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Literatura académica sobre el tema "Forward-And-Backward algorithm"

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Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Forward-And-Backward algorithm"

Wang, Feng, Jianping Zhang, Guiling Sun y Tianyu Geng. "Iterative Forward-Backward Pursuit Algorithm for Compressed Sensing". Journal of Electrical and Computer Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/5940371.

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It has been shown that iterative reweighted strategies will often improve the performance of many sparse reconstruction algorithms. Iterative Framework for Sparse Reconstruction Algorithms (IFSRA) is a recently proposed method which iteratively enhances the performance of any given arbitrary sparse reconstruction algorithm. However, IFSRA assumes that the sparsity level is known. Forward-Backward Pursuit (FBP) algorithm is an iterative approach where each iteration consists of consecutive forward and backward stages. Based on the IFSRA, this paper proposes the Iterative Forward-Backward Pursuit (IFBP) algorithm, which applies the iterative reweighted strategies to FBP without the need for the sparsity level. By using an approximate iteration strategy, IFBP gradually iterates to approach the unknown signal. Finally, this paper demonstrates that IFBP significantly improves the reconstruction capability of the FBP algorithm, via simulations including recovery of random sparse signals with different nonzero coefficient distributions in addition to the recovery of a sparse image.

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Renugadevi, S., Aayush S. Rawat, Suraj Swaminathan, P. Arulmozhivarman y R. Selvakumar. "Perimeter coverage using backward and forward greedy algorithm". Applied Mathematical Sciences 7 (2013): 3883–96. http://dx.doi.org/10.12988/ams.2013.34203.

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Moursi, Walaa M. "The Forward–Backward Algorithm and the Normal Problem". Journal of Optimization Theory and Applications 176, n.º 3 (6 de febrero de 2018): 605–24. http://dx.doi.org/10.1007/s10957-017-1113-4.

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Chen, Jinjian, Xingyu Luo, Yuchao Tang y Qiaoli Dong. "Primal-Dual Splitting Algorithms for Solving Structured Monotone Inclusion with Applications". Symmetry 13, n.º 12 (13 de diciembre de 2021): 2415. http://dx.doi.org/10.3390/sym13122415.

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This work proposes two different primal-dual splitting algorithms for solving structured monotone inclusion containing a cocoercive operator and the parallel-sum of maximally monotone operators. In particular, the parallel-sum is symmetry. The proposed primal-dual splitting algorithms are derived from two approaches: One is the preconditioned forward–backward splitting algorithm, and the other is the forward–backward–half-forward splitting algorithm. Both algorithms have a simple calculation framework. In particular, the single-valued operators are processed via explicit steps, while the set-valued operators are computed by their resolvents. Numerical experiments on constrained image denoising problems are presented to show the performance of the proposed algorithms.

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Tottori, Takehiro y Tetsuya J. Kobayashi. "Forward and Backward Bellman Equations Improve the Efficiency of the EM Algorithm for DEC-POMDP". Entropy 23, n.º 5 (29 de abril de 2021): 551. http://dx.doi.org/10.3390/e23050551.

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Decentralized partially observable Markov decision process (DEC-POMDP) models sequential decision making problems by a team of agents. Since the planning of DEC-POMDP can be interpreted as the maximum likelihood estimation for the latent variable model, DEC-POMDP can be solved by the EM algorithm. However, in EM for DEC-POMDP, the forward–backward algorithm needs to be calculated up to the infinite horizon, which impairs the computational efficiency. In this paper, we propose the Bellman EM algorithm (BEM) and the modified Bellman EM algorithm (MBEM) by introducing the forward and backward Bellman equations into EM. BEM can be more efficient than EM because BEM calculates the forward and backward Bellman equations instead of the forward–backward algorithm up to the infinite horizon. However, BEM cannot always be more efficient than EM when the size of problems is large because BEM calculates an inverse matrix. We circumvent this shortcoming in MBEM by calculating the forward and backward Bellman equations without the inverse matrix. Our numerical experiments demonstrate that the convergence of MBEM is faster than that of EM.

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Jannati, Mohammad, Tole Sutikno, Nik Rumzi Nik Idris y Mohd Junaidi Abdul Aziz. "High Performance Speed Control of Single-Phase Induction Motors Using Switching Forward and Backward EKF Strategy". International Journal of Power Electronics and Drive Systems (IJPEDS) 7, n.º 1 (1 de marzo de 2016): 17. http://dx.doi.org/10.11591/ijpeds.v7.i1.pp17-27.

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<p>The aim of this research is to provide a high performance vector control of single-phase Induction Motor (IM) drives. It is shown that in the rotating reference frame, the single-phase IM equations can be separated into forward and backward equations with the balanced structure. Based on this, a method for vector control of the single-phase IM, using two modified Rotor Field-Oriented Control (RFOC) algorithms is presented. In order to accommodate forward and backward rotor fluxes in the presented controller, an Extended Kalman Filter (EKF) with two different forward and backward currents that are switched interchangeably (switching forward and backward EKF), is proposed. Simulation results illustrate the effectiveness of the proposed algorithm.</p>

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Liu, Liya, Xiaolong Qin y Jen-Chih Yao. "A Hybrid Forward–Backward Algorithm and Its Optimization Application". Mathematics 8, n.º 3 (19 de marzo de 2020): 447. http://dx.doi.org/10.3390/math8030447.

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In this paper, we study a hybrid forward–backward algorithm for sparse reconstruction. Our algorithm involves descent, splitting and inertial ideas. Under suitable conditions on the algorithm parameters, we establish a strong convergence solution theorem in the framework of Hilbert spaces. Numerical experiments are also provided to illustrate the application in the field of signal processing.

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Stankovic, Marko, Miroslav Ciric y Jelena Ignjatovic. "Simulations and bisimulations for fuzzy multimodal logics over Heyting algebras". Filomat 37, n.º 3 (2023): 711–43. http://dx.doi.org/10.2298/fil2303711s.

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In the present paper, we study fuzzy multimodal logics over complete Heyting algebras and Kripke models for these logics. We introduce two types of simulations (forward and backward) and five types of bisimulations (forward, backward, forward-backward, backward-forward and regular) between Kripke models, as well as the corresponding presimulations and prebisimulations, which are simulations and bisimulations with relaxed conditions. For each type of simulations and bisimulations an efficient algorithm has been provided that works as follows: it computes the greatest presimulation/prebisimulation of that type, and then checks whether it meets the additional condition: if it does, then it is also the greatest simulation/ bisimulation of that type, otherwise, there is not any simulation/bisimulation of that type. The algorithms are inspired by algorithms for checking the existence and computing the greatest simulations and bisimulations between fuzzy automata. We also demonstrate the application of these algorithms in the state reduction of Kripke models. We show that forward bisimulation fuzzy equivalences on the Kripke model provide reduced models equivalent to the original model concerning plus-formulas, backward bisimulation fuzzy equivalences provide reduced models equivalent concerning minus-formulas, while regular bisimulation fuzzy equivalences provide reduced models equivalent concerning all modal formulas.

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Bussaban, Limpapat, Attapol Kaewkhao y Suthep Suantai. "Inertial s-iteration forward-backward algorithm for a family of nonexpansive operators with applications to image restoration problems". Filomat 35, n.º 3 (2021): 771–82. http://dx.doi.org/10.2298/fil2103771b.

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Image restoration is an important branch of image processing which has been studied extensively while there are several methods to solve this problem by many authors with the challenges of computational speed and accuracy of algorithms. In this paper, we present two methods, called ?Inertial S-iteration forward-backward algorithm (ISFBA)? and ?A fast iterative shrinkage-thresholding algorithm-Siteration (FISTA-S)?, for finding an approximate solution of least absolute shrinkage and selection operator problem by using a special technique in fixed point theory and prove weak convergence of the proposed methods under some suitable conditions. Moreover, we apply our main results to solve image restoration problems. It is shown by some numerical examples that our algorithms have a good behavior compared with forward-backward algorithm (FBA), a new accelerated proximal gradient algorithm (nAGA) and a fast iterative shrinkage-thresholding algorithm (FISTA).

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MUNEHISA, TOMO y HIDEKAZU TANAKA. "ALGORITHM DEPENDENCE OF PARTON SHOWERS IN DEEP INELASTIC SCATTERING". International Journal of Modern Physics A 06, n.º 06 (10 de marzo de 1991): 989–1002. http://dx.doi.org/10.1142/s0217751x9100054x.

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Parton showers for deep inelastic electron-proton scattering are studied. Forward and backward evolution algorithms are constructed using the same definitions for splitting and evolution variables. We discuss features of two algorithms and examine algorithm dependence of final parton distributions.

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Más fuentes

Tesis sobre el tema "Forward-And-Backward algorithm"

Cerqueira, Andressa. "Statistical inference on random graphs and networks". Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-04042018-094802/.

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In this thesis we study two probabilistic models defined on graphs: the Stochastic Block model and the Exponential Random Graph. Therefore, this thesis is divided in two parts. In the first part, we introduce the Krichevsky-Trofimov estimator for the number of communities in the Stochastic Block Model and prove its eventual almost sure convergence to the underlying number of communities, without assuming a known upper bound on that quantity. In the second part of this thesis we address the perfect simulation problem for the Exponential random graph model. We propose an algorithm based on the Coupling From The Past algorithm using a Glauber dynamics. This algorithm is efficient in the case of monotone models. We prove that this is the case for a subset of the parametric space. We also propose an algorithm based on the Backward and Forward algorithm that can be applied for monotone and non monotone models. We prove the existence of an upper bound for the expected running time of both algorithms.
Nessa tese estudamos dois modelos probabilísticos definidos em grafos: o modelo estocástico por blocos e o modelo de grafos exponenciais. Dessa forma, essa tese está dividida em duas partes. Na primeira parte nós propomos um estimador penalizado baseado na mistura de Krichevsky-Trofimov para o número de comunidades do modelo estocástico por blocos e provamos sua convergência quase certa sem considerar um limitante conhecido para o número de comunidades. Na segunda parte dessa tese nós abordamos o problema de simulação perfeita para o modelo de grafos aleatórios Exponenciais. Nós propomos um algoritmo de simulação perfeita baseado no algoritmo Coupling From the Past usando a dinâmica de Glauber. Esse algoritmo é eficiente apenas no caso em que o modelo é monotóno e nós provamos que esse é o caso para um subconjunto do espaço paramétrico. Nós também propomos um algoritmo de simulação perfeita baseado no algoritmo Backward and Forward que pode ser aplicado à modelos monótonos e não monótonos. Nós provamos a existência de um limitante superior para o número esperado de passos de ambos os algoritmos.

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Vaillaud, Hugo. "Algorithms for the Search of a Moving Air Target with a Radar Onboard an Airborne Platform". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS695.

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Dans le contexte actuel des missions aériennes, les pilotes et opérateurs à bord d'une plateforme aéroportée sont confrontés à des situations tactiques de plus en plus complexes.Outre la trajectoire de l'appareil, ils doivent aussi utiliser au mieux plusieurs capteurs pour effectuer de multiples tâches essentielles à une bonne représentation de la situation tactique — allant de la surveillance et de la poursuite des cibles à l'identification des cibles, ainsi que la conduite de tir.Ils doivent également faire face à de nouveaux types de cibles plus difficiles à détecter, connectées en réseau et capables de se coordonner de plus en plus efficacement.Lors d'une mission à haute intensité, il peut être difficile d'exécuter toutes les tâches simultanément, car des prises de décision complexes en un très court laps de temps sont nécessaires.Les travaux présentés dans ce manuscrit sont motivés par un besoin de l'amélioration et l'évaluation de la planification pour la surveillance air-air avec un radar embarqué.Une part importante de cette thèse est consacrée à l'adaptation d'un modèle général pour la recherche de cible, ainsi que l'algorithme Forward And Backward (FAB) pour la tâche de surveillance air-air avec un radar. De nouveaux algorithmes sont également proposés.L'étude s'étend à l'intégration progressive des caractéristiques de la technologie radar au modèle de recherche de cible. Par exemple, l'effort de recherche est ici alloué à des cônes d'observation afin de représenter la forme d'un faisceau radar. Des cônes d'observations disjoints sont d'abord considérés, puis le modèle est complexifié en considérant des cônes d'observation recouvrants afin d'augmenter la qualité des plans calculés. Le modèle de détection est lui aussi graduellement amélioré afin de refléter le fonctionnement du radar.Un cadre robuste pour l'évaluation des stratégies de pointage radar est proposé. L'outil d'évaluation des algorithmes permet d'une part la comparaison des différents algorithmes sur différents critères clairement définis, et d'autre part de mesurer leur optimalité avec des bornes théoriques sur les performances atteignables.Grâce à ce cadre expérimental, nous validons la supériorité des algorithmes proposés par rapport à une heuristiques de la littérature ouverte utilisée dans l'industrie, et proposons ainsi un nouveau point de comparaison à l'état de l'art de la surveillance air-air.À travers des expérimentations, l'efficacité de ces algorithmes est validée, notamment en explorant les compromis entre la qualité de la solution et le temps de calcul afin de considérer des contraintes d'exécution en temps réel.En définitive, cette recherche représente une avancée dans l'optimisation de la surveillance air-air avec un radar embarqué. En effet les algorithmes proposés démontrent une performance supérieure face à l'heuristique existante, et un cadre d'évaluation robuste est introduit pour une comparaison méthodique. Ces contributions forment la base pour des études ultérieures dans des scénarios plus complexes, envisageant l'utilisation de multiples capteurs embarqués sur plusieurs plateformes, coordonnés pour exécuter simultanément des tâches diverses. Ce travail s'inscrit dans une démarche plus large visant à développer des outils qui permettront d'alléger la charge mentale des opérateurs, leur permettant ainsi de se concentrer sur les aspects opérationnels cruciaux de leurs missions
In the current context of aerial missions, pilots and operators aboard airborne platforms are facing increasingly complex tactical situations. Apart from managing the aircraft's trajectory, they must also use multiple sensors to complete various essential tasks for a comprehensive representation of the tactical situation. These tasks range from surveillance and tracking of targets to target identification and fire control. They must also deal with new, harder-to-detect targets that are networked and capable of more efficient coordination. During high-intensity missions, performing all these tasks simultaneously can be challenging due to the need for complex decision-making within very short timeframes.The work presented in this manuscript is driven by the need to enhance and evaluate air-to-air surveillance planning with an onboard radar. A significant portion of this thesis is dedicated to adapting a general target search model and the Forward And Backward (FAB) algorithm for the specific task of air-to-air surveillance using radar. New algorithms are also introduced. The study extends to the gradual integration of radar technology features into the target search model. For instance, research efforts are allocated to observation cones to represent radar beam shape. Initially, disjoint observation cones are considered, and the model is further enriched by incorporating overlapping observation cones to enhance the quality of computed plans. The detection model is also progressively refined to accurately reflect radar operation.A robust framework for evaluating radar pointing strategies is proposed. The algorithm evaluation tool allows for both the comparison of different algorithms based on clearly defined criteria and the measurement of their optimality against theoretical performance bounds. Through this experimental framework, we validate the superiority of the proposed algorithms over a heuristic from the open literature used in the industry, thus providing a new benchmark in the field of air-to-air surveillance. Through experimentation, the effectiveness of these algorithms is confirmed, particularly by exploring the trade-offs between solution quality and computation time to accommodate real-time execution constraints.Ultimately, this research represents a step forward in optimizing air-to-air surveillance with onboard radar. The proposed algorithms demonstrate superior performance compared to existing heuristics, and a robust evaluation framework is introduced for systematic comparison. These contributions serve as the basis for future studies in more complex scenarios, envisioning the use of multiple onboard sensors across various platforms, coordinated to perform diverse tasks simultaneously. This work aligns with a broader objective of developing tools to reduce the cognitive load on operators, allowing them to focus on the critical operational aspects of their missions

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Noun, Nahla. "Convergence et stabilisation de systèmes dynamiques couplés et multi-échelles vers des équilibres sous contraintes : application à l’optimisation hiérarchique". Thesis, Montpellier 2, 2013. http://www.theses.fr/2013MON20011/document.

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Nous étudions la convergence de systèmes dynamiques vers des équilibres. En particulier, nous nous intéressons à deux types d'équilibres. D'une part, les solutions d'inéquations variationnelles sous contraintes qui interviennent aussi dans la résolution de problèmes d'optimisation hiérarchique. D'autre part l'état stable d'un système dynamique, c'est à dire l'état où l'énergie du système est nulle. Cette thèse est divisée en deux parties principales, chacune focalisée sur la recherche d'un de ces équilibres. Dans la première partie nous étudions une classe d'algorithmes explicite-implicites pour résoudre certaines inéquations variationnelles sous contraintes. Nous introduisons un algorithme proximal-gradient pénalisé, "splitting forward-backward penalty scheme". Ensuite, nous prouvons sa convergence ergodique faible vers un équilibre dans le cas général d'un opérateur maximal monotone, et sa convergence forte vers l'unique équilibre si l'opérateur est de plus fortement monotone. Nous appliquons aussi notre algorithme pour résoudre des problèmes d'optimisation sous contrainte ou hiérarchique dont les fonctions objectif et de pénalisation sont formées d'une partie lisse et d'une autre non lisse. En effet, nous démontrons la convergence faible de l'algorithme vers un optimum hiérarchique lorsque l'opérateur est le sous-différentiel d'une fonction convexe semi-continue inférieurement et propre. Nous généralisons ainsi plusieurs algorithmes connus et nous retrouvons leurs résultats de convergence en affaiblissant les hypothèses utilisées dans nombre d'entre eux.Dans la deuxième partie, nous étudions l'action d'un contrôle interne local sur la stabilisation indirecte d'un système dynamique couplé formé de trois équations d'ondes, le système de Bresse. Sous la condition d'égalité des vitesses de propagation des ondes, nous montrons la stabilité exponentielle du système. En revanche, quand les vitesses sont différentes, nous prouvons sa stabilité polynomiale et nous établissons un nouveau taux de décroissance polynomial de l'énergie. Ceci étend des résultats présents dans la littérature au sens où le contrôle est localement distribué (et non pas appliqué à tout le domaine) et nous améliorons le taux de décroissance polynomial de l'énergie pour des conditions au bord de type Dirichlet et Dirichlet-Neumann
We study the convergence of dynamical systems towards equilibria. In particular, we are interested in two types of equilibria. On one hand solutions of constrained variational inequations that are also involved in the resolution of hierarchical optimization problems. On the other hand the stable state of a dynamical system, i.e. the state when the energy of the system is zero. The thesis is divided into two parts, each focused on one of these equilibria. In the first part, we study a class of forward-backward algorithms for solving constrained variational inequalities. We consider a splitting forward-backward penalty scheme. We prove the weak ergodic convergence of the algorithm to an equilibrium for a general maximal monotone operator, and the strong convergence to the unique equilibrium if the operator is an addition strongly monotone. We also apply our algorithm for solving constrained or hierarchical optimization problems whose objective and penalization functions are formed of a smooth and a non-smooth part. In fact, we show the weak convergence to a hierarchical optimum when the operator is the subdifferential of a closed convex proper function. We then generalize several known algorithms and we find their convergence results by weakening assumptions used in a number of them. In the second part, we study the action of a locally internal dissipation law in the stabilization of a linear dynamical system coupling three wave equations, the Bresse system. Under the equal speed wave propagation condition we show that the system is exponentially stable. Otherwise, when the speeds are different, we prove the polynomial stability and establish a new polynomial energy decay rate. This extends results presented in the literature in the sense that the dissipation law is locally distributed (and not applied in the whole domain) and we improve the polynomial energy decay rate with both types of boundary conditions, Dirichlet and Dirichlet-Neumann

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Luu, Duy tung. "Exponential weighted aggregation : oracle inequalities and algorithms". Thesis, Normandie, 2017. http://www.theses.fr/2017NORMC234/document.

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Dans plusieurs domaines des statistiques, y compris le traitement du signal et des images, l'estimation en grande dimension est une tâche importante pour recouvrer un objet d'intérêt. Toutefois, dans la grande majorité de situations, ce problème est mal-posé. Cependant, bien que la dimension ambiante de l'objet à restaurer (signal, image, vidéo) est très grande, sa ``complexité'' intrinsèque est généralement petite. La prise en compte de cette information a priori peut se faire au travers de deux approches: (i) la pénalisation (très populaire) et (ii) l'agrégation à poids exponentiels (EWA). L'approche penalisée vise à chercher un estimateur qui minimise une attache aux données pénalisée par un terme promouvant des objets de faible complexité (simples). L'EWA combine une famille des pré-estimateurs, chacun associé à un poids favorisant exponentiellement des pré-estimateurs, lesquels privilègent les mêmes objets de faible complexité.Ce manuscrit se divise en deux grandes parties: une partie théorique et une partie algorithmique. Dans la partie théorique, on propose l'EWA avec une nouvelle famille d'a priori favorisant les signaux parcimonieux à l'analyse par group dont la performance est garantie par des inégalités oracle. Ensuite, on analysera l'estimateur pénalisé et EWA, avec des a prioris généraux favorisant des objets simples, dans un cardre unifié pour établir des garanties théoriques. Deux types de garanties seront montrés: (i) inégalités oracle en prédiction, et (ii) bornes en estimation. On les déclinera ensuite pour des cas particuliers dont certains ont été étudiés dans littérature. Quant à la partie algorithmique, on y proposera une implémentation de ces estimateurs en alliant simulation Monte-Carlo (processus de diffusion de Langevin) et algorithmes d'éclatement proximaux, et montrera leurs garanties de convergence. Plusieurs expériences numériques seront décrites pour illustrer nos garanties théoriques et nos algorithmes
In many areas of statistics, including signal and image processing, high-dimensional estimation is an important task to recover an object of interest. However, in the overwhelming majority of cases, the recovery problem is ill-posed. Fortunately, even if the ambient dimension of the object to be restored (signal, image, video) is very large, its intrinsic ``complexity'' is generally small. The introduction of this prior information can be done through two approaches: (i) penalization (very popular) and (ii) aggregation by exponential weighting (EWA). The penalized approach aims at finding an estimator that minimizes a data loss function penalized by a term promoting objects of low (simple) complexity. The EWA combines a family of pre-estimators, each associated with a weight exponentially promoting the same objects of low complexity.This manuscript consists of two parts: a theoretical part and an algorithmic part. In the theoretical part, we first propose the EWA with a new family of priors promoting analysis-group sparse signals whose performance is guaranteed by oracle inequalities. Next, we will analysis the penalized estimator and EWA, with a general prior promoting simple objects, in a unified framework for establishing some theoretical guarantees. Two types of guarantees will be established: (i) prediction oracle inequalities, and (ii) estimation bounds. We will exemplify them for particular cases some of which studied in the literature. In the algorithmic part, we will propose an implementation of these estimators by combining Monte-Carlo simulation (Langevin diffusion process) and proximal splitting algorithms, and show their guarantees of convergence. Several numerical experiments will be considered for illustrating our theoretical guarantees and our algorithms

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Li, Du-Hsiu y 李讀修. "Sparse Multi-Camera Virtual View Synthesis Using Forward and Backward Depth Warping Algorithms". Thesis, 2012. http://ndltd.ncl.edu.tw/handle/15134353059526684064.

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碩士
國立交通大學
電子研究所
100
Recently, multiview video technology receives a lot of attention. Because of its potential wide applications and large market, the so-called free-view system or virtual view system becomes a standardization item (3DVC) of the international MPEG committee. The current focus of MPEG 3DVC project is on the parallel and dense camera array system. The distance between two nearby cameras is about of less than 10 cm. In contrast, our focus in this study is on the so-called sparse multi camera systems, of which the cameras are located farther away. Our target is to synthesize a virtual view based on the recorded sparse camera pictures. We first study the causes of the depth map artifacts produced in the process of forward warping. And then to reduce these artifacts and the artifacts in the texture image synthesized stage, we propose a few refinement tools described below. Four techniques have been developed and presented in this thesis. They are depth map up-sampling, backward depth map warping, pyramid-based hole filling, and post artifact reduction. At the up-sampling stage, we find empirically that the duplicated interpolation produces better depth map as compared to the other interpolation methods. The ordinary forward warping produces many types of artifacts. We propose an instrumental backward warping algorithm on the depth map. It is able to reduce most artifacts in depth warping due to the more accurate geometric relations. The occlusion regions can be eliminated by using our proposed pyramid-based hole filling method. One of its nice features is that it can suppress the noise when filling in the occlusion area. Finally, with the aid of artifact reduction techniques, the synthesized virtual view is more vivid and natural. All the above techniques have been tested on the test images captured by a set of sparsely located 3-camera array. The results show that every of them can prove visible subjective quality improvement on the synthesized virtual view images.

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Po-YinCheng y 鄭博尹. "An Efficient Hardware Accelerator for Arbitrary-Length Forward and Backward MDCT Algorithms Based on IDCT-II Kernel". Thesis, 2010. http://ndltd.ncl.edu.tw/handle/30842273024859663332.

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碩士
國立成功大學
電機工程學系碩博士班
98
In audio coding and decoding flow, the major purpose of MDCT and IMDCT are to convert time domain into frequency domain and to convert frequency domain into time domain, respectively, and this step accounts great part of the computation in audio codec. Derived from previous experience, we can found that both MDCT and IMDCT can be found to a kernel type of DCT-IV, which can be employed a single hardware accelerator through this IDCT-II kernel to share the hardware resources. Fast algorithm based on a unified recursive IDCT-II is derived for MDCT and IMDCT, and we can reduce the kernel consideration by using pre-processing steps. Hence, the proposed design would reduce the hardware costs in implementation of MDCT and IMDCT on a platform of audio codec. The proposed algorithm takes (N2/64 + N/4) computational cycles for computing all output sequences, which achieve a great improvement than original definition algorithm that needs (N2/2) computational cycles. The proposed algorithm also has a significant improvement than other previous algorithm.

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Fernández, Cristián Fabio. "Modelos ocultos de Markov aplicados al reconocimiento de patrones del análisis técnico bursátil". Bachelor's thesis, 2008. http://hdl.handle.net/11086/13.

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Tesis (Lic. en Ciencias de la Computación)--Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física, 2008.
El análisis técnico bursátil desde hace más de un siglo se ha utilizado para intentar predecir el comportamiento futuro de los precios de un activo o índice bursátil. Dicho análisis se basa en la detección de patrones en series temporales financieras. Esta detección o reconocimiento ha sido, y es hasta nuestros días, realizada en forma manual y de manera artesanal.
Cristián Fabio Fernández ; dir. por Oscar Humberto Bustos.

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Libros sobre el tema "Forward-And-Backward algorithm"

Sangeetha, V. y S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip its readers with a comprehensive understanding of AI and its subsets, machine learning and deep learning, with a particular emphasis on neural networks. It is designed for novices venturing into the field, as well as experienced learners who desire to solidify their knowledge base or delve deeper into advanced topics. In Chapter 1, we provide a thorough introduction to the world of AI, exploring its definition, historical trajectory, and categories. We delve into the applications of AI, and underscore the ethical implications associated with its proliferation. Chapter 2 introduces machine learning, elucidating its types and basic algorithms. We examine the practical applications of machine learning and delve into challenges such as overfitting, underfitting, and model validation. Deep learning and neural networks, an integral part of AI, form the crux of Chapter 3. We provide a lucid introduction to deep learning, describe the structure of neural networks, and explore forward and backward propagation. This chapter also delves into the specifics of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). In Chapter 4, we outline the steps to train neural networks, including data preprocessing, cost functions, gradient descent, and various optimizers. We also delve into regularization techniques and methods for evaluating a neural network model. Chapter 5 focuses on specialized topics in neural networks such as autoencoders, Generative Adversarial Networks (GANs), Long Short-Term Memory Networks (LSTMs), and Neural Architecture Search (NAS). In Chapter 6, we illustrate the practical applications of neural networks, examining their role in computer vision, natural language processing, predictive analytics, autonomous vehicles, and the healthcare industry. Chapter 7 gazes into the future of AI and neural networks. It discusses the current challenges in these fields, emerging trends, and future ethical considerations. It also examines the potential impacts of AI and neural networks on society. Finally, Chapter 8 concludes the book with a recap of key learnings, implications for readers, and resources for further study. This book aims not only to provide a robust theoretical foundation but also to kindle a sense of curiosity and excitement about the endless possibilities AI and neural networks offer. The journ

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Capítulos de libros sobre el tema "Forward-And-Backward algorithm"

Jiménez-Pastor, A., K. G. Larsen, M. Tribastone y M. Tschaikowski. "Forward and Backward Constrained Bisimulations for Quantum Circuits". En Tools and Algorithms for the Construction and Analysis of Systems, 343–62. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57249-4_17.

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AbstractEfficient methods for the simulation of quantum circuits on classic computers are crucial for their analysis due to the exponential growth of the problem size with the number of qubits. Here we study lumping methods based on bisimulation, an established class of techniques that has been proven successful for (classic) stochastic and deterministic systems such as Markov chains and ordinary differential equations. Forward constrained bisimulation yields a lower-dimensional model which exactly preserves quantum measurements projected on a linear subspace of interest. Backward constrained bisimulation gives a reduction that is valid on a subspace containing the circuit input, from which the circuit result can be fully recovered. We provide an algorithm to compute the constraint bisimulations yielding coarsest reductions in both cases, using a duality result relating the two notions. As applications, we provide theoretical bounds on the size of the reduced state space for well-known quantum algorithms for search, optimization, and factorization. Using a prototype implementation, we report significant reductions on a set of benchmarks. Furthermore, we show that constraint bisimulation complements state-of-the-art methods for the simulation of quantum circuits based on decision diagrams.

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Kori, Mayuko, Flavio Ascari, Filippo Bonchi, Roberto Bruni, Roberta Gori y Ichiro Hasuo. "Exploiting Adjoints in Property Directed Reachability Analysis". En Computer Aided Verification, 41–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37703-7_3.

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AbstractWe formulate, in lattice-theoretic terms, two novel algorithms inspired by Bradley’s property directed reachability algorithm. For finding safe invariants or counterexamples, the first algorithm exploits over-approximations of both forward and backward transition relations, expressed abstractly by the notion of adjoints. In the absence of adjoints, one can use the second algorithm, which exploits lower sets and their principals. As a notable example of application, we consider quantitative reachability problems for Markov Decision Processes.

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Fan, John L. "Forward-Backward Algorithm". En Constrained Coding and Soft Iterative Decoding, 97–116. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1525-8_3.

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Prakruthi, G. B. y K. T. Veeramanju. "Backward – Forward Algorithm Approach for Computation of Losses in LVDS and Proposed HVDS - Towards Loss Minimization and Voltage Improvement in Agricultural Sector". En Communications in Computer and Information Science, 415–29. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-9059-2_37.

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Azuma, Ai y Yuji Matsumoto. "A Generalization of Forward-Backward Algorithm". En Machine Learning and Knowledge Discovery in Databases, 99–114. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04180-8_24.

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Varadarajan, V., S. V. Lokesh, A. Ramesh, A. Vanitha y V. Vaidehi. "Face Tracking Using Modified Forward-Backward Mean-Shift Algorithm". En Communications in Computer and Information Science, 46–59. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8603-8_5.

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Jabari, Farkhondeh, Farnaz Sohrabi, Pouya Pourghasem y Behnam Mohammadi-Ivatloo. "Backward-Forward Sweep Based Power Flow Algorithm in Distribution Systems". En Studies in Systems, Decision and Control, 365–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34050-6_14.

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Bogdan, Karina Olga Maizman y Valdinei Freire da Silva. "Forward and Backward Feature Selection in Gradient-Based MDP Algorithms". En Advances in Artificial Intelligence, 383–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37807-2_33.

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Receveur, Simon, David Scheler y Tim Fingscheidt. "A Turbo-Decoding Weighted Forward-Backward Algorithm for Multimodal Speech Recognition". En Signals and Communication Technology, 179–92. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-21834-2_16.

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Sarada Devi, T. S. N. G. y T. V. L. N. Pavan Phani Kumar. "System Modification Process to Reduce Calculation Complexity in Backward Forward Sweep Algorithm". En Proceedings of Fifth International Conference on Computer and Communication Technologies, 115–24. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9704-6_10.

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Actas de conferencias sobre el tema "Forward-And-Backward algorithm"

Bittar, Alexandre y Philip N. Garner. "Bayesian Recurrent Units and the Forward-Backward Algorithm". En Interspeech 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/interspeech.2022-11035.

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Hong, I. K., S. T. Chung, H. K. Kim, Y. B. Kim, Y. D. Son y Z. H. Cho. "Fast forward projection and backward projection algorithm using SIMD". En 2006 IEEE Nuclear Science Symposium Conference Record. IEEE, 2006. http://dx.doi.org/10.1109/nssmic.2006.353723.

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Tomoiaga, Bogdan, Mircea Chindris, Antoni Sudria-Andreu y Andreas Sumper. "Object oriented backward/forward algorithm for unbalanced and harmonic polluted distribution systems". En 2011 11th International Conference on Electrical Power Quality and Utilisation - (EPQU). IEEE, 2011. http://dx.doi.org/10.1109/epqu.2011.6128909.

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Abeygunawardana, Anula, Ali Arefi y Gerard Ledwich. "An efficient forward-backward algorithm to MSDEPP including batteries and voltage control devices". En 2014 IEEE Power & Energy Society General Meeting. IEEE, 2014. http://dx.doi.org/10.1109/pesgm.2014.6939812.

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Willkommen, Th, H. J. Kretzschmar y A. Dittmann. "AN ALGORITHM FOR SETTING UP NUMERICALLY CONSISTENT FORWARD AND BACKWARD EQUATIONS FOR PROCESS MODELLING". En Physical Chemistry of Aqueous Systems: Meeting the Needs of Industry. Connecticut: Begellhouse, 2023. http://dx.doi.org/10.1615/icpws-1994.260.

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He, Jun, Bu-xiang Zhou, Qin Zhang, Yang-chun Zhao y Jin-hua Liu. "An Improved Power Flow Algorithm for Distribution Networks Based on Zbus Algorithm and Forward/Backward Sweep Method". En 2012 International Conference on Control Engineering and Communication Technology (ICCECT). IEEE, 2012. http://dx.doi.org/10.1109/iccect.2012.8.

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Azmi, Muhammad Hafidz Bin y Sergey Akhramovich. "Inverse kinematics algorithm in dual quaternion form based on FABRIK (Forward and Backward Reach Inverse Kinematics) algorithm". En INTERNATIONAL CONFERENCE ON INFORMATICS, TECHNOLOGY, AND ENGINEERING 2021 (InCITE 2021): Leveraging Smart Engineering. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0075431.

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Iwai, Toshiki, Naoki Hirose, Nobuyoshi Kikuma, Kunio Sakakibara y Hiroshi Hirayama. "DOA estimation by MUSIC algorithm using forward-backward spatial smoothing with overlapped and augmented arrays". En 2014 International Symposium on Antennas & Propagation (ISAP). IEEE, 2014. http://dx.doi.org/10.1109/isanp.2014.7026687.

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Murari, Krishna, Narayana Prasad Padhy y Sukumar Kamalasadan. "Backward-Forward Sweep Based Power Flow Algorithm for Radial and Meshed AC-DC Distribution System". En 2021 IEEE Industry Applications Society Annual Meeting (IAS). IEEE, 2021. http://dx.doi.org/10.1109/ias48185.2021.9677175.

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Haque, F., K. Reaz y M. A. Matin. "Enhanced fast DOA algorithm through shrinking signal subspace and noise pseudo eigenvector using forward-backward method". En 2011 IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE). IEEE, 2011. http://dx.doi.org/10.1109/iccaie.2011.6162188.

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