Scheduling for Multi-Camera Surveillance in LTE Networks

Wireless surveillance in cellular networks has become increasingly important, while commercial LTE surveillance cameras are also available nowadays. Nevertheless, most scheduling algorithms in the literature are throughput, fairness, or profit-based approaches, which are not suitable for wireless surveillance. In this paper, therefore, we explore the resource allocation problem for a multi-camera surveillance system in 3GPP Long Term Evolution (LTE) uplink (UL) networks. We minimize the number of allocated resource blocks (RBs) while guaranteeing the coverage requirement for surveillance systems in LTE UL networks. Specifically, we formulate the Camera Set Resource Allocation Problem (CSRAP) and prove that the problem is NP-Hard. We then propose an Integer Linear Programming formulation for general cases to find the optimal solution. Moreover, we present a baseline algorithm and devise an approximation algorithm to solve the problem. Simulation results based on a real surveillance map and synthetic datasets manifest that the number of allocated RBs can be effectively reduced compared to the existing approach for LTE networks.Comment: 9 pages, 10 figure

Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector

We provide a fast approach incorporating the usage of deep learning for evaluating the effects of photon sensors in an antineutrino detector on the event reconstruction performance therein. This work is an attempt to harness the power of deep learning for detector designing and upgrade planning. Using the Daya Bay detector as a benchmark case and the vertex reconstruction performance as the objective for the deep neural network, we find that the photomultiplier tubes (PMTs) have different relative importance to the vertex reconstruction. More importantly, the vertex position resolutions for the Daya Bay detector follow approximately a multi-exponential relationship with respect to the number of PMTs and hence, the coverage. This could also assist in deciding on the merits of installing additional PMTs for future detector plans. The approach could easily be used with other objectives in place of vertex reconstruction

Deep neural network approach in human-like redundancy optimization for anthropomorphic manipulators

© 2013 IEEE. Human-like behavior has emerged in the robotics area for improving the quality of Human-Robot Interaction (HRI). For the human-like behavior imitation, the kinematic mapping between a human arm and robot manipulator is one of the popular solutions. To fulfill this requirement, a reconstruction method called swivel motion was adopted to achieve human-like imitation. This approach aims at modeling the regression relationship between robot pose and swivel motion angle. Then it reaches the human-like swivel motion using its redundant degrees of the manipulator. This characteristic holds for most of the redundant anthropomorphic robots. Although artificial neural network (ANN) based approaches show moderate robustness, the predictive performance is limited. In this paper, we propose a novel deep convolutional neural network (DCNN) structure for reconstruction enhancement and reducing online prediction time. Finally, we utilized the trained DCNN model for managing redundancy control a 7 DoFs anthropomorphic robot arm (LWR4+, KUKA, Germany) for validation. A demonstration is presented to show the human-like behavior on the anthropomorphic manipulator. The proposed approach can also be applied to control other anthropomorphic robot manipulators in industry area or biomedical engineering

Expression of c-Kit, Flk-1, and Flk-2 Receptors in Benign and Malignant Tumors of Follicular Epithelial Origin

BackgroundVascular endothelial growth factor (VEGF) is a key regulator of physiologic as well as pathologic angiogenesis. The response of VEGF to endothelial cell mitogenesis and survival, as well as angiogenesis and microvascular permeability, is mainly mediated through its receptor-2, VEGFR2 (kinase domain receptor or fetal liver kinase-1, KDR or Flk-1). This study aimed to detect the expression of VEGFR2 in various forms of thyroid tumors. In addition, the expression of Flk-2 (receptor for Flt-3) and c-Kit (receptor for steel locus factor), which shows strong similarity to Flk-1, was also examined in thyroid tumors.MethodsRT-PCR analyses of c-Kit and immunohistochemical staining of c-Kit, Flk-1, and Flk-2 were performed in archived samples of 18 papillary thyroid carcinoma (PTC), 9 follicular thyroid carcinoma (FTC), 12 follicular adenoma (FA), and 7 nodular goiter (NG) samples. The data were correlated to clinicopathologic features.ResultsBy RT-PCR analyses, c-Kit expression was detected in 22% (4/18) of PTC, 22% (2/9) of FTC, 25% (3/12) of FA, and 57% (4/7) of NG samples. However, positive immunostaining signals of c-Kit were only observed in 17% (3/18) of PTC samples, and not in the others. Similarly, Flk-1 expression was only detected by immunohistochemistry in 67% (12/18) of PTC and 43% (3/7) of NG samples, and not in the others. Interestingly, the expression of Flk-2 was found in 89% (16/18) of PTC, 89% (8/9) of FTC, 75% (9/12) of FA, and 29% (2/7) of NG samples. An inverse relationship of thyroid cancer size with Flk-2 expression was found.ConclusionFlk-2 expression was detected in various forms of thyroid tumors and increased Flk-2 expression was correlated with thyroid tumors with increased transforming activity, suggesting that Flk-2 is involved in pathogenic development of thyroid malignancy. Similarly, Flk-1 expression was also found in some thyroid tumors, while the expression of c-Kit-mediated pathways may not play a major role in thyroid tumorigenesis

Study on Perfume Stimulating Olfaction with Volatile Oil of Acorus Gramineus for Treatment of the Alzheimer's Disease Rat

ObjectiveTo probe into the therapeutic efect of perfume stimulating olfaction with volatile oil of Acorus Gramineus on the Alzheimer's disease (AD) rat.MethodsTotally 50 adult SD rats, male, weighing 300±10 g, were randomly divided into 5 groups, normal group (group A), olfactory nerve severing model group (group B), AD model group (group C), AD model plus perfume stimulation group (group D), AD model olfactory nerve severing plus perfume stimulation group (group E), 10 rats in each group. After perfume stimulation, Morris maze test was conducted for valuating the learning and memory ability; Malondaldehyde (MDA) content, and superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activities in the brain, and the brain weight were detected.ResultsCompared with the AD model group, the average escape latency and swimming distance in 6 days were significantly shorter than those in the group A, B, D (P<0.01), with no significant differences between the group C and the group E (P>0.05); Compared with the group A, B and D, MDA content in the group C significantly increased (P<0.01), and SOD and GSH-Px activities significantly decreased (P<0.01), and brain weight/body weight decreased significantly in the group C (P<0.01), with no significant differences between the group C and the group E (P>0.05).ConclusionPerfume stimultating olfaction with volatile oil of Acorus Gramineus can significantly increase the learning-memory ability, decrease MDA content and increase SOD and GSH-Px activities and weight of brain in AD rats

Poly[(5,5′-dimethyl-2,2′-bipyridine-κ2 N,N′)(μ3-5-hy­droxy­isophthalato-κ4 O 1:O 3,O 3′:O 3′)cadmium]

In the title compound, [Cd(C8H4O5)(C12H12N2)], the CdII cation is coordinated by three 5-hy­droxy­isophthalate anions and one 5,5′-bimethyl-2,2′-bipyridine ligand in a distorted CdO4N2 octa­hedral geometry. The 5-hy­droxy­isophthalate anions bridge the Cd cations, forming a two-dimensional polymeric complex parallel to (100). In the complex, the hy­droxy group is linked to the uncoordinated carb­oxy-O atom via an O—H⋯O hydrogen bond. Weak C—H⋯O hydrogen bonds are also present in the crystal structure. One of the methyl groups is disordered over two positions in a 0.536 (11):0.464 (11) ratio