In this letter, the validity of lumped element class-F amplifier circuit design approaches, which were previously proposed by the same authors, has been demonstrated experimentally using microwave InGaP/GaAs HBT. By means of the proposed class-F amplifier design method, more than 4th order higher harmonic frequencies can be taken into account in class-F microwave amplifier design using only lumped element components. In this approach, miniaturization of class-F amplifier circuit has also been realized. A collector efficiency of 71.2% and a power-added efficiency of 69.2% have been measured at an operating fundamental frequency of 1 GHz considering up to the 4th order higher harmonic frequency.

Driving methods for TFT-OLEDs are explained with their features and classified from the viewpoints of grayscale methods and uniformizing methods. This classification leads us to a novel proposal using time ratio grayscale and current uniformization. This driving method maintains current uniformity and simultaneously overcomes charging shortage of the pixel circuit for low grayscale levels and current variation due to the shift of operating points. Tolerance toward degraded characteristics, linearity of grayscale and luminance uniformity against degraded characteristics are confirmed using circuit simulation.

IP-over-optical multilayer networks are capable of flexibly dealing with traffic increases and fluctuations because they support both high-speed transmission using lightpaths and scalable IP hop-by-hop transmission. This paper introduces an architecture for quality of service (QoS) control in such networks, based on the differentiated services (DiffServ) concept. The architecture supports both class-based queues and class-based lightpaths to efficiently handle multiple-QoS-class traffic. QoS schemes based on the proposed architecture are categorized into four types according to their traffic-differentiation and transmission mechanisms. Through simulation, the schemes are evaluated in terms of measures that largely determines network costs. Finally, the conditions under which each scheme is feasible are clarified in terms of the traffic volume and the cost of class-based queues for DiffServ.

This paper describes a pattern classifier for detecting frontal-view faces via learning a decision boundary. The proposed classifier consists of two major parts for improving classification accuracy: the implicit modeling of both the face and the near-face classes resulting in an extended discriminative feature set, and the subsequent composite Support Vector Machines (SVMs) for speeding up the classification. For the extended discriminative feature set, Principal Component Analysis (PCA) or Independent Component Analysis (ICA) is performed for the face and near-face classes separately. The projections and distances to the two different subspaces are complementary, which significantly enhances classification accuracy of SVM. Multiple nonlinear SVMs are trained for the local facial feature spaces considering the general multi-modal characteristic of the face space. Each component SVM has a simpler boundary than that of a single SVM for the whole face space. The most appropriate component SVM is selected by a gating mechanism based on clustering. The classification by utilizing one of the multiple SVMs guarantees good generalization performance and speeds up face detection. The proposed classifier is finally implemented to work in real-time by cascading a boosting based face detector.

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.

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.

This paper present three characteristic functions which can express the luminance distribute characteristic much better. Based on these functions a region classification algorithm is presented. The algorithm can offer more information on regions' similarity and greatly improve the efficiency and performance of match seeking in fractal coding. It can be widely applied to many kinds of fractal coding algorithms. Analysis and experimental results proved that it can offer more information on luminance distribute characteristics among regions and greatly improve the decoding quality and compression ratio with holding the running speed.

This paper incorporates statistical language models into an on-line handwriting recognition system for devices with limited memory and computational resources. The objective is to minimize the error recognition rate by taking into account the sentence context to disambiguate poorly written texts. Probabilistic word n-grams have been first investigated, then to fight the curse of dimensionality problem induced by such an approach and to decrease significantly the size of the language model an extension to class-based n-grams has been achieved. In the latter case, the classes result either from a syntactic criterion or a contextual criteria. Finally, a composite model is proposed; it combines both previous kinds of classes and exhibits superior performances compared with the word n-grams model. We report on many experiments involving different European languages (English, French, and Italian), they are related either to language model evaluation based on the classical perplexity measurement on test text corpora but also on the evolution of the word error rate on test handwritten databases. These experiments show that the proposed approach significantly improves on state-of-the-art n-gram models, and that its integration into an on-line handwriting recognition system demonstrates a substantial performance improvement.

This paper introduces the multiple signal classification (MUSIC) method that utilizes the transfer characteristics of microphones located at the same place, namely aggregated microphones. The conventional microphone array realizes a sound localization system according to the differences in the arrival time, phase shift, and the level of the sound wave among each microphone. Therefore, it is difficult to miniaturize the microphone array. The objective of our research is to build a reliable miniaturized sound localization system using aggregated microphones. In this paper, we describe a sound system with N microphones. We then show that the microphone array system and the proposed aggregated microphone system can be described in the same framework. We apply the multiple signal classification to the method that utilizes the transfer characteristics of the microphones placed at a same location and compare the proposed method with the microphone array. In the proposed method, all microphones are placed at the same place. Hence, it is easy to miniaturize the system. This feature is considered to be useful for practical applications. The experimental results obtained in an ordinary room are shown to verify the validity of the measurement.

In the adaptive modulation and coding (AMC)-based orthogonal frequency division multiple access (OFDMA) system for broadband wireless service, a large number of users with short packets cause a serious capacity mismatch problem, which incurs resource under-utilization when the data rate of subchannel increases with a better channel condition. To handle the capacity mismatch problem, we propose an AMC-based subchannel multiplexing (ASM) scheme, which allows for sharing the same subchannel among the different simultaneous flows of the same user. Along with the ASM scheme, we also consider multi-class scheduling scheme, which employs the different packet scheduling algorithm for the different service class, e.g., packet loss rate-based (PLR) scheduling algorithm for real-time (RT) service and modified minimum bit rate-based (MMBR) scheduling algorithm for non-real-time (NRT) service. In the typical integrated service scenario with voice, video, and data traffic, we have shown that the proposed schemes significantly improve the overall system capacity.

Larger object classes often become more costly classes in the maintenance phase of object-oriented software. Consequently class would have to be constructed in a medium or small size. In order to discuss such desirable size, this paper proposes a simple method for predictively discriminating costly classes in version-upgrades, using a class size metric, Stmts. Concretely, a threshold value of class size (in Stmts) is provided through empirical studies using many Java classes. The threshold value succeeded as a predictive discriminator for about 73% of the sample Java classes.

The multinomial naive Bayes model has been widely used for probabilistic text classification. However, the parameter estimation for this model sometimes generates inappropriate probabilities. In this paper, we propose a topic document model for the multinomial naive Bayes text classification, where the parameters are estimated from normalized term frequencies of each training document. Experiments are conducted on Reuters 21578 and 20 Newsgroup collections, and our proposed approach obtained a significant improvement in performance compared to the traditional multinomial naive Bayes.

In a switched-capacitor (SC) circuit, the major block is an operational transconductance amplifier (OTA) designed in order to form a feedback loop. However, the OTA is the block that consumes most of the power in SC circuits. This paper proposes the use of a class-C inverter instead of the OTA in SC circuits and a corresponding switches configuration for extremely low power applications. A detailed analysis and design trade-offs are also provided. Simulation and experimental results show that sufficient performance can be obtained even though a class-C inverter is used. The second-order biquad filter and the second-order SC sigma-delta (ΣΔ) modulator based on a class-C inverter are designed. These circuits have been fabricated with a 0.35-µm CMOS process. The measurement results of the fabricated SC biquad filter show a 59-dB signal-to-noise-plus-distortion ratio (SNDR) for a 0.2-Vp-p input signal and 0.9-V dynamic ranges. The power consumption of the biquad filter is only 0.4 µW with a 1-V power supply. The measurement results of the fabricated ΣΔ modulator show a 61-dB peak SNR for a 1.6-kHz bandwidth with a sample rate of 200 kHz. The modulator consumes 0.8 µW with a 1-V power supply.

In the next-generation networks, ultra high-speed data transmission will become necessary to support a variety of advanced point-to-point and multipoint multimedia services with stringent quality-of-service (QoS) constraints. Such a requirement desires the realization of optical WDM networks. Researches on multicast in optical WDM networks have become active for the purpose of efficient use of wavelength resources. Since multiple channels are more likely to share the same links in WDM multicast, effective routing and wavelength assignment (RWA) technology becomes very important. The introduction of the wavelength conversion technology leads to more efficient use of wavelength resources. This technology, however, has problems to be solved, and the number of wavelength converters will be restricted in the network. In this paper, we propose an effective WDM multicast design method on condition that wavelength converters on each switching node are restricted, which consists of three separate steps: routing, wavelength converter allocation, and wavelength assignment. In our proposal, preferentially available waveband is classified according to the scale of multicast group. Assuming that the number of wavelength converters on each switching node is limited, we evaluate its performance from a viewpoint of the call blocking probability.

We developed a Web-based education system called "Web-Com". It supports synchronous and asynchronous learning. It consists of an interactive web browser and voice server. Web-Com provides a multi-layer drawable canvas on which the user can draw annotations. Each layer can be shared with other users in real-time via the Internet to enable synchronous learning. In conjunction with the voice server, Web-Com can support voice communication. It can also replay the process of annotation in order, which enables asynchronous learning. Finally, a subject experiment is conducted to evaluate the scheme's workability and explore various issues that arise during the course of learning. The experimental results show that learners can learn fairly interactively with an instructor in a Web-Based class using Web-Com's synchronous style.

This paper investigates the application of the computational model of Cellular Automata (CA) for pattern classification of real valued data. A special class of CA referred to as Fuzzy CA (FCA) is employed to design the pattern classifier. It is a natural extension of conventional CA, which operates on binary string employing boolean logic as next state function of a cell. By contrast, FCA employs fuzzy logic suitable for modeling real valued functions. A matrix algebraic formulation has been proposed for analysis and synthesis of FCA. An efficient formulation of Genetic Algorithm (GA) is reported for evolution of desired FCA to be employed as a classifier of datasets having attributes expressed as real numbers. Extensive experimental results confirm the scalability of the proposed FCA based classifier to handle large volume of datasets irrespective of the number of classes, tuples, and attributes. Excellent classification accuracy has established the FCA based pattern classifier as an efficient and cost-effective solutions for the classification problem.

Active Queue Management (AQM) can maintain smaller queuing delay and higher throughput by purposefully dropping packets at the intermediate nodes. Most of the existing AQM schemes follow the probability dropping mechanism originated from Random Early Detection (RED). In this paper, we develop a novel packet dropping mechanism for AQM through designing a two-category classifier based on the Fisher Linear Discriminate approach. The simulation results show that the new scheme outperforms other well-known AQM schemes, such as RED, AdaptiveRED, AVQ, PI, REM etc., in the integrated performance. Additionally, our mechanism is simple since it requires few CPU cycles, which makes it suitable for the high-speed routers.

Packet classification is important in fulfilling the requirements of differentiated services in next generation networks. One of interesting hardware solutions proposed to solve the packet classification problem is bit vector algorithm. Different from other hardware solutions such as ternary CAM, it efficiently utilizes the memories to achieve an excellent performance in medium size policy database; however, it exhibits poor worst-case performance with a potentially large number of policies. In this paper, we proposed an improved bit-vector algorithm named Condensate Bit Vector which can be adapted to large policy databases in the backbone network. Experiments showed that our proposed algorithm drastically improves in the storage requirements and search speed as compared to the original algorithm.

This paper investigates the parameter tying structures of a mixture of factor analyzers (MFA) and discriminative training of MFA for speaker identification. The parameters of factor loading matrices or diagonal matrices are shared in different mixtures of MFA. Then, minimum classification error (MCE) training is applied to the MFA parameters to enhance the discrimination ability. The result of a text-independent speaker identification experiment shows that MFA outperforms the conventional Gaussian mixture model (GMM) with diagonal or full covariance matrices and achieves the best performance when sharing the diagonal matrices, resulting in a relative gain of 26% over the GMM with diagonal covariance matrices. The improvement is more significant especially in sparse training data condition. The recognition performance is further improved by MCE training with an additional gain of 3% error reduction.

We investigate strategies to improve the utterance verification performance using a 2-class pattern classification approach, including: utilizing N-best candidate scores, modifying segmentation boundaries, applying background and out-of-vocabulary filler models, incorporating contexts, and minimizing verification errors via discriminative training. A connected-digit database recorded in a noisy, moving car with a hands-free microphone mounted on the sun-visor is used to evaluate the verification performance. The equal error rate (EER) of word verification is employed as the sole performance measure. All factors and their effects on the verification performance are presented in detail. The EER is reduced from 29%, using the standard likelihood ratio test, down to 21.4%, when all features are properly integrated.