Replicas Strategy and Cache Optimization of Video Surveillance Systems Based on Cloud Storage

With the rapid development of video surveillance technology, especially the popularity of cloud-based video surveillance applications, video data begins to grow explosively. However, in the cloud-based video surveillance system, replicas occupy an amount of storage space. Also, the slow response to video playback constrains the performance of the system. In this paper, considering the characteristics of video data comprehensively, we propose a dynamic redundant replicas mechanism based on security levels that can dynamically adjust the number of replicas. Based on the location correlation between cameras, this paper also proposes a data cache strategy to improve the response speed of data reading. Experiments illustrate that: (1) our dynamic redundant replicas mechanism can save storage space while ensuring data security; (2) the cache mechanism can predict the playback behaviors of the users in advance and improve the response speed of data reading according to the location and time correlation of the front-end cameras; and (3) in terms of cloud-based video surveillance, our proposed approaches significantly outperform existing methods

Directional Light Scattering and Absorption Enhancement by Nanoparticles and Nanoparticle Clusters with their Applications in Solar Energy Harvesting

This thesis presents a computational study of the directional light scattering and absorption enhancement by nanoparticles and clusters of nanoparticles. The computational algorithm is based on the Mie electromagnetic-scattering theory with the addition-theorem recursive algorithm for electromagnetic scattering particles. Nanoparticles or nanoparticle aggregates are made into forward scattering of incident light by tuning the electric and corresponding magnetic modes of each particles in the cluster. Lossless broaden band forward scattering nanoparticle cluster is designed for the photovoltaic application. These nanoparticles and nanoparticle aggregates are incorporated into thin film solar cells and their performances in harvesting solar energy are evaluated and optimized by the use of Finite-Difference Time-Domain (FDTD) numerical method. Extensive simulations were carried out to identify the directional scattering nanoparticles and/or nanoparticle aggregates and to design solar cells incorporated with these nanoparticles. Analysis was conducted, with help of both analytical and numerical solutions, to explore fundamental physical mechanism governing light-particle and inter-particle interactions in nanoparticles and nanoparticle-thin film solar systems. Computed results for various nanoparticle-thin film solar configurations demonstrate that significant enhancement of light absorption in solar cell absorption can be obtained with the directional scattering nanoparticle and/or nanoparticle clusters over the solar cells enhanced by non-directional scattering particles reported in literature.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/151939/1/Rongheng Li Final Dissertation.pdfDescription of Rongheng Li Final Dissertation.pdf : Restricted to UM users only

Replicas Strategy and Cache Optimization of Video Surveillance Systems Based on Cloud Storage

With the rapid development of video surveillance technology, especially the popularity of cloud-based video surveillance applications, video data begins to grow explosively. However, in the cloud-based video surveillance system, replicas occupy an amount of storage space. Also, the slow response to video playback constrains the performance of the system. In this paper, considering the characteristics of video data comprehensively, we propose a dynamic redundant replicas mechanism based on security levels that can dynamically adjust the number of replicas. Based on the location correlation between cameras, this paper also proposes a data cache strategy to improve the response speed of data reading. Experiments illustrate that: (1) our dynamic redundant replicas mechanism can save storage space while ensuring data security; (2) the cache mechanism can predict the playback behaviors of the users in advance and improve the response speed of data reading according to the location and time correlation of the front-end cameras; and (3) in terms of cloud-based video surveillance, our proposed approaches significantly outperform existing methods

Modeling and Simulation of a Hybrid Battery Pack Using Li-Ion Battery and Supercapacitors for Class 2A Light Duty Pick-Up Truck Application

This paper investigates the battery pack capacity optimization problem for a full-size electric pick-up truck using a hybrid battery pack consisting of supercapacitors and Li-Ion batteries. It also aims to simulate and study the thermal and ageing effects on the battery pack. A simplified mathematical model of an electric pick-up truck was developed using the power consumption-based approach which includes roadway grade, rolling resistance and wind resistance as the vehicle model along with modelling the electric powertrain. The proposed model takes into account the dynamic behavior of the system resulting in real time power demand by the electric motor and energy delivered by the storage system. The validation of the model is done by running the model through different US EPA Drive Cycles which provide the second-by-second velocity and time data inputs. The energy consumed by the vehicle model is then subsequently used to compute the battery ageing effects of the battery pack

Effect of chinese traditional herb<i style="mso-bidi-font-style:normal"> Epimedium grandiflorum</i> C.Morren and its extract Icariin on osteoarthritis via suppressing NF-<span style="font-family:Symbol;mso-ascii-font-family:"Times New Roman";mso-hansi-font-family: "Times New Roman";mso-char-type:symbol;mso-symbol-font-family:Symbol" lang="EN-US"><img src='/image/spc_char/k.gif' border=0>B pathway </span>

313-321<span style="font-size:11.0pt;font-family: " times="" new="" roman";mso-fareast-font-family:"times="" roman";mso-bidi-font-family:="" mangal;mso-ansi-language:en-gb;mso-fareast-language:en-us;mso-bidi-language:="" hi"="" lang="EN-GB">Osteoarthritis (OA), which is also called degenerative arthritis, is the leading cause of disabilities in the old people. The Chinese traditional herb Epimedium grandiflorum had long been found to attenuate osteoarthritis process, but the detailed mechanism was not clear. To study the mechanisms of E. grandiflorum in the treatment of osteoarthritis, rabbit osteoarthritis model combined with D-galactose was used. After different treatments for 10 weeks, cartilage sections were analyzed by immunohistochemistry for uPA, uPAR and PAI expression level. E. grandiflorum could significantly attenuate OA condition and decrease uPA, uPAR and PAI expression. The extract of E. <i style="mso-bidi-font-style: normal">grandiflorum, icariin also had a similar effect when compared with E. grandiflorum treatment alone. Rabbit chondrocytes were further isolated to be  stimulated by TNFα combined with different reagents treatment. Here, icariin treatment significantly reduced nuclear factor kappa B NF-B (P65) activity, decreased uPA expression level and increased IBα protein level. The results indicated that E. grandiflorum and its extract icariin could attenuate OA condition, reduce the expression of uPA and uPAR and increase PAI in experimental rabbit model and this effect may be conducted by suppressing NF-kB activity by increasing IBα level. </span

An FCM–GABPN Ensemble Approach for Material Feeding Prediction of Printed Circuit Board Template

Accurate prediction of material feeding before production for a printed circuit board (PCB) template can reduce the comprehensive cost caused by surplus and supplemental feeding. In this study, a novel hybrid approach combining fuzzy c-means (FCM), feature selection algorithm, and genetic algorithm (GA) with back-propagation networks (BPN) was developed for the prediction of material feeding of a PCB template. In the proposed FCM&ndash;GABPN, input templates were firstly clustered by FCM, and seven feature selection mechanisms were utilized to select critical attributes related to scrap rate for each category of templates before they are fed into the GABPN. Then, templates belonging to different categories were trained with different GABPNs, in which the separately selected attributes were taken as their inputs and the initial parameter for BPNs were optimized by GA. After training, an ensemble predictor formed with all GABPNs can be taken to predict the scrap rate. Finally, another BPN was adopted to conduct nonlinear aggregation of the outputs from the component BPNs and determine the predicted feeding panel of the PCB template with a transformation. To validate the effectiveness and superiority of the proposed approach, the experiment and comparison with other approaches were conducted based on the actual records collected from a PCB template production company. The results indicated that the prediction accuracy of the proposed approach was better than those of the other methods. Besides, the proposed FCM&ndash;GABPN exhibited superiority to reduce the surplus and/or supplemental feeding in most of the case in simulation, as compared to other methods. Both contributed to the superiority of the proposed approach