Relevant bibliographies by topics / GENETIC PARTICLE FILTER

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'GENETIC PARTICLE FILTER'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "GENETIC PARTICLE FILTER"

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Wang, Er Shen, Tao Pang, and Zhi Xian Zhang. "Accuracy Improvement of GPS Positioning Based on GA-Aided Particle Filter." Applied Mechanics and Materials 719-720 (January 2015): 737–43. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.737.

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Aiming at the weight degeneracy phenomena in particle filter algorithm, a resampling method improving the diversity based on GA-aided particle filter was presented. Taking the advantage of genetic algorithm ( GA ) in selection ,crossover and inheritance to make up for the shortcoming of resampling. Genetic operation on particles in real number domain is adapted to reduce the complex of the genetic algorithm. And the evolutionary idea of genetic algorithm was combined with particle filter, by using selection, and mutation to improve the weight degeneracy and diversity of particle filter. This GA-aided particle filter was applied in the established GPS system nonlinear dynamic state space model. The experimental results based on the collected real GPS data is compared with the tradition particle filter, and compared with the effective number of particles and particle distribution. The experimental results indicated that the GA-aided particle filter can increase the number of particle, and effectively solve the particle degradation phenomena, the estimation accuracy of GA-aided particle filter is better than that of particle filter (PF).
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Liu, Zhigang, Jin Shang, and Xufen Hua. "Smart City Moving Target Tracking Algorithm Based on Quantum Genetic and Particle Filter." Wireless Communications and Mobile Computing 2020 (June 20, 2020): 1–9. http://dx.doi.org/10.1155/2020/8865298.

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In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method of combining evolutionary algorithm. Each particle in particle filter is regarded as a chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle. For each particle state with weight, the particle is first binary coded with qubit and quantum superposition state, and then quantum rotation gate is used for selection, crossing, mutation, and other operations, after a set number of iterations, the final particle set with accuracy and better diversity. In this paper, the filter state estimation and RMSF of N=50 and N=100 for nonlinear target tracking and the comparison of real state and state estimation trajectory in time-constant model under nonlinear target tracking are given. It can be seen that in nonlinear state, the quantum genetic and particle filter (QGPF) algorithm can achieve a higher accuracy of state estimation, and the filtering error of QGPF algorithm at each time is relatively uniform, which shows that the algorithm in this paper has better algorithm stability. Under the time-constant model, the algorithm fits the real state and realizes stable and accurate tracking.
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You, Yuanhong, Chunlin Huang, Zuo Wang, Jinliang Hou, Ying Zhang, and Peipei Xu. "A genetic particle filter scheme for univariate snow cover assimilation into Noah-MP model across snow climates." Hydrology and Earth System Sciences 27, no. 15 (August 9, 2023): 2919–33. http://dx.doi.org/10.5194/hess-27-2919-2023.

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Abstract. Accurate snowpack simulations are critical for regional hydrological predictions, snow avalanche prevention, water resource management, and agricultural production, particularly during the snow ablation period. Data assimilation methodologies are increasingly being applied for operational purposes to reduce the uncertainty in snowpack simulations and to enhance their predictive capabilities. This study aims to investigate the feasibility of using a genetic particle filter (GPF) as a snow data assimilation scheme designed to assimilate ground-based snow depth (SD) measurements across different snow climates. We employed the default parameterization scheme combination within the Noah-MP (with multi-parameterization) model as the model operator in the snow data assimilation system to evolve snow variables and evaluated the assimilation performance of the GPF using observational data from sites with different snow climates. We also explored the impact of measurement frequency and particle number on the filter updating of the snowpack state at different sites and the results of generic resampling methods compared to the genetic algorithm used in the resampling process. Our results demonstrate that a GPF can be used as a snow data assimilation scheme to assimilate ground-based measurements and obtain satisfactory assimilation performance across different snow climates. We found that particle number is not crucial for the filter's performance, and 100 particles are sufficient to represent the high dimensionality of the point-scale system. The frequency of measurements can significantly affect the filter-updating performance, and dense ground-based snow observational data always dominate the accuracy of assimilation results. Compared to generic resampling methods, the genetic algorithm used to resample particles can significantly enhance the diversity of particles and prevent particle degeneration and impoverishment. Finally, we concluded that the GPF is a suitable candidate approach for snow data assimilation and is appropriate for different snow climates.
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Li, Tao, and Qi Yuan Sun. "A Visual Tracking Based on Particle Filter of Multi-Algorithm Fusion." Applied Mechanics and Materials 513-517 (February 2014): 2893–96. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2893.

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A novel visual tracking algorithm based on particle filter with multi-algorithm fusion is proposed. Mean shift is employed to make particles distribute more reasonably in order to maintain tracking accuracy by using fewer particles, and the genetic evolution ideas is introduced to increase the diversity of samples by applying selection, crossover and mutation operator to achieve particles resampling. The experiments show that the tracking performance of the proposed method, compared with Mean Shift Embedded Particle Filter (MSEPF), is significantly improved.
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Huo, Lina. "Intelligent Recognition Method of Vehicle Path with Time Window Based on Genetic Algorithm." Security and Communication Networks 2021 (August 19, 2021): 1–11. http://dx.doi.org/10.1155/2021/3614291.

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Based on particle filter and improved cuckoo genetic algorithm, an algorithm for intelligent vehicle path recognition with a time window is designed. Particle filter (PF) is an influential visual tracking tool; it relies on the Monte Carlo Chain framework and Bayesian probability, which are essential for intelligent monitoring systems. The algorithm first uses particle filters for visual tracking and then obtains the current operating environment of the vehicle, then performs cluster analysis on customer locations, and finally performs path recognition in each area. The algorithm not only introduces particle filters, which are advanced visual tracking, but also improves the cuckoo search algorithm; when the bird’s egg is found by the bird’s nest owner, it needs to randomly change the position of the entire bird’s nest, which speeds up the search speed of the optimal delivery route. Analyze and compare the hybrid intelligent algorithm and the cuckoo search algorithm. Finally, the international standard test set Benchmark Problems is used for testing. The experimental outcomes indicated that the new hybrid intelligent approach is an effective algorithm for handling vehicle routing tasks with time windows.
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Yang, Jin, Xuerong Cui, Juan Li, Shibao Li, Jianhang Liu, and Haihua Chen. "Particle filter algorithm optimized by genetic algorithm combined with particle swarm optimization." Procedia Computer Science 187 (2021): 206–11. http://dx.doi.org/10.1016/j.procs.2021.04.052.

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Mechri, Rihab, Catherine Ottlé, Olivier Pannekoucke, and Abdelaziz Kallel. "Genetic particle filter application to land surface temperature downscaling." Journal of Geophysical Research: Atmospheres 119, no. 5 (March 6, 2014): 2131–46. http://dx.doi.org/10.1002/2013jd020354.

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Chen, Xiyuan, Chong Shen, and Yuefang Zhao. "Study on GPS/INS System Using Novel Filtering Methods for Vessel Attitude Determination." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/678943.

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Any vehicle such as vessel has three attitude parameters, which are mostly defined as pitch, roll, and heading from true north. In hydrographic surveying, determination of these parameters by using GPS or INS technologies is essential for the requirements of vehicle measurements. Recently, integration of GPS/INS by using data fusion algorithm became more and more popular. Therefore, the data fusion algorithm plays an important role in vehicle attitude determination. To improve attitude determination accuracy and efficiency, two improved data fusion algorithms are presented, which are extended Kalman particle filter (EKPF) and genetic particle filter (GPF). EKPF algorithm combines particle filter (PF) with the extended Kalman filter (EKF) to avoid sample impoverishment during the resampling process. GPF is based on genetic algorithm and PF; several genetic operators such as selection, crossover, and mutation are adopted to optimize the resampling process of PF, which can not only reduce the particle impoverishment but also improve the computation efficiency. The performances of the system based on the two proposed algorithms are analyzed and compared with traditional KF. Simulation results show that, comprehensively considering the determination accuracy and consumption cost, the performance of the proposed GPF is better than EKPF and traditional KF.
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Sun, Meng, Yunjia Wang, Shenglei Xu, Hongji Cao, and Minghao Si. "Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter." Applied Sciences 10, no. 2 (January 17, 2020): 668. http://dx.doi.org/10.3390/app10020668.

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This paper proposes a fusion indoor positioning method that integrates the pedestrian dead-reckoning (PDR) and geomagnetic positioning by using the genetic-particle filter (GPF) algorithm. In the PDR module, the Mahony complementary filter (MCF) algorithm is adopted to estimate the heading angles. To improve geomagnetic positioning accuracy and geomagnetic fingerprint specificity, the geomagnetic multi-features positioning algorithm is devised and five geomagnetic features are extracted as the single-point fingerprint by transforming the magnetic field data into the geographic coordinate system (GCS). Then, an optimization mechanism is designed by using gene mutation and the method of reconstructing a particle set to ameliorate the particle degradation problem in the GPF algorithm, which is used for fusion positioning. Several experiments are conducted to evaluate the performance of the proposed methods. The experiment results show that the average positioning error of the proposed method is 1.72 m and the root mean square error (RMSE) is 1.89 m. The positioning precision and stability are improved compared with the PDR method, geomagnetic positioning, and the fusion-positioning method based on the classic particle filter (PF).
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Bi, Jun, Wei Guan, and Long-Tao Qi. "A genetic resampling particle filter for freeway traffic-state estimation." Chinese Physics B 21, no. 6 (June 2012): 068901. http://dx.doi.org/10.1088/1674-1056/21/6/068901.

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Dissertations / Theses on the topic "GENETIC PARTICLE FILTER"

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Mullen, Patrick Bowen. "Learning in Short-Time Horizons with Measurable Costs." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/808.

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Dynamic pricing is a difficult problem for machine learning. The environment is noisy, dynamic and has a measurable cost associated with exploration that necessitates that learning be done in short-time horizons. These short-time horizons force the learning algorithms to make pricing decisions based on scarce data. In this work, various machine learning algorithms are compared in the context of dynamic pricing. These algorithms include the Kalman filter, artificial neural networks, particle swarm optimization and genetic algorithms. The majority of these algorithms have been modified to handle the pricing problem. The results show that these adaptations allow the learning algorithms to handle the noisy dynamic conditions and to learn quickly.
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Hussain, M. S. "Real-coded genetic algorithm particle filters for high-dimensional state spaces." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1426733/.

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This thesis successfully addresses the issues faced by particle filters in high-dimensional state-spaces by comparing them with genetic algorithms and then using genetic algorithm theory to address these issues. Sequential Monte Carlo methods are a class of online posterior density estimation algorithms that are suitable for non-Gaussian and nonlinear environments, however they are known to suffer from particle degeneracy; where the sample of particles becomes too sparse to approximate the posterior accurately. Various techniques have been proposed to address this issue but these techniques fail in high-dimensions. In this thesis, after a careful comparison between genetic algorithms and particle filters, we posit that genetic algorithm theoretic arguments can be used to explain the working of particle filters. Analysing the working of a particle filter, we note that it is designed similar to a genetic algorithm but does not include recombination. We argue based on the building-block hypothesis that the addition of a recombination operator would be able to address the sample impoverishment phenomenon in higher dimensions. We propose a novel real-coded genetic algorithm particle filter (RGAPF) based on these observations and test our hypothesis on the stochastic volatility estimation of financial stocks. The RGAPF successfully scales to higher-dimensions. To further strengthen our argument that whether building-block-hypothesis-like effects are due to the recombination operator, we compare the RGAPF with a mutation-only particle filter with an adjustable mutation rate that is set to equal the population-to-population variance of the RGAPF. The latter significantly and consistently performs better, indicating that recombination is having a subtle and significant effect that may be theoretically explained by genetic algorithm theory. After two successful attempts at validating our hypothesis we compare the performance of the RGAPF using different real-recombination operators. Observing the behaviour of the RGAPF under these recombination operators we propose a mean-centric recombination operator specifically for high-dimensional particle filtering. This recombination operator is successfully tested and compared with benchmark particle filters and a hybrid CMA-ES particle filter using simulated data and finally on real end-of-day data of the securities making up the FTSE-100 index. Each experiment is discussed in detail and we conclude with a brief description of the future direction of research.
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Boudjelaba, Kamal. "Contribution à la conception des filtres bidimensionnels non récursifs en utilisant les techniques de l’intelligence artificielle : application au traitement d’images." Thesis, Orléans, 2014. http://www.theses.fr/2014ORLE2015/document.

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La conception des filtres a réponse impulsionnelle finie (RIF) peut être formulée comme un problème d'optimisation non linéaire réputé pour être difficile sa résolution par les approches conventionnelles. Afin d'optimiser la conception des filtres RIF, nous explorons plusieurs méthodes stochastiques capables de traiter de grands espaces. Nous proposons un nouvel algorithme génétique dans lequel certains concepts innovants sont introduits pour améliorer la convergence et rendre son utilisation plus facile pour les praticiens. Le point clé de notre approche découle de la capacité de l'algorithme génétique (AG) pour adapter les opérateurs génétiques au cours de la vie génétique tout en restant simple et facile à mettre en oeuvre. Ensuite, l’optimisation par essaim de particules (PSO) est proposée pour la conception de filtres RIF. Finalement, un algorithme génétique hybride (HGA) est proposé pour la conception de filtres numériques. L'algorithme est composé d'un processus génétique pur et d’une approche locale dédiée. Notre contribution vise à relever le défi actuel de démocratisation de l'utilisation des AG’s pour les problèmes d’optimisation. Les expériences réalisées avec différents types de filtres mettent en évidence la contribution récurrente de l'hybridation dans l'amélioration des performances et montrent également les avantages de notre proposition par rapport à d'autres approches classiques de conception de filtres et d’autres AG’s de référence dans ce domaine d'application
The design of finite impulse response (FIR) filters can be formulated as a non-linear optimization problem reputed to be difficult for conventional approaches. In order to optimize the design of FIR filters, we explore several stochastic methodologies capable of handling large spaces. We propose a new genetic algorithm in which some innovative concepts are introduced to improve the convergence and make its use easier for practitioners. The key point of our approach stems from the capacity of the genetic algorithm (GA) to adapt the genetic operators during the genetic life while remaining simple and easy to implement. Then, the Particle Swarm Optimization (PSO) is proposed for FIR filter design. Finally, a hybrid genetic algorithm (HGA) is proposed for the design of digital filters. The algorithm is composed of a pure genetic process and a dedicated local approach. Our contribution seeks to address the current challenge of democratizing the use of GAs for real optimization problems. Experiments performed with various types of filters highlight the recurrent contribution of hybridization in improving performance. The experiments also reveal the advantages of our proposal compared to more conventional filter design approaches and some reference GAs in this field of application
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SNEKHA. "GENETIC ALGORITHM BASED ECG SIGNAL DE-NOISING USING EEMD AND FUZZY THRESHOLDING." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15346.

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ElectroCardioGram (ECG) signal records electrical conduction activity of heart. These are very small signals in strength with narrow bandwidth of 0.05-120 Hz. Physicians especially cardiologist use these signals for diagnosis of the heart’s condition or heart diseases. ECG signal is contaminated with various artifacts such as Power Line Interference (PLI), Patient–electrode motion artifacts, Electrode-pop or contact noise, and Baseline Wandering and ElectroMyoGraphic (EMG) noise during acquisition. Analysis of ECG signals becomes difficult to inspect the cardiac activity in the presence of such unwanted signals. So, de-noising of ECG signal is extremely important to prevent misinterpretation of patient’s cardiac activity. Various method are available for de-noising the ECG signal such as Hybrid technique, Empirical Mode Decomposition, Un-decimated Wavelet Transform, Hilbert-Hung Transform, Adaptive Filtering, FIR Filtering, Morphological Filtering, Noise Invalidation Techniques, Non- Local Means Technique and S-Transform etc. All these techniques have some limitations such as mode mixing problem, oscillation in the reconstructed signals, reduced amplitude of the ECG signal and problem of degeneracy etc. To overcome the above mentioned limitations, a new technique is proposed for denoising of ECG signal based on Genetic Algorithm and EEMD with the help of Fuzzy Thresholding. EEMD methods are used to decompose the electrocardiogram signal into true Intrinsic Mode Functions (IMFs).Then the IMFs which are ruled by noise are automatically determined using Fuzzy Thresholding and then filtered using Genetic Particle Algorithms to remove the noise. Use of Genetic Particle Filter mitigates the sample degeneracy of Particle Filter (PF).EEMD is used in this thesis instead of EMD because it solves the EMD mode mixing problem. EEMD represents a major improvement with great versatility and robustness in noisy ECG signal filtering.
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Book chapters on the topic "GENETIC PARTICLE FILTER"

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Wang, Qicong, Jilin Liu, and Zhigang Wu. "Object Tracking Using Genetic Evolution Based Kernel Particle Filter." In Lecture Notes in Computer Science, 466–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11774938_37.

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Liang, Yue, Zhong Liu, and Guodong Zhang. "Passive Target Tracking Using an Improved Particle Filter Algorithm Based on Genetic Algorithm." In Lecture Notes in Electrical Engineering, 559–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12990-2_65.

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He, Pan, Chun Tan, and Huawen Huang. "Research on Receiver Autonomous Integrity Monitoring Algorithm Using Genetic Algorithm Resampling Particle Filter." In Lecture Notes in Electrical Engineering, 221–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37404-3_20.

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Nie, Lixia, Xuguang Yang, Jinglin He, Yaya Mu, and Likang Wang. "Research on the Elite Genetic Particle Filter Algorithm and Application on High-Speed Flying Target Tracking." In Lecture Notes in Electrical Engineering, 792–98. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8411-4_105.

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Lundén, Daniel, Johannes Borgström, and David Broman. "Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages." In Programming Languages and Systems, 404–31. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_15.

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AbstractProbabilistic programming is an approach to reasoning under uncertainty by encoding inference problems as programs. In order to solve these inference problems, probabilistic programming languages (PPLs) employ different inference algorithms, such as sequential Monte Carlo (SMC), Markov chain Monte Carlo (MCMC), or variational methods. Existing research on such algorithms mainly concerns their implementation and efficiency, rather than the correctness of the algorithms themselves when applied in the context of expressive PPLs. To remedy this, we give a correctness proof for SMC methods in the context of an expressive PPL calculus, representative of popular PPLs such as WebPPL, Anglican, and Birch. Previous work have studied correctness of MCMC using an operational semantics, and correctness of SMC and MCMC in a denotational setting without term recursion. However, for SMC inference—one of the most commonly used algorithms in PPLs as of today—no formal correctness proof exists in an operational setting. In particular, an open question is if the resample locations in a probabilistic program affects the correctness of SMC. We solve this fundamental problem, and make four novel contributions: (i) we extend an untyped PPL lambda calculus and operational semantics to include explicit resample terms, expressing synchronization points in SMC inference; (ii) we prove, for the first time, that subject to mild restrictions, any placement of the explicit resample terms is valid for a generic form of SMC inference; (iii) as a result of (ii), our calculus benefits from classic results from the SMC literature: a law of large numbers and an unbiased estimate of the model evidence; and (iv) we formalize the bootstrap particle filter for the calculus and discuss how our results can be further extended to other SMC algorithms.
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Goudos, Sotirios K. "Application of Multi-Objective Evolutionary Algorithms to Antenna and Microwave Design Problems." In Multidisciplinary Computational Intelligence Techniques, 75–101. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1830-5.ch006.

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Antenna and microwave design problems are, in general, multi-objective. Multi-objective Evolutionary Algorithms (MOEAs) are suitable optimization techniques for solving such problems. Particle Swarm Optimization (PSO) and Differential Evolution (DE) have received increased interest from the electromagnetics community. The fact that both algorithms can efficiently handle arbitrary optimization problems has made them popular for solving antenna and microwave design problems. This chapter presents three different state-of-the-art MOEAs based on PSO and DE, namely: the Multi-objective Particle Swarm Optimization (MOPSO), the Multi-objective Particle Swarm Optimization with fitness sharing (MOPSO-fs), and the Generalized Differential Evolution (GDE3). Their applications to different design cases from antenna and microwave problems are reported. These include microwave absorber, microwave filters and Yagi-uda antenna design. The algorithms are compared and evaluated against other evolutionary multi-objective algorithms like Nondominated Sorting Genetic Algorithm-II (NSGA-II). The results show the advantages of using each algorithm.
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Gorman, Sara E., and Jack M. Gorman. "Causality and Filling the Ignorance Gap." In Denying to the Grave. Oxford University Press, 2016. http://dx.doi.org/10.1093/oso/9780199396603.003.0008.

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There is an old adage: “What you don’t know can’t hurt you.” In the science denial arena, however, this adage seems to have been recrafted to something like: “What you don’t know is an invitation to make up fake science.” Before it was dis¬covered that tuberculosis is caused by a rather large bacteria called Mycobacterium tuberculosis it was widely believed to be the result of poor moral character. Similarly, AIDS was attributed to “deviant” lifestyles, like being gay or using intravenous drugs. When we don’t know what causes something, we are pummeled by “experts” telling us what to believe. Vaccines cause autism. ECT causes brain damage. GMOs cause cancer. Interestingly, the leap by the public to latch onto extreme theories does not extend to all branches of science. Physicists are not certain how the force of gravity is actually conveyed between two bodies. The theoretical solutions offered to address this question involve mind-boggling mathematics and seemingly weird ideas like 12 dimensional strings buzzing around the universe. But we don’t see denialist theories about gravity all over the Internet. Maybe this is simply because the answer to the question does not seem to affect our daily lives one way or the other. But it is also the case that even though particle physics is no more or less complex than molecular genetics, we all believe the former is above our heads but the latter is within our purview. Nonphysicists rarely venture an opinion on whether or not dark matter exists, but lots of nonbiologists will tell you exactly what the immune system can and cannot tolerate. Even when scientific matters become a little more frightening, when they occur in some branches of science, they register rather mild atten¬tion. Some people decided that the supercollider in Switzerland called the Large Hadron Collider (LHC) might be capable of producing black holes that would suck in all of Earth. Right before the LHC was scheduled to be tested at full capacity, there were a few lawsuits filed around the world trying to stop it on the grounds that it might induce the end of the world.
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Conference papers on the topic "GENETIC PARTICLE FILTER"

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Cong Li, Qin Honglei, and Xing Juhong. "Distributed genetic resampling particle filter." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579807.

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Zhao Juan and Dong-feng Li. "The ant system-genetic algorithm particle filter." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5690150.

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Liu, Yanli, and Heng Zhang. "Real time face tracking by genetic particle filter." In 2009 Chinese Control and Decision Conference (CCDC 2009). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5192407.

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Wang, W., Q. K. Tan, J. Chen, and Z. Ren. "Particle filter based on improved genetic algorithm resampling." In 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC). IEEE, 2016. http://dx.doi.org/10.1109/cgncc.2016.7828809.

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Zhao, Bin, Jian-wang Hu, and Bing Ji. "An improved particle filter based on genetic resampling." In 2015 International Conference on Automation, Mechanical Control and Computational Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/amcce-15.2015.125.

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Ho, Ming-Che, Cheng-Chin Chiang, and Yu-Long Chen. "A Genetic Particle Filter for Moving Object Tracking." In Fourth International Conference on Image and Graphics (ICIG 2007). IEEE, 2007. http://dx.doi.org/10.1109/icig.2007.165.

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Ziyu, Li, Liu Yan, Song Lei, and Cheng Ying. "Particle Filter Based on Pseudo Parallel Genetic Algorithm." In 2013 Fifth International Conference on Computational and Information Sciences (ICCIS). IEEE, 2013. http://dx.doi.org/10.1109/iccis.2013.59.

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Ye, Zhu, and Zhi-Qiang Liu. "Tracking Human Hand Motion Using Genetic Particle Filter." In 2006 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icsmc.2006.385089.

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Kalami Heris, S. Mostapha, and Hamid Khaloozadeh. "Non-dominated sorting genetic filter a multi-objective evolutionary particle filter." In 2014 Iranian Conference on Intelligent Systems (ICIS). IEEE, 2014. http://dx.doi.org/10.1109/iraniancis.2014.6802580.

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Xu, Zhenyuan, Junzo Watada, and Zalili Binti Musa. "Particle Filter-Based Height Estimation in Human Tracking." In 2011 Fifth International Conference on Genetic and Evolutionary Computing (ICGEC). IEEE, 2011. http://dx.doi.org/10.1109/icgec.2011.94.

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Reports on the topic "GENETIC PARTICLE FILTER"

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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.
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2

Sela, Hanan, Eduard Akhunov, and Brian J. Steffenson. Population genomics, linkage disequilibrium and association mapping of stripe rust resistance genes in wild emmer wheat, Triticum turgidum ssp. dicoccoides. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598170.bard.

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The primary goals of this project were: (1) development of a genetically characterized association panel of wild emmer for high resolution analysis of the genetic basis of complex traits; (2) characterization and mapping of genes and QTL for seedling and adult plant resistance to stripe rust in wild emmer populations; (3) characterization of LD patterns along wild emmer chromosomes; (4) elucidation of the multi-locus genetic structure of wild emmer populations and its correlation with geo-climatic variables at the collection sites. Introduction In recent years, Stripe (yellow) rust (Yr) caused by Pucciniastriiformis f. sp. tritici(PST) has become a major threat to wheat crops in many parts of the world. New races have overcome most of the known resistances. It is essential, therefore, that the search for new genes will continue, followed by their mapping by molecular markers and introgression into the elite varieties by marker-assisted selection (MAS). The reservoir of genes for disease and pest resistance in wild emmer wheat (Triticumdicoccoides) is an important resource that must be made available to wheat breeders. The majority of resistance genes that were introgressed so far in cultivated wheat are resistance (R) genes. These genes, though confering near-immunity from the seedling stage, are often overcome by the pathogen in a short period after being deployed over vast production areas. On the other hand, adult-plant resistance (APR) is usually more durable since it is, in many cases, polygenic and confers partial resistance that may put less selective pressure on the pathogen. In this project, we have screened a collection of 480 wild emmer accessions originating from Israel for APR and seedling resistance to PST. Seedling resistance was tested against one Israeli and 3 North American PST isolates. APR was tested on accessions that did not have seedling resistance. The APR screen was conducted in two fields in Israel and in one field in the USA over 3 years for a total of 11 replicates. We have found about 20 accessions that have moderate stripe rust APR with infection type (IT<5), and about 20 additional accessions that have novel seedling resistance (IT<3). We have genotyped the collection using genotyping by sequencing (GBS) and the 90K SNP chip array. GBS yielded a total 341K SNP that were filtered to 150K informative SNP. The 90K assay resulted in 11K informative SNP. We have conducted a genome-wide association scan (GWAS) and found one significant locus on 6BL ( -log p >5). Two novel loci were found for seedling resistance. Further investigation of the 6BL locus and the effect of Yr36 showed that the 6BL locus and the Yr36 have additive effect and that the presence of favorable alleles of both loci results in reduction of 2 grades in the IT score. To identify alleles conferring adaption to extreme climatic conditions, we have associated the patterns of genomic variation in wild emmer with historic climate data from the accessions’ collection sites. The analysis of population stratification revealed four genetically distinct groups of wild emmer accessions coinciding with their geographic distribution. Partitioning of genomic variance showed that geographic location and climate together explain 43% of SNPs among emmer accessions with 19% of SNPs affected by climatic factors. The top three bioclimatic factors driving SNP distribution were temperature seasonality, precipitation seasonality, and isothermality. Association mapping approaches revealed 57 SNPs associated with these bio-climatic variables. Out of 21 unique genomic regions controlling heading date variation, 10 (~50%) overlapped with SNPs showing significant association with at least one of the three bioclimatic variables. This result suggests that a substantial part of the genomic variation associated with local adaptation in wild emmer is driven by selection acting on loci regulating flowering. Conclusions: Wild emmer can serve as a good source for novel APR and seedling R genes for stripe rust resistance. APR for stripe rust is a complex trait conferred by several loci that may have an additive effect. GWAS is feasible in the wild emmer population, however, its detection power is limited. A panel of wild emmer tagged with more than 150K SNP is available for further GWAS of important traits. The insights gained by the bioclimatic-gentic associations should be taken into consideration when planning conservation strategies.
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