Cell Based QoSE Centric Topology Control Algorithm for Wireless Sensor Networks

The central issue of Topology control （TC） in wireless sensor networks is how to select the appropriate active nodes. This paper proposes a Quality of SEnsing （QoSE） centric TC algorithm CQCTC： it combines a node＇s sensed frequency and signal strength of physical events to define QoSE and uses the predicted QoSE as the metric for WN selection; therefore CQCTC could form proper topology structures dynamically according to the distribution of the targets/events. Simulation experiments indicate that, compared with the existing TC algorithms, CQCTC decreases network energy cost and extends net- work lifetime.     

A Scalable Peer-to-Peer Web Services Overlay for Semantic Discovery

The growing number of web services advocates distributed discovery infrastructures which are semantics-enabled. We introduce a novel approach for semantic discovery of web services in structured P2P-based （Peer-to-Peer） registries. We partition concepts into different CGs （Concept groups） and index these ordered concept sets in a two-phase semantic routing mechanism. Each node and query has one concept set as its unique ID. For each concept in the query, current node first route the query to a node with nodeID that shares a prefix as long as with the query, but a longer CG. Second, the query will be routed to a node sharing a longer prefix than that of the current node. Combining with semantic similarity calculation, our proposal guarantees that most existing services matching a query will be found with bounded costs in terms of number of nodes involved. The scalability and reliability of our approach are also confirmed through emulation tests.     

Arithmetic Expression Evaluations with Membranes

Arithmetic operations and expression eval- uations are fundamental in computing models. This paper firstly designs arithmetic membranes without priority rules for basic arithmetic operations, and then proposes an algo- rithm to construct expression P systems based on several of such membranes after designing synchronous and asyn- chronous transmission strategies among the membranes. For any arithmetic expression, an expression P system can be built to evaluate it effectively. Finally, we discuss differ- ent parallelism strategies through which different expres- sion P systems can be built for an arithmetic expression.     

Fraction Arithmetic Operations Performed by P Systems

Arithmetical operations are fundamental in computing models. But arithmetic operations in mem- brane computing are restricted in integer field. In this paper, we present fraction arithmetic P systems for per- forming addition, subtraction, multiplication＇s division on fractions through designing the rules with priority. Some examples are given to illustrate how to compute the arith- metical fractions in these systems and show that the de- signed rules can carry out correct arithmetic computations of fractions.     

A Population-Membrane-System-Inspired Evolutionary Algorithm for Distribution Network Reconfiguration

This paper proposes a Population- membrane-system-inspired evolutionary algorithm （PM- SIEA）, which is designed by using a population P system and a Quantum-inspired evolutionary algorithm （QIEA）. PMSIEA uses the population P system with three cells to organize three variants of QIEAs, where communications between cells are performed at the level of genes, instead of the level of individuals reported in the existing membrane algorithms. This work provides a useful framework for synthesizing different algorithms at macro level and exchanging genic information at micro scale. Experimental results conduced on knapsack problems show that PMSIEA is superior to four representative QIEAs and our previous work with respect to the quality of solutions and the elapsed time. We also use PMSIEA to solve the optimal distribution system reconfiguration problem in power systems for minimizing the power loss.     

Metabolic P Systems： A Discrete Model for Biological Dynamics

A recent methodology to model biochem- ical systems is here presented. It is based on a concep- tual framework rooted in membrane computing and de- veloped with concepts typical of discrete dynamical sys- tems. According to our approach, from data observed at suitable macroscopic temporal scales, one can deduce, by means of algebraic and algorithmic procedures, a dis- crete model （called Metabolic P system） which accounts for the experimental data, and opens the possibility to under- stand the systemic logic of the investigated phenomenon. The procedures of such a method have been implemented within a computational platform, a Java software called MetaPlab, processing data and simulating behaviors of metabolic models. In the paper, we briefly describe the theory underlying the modeling of biochemical systems by Metabolic P systems, along with its development stages and the related extensive literature.     

Evaluating Logical Expressions by Membrane Systems

Boolean and relational operations, which are defined for solving mathematically logical problems, are always required in computing models. Membrane com- puting is a kind of distributed parallel computing model. In this paper, we design different membranes for imple- menting primary Boolean and relational operations respec- tively. And based on these membranes, a membrane sys- tem can be constructed by a present algorithm for eval- uating a logical expression. Some examples are given to illustrate how to perform the Boolean, relational opera- tions and evaluate the logical expression correctly in these membrane systems.     

Automatic Design of P Systems for Five Basic Arithmetic Operations within One Framework

To solve the programmability of a mem- brane system, this paper presents an automatic design method of a cell-like P system framework for performing five basic arithmetic operations. This method proved that different P systems can be designed with same framework by programming. A technique is introduced to remove re- dundant rules in the process of the design of a P system. The effectiveness and feasibility of this method is verified by experimental results.     

Energy-Efficient Mapping for 3D NoC Using Logistic Function Based Adaptive Genetic Algorithms

The problem of mapping application tasks is one of key issues in 3D Network on chip （3D NoC） de- sign. A novel Logistic function based adaptive genetic al- gorithm （LFAGA） is proposed for energy-aware mapping of homogeneous 3D NoC. We formulate the mapping prob- lem and show the Standard genetic algorithm （SGA）. The LFAGA is presented in detail with the goal of obtaining higher convergence speed while preventing the premature convergence. Experimental results indicate that the pro- posed LFAGA is more efficient than previously proposed Chaos-genetic mapping algorithm （CGMAP）. In the ex- periments, a randomly generated task graph of size 27 is mapped to a 3D NoC of size 3×3×3, the convergence speed of LFAGA is 2.55 times faster than CGMAP in the best condition. When the task size increases to 64 and the 3D NoC size extends to 4×4×4, LFAGA is 2.31 times faster compared to CGMAP. For the NoC sizes in the range from 3×3×2 to 4×4×4, solutions obtained by the LFAGA are consistently better than the CGMAP. For example, in the experiment of size 4×4×4, the improvement of final result reaches 30.0% in term of energy consumption. For a real application of size 3×4×2, 18.6% of energy saving can be achieved and the convergence speed is 1.58 times faster than that of the CGMAP.     

An Area Weighted Surface Sampling Method for 3D Model Retrieval

3D model feature extraction is a key step for geometric content based 3D model retrieval. A 3D model is usually expressed by patches and it has large amount of data. Pseudo-random sequential Monte Carlo surface sampling method is used to sample 3D model data. It has strong randomness. The sampling results are not uniform, which worsen the efficiency and precision of feature extraction. An area weighted surface sampling method for 3D Models is proposed. The larger area patches get the higher probability of sampling. The sampling points are evenly distributed in sampling space, which represent more accurate 3D model features. Experiments show that the proposed method needs less computing resources. Even when there are less sampling points, it can still keep high calculation accuracy and stability.     

A Dynamic Quantitative Analysis Model for Network Survivability

A Dynamic quantitative analysis model for network survivability （DQAMNS） is proposed to measure survivability of large-scale distributed information systems based on ANP. Starting with the quantification and normalization of survivability factors （Resistance, recognition, recovery and adaptation, R3A）, DQAMNS makes use of probability model and dimensionless representation to ac- cess the degree of each factor. Then, DQAMNS applies ANP to measure the weights of survivability factors based on experts＇ experience and gets a number between 0 and 1 which synthetically indicates network survivability. Analysis results show that DQAMNS can actually evaluate the four survivability properties, and precisely access survivability in different environments, thus they could be theoretical instructions to improve survivability in designing and implementing LSDIS.     

3D Reconstruction of Periodic Human Walking Trajectories Based on Single View

Periodicity of a moving body is one of important characteristics in activity monitoring. We present a method to estimate the trajectory of human gait in 3D space from a single camera by exploring the periodicity of the human movement. Geometric constraints are established to characterize the periodic motion, which are invariant under viewing geometry variations. According to our analysis, these geometric constraints reduce the overall computational complexity of evaluating periodicity. Meanwhile, with the help of these geometric constraints, we develop a novel method to reconstruct the 3D motion trajectory from a single camera. Experimental results demonstrate the accuracy and robustness of the algorithm.     

Key Factor-Based Initialization for 3D Hand Tracking

The discovery of three-dimensional （3D） hand models corresponding to the user＇s 3D hand pose in initial frames is significant in 3D human hand tracking and interacting. This study proposes an approach to initial- ize 3D hand gestures for the user to have an easier, more pleasurable, and satisfactory experience. This paper cov- ers the following points. First, a new approach to selecting a human hand gesture from the hand postures database is presented. Second, both techniques of visualization and human-computer interaction are used in the initialization process, through which the 3D human hand model is fine- tuned time after time until the required accuracy is satis- fied. Lastly, the proposed initialization method is applied to our online virtual assembling system. We introduce a key factor to improve time cost of initialization. Our ex- perimental results show that the proposed approach is not only fast, accurate, and robust hut also direct, natural, and convenient for operators to handle.     

Viewpoint Optimization Using Genetic Algorithm for Flying Robot Inspection of Electricity Transmission Tower Equipment

To fully exploit the limited flight-time of the flying robot, and ensure the successful visibility of target, viewpoint optimization is proposed in this paper for the inspection of electricity transmission tower equipment with an optimization function to determine the best view- points in a local viewpoint region. The local viewpoint regions are generated from the local objective regions which are determined by the geometrical structure of a priori 3D model for the electricity transmission tower equipment. The optimization function is structured based on three factors including visibility, viewing quality and observation distance. In addition, the fitness function of genetic algo-rithm is used to find the optimal viewpoint. The experimental results demonstrate the effectiveness and efficiency of the proposed viewpoint selection algorithm.     

Technique of Embedding Depth Maps into 2D Images

This paper proposes a new technique that is used to embed depth maps into corresponding 2-dimensional(2D) images. Since a 2D image and its depth map are integrated into one type of image format, they can be treated as if they were one 2D image. Thereby, it can reduce the amount of data in 3D images by half and simplify the processes for sending them through networks because the synchronization between images for the left and right eyes becomes unnecessary. We embed depth maps in the quantized discrete cosine transform(DCT) data of 2D images. The key to this technique is whether the depth maps could be embedded into 2D images without perceivably deteriorating their quality. We try to reduce their deterioration by compressing the depth map data by using the differences from the next pixel to the left. We assume that there is only one non-zero pixel at most on one horizontal line in the DCT block because the depth map values change abruptly. We conduct an experiment to evaluate the quality of the 2D images embedded with depth maps and find that satisfactory quality could be achieved.     

An Energy Balancing Adaptive Routing Strategy Based on Interference Model in 6LoWPANs

IPv6-enabled low-power wireless personal area network （6LoWPAN） is an important part of the Internet of Things which will drastically transform the way our society functions. Designing an efficient routing strategy is crucial for optimizing traffic over 6LoWPAN resources and extending the network lifetime. This paper proposes an energy balancing adaptive routing strategy named EBAR. Considering the dynamic traffic and constrained energy in 6LoWPAN, EBAR adaptively updates paths between different source-destination pairs and balances the energy in 6LoWPAN. Simulation results indicate that EBAR can promote network performance and balance the energy in the network.     

3D Freehand Tracking Based on Relevancy Among Local Motion Models

A novel 3D freehand tracking algorithm based on relevancy among local motion models is put forward. Firstly, a specification of the Cognitive and behavioral model （CBM） called PAMT is proposed. Secondly we regard PAMT as a data structure upon which freehand tracking algorithm is designed, and we describe the PAMT in detail. Lastly, the experimental results are provided. The proposed algorithm is tested in a virtual assembly platform and two other application systems. The highlights of this paper are as follows： （1） A new cognitive and behavioral model, called PAMT, is presented; （2） The PAMT is explained with cognitive model; （3） Focus on describing ＇Attractor in PAMT with the relevancy among local motion models; （4） Shows us how the PAMT is shaped and used to design the 3D freehand tracker. One of the advantages of PAMT and RLMM model is that it is easier to explore some of the complex correlations among the variables of the 3D hand model. Our experimental results show that, compared with the particle filter and the annealed particle filter, our algorithm effectively reduces dimensionality and can track 3D hand in real-time.     

In基复合半导体氧化物Cl2敏感特性的研究

Aiming at detecting Cl2 gas, this study was made on how to make In-based compound semiconductor oxide gas sensor. The micro-property and sensitivity of In-based gas sensing material were analyzed and its gas sensitive mechanism was also discussed. Adopting constant temperature chemical coprecipitation, the compound oxides such as In-Nb, In-Cd and In-Mg were synthesized, respectively. The products were sintered at 600 ℃ and characterized by the Scanning Electron Microscope （SEM）, showing the grain size almost about 50-60 nm. The test results show that the sensitivities of In-Nb, In-Cd and In-Mg materials under the concentration of 50 × 10^-6 in Cl2 gas are above 100 times, 4 times and 10 times, respectively. The response time of In-Nb, In-Cd and In-Mg materials is about 30, 60 and 30 s, and the recovery time less than 2, 10 and 2 min, respectively. Among them, the In-Nb material was found to have a relatively high conductivity and ideal sensitivity to Cl2 gas, which showed rather good selectivity and stability, and could detect the minimum concentration of 0.5 × 10^-6 with the sensitivity of 2.2, and the upper limit concentration of 500 × 10^-6. The power loss of the device is around 220 mW under the heating voltage of 3 V.     

Analysis of Two Public Key Cryptosystems Based on Randomized Knapsack Sequences

Two knapsack public key cryptosystems based on randomized knapsack sequences were proposed in 2009 and 2011 respectively, where the secret knapsack se- quence is transformed by secret modular linear transforms into one or three public randomized knapsack sequences with appropriate density. In this paper, we recover the se- cret modular linear transforms by simultaneous Diophan- tine approximation and propose secret key recovery attacks on these two cryptosystems. Practical attack experiments are done within one minute.     

Joint Optimization of Subchannel Reassignment and Power Adaptation for Multicarrier Cooperative Systems

An amplify-and-forward multicarrier cooperative system was proposed in this paper. The optimal resource-allocation problem was investigated in terms of improving data rate. An effective resource-allocation algorithm was proposed based on three techniques, adaptive modulation, subchannel reassignment design and transmit power adaptation. The designed subchannel reassignment not only increases the average data rate, but also raises the threshold of ＂water-filling＂ in optimizing power allocation that can further increase data rate. Since the power allocation is based on the designed subchannel reassignment, the complexity of the proposed algorithm is reduced greatly. Simulation results show that the proposed algorithm effectively increases the system＇s average data rate.