Relevant bibliographies by topics / Automatic identification sensor

Academic literature on the topic 'Automatic identification sensor'

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Published: 14 March 2023

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Journal articles on the topic "Automatic identification sensor"

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Álvarez-Bazo, Fernando, Santos Sánchez-Cambronero, David Vallejo, Carlos Glez-Morcillo, Ana Rivas, and Inmaculada Gallego. "A Low-Cost Automatic Vehicle Identification Sensor for Traffic Networks Analysis." Sensors 20, no. 19 (September 29, 2020): 5589. http://dx.doi.org/10.3390/s20195589.

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In recent years, different techniques to address the problem of observability in traffic networks have been proposed in multiple research projects, being the technique based on the installation of automatic vehicle identification sensors (AVI), one of the most successful in terms of theoretical results, but complex in terms of its practical application to real studies. Indeed, a very limited number of studies consider the possibility of installing a series of non-definitive plate scanning sensors in the elements of a network, which allow technicians to obtain a better conclusions when they deal with traffic network analysis such as urbans mobility plans that involve the estimation of traffic flows for different scenarios. With these antecedents, the contributions of this paper are (1) an architecture to deploy low-cost sensors network able to be temporarily installed on the city streets as an alternative of rubber hoses commonly used in the elaboration of urban mobility plans; (2) a design of the low-cost, low energy sensor itself, and (3) a sensor location model able to establish the best set of links of a network given both the study objectives and of the sensor needs of installation. A case of study with the installation of as set of proposed devices is presented, to demonstrate its viability.
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Kushwaha, Ruchi, Rohit Shambharkar, Suyash Gupta, and Monika Malik. "Integration of Block chain Model for Energy Efficient WSN for IOT Application." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (February 28, 2023): 34–37. http://dx.doi.org/10.22214/ijraset.2023.48942.

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Abstract: Wireless Sensor Networks (WSNs) play a key role in the Internet of Sensor Things (IoST).IoST helps collect data from environments and is used in energy trading, monitoring, smart grids, and more. Connect to the Internet and automate your surveillance system without third-party involvement. An IoST network consists of Sensor nodes that perform environmental monitoring. Wireless Sensor Networks (WSN) and the Internet of Things (I.o.T) have gained popularity in recent years as the underlying infrastructure for connected devices and sensors in a variety of sectors. The data generated by these sensors are used in smart cities, agriculture, transportation systems, healthcare systems, toll collection systems, automatic identification of road data, automatic identification of vehicle license plates, and more. It has become a proposed blockchain mechanism. The main problems and challenges of WSN are effectively reduced by using the LEACH protocol with efficient cluster head selection.
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Giurgiutiu, Victor, and Andrei N. Zagrai. "Embedded Self-Sensing Piezoelectric Active Sensors for On-Line Structural Identification." Journal of Vibration and Acoustics 124, no. 1 (July 1, 2001): 116–25. http://dx.doi.org/10.1115/1.1421056.

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The benefits and limitations of using embedded piezoelectric active sensors for structural identification at ultrasonic frequency are highlighted. An analytical model based on structural vibration theory and theory of piezoelectricity was developed and used to predict the electro-mechanical (E/M) impedance response, as it would be measured at the piezoelectric active sensor’s terminals. The model considers one-dimension structures and accounts for both axial and flexural vibrations. Experiments were conducted on simple specimens in support of the theoretical investigation, and on realistic turbine blade specimen to illustrate the method’s potential. It was shown that E/M impedance spectrum recorded by the piezoelectric active sensor accurately represents the mechanical response of a structure. It was further proved that the response of the structure is not modified by the presence of the sensor, thus validating the latter’s noninvasive characteristics. It is shown that such sensors, of negligible mass, can be permanently applied to the structure creating a nonintrusive sensor array adequate for on-line automatic structural identification and health monitoring. The sensor calibration procedure is outlined. Numerical estimation of the noninvasive properties of the proposed active sensors in comparison with conventional sensors is presented. Self-diagnostics capabilities of the proposed sensors were also investigated and methods for automatic self-test implementation are discussed. The paper underlines that the use of piezoelectric wafer active sensors is not only advantageous, but, in certain situations, may be the sole investigative option, as in the case of precision machinery, small but critical turbine-engine parts, and computer industry components.
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Liu, Li Min. "Internet of Things and RFID Technology." Applied Mechanics and Materials 336-338 (July 2013): 2512–15. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2512.

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The internet of things is a foundation for connecting things, sensors, actuators, and other smart technologies, thus enabling person-to-object and object-to-object communications. Its applications are concerned to emergency response, intelligent shopping, smart product management, smart meters, home automation, waste management, sustainable urban environment, continuous care and so on. As automatic identification sensor, RFID is a foundational component for the internet of things. In this paper, internet of things, RFID and technical analysis for IoT and RFID are discussed.
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Zhou, Guang-Dong, Mei-Xi Xie, Ting-Hua Yi, and Hong-Nan Li. "Optimal wireless sensor network configuration for structural monitoring using automatic-learning firefly algorithm." Advances in Structural Engineering 22, no. 4 (October 4, 2018): 907–18. http://dx.doi.org/10.1177/1369433218797074.

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Wireless sensor networks are becoming attractive data communication patterns in structural health monitoring systems. Designing and applying effective wireless sensor network–based structural health monitoring systems for large-scale civil infrastructure require a great number of wireless sensors and the optimal wireless sensor networks configuration becomes critical for such spatially separated large structures. In this article, optimal wireless sensor network configuration for structural health monitoring is treated as a discrete optimization problem, where parameter identification and network performance are simultaneously addressed. To solve this rather complicated optimization problem, a novel swarm intelligence algorithm called the automatic-learning firefly algorithm is proposed by integrating the original firefly algorithm with the Lévy flight and the automatic-learning mechanism. In the proposed algorithm, the Lévy flight is adopted to maximize the searching capability in unknown solution space and avoid premature convergence and the automatic-learning mechanism is designed to drive fireflies to move toward better locations at high speed. Numerical experiments are performed on a long-span bridge to demonstrate the effectiveness of the proposed automatic-learning firefly algorithm. Results indicate that automatic-learning firefly algorithm can find satisfactory wireless sensor network configurations, which facilitate easy discrimination of identified mode vectors and long wireless sensor network lifetime, and the innovations in automatic-learning firefly algorithm make it superior to the simple discrete firefly algorithm as to solution quality and convergence speed.
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Beligni, Alessio, Claudio Sbarufatti, Andrea Gilioli, Francesco Cadini, and Marco Giglio. "Robust Identification of Strain Waves due to Low-Velocity Impact with Different Impactor Stiffness." Sensors 19, no. 6 (March 14, 2019): 1283. http://dx.doi.org/10.3390/s19061283.

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Low-velocity impacts represent a major concern for aeronautical structures, sometimes producing barely detectable damage that could severely hamper the aircraft safety, even with regards to metallic structures. For this reason, the development of an automated impact monitoring system is desired. From a passive monitoring perspective, any impact generates a strain wave that can be acquired using sensor networks; signal processing techniques allow for extracting features useful for impact identification, possibly in an automatic way. However, impact wave characteristics are related to the impactor stiffness; this presents a problem for the evaluation of an impact-related feature and for the development of an automatic approach to impact identification. This work discusses the problem of reducing the influence of the impactor stiffness on one of the features typically characterizing the impact event, i.e., the time of arrival (TOA). Two passive sensor networks composed of accelerometers and piezoelectric sensors are installed on two metallic specimens, consisting of an aluminum skin and a sandwich panel, with aluminum skins and NOMEXTM honeycomb core. The effect of different impactor stiffnesses is investigated by resorting to an impact hammer, equipped with different tips. Subsequently, a method for data processing is defined to obtain a feature insensitive to the impactor stiffness, and this method is applied to multiple impact signals for feature uncertainty evaluation.
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Zheng, Jun Hui, and Bing Li. "Fire Seat Intelligent Identification System." Applied Mechanics and Materials 536-537 (April 2014): 421–25. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.421.

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Current fire alarm system can only send fire alarms but failed to report accurately ignition point. So a fire seat intelligent identification system was designed based on wireless sensor networks. The system collects data from wireless sensors using ZigBee wireless communication technology, and carries on cluster analysis on the data set using the improved fuzzy kernel clustering algorithm, and gets accurate clustering results. Finally the ignition source location information is reported to the fire alarm control center. The experimental results show that, compared with other methods, this system realizes real-time monitoring and automatic identification of fire seats, which has virtues of high sensitivity and accuracy and high-speed data transfer.
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Zheng, Fu. "Design of Auto Route Identified Vehicle Model Based on MC9S12XS128." Applied Mechanics and Materials 187 (June 2012): 146–50. http://dx.doi.org/10.4028/www.scientific.net/amm.187.146.

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A vehicle based on automatic route identification technology by using modulated infrared photoelectric sensors was introduced. Circuits for path detecting , motor drive, speed sensor and servo drive were designed. And the controlling strategy was introduced. At last, the assembled model car could run along the black line stably and fast.
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Beiderman, Yevgeny, Mark Kunin, Eli Kolberg, Ilan Halachmi, Binyamin Abramov, Rafael Amsalem, and Zeev Zalevsky. "Automatic solution for detection, identification and biomedical monitoring of a cow using remote sensing for optimised treatment of cattle." Journal of Agricultural Engineering 45, no. 4 (December 21, 2014): 153. http://dx.doi.org/10.4081/jae.2014.418.

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In this paper we show how a novel photonic remote sensing system assembled on a robotic platform can extract vital biomedical parameters from cattle including their heart beating, breathing and chewing activity. The sensor is based upon a camera and a laser using selfinterference phenomena. The whole system intends to provide an automatic solution for detection, identification and biomedical monitoring of a cow. The detection algorithm is based upon image processing involving probability map construction. The identification algorithms involve well known image pattern recognition techniques. The sensor is used on top of an automated robotic platform in order to support animal decision making. Field tests and computer simulated results are presented.
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Li, Dongya, Wei Wang, and De Zhao. "A Practical and Sustainable Approach to Determining the Deployment Priorities of Automatic Vehicle Identification Sensors." Sustainability 14, no. 15 (August 2, 2022): 9474. http://dx.doi.org/10.3390/su14159474.

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Monitoring vehicles’ paths is important for the management and governance of smart sustainable cities, where traffic sensors play a significant role. As a typical sensor, an automatic vehicle identification (AVI) sensor can observe the whereabouts and movements of vehicles. In this article, we introduced an indicator called the deployment score to present the deployment priorities of AVIs for a better reconstruction of vehicles’ paths. The deployment score was obtained based on a programming method for maximizing the accuracy of a recurring vehicle’s path and minimizing the number of AVI sensors. The calculation process is data-driven, where a random-work method was developed to simulate massive path data (tracks of vehicles) according to travel characteristics extracted from finite GPS data. Then, for each simulated path, a path-level bi-level programming model (P-BPM) was constructed to find the optimal layout of the AVI sensors. The solutions of the P-BPM proved to be approximate Pareto optima from a data-driven perspective. Furthermore, the PageRank method was presented to integrate the solutions; thus, the deployment score was obtained. The proposed method was validated in Chengdu City, whose results demonstrated the remarkable value of our approach.
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Dissertations / Theses on the topic "Automatic identification sensor"

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Ammineni, Chandini Muniratnam. "Design of Lignin Sensor for Identification of Paper Grades for an Automatic Waste Paper SortingSystem." NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20010907-181312.

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AMMINENI, CHANDINI MUNIRATNAM. Design of Lignin Sensor forIdentification of Paper Grades for an Automatic Waste Paper SortingSystem. (Under the direction of Dr. M. K. Ramasubramanian.)The purpose of this research has been to design a lignin sensor fornon-destructive, real-time identification of waste paper grades, toaid in automating a waste paper sorting process. The sensor iscapable of identifying about 500 papers in one second. It is based onthe principle that fluorescence light emitted from paper followingabsorption of visible light has a wavelength distribution determinedby the chemical composition of the paper. The sensor is the most critical part in waste paper sorting, whichhas hitherto not been automated due to the inability to design asensor that distinguishes paper grades. This sensor is vastlysuperior to all other sensors previously designed for this purposebecause, it does not use the conventional reflective type opticalproperties of paper, and this is the only sensor that can identifyall grades unlike the previous sensors that could identify only whiteledger papers.

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Narby, Erik. "Modeling and Estimation of Dynamic Tire Properties." Thesis, Linköping University, Department of Electrical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-6153.

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Information about dynamic tire properties has always been important for drivers of wheel driven vehicles. With the increasing amount of systems in modern vehicles designed to measure and control the behavior of the vehicle information regarding dynamic tire properties has grown even more important.

In this thesis a number of methods for modeling and estimating dynamic tire properties have been implemented and evaluated. The more general issue of estimating model parameters in linear and non-linear vehicle models is also addressed.

We conclude that the slope of the tire slip curve seems to dependent on the stiffness of the road surface and introduce the term combined stiffness. We also show that it is possible to estimate both longitudinal and lateral combined stiffness using only standard vehicle sensors.

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Souza, Vinicius Mourão Alves de. "Classificação de fluxo de dados não estacionários com aplicação em sensores identificadores de insetos." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-13122016-113648/.

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Diversas aplicações são responsáveis por gerar dados ao longo do tempo de maneira contínua, ordenada e ininterrupta em um ambiente dinâmico, denominados fluxo de dados. Entre possíveis tarefas que podem ser realizadas com estes dados, classificação é uma das mais proeminentes. Devido à natureza não estacionária do ambiente responsável por gerar os dados, as características que descrevem os conceitos das classes do problema de classificação podem se alterar ao longo do tempo. Por isso, classificadores de fluxo de dados requerem constantes atualizações em seus modelos para que a taxa de acerto se mantenha estável ao longo do tempo. Na etapa de atualização a maior parte das abordagens considera que, após a predição de cada exemplo, o seu rótulo correto é imediatamente disponibilizado sem qualquer atraso de tempo (latência nula). Devido aos altos custos do processo de rotulação, os rótulos corretos nem sempre podem ser obtidos para a maior parte dos dados ou são obtidos após um considerável atraso de tempo. No caso mais desafiador, encontram-se as aplicações em que após a etapa de classificação dos exemplos, os seus respectivos rótulos corretos nunca sã disponibilizados para o algoritmo, caso chamado de latência extrema. Neste cenário, não é possível o uso de abordagens tradicionais, sendo necessário o desenvolvimento de novos métodos que sejam capazes de manter um modelo de classificação atualizado mesmo na ausência de dados rotulados. Nesta tese, além de discutir o problema de latência na tarefa de classificação de fluxo de dados não estacionários, negligenciado por boa parte da literatura, também sã propostos dois algoritmos denominados SCARGC e MClassification para o cenário de latência extrema. Ambas as propostas se baseiam no uso de técnicas de agrupamento para a adaptação à mudanças de maneira não supervisionada. Os algoritmos propostos são intuitivos, simples e apresentam resultados superiores ou equivalentes a outros algoritmos da literatura em avaliações com dados sintéticos e reais, tanto em termos de acurácia de classificação como em tempo computacional. Aléem de buscar o avanço no estado-da-arte na área de aprendizado em fluxo de dados, este trabalho também apresenta contribuições para uma importante aplicação tecnológica com impacto social e na saúde pública. Especificamente, explorou-se um sensor óptico para a identificação automática de espécies de insetos a partir da análise de informações provenientes do batimento de asas dos insetos. Para a descrição dos dados, foi verificado que os coeficientes Mel-cepstrais apresentaram os melhores resultados entre as diferentes técnicas de processamento digital de sinais avaliadas. Este sensor é um exemplo concreto de aplicação responsável por gerar um fluxo de dados em que é necessário realizar classificações em tempo real. Durante a etapa de classificação, este sensor exige a adaptação a possíveis variações em condições ambientais, responsáveis por alterar o comportamento dos insetos ao longo do tempo. Para lidar com este problema, é proposto um Sistema com Múltiplos Classificadores que realiza a seleção dinâmica do classificador mais adequado de acordo com características de cada exemplo de teste. Em avaliações com mudanças pouco significativas nas condições ambientais, foi possível obter uma acurácia de classificação próxima de 90%, no cenário com múltiplas classes e, cerca de 95% para a identificação da espécie Aedes aegypti, considerando o treinamento com uma única classe. No cenário com mudanças significativas nos dados, foi possível obter 91% de acurácia em um problema com 5 classes e 96% para a classificação de insetos vetores de importantes doenças como dengue e zika vírus.
Many applications are able to generate data continuously over t ime in an ordered and uninterrupted way in a dynamic environment , called data streams. Among possible tasks that can be performed with these data, classification is one of the most prominent . Due to non-stationarity of the environment that generates the data, the features that describe the concepts of the classes can change over time. Thus, the classifiers that deal with data streams require constants updates in their classification models to maintain a stable accuracy over time. In the update phase, most of the approaches assume that after the classification of each example from the stream, their actual class label is available without any t ime delay (zero latency). Given the high label costs, it is more reasonable to consider that this delay could vary for the most portion of the data. In the more challenging case, there are applications with extreme latency, where in after the classification of the examples, heir actual class labels are never available to the algorithm. In this scenario, it is not possible to use traditional approaches. Thus, there is the need of new methods that are able to maintain a classification model updated in the absence of labeled data. In this thesis, besides to discuss the problem of latency to obtain actual labels in data stream classification problems, neglected by most of the works, we also propose two new algorithms to deal with extreme latency, called SCARGC and MClassification. Both algorithms are based on the use of clustering approaches to adapt to changes in an unsupervised way. The proposed algorithms are intuitive, simpleand showed superior or equivalent results in terms of accuracy and computation time compared to other approaches from literature in an evaluation on synthetic and real data. In addition to the advance in the state-of-the-art in the stream learning area, this thesis also presents contributions to an important technological application with social and public health impacts. Specifically, it was studied an optical sensor to automatically identify insect species by the means of the analysis of information coming from wing beat of insects. To describe the data, we conclude that the Mel-cepst ral coefficients guide to the best results among different evaluated digital signal processing techniques. This sensor is a concrete example of an applicat ion that generates a data st ream for which it is necessary to perform real-time classification. During the classification phase, this sensor must adapt their classification model to possible variat ions in environmental conditions, responsible for changing the behavior of insects. To address this problem, we propose a System with Multiple Classifiers that dynamically selects the most adequate classifier according to characteristics of each test example. In evaluations with minor changes in the environmental conditions, we achieved a classification accuracy close to 90% in a scenario with multiple classes and 95% when identifying Aedes aegypti species considering the training phase with only the positive class. In the scenario with considerable changes in the environmental conditions, we achieved 91% of accuracy considering 5 species and 96% to classify vector mosquitoes of important diseases as dengue and zika virus.
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Skoglar, Per. "Modelling and control of IR/EO-gimbal for UAV surveillance applications." Thesis, Linköping University, Department of Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1281.

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This thesis is a part of the SIREOS project at Swedish Defence Research Agency which aims at developing a sensor system consisting of infrared and video sensors and an integrated navigation system. The sensor system is placed in a camera gimbal and will be used on moving platforms, e.g. UAVs, for surveillance and reconnaissance. The gimbal is a device that makes it possible for the sensors to point in a desired direction.

In this thesis the sensor pointing problem is studied. The problem is analyzed and a system design is proposed. The major blocks in the system design are gimbal trajectory planning and gimbal motion control. In order to develop these blocks, kinematic and dynamic models are derived using techniques from robotics. The trajectory planner is based on the kinematic model and can handle problems with mechanical constraints, kinematic singularity, sensor placement offset and reference signal transformation.

The gimbal motion controller is tested with two different control strategies, PID and LQ. The challenge is to perform control that responds quickly, but do not excite the damping flexibility too much. The LQ-controller uses a linearization of the dynamic model to fulfil these requirements.

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Abdul, Nour Charles. "Identification de paramètres optiques de structures tissulaires : instrumentation prototype associée : application à la dosimétrie de la thérapie photo-dynamique." Vandoeuvre-les-Nancy, INPL, 1994. http://www.theses.fr/1994INPL006N.

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La thérapie photodynamique anticancéreuse repose sur la production d'oxygène singulet lors d'une réaction photochimique entre un agent photosensibilisant fixé sur les cellules cancéreuses et une irradiation lumineuse dans le rouge. Pour avoir une efficacité thérapeutique, il est important de connaitre l'atténuation lumineuse dans les tissus et ainsi la profondeur de pénétration de la lumière afin de définir une dosimétrie optique précise. La connaissance de ce paramètre doit s'effectuer en continu et in situ. Les mesures préliminaires de la rétrodiffusion et de la transmission lumineuse dans des solutions biologiques ainsi que le développement d'une simulation numérique décrivant les deux phénomènes ont été des bases solides pour la réalisation d'un capteur à fibres optiques capable d'estimer la profondeur de pénétration de la lumière à partir de la rétrodiffusion de celle-ci dans ces milieux. Une instrumentation est associée au capteur pour permettre l'estimation de la profondeur de pénétration de la lumière dans les tissus en continu et in situ. Les résultats des mesures avec le capteur dans des milieux liquides et solides sont rapportés puis comparés aux valeurs de référence. Le dispositif réalisé peut être utilisé pour des mesures cliniques directes
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Harichandran, Aparna. "Sensor Placement, Operation Identification and Fault Detection for Automated Construction Monitoring." Thesis, Curtin University, 2022. http://hdl.handle.net/20.500.11937/87927.

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CURRERI, Francesco. "Soft Sensor Design, Transferability and Causality through Machine Learning Techniques." Doctoral thesis, Università degli Studi di Palermo, 2023. https://hdl.handle.net/10447/582112.

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Dann, Aaron. "Identification and simulation of an automated guided vechile for minimal sensor applications." Thesis, University of Canterbury. Mechanical Engineering, 1996. http://hdl.handle.net/10092/6410.

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The problem of controlling an Automated Guided Vehicles (AGV) with the minimum number of sensors is considered. Sensors add cost and complexity to an AGV both electrically and in terms of increased computational requirements of the controller. Computer simulations are proposed to model the behaviour of the AGV Models of the dynamics of an AGV are proposed and simulated at varying levels of complexity using commercially available numerical software. In order to model the AGV accurately, aspects of the control system and the physical system had to be analysed. Laboratory experiments were designed and performed, and the results were analysed to determine the dynamic properties of sub-systems of the AGV To provide a datum for comparison to the simulations, measurements were made of the performance of an AGV under a variety of control conditions corresponding to the computer models. Comparisons of the simulations and the AGV performance are discussed and suggestions are made for improving the AGV and its control system. The models presented in this thesis demonstrate a good correlation for low performance AGV s in non-rigorous conditions, or well loaded AGV s on good traction surfaces. However they do not accurately represent the AGV at the limits of traction. Two mechanical improvements to the University of Canterbury (UOC) Mk-II AGV are suggested, including the addition of softer compound tyres for use on hard, painted surfaces, and the design of a gear train with lower backlash.
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Šíbl, Josef. "Studie řízení plynulých materiálových toků s využitím značení produktů." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2009. http://www.nusl.cz/ntk/nusl-222051.

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Introduce grauation work contains the detail characteristic of the marking of material elements incoming to store and work out from store. The main part of the work is aimed to shortening working time during material entry and work out from store. On ground ride out analyse has been suggested optimal soluti-on implementation marking of material elements into production company. Optimal type barcode has been designed and time schedule to implementation marking of material elements has been described. The technic-economical analysis proposed solution SAP console describing type and price component need to implementation this solution.
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Bayram, Alican. "Identification Of Kinematic Parameters Using Pose Measurements And Building A Flexible Interface." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614819/index.pdf.

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Robot manipulators are considered as the key element in flexible manufacturing systems. Nonetheless, for a successful accomplishment of robot integration, the robots need to be accurate. The leading source of inaccuracy is the mismatch between the prediction made by the robot controller and the actual system. This work presents techniques for identification of actual kinematic parameters and pose accuracy compensation using a laser-based 3-D measurement system. In identification stage, both direct search and gradient methods are utilized. A computer simulation of the identification is performed using virtual position measurements. Moreover, experimentation is performed on industrial robot FANUC Robot R-2000iB/210F to test full pose and relative position accuracy improvements. In addition, accuracy obtained by classical parametric methodology is improved by the implementation of artificial neural networks. Neuro-parametric method proves an enhanced improvement in simulation results. The whole proposed theory is reflected by developed simulation software throughout this work while achieving accuracy nine times better when comparing before and after implementation.
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Books on the topic "Automatic identification sensor"

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Piramuthu, Selwyn. RFID & sensor network automation in the food industry: Ensuring quality and safety through supply chain visibility. Hoboken: John Wiley & Sons Inc., 2015.

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Piramuthu, Selwyn, and Weibiao Zhou. RFID and Sensor Network Automation in the Food Industry: Ensuring Quality and Safety Through Supply Chain Visibility. Wiley & Sons, Limited, John, 2016.

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Piramuthu, Selwyn, and Weibiao Zhou. RFID and Sensor Network Automation in the Food Industry: Ensuring Quality and Safety Through Supply Chain Visibility. Wiley & Sons, Incorporated, John, 2016.

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Piramuthu, Selwyn, and Weibiao Zhou. RFID and Sensor Network Automation in the Food Industry: Ensuring Quality and Safety Through Supply Chain Visibility. Wiley & Sons, Incorporated, John, 2016.

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Book chapters on the topic "Automatic identification sensor"

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Lambrecht, S., and J. L. Pons. "Automatic Identification of Sensor Localization on the Upper Extremity." In IFMBE Proceedings, 1497–500. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00846-2_370.

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Ntalampiras, Stavros, and Georgios Giannopoulos. "Automatic Fault Identification in Sensor Networks Based on Probabilistic Modeling." In Critical Information Infrastructures Security, 344–54. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31664-2_35.

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Bradley, Elizabeth, and Matthew Easley. "Reasoning about sensor data for automated system identification." In Advances in Intelligent Data Analysis Reasoning about Data, 561–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0052871.

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Catucci, Antonella, Alessia Tricomi, Laura De Vendictis, Savvas Rogotis, and Nikolaos Marianos. "Farm Weather Insurance Assessment." In Big Data in Bioeconomy, 247–63. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_19.

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AbstractThe pilot aimed to develop services supporting both the risk and the damage assessment in the agro-insurance domain. It is based on the use of remotely sensed data, integrated with meteorological data, and adopts machine learning and artificial intelligence tools. Netherlands and Greece have been selected as pilot areas . In the Netherlands, the pilot was focused on potato crops for the identification of areas with higher risk, based on the historical analysis of heavy rains. In addition, it covered automated detection of potato parcels with anomalous behaviours (damage assessment) from satellite data, meteorological parameters and soil characteristics. In Greece, the pilot worked with 7 annual crops of high economic interest to the national agricultural sector. The crops have been modelled exploiting the last 3-year NDVI measurements to identify their deviations from the normal crop health behaviour for an early identification of affected parcels in case of adverse events. The models were successfully tested on a flooding event that occurred in 2019 in the Komotini region. Even though the proposed methodologies should be tested over larger areas and compared against a larger validation dataset, the results already now demonstrate how to reduce the operating costs of damage assessors through a more precise and automatic risk assessment. Additionally, the identification of parameters that most affect the crop yield could transform the insurance industry through index-based solutions allowing to dramatically cut costs.
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Javidi, Bahram, Timothy O’Connor, Arun Anand, Inkyu Moon, Adrian Stern, and Manuel Martinez-Corral. "Compact and Field Portable Biophotonic Sensors for Automated Cell Identification (Plenary Address)." In Springer Proceedings in Physics, 15–18. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9259-1_4.

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Pea, Roy D., Paulina Biernacki, Maxwell Bigman, Kelly Boles, Raquel Coelho, Victoria Docherty, Jorge Garcia, et al. "Four Surveillance Technologies Creating Challenges for Education." In AI in Learning: Designing the Future, 317–29. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09687-7_19.

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Abstract“Ubiquitous AI”—embodied in cloud computing web services, coupled with sensors in phones and the physical world—is becoming infrastructural to cultural practices. It creates a surveillance society. We review the capabilities of four core surveillance technologies, all making headway into universities and PreK-12 schools: (1) location tracking, (2) facial identification, (3) automated speech recognition, and (4) social media mining. We pose primary issues educational research should investigate on cultural practices with these technologies. We interweave three priority themes: (1) how these technologies are shaping human development and learning; (2) current algorithmic biases and access inequities; and (3) the need for learners’ critical consciousness concerning their data privacy. We close with calls to action—research, policy and law, and practice.
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Malhotra, Baljeet, Hoyoung Jeung, Thomas Kister, Stéphane Bressan, and Kian-Lee Tan. "Maritime Data Management and Analytics: A Survey of Solutions Based on Automatic Identification System." In Building Sensor Networks, 249–70. CRC Press, 2017. http://dx.doi.org/10.1201/b15479-11.

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TONGCO, E. C., and D. R. MELDRUM. "OPTIMAL SENSOR PLACEMENT FOR IDENTIFICATION OF LARGE FLEXIBLE SPACE STRUCTURES." In Automatic Control in Aerospace 1994 (Aerospace Control '94), 249–54. Elsevier, 1995. http://dx.doi.org/10.1016/b978-0-08-042238-1.50042-2.

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Chowdhury, Dhrubajit, Alexander Melin, and Kris Villez. "Method for automatic correction of offset drift in online sensors." In Celebrating passion for Water, Science and Technology, 17–42. IWA Publishing, 2022. http://dx.doi.org/10.2166/9781789063370_0017.

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Abstract Successful operation and optimization of water treatment systems hinge on the availability of high-quality online sensor measurements. Ideally, the available measurements should be simultaneously accurate (i.e., unbiased and precise), representative, voluminous, and timely. This remains a pain-point in current water infrastructures, forming a barrier to a wider adoption of advanced and autonomous control systems. While short-lived symptoms, such as outliers and spikes, can be detected or corrected with state-of-the-art tools for fault detection and identification, it is much more difficult to detect, diagnose, and correct the symptoms of slow faults, such as changes in offset or sensitivity due to drift. The time scale of drift is often longer than the time scales of the system dynamics of interest. Moreover, sensor drift has been shown to occur at the same time and with similar rates when sensors are exposed to the same conditions. This challenges data quality management strategies based on redundancy. In this contribution, we develop a new method, including both a hands-off sensor calibration mechanism and an information-seeking control architecture that can handle the unique challenge of simultaneous and similar drift in online sensors.
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Joshi, Deepak, and Michael E. Hahn. "Electromyogram and Inertial Sensor Signal Processing in Locomotion and Transition Classification." In Computational Tools and Techniques for Biomedical Signal Processing, 195–211. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0660-7.ch009.

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Signal processing in biomedical engineering is essentially required for classification while serving mainly two aims. The first is noise removal and the second is signal representation. Signal representation deals with transforming the signal in such a way that the signal is most informative in that particular domain for the application at hand. This chapter will describe signal processing methods like spectrogram with specific applications to locomotion and transition classification using Electromyography (EMG) data. A wavelet analysis application on foot acceleration signals for automatic identification of toe off in locomotion and the ramp transition is also shown. Finally, the performance of EMG and accelerometer performance across different time windows of a gait cycle in locomotion and transition classification is presented with an emphasis on fusing the data from both sensors for better classification.
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Conference papers on the topic "Automatic identification sensor"

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Iwamoto, Takashi. "Practical Identification of Specific Emitters Used in the Automatic Identification System." In 2015 Sensor Signal Processing for Defence (SSPD). IEEE, 2015. http://dx.doi.org/10.1109/sspd.2015.7288518.

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Li, Hongyu, Hairong Wang, Luyang Liu, and Marco Gruteser. "Automatic Unusual Driving Event Identification for Dependable Self-Driving." In SenSys '18: The 16th ACM Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3274783.3274838.

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Wang, Yicheng, and Murat Uney. "Fast Trajectory Forecasting With Automatic Identification System Broadcasts." In 2022 Sensor Signal Processing for Defence Conference (SSPD). IEEE, 2022. http://dx.doi.org/10.1109/sspd54131.2022.9896218.

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Gafurov, Davrondzhon, Einar Snekkenes, and Patrick Bours. "Gait Authentication and Identification Using Wearable Accelerometer Sensor." In 2007 IEEE Workshop on Automatic Identification Advanced Technologies. IEEE, 2007. http://dx.doi.org/10.1109/autoid.2007.380623.

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Wisanmongkol, J., T. Sanpechuda, and U. Ketprom. "Automatic vehicle identification with sensor-integrated RFID system." In 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2008. http://dx.doi.org/10.1109/ecticon.2008.4600541.

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Lin, Chung-Yen, Wenjie Chen, and Masayoshi Tomizuka. "Automatic Sensor Frame Identification in Industrial Robots With Joint Elasticity." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3836.

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For robots with joint elasticity, discrepancies exist between the motor side information and the load side (i.e., end-effector) information. Therefore, high tracking performance at the load side can hardly be achieved when the estimate of load side information is inaccurate. To minimize such inaccuracies, it is desired to calibrate the load side sensor (in particular, the exact sensor location). In practice, the optimal placement of the load side sensor often varies due to the task variation necessitating frequent sensor calibrations. This frequent calibration need requires significant effort and hence is not preferable for industries which have relatively short product cycles. To solve this problem, this paper presents a sensor frame identification algorithm to automate this calibration process for the load side sensor, in particular the accelerometer. We formulate the calibration problem as a nonlinear estimation problem with unknown parameters. The Expectation-Maximization algorithm is utilized to decouple the state estimation and the parameter estimation into two separated optimization problems. An overall dual-phase learning structure associated with the proposed approach is also studied. Experiments are designed to validate the effectiveness of the proposed algorithm.
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Heydary, Mohammadreza Hajy, Pritesh Pimpale, and Anand Panangadan. "Automatic Identification of Use of Public Transportation from Mobile Sensor Data." In 2018 IEEE Green Technologies Conference (GreenTech). IEEE, 2018. http://dx.doi.org/10.1109/greentech.2018.00042.

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Kuzume, Koichi, Yoshitugu Watanabe, Haruko Masuda, and Tomonari Masuzaki. "Inference System for Automatic Identification of Braille Blocks Using a Pressure Sensor Array." In 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2022. http://dx.doi.org/10.1109/percomworkshops53856.2022.9767257.

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Liu, Zhongdi, Xiang'ao Meng, Jiajia Cui, Zhipei Huang, and Jiankang Wu. "Automatic Identification of Abnormalities in 12-Lead ECGs Using Expert Features and Convolutional Neural Networks." In 2018 International Conference on Sensor Networks and Signal Processing (SNSP). IEEE, 2018. http://dx.doi.org/10.1109/snsp.2018.00038.

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Kuzume, Koichi, Haruko Masuda, and Yudai Murakami. "Automatic Identification of Braille Blocks by Neural Network Using Multi-Channel Pressure Sensor Array." In CIIS 2020: 2020 The 3rd International Conference on Computational Intelligence and Intelligent Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3440840.3440858.

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Reports on the topic "Automatic identification sensor"

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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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,
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