Solving minimum spanning tree problem with DNA computing

Molecular programming is applied to minimum spanning problem whose solution requires encoding of real values in DNA strands. A new encoding scheme is proposed for real values that is biologically plausible and has a fixed code length. According to the characteristics of the problem, a DNA algorithm solving the minimum spanning tree problem is given. The effectiveness of the proposed method is verified by simulation. The advantages and disadvantages of this algorithm are discussed.     

Efficient channel blind estimator for multiuser system with conjugate cyclostationarity

The problem of blind channel identification in a multiuser system is considered in this article. For this purpose, a blind identification algorithm is proposed based on the conjugate cyclostationarity of the received signal. The new approach contains a two-stage identification procedure. First, the separation technique in the cyclic domain is used to separate the second-order cyclic statistics for each user. Second, a subspace algorithm based on the rational subspace theory is exploited to estimate the desired channel. Theoretical analysis and simulation results show that this algorithm is suitable for a multiuser system. Compared with other methods, the algorithm shows good performance even in a bad situation when the number of users is large and the diversity condition is unavailable.     

Research on unified recognition model and algorithm for multi-modal gestures

In gesture recognition,static gestures,dynamic gestures and trajectory gestures are collectively known as multi-modal gestures.To solve the existing problem in different recognition methods for different modal gestures,a unified recognition algorithm is proposed.The angle change data of the finger joints and the movement of the centroid of the hand were acquired respectively by data glove and Kinect.Through the preprocessing of the multi-source heterogeneous data,all hand gestures were considered as curves while solving hand shaking,and a uniform hand gesture recognition algorithm was established to calculate the Pearson correlation coefficient between hand gestures for gesture recognition.In this way,complex gesture recognition was transformed into the problem of a simple comparison of curves similarities.The main innovations:1) Aiming at solving the problem of multi-modal gesture recognition,an unified recognition model and a new algorithm is proposed;2) The Pearson correlation coefficient for the first time to construct the gesture similarity operator is improved.By testing 50 kinds of gestures,the experimental results showed that the method presented could cope with intricate gesture interaction with the 97.7% recognition rate.     

Joint Maximum-Likelihood Estimation of Frequency Offset and Channel in MIMO Systems

This paper addresses the problem of frequency offsets and channel gains estimation for a Multiinput multi-output （MIMO） system in fiat-fading channels. The general case where the frequency offsets are possibly different for each transmit antenna is considered, The Maximum-likelihood （ML） estimation of the carrier frequency offset for each transmit antenna in a MIMO system is investigated in this paper. The exact solution to this estimation problem turns out to be too complex as it involves a search over a multi-dimensional domain. However, by making use of the Particle swarm optimization （PSO） method, we efficiently solve the above complex problem. So based on the PSO theory, a novel joint estimation algorithm of frequency offsets and channel gains is proposed. Simulation results show that the proposed algorithm has better performance as compared with the correlation-based estimation algorithm and asymptotically achieves the Cramer-Rao lower bound （CRLB）, which provides a new idea to solve the problem of joint frequency offsets and channel gains estimation for MIMO systems.     

Tree-based differential evolution algorithm for QoS multicast routing

Differential evolution(DE)algorithm has attracted more and more attention due to its fast optimization performance and good stability.When DE algorithm is applied into multi-constrained multicast routing optimization problem,a common solution to such problem is to merge the paths into a tree after finding paths from the source node to each destination node.This method maybe obtains the better result,but it can consume a lot of computational time.To solve the problem,a tree-based DE algorithm is introduced in this paper.The central operations of the algorithm are realized with tree structure.This method saves the time of finding paths and integrating them to construct a multicast tree.The experiments show that the proposed algorithm can achieve higher success rate than several common algorithms with much smaller running time for different networks.     

Power Allocation for Video Segment Based Caching Strategy in F-RAN Architecture

The concept of edge network caching has been proposed to alleviate the excessive pressure on the core networks.Furthermore,video segment caching technology,a method to cut the whole video into segments and cache them separately,has brought a novel idea to solve the caching problem in the smaller space for massive data.The adoption of segment caching in edge networks will divide the simple video transmission process into two coupling stages because of separate data caching,which leads to more complicated resource allocation.In this paper,this problem is discussed,and its mathematical model is established to minimize the energy consumption of video transmissions.By introducing an efficient prediction window of channel fading,an optimal dynamic scheduling algorithm based on Qlearning is proposed to minimize power consumption while ensuring smooth video streaming.The proposed Q-learning algorithm is simulated and the impacts of channel state,target video bit rate and largescale channel parameter are evaluated.Simulation results show that the proposed method can effectively reduce the total power consumption while ensuring the smooth playback of video service,thanks to the fact that the proposed method is intelligent which can effectively utilize idle resources in favorable channel states.     

FILTERING OF MEDICAL ULTRASONIC IMAGES BASED ON A MODIFIED ANISTROPIC DIFFUSION EQUATION

Speckle noise reduction is a key problem of the image analysis of medical UltraSound images. In this paper, two important improvements have been developed to a fast anisotropic diffusion algorithm for speckle noise reduction. The Gaussian filter is firstly used before gradient calculation, and then the adaptive algorithm of the factor k is proposed. Numerous experimental results show that the proposed model is superior to other methods in noise removal, fidelity and edge preservation. It is suitable for the preprocessing of a great number of medical UltraSound images, such as three dimen- sional reconstruction.     

A Topology-Cognitive Algorithm Framework for Virtual Network Embedding Problem

The virtual network embedding/ mapping problem is an important issue in network virtualization in Software-Defined Networking （SDN）. It is mainly concerned with mapping virtual network requests, which could be a set of SDN flows, onto a shared substrate network automatically and efficiently. Previous researches mainly focus on developing heuristic algorithms for general topology virtual network. In practice however, the virtual network is usually generated with specific topology for specific purpose. Thus, it is a challenge to optimize the heuristic algorithms with these topology information. In order to deal with this problem, we propose a topology-cognitive algorithm framework, which is composed of a guiding principle for topology algorithm developing and a compound algorithm. The compound algorithm is composed of several sub- algorithms, which are optimized for specific topologies. We develop star, tree, and ring topology algorithms as examples, other sub- algorithms can be easily achieved following the same framework. The simulation results show that the topology-cognitive algorithm framework is effective in developing new topology algorithms, and the developed compound algorithm greatly enhances the performance of the Revenue/Cost （R/C） ratio and the Runtime than traditional heuristic algorithms for multi-topology virtual network embedding problem.     

Artificial Neural Network Based Trilogic SVM Control in Current Source Rectifier

Various modulation methods for the Current Source Rectifier （CSR） controlling scheme have been investigated in recent years. The traditional modulation methods have the disadvantages such as the great computing cost, sensitivity to load and system parameter variation. In this study, an Artificial neural network （ANN） based algorithm is adopted to tackle the problem. This algorithm features parallel computation and self-tunlng. The Random weight change （RWC） algorithm is employed for on-line parameter tuning to achieve better performance. The principle of the trilogic Space vector modulation （SVM） for CSR is introduced as the theoretical foun- dation. The proposed method is introduced in two parts, the constructing of the neural network and the designing of an on-line parameter tuning algorithm. The simulation results based on SABER software show that the new algorithm has a good performance, especially under a nonrated system load.     

ROUTE SELECTION IN VIRTUAL CIRCUIT DATA NETWORKS BASED ON GENETIC ALGORITHMS

This paper addresses the problem of selecting a route for every pair of communicating nodes in a virtual circuit data network in order to minimize the average delay encountered by messages. The problem was previously modeled as a network of M/M/1 queues. Agenetic algorithm to solve this problem is presented. Extensive computational results across a variety of networks are reported. These results indicate that the presented solution procedure outperforms the other methods in the literature and is effective for a wide range of traffic loads.     

Micro-vibration parameters fast demodulation algorithm and experiment of self-mixing interference

Self-mixing interference （SMI） technique can be used for measuring vibration, displacement, velocity and absolute distance. In this paper, a simple demodulation algorithm for fast measuring frequency and amplitude of a simple harmonic vibration target is proposed based on the basic theoretical model of self-mixing interference effects. The simulative results show that the error between the vibration parameters which are demodulated by this algorithm and initial settings merely results from the sample rate. Further, the experimental system of self-mixing vibration measurement is built. The experimental results have a good agreement with simulation analyses. The maximum error of frequency demodulation is less than 1 Hz in our experiment.     

MULTIPLE ATTRIBUTE NETWORK SELECTION ALGORITHM BASED ON AHP AND SYNERGETIC THEORY FOR HETEROGENEOUS WIRELESS NETWORKS简

It is a hot issue in communication research field to select the best network for Heteroge- neous Wireless Networks （HWNs）, and it is also a difficult problem to reduce the handoff number of vertical handoff. In order to solve this problem, the paper proposes a multiple attribute network se- lection algorithm based on Analytic Hierarchy Process （AHP） and synergetic theory. The algorithm applies synergetics to network selection, considering the candidate network as a compound system composed of multiple attribute subsystems, and combines the subsystem order degree with AHP weight to obtain entropy of the compound system, which is opposite the synergy degree of a network system. The greater the synergy degree, the better the network performance. The algorithm takes not only the coordination of objective attributes but also Quality of Service （QoS） requirements into consideration, ensuring that users select the network with overall good performance. The simulation results show that the proposed algorithm can effectively reduce the QoS according to different services. handoff number and provide uses with satisfactory     

Dynamic computation offloading in time-varying environment forultra-dense networks: a stochastic game approach

To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications, combining ultra-dense networks (UDNs) and mobile edge computing (MEC)are considered as important solutions. In the MEC enabled UDNs, one of the most important issues is computation offloading. Although a number of work have been done toward this issue, the problem of dynamic computation offloading in time-varying environment, especially the dynamic computation offloading problem for multi-user, has not been fully considered. Therefore, in order to fill this gap, the dynamic computation offloading problem in time-varying environment for multi-user is considered in this paper. By considering the dynamic changes of channel state and users queue state, the dynamic computation offloading problem for multi-user is formulated as a stochastic game, which aims to optimize the delay and packet loss rate of users. To find the optimal solution of the formulated optimization problem, Nash Q-learning (NQLN) algorithm is proposed which can be quickly converged to a Nash equilibrium solution. Finally, extensive simulation results are presented to demonstrate the superiority of NQLN algorithm. It is shown that NQLN algorithm has better optimization performance than the benchmark schemes.     

A MATRIX ALGORITHM FOR COMPUTING THE FREE SPACE DISTANCE OF TCM SIGNAL SEQUENCES

In this paper, the problem of computing the free distance of Trellis Coded Modulation (TCM) signal sequence has been discussed; a new algorithm-the matrix algorithm is proposed; and the step-number estimation problem for state transmission to compute the free distance of TCM signal sequence has been theoretically solved. The matrix algorithm is derived from the Viterbi algorithm, and is an implementation of Viterbi algorithm in the form of matrix. Compared with other algorithms, the matrix algorithm gains two advantages: (1) The explicit solution, and its relatively less complexity. (2) More reflexible ability to the signal space distance variation. As examples, the results of some TCM signal sequence on AWGN channel and fading channels have been presented.     

THE Q-MATRIX LOW-DENSITY PARITY-CHECK CODES

This paper presents a matrix permuting approach to the construction of Low-Density Parity-Check （LDPC） code. It investigates the structure of the sparse parity-check matrix defined by Gallager. It is discovered that the problem of constructing the sparse parity-check matrix requires an algorithm that is efficient in search environments and also is able to work with constraint satisfaction problem. The definition of Q-matrix is given, and it is found that the queen algorithm enables to search the Q-matrix. With properly permuting Q-matrix as sub-matrix, the sparse parity-check matrix which satisfied constraint condition is created, and the good regular-LDPC code that is called the Q-matrix LDPC code is generated. The result of this paper is significant not only for designing low complexity encoder, improving performance and reducing complexity of iterative decoding arithmetic, but also for building practical system of encodable and decodable LDPC code.     

Frequency-coded optimization of hopped-frequency pulse signal based on genetic algorithm

The Frequency-Coded Pulse (FCP) signal has good performance of range and Doppler resolution. This paper first gives the mathematical expression of the ambiguity function for FCP signals, and then presents a coding rule for optimizing FCP signal. The genetic algorithm is presented to solve this kind of problem for optimizing codes. Finally, an example for optimizing calculation is illustrated and the optimized frequency coding results are given with the code length N=64 and N=128 respectively.     

Graph coloring based channel assignment framework for rural wireless mesh networks

IEEE 802.11 based wireless mesh networks with directional antennas are expected to be a new promising technology and an economic approach for providing wireless broadband services in rural areas. In this paper, we discuss interference models and address how they can affect the design of channel assignment in rural mesh networks. We present a new channel assignment framework based on graph coloring for rural wireless mesh networks. The goal of the framework is to allow synchronously transmitting or receiving data from multiple neighbor links at the same time, and continuously doing full-duplex data transfer on every link, creating an efficient rural mesh network without interference. Channel assignment is shown to be NP-hard. We frame this channel allocation problem in terms of Adjacent Vertex Distinguishing Edge Coloring （AVDEC）. Detailed assignment results on grid topology are presented and discussed. Furthermore, we design an algorithm. Finally, we evaluate the perform- ance of the proposed algorithm through extensive simulations and show the algorithm is effective to the regular grid topologies, and the number of colors used by the algorithm is upper bounded by A ~ 1. Hence the algorithm guarantees that the number of channels available in standards such as IEEE 802.11a is sufficient to have a valid AVDEC for many grid topologies. We also evaluate the proposed algorithm for arbitrary graphs. The algorithm provides a lower upper bound on the minimum number of channels to the AVDEC index channel assignment problem.     

Optimal distributed fusion algorithm with one-step out-of-sequence estimates

The transmission modes of multi-hop and broadcasting for Wireless Sensor Networks（WSN）often make random and unknown transmission delays appear,so multisensor data fusion based on delayed systems attracts intense attention from lots of researchers.The existing achievements for the delayed fusion all focus on Out-Of-Sequence Measurements（OOSM）problem which has many disadvantages such as high communication cost,low computational efficiency,huge computational complexity and storage requirement,bad real-time performance and so on.In order to overcome these problems occurred in the OOSM fusion,the Out-Of-Sequence Estimates（OOSE）are considered to solve the delayed fusion for the first time.Different from OOSM which belongs to the centralized fusion,the OOSE scheme transmits local estimates from local sensors to the central processor and is thus the distributed fusion;thereby,the OOSE fusion can not only avoid the problems suffered in the OOSM fusion but also make the design of fusion algorithm highly simple and easy.Accordingly,a novel optimal linear recursive prediction weighted fusion method is proposed for one-step OOSE problem in this letter.As a tradeoff,its fusion accuracy is slightly lower than that of the OOSM method because the current OOSM fusion is a smooth estimate and OOSE gets a prediction estimate.But,the smooth result of the OOSE problem also has good fusion accuracy.Performance analysis and computer simulation show that the total performance of the proposed one-step OOSE fusion algorithm is better than the current one-step OOSM fusion in the practical tracking systems.     

Unvoiced/voiced classification and voiced harmonic parameters estimation using the third-order statistics

Unvoiced/voiced classification of speech is a challenging problem especially under conditions of low signal-to-noise ratio or the non-white-stationary noise environment. To solve this problem, an algorithm for speech classification, and a technique for the estimation of pairwise magnitude frequency in voiced speech are proposed. By using third order spectrum of speech signal to remove noise, in this algorithm the least spectrum difference to get refined pitch and the max harmonic number is given. And this algorithm utilizes spectral envelope to estimate signal-to-noise ratio of speech harmonics. Speech classification, voicing probability, and harmonic parameters of the voiced frame can be obtained. Simulation results indicate that the proposed algorithm, under complicated background noise, especially Gaussian noise, can effectively classify speech in high accuracy for voicing probability and the voiced parameters.     

Coalition-based downlink resource allocation for LTE system with divide-and-conquer approach

To take advantage of the multiuser diversity resulted from the variation in channel conditions among the users,it has become an interesting and challenging problem to efficiently allocate the resources such as subcarriers,bits,and power.Most of current research concentrates on solving the resource-allocation problem for all users together in a centralized way,which brings about high computational complexity and makes it impractical for real system.Therefore,a coalitional game framework for downlink multi-user resource allocation in long term evolution(LTE) system is proposed,based on the divide-and-conquer idea.The goal is to maximize the overall system data rate under the constraints of each user’s minimal rate requirement and maximal transmit power of base station while considering the fairness among users.In this framework,a coalitional formation algorithm is proposed to achieve optimal coalition formation and a two-user bargaining algorithm is designed to bargain channel assignment between two users.The total computational complexity is greatly reduced in comparison with conventional methods.The simulation results show that the proposed algorithms acquire a good tradeoff between the overall system throughout and fairness,compared to maximal rate and max-min schemes.