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In this paper, the research status of distributed control system of aero engine were briefly analyzed. It is reasonable to believe that the studies of distributed control system of aero engine are at the transition phase from theoretical research to engineering application with the development of related theories, yet there still exists problems such as the intelligence of the system still needs to be improved, and the lack of standardized interface which leads to diversification and complexity of equipment. Aiming at the problems mentioned above and taking a domestic turboprop engine as the research object, a multi-smart-agents based partially distributed control system architecture is proposed and then the function division and data transmission are described. The practical method of the concept of “smart” in the architecture of partial distributed control system is explored in order to give full play to the advantages of distributed control system. The results show that the structure of the partially distributed control system based on multi-smart-agents is feasible, which provides a reference for the further research of the distributed control theory of aero engine.

In order to solve the problem of reduced classification accuracy caused by the different spatial features of EEG motor imagery signals between the same subjects on different days, this paper proposes an EEG feature extraction and recognition method based on common spatial pattern (CSP) and decision surface adaptive linear discriminant analysis (DSALDA). The decision surface threshold of the linear discriminant analysis (LDA) classifier was updated by saving the CSP spatial feature of the different day's test data, and use parameters to control the proportion of CSP spatial features between the training data set and the different day’ test data set. The experiment collected EEG data of 24 subjects, each subject collected data on different days. The results show that the average accuracy of this method is improved by 6.35% compared with the CSP-LDA method, which effectively improves the accuracy and stability of the different day's motor imagery EEG classification. It has created conditions for the wide application of motor imagery brain-computer interface (BCI) system in the field of rehabilitation.

In order to overcome the security and integrity problems of the traditional university file information resource sharing model, the paper proposes a new cloud computing-based university file information resource sharing model. Supported by the cloud computing system architecture, the use of cloud computing search algorithms to fully excavate university archives information resources to realize the comprehensive sharing of university information archives, and calculate the utility parameters of university archives information resource sharing based on the mining results of cloud computing, and build a communication consumption matrix for the sharing of archives information resources in colleges and universities, and complete the construction of the sharing model of archives information resources in colleges. The comparative experiment results show that compared with the traditional sharing model, the intrusion rate and sharing transmission loss of the constructed sharing model are significantly reduced, indicating that the constructed model can effectively realize the sharing of archive information resources in universities and ensure the integrity and safety of archives.

There is currently few related research in the field of drug detoxification to objectively quantify the degree of drug addiction among drug addicts. This paper designs an experimental paradigm, using a high-density near infrared spectroscopy device to collect forehead near infrared data on 30 drug addicts, and has sorted drug addicts through scientific researchers and hospital experts (10 mild, 10 moderate, and 10 severe).Data were obtained from subjects exposed to pictures of drugs. Aiming at these data sets, a drug addiction detection algorithm based on convolutional neural network and batch normalization (CNN-BN) is designed in this paper to classify mild, moderate and severe addiction levels. we apply the convolutional neural network to extract features in the near-infrared spectroscopy data of drug addicts. In addition, batch normalization layer is added to the convolutional neural network, which greatly improves the training speed and reduces the sensitivity to parameters, and the system's robustness is improved. The final three classification results stabilized at 72%. It provides a theoretical basis for the objective quantitative classification of drug addiction degree and the design of corresponding rehabilitation treatment programs based on the degree of drug addiction.

Computerized ionospheric tomography (CIT) can image 3-D ionospheric variations from ground Global Navigation Satellite System (GNSS) observations. However, due to the sparse and inhomogeneous ground GNSS observations, the prior information from the empirical models (e.g., IRI-2012 and NeQuick 2) have to be used, which affect the reconstructed results in the CIT, particularly the peak parameters of the F2 layer, namely foF2 and M(3000)F2. In this paper, an improved two-step CIT algorithm is developed to image the ionospheric electron density (IED) distribution up to 2000 km over Japan using dense GNSS Earth Observation Network (GEONET) observations operated by the Geospatial Information Authority (GSI) of Japan. Firstly, we update the IRI-2012 using the ground-based GNSS observations. The NeQuick-2 is used to provide the vertical ionospheric profile, and foF2 and M(3000)F2 derived from CCIR model in NeQuick 2 are improved by the B-spline modeling methods. Secondly, the updated IRI-2012 is input as a prior value and the multiplicative algebraic reconstruction technique (MART) is implemented to reconstruct the IED distribution. The reconstructed results are validated by the simulation and observation results from ground-based GNSS, GNSS Radio Occultation (GNSS-RO) observations and ionosonde measurements. The results demonstrate the feasibility and superiority of our new method. The standard deviations of TEC residual errors from the updated NeQuick 2 are at least 20% less than those from the original NeQuick 2 model, and the updated NeQuick 2 has better performance for the ionospheric correction.

The development of machine vision-based technologies to replace human labor for rapid and exact detection of agricultural product quality has received extensive attention. In this study, we describe a low-rank representation of jointly multi-modal bag-of-feature (JMBoF) classification framework for inspecting the appearance quality of postharvest dry soybean seeds. Two categories of speeded-up robust features and spatial layout of L*a*b* color features are extracted to characterize the dry soybean seed kernel. The bag-of-feature model is used to generate a visual dictionary descriptor from the above two features, respectively. In order to exactly represent the image characteristics, we introduce the low-rank representation (LRR) method to eliminate the redundant information from the long joint two kinds of modal dictionary descriptors. The multiclass support vector machine algorithm is used to classify the encoding LRR of the jointly multi-modal bag of features. We validate our JMBoF classification algorithm on the soybean seed image dataset. The proposed method significantly outperforms the state-of-the-art single-modal bag of features methods in the literature, which could contribute in the future as a significant and valuable technology in postharvest dry soybean seed classification procedure.

With the ongoing development of electric technology, the complexity of the system has extremely raised, which brings challenges for the design of test strategies. Over decades, numbers of researches has dedicated for providing efficient approaches for diagnosis with high FIR and low cost. However, most of the methods only considers sequential or paralleling test strategies, which influence the estimation on the time consumption of the optimal solution. Therefore, in this paper, a novel test optimization method is proposed for combining the sequential test strategy and parallel test strategy with a new developed technique called heuristic programming, which aims at providing the mixture test strategies for the systems. Also, the decision graph is introduced to give more accurate estimation on HEF and defines the budget impact into the heuristic programming to further improve the searching efficiency. The experiments shown that comparing with state-of-art test optimization method, the proposed method is able to provide efficient test strategies in compatible time consumption with lower time cost of the strategies. Also, with the improvement of the HEF calculation, the proposed method has high efficiency on optimization process.

Distributed file systems have been widely used in many applications to provide high performance. However, large amounts of data-intensive applications often access the data server concurrently, the average completion time is enlarged due to the long request queue on data servers, especially when requests of applications cause a series of synchronous I/O requests. This paper proposes an I/O scheduling algorithm, called EML (equal-length multi-level algorithm) to solve this problem, it can reduce the average response time significantly. We demonstrate the performance improvement versus multi-level queue through both theoretical and experimental analysis. The experiments show that EML algorithm can effectively reduce the average completion time by 30% in 64 concurrent write applications and 50% in 64 concurrent read applications.

In recent years, the correntropy instead of the mean squared error has been widely taken as a powerful tool for enhancing the robustness against noise and outliers by forming the local similarity measurements. However, most correntropy-based models either have too simple descriptions of the correntropy or require too many parameters to adjust in advance, which is likely to cause poor performance since the correntropy fails to reflect the probability distributions of the signals. Therefore, in this paper, a novel correntropy-based extreme learning machine (ELM) called ECC-ELM has been proposed to provide a more robust training strategy based on the newly developed multi-kernel correntropy with the parameters that are generated using cooperative evolution. To achieve an accurate description of the correntropy, the method adopts a cooperative evolution which optimizes the bandwidths by switching delayed particle swarm optimization (SDPSO) and generates the corresponding influence coefficients that minimizes the minimum integrated error (MIE) to adaptively provide the best solution. The simulated experiments and real-world applications show that cooperative evolution can achieve the optimal solution which provides an accurate description on the probability distribution of the current error in the model. Therefore, the multi-kernel correntropy that is built with the optimal solution results in more robustness against the noise and outliers when training the model, which increases the accuracy of the predictions compared with other methods.

Glycine max (L.) Merrill is one of the most important economic and food crops in the world. Defective rate is an important indicator affecting Glycine max (L.) Merrill surface quality factors. In this study, we introduce two kinds of color- and gradient-related visual dictionary models for discrimination of Glycine max (L.) Merrill quality. The RGB color components is firstly transferred to L*a*b* color domain. The speeded-up robust features (SURF) is performed to obtain the local gradient-related characteristic representation. The KNN clustering method is individually performed to cluster the global L*a*b* color features and local gradient-related SURF features. The clustering centers can be viewed as visual words. All the visual words are collected to generate the visual dictionary. The visual clustering features are finally discriminated by the support vector machine algorithm. The result shows that the gradient-related model achieves higher discrimination accuracy than the color-related visual dictionary model.

Over decades, testing sequence optimization has been an important part for the design of testability (DFT). However, most existed method suffers from high searching complexity. Meanwhile, with the high speed of updating with electronic technology, the cost for test points and the probability of the fault state varies due to the development of the systems. Therefore, how to update the testing sequential model comes to be important to improve the efficiency on diagnosis. Since that, in this paper, we proposed a new testing sequence optimization method based on AO* and dynamic programming to improve the efficiency on generate solutions. Also, we develop the updating method for the problem under three dynamic conditions which may change the optima solution. The experiments shows that our method can achieve better performance than several state-of-art algorithms.

Recent studies have found that parallel garbage collection performs worse with more CPUs and more collector threads. As part of this work, we further investigate this enomenon and find that poor scalability is worst in highly scalable Java applications. Our investigation to find the causes clearly reveals that efficient multi-threading in an application can prolong the average object lifespan, which results in less effective garbage collection. We also find that prolonging lifespan is the direct result of Linux's Completely Fair Scheduler due to its round-robin like behavior that can increase the heap contention between the application threads. Instead, if we use pseudo first-in-first-out to schedule application threads in large multicore systems, the garbage collection scalability is significantly improved while the time spent in garbage collection is reduced by as much as 21%. The average execution time of the 24 Java applications used in our study is also reduced by 11%. Based on this observation, we propose two approaches to optimally select scheduling policies based on application scalability profile. Our first approach uses the profile information from one execution to tune the subsequent executions. Our second approach dynamically collects profile information and performs policy selection during execution.

Order of shared memory accesses, known as the shared memory dependence, is the cornerstone of dynamic analyses of concurrent programs. In this paper, we study the problem of reducing shared memory dependences. We present the first online software-only algorithm to reduce shared memory dependences without vector clock maintenance, opening a new direction to a broad range of applications (e.g., deterministic replay and data race detection). Our algorithm exploits a simple yet effective observation, that adaptive variable grouping can recognize and match spatial locality in shared memory accesses, to reduce shared memory dependences. We designed and implemented the bisectional coordination protocol, which dynamically maintains a partition of the program's address space without its prior knowledge, such that shared variables in each partitioned interval have consistent thread and spatial locality properties. Evaluation on a set of real-world programs showed that by paying a 0--54.7% (median 21%) slowdown, bisectional coordination reduced 0.95--97% (median 55%) and 16--99.99% (median 99%) shared memory dependences compared with RWTrace and LEAP, respectively.

Small talk is an important social lubricant that helps people, especially strangers, initiate conversations and make friends with each other in physical proximity. However, due to difficulties in quickly identifying significant topics of common interest, real-world small talk tends to be superficial. The mass popularity of mobile phones can help improve the effectiveness of small talk. In this paper, we present E-SmallTalker, a distributed mobile communications system that facilitates social networking in physical proximity. It automatically discovers and suggests topics such as common interests for more significant conversations. We build on Bluetooth Service Discovery Protocol (SDP) to exchange potential topics by customizing service attributes to publish nonservice-related information without establishing a connection. We propose a novel iterative Bloom filter (IBF) protocol that encodes topics to fit in SDP attributes and achieves a low false positive rate. We have implemented the system in Java ME for ease of deployment. Our experiments on real-world phones show that it is efficient enough at the system level to facilitate social interactions among strangers in physical proximity. To the best of our knowledge, E-SmallTalker is the first distributed mobile system to achieve the same purpose.

Inter-thread shared memory dependences are crucial to understanding the behavior of concurrent systems, as such dependences are the cornerstone of time-travel debugging and further predictive trace analyses. To enable effective and efficient shared memory dependence tracing, we present an optimistic scheme addressing the challenge of capturing exact dependences between unsynchronized events to reduce the probe effect of program instrumentation. Specifically, our approach achieved a wait-free fast path for thread-local reads on x86-TSO relaxed memory systems, and simultaneously achieved precise tracing of exact read-after-write, write-after-write and write-after-read dependences on the fly. We implemented an open-source RWTrace tool, and evaluation results show that our approach not only achieves efficient shared memory dependence tracing, but also scales well on a multi-core computer system.

In a mobile collaboration environment, the shared files are often replicated so that the users can work in parallel during periods of disconnection. When reconnected, sequences of updates made by different users are merged to produce a consistent view of shared files. This paper presents a novel transformation-based merging algorithm for supporting mobile collaboration. Compared to alternative optimistic consistency control methods, it can merge any updates to the same file automatically at the finest granularity without causing loss of work. Moreover, it improves the time complexity of the state-of-the-art transformation-based merging algorithms from O(n3) to O(n), where n is the size of either sequence when their sizes are comparable. This improvement is drastic and important especially for mobile devices that run on batteries and have limited processing power.

Operational transformation (OT) as a consistency control method has been well accepted in group editors. With OT, the users can edit any part of a shared document at any time and local responsiveness is not sensitive to communication latencies. However, established theoretical frameworks for developing OT algorithms either require transformation functions to work in all possible cases, which complicates the design of transformation functions, or include an under-formalized condition of intention preservation, which results in algorithms that cannot be formally proved and must be fixed over time to address newly discovered counterexamples. To address those limitations, this paper proposes an alternative framework, called admissibility-based transformation (ABT), that is theoretically based on formalized, provable correctness criteria and practically no longer requires transformation functions to work under all conditions. Compared to previous approaches, ABT simplifies the design and proofs of OT algorithms.

Parallel garbage collection has been used to speedup the collection process on multicore architectures. Similar to other parallel techniques, balancing the workload among threads is critical to ensuring good overall collection performance. To this end, work stealing is employed by the current state-of-the-art Java Virtual Machine, OpenJDK, to keep GC threads from idling during a collection process. However, we found that the current algorithm is not efficient. Its usage can often cause GC performance to be worse than when work stealing is not used.In this paper, we identify three factors that affect work stealing efficiency: determining tasks that can benefit from stealing, frequency with which to attempt stealing, and performance impacts of failed stealing attempts. Based on this analysis, we propose SmartStealing, a new algorithm that can automatically decide whether to attempt stealing at a particular point during execution. If stealing is attempted, it can efficiently identify a task to steal from. We then compare the collection performances when (i) the default work stealing algorithm is used, (ii) work stealing is not used at all, and (iii) the SmartStealing approach is used. Without modifying the remaining garbage collection system, the evaluation result shows that SmartStealing can reduce the parallel GC execution time for 19 of the 21 benchmarks. The average reduction is 50.4% and the highest reduction is 78.7%. We also investigate the performances of SmartStealing on NUMA and UMA architectures.

Modern Java applications employ multithreading to improve performance by harnessing execution parallelism available in today’s multicore processors. However, as the numbers of threads and processing cores are scaled up, many applications do not achieve the desired level of performance improvement. In this paper, we explore two factors, lock contention and garbage collection performance that can affect scalability of Java applications. Our initial result reveals two new observations. First, applications that are highly scalable may experience more instances of lock contention than those experienced by applications that are less scalable. Second, efficient multithreading can make garbage collection less effective, and therefore, negatively impacting garbage collection performance.

This paper studied the congestion control algorithm in the IONs and put forward two innovative algorithms. Firstly, designed a wavelength conversion algorithm which based on Partial-sparse-Limited network model (for short PLWC); Secondly, presented another wavelength conversion algorithm which based on Partialsparse-Limited network model and Wavelength Buffering (for short PLWC_WB); Finally, analyzed and verified PLWC and PLWC_WB algorithm by system simulation. The simulation results shown that PLWC algorithm achieved an outstanding performance of the network improvement compared with the existing SPWC algorithm, and comparing with the PLWC algorithm the PLWC_WB algorithm can decrease the blocking probability about 13% and increase network resource utilization about 10%. Keywordsintelligent optical networks; congestion control; wavelength conversion; wavelength buffering.

Operational transformation (OT) is an optimistic concurrency control method that has been well established in realtime group editors and has drawn significant research attention in the past decade. It is generally believed that the use of OT automatically achieves high local responsiveness in group editors. However, no performance study has been reported previously on OT algorithms to the best of our knowledge. This paper extends a recent OT algorithm and studies its performance. By theoretical analyses and performance experiments, this paper proves that the worst-case execution time of OT only appears in rare cases, and shows that local responsiveness of OT-based group editors in fact depends on a number of factors such as the size of the operation log. The paper also reveals that these two results have general implications on OT algorithms and hence the design of OT-based group editors must pay attention to performance issues.

Real-time group editors allow distributed users to edit a shared document at the same time over a computer network. Operational transformation (OT) is a well accepted consistency control method in state-of-the-art group editors. Significant progress has been made in this field but there are still many open issues and research opportunities. In particular, established theoretic OT frameworks all require that OT algorithms be able to converge along arbitrary transformation paths. This property is desirable because group editors that implement such algorithms will not rely on a central component for achieving convergence. However, this has not been achieved in any published work to our knowledge. We analyze the root of this problem and propose a novel state difference based transformation (SDT) approach which ensures convergence in the presence of arbitrary transformation paths. Our approach is based on a novel consistency model that is more explicitly formulated than previously established models for proving correctness. SDT is the first and the only OT algorithm proved to converge in peer-to-peer group editors.

Although fingerprint based localization is promising for indoor applications, its accuracy still remains a huge challenge. Most of existing approaches rely on the Radio Signal Strength (RSS) to generate fingerprints. However, merely using RSS is unable to accurately localize objects since such an one-dimensional fingerprint will be seriously influenced by the interference and multi-path effect in the indoor environment. In this paper, we propose a new localization approach based on multidimensional Wi-Fi fingerprint. Instead of only using RSS to construct fingerprint, we employ RSS, transmitted power, and channel information to construct an integrated fingerprint. The extended fingerprint enables fine-grained localization and tracking services. We also deign a cosine similarity based matching algorithm and enhanced particle filter mechanism to achieve accurate localization and tracking. Extensive experiment and implementation results show that the new fingerprint and proposed algorithms can achieve an accuracy within two meters in 90% of testing points, while demonstrating a good adaptability to complex indoor environments.

Consistency control is a critical issue in collaborative systems. Supporting flexible consistency control in particular matches the dynamic and situated needs of cooperative work. However, previous approaches generally provide only limited flexibility due to their static binding between shared data objects and consistency control protocols. We propose a component-based framework that allows for the runtime plug-n-play of consistency protocols in collaborative systems. We model data and protocols in a way such that they are cleanly separated and can be dynamically bound at run time to achieve flexible control. Data-protocol bindings can happen at the property, object, and workspace levels. The framework provides reusable services for implementing adaptable consistency control in a range of collaborative workspace applications.

The Internet of Things (IoT) aims to integrate the digital world of the Internet with our encompassing physical world. However, existing IoT lacks to provide considerate services, meaning that sensors dynamically "collaborate" to provide context-aware federated sensor data. This paper reports our on-going work developing a sensor service federation and provisioning infrastructure. A novel approach has been presented to build social sensor networks to record and study historical interaction patterns among sensors. A case study is reported to leverage in-memory database to monitor and manage real-time sensor service provisioning. A two-way publish/subscribe pattern on top of a message bus is established to assure scalability of sensor service communication. Workflow provenance is carried by a dynamic virtual device concept that we have introduced. A case study is reported to leverage in-memory database to monitor and manage real-time sensor service provisioning.

Operation propagation refers to the mechanisms and policies used in group editors for the participants to notify each other of their individual editing activities. The timing and granularity of propagation depend on different requirements, such as network bandwidth, session scale, the computational power of the involved sites and the preferences of each user. In this article, we draw on some common design principles in two group editors we have implemented.

According to Daniel Kahneman, there are two systems that drive the human decision making process: The intuitive system that performs the fast thinking, and the deliberative system that does more logical and slower thinking. Inspired by this model, we propose a framework for implementing human activity recognition on mobile devices. In this area, the mobile app is usually always-on and the general challenge is how to balance accuracy and energy consumption. However, among existing approaches, those based on cellular IDs consume little power but are less accurate; those based on GPS/WiFi sampling are accurate often at the costs of battery drainage; moreover, previous methods in general do not improve over time. To address these challenges, our framework consists of two modes: In the deliberation mode, the system learns cell ID patterns that are trained by existing GPS/WiFi based methods; in the intuition mode, only the learned cell ID patterns are used for activity recognition, which is both accurate and energy-efficient; system parameters are learned to control the transition from deliberation to intuition, when sufficient confidence is gained, and the transition from intuition to deliberation, when more training is needed. For the scope of this paper, we first elaborate our framework in a subproblem in activity recognition, trip detection, which recognizes significant places and trips between them. For evaluation, we collected real-life traces of six participants over five months. Our experiments demonstrated consistent results across different participants in terms of accuracy and energy efficiency, and, more importantly, its fast improvement on energy efficiency over time due to regularities in human daily activities.

This paper uses a simplified mathematical model of engineering panels to accurately simulate the output characteristic of the solar cells only to know individual panels' parameters. On photovoltaic characteristic curve fitting timeliness, the four broken-line method combined with linear interpolation and parabolic interpolation is adopted to accurately simulate the output characteristics of photovoltaic battery. Based on this method, the main circuits to Buck circuit and control section which use the current closed-loop PI control are introduced. The simulation results show that using the four broken line method to piecewise fit photovoltaic battery output characteristics having a high precision. Finally, according to the nonlinear output use the MPPT control the photovoltaic battery simulator and the simulation results show the control precision.

For new social service providers (freshmen), it is critical to determine where to deploy the computational resources to best accommodate future client requests. Existing proposals on server placement rely on collecting and analyzing request history from servers that are already running, which are not so useful to those starting new online social network services (OSNs). In this work, we aim at helping new OSN providers with intelligent server placement by exploring available public information from existing social network communities. We explore the commonality between the selected set of server locations from multiple OSNs and utilize such similarity for future OSNs to select their server locations. The similarity is ultimately due to the fact that the underlying human relationship is relatively consistent across OSNs.

Trip detection is a fundamental issue in many context-sensitive information services on mobile devices. It aims to automatically recognize significant places and trips between them. The key challenge is how to minimize energy consumption while maintaining high accuracy. Previous works that use GPS/WiFi sampling are accurate but energy efficiency is low and does not improve over time. Learning from the human decision making process, we propose an energy-efficient trip detection framework that consists of two modes: The deliberation mode learns cell-id patterns using GPS/WiFi based localization methods; the intuition mode only uses cell-ids and learned patterns for trip detection; transition between the two modes is controlled by parameters that are also learned. We evaluated our framework using real-life traces of six people over five months. Our experiments demonstrate that its energy consumption decreases rapidly as users' activities manifest regularity over time.

By using Java Native Interface (JNI), programmers can integrate Java programs with legacy applications or third-party libraries written in other languages (e.g., C, C++, and Pascal). However, the use of JNI can bypass the Java boundary checking and exception-handling mechanisms. Furthermore, its use can violate Java's type-safety feature because of the type-mismatches between native programs and Java programs. As a result, such integration can cause various security issues including heap errors that can be dangerous and difficult to detect.In this paper, we introduce Quarantine, a runtime system that can identify objects accessible by native methods and then migrate these objects to a quarantine area, which is used specifically to host this type of object. The goal of Quarantine is to create a runtime platform that allows programmers to apply existing heap protection techniques that have been designed for native languages but do not work well in the Java domain. We implemented Quarantine in Jikes RVM and evaluated its performance using the optimizing compiler. Our results using nine benchmark programs indicate that Quarantine, on average, incurs execution overhead of 14% and 13% when the heap is two and five times larger than the minimum heap requirement for an application, respectively.

It is significant of applying error concealment in video transmission in wireless network. This report compares the new FMO (Flexible Macroblock Ordering) model based on H.264 to the traditional FMO model in error concealment. The author test different FMO model and make the contrast of results through selection of different standard test video sequences. And the testing result indicates that different FMO model selection for different transmission environment can reach the best error concealment effect, which provided a new concept of adaptive error concealment.

With the concept of smart grid appear, the power grid is strengthening their own grid monitoring maintenance gradually by technology, specially to its built on overhead transmission line's construction, maintenance and monitoring. Because the working places are usually in the wild and remote areas, so how to keep the communication link unobstructed, and how to keep a real-time communication between the job site with the background information communication command center is the urgent problem for the electrical power system. This paper presents a new wireless communication platform, applying wireless IP digit spread spectrum wireless network combined with 3G wireless public network, this kind of design can provide a wireless communication platform for the electric power field, with the characteristics of high transmission rate, unaffected by topography influence, simple erected and low cost. It can solve the problem of communications disconnected in fieldwork thoroughly.

As the contour error on cutting part is closely related to the dynamic parameters of CNC machine tool, which are usually refered to the dynamic accuracy, this paper presents a way to identify the shift of dynamic parameters with special shape feature. By modeling control system of CNC machine tool, the dynamic accuracy parameters of CNC machine tool are selected and the contour error curves are simulated. The contour errors are the results of unsuitable machine parameters and there is certain regularity. Using multiple shape features, the uncoordinated parameters of CNC machine tool causing the contour error are identified. The simulation shows that it is an effective way to identify the machine dynamic accuracy with the characteristic of contour error.

A novel ultra-wideband filter with wide stopband is developed in this paper. The filter is based on the shunt stub-loaded bandpass structure. In order to suppress the upper-band spurious response, lowpass sections are embedded into the shunt stub-loaded bandpass filter. The good performance in passband and stopband is achieved. The design is successfully verified by both simulation and measurement. The designed filter has an ultra-wide multiple-octave bandwidth (2.2–10.2GHz) and a wide stopband which is up to 20GHz. Due to its simple structure and compact size, the ultra-wideband bandpass filter introduced in this paper is suitable for portable applications.

This paper presents a collaboration transparency system that allows for distributed sharing of legacy collocated video games in real time without modifying the original games. Our system has attracted two million registered players since it was launched in late 2008. As the system went beyond a research prototype and reached a large number of real users, we observed performance issues that were mainly caused by unpredicted networking latencies over the public Internet and opportunistic distinction of roles such as active players and passive observers. To address these issues, we propose a novel hybrid consistency control approach that combines the merits of optimistic methods, which help achieve interactivity, and pessimistic methods, which guarantee consistency. As a result, the system can tolerate network round-trip time up to 230 ms, improving the latest work by 43.7%. In addition, the system can support over 1,000 simultaneous observers when a video game is played over the Internet.

Dynamic tainting is a powerful technique that has been used to detect computer attacks, generate test cases analyze data scopes, and protect memory. However, existing tainting techniques suffer from excessive runtime overheads that can be as high as 30 to 50 times, making them unsuitable for applications in deployed systems. The goal of our work is to provide as efficient and low-overhead tainting framework that can be used in deployed environments. To accomplish this goal, we propose to implement framework that supports dynamic tainting as a feature of a Java Virtual Machine (JVM). In this approach, the tainting code can be injected by the JVM without needing to instrument the source code. It can also support customizable and configurable tainting. The overhead of tainting can be controlled by sampling and different tainting granularity. For example, the framework can taint all the data as needed when the workload is low. It can also taint only a subset of interesting data to reduce the overhead. Ultimately, we envision that our proposed framework will be instrumental in various dynamic monitoring methodologies including runtime verification.

Consistent with the characteristics of mobile grid, super-node based overlay network model is proposed in this paper. Clustering problem of nodes is described formally. Based on vivaldi a distributed network coordinate system, the proposed algorithm takes the super-node as core and gathers physically adjacent nodes into a cluster, showing some good characteristics such as distributed, adaptive, lightweight, proximity-aware and load-balancing. We have also improved algorithm for better performance. Finally, pseudo-code and simulation results of proposed algorithm are presented in this paper.

Aimed at optical networks with wavelength conversion capacity, an algorithm for dynamic routing and wavelength assignment named WIC-RWA was proposed. Extended the layered-graph model to optical networks with limited-range wavelength conversion and limited-count wavelength converters; Designed key degree and influence weight value for links in order to allocate the network resources reasonably; At the same time reduced the blocking probability of the network and the number of wavelength converters by setting wavelength conversion cost weight value and computing the path of minimum weight value. The simulation results showed that the new algorithm can effectively reduce the blocking probability of the entire network and limit the used number of wavelength converters.

Intelligent Tutoring System (ITS) is the result of the application of artificial intelligence in education. This paper briefly introduced the concept of ITS; detailed its structure of modules and features; and focused on the application of AI technology; several trends of ITS development were analyzed in the end.

Distance education that transmits information on the global Internet has become the trend of educational development in the coming years; however, it still has some drawbacks and shortcomings. This article focuses on how to apply Multi-Agent technology in distance learning systems. The systems are supposed to teach students individualized according to their personality characteristics and cognitive abilities by establishing Student Agent and Teacher Agent, thus, to improve the intelligence and personalization of distance education system, in order to fully tap the potential of learners and improve teaching effectiveness and learning efficiency.

Operational transformation (OT) is a promising technique for supporting collaboration using mobile devices because it allows users to work on local data replicas even in a disconnected mode. However, as work goes mobile, a large number of operations may accumulate, defying the capacity of current OT algorithms that are mostly designed for real-time group editing. Since their assumption is that operations are propagated frequently, they generally only address how to integrate one remote operation at a time. As a consequence, most algorithms take O(|H|2) to integrate one operation and thus O(|H|3) to integrate a long sequence, where H is the operation history. This paper proposes a novel algorithm that provides optimized transformation of long sequences, improving the time complexity to O(|H|). Our experiments will show that it takes 59 minutes in a recent algorithm versus 1.5 seconds in this work to integrate two long sequences on a mobile device. The performance improvement is critical towards achieving desired responsiveness and group productivity in a class of mobile collaborative applications.

A new technique of camera calibration based on X-target is proposed. Calibration steps include: judging gray saturation, adjusting verticality between optical axis and object surface, two-dimensional plane camera calibration. In the calibration process, the improved Harris operator and spatial moment are used to detect sub-pixel X-target corners, and the accuracy achieves up to 0.1 pixels. It does not need to calculate the specific internal and external camera parameters, and only calculates the relations of world coordinates and image pixel coordinates, and the distortion model. The method has better accuracy and stability, and has been applied in the industrial field of embedded machine vision.

Selective undo allows users to undo any operation in the history and is considered a key feature in collaborative applications. Operational transformation (OT) is a powerful tool for implementing selective undo because it can be used to rearrange operations in a history in arbitrary orders. Despite the significant progress over the past two decades, however, there is still a space for improvements. Most existing works take time quadratic or even exponential in the size of the operation history H to undo an operation. Although this might be acceptable for real-time collaboration, it would be suboptimal in mobile and asynchronous collaborative applications in which a long history may accumulate. In addition, it is important to prove an algorithm with regard to the correctness criteria it assumes. This paper proposes a novel OT-based algorithm that provides integrated do and selective undo. The algorithm achieves time complexity of O(|H|) in both do and undo by keeping the history in a special operation effects relation order. Its correctness is formally proved with regard to formalized, provable conditions that are extended from a recent theoretical framework.

In the DiffServ architecture, a queue buffer management algorithm-PARIO-C algorithm based on traffic prediction was proposed. The algorithm used Recursive Least Squares (RLS) method to predict the change of average queue length in the network, and combined Adaptive Random Early Detection (ARED) algorithm to adjust the parameter adaptively, thus achieved better control of network congestion. Experimental comparison with Random Early Detection with In/Out and Couple queue (RIO-C) algorithm on latency, jitter, packet loss rate, the results show that PARIO-C algorithm lower the scope of network jitter, and reduce the network latency and packet loss rate, so improve network QoS to a certain extent.

The Web has become the host platform for interactive and collaborative applications that usually consist of multiple Web services. This transition is pushing the browsers to perform operating system like functionalities for better resource utilization. Unfortunately, there exists limited support for resource management on current Web platforms. This problem is further aggravated on mobile devices because of their limited resources. If current Web communication APIs were used as-is, mobile users would suffer from low responsiveness, battery drainage, and expensive charges.This paper describes the design and implementation of Mobile Ajax Broker (or MaJaB), a middleware layer that performs management and coordination of networking activities of multiple Web services residing in a Web page. MaJaB integrates a range of optimization techniques that were typically applied at the OS level including scheduling, multiplexing, piggybacking, and priority-switching. This work has been implemented as part of EasyMeet, a Web service that enables anytime, anywhere conference participation using any device. MaJaB is built using standard Web technologies to promote easy deployment across platforms.We have evaluated MaJaB over a real-world workload that we observed during our four-month pilot of EasyMeet. Our results show that MaJaB was successful in achieving its goal and, compared to a naive implementation, reduced the percentage of CPU busy periods by 99%, energy consumption by 21%, and network traffic by 25-81% for different participants. While all those improvements result in better user experience, the reduction in network traffic in particular can translate into over $800 saving in international roaming even for a two-user meeting that lasts only for an hour.