure of the MNN is composed of several sub-classifiers using neural networks and a decision unit. Two types of the MNN are proposed: a primary MNN and a twofold MNN. In the primary MNN, the sub-classifier is composed of a conventional three-layer neural network. The decision unit uses the majority decision to produce the final decisions from the outputs of the sub-classifiers. In the twofold MNN, the sub-classifier is composed of the primary MNN for partial classification. The decision unit uses a three-layer neural network to produce the final decisions. In the latter type of the MNN, since the structure of the primary MNN is folded into the sub-classifier, the basic structure of the MNN is used twice, which is the reason why we call the method twofold MNN. The MNN is validated with two benchmark tests: EPR (English Pronunciation Reasoning) and prediction of protein secondary structure. The reasoning accuracy of EPR is improved from 85.4% by using a three-layer neural network to 87.7% by using the primary MNN. In the prediction of protein secondary structure, the average accuracy is improved from 69.1% of a three-layer neural network to 74.6% by the primary MNN and 75.6% by the twofold MNN. The prediction test is based on a database of 126 non-homologous protein sequences.

This paper proposes the use of the ratio of wavelet extrema numbers taken from the horizontal and vertical counts respectively as a texture feature, which is called aspect ratio of extrema number (AREN). We formulate the classification problem upon natural and synthesized texture images as an optimization problem and develop a coevolving approach to select both scalar wavelet and multiwavelet feature spaces of greater discriminatory power. Sequential searches and genetic algorithms (GAs) are comparatively investigated. The experiments using wavelet packet decompositions with the innovative packet-tree selection scheme ascertain that the classification accuracy of coevolutionary genetic algorithms (CGAs) is acceptable enough.

In this paper, we propose a method of linear time-varying filtering of discrete time signals. The objective of this method is to derive a component, of an input signal, whose alias-free generalized discrete time-frequency distribution [Jeong & Williams 1992] concentrates on a specific region of a time-frequency plane. The method is essentially realized by computing an orthogonal projection of an input onto a subspace that is spanned by orthonormal signals, whose distributions concentrate on the region. We show that such orthonormal signals can be derived as eigenvectors of a matrix whose components are explicitly expressed by using the kernel of the distribution and the regions. This result shows that we can design such a filter prior to processing of the input if the specific region is given as a priori. This result is a generalization of [Hlawatsch & Kozek 1994], that is originally derived for the continuous Wigner distributions, to the discrete distributions.

It is generally believed that one major function of the immune system is helping to protect multicellular organisms from foreign pathogens, especially replicating pathogens such as viruses, bacteria and parasites. The relevant events in the immune system are not only the molecules, but also their interactions. The immune cells can respond either positively or negatively to the recognition signal. A positive response would result in cell proliferation, activation and antibody secretion, while a negative response would lead to tolerance and suppression. Depending upon these immune mechanisms, an immune network model (here, we call it the binary immune network) based on the biological immune response network was proposed in our previous work. However, there are some problems like that input and memory were all binary and it did not consider the antigen diversity of immune system. To improve these problems, in this paper we propose a fuzzy immune network model by considering the antigen diversity of immune system that is the most important property to be exhibited in the immune system. As an application, the proposed fuzzy immune network is applied to pattern recognition problem. Computer simulations illustrate that the proposed fuzzy immune network model not only can improve the problems existing in the binary immune network but also is capable of clustering arbitrary sequences of large-scale analog input patterns into stable recognition categories.

This paper presents a novel class of sigma-delta modulator topologies for low-voltage, high-speed, and high-resolution applications with low oversampling ratios (OSRs). The main specifications of these architectures are the reduced analog circuit requirements, large out-of-band gain in the noise transfer function (NTF) without any stability concerns to achieve high signal to noise ratio (SNR) with a low OSR, and unity-gain signal transfer function (STF) to reduce the harmonic distortions resulted from the analog circuit imperfections. To demonstrate the efficiency of the proposed modulator architectures a prototype with HSPICE is implemented. A low-power two-stage class A/AB OTA with modified common mode feedback (CMFB) circuit in the first stage is used to implement the fourth order modulator. Simulation results with OSR of 16 give signal to noise plus distortion ratio (SNDR) and dynamic range (DR) of 90-dB and 92.5-dB including the circuit noise in the 1.25-MHz signal bandwidth, respectively. The circuit is implemented in a 0.13-µm standard CMOS technology. It dissipates about 40-mW from a single 1.2-V power supply voltage.

Applying the formerly proposed classification framework for first order line search optimization techniques we introduce novel superlinear first order line search methods. Novelty of the methods lies in the line search subproblem. The presented line search subproblem features automatic step length and momentum adjustments at every iteration of the algorithms realizable in a single step calculation. This keeps the computational complexity of the algorithms linear and does not harm the stability and convergence of the methods. The algorithms have none or linear memory requirements and are shown to be convergent and capable of reaching the superlinear convergence rates. They were practically applied to artificial neural network training and compared to the relevant training methods within the same class. The simulation results show satisfactory performance of the introduced algorithms over the standard and previously proposed methods.

This paper presents a new submesh allocation scheme for mesh connected multicomputer systems, called CFSL-TR (Classified Free Submesh List-Task Relocation), which reduces task waiting time in two aspects, shortening submesh search time and reducing the submesh allocation delay caused by external fragmentation. This scheme classifies independent free submeshes by their types: square, horizontal rectangle, or vertical rectangle. Then it searches for the best-fit submesh only from one list depending on the type of the given task, thus saving submesh searching time. If no suitable submeshes are found, it is most likely caused by external fragmentation. In such a case, our scheme relocates the tasks being executed to free submeshes and combines the newly available submesh with other fragmented ones to form a larger submesh. This allows allocation of the task, otherwise to be put on the queue, hence reducing the submesh allocation delay. Through simulation, we show that our scheme helps reduce task waiting time and that it is by far more effective to reduce the submesh allocation delay caused by external fragmentation rather than to reduce submesh search time for reduction of the task waiting time.

In this letter, the concept of cross-entropy is introduced for unsupervised polarimetric synthetic aperture radar (SAR) image classification. The difference between two scatterers is decomposed into three parts, i.e., the difference of average scattering characteristic, the difference of scattering randomness and the difference of scattering matrix span. All these three parts are expressed in cross-entropy formats. The minimum cross-entropy principle is adopted to make classification decision. It works well in unsupervised terrain classification with a NASA/JPL AIRSAR image.

The classification time required by conventional multi-class SVMs greatly increases as the number of pattern classes increases. This is due to the fact that the needed set of binary class SVMs gets quite large. In this paper, we propose a method to reduce the number of classes by using nearest neighbor rule (NNR) in the principle component analysis and linear discriminant analysis (PCA+LDA) feature subspace. The proposed method reduces the number of face classes by selecting a few classes closest to the test data projected in the PCA+LDA feature subspace. Results of experiment show that our proposed method has a lower error rate than nearest neighbor classification (NNC) method. Though our error rate is comparable to the conventional multi-class SVMs, the classification process of our method is much faster.

A novel fast KNN classification algorithm is proposed for pattern recognition. The technique uses one important feature, mean of the vector, to reduce the search space in the wavelet domain. Since the proposed algorithm rejects those vectors that are impossible to be the k closest vectors in the design set, it largely reduces the classification time and holds the classification performance as that of the original classification algorithm. The simulation on texture image classification confirms the efficiency of the proposed algorithm.

The paper discusses the spatio-temporal phenomena in autonomous two-layer Cellular Neural Networks (CNNs) with mutually coupled templates between two layers. By computer calculations, we show how pattern formations, autowaves and classical waves can be regenerated in the networks, and describe the properties of these phenomena in detail. In particular, we focus our discussion on the necessary conditions for generating these spatio-temporal phenomena. In addition, the influences of the template parameters and initial state conditions of CNNs on the spatio-temporal phenomena are investigated.