This paper studies the robustness of message masking communication over noisy channels using modified chaotic systems. First, the modified chaotic systems are introduced with a higher capability of transmitting messages than typical chaotic systems. Then, assuming an ideal channel, the chaotic message masking scheme is derived which achieves asymptotic convergence or dead-beat performance for recovering messages. Next, considering the case of noisy channels, an H∞ performance and an L2-gain optimal noise rejection are achieved by solving linear matrix inequality (LMI) problems. Furthermore, the ultimate bound of synchronization error and recovered message error can be adjusted by both design gains and the system parameter of the modified chaos. Using the proposed method, the bit-error-ratio and noise tolerance are improved. Finally, numerical simulations and DSP experiments are carried out to verify the theoretical derivations.

In software development, comprehensive software reviews and testings are important activities to preserve high quality and to control maintenance cost. However it would be actually difficult to perform comprehensive software reviews and testings because of a lot of components, a lack of manpower and other realistic restrictions. To improve performances of reviews and testings in object-oriented software, this paper proposes a novel model for detecting cost-prone classes; the model is based on Mahalanobis-Taguchi method--an extended statistical discriminant method merging with a pattern recognition approach. Experimental results using a lot of Java software are provided to statistically demonstrate that the proposed model has a high ability for detecting cost-prone classes.

This paper proposes a robust fuzzy integral controller for output regulating a class of affine nonlinear systems subject to a bias reference to the origin. First, a common biased fuzzy model is introduced for a class of continuous/discrete-time affine nonlinear systems, such as dc-dc converters, robotic systems. Then, combining an integrator and parallel distributed compensators, the fuzzy integral regulator achieves an asymptotic regulation. Moreover, when considering disturbances or unstructured certainties, a virtual reference model is presented and provides a robust gain design via LMI techniques. In this case, H∞ performances is guaranteed. Note that the information regarding the operational point and bias terms are not required during the controller implementation. Thus, the controller can be applied to a multi-task regulation. Finally, three numerical simulations show the expected results.

Minimum Bayes risk estimation and decoding strategies based on lattice segmentation techniques can be used to refine large vocabulary continuous speech recognition systems through the estimation of the parameters of the underlying hidden Markov models and through the identification of smaller recognition tasks which provides the opportunity to incorporate novel modeling and decoding procedures in LVCSR. These techniques are discussed in the context of going 'beyond HMMs', showing in particular that this process of subproblem identification makes it possible to train and apply small-domain binary pattern classifiers, such as Support Vector Machines, to large vocabulary continuous speech recognition.

A discriminative modeling is applied to optimize the structure of a Partly-Hidden Markov Model (PHMM). PHMM was proposed in our previous work to deal with the complicated temporal changes of acoustic features. It can represent observation dependent behaviors in both observations and state transitions. In the formulation of the previous PHMM, we used a common structure for all models. However, it is expected that the optimal structure which gives the best performance differs from category to category. In this paper, we designed a new structure optimization method in which the dependence of the states and the observations of PHMM are optimally defined according to each model using the weighted likelihood-ratio maximization (WLRM) criterion. The WLRM criterion gives high discriminability between the correct category and the incorrect categories. Therefore it gives model structures with good discriminative performance. We define the model structure combination which satisfy the WLRM criterion for any possible structure combinations as the optimal structures. A genetic algorithm is also applied to the adequate approximation of a full search. With results of continuous lecture talk speech recognition, the effectiveness of the proposed structure optimization is shown: it reduced the word errors compared to HMM and PHMM with a common structure for all models.

The objective of this paper is to present a decision support system which uses a computer-based procedure to detect tumor blocks or lesions in digitized medical images. The authors developed a simple method with a low computation effort to detect tumors on T2-weighted Magnetic Resonance Imaging (MRI) brain images, focusing on the connection between the spatial pixel value and tumor properties from four different perspectives: 1) cases having minuscule differences between two images using a fixed block-based method, 2) tumor shape and size using the edge and binary images, 3) tumor properties based on texture values using spatial pixel intensity distribution controlled by a global discriminate value, and 4) the occurrence of content-specific tumor pixel for threshold images. Measurements of the following medical datasets were performed: 1) different time interval images, and 2) different brain disease images on single and multiple slice images. Experimental results have revealed that our proposed technique incurred an overall error smaller than those in other proposed methods. In particular, the proposed method allowed decrements of false alarm and missed alarm errors, which demonstrate the effectiveness of our proposed technique. In this paper, we also present a prototype system, known as PCB, to evaluate the performance of the proposed methods by actual experiments, comparing the detection accuracy and system performance.

In this paper, we introduce a new method for depth perception from a 2D natural scene using scale variation of patterns. As the surface from a 2D scene gets farther away from us, the texture appears finer and smoother. Texture gradient is one of the monocular depth cues which can be represented by gradual scale variations of textured patterns. To extract feature vectors from textured patterns, higher order local autocorrelation functions are utilized at each scale step. The hierarchical linear discriminant analysis is employed to classify the scale rate of the feature vector which can be divided into subspaces by recursively grouping the overlapped classes. In the experiment, relative depth perception of 2D natural scenes is performed on the proposed method and it is expected to play an important role in natural scene analysis.

This paper describes a soft error hardened latch (SEH-Latch) scheme that has an error correction function in the fine process. The storage node of the latch is separated into three electrodes and a soft error on one node is collected by the other two nodes despite the large amount and long-lasting influx of radiation-induced charges. To achieve this, we designed two types of SEH-Latch circuits and a standard latch circuit using 130-nm 2-well, 3-well, and also 90-nm 2-well CMOS processes. The proposed circuit demonstrated immunity that was two orders higher through an irradiation test using alpha-particles, and immunity that was one order higher through neutron irradiation. We also demonstrated forward body bias control, which improves alpha-ray immunity by 26% for a standard latch and achieves 44 times improvement in the proposed latch.

In this paper, we propose alternate self shielding to remove critical transitions of on-chip global interconnect. Our proposed method alternates shield and signal wires cycle by cycle. The conventional self-shielding methods need additional wires to remove critical transition by encoding. The proposed alternate self-shielding, however, requires no additional wires. We evaluate our method by simulating signal transimission with a circuit simulator. As a result, our proposed method is superior in bit rate compared to others from 10% to 75%.

This paper presents a personal identification method based on BMME and LDA for images acquired at anterior and posterior occlusion expression of teeth. The method consists of teeth region extraction, BMME, and pattern recognition for the images acquired at the anterior and posterior occlusion state of teeth. Two occlusions can provide consistent teeth appearance in images and BMME can reduce matching error in pattern recognition. Using teeth images can be beneficial in recognition because teeth, rigid objects, cannot be deformed at the moment of image acquisition. In the experiments, the algorithm was successful in teeth recognition for personal identification for 20 people, which encouraged our method to be able to contribute to multi-modal authentication systems.

This paper presents a way of using a linear regression model to produce a single-valued criterion that indicates the perceived importance of each block in a stream of speech blocks. This method is superior to the conventional approach, voice activity detection (VAD), in that it provides a dynamically changing priority value for speech segments with finer granularity. The approach can be used in conjunction with scalable speech coding techniques in the context of IP QoS services to achieve a flexible form of quality control for speech transmission. A simple linear regression model is used to estimate a mean opinion score (MOS) of the various cases of missing speech segments. The estimated MOS is a continuous value that can be mapped to priority levels with arbitrary granularity. Through subjective evaluation, we show the validity of the calculated priority values.

With VLSI design development, the increasingly severe power problem requests to minimize clock routing wirelength so that both power consumption and power supply noise can be alleviated. In contrast to most of traditional works that handle this problem only in clock routing, we propose to navigate standard cell register placement to locations that enable further less clock routing wirelength and power. To minimize adverse impacts to conventional cell placement goals such as signal net wirelength and critical path delay, the register placement is carried out in the context of a quadratic placement. The proposed technique is particularly effective for the recently popular prescribed skew clock routing. Experiments on benchmark circuits show encouraging results.

Improving GPS positioning accuracy requires an understanding of the inner workings of GPS receivers. However, the necessary hardware and software for research is prohibitively expensive. It is almost impossible to modify the correlator and functions of signal acquisition and tracking in commercial GPS hardware. The software GPS receiver allows us to access the inner workings of the receiver without significant time or expense. The present paper introduces a prototype software GPS receiver developed by us, which consists of a commercial RF-module and PC-based signal processing software. In addition, the software GPS receiver is shown herein to enable evaluation and mitigation of the code multipath error with the outputs of a software multi-correlator, which can be implemented easily in a software GPS receiver, with the aid of maximum likelihood criteria.

As a representative of the linear discriminant analysis, the Fisher method is most widely used in practice and it is very effective in two-class classification. However, when it is expanded to a multi-class classification problem, the precision of its discrimination may become worse. A main reason is an occurrence of overlapped distributions on the discriminant space built by Fisher criterion. In order to take such overlaps among classes into consideration, our approach builds a new discriminant space by hierarchically classifying the overlapped classes. In this paper, we propose a new hierarchical discriminant analysis for texture classification. We divide the discriminant space into subspaces by recursively grouping the overlapped classes. In the experiment, texture images from many classes are classified based on the proposed method. We show the outstanding result compared with the conventional Fisher method.

In this paper, a source coding model for learning multiple concept descriptions of data is proposed. Our source coding model is based on the concept of transmitting data over multiple channels, called multiple description (MD) coding. In particular, frame expansions have been used in our MD coding models for pattern classification. Using this model, there are several interesting properties within a class of multiple classifier algorithms that share with our proposed scheme. Generalization of the MD view under an extension of local discriminant basis towards the theory of frames allows the formulation of a generalized class of low-complexity learning algorithms applicable to high-dimensional pattern classification. To evaluate this approach, performance results for automatic target recognition (ATR) are presented for synthetic aperture radar (SAR) images from the MSTAR public release data set. From the experimental results, our approach outperforms state-of-the-art methods such as conditional Gaussian signal model, Adaboost, and ECOC-SVM.

A timing-driven placement algorithm based on path topology analysis is presented. The optimization for path delay is transformed into cell location optimization. The algorithm pays much attention on path topologies and applies an effective force directed method to find cell target locations. Total wire length optimization is combined with the timing-driven placement algorithm. MCNC (Microelectronics Centre of North-Carolina) standard cell benchmarks are experimented and results show that our timing-driven placement algorithm can make the longest path delay improve up to 13% compared with wirelength driven placement.

The scaled down feature size and the increased frequency of today's deep sub-micron region call for fundamental changes in driver-load models. To be more specific, new driver-load models need to take into consideration the nonlinear behavior of the drivers, the inductance effects of the loads, and the slew rates of the output waveforms. Current driver-load models use the conventional single Ceff (one-ramp) approach and treat the interconnect load as lumped RC networks. Neither the nonlinear property nor the inductance effects were considered. The accuracy of these existing models is therefore questionable. This paper introduces a new multi-ramp driver model that represents the interconnect load as a distributed RLC network. The employed two effective capacitance values capture the nonlinear behavior of the driver. The lossy transmission line approach accounts for the impact of inductance when modeling the driving point interconnect load. The new model shows improvements of 9% in the average delay error and 2.2% in the slew rate error compared to SPICE.

This paper presents a 2-D model for calculating the current density distribution and the flux-flow resistivity of a Melt Cast Process BSCCO 2212 rod during the quenching process in self field with large current density. Based on the forces analysis of the flux-line lattice, the equilibrium equation for the 2-D viscous flux motion is derived from the model. With this equation, the current density distribution and the flux density distribution are obtained in not only the critical state but also the flux-flow state. Subsequently, the average flux-flow resistivity is calculated with the knowledge of the 2-D field distribution. The calculation results are in accordance with the experimental results. Finally, the applications of the 2-D model are extended to the superconducting tube and the low-Tc superconductor.

This paper proposes a cell layout synthesis technique to minimize the sensitivity to wiring faults due to spot defects. We modeled the sensitivity to faults on intra-cell routings with consideration to the spot defects size distribution and the end effect of critical areas. The effect of the sensitivity reduction on the yield is also discussed. By using the model as a cost function, we comprehensively generate the minimum width layout of CMOS logic cells and select the optimal layouts. Experimental results show that our technique reduces about 15% of the fault sensitivities compared with the wire-length-minimum layouts for benchmark CMOS logic circuits which have up to 14 transistors.

In this study, we have proposed an efficient automatic multilevel thresholding method for image segmentation. An effective criterion for measuring the separability of the homogenous objects in the image, based on discriminant analysis, has been introduced to automatically determine the number of thresholding levels to be performed. Then, by applying this discriminant criterion, the object regions with homogeneous illuminations in the image can be recursively and automatically thresholded into separate segmented images. The proposed method is fast and effective in analyzing and thresholding the histogram of the image. In order to conduct an equitable comparative performance evaluation of the proposed method with other thresholding methods, a combinatorial scheme is also introduced to properly reduce the computational complexity of performing multilevel thresholding. The experimental results demonstrated that the proposed method is feasible and computationally efficient in automatic multilevel thresholding for image segmentation.