A Novel Object Segmentation Method for Silhouette Tracker in Video Surveillance Application

In recent years, surveillance cameras are deployed almost everywhere. More and more video analytics features have been developed and incorporated with video surveillance system for conducting intelligence tasks, such as motion detection, human identification, etc. One typical requirement is to track suspicious humans or vehicles in the cameras' live or recorded footages, and over the years researchers have proposed different tracking methods, such as point tracking, kernel tracking and silhouette tracking to support this requirement. In particular, silhouette tracker has received considerable attention because it works well for objects with a large variety of shape, provided that reasonably good object masks or contours are initialized properly for the silhouette tracker. A properly initialized object mask and contour, however, cannot be obtained easily. On one hand, a simple bounding box contains too much irrelevant background objects, while a manually specified mask could provide accurate silhouette but this also requires lots of interactive which greatly limits its practicality. In this paper, we present a novel block based object mask segmentation method for silhouette tracker initialization. Essentially, the proposed method re-uses the motion information extracted during the video encoding phase, which provides approximated object masks for silhouette tracker. Experimental results confirm that such a block-based object masks is sufficient for a robust silhouette tracker to reliably track moving objects. © 2014 IEEE.published_or_final_versio

Selection of Temporal Aligned Video Frames for Video Stitching Application

Multi-view image/video stitching algorithm is an extensive research area in computer vision and image based rendering. Most researches focus on stitching the images from different views with assumption that those images have been already aligned in temporal domain. However it is not the case in real application. If the images from different views are not aligned in temporal domain, or in another words, not time synchronized, the corresponding feature points or regions will not be located correctly among different views, which will result in ghost objects appearing in the final stitching/rendering result. In this paper, we present an epipolar geometry consistency scoring scheme to guide temporal aligned video frame pair selection for multi-view video stitching application. Essentially, the proposed scheme allows us to determine whether a given pair of video frames is temporally aligned well for video stitching. Experimental results confirm that better video stitching results can be obtained with the proposed scheme in place.published_or_final_versio

New genomic resources for switchgrass: a BAC library and comparative analysis of homoeologous genomic regions harboring bioenergy traits

<p>Abstract</p> <p>Background</p> <p>Switchgrass, a C4 species and a warm-season grass native to the prairies of North America, has been targeted for development into an herbaceous biomass fuel crop. Genetic improvement of switchgrass feedstock traits through marker-assisted breeding and biotechnology approaches calls for genomic tools development. Establishment of integrated physical and genetic maps for switchgrass will accelerate mapping of value added traits useful to breeding programs and to isolate important target genes using map based cloning. The reported polyploidy series in switchgrass ranges from diploid (2X = 18) to duodecaploid (12X = 108). Like in other large, repeat-rich plant genomes, this genomic complexity will hinder whole genome sequencing efforts. An extensive physical map providing enough information to resolve the homoeologous genomes would provide the necessary framework for accurate assembly of the switchgrass genome.</p> <p>Results</p> <p>A switchgrass BAC library constructed by partial digestion of nuclear DNA with <it>Eco</it>RI contains 147,456 clones covering the effective genome approximately 10 times based on a genome size of 3.2 Gigabases (~1.6 Gb effective). Restriction digestion and PFGE analysis of 234 randomly chosen BACs indicated that 95% of the clones contained inserts, ranging from 60 to 180 kb with an average of 120 kb. Comparative sequence analysis of two homoeologous genomic regions harboring orthologs of the rice <it>OsBRI1 </it>locus, a low-copy gene encoding a putative protein kinase and associated with biomass, revealed that orthologous clones from homoeologous chromosomes can be unambiguously distinguished from each other and correctly assembled to respective fingerprint contigs. Thus, the data obtained not only provide genomic resources for further analysis of switchgrass genome, but also improve efforts for an accurate genome sequencing strategy.</p> <p>Conclusions</p> <p>The construction of the first switchgrass BAC library and comparative analysis of homoeologous harboring <it>OsBRI1 </it>orthologs present a glimpse into the switchgrass genome structure and complexity. Data obtained demonstrate the feasibility of using HICF fingerprinting to resolve the homoeologous chromosomes of the two distinct genomes in switchgrass, providing a robust and accurate BAC-based physical platform for this species. The genomic resources and sequence data generated will lay the foundation for deciphering the switchgrass genome and lead the way for an accurate genome sequencing strategy.</p

Design of an electrochemical micromachining machine

Electrochemical micromachining (μECM) is a non-conventional machining process based on the phenomenon of electrolysis. μECM became an attractive area of research due to the fact that this process does not create any defective layer after machining and that there is a growing demand for better surface integrity on different micro applications including microfluidics systems, stress-free drilled holes in automotive and aerospace manufacturing with complex shapes, etc. This work presents the design of a next generation μECM machine for the automotive, aerospace, medical and metrology sectors. It has three axes of motion (X, Y, Z) and a spindle allowing the tool-electrode to rotate during machining. The linear slides for each axis use air bearings with linear DC brushless motors and 2-nm resolution encoders for ultra precise motion. The control system is based on the Power PMAC motion controller from Delta Tau. The electrolyte tank is located at the rear of the machine and allows the electrolyte to be changed quickly. This machine features two process control algorithms: fuzzy logic control and adaptive feed rate. A self-developed pulse generator has been mounted and interfaced with the machine and a wire ECM grinding device has been added. The pulse generator has the possibility to reverse the pulse polarity for on-line tool fabrication.The research reported in this paper is supported by the European Commission within the project “Minimizing Defects in Micro-Manufacturing Applications (MIDEMMA)” (FP7-2011-NMPICT- FoF-285614)

Stimulation of Na⁺/H⁺ Exchanger Isoform 1 Promotes Microglial Migration

Regulation of microglial migration is not well understood. In this study, we proposed that Na+/H+ exchanger isoform 1 (NHE-1) is important in microglial migration. NHE-1 protein was co-localized with cytoskeletal protein ezrin in lamellipodia of microglia and maintained its more alkaline intracellular pH (pHi). Chemoattractant bradykinin (BK) stimulated microglial migration by increasing lamellipodial area and protrusion rate, but reducing lamellipodial persistence time. Interestingly, blocking NHE-1 activity with its potent inhibitor HOE 642 not only acidified microglia, abolished the BK-triggered dynamic changes of lamellipodia, but also reduced microglial motility and microchemotaxis in response to BK. In addition, NHE-1 activation resulted in intracellular Na+ loading as well as intracellular Ca2+ elevation mediated by stimulating reverse mode operation of Na+/Ca2+ exchange (NCXrev). Taken together, our study shows that NHE-1 protein is abundantly expressed in microglial lamellipodia and maintains alkaline pHi in response to BK stimulation. In addition, NHE-1 and NCXrev play a concerted role in BK-induced microglial migration via Na+ and Ca2+ signaling. © 2013 Shi et al

Microbial fuel cells: a green and alternative source for bioenergy production

Microbial fuel cell (MFC) represents one of the green technologies for the production of bioenergy. MFCs using microalgae produce bioenergy by converting solar energy into electrical energy as a function of metabolic and anabolic pathways of the cells. In the MFCs with bacteria, bioenergy is generated as a result of the organic substrate oxidation. MFCs have received high attention from researchers in the last years due to the simplicity of the process, the absence in toxic by-products, and low requirements for the algae growth. Many studies have been conducted on MFC and investigated the factors affecting the MFC performance. In the current chapter, the performance of MFC in producing bioenergy as well as the factors which influence the efficacy of MFCs is discussed. It appears that the main factors affecting MFC’s performance include bacterial and algae species, pH, temperature, salinity, substrate, mechanism of electron transfer in an anodic chamber, electrodes materials, surface area, and electron acceptor in a cathodic chamber. These factors are becoming more influential and might lead to overproduction of bioenergy when they are optimized using response surface methodology (RSM)

Population seroprevalence of antibody to influenza A(H7N9) virus, Guangzhou, China

BACKGROUND: Since the identification in early 2013 of severe disease caused by influenza A(H7N9) virus infection, there have been few attempts to characterize the full severity profile of human infections. Our objective was to estimate the number and severity of H7N9 infections in Guangzhou, using a serological study. METHODS: We collected residual sera from patients of all ages admitted to a hospital in the city of Guangzhou in southern China in 2013 and 2014. We screened the sera using a haemagglutination inhibition assay against a pseudovirus containing the H7 and N9 of A/Anhui/1/2013(H7N9), and samples with a screening titer ≥10 were further tested by standard hemagglutination-inhibition and virus neutralization assays for influenza A(H7N9). We used a statistical model to interpret the information on antibody titers in the residual sera, assuming that the residual sera provided a representative picture of A(H7N9) infections in the general population, accounting for potential cross-reactions. RESULTS: We collected a total of 5360 residual sera from December 2013 to April 2014 and from October 2014 to December 2014, and found two specimens that tested positive for H7N9 antibody at haemagglutination inhibition titer ≥40 and a neutralization titer ≥40. Based on this, we estimated that 64,000 (95 % credibility interval: 7300, 190,000) human infections with influenza A(H7N9) virus occurred in Guangzhou in early 2014, with an infection-fatality risk of 3.6 deaths (95 % credibility interval: 0.47, 15) per 10,000 infections. CONCLUSIONS: Our study suggested that the number of influenza A(H7N9) virus infections in Guangzhou substantially exceeded the number of laboratory-confirmed cases there, albeit with considerable imprecision. Our study was limited by the small number of positive specimens identified, and larger serologic studies would be valuable. Our analytic framework would be useful if larger serologic studies are done.published_or_final_versio

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Micro-manufacturing : research, technology outcomes and development issues

Besides continuing effort in developing MEMS-based manufacturing techniques, latest effort in Micro-manufacturing is also in Non-MEMS-based manufacturing. Research and technological development (RTD) in this field is encouraged by the increased demand on micro-components as well as promised development in the scaling down of the traditional macro-manufacturing processes for micro-length-scale manufacturing. This paper highlights some EU funded research activities in micro/nano-manufacturing, and gives examples of the latest development in micro-manufacturing methods/techniques, process chains, hybrid-processes, manufacturing equipment and supporting technologies/device, etc., which is followed by a summary of the achievements of the EU MASMICRO project. Finally, concluding remarks are given, which raise several issues concerning further development in micro-manufacturing

How large should whales be?

The evolution and distribution of species body sizes for terrestrial mammals is well-explained by a macroevolutionary tradeoff between short-term selective advantages and long-term extinction risks from increased species body size, unfolding above the 2g minimum size induced by thermoregulation in air. Here, we consider whether this same tradeoff, formalized as a constrained convection-reaction-diffusion system, can also explain the sizes of fully aquatic mammals, which have not previously been considered. By replacing the terrestrial minimum with a pelagic one, at roughly 7000g, the terrestrial mammal tradeoff model accurately predicts, with no tunable parameters, the observed body masses of all extant cetacean species, including the 175,000,000g Blue Whale. This strong agreement between theory and data suggests that a universal macroevolutionary tradeoff governs body size evolution for all mammals, regardless of their habitat. The dramatic sizes of cetaceans can thus be attributed mainly to the increased convective heat loss is water, which shifts the species size distribution upward and pushes its right tail into ranges inaccessible to terrestrial mammals. Under this macroevolutionary tradeoff, the largest expected species occurs where the rate at which smaller-bodied species move up into large-bodied niches approximately equals the rate at which extinction removes them.Comment: 7 pages, 3 figures, 2 data table