We present circuit implementations for computing exponentials and logarithms suitable for rapid single-flux-quantum (RSFQ) logic. We propose hardware algorithms based on the sequential table-lookup (STL) method using the radix-2 signed-digit representation that achieve high-throughput, digit-serial calculations. The circuits are implemented by processing elements formed in systolic-array-like, regularly-aligned pipeline structures. The processing elements are composed of adders, shifters, and readouts of precomputed constants. The iterative calculations are fully overlapped, and throughputs approach the maximum throughput of serial processing. The circuit size for calculating significand parts is estimated to be approximately 5-10 times larger than that of a bit-serial floating-point adder or multiplier.

This paper presents a method for registering retinal images. Retinal image registration is crucial for the diagnoses and treatments of various eye conditions and diseases such as myopia and diabetic retinopathy. Retinal image registration is challenging because the images have non-uniform contrasts and intensity distributions, as well as having large homogeneous non-vascular regions. This paper provides a new retinal image registration method by effectively combining expectation maximization principal component analysis based mutual information (EMPCA-MI) with salient features. Experimental results show that our method is more efficient and robust than the conventional EMPCA-MI method.

Recent studies have obtained superior performance in image recognition tasks by using, as an image representation, the fully connected layer activations of Convolutional Neural Networks (CNN) trained with various kinds of images. However, the CNN representation is not very suitable for fine-grained image recognition tasks involving food image recognition. For improving performance of the CNN representation in food image recognition, we propose a novel image representation that is comprised of the covariances of convolutional layer feature maps. In the experiment on the ETHZ Food-101 dataset, our method achieved 58.65% averaged accuracy, which outperforms the previous methods such as the Bag-of-Visual-Words Histogram, the Improved Fisher Vector, and CNN-SVM.

This paper proposes two linearity enhancement techniques for open-loop amplifiers. One technique is nonlinearity cancellation. An amplifier with reversed nonlinearity is proposed to cascade with a conventional common source amplifier. The product of these two nonlinear gains demonstrates much higher linearity. It achieves a SFDR of 71 dB when differential output range is 600 mV. Compared with the conventional common source amplifier, about 24 dB improvement is achieved. Another proposed technique is gain adapting. An input amplitude detector utilizing second order nonlinearity is combined with a source-degenerated amplifier. It can adjust the gain automatically according to the input amplitude, and compensate the gain compression when the input amplitude becomes larger. A SFDR of 69 dB is realized when the differential output range is 600 mV. An improvement of 23 dB is achieved after gain is adapted. Furthermore, mismatch calibration for the two proposed linearity enhancement techniques is investigated. Finally, comparison between two proposed amplifiers is introduced. The amplifier with nonlinearity cancellation has advantage in large signal range while the amplifier utilizing gain adapting is more competitive on accurate calibration, fast response and low noise.

This paper considers an optimal stabilization problem of quantitative discrete event systems (DESs) under the influence of disturbances. We model a DES by a deterministic weighted automaton. The control cost is concerned with the sum of the weights along the generated trajectories reaching the target state. The region of weak attraction is the set of states of the system such that all trajectories starting from them can be controlled to reach a specified set of target states and stay there indefinitely. An optimal stabilizing controller is a controller that drives the states in this region to the set of target states with minimum control cost and keeps them there. We consider two control objectives: to minimize the worst-case control cost (1) subject to all enabled trajectories and (2) subject to the enabled trajectories starting by controllable events. Moreover, we consider the disturbances which are uncontrollable events that rarely occur in the real system but may degrade the control performance when they occur. We propose a linearithmic time algorithm for the synthesis of an optimal stabilizing controller which is robust to disturbances.

Object tracking is one of the fundamental problems in computer vision. However, there is still a need to improve the overall capability in various tracking circumstances. In this letter, a patches-collaborative compressive tracking (PCCT) algorithm is presented. Experiments on various challenging benchmark sequences demonstrate that the proposed algorithm performs favorably against several state-of-the-art algorithms.

We propose an exact algorithm to determine the satisfiability of oblivious read-twice branching programs. Our algorithm runs in $2^{left(1 - Omega(rac{1}{log c}) ight)n}$ time for instances with n variables and cn nodes.

A striped inductor and its utilization of a voltage-controlled oscillator (VCO) are studied with the aim of suppressing phase noise degradation in K- and Ka-bands. The proposed striped inductor exhibits reduced series resistance in the high frequency region by increasing the cross-sectional peripheral length, as with the Litz wire, and the VCO of the striped inductor simultaneously exhibits a lower phase noise than that of the conventional inductor. Striped and conventional inductors and VCOs are designed and fabricated, and their use of K- and Ka-bands is measured. Results show that the Q factor and corner frequency of the striped inductor are approximately 1.3 and 1.6 times higher, respectively, than that of the conventional inductor. Moreover, the 1-MHz-offset phase noise of the striped inductor's VCO in the K- and Ka-bands was approximately 3.5 dB lower than that of the conventional inductor. In this study, a 65-nm standard CMOS process was used.

This paper aims to support the cultivation of proper cognitive skills for academic English listening. First of all, this paper identified several listening strategies proved to be effective for cultivating listening skills through past research and builds up the respective strategy models, based on which we designed and developed various functional units as strategy objects, and the mashup environment where these function units can be assembled to serve as a personal learning environment. We also attached listening strategies and tactics to each object, in order to make learners aware of the related strategies and tactics applied during learning. Both short-term and mid-term case studies were carried out, and the data collected showed several positive results and some interesting indications.

Linked data entity resolution is the detection of instances that reside in different repositories but co-describe the same topic. The quality of the resolution result depends on the appropriateness of the configuration, including the selected matching properties and the similarity measures. Because such configuration details are currently set differently across domains and repositories, a general resolution approach for every repository is necessary. In this paper, we present cLink, a system that can perform entity resolution on any input effectively by using a learning algorithm to find the optimal configuration. Experiments show that cLink achieves high performance even when being given only a small amount of training data. cLink also outperforms recent systems, including the ones that use the supervised learning approach.

Controlling the peak-to-mean envelope power ratio (PMEPR) of orthogonal frequency-division multiplexed (OFDM) transmissions is a significant obstacle in many low-cost applications of OFDM. An coding approach proposed by H.R. Sadjadpour presents non-square M-QAM symbols as a combination of QPSK and BPSK signals when M=22n+1, and then uses QPSK and BPSK Golay (or Golay-like) sequences with a constant PMEPR to generate M-QAM sequences. This paper proposes a new scheme in which M-QAM sequences are generated by QPSK and BPSK sequences with variable PMEPRs. In other words, this new scheme is a general case of the existing approach. As a result, the code rate of the new sequence is significantly improved, while the upper bound of its PMEPR remains at a comparative level.

The Building puzzle is played on an N×N grid of cells. Initially, some numbers are given around the border of the grid. The object of the puzzle is to fill out blank cells such that every row and column contains the numbers 1 through N. The number written in each cell represents the height of the building. The numbers around the border indicate the number of buildings which a person can see from that direction. A shorter building behind a taller one cannot be seen by him. It is shown that deciding whether the Building puzzle has a solution is NP-complete.

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.

In this letter, a micro-expression recognition method is investigated by integrating both spatio-temporal facial features and a regression model. To this end, we first perform a multi-scale facial region division for each facial image and then extract a set of local binary patterns on three orthogonal planes (LBP-TOP) features corresponding to divided facial regions of the micro-expression videos. Furthermore, we use GSLSR model to build the linear regression relationship between the LBP-TOP facial feature vectors and the micro expressions label vectors. Finally, the learned GSLSR model is applied to the prediction of the micro-expression categories for each test micro-expression video. Experiments are conducted on both CASME II and SMIC micro-expression databases to evaluate the performance of the proposed method, and the results demonstrate that the proposed method is better than the baseline micro-expression recognition method.

This paper newly proposes and theoretically analyzes the performance of multi-hop free-space optical (FSO) systems employing optical amplify-and-forward (OAF) relaying technique and wavelength division multiplexing (WDM). The proposed system can provide a low cost, low latency, high flexibility, and large bandwidth access network for multiple users in areas where installation of optical fiber is unfavorable. In WDM/FSO systems, WDM channels suffer from the interchannel crosstalk while FSO channels can be severely affected by the atmospheric turbulence. These impairments together with the accumulation of background and amplifying noises over multiple relays significantly degrade the overall system performance. To deal with this problem, the use of the M-ary pulse position modulation (M-PPM) together with the OAF relaying technique is advocated as a powerful remedy to mitigate the effects of atmospheric turbulence. For the performance analysis, we use a realistic model of Gaussian pulse propagation to investigate major atmospheric effects, including signal turbulence and pulse broadening. We qualitatively discuss the impact of various system parameters, including the required average transmitted powers per information bit corresponding to specific values of bit error rate (BER), transmission distance, number of relays, and turbulence strength. Our numerical results are also thoroughly validated by Monte-Carlo (M-C) simulations.

Because the perceptual compressive sensing framework can achieve a much better performance than the legacy compressive sensing framework, it is very promising for the compressive sensing based image compression system. In this paper, we propose an innovative adaptive perceptual block compressive sensing scheme. Firstly, a new block-based statistical metric which can more appropriately measure each block's sparsity and perceptual sensibility is devised. Then, the approximated theoretical minimum measurement number for each block is derived from the new block-based metric and used as weight for adaptive measurements allocation. The obtained experimental results show that our scheme can significantly enhance both objective and subjective performance of a perceptual compressive sensing framework.

This study focuses on modeling the storytelling performance of the participants in a group conversation. Storytelling performance is one of the fundamental communication techniques for providing information and entertainment effectively to a listener. We present a multimodal analysis of the storytelling performance in a group conversation, as evaluated by external observers. A new multimodal data corpus is collected through this group storytelling task, which includes the participants' performance scores. We extract multimodal (verbal and nonverbal) features regarding storytellers and listeners from a manual description of spoken dialog and from various nonverbal patterns, including each participant's speaking turn, utterance prosody, head gesture, hand gesture, and head direction. We also extract multimodal co-occurrence features, such as head gestures, and interaction features, such as storyteller utterance overlapped with listener's backchannel. In the experiment, we modeled the relationship between the performance indices and the multimodal features using machine-learning techniques. Experimental results show that the highest accuracy (R2) is 0.299 for the total storytelling performance (sum of indices scores) obtained with a combination of verbal and nonverbal features in a regression task.

This article presents a novel, approximate method that makes it possible to analyse multi-service switching networks. The method belongs to the group of the so-called effective availability methods and is characterized by very high accuracy for single-service and multi-service switching networks alike. The operation of the proposed method is presented with an example of a number of three-stage switching networks with different ways of the execution of inter-stage connections. A comparison of analytical and simulation results confirms high accuracy of the proposed method that is independent of the structure of a switching network.

Today, computer vision is used in different applications for intelligent transportation systems like: traffic surveillance, driver assistance, law enforcement etc. Amongst these applications, we are concentrating on speed measurement for law enforcement. In law enforcement applications, the presence of the license plate in the scene is a presupposition and metric parameters like vehicle's speed are to be estimated with a high degree of precision. The novelty of this paper is to propose a new precise, practical and fast procedure, with hierarchical architecture, to estimate the homraphic transform of the license plate and using this transform to estimate the vehicle's speed. The proposed method uses the RANSAC algorithm to improve the robustness of the estimation. Hence, it is possible to replace the peripheral equipment with vision based systems, or in conjunction with these peripherals, it is possible to improve the accuracy and reliability of the system. Results of experiments on different datasets, with different specifications, show that the proposed method can be used in law enforcement applications to measure the vehicle's speed.

We have developed an automatic network configuration technology for flexible and robust network construction. In this paper, we propose a two-or-more-level hierarchical link-state routing protocol in Hierarchical QoS Link Information Protocol (HQLIP). The hierarchical routing easily scales up the network by combining and stacking configured networks. HQLIP is designed not to recompute shortest-path trees from topology information in order to achieve a high-speed convergence of forwarding information base (FIB), especially when renumbering occurs in the network. In addition, we propose a fixed-midfix renumbering (FMR) method. FMR enables an even faster convergence when HQLIP is synchronized with Hierarchical/Automatic Number Allocation (HANA). Experiments demonstrate that HQLIP incorporating FMR achieves the convergence time within one second in the network where 22 switches and 800 server terminals are placed, and is superior to Open Shortest Path First (OSPF) in terms of a convergence time. This shows that a combination of HQLIP and HANA performs stable renumbering in link-state routing protocol networks.