The major application of digital data hiding techniques is to deliver confidential data secretly via public but unreliable computer networks. Most of the existing data hiding schemes, however, exploit the raw data of cover images to perform secret communications. In this paper, a novel data hiding scheme was presented with the manipulation of images based on the compression of side-match vector quantization (SMVQ). This proposed scheme provided adaptive alternatives for modulating the quantized indices in the compressed domain so that a considerable quantity of secret data could be artfully embedded. As the experimental results demonstrated, the proposed scheme indeed provided a larger payload capacity without making noticeable distortions in comparison with schemes proposed in earlier works. Furthermore, this scheme also presented a satisfactory compression performance.

Reliable Multicast has been applied to large-scale contents delivery systems for distributing digital contents to a large number of users without data loss. Reliable contents distribution is indispensable for software updates and management data sharing in actual delivery services. This paper evaluates the implementation and performance of RMTP; a reliable multicast protocol for bulk-data transfer, through the developments of contents delivery systems. Software configuration is also examined including operation functions such as delivery scheduling. Furthermore, applicability of reliable multicast to emerging broadband networks is also discussed based on the experimentation results. Through the deployment of the protocol and the software, performance estimation has played a key role for constructing the delivery systems as well as for designing the communication protocol.

This study initiates a systematic evaluation of web search engine performance using queries written in Thai. Statistical testing indicates that there are some significant differences in the performance of search engines. In addition to compare the search performance, an analysis of the returned results is carried out. The analysis of the returned results shows that the majority of returned results are unique to a particular search engine and each system provides quite different results. This encourages the use of metasearch techniques to combine the search results in order to improve the performance and reliability in finding relevant documents. We examine several metasearch models based on the Borda count and Condorcet voting schemes. We also propose the use of Evolutionary Programming (EP) to optimize weight vectors used by the voting algorithms. The results show that the use of metasearch approaches produces superior performance compared to any single search engine on Thai queries.

It has been shown that the output information produced by the soft output Viterbi algorithm (SOVA) is too optimistic. To compensate for this, the output information should be normalized. This letter proposes a simple normalization technique that extends the existing sign difference ratio (SDR) criterion. The new normalization technique counts the sign differences between the a-priori information and the extrinsic information, and then adaptively determines the corresponding normalization factor for each data block. Simulations comparing the new technique with other well-known normalization techniques show that the proposed normalization technique can achieve about 0.2 dB coding gain improvement on average while reducing up to about 1/2 iteration for decoding.

Machine learning techniques have long been applied in many fields and have gained a lot of success. The purpose of learning processes is generally to obtain a set of parameters based on a given data set by minimizing a certain objective function which can explain the data set in a maximum likelihood or minimum estimation error sense. However, most of the learned parameters are highly data dependent and rarely reflect the true physical mechanism that is involved in the observation data. In order to obtain the inherent knowledge involved in the observed data, it is necessary to combine physical models with learning process rather than only fitting the observations with a black box model. To reveal underlying properties of human speech production, we proposed a learning process based on a physiological articulatory model and a coarticulation model, where both of the models are derived from human mechanisms. A two-layer learning framework was designed to learn the parameters concerned with physiological level using the physiological articulatory model and the parameters in the motor planning level using the coarticulation model. The learning process was carried out on an articulatory database of human speech production. The learned parameters were evaluated by numerical experiments and listening tests. The phonetic targets obtained in the planning stage provided an evidence for understanding the virtual targets of human speech production. As a result, the model based learning process reveals the inherent mechanism of the human speech via the learned parameters with certain physical meaning.

We propose feedback-limited NFSRs (nonlinear feedback shift registers) which can generate periodic sequences of period 2k-1, where k is the length of the register. We investigate some characteristics of such periodic sequences. It is also shown that the scale of such NFSRs can be reduced by the feedback limitation. Some simulation and experimental results are shown including comparison with LFSRs (linear feedback shift registers) for conventional M-sequences and Gold sequences.

Since we can accumulate a large amount of data including useless information in recent years, it is important to investigate various extraction method of clusters from data including much noises. The Self-Organizing Map (SOM) has attracted attention for clustering nowadays. In this study, we propose a method of using plural SOMs (TSOM: Tentacled SOM) for effective data extraction. TSOM consists of two kinds of SOM whose features are different, namely, one self-organizes the area where input data are concentrated, and the other self-organizes the whole of the input space. Each SOM of TSOM can catch the information of other SOMs existing in its neighborhood and self-organizes with the competing and accommodating behaviors. We apply TSOM to data extraction from input data including much noise, and can confirm that TSOM successfully extracts only clusters even in the case that we do not know the number of clusters in advance.

This paper is to investigate the application of fuzzy c-means clustering to the direct identification of coherent synchronous generators in power systems. Because of the conceptual appropriateness and computational simplicity, this approach is essentially a fast and flexible method. At first, the coherency measures are derived from the time-domain responses of generators in order to reveal the relations between any pair of generators. And then they are used as initial element values of the membership matrix in the clustering procedures. An application of the proposed method to the Taiwan power (Taipower) system is demonstrated in an attempt to show the effectiveness of this clustering approach. The effects of short circuit fault locations, operating conditions, data sampling interval, and power system stabilizers are also investigated, as well. The results are compared with those obtained from the similarity relation method. And thus it is found that the presented approach needs less computation time and can directly initialize a clustering process for any number of clusters.

Many researchers have attempted to solve the combinatorial optimization problems, that are NP-hard or NP-complete problems, by using neural networks. Though the method used in a neural network has some advantages, the local minimum problem is not solved yet. It has been shown that the Inverse Function Delayed (ID) model, which is a neuron model with a negative resistance on its dynamics and can destabilize an intended region, can be used as the powerful tool to avoid the local minima. In our previous paper, we have shown that the ID network can separate local minimum states from global minimum states in case that the energy function of the embed problem is zero. It can achieve 100% success rate in the N-Queen problem with the certain parameter region. However, for a wider parameter region, the ID network cannot reach a global minimum state while all of local minimum states are unstable. In this paper, we show that the ID network falls into a particular permanent oscillating state in this situation. Several neurons in the network keep spiking in the particular permanent oscillating state, and hence the state transition never proceed for global minima. However, we can also clarify that the oscillating state is controlled by the parameter α which affects the negative resistance region and the hysteresis property of the ID model. In consequence, there is a parameter region where combinatorial optimization problems are solved at the 100% success rate.

Radio communications via channels already occupied by traditional telecommunication systems can be achieved by using ultra-wideband (UWB) radio where extremely wideband wavelets are used in order to reduce the power spectral density (psd) of transmitted signal. Since the recovery of these UWB carriers is not feasible, noncoherent demodulation techniques have to be used. The letter evaluates and compares the noise performances of the feasible noncoherent UWB modulation schemes, namely, that of the noncoherent pulse polarity modulation and the transmitted reference system.

This letter presents a simple maximum likelihood (ML) estimation method with one training symbol for the integer frequency offset (IFO) in orthogonal frequency division multiplexing (OFDM) systems. We show that the proposed method performs better than the conventional one through Monte Carlo simulations.

This letter studies frequency-locked rotations in a phase-locked loop (PLL) circuit as FM demodulator. A rotation represents a desynchronized steady state in the PLL circuit and is regarded as another type of self-excited oscillations with natural rotation frequencies. The rotation frequency can be locked at driving frequencies of modulation signals. This letter shows response curves for harmonic amplitude of frequency-locked rotations. They have several different features from response curves of van der Pol oscillator.

Although subspace-based methods for estimating the Angle of Arrival (AOA) require a precise array response to achieve highly accurate results, it is difficult to obtain this response in practice even though the antennas are calibrated. Therefore, a method of compensating for errors in calibration is required. This paper proposes a procedure to enable precise AOA estimates to be obtained in a real system by applying array calibration and spatial smoothing preprocessing (SSP). Measured data were collected from experiments using two scenarios, i.e., in an anechoic chamber and at an open site, where a single source signal arrived at the array antenna. All measured data were then calibrated by using data obtained at 0 deg in an anechoic chamber before the AOAs were estimated. Nevertheless, errors in the array response remained after calibration because errors in the AOA estimates could still be observed. SSP was then applied to the calibrated data to obtain more accurate AOA estimates. We found that SSP can reduce the random error in an array response obtained in a real system, leading to reduced errors in AOA estimates in the observed data. To generalize the problem that SSP can reduce random perturbation in the array response, simple expressions are illustrated and verified by Monte-Carlo simulation. Random gain and phase errors in the array response are only considered in this paper and ESPRIT was used to estimate the AOAs.

In order to support decision making, text classification is an important tool. Recently, in addition to term frequency and inverse document frequency, term distributions have been shown to be useful to improve classification accuracy in multi-class classification. This paper investigates the performance of these term distributions on binary classification using a centroid-based approach. In such one-against-the-rest, there are only two classes, the positive (focused) class and the negative class. To improve the performance, a so-called hierarchical EM method is applied to cluster the negative class, which is usually much larger and more diverse than the positive one, into several homogeneous groups. The experimental results on two collections of web pages, namely Drug Information (DI) and WebKB, show the merits of term distributions and clustering on binary classification. The performance of the proposed method is also investigated using the Thai Herbal collection where the texts are written in Thai language.

In this paper, we shall describe about a basic theory based on the concept of set-valued operators, suitable for available operation of extremely complicated large-scale network systems. Fundamental conditions for availability of system behaviors of such network systems are clarified in a form of fixed point theorem for system of set-valued operators. Here, the proof of this theorem is accomplished by the concept of Hausdorff's ball measure of non-compactness.

This paper proposes to use eye movements to characterize the performance of individuals in reviewing software documents. We design and implement a system called DRESREM, which measures and records eye movements of document reviewers. Based on the eye movements captured by eye tracking device, the system computes the line number of the document that the reviewer is currently looking at. The system can also record and play back how the eyes moved during the review process. To evaluate the effectiveness of the system we conducted an experiment to analyze 30 processes of source code review (6 programs, 5 subjects) using the system. As a result, we have identified a particular pattern, called scan, in the subject's eye movements. Quantitative analysis showed that reviewers who did not spend enough time on the scan took more time to find defects on average.

Service differentiation is one of the key issues in the current Internet. In this paper, we focus on a recent proposal for proportional loss rate differentiation which employs a single FIFO queue, an AQM algorithm for computing the packet drop probability, and a counter-based packet dropping routine for achieving the intended proportional loss rate differentiation among classes. It is first shown that, when the target dropping probability of a class is large, the counter-based packet dropping routine may yield a significant amount of error between the target and measured drop probabilities for the class, and subsequently, fails to maintain the loss rate ratios between classes as intended. To avoid this problem, a new compensatory packet dropping routine is developed in this paper. Then, a series of simulation experiments are conducted using the ns-2 simulator to assess the performances of the two dropping routines under various congestion conditions and quality spacings between classes. The simulation results show that, unlike the counter-based dropping routine, the proposed compensatory dropping routine is effective in keeping the loss rate ratios between classes closely on target regardless of the degree of congestion and quality spacing between classes, while the two dropping routines perform similarly in terms of throughput and queueing delay in the bottleneck link. In addition, such robustness of the proposed routine is achieved without any additional control parameter or computational effort compared to the counter-based routine.

The present paper introduces an integrated construction of binary sequences having a zero-correlation zone. The cross-correlation function and the side-lobe of the auto-correlation function of the proposed sequence set is zero for the phase shifts within the zero-correlation zone. The proposed method enables more flexible design of the binary zero-correlation zone sequence set with respect to its member size, length, and width of zero-correlation zone. Several previously reported sequence construction methods of binary zero-correlation zone sequence sets can be explained as special cases of the proposed method.

In mobile ad hoc networks, as the communications have to be achieved autonomously and via third parties, a variety of risks against stable communication and session completion can be predicted. Many researchers have been studying these issues, i.e., mobility, broadcast storm, selfish behavior on data forwarding, security, and so forth. However, availability of routes against selfish power off is still to be addressed. This paper defines a new, realistic and practical problem beyond the scope of malicious program modification, called selfish power off, and evaluates the influence of it. Here, a selfish user turns his terminal on only when it has some data to send or receive and decline to work as an intermediate user by turning its terminal power off. In addition, this paper proposes a solution, called Proactive COoperation Mechanism (PCOM) against selfish power off in order to eliminate the influence of selfish power off. In PCOM, each user holds the cooperation records of its adjacent users, and forwards only those packets that are generated by users with good cooperation records. PCOM thus prevents SUs from joining the network. Extensive simulation shows the efficiency of our proposal in maintaining network connectivity and fairness in resource consumption.

A passive optical network (PON) that provides fiber to the home (FTTH) services is a fundamental access network topology in Japan. An optical fiber line monitoring and testing system is essential if we are to improve service reliability and reduce the maintenance costs of optical access networks. PONs have optical splitters in their optical fiber lines. It is difficult to find a fault in an optical fiber line equipped with an optical splitter by using a conventional optical fiber line testing system, which uses optical time-domain reflectometer (OTDR) in a central office (CO), because Rayleigh backscattering from the branched fibers accumulates in the OTDR trace. This paper describes a newly developed optical fiber line testing method that employs bi-directional OTDRs with two wavelengths at branched fiber regions in a PON to locate a fault precisely. Optical fiber line testing is conducted by two OTDRs that are installed in a CO and on a customer's premises, respectively. The OTDR in the CO has a U-band maintenance wavelength. We present two kinds of maintenance wavelength allocation for OTDRs on a customer's premises, which are in the U-band and C-band respectively. An OTDR whose maintenance wavelength is in the U-band enables us to test in-service PON lines simply by filtering the U-band wavelength. For the maintenance wavelengths in the C-band, we can use a cost-effective conventional OTDR to test the PON from the customer's premises on condition that we clarify the peak pulse power limit and dynamic range. We describe the test procedures for both cases. We also clarify the insertion loss design for an optical filter in the CO when using the U-band to provide the maintenance wavelength and the criteria for in-service line testing when the using C-band to provide the maintenance wavelength. To confirm the feasibility of our approach, we demonstrate a bi-directional OTDR method using the U-band and the C-band, and the test procedure, which successfully detected fault locations in branched fiber regions. We also describe the use of packet loss measurements to investigate the effect of in-service line testing with an OTDR in the C-band on data communication quality.