A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor

Internet of Things (IoT) is driving innovation in an ever-growing set of application domains such as intelligent processing for autonomous robots. For an autonomous robot, one grand challenge is how to sense its surrounding environment effectively. The Simultaneous Localization and Mapping with RGB-D Kinect camera sensor on robot, called RGB-D SLAM, has been developed for this purpose but some technical challenges must be addressed. Firstly, the efficiency of the algorithm cannot satisfy real-time requirements; secondly, the accuracy of the algorithm is unacceptable. In order to address these challenges, this paper proposes a set of novel improvement methods as follows. Firstly, the ORiented Brief (ORB) method is used in feature detection and descriptor extraction. Secondly, a bidirectional Fast Library for Approximate Nearest Neighbors (FLANN) k-Nearest Neighbor (KNN) algorithm is applied to feature match. Then, the improved RANdom SAmple Consensus (RANSAC) estimation method is adopted in the motion transformation. In the meantime, high precision General Iterative Closest Points (GICP) is utilized to register a point cloud in the motion transformation optimization. To improve the accuracy of SLAM, the reduced dynamic covariance scaling (DCS) algorithm is formulated as a global optimization problem under the G2O framework. The effectiveness of the improved algorithm has been verified by testing on standard data and comparing with the ground truth obtained on Freiburg University’s datasets. The Dr Robot X80 equipped with a Kinect camera is also applied in a building corridor to verify the correctness of the improved RGB-D SLAM algorithm. With the above experiments, it can be seen that the proposed algorithm achieves higher processing speed and better accuracy

Information Fields Navigation with Piece-Wise Polynomial Approximation for High-Performance OFDM in WSNs

Since Wireless sensor networks (WSNs) are dramatically being arranged in mission-critical applications,it changes into necessary that we consider application requirements in Internet of Things. We try to use WSNs to assist information query and navigation within a practical parking spaces environment. Integrated with high-performance OFDM by piece-wise polynomial approximation, we present a new method that is based on a diffusion equation and a position equation to accomplish the navigation process conveniently and efficiently. From the point of view of theoretical analysis, our jobs hold the lower constraint condition and several inappropriate navigation can be amended. Information diffusion and potential field are introduced to reach the goal of accurate navigation and gradient descent method is applied in the algorithm. Formula derivations and simulations manifest that the method facilitates the solution of typical sensor network configuration information navigation. Concurrently, we also treat channel estimation and ICI mitigation for very high mobility OFDM systems, and the communication is between a BS and mobile target at a terrible scenario. The scheme proposed here combines the piece-wise polynomial expansion to approximate timevariations of multipath channels. Two near symbols are applied to estimate the first-and second-order parameters. So as to improve the estimation accuracy and mitigate the ICI caused by pilot-aided estimation, the multipath channel parameters were reestimated in timedomain employing the decided OFDM symbol. Simulation results show that this method would improve system performance in a complex environment

trans-Trismethoxy Resveratrol Decreased Fat Accumulation Dependent on Fat-6 and Fat-7 in Caenorhabditis Elegans

trans-Trismethoxy resveratrol (TMR) is a methyl analog of resveratrol. It is found to exhibit enhanced biological effects compared to resveratrol, such as inhibition of cancer cell growth and pro-apoptotic activities. However, the role of TMR in lipid metabolism is not fully understood. In this study, we used Caenorhabditis elegans, an in vivo nematode model which has been widely applied in disease research, including research on obesity, to investigate the effect of TMR on lipid metabolism. Treatment with TMR (100 and 200 μM) for 4 days significantly reduced triglyceride accumulation (14% and 20% reduction over the control, respectively) of C. elegans, without affecting nematode growth, food intake and reproduction. Treatment with TMR significantly downregulated stearoyl-CoA desaturase genes, fat-6 and fat-7, accompanied by a decrease in the desaturation index of fatty acids, the ratio of oleic acid to stearic acid. These results suggest that TMR inhibits fat accumulation by downregulating stearoyl-CoA desaturase in C. elegans

Efficient Scene Text Detection with Textual Attention Tower

Scene text detection has received attention for years and achieved an impressive performance across various benchmarks. In this work, we propose an efficient and accurate approach to detect multioriented text in scene images. The proposed feature fusion mechanism allows us to use a shallower network to reduce the computational complexity. A self-attention mechanism is adopted to suppress false positive detections. Experiments on public benchmarks including ICDAR 2013, ICDAR 2015 and MSRA-TD500 show that our proposed approach can achieve better or comparable performances with fewer parameters and less computational cost.Comment: Accepted by ICASSP 202

Are prominent medullary veins better than prominent cortical veins as predictors of early clinical outcome in patients with acute ischemic stroke?

PURPOSEThe prominent vessel sign (PVS) on susceptibility-weighted imaging (SWI) can be dichotomized into prominent cortical veins (PCV) and prominent medullary veins (PMV). This study was designed to compare the predictive value of PCV and PMV in the evaluation of the severity of acute ischemic stroke (AIS) in patients within the reperfusion window.METHODSForty-seven consecutive patients with AIS within the middle cerebral artery territory were recruited. Magnetic resonance imaging was performed within 8 hours of symptom onset and at 7 days after stroke onset. Infarct volume was measured, and the early clinical outcome at 7 days was assessed using the modified Rankin Scale. PVS was dichotomized into cases with both PCV and PMV and cases with only PCV according to location.RESULTSPatients with both PCV and PMV (n=32) had higher admission National Institutes of Health Stroke Scale scores (p = 0.020), larger infarct volumes at baseline (p = 0.026) and 7 days (p = 0.007), and larger infarct growth at 7 days (p = 0.050) than those with PCV only. Multivariate regression analysis showed that both the time of onset at baseline (p = 0.013) and infarct growth at 7 days (p = 0.014) could independently predict poor early clinical outcome.CONCLUSIONPMV may predict poor early clinical outcome in AIS patients, and reperfusion therapy may, therefore, be required more urgently in patients with PMV

Structure-Feature based Graph Self-adaptive Pooling

Various methods to deal with graph data have been proposed in recent years. However, most of these methods focus on graph feature aggregation rather than graph pooling. Besides, the existing top-k selection graph pooling methods have a few problems. First, to construct the pooled graph topology, current top-k selection methods evaluate the importance of the node from a single perspective only, which is simplistic and unobjective. Second, the feature information of unselected nodes is directly lost during the pooling process, which inevitably leads to a massive loss of graph feature information. To solve these problems mentioned above, we propose a novel graph self-adaptive pooling method with the following objectives: (1) to construct a reasonable pooled graph topology, structure and feature information of the graph are considered simultaneously, which provide additional veracity and objectivity in node selection; and (2) to make the pooled nodes contain sufficiently effective graph information, node feature information is aggregated before discarding the unimportant nodes; thus, the selected nodes contain information from neighbor nodes, which can enhance the use of features of the unselected nodes. Experimental results on four different datasets demonstrate that our method is effective in graph classification and outperforms state-of-the-art graph pooling methods.Comment: 7 pages, 4 figures, The Web Conference 202

A LIVING MODEL FOR OBESITY AND AGING RESEARCH: CAENORHABDITIS ELEGANS

Caenorhabditis elegans (C. elegans) is a free-living nematode that has been extensively utilized as an animal model for the research involving aging and neurodegenerative diseases, like Alzheimer and Parkinson, etc. Compared with the traditional animal models, this nematode possesses many benefits, such as small body size, short lifespan, complete sequenced genome and more than 65% of the genes associated with human diseases. All these characteristics enable this organism to be an ideal living system for obesity and aging studies. Piceatannol is a natural stilbene with many beneficial effects, such as anti-oxidative, anti-inflammatory, anti-atherogenic activities, however, its role on fat accumulation and aging of whole organism is not known. Our results showed that piceatannol (50 and 100 µM) significantly reduced fat accumulation of wild type worms grown in normal and high glucose conditions without altering the growth rate, worm length, pumping rate, or moving speed. The current study further indicated that piceatannol decreased the expression of sbp-1 (encodes an ortholog of mammalian sterol-regulatory-element-binding protein) and its target gene fasn-1 (encodes fatty acid synthase) as well as increased the expression of hosl-1 (encodes an ortholog of hormone-sensitive lipase) in glucose-treated worms. These data suggested that piceatannol reduced fat accumulation in C. elegans by suppression of genes involved in lipid synthesis and possibly through stimulation of lipolysis. Furthermore, piceatannol (50 and 100 µM) significantly extended the lifespan of C. elegans and delayed the age-related decline of pumping rate and locomotive activity, and protected the worms from heat and oxidative stress. The current study further indicated that lifespan extension and enhanced stress resistance induced by piceatannol requires the insulin/IGF-1 signaling (IIS) pathway and sir-2.1 (encodes sirtuin). Research has shown that permethrin, a Type-I pyrethroid, increases triglyceride (fat) accumulation in adipocytes. Little is known, however, about any similar effect of deltamethrin, a Type-II pyrethroid, which produces a distinct syndrome of poisoning in mammals compared with permethrin. Our study indicated that deltamethrin (10 µM) significantly increased the fat accumulation in wild type C. elegans and concomitantly reduced the total progeny number and locomotive activities in a dose-dependent manner. Deltamethrin increased fat accumulation via aak-2 (an ortholog of AMPKα) and nhr-49 (downstream target of aak-2 with function close to peroxisome proliferator-activated receptor-a) mediated mechanisms. The current study may have important implications in understanding effect of bioactive compounds as well as insecticides on the regulation of fat storage and healthy aging in humans

Two-dimensional ATRFTIR spectroscopic study on the water diffusion behavior in polyimide/silica nanocomposite

Two-dimensional (2D) correlation ATR-FTIR spectroscopy was used to study the dynamic diffusion behavior and state of water in syndiotactic polypropylene (S-PP). The 2D asynchronous spectra of water bending band clearly reveal that there are three separate bands in the 1800-1500 cm -1 region. These three bands at 1676, 1645, and 1592 cm -1 are assigned, respectively, to the aggregated water with strong hydrogen bond, the aggregated water with moderate strong hydrogen bond, and the "free water". On the basis of this approach, the following mechanism for the diffusion process of water in S-PP has been proposed: the aggregated water molecules first diffuse into the polymer during the diffusion process. The water molecules with strong hydrogen bond will diffuse slower than water with moderate strong hydrogen bond because of their large size. In the late stage of the diffusion process, some aggregated water molecules in the PP matrix are forced to dissolve into the "free water" form