This paper proposes an acoustic modeling technique based on Bayesian framework using multiple model structures for speech recognition. The aim of the Bayesian approach is to obtain good prediction of observation by marginalizing all variables related to generative processes. Although the effectiveness of marginalizing model parameters was recently reported in speech recognition, most of these systems use only “one” model structure, e.g., topologies of HMMs, the number of states and mixtures, types of state output distributions, and parameter tying structures. However, it is insufficient to represent a true model distribution, because a family of such models usually does not include a true distribution in most practical cases. One of solutions of this problem is to use multiple model structures. Although several approaches using multiple model structures have already been proposed, the consistent integration of multiple model structures based on the Bayesian approach has not seen in speech recognition. This paper focuses on integrating multiple phonetic decision trees based on the Bayesian framework in HMM based acoustic modeling. The proposed method is derived from a new marginal likelihood function which includes the model structures as a latent variable in addition to HMM state sequences and model parameters, and the posterior distributions of these latent variables are obtained using the variational Bayesian method. Furthermore, to improve the optimization algorithm, the deterministic annealing EM (DAEM) algorithm is applied to the training process. The proposed method effectively utilizes multiple model structures, especially in the early stage of training and this leads to better predictive distributions and improvement of recognition performance.

Root cause analysis of BGP updates is the key to debug and troubleshoot BGP routing problems. However, it is a challenge to precisely diagnose the cause and the origin of routing instability. In this paper, we are the first to distinguish link failure events from policy change events based on BGP updates from single vantage points by analyzing the relationship of the closed loops formed through intersecting all the transient paths during instability and the length variation of the stable paths after instability. Once link failure events are recognized, their origins are precisely inferred with 100% accuracy. Through simulation, our method is effective to distinguish link failure events from link restoration events and policy related events, and reduce the size of candidate set of origins.

An artificial fish swarm algorithm for solving symbolic regression problems is introduced in this paper. In the proposed AFSA, AF individuals represent candidate solutions, which are represented by the gene expression scheme in GEP. For evaluating AF individuals, a penalty-based fitness function, in which the node number of the parse tree is considered to be a constraint, was designed in order to obtain a solution expression that not only fits the given data well but is also compact. A number of important conceptions are defined, including distance, partners, congestion degree, and feature code. Based on the above concepts, we designed four behaviors, namely, randomly moving behavior, preying behavior, following behavior, and avoiding behavior, and present their respective formalized descriptions. The exhaustive simulation results demonstrate that the proposed algorithm can not only obtain a high-quality solution expression but also provides remarkable robustness and quick convergence.

Identification of early aspects is the critical problem in aspect-oriented requirement engineering. But the representation of crosscutting concerns is various, which makes the identification difficult. To address the problem, this paper proposes the AspectQuery method based on goal model. We analyze four kinds of goal decomposition models, then summarize the main factors about identification of crosscutting concerns and conclude the identification rules based on a goal model. A goal is crosscutting concern when it satisfies one of the following conditions: i) the goal is contributed to realize one soft-goal; ii) parent goal of the goal is candidate crosscutting concern; iii) the goal has at least two parent goals. AspectQuery includes four steps: building the goal model, transforming the goal model, identifying the crosscutting concerns by identification rules, and composing the crosscutting concerns with the goals affected by them. We illustrate the AspectQuery method through a case study (a ticket booking management system). The results show the effectiveness of AspectQuery in identifying crosscutting concerns in the requirement phase.

This paper presents a low-power LDPC decoder that can be used in Multimedia Wireless Sensor Networks. Three low power design techniques are proposed in the decoder design: a layered decoding algorithm, a modified Benes network and a modified memory bypassing scheme. The proposed decoder is implemented in TSMC 0.13 µm, 1.2 V CMOS process. Experiments show that when the clock frequency is 32 MHz, the power consumption of the proposed decoder is 38.4 mW, the energy efficiency is 53.3 pJ/bit/ite and the core area is 1.8 mm2.

A precoding design for double space-time block coding (STBC) system is investigated in this paper, i.e., the joint processing of STBC and dirty paper coding (DPC) techniques. These techniques are used for avoiding dual spatial streams interference and improving the transmitter diversity. The DPC system is interference free on multi-user or multi-antenna. The STBC transceiver can provide the transmit diversity. Due to the benefits about offered by the STBC and DPC techniques, we propose a new scheme called STBC-DPC system. The transceiver design involves the following procedures. First, the ordering QR decomposition of channel matrix and the maximum likelihood (ML) one-dimensional searching algorithm are proposed to acquire reliable performance. Next, the channel on/off assignment using the water filling algorithm, i.e., maximum capacity criterion, is proposed to overcome the deep fading channel problem. Finally, the STBC-DPC system with the modulus operation to limit the transmit signal level, i.e., the Tomlinson-Harashima precoding (THP) scheme, is proposed to achieve low peak-to-average power ratio (PAPR) performance. Simulation results confirm that the proposed STBC-DPC/THP with water filling ML algorithm can provide the low PAPR and excellent bit error rate (BER) performances.

In this paper, performances of two different virtual multiple-input multiple-output (MIMO) transmission schemes — spatial multiplexing (SM) and space-time block coding (STBC) — in a correlated wireless sensor network are analyzed. By utilizing a complex Wishart distribution, we investigate the statistical properties of a correlated virtual MIMO channel between the sensors and data collector that is used in the performance analysis of each MIMO transmission mode. Distributed sensors then transmit their data cooperatively to the data collector by choosing a proper transmission mode adaptively based on the channel conditions and spatial correlation among the sensors. Furthermore, after analyzing the energy efficiencies of SM and STBC, we propose a new energy efficient mode switching rule between SM and STBC. Finally, by analytically deriving the required transmit energy of the proposed adaptive transmission scheme, the manner in which the spatial correlation influences the energy consumption is shown. This suggests a cooperating node scheduling protocol that makes energy consumption less sensitive to the variation of the spatial correlation.

We propose a novel acceleration scheme for Monte Carlo based statistical static timing analysis (MC-SSTA). MC-SSTA, which repeatedly executes ordinary STA using a set of randomly generated gate delay samples, is widely accepted as an accuracy reference. A large number of random samples, however, should be processed to obtain accurate delay distributions, and software implementation of MC-SSTA, therefore, takes an impractically long processing time. In our approach, a generalized hardware module, the STA processing element (STA-PE), is used for the delay evaluation of a logic gate, and netlist-specific information is delivered in the form of instructions from an SRAM. Multiple STA-PEs can be implemented for parallel processing, while a larger netlist can be handled if only a larger SRAM area is available. The proposed scheme is successfully implemented on Altera's Arria II GX EP2AGX125EF35C4 device in which 26 STA-PEs and a 624-port Mersenne Twister-based random number generator run in parallel at a 116 MHz clock rate. A speedup of far more than10 is achieved compared to conventional methods including GPU implementation.

Novel joint motion-compensated interpolation using eight-neighbor block motion vectors (8J-MCI) is presented. The proposed method uses bi-directional motion estimation (BME) to obtain the motion vector field of the interpolated frame and adopts motion vectors of the interpolated block and its 8-neighbor blocks to jointly predict the target block. Since the smoothness of the motion vector filed makes the motion vectors of 8-neighbor blocks quite close to the true motion vector of the interpolated block, the proposed algorithm has the better fault-tolerancy than traditional ones. Experiments show that the proposed algorithm outperforms the motion-aligned auto-regressive algorithm (MAAR, one of the state-of-the-art frame rate up-conversion (FRUC) schemes) in terms of the average PSNR for the test image sequence and offers better subjective visual quality.

In this paper, we present an algorithm to segment the liver in low-contrast CT images. As the first step of our algorithm, we define a search range for the liver boundary. Then, the EM algorithm is utilized to estimate parameters of a 'Gaussian Mixture' model that conforms to the intensity distribution of the liver. Using the statistical parameters of the intensity distribution, we introduce a new thresholding technique to classify image pixels. We assign a distance feature vectors to each pixel and segment the liver by a K-means clustering scheme. This initial boundary of the liver is conditioned by the Fourier transform. Then, a Geodesic Active Contour algorithm uses the boundaries to find the final surface. The novelty in our method is the proper selection and combination of sub-algorithms so as to find the border of an object in a low-contrast image. The number of parameters in the proposed method is low and the parameters have a low range of variations. We applied our method to 30 datasets including normal and abnormal cases of low-contrast/high-contrast images and it was extensively evaluated both quantitatively and qualitatively. Minimum of Dice similarity measures of the results is 0.89. Assessment of the results proves the potential of the proposed method for segmentation in low-contrast images.

A motion estimation (ME) multimedia processor was developed by employing dynamic voltage and frequency scaling (DVFS) technique to greatly reduce the power dissipation. To make full use of the advantages of DVFS technique, a fast motion estimation (ME) algorithm was also developed. It can adaptively predict the optimum supply voltage and the optimum clock frequency before ME process starts for each macro-block for encoding. Power dissipation of the 90-nm CMOS DVFS controlled multimedia processor, which contained an absolute difference accumulator as well as a small on-chip DC/DC level converter, a minimum value detector and DVFS controller, was reduced to 38.48 µW, which was only 3.261% that of a conventional multimedia processor.

Computer-aided detection (CADe) and diagnosis (CAD) has been a rapidly growing, active area of research in medical imaging. Machine leaning (ML) plays an essential role in CAD, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require “learning from examples.” One of the most popular uses of ML is the classification of objects such as lesion candidates into certain classes (e.g., abnormal or normal, and lesions or non-lesions) based on input features (e.g., contrast and area) obtained from segmented lesion candidates. The task of ML is to determine “optimal” boundaries for separating classes in the multi-dimensional feature space which is formed by the input features. ML algorithms for classification include linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), multilayer perceptrons, and support vector machines (SVM). Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which uses pixel/voxel values in images directly, instead of features calculated from segmented lesions, as input information; thus, feature calculation or segmentation is not required. In this paper, ML techniques used in CAD schemes for detection and diagnosis of lung nodules in thoracic CT and for detection of polyps in CT colonography (CTC) are surveyed and reviewed.

In this letter, we consider the ultimate boundedness of the singularly perturbed system with measurement noise. The composite controller is commonly used to regulate the singularly perturbed system. However, in the presence of measurement noise, the composite controller does not guarantee the ultimate boundedness of the singularly perturbed system. Thus, we propose the modified composite controller to show the ultimate boundedness of the singularly perturbed system with measurement noise.

In object tracking, a recent trend is using “Tracking by Detection” technique which trains a discriminative online classifier to detect objects from background. However, the incorrect updating of the online classifier and insufficient features used during the online learning often lead to the drift problems. In this work we propose an online random fern classifier with a simple but effective compressive feature in a framework integrating the online classifier, the optical-flow tracker and an update model. The compressive feature is a random projection from highly dimensional multi-scale image feature space to a low-dimensional representation by a sparse measurement matrix, which is expect to contain more information. An update model is proposed to detect tracker failure, correct tracker result and constrain the updating of online classifier, thus reducing the chance of wrong updating in online training. Our method runs at real-time and the experimental results show performance improvement compared to other state-of-the-art approaches on several challenging video clips.

Crohn's disease commonly affects the small and large intestines. Its symptoms include ulcers and intestinal stenosis, and its diagnosis is currently performed using an endoscope. However, because the endoscope cannot pass through the stenosed parts of the intestines, diagnosis of the entire intestines is difficult. A CT image-based method is expected to become an alternative way for the diagnosis of Crohn's disease because it enables observation of the entire intestine even if stenosis exists. To achieve efficient CT image-based diagnosis, diagnostic-aid by computers is required. This paper presents an automated detection method of the surface of ulcers in the small and large intestines from fecal tagging CT images. Ulcers cause rough surfaces on the intestinal wall and consist of small convex and concave (CC) regions. We detect them by blob and inverse-blob structure enhancement filters. A roughness value is utilized to reduce the false positives of the detection results. Many CC regions are concentrated in ulcers. The roughness value evaluates the concentration ratio of the detected regions. Detected regions with low roughness values are removed by a thresholding process. The thickness of the intestinal lumen and the CT values of the surrounding tissue of the intestinal lumen are also used to reduce false positives. Experimental results using ten cases of CT images showed that our proposed method detects 70.6% of ulcers with 12.7 FPs/case. The proposed method detected most of the ulcers.

In this paper, a direct/cooperative relay switched single carrier-frequency division multiple access (SC-FDMA) using amplify-and-forward (AF) protocol and spectrum division/adaptive subcarrier allocation (SDASA) is proposed. Using SDASA, the transmit SC signal spectrum is divided into sub-blocks, to each of which a different set of subcarriers (resource block) is adaptively allocated according to the channel conditions of mobile terminal (MT)-relay station (RS) link, RS-base station (BS) link, and MT-BS link. Cooperative relay does not always provide higher capacity than the direct communication. Switching between direct communication and cooperative relay is done depending on the channel conditions of MT-RS, RS-BS, and MT-BS links. We evaluate the achievable channel capacity by the Monte-Carlo numerical computation method. It is shown that the proposed scheme can reduce the transmit power by about 6.0 (2.0) dB compared to the direct communication (the cooperative AF relay) for a 1%-outage capacity of 3.0 bps/Hz.

According to the process scaling, radiation-hard devices are becoming sensitive to soft errors caused by Multiple Cell Upset (MCUs). In this paper, the parasitic bipolar effects are utilized to suppress MCUs of the radiation-hard dual-modular flip-flops. Device simulations reveal that a simultaneous flip of redundant latches is suppressed by storing opposite values instead of storing the same value due to its asymmetrical structure. The state of latches becomes a specific value after a particle hit due to the bipolar effects. Spallation neutron irradiation proves that MCUs are effectively suppressed in the D-FF arrays in which adjacent two latches in different FFs store opposite values. The redundant latch structure storing the opposite values is robust to the simultaneous flip.

This paper presents an adaptive cache architecture for wide-range reliable low-voltage operations. The proposed associativity-reconfigurable cache consists of pairs of cache ways so that it can exploit the recovery feature of the novel 7T/14T SRAM cell. Each pair has two operating modes that can be selected based upon the required voltage level of current operating conditions: normal mode for high performance and dependable mode for reliable low-voltage operations. We can obtain reliable low-voltage operations by application of the dependable mode to weaker pairs that cannot operate reliably at low voltages. Meanwhile leaving stronger pairs in the normal mode, we can minimize performance losses. Our chip measurement results show that the proposed cache can trade off its associativity with the minimum operating voltage. Moreover, it can decrease the minimum operating voltage by 140 mV achieving 67.48% and 26.70% reduction of the power dissipation and energy per instruction. Processor simulation results show that designing the on-chip caches using the proposed scheme results in 2.95% maximum IPC losses, but it can be chosen various performance levels. Area estimation results show that the proposed cache adds area overhead of 1.61% and 5.49% in 32-KB and 256-KB caches, respectively.

We propose a novel propagation measurement scheme for terrestrial TV signal indoor reception based on a virtual array technique. The system proposed in this paper carries out two-branch recording of target signals and the reference signal. By using the signal phase reference in the reference signal, we clarify the spatial propagation characteristics obtained from the two-dimensional virtual array outputs. Outdoor measurements were performed first to investigate the validity of the proposed measurement system. The results confirm its effectiveness in accurately determining the direction-of-arrival (DOA). We then investigated the propagation characteristics in an indoor environment. The angular spectrum obtained showed clear wave propagation structure. Thus, our proposed system is promising as a very accurate measurement tool for indoor propagation analysis.

Objective: The virtual slides are high-magnification whole digital images of histopathological tissue sections. The existing virtual slide system, which is optimized for scanning flat and smooth plane slides such as histopathological paraffin-embedded tissue sections, but is unsuitable for scanning irregular plane slides such as cytological smear slides. This study aims to develop a virtual slide system suitable for cytopathology slide scanning and to evaluate the effectiveness of multi-focus image fusion (MF) in cytopathological diagnosis. Study Design: We developed a multi-layer virtual slide scanning system with MF technology. Tumors for this study were collected from 21 patients diagnosed with primary breast cancer. After surgical extraction, smear slide for cytopathological diagnosis were manufactured by the conventional stamp method, fine needle aspiration method (FNA), and tissue washing method. The stamp slides were fixed in 95% ethanol. FNA and tissue washing samples were fixed in CytoRich RED Preservative Fluid, a liquid-based cytopathology (LBC). These slides were stained with Papanicolaou stain, and scanned by virtual slide system. To evaluate the suitability of MF technology in cytopathological diagnosis, we compared single focus (SF) virtual slide with MF virtual slide. Cytopathological evaluation was carried out by 5 pathologists and cytotechnologists. Results: The virtual slide system with MF provided better results than the conventional SF virtual slide system with regard to viewing inside cell clusters and image file size. Liquid-based cytology was more suitable than the stamp method for virtual slides with MF. Conclusion: The virtual slide system with MF is a useful technique for the digitization in cytopathology, and this technology could be applied to tele-cytology and e-learning by virtual slide system.