Technique of Error Concealment for Block-Based Image Coding Using Genetic Algorithm

Since real world communication channels are not error free, the coded data transmitted on them may be corrupted, and block based image coding systems are vulnerable to transmission impairment. So the best neighborhood match method using genetic algorithm is used to conceal the error blocks. Experimental results show that the searching space can be greatly reduced by using genetic algorithm compared with exhaustive searching method, and good image quality is achieved. The peak signal noise ratios(PSNRs) of the restored images are increased greatly.

Interleaver Design Method for Turbo Codes Based on Genetic Algorithm

This paper describes a new interleaver construction technique for turbo code. The technique searches as much as possible pseudo-random interleaving patterns under a certain condition using genetic algorithms(GAs). The new interleavers have the superiority of the S-random interleavers and this interleaver construction technique can reduce the time taken to generate pseudo-random interleaving patterns under a certain condition. The results obtained indicate that the new interleavers yield an equal to or better performance than the S-random interleavers. Compared to the S-random interleaver, this design requires a lower level of computational complexity. Key words interleaver - Turbo codes - genetic algorithm CLC number TN 911. 22 Foundation item: Supported by the National Natural Science Foundation of China (60372057) and the Key Open Laboratory on Information Science and Engineering of Railway Transportation Ministry of Beijing Jiaotong University of China (KLISAE-0103)Biography: Tan Ying (1963-), female, Associate professor, Ph. D, research direction: error control coding, analysis and design of wireless communication system.

Genetic Searching Algorithm for Optimal Runlength-Limited Codes with Error Control

ＧｅｎｅｔｉｃＳｅａｒｃｈｉｎｇＡｌｇｏｒｉｔｈｍｆｏｒＯｐｔｉｍａｌＲｕｎｌｅｎｇｔｈ┐ＬｉｍｉｔｅｄＣｏｄｅｓｗｉｔｈＥｒｒｏｒＣｏｎｔｒｏｌ＊＊ＴｈｉｓｗｏｒｋｗａｓｓｕｐｐｏｒｔｅｄｂｙｔｈｅＣｈｉｎｅｓｅＫｅｙＰｒｏｊｅｃｔｏｆＦｕｎｄａｍ...

Application of hybrid coded genetic algorithm in fuzzy neural network controller

Presents the fuzzy neural network optimized by hybrid coded genetic algorithm of decimal encoding and binary encoding, the searching ability and stability of genetic algorithms enhanced by using binary encoding during the crossover operation and decimal encoding during the mutation operation, and the way of accepting new individuals by probability adopted, by which a new individual is accepted and its parent is discarded when its fitness is higher than that of its parent, and a new individual is accepted by probability when its fitness is lower than that of its parent. And concludes with calculations made with an example that these improvements enhance the speed of genetic algorithms to optimize the fuzzy neural network controller.

Real-coded genetic algorithm for optimal vibration controlof flexible structure

Presents the study on the optimum location of actuators/sensors for active vibration control in aerospace flexible structures with the performance function first built by maximization of dissipation energy due to control action and a real coded genetic algorithm then proposed to produce a global optimum solution, and proves the feasibility and advantages of this algorithm with the example of a standard test function and a two collocated actuators/sensors cantilever, and comparing the results with those given in the literatures.

Efficient Numerical Optimization Algorithm Based on New Real-Coded Genetic Algorithm, AREX + JGG, and Application to the Inverse Problem in Systems Biology

In Systems Biology, system identification, which infers regulatory network in genetic system and metabolic pathways using experimentally observed time-course data, is one of the hottest issues. The efficient numerical optimization algorithm to estimate more than 100 real-coded parameters should be developed for this purpose. New real-coded genetic algorithm (RCGA), the combination of AREX (adaptive real-coded ensemble crossover) with JGG (just generation gap), have applied to the inference of genetic interactions involving more than 100 parameters related to the interactions with using experimentally observed time-course data. Compared with conventional RCGA, the combination of UNDX (unimodal normal distribution crossover) with MGG (minimal generation gap), new algorithm has shown the superiority with improving early convergence in the first stage of search and suppressing evolutionary stagnation in the last stage of search.

Efficient Information Set Decoding Based on Genetic Algorithms

In this paper, we describe a hard-decision decoding technique based on Genetic Algorithms (HDGA), which is applicable to the general case of error correcting codes where the only known structure is given by the generating matrix G. Then we present a new soft-decision decoding based on HDGA and the Chase algorithm (SDGA). The performance of some binary and non-binary Linear Block Codes are given for HDGA and SDGA over Gaussian and Rayleigh channels. The performances show that the HDGA decoder has the same performances as the Berlekamp-Massey Algorithm (BMA) in various transmission channels. On the other hand, the performances of SDGA are equivalent to soft-decision decoding using Chase algorithm and BMA (Chase-BMA). The complexity of decoders proposed is also discussed and compared to those of other decoders.

Serial Genetic Algorithm Decoder for Low Density Parity Check Codes

Genetic algorithms are successfully used for decoding some classes of error correcting codes, and offer very good performances for solving large optimization problems. This article proposes a new decoder based on Serial Genetic Algorithm Decoder (SGAD) for decoding Low Density Parity Check (LDPC) codes. The results show that the proposed algorithm gives large gains over sum-product decoder, which proves its efficiency.

A Novel Decoder Based on Parallel Genetic Algorithms for Linear Block Codes

Genetic algorithms offer very good performances for solving large optimization problems, especially in the domain of error-correcting codes. However, they have a major drawback related to the time complexity and memory occupation when running on a uniprocessor computer. This paper proposes a parallel decoder for linear block codes, using parallel genetic algorithms (PGA). The good performance and time complexity are confirmed by theoretical study and by simulations on BCH(63,30,14) codes over both AWGN and flat Rayleigh fading channels. The simulation results show that the coding gain between parallel and single genetic algorithm is about 0.7 dB at BER = 10﹣5 with only 4 processors.

A Cooperative Evolution of Multiple Operators Based Adaptive Quantum Genetic Algorithm for Network Coding Resources Optimization

In order to optimize the network coding resources in a multicast network, an improved adaptive quantum genetic algorithm (AM-QEA) was proposed. Firstly, the optimization problem was translated into a graph decomposition problem. Then the graph decomposition problem was represented by the binary coding, which can be processed by quantum genetic algorithm. At last, a multiple-operators based adaptive quantum genetic algorithm was proposed to optimize the network coding resources. In the algorithm, the individual fitness evaluation operator and population mutation adjustment operator were employed to solve the shortcomings of common quantum genetic algorithm, such as high convergence rate, easy to fall into local optimal solution and low diversity of the population in later stage. The experimental results under various topologies show that the proposed algorithm has the advantages of high multicast success rate, fast convergence speed and strong global search ability in resolving the network coding resource optimization problems.

Genetic Algorithms for Perceptual Codes Extraction

In this work a new technique for global perceptual codes (GPCs) extraction using genetic algorithms (GA) is presented. GAs are employed to extract the GPCs in order to reduce the original number of features and to provide meaningful representations of the original data. In this technique the GPCs are build from a certain combination of elementary perceptual codes (EPCs) which are provided by the Beta-elliptic model for the generation of complex handwriting movements. Indeed, in this model each script is modelled by a set of elliptic arcs. We associate to each arc an EPC. In the proposed technique we defined four types of EPCs. The GPCs can be formed by many possible combinations of EPCs depending on their number and types. So that, the problem of choosing the right combination for each GPC can be regarded as a global optimization problem which is treated in this work using the GAs. Several simulation examples are presented to evaluate the interest and the efficiency of the proposed technique.

Bandwidth optimization of a Planar Inverted-F Antenna using binary and real coded genetic algorithms

With the exponential development of mobile communications and the miniaturization of radio frequency transceivers, the need for small and low profile antennas at mobile frequencies is constantly growing. Therefore, new antennas should be developed to provide larger bandwidth and at the same time small dimensions. Although the gain in bandwidth performances of an antenna are directly related to its dimensions in relation to the wavelength, the aim is to keep the overall size of the antenna constant and from there, find the geometry and structure that give the best performance. The design and bandwidth optimization of a Planar Inverted-F Antenna (PIFA) were introduced in order to achieve a larger bandwidth in the 2 GHz band, using two optimization techniques based upon genetic algorithms (GA), namely the Binary Coded GA (BCGA) and Real-Coded GA (RCGA). During the optimization process, the different PIFA models were evaluated using the finite-difference time domain (FDTD) method-a technique belonging to the general class of differential time domain numerical modeling methods.

Adaptive Real-Coded Genetic Algorithm for Identifying Motor Systems

In this paper, the main objective is to identify the parameters of motors, which includes a brushless direct current (BLDC) motor and an induction motor. The motor systems are dynamically formulated by the mechanical and electrical equations. The real-coded genetic algorithm (RGA) is adopted to identify all parameters of motors, and the standard genetic algorithm (SRGA) and various adaptive genetic algorithm (ARGAs) are compared in the rotational angular speeds and fitness values, which are the inverse of square differences of angular speeds. From numerical simulations and experimental results, it is found that the SRGA and ARGA are feasible, the ARGA can effectively solve the problems with slow convergent speed and premature phenomenon, and is more accurate in identifying system’s parameters than the SRGA. From the comparisons of the ARGAs in identifying parameters of motors, the best ARGA method is obtained and could be applied to any other mechatronic systems.

Dynamic airspace sectorization via improved genetic algorithm

This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.

A Genetic Algorithm Based Approach to Pipe Routing Design

To solve the problem of low efficiency in pipe routing design, an improved genetic algorithm based approach is proposed. To present this approach, the paper mainly describes a generation method of nodes considering the safety distance of pipes and the directional constraints at terminals, the definition of a double coding technique, the collision detection method, the concept of energy and the definition of fitness functions. The similarity detection is introduced to prevent close breeding in the crossover operator, the selection pressure is controlled according to the evolution situation and a heuristic mutation method is used to boost the evolution. Simulation case shows that this approach is more practical and can satisfy different design requirements by changing algorithm parameters.

A NOVEL 2-D GENETIC ALGORITHM FOR STANDARD PE PLACEMENT IN OPTOELECTRONIC MCM

In order for optical interconnection technologies to be incorporated into the next generation parallel computers, new optoelectronic computer aided design, integration, and packaging technologies must be investigated. One of the key issues in designing is the system volume, which is determined by maximum interconnection distance(MID) between PEs. A novel 2 D genetic algorithm was presented in this paper at the first time, and used to solve the placement of twin butterfly multistage networks based on transmissive physical model. The experiment result shows that this algorithm case works better than other algorithm cases.

A genetic algorithm based hybrid non-orthogonal multiple access protocol

Both high-dense wireless connectivity and ultra-huge network capacity are main challenges of next generation broadband networks.As one of its key promising technologies,non-orthogonal multi-ple access(NOMA)scheme can solve those challenges and meet those needs to some extent,in the way that different user equipments(UEs)multiplex on the same resource.Researchers around the world have presented numerous NOMA solutions.Among those,sparse code multiple access(SC-MA)technology is a typical NOMA solution.It supports scheduled access and random access which can be called granted access and grant-free access respectively.But resources allocated to granted UEs and grant-free UEs are strictly separated.In order to improve resource utilization,a hybrid non-orthogonal multiple access scheme is proposed.It allows granted UEs and grant-free UEs sharing the same resource unit in terms of fine-grained integration.On the basis,a resource allocation method is further brought forward based on genetic algorithm.It optimizes resource allocation of all UEs by mapping resource distribution issue to an optimization problem.Comparing throughputs of four meth-ods,simulation results demonstrate the proposed genetic algorithm has better throughput gain.

Using Genetic Algorithm for Identification of Diabetic Retinal Exudates in Digital Color Images

Blood vessels in ophthalmoscope images play an important role in diagnosis of some serious pathology on retinal images. Hence, accurate extraction of vessels is becoming a main topic of this research area. In this paper, a new hybrid approach called the (Genetic algorithm and vertex chain code) for blood vessel detection. And this method uses geometrical parameters of retinal vascular tree for diagnosing of hypertension and identified retinal exudates automatically from color retinal images. The skeletons of the segmented trees are produced by thinning. Three types of landmarks in the skeleton must be detected: terminal points, bifurcation and crossing points, these points are labeled and stored as a chain code. Results of the proposed system can achieve a diagnostic accuracy with 96.0% sensitivity and 98.4% specificity for the identification of images containing any evidence of retinopathy.

A Self-Adaptive Quantum Genetic Algorithm for Network Flow Vehicle Scheduling Problem

Bicycle sharing scheduling is a complex mathematical optimization problem, and it is challenging to design a general algorithm to solve it well due to the uncertainty of its influencing factors. This paper creatively establishes a new mathematical model to determine the appropriate number of vehicles to be placed at each placement point by calculating the traffic weights of the placement points and optimizes the hyperparameters in the algorithm by adaptive quantum genetic algorithm, and at the same time combines the network flow algorithm in graph theory to calculate the most suitable scheduling scheme for shared bicycles by establishing the minimum cost maximum flow network. Through experimental validation, the network flow-based algorithm proposed in this paper allows for a more convenient calculation of the daily bike-sharing scheduling scheme compared to previous algorithms. An adaptive quantum genetic algorithm optimizes the hyperparameters appearing in the algorithm. The experimental results show that the algorithm achieves good results as the transportation cost is only 1/15th of the GA algorithm and 1/9th of the QGA algorithm.

Key Frames Extraction Based on the Improved Genetic Algorithm

In order toovercomethe poor local search ability of genetic algorithm, resulting in the basic genetic algorithm is time-consuming, and low search abilityin the late evolutionary, we use thegray coding instead ofbinary codingatthebeginning of the coding;we use multi-point crossoverto replace the originalsingle-point crossoveroperation.Finally, theexperimentshows that the improved genetic algorithmnot only has a strong search capability, but also thestability has been effectively improved.