Earticle

A feature parameter modification algorithm is proposed to increase the accuracy of voice activity detection that based on support vector machine and energy acceleration parameters. The three energy acceleration parameters should have the equal importance for voice activity detection, in that all the three parameters can suitably characterize the classification features of speech and non-speech frames. For the three energy acceleration parameters, its minimum values vary a little; but its maximum values are greatly different. When radial basis function is chosen as the kernel function for voice activity detection, the coordinate values in Hilbert space is dominative determined by the energy values over a sub-region spectrum, the other two parameters only have a few contributions to it. The proposed algorithm extends the three energy acceleration parameters into the nearby or same order of magnitude. It make the three energy acceleration parameters at the same importance level in the calculation of coordinate values in Hilbert space, and can finally increase the accuracy of voice activity detection. The experimental results show that this algorithm can increase the accuracy of voice activity detection in the absence of noise and noise conditions.

Cloud Computing is one of the major trends in today’s information technology and the most likely research area. Cloud Computing is a model of sharing resources, software, information. The user access the cloud via internet and all the resources are open to all. Thus, cloud security is now a challenging problem for the cloud service providers. Cryptography is a good solution of the cloud security and several research works are published to ensure secure cloud using cryptographic tools. This paper proposes an authentic communication between a customer and cloud service provider using encryption, decryption and message digests.

Load balancing of virtual machines is one of the most significant issues in cloud computing research. A common approach is to employ intelligent algorithms such as Ant Colony Optimization (ACO). However, there are two main issues with traditional ACO. First, ACO is very dependent on the initial conditions, which might affect the final optimal solution and the convergence speed. To solve this problem, we propose to employ Genetic Algorithm (GA) for ACO initialization. Second, ACO could arrive at local optimal point, and the convergence speed is typically low. Along this line, we introduce the idea of Simulated Annealing (SA) to avoid local optimal and accelerate the convergence. Lastly, our experiments show that our improved ACO achieves good performance in load balancing.

The limitation of terminal equipment and the scarcity of mobile learning resource are key constrains of mobile learning development. However, cloud computing supplies a new way of solving the problem. For cloud computing, the calculation and the storage of task are conducted in the cloud server. As a result, computing and storage work of mobile phone are separated from the machine. So user can customize through cloud computing services according to personal need. Thus a lot of works that are realized only in high-performance computers before, but now can be finished through the mobile phones with poor configuration. This paper through theoretical study and based on the theory of mobile learning and cloud computing, it points out the disadvantages of mobile learning and puts forward the solution of cloud computing technology.

This paper analyzes the technology of cloud computing and the challenges that occur when connection is established through the Internet and public devices. Devices used for connection to the secured network very often don’t meet the security levels of the network that they connect to. To ensure that the connection is secured even from a public device and through the Internet major role is played by older technology, VPN. Therefore, we will present the security measures taken to secure the remote connection and the challenges faced to meet such a level, benefits, reliability, performance and latency of an enterprise network. These issues must be solved appropriately to ensure the confidentiality and integrity of data and information, as well as overall corporate network security. Obtained analysis in this work also presents the evolution of the cloud related to VPN, addressing the general security risks associated when using devices via VPN to access to an enterprise internal network. Furthermore, we analyze SSL VPN security risks in this context.

Recent advances in micro electro mechanical systems (MEMS), wireless communication, and network technology, wireless sensor networks (WSNs) have become technically and economically feasible. In most case, WSNs are usually applied to many applications that require unattended operation. However unlike traditional networks, WSNs are resource constrained distributed networks. The delay latency, uncertainty and dynamic reliability of the WSNs may strongly impact on the WSNs applications. The information content or meaning of sending and receiving data will happened frequency when exchanging data among these sensor nodes over the WSNs. In addition, the application protocol themselves for data exchange may also impact on the WSNs applications. An inappropriate protocol will result in serious inefficiency so as not to meet application requirement. In order to ensure the reliability, integrity, and the continuity of data operations on the WSNs, this paper presents a delay-aware application protocol for WSNs applications. This protocol is based on ZigBee protocol that is suited for remotely monitor and control because of its low-power. Simulation results show that the protocol is feasible for the wireless sensor network in terms of the reliability, integrity and continuity of data.

Recent advancements in MEMSs technology, VLSI and wireless private area networks have contributed in developing enormous interest in wireless sensor networks by both academia and research community. WSN is formed by large number of small, cheap and low energy sensors that work together in monitoring region. These sensors sense, collect and forward data within the network through wireless medium. WSN find its application in many areas such as object tracking, disaster relief and smart environments etc. Most of these applications need location awareness of sensor nodes to estimate the place of origin of events. The task of determining physical coordinates of sensor nodes within the network is known as localization. Localization has always remained a challenge in successful implementation of WSN and so, it is a hotspot of research. Localization techniques are broadly categorized as Range based techniques and Range-free techniques based on their dependency on range measurements. Because of constraints on cost, energy, computation complexity and infrastructure requirements, range free localization techniques are given importance in recent research works. Considering the latest trends in localization, a thorough analysis for major range free techniques is done in this paper and accordingly their pros and cons are highlighted. As the existing range free techniques are not fully efficient so, open issues are also raised to give directions to future research.

Mobile Internet make human whenever and wherever access to the Internet possibly, achieving online booking, according to the mobile phone location data, we can acquire the relevant data tourist attractions , through the real data of tourist arrivals, traffic load data, according to the computing resources of mobile Internet, and the data on the travel data mining algorithms to predict , in order to achieve tourism management ; this article aim at mobile Internet cloud computing resource allocation and data mining algorithms to predict data, the simulation results can be obtained ,the accuracy of the data is more stronger.

Service process management is a cross-discipline research topic, including computer science, management technology and economics. How to effectively integrate service resources to meet customer demands becomes a key problem to service-oriented software development. Most of existing works are in the early research stage. The scope of process management consists of process design, process execution, process mining, and process intelligence. However, it requires process modeling, which is a crucial precondition in the lifecycle of service process management. In this paper, it reviews workflow verifications of Petri Nets based service process management. First, the workflow and description supporting tools are introduced. Then, considering the composite service is an effective way to organize third-party business functions seamlessly, the Petri Nets based service workflow employed to formalizing composition behaviors is discussed. Third, components encapsulated in the composite service may be encountered functional and non-functional problems since services are offered by different organizations. The functional verification includes the correctness, reachability and safety. The non-functional verification is to evaluate the performance of Petri Nets based workflow model ranging over timed constraints and probabilistic behaviors. Therefore, it summarizes Petri Nets based workflow verification techniques to guarantee the quality of services composition. In conclusion, this survey provides academic references to researchers and developers in service computing and process management.

Regarding the increasingly expanded utility of Cloud storage, the improvement of resources management in the shortest time to respond upon the users’ requests and the geographical constraints is of prime importance to both the Cloud service providers and the users. Since the Cloud storage systems are exposed to failure, fault-tolerance is appraised by Cloud storage systems’ capability for responding to unexpected fault through software or hardware. This paper represents an algorithm based on Learning Automata–oriented approach to fault tolerance data in Cloud storage regarding traffic and query loads dispatched on data centers and learning automata that provides the best possible status for scaling up or down of data nodes. Based on appraisal of traffic on nodes, the node with the highest traffic is chosen for coping among physical nodes. The experimental results indicate that the proposed Learning Automata Fault-Tolerant and High-efficient Replication algorithm (LARFH) has utilization high replication, high query efficiency, low cost and high availibility in comparison with other similar approaches.

Optimal allocation of distributed generations (DGs) integrated into micro grids can significantly improve the stability and benefit the economy of micro grid operation. However, optimal micro grid planning is a kind of multi-dimensional and non-linear optimization problem. In this study, a multi-objective model is established by adopting the objective function which minimizes network loss, electricity price and operation cost; an improved particle swarm optimization (IPSO) algorithm with better optimizing performance is proposed by improving the initializing method and parameter control as well as average minimum and mutation factor are introduced. The proposed IPSO algorithm is then applied to a 29-node micro grid network structure. The comparison between different optimization schemes demonstrates the significance of optimal placement of DGs in micro grids. And it is also clear that the IPSO algorithm proposed in this study can effectively solve such problems.

The double Y shift 3 0 PMSM two-motor series-connected system is a novel multi-machines system, which PMSM1 operates on d q plane and PMSM2 operates on 1 2 z z plane. To overcome the poor robustness and static and dynamic performances of PI controller, the backstepping control approach suitable for the two-motor series-connected system is researched. And the backstepping controller of PMSM1 is deduced on d q plane. The simulation contrasting with PI controller show that backstepping can realize the decouple control of the two series-connected motors and improve the dynamic and static performances.

Scheduling jobs on computational grids is identified as NP-complete problem due to the heterogeneity of resources; the resources belong to different administrative domains and apply different management policies. This paper conducted an extensive and wide literature review to study the state of the art of grid scheduling algorithms. This review starts with an overview of the grid technologies and a description of the grid resource management systems. The evolution of the grid scheduling mechanisms is illustrated in this paper started from basic scheduling mechanisms such as Min-Min and Max-Min approaches ending with the swarm intelligence optimization methods. The swarm intelligence and evolutionary mechanisms are also presented and critically analyzed.

Cloud computing is the delivery of computing services over the internet. Cloud services allow individuals and other businesses organization to use data that are managed by third parties or another person at remote locations. In cloud computing, Process and virtual machine migration are two migration techniques. Process migration is a technique in which an active process is moved from one machine to another of possibly different architecture and Virtual Machine (VM) is an emulation of a particular computer system. Virtual machine migration is a useful tool for migrating OS instances across multiple physical machines. Both techniques are used to load balancing, fault management, low-level system maintenance, better communication and reduce energy consumption. This paper presents the various virtual machine and process migration techniques.

Impellers of helicon-axial multiphase pump are optimized based on CFD and genetic algorithm. The method mainly includes: CFD numerical calculation, to establish nonlinear relation through neural network, and genetic algorithm optimization extreme. Firstly, the profile of blades is parametric by spline surface and Choose 12 control points as optimization variables. Then, every optimization variable is given optimal dimension. Finally, sample database is got by using standard L27_3_13 orthogonal design table. Next, output values are got by modeling every sample, meshing generation and using CFD numerical calculation. Train neural networks through the database; thus the nonlinear relation between the blade parameter and pump performance parameters is built by applying the nonlinear fitting ability of BP neural networks. Regard the trained neural network as a fitness function of the genetic algorithm and use the characteristic of nonlinear global optimization of genetic algorithm to optimize the multiphase pump. Optimization result shows that the hydraulic efficiency of the multiphase pump is increased by 1.91%.

In order to speed up data processing in a signal monitoring and evaluation system, we need to use a parallel method. It is obvious that the traditional stand-alone store has no ability to satisfy the performance requirements, and the use of single core CPU is unable to content the severe requirement of speed. Consequently, multi-machine parallel acceleration technique based on MPI (cooperated with multi-core parallel acceleration technique based on OpenMP) can effectively solve all above problems. In this paper, a parallel distributed acceleration framework based on MPI and Open MP technology was given. Experimental tests were carried to verify our proposal. Finally, some suggestions to speed up the data processing was given.

SRM drives might be used in distant viable applications owing to their simplicity and low cost. In this paper, the SRM Drive is described as that which is used for fuzzy modeling, estimation, prediction and control. A fuzzy-logic-based model is constructed from both static and real-time motor data, and from this model the rotor position is estimated and controlled. The system also incorporates fuzzy logic-based methods to provide a high robustness against noise. In addition, the method uses heuristic knowledge to choose the most desirable phase for angle estimation in order to minimize the effect of feedback error. It is also shown that, by using fuzzy logic, the drive control scheme offers a high robustness and reliability and be suited to a wide range of applications. Simulation results are presented to verify the proposed scheme by using in simulink /mat lab.

Message middleware is an important branch of middleware, it provides the development of distributed application system with an asynchronous, pine for programming architecture, the consistent thought and the development trend of modern software technology, which is widely used in industry in the enterprise application integration and distributed computing. Message bus message middleware is a kind of simple implementation, but with its unique advantages for a wide variety of distributed applications development injected with strong power, greatly promote the development of application system integration. This paper is mainly focusing on the key issues involved in the middleware, message bus, and concurrency model for further research.

Pipeline is one of the classic technologies to enhance efficiency. When the amount of data is becoming more and more big when processing, it becomes a big problem that how to improve the efficiency with existing resources. In the meantime, a large number of older hardware and software feel difficult to deal with new task in new environment with the accumulation of time. In order to ameliorate efficiency of web GIS with low cost, this paper propose a service pipelining model for Web GIS based on distributed environment. The system build a basic service library to reuse plenty of existing resource, and design a segment system to organize pipeline in level of service to improve efficiency. The model can be used in many platforms and occasions, not limited to the application we proposed in this paper. The model we proposed has been proved work well in our experiments and test.

This paper analysis the electric power communication network structure and performance requirements. To investigate the power communication backbone network and network power substation general way, and in electric power communication network delay, error rate, network bandwidth demand were analyzed. The for prevalent in electric power communication network is not a fully meshed network, explore artificially cobweb logic change principle and system method, artificial transform sheet mesh to the standard web, and making use of the cobweb, based on the characteristics of network fast routing. In order to better in the smart grid communication network using artificially cobweb routing algorithm, for the smart grid in the presence of irregular mesh network, this paper presents a kind of the mesh network into logical artificially cobweb network general standardization method. The principle of combination of label switching, this paper presents artificially cobweb source routing algorithm, expounds the artificially cobweb routing algorithm of label switching and label update mechanism, and artificially cobweb routing algorithm balancing routing method and routing method is described in detail.

Wireless Sensor Network is built of more than a few nodes where each node is linked to one sensor. The main challenging task in this network is lifetime and energy consumption. The LEACH protocol is energy efficient protocol to reduce their energy consumption different modes is applied on the sensor nodes. These modes are sleep, Active and ready mode. These modes are applied on LEACH protocol and this enhancement is called RFID protocol. The main problem exists in RFID protocol is of clock synchronization due which packet loss happened in the network which reduce network performance. To overcome this problem, proposed contention Avoidance Algorithm (RTS /CTS). In this technique cluster, head nodes send RTS packets containing a NONCE feed to all its cluster members. The member nodes would adjust their clocks according to the feed and revert back with CTS packets are synchronization their clocks. The proposed technique has been implemented in Network Simulator. The graphical results show that proposed technique performs batter than LEACH, RFID protocol in terms of throughput, energy and packet loss, Delay and control overhead in the network.

Lifetime optimization and security are two important design issues for WSNs with non-replenishable energy resources. Routing protocols in wireless sensor networks (WSNs) are susceptible to a number of attacks depending on the nature of the protocol, its application, and the environment in which the protocol is intended to be used. Routing protocols that do not take the malicious attacks into account can not be easily temper proofed. The longevity of WSNs is a crucial concern that significantly influences their applicability in a specific context. In this paper, we present a Load Balancing Multi-path Secure Routing (LBMSR) protocol to address these two issues concurrently through balanced energy consumption and one-way hash key chain and symmetric key cryptography. LBMSR is designed with two configurable parameters, load balance control and security level. Load balance is used to avoid the problem of energy consuming imbalance and the formation of energy holes. Security level is designed to determine the probabilistic distribution of the random walking that provides routing security. Simulation results and security analyses show that the proposed algorithm can provide excellent balance between routing efficiency and energy consumption while preventing routing attacks.

This paper proposed a circular particle swarm optimization least squares (CPSOLS) method which is consisted of the regularized least squares (RLS) method and the adaptive particle swarm optimization (APSO) algorithm. The RLS algorithm optimized the parameters of the RBF network, aiming at the phenomenon of RLS trapping in the local minimum, introduced the penalty factor and used the global optimization ability of the particle swarm optimization algorithm to make it out of the local minimum; simplified the structure of the RBF network and improved the generalization ability of the network. The APSO algorithm weakened the precocious converge phenomena of the particle swarm optimization algorithm, adopted the adaptive selection of the nonlinear dynamic inertia weight which is guided by the control factor of the battery external characteristic temperature parameters, optimized the link weight of the RBF network, improved the state of charge (SOC) estimation accuracy and real-time performance of the RBF network. Using the Arbin multifunctional battery test system BT2000 to collect the sample data of the battery external characteristic parameters, and using the sample data to train and optimize the RBF neural network, and estimate the SOC of the batteries. The results showed that the optimized RBF network improved the SOC estimation accuracy and real-time performance.

Based on the study of traditional min-min scheduling algorithm, the paper proposed a min-min task scheduling algorithm based on QOS constraints in cloud computing. According to the vector which is generated by QOS parameters, the algorithm processes the matching of resources and tasks, and then provides users with resources which meet their requirements. Experimental results show that the min-min task scheduling algorithm for cloud computing has better performance in such aspects as task execution time, rate of discarding task and QOS satisfaction compared with traditional min-min scheduling algorithm.

In this paper, a data transmission model is proposed to simulate the way of transmitting resource status information in IaaS cloud computing platforms. An adaptive periodic push strategy is also proposed to improve the performance of the data transmission model. The time interval in the adaptive periodic push strategy is dynamically updated by considering the change degree of resource status information. A series of experiments are conducted on a cloud computing testbed and experimental results show that the adaptive periodic push strategy performs better than previous data transmission strategies in terms of the number of data transmissions and data coherence.