Relevant bibliographies by topics / Multisensor

Academic literature on the topic 'Multisensor'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Multisensor"

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Zenevich, A. O., S. V. Zhdanovich, H. V. Vasilevski, A. A. Lagutik, T. G. Kovalenko, and T. M. Lukashik. "Research of multisensor characteristics based on optical fiber." Doklady BGUIR 19, no. 1 (February 23, 2021): 70–78. http://dx.doi.org/10.35596/1729-7648-2021-19-1-70-78.

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The research results of multisensors based on optical fiber, the principle of which is to change the conditions of propagation of optical radiation in the optical fiber in the places where macro-bends are formed at the points of impact, are presented in the paper. The formation of macro-bends leads to an additional attenuation of the power of optical radiation propagating through the optical fiber. A single-mode optical fiber was used with the parameters, which are supported by numerous manufacturers and comply with the recommendations of ITU-T G.655. The measurements were carried out for four wavelengths of optical radiation (1310, 1490, 1550, 1625 nm), corresponding to the transparency windows of the optical loss spectrum of the optical fiber. Using optical reflectometry methods, it was determined that the amount of attenuation of optical radiation of each macro-bend formed at the point of action of the multisensor does not depend on the number of simultaneously formed macro-bends and also does not depend on the location of the point of action along the length of the multisensor. The dependences of the attenuation of the optical radiation power introduced by the macro-bends of the optical fiber on the radius, length, or angle of the macro-bends formed at the multisensory impact points are determined experimentally. The obtained dependences also allow one to determine the optimal parameters of the formed macro-bends of the multisensor to obtain the maximum range of attenuation change for each value of the wavelength. The values of the minimum distance between the impact points, the maximum number of impact points and the optimal values of the radius and angle of the optical fiber macro-bend at the impact points are determined. The results obtained provide opportunities to continue the development of multisensors that allow us to receive information about parameters from several impact points, that are located on a single optical fiber, simultaneously.
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Lin, Tsun-Kuo. "PCA/SVM-Based Method for Pattern Detection in a Multisensor System." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/6486345.

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This paper presents a multivariate analysis framework for pattern detection in a multisensor system; the proposed principal component analysis (PCA)/support vector machine- (SVM-) based supervision scheme can identify patterns in the multisensory system. Although the PCA and SVM are commonly used in pattern recognition, an effective methodology using the PCA/SVM for multisensory system remains unexplored. Pattern detection in a multisensor system has long been a challenge. For example, object inspections in multisensor systems are difficult to perform because inspectors might fail to use multiple sensing devices when concurrently detecting different patterns. Therefore, to resolve this issue, this study proposes a novel framework for establishing indicators and corresponding thresholds to identify patterns in the system; it employs a feature-based scheme that integrates principal component analysis (PCA) with an SVM for effectively detecting patterns in the system. Experiments were conducted using a tactile and optical measurement system. The experimental results demonstrated that the proposed method can effectively identify patterns in multisensor systems by using a feature-based algorithm that combines PCA and SVM classification for detecting various patterns. Moreover, the proposed framework established alarm indicators and corresponding thresholds that can be used for pattern detection.
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Sanjaya, Muhammad Fahyu, Ummu Kalsum, and Andi Rosman N. "PENERAPAN TEKNOLOGI CERDAS PENYIRAMAN TANAMAN HIDROPONIK BERBASIS MIKROKONTROLER DAN MULTISENSOR PADA PEMBUDIDAYA TANAMAN HIDROPONIK KABUPATEN MAJENE." Jurnal Abdi Insani 10, no. 3 (September 13, 2023): 1880–89. http://dx.doi.org/10.29303/abdiinsani.v10i3.1113.

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Smart technology has provided many benefits for all human activities, including hydroponic farming systems. The consistent challenge in the process of cultivating plants using hydroponic systems is the monitoring of partner farmers in managing plant nutrition. Smart technology, utilizing microcontrollers and multisensors, can enhance hydroponic management, particularly concerning the control of nutrient availability, including saturation, water pH, and water availability in the hydroponic system. The purpose of this activity is to address partner-related issues regarding their lack of knowledge and skills in managing businesses aided by smart technology such as microcontrollers and multisensors and to improve the economic viability of partner businesses by producing nutritious and high-quality vegetables. The method used in this community engagement activity involves initial observation at the partner's business location, theoretical instruction to enhance partner knowledge, and finally, training in the creation and operation of smart technology involving microcontrollers and multisensors in the partner's hydroponic area. Questionnaires were distributed to assess the initial conditions before the training and to measure the success of the training after its completion. The initial observation showed a lack of knowledge about smart technology using microcontrollers and multisensors. Based on this, theoretical training was conducted to improve partner knowledge about smart technology using microcontrollers and multisensors. The theoretical content included 1) technology and innovation in hydroponic plant nutrition, 2) an introduction to microcontrollers and sensors in agriculture/horticulture technology, and 3) a demonstration of monitoring physical parameters and nutrient content of hydroponic plants using the Wokwi simulator. Subsequently, a simulation of microcontroller and multisensory device use was carried out, resulting in an enhancement of partner skills in using microcontrollers and multisensors. The improvement in partner knowledge and skills after participating in this training is evident from the questionnaire results, and partner satisfaction with the conducted training is also evident. The conclusion of this community engagement activity is that partners have gained knowledge and skills after participating in the training on the application of smart technology for microcontroller-based irrigation of hydroponic plants with multisensor systems in hydroponic plant cultivation.
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Bogomolov, Andrey. "Developing Multisensory Approach to the Optical Spectral Analysis." Sensors 21, no. 10 (May 19, 2021): 3541. http://dx.doi.org/10.3390/s21103541.

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This article presents an overview of research aimed at developing a scientific approach to creating multisensor optical systems for chemical analysis. The review is mainly based on the author’s works accomplished over the recent 10 years at Samara State Technical University with broad international cooperation. It consists of an introduction and five sections that describe state of the art in the field of optical sensing, suggested development methodology of optical multisensor systems, related aspects of experimental design and process analytical technology followed by a collection of practical examples in different application fields: food and pharmaceutical production, medical diagnostics, and ecological monitoring. The conclusion summarizes trends and prospects of the multisensory approach to optical spectral analysis.
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LIU, QING (CHARLIE), and HSU-PIN (BEN) WANG. "A case study on multisensor data fusion for imbalance diagnosis of rotating machinery." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 3 (June 2001): 203–10. http://dx.doi.org/10.1017/s0890060401153011.

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Techniques for machine condition monitoring and diagnostics are gaining acceptance in various industrial sectors. They have proved to be effective in predictive or proactive maintenance and quality control. Along with the fast development of computer and sensing technologies, sensors are being increasingly used to monitor machine status. In recent years, the fusion of multisensor data has been applied to diagnose machine faults. In this study, multisensors are used to collect signals of rotating imbalance vibration of a test rig. The characteristic features of each vibration signal are extracted with an auto-regressive (AR) model. Data fusion is then implemented with a Cascade-Correlation (CC) neural network. The results clearly show that multisensor data-fusion-based diagnostics outperforms the single sensor diagnostics with statistical significance.
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Han, Youkyung, Jaewan Choi, Jinha Jung, Anjin Chang, Sungchan Oh, and Junho Yeom. "Automated Coregistration of Multisensor Orthophotos Generated from Unmanned Aerial Vehicle Platforms." Journal of Sensors 2019 (April 14, 2019): 1–10. http://dx.doi.org/10.1155/2019/2962734.

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Image coregistration is a key preprocessing step to ensure the effective application of very-high-resolution (VHR) orthophotos generated from multisensor images acquired from unmanned aerial vehicle (UAV) platforms. The most accurate method to align an orthophoto is the installation of air-photo targets at a test site prior to flight image acquisition, and these targets were used as ground control points (GCPs) for georeferencing and georectification. However, there are time and cost limitations related to installing the targets and conducting field surveys on the targets during every flight. To address this problem, this paper presents an automated coregistration approach for orthophotos generated from VHR images acquired from multisensors mounted on UAV platforms. Spatial information from the orthophotos, provided by the global navigation satellite system (GNSS) at each image’s acquisition time, is used as ancillary information for phase correlation-based coregistration. A transformation function between the multisensor orthophotos is then estimated based on conjugate points (CPs), which are locally extracted over orthophotos using the phase correlation approach. Two multisensor datasets are constructed to evaluate the proposed approach. These visual and quantitative evaluations confirm the superiority of the proposed method.
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Tibbetts, Jake, Bethany L. Goldblum, Christopher Stewart, and Arman Hashemizadeh. "Classification of Nuclear Reactor Operations Using Spatial Importance and Multisensor Networks." Journal of Nuclear Engineering 3, no. 4 (September 22, 2022): 243–62. http://dx.doi.org/10.3390/jne3040014.

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Distributed multisensor networks record multiple data streams that can be used as inputs to machine learning models designed to classify operations relevant to proliferation at nuclear reactors. The goal of this work is to demonstrate methods to assess the importance of each node (a single multisensor) and region (a group of proximate multisensors) to machine learning model performance in a reactor monitoring scenario. This, in turn, provides insight into model behavior, a critical requirement of data-driven applications in nuclear security. Using data collected at the High Flux Isotope Reactor at Oak Ridge National Laboratory via a network of Merlyn multisensors, two different models were trained to classify the reactor’s operational state: a hidden Markov model (HMM), which is simpler and more transparent, and a feed-forward neural network, which is less inherently interpretable. Traditional wrapper methods for feature importance were extended to identify nodes and regions in the multisensor network with strong positive and negative impacts on the classification problem. These spatial-importance algorithms were evaluated on the two different classifiers. The classification accuracy was then improved relative to baseline models via feature selection from 0.583 to 0.839 and from 0.811 ± 0.005 to 0.884 ± 0.004 for the HMM and feed-forward neural network, respectively. While some differences in node and region importance were observed when using different classifiers and wrapper methods, the nodes near the facility’s cooling tower were consistently identified as important—a conclusion further supported by studies on feature importance in decision trees. Node and region importance methods are model-agnostic, inform feature selection for improved model performance, and can provide insight into opaque classification models in the nuclear security domain.
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Chen, Guo, Zhigui Liu, Guang Yu, and Jianhong Liang. "A New View of Multisensor Data Fusion: Research on Generalized Fusion." Mathematical Problems in Engineering 2021 (October 15, 2021): 1–21. http://dx.doi.org/10.1155/2021/5471242.

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Multisensor data generalized fusion algorithm is a kind of symbolic computing model with multiple application objects based on sensor generalized integration. It is the theoretical basis of numerical fusion. This paper aims to comprehensively review the generalized fusion algorithms of multisensor data. Firstly, the development and definition of multisensor data fusion are analyzed and the definition of multisensor data generalized fusion is given. Secondly, the classification of multisensor data fusion is discussed, and the generalized integration structure of multisensor and its data acquisition and representation are given, abandoning the research characteristics of object oriented. Then, the principle and architecture of multisensor data fusion are analyzed, and a generalized multisensor data fusion model is presented based on the JDL model. Finally, according to the multisensor data generalized fusion architecture, some related theories and methods are reviewed, and the tensor-based multisensor heterogeneous data generalized fusion algorithm is proposed, and the future work is prospected.
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Semenova, V. A., and E. M. Petrenko. "Identification and Comparative Analysis of Alkaloids by Inversion Voltammetry Method." Herald of the Bauman Moscow State Technical University. Series Natural Sciences, no. 6 (87) (December 2019): 113–21. http://dx.doi.org/10.18698/1812-3368-2019-6-113-121.

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The alkaloids concurrent detection and identification in the samples by the method of multisensory inversion voltammetry is the purpose of the work. To achieve this goal, the proposed method has been substantiated, the composition of the electrochemical test system has been optimized taking into account the specifics of the detected substances, and informative signs that characterize the presence of alkaloids in the studied sample have been found. A new approach, consisting in the use of an electrochemical multisensor test system in the form of a solution containing a set of metal ions that can form complex compounds with organic substances, has been developed and scientifically approved. The results showed that each organic substance has a different effect on the electrochemical behavior of the multisensory test system. The use of such a test system made it possible to model the principle currently defined by the term "Electronic tongue". An electronic database has been prepared according to the results of the electroanalytical studies, which made it possible to identify the detected substance by comparing it with analyzed sample. The proposed electrochemical method, which is based on multisensor inversion voltammetry, allows the detection and identification of both narcotic drugs and psychotropic substances with high confidence for a small mass of the sample.
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Liu, Bao Jun. "Study on Multisensor Data Fusion of Ultrasonic Sensor." Advanced Materials Research 722 (July 2013): 44–48. http://dx.doi.org/10.4028/www.scientific.net/amr.722.44.

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Aiming at data fusion of Autonomous car multisensors experiment at many times on distances, a novel fusion method is proposed based on the approach degree and weights. The method calculate mean and variance based on the measured sensors data, Using the maximum and minimum approach degree of this fuzzy set , the approach degree of the measured data from various sensors is processed quantitatively ,eliminating outlier data by Grubbs method, assigned the weights of data measured in the fusion process reasonably , so that the final expression of the data fusion is obtained, thus the data fusion of multisensor is realized.Test results demonstrate that this method can bring higher fusion precision, and more suitable for microcontroller and embedded systems applications.
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Dissertations / Theses on the topic "Multisensor"

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Filippidis, Arthur. "Multisensor data fusion." Title page, contents and abstract only, 1993. http://web4.library.adelaide.edu.au/theses/09ENS/09ensf482.pdf.

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Purohit, Madhavi. "Multisensor integration for a robot." Ohio : Ohio University, 1989. http://www.ohiolink.edu/etd/view.cgi?ohiou1182456473.

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Petrovic, Vladimir. "Multisensor pixel-level image fusion." Thesis, University of Manchester, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.715412.

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Pradhan, Pushkar S. "Multiresolution based, multisensor, multispectral image fusion." Diss., Mississippi State : Mississippi State University, 2005. http://library.msstate.edu/etd/show.asp?etd=etd-07082005-140541.

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Jeong, Soonho Tugnait Jitendra K. "Topics in multisensor maneuvering target tracking." Auburn, Ala., 2005. http://repo.lib.auburn.edu/2005%20Summer/doctoral/JEONG_SOONHO_43.pdf.

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Bonaccorso, Filippo. "Multisensor data fusion for robotic control." Doctoral thesis, Università di Catania, 2012. http://hdl.handle.net/10761/1081.

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Nowadays robots perform more and more tasks, from simpler ones such as automatic domestic vacuum cleaner, to highly skilled ones such as tele-surgery. To do so a robot continuously needs to know what to do and what has been done. A user controls robots through a system controller, which processes both his commands and information from the environment. These information are collected by a measurement system, often called observer, which bring back them to the system controller. Measures are the information used to perform the control. The simpler measurement system can, in general, be considered as made up of two elements: 1. A sensor or transducer which is an element that produces a signal relating the quantity being measured. Sensors are elements that when subject to some physical change experience a related change. 2. A signal conditioner which takes the signal from the sensor and processes it to make it suitable for the specific application. The signal may be, for example, too small or too noisy. Different kinds of sensors are used in robotics, such as position, velocity, force sensors and so on. Each application needs the right sensor to be chosen, the choice must satisfy different criterions such as performance, cost and feasibility. In this way both static, as range or sensitivity, and dynamic characteristics, as response time, of the sensor have to be considered. In robotic applications there are two main subclasses of sensors: internal and external. The former bring information about the robot; the latter bring information about the environment. In a mobile robot, for example, encoders are internal sensors, while ultrasound sensors are external. Multisensor data fusion seeks to combine data from multiple sensors to perform inferences that may not be possible from a single sensor. Different sensors have different strengths and weaknesses, so fused data from multiple sensors provides several advantages over data from a single sensor: A wider and more accurate range of information by combining data from different types of sensors. Redundancy. Increased reliability. Weaknesses compensation. Sensors failure detection and handling. Typical applications that can benefit from multiple sensors are, first of all, mobile robot navigation, target tracking, aircraft navigation and industrial tasks control. There are a number of different ways to integrate or fuse information provided by multiple sensors. Multisensor input system controller The simplest approach to multisensor data fusion, is to use the information from each sensor as a separate input to the system controller. This approach may be the most appropriate if each sensor is providing information concerning completely different aspects of the environment. The major benefit gained through this approach is the increase in the extent of the environment that is able to be sensed. The only interaction between the sensors is indirect and based on the individual effect each sensor has on the controller and so on the whole robot. If there is some degree of overlap between sensors, concerning some aspect of the environment they are able to sense, it may be possible for a sensor to directly influence the operation of another one. In this way the value of the combined information that the sensors provide, is greater than the sum of the value of the information provided by each sensor separately. This synergistic effect can be achieved either by using the information from one sensor to provide cues, or guide the operation of other sensors, or by actually combining or fusing the information from multiple sensors. The whole research activity has dealt with two main topics: the RAPOLAC Project and the Mobile Robots. The former are discussed in the first four chapters while the latter in the following four.
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PINARDI, STEFANO. "Movements recognition with intelligent multisensor analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19297.

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In movement science with inertial sensor many different methodologies resolving specific aspects of movement recognition have been proposed. They are very interesting, and useful, but none of them are generally explicative of what is going on in the semantic sense. When we go down to the movement recognition/classification area (for example in Ambient Intelligence) we do not have a feasible model that can be considered generally predictive or usable for activity recognition. Also, in the field of movement recognition with inertial sensors many technological issues arise: technological diversity, calibration matters, sensor model problems, orientation and position of sensors, and a lot of numerous specificities that, with all the above aspects, and the lack of public dataset of movements sufficiently generic and semantically rich, contribute to create a strong barrier to any approach to a classification matters with wearable sensors. We have also to notice that a movement is a phenomenon explicitly or implicitly (voluntary or involuntary) controlled by brain. The individual free-will introduce a further matter when we want to temporary predict the movements looking at the close past. Pattern can change at any time when ambient, psychological context, age of the subject change. Also, pathological issues, and physiological differences and the will of the subject, introduce important differences. For all these reasons I considered that a semantical /lexical approach to movement recognition with sensors, driven by machine learning techniques could be a promising way to solve some of these challenge and problems. In this Ph.D. Thesis wearable inertial sensors has been used to classify movements, the choice of inertial sensors has been driven by technological and practical advantages, they are cheap, lightweight, and - differently from video cameras - are not prone to the hidden face, or luminance problems. The main idea is to use inertial sensor to understand what a person is doing for ambient-intelligent, healthcare, medical-sport applications. My principal concerns was to propose a method that was not centered on technology issues but on data analysis, that could be a general framework and could also create a general representation of movement,that could be useful also in other area of research, like reasoning. Inertial sensors are treated just as an example, a particular type of sensors, the method is new, reusable, algorithmically simple, net and easy to understand. Accuracy is very high outperforming the best results given in literature, reducing the error rate of 4 times.
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Rødningsby, Anders. "Multitarget Multisensor Trackingin the Presence of Wakes." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11913.

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TARGET tracking is an essential requirement for surveillance and control systems to interpret the environment. This environment may contain multiple targets, and the environmental information may be obtained by multiple sensors in a multitarget multisensor tracking system. In this thesis we focus on targets which, in addition to reflecting signals themselves, also have a trailing path behind them, called a wake. This wake causes additional measurements to those originating from the target. When the measurements are processed, the estimated track can be misled and sometimes lose the real target because of the wake. This problem becomes even more severe in multitarget environments where targets are operating close to each other in the presence of wakes. In this thesis a probabilistic model is developed which reflects the probability that a false measurement originates from the wake behind a target. This wake model is integrated in the probabilistic data association filter (PDAF) to improve the track continuity for tracking single targets. The modified PDAF is further extended to handle multiple targets in the presence of wakes by using a probabilistic wake model for each of the targets in the multitarget environment that has a wake behind it. These single wake models are combined to form a joint wake model which augments the joint probabilistic data association filter (JPDAF) for both coupled and decoupled filtering. The wake-originated measurements may also cause confusion in the track initiation. To prevent this problem, a clustering method is proposed based on morphological operators which allows tracks to be initialized based on two-point differencing of the cluster centroids from succeeding scans. The modified PDAF is tested on data of a real scuba diver with an open breathing system. In this case the air bubbles produced by the diver form a wake which extends far behind the diver. The experiment showed that the above modifications of the PDAF improved the track continuity significantly. Finally, a relatively extensive simulation, based on real scuba diver data, is presented. Four different multitarget multisensor tracking scenarios are simulated, considering two targets with wakes that are: 1. Crossing each other. 2. Moving in parallel to each other. 3. One following after another. 4. Meeting and then passing each other. The results of these simulation scenarios show that the presented modifications improve the tracking performance, and the probability of lost tracks is significantly reduced. The targets are observed by two sensors, and it is shown that tracks estimated in a centralized fusion configuration are better than the local tracks estimated using data from individual sensors only. It is also shown that applying the wake model to targets that do not generate a wake, yields almost no deterioration of the tracking performance.
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Berg, Timothy Martin. "Model distribution in decentralized multisensor data fusion." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317852.

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Hilmersson, Anette. "Multisensor Stress Monitoring For Non-Stationary Subjects." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-28340.

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Monitoring stress in real-time, in a non-laboratory environment can be benecial in several applications. One of these, which have been the motivation for this thesis, is to to perform this measurement during Attention decit hyperactivity disorder (ADHD) diagnosis. Monitoring several physiological responses to internal or external stimuli in a single soft-real-time system is nota solution widely used in an application like this. The thesis starts by studying several stress related responses in detail. Sensors for all of the responses are not implemented nor is it possible toimplement in to the desired system. After the study is was decided to implement two measurement modules. The first a Photo-plethysmogrophy (PPG) measurement module to measure heart rate and also estimate breathing. This module is prepared for estimating arterial blood oxygen levels but the calculation or verification have not been done. The second is Skin Conductance (SC) measurement module and in to both ofthese add a temperature sensor to measure the temperature of the skin. Time constraints limit the SC module to only be presented in theory. The PPG module on the other hand have been realisedin a prototype. This prototype performs the measurement in transmissive mode on the left earlobe, which leaves the hands free and it does not affect the hearing on that ear. The prototype giveout acceptable signal quality when good contact with the measurement site is achieved. The signalinterpretation, such as performing the signal analysis to count the beats per minute, is outside thescope of this thesis and will therefore not be presented but the signals can be seen in figures.
Att mäta stress i realtid i verkliga situationer kan vara fördelaktigt för flera applikationer. Det som har legat som grund för denna uppsats är att kunna mäta stress under ADHD diagnostisering. Genom att kombinera de vanliga testerna med stressnivåer hos patienten hoppas man kunna utveckla nya metoder för diagnostisering. Att mäta fera parametrar samtidigt i realtid är inte något ofta utförs idag. För att komma igång har fera kroppsliga funktioner som påverkas på olika sätt av stress studerats. Alla dessa funktioner kan inte inkluderas i det system som önskas konstrueras antingen på grund av systemets karaktär eller på grund tidsbrist. Efter att undersökningen var klar beslutades det att konstruera två moduler. Den första använder en mätteknik som kallas PPG och används för att mäta hjärtfrekvens, även andningsfrekvensen estimeras och modulen är förberedd för att estimera blodsyre nivåa men signalbehandling och validering för detta är inte gjord. Den andra modulen mäter resistans i huden. I dessa moduler lades även till en temperatur sensor för att mäta hudtemperaturen. Tidsbrist har gjort att endast en av dem två modulerna kunnat realiserats. Den som realiserat är PPG modulen och modulen för hudresistans presenteras endast teoretiskt. PPG modulen genomför matningen med en transmissiv teknik på vänster öra och ger ut en acceptabel signal kvalité om sensorn får bra kontakt. Arbetet är avgränsat och inkluderar inte signalanalysen av signalen däremot visualiseras signalen i figurer.
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Books on the topic "Multisensor"

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Hyder, A. K., E. Shahbazian, and E. Waltz, eds. Multisensor Fusion. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2.

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NATO Advanced Study Institute on Multisensor Data Fusion (2000 Pitlochry, Scotland). Multisensor fusion. Dordrecht: Kluwer Academic Publishers, 2002.

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Foresti, Gian Luca, Carlo S. Regazzoni, and Pramod K. Varshney, eds. Multisensor Surveillance Systems. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0371-2.

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James, Llinas, ed. Multisensor data fusion. Boston: Artech House, 1990.

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Aggarwal, J. K., ed. Multisensor Fusion for Computer Vision. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-02957-2.

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Zhu, Yunmin. Multisensor Decision And Estimation Fusion. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1045-1.

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1936-, Aggarwal J. K., North Atlantic Treaty Organization. Scientific Affairs Division., and NATO Advanced Research Workshop on Multisensor Fusion for Computer Vision (1989 : Grenoble, France), eds. Multisensor fusion for computer vision. Berlin: Springer-Verlag, 1993.

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Zhu, Yunmin. Multisensor Decision And Estimation Fusion. Boston, MA: Springer US, 2003.

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Multisensor decision and estimation fusion. Boston: Kluwer Academic Publishers, 2003.

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Appriou, Alain. Uncertainty Theories and Multisensor Data Fusion. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118578636.

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Book chapters on the topic "Multisensor"

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Grejner-Brzezinska, Dorota A. "Multisensor Systems." In Encyclopedia of Geodesy, 1–6. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-02370-0_12-1.

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Hanwehr, R. "Information Fusion in the Human Brain." In Multisensor Fusion, 1–36. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_1.

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Valin, P. "Random Sets and Unification." In Multisensor Fusion, 247–66. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_10.

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Bloch, I. "Fusion of Information under Imprecision and Uncertainty, Numerical Methods, and Image Information Fusion." In Multisensor Fusion, 267–93. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_11.

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Rao, N. S. V. "Multisensor Fusion under Unknown Distributions Finite-Sample Performance Guarantees." In Multisensor Fusion, 295–329. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_12.

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Cadre, J. P. "Data Association and Multitarget Tracking." In Multisensor Fusion, 331–49. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_13.

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Ranchin, T. "Wavelets for Modeling and Data Fusion in Remote Sensing." In Multisensor Fusion, 351–63. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_14.

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Nimier, V. "Soft Sensor Management for Multisensor Tracking Algorithm." In Multisensor Fusion, 365–79. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_15.

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Bekmuratov, T. E. "Intellectualization of Multi-Sensor Systems for Decision Making Process." In Multisensor Fusion, 381–85. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_16.

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Reynaud, R. "Implementing Data Fusion Systems." In Multisensor Fusion, 387–418. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_17.

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Conference papers on the topic "Multisensor"

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Pate, Michael A. "Multisensor Boresighting." In OPTCON '88 Conferences--Applications of Optical Engineering, edited by Thomas C. Bristow and Alson E. Hatheway. SPIE, 1989. http://dx.doi.org/10.1117/12.950992.

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Pao, Lucy. "Distributed multisensor fusion." In Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-3549.

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Romeo, Katerin, Piet B. W. Schwering, and Marcel G. J. Breuers. "Multisensor track fusion." In Photonics West 2001 - Electronic Imaging, edited by Bernd Girod, Charles A. Bouman, and Eckehard G. Steinbach. SPIE, 2000. http://dx.doi.org/10.1117/12.411821.

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Sienski, Kenneth T., and Stephen T. Makrinos. "Airborne multisensor system." In Aerospace/Defense Sensing and Controls, edited by Raja Suresh and William Langford. SPIE, 1996. http://dx.doi.org/10.1117/12.242075.

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de Salabert, Arturo, Timothy K. Pike, F. G. Sawyer, I. H. Jones-Parry, A. J. Rye, Clare J. Oddy, D. G. Johnson, et al. "Multisensor image processing." In Munich '91 (Lasers '91), edited by Hatem N. Nasr. SPIE, 1991. http://dx.doi.org/10.1117/12.46065.

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Colombi, John M., D. Krepp, Steven K. Rogers, Dennis W. Ruck, and Mark E. Oxley. "Multisensor user authentication." In Optical Engineering and Photonics in Aerospace Sensing, edited by Steven K. Rogers. SPIE, 1993. http://dx.doi.org/10.1117/12.152518.

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Colombi, John M., D. Krepp, Steven K. Rogers, Dennis W. Ruck, and Mark E. Oxley. "Multisensor user authentication." In Optical Engineering and Photonics in Aerospace Sensing, edited by Dennis W. Ruck. SPIE, 1993. http://dx.doi.org/10.1117/12.152612.

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Mitchell, Horace. "Multisensor fire observations." In ACM SIGGRAPH 2003 video review. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/1006114.1006117.

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Henderson, Thomas C. "Multisensor Knowledge Systems." In 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, edited by Charles B. Weaver. SPIE, 1988. http://dx.doi.org/10.1117/12.946669.

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Vidmar, Jr., Anthony, and Kourken Malakian. "Multisensor predetection fusion." In Aerospace Sensing, edited by Oliver E. Drummond. SPIE, 1992. http://dx.doi.org/10.1117/12.139374.

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Reports on the topic "Multisensor"

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Foyle, David C. Multisensor Evaluation Framework. Fort Belvoir, VA: Defense Technical Information Center, September 1989. http://dx.doi.org/10.21236/ada224271.

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Blair, William Dale. Information-Based Multisensor Detecton. Fort Belvoir, VA: Defense Technical Information Center, December 2001. http://dx.doi.org/10.21236/ada397420.

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Blair, William Dale. Information-Based Multisensor Detection. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada369381.

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Bar-Shalom, Yaakov, K. R. Pattipati, and P. K. Willett. Estimation With Multisensor Fusion. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada416565.

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Bar-Shalom, Y., and K. R. Pattipati. Multisensor/Multiscan Detection Fusion. Fort Belvoir, VA: Defense Technical Information Center, April 1997. http://dx.doi.org/10.21236/ada336763.

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Blair, William D. Information-Based Multisensor Detection. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada383793.

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Aggarwal, R. K., M. Bazakos, J. Budenske, Y. Kim, and S. Mader. Hierarchical Multisensor Image Understanding. Fort Belvoir, VA: Defense Technical Information Center, August 1985. http://dx.doi.org/10.21236/ada160324.

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Minor, Christian P., Joseph C. Gezo, and Kevin J. Johnson. Information Measures for Multisensor Systems. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada591226.

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Yocky, D. A., M. D. Chadwick, S. P. Goudy, and D. K. Johnson. Multisensor data fusion algorithm development. Office of Scientific and Technical Information (OSTI), December 1995. http://dx.doi.org/10.2172/172138.

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Hall, David L., and Alan Steinberg. Dirty Secrets in Multisensor Data Fusion. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada394631.

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