基于FPGA的字符串匹配算法

在全字节比较法的基础上提出了一种基于FPGA的子字符串LUT重用算法。该算法通过位宽扩展．以及流水线问字符串、LUT共享，用低端FPGA成功解决了高速字符串匹配问题，与传统字符串匹配算法相比．该算法大幅缩小了匹配算法芯片资源的占用率，是一种高效的并行多模式字符串匹配算法。     

eXiTCDSS: A framework for a workflow-based CBR for interventional Clinical Decision Support Systems and its application to TAVI

Clinical Decision Support System (CDSSs) should form an important part of the field of clinical knowledge management technologies through their capacity to support the clinical process and use of knowledge, including knowledge maintenance and continuous learning, from diagnosis and investigation through surgery, treatment and long-term care. The work presented shows a workflow-based CDSS designed to give case-specific assessment to clinicians during complex surgery or Minimally Invasive Surgery (MISs). Following a perioperative workflow, the designed software will use a Case-Based Reasoning (CBR) methodology to retrieve similar past cases from a case base to provide support at any particular point of the process. The graphical user interface allows easy navigation through the whole support progress, from the initial configuration steps to the final results organized as sets of experiments easily visualized in a user-friendly way. The eXiTCDSS tool is presented giving support to a recent complex minimally invasive surgery which is receiving growing attention lately, the Transcatheter Aortic Valve Implantation (TAVI). The results obtained are presented on a basis of a real TAVI case base of 82 patients operated at Rennes University Hospital.     

The influence of temperature and humidity on the sensitivity of torque transducers

This paper presents a study of the effect of temperature and relative humidity on the sensitivity and the characteristics of torque transducers such as residual zero torque, reversibility and creep. Temperature and humidity coefficients of the sensitivity take positive as well as negative values. Linear dependence is found between the influence of temperature/relative humidity and the applied load. For some torque transducers, under temperature change the sensitivity has a transient overshoot reaching up to 3 times the steady amplitude. Also there is a small effect of temperature/relative humidity on the transducer’s parameters such as residual zero torque, reversibility and creep. Equations are presented to predict the effect of temperature/relative humidity changes on the sensitivity of torque transducers.     

Street level urban design qualities for walkability: Combining 2D and 3D GIS measures

Much of the physical activity and built environment literature has focused on composite walkability indices based on the D variables– design, density, diversity, destination accessibility, and distance to transit. This literature, however, has largely ignored the microscale streetscape features that affect the pedestrian experience. Five street level urban design qualities were recently identified and defined for quantitative measures although these measures are mostly through subjective field observation. View related features such as long sight line and proportion of sky have not yet been objectively measured due to the limitation of data and method. This study uses both 2D and 3D GIS to objectively measure street level urban design qualities in Buffalo, New York and tests their correlation with observed pedestrian counts and Walk Scores. Our results showed that 3D GIS helped to generate objective measures on view related features. These objective measures can help us better understand the influence of street level urban design features on walkability for designing and planning healthy cities.     

Simultaneous segmentation of prostatic zones using Active Appearance Models with multiple coupled levelsets

In this work we present an improvement to the popular Active Appearance Model (AAM) algorithm, that we call the Multiple-Levelset AAM (MLA). The MLA can simultaneously segment multiple objects, and makes use of multiple levelsets, rather than anatomical landmarks, to define the shapes. AAMs traditionally define the shape of each object using a set of anatomical landmarks. However, landmarks can be difficult to identify, and AAMs traditionally only allow for segmentation of a single object of interest. The MLA, which is a landmark independent AAM, allows for levelsets of multiple objects to be determined and allows for them to be coupled with image intensities. This gives the MLA the flexibility to simulataneously segmentation multiple objects of interest in a new image.In this work we apply the MLA to segment the prostate capsule, the prostate peripheral zone (PZ), and the prostate central gland (CG), from a set of 40 endorectal, T2-weighted MRI images. The MLA system we employ in this work leverages a hierarchical segmentation framework, so constructed as to exploit domain specific attributes, by utilizing a given prostate segmentation to help drive the segmentations of the CG and PZ, which are embedded within the prostate. Our coupled MLA scheme yielded mean Dice accuracy values of .81, .79 and .68 for the prostate, CG, and PZ, respectively using a leave-one-out cross validation scheme over 40 patient studies. When only considering the midgland of the prostate, the mean DSC values were .89, .84, and .76 for the prostate, CG, and PZ respectively.     

Large scale automatic image annotation based on convolutional neural network

Automatic image annotation is one of the most important challenges in computer vision, which is critical to many real-world researches and applications. In this paper, we focus on the issue of large scale image annotation with deep learning. Firstly, considering the existing image data, especially the network images, most of the labels of themselves are inaccurate or imprecise. We propose a Multitask Voting (MV) method, which can improve the accuracy of original annotation to a certain extent, thereby enhancing the training effect of the model. Secondly, the MV method can also achieve the adaptive label, whereas most existing methods pre-specify the number of tags to be selected. Additionally, based on convolutional neural network, a large scale image annotation model MVAIACNN is constructed. Finally, we evaluate the performance with experiments on the MIRFlickr25K and NUS-WIDE datasets, and compare with other methods, demonstrating the effectiveness of the MVAIACNN.     

The hasty wisdom of the mob: How market sentiment predicts stock market behavior

We explore the ability of sentiment metrics, extracted from micro-blogging sites, to predict stock markets. We also address sentiments’ predictive time-horizons. The data concern bloggers’ feelings about five major stocks. Taking independent bullish and bearish sentiment metrics, granular to two minute intervals, we model their ability to forecast stock price direction, volatility, and traded volume. We find evidence of a causal link from sentiments to stock price returns, volatility and volume. The predictive time-horizon is minutes, rather than hours or days. We argue that diverse and high volume sentiment is more predictive of price volatility and traded volume than near-consensus is predictive of price direction. Causality is ephemeral. In this sense, the crowd is more a hasty mob than a source of wisdom.     

Error modeling for an untethered ultra-wideband system for construction indoor asset tracking

This paper analyzes the performance of an ultra-wideband (UWB) wireless network system for mobile asset tracking at a dynamic construction site. Static and dynamic error tests were performed on a commercial UWB system in different building spaces including open/closed spaces, steel/wood framed construction sites, and a closed office area. All tests were carried out with an untethered UWB configuration for more flexible deployment of the UWB system at construction sites. Statistical approaches including regression analysis, outlier detection, and Kalman filtering were used to build an error model. The research found that each site has a unique pattern of producing errors caused by various types of interference, e.g., electromagnetic interference, multi-path propagation, fading and scattering of signals. Approximately 25% of the errors were reduced by using the proposed error modeling process. The paper concludes that a statistically developed error model process can significantly reduce random errors and improve position accuracy for indoor mobile asset tracking applications in construction.     

Not all PBILs are the same: Unveiling the different learning mechanisms of PBIL variants

Model-based optimization using probabilistic modeling of the search space is one of the areas where research on evolutionary algorithms (EAs) has considerably advanced in recent years. The population-based incremental algorithm (PBIL) is one of the first algorithms of its kind and it has been extensively applied to many optimization problems. In this paper we show that the different applications of PBIL reported in the literature correspond, in fact, to two essentially different algorithms, which are defined by the way the learning step is implemented. We analytically and empirically study the impact of the learning method on the search behavior of the algorithm. As a result of our research, we show examples in which the choice of a PBIL variant can produce qualitatively different outputs of the search process.     

Symbiotic organisms search and two solution representations for solving the capacitated vehicle routing problem

This paper presents the symbiotic organisms search (SOS) heuristic for solving the capacitated vehicle routing problem (CVRP), which is a well-known discrete optimization problem. The objective of CVRP is to decide the routes for a set of vehicles to serve a set of demand points while minimizing the total routing cost. SOS is a simple and powerful metaheuristic that simulates the symbiotic interaction strategies adopted by an organism for surviving in an ecosystem. As SOS is originally developed for solving continuous optimization problems, we therefore apply two solution representations, SR-1 and SR-2, to transform SOS into an applicable solution approach for CVRP and then apply a local search strategy to improve the solution quality of SOS. The original SOS uses three interaction strategies, mutualism, commensalism, and parasitism, to improve a candidate solution. In this improved version, we propose two new interaction strategies, namely competition and amensalism. We develop six versions of SOS for solving CVRP. The first version, SOS_Canonical, utilizes a commonly used continuous to discrete solution representation transformation procedure. The second version is an improvement of canonical SOS with a local search strategy, denoted as SOS_Basic. The third and fourth versions use SR-1 and SR-2 with a local search strategy, denoted as SOS_SR-1 and SOS_SR-2. The fifth and sixth versions, denoted as ISOS_SR-1 and ISOS_SR-2, improve the implementation of SOS_SR-1 and SOS_SR-2 by adding the newly proposed competition and amensalism interaction strategies. The performances of SOS_Canonical, SOS_Basic, SOS_SR-1, and SOS_SR-2 are evaluated on two sets of benchmark problems. First, the results of the four versions of SOS are compared, showing that the preferable result was obtained from SOS_SR-1 and SOS_SR-2. The performances of SOS_SR-1, SOS_SR-2, ISOS_SR-1, and ISOS_SR-2 are then compared, presenting that ISOS_SR-1 and ISOS_SR-2 offer a better performance. Next, the ISOS_SR-1 and ISOS_SR-2 results are compared to the best-known solutions. The results show that ISOS_SR-1 and ISOS_SR-2 produce good VRP solutions under a reasonable computational time, indicating that each of them is a good alternative algorithm for solving the capacitated vehicle routing problem.