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Continuous Attacks are common cross-core cache side-channel attack scenarios that we observed, where adversaries frequently probe-target cache lines in a short time. Under Continuous Attacks, the attacked lines go through multiple load-evict processes between different cache (or memory) hierarchies, exhibiting Ping-Pong patterns. Identifying and obscuring these abnormal patterns effectively interfere with the attacker’s probe and mitigate such attacks. Our recent proposal, Ping-Pong regulator (PPR), captures multiple Ping-Pong patterns by counting the reaccesses per cache line and blocks them with different obscuring actions (preload or lock). Although PPR mitigates Continuous Attacks, the added regulator directory (RDir) is vulnerable because it cannot record all cache lines simultaneously. Sophisticated attackers can evict the records of the attacked line from the RDir to avoid triggering defensive actions, thereby bypassing PPR. To improve robustness, we further propose PPR+, which dynamically changes the mapping of physical addresses to RDir locations by encryption and periodically changing keys. This randomness makes it difficult for attackers to evict target entries out of the RDir within a limited time. We show that PPR+ tolerates more than 100 years of attacks, induces negligible performance impacts (improves 0.13%), requires acceptable storage overhead (3.15%), and does not need any software support.

With the sustainable development of mobile communication technology and the increasing demand for indoor services, Location-based Service (LBS) is attracting more and more attention. Determining the mobile target’s location is a core problem of LBS. The traditional WiFi signal fingerprint-based positioning technology mainly determines the location information of the mobile target by received RSS, which has high real-time positioning but low positioning accuracy. The fingerprint-based positioning technology using image mainly determines the location information of the mobile target by matching the features of the foreground images, which has the high positioning accuracy but low real-time positioning. This paper presents an indoor positioning method fusing information of the WiFi signal and RGB image to improve the positioning performance. The WiFi signal is transformed into the W-image according to indoor space and correction radius parameters, then the W-image and RGB image information are fused with LBP feature by the uniform-LBP algorithm. A fusion positioning model based on the sparse representation is established and solved using Lasso and BPDN positioning method. The positioning methods are tested in manufacturing workshop, and the experimental results show that the proposed method can reduce the complexity of the positioning method and achieve the higher positioning accuracy under same conditions.

Big data values data processing to ensure effective value-added data. With the rapid development of the cloud era, the coverage of big data has gradually expanded, and it has received wide attention from all walks of life. In the process of modern social development, big data analysis is gradually applied to the future development planning, risk evaluation and integration of market development status. With the rapid development of many fields of society, the flow of information has gradually expanded, and the Internet has developed more rapidly, prompting the application of big data in various fields. Machine learning is a multidisciplinary study of how computers use data or past experience. With the ability to independently improve specific algorithms, the computer acquires knowledge through learning and achieves the goal of artificial intelligence. Big data and machine learning are the major technological changes in the modern computer world, and these technologies have had a huge impact on all walks of life. At present, with the rapid development of the Internet, mobile communications, social networks and the Internet of Things, these networks generate large amounts of data every day, and data become the most important information resource of today. Some studies have shown that in many cases, the larger the amount of data, the better the data will be for machine learning. On this basis, this paper proposes an online client algorithm based on machine learning algorithm for IoT unstructured big data analysis and uses it in other big data analysis scenarios. Use the online data entered by the customer to implement background data mining, the parallel way to verify its efficiency through machine learning algorithms such as K-nearest neighbor algorithm.

Sonodynamic therapy (SDT) is a prospective therapy for many tumors by activation of sonosensitizers to produce reactive oxygen species (ROS) by ultrasound (US). However, limited generation of ROS and low drug delivery efficiency of sonosensitizers to the tumor tissue still hinder the application of SDT. Herein, an amphiphilic rose bengal (ARB) conjugate was designed to fabricate rose bengal microbubbles (RB-MBs) with high drug-loading contents (∼6.8%) and excellent contrast enhancement capability for US imaging, well suited for detecting tumor location and size. More importantly, RB-MBs could be successfully converted into RB-NPs by local US exposure, resulting in ∼7.5 times higher drug accumulation at the tumor tissue through the sonoporation effect as compared to RB-NPs and RB-MBs without US sonication. Meanwhile, using RB as the MB shell facilitated US energy transfer by the US mediated collapse of MBs through either a sonoluminescence or pyrolysis process; thus, the ROS generation efficiency could be greatly enhanced, resulting in a significantly higher tumor inhibition rate for the RB-MBs + US (∼76.5%) in the HT-29 tumor model as compared to conventional MBs + US and RB-NPs + US (∼23.8% and ∼49.2%), respectively. All these results suggested that this novel sonosensitizer delivery system of RB-MBs combined with US is a powerful strategy for remarkably enhancing SDT therapeutic efficacy with minimal side effects, showing great potential in cancer theranostics.

To guarantee the safety of big data calculation, the concept of security research and trusted cloud combined with large data security, a two-way defense system model of a large data calculation of SAT. The model of user data evidence through the dynamic safety factor; gradually determine the behavior of security permissions according to the reliability test implementation behavior. Provide the maximum-security defense for users. the experimental results show that the model can effectively improve the data big data security, reduce malicious hackers, to improve the efficiency of data analysis.

According to different control objectives, shift schedules of dynamical and economical mode for electric-drive AMT vehicles is presented and a comprehensive shift schedule model is established with precise traction motor torque and speed experimental data. The state function and objective functional are established by using dynamic programming and variety of optimal shift schedules are solved for different control weights. The simulation results indicate that the vehicle performance with comprehensive shift schedule is better than the traditional method.

In order to alleviate the overfitting problem caused by image quality evaluation (IQA) model learning under intolerably small dataset, this paper proposes a multi-feature fusion-based deep architecture for hyperspectral image quality assessment. First, eight key IQA-related features, which are descriptive to the mean noise of multi-band images, spatial correlation, inter-spectral correlation, blur, and the phase-consistent map of images, are extracted from each hyperspectral image within the dataset. Based on this, a carefully-designed generalized regression neural network (GRNN) with a limited number of parameters is hierarchically trained by the feature vectors from samples in the training IQA data set. Comprehensive experimental evaluations on the hyperspectral IQA images from the DOTA dataset and the EO-1 Hyperion dataset have shown that the proposed model can indicate the subjective/objective quality-aware images regions precisely In addition, we observe that our designed IQA method has received impressive IQA performance than the other state-of-the-art non-reference methods.

Image quality assessment (IQA) attempts to quantify the quality-aware visual attributes perceived by humans. They can be divided into subjective and objective image quality assessment. Subjective IQA algorithms rely on human judgment of image quality, where the human visual perception functions as the dominant factor However, they cannot be widely applied in practice due to the heavy reliance on different individuals. Motivated by the fact that objective IQA largely depends on image structural information, we propose a structural cues-based full-reference IPTV IQA algorithm. More specifically, we first design a grid-based object detection module to extract multiple structural information from both the reference IPTV image (i.e., video frame) and the test one. Afterwards, we propose a structure-preserved deep neural networks to generate the deep representation for each IPTV image. Subsequently, a new distance metric is proposed to measure the similarity between the reference image and the evaluated image. A test IPV image with a small calculated distance is considered as a high quality one. Comprehensive comparative study with the state-of-the-art IQA algorithms have shown that our method is accurate and robust.

The standard no-reference video quality assessment (NR-VQA) is designed for a specific type of distortion. It quantifies the visual quality of a distorted video without the reference one. Practically, there is a deviation between the result of NR-VQA and human subjective perception. To tackle this problem, we propose a 3D deep convolutional neural network (3D CNN) to evaluate video quality without reference by generating spatial/temporal deep features within different video clips 3D CNN is designed by collaboratively and seamlessly integrating the features output from VGG-Net on video frames. To prevent our adopted VGG-Net from overfitting, the parameters are transferred from the deep architecture learned from the ImageNet dataset. Extensive IQA/VQA experimental results based on the LIVE, TID, and the CSIQ video quality databases have demonstrated that the proposed IQA/VQA model performs competitively the conventional methods.

Internet of Things (IoT) is widely applied in modern power systems, which could establish the intelligent power grid systems and obtain considerable social and economic benefits. IoT plays an important role in power grid safety production, user interaction, and information collection. However, existing methods cannot address problems of IoT devices accurately and quickly, such as fault detection. Aiming at the shortcomings of current power IoT equipment fault detection methods, this paper proposes a multi-spectral image fusion based on deep learning to detect fault points of power IoT equipment. The deep convolutional neural network is trained by simulating the image of the power device. The results show that the multi-spectral image descriptor based on deep learning presented in this paper shows very high accuracy in block matching, and the effect of image fusion is remarkable. This indicates that the proposed method can accurately integrate multi-spectral images of power equipment, helping to locate fault points quickly and accurately.

Despite the good prognosis of the low-risk thyroid cancer, there are no truly effective treatments for radioactive iodine-refractory thyroid cancer. Herein, a novel theranostic nanoplatform, as well as a smart doxorubucin (DOX) delivery system is fabricated. Gelatin-stabilized polypyrrole nanoparticles are reported for the first time. The combination of gelatin-stabilized polypyrrole and cyclodextrin-DOX complexes enabling three-stimuli-controlled drug delivery, including the enzyme-sensitive, pH-sensitive and photothermal-sensitive drug release, exhibiting a new way to equip photothermal agents with precisely controlled drug delivery. Anti-galectin-3 antibodies are utilized as the targeting molecules of nanoparticles in the first time, which surprisingly increase intracellular DOX uptake by enhanced clathrin-mediated endocytosis, showing galectin-3 can be employed as a highly efficient target of drug delivery systems. The nanoparticles achieve excellent photoacoustic imaging effect, enabled chemo-photothermal combined therapy with pinpointed drug delivery. Compared to free DOX, these multifunctional nanoparticles decrease the heart damage, but greatly increase the tumor/heart ratio of DOX concentration by 12.9-fold. The tumors are completely eradicated without any recurrence after the in vivo combined therapy. To the best of the authors' knowledge, this is also the first report to apply photoacoustic imaging-guided chemo-photothermal therapy for thyroid cancer, showing great potential to solve the dilemma in thyroid cancer therapy.

The process of urbanization has significant and various impacts on the environment of the earth, either positive or negative. In this paper, multi-disciplined methods such as GIS and bird survey were used to monitor the landscape change and ecosystem transformation by urbanization. The case study in Xiaguanyin Town of Northwest China demonstrated that urbanization can probably have positive environmental influences on a relatively arid place.

The exponential growth of data has brought a tremendous challenge on the storage system in data center. Data deduplication technology which detects and eliminates redundant data in the dataset can greatly reduce the quantity of data and optimize the utilization of storage space. This paper presented a scalable and reliable cluster deduplication system Halodedu over the Hadoop-based cloud computing platform. Halodedu used MapReduce and HDFS to realize parallel deduplication processing and manage data storage, respectively. Intra-node local database was used to build up a fast and distributed chunk fingerprint index management. In order to maintain the availability and reliability of metadata, HBase was utilized to store the metadata of backup files. We further used virtual machine images as input dataset to evaluate Halodedu. The comparative experiments demonstrated that Halodedu has improvements on deduplication speed and system scalability.

To complete a successful laser jamming to imaging charge coupled device (CCD) based on combined fiber lasers, the interactions between CCD and combined fiber lasers were analyzed in detail. The saturation and crosstalk thresholds of CCD were achieved, which are lower than 10 mW/cm 2 . Through theoretical analysis and numerical simulations, the thermal processes under single pulse, multi-pulses and continuous laser irradiations were developed. The simulation results have proved the possibility of hard damage caused by multi-pulses and continuous laser irradiations. The combined fiber lasers is suitable to deploy optical saturation jamming at present. The further applications of combined fiber lasers need a more powerful laser source and a more accurate tracking and pointing system.

A compact and light laser jamming source is needed to increase the flexibility of laser jamming technique. A novel laser jamming source based on combined fiber lasers is proposed. Preliminary experimental results show that power levels in excess of 10 kW could be achieved. An example of laser jamming used for an air-to-air missile is given. It shows that the tracking system could complete tracking in only 4 s and came into a steady state with its new tracking target being the laser jamming source.

A Dynamic Prefetching Thread scheme is proposed in this paper to accelerate sequential programs on Chip Multiprocessors. This scheme belongs to the hardware-generated thread-based prefetching technique and can decouple the performance and correctness to some extent. This paper describes the necessary hardware infrastructure supporting Dynamic Prefetching Thread on traditional Chip Multiprocessors. Aiming at the loosely coupled feature of Chip Multiprocessors, we present the ''Shadow Register'' mechanism to support rapid register transportation among multi-cores and discuss the selection of thread spawn time. Furthermore, two aggressive thread construction policies, known as ''Self-Loop'' and ''Fork-on-Recursive-Call'', are proposed. ''Self-Loop'' policy can greatly enlarge the prefetching range and issue more timely prefetches. ''Fork-on-Recursive-Call'' policy can effectively accelerate applications accessing trees or graphs via recursive calls. For a set of memory limited benchmarks selected from Olden benchmark, SPEC CPU2000 as well as Stream benchmark, an average speedup of 3.8% is achieved on dual-core CMP when constructing basic Dynamic Prefetching Threads, and this gain grows to 29.6% when adopting our aggressive thread construction policies.

Smoking leads to the occurrence and development of a variety of diseases. Most importantly, it is an independent risk factor of cardiovascular atherosclerosis. In recent years, electronic cigarettes have become a popular alternative to traditional cigarettes, since modern micro-electronic techniques provide the possibility of simulating the process of traditional smoking. Additionally, it is convenient and fashionable. Nevertheless, comments about the safety of electronic cigarettes remain controversial. Although the research about electronic cigarettes increased exponentially, there has been no systematic study of its safety. The aim of the current study is to review the literature reports about the safety of electronic cigarettes, and to understand their hazards and disadvantages. It was found that most of the current research about electronic cigarettes comprises short-term and in vitro studies. There are few reports of in vivo and long-term studies. Notably, the level of harmful components such as volatile organic compounds, tobacco-specific nitrosamines and heavy metals in electronic cigarettes are even higher than in traditional cigarettes. Therefore, the harm of electronic cigarettes should not be underestimated. In conclusion, the question of whether electronic cigarettes are a safe and sufficient substitute for traditional smoking needs further investigation.

Active power filter (APF) often combines with thyristor switched capacitor (TSC) for the purpose of reducing cost. Traditional full-bandwidth control method of APF contains system's parallel resonant point and the harmonic of power grid will be amplified. To address this problem, a novel fractional frequency control strategy with lead compensation is proposed. This method can obtain arbitrary order harmonic reference to compensate using synchronous rotating coordinate transform method. Instruction value of APF can be achieved by adding the amplitude limited fundamental reactive power reference to the finite harmonics reference. In this way which can improve the stability of hybrid compensation system, output spectrum of APF will become numbered discrete spectral line. And a synchronous rotating coordinate transformation based lead compensation is also proposed. By adding a leading angle in the inverse transformation matrix, compensation effect is improved. And proposed methods have all been verified in experiments and practical debugging in field.

With the development and wide application of power electronic technology, grid pollution like harmonics and reactive power is becoming aggravated. And with the increasing capacity and voltage grade of devices, traditional active power filter (APF) with 2-level structure is not suitable, because of the slow development of power electronic devices. The voltage on the switches can be reduced by APF with 3-level structure effectively with the use of switches connected in series. 3-level APF based on the time-fixed hysteresis comparison control is proposed and then tested in the three-phase three-wire system by simulation. Relative parameters are also obtained

Smart phone have become a very important machine of communication, the security performance of smart phone is obvious, it needs to solve the problem of mobile internet. Andriod & Symbian is a major and open-source platform of smart phone which marketing share is more than 80%. Due to open-source specialty, the security performance has suffered suspected by end user. This article starts from the Android & Symbian system architecture, in-depth analysis of various components of its security mechanisms in which you can get a clear conception of security mechanism around Andriod OS & Symbian OS, at the same time, you can view the difference of them.

Static-synchronous-series-compensator has a broad prospect of a controllable series compensation technology which can improve the performance of power system, arousing increasing attention, this paper from the angles of constant voltage control of SSSC analyzes its characteristic, then the three-order mathematical model is established in reference to the single machine infinite bus system with SSSC. The theory of inverse system and sliding mode control are used for the design of control strategy for multi-order nonlinear systems. Finial, the results of MATLAB prove the viability and robustness of the SSSC control strategy.

While the rapid development of computer and network technology has brought strong support for improving teaching environment and teaching means, it has brought a lot of lures for university students. The problem that university students only pay attention to the software and hardware technology of the computer and ignore traditional technology courses is urgent to be solved. In the process of traditional technology course teaching, the authors realized that striving for guiding students to correct students' studying attitudes, enriching teaching contents to arouse their interests, introducing the most recent technology to traditional technology course to increase the technology contents and reforming teaching methods and means to enhance the modern sense of traditional technology course were effective methods to improve the teaching quality of traditional technology course in cyber era.

The existing two main ways to interpret the formation of merge waves are neo-classical approach and behavior finance approach. Both of these two methods focus on exogenous variables influence, such as the industry shock, shakeout, stock price fluctuation, manager's behaviors and so forth, while they neglect effects of endogenous variables, such as the interactive competition among enterprises and market structure. In this study, the avalanche effect theory of complex network, industry competitive pressure theory and Mergers and Acquisitions (M&A) theory are skillfully combined. It puts forward new ideas that competitive pressures diffusion leads to merger waves. The ideas and theoretical framework is expected to explain the formation process of merger waves from the perspective of industry competition structure. Meanwhile, this method will transfer the research object from individual enterprise to the relationship among enterprises, and find a comprehensive and integrated research framework for the analysis between enterprise-level competitive behavior of individual micro-analysis and industry-level statistical behavior of macro-analysis.

Through building PDM managerial classes' model, authorization management is organized based on PDM system's function modules. Inheritance methods of PDM managerial classes and the operations assign modes of PDM managerial classes are discussed. Access control model is organized. Through discussing the relationship of mutually tables in database, rules of access authorization control is analyzed. Class-oriented access control and object-oriented access control are realized, and mult-authorization setting for a class data and a instance are completed. The flow chart of roles assignment, the flow chart of the user's activities in PDM system and the flow chart of authorization validation are given. The methods of authorization setting are simple and criterion, and the operational efficiency of PDM system is improved.

The laser-jamming effectiveness of combined fiber lasers for airborne defense systems is analyzed in detail. Our preliminary experimental results are proof of the concept of getting a high-power laser through a beam combination technique. Based on combined fiber lasers, the jamming effectiveness of four-quadrant guidance and imaging guidance systems are evaluated. The simulation results have proved that for a four-quadrant guidance system, the tracking system takes only two seconds to complete tracking, and the new tracking target is the jamming laser; for the imaging guidance system, increasing the power of the jamming laser or the distance between the target and the jamming laser are both efficient ways to achieve a successful laser jamming.

Hardware transactional memory (HTM) is an attractive research topic in recent years. It has great potential to simplify parallel programming on the soon-to-be-ubiquitous multi-core systems. In this paper, a HTM design is proposed, and overall performance is evaluated. This HTM design distinguishes itself from others by its best effort philosophy. The hardware makes best effort to complete each transaction and software handles those transactions that cannot be completed by hardware. This design seeks a balance between application performance and hardware implementation complexity, and tries to answer the question: what should be done by hardware and what should be done by software. The overall performance of benchmarks is also evaluated by simulation.

This paper put forward a new method to analysis optical waveguide directional couplers, which dependent on modified effective-index method. The results of the modified effective-index method are compared with the conventional effective-index method and the accurate numerical results in this paper. The results of the modified effective-index method are nearly close to the exact results, and some valuable conclusions will help for analysis and design optical waveguide directional couplers in practice.

Optical burst switching (OBS) is a promised newly technology to implemented the all-optical transmission for Internet protocol (IP) traffic. How to effectively resolve the burst contention at the core router and propose a QoS (Quality of Service) supported burst assembly technique is a critical issue in nowadays. In this paper, a novel burst contention resolution scheme for OBS network has been proposed, this scheme based on two main aspects. Firstly, we propose a new burst segmentation approach named both-direction-segmentation (BDS), which could subtly take the advantages of burst head-segmentation and burst tail-segmentation approach. Secondly, corresponding to BDS, a novel burst assembly technique, which we called hybrid burst assembly (HBA) also has been proposed. Different from several existing burst assembly techniques, which only protect high-priority bursts. HBA has also been considered to protect the relatively high-class packets in low-priority bursts. A simulation is given out at the end of this paper, we observe that the proposed contention resolution scheme not only could minimize the losing of packets remarkably but also could efficiently and veritably support QoS for OBS network. The results have some valuable theoretic significances to design the real network in practice.

This paper proposes a hybrid hardware/software generated prefetching thread mechanism on Chip Multiprocessors(CMP). Two kinds of prefetching threads appear in our hybrid mechanism. Most threads belong to Dynamic Prefetching Thread, which are automatically generated, triggered, spawn and managed by hardware; The others are of Static Prefetching Thread, targeting at the critical delinquent loads which can not be accurately or timely predicted by Dynamic Prefetching Thread. Static Prefetching Threads are statically generated by binary-level optimization tool with the guide of profiling information. Also, some aggressive thread construction policies are proposed. Furthermore, the necessary hardware infrastructure for CMP supporting this hybrid mechanism are described. For a set of memory limited benchmarks with complicated access patterns, an average speedup of 3.1% is achieved on dual-core CMP when constructing basic hardware-generated prefetching thread, and this gain grows to 31% when adopting our hybrid mechanism.

Image edge detection is an important tool of image processing, in which edge representation and extraction with uncertainty is one of key issues. Based on the physics-like methods for image edge representation and extraction, a novel cognitive physics-based method with uncertainty is proposed. The method uses data field to discover the global information from the image and then to map it from grayscale space to the appropriate potential space. From the point of view of the field theory, the method establishes an extensible theoretical framework and unifies the existing physics-like methods. On the other hand, the method defines the ascending half-cloud to construct the internal relationship between the range of cloud uncertainty degree and the edge representation and extraction. Finally, the method achieves image edge representation and extraction with uncertainty using the cognitive physics. The time complexity of the proposed algorithm is approximately linear in the size of the original image. It is indicated by the quantitative and qualitative experiments that the proposed method yields accurate and robust result, and is reasonable and effective.