Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).

QTL Mapping for Fiber Quality Traits Based on a Dense Genetic Linkage Map with SSR,TRAP,SRAP and AFLP Markers in Cultivated Tetraploid Cotton

Cotton is one of the most important economic crops in the world,and it provides natural fiber for the textile industry.With the advancement of the textile technology and increased consumption demands on cotton fiber,both cotton yield and quality should be enhanced.However,cotton yield

Hybrid orthogonal and non-orthogonal pilot distribution based channel estimation in massive MIMO system

How to obtain accurate channel state information(CSI)at the transmitter with less pilot overhead for frequency division duplexing(FDD) massive multiple-input multiple-output(MIMO)system is a challenging issue due to the large number of antennas. To reduce the overwhelming pilot overhead, a hybrid orthogonal and non-orthogonal pilot distribution at the base station(BS),which is a generalization of the existing pilot distribution scheme,is proposed by exploiting the common sparsity of channel due to the compact antenna arrangement. Then the block sparsity for antennas with hybrid pilot distribution is derived respectively and can be used to obtain channel impulse response. By employing the theoretical analysis of block sparse recovery, the total coherence criterion is proposed to optimize the sensing matrix composed by orthogonal pilots. Due to the huge complexity of optimal pilot acquisition, a genetic algorithm based pilot allocation(GAPA) algorithm is proposed to acquire optimal pilot distribution locations with fast convergence. Furthermore, the Cramer Rao lower bound is derived for non-orthogonal pilot-based channel estimation and can be asymptotically approached by the prior support set, especially when the optimized pilot is employed.

Apple leaf disease identification using genetic algorithm and correlation based feature selection method

Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best time to prevent and treat the diseases.Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision,where the key task is to find an effective way to represent the diseased leaf images.In this research,based on image processing techniques and pattern recognition methods,an apple leaf disease recognition method was proposed.A color transformation structure for the input RGB(Red,Green and Blue)image was designed firstly and then RGB model was converted to HSI(Hue,Saturation and Intensity),YUV and gray models.The background was removed based on a specific threshold value,and then the disease spot image was segmented with region growing algorithm(RGA).Thirty-eight classifying features of color,texture and shape were extracted from each spot image.To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification,the most valuable features were selected by combining genetic algorithm(GA)and correlation based feature selection(CFS).Finally,the diseases were recognized by SVM classifier.In the proposed method,the selected feature subset was globally optimum.The experimental results of more than 90%correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases,powdery mildew,mosaic and rust,demonstrate that the proposed method is feasible and effective.

An adaptive laser beam shaping technique based on a genetic algorithm

A new adaptive beam intensity shaping technique based on the combination of a 19-element piezo-electricity deformable mirror （DM） and a global genetic algorithm is presented. This technique can adaptively adjust the voltages of the 19 actuators on the DM to reduce the difference between the target beam shape and the actual beam shape. Numerical simulations and experimental results show that within the stroke range of the DM, this technique can be well used to create the given beam intensity profiles on the focal plane.

Spatial phase-shifting interferometry with compensation of geometric errors based on genetic algorithm(Invited Paper)

We propose a novel spatial phase-shifting interferometry that exploits a genetic algorithm to compensate for geometric errors. Spatial phase-shifting interferometry is more suitable for measuring objects with properties that change rapidly in time than the temporal phase-shifting interferometry. However, it is more susceptible to the geometric errors since the positions at which interferograms are collected are different. In this letter, we propose a spatial phase-shifting interferometry with separate paths for object and reference waves. Also, the object wave estimate is parameterized in terms of geometric errors, and the error is compensated by using a genetic algorithm.

Efficient and optimized approximate GDI full adders based on dynamic threshold CNTFETs for specific least significant bits

Carbon nanotube field-effect transistors(CNTFETs) are reliable alternatives for conventional transistors, especially for use in approximate computing(AC) based error-resilient digital circuits. In this paper, CNTFET technology and the gate diffusion input(GDI) technique are merged, and three new AC-based full adders(FAs) are presented with 6, 6, and 8 transistors, separately. The nondominated sorting based genetic algorithm II(NSGA-II) is used to attain the optimal performance of the proposed cells by considering the number of tubes and chirality vectors as its variables. The results confirm the circuits' improvement by about 50% in terms of power-delay-product(PDP) at the cost of area occupation. The Monte Carlo method(MCM) and 32-nm CNTFET technology are used to evaluate the lithographic variations and the stability of the proposed circuits during the fabrication process, in which the higher stability of the proposed circuits compared to those in the literature is observed. The dynamic threshold(DT) technique in the transistors of the proposed circuits amends the possible voltage drop at the outputs. Circuitry performance and error metrics of the proposed circuits nominate them for the least significant bit(LSB) parts of more complex arithmetic circuits such as multipliers.

CRISPR/Cas9-based functional analysis of yellow gene in the diamondback moth,Plutella xylostella

The diamondback moth,Plutella xylostella(L.),is an economically important pest of cruciferous crops worldwide.This pest is notorious for rapid evolution of the resistance to diferent classes of insecticides,making it increasingly dificult to control.Genetics-based control approaches,through manipulation of target genes,have been reported as promising supplements or alternatives to traditional methods of pest management.Here we identified a gene of pigmentation(yellow)in P.xylostella,Pxyellow,which encodes 1674 bp complementary DNA sequence with four exons and three introns.Using the clustered regularly interspersed palindromic repeats(CRISPR)CRISPR-associated protein 9 system,we knocked out Pxyellow,targeting two sites in Exon III,to generate 272 chimeric mutants(57%of the CRISPR-treated individuals)with color-changed phenotypes of the Ist to 3rd instar larvae,pupae,and adults,indicating that Pxyellow plays an essential role in the body pigmentation of P xlostella.Fitness analysis revealed no significant difference in the oviposition of adults,the hatchability of eggs,and the weight of pupac between homozygous mutants and wildtypes,suggesting that Pxyellow is not directly involved in regulation of growth,development,or reproduction.This work advances our understanding of the genetic and insect science molecular basis for body pigmentation of P xylostella,and opens a wide avenue for development of the genctcally based pest control techniques using Pxyellow as a screening marker.

Parameter influence law analysis and optimal design of a dual mass flywheel

The influence of the dynamic parameters of a dual mass flywheel(DMF)on its vibration reduction performance is analyzed,and several optimization algorithms are used to carry out multiobjective DMF optimization design.First,the vehicle powertrain system is modeled according to the parameter configuration of the test vehicle.The accuracy of the model is verified by comparing the simulation data with the test results.Then,the model is used to analyze the influence of the moment of inertia ratio,torsional stiffness,and damping in reducing DMF vibration.The speed fluctuation amplitude at the transmission input shaft and the natural frequency of the vehicle are taken as the optimization objectives.The passive selection method,multiobjective particle swarm optimization,and the nondominated sorting genetic algorithm based on an elite strategy are used to carry out DMF multiobjective optimization design.The advantages and disadvantages of these algorithms are evaluated,and the best optimization algorithm is selected.