Based on Radon transform, a novel method for registering a periodic (self-referencing) watermark is presented. Although the periodic watermark is widely used as a countermeasure for affine transformation, there is no known efficient method to register it. Experimental results show that the proposed method is effective for registering the watermark from an image that had undergone both affine transformations and severe lossy compression.

A high-attenuation waveguide filter using electromagnetic bandgap (EBG) substrates is introduced. With a simple design modification on the EBG covers, the waveguide filter produced an almost full Ku-band rejection bandwidth showing better than 20 dB input-to-output isolation from 12.3 to 17.2 GHz.

The DAPDNA®-2 is the world's first general purpose dynamically reconfigurable processor for commercial usage. It is a dual-core processor consisting of a custom RISC core called the Digital Application Processor (DAP), and a two dimensional array of dynamically reconfigurable processing elements referred to as the Distributed Network Architecture (DNA). The DAP has a 32 bit instruction set architecture with an 8 KB instruction cache and 8 KB data cache that can be accessed in one clock cycle. It has an interrupt control function to detect data processing completion in the DNA-Matrix. The DNA-Matrix has different types of data processing elements such as ALU, delay, and memory elements to process fully parallel computations. The DNA-Matrix includes 32 independent 16 KB high speed SRAM elements (in total 512 KB). The DNA-Matrix, even with its parallel computational capability, can be synchronized and co-work at the same clock frequency as the DAP. The processor operates at a 166 MHz working frequency and fabricated with a 0.11 µm CMOS process. The DAPDNA-2 device can be connected directly with up to 16 units with linear scalability in processing performance, provided the bandwidth requirement is within the maximum communication speed between DNAs, which is 32 Gbps. The DAPDNA-2 performs at a level that is two orders of magnitude higher than conventional high performance processors.

This paper proposes an alternative learning algorithm for a stereophonic acoustic echo canceller without pre-processing which can identify the correct echo-paths. By dividing the filter coefficients into the former/latter parts and updating them alternatively, conditions both for unique solution and for perfect echo cancellation are satisfied. The learning for each part is switched from one part to the other when that part converges. Convergence analysis clarifies the condition for correct echo-path identification. For fast and stable convergence, a convergence detection and an adaptive step-size are introduced. The modification amount of the filter coefficients determines the convergence state and the step-size. Computer simulations show 10 dB smaller filter coefficient error than those of the conventional algorithms without pre-processing.

A large body of research in the measurement of software complexity at code level has been conducted, but little effort has been made to measure the architectural-level complexity of a software system. In this paper, we propose some architectural-level metrics which are appropriate for evaluating the architectural attributes of a software system. The main feature of our approach is to assess the architectural-level complexity of a software system by analyzing its formal architectural specification, and therefore the process of metric computation can be automated completely.

In order to get the knowledge of gas dynamics in interruption process of molded case circuit breakers, a quenching chamber model with gas-driven arc is proposed. The two-dimensional optical fiber digital testing system has been used to measure the arc current, arc voltage, pressure in the quenching chamber, and the movement of the arc when interrupting the 10 kA prospective current in different conditions. The influence of venting conditions, the configuration of splitter plates and gassing material characteristics on the performance of gas-driven arc has investigated. It demonstrates that the performance can be improved effectively by the ways of closing the bottom venting, adopting shorter splitter plate configuration, and POM and Nylon gassing materials.

Motion estimation is the key issue in video compressing. Several methods for motion estimation based on the center biased strategy and minimum mean square error trend searching have been proposed, such as TSS, FSS, UCBDS and MIBAS, but these methods yield poor estimates or find local minima. Many other methods predict the starting point for the estimation; such methods include PMEA, PSA and GPS: these can be fast but are inaccurate. This study addresses the causes of wrong estimates, local minima and incorrect predictions in the prior estimation methods. The Multiple Searching Trend (MST) is proposed to overcome the problems of ineffective searches and local minima, and the Adaptive Dilated Searching Field (ADSF) is described to prevent prediction from wrong location. Applying MST and ADSF to the listed estimating methods, such as UCBDS, a fast and accurate can be reached. For this this reason, the method is called CockTail Searching (CTS).

This paper analyzes the timing of eyeblink during visual identification of katakana characters on a display, which were presented under the constraint of a restricted visual field (R.V.F.). Blinks frequently occurred when the subject slowly brought the R.V.F. near a feature point (e.g., terminal point, crossing point).

This paper deals with a design problem of an adaptive robust control system for linear systems with structured uncertainties. The control law consists of a state feedback with a fixed gain designed by using the nominal system, a state feedback with an adaptive gain tuned by a parameter adjustment law and a compensation input. We show the parameter adjustment law and that sufficient conditions for the existence of the compensation input are given in terms of linear matrix inequalities (LMIs). Finally, a numerical example is included.

The Camera sensor network is a new advent of technology in which each sensor node can capture video signal, process and communicate with other nodes. We have investigated a dense node configuration. The requested processing task in this network is arbitrary view generation among nodes view. To avoid unnecessary communication between nodes in this network and to speed up the processing time, we propose a distributed processing architecture where the number of nodes sharing image data are optimized. Therefore, each sensor node processes part of the interpolation algorithm with local communication between sensor nodes. Two processing methods are used based on the image size shared. These two methods are F-DP (Fully image shared Distributed Processing) and P-DP (Partially image shared Distributed Processing). In this research, the network processing time has been theoretically analyzed for one user. The theoretical results are compatible with the experimental results. In addition, the performance of proposed DP methods were compared with Centralized Processing (CP). As a result, the best processing method for optimum number of nodes can be chosen based on (i) communication delay of the network, (ii) whether the network has one or more channels for communication among nodes and (iii) the processing ability of nodes.

All power electronic circuits with state feedback controlled switching can be described as nonlinear time-varying dynamical systems. The occurrence of chaos--where the ripple waveforms become aperiodic--is common in such systems. It is shown here that this natural phenomenon may be effectively used in minimizing electromagnetic interference problems in power electronic circuits. This is because converters operating chaotically tend to spread the spectrum, thereby reducing the interference power at any target frequency. We also present the ways of calculating the average values of state variables and the power spectrum under chaotic operation.

In uplink multicarrier code division multiple access (MC-CDMA), the inter-code interference (ICI) caused by the independent and frequency-selective fading channel of each user and the inter-carrier interference caused by the asynchronous reception of each user's OFDM symbols result in multiple access interference (MAI). This paper evaluates the ICI in frequency-selective Rayleigh fading channels for uplink MC-CDMA. We derive theoretical expressions for the desired-to-undesired signal power ratio (DUR) as a quantitative representation of ICI, and validate them by comparison with computer simulations using a Walsh-Hadamard (WH) code. Based on the analytical results, we obtain the optimum spreading sequence that minimizes the ICI (in short, maximizes the multiplexing performance); this sequence appears to be orthogonal. Three equalization combining methods are examined; equal gain combining (EGC), orthogonality restoring combining (ORC), and maximum ratio combining (MRC).

We propose a novel handover initiation algorithm based on available bit rate and timing constraint criterion for multimedia capable cellular systems. Computer simulations are performed to evaluate the handover rate and handover initiation delay. Numerical results show that handover must be initiated at different positions for different services to maintain the required quality of service requirements.

Matching Pursuits (MP), a technique for signal decomposition using a dictionary of functions, is applied to ground penetrating radar (GPR) signals in order to remove noise and clutter included in the signals and to extract target responses. A wave-based dictionary composed of wavefronts and resonances is employed. Noise reduction performance and the removal of ground-surface reflection are evaluated through numerical simulations. The results show that the MP approach performs well and offers an effective method for feature extraction from GPR signals.

This paper proposes a learning classifier based on Support Vector Domain Description (SVDD) for two-class problem. First, by the description of the training samples from one class, a sphere boundary containing these samples is obtained; then, this boundary is used to classify the test samples. In addition, instead of the traditional quadratic programming, multiplicative updates is used to solve the Lagrange multiplier in optimizing the solution of the sphere boundary. The experiment on CBCL face database illustrates the effectiveness of this learning algorithm in comparison with Support Vector Machine (SVM) and Sequential Minimal Optimization (SMO).

The present report introduces a new construction of sequences having both a low peak factor (crest factor) and a flat power spectrum. Since the proposed sequence has a flat power spectrum, its auto-correlation is zero except for the zero shift. The proposed construction uses a systematic scheme and no search method. The length of the proposed sequence is (2n+1)(4n+1) for an arbitrary integer n. The sequence construction presented herein provides a means for generating various sequences at the lengths required for such applications as system measurement (which requires uncorrelated test signals), and audio signal processing for sound production (for enhancing spatial imagery in stereo signals synthesized from mono sources).

In this paper, we propose two adaptive filtering schemes for Stereophonic Acoustic Echo Cancellation (SAEC), which are based on the adaptive projected subgradient method (Yamada et al., 2003). To overcome the so-called non-uniqueness problem, the schemes utilize a certain preprocessing technique which generates two different states of input signals. The first one simultaneously uses, for fast convergence, data from two states of inputs, meanwhile the other selects, for stability, data based on a simple min-max criteria. In addition to the above difference, the proposed schemes commonly enjoy (i) robustness against noise by introducing the stochastic property sets, and (ii) only linear computational complexity, since it is free from solving systems of linear equations. Numerical examples demonstrate that the proposed schemes achieve, even in noisy situations, compared with the conventional technique, (i) much faster and more stable convergence in the learning process as well as (ii) lower level mis-identification of echo paths and higher level Echo Return Loss Enhancement (ERLE) around the steady state.

The enlargement of the left lobe of the liver and the shrinkage of the right lobe are helpful signs at MR imaging in diagnosis of cirrhosis of the liver. To investigate whether the volume ratio of left-to-whole (LTW) is effective to differentiate cirrhosis from a normal liver, we developed an automatic algorithm for three-dimensional (3D) segmentation and volume calculation of the liver region in multi-detector row CT scans and MR imaging. From one manually selected slice that contains a large liver area, two edge operators are applied to obtain the initial liver area, from which the mean gray value is calculated as threshold value in order to eliminate the connected organs or tissues. The final contour is re-confirmed by using thresholding technique. The liver region in the next slice is generated by referring to the result from the last slice. After continuous procedure of this segmentation on each slice, the 3D liver is reconstructed from all the extracted slices and the surface image can be displayed from different view points by using the volume rendering technique. The liver is then separated into the left and the right lobe by drawing an inter-segmental plane manually, and the volume in each part is calculated slice by slice. The degree of cirrhosis can be defined as the ratio of volume in these two lobes. Four cases including normal and cirrhotic liver with MR and CT slices are used for 3D segmentation and visualization. The volume ratio of LTW was relatively higher in cirrhosis than in the normal cases in both MR and CT cases. The average error rate on liver segmentation was within 5.6% after employing in 30 MR cases. These results demonstrate that the performance in our 3D segmentation was satisfied and the LTW ratio may be effective to differentiate cirrhosis.

This paper proposes a new implementation of an adaptive noise canceller based upon a parallel block structure, which aims to raise the processing and convergence rates and to improve the steady-state performance. The procedure is as follows: First, an IIR bandpass filter with a variable center angular frequency using adaptive Q-factor control and two adaptive control signal generators are realized by the parallel block structure. Secondly, a new algorithm for adaptive Q-factor control with parallel block structure is proposed to improve the convergence characteristic. In addition, the steady-state performance of the filter is stabilized by using the variable step size parameter in adaptive control of the center frequency and the speed up of the convergence rate is achieved by adopting a normalized gradient algorithm for adaptive control. Finally, simulation results are given to demonstrate the convergence performance.

A fast motion estimation algorithm is proposed, which performs a tree-structured motion vector search for variable blocks in the integer-pixel unit. The proposed method is based on the inequality of sum norms to find the best estimate of the motion vectors for variable blocks. The proposed motion estimation algorithm is applied to the Joint Video Team (JVT) encoder that performs variable-block motion estimation (ME) with quarter-pixel unit. In terms of computational complexity, the proposed motion estimation algorithm searches motion vectors about 10.7 times as fast as the spiral full search with early termination and 6.6 times as fast as the fast full search using the hierarchical sum of absolute difference (SAD), while the PSNR (peak signal-to-noise ratio) of the reconstructed image is slightly degraded.