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In the paper, aperture averaging of Modulating Retro-Reflector (MRR) optical communication is studied. The simulation model based on multi-phase screen method is established to analyze the scintillation index of MRR with different aperture. Also experiments are demonstrated to verify the simulation results. The results show that not only the aperture of receiver in interrogator but also the aperture of retro-reflector will affect the fluctuation of power, and these will be helpful to design the system of MRR.

In the practical applications of the Unmanned Aerial Vehicle (UAV) ultraviolet (UV) communication networks, it is necessary and important to set the transceiver elevation angle, and to achieve better network connectivity. In this manuscript, the relationship between the transceiver elevation angles and the communication radii of the UAV nodes is investigated, the connection between the transceiver elevation angles and the network connectivity is established, and then the effects of node density, transmission power and data rate on the connectivity probability of k-connected networks (k=1, 2) are respectively analyzed and numerically researched in the three types of UV non-line-of-sight (NLOS) communication modes. The results show that the smaller the transceiver angle is, the larger the node communication radius is. Under the same conditions, the connectivity probability of NLOS (c) communication mode is higher than that of NLOS (a) and (b) modes. When the transmitting end elevation angle is the same and the receiving end elevation angle is 50° in NLOS (c) mode, the network can still achieve a higher connectivity probability under the condition of lower node density or transmission power. In addition, the specific parameter settings of the 2connected network with better robustness are given, which can provide the reference and guidance for practical applications.

Afterpulsing is a critical non-ideal factor of single photon avalanche diode (SPAD) at telecommunication wavelength, which limits its performance. In order to optimize the dead time in SPAD operation, a multiple exponential model of afterpulsing probability (AP) is built and fitted with the testing data of QCD-300 single photon detector. Furthermore, a new bit error rate (BER) model of photon-counting communication system with afterpulsing is studied. Based on the joint model of AP and BER, the optimization of dead time in different bit times and background photon levels is investigated. The results indicate that the optimal dead time is constant with the variation of bit time. When background photon level increases, the optimal dead time will be increased quickly. And keeping the background photon level below 0.5-c/ns, the optimal dead time is below 32-ns.

Conventionally, voltage control in distribution grids has been achieved through load tap changers, capacitor banks, and network switches. With the advancements in on-board electric vehicle (EV) chargers, voltage control can be achieved through control of EV charging/discharging as EVs can operate in any of the four P-Q quadrants with slight modifications to the charger. In this work, we use a high-fidelity on-board EV charger model, which has been validated using actual measurements. Then, we coordinate the four quadrant operation of EVs and distribution feeder to support voltage control in the grid. The efficacy of the developed models are demonstrated by using an LV secondary feeder. Operation of EVs in all four quadrants are shown to compensate for the feeder voltage fluctuations caused by daily time varying residential loads, while satisfying operational constraints of the feeder.

This paper first presents a brief review of existing computation techniques and challenges in distribution system state estimation. Then, the impacts of measurement points, network configuration, load model, distributed generation, real-time measurement data, and computational parameters on the state estimation of distribution grids are investigated. Simulations are carried out on an example utility distribution network which is fed by a large high voltage transmission network at multiple points. The results show that greater observability of downstream distribution network enhances accurate estimation of network states. Similarly, load models and distributed generations have significant impact on state estimation. The results also show that the accuracy of state estimation depends on the choice of computational parameters.

Nowadays, with the size and complexity of software increasing rapidly, vulnerabilities are becoming diversified and hard to identify. It is unpractical to detect and exploit vulnerabilities by manual construction. Therefore, an efficient automatic method of detecting and exploiting software vulnerability is in critical demand. This paper implements Pangr, an entire system for automatic vulnerability detection, exploitation, and patching. Pangr builds a complete vulnerability model based on its triggering behavior to identify vulnerabilities and generate exp or exploit schemes. According to the type and feature of the vulnerability, Pangr can generate the specific patch for the software. In the experiment, we tested 20 vulnerable programs on 32-bit Linux machine. Pangr detected 16 vulnerabilities, generated 10 exp, and patched 14 programs.

The return-oriented programming(ROP) attack has been a common access to exploit software vulnerabilities in the modern operating system(OS). An attacker can execute arbitrary code with the aid of ROP despite security mechanisms are involved in OS. In order to mitigate ROP attack, defense mechanisms are also drawn researchers' attention. Besides, research on the benign use of ROP become a hot spot in recent years, since ROP has a perfect resistance to static analysis, which can be adapted to hide some important code. The results in benign use also benefit from a low overhead on program size. The paper discusses the concepts of ROP attack as well as extended ROP attack in recent years. Corresponding defense mechanisms based on randomization, frequency, and control flow integrity are analyzed as well, besides, we also analyzed limitations in this defense mechanisms. Later, we discussed the benign use of ROP in steganography, code integrity verification, and software watermarking, which showed the significant promotion by adopting ROP. At the end of this paper, we looked into the development of ROP attack, the future of possible mitigation strategies and the potential for benign use.

With the increase of financial transactions in recent decades, forecasting risk of financial products has become more and more challenging, though it's of great significance to financial companies and investors. Fortunately, the accumulation of huge valuable records from trading platforms has provided strong support for solving this issue. In this paper, we proposed a GARCH-VaR model for forecasting the risk of investors' portfolios. We consider both of the volatility of futures and the available fund for each account in GARCH-VaR. Specifically, we first preprocessed settlement prices and holding positions data, and then labeled the samples with whether extreme losses occur. In the second step, we calculated the volatility of all futures and their correlation coefficient matrix. Finally, we forecasted the risk probability of investors' daily positions. Experimental results on the transaction data of the futures company showed the effectiveness of our proposed approach.

In this paper, we explore the methods of medical data mining. The medical data usually have some unique characteristics such as sparseness, highly correlated features and unbalanced sample categories. After researching the models commonly used in current medical data mining, we use the topic-based model for medical data mining. We build a supervised topic model (the SLDA model) and use Gibbs sampling to estimate parameters. From the results of the model, we can find some important relationships among features in our medical data. Finally, the SLDA model was combined with a Random Forest classifier, which gets good predictive performance in disease prediction.

This paper proposed a design of micro-strip line class-E power amplifier based on GaN HEMT devices working at 2.2GHz. By means of part harmonics suppression and inductance effect enhancing in the drain bias circuit, ideal class-E voltage and current waveform at drain and high efficiency were achieved. The combining simulation results demonstrate 74.8% PAE, 15.1dB power gain and 41.16dBm output power with 26dBm input power.

At present, recommender system has become a hot research area. It has been widely used, especially in e-commerce, news promotion, online education and other fields. Collaborative filtering algorithm based on user behavior analysis is a popular algorithm in recommender system research. In the research of user taste evolution, most of the work considered from the perspective of the group, thus ignoring the difference of individual learning ability of users. On the other hand, how to effectively mine long tail items has become one of the problems that need to be solved in recommender systems. In this paper, we model the evolution of user tastes from the individual level by user rating sequence. Then we build the recommendation scoring model according to the user experience level and the corresponding popularity of the items, and make reasonable topN recommendation. Experiments show that our model can achieve good results. In addition, the recommendation based on user experience level can effectively solve the long tail mining problem.

The way of using Modulating Retro-Reflector (MRR) to communicate is appealing. However, in the long-range MRR communication system, the retro-reflective signal which falls off as 1/R4 is weak and difficult to be detected. In this manuscript, A novel detection mode of MRR communication based on speckle technology is proposed, which can detect small fluctuation of the light from the modulator. The novel detection mode is simple, modular, inexpensive and suitable for low speed MRR communication. System design as well as the date processing is descripted and the experiment is demonstrated in the paper.

The project named after City-Eyes is based on the HBASE big data analyzing platform and the real-time-display technology with H5/CSS3, which could analyze data and result dynamically. The project is devoted to designing the online city-data-analyzing platform, with interacting in real time, filtering, analyzing and displaying data. In the view of filtering, we sample Wuxi and collect much information, including phone signaling, railway, airports, Weibo, population mobility and etc. Moreover, we write enough data mining scripts for varieties of data to extract the value of them. In the view of analyzing, we build an efficient analyzing model, which divides the task into two parts, which includes the front end and the back end. The back end is focusing on compressing data and extracting valid information by clustering the data streams. At the same time, the front-end applies the real-time-display technology to provide an interactive and nice-looking data visualizing solution for our users [1]. In the view of displaying, we come up with various solutions from two angles for every variety of data: ‘distribution and network'. With svg/canvas animation tech, the data can be mixed into the map and display itself dynamically [2].

With the overload of information on the Web, Recommender Systems (RSs) are becoming increasingly popular and have been employed to provide suggestions to meet different requirements. RSs are utilized in a variety of areas including movies, music, social tags, user group and products as Web services evoked on the Internet either as mobile Apps or PC-based applications. However, it is challenging to achieve personalized recommendations instead of offering up too many lowest common denominator recommendations. Understanding how products relate to each other is important because it has great impact on the performance. Furthermore, the personalized sequential behavior, which is closely related to a particular product, is essential for recommender systems. Most models simply integrate features from users and items without considering potential product bundle relationships between products exposed by users' personalized sequential behaviors. In this paper, a novel method based on Factorizing Personalized Markov Chain (FPMC) is proposed to comprehensively explore the latent bundle relations from users perspective, along with the hidden correlative semantics between products obtained from logic regression method, which provides a unified view to describe the user preferences, product/item features, and the user sequential patterns in timely manner. The involved semantic features are extracted using deep learning models. We evaluate our method on real-world Amazon datasets and our framework significantly outperforms other baseline models, especially on sparse datasets. The experimental results show that our approach qualitatively captures personalized behaviors with superior recommendation performance.

In this work, we focus on the problem of city security based on the massive amount of crime statistics data. Combined with city crime statistics data, fuzzy comprehensive evaluation method is used to judge the safety grade of the city, and the weight of the evaluation set is calculated based on the analytic hierarchy process. The experiment results show that fuzzy comprehensive evaluation method is effective and feasible, and it can be widely used in the evaluation of general city security level.

User number prediction in cell phone base station is a very important problem for cell phone communication system design and base station location selection. Recent years, we have witnessed the encouraging potentials of deep neural networks for real-life applications of various domains. User number prediction, however, is still in its initial stage. In this paper, we propose a wavelet-based stacked denoising autoencoder deep learning framework, named as Wavelet-SDA, which adopted wavelet to decompose the user volume signal as several sub channels, for each channel, an independent SDA model is introduce to achieve accurately signal prediction. In order to exploit the correlations between different base stations, a transfer entropy based knowledge transfer is also adopted by the proposed framework. Extensive experiments on real-life cell phone base station log dataset of Wuxi city demonstrate the strong predictive power of Wavelet-SDA comparison to some state-of-the-art competitors.

Traffic speed prediction is a long-standing and critically important topic in the area of Intelligent Transportation Systems (ITS). Recent years have witnessed the encouraging potentials of deep neural networks for real-life applications of various domains. Traffic speed prediction, however, is still in its initial stage without making full use of spatio-temporal traffic information. In light of this, in this paper, we propose a deep learning method with an Error-feedback Recurrent Convolutional Neural Network structure (eRCNN) for continuous traffic speed prediction. By integrating the spatio-temporal traffic speeds of contiguous road segments as an input matrix, eRCNN explicitly leverages the implicit correlations among nearby segments to improve the predictive accuracy. By further introducing separate error feedback neurons to the recurrent layer, eRCNN learns from prediction errors so as to meet predictive challenges rising from abrupt traffic events such as morning peaks and traffic accidents. Extensive experiments on real-life speed data of taxis running on the 2nd and 3rd ring roads of Beijing city demonstrate the strong predictive power of eRCNN in comparison to some state-of-the-art competitors. The necessity of weight pre-training using a transfer learning notion has also been testified. More interestingly, we design a novel influence function based on the deep learning model, and showcase how to leverage it to recognize the congestion sources of the ring roads in Beijing.

Based on the line-of-sight channel model of atmosphere established through the photon tracing method, the impact of SNR, bit rate and visibility to the bit error rate of free space optical communication (FSO) is analyzed. The comparative analysis is accomplished by using two wavelengths laser 1.55μm and 10.6μm. The results show that the error bit rates of 1.55μm and 10.6μm increase with SNR decreasing or bit rate increasing. In low visibility, the error performance of 10.6μm laser is better than that of 1.55μm, and the superiority is much more obvious in lower SNR and higher bit rate. Furthermore, error performances of the two wavelengths are almost the same.

In order to satisfy the source demand of long-distance scattering optical communication system, pulse width modulation scheme is proposed, which aim at the characteristics of high power laser. A wireless optical communication experiment system is built and the pulse width which generated by the same drive electric is tested. Based on the experiment, the distribution of the optical pulse width which is obtained by fitting method is presented. Furthermore, the performance of pulse width modulation and demodulation which is based on pulse width distribution is researched.

This paper studies the planning problem of hydro-thermal system, which is motivated by the practical system in the Yellow River Basin of China. The problem is formulated as one multistage stochastic optimization problem. It has the high-dimension, dynamic, nonlinear and stochastic characteristics. The solution approach is nonlinear stochastic dual dynamic programming(NSDDP) which is based on the approximation of the expected-cost-to-go function of stochastic dynamic programming by a piecewise linear function. For the sake of easy practical management, two coordination mechanisms, annual consumption quota and minimum flow limit, are explored. These two mechanisms also enable to solve the problem in a distributed way and hence improve the scalability of the NSDDP algorithms. Numerical results in case study are presented to illustrate the effectiveness and efficiency of the coordination mechanisms for hydro-thermal system planning.

FAST TCP is an outstanding TCP congestion control algorithm that has great performance over ultra high speed networks. However, the unfairness problem of FAST TCP with TCP Reno blocks widely the deployment of FAST TCP over wide area networks. In this paper, we proposed a novel delay-based congestion control algorithm, named FAST-FIT, which could perform gracefully in both ultra high speed networks and wide area networks, as well as keep graceful fairness with widely deployed TCP Reno hosts. Extensive experimental results have been obtained by using network simulators.

Selecting meaningful features is central in the analysis of scientific data. Today's multivariate scientific datasets are often large and complex making it difficult to define general features of interest significant to scientific applications. To address this problem, we propose three general, spatiotemporal metrics to quantify the significant properties of data features-concentration, continuity and co-occurrence, named collectively as CO3. We implemented an interactive visualization system to investigate complex multivariate time-varying data from satellite remote sensing with great spatial resolutions, as well as from real-time continental-scale power grid monitoring with great temporal resolutions. The system integrates CO3 metrics with an elegant multi-space user interaction tool to provide various forms of quantitative user feedback. Through these, the system supports an iterative user-driven analysis process. Our findings demonstrate that the CO3 metrics are useful for simplifying the problem space and revealing potential unknown possibilities of scientific discoveries by assisting users to effectively select significant features and groups of features for visualization and analysis. Users can then comprehend the problem better and design future studies using newly discovered scientific hypotheses.

TCP incast is an issue of great importance to the performance of many-to-one communication sessions in data centers. It is resulted from the mismatch between the RTO mechanism of existing TCP congestion control algorithms and the requirements and parameters in data center applications, leading to gross under-utilization of link capacity, high latency and packets loss rates. In this paper, we propose a novel and adaptive delay-based mechanism for TCP incast prevention in data center applications. Theoretical analysis and simulation results show significant performance improvement as compared with existing methods.

Mesh tessellations are indispensable tools for analyzing point data because they transform sparse discrete samples into dense continuous functions. Meshing the output of petascale simulations, however, can be as data-intensive as the simulations themselves and often must be executed in parallel on the same supercomputers in order to fit in memory. To date, however, no general-purpose large-scale parallel tessellation tools exist. We present a prototype method for computing such a Voronoi tessellation in situ during cosmological simulations. In principle, similar methods can be applied to other computational geometry problems such as Delaunay tetrahedralizations and convex hulls in other science domains. We demonstrate the utility of our approach as part of an in situ cosmology tools framework that runs various analysis tools at selected time steps, saves results to parallel storage, and includes visualization and further analysis in a widely used visualization package. In the example highlighted in this paper, connected components of Voronoi cells are interrogated to detect and characterize cosmological voids.

Right now, the diagnosis of coronary heart disease is mostly from experienced physician's judgment. How to use computer intelligent algorithms to aid in diagnosing of coronary heart disease has been a hot research of machine learning. This article will apply support vector machine (SVM) method which is based on the statistical learning theory to the diagnosis of coronary heart disease. On the basis of original data pre-processing and feature extraction, classifiers with different kernel are selected to classify the test data, followed by a comparison of classification results which show that the accuracy of classifiers with radial basis function is the highest. To select the best parameters of kernel function and penalty factor, Grid Search Method of optimizing parameters is used, which makes the classifier achieve the highest classification accuracy.

HTTP Streaming is one type of video streaming technology, delivering multimedia data using HTTP/TCP. Due to the strict boundaries between layers of the OSI communication model, HTTP Streaming client at the Application Layer can only see the bandwidth that TCP at the Transport Layer supplies. How to estimate the upcoming bandwidth in the future according to the bandwidth history HTTP Streaming client has seen is the first problem this paper wants to address. Since the network bandwidth represents the data producing rate for the media player at the client and the media bitrate represents the data consuming rate of the media player, the quality of user experience of HTTP Streaming would be at a low level, if there is mismatch between the network bandwidth and the media bitrate. How to make rate adaptation for HTTP Streaming system based on the bandwidth estimation results is the second problem this paper wants to address. From the experimental results, we can see our bandwidth estimation and rate adaptation algorithm proposed to tackle the above two problems can provide a better enhancement for the prevalent HTTP Streaming system, comparing with the rate adaptation algorithm using common throughput smoothing strategy.

In same coordinate system with discharge current as x-axis and vacuum level as y-axis, calibration curves of different vacuum interrupters are similar in shapes; therefore, a calibration curve for an idiographic vacuum interrupter can be obtained by shifting the general calibration curve along x-axis. Because the vacuum level measured by the manufacturer is the most accurate, its level is set as the base value. Then an increment of shift should be found: it is the x-axis difference value between the general calibration curve and the value measured when this vacuum interrupter leaves factory. After which, an idiographic calibration curve can be obtained by shifting the general calibration curve on x-axis by the increment. Thus, a self-calibration method is realized, which leads to an improvement of measurement accuracy. In this paper, the shifting of curves is realized by two kinds of methods: direct increment and curve fitting. This self-calibration method can be applied to various vacuum level measuring systems of vacuum interrupters.

HTTP Streaming plays an important role in today's multimedia distribution over internet. Besides the data rate of network, the quality of user experience of HTTP Streaming is also related to multimedia's distortion that server could limit under a certain bit-rate. Given a specific bandwidth, how to provide the HTTP Streaming client the multimedia content with the least distortion in HTTP Streaming system is what this paper want to discuss, which we call Rate-Distortion Optimization problem in HTTP Streaming.

We propose a dispersion-compensating fiber based on hybrid lighting guide mechanism, which value of negative dispersion can reach to −1×105ps/nm/km. This type of DCF gets a large negative dispersion by a coupling between the guiding mode in the edge of the band-gap and the cladding deficit, which is different from the ordinary dual-core mechanism. The proposed DCF can be used in dispersion compensating system and can be potentially used in pulse compress system.

HTTP Streaming is a prevalent multimedia streaming service over TCP/HTTP. Its quality is mainly determined by the efficiency of TCP. In this paper, we propose an improved TCP congestion control algorithm named TCP-MW for HTTP Streaming in wireless environments with high packet loss rate. Our experiment results show a significant performance improvement and a good fairness feature for TCP-MW.

A novel large negative dispersion photonic crystal Fiber (PCF) with dual-core structure is proposed. A new outer core is formed by replacing or filling the 3rd ring air-holes around the inner core with high refractive index material. The dispersion property is investigated numerically and the results show that large negative dispersion above an order of 104 can be obtained over a wide wavelength range, and the peak value and its wavelength can be well tuned by varying the diameter, the relative lattice constant, and the refractive index of filled material. It can be used as novel dispersion compensation PCF.

The Transport Control Protocol (TCP) has been widely used by wired and wireless Internet applications such as FTP, email and HTTP. Numerous congestion algorithms have been proposed to improve the performance of TCP in various scenarios, especially for high bandwidth-delay product (BDP) and wireless networks. Although different algorithms may achieve different performance improvements under different network conditions, designing a congestion algorithm that performs well across a wide spectrum of network conditions remains a great challenge. In this paper, we propose a novel congestion control algorithm, named TCP-FIT, which could perform gracefully in both wireless and high BDP networks. The algorithm was inspired by parallel TCP, but with the important distinctions that only one TCP connection with one congestion window is established for each TCP session, and that no modifications to other layers (e.g. the application layer) of the end-to-end system need to be made. Extensive experimental results obtained using both network simulators as well as over “live” wired line, WiFi and 3G networks at different geographical locations and at different times of the day are presented. The performance of the algorithm shown in the experiment results is significantly improved as compared to other state-of-the-art algorithms, while maintaining good fairness.

An improved 800bps speech coding algorithm is presented in this paper. Several methods are exploited to reduce the bit rate, such as dynamic bit allocation, multi-frame joint vector quantization and parameter interpolation. The 800 bps speech coding algorithm has been implemented on TMS320VC5416. The PESQ scores obtained with GL VQT are over 2.9, which show that the speech coding system can meet actual communication requirements.

The Transport Control Protocol (TCP) has been widely used in wired and wireless Internet applications such as FTP, email and HTTP. However, performances of traditional TCP congestion algorithms degrade significantly when deployed over wireless networks. In this paper, we proposed an improved TCP congestion control algorithm for wireless networks, named TCP-FIT, and compared its performance with existing state-of-the-art congestion control algorithms as well as an application layer Parallel TCP scheme. Experiment results show significant performance improvement, good fairness and lower end-to-end latency for TCP-FIT.

This paper analyzes the relevant factors influencing returns of B2C e-commerce companies from the micro and macro levels and establishes function model of enterprise' profit and obtain the optimal mix of return price and return policy. Meanwhile, MATLAB software is utilized to conduct sensitivity analysis of relevant parameters and assists the development of companies' return strategies.

A temperature sensor based on fibers Bragg grating in photonic crystal fibers is simulated. Fibers Bragg grating has been widely used in the field of sensor. The temperature property of uniform fibers Bragg grating in photonic crystal fibers was studied. By finite element method, the relation between the birefringence in photonic crystal fibers and the temperature was analyzed. Numerical results show that the birefringence in photonic crystal fibers is in proportion to the temperature. Still, a new measurement dependent on polarization was also presented. This measurement was proved that it is applicable for the proposed temperature sensor.

In this paper, we propose a novel scheme using computing complexity layered scalable video coding (CCLSVC) to optimize the user experience of broadcasting video in the computing capability limited handheld terminals. To address the heterogeneity of computing capability among different handheld devices, we employ hierarchal B reference structure of SVC to divide the frames into multiple computing complexity layers (CC Layers) in server side. The handheld clients simply choose to decode the frames in their corresponding layers in terms of their computation capability to maximize the video PSNR. We have proved that the optimal CC Layers division problem is a precedence constrained scheduling problem, which is an NP-complete problem. And we further propose our fast greedy method to approximately get optimized broadcasting video playback PSNR. The simulation shows that our method is superior to temporal SVC and random frame discarding method.

In order to overcome the shortages of the conventional bus route generation algorithm which aims to maximize the number of direct travelers on the shortest path, this paper present an improvement route generation algorithm for the design of bus network, which aims to maximize the direct passenger-kilometers per unit length and minimize the average travel time of direct travelers on each route at the same time. At first, the paper analyzed the irrationality of the conventional bus network design model developed by others, revised the objective function appropriately, and built a new model of bus network design. Then a modified algorithm is developed particularly to solve this problem, this design algorithm is heavily guided by the demand matrix and allows the designer's knowledge to be implemented so as to reduce the search space. Finally, the applications of the new algorithm are illustrated with a numerical example. Numerical results indicate that the proposed algorithm would be efficient in practice.

In this paper, an improved differential pulse position modulation (IDPPM) for optical wireless communications is presented. The soft-decision decoding technique for it is discussed. Compared with OOK, PPM, and DPPM, IDPPM achieved better compromise between bandwidth and power requirement. Our results show the slot error rate of system being improved by Soft-decision decoding technique of IDPPM.

In this paper, we propose a scalable video transmission strategy over multistandard integrated networks based on H.264/AVC SVC extension streaming. In this proposed transmission strategy, SVC streams are divided into several subflows and different flows have different temporal resolution and delay constraints. For broadcasting network with hierarchical modulation scheme in the multistandard integrated networks, such as DVB-H and DVB-T, the proposed subflows division strategy can enable a smart use of the available bandwidth. For wireless network transmission standards with the resource reserved QoS assurance in the multistandard integrated networks, such as IEEE 802.11e, transmission of suitable subflows subset over the channels can decrease the delay and admit more users without much compromise in the video quality for the already admitted users.