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Auteur : Grafiati
Publié le 10 mars 2023

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1

PÉREZ ARCILA, MAURICIO, et 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 (11 novembre 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 et Jin Yong. « Sensor Selection Scheme considering Uncertainty Disturbance ». Journal of Sensors 2022 (16 février 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 et 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 et 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 (octobre 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 et Hailong Pei. « Non-Uniform Discretization-based Ordinal Regression for Monocular Depth Estimation of an Indoor Drone ». Electronics 9, no 11 (23 octobre 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 et Kibum Kim. « Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model ». Entropy 22, no 5 (20 mai 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 et 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 et Klaus Dietmayer. « A random finite set approach for dynamic occupancy grid maps with real-time application ». International Journal of Robotics Research 37, no 8 (juillet 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 et 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 (18 mars 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 et Jinhui Li. « Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field ». Sensors 19, no 2 (21 janvier 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.
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Huang, Junhao, Zhicheng Zhang, Guoping Xie et Hui He. « Real-Time Precise Human-Computer Interaction System Based on Gaze Estimation and Tracking ». Wireless Communications and Mobile Computing 2021 (8 novembre 2021) : 1–10. http://dx.doi.org/10.1155/2021/8213946.

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Noncontact human-computer interaction has an important value in wireless sensor networks. This work is aimed at achieving accurate interaction on a computer based on auto eye control, using a cheap webcam as the video source. A real-time accurate human-computer interaction system based on eye state recognition, rough gaze estimation, and tracking is proposed. Firstly, binary classification of the eye states (opening or closed) is carried on using the SVM classification algorithm with HOG features of the input eye image. Second, rough appearance-based gaze estimation is implemented based on a simple CNN model. And the head pose is estimated to judge whether the user is facing the screen or not. Based on these recognition results, noncontact mouse control and character input methods are designed and developed to replace the standard mouse and keyboard hardware. Accuracy and speed of the proposed interaction system are evaluated by four subjects. The experimental results show that users can use only a common monocular camera to achieve gaze estimation and tracking and to achieve most functions of real-time precise human-computer interaction on the basis of auto eye control.
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Dzubur, Eldin, Aditya Ponnada, Rachel Nordgren, Chih-Hsiang Yang, Stephen Intille, Genevieve Dunton et Donald Hedeker. « MixWILD : A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data ». Behavior Research Methods 52, no 4 (2 janvier 2020) : 1403–27. http://dx.doi.org/10.3758/s13428-019-01322-1.

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AbstractThe use of intensive sampling methods, such as ecological momentary assessment (EMA), is increasingly prominent in medical research. However, inferences from such data are often limited to the subject-specific mean of the outcome and between-subject variance (i.e., random intercept), despite the capability to examine within-subject variance (i.e., random scale) and associations between covariates and subject-specific mean (i.e., random slope). MixWILD (Mixed model analysis With Intensive Longitudinal Data) is statistical software that tests the effects of subject-level parameters (variance and slope) of time-varying variables, specifically in the context of studies using intensive sampling methods, such as ecological momentary assessment. MixWILD combines estimation of a stage 1 mixed-effects location-scale (MELS) model, including estimation of the subject-specific random effects, with a subsequent stage 2 linear or binary/ordinal logistic regression in which values sampled from each subject’s random effect distributions can be used as regressors (and then the results are aggregated across replications). Computations within MixWILD were written in FORTRAN and use maximum likelihood estimation, utilizing both the expectation-maximization (EM) algorithm and a Newton–Raphson solution. The mean and variance of each individual’s random effects used in the sampling are estimated using empirical Bayes equations. This manuscript details the underlying procedures and provides examples illustrating standalone usage and features of MixWILD and its GUI. MixWILD is generalizable to a variety of data collection strategies (i.e., EMA, sensors) as a robust and reproducible method to test predictors of variability in level 1 outcomes and the associations between subject-level parameters (variances and slopes) and level 2 outcomes.
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Obukhov, Sergey A., Valery P. Stepanov et Igor V. Rudakov. « MATHEMATICAL MODEL OF BRAIN-COMPUTER INTERFACE BASED ON THE ANALYSIS OF P300 EVENT RELATED POTENTIALS ». RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no 2 (2021) : 48–67. http://dx.doi.org/10.28995/2686-679x-2021-2-48-67.

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The evoked potentials (EP) method consists in recording bioelectric reactions of the brain in response to external stimulation or while performing cognitive tasks. The goal of the work is to develop a mathematical model of the system for detection and classification of evoked potentials on the electroencephalogram (EEG). The main odd of the machine EP detection are artifacts from EEG recordings and the high variability of potentials. EP detection and classification algorithm includes three stages. At the preliminary stage, the frequency-time and spatial signal transformations – a set of Butterworth frequency filters, linear composition and averaging of the recorded signals from different sensors are used to remove noise and uninformative EEG components. The next step is the direct fixation and averaging of the evoked potentials. At the final stage, to reduce the dimension of the problem, the information features vector is formed. The parameterized image is used as input of the binary classifier. The support vector method is used to construct the classifier. During the study, the optimization of the regularization C parameter of the classifier was carried out using the estimation of sliding control. The proposed solution is useful for human-machine interaction and for medical procedures with biofeedback.
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Zhang, Yuchen, Bo Chen et Li Yu. « Fusion estimation under binary sensors ». Automatica 115 (mai 2020) : 108861. http://dx.doi.org/10.1016/j.automatica.2020.108861.

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Wang, Leyi, Gang George Yin, Chanying Li et Weixing Zheng. « Signal estimation with binary-valued sensors ». Journal of Systems Science and Complexity 23, no 3 (juin 2010) : 622–39. http://dx.doi.org/10.1007/s11424-010-0149-4.

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Masry, Elias, et Prakash Ishwar. « Field Estimation From Randomly Located Binary Noisy Sensors ». IEEE Transactions on Information Theory 55, no 11 (novembre 2009) : 5197–210. http://dx.doi.org/10.1109/tit.2009.2030469.

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Lobashev, Alexander A., Nikita A. Turko, Konstantin V. Ushakov, Maxim N. Kaurkin et Rashit A. Ibrayev. « Concrete Autoencoder for the Reconstruction of Sea Temperature Field from Sparse Measurements ». Journal of Marine Science and Engineering 11, no 2 (12 février 2023) : 404. http://dx.doi.org/10.3390/jmse11020404.

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This paper presents a new method for finding the optimal positions for sensors used to reconstruct geophysical fields from sparse measurements. The method is composed of two stages. In the first stage, we estimate the spatial variability of the physical field by approximating its information entropy using the Conditional Pixel CNN network. In the second stage, the entropy is used to initialize the distribution of optimal sensor locations, which is then optimized using the Concrete Autoencoder architecture with the straight-through gradient estimator for the binary mask and with adversarial loss. This allows us to simultaneously minimize the number of sensors and maximize reconstruction accuracy. We apply our method to the global ocean under-surface temperature field and demonstrate its effectiveness on fields with up to a million grid cells. Additionally, we find that the information entropy field has a clear physical interpretation related to the mixing between cold and warm currents.
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Vazquez, Rafael, Eugenio Schuster et Miroslav Krstic. « Magnetohydrodynamic state estimation with boundary sensors ». Automatica 44, no 10 (octobre 2008) : 2517–27. http://dx.doi.org/10.1016/j.automatica.2008.02.022.

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Ye Wang et P. Ishwar. « Distributed Field Estimation With Randomly Deployed, Noisy, Binary Sensors ». IEEE Transactions on Signal Processing 57, no 3 (mars 2009) : 1177–89. http://dx.doi.org/10.1109/tsp.2008.2008535.

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Movaghati, Sahar, et Masoud Ardakani. « Distributed Binary Quantization of a Noisy Source in Wireless Sensor Networks ». Journal of Sensors 2014 (2014) : 1–11. http://dx.doi.org/10.1155/2014/368643.

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In distributed (decentralized) estimation in wireless sensor networks, an unknown parameter must be estimated from some noisy measurements collected at different sensors. Due to limited communication resources, these measurements are typically quantized before being sent to a fusion center, where an estimation of the unknown parameter is calculated. In the most stringent condition, each measurement is converted to a single bit. In this study, we propose a distributed quantization scheme which is based on single-bit quantized data from each sensor and achieves high estimation accuracy at the fusion centre. We do this by designing some local binary quantizers which define a multithreshold quantization rule for each sensor. These local binary quantizers are initially designed so that together they mimic the functionality of a multilevel quantizer. Later, their design is improved to include some error-correcting capability, which further improves the estimation accuracy from the sensors’ binary data. The distributed quantization formed by such local binary quantizers along with the proper estimator proposed in this work achieves better performance, compared to the existing distributed binary quantization methods, specially when fewer sensors with low measurement noise are available.
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Hill, David J. « Assimilation of weather radar and binary ubiquitous sensor measurements for quantitative precipitation estimation ». Journal of Hydroinformatics 17, no 4 (20 janvier 2015) : 598–613. http://dx.doi.org/10.2166/hydro.2015.072.

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Assimilation of data from heterogeneous sensors and sensor networks is critical for achieving accurate measurements of environmental processes at the time and space scales necessary to improve forecasting and decision-making. Owing to different measurement accuracies and types of spatial and/or temporal measurement support of the component sensors, it is often unclear how best to combine these data. This study explores the utility of ubiquitous sensors producing categorical wet/dry rainfall measurements for improving the resolution of areal quantitative precipitation estimates through fusion with weather radar observations. The model developed in this study employs a Markov random field model to compute the probability of rainfall at sub-grid pixels. These likelihoods are used to ‘unmix’ the cell-averaged rainfall rate measured by the radar. Simulation studies using synthetic and known rainfall fields reveal that the model can improve remotely sensed quantitative rainfall intensity measurements by 40% using networks of ubiquitous sensors with a density of 56 sensors per square kilometer, and for denser networks, the accuracy can increase by as much as 50%.
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Carnevale, Daniele, et Francesco Martinelli. « State Estimation for Robots with Complementary Redundant Sensors ». International Journal of Advanced Robotic Systems 12, no 10 (6 octobre 2015) : 138. http://dx.doi.org/10.5772/60528.

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Leong, Alex S., Subhrakanti Dey, Girish N. Nair et Priyank Sharma. « Outage Minimization For State Estimation Using Multiple Sensors* ». IFAC Proceedings Volumes 42, no 20 (septembre 2009) : 216–21. http://dx.doi.org/10.3182/20090924-3-it-4005.00037.

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Battistelli, G., A. Benavoli et L. Chisci. « State estimation with remote sensors and intermittent transmissions ». Systems & ; Control Letters 61, no 1 (janvier 2012) : 155–64. http://dx.doi.org/10.1016/j.sysconle.2011.10.005.

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SAITO, Hiroshi, Sadaharu TANAKA et Shigeo SHIODA. « Parameter Estimation for Non-convex Target Object Using Networked Binary Sensors ». IEICE Transactions on Information and Systems E94-D, no 4 (2011) : 772–85. http://dx.doi.org/10.1587/transinf.e94.d.772.

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Csáji, Balázs Csanád, et Erik Weyer. « Recursive Estimation of ARX Systems Using Binary Sensors with Adjustable Thresholds ». IFAC Proceedings Volumes 45, no 16 (juillet 2012) : 1185–90. http://dx.doi.org/10.3182/20120711-3-be-2027.00264.

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Hara, Yushin, Yuta Yamamoto et Kanjuro Makihara. « Self-sensing state estimation of switch-controlled energy harvesters ». Journal of Intelligent Material Systems and Structures 31, no 20 (3 août 2020) : 2326–41. http://dx.doi.org/10.1177/1045389x20943944.

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Vibration energy harvesters are expected to become a new source of electrical power. Piezoelectric vibration energy harvesters that employ a piezoelectric transducer, a rectifier, and a storage capacitor are being used widely as electro-mechanical harvesters. Synchronized switch harvesting on inductor enhances harvesting performance due to employing a simple additional circuit and incorporating suitable switch control functionality. Switching is usually based on the displacement of a vibrating structure; hence, sensing the vibrational states is of critical importance. Conventionally, the structural displacement is measured by displacement sensors or accelerometers attached to the target vibrating structure. Although enhancement of performance through synchronized switch harvesting on inductor equipped with sensors is important, the arrangement requirements of sensors have adverse effects on the compactness and usability of the harvesters. This study aimed to eliminate the use of sensors from switch-controlled harvesters. We developed a new state estimation method that uses the piezoelectric transducer’s voltage as an observation value. Using the proposed state estimation method, the modal state values of the vibrating structure can be determined by simply measuring the voltage of the transducer. With the switch device being controlled by the estimated modal state values, no sensors are required for ensuring effective harvesting. A comparison of the harvesting performances by the proposed self-sensing state estimation method and the conventional sensor-equipped state estimation method showed that there is little difference in harvested power between the two methods over a wide range of load resistances. The proposed method is superior to the sensor-equipped method in terms of compactness and usability as it does not require any external sensors.
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Battistelli, Giorgio, Alessio Benavoli et Luigi Chisci. « State Estimation with Remote Sensors and Data-Driven Communication ». IFAC Proceedings Volumes 42, no 20 (septembre 2009) : 364–69. http://dx.doi.org/10.3182/20090924-3-it-4005.00062.

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Ren, Xiaoqiang, Yilin Mo, Jie Chen et Karl Henrik Johansson. « Secure State Estimation With Byzantine Sensors : A Probabilistic Approach ». IEEE Transactions on Automatic Control 65, no 9 (septembre 2020) : 3742–57. http://dx.doi.org/10.1109/tac.2020.2982589.

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de Assis, Adilson José, et Rubens Maciel Filho. « Soft sensors development for on-line bioreactor state estimation ». Computers & ; Chemical Engineering 24, no 2-7 (juillet 2000) : 1099–103. http://dx.doi.org/10.1016/s0098-1354(00)00489-0.

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Mahmoud, Magdi S., et Muhammad F. Emzir. « State estimation with asynchronous multi-rate multi-smart sensors ». Information Sciences 196 (août 2012) : 15–27. http://dx.doi.org/10.1016/j.ins.2012.01.034.

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Ku, Do Yeou, et Amir Patel. « Kinematic State Estimation Using Multiple DGPS/MEMS-IMU Sensors ». IEEE Sensors Letters 4, no 12 (décembre 2020) : 1–4. http://dx.doi.org/10.1109/lsens.2020.3040661.

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Baratti, Roberto, Massimiliano Barolo, Fabrizio Bezzo et Stefania Tronci. « Robust Dynamic State Estimation of a Binary Distillation Column ». IFAC Proceedings Volumes 37, no 9 (juillet 2004) : 971–76. http://dx.doi.org/10.1016/s1474-6670(17)31934-1.

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Solares, C., A. J. Conejo, E. Castillo et R. E. Pruneda. « Binary-arithmetic approach to observability checking in state estimation ». IET Generation, Transmission & ; Distribution 3, no 4 (2009) : 336. http://dx.doi.org/10.1049/iet-gtd.2008.0248.

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Su, Yi-Zhu, et Wei-Chang Yeh. « Binary-Addition Tree Algorithm-Based Resilience Assessment for Binary-State Network Problems ». Electronics 9, no 8 (27 juillet 2020) : 1207. http://dx.doi.org/10.3390/electronics9081207.

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Applications in real life are composed of different kinds of network systems; these networks may be interfered by uncontrollable or unpredictable disruptive events involving natural disasters, human errors, evil-intentioned attacks, or other disturbances. Any of these disruptive events will cause networks to malfunction and possibly result in large economic losses. As a result, it is important to assess network resilience which is a measure to describe how a network system recovers its performance and functionality to a satisfactory level from a disruptive event. Inspired by the measures of reliability evaluation used in binary-state networks, this paper proposes a binary-addition tree algorithm-based resilience assessment for binary-state networks and applies it on a wildfire network with wireless sensors. Considering the stochastic nature of disruptive events, the proposed binary-addition tree algorithm-based resilience assessment comprehensively enumerates all the possible disruptive events and all the corresponding recovery strategies, and then calculate the network resilience. Furthermore, recovery cost limit is concerned in this paper for decision makers who choose the recovery strategies with their recovery cost limit and resilience requirement.
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Ristic, Branko, Ajith Gunatilaka et Ralph Gailis. « Achievable accuracy in Gaussian plume parameter estimation using a network of binary sensors ». Information Fusion 25 (septembre 2015) : 42–48. http://dx.doi.org/10.1016/j.inffus.2014.10.007.

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Kumar, Yash, Pranav Bahl et Souvik Chakraborty. « State estimation with limited sensors – A deep learning based approach ». Journal of Computational Physics 457 (mai 2022) : 111081. http://dx.doi.org/10.1016/j.jcp.2022.111081.

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Malhara, Sunita, et Vijay Vittal. « Mechanical State Estimation of Overhead Transmission Lines Using Tilt Sensors ». IEEE Transactions on Power Systems 25, no 3 (août 2010) : 1282–90. http://dx.doi.org/10.1109/tpwrs.2009.2038703.

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Xu, DongWei, HongHui Dong, LiMin Jia et Yong Qin. « Virtual speed sensors based algorithm for expressway traffic state estimation ». Science China Technological Sciences 55, no 5 (20 mars 2012) : 1381–90. http://dx.doi.org/10.1007/s11431-012-4814-9.

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Gish, Herbert, et Ronald Mucci. « Target State Estimation in a Multitarget Environment Using Multiple Sensors ». IEEE Transactions on Aerospace and Electronic Systems AES-23, no 1 (janvier 1987) : 60–72. http://dx.doi.org/10.1109/taes.1987.313336.

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Bogaerts, Ph, et A. Vande Wouwer. « Parameter identification for state estimation—application to bioprocess software sensors ». Chemical Engineering Science 59, no 12 (juin 2004) : 2465–76. http://dx.doi.org/10.1016/j.ces.2004.01.066.

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Shao, Haiyan, Qingshuai Zhao, Bin Chen, Xiankun Liu et Zhiquan Feng. « Analysis of Position and State Estimation of Quadruped Robot Dog Based on Invariant Extended Kalman Filter ». International Journal of Robotics and Automation Technology 9 (30 août 2022) : 17–25. http://dx.doi.org/10.31875/2409-9694.2022.09.03.

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Abstract: Compared with the state estimation of quadruped robots based on external sensors such as camera and lidar, the state estimation based on body sensors can provide high-frequency and stable odometer estimation. By analyzing the state estimation methods of the legged robot based on the body sensor, the invariant extended Kalman filter (IEKF) based on the body sensor is determined to conduct the state estimation analysis of the quadruped robot. Through various path tracking experiments in simulation and real environment, the influence of travel speed, travel distance and different steering angles on the position state estimation results was analyzed, and the IEKF model was optimized by compensating the angular velocity. Experiments show that within the set speed range, after adding angular velocity compensation, the position estimation accuracy error of the robot dog is well controlled and is less than 1%.
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Buller, Mark, William Tharion, Reed Hoyt et Odest Jenkins. « Estimation of Human Internal Temperature from Wearable Physiological Sensors ». Proceedings of the AAAI Conference on Artificial Intelligence 24, no 2 (7 octobre 2021) : 1763–68. http://dx.doi.org/10.1609/aaai.v24i2.18809.

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We evaluated a Kalman filter (KF) approach to modeling the physiology of internal temperature viewed through “noisy” non-invasive observations of heart rate. Human core body temperature (Tcore) is an important measure of thermal state, e.g., hypo- or hyperthermia, but is difficult to measure using non-invasive wearable sensors. We estimated parameters for a discrete KF model from data collected during several Military training events and from distance runners (n=38). Model performance was evaluated in 25 physically-active subjects who participated in various laboratory and field studies involving exercise of 2-to-8 h duration at ambient temperatures of 20 to 40°C. Overall, the KF model’s estimate of Tcore had a root mean square error of 0.30±0.13 ºC from the observed Tcore, and was within ± 0.5 ºC over 85% of the time. The benefit of the KF approach is that it requires only one input while current state of the art models typically require multiple inputs including individual anthropometrics, metabolic rate, clothing characteristics, and environmental conditions. This state estimation problem in computational physiology illustrates the potential for collaboration between the artificial intelligence and ambulatory physiological monitoring communities.
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Wen, C. L., D. H. Zhou et A. S. Morse. « THE MULTISCALE STATE FUSION ESTIMATION FOR NONLINEAR SYSTEMS WITH MULTIRATE SENSORS ». IFAC Proceedings Volumes 35, no 1 (2002) : 181–86. http://dx.doi.org/10.3182/20020721-6-es-1901.00441.

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Staufer, Peter, et Hubert Gattringer. « State estimation on flexible robots using accelerometers and angular rate sensors ». Mechatronics 22, no 8 (décembre 2012) : 1043–49. http://dx.doi.org/10.1016/j.mechatronics.2012.08.009.

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Rehbinder, Henrik, et Xiaoming Hu. « Nonlinear state estimation for rigid-body motion with low-pass sensors ». Systems & ; Control Letters 40, no 3 (juillet 2000) : 183–90. http://dx.doi.org/10.1016/s0167-6911(00)00022-0.

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Ai, Zidong, Lianghong Peng et Menglong Cao. « Optimal Attack Schedule for Two Sensors State Estimation Under Jamming Attack ». IEEE Access 7 (2019) : 75741–48. http://dx.doi.org/10.1109/access.2019.2922272.

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Quevedo, Daniel E., Jan Ostergaard et Anders Ahlen. « Power Control and Coding Formulation for State Estimation With Wireless Sensors ». IEEE Transactions on Control Systems Technology 22, no 2 (mars 2014) : 413–27. http://dx.doi.org/10.1109/tcst.2013.2253464.

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Peng, Lianghong, Xianghui Cao, Hongbao Shi et Changyin Sun. « Optimal jamming attack schedule for remote state estimation with two sensors ». Journal of the Franklin Institute 355, no 14 (septembre 2018) : 6859–76. http://dx.doi.org/10.1016/j.jfranklin.2018.06.016.

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Wachlin, Jacob, Michael Ward et Mark Costello. « In-canopy sensors for state estimation of precision guided airdrop systems ». Aerospace Science and Technology 90 (juillet 2019) : 357–67. http://dx.doi.org/10.1016/j.ast.2019.04.036.

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