As technology nodes continue to shrink, the impact of radiation-induced soft error on processor reliability increases. Estimation of processor reliability and identification of vulnerable flip-flops requires accurate soft error rate (SER) analysis techniques. This paper presents a proposal for a soft error propagation analysis technique. We specifically examine single event upset (SEU) occurring at a flip-flop in sequential circuits. When SEUs propagate in sequential circuits, the faults can be masked temporally and logically. Conventional soft error propagation analysis techniques do not consider error convergent timing on re-convergent paths. The proposed technique can analyze soft error propagation while considering error-convergent timing on a re-convergent path by combinational analysis of temporal and logical effects. The proposed technique also considers the case in which the temporal masking is disabled with an enable signal of the erroneous flip-flop negated. Experimental results show that the proposed technique improves inaccuracy by 70.5%, on average, compared with conventional techniques using ITC 99 and ISCAS 89 benchmark circuits when the enable probability is 1/3, while the runtime overhead is only 1.7% on average.

Sentence similarity computation is an increasingly important task in applications of natural language processing such as information retrieval, machine translation, text summarization and so on. From the viewpoint of information theory, the essential attribute of natural language is that the carrier of information and the capacity of information can be measured by information content which is already successfully used for word similarity computation in simple ways. Existing sentence similarity methods don't emphasize the information contained by the sentence, and the complicated models they employ often need using empirical parameters or training parameters. This paper presents a fully unsupervised computational model of sentence semantic similarity. It is also a simply and straightforward model that neither needs any empirical parameter nor rely on other NLP tools. The method can obtain state-of-the-art experimental results which show that sentence similarity evaluated by the model is closer to human judgment than multiple competing baselines. The paper also tests the proposed model on the influence of external corpus, the performance of various sizes of the semantic net, and the relationship between efficiency and accuracy.

We present a hierarchical replicated state machine (H-RSM) and its corresponding consensus protocol D-Paxos for replication across multiple data centers in the cloud. Our H-RSM is based on the idea of parallel processing and aims to improve resource utilization. We detail D-Paxos and theoretically prove that D-Paxos implements an H-RSM. With batching and logical pipelining, D-Paxos efficiently utilizes the idle time caused by high-latency message transmission in a wide-area network and available bandwidth in a local-area network. Experiments show that D-Paxos provides higher throughput and better scalability than other Paxos variants for replication across multiple data centers. To predict the optimal batch sizes when D-Paxos reaches its maximum throughput, an analytical model is developed theoretically and validated experimentally.

The separation of signals with temporal structure from mixed sources is a challenging problem in signal processing. For this problem, blind source extraction (BSE) is more suitable than blind source separation (BSS) because it has lower computation cost. Nowadays many BSE algorithms can be used to extract signals with temporal structure. However, some of them are not robust because they are too dependent on the estimation precision of time delay; some others need to choose parameters before extracting, which means that arbitrariness can't be avoided. In order to solve the above problems, we propose a robust source extraction algorithm whose performance doesn't rely on the choice of parameters. The algorithm is realized by maximizing the objective function that we develop based on the non-Gaussianity and the temporal structure of source signals. Furthermore, we analyze the stability of the algorithm. Simulation results show that the algorithm can extract the desired signal from large numbers of observed sensor signals and is very robust to error in the estimation of time delay.

Some diffusive and recurrence properties of Lorentz Lattice Gas Cellular Automata (LLGCA) have been expensively studied in terms of the densities of some of the left/right static/flipping mirrors/rotators. In this paper, for any combination S of these well known scatters, we study the computational complexity of the following problem which we call PERIODICITY on the S-model: given a finite configuration that distributes only those scatters in S, whether a particle visits the starting position periodically or not. Previously, the flipping mirror model and the occupied flipping rotator model have been shown unbounded, i.e. the process is always diffusive [17]. On the other hand, PERIODICITY is shown PSPACE-complete in the unoccupied flipping rotator model [21]. In this paper, we show that PERIODICITY is PSPACE-compete in any S-model that is neither occupied, unbounded, nor static. Particularly, we prove that PERIODICITY in any unoccupied and bounded model containing flipping mirror is PSPACE-complete.

Distributed compressive video sensing (DCVS) is an emerging low-complexity video coding framework which integrates the merits of distributed video coding (DVC) and compressive sensing (CS). In this paper, we propose a novel rate-distortion optimized DCVS codec, which takes advantage of a rate-distortion optimization (RDO) model based on the estimated correlation noise (CN) between a non-key frame and its side information (SI) to determine the optimal measurements allocation for the non-key frame. Because the actual CN can be more accurately recovered by our DCVS codec, it leads to more faithful reconstruction of the non-key frames by adding the recovered CN to the SI. The experimental results reveal that our DCVS codec significantly outperforms the legacy DCVS codecs in terms of both objective and subjective performance.

Designing a backbone IP network, especially to support both unicast and multicast traffic under delay constraints, is a difficult problem. Real network design must consider cost, performance and reliability. Therefore, a simulator can help a network designer to test the functionality of the network before the implementation. This paper proposes a heuristic design algorithm called D-MENTOR, and the algorithm was developed by programming based on Mesh Network Topological Optimization and Routing Version 2 (MENTOR-II) to integrate as a new module of DElite tool. The simulation results show that, in almost all test cases, the proposed algorithm yields lower installation cost.

A method for increasing alignment tolerance in simple multiple-stream transmission is described. Its use of π-shifted antenna directivity phase enables it to cancel interference even when antenna placement deviations occur. The interference cancellation by using π-shifted directivities provides higher alignment tolerance than that with conventional fixed weight methods. It also provides smaller channel gain variation than can be obtained using fixed weights even when antenna displacement occurs. An objective function is described that is determined by the alignment tolerance. The function is defined to maximize the alignment tolerance. The method's validity is confirmed by an experimental analysis of two-stream transmission in which the alignment tolerance of the proposed method is compared to that of conventional fixed weight methods.

Optimizating the deployment of wireless sensor networks, which is one of the key issues in wireless sensor networks research, helps improve the coverage of the networks and the system reliability. In this paper, we propose an evolutionary algorithm based on modified t-distribution for the wireless sensor by introducing a deployment optimization operator and an intelligent allocation operator. A directed perturbation operator is applied to the algorithm to guide the evolution of the node deployment and to speed up the convergence. In addition, with a new geometric sensor detection model instead of the old probability model, the computing speed is increased by 20 times. The simulation results show that when this algorithm is utilized in the actual scene, it can get the minimum number of nodes and the optimal deployment quickly and effectively.Compared with the existing mainstream swarm intelligence algorithms, this method has satisfied the need for convergence speed and better coverage, which is closer to the theoretical coverage value.

A 10-bit 20-MS/s asynchronous SAR ADC with a meta-stability detector using replica comparators is proposed. The proposed SAR ADC with the area of 0.093mm2 is implemented using a 130-nm CMOS process with a 1.2-V supply. The measured peak ENOBs for the full rail-to-rail differential input signal is 9.6bits.

In this study, a well-shaped microelectrode array (MEA) for fabricating a high-density complementary metal-oxide semiconductor amperometric electrochemical sensor array was designed and verified. By integrating an auxiliary electrode with the well-shaped structure of the MEA, the footprint was reduced and high density and high resolution were also achieved. The results of three-dimensional electrochemical simulations confirmed the effectiveness of the proposed MEA structure and possibility of increasing the density to four times than that achieved by the conventional two-dimensional structure.

A non-linear extension of generalized hyperplane approximation (GHA) method is introduced in this letter. Although GHA achieved a high-confidence result in motion parameter estimation by utilizing the supervised learning scheme in histogram of oriented gradient (HOG) feature space, it still has unstable convergence range because it approximates the non-linear function of regression from the feature space to the motion parameter space as a linear plane. To extend GHA into a non-linear regression for larger convergence range, we derive theoretical equations and verify this extension's effectiveness and efficiency over GHA by experimental results.

A non-photorealistic rendering method creates oil-film-like images, expressed with colorful, smooth curves similar to the oil films generated on the surface of glass or water, from color photo images. The proposed method generates oil-film-like images through iterative processing between a bilateral infra-envelope filter and an unsharp mask. In order to verify the effectiveness of the proposed method, tests using a Lena image were performed, and visual assessment of oil-film-like images was conducted for changes in appearance as the parameter values of the proposed method were varied. As a result of tests, the optimal value of parameters was found for generating oil-film patterns.

Based on the completeness of the real-valued discrete Gabor transform, a new biorthogonal relationship between analysis window and synthesis window is derived and a fast algorithm for computing the analysis window is presented for any given synthesis window. The new biorthogonal relationship can be expressed as a linear equation set, which can be separated into a certain number of independent sub-equation sets, where each of them can be fast and independently solved by using convolution operations and FFT to obtain the analysis window for any given synthesis window. Computational complexity analysis and comparison indicate that the proposed algorithm can save a considerable amount of computation and is more efficient than the existing algorithms.

Fine-grained visual categorization (FGVC) has drawn increasing attention as an emerging research field in recent years. In contrast to generic-domain visual recognition, FGVC is characterized by high intra-class and subtle inter-class variations. To distinguish conceptually and visually similar categories, highly discriminative visual features must be extracted. Moreover, FGVC has highly specialized and task-specific nature. It is not always easy to obtain a sufficiently large-scale training dataset. Therefore, the key to success in practical FGVC systems is to efficiently exploit discriminative features from a limited number of training examples. In this paper, we propose an efficient two-step dimensionality compression method to derive compact middle-level part-based features. To do this, we compare both space-first and feature-first convolution schemes and investigate their effectiveness. Our approach is based on simple linear algebra and analytic solutions, and is highly scalable compared with the current one-vs-one or one-vs-all approach, making it possible to quickly train middle-level features from a number of pairwise part regions. We experimentally show the effectiveness of our method using the standard Caltech-Birds and Stanford-Cars datasets.

In this paper, we propose improved methods of liquid-phase detection of biological targets utilizing magnetic markers and a high-critical-temperature superconducting quantum interference device (SQUID). For liquid-phase detection, the bound and unbound (free) markers are magnetically distinguished by using Brownian relaxation of free markers. Although a signal from the free markers is zero in an ideal case, it exists in a real sample on account of the aggregation and precipitation of free markers. This signal is called a blank signal, and it degrades the sensitivity of target detection. To solve this problem, we propose improved detection methods. First, we introduce a reaction field, Bre, during the binding reaction between the markers and targets. We additionally introduce a dispersion process after magnetization of the bound markers. Using these methods, we can obtain a strong signal from the bound markers without increasing the aggregation of the free markers. Next, we introduce a field-reversal method in the measurement procedure to differentiate the signal from the markers in suspension from that of the precipitated markers. Using this procedure, we can eliminate the signal from the precipitated markers. Then, we detect biotin molecules by using these methods. In an experiment, the biotins were immobilized on the surfaces of large polymer beads with diameters of 3.3 µm. They were detected with streptavidin-conjugated magnetic markers. The minimum detectable molecular number concentration was 1.8×10-19 mol/ml, which indicates the high sensitivity of the proposed method.

Network virtualization (NV) provides a promising solution to overcome the resistance of the current Internet in aspects of architecture change, and virtual network embedding (VNE) has been recognized as a core component in NV. In this paper, the current advances in exploring model, methods and technologies for embedding the virtual network into the substrate network, are summarized. Furthermore, the future research trends are drawn. The main distinctive aspects of this survey with early ones include that it is mainly contributed to simplify the VNE problem on large networks, and that more recent publications in this field are introduced. In addition, the suggestions to the future investigation will concern some new terms of the VNE optimization.

In this paper a new method for noise removal from single-trial event-related potentials recorded with a multi-channel electroencephalogram is addressed. An observed signal is separated into multiple signals with a multi-channel Wiener filter whose coefficients are estimated based on parameter estimation of a probabilistic generative model that locally models the amplitude of each separated signal in the time-frequency domain. Effectiveness of using prior information about covariance matrices to estimate model parameters and frequency dependent covariance matrices were shown through an experiment with a simulated event-related potential data set.

In recent years, optical music recognition (OMR) has been extensively developed, particularly for use with mobile devices that require fast processing to recognize and play live the notes in images captured from sheet music. However, most techniques that have been developed thus far have focused on playing back instrumental music and have ignored the importance of lyric extraction, which is time consuming and affects the accuracy of the OMR tools. The text of the lyrics adds complexity to the page layout, particularly when lyrics touch or overlap musical symbols, in which case it is very difficult to separate them from each other. In addition, the distortion that appears in captured musical images makes the lyric lines curved or skewed, making the lyric extraction problem more complicated. This paper proposes a new approach in which lyrics are detected and extracted quickly and effectively. First, in order to resolve the distortion problem, the image is undistorted by a method using information of stave lines and bar lines. Then, through the use of a frequency count method and heuristic rules based on projection, the lyric areas are extracted, the cases where symbols touch the lyrics are resolved, and most of the information from the musical notation is kept even when the lyrics and music notes are overlapping. Our algorithm demonstrated a short processing time and remarkable accuracy on two test datasets of images of printed Korean musical scores: the first set included three hundred scanned musical images; the second set had two hundred musical images that were captured by a digital camera.

The high-availability seamless redundancy (HSR) protocol is a representative protocol that fulfills the reliability requirements of the IEC61850-based substation automation system (SAS). However, it has the drawback of creating unnecessary traffic in a network. To solve this problem, a dual virtual path (DVP) algorithm based on HSR was recently presented. Although this algorithm dramatically reduces network traffic, it does not consider the substation timing requirements of messages in an SAS. To reduce unnecessary network traffic in an HSR ring network, we introduced a novel packet transmission (NPT) algorithm in a previous work that considers IEC61850 message types. To further reduce unnecessary network traffic, we propose an extended dual virtual paths (EDVP) algorithm in this paper that considers the timing requirements of IEC61850 message types. We also include sending delay (SD), delay queue (DQ), and traffic flow latency (TFL) features in our proposal. The source node sends data frames without SDs on the primary paths, and it transmits the duplicate data frames with SDs on the secondary paths. Since the EDVP algorithm discards all of the delayed data frames in DQs when there is no link or node failure, unnecessary network traffic can be reduced. We demonstrate the principle of the EDVP algorithm and its performance in terms of network traffic compared to the standard HSR, NPT, and DVP algorithm using the OPNET network simulator. Throughout the simulation results, the EDVP algorithm shows better traffic performance than the other algorithms, while guaranteeing the timing requirements of IEC61850 message types. Most importantly, when the source node transmits heavy data traffic, the EDVP algorithm shows greater than 80% and 40% network traffic reduction compared to the HSR and DVP approaches, respectively.