Asymmetric slope differential CMOS (ASD-CMOS) and asymmetric slope differential dynamic logic (ASDDL) surpass the highest speed that conventional CMOS logic circuits can achieve, resulting from deeply shortened rise time along with relatively prolonged fall time. ASD-CMOS is a static logic and ASDDL is a dynamic logic without per-gate synchronous clock signal, each of which needs two-phase operation as well as differential signaling, however, interleaved precharging hides the prolonged fall time and BDD-based compound logic design mitigates area increase. ASD-CMOS 16-bit multiplier in a 0.18-µm CMOS technology demonstrates 1.78 nsec per an operation, which reaches 34% reduction of the best delay time achieved by a multiplier using a CMOS standard cell library that is conventional yet tuned to the optimum in energy-delay products. ASDDL can be superior to DCVS-DOMINO circuits not only in delay time but also in area and even in power. ASDDL 16-bit multiplier achieves delay and power reduction of 4% and 20%, respectively, compared with DCVS-DOMINO realization. A prototype ASD-CMOS 16-bit multiplier with built-in test circuitry fabricated in a 0.13-µm CMOS technology operates with the delay time of 1.57 nsec at 1.2 V.

This investigation proposes a fast wavelet-based color segmentation (FWCS) technique and a modified directional region-growing (DRG) technique for semantic image segmentation. The FWCS is a subsequent combination of progressive color truncation and histogram-based color extraction processes for segmenting color regions in images. By exploring specialized centroids of segmented fragments as initial growing seeds, the proposed DRG operates a directional 1-D region growing on pairs of color segmented regions based on those centroids. When the two examined regions are positively confirmed by DRG, the proposed framework subsequently computes the texture features extracted from these two regions to further check their relation using texture similarity testing (TST). If any pair of regions passes double checking with both DRG and TST, they are identified as associated regions. If two associated regions/areas are connective, they are unified to a union area enclosed by a single contour. On the contrary, the proposed framework merely acknowledges a linking relation between those associated regions/areas highlighted with any linking mark. Particularly, by the systematic integration of all proposed processes, the critical issue to decide the ending level of wavelet decomposition in various images can be efficiently solved in FWCS by a quasi-linear high-frequency analysis model newly proposed. The simulations conducted here demonstrate that the proposed segmentation framework can achieve a quasi-semantic segmentation without priori a high-level knowledge.

Phonetic transcription of text is an indispensable component of text-to-speech (TTS) systems and is used in acoustic modeling for speech recognition and other natural language processing applications. One approach to the transcription of written text into phonetic entities or sounds is to use a set of well-defined context and language-dependent rules. The process of transcribing text into sounds starts by preprocessing the text and representing it by lexical items to which the rules are applicable. The rules can be segregated into phonemic and phonetic rules. Phonemic rules operate on graphemes to convert them into phonemes. Phonetic rules operate on phonemes and convert them into context-dependent phonetic entities with actual sounds. Converting from written text into actual sounds, developing a comprehensive set of rules, and transforming the rules into implementable algorithms for any language cause several problems that have their origins in the relative lack of correspondence between the spelling of the lexical items and their sound contents. For Standard Malay (SM) these problems are not as severe as those for languages of complex spelling systems, such as English and French, but they do exist. In this paper, developing a comprehensive computerized system for processing SM text and transcribing it into phonetic entities and evaluating the performance of this system, irrespective of the application, is discussed. In particular, the following issues are dealt with in this paper: (1) the spelling and other problems of SM writing and their impact on converting graphemes into phonemes, (2) the development of a comprehensive set of grapheme-to-phoneme rules for SM, (3) a description of the phonetic variations of SM or how the phonemes of SM vary in context and the development of a set of phoneme-to-phonetic transcription rules, (4) the formulation of the phonemic and phonetic rules into algorithms that are applicable to the computer-based processing of input SM text, and (5) the evaluation of the performance of the process of converting SM text into actual sounds by the above mentioned methods.

As a representative of the linear discriminant analysis, the Fisher method is most widely used in practice and it is very effective in two-class classification. However, when it is expanded to a multi-class classification problem, the precision of its discrimination may become worse. A main reason is an occurrence of overlapped distributions on the discriminant space built by Fisher criterion. In order to take such overlaps among classes into consideration, our approach builds a new discriminant space by hierarchically classifying the overlapped classes. In this paper, we propose a new hierarchical discriminant analysis for texture classification. We divide the discriminant space into subspaces by recursively grouping the overlapped classes. In the experiment, texture images from many classes are classified based on the proposed method. We show the outstanding result compared with the conventional Fisher method.

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.

We propose a system that enables us to gather hundreds of images related to one set of keywords provided by a user from the World Wide Web. The system is called Image Collector II. The Image Collector, which we proposed previously, can gather only one or two hundreds of images. We propose the two following improvements on our previous system in terms of the number of gathered images and their precision: (1) We extract some words appearing with high frequency from all HTML files in which output images are embedded in an initial image gathering, and using them as keywords, we carry out a second image gathering. Through this process, we can obtain hundreds of images for one set of keywords. (2) The more images we gather, the more the precision of gathered images decreases. To improve the precision, we introduce word vectors of HTML files embedding images into the image selecting process in addition to image feature vectors.

In this paper, it is proved that there is a transformation between two types of nonlinear feedback shift register which can be regarded as implementation of the Bernoulli and tent maps with finite precision. This transformation can be interpreted as a sort of finite dimension version of topological conjugation between the Bernoulli and tent maps on continuous phase space. Several properties of periodic sequences generated from extended NFSRs are derived from the relation.

In this paper, a multi-Gbps pre-emphasis design methodology and circuits for a 4/2 Pulse Amplitude Modulation (PAM) transmitter of high-speed data serial link over cable are proposed. Theoretically analysis of the total frequency response including pre-emphasis, package, cable loss and termination are first carried out. In order to gain higher data rates without increasing of symbol rate, we use 4 PAM in our system. Then, we propose a pre-emphasis architecture and algorithm that can enlarge the high frequency response so the overall frequency response in the receiver side is uniform within the desired frequency range. The overall circuit is implemented in TSMC 0.18 µm 1P6M 1.8 V CMOS process. A test chip of this transmitter with pre-emphasis, PLL circuit and on-chip termination resistors is implemented by full custom flow to verify the design methodology. The measurement results of 10/5 Gbps (4/2 PAM) are carried out over 5 meter (m) long cable and is in agreement with our analysis and simulation results.

Anycast refers to the transmission of data from a source node to (any) one member in the group of designed recipients in a network. When the physical network and the set of anycast requests are given, the Anycast Routing and Wavelength Assignment (ARWA) problem is to find a set of light-paths, one for each source, for anycasting messages to any one of the member in the anycast destination group such that not any path using the same wavelength passes through the same link. The goal of the ARWA problem is to minimize the number of used wavelengths. In this paper, the ARWA problem is formulated and studied; since ARWA problem is NP-hard, a three-phase genetic algorithm is proposed to solve it. This algorithm is used to find the close-to-optimal solution. Simulated results show that the proposed algorithm is able to achieve good performance.

In this paper, a source coding model for learning multiple concept descriptions of data is proposed. Our source coding model is based on the concept of transmitting data over multiple channels, called multiple description (MD) coding. In particular, frame expansions have been used in our MD coding models for pattern classification. Using this model, there are several interesting properties within a class of multiple classifier algorithms that share with our proposed scheme. Generalization of the MD view under an extension of local discriminant basis towards the theory of frames allows the formulation of a generalized class of low-complexity learning algorithms applicable to high-dimensional pattern classification. To evaluate this approach, performance results for automatic target recognition (ATR) are presented for synthetic aperture radar (SAR) images from the MSTAR public release data set. From the experimental results, our approach outperforms state-of-the-art methods such as conditional Gaussian signal model, Adaboost, and ECOC-SVM.

In deep submicron designs, the interconnect wires play a major role in the timing behavior of logic gates. The effective capacitance Ceff concept is usually used to calculate the delay of gate with interconnect loads. In this paper, we present a new method of Integration Approximation to calculate Ceff. In this new method, the complicated nonlinear gate output is assumed as a piecewise linear (PWL) waveform. A new model is then derived to compute the value of Ceff. The introduction of Integration Approximation results in Ceff being insensitive to output waveform shape. Therefore, the new method can be applied to various output waveforms of CMOS gates with RC-π loads. Experimental results show a significant improvement in accuracy.

Though block-based motion estimation techniques are primarily designed for video coding applications, they are increasingly being used in other video analysis applications due to their simplicity and ease of implementation. The major drawback associated with these techniques is that noises, in the form of false motion vectors, cannot be avoided while capturing block motion vectors. Similar noises may further be introduced when the technique of global motion compensation is applied to obtain true object motion from video sequences where both the camera and object motions are present. This paper presents a new technique for capturing large number of true object motion vectors by eliminating the false motion vector fields, for use in the application of object motion based video indexing and retrieval applications. Experimental results prove that our proposed technique significantly increases the percentage of retained true object motion vectors while eliminating all false motion vectors for variety of standard and non-standard video sequences.

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.

Suitably aggregated data burst enhances link utilization and reduces data processing complexity of optical transport networks rather than just transmitting each bursty input traffic from access networks. This data burst generation method is called as a burst assembly process and has two assembly parameters, timer and threshold, for regulating burst release time and burst size. Since the traffic characteristics of data burst generated at the burst assembler may affect network performance, the decision of burst assembly parameters should be carefully designed. Thus, in this paper we study the dimensioning burst assembly process to find the burst assembly parameter values satisfying target performance. For this purpose, we first analyze timer-based and threshold-based burst assembly processes, respectively. As constraints on the dimensioning burst assembly process, we consider the following performance metrics: 1) processing delay of control packet, 2) burst loss at control plane, and 3) link utilization. Based on these constraints, a decision mechanism of the burst assembly parameters is proposed. From numerical analysis, we suggest a possible lower boundary value for the burst assembly parameters satisfying the target burst loss rate and delay time at the control plane.

We propose an accurate and efficient model of having an unbalanced differential line structure, where mode-conversion and frequency dependent loss effects are considered in above the GHz frequency range. To extract model parameters of the proposed unbalanced differential line model, we measured s-parameters of test patterns using a 2-port VNA and defined a new type of mixed-mode s-parameter. The model parameters were obtained and are described for various types of the unbalanced differential line structures. Finally, the validity of the proposed model and the model parameters were successfully confirmed by a series of time-domain measurements and a lattice diagram analysis.

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 presents properties of role-based access control which were obtained through a development of a prototype of a teaching management system. These properties are related to assignment of temporal constraints and access control procedure in terms of the corresponding flow of user's view and considered to be suitable to other information systems.

This paper clarifies a necessary condition and a sufficient condition for transmissibility for a given set of general sources and a given general broadcast channel. The approach is based on the information-spectrum methods introduced by Han and Verdu. Moreover, we consider the capacity region of the general broadcast channel with arbitrarily fixed error probabilities if we send independent private and common messages over the channel. Furthermore, we treat the capacity region for mixed broadcast channel.

Seasonal ARIMA model is a good traffic model capable of capturing the behavior of a network traffic stream. In this paper, we give a general expression of seasonal ARIMA models with two periodicities and provide procedures to model and to predict traffic using seasonal ARIMA models. The experiments conducted in our feasibility study showed that seasonal ARIMA models can be used to model and predict actual wireless traffic such as GSM traffic in China.

This paper presents designs and performances of 76 GHz band alternating-phase fed single-layer slotted waveguide arrays. Two kinds of design, that is, uniform aperture illumination for maximum gain and Taylor distribution for sidelobe suppression of -25 dB, are conducted. High gain and high efficiency performance of 34.8 dBi with 57% is achieved for the former, while satisfactory sidelobe suppression of -20 dB in the H-plane and -23 dB in the E-plane with high efficiency is confirmed for the latter. The simple structure dispensing with electrical contact between the slotted plate and the groove feed structure is the key advantage of alternating-phase fed arrays and the slotted plate is just tacked on the feed structure with screws at the periphery. High gain and high efficiency performances predicted theoretically as well as design flexibility of the alternating-phase fed array are demonstrated in the millimeter wave frequency.