Academic literature on the topic 'State estimation with binary sensors'
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Journal articles on the topic "State estimation with binary sensors"
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PÉREZ ARCILA, MAURICIO, and MARTIN ALONSO TAMAYO VELEZ. "IMPLEMENTATION OF LEBESGUE SAMPLING METHOD AND DIGITAL SENSORS FOR CONTROLLING THE LEVEL VARIABLE IN A CONTINUOUS SYSTEM." DYNA NEW TECHNOLOGIES 8, no. 1 (November 11, 2021): [14 P.]. http://dx.doi.org/10.6036/nt10248.
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This study aims to show that the continuous control from a level system can be efficiently measured and controlled using capacitive digital binary sensors, which in this case, replace the measurement signal from an analog differential pressure transmitter in a level control system. The binary sensors low cost and the digital output they process allow the reproduction of a correct signal and the estimation of a variable for controlling the water level inside the process tank through a proportional pneumatic level control valve, which receives the control signal from the Lebesgue sampling estimation algorithm applied herein for processing digital measurements. In this particular case, the Lebesgue algorithm is applied to reproduce the estimation of values obtained from the continuous signal in the real level process for the measurement and control. Also, are compared both, simulated and real outputs obtained using the Lebesgue algorithm and digital sensors, which were applied to a state observer controller that relates digital signals for controlling the real level system output. The application of the Lebesgue algorithm in the real level process concludes that the analog level signal can be efficiently reproduced using this method. In addition, the controller enables the system to smoothly conduct digital output processing using digital sensors to control the system output correctly, validating that not only analog sensors should be applied for controlling the output of proportional actuators, because it is shown that digital binary signals can be used for controlling and emulating continuous signals, which were processed and applied to the pneumatic valve. Keywords: Lebesgue sampling, estimation, binary sensor, observer controller, finite state machine, continuous system, control, LTI systems, identification, state variable, estimated output, proportional actuator
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Wentao, Shi, Chen Dong, Zhou Lin, Bai Ke, and Jin Yong. "Sensor Selection Scheme considering Uncertainty Disturbance." Journal of Sensors 2022 (February 16, 2022): 1–11. http://dx.doi.org/10.1155/2022/2488907.
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In multisensor cooperative detection network, some random disturbances, energy carried by sensor, distance between target and sensor node, and so on all affect the sensor selection scheme. To effectively select some sensors for detecting the target, a novel sensor selection method considering uncertainty disturbance is proposed under constraints of estimation accuracy and energy consumption. Firstly, the sensor selection problem is modeled as a binary form optimization problem with a penalty term to minimize the number of sensors. Secondly, some factors (precision, energy, and distance, etc.) affecting the sensor selection scheme are analyzed and quantified, and energy consumption matrix and estimation precision threshold are given by matrix tra‘nsformation. Finally, the problem of minimizing sensor number after relaxation is solved by convex optimization method, obtaining sensor selection scheme by discretization and legitimization of the suboptimal solution after convex relaxation. Simulation results show that the proposed algorithm can ensure the minimum number of sensors, improving accuracy of state estimation and saving network energy.
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Posada, Juan C., Manuel J. Betancur, Jaime A. Moreno, Rubén D. Guerra, and Martin A. Tamayo. "Study of the Convergence in State Estimators for LTI Systems with Event Detection." Journal of Control Science and Engineering 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/4281786.
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The methods frequently used to estimate the state of an LTI system require that the precise value of the output variable is known at all times, or at equidistant sampling times. In LTI systems, in which the output signal is measured through binary sensors (detectors), the traditional way of state observers design is not applicable even though the system has a complete observability matrix. This type of state observers design is known as passive. It is necessary, then, to introduce a new state estimation technique, which allows reckoning the state from the information of the variable’s crossing through a detector’s action threshold (switch). This paper seeks, therefore, to study the convergence in this type of estimators in finite time, allowing establishing, theoretically, whether some family of the proposed models can be estimated in a convergent way through the use of the estimation technique based on events.
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Gao, Xiang, Yin Tang Yang, Duan Zhou, Jian Xian Zhang, and Chang Chun Chai. "Target Tracking Approximation Algorithms Based on Particle Filters and near-Linear Curve Simplified Optimization in WSN." Applied Mechanics and Materials 128-129 (October 2011): 1079–84. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.1079.
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In order to process target tracking approximation with unknown motion state models beforehand in a two-dimensional field of binary proximity sensors, the algorithms based on cost functions of particle filters and near-linear curve simple optimization are proposed in this paper. Through moving target across detecting intersecting fields of sensors sequentially, cost functions are introduced to solve target tracking approximation and velocity estimation which is not similar to traditional particle filters that rely on probabilistic assumptions about the motion states. Then a near-linear curve geometric approach is used to simplify and easily describe target trajectories that are below a certain error measure. The validity of our algorithms is demonstrated through simulation results.
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Zhang, Xiangzhu, Lijia Zhang, Frank L. Lewis, and Hailong Pei. "Non-Uniform Discretization-based Ordinal Regression for Monocular Depth Estimation of an Indoor Drone." Electronics 9, no. 11 (October 23, 2020): 1767. http://dx.doi.org/10.3390/electronics9111767.
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At present, the main methods of solving the monocular depth estimation for indoor drones are the simultaneous localization and mapping (SLAM) algorithm and the deep learning algorithm. SLAM requires the construction of a depth map of the unknown environment, which is slow to calculate and generally requires expensive sensors, whereas current deep learning algorithms are mostly based on binary classification or regression. The output of the binary classification model gives the decision algorithm relatively rough control over the unmanned aerial vehicle. The regression model solves the problem of the binary classification, but it carries out the same processing for long and short distances, resulting in a decline in short-range prediction performance. In order to solve the above problems, according to the characteristics of the strong order correlation of the distance value, we propose a non-uniform spacing-increasing discretization-based ordinal regression algorithm (NSIDORA) to solve the monocular depth estimation for indoor drone tasks. According to the security requirements of this task, the distance label of the data set is discretized into three major areas—the dangerous area, decision area, and safety area—and the decision area is discretized based on spacing-increasing discretization. Considering the inconsistency of ordinal regression, a new distance decoder is produced. Experimental evaluation shows that the root-mean-square error (RMSE) of NSIDORA in the decision area is 33.5% lower than that of non-uniform discretization (NUD)-based ordinal regression methods. Although it is higher overall than that of the state-of-the-art two-stream regression algorithm, the RMSE of the NSIDORA in the top 10 categories of the decision area is 21.8% lower than that of the two-stream regression algorithm. The inference speed of NSIDORA is 3.4 times faster than that of two-stream ordinal regression. Furthermore, the effectiveness of the decoder has been proved through ablation experiments.
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Tahir, Sheikh Badar ud din, Ahmad Jalal, and Kibum Kim. "Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model." Entropy 22, no. 5 (May 20, 2020): 579. http://dx.doi.org/10.3390/e22050579.
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Advancements in wearable sensors technologies provide prominent effects in the daily life activities of humans. These wearable sensors are gaining more awareness in healthcare for the elderly to ensure their independent living and to improve their comfort. In this paper, we present a human activity recognition model that acquires signal data from motion node sensors including inertial sensors, i.e., gyroscopes and accelerometers. First, the inertial data is processed via multiple filters such as Savitzky–Golay, median and hampel filters to examine lower/upper cutoff frequency behaviors. Second, it extracts a multifused model for statistical, wavelet and binary features to maximize the occurrence of optimal feature values. Then, adaptive moment estimation (Adam) and AdaDelta are introduced in a feature optimization phase to adopt learning rate patterns. These optimized patterns are further processed by the maximum entropy Markov model (MEMM) for empirical expectation and highest entropy, which measure signal variances for outperformed accuracy results. Our model was experimentally evaluated on University of Southern California Human Activity Dataset (USC-HAD) as a benchmark dataset and on an Intelligent Mediasporting behavior (IMSB), which is a new self-annotated sports dataset. For evaluation, we used the “leave-one-out” cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving an improved recognition accuracy of 91.25%, 93.66% and 90.91% when compared with USC-HAD, IMSB, and Mhealth datasets, respectively. The proposed system should be applicable to man–machine interface domains, such as health exercises, robot learning, interactive games and pattern-based surveillance.
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Caballero-Águila, R., A. Hermoso-Carazo, and J. Linares-Pérez. "Covariance-Based Estimation from Multisensor Delayed Measurements with Random Parameter Matrices and Correlated Noises." Mathematical Problems in Engineering 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/958474.
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The optimal least-squares linear estimation problem is addressed for a class of discrete-time multisensor linear stochastic systems subject to randomly delayed measurements with different delay rates. For each sensor, a different binary sequence is used to model the delay process. The measured outputs are perturbed by both random parameter matrices and one-step autocorrelated and cross correlated noises. Using an innovation approach, computationally simple recursive algorithms are obtained for the prediction, filtering, and smoothing problems, without requiring full knowledge of the state-space model generating the signal process, but only the information provided by the delay probabilities and the mean and covariance functions of the processes (signal, random parameter matrices, and noises) involved in the observation model. The accuracy of the estimators is measured by their error covariance matrices, which allow us to analyze the estimator performance in a numerical simulation example that illustrates the feasibility of the proposed algorithms.
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Nuss, Dominik, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, and Klaus Dietmayer. "A random finite set approach for dynamic occupancy grid maps with real-time application." International Journal of Robotics Research 37, no. 8 (July 2018): 841–66. http://dx.doi.org/10.1177/0278364918775523.
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Grid mapping is a well-established approach for environment perception in robotic and automotive applications. Early work suggests estimating the occupancy state of each grid cell in a robot’s environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter. A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a real-time application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.
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Bosse, Stefan, Dennis Weiss, and Daniel Schmidt. "Supervised Distributed Multi-Instance and Unsupervised Single-Instance Autoencoder Machine Learning for Damage Diagnostics with High-Dimensional Data—A Hybrid Approach and Comparison Study." Computers 10, no. 3 (March 18, 2021): 34. http://dx.doi.org/10.3390/computers10030034.
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Structural health monitoring (SHM) is a promising technique for in-service inspection of technical structures in a broad field of applications in order to reduce maintenance efforts as well as the overall structural weight. SHM is basically an inverse problem deriving physical properties such as damages or material inhomogeneity (target features) from sensor data. Often models defining the relationship between predictable features and sensors are required but not available. The main objective of this work is the investigation of model-free distributed machine learning (DML) for damage diagnostics under resource and failure constraints by using multi-instance ensemble and model fusion strategies and featuring improved scaling and stability compared with centralised single-instance approaches. The diagnostic system delivers two features: A binary damage classification (damaged or non-damaged) and an estimation of the spatial damage position in case of a damaged structure. The proposed damage diagnostics architecture should be able to be used in low-resource sensor networks with soft real-time capabilities. Two different machine learning methodologies and architectures are evaluated and compared posing low- and high-resolution sensor processing for low- and high-resolution damage diagnostics, i.e., a dedicated supervised trained low-resource and an unsupervised trained high-resource deep learning approach, respectively. In both architectures state-based recurrent artificial neural networks are used that process spatially and time-resolved sensor data from experimental ultrasonic guided wave measurements of a hybrid material (carbon fibre laminate) plate with pseudo defects. Finally, both architectures can be fused to a hybrid architecture with improved damage detection accuracy and reliability. An extensive evaluation of the damage prediction by both systems shows high reliability and accuracy of damage detection and localisation, even by the distributed multi-instance architecture with a resolution in the order of the sensor distance.
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Lin, Guichao, Yunchao Tang, Xiangjun Zou, Juntao Xiong, and Jinhui Li. "Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field." Sensors 19, no. 2 (January 21, 2019): 428. http://dx.doi.org/10.3390/s19020428.
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Fruit detection in real outdoor conditions is necessary for automatic guava harvesting, and the branch-dependent pose of fruits is also crucial to guide a robot to approach and detach the target fruit without colliding with its mother branch. To conduct automatic, collision-free picking, this study investigates a fruit detection and pose estimation method by using a low-cost red–green–blue–depth (RGB-D) sensor. A state-of-the-art fully convolutional network is first deployed to segment the RGB image to output a fruit and branch binary map. Based on the fruit binary map and RGB-D depth image, Euclidean clustering is then applied to group the point cloud into a set of individual fruits. Next, a multiple three-dimensional (3D) line-segments detection method is developed to reconstruct the segmented branches. Finally, the 3D pose of the fruit is estimated using its center position and nearest branch information. A dataset was acquired in an outdoor orchard to evaluate the performance of the proposed method. Quantitative experiments showed that the precision and recall of guava fruit detection were 0.983 and 0.948, respectively; the 3D pose error was 23.43° ± 14.18°; and the execution time per fruit was 0.565 s. The results demonstrate that the developed method can be applied to a guava-harvesting robot.
Dissertations / Theses on the topic "State estimation with binary sensors"
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Jones, Caitlin Dawn. "Stellar variance for asteroseismic parameter estimation and inferences on the evolutionary state and binary population of red giant stars." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8444/.
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The latest generation of space missions have performed large scale observations of stars and this has been revolutionary in the field of asteroseismology. The ability to characterise thousands of stars has been instrumental in understanding the interiors of stars and the evolution of the Galaxy. This thesis focuses on studying red giant stars, both on an individual basis and as a population, using a robust asteroseismic metric we define based on the bandpass filtered estimate of the stellar variance. Here we present results of testing asteroseismic scaling relations, and the assumptions needed to create realistic simulated power spectra. The resulting synthetic datasets then inform three other investigations. We present the results of an investigation into determining the binary population of Kepler red giant branch stars using our variance metric. The inferred fraction of 57.4 +/- 2.5% is consistent with previous work on main sequence stars. Results of using our variance metric as part of an analysis pipeline, designed to automate the detection of solar-like oscillations and determine global asteroseismic parameters in K2 and CoRoT data are presented. Finally, we present a discussion of using ourvariance metric to highlight structural differences between red giant branch and red clump stars.
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Jiang, Kun. "Real-time estimation and diagnosis of vehicle's dynamics states with low-cost sensors in different driving condition." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2292/document.
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Le développement des systèmes intelligents pour contrôler la stabilité du véhicule et éviter les accidents routier est au cœur de la recherche automobile. L'expansion de ces systèmes intelligents à l'application réelle exige une estimation précise de la dynamique du véhicule dans des environnements diverses (dévers et pente). Cette exigence implique principalement trois problèmes : ⅰ), extraire des informations non mesurées à partir des capteurs faible coût; ⅱ), rester robuste et précis face aux les perturbations incertaines causées par les erreurs de mesure ou de la méconnaissance de l'environnement; ⅲ), estimer l'état du véhicule et prévoir le risque d'accident en temps réel. L’originalité de cette thèse par rapport à l’existant, consiste dans le développement des nouveaux algorithmes, basés sur des nouveaux modèles du véhicule et des différentes techniques d'observation d'état, pour estimer des variables ou des paramètres incertains de la dynamique du véhicule en temps réel. La première étape de notre étude est le développement de nouveaux modèles pour mieux décrire le comportement du véhicule dans des différentes situations. Pour minimiser les erreurs de modèle, un système d'estimation composé de quatre observateurs est proposé pour estimer les forces verticales, longitudinales et latérales par pneu, ainsi que l'angle de dérive. Trois techniques d'observation non linéaires (EKF, UKF et PF) sont appliquées pour tenir compte des non-linéarités du modèle. Pour valider la performance de nos observateurs, nous avons implémenté en C++ des modules temps-réel qui, embarqué sur le véhicule, estiment la dynamique du véhicule pendant le mouvementEnhancing road safety by developing active safety system is the general purpose of this thesis. A challenging task in the development of active safety system is to get accurate information about immeasurable vehicle dynamics states. More specifically, we need to estimate the vertical load, the lateral frictional force and longitudinal frictional force at each wheel, and also the sideslip angle at center of gravity. These states are the key parameters that could optimize the control of vehicle's stability. The estimation of vertical load at each tire enables the evaluation of the risk of rollover. Estimation of tire lateral forces could help the control system reduce the lateral slip and prevent the situation like spinning and drift out. Tire longitudinal forces can also greatly influence the performance of vehicle. The sideslip angle is one of the most important parameter to control the lateral dynamics of vehicle. However, in the current market, very few safety systems are based on tire forces, due to the lack of cost-effective method to get these information. For all the above reasons, we would like to develop a perception system to monitor these vehicle dynamics states by using only low-cost sensor. In order to achieve this objective, we propose to develop novel observers to estimate unmeasured states. However, construction of an observer which could provide satisfactory performance at all condition is never simple. It requires : 1, accurate and efficient models; 2, a robust estimation algorithm; 3, considering the parameter variation and sensor errors. As motivated by these requirements, this dissertation is organized to present our contribution in three aspects : vehicle dynamics modelization, observer design and adaptive estimation. In the aspect of modeling, we propose several new models to describe vehicle dynamics. The existent models are obtained by simplifying the vehicle motion as a planar motion. In the proposed models, we described the vehicle motion as a 3D motion and considered the effects of road inclination. Then for the vertical dynamics, we propose to incorporate the suspension deflection to calculate the transfer of vertical load. For the lateral dynamics, we propose the model of transfer of lateral forces to describe the interaction between left wheel and right wheel. With this new model, the lateral force at each tire can be calculated without sideslip angle. Similarly, for longitudinal dynamics, we also propose the model of transfer of longitudinal forces to calculate the longitudinal force at each tire. In the aspect of observer design, we propose a novel observation system, which is consisted of four individual observers connected in a cascaded way. The four observers are developed for the estimation of vertical tire force, lateral tire force and longitudinal tire force and sideslip angle respectively. For the linear system, the Kalman filter is employed. While for the nonlinear system, the EKF, UKF and PF are applied to minimize the estimation errors. In the aspect of adaptive estimation, we propose the algorithms to improve sensor measurement and estimate vehicle parameters in order to stay robust in presence of parameter variation and sensor errors. Furthermore, we also propose to incorporate the digital map to enhance the estimation accuracy. The utilization of digital map could also enable the prediction of vehicle dynamics states and prevent the road accidents. Finally, we implement our algorithm in the experimental vehicle to realize real-time estimation. Experimental data has validated the proposed algorithm
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Arthur, Paul Edwin Solomon, and Sanjay Varadharajan. "Sensor fusion for estimating vehicle chassis movement." Thesis, KTH, Fordonsdynamik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302285.
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The aim of this thesis work is to investigate the possibility of applying a sensor fusion algorithm with a focus on estimating vehicle dynamic states, mainly the vehicle body accelerations. Modern passenger vehicles have several mechatronic systems such as active safety, comfort, driver assistance etc., which are highly dependant on accurate knowledge of such states. This work focuses on the mechatronic suspension system, which makes use of the body accelerations measurements to control the dynamics of the vehicle body in order to provide an improved driving experience. This work can be split up into two major parts, the first being the identification of available onboard sensors for measuring the vehicle body accelerations. Five different sensor combinations are considered and compared with each other. The next part is to develop a sensor fusion algorithm, in this case, a Kalman Filter (KF) based algorithm, which uses vehicle dynamic modelling knowledge to obtain accurate, reliable and less uncertain estimates of the states. Specifically, an Unscented Kalman Filter (UKF) and Cubature Kalman Filter (CKF) were built and compared with each other. Two different vehicle dynamic models, a vehicle planar dynamic model and a full car suspension model, were implemented to capture both the effects of road disturbances and drivingmanoeuvres on the vehicle body dynamics. Both these fusion algorithms were tested using simulation data and logged data and validated by comparing with an ideal sensing method to measure the body accelerations used currently at Volvo Car Corporation.Syftet med detta examensarbete är att undersöka möjligheten att tillämpa en sensorfusionsalgoritm med fokus på att uppskatta fordonets dynamiska tillstånd, främst karossens acceleration. Moderna personbilar har flera mekatroniska system som aktiv säkerhet, komfort, förarassistans etc., som är mycket beroende av exakt kunskap om sådana tillstånd. Detta arbete fokuserar på det mekatroniska fjädringssystemet, som använder karossens accelerationsmätningar för att styra fordonets dynamik och för att ge en förbättrad körupplevelse. Detta arbete kan delas upp i två huvuddelar, den första är identifiering av tillgängliga inbyggda sensorer för mätning av fordonets accelerationer. Fem olika sensorkombinationer övervägs och jämförs med varandra. Nästa del är att utveckla en sensorfusionsalgoritm, i detta fall en kalmanfilter baserad algoritm, som använder kunskap om fordonets dynamik för att få exakta, pålitliga och mindre osäkra uppskattningar av tillstånden. Specifikt byggdes en UKF och CKF som jämfördes med varandra. Två olika fordonsdynamiska modeller, en plan dynamisk modell och en full hjulupphängningsmodell, implementerades för att fånga både effekterna av vägstörningar och körmanövrer på fordonets karossdynamik. Båda dessa fusionsalgoritmer testades med hjälp av simuleringsdata och loggade data och validerades genom att jämföra med en idealisk avkänningsmetod för att mäta karossaccelerationerna som används för närvarande på Volvo Car Corporation.
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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.
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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.
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Nguyen, Van Tri. "Adjoint-based approach for estimation & sensor location on 1D hyperbolic systems with applications in hydrology & traffic." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT063/document.
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Ce travail de thèse propose une approche générique pour l'estimation de l'état/ des paramètres et pour le placement de capteurs de systèmes hyperboliques non linéaires en dimension infinie. Le travail est donc divisé en deux parties principales : une partie consacrée à l'estimation optimale et une partie dédiée au placement optimal de capteurs. La méthode d'estimation optimale utilise une approche par calcul des variations et utilise la méthode des multiplicateurs de Lagrange. Ces multiplicateurs jouent un rôle important en donnant accès aux sensibilités des mesures par rapport aux variables qui doivent être estimées. Ces sensibilités, décrites par les équations adjointes, sont aussi à l'origine d'une nouvelle approche, dite méthode de l'adjoint, pour le placement optimal de capteurs. Divers exemples, construits sur la base de simulations mais également de données réelles et pour différents scénarios, sont aussi étudiées afin d'illustrer l'efficacité des approches développées. Ces exemples concernent les écoulements à surface libre (en hydrologie des bassins versants) et le trafic routier représentés par des équations aux dérivées partielles hyperboliques non linéairesThe thesis proposes a general framework for both state/parameters estimation and sensor placement in nonlinear infinite dimensional hyperbolic systems. The work is therefore divided into two main parts: a first part devoted to the optimal estimation and a second one to optimal sensor location. The estimation method is based on the calculus of variations and the use of Lagrange multipliers. The Lagrange multipliers play an important role in giving access to the sensitivities of the measurements with respect to the variables to be estimated. These sensitivities, described by the adjoint equations, are also the key idea of a new approach, so-called the adjoint-based approach, for the optimal sensor placement. Various examples, either based on some simulations with synthetic measurements or real data sets and for different scenarios, are also studied to illustrate the effectiveness of the developed approaches. Theses examples concern the overland flow systems and the traffic flow, which are both governed by nonlinear hyperbolic partial differential equations
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Alatorre, Vazquez Angel Gabriel. "Robust estimation of dynamics behavior and driving diagnosis applied to an intelligent MAGV." Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2554.
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Cette thèse présente une série de stratégies pour estimer la dynamique des véhicules. Le but de ce travail est de développer une stratégie d'observation qui peut être appliquée aux véhicules de série. L'idée d'avoir un algorithme dans une voiture produite en série pose un grand nombre de défis,' ceux que nous considérons dans ce travail sont la robustesse et le coût. Nous avons proposé des modèles et des stratégies d'observateurs capables de faire face à des niveaux d'excitation élevés et faibles. Nous avons validé la robustesse de l'algorithme avec de nombreux tests, des petites pistes à faible vitesse aux manœuvres de changement de voie. Nos algorithmes ont été retravaillés plusieurs fois pour atteindre un degré de précision qui peut être utile pour l'intégration dans ADAS. Nous avons également proposé des stratégies d'observateurs qui permettent ce degré de robustesse tout en conservant une grille de capteurs à faible coût. Les principales contributions sont au nombre de trois : 1 - Estimation de la vitesse latérale et longitudinale,' ces variables sont essentielles pour une bonne rétroaction des régulateurs de stabilisation et de vitesse de croisière. Notre proposition utilise comme base un modèle cinématique pour éviter d'utiliser des paramètres liés à la masse dans notre modèle; cela est possible puisque notre grille de capteurs comprend des accéléromètres et des gyroscopes. L'une des principales contributions de cette section est la compensation de la gravité,' une équation différentielle de quaternions définit l'attitude de notre système. Plus de 100 tests valident la robustesse de l'algorithme, et nous obtenons des résultats cohérents dans chacun d'eux. 2- Estimation de l'estimation de la force pneu-sol normale. Cette variable est, à notre avis, la plus difficile à estimer car la grille de capteurs des véhicules de série ne contient pas beaucoup de capteurs mesurant la dynamique verticale. Cette section doit étendre notre solution aux véhicules de série avec des systèmes de suspension améliorés, y compris des capteurs de déflexion. Nous pouvons estimer la masse, la distribution de masse et le centre de gravité avec ces capteurs en place et transmettre l'estimation normale de la force pneu-sol en utilisant la fusion de modèles et le filtre de Kalman. 3 - Stratégies d'estimation des forces longitudinales et latérales pneu-sol. La première méthode utilise les modèles bicycle et hoverboard connus et les filtres de Kalman pour estimer les TGFs, et d'autres modèles sont introduits pour répartir ces forces sur le pneu adéquat. Cette méthode doit gérer la saturation des pneus, pour séparer correctement les TGF virtuels. La deuxième méthode utilise les lois de Newton du mouvement; ici, nous calculons les accélérations locales en utilisant l'accélération et les rotations d'un corps rigide. Étant donné que nous connaissons déjà les TGF normaux à chaque pneu, nous pouvons calculer les TGFs latéraux et longitudinaux avec précision. Cette dernière méthode est plus précise et robuste que la première méthode. Enfin, au final, nous proposons une série de systèmes qui bénéficieront des estimations antérieuresThe context of this thesis is the improvement of road safety through the development of active safety systems. One challenge in the development of active safety systems is obtaining accurate information about unmeasurable vehicle dynamic states. Specifically, the necessity to estimate the vertical load, frictional forces at each wheel (longitudinal and lateral), and also the sideslip angle at the center of gravity. These states are the critical parameters for optimizing the control of a vehicle’s stability. If the vertical load on each tire can be estimated, then the risk of rollover can be evaluated. Estimating tire lateral forces can help to reduce lateral slip and prevent dangerous situations like spinning and drifting out the road. Tire longitudinal forces influence the performance of a vehicle. Sideslip angle is one of the essential parameters for controlling the lateral dynamics of a vehicle. However, the different technologies that the market offers, are not based on tire-ground forces due to the lack of cost-effective methods for obtaining the required information. For the above mentioned reasons, we want to develop a system that monitors these dynamic vehicle states using only low-cost sensors. To accomplish our endeavor, we propose developing novel observers to estimate unmeasured states. Constructing an observer that met the reliability, robustness and accuracy requirements is not an easy task. It requires one the one hand, accurate and efficient models, and on the other hand, robust estimation algorithms that take into account variations in parameters and measurement errors. The present thesis has consequently been structured around the following two aspects: modeling of vehicle dynamics, and design of observers. Under the heading of modeling, we propose new models to describe vehicle dynamics. Current models simplify the vehicle motion as a planar motion. In our proposal, our models describe vehicle motion as a 3D motion, including the effects of road inclination. Regarding vertical dynamics, we propose incorporating the suspension deflection to calculate the transfer of vertical load. Regarding lateral dynamics, we propose a model for the lateral forces transfer to describe the interaction between the left wheel and the right wheel. With this relationship, the lateral force on each tire is computed without using the sideslip angle. Similarly, for longitudinal dynamics, we also propose a model for the transfer of longitudinal forces to calculate the longitudinal force at each tire. Under the heading of observer design, we propose a novel observation system consisting of four individual observers connected in cascade. The four observers are developed for estimating vertical tire force, lateral tire force, longitudinal tire force, and sideslip angle, respectively. For the linear system, the Kalman filter is employed, while for the nonlinear system, the EKF applied to reduce estimation errors. Finally, we implement our algorithm in an experimental vehicle to perform estimation in real-time, and we validate our proposed algorithm using experimental data
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Dittrich, Petr. "Odhad Letových Parametrů Malého Letounu." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-412582.
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Tato práce je zaměřena na odhad letových parametrů malého letounu, konkrétně letounu Evektor SportStar RTC. Pro odhad letových parametrů jsou použity metody "Equation Error Method", "Output Error Method" a metody rekurzivních nejmenších čtverců. Práce je zaměřena na zkoumání charakteristik aerodynamických parametrů podélného pohybu a ověření, zda takto odhadnuté letové parametry odpovídají naměřeným datům a tudíž vytvářejí předpoklad pro realizaci dostatečně přesného modelu letadla. Odhadnuté letové parametry jsou dále porovnávány s a-priorními hodnotami získanými s využitím programů Tornado, AVL a softwarovéverze sbírky Datcom. Rozdíly mezi a-priorními hodnotami a odhadnutými letovými paramatery jsou porovnány s korekcemi publikovanými pro subsonické letové podmínky modelu letounu F-18 Hornet.
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Gherardini, Stefano. "Noise as a resource - Probing and manipulating classical and quantum dynamical systems via stochastic measurements." Doctoral thesis, 2018. http://hdl.handle.net/2158/1120060.
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In this thesis, common features from the theories of open quantum systems, estimation of state dynamics and statistical mechanics have been integrated in a comprehensive framework, with the aim to analyze and quantify the energetic and information contents that can be extracted from a dynamical system subject to the external environment. The latter is usually assumed to be deleterious for the feasibility of specic control tasks, since it can be responsible for uncontrolled time-dependent (and even discontinuous) changes
of the system. However, if the effects of the random interaction with a noisy environment are properly modeled by the introduction of a given stochasticity within the dynamics of the system, then even noise contributions might be seen as control knobs. As a matter of fact, even a partial knowledge of the environment can allow to set the system in a dynamical condition in which the response is optimized by the presence of noise sources. In particular, we have investigated what kind of measurement devices can work better in
noisy dynamical regimes and studied how to maximize the resultant information via the adoption of estimation algorithms. Moreover, we have shown the optimal interplay between quantum dynamics, environmental noise and complex network topology in maximizing the energy transport efficiency. Then, foundational scientic aspects, such as the occurrence of an ergodic property for the system-environment interaction modes of a randomly perturbed quantum system or the characterization of the stochastic quantum Zeno phenomena, have been analyzed by using the predictions of the large deviation theory. Finally, the energy cost in maintaining the system in the non-equilibrium regime due to the presence of the environment is evaluated by reconstructing the corresponding thermodynamics entropy production. In conclusion, the present thesis can constitute the basis for an effective
resource theory of noise, which is given by properly engineering the interaction between a dynamical (quantum or classical) system and its external environment.
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Serpas, Mitchell Roy. "Soft Sensors for Process Monitoring of Complex Processes." Thesis, 2012. http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11639.
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Soft sensors are an essential component of process systems engineering schemes. While soft sensor design research is important, investigation into the relationships between soft sensors and other areas of advanced monitoring and control is crucial as well. This dissertation presents two new techniques that enhance the performance of fault detection and sensor network design by integration with soft sensor technology. In addition, a chapter is devoted to the investigation of the proper implementation of one of the most often used soft sensors. The performance advantages of these techniques are illustrated with several cases studies.
First, a new approach for fault detection which involves soft sensors for process monitoring is developed. The methodology presented here deals directly with the state estimates that need to be monitored. The advantage of such an approach is that the nonlinear effect of abnormal process conditions on the state variables can be directly observed. The presented technique involves a general framework for using soft sensor design and computation of the statistics that represent normal operating conditions.
Second, a method for determining the optimal placement of multiple sensors for processes described by a class of nonlinear dynamic systems is described. This approach is based upon maximizing a criterion, i.e., the determinant, applied to the empirical observability gramian in order to optimize certain properties of the process state estimates. The determinant directly accounts for redundancy of information, however, the resulting optimization problem is nontrivial to solve as it is a mixed integer nonlinear programming problem. This paper also presents a decomposition of the optimization problem such that the formulated sensor placement problem can be solved quickly and accurately on a desktop PC.
Many comparative studies, often based upon simulation results, between Extended Kalman filters (EKF) and other estimation methodologies such as Moving Horizon Estimation or Unscented Kalman Filter have been published over the last few years. However, the results returned by the EKF are affected by the algorithm used for its implementation and some implementations may lead to inaccurate results. In order to address this point, this work provides a comparison of several different algorithms for implementation.
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Reddy, T. Mohan. "Capacity and Life Estimation of Flooded Lead Acid Batteries using Eddy Current Sensors." Thesis, 2016. http://etd.iisc.ernet.in/handle/2005/2971.
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Lead acid batteries are widely used in domestic, industrial and automotive applications. Even after lot of advancements in battery technologies, lead acid cells are still in use because of their high capacity and low cost. To use any battery effectively, first we should be able to identify the available capacity or State of Charge (SoC). There are many techniques available to measure SoC of a lead acid battery. One such unique method is to measure the capacity using eddy current sensors. This method is unique because it is non-obtrusive and online. Eddy current sensors (ECS) are wire wound inductors which work on the principle of electromagnetic induction. Eddy currents are the currents generated on a conductive material when it is kept in a varying magnetic. Eddy current sensors generate varying magnetic eldest and will be able to identify the properties of conductive materials like thickness, conductivity, material composition etc. Also they can be used as proximity sensors.
Lead acid batteries use lead metal as cathode. Upon usage(discharge) the lead metal converts to lead sulfate and revert back to lead after charging. These changes in lead electrode can be monitored using eddy current sensors. The impedance of an eddy current sensor will change when it is kept close to the lead electrode when the battery is charging or discharging. These impedance parameters can be monitored to determine the battery SoC. When lead is deposited on cathode, there will be more eddy current loss in the target and the total resistance of coil increases. On the other hand, when lead is deposited on the electrode because of increase in the magnitude of eddy currents which oppose the source magnetic, the total inductance of coil decreases. We can observe exactly opposite behaviour of coil resistance and inductance when the lead electrode is converted to less conductive lead sulfate. There is a lot of research on using ECS to measure SoC of lead acid batteries and there are still many challenges to be addressed.
First we have explained about different circuit designs we have used to monitor the battery capacity using eddy current sensors. After that, we have explained about our complete experimental setup and the procedure to measure the sensor parameters using the setup. Then, we have discussed about different issues involved in the eddy current sensing based state of charge measurement.
Eddy current sensors are affected by temperature variations. We have studied the coil resistance behaviour with temperature at different frequencies using simulations and experiments. We have obtained the conditions for linear variation of coil resistance with temperature. The measured temperature compensation scheme is applied and the results are discussed. We have also modified the measurement system design in order to minimize the lift o errors. We have used a metallic clamp structure to minimize the lift o errors.
We have used finite element analysis based simulations to study different design parameters and their effect on the sensitivity of eddy current sensor. We have created 2D eddy current models and the sensitivity of coil resistance is computed by changing the coil dimensions and the core permeability. We have also performed error analysis and computed the error due to the tilt angle shift between coil and electrode. We have also computed the error due to the internal heating of battery.
We have also studied the effect of acid strati cation on state of charge for both sealed and hooded batteries. We have proposed a multi coil method to minimize the errors in SoC measurement due to acid strati cation for Flooded type batteries. We have used finite element analysis based simulations to compute the error due to acid strati cation by increasing the number of coils.
Finally we have derived the equation for electrode Q factor using the transformer model of eddy current sensor. The derived Q factor equation is then used to study the aging of lead acid batteries both by using experiments and simulations. Finally we have explained a detail procedure to measure the state of charge(SoC) and state of health(SoH) of a hooded lead acid battery using eddy current sensing method.
Books on the topic "State estimation with binary sensors"
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Vaez-Zadeh, Sadegh. Rotor Position and Speed Estimation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198742968.003.0006.
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The ultimate importance of rotor position and speed information in permanent magnet synchronous (PMS) machines control, and the industry interest to the rotor and speed sensorless systems as a cost-saving and practical alternative to the motor control with mechanical sensors are emphasized. Major position and speed estimation schemes are then presented in detail. These are the: back electromotive force (EMF)-based method; flux linkage method; hypothesis rotor position method; saliency-based method, including high frequency signal injection and inverter switching harmonics schemes; and finally, the observer-based method, including state observer and extended Kalman filter-based schemes. Each scheme was discussed by presenting the corresponding fundamental principles, followed by the appropriate motor model, estimation procedure, and the implementation. Demanding criteria such as accuracy, robustness, swiftness, and capability of working over the entire range of motor operation are discussed with each method.
Book chapters on the topic "State estimation with binary sensors"
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García Carrillo, Luis Rodolfo, Alejandro Enrique Dzul López, Rogelio Lozano, and Claude Pégard. "Imaging Sensors for State Estimation." In Advances in Industrial Control, 71–102. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4399-4_5.
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Sturm, Jürgen. "Object State Estimation Using Tactile Sensors." In Springer Tracts in Advanced Robotics, 141–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37160-8_7.
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Matveev, Alexey S., and Andrey V. Savkin. "Robust Kalman State Estimation with Switched Sensors." In Estimation and Control over Communication Networks, 1–11. Boston: Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4607-3_15.
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Cohen, Albert, Wolfgang Dahmen, and Ron DeVore. "State Estimation—The Role of Reduced Models." In SEMA SIMAI Springer Series, 57–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-86236-7_4.
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AbstractThe exploration of complex physical or technological processes usually requires exploiting available information from different sources: (i) physical laws often represented as a family of parameter dependent partial differential equations and (ii) data provided by measurement devices or sensors. The amount of sensors is typically limited and data acquisition may be expensive and in some cases even harmful. This article reviews some recent developments for this “small-data” scenario where inversion is strongly aggravated by the typically large parametric dimensionality. The proposed concepts may be viewed as exploring alternatives to Bayesian inversion in favor of more deterministic accuracy quantification related to the required computational complexity. We discuss optimality criteria which delineate intrinsic information limits, and highlight the role of reduced models for developing efficient computational strategies. In particular, the need to adapt the reduced models—not to a specific (possibly noisy) data set but rather to the sensor system—is a central theme. This, in turn, is facilitated by exploiting geometric perspectives based on proper stable variational formulations of the continuous model.
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Matveev, Alexey S., and Andrey V. Savkin. "Kalman State Estimation in Networked Systems with Asynchronous Communication Channels and Switched Sensors." In Estimation and Control over Communication Networks, 1–24. Boston: Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4607-3_14.
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Unhelkar, Vaibhav V., and Hari B. Hablani. "Spacecraft Attitude Determination with Sun Sensors, Horizon Sensors and Gyros: Comparison of Steady-State Kalman Filter and Extended Kalman Filter." In Advances in Estimation, Navigation, and Spacecraft Control, 413–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44785-7_22.
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Kieffer, Michel. "Distributed Bounded-Error Parameter and State Estimation in Networks of Sensors." In Numerical Validation in Current Hardware Architectures, 189–202. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01591-5_12.
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Chaturvedi, Sarthak, S. Deepak, Dhivya Bharathi, and Bhargava Rama Chilukuri. "Data Imputation for Traffic State Estimation and Pre-diction Using Wi-Fi Sensors." In Proceedings of the Sixth International Conference of Transportation Research Group of India, 385–95. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4204-4_23.
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Mwaffo, Violet, Jackson S. Curry, Francesco Lo Iudice, and Pietro DeLellis. "Experiments on Pause and Go State Estimation and Control with Uncertain Sensors Feedback." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 87–101. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92163-7_8.
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Bauer, Markus, Carlo Ackermann, and Rolf Isermann. "Integrated State Estimation with Driving Dynamic Sensors and GPS Data to Evaluate Driving Dynamics Control Functions." In Lecture Notes in Electrical Engineering, 1797–806. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33738-3_73.
Full textConference papers on the topic "State estimation with binary sensors"
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Battistelli, Giorgio, Luigi Chisci, and Stefano Gherardini. "Moving horizon state estimation for discrete-time linear systems with binary sensors." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402569.
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Laoudias, Christos, Michalis P. Michaelides, and Christos Panayiotou. "Sensor health state estimation for target tracking with binary sensor networks." In ICC 2013 - 2013 IEEE International Conference on Communications. IEEE, 2013. http://dx.doi.org/10.1109/icc.2013.6654795.
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Laoudias, Christos, Michalis P. Michaelides, and Christos Panayiotou. "Fault tolerant target localization and tracking in binary WSNs using sensor health state estimation." In ICC 2013 - 2013 IEEE International Conference on Communications. IEEE, 2013. http://dx.doi.org/10.1109/icc.2013.6654719.
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Saito, H., S. Shimogawa, S. Shioda, and J. Harada. "Shape Estimation Using Networked Binary Sensors." In 2009 Proceedings IEEE INFOCOM. IEEE, 2009. http://dx.doi.org/10.1109/infcom.2009.5062255.
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Huang, Xiaonan, William R. Johnson, Joran Booth, and Rebecca Kramer-Bottiglio. "Live Demonstration: Tensegrity State Estimation." In 2022 IEEE Sensors. IEEE, 2022. http://dx.doi.org/10.1109/sensors52175.2022.9967005.
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Wang, Yuquan, Jan Mangnus, Dragan Kostic, Henk Nijmeijer, and Sven T. H. Jansen. "Vehicle state estimation using GPS/IMU integration." In 2011 IEEE Sensors. IEEE, 2011. http://dx.doi.org/10.1109/icsens.2011.6127142.
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Veillard, Damien, Frederick Mailly, and Philippe Fraisse. "EKF-based state estimation for train localization." In 2016 IEEE SENSORS. IEEE, 2016. http://dx.doi.org/10.1109/icsens.2016.7808726.
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Le Yi Wang, G. George Yin, and Guohua Xu. "State estimation of systems with binary-valued observations." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434187.
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Battistelli, Giorgio, Luigi Chisci, Nicola Forti, and Stefano Gherardini. "MAP Moving Horizon state estimation with binary measurements." In 2016 American Control Conference (ACC). IEEE, 2016. http://dx.doi.org/10.1109/acc.2016.7526518.
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Moussakhani, Babak, Ilangko Balasingham, and Tor Ramstad. "Distributed Signal Estimation Using Binary Sensors with Multiple Thresholds." In 2010 IEEE 71st Vehicular Technology Conference. IEEE, 2010. http://dx.doi.org/10.1109/vetecs.2010.5494193.
Full textReports on the topic "State estimation with binary sensors"
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Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs, and Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, October 1994. http://dx.doi.org/10.32747/1994.7570549.bard.
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The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the project, to develop and apply the two complementary methods for quality control of agricultural commodities. In Israel: An improved firmness testing system was developed and tested with tropical fruits. The new system included an instrumented fruit-bed of three flexible piezoelectric sensors and miniature electromagnetic hammers, which served as fruit support and low-energy excitation device, respectively. Resonant frequencies were detected for determination of firmness index. Two new acoustic parameters were developed for evaluation of fruit firmness and maturity: a dumping-ratio and a centeroid of the frequency response. Experiments were performed with avocado and mango fruits. The internal damping ratio, which may indicate fruit ripeness, increased monotonically with time, while resonant frequencies and firmness indices decreased with time. Fruit samples were tested daily by destructive penetration test. A fairy high correlation was found in tropical fruits between the penetration force and the new acoustic parameters; a lower correlation was found between this parameter and the conventional firmness index. Improved table-top firmness testing units, Firmalon, with data-logging system and on-line data analysis capacity have been built. The new device was used for the full-scale experiments in the next two years, ahead of the original program and BARD timetable. Close cooperation was initiated with local industry for development of both off-line and on-line sorting and quality control of more agricultural commodities. Firmalon units were produced and operated in major packaging houses in Israel, Belgium and Washington State, on mango and avocado, apples, pears, tomatoes, melons and some other fruits, to gain field experience with the new method. The accumulated experimental data from all these activities is still analyzed, to improve firmness sorting criteria and shelf-life predicting curves for the different fruits. The test program in commercial CA storage facilities in Washington State included seven apple varieties: Fuji, Braeburn, Gala, Granny Smith, Jonagold, Red Delicious, Golden Delicious, and D'Anjou pear variety. FI master-curves could be developed for the Braeburn, Gala, Granny Smith and Jonagold apples. These fruits showed a steady ripening process during the test period. Yet, more work should be conducted to reduce scattering of the data and to determine the confidence limits of the method. Nearly constant FI in Red Delicious and the fluctuations of FI in the Fuji apples should be re-examined. Three sets of experiment were performed with Flandria tomatoes. Despite the complex structure of the tomatoes, the acoustic method could be used for firmness evaluation and to follow the ripening evolution with time. Close agreement was achieved between the auction expert evaluation and that of the nondestructive acoustic test, where firmness index of 4.0 and more indicated grade-A tomatoes. More work is performed to refine the sorting algorithm and to develop a general ripening scale for automatic grading of tomatoes for the fresh fruit market. Galia melons were tested in Israel, in simulated export conditions. It was concluded that the Firmalon is capable of detecting the ripening of melons nondestructively, and sorted out the defective fruits from the export shipment. The cooperation with local industry resulted in development of automatic on-line prototype of the acoustic sensor, that may be incorporated with the export quality control system for melons. More interesting is the development of the remote firmness sensing method for sealed CA cool-rooms, where most of the full-year fruit yield in stored for off-season consumption. Hundreds of ripening monitor systems have been installed in major fruit storage facilities, and being evaluated now by the consumers. If successful, the new method may cause a major change in long-term fruit storage technology. More uses of the acoustic test method have been considered, for monitoring fruit maturity and harvest time, testing fruit samples or each individual fruit when entering the storage facilities, packaging house and auction, and in the supermarket. This approach may result in a full line of equipment for nondestructive quality control of fruits and vegetables, from the orchard or the greenhouse, through the entire sorting, grading and storage process, up to the consumer table. The developed technology offers a tool to determine the maturity of the fruits nondestructively by monitoring their acoustic response to mechanical impulse on the tree. A special device was built and preliminary tested in mango fruit. More development is needed to develop a portable, hand operated sensing method for this purpose. In North Carolina: Analysis method based on an Auto-Regressive (AR) model was developed for detecting the first resonance of fruit from their response to mechanical impulse. The algorithm included a routine that detects the first resonant frequency from as many sensors as possible. Experiments on Red Delicious apples were performed and their firmness was determined. The AR method allowed the detection of the first resonance. The method could be fast enough to be utilized in a real time sorting machine. Yet, further study is needed to look for improvement of the search algorithm of the methods. An impact contact-pressure measurement system and Neural Network (NN) identification method were developed to investigate the relationships between surface pressure distributions on selected fruits and their respective internal textural qualities. A piezoelectric dot-matrix pressure transducer was developed for the purpose of acquiring time-sampled pressure profiles during impact. The acquired data was transferred into a personal computer and accurate visualization of animated data were presented. Preliminary test with 10 apples has been performed. Measurement were made by the contact-pressure transducer in two different positions. Complementary measurements were made on the same apples by using the Firmalon and Magness Taylor (MT) testers. Three-layer neural network was designed. 2/3 of the contact-pressure data were used as training input data and corresponding MT data as training target data. The remaining data were used as NN checking data. Six samples randomly chosen from the ten measured samples and their corresponding Firmalon values were used as the NN training and target data, respectively. The remaining four samples' data were input to the NN. The NN results consistent with the Firmness Tester values. So, if more training data would be obtained, the output should be more accurate. In addition, the Firmness Tester values do not consistent with MT firmness tester values. The NN method developed in this study appears to be a useful tool to emulate the MT Firmness test results without destroying the apple samples. To get more accurate estimation of MT firmness a much larger training data set is required. When the larger sensitive area of the pressure sensor being developed in this project becomes available, the entire contact 'shape' will provide additional information and the neural network results would be more accurate. It has been shown that the impact information can be utilized in the determination of internal quality factors of fruit. Until now,