A new analog mismatch model in circuit level has been developed. MOS transistor's small signal parameters are modeled in term of their matching character for both strong- and weak-inversion operations. Mismatch analysis on basic CMOS amplifiers are conducted with proposed model and Monte Carlo SPICE simulations. We successfully derived simple analytical formula on performance mismatch for analog CMOS circuits, which is verified to be accurate in using actual analog circuit design, within an average error of less than 10%.

Circuits utilizing advanced process technologies have to correctly account for device parameter variation to optimize its performance. In this paper, analytical formulas for evaluating path delay variation of Multi-Threshold CMOS (MTCMOS) circuits are proposed. The proposed formulas express path delay and its variation as functions of process parameters that are determined by fabrication technology (threshold voltage, carrier mobility, etc.) and the circuit parameters that are determined by circuit structure (equivalent load capacitance and the concurrently switching gates). Two procedures to obtain the circuit parameter sets necessary in the calculation of the proposed formulas are also defined. With the proposed formulas, calculation time of a path delay variation becomes three orders faster than that of Monte-Carlo simulation. The proposed formulas are suitably applied for efficient design of MTCMOS circuits considering process variation.

A deep submicron semiconductor technology has increased process variations. This fact makes the estimate of the worst-case design margin difficult. In order to realize robust designs, we are investigating such a typical-case design methodology, which we call Constructive Timing Violation (CTV). In the CTV-based design, we can relax timing constraints. However, relaxing timing constraints might cause some timing errors. While we have applied the CTV-based design to a processor, unfortunately, the timing error recovery has serious impact on processor performance. In this paper, we investigate enhancement techniques of the CTV-based design. In addition, in order to accurately evaluate the CTV-based design, we build a co-simulation framework to consider circuit delay at the architectural level. From the co-simulation results, we find the performance penalty is significantly reduced by the enhancement techniques.

Sensor networks have promising applications such as battlefield surveillance, biological detection, and emergency navigation, etc. Crucial problems in sensor networks are energy-efficiency and collision avoidance in wireless communication. To deal with the problems, we consider a self-stabilizing solution to the construction of k disjoint sense-sleep trees, where range adjustment and the use of GPS are allowed. Each root is determined by its identifier and is distinguished by its color, the identification of a tree. Using a dominating k-partition rule, each non-root node first determines a color irrelevant to the root. Then, the non-root node determines a parent node that is equally colored with minimal distance. If there is no appropriate parent, the range is extended or shrunk until the nearest parent is determined. Finally, we perform a simulation.

We propose a polynomial-time algorithm for the scheduling of real-time parallel tasks on multicore processors. The proposed algorithm always finds a feasible schedule using the minimum number of processing cores, where tasks have properties of linear speedup, flexible preemption, arbitrary deadlines and arrivals, and parallelism bound. The time complexity of the proposed algorithm is O(M3log N) for M tasks and N processors in the worst case.

We introduce a novel power allocation scheme for decode-and-forward relaying system with partial channel state (CSI) information, i.e., the source knows full CSI of source-relay link but only statistical CSI of source-destination and relay-destination links. Our objective is to minimize the outage probability by jointly allocating the transmit power between the source and relays. To avoid exhaustive search, the MAOP scheme and the MMS scheme are proposed to approach the optimal allocation in the high and low signal-to-noise ratio regimes, respectively.

Liquid-phase detection of biological targets utilizing magnetic marker and superconducting quantum interference device (SQUID) magnetometer is shown. In this method, magnetic markers are coupled to the biological targets, and the binding reaction between them is detected by measuring the magnetic signal from the bound markers. Detection can be done in the liquid phase, i.e., we can detect only the bound markers even in the presence of unbound (free) markers. Since the detection principle is based on the different magnetic properties between the free and bound markers, we clarified the Brownian relaxation of the free markers and the Neel relaxation of the bound markers. Usefulness of the present method is demonstrated from the detection of the biological targets, such as biotin-coated polymer beads, IgE and Candida albicans.

A sound field reproduction method is proposed that uses blind source separation and a head-related transfer function. In the proposed system, multichannel acoustic signals captured at distant microphones are decomposed to a set of location/signal pairs of virtual sound sources based on frequency-domain independent component analysis. After estimating the locations and the signals of the virtual sources by convolving the controlled acoustic transfer functions with each signal, the spatial sound is constructed at the selected point. In experiments, a sound field made by six sound sources is captured using 48 distant microphones and decomposed into sets of virtual sound sources. Since subjective evaluation shows no significant difference between natural and reconstructed sound when six virtual sources and are used, the effectiveness of the decomposing algorithm as well as the virtual source representation are confirmed.

In this paper, a design method of two-dimensional (2-D) orthogonal symmetric wavelets is proposed by using a lattice structure for multi-dimensional (M-D) linear-phase paraunitary filter banks (LPPUFB), which the authors have proposed as a previous work and then modified by Lu Gan et al. The derivation process for the constraints on the second-order vanishing moments is shown and some design examples obtained through optimization with the constraints are exemplified. In order to verify the significance of the constraints, some experimental results are shown for Lena and Barbara image.

In laparoscopic surgery, the lack of tactile sensation and 3D visual feedback make it difficult to identify the position of a blood vessel intraoperatively. An unintentional partial tear or complete rupture of a blood vessel may result in a serious complication; moreover, if the surgeon cannot manage this situation, open surgery will be necessary. Differentiation of arteries from veins and other structures and the ability to independently detect them has a variety of applications in surgical procedures involving the head, neck, lung, heart, abdomen, and extremities. We have used the artery's pulsatile movement to detect and differentiate arteries from veins. The algorithm for change detection in this study uses edge detection for unsupervised image registration. Changed regions are identified by subtracting the systolic and diastolic images. As a post-processing step, region properties, including color average, area, major and minor axis lengths, perimeter, and solidity, are used as inputs of the LVQ (Learning Vector Quantization) network. The output results in two object classes: arteries and non-artery regions. After post-processing, arteries can be detected in the laparoscopic field. The registration method used here is evaluated in comparison with other linear and nonlinear elastic methods. The performance of this method is evaluated for the detection of arteries in several laparoscopic surgeries on an animal model and on eleven human patients. The performance evaluation criteria are based on false negative and false positive rates. This algorithm is able to detect artery regions, even in cases where the arteries are obscured by other tissues.

Nonnegative matrix factorization (NMF) and its extensions such as Nonnegative Tensor Factorization (NTF) have become prominent techniques for blind sources separation (BSS), analysis of image databases, data mining and other information retrieval and clustering applications. In this paper we propose a family of efficient algorithms for NMF/NTF, as well as sparse nonnegative coding and representation, that has many potential applications in computational neuroscience, multi-sensory processing, compressed sensing and multidimensional data analysis. We have developed a class of optimized local algorithms which are referred to as Hierarchical Alternating Least Squares (HALS) algorithms. For these purposes, we have performed sequential constrained minimization on a set of squared Euclidean distances. We then extend this approach to robust cost functions using the alpha and beta divergences and derive flexible update rules. Our algorithms are locally stable and work well for NMF-based blind source separation (BSS) not only for the over-determined case but also for an under-determined (over-complete) case (i.e., for a system which has less sensors than sources) if data are sufficiently sparse. The NMF learning rules are extended and generalized for N-th order nonnegative tensor factorization (NTF). Moreover, these algorithms can be tuned to different noise statistics by adjusting a single parameter. Extensive experimental results confirm the accuracy and computational performance of the developed algorithms, especially, with usage of multi-layer hierarchical NMF approach [3].

Image restoration based on Bayesian estimation in most previous studies has assumed that the noise accumulated in an image was independent for each pixel. However, when we take optical effects into account, it is reasonable to expect spatial correlation in the superimposed noise. In this paper, we discuss the restoration of images distorted by noise which is spatially correlated with translational symmetry in the realm of probabilistic processing. First, we assume that the original image can be produced by a Gaussian model based on only a nearest-neighbor effect and that the noise superimposed at each pixel is produced by a Gaussian model having spatial correlation characterized by translational symmetry. With this model, we can use Fourier transformation to calculate system characteristics such as the restoration error and also minimize the restoration error when the hyperparameters of the probabilistic model used in the restoration process coincides with those used in the formation process. We also discuss the characteristics of image restoration distorted by spatially correlated noise using a natural image. In addition, we estimate the hyperparameters using the maximum marginal likelihood and restore an image distorted by spatially correlated noise to evaluate this method of image restoration.

An algorithm for blind signal separation (BSS) of convolutive mixtures is presented. In this algorithm, the BSS problem is treated as multidimensional independent component analysis (ICA) by introducing an extended signal vector which is composed of current and previous samples of signals. It is empirically known that a number of conventional ICA algorithms solve the multidimensional ICA problem up to permutation and scaling of signals. In this paper, we give theoretical justification for using any conventional ICA algorithm. Then, we discuss the remaining problems, i.e., permutation and scaling of signals. To solve the permutation problem, we propose a simple algorithm which classifies the signals obtained by a conventional ICA algorithm into mutually independent subsets by utilizing temporal structure of the signals. For the scaling problem, we prove that the method proposed by Koldovský and Tichavský is theoretically proper in respect of estimating filtered versions of source signals which are observed at sensors.

For the purpose of the application of odor to information technology, we have developed an odor-emitting apparatus coupled with chemical capsules made of alginic acid polymer. This apparatus consists of a chemical capsule cartridge including chemical capsules of odor ingredients, valves to control odor emission, and a temperature control unit. Different odors can be easily emitted by using the apparatus. We have developed an integrated system of vision, audio and olfactory information in which odor strength can be controlled coinciding with on-screen moving images based on analytical results from the odor scanner.

The spanning tree problem is to find a tree that connects all the vertices of G. This problem has many applications, such as electric power systems, computer network design and circuit analysis. Klein and Stein demonstrated that a spanning tree can be found in O(log n) time with O(n+m) processors on the CRCW PRAM. In general, it is known that more efficient parallel algorithms can be developed by restricting classes of graphs. Circular permutation graphs properly contain the set of permutation graphs as a subclass and are first introduced by Rotem and Urrutia. They provided O(n2.376) time recognition algorithm. Circular permutation graphs and their models find several applications in VLSI layout. In this paper, we propose an optimal parallel algorithm for constructing a spanning tree on circular permutation graphs. It runs in O(log n) time with O(n/log n) processors on the EREW PRAM.

The purpose of this study is the real-time estimation of Doppler spectral moments for precipitation in the presence of ground clutter overlap. The proposed method is a frequency domain approach that uses a Gaussian model both to remove clutter spectrum and to estimate weather spectrum. The main advantage of this method is that it does not use processes like several fitting procedures and enables to estimate profiles of precipitation in a short processing time. Therefore this method is efficient for real-time radar observation with high range and time resolution. The performance of this method is evaluated based on simulation data and the observation data acquired by the Ku-band broad band radar (BBR) [1].

An AuSn reflow process using hydrogen radicals as a way to avert the cleaning of flux residues was investigated for its application to solder bumping. AuSn particles (manufactured by a gas atomizer) smaller than 5 µm, which are difficult to reflow by conventional methods that use rosin mildly activated (RMA) flux, were used for the experiments. In this process, the reduction effect by the hydrogen radicals removes the surface oxides of the AuSn particles. Excellent wetting between 1-µm-diameter AuSn particles and Ni metallization occurred in hydrogen plasma. Using hydrogen radicals, 100 µm-diameter AuSn bumps without voids were successfully formed at a peak temperature of 300. The average bump shear strength was approximately 73 gf/bump. Bump inspection after shear testing showed that a fracture had occurred between the Au/Ni/Cr under bump metallurgy (UBM) and Si substrate, suggesting sufficient wetting between the AuSn bump and the UBM.

In this paper, an efficient partial incremental redundancy (P-IR) scheme is proposed for an H-ARQ using block type low density parity check (B-LDPC) codes. The performance of the proposed P-IR scheme is evaluated in an HSDPA system using IEEE 802.16e B-LDPC codes. Simulation results show that the proposed H-ARQ using IEEE 802.16e B-LDPC codes outperforms the H-ARQ using 3GPP turbo codes.

Particle Image Velocimetry (PIV) is a widely used tool for the measurement of the different kinematic properties of the fluid flow. In this measurement technique, a pulsed laser light sheet is used to illuminate a flow field seeded with tracer particles and at each instance of illumination, the positions of the particles are recorded on digital CCD cameras. The resulting two camera frames can then be processed by various techniques to obtain the velocity vectors. One such techniques involve the tracking of the individual particles so as to identify the displacement of the every particles present in the flow field. The displacement of individual particles thus determined gives the velocity information if divided by known time interval. The accuracy as well as efficiency of such measurement systems depend upon the reliability of the algorithms to track those particles. In the present work, a cellular neural network based algorithm has been proposed. Performance test has been carried out using the standard flow images. It performs well in comparison to the existing algorithms in terms of reliability, accuracy and processing time.

Cluster systems are prevalent infrastructures for offering e-services because of their cost-effectiveness. The objective of our research is to enhance their cost-effectiveness by reducing the minimum number of nodes to meet a given target performance. To achieve the objective, we propose a load balancing algorithm, the Nearest Underloaded algorithm (N algorithm). The N algorithm aims at quick solution of load imbalance caused by request departures while also preventing herd effect. The performance index in our evaluation is the xth percentile capacity which we define based on throughputs and the xth percentile response times. We measured the capacity of 8- to 16-node cluster systems under the N algorithm and existing Least-Loaded (LL) algorithms, which dispatch or transfer requests to the least-loaded node. We found that the N algorithm could achieve larger capacity or could achieve the target capacity with fewer nodes than LL algorithms could.