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.

The electrical parameters (88 LRGC matrices) of 8-coupled uniform lossy transmission lines were extracted from 40 S-parameter values measured by using 2-port VNA measurements, where all the ports other than 2 VNA ports were terminated by 50 ohm chip resistors. It was assumed in the extraction step that the transmission lines are weakly-coupled, and that the resistance values of all the termination chip resistors are exactly 50 ohms with the second reflections neglected. Comparison of the extracted LRGC matrix components with those from a commercial 3D field solver revealed on average and a maximum relative difference of 2.45% and 7.66%, respectively. In addition, the time-domain crosstalk voltage waveforms in the measured data and those in the SPICE simulation results using the extracted LRGC parameters agreed very well with the average difference and the maximum relative difference in peak crosstalk voltages of 4.15% and 9.68%, respectively.

In this paper, we propose a hybrid fine-tuned multi-objective memetic algorithm hybridizing different solution fitness evaluation methods for global exploitation and exploration. To search across all regions in objective space, the algorithm uses a widely diversified set of weights at each generation, and employs a simulated annealing to optimize each utility function. For broader exploration, a grid-based technique is adopted to discover the missing nondominated regions on existing tradeoff surface, and a Pareto-based local perturbation is performed to reproduce incrementing solutions trying to fill up the discontinuous areas. Additional advanced feature is that the procedure is made dynamic and adaptive to the online optimization conditions based on a function of improvement ratio to obtain better stability and convergence of the algorithm. Effectiveness of our approach is shown by applying it to multi-objective 0/1 knapsack problem (MOKP).

Text detection in color images has become an active research area in the past few decades. In this paper, we present a novel approach to accurately detect text in color images possibly with a complex background. The proposed algorithm is based on the combination of connected component and texture feature analysis of unknown text region contours. First, we utilize an elaborate color image edge detection algorithm to extract all possible text edge pixels. Connected component analysis is performed on these edge pixels to detect the external contour and possible internal contours of potential text regions. The gradient and geometrical characteristics of each region contour are carefully examined to construct candidate text regions and classify part non-text regions. Then each candidate text region is verified with texture features derived from wavelet domain. Finally, the Expectation maximization algorithm is introduced to binarize each text region to prepare data for recognition. In contrast to previous approach, our algorithm combines both the efficiency of connected component based method and robustness of texture based analysis. Experimental results show that our proposed algorithm is robust in text detection with respect to different character size, orientation, color and language and can provide reliable text binarization result.

In this paper, we derive an analytical result for channel holding time distribution in mobile satellite networks under general call holding time distribution.

In closed-loop multiple-input and multiple-output space-division multiplexing (MIMO-SDM) systems, allocating power among multiple transmit data streams improves the channel capacity. However, the optimum power allocation values are not always available in closed-form. For instance, when we use transmission and reception schemes that do not transfer the MIMO channel into parallel orthogonal channels (e.g., eigen-mode SDM), the signal to interference plus noise ratio (SINR) of each data stream at the output of the receiver is not proportional to its corresponding transmit power. This feature makes it difficult to obtain the optimal closed-form power allocation value for each data stream. Thus, in this paper, we propose an iterative power allocation scheme for MIMO-SDM systems where the SINR is not proportional to the transmit power. Furthermore, we incorporate a transmit antenna selection scheme into the proposed power allocation scheme in order to attain further capacity enhancement. Computer simulation results are provided to show the effectiveness of the proposed power allocation schemes.

As the technology scaling approaching nano-scale region, variability in device performance becomes a major issue in the design of integrated circuits. Besides the growing amount of variability, the statistical nature of the variability is changing as the progress of technology generation. In the past, die-to-die variability, which is well managed by the worst case design technique, dominates over within-die variability. In present and the future, the amount of within-die variability is increasing and it casts a challenge in design methodology. This paper first shows measured results of variability in three different processes of 0.35, 0.18, and 0.13 µm technologies, and explains the above mentioned trend of variability. An example of modeling for the within-die variability is explained. The impact of within-die random variability on circuit performance is demonstrated using a simple numerical example. It shows that a circuit that is designed optimally under the assumption of deterministic delay is now most susceptible to random fluctuation in delay, which clearly indicates the requirement of statistical design methodology.

We developed two types of practical maximum-likelihood detectors (MLD) for multiple-input multiple-output (MIMO) systems, using a field programmable gate array (FPGA) device. For implementations, we introduced two simplified metrics called a Manhattan metric and a correlation metric. Using the Manhattan metric, the detector needs no multiplication operations, at the cost of a slight performance degradation within 1 dB. Using the correlation metric, the MIMO-MLD can significantly reduce the complexity in both multiplications and additions without any performance degradation. This paper demonstrates the bit-error-rate performance of these MLD prototypes at a 1 Gbps-order real-time processing speed, through the use of an all-digital baseband 44 MIMO testbed integrated on the same FPGA chip.

We clarify the effective range of distance between the front and rear images of the depth-fused 3-D (DFD) visual illusion. The DFD visual illusion is perceived when two images with many edges in the front and rear frontal-parallel planes at different depths are overlapped from the viewpoint of an observer. We evaluated how the fusion of the DFD visual illusion depended on the difference in distance between the front and rear images when the distance between the two images was changed. Subjective tests clarified the cases where DFD can be applied.

This letter studies the effect of node mobility on application-level QoS of audio-video multipath streams in wireless ad hoc networks. The audio-video streams are transmitted with the MultiPath streaming scheme with Media Synchronization control (MPMS), which was previously proposed by the authors. We perform computer simulation with a grid topology network of IEEE 802.11b including two mobile nodes. The simulation results show that MPMS is effective in achieving high application-level QoS in mobile networks as well.

This paper discusses propagation delay error, transient response, and power consumption distribution due to inductive effects in optimal buffered on-chip interconnects. Inductive effect is said to be important to consider in deep submicron (DSM) VLSI design. However, study shows that the effect decreases and can be neglected in next technology nodes for such conditions.

To realize a secure networking infrastructure, the author is carrying out CUE (Coordinating Users' requirements and Engineering constraints) project with a network carrier and a VLSI manufacture. Since CUE-series data-driven processors developed in the project were specifically designed to be an embedded programmable component as well as a multi-processor element, particular design considerations were taken to achieve real-time multiprocessing capabilities essentially needed in multi-media communication environment. A novel data-driven paradigm is first introduced with special emphasis on VLSI-oriented parallel processing architectures. Data-driven protocol handlings on CUE-p and CUE-v1 are then discussed for their real-time multiprocessing capability without any runtime overheads. The emulation facility RESCUE (Real-time Execution System for CUE-series data-driven processors) was also built to develop scalable chip multi-processors in self-evolutional manner. Based on emulation results, the latest version named CUE-v2 was realized as a hybrid processor enabling simultaneous processing of data-driven and control-driven threads to achieve higher performance for inline processing and to avoid any bottlenecks in sequential parts of real-time programs frequently encountered in actual time-sensitive applications. Effectiveness of the data-driven chip multi-processor architecture will finally be addressed for lower power consumption and scalability to realize future VLSI processors in the sub-100 nm era.

We present a training algorithm to create a neural network (NN) ensemble that performs classification tasks. It employs a competitive decay of hidden nodes in the component NNs as well as a selective deletion of NNs in ensemble, thus named a pruning algorithm for NN ensembles (PNNE). A node cooperation function of hidden nodes in each NN is introduced in order to support the decaying process. The training is based on the negative correlation learning that ensures diversity among the component NNs in ensemble. The less important networks are deleted by a criterion that indicates over-fitting. The PNNE has been tested extensively on a number of standard benchmark problems in machine learning, including the Australian credit card assessment, breast cancer, circle-in-the-square, diabetes, glass identification, ionosphere, iris identification, and soybean identification problems. The results show that classification performances of NN ensemble produced by the PNNE are better than or competitive to those by the conventional constructive and fixed architecture algorithms. Furthermore, in comparison to the constructive algorithm, NN ensemble produced by the PNNE consists of a smaller number of component NNs, and they are more diverse owing to the uniform training for all component NNs.

Modern digital systems design requires us to explore a large and complex design space to find a best configuration which satisfies design requirements. Such exploration requires a sound representation of design space from which design candidates are efficiently generated, each of which then is evaluated. This paper proposes a plan-generation-evaluation framework which supports a complete process of such design space exploration. The plan phase constitutes a design space of all possible design alternatives by means of a formally defined representation scheme of attributed AND-OR graph. The generation phase generates a set of candidates by algorithmic pruning of the design space in an attributed AND-OR graph with respect to design requirements as well as architectural constraints. Finally, the evaluation phase measures performance of design candidates in a pruned graph to select a best one. A complete process of cache design is exemplified to show the effectiveness of the proposed framework.

This letter considers the signal design problems for quaternary digital communications with nonuniform sources. The designs are considered for both the average and equal energy constraints and for a two-dimensional signal space. A tight upper bound on the bit error probability (BEP) is employed as the design criterion. The optimal quarternary signal sets are presented and their BEP performance is compared with that of the standard QPSK and the binary signal set previously designed for nonuniform sources. Results shows that a considerable saving in the transmitted power can be achieved by the proposed average-energy signal set for a highly nonuniform source.

The module-wise dynamic voltage and frequency scaling (MDVFS) scheme is applied to a single-chip H.264/MPEG-4 audio/visual codec LSI. The power consumption of the target module with controlled supply voltage and frequency is reduced by 40% in comparison with the operation without voltage or frequency scaling. The consumed power of the chip is 63 mW in decoding QVGA H.264 video at 15 fps and MPEG-4 AAC LC audio simultaneously. This LSI keep operating continuously even during the voltage transition of the target module by introducing the newly developed dynamic de-skewing system (DDS) which watches and control the clock edge of the target module.

A pitch-synchronous (PS) auditory feature extraction method based on ZCPA (Zero-Crossings Peak-Amplitudes) was proposed previously and showed more robustness over a conventional ZCPA and MFCC based features. In this paper, firstly, a non-linear adaptive threshold adjustment procedure is introduced into the PS-ZCPA method to get optimal results in noisy conditions with different signal-to-noise ratio (SNR). Next, auditory masking, a well-known auditory perception, and modulation enhancement that simulates a strong relationship between modulation spectrums and intelligibility of speech are embedded into the PS-ZCPA method. Finally, a Wiener filter based noise reduction procedure is integrated into the method to make it more noise-robust, and the performance is evaluated against ETSI ES202 (WI008), which is a standard front-end for distributed speech recognition. All the experiments were carried out on Aurora-2J database. The experimental results demonstrated improved performance of the PS-ZCPA method by embedding auditory masking into it, and a slightly improved performance by using modulation enhancement. The PS-ZCPA method with Wiener filter based noise reduction also showed better performance than ETSI ES202 (WI008).

We introduce a robust classification method based on the Bayesian predictive distribution (Bayesian Predictive Classification, referred to as BPC) for speech recognition. We and others have recently proposed a total Bayesian framework named Variational Bayesian Estimation and Clustering for speech recognition (VBEC). VBEC includes the practical computation of approximate posterior distributions that are essential for BPC, based on variational Bayes (VB). BPC using VB posterior distributions (VB-BPC) provides an analytical solution for the predictive distribution as the Student's t-distribution, which can mitigate the over-training effects by marginalizing the model parameters of an output distribution. We address the sparse data problem in speech recognition, and show experimentally that VB-BPC is robust against data sparseness.

We propose an ant-based algorithm to improve the alternate routing scheme for dynamic Routing and Wavelength Assignment (RWA) in all-optical wavelength-division- multiplexing (WDM) networks. In our algorithm, we adopt a novel twin routing table structure that comprises both a P-route table for connection setup and a pheromone table for ants' foraging. The P-route table contains P alternate routes between a source-destination pair, which are dynamically updated by ant-based mobile agents based on current network congestion information. Extensive simulation results upon the ns-2 network simulator indicate that by keeping a suitable number of ants in a network to proactively and continually update the twin routing tables in the network, our new ant-based alternate routing algorithm can result in a small setup time and achieve a significantly lower blocking probability than the promising alternate shortest-path (ASP) algorithm and the fixed-paths least congestion (FPLC) algorithm for dynamic RWA even with a small value of P.

This paper describes a method of generating F0 contours from natural F0 segmental shapes for speech synthesis. The extracted shapes of the F0 units are basically held invariant by eliminating any averaging operations in the analysis phase and by minimizing modification operations in the synthesis phase. The use of natural F0 shapes has great potential to cover a wide variety of speaking styles with the same framework, including not only read-aloud speech, but also dialogues and emotional speech. A linear-regression statistical model is used to "manipulate" the stored raw F0 shapes to build them up into a sentential F0 contour. Through experimental evaluations, the proposed model is shown to provide stable and robust F0 contour prediction for various speakers. By using this model, linguistically derived information about a sentence can be directly mapped, in a purely data-driven manner, to acoustic F0 values of the sentential intonation contour for a given target speaker.