Relevant bibliographies by topics / Automatic Aircraft Recognition System

Academic literature on the topic 'Automatic Aircraft Recognition System'

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Published: 11 September 2023

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Journal articles on the topic "Automatic Aircraft Recognition System"

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Jia, Jiaqi, and Haibin Duan. "Automatic target recognition system for unmanned aerial vehicle via backpropagation artificial neural network." Aircraft Engineering and Aerospace Technology 89, no. 1 (January 3, 2017): 145–54. http://dx.doi.org/10.1108/aeat-07-2015-0171.

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Purpose The purpose of this paper is to propose a novel target automatic recognition method for unmanned aerial vehicle (UAV), which is based on backpropagation – artificial neural network (BP-ANN) algorithm, with the objective of optimizing the structure of backpropagation network, to increase the efficiency and decrease the recognition time. A hardware-in-the-loop system for UAV target automatic recognition is also developed. Design/methodology/approach The hybrid model of BP-ANN structure is established for aircraft automatic target recognition. This proposed method identifies controller parameters and reduces the computational complexity. Approaching speed of the network is faster and recognition accuracy is higher. This kind of network combines or better fuses the advantages of backpropagation artificial neural algorithm and Hu moment. with advantages of two networks and improves the speed and accuracy of identification. Finally, a hardware-in-the-loop system for UAV target automatic recognition is also developed. Findings The double hidden level backpropagation artificial neural can easily increase the speed of recognition process and get a good performance for recognition accuracy. Research limitations/implications The proposed backpropagation artificial neural algorithm can be ANN easily applied to practice and can help the design of the aircraft automatic target recognition system. The standard backpropagation algorithm has some obvious drawbacks, namely, converging slowly and falling into the local minimum point easily. In this paper, an improved algorithm based on the standard backpropagation algorithm is constructed to make the aircraft target recognition more practicable. Originality/value A double hidden levels backpropagation artificial neural algorithm is presented for automatic target recognition system of UAV.
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Bobin, A. V., V. A. Azarov, S. A. Bulgakov, and D. A. Savin. "Technique for recognition of aircrafts and radar traps in the control circuit of airspace control system based on neural network technology." Izvestiya MGTU MAMI 7, no. 1-4 (July 10, 2013): 124–30. http://dx.doi.org/10.17816/2074-0530-67843.

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The paper proposes a method for building of automatic recognizers of aircrafts on a set of radar measurements based on the cascade of multilayer feedforward neural networks. The practical application of this technique in recognizing of three types of aircraft is presented as well.
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Silva Filho, P., E. H. Shiguemori, and O. Saotome. "UAV VISUAL AUTOLOCALIZATON BASED ON AUTOMATIC LANDMARK RECOGNITION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W3 (August 18, 2017): 89–94. http://dx.doi.org/10.5194/isprs-annals-iv-2-w3-89-2017.

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Deploying an autonomous unmanned aerial vehicle in GPS-denied areas is a highly discussed problem in the scientific community. There are several approaches being developed, but the main strategies yet considered are computer vision based navigation systems. This work presents a new real-time computer-vision position estimator for UAV navigation. The estimator uses images captured during flight to recognize specific, well-known, landmarks in order to estimate the latitude and longitude of the aircraft. The method was tested in a simulated environment, using a dataset of real aerial images obtained in previous flights, with synchronized images, GPS and IMU data. The estimated position in each landmark recognition was compatible with the GPS data, stating that the developed method can be used as an alternative navigation system.
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Roopa, K., T. V. Rama Murthy, and P. Cyril Prasanna Raj. "Neural Network Classifier for Fighter Aircraft Model Recognition." Journal of Intelligent Systems 27, no. 3 (July 26, 2018): 447–63. http://dx.doi.org/10.1515/jisys-2016-0087.

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Abstract Fighter aircraft recognition is important in military applications to make strategic decisions. The complexity lies in correctly identifying the unknown aircraft irrespective of its orientations. The work reported here is a research initiative in this regard. The database used here was obtained by using rapid prototyped physical models of four classes of fighter aircraft: P51 Mustang, G1-Fokker, MiG25-F, and Mirage 2000. The image database was divided into the training set and test set. Two feature sets, Feature Set1 (FS1) and FS2, were extracted for the images. FS1 consisted of 15 general features and FS2 consisted of 14 invariant moment features. Four multilayered feedforward backpropagation neural networks were designed and trained optimally with the normalized feature sets. The neural networks were configured to classify the test aircraft image. An overall accuracy of recognition of 91% and a response time of 3 s were achieved for the developed automatic fighter aircraft model image recognition system.
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Сафтли, Ф. Х. А., and С. Т. Баланян. "Methodology for assessing the control system of aircraft weapons in the process of aiming controlled aircraft weapons equipped with optelectronic homing heads." Vestnik of Russian New University. Series «Complex systems: models, analysis, management», no. 1 (March 23, 2022): 64–75. http://dx.doi.org/10.18137/rnu.v9187.22.01.p.064.

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Рассматривается повышение эффективности боевого применения управляемой авиационной ракеты класса «воздух – поверхность» по полученным целеуказаниям с беспилотного летательного аппарата, позволяющее на больших расстояниях автоматически решать задачи поиска, обнаружения и распознавания целей в условиях реального масштаба времени при сложной фоноцелевой обстановке в зоне боевых действий, тем самым уменьшая риск попадания самолета-носителя в зону действия ПВО противника. Разработана оптимизированная архитектура сверточной нейронной сети для сегментации изображений и распознавания наземных целей в оптико-электронной системе беспилотного летательного аппарата, а также разработанный алгоритм автоматического распознавания наземной цели искусственной нейронной сетью в телевизионной головке самонаведения управляемых авиационных средств поражения класса «воздух – поверхность». Проведено аналитическое сравнительное исследование по вероятности поражения наземной цели типа танка между разработанными алгоритмами автоматического распознавания наземной цели и использованием визуального (оптического) обнаружения и распознавания наземной цели летчиком (штурманом) при разных значениях средней интенсивности потока огневого воздействия ракет противовоздушной обороны противника. Осуществлена программная реализация алгоритмов автоматического распознавания наземной цели и обучения оптимизированной нейронной сети с использованием объектно ориентированного языка программирования Matlab. This article discusses the increase in the effectiveness of the combat use of an air-to-surface guided missile based on the received target designations from an unmanned aerial vehicle, which makes it possible to automatically solve the tasks of searching, detecting and recognizing targets in real time conditions at large distances in a complex background-target situation in the combat zone actions, thereby reducing the risk of the carrier aircraft falling into the enemy air defense coverage area. An optimized architecture of a convolutional neural network has been developed for image segmentation and ground target recognition in the optoelectronic system of an unmanned aerial vehicle, as well as an algorithm for automatic recognition of a ground target by an artificial neural network in a television homing head of controlled air-to-surface weapons. An analytical comparative study was carried out on the probability of hitting a ground target such as a tank between the developed algorithms for automatic recognition of a ground target and the use of visual (optical) detection and recognition of a ground target by a pilot (navigator) at different values of the average intensity of the flow of fire from enemy air defense missiles. The software implementation of algorithms for automatic recognition of a ground target and training of an optimized neural network using the object-oriented programming language Matlab has been implemented.
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Zhang, Li Ping, Chao Wang, Hong Zhang, and Bo Zhang. "Aircraft Type Recognition in High-Resolution SAR Images Using Multi-Scale Autoconvolution." Key Engineering Materials 439-440 (June 2010): 1475–80. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.1475.

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Automatic target recognition is the key stage of SAR image interpretation system and has been taking a great interest to the researchers in recent years. Aiming at the issue of aircraft type recognition in high-resolution SAR images, a novel method based on multi-scale autoconvolution (MSA) affine invariant moment is proposed. First, the texture analysis and clustering method are used to segment the SAR images and then the denoising algorithm and morphological processing are applied to segmented results. Second, 29 MSA features are extracted and form a feature vector to represent the target, then the vector components are standardized by gauss normalization. In the final, the vectors are classified by using the nearest neighbor classifier and template library constructed previously. Experimental results show that the proposed method can obtain high accuracy rate with high processing speed, in which the accuracy rate of two type aircrafts with real data arrives at 85.17% and the accuracy rate of four type aircrafts with simulated data arrives at 87.85%.
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Bohouta, Gamal. "Automatic speech recognition for unmanned aerial vehicles." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A98. http://dx.doi.org/10.1121/10.0015671.

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Unmanned aerial vehicles (UAVs), also known as unmanned aerial systems (UASs), are quickly becoming a ubiquitous technology, poised to enter some key large-scale markets in the very near future. Fleets of such vehicles will be required in these large-scale deployments for commercial, industrial, and emergency response, along with the ability to efficiently control these fleets. Voice control and communication between human operators and these fleets will become imperative. This paper explores the framework for building an automatic speech recognition (ASR) use to the control of unmanned aerial vehicles (UAVs). The ARS system will be used by Aeronyde Corporation to fully-autonomous fleets with minimal human intervention. Aeronyde Corporation is working to shift and advance the current industry thinking of unmanned platforms from Remotely Piloted Aircraft (RPA) to fully-autonomous fleets with minimal human intervention. The Aeronyde Avionics package enables a single operator to control and monitor missions of many drones in real time anywhere in the world. The “1 Pilot – Many Drones” approach to aerial data collection is revolutionary for Big Data aggregation and analytics of the 4th Industrial Revolution. Multi-UAV autonomous aerial systems will transform data acquisition for many commercial applications, including: agriculture and forestry, railroad inspections, pipeline inspections, powerline inspections, windmill inspections, terrain mapping, search and rescue, firefighting and police work, and border patrol.
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Shabelnik, Tetyana, Serhii Krivenko, and Olena Koneva. "AUTOMATIC PILOT SYSTEM FOR UNMANNED OF AIRCRAFT IN THE ABSENCE OF RADIO COMMUNICATION." Cybersecurity: Education, Science, Technique 1, no. 9 (2020): 93–103. http://dx.doi.org/10.28925/2663-4023.2020.9.93103.

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One of the most pressing problems of piloting unmanned aerial vehicles (UAV) in the absence of radio communication is considered in the article. Therefore, the aim of the article is to develop an algorithm and method of automatic piloting of UAV in terms of loss of radio control signal using the methods of technical vision. The most effective methods of tracking, identification and detection of landmarks are based on the comparison of reference information (database of known navigation objects) with the observation scene in real time.Working system of automatic piloting of UAVs in the conditions of loss of radio control signal or GPS-navigation developed. The hardware and software of the UAV provides full automatic control. The programming of the system consists of two stages: planning the flight task and calculating the trajectory of the UAV in flight. The planning of the flight task is carried out by setting the topographic landmarks and flight parameters in relation to them. At this stage, the criteria for the generalization of the various components of the landscape are formed and their division by gradations. This work is combined with the recognition of points with altitude marks, and fixing the heights of horizontal surfaces available in the area. All horizontal surfaces are tied with the shortest shooting strokes to at least of three points with elevations. The process of topography-based object selection is directly related to its segmentation, the results of which significantly affect the further process of image analysis and UAV control. The calibration of the starting point of the route occurs during the launch of the UAV. The control system automatically monitors the location of the UAV throughout the trajectory of the movement on a topographic basis relative to the prespecified landmarks. Structured shots of the terrain and topographic bases are compared during the flight. The algorithm is based on the comparison of geometric parameters of landmarks. The parameters of the geometric center O(x, y) and the area S are taken into account by such parameters. The control signal in the three axes OX, OY and OZ is determined for the first time by the method of least squares depending on the values ​​of the calculated coefficients of the original equations.
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Kniaz, V. V. "A Fast Recognition Algorithm for Detection of Foreign 3D Objects on a Runway." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (August 11, 2014): 151–56. http://dx.doi.org/10.5194/isprsarchives-xl-3-151-2014.

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!The systems for detection of foreign objects on a runway during the landing of an aircraft are highly demanded. Such systems could be installed in the airport or could be mounted on the board of an aircraft. This work is focused on a fast foreign object recognition algorithm for an onboard foreign object detection system. <br><br> The algorithm is based on 3D object minimal boundary extraction. The boundary is estimated through an iterative process of minimization of a difference between a pair of orthophotos. During the landing an onboard camera produces a sequence of images from which a number of stereo pair could be extracted. For each frame the runway lines are automatically detected and the external orientation of the camera relative to the runway is estimated. Using external orientation parameters the runway region is projected on an orthophoto to the runway plane. The difference of orthophotos shows the objects that doesn't coincide with the runway plane. After that the position of the foreign object relative to the runway plane and its minimal 3D boundary could be calculated. The minimal 3D boundary for each object is estimated by projection of a runway region on a modified model of the runway. The extracted boundary is used for an automatic recognition of a foreign object from the predefined bank of 3D models.
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Sun, Yuchuang, Wen Jiang, Jiyao Yang, and Wangzhe Li. "SAR Target Recognition Using cGAN-Based SAR-to-Optical Image Translation." Remote Sensing 14, no. 8 (April 8, 2022): 1793. http://dx.doi.org/10.3390/rs14081793.

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Target recognition in synthetic aperture radar (SAR) imagery suffers from speckle noise and geometric distortion brought by the range-based coherent imaging mechanism. A new SAR target recognition system is proposed, using a SAR-to-optical translation network as pre-processing to enhance both automatic and manual target recognition. In the system, SAR images of targets are translated into optical by a modified conditional generative adversarial network (cGAN) whose generator with a symmetric architecture and inhomogeneous convolution kernels is designed to reduce the background clutter and edge blur of the output. After the translation, a typical convolutional neural network (CNN) classifier is exploited to recognize the target types in translated optical images automatically. For training and testing the system, a new multi-view SAR-optical dataset of aircraft targets is created. Evaluations of the translation results based on human vision and image quality assessment (IQA) methods verify the improvement of image interpretability and quality, and translated images obtain higher average accuracy than original SAR data in manual and CNN classification experiments. The good expansibility and robustness of the system shown in extending experiments indicate the promising potential for practical applications of SAR target recognition.
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Dissertations / Theses on the topic "Automatic Aircraft Recognition System"

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Kim, Jijoong. "Automatic aircraft recognition and identification." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060808.161115/index.html.

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Wei, Yi. "Statistical methods on automatic aircraft recognition in aerial images." Thesis, University of Strathclyde, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248947.

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Campbell, Larry W. "An intelligent tutor system for visual aircraft recognition." Thesis, Monterey, California: Naval Postgraduate School, 1990. http://hdl.handle.net/10945/27723.

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Approved for public release; distribution is unlimited.
Visual aircraft recognition (VACR) is a critical skill for U.S. Army Short Range Air Defense (SHORAD) soldiers. It is the most reliable means of identifying aircraft, however VACR skills are not easy to teach or learn, and once learned they are highly degradable. The numerous training aids that exist to help units train soldiers require qualified instructors who are not always available. Also, the varying degrees of proficiency among soldiers make group training less than ideal. In an attempt to alleviate the problems in most VASC training programs, an intelligent tutor system has been developed to teach VACR in accordance with the Wings, Engine, Fuselage, Tail (WEFT) cognitive model. The Aircraft Recognition Tutor is a graphics based, object oriented instructional program that teaches, reviews and tests VACR skills at a level appropriate to the student. The tutor adaptively coaches the student from the novice level, through the intermediate level, to the expert level. The tutor was provided to two U.S. Army Air Defense Battalions for testing and evaluation. The six month implementation, testing, and evaluation process demonstrated that, using existing technology in Computer Science and Artificial Intelligence, useful training tools could be developed quickly and inexpensively for deployment on existing computers in field.
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Higgins, C. A. "Automatic recognition of handwritten script." Thesis, University of Brighton, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.372081.

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Sherrah, Jamie. "Automatic feature extraction for pattern recognition /." Title page, contents and abstract only, 1998. http://web4.library.adelaide.edu.au/theses/09PH/09phs553.pdf.

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Thesis (Ph. D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1999.
CD-ROM in back pocket comprises experimental results and executables. Includes bibliographical references (p. 251-261).
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Benkhedda, Hassen. "Design of a transport aircraft automatic flight control system with analytical redundancy." Thesis, University of Southampton, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.241599.

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Bennamoun, Mohammed. "An adaptive vision system for automatic object recognition." Thesis, Queensland University of Technology, 1996. https://eprints.qut.edu.au/107120/1/T%28BE%26E%29%201022%20An%20adaptive%20vision%20system%20for%20automatic%20object%20recognition.pdf.

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The aim of this thesis is to propose a modular vision system for automatic 2D object recognition, to determine its performance (with respect to accuracy, robustness, and efficiency), and to compare its performance to the performance of the neocognitron. Most of the existing vision systems assume that a preprocessed and clean image is provided, and that the parameters of the used algorithms are chosen by the user, depending on the scene under consideration. On this basis it is desired from the proposed system to be: 1)accurate, 2) robust, 3) efficient, and 4) adaptive. The system works under the following assumptions: 1) a single object is present in the field of view, 2) a single grey level image of the scene is provided, 3) the contrast in intensity between the object and the background is enough to be able to separate them. Note that we also extended this system to assume multiple moving objects in the scene.
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Tran, Michael. "An approach to a robust speaker recognition system." Diss., This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06062008-164814/.

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Bengio, Yoshua. "Connectionist models applied to automatic speech recognition." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63920.

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Zaludin, Zairil A. "Flight dynamics and automatic flight control system of an hypersonic transport aircraft." Thesis, University of Southampton, 1999. https://eprints.soton.ac.uk/47120/.

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Books on the topic "Automatic Aircraft Recognition System"

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Center, Langley Research, ed. Automatic braking system modification for the Advanced Transport Operating System (ATOPS) Transportation System Research Vehicle (TSRV). Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1986.

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Lee, Kai-Fu. Automatic speech recognition: The development of the SPHINX system. New York: Springer Science+Business Media, LLC, 1989.

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Lee, Kai-Fu. Automatic speech recognition: The development of the SPHINX system. Boston: Kluwer Academic Publishers, 1989.

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Russell, M. J. The development of the speaker independent ARM continuous speech recognition system. [London: Controller, H.M.S.O., 1992.

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Center, Ames Research, ed. Rotary-wing aircraft terrain-following/terrain-avoidance system development. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1986.

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Center, Ames Research, ed. Rotary-wing aircraft terrain-following/terrain-avoidance system development. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1986.

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C, Chenoweth C., ed. Aircraft flight control actuation system design. Warrendale, PA: Society of Automotive Engineers, 1993.

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Alphen, Paul van. HMM-based continuous-speech recognition: Systematic evaluation of various system components. [Leidshcendam: PTT Research,], 1992.

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Franz, Gerl, Minker Wolfgang, and SpringerLink (Online service), eds. Self-Learning Speaker Identification: A System for Enhanced Speech Recognition. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Peeling, S. M. The use of linear discriminant analysis in the ARM continuous speech recognition system. London: Controller H.M.S.O., 1992.

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Book chapters on the topic "Automatic Aircraft Recognition System"

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Lee, Kai-Fu. "The Baseline SPHINX System." In Automatic Speech Recognition, 51–62. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4615-3650-5_4.

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Ghate, P. M., Shraddha Chadha, Aparna Sundar, and Ankita Kambale. "Automatic Speaker Recognition System." In Advances in Intelligent Systems and Computing, 1037–44. New Delhi: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-0740-5_126.

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Sun, Qi Bin, Chian Prong Lam, and Jian Kang Wu. "A Practical Automatic Face Recognition System." In Face Recognition, 537–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_33.

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Roopa, K., and T. V. Ramamurthy. "Aircraft Recognition System Using Image Analysis." In Lecture Notes in Electrical Engineering, 195–204. New Delhi: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1157-0_21.

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Meenakshi, Durga Puja, Mukesh Saraswat, and K. V. Arya. "Automatic Agricultural Leaves Recognition System." In Advances in Intelligent Systems and Computing, 123–31. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-1038-2_11.

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Yi, Wei. "Automatic Aircraft Recognition Using Maximum Likelihood Ratio Test." In Active Media Technology, 321–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45336-9_37.

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Murray-Smith, D. J. "Case Study II — An Aircraft Automatic Landing System." In Continuous System Simulation, 163–73. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2504-2_11.

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Agarwal, Suchit, Akshay Chatterjee, and Ghazaala Yasmin. "Automatic Multilingual System from Speech." In Computational Intelligence in Pattern Recognition, 145–55. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9042-5_13.

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Shirai, K., and T. Kobayashi. "Speech Production Model and Automatic Recognition." In Nature, Cognition and System I, 3–14. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-2991-3_1.

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Odriozola, Igor, Luis Serrano, Inma Hernaez, and Eva Navas. "The AhoSR Automatic Speech Recognition System." In Advances in Speech and Language Technologies for Iberian Languages, 279–88. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13623-3_29.

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Conference papers on the topic "Automatic Aircraft Recognition System"

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Kamgar-Parsi, B., and A. K. Jain. "Automatic aircraft recognition: toward using human similarity measure in a recognition system." In Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 1999. http://dx.doi.org/10.1109/cvpr.1999.786950.

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Ali, Syed Faisal, Jafreezal Jaafar, and Aamir Saeed Malik. "Proposed technique for aircraft recognition in Intelligent Video Automatic Target Recognition System (IVATRs)." In 2010 International Conference on Computer Applications and Industrial Electronics (ICCAIE). IEEE, 2010. http://dx.doi.org/10.1109/iccaie.2010.5735070.

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Vazquez-Nicolas, J. M., Erik Zamora, I. Gonzalez-Hernandez, Rogelio Lozano, and Humberto Sossa. "Towards automatic inspection: crack recognition based on Quadrotor UAV-taken images." In 2018 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2018. http://dx.doi.org/10.1109/icuas.2018.8453390.

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Bateman, Alec J., Stephan De Wekker, Keith D. Hoffler, Jesse Couch, and Eugene A. Morelli. "Flight Testing an Automated Turbulence Recognition System for Unmanned Aircraft." In AIAA AVIATION 2022 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2022. http://dx.doi.org/10.2514/6.2022-3407.

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Guoxiong Luo, Jun Liu, Wenbo Shen, and Hongyi Gao. "A New Automatic Tracking and Shooting Technique Used in Seat Ejection Test Based on Image Recognition." In CSAA/IET International Conference on Aircraft Utility Systems (AUS 2018). Institution of Engineering and Technology, 2018. http://dx.doi.org/10.1049/cp.2018.0275.

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Shetty, Shruthi, Hartmut Helmke, Matthias Kleinert, and Oliver Ohneiser. "Early Callsign Highlighting using Automatic Speech Recognition to Reduce Air Traffic Controller Workload." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002493.

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The primary task of an air traffic controller (ATCo) is to issue instructions to pi-lots. However, the first verbal communication contact is often initiated by the pi-lot. Hence, the ATCo needs to search for the aircraft radar label that corresponds to the callsign uttered by the pilot. Therefore, it would be useful to have a control-ler assistance system, which recognizes and highlights the spoken callsign in the ATCo display as early as possible, directly from the speech data. Therefore, we propose to use an automatic speech recognition (ASR) system to first obtain the speech-to-text transcription, followed by extracting the spoken callsign from the transcription. As a high performance in callsign recognition is required, we use surveillance data, which significantly reduces callsign recognition error rates. When using ASR transcriptions for ATCo utterances of Isavia data (HAAWAII project ), we initially obtain a callsign recognition error rate of 6.2%, which im-proves to 2.8% when surveillance data information is used.For the ATC operational speech data obtained from NATS air navigation service provider for London approach area, currently we obtain a callsign recognition rate of 93.8% for both ATCo and pilot utterances on automatic transcriptions which are generated by an ASR system with a word error rate of 5.1%. However, when surveillance data is not used, the callsign recognition rate drops significantly to 82.7%, indicating the importance of using surveillance data while recognizing callsigns. Once the callsign is spoken, we are able to recognize it within a second, which would be of great value to ATCos especially in situations of high traffic constituting high workload.
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Grabill, Paul, Jerry Jacobs, Tom Johnson, Tom Brotherton, and Jon Keller. "Helicopter Structural Life Modeling: Flight Regime and Gross Weight Estimation." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27894.

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Accurate usage information collected by Health and Usage Monitoring Systems (HUMS) coupled with improved structural fatigue life calculation methodologies promise to reduce helicopter operational and support costs while maintaining current flight safety levels. Current fatigue life calculations assume worst-case flight profiles in determining component life. This approach may be outdated or not reflective of actual aircraft usage. On a small business innovative research (SBIR) contract the Intelligent Automation Corporation (IAC) has developed processing to include a low cost regime recognition and aircraft gross weight estimation capability as an extension to the US Army’s Vibration Management Enhancement Program (VMEP). IAC’s approach relies on multi-sensor data fusion technology and flight parameters collected by VMEP to provide an accurate flight regime calculation. Recording time in particular flight regimes has the potential of extending aircraft component life without changing proven lifing models. The regime recognition system has been implemented with the current release of IAC’s VMEP systems. Presented here are the methodology, development and visualization tools developed on the SBIR as well as results for using the system on AH-64 aircraft in follow-on work.
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Dierking, M., F. Heitkamp, and L. Barnes. "High temporal resolution laser radar tomography for long range target identification." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1998. http://dx.doi.org/10.1364/srs.1998.sthc.2.

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The Air Force Electro Optics Division is pursuing advanced laser target identification technologies under its Enhanced Recognition and Sensing Ladar (ERASER) program. Long range, eyesafe target identification is of central importance in many regional operations. Building on advancements in laser sources, detectors1 and signal processing, direct detection ladar can provide ID well beyond the range of conventional imaging sensors. High temporal resolution laser radar can produce range resolved (ID) scattering profiles, which are unique to each aircraft relatively independent of target range2,3. This allows a variety of ID techniques to be applied at ranges well beyond the resolution limits of comparably sized imaging apertures. The ID profiles can be applied to automatic target recognition systems for classification and identification. However, in-class variation as well as large numbers of unclassified targets may limit these techniques. In this paper, we investigate the use of tomographic reconstruction to enhance long range identification. The reconstruction is based on compilations of ID signatures obtained at various angles due to relative target sensor motion. We describe the ladar system used to measure the ID returns of an F-4 aircraft. Based on the measured capabilities of the ERASER ladar, we use the IRMA computer model to generate sets of data consistent with the measured ladar data. Finally, we utilize the filtered back-projection method to investigate the reconstruction model data for both the ideal and limited-data cases.
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Hmam, Hatem, and Jijoong Kim. "Automatic aircraft recognition." In AeroSense 2002, edited by Raja Suresh and William E. Roper. SPIE, 2002. http://dx.doi.org/10.1117/12.478717.

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Kallappa, Pattada, and Haftay Hailu. "Automated Contingency and Life Management for Integrated Power and Propulsion Systems." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68587.

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Impact Technologies, LLC has developed a prototype Automated Contingency and Life Management (ACLM) System for aircraft propulsion systems. Using a combination of on-board diagnostic and control modules, the ACLM system implements real-time and near real-time strategies to detect critical and life limiting engine faults and take appropriate control actions to ensure optimal performance. The modular hierarchical ACLM architecture is populated with Prognostics and Health Monitoring (PHM), and Adaptive Intelligent Control (AIC) algorithms. The PHM algorithms include Statistical Fault Pattern Recognition, Probabilistic Neural Network and Model-Based modules. These algorithms work in parallel and their outcomes are fused to provide a comprehensive fault diagnosis with an associated level of confidence. The AIC consists of a hierarchical framework for the implementation of the control strategy. The strategy incorporates varying control modes, such as life extending, performance and safe/abort/contingency management. This ACLM system has been demonstrated on an Air Force Research Laboratory generic two-spool turbofan engine computer simulation model.
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Reports on the topic "Automatic Aircraft Recognition System"

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Lassahn, G. D. Automatic TLI recognition system, general description. Office of Scientific and Technical Information (OSTI), February 1997. http://dx.doi.org/10.2172/481877.

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Partin, J. K., G. D. Lassahn, and J. R. Davidson. Automatic TLI recognition system. Part 1: System description. Office of Scientific and Technical Information (OSTI), May 1994. http://dx.doi.org/10.2172/10166991.

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Lassahn, G. D. Automatic TLI recognition system, programmer`s guide. Office of Scientific and Technical Information (OSTI), February 1997. http://dx.doi.org/10.2172/481865.

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Lassahn, G. D. Automatic TLI recognition system, user`s guide. Office of Scientific and Technical Information (OSTI), February 1997. http://dx.doi.org/10.2172/481878.

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Lassahn, G. D. Automatic TLI recognition system beta prototype testing. Office of Scientific and Technical Information (OSTI), June 1996. http://dx.doi.org/10.2172/266745.

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Lassahn, G. D., J. K. Partin, and J. R. Davidson. Automatic TLI recognition system evaluation using AMPS data. Office of Scientific and Technical Information (OSTI), March 1995. http://dx.doi.org/10.2172/130622.

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Chun, Cornell S., and Firooz A. Sadjadi. Polarimetric Imaging System for Automatic Target Detection and Recognition. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada395219.

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Partin, J. K., G. D. Lassahn, and J. R. Davidson. Automatic TLI recognition system. Part 2: User`s guide. Office of Scientific and Technical Information (OSTI), May 1994. http://dx.doi.org/10.2172/10166988.

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Thomas, Michael J. Combining Facial Recognition, Automatic License Plate Readers and Closed Circuit Television to Create an Interstate Identification System for Wanted Subjects. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ad1009302.

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Gaponenko, Artiom, and Andrey Golovin. Electronic magazine with rating system of an estimation of individual and collective work of students. Science and Innovation Center Publishing House, October 2017. http://dx.doi.org/10.12731/er0043.06102017.

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«The electronic magazine with rating system of an estimation of individual and collective work of students» (EM) is developed in document Microsoft Excel with use of macros. EM allows to automate all the calculated operations connected with estimation of amount scored by students in each form of the current control. EM provides automatic calculation of rating of the student with reflection of a maximum quantity of the points received in given educational group. The rating equal to “1” is assigned to the student who has got a maximum quantity of points for the certain date. For the other students the share of their points in this maximum size is indicated. The choice of an estimation is made in an alphabetic format according to requirements of the European translation system of test units for the international recognition of results of educational outcomes (ECTS - European Credit Transfer System), by use of a corresponding scale of an estimation. The list of students is placed on the first page of magazine and automatically displayed on all subsequent pages. For each page of magazine the optimal size of document printing is set with automatic enter of current date and time. Owing to accounting rate of complexity of task EM is the universal technical tool which can be used for any subject matter.
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