A method was developed for automatic recognition of syllable tone types in continuous speech of Mandarin by integrating two techniques, tone nucleus modeling and neural network classifier. The tone nucleus modeling considers a syllable F0 contour as consisting of three parts: onset course, tone nucleus, and offset course. Two courses are transitions from/to neighboring syllable F0 contours, while the tone nucleus is intrinsic part of the F0 contour. By viewing only the tone nucleus, acoustic features less affected by neighboring syllables are obtained. When using the tone nucleus modeling, automatic detection of tone nucleus comes crucial. An improvement was added to the original detection method. Distinctive acoustic features for tone types are not limited to F0 contours. Other prosodic features, such as waveform power and syllable duration, are also useful for tone recognition. Their heterogeneous features are rather difficult to be handled simultaneously in hidden Markov models (HMM), but are easy in neural networks. We adopted multi-layer perceptron (MLP) as a neural network. Tone recognition experiments were conducted for speaker dependent and independent cases. In order to show the effect of integration, experiments were conducted also for two baselines: HMM classifier with tone nucleus modeling, and MLP classifier viewing entire syllable instead of tone nucleus. The integrated method showed 87.1% of tone recognition rate in speaker dependent case, and 80.9% in speaker independent case, which was about 10% relative error reduction as compared to the baselines.

For wireless receivers, low-power 1.2-V 12-bit 100-MSPS pipeline ADCs are fabricated in 90-nm CMOS technology. To achieve low-power dissipation at 1.2 V without the degradation of SNR, the configuration of 2.5 bit/stage is employed with an I/Q amplifier sharing technique. Furthermore, single-stage pseudo-differential amplifiers are used in a Sample-and-Hold (S/H) circuit and a 1st Multiplying Digital-to-Analog Converter (MDAC). The pseudo-differential amplifier with two-gain-stage transimpedance gain-boosting amplifiers realizes high DC gain of more than 90 dB with low power. The measured SNR of the 100-MSPS ADC is 66.7 dB at 1.2-V supply. Under that condition, each ADC dissipates only 55 mW.

Intrusion detection system (IDS) has played a central role as an appliance to effectively defend our crucial computer systems or networks against attackers on the Internet. The most widely deployed and commercially available methods for intrusion detection employ signature-based detection. However, they cannot detect unknown intrusions intrinsically which are not matched to the signatures, and their methods consume huge amounts of cost and time to acquire the signatures. In order to cope with the problems, many researchers have proposed various kinds of methods that are based on unsupervised learning techniques. Although they enable one to construct intrusion detection model with low cost and effort, and have capability to detect unforeseen attacks, they still have mainly two problems in intrusion detection: a low detection rate and a high false positive rate. In this paper, we present a new clustering method to improve the detection rate while maintaining a low false positive rate. We evaluated our method using KDD Cup 1999 data set. Evaluation results show that superiority of our approach to other existing algorithms reported in the literature.

Wireless Mesh Network (WMN) is a promising model with benefits in coverage extension and throughput improvement. In WMN, multiple channels are available for improving system performance through concurrent transmission. For maximum utilization, per-node channel quality and inter-channel interference should be considered in multi-channel assignment. We propose a new multi-channel assignment method. First, we model the mesh network connectivity after a multi-graph which has multiple edges between two nodes. From this connectivity graph, we generate a multi-channel conflict graph, then we allocate multiple channels so that they do not overlap, using list coloring algorithm. We also propose a new sub-graph list coloring algorithm to enhance channel allocation performance. From computer simulations, we verify the performance of the algorithm.

MUSIC-based estimation of direction of arrival (DOA) using universal steering vector (USV) is experimentally studied. A four-element array antenna and a four-channel receiver are employed for the experiment. In order to improve the accuracy of DOA estimation, USV which has already included the effect of mutual coupling between array elements and effect of array elements themselves is compensated to further include the electric delay and loss of four channels in the receiver. The compensated USV (C-USV) approach proposed in this paper does not need the time-consuming measurement of array element pattern because the compensating matrix for USV is obtained by measuring the S parameters between RF input ports of the feeding cables and IF output ports of the receiver. The experimental results of MUSIC-based DOA estimation show that C-USV approach is an accurate, effective and practical method for the MUSIC-based DOA estimation.

PIN diodes for digital X-ray detection as a single photon counting sensor were fabricated on a floating-zone (FZ) n-type (111), high resistivity (5-10 kΩcm) silicon substrates (500 µm thickness). Its electrical properties such as the leakage current and the breakdown voltage were characterized. The size of pixels was 100 µm100 µm. The p+ guard-ring was formed around the active area to reduce the leakage current. After the p+ active area and guard-ring were fabricated by the ion-implantation, the extrinsic-gettering on the wafer backside was performed to reduce the leakage current by n+ ion-implantation. PECVD oxide was deposited as an IMD layer on front side and then, metal lines were formed on both sides of wafers. The leakage current of detectors was significantly reduced with a guard-ring when compared with that without a guard ring. The leakage current showed the strong dependency on the gap distance between the active area and the guard ring. It was possible to achieve the leakage current lower than 0.2 nA/cm2.

In this letter we propose a simple adaptive algorithm which solves the unit-norm constrained optimization problem. Instead of conventional parameter norm based normalization, the proposed algorithm incorporates single parameter normalization which is computationally much simpler. The simulation results illustrate that the proposed algorithm performs as good as conventional ones while being computationally simpler.

With the multiplication of attacks against computer networks, system administrators are required to monitor carefully the traffic exchanged by the networks they manage. However, that monitoring task is increasingly laborious because of the augmentation of the amount of data to analyze. And that trend is going to intensify with the explosion of the number of devices connected to computer networks along with the global rise of the available network bandwidth. So system administrators now heavily rely on automated tools to assist them and simplify the analysis of the data. Yet, these tools provide limited support and, most of the time, require highly skilled operators. Recently, some research teams have started to study the application of visualization techniques to the analysis of network traffic data. We believe that this original approach can also allow system administrators to deal with the large amount of data they have to process. In this paper, we introduce a tool for network traffic monitoring using visualization techniques that we developed in order to assist the system administrators of our corporate network. We explain how we designed the tool and some of the choices we made regarding the visualization techniques to use. The resulting tool proposes two linked representations of the network traffic and activity, one in 2D and the other in 3D. As 2D and 3D visualization techniques have different assets, we resulted in combining them in our tool to take advantage of their complementarity. We finally tested our tool in order to evaluate the accuracy of our approach.

Pairing based cryptosystems can accomplish novel security applications such as ID-based cryptosystems, which have not been constructed efficiently without the pairing. The processing speed of the pairing based cryptosystems is relatively slow compared with the other conventional public key cryptosystems. However, several efficient algorithms for computing the pairing have been proposed, namely Duursma-Lee algorithm and its variant ηT pairing. In this paper, we present an efficient implementation of the pairing over some mobilephones. Moreover, we compare the processing speed of the pairing with that of the other standard public key cryptosystems, i.e. RSA cryptosystem and elliptic curve cryptosystem. Indeed the processing speed of our implementation in ARM9 processors on BREW achieves under 100 milliseconds using the supersingular curve over F397. In addition, the pairing is more efficient than the other public key cryptosystems, and the pairing can be achieved enough also on BREW mobilephones. It has become efficient enough to implement security applications, such as short signature, ID-based cryptosystems or broadcast encryption, using the pairing on BREW mobilephones.

The MIMO system can meet the growing demand for higher capacity in wireless communication fields. So far, the authors have reported that, based on channel measurements, uncoded performance of narrowband MIMO spatial multiplexing in indoor line-of-sight (LOS) environments generally outperforms that in non-LOS (NLOS) ones under the same transmit power condition. In space-frequency coded MIMO-OFDM spatial multiplexing, however, we cannot expect high space-frequency diversity gain in LOS environments because of high fading correlations and low frequency selectivity of channels so that the performance may degrade unlike uncoded cases. In this letter, we present the practical performance of coded MIMO-OFDM spatial multiplexing based on indoor channel measurements. The results show that an LOS environment tends to provide lower space-frequency diversity effect whereas the MIMO-OFDM spatial multiplexing performance is still better in the environment compared with an NLOS environment.

In this paper, we present a multiple capture approach to reducing the peak power as well as average power consumption during testing. The basic idea behind is to divide a scan chain into two sub-scan chains, and only one sub-scan chain will be enabled at a time during the scan shift or capture operations. We develop a pattern insertion technique to efficiently deal with the capture violation problem during the capture cycle. In order to alleviate the timing cost due to the insertion of redundant patterns, a scan chain partitioning method incorporated with test pattern reordering is developed to reduce the testing time. Experimental results for large ISCAS'89 benchmark circuits show that the proposed approach can efficiently reduce peak and average power with little timing overhead.

In this paper, we present an efficient fingerprint classification algorithm which is an essential component in many critical security application systems e.g. systems in the e-government and e-finance domains. Fingerprint identification is one of the most important security requirements in homeland security systems such as personnel screening and anti-money laundering. The problem of fingerprint identification involves searching (matching) the fingerprint of a person against each of the fingerprints of all registered persons. To enhance performance and reliability, a common approach is to reduce the search space by firstly classifying the fingerprints and then performing the search in the respective class. Jain et al. proposed a fingerprint classification algorithm based on a two-stage classifier, which uses a K-nearest neighbor classifier in its first stage. The fingerprint classification algorithm is based on the fingercode representation which is an encoding of fingerprints that has been demonstrated to be an effective fingerprint biometric scheme because of its ability to capture both local and global details in a fingerprint image. We enhance this approach by improving the efficiency of the K-nearest neighbor classifier for fingercode-based fingerprint classification. Our research firstly investigates the various fast search algorithms in vector quantization (VQ) and the potential application in fingerprint classification, and then proposes two efficient algorithms based on the pyramid-based search algorithms in VQ. Experimental results on DB1 of FVC 2004 demonstrate that our algorithms can outperform the full search algorithm and the original pyramid-based search algorithms in terms of computational efficiency without sacrificing accuracy.

It is said that technology comes out from humanity. What is humanity? The very definition of humanity is emotion. Emotion is the basis for all human expression and the underlying theme behind everything that is done, said, thought or imagined. Making computers being able to perceive and respond to human emotion, the human-computer interaction will be more natural. Several classifiers are adopted for automatically assigning an emotion category, such as anger, happiness or sadness, to a speech utterance. These classifiers were designed independently and tested on various emotional speech corpora, making it difficult to compare and evaluate their performance. In this paper, we first compared several popular classification methods and evaluated their performance by applying them to a Mandarin speech corpus consisting of five basic emotions, including anger, happiness, boredom, sadness and neutral. The extracted feature streams contain MFCC, LPCC, and LPC. The experimental results show that the proposed WD-MKNN classifier achieves an accuracy of 81.4% for the 5-class emotion recognition and outperforms other classification techniques, including KNN, MKNN, DW-KNN, LDA, QDA, GMM, HMM, SVM, and BPNN. Then, to verify the advantage of the proposed method, we compared these classifiers by applying them to another Mandarin expressive speech corpus consisting of two emotions. The experimental results still show that the proposed WD-MKNN outperforms others.

Fair queueing is a service scheduling discipline to pursue the fairness among users in packet communication networks. Many fair queueing algorithms, however, have problems of computational overhead since the central scheduler has to maintain a certain performance counter for each flow of user packets based on the global virtual time. Moreover, they are not suitable for wireless networks with high probability of input channel errors due to the lack or complexity in the compensation mechanism for the recovery from the error state. In this paper, we propose a new, computationally efficient, distributed fair queueing scheme, which we call Channel-Aware Throughput Fair Queueing (CATFQ), that is applicable to both wired and wireless packet networks. In our CATFQ scheme, each flow is equipped with a counter that measures the weighted throughput achievement while it has a backlog of packets. At the end of every service to a packet, the scheduler simply selects a flow with the minimum counter value as the one from which a packet is served next. We show that the difference between any two throughput counters is bounded. Our scheme significantly reduces the scheduler's computational overhead and guarantees fair throughput for all flows. For wireless networks with error-prone channels, the service chance lost in bad channel condition is compensated quickly as the channel recovers. Our scheme suppresses the service for leading flows, brings short-term fairness for flows without channel errors, and achieves long-term fairness for all flows. These merits are verified by simulation.

This paper presents a knowledge acquisition method using sentence topics for question answering. We define templates for information extraction by the Korean concept network semi-automatically. Moreover, we propose the two-phase information extraction model by the hybrid machine learning such as maximum entropy and conditional random fields. In our experiments, we examined the role of sentence topics in the template-filling task for information extraction. Our experimental result shows the improvement of 18% in F-score and 434% in training speed over the plain CRF-based method for the extraction task. In addition, our result shows the improvement of 8% in F-score for the subsequent QA task.

This letter presents the first experimental results that confirm the restorability of Rayleigh backscatter traces from a single-mode fiber measured by using a coherent optical time domain reflectometer (OTDR) with a precisely frequency-controlled light source. Based on this restorability, we can measure the distributed strain and temperature along the fiber with a very high measurand resolution that is one to two orders of magnitude better than that provided by Brillouin-based techniques for a long length of fiber.

This paper presents a palmprint recognition algorithm using Phase-Only Correlation (POC). The use of phase components in 2D (two-dimensional) discrete Fourier transforms of palmprint images makes it possible to achieve highly robust image registration and matching. In the proposed algorithm, POC is used to align scaling, rotation and translation between two palmprint images, and evaluate similarity between them. Experimental evaluation using a palmprint image database clearly demonstrates efficient matching performance of the proposed algorithm.

Feature transformation in automatic text classification (ATC) can lead to better classification performance. Furthermore dimensionality reduction is important in ATC. Hence, feature transformation and dimensionality reduction are performed to obtain lower computational costs with improved classification performance. However, feature transformation and dimension reduction techniques have been conventionally considered in isolation. In such cases classification performance can be lower than when integrated. Therefore, we propose an integrated feature analysis approach which improves the classification performance at lower dimensionality. Moreover, we propose a multiple feature integration technique which also improves classification effectiveness.

We have developed a visual entertainment system called "Future Cast" which enables anyone to easily participate in a pre-recorded or pre-created film as an instant CG movie star. This system provides audiences with the amazing opportunity to join the cast of a movie in real-time. The Future Cast System can automatically perform all the processes required to make this possible, from capturing participants' facial characteristics to rendering them into the movie. Our system can also be applied to any movie created using the same production process. We conducted our first experimental trial demonstration of the Future Cast System at the Mitsui-Toshiba pavilion at the 2005 World Exposition in Aichi Japan.

An effective feature compensation method is developed for reliable speech recognition in real-life in-vehicle environments. The CU-Move corpus, used for evaluation, contains a range of speech and noise signals collected for a number of speakers under actual driving conditions. PCGMM-based feature compensation, considered in this paper, utilizes parallel model combination to generate noise-corrupted speech model by combining clean speech and the noise model. In order to address unknown time-varying background noise, an interpolation method of multiple environmental models is employed. To alleviate computational expenses due to multiple models, an Environment Transition Model is employed, which is motivated from Noise Language Model used in Environmental Sniffing. An environment dependent scheme of mixture sharing technique is proposed and shown to be more effective in reducing the computational complexity. A smaller environmental model set is determined by the environment transition model for mixture sharing. The proposed scheme is evaluated on the connected single digits portion of the CU-Move database using the Aurora2 evaluation toolkit. Experimental results indicate that our feature compensation method is effective for improving speech recognition in real-life in-vehicle conditions. A reduction of 73.10% of the computational requirements was obtained by employing the environment dependent mixture sharing scheme with only a slight change in recognition performance. This demonstrates that the proposed method is effective in maintaining the distinctive characteristics among the different environmental models, even when selecting a large number of Gaussian components for mixture sharing.