Adaptive streaming of multi-view video over P2P networks

In this paper, we propose a novel solution for the adaptive streaming of 3D representations in the form of multi-view video by utilizing P2P overlay networks to assist the media delivery and minimize the bandwidth requirement at the server side. Adaptation to diverse network conditions is performed regarding the features of human perception to maximize the perceived 3D. We have performed subjective tests to characterize these features and determined the best adaptation method to achieve the highest possible perceived quality. Moreover, we provide a novel method for mapping from scalable video elementary stream to torrent-like data chunks for adaptive video streaming and provide an optimized windowing mechanism that ensures timely delivery of the content over yanl?? gibi. The paper also describes techniques generating scalable video chunks and methods for determining system parameters such as chunk size and window length.     

奥氏体不锈钢在干切削和微量润滑条件下的切削力的研究

金属切削加工中冷却润滑液的使用会增加加工成本,污染环境,而干切削和近干切削可以彻底消除或极大地减少冷却润滑液的使用,所以其成为许多单位研究热点.近来奥氏体不锈钢在各行业内应用越来越广,为了充分了解奥氏体不锈钢的切削性能,本文对奥氏体不锈钢0Cr18Ni9在传统乳化剂、干车削和微量润滑车削条件下进行切削试验,通过设计多因素正交试验方法,建立压电式测力系统测量切削过程中的动态平均切削力,在不同的冷却润滑方式下,比较背吃刀量、切削速度、进给量等切削用量对切削力的影响,得出各个因素的影响曲线图,从而揭示奥氏体不锈钢0Cr18Ni9在车削过程中的切削力变化情况,为实际的生产加工积累和提供基础数据.     

Modelling of the hysteresis effect of target voltage in reactive magnetron sputtering process by using neural networks

Reactive sputtering process is very non-linear and usually exhibits hysteresis behaviour with respect to the reactive gas flow. Most of the problems encountered in the preparation of non-stoichiometric compound films by reactive sputtering are due to the hysteresis effect. Therefore, a considerable amount of effort has been devoted to find means for its elimination or ensuring a stable sputtering in the transition mode. This paper presents a new approach based on Artificial Neural Networks (ANNs) for modelling of the hysteresis effect of target voltage at different target power levels and reactive gas flow rates in reactive sputtering. Based on this model, it is possible to predict the target voltage in reactive magnetron sputtering processes, when the target power level, reactive gas flow rate and its direction are used as inputs to the artificial neural network (ANN). The proposed ANN is trained in different structures with the use of learning algorithms to obtain better performance and faster error convergence. Broyden–Fletcher–Goldfarb Shanno (BFGS) algorithm gives the best result among other learning algorithms used in the analysis. The training and test data required to develop the ANN model are obtained from the experimental studies. Both the training and the test results are in very good agreement with the experimental results obtained in this work.     

Interferon-induced vitiligo in a patient with chronic viral hepatitis C infection

Recurrent abdominal pain (RAP) syndrome is described by Apley 40 years ago. The definition of condition, still generally accepted, is at least three episodes of abdominal pain over a period of three months, with pain of intensity which affects the behaviour of the child. The prevalence of condition among school children is 10-15%. Apley's classic studies demonstrated organic disease in only 10% of the children. Apley's conclusions have dominated pediatric writing through present era. In recent years, however, a number of reports have appeared in the medical literature that have suggested that careful investigation of children with RAP may reveal previously unsuspected functional or morphologic abnormalities of the gastrointestinal tract. These have included reports of peptic disease and Helicobacter Pylori infection, abnormal antro-duodenal motility, lactase malabsorption, gastro-esophageal reflux. Nevertheless these abnormalities cannot be correlated always with specific complaints. Therefore pathogenetic background is not clarified. Despite greater understanding of these disorders the enigme remains. There is a need for controlled studies in non selected patients.     

Coreless Fiber‐Based Whispering‐Gallery‐Mode Assisted Lasing from Colloidal Quantum Well Solids

Whispering gallery mode (WGM) resonators are shown to hold great promise to achieve high‐performance lasing using colloidal semiconductor nanocrystals (NCs) in solution phase. However, the low packing density of such colloidal gain media in the solution phase results in increased lasing thresholds and poor lasing stability in these WGM lasers. To address these issues, here optical gain in colloidal quantum wells (CQWs) is proposed and shown in the form of high‐density close‐packed solid films constructed around a coreless fiber incorporating the resulting whispering gallery modes to induce gain and waveguiding modes of the fiber to funnel and collect light. In this work, a practical method is presented to produce the first CQW‐WGM laser using an optical fiber as the WGM cavity platform operating at low thresholds of ≈188 µJ cm^?2 and ≈1.39 mJ cm^?2 under one‐ and two‐photon absorption pumped, respectively, accompanied with a record low waveguide loss coefficient of ≈7 cm^?1 and a high net modal gain coefficient of ≈485 cm^?1. The spectral characteristics of the proposed CQW‐WGM resonator are supported with a numerical model of full electromagnetic solution. This unique CQW‐WGM cavity architecture offers new opportunities to achieve simple high‐performance optical resonators for colloidal lasers.     

SpyDetector: An approach for detecting side-channel attacks at runtime

International Journal of Information Security - In this work, we first present a low-cost, anomaly-based semi-supervised approach, which is instrumental in detecting the presence of ongoing...     

A practical privacy-preserving targeted advertising scheme for IPTV users

In this work, we present a privacy-preserving scheme for targeted advertising via the Internet Protocol TV (IPTV). The scheme uses a communication model involving a collection of subscribers, a content provider (IPTV), advertisers and a semi-trusted server. To target potential customers, the advertiser can utilize not only demographic information of subscribers, but also their watching habits. The latter includes watching history, preferences for IPTV content and watching rate, which are periodically (e.g., weekly) published on a semi-trusted server (e.g., cloud server) along with anonymized demographics. Since the published data may leak sensitive information about subscribers, it is safeguarded using cryptographic techniques in addition to the anonymization of demographics. The techniques used by the advertiser, which can be manifested in its queries to the server, are considered (trade) secrets and therefore are protected as well. The server is oblivious to the published data and the queries of the advertiser as well as its own responses to these queries. Only a legitimate advertiser, endorsed with so-called trapdoors by the IPTV, can query the cloud server and access the query results. Even when some background information about users is available, query responses do not leak sensitive information about the IPTV users. The performance of the proposed scheme is evaluated with experiments, which show that the scheme is practical. The algorithms demonstrate both weak and strong scaling property and take advantage of high level of parallelism. The scheme can also be applied as a recommendation system.     

Fault detection on robot manipulators using artificial neural networks

Nowadays, gas welding applications on vehicle’s parts with robot manipulators have increased in automobile industry. Therefore, the speed of end-effectors of robot manipulator is affected on each joint during the welding process with complex trajectory. For that reason, it is necessary to analyze the noise and vibration of robot’s joints for predicting faults. This paper presents an experimental investigation on a robot manipulator, using neural network for analyzing the vibration condition on joints. Firstly, robot manipulator’s joints are tested with prescribed of trajectory end-effectors for the different joints speeds. Furthermore, noise and vibration of each joint are measured. And then, the related parameters are tested with neural network predictor to predict servicing period. In order to find robust and adaptive neural network structure, two types of neural predictors are employed in this investigation. The results of two approaches improved that an RBNN type can be employed to predict the vibrations on industrial robots.     

Diffraction of 0.5keV electrons from free-standing transmission gratings

A nanostructured grating was used to diffract a low-energy (500 eV) electron beam, and the current transmitted into the zeroth diffraction order was greater than 5% of the incident beam current. This diffraction efficiency indicates that the 55-nm-wide grating bars absorb electrons but the 45-nm-wide slots between bars transmit electron de Broglie waves coherently. The diffraction patterns can be asymmetric, and can be explained by a model that incorporates an electrostatic potential energy for electrons within 20 nm of the grating structure calculated by the method of images.