Similarity search in time-series databases finds such data sequences whose changing patterns are similar to that of a query sequence. For efficient processing, it normally employs a multi-dimensional index. In order to alleviate the well-known dimensionality curse, the previous methods for similarity search apply the Discrete Fourier Transform (DFT) to data sequences, and take only the first two or three DFT coefficients as organizing attributes. Other than this ad-hoc approach, there have been no research efforts on devising a systematic guideline for choosing the best organizing attributes. This paper first points out the problems occurring in the previous methods, and proposes a novel solution to construct optimal multi-dimensional indexes. The proposed method analyzes the characteristics of a target time-series database, and identifies the organizing attributes having the best discrimination power. It also determines the optimal number of organizing attributes for efficient similarity search by using a cost model. Through a series of experiments, we show that the proposed method outperforms the previous ones significantly.

This paper presents discussion about channel fluctuation on channel estimation in digital terrestrial television broadcasting. This channel estimation uses a two-dimensional (2D) filter. In our previous work, only a structure of a lattice is considered for generation of nonrectangular 2D filter. We investigate generation of nonrectangular 2D filter with adaptive method, because we should refer to not only a lattice but also channel conditions. From the computer simulations, we show that bit error rate of the proposed filter is improved compared to that of the filter depending on only lattices.

To precisely model the time dependency of features is one of the important issues for speech recognition. Segmental unit input HMM with a dimensionality reduction method has been widely used to address this issue. Linear discriminant analysis (LDA) and heteroscedastic extensions, e.g., heteroscedastic linear discriminant analysis (HLDA) or heteroscedastic discriminant analysis (HDA), are popular approaches to reduce dimensionality. However, it is difficult to find one particular criterion suitable for any kind of data set in carrying out dimensionality reduction while preserving discriminative information. In this paper, we propose a new framework which we call power linear discriminant analysis (PLDA). PLDA can be used to describe various criteria including LDA, HLDA, and HDA with one control parameter. In addition, we provide an efficient selection method using a control parameter without training HMMs nor testing recognition performance on a development data set. Experimental results show that the PLDA is more effective than conventional methods for various data sets.

In this study, we propose a method of classifying a spontaneous electroencephalogram (EEG) approach to a brain-computer interface. Ten subjects, aged 21-32 years, volunteered to imagine left- and right-hand movements. An independent component analysis based on a fixed-point algorithm is used to eliminate the activities found in the EEG signals. We use a fractal dimension value to reveal the embedded potential responses in the human brain. The different fractal dimension values between the relaxing and imaging periods are computed. Featured data is classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Two conventional methods, namely, the use of the autoregressive (AR) model and the band power estimation (BPE) as features, and the linear discriminant analysis (LDA) as a classifier, are selected for comparison in this study. Experimental results show that the proposed method is more effective than the conventional methods.

Recently, we provided closed-form expressions involving two-dimensional (2-D) joint Gaussian Q-function for the symbol error rate (SER) and bit error rate (BER) of an arbitrary 2-D signal with I/Q unbalances over an additive white Gaussian noise (AWGN) channel [1]. In this letter, we extend the expressions to Nakagami-m fading channels. Using Craig representation of the 2-D joint Gaussian Q-function, we derive an exact and general expression for the error probabilities of arbitrary 2-D signaling with I/Q phase and amplitude unbalances over Nakagami-m fading channels.

There is an increasing requirement for supporting complex multidimensional queries in Peer-to-Peer systems. In the centralized spatial database world, R-Trees and its variant structures are widely accepted due to their capabilities to manage complex multidimensional queries. In this paper, we propose a new multidimensional indexing structure for P2P systems, called Distributed Hilbert R-Trees (DHR-Trees), in which peers organize themselves into an overlay network, dynamically maintain routing tables with region information and collaboratively execute complex multidimensional queries, such as range query and k-nearest neighbors query, efficiently. DHR-Trees has similar topology to the P-Trees P2P system. The peers' routing tables are enhanced with spatial region information, which allow multidimensional query predicates to be adapted into P2P systems with minor modification. The structure design and two major multidimensional query algorithms are presented. Our experimental results demonstrate that it performs well on range queries and k-nearest neighbors queries with multidimensional data set.

A novel ratio-oriented definition based on 2T2R (Two transistors & two phase change resistors) phase change memory (PCM) cell structure is proposed to gain a high density by multilevel storage. In this novel solution, no reference is needed and good robustness remains still as conventional 2T2R, which is crucial when feature size scales to nanometer technology node. A behavioral SPICE model together with a preliminary simulation proves the idea to be feasible, and further optimization has been carried out. In addition, based on the ratio-oriented definition, a simpler and faster Error Control Coding (ECC) can be realized with n-Error-detection feasible.

This paper describes a method of free iris and focus image generation based on transformation integrating multiple differently focused images. First, we assume that objects are defocused by a geometrical blurring model. And we combine acquired images on certain imaging planes and spatial information of objects by using a convolution of a three-dimensional blur. Then, based on spatial frequency analysis of the blur, we design three-dimensional filters that generate free iris and focus images from the acquired images. The method enables us to generate not only an all-in-focus image corresponding to an ideal pin-hole iris but also various images, which would be acquired with virtual irises whose sizes are different from the original one. In order to generate a certain image by using multiple differently focused images, especially very many images, conventional methods usually analyze focused regions of each acquired image independently and construct a depth map. Then, based on the map, the regions are merged into a desired image with some effects. However, generally, it is so difficult to conduct such depth estimation robustly in all regions that these methods cannot prevent merged results from including visible artifacts, which decrease the quality of generated images awfully. In this paper, we propose a method of generating desired images directly and robustly from very many differently focused images without depth estimation. Simulations of image generation are performed utilizing synthetic images to study how certain parameters of the blur and the filter affect the quality of generated images. We also introduce pre-processing that corrects the size of acquired images and a simple method for estimating the parameter of the three-dimensional blur. Finally, we show experimental results of free iris and focus image generation from real images.

Guiding and nanofocusing of a two-dimensional (2D) optical beam in a negative-dielectric-gap waveguide is studied theoretically. An index-guiding method along the dielectric core embedded in the negative-dielectric-gap is proposed and the confinement properties of the 2D optical beam are studied by the effective-refractive-index method and FDTD simulations. We have shown that the lateral beam width of the 2D optical beam can be shrunk to zero beyond the diffraction limit. A tapered negative-dielectric-gap waveguide using adiabatic propagation achieves nano-focusing and can be applied to nano-optical couplers. This is a gateway from conventional dielectric waveguides to nano-optical integrated circuits.

The element-by-element finite element method (EBE-FEM) combined with the preconditioned conjugate gradient (PCG) technique is employed in this paper to calculate the coupling capacitances of multi-level high-density three-dimensional interconnects (3DIs). All capacitive coupling 3DIs can be captured, with the effects of all geometric and physical parameters taken into account. It is numerically demonstrated that with this hybrid method in the extraction of capacitances, an effective and accurate convergent solution to the Laplace equation can be obtained, with less memory and CPU time required, as compared to the results obtained by using the commercial FEM software of either MAXWELL 3D or ANSYS.

Multiple-access interference (MAI) limits the bit error rate (BER) performance of CDMA uplink transmission. In this paper, we propose a generalized chip-interleaved CDMA with 2-dimensional (2D) spreading using orthogonal variable spreading factor (OVSF) codes to minimize the MAI effects and achieve the maximum available time- and frequency-domain diversity gains. We present the code assignment for 2D spreading to provide users with flexible multi-rate data transmission. A computer simulation shows that by the joint use of 2D OVSF spreading and chip-interleaving, MAI-free transmission is possible for the quasi-synchronous DS- or MC-CDMA uplink, and hence the single-user frequency-domain equalization based on the MMSE criterion can be applied for signal detection. The BER performance in a time- and frequency-selective fading multiuser channel is theoretically analyzed and evaluated by both numerical computation and computer simulation.

The I/Q unbalance which is generated by a non-ideal component is an inevitable physical phenomenon and leads to performance degradation when we implement a practical two-dimensional (2-D) modulation system. In this paper, we provide an exact and general expression involving the 2-D Gaussian Q-function for the SER/BER of arbitrary 2-D signaling with I/Q amplitude and phase unbalances over an additive white Gaussian noise (AWGN) channel by using the coordinate rotation and shifting technique. Through Monte Carlo simulations we verify our expression provided here for 16-star Quadrature Amplitude Modulation (QAM).

In real-time multimedia processing systems a very large part of the power consumption is due to the data storage and data transfer. Moreover, the area cost is often largely dominated by the memory modules. In deriving an optimized (for area and/or power) memory architecture, memory size computation is an important step in the exploration of the possible algorithmic specifications of multimedia applications. This paper presents a novel non-scalar approach for computing exactly the memory size in real-time multimedia algorithms. This methodology uses both algebraic techniques specific to the data-flow analysis used in modern compilers and, also, more recent advances in the theory of polyhedra. In contrast with all the previous works which are only estimation methods, this approach performs exact memory computations even for applications significantly large in terms of the code size, number of scalars, and number of array references.

We have researched high dimensional parity-check (HDPC) codes that give good performance over a channel that has a very high error rate. HDPC code has a little coding overhead because of its simple structure. It has hard-in, maximum detected bit flipping (MDBF) decoding that has reasonable decoding performance and computational cost. In this paper, we propose a modified algorithm for MDBF decoding and compare the proposed MDBF decoding with conventional hard-in decoding.

In this paper an analysis of component and system reliability for lattice systems is proposed when component failures are not statistically independent. We deal the case that the failure rate of a component depends on the number of the adjacent failed components. And we discuss the maintainability of the system when a failed component is replaced by a spare component. At first we discuss the approximated reliability of each component. Then we estimate the mean number of failed components. Furthermore, the system reliability is approximated by using the component reliability.

Visual criteria for diagnosing liver diseases, such as cirrhosis, from ultrasound images can be assisted by computerized texture classification. This paper proposes a system applying a PNN (Pyramid Neural Network) for classifying the hepatic parenchymal diseases in ultrasonic B-scan texture. In this study, we propose a multifractal-dimensions method to select the patterns for the training set and the validation sets. A modified box-counting algorithm is used to calculate the dimensions of the B-scan images. FDWT (Fast Discrete Wavelet Transform) is applied for feature extraction during the preprocessing. The structure of the proposed neural network is testified by training and validation sets by cross-validation method. The performance of the proposed system and a system based on the conventional multilayer network architecture is compared. The results show that, compared with the conventional 3-layer neural network, the performance of the proposed pyramid neural network is improved by efficiently utilizing the lower layer of the neural network.

A state space compression method based on multivariate analysis was developed and applied to reinforcement learning for high-dimensional continuous state spaces. First, useful components in the state variables of an environment are extracted and meaningless ones are removed by using multiple regression analysis. Next, the state space of the environment is compressed by using principal component analysis so that only a few principal components can express the dynamics of the environment. Then, a basis of a feature space for function approximation is constructed based on orthonormal bases of the important principal components. A feature space is thus autonomously construct without preliminary knowledge of the environment, and the environment is effectively expressed in the feature space. An example synchronization problem for multiple logistic maps was solved using this method, demonstrating that it solves the curse of dimensionality and exhibits high performance without suffering from disturbance states.

This paper studies bit-interleaved coded modulation with iterative decoding (BICM-ID) systems that employ multi-dimensional mappings of M-ary constellations to improve the error performance over Rayleigh fading channels. Based on the analytical evaluations of the asymptotic bit error probability (BEP), the distance criteria for the mapping designs can be obtained. A binary switching algorithm (BSA) is then applied to find the optimal mappings with respect to the asymptotic performance. Simulation and analytical results show that the use of multi-dimensional mappings of M-ary constellations can significantly improve the error performance.

Accumulation of non-equilibrium longitudinal optical (LO) phonons (termed hot phonons) is considered as a possible cause for limitation of frequency of operation of GaN-based high-electron-mobility transistors (HEMTs). The experimental data on noise temperature of hot electrons at a microwave frequency as a function of supplied electric power is used to extract information on hot phonons: the hot-phonon lifetime, the equivalent hot-phonon temperature, the effective occupancy of hot-phonon states involved into electron-LO-phonon interaction. The possible ways for controlling the hot-phonon effect on electron drift velocity through variation of electron density, channel composition, and hot-phonon lifetime are discussed. The expected dependence of hot-electron drift velocity on hot-phonon lifetime is confirmed experimentally. A self-consistent explanation of different frequency behaviour of InP-based and GaN-based HEMTs is obtained from a comparative study of hot-phonon effects.

One of the challenging issues in affective computing is to give a machine the ability to recognize the affective states with intensity of a person. Few studies are directed toward this goal by categorizing affective behavior of the person into a set of discrete categories. But still two problems exist: gesture is not yet a concern as a channel of affective communication in interactive technology, and existing systems only model discrete categories but not affective dimensions, e.g., intensity. Modeling the intensity of emotion has been well addressed in synthetic autonomous agent and virtual environment literature, but there is an evident lack of attention in other important research areas such as affective computing, machine vision, and robotic. In this work, we propose an affective gesture recognition system that can recognize the emotion of a child and the intensity of the emotion states in a scenario of game playing. We used levels of cognitive and non-cognitive appraisal factors to estimate intensity of emotion. System has an intelligent agent (called Mix) that takes these factors into consideration and adapt the game state to create a more positive interactive environment for the child.