＂Smart Cafe＂： A Mobile Local Computing System Based On Indoor Virtual Cloud

With network developing and virtualization rising, more and more indoor environment （POIs） such as care, library, office, even bus and subway can provide plenty of bandwidth and computing resources. Meanwhile many people daily spending much time in them are still suffering from the mobile device with limited resources. This situation implies a novel local cloud computing paradigm in which mobile device can leverage nearby resources to facilitate task execution. In this paper, we implement a mobile local computing system based on indoor virtual cloud. This system mainly contains three key components： 1）As to application, we create a parser to generate the ＂method call and cost tree＂ and analyze it to identify resource- intensive methods. 2） As to mobile device, we design a self-learning execution controller to make offtoading decision at runtime. 3） As to cloud, we construct a social scheduling based application-isolation virtual cloud model. The evaluation results demonstrate that our system is effective and efficient by evaluating CPU- intensive calculation application, Memory- intensive image translation application and I/ O-intensive image downloading application.

A New Method of 3-Dimensional Localization of Intraocular Foreign Bodies Using CT Imaging:A Role of Optic Nerve

Computed tomography（CT） is considered the most sensitive method for the detection of intraocular foreign bodies（IOFBs）. The purpose of this study was to evaluate a new method of 3-dimentional（3D） localization of IOFBs that takes advantage of the anatomical structure of the optic nerve and to assess the clinical outcomes using this new method. Twenty-two trauma patients with IOFBs or suspected IOFBs admitted to our hospital were scanned with multislice CT（MSCT） between July and December 2003. All scanning was performed with a 16-row spiral CT in axial plane using a sequential scanning protocol. During the scanning, the eyeball of the patient was kept stable and was not allowed to rotate internally or externally. Section collimation was set at 16 mm × 0.75 mm. Table feed was 12 mm. Reconstruction index was 0.75 mm. After scanning, the reconstructed images were loaded into a workstation to create the multiplanar reconstruction images with the aid of the 3D software. We compared the localization results with the operative findings. Multiplanar reconstruction images showed IOFBs in all 22 patients. IOFBs occurred in the eyeball of 14 patients, in the wall of the eyeball of 5 patients and in the posterior orbits of 3 patients. Different surgical procedures were designed according to the localization by this new method and all IOFBs were successfully removed. All of these foreign bodies were metallic and the localization of IOFB using MSCT was consistent with that found by operative findings. It was suggested that MSCT is a simple and effective imaging modality for the localization of IOFBs. In our study, we localized the IOFBs more quickly and accurately by taking advantage of the fixed position of the intraocular segment of the optic nerve, and determined the necessary surgical parameters.

Computer Geometries for Finding All Real Zeros of Polynomial Equations Simultaneously

In this research article,we construct a family of derivative free simultaneous numerical schemes to approximate all real zero of non-linear polynomial equation.We make a comparative analysis of the newly constructed numerical schemes with a well-known existing simultaneous method for determining all the distinct real zeros of polynomial equations using computer algebra system Mat Lab.Lower bound of convergence of simultaneous schemes is calculated using Mathematica.Global convergence property of the numerical schemes is presented by taking random starting initial approximation and their convergence history are graphically presented.Some real life engineering applications along with some higher degree polynomials are considered as numerical test problems to show performance and efficiency of the derivative free family of numerical methods with comparison of an existing method of same order in literature.Local computational order of convergence,CPU time,graph of computational order of convergence and residual error graphs elaborate efficiency,robustness and authentication of the suggested family of numerical methods in its domain.

全局与局部模型交替辅助的差分进化算法

为求解实际复杂工程应用中的高维计算费时优化问题,提出一种全局与局部代理模型交替辅助的差分进化算法。利用历史样本训练全局和局部代理模型,通过交替搜索全局和局部代理模型得到模型最优解并对其进行真实目标函数评价,实现探索和开采的平衡以减少真实目标函数的计算次数,同时通过针对性地选择个体进行真实目标函数计算,辅助算法快速找到目标函数的较优解。在15个低维测试问题和14个高维测试问题上的实验结果表明,在有限的计算资源情况下,该算法在12个低维测试问题上相较于最优重启策略代理辅助的社会学习粒子群优化算法、基于主动学习的代理模型辅助的粒子群优化算法等表现更好,在7个高维测试问题上相较于高斯过程辅助的进化算法、代理模型辅助的分层粒子群优化算法、求解高维费时问题的代理辅助的多种群优化算法等能找到目标函数的更优解。

Mapping the Information Trace in Local Field Potentials by a Computational Method of Two-Dimensional Time-Shifting Synchronization Likelihood Based on Graphic Processing Unit Acceleration

The local field potential（LFP） is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are organized. Synchronization between two distant brain regions is hard to detect using linear synchronization algorithms like correlation and coherence. Synchronization likelihood（SL） is a non-linear synchronization-detecting algorithm widely used in studies of neural signals from two distant brain areas. One drawback of non-linear algorithms is the heavy computational burden. In the present study, we proposed a graphic processing unit（GPU）-accelerated implementation of an SL algorithm with optional 2-dimensional time-shifting. We tested the algorithm with both artificial data and raw LFP data. The results showed that this method revealed detailed information from original data with the synchronization values of two temporal axes,delay time and onset time, and thus can be used to reconstruct the temporal structure of a neural network. Our results suggest that this GPU-accelerated method can be extended to other algorithms for processing time-series signals（like EEG and f MRI） using similar recording techniques.

Sensorless Sensing with WiFi

Can WiFi signals be used for sensing purpose？ The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing without wearable sensors, simultaneous perception and data transmission without extra communication infrastructure, and contactless sensing in privacy-preserving mode. Due to the popularity of WiFi devices and the ubiquitous deployment of WiFi networks, WiFi-based sensing networks, if fully connected, would potentially rank as one of the world＇s largest wireless sensor networks. Yet the concept of wireless and sensorless sensing is not the simple combination of WiFi and radar. It seeks breakthroughs from dedicated radar systems, and aims to balance between low cost and high accuracy, to meet the rising demand for pervasive environment perception in everyday life. Despite increasing research interest, wireless sensing is still in its infancy. Through introductions on basic principles and working prototypes, we review the feasibilities and limitations of wireless, sensorless, and contactless sensing via WiFi. We envision this article as a brief primer on wireless sensing for interested readers to explore this open and largely unexplored field and create next-generation wireless and mobile computing applications.