Using Data Mining to Find Patterns in Ant Colony Algorithm Solutions to the Travelling Salesman Problem

Travelling Salesman Problem （TSP） is a classical optimization problem and it is one of a class of NP- Problem. The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an Ant Colony Algorithm （ACA） performing a searching operation and to develop a rule set searcher which approximates the ACA＇s searcher. An attribute - oriented induction methodology was used to explore the relationship between an operations＇ sequence and its attributes and a set of rules has been developed. At the end of this paper,the experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.     

A K-Median Facility Location Agent for Low-Cost Service Selection in Digital Community Network

With the increasing number of web services, it becomes a difficult task for an ordinary user to select an appropriate service. Hence, it is conventional that users in a digital community network take part in a collaborative mechanism for the purpose of service selection. The participation usually brings unnecessary burdens for users, such as giving opinions, storing service information. Extra communication overhead hinders the performance of the network. Thus, the community administrators are facing a problem of how to obtain an overall service selection result for the whole community readily and effectively. To address this problem, we present a k-median facility location agent model. The model analyzes the procedure of service selection through five entities and six types of messages. Two algorithms are elaborated in pursuit of a global optimization concerning connection costs between users and facilities where services are deployed. To evaluate our model, we conduct extensive simulations and present a detailed analysis of the simulation results.     

Intelligent test case generation based on branch and bound

Path-oriented test case generation is in essence a constraint satisfaction problem （CSP） solved by search strategies, among which backtracking algorithms are widely used. In this article, the backtracking algorithm branch and bound （BB） is introduced to generate path-oriented test cases automatically. A model based on state space search is proposed to construct the search tree dynamically. The BB is optimized from two perspectives. Variable permutation with a heuristic rule to break ties is adopted for the branching operation, and interval computation with analysis on the monotony of branching conditions is utilized for the bounding operation. Empirical experiments show that the proposed method performs well with linear complexity, and reaches 100% coverage on some benchmark programs with an advantage over some static and dynamic algorithms.     

Multi-user selection based on particle swarm optimization and power control scheme in the uplink CoMP-MU-MIMO system

Orthogonal frequency division multiplexing （OFDM） which has been adopted in the long-term evolution （LTE） system can improve the system capacity obviously. However, it also brings about severe inter-cell interference （ICI） for cell-edge users （CEUs）. To tackle this problem, multi-user selection and power control （MuS-PC） is proposed as an efficient scheme in uplink coordinated multi-point multi-user multi-input multi-output （CoMP-MU-MIMO） transmission/reception. This paper jointly considers user＇s signal to interference plus noise ratio （S1NR） and proportional fairness （PF） to maximize the total channel capacity in multi-user selection by formulating a penalty function. To simplify the penalty function＇s computation, particle swarm optimization （PSO） algorithm is introduced. In addition, power control is adopted to maximize overall energy efficiency. Simulation results demonstrate that the MuS-PC scheme can not only obtain the optimal total channel capacity while guarantee each user＇s quality of service （QoS） and PF, but also largely reduce computational complexity and improve energy efficiency. As a result, the poor communication quality of CEUs can be enhanced.     

Data management solutions based on the data distribution service communication model

This article presents a data management solution based on the data distribution service （DDS） communication model. The basic DDS communication model consists of a unidirectional data exchange where applications that publish data ＇push＇ the relevant data, which is updated to the local caches of co-located subscribers to the data [1]. DDS has no specified center node to forward data packets and maintain the communication data. This type of publish-subscribe （P/S） model presents integrity and consistency challenges in data management. Unlike peer-to-peer （P2P） distributed storage, DDS applications have a hard real-time environment and fewer data features, and the core problem is ensuring the integrity and consistency of data in distributed systems under this hard real-time environment. This article begins with a brief introduction of the communication model used by DDS, then analyzes persistent data management problems caused by such model, and provides an appropriate solution to these problems. This solution has been implemented in a prototype system of the real-time service bus （RTSB） of Tsinghua University.     

Joint power allocation and multi-relay selection scheme based on system outage probability

An amplify-and-forward （AF） based multi-relay network is studied. In order to minimize the system outage probability with a required transmission rate, a joint power allocation （PA） and multi-relay selection scheme is proposed under both total and individual power constraints （TIPC）. In the proposed scheme, the idea of ordering is adopted to avoid exhaustive search without losing much system performance. Besides the channel quantity, the ordering algorithm proposed in this article also takes relays＇ maximal output ability into consideration, which is usually ignored in traditional relay ordering algorithms. In addition, simple power reallocation method is provided to avoid repetitive PA operation during the process of searching all possible relay subsets. By Adopting the idea of ordering and using the proposed power reallocation method lead to remarkable decrease of the computation complexity, making the scheme easier and more feasible to implement in practical communication scenarios. Simulations show that the proposed multi-relay selection scheme provides similar performance compared to the optimal scheme with optimal PA and exhaustive search （OPAES） but with much lower complexity.     

A Secure Adaptive Routing Strategy Using Immune Mechanism in MANET

Many protocols have been proposed to in- crease efficiency and security of traditional protocols in Mobile ad-hoc networks （MANET）, but they are all facing the problem of computation overhead which leads to im- practicability. Adaptive routing strategy （ARS） is a novel routing strategy that switches routing protocol according to the network condition. Though this method alleviates the problem of efficiency, it doesn＇t cover the secure issues. We integrate our security mechanism based on Artificial immune system （AIS）, with adaptive routing strategy to enhance efficiency and security. First we presented a more appropriate definition to the measurement of network con- dition, then we designed a secure adaptive routing strategy based on the definition. At last, we gave a performance analysis to validate the correctness and reliability of our scheme.     

Seed region selection and homogeneity criterion for doorplate image segmentation in mobile robot navigation

Focused on the seed region selection and homogeneity criterion in Seeded Region Growing （SRG）, an unsupervised seed region selection and a polynomial fitting homogeneity criterion for SRG are proposed in this paper. First of all, making use of Peer Group Filtering （PGF） techniques, an unsupervised seed region selection algorithm is presented to construct a seed region. Then based on the constructed seed region a polynomial fitting homogeneity criterion is applied to solve the concrete problem of doorplate segmentation appearing in the robot navigation along a corridor. At last, experiments are performed and the results demonstrate the effectiveness of the proposed algorithm.     

Collaborative tracking via particle filter in wireless sensor networks

Target tracking is one of the main applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of the sensor nodes. A framework and analysis for collaborative tracking via particle filter are presented in this paper Collaborative tracking is implemented through sensor selection, and results of tracking are propagated among sensor nodes. In order to save communication resources, a new Gaussian sum particle filter, called Gaussian sum quasi particle filter, to perform the target tracking is presented, in which only mean and covariance of mixands need to be communicated. Based on the Gaussian sum quasi particle filter, a sensor selection criterion is proposed, which is computationally much simpler than other sensor selection criterions. Simulation results show that the proposed method works well for target tracking.     

Research on motion compensation for high resolution profile of extended target for stepped chirp radar and DSP implementation

A discrete model is set up for High Resolution Range Profile （HRRP） of an extended target and the model of echo from an extended target for a Stepped Chirp Radar （SCR） is proposed. The effect of target motion on a range profile is thoroughly analyzed, and based on which precision requirement is developed for motion compensation. By studying the time domain correlation and the rule based on the least burst error, a motion compensation algorithm which satisfies the project requirement is presented, and the cyber-emulation confirms the conclusion. At last the processor is designed by using DSP devices to realize motion compensation and target recognition.     

A Practical Parallel Algorithm for All-Pair Shortest Path Based on Pipelining

On the basis of Floyd algorithm with the extended path matrix, a parallel algorithm which resolves all-pair shortest path （APSP） problem on cluster environment is analyzed and designed. Meanwhile, the parallel APSP pipelining algorithm makes full use of overlapping technique between computation and communication. Compared with broadcast operation, the parallel algorithm reduces communication cost. This algorithm has been implemented on MPI on PC-cluster. The theoretical analysis and experimental results show that the parallel algorithm is an efficient and scalable algorithm.     

Combination Rule Based on Proportional Redistribution of Generalized Conflict for Evidence Theory

Dempster＇s rule is a powerful tool for combining multiple distinct pieces of evidence in evidence theory. When conflict between pieces of evidence is high, the combination result of Dempster＇s rule is counterintuitive. Many alternative combination rules have been proposed. Most of these alternative combination rules are based on the redistribution of Shafer＇s conflicting mass. The quantitative definition of generalized conflict is given. Based on Proportional generalized conflict redistribution （PGCR）, a new class of evidence combination rules is proposed and the links with some existed rules are established. A special instance of the PGCR rules referred to as PGCR-A is proposed by using the subsets＇ average support degrees as the proportional coefficients. PGCR-A rule can deal with the combination of high conflict evidence. During the process of combination, PGCR-A rule can also automatically determine the weights of the basic belief mass converging to the more specific subsets according to the focal elements＇ cardinalities. Since PGCR-A rule is not associative, a modified version is proposed to make it quasi-associative.     

Enabling Energy Efficient Sensory Data Collection Using Multiple Mobile Sink

Mobile sink is the challenging task for wireless sensor networks （WSNs）. In this paper we propose to design an efficient routing protocol for single mobile sink and multiple mobile sink for data gathering in WSN. In this process, a biased random walk method is used to determine the next position of the sink. Then, a rendezvous point selection with splitting tree technique is used to find the optimal data transmission path. If the sink moves within the range of the rendezvous point, it receives the gathered data and if moved out, it selects a relay node from its neighbours to relay packets from rendezvous point to the sink. Proposed algorithm reduces the signal overhead and improves the triangular routing problem. Here the sink acts as a vehicle and collect the data from the sensor. The results show that the proposed model effectively supports sink mobility with low overhead and delay when compared with Intelligent Agent-based Routing protocol （IAR） and also increases the reliability and delivery ratio when the number of sources increases.     

Cooperative Anti-Jamming and Interference Mitigation for UAV Networks: A Local Altruistic Game Approach

To improve the anti-jamming and interference mitigation ability of the UAV-aided communication systems, this paper investigates the channel selection optimization problem in face of both internal mutual interference and external malicious jamming. A cooperative anti-jamming and interference mitigation method based on local altruistic is proposed to optimize UAVs’ channel selection. Specifically, a Stackelberg game is modeled to formulate the confrontation relationship between UAVs and the jammer...     

A Throughput-Aware Joint Vehicle Route and Access Network Selection Approach Based on SMDP

In intelligent transportation system(ITS), the interworking of vehicular networks(VN) and cellular networks(CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN is location related, mobile data offloading(MDO), which dynamically selects access networks for vehicles, should be considered with vehicle route planning to further improve the wireless data throughput of individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint MDO and route selection for an individual vehicle in a metropolitan scenario. We aim to improve the throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process(SMDP). Then we propose an optimal algorithm to calculate its optimal policy. To further reduce the computation complexity, we derive a suboptimal algorithm which reduces the action space. Simulation results demonstrate that the proposed optimal algorithm significantly outperforms the existing work in total throughput and the late arrival ratio.Moreover, the heuristic algorithm is able to substantially reduce the computation time with only slight performance degradation.     

A New Word Clustering Algorithm Based on Word Similarity

Category-based statistic language model is an important method to solve the problem of sparse data in statistical language models. But there are two bottlenecks about this model: 1) The problem of word clustering, it is hard to find a suitable clustering method that has good performance and has not large amount of computation; 2) Class-based method always loses some prediction ability to adapt the text of different domain. In order to solve above problems, a novel definition of word similarity by utilizing mutual information was presented. Based on word similarity, the definition of word set similarity was given and a bottom-up hierarchical clustering algorithm was proposed. Experimental results show that the word clustering algorithm based on word similarity is better than conventional greedy clustering method in speed and performance, the perplexity is reduced from 283 to 207.8.     

An Efficient Trajectory Planning for Cellular-Connected UAV under the Connectivity Constraint

Unmanned Aerial Vehicles(UAVs)acting as aerial users to access the cellular network form a promising solution to guarantee its safe and efficient operations via the high-quality communication.Due to the flexible mobility of UAVs and the coverage range limits of ground base station(GBS),the signalto-noise ratio(SNR)of the communication link between UAVs and GBS will fluctuate.It is an important requirement to maintain the UAV’s cellular connection to meet a certain SNR requirement during the mission for UAV flying from take off to landing.In this paper,we study an efficient trajectory planning method that can minimize a cellular-connected UAV’s mission completion time under the connectivity requirement.The conventional method to tackle this problem adopts graph theory or a dynamic programming method to optimize the trajectory,which generally incurs high computational complexities.Moreover,there is a nonnegligible performance gap compared to the optimal solution.To this end,we propose an iterative trajectory optimizing algorithm based on geometric planning.Firstly,we apply graph theory to obtain all the possible UAV-GBS association sequences and select the candidate association sequences based on the topological relationship among UAV and GBSs.Next,adopting the triangle inequality property,an iterative handover location design is proposed to determine the shortest flight trajectory with fast convergence and low computation complexity.Then,the best flight trajectory can be obtained by comparing all the candidate trajectories.Lastly,we revealed the tradeoff between mission completion time and flight energy consumption.Numerical results validate that our proposed solution can obtain the effectiveness with set accuracy and outperform against the benchmark schemes with affordable computation time.     

FPGA implementation of RSA public-key cryptographic coprocessor based on systolic linear array architecture

In order to make the typical Montgomery＇s algorithm suitable for implementation on FPGA, a modified version is proposed and then a high-performance systolic linear array architecture is designed for RSA cryptosystem on the basis of the optimized algorithm. The proposed systolic array architecture has distinctive features, i,e, not only the computation speed is significantly fast but also the hardware overhead is drastically decreased. As a major practical result, the paper shows that it is possible to implement public-key cryptosystem at secure bit lengths on a single commercially available FPGA.     

A Classification Algorithm Based on Artificial Immune

Based on the natural characteristic of artificial immune system in the field of pattern recognition, the paper proposes a novel Classification algorithm （CAAI）. According to the principle of gene revolution, negative selection and clonal selection, a classification algorithm which is based on AIS is designed and implemented. The algo- rithm consists of two phases. For each word, in the antibody libraries initialization phase, characteristic vectors of the training datasets are extracted and antibody libraries continually evolve using the negative selection. In the clas-sification phase, a clonal selection is introduced to clas- sify the test sample into different categories. Then, the proposed algorithm is validated using classical handwritten digit recognition problem, and the experimental results indicate the robustness and accuracy of the proposed algorithm. Additionally, the comparison with other classification algorithm showed the proposed algorithm has a great competitiveness on recognition rate and recognition performance.     

Reservation Based Optimal Parking Lot Recommendation Model in Internet of Vehicle Environment

In order to solve the problem that the drivers can＇t find the optimal parking lot timely, a reservation based optimal parking lot recommendation model in Internet of Vehicle （IoV） environment is designed. Based on the users oriented parking information recommendation system, the model considers subjective demands of drivers comprehensively, makes a deeply analysis of the evaluation indicators. This recommendation model uses a phased selection method to calculate the optimal objective parking lot. The first stage is screening which based on the users＇ subjective parking demands; the second stage is processing the candidate parking lots through multiple attribute decision making. Simulation experiments show that this model can effectively solve the problems encountered in the process of finding optimal parking lot, save the driver＇s parking time and parking costs and also improve the overall utilization of parking facilities to ease the traffic congestion caused by vehicles parked patrol.