Academic literature on the topic 'Forward detection systems'
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Journal articles on the topic "Forward detection systems"
Kabakchiev, Hristo, Vera Behar, Ivan Garvanov, Dorina Kabakchieva, Avgust Kabakchiev, and Hermann Rohling. "FSR Systems for Detection of Air Objects Using Cosmic Radio Emissions." Sensors 21, no. 2 (January 11, 2021): 465. http://dx.doi.org/10.3390/s21020465.
Full textKabakchiev, Hristo, Vera Behar, Ivan Garvanov, Dorina Kabakchieva, Avgust Kabakchiev, and Hermann Rohling. "FSR Systems for Detection of Air Objects Using Cosmic Radio Emissions." Sensors 21, no. 2 (January 11, 2021): 465. http://dx.doi.org/10.3390/s21020465.
Full textRay, Loye Lynn, and Henry Felch. "Improving Performance and Convergence Rates in Multi-Layer Feed Forward Neural Network Intrusion Detection Systems." International Journal of Strategic Information Technology and Applications 5, no. 3 (July 2014): 24–36. http://dx.doi.org/10.4018/ijsita.2014070102.
Full textde Almeida, A. L. F., C. A. R. Fernandes, and Daniel Benevides da Costa. "Multiuser Detection for Uplink DS-CDMA Amplify-and-Forward Relaying Systems." IEEE Signal Processing Letters 20, no. 7 (July 2013): 697–700. http://dx.doi.org/10.1109/lsp.2013.2260738.
Full textMao, Minghe, Ning Cao, Yunfei Chen, and Haobing Chu. "Novel noncoherent detection for multi-hop amplify-and-forward relaying systems." International Journal of Communication Systems 29, no. 7 (December 18, 2015): 1293–304. http://dx.doi.org/10.1002/dac.3099.
Full textHindriks, Rikkert. "Lag-invariant detection of interactions in spatially-extended systems using linear inverse modeling." PLOS ONE 15, no. 12 (December 11, 2020): e0242715. http://dx.doi.org/10.1371/journal.pone.0242715.
Full textSchreiner, K. "Landmine detection research pushes forward, despite challenges." IEEE Intelligent Systems 17, no. 2 (2002): 4–7. http://dx.doi.org/10.1109/5254.995459.
Full textLu, You Li, and Jun Luo. "Imbalanced Data Detection Kernel Method in Closed Systems." Advanced Materials Research 756-759 (September 2013): 3652–58. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3652.
Full textVeselov, V. V., A. M. Nechipai, E. A. Poltoryhina, and A. V. Vasilchenko. "FIRST EXPERIENCE IN FULL-SPECTRUM COLONOSCOPY." Koloproktologia, no. 2 (June 30, 2017): 36–46. http://dx.doi.org/10.33878/2073-7556-2017-0-2-36-46.
Full textHe Yucheng, Qiao Ying, and Zhou Lin. "Generalized maximum likelihood noncoherent block detection for decode-and-forward relay systems." China Communications 11, no. 4 (April 2014): 163–71. http://dx.doi.org/10.1109/cc.2014.6827578.
Full textDissertations / Theses on the topic "Forward detection systems"
Mukhopadhyay, Shayok. "Robust forward invariant sets for nonlinear systems." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52311.
Full textMa, Jun. "Channel estimation and signal detection for wireless relay." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37082.
Full textWen, Wen. "Forward Leading Vehicle Detection for Driver Assistant System." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42127.
Full textPaulo, Eugene P. "Comparison of Janus and field test aircraft detection ranges for the line-of-sight forward heavy system." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26417.
Full textWåhlin, Peter. "Enhanching the Human-Team Awareness of a Robot." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-16371.
Full textAnvändningen av autonoma robotar i vårt samhälle ökar varje dag och en robot ses inte längre som ett verktyg utan som en gruppmedlem. Robotarna arbetar nu sida vid sida med oss och ger oss stöd under farliga arbeten där människor annars är utsatta för risker. Denna utveckling har i sin tur ökat behovet av robotar med mer människo-medvetenhet. Därför är målet med detta examensarbete att bidra till en stärkt människo-medvetenhet hos robotar. Specifikt undersöker vi möjligheterna att utrusta autonoma robotar med förmågan att bedöma och upptäcka olika beteenden hos mänskliga lag. Denna förmåga skulle till exempel kunna användas i robotens resonemang och planering för att ta beslut och i sin tur förbättra samarbetet mellan människa och robot. Vi föreslår att förbättra befintliga aktivitetsidentifierare genom att tillföra förmågan att tolka immateriella beteenden hos människan, såsom stress, motivation och fokus. Att kunna urskilja lagaktiviteter inom ett mänskligt lag är grundläggande för en robot som ska vara till stöd för laget. Dolda markovmodeller har tidigare visat sig vara mycket effektiva för just aktivitetsidentifiering och har därför använts i detta arbete. För att en robot ska kunna ha möjlighet att ge ett effektivt stöd till ett mänskligtlag måste den inte bara ta hänsyn till rumsliga parametrar hos lagmedlemmarna utan även de psykologiska. För att tyda psykologiska parametrar hos människor förespråkar denna masteravhandling utnyttjandet av mänskliga kroppssignaler. Signaler så som hjärtfrekvens och hudkonduktans. Kombinerat med kroppenssignalerar påvisar vi möjligheten att använda systemdynamiksmodeller för att tolka immateriella beteenden, vilket i sin tur kan stärka människo-medvetenheten hos en robot.
The thesis work was conducted in Stockholm, Kista at the department of Informatics and Aero System at Swedish Defence Research Agency.
Huang, Shun-Wei, and 黃舜暐. "Weather-adapted Vehicle Detection and Verification For Forward Collision Warning Systems." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/ga43cj.
Full text國立中央大學
資訊工程學系
104
There were lots of deaths caused by traffic accidents of rear-end collision, advanced driver assistance systems (ADASs) has become an important research topic in recent years. To prevent these fatalities, forward collision warning (FCW) systems have been proposed to protect drivers from the danger due to paying no attention to forward traffic situations. Not only FCW systems but also many other ADASs such as lane departure warning (LDW), blind spot detection (BSD), pedestrian collision warning (PCW), etc. have been developed to assist drivers. In this thesis, we present a weather-adaptive forward collision warning system, which would help drivers to avoid collisions to the preceding vehicles or obstacles. In the proposed FCW system, the local features such as horizontal and vertical edges are first calculated. Then edge maps are bi-leveled using a learning thresholding method to adapt the intensity variation of captured images, so that the extraction of edge points is less influenced by bad weather conditions. Third, the preserved edge points are used to generate possible objects . Fourth, the objects are selected based on edge response, location, and symmetry of object candidates to generate vehicle candidates. Three candidate generation schemes are hierarchically designed to extract vehicle candidates in various weather conditions. At last, a method based on principal component analysis (PCA) is proposed to verify the vehicle candidates. PCA is a technique used to extract the important features of a set of vehicle images. Each extracted feature describes a characteristic of vehicle appearance which is defined as a global feature. Depending on the extracted features, a candidate region can be decomposed and reconstructed. The similarity between the original regions and the reconstructed regions are measured to verify the vehicle candidates. Theoretically, PCA method is used to remove the non-vehicle candidates to reduce the false alarm. The proposed FCW system has been test and evaluated on various weather conditions. The average accuracies of the proposed FCW system in clear and bad weather conditions are 98.5% and 71.8%, respectively. In our experiment, the system execution speed of approximately 50 frames per second and camera captured 30 frames per second, so the system can achieve real-time vehicle detection.
Liu, Wen-Tong, and 劉文通. "Vision‐based Lane Tracking and Forward Vehicle Detection Advanced Driver Assistance Systems." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/32000490219636445352.
Full text國立臺灣科技大學
電機工程系
102
Advanced driver assist systems (ADAS) are technologies that provide the driver with essential information when driving and lead to an increase in safe driving. On a long stretch of highway, the lane departure and forward vehicle collision are very important issues for drivers. The proposed ADAS focuses on implementing lane tracking, lane departure detection and forward vehicle detection by using the image processing techniques. In this thesis, our algorithms can provide the following basic information: (1) Lane detection, (2) Lane tracking, (3) Forward vehicle detection. In lane detection, our algorithms first combine the edge distribution function (EDF) with lane marking filter to improve the effects of non-lane marking noise and then detect the lane marking correctly. In lane tracking, the proposed method can predict the region of interest (ROI) of lane marking by the properties of continuous video in the next frame. Then, ROI is examined to track lane marking by Hough transform. In addition, no matter whether a vehicle is changing lane or passing exit-ramp, our algorithms can keep track of the lane marking smoothing. In forward vehicle detection, the position of the forward vehicle inside its own lane is determined by the features of rear shapes of vehicle, such as shadow, horizontal edge, vertical edge, symmetry and image density. Through video streams with resolution of 640×360 pixels, the experimental results achieve the average detection rate of lane tracking at 97.00% with the average processing time 5.4 ms per frame and the average detection rate of forward vehicle at 84.86% with average processing time 2 ms per frame.
Hamdane, Hédi. "Improvement of pedestrian safety: response of detection systems to real accident scenarios." Thesis, 2016. http://hdl.handle.net/2440/119694.
Full textThesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 2016.
Chuang, Shih-Hsien, and 莊士賢. "Nighttime Forward Vehicle Deceleration Detection System for Motorcycle." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/23198101113657008169.
Full text國立臺灣師範大學
資訊工程學系
104
Vision-based driver assistance systems and its related technologies were started to pay attention and develop from about 20 years ago. Visual analyzing the road situation in front of vehicles through camera to assist drivers. Vision-based driver assistance systems for automobile has been gradually consummated. In contrast, vision-based driver assistance systems for motorcycle went unheeded. The quantity of motorcycle and automobile increases year by year, and the quantity of motorcycle is fifty thousand more than automobile per year. Summarizes the above situation causes that automobile traffic accident rate reduces year after year, but motorcycle traffic accident rate rises every year. Daytime forward vehicle detection technology has been matured by degrees, but there is not so much researchers developing and researching at nighttime. By literatures in recent years of nighttime forward vehicle detection technology, many researches confirm the location of vehicle through related technologies about taillight detection. Therefore this study will use taillight detection to confirm the location of vehicle. Because it has to do a forward vehicle deceleration detection, forward vehicle decelerates or not will be determined by the brake-lights activates or not. When the motorcycle turns a corner, this study will adjust Region of Interest (ROI). There will not be traffic accidents with the forward vehicle when the vehicle stop moving as the red traffic light shows. So it hasn’t to do a taillight detection and brake-light detection. Therefore our system needs to detect forward vehicle move or not. The shape of taillight in the recent years is not only traditional circle but also irregular shape or elongated shape, and therefore this study will aim at the characteristic of surrounding light source around the taillight to do a taillight detection. This study will use illumination and threshold to determine brake-light on or off, and this dynamic threshold according to the distance between taillight and camera. The illumination of some activated brake-lights is lower than our determined threshold, and some non-brake of taillights are higher than it. It will lead to failure of brake-light detection. So our system will adjust threshold specifically to increase the accuracy rate of brake-light detection. Our system experiments on sunny day, rainy day, in the tunnel, and on many kinds of roads. The experiment result shows that it will get the higher accuracy rate without considering the consequence of taillight detection with the reflection of red lights. And our system expects that the reflection of red lights can be filter in the future to increase the accuracy rate of taillight detection. In this study, if it doesn’t consider raindrop dripping on the camera lens on rainy day, the accuracy rate of brake-light detection is about 90%
Chiu, Shan-Ting, and 邱慎廷. "The Study of a Forward Vehicle Detection Warning System with Multiple-lane Detection." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/51565014465278631081.
Full text國立交通大學
電機與控制工程系所
98
In This thesis the technique of image processing and computer vision theorem are applied to lane detection and vehicle detection. In the meantime, the algorithm is also applied on TI-DM642 for driving assistance system (DAS). The system has been working in different environment such as expressway, urban area and tunnel. Furthermore the algorithm is such robust to be verified with all weather condition like sunny day, cloudy day, evening, morning, night and rainy day. In the lane detection, CCD camera is used to grab the front view, and then the algorithm detects the lane making to contribute a real lane model. This model is applied to estimate and narrow the searching area in order to increase the accuracy and reduce the computation. The lane detection system has been verified successfully in expressway and urban road. Moreover, the system has been equipped on Taiwan iTS-1 (the first intelligence car of Taiwan) as vision system and combines with control system to accomplish lane keeping and lane change. The vehicle detection uses the lane model to build basic vehicle parameters and sets the ROI from the result of lane detection. Algorithm will select multiple possible objects those have strong vehicle characteristics. After feature extraction, the vehicles will be verified with the classify rules. The thesis considers kinds of the vehicle features in different weathers condition and overcomes lots of influence from environment like the text on the road, the strong shadows and light, and the reflected light from the road surface. Even with rainfall and windscreen wiper, the system works successfully. The detection has no relationship with the day or night, so it has good performance at smooth light changing or sudden light changing. With the lane model from lane detection, the algorithm could establish the multiple lane boundaries and finish the multiple forward car detection. At same time, the distance of the forward vehicle can be calculated, and the systems will warn driver that he is in the dangerous distance.
Books on the topic "Forward detection systems"
D, Anderson Charles. Flight crew interface aspects of forward-looking airborne windshear detection systems: Final report. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1993.
Find full textPaulo, Eugene P. Comparison of Janus and field test aircraft detection ranges for the line-of-sight forward heavy system. Monterey, Calif: Naval Postgraduate School, 1991.
Find full textAllsop, Cheryl. Organizing the Organizational Memory. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198747451.003.0007.
Full textWalczak, Jean-Sébastien. Understanding the responsiveness of C-fibres. Edited by Paul Farquhar-Smith, Pierre Beaulieu, and Sian Jagger. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198834359.003.0006.
Full textBook chapters on the topic "Forward detection systems"
Wang, Zhangyu, Tony Lee, Michael Leung, Simon Tang, Qiang Zhang, Zining Yang, and Virginia Cheung. "A Forward Train Detection Method Based on Convolutional Neural Network." In Advances in Intelligent Systems and Computing, 129–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39512-4_21.
Full textAhmed, Abdulghani Ali. "Botnet Detection Using a Feed-Forward Backpropagation Artificial Neural Network." In Advances in Intelligent Systems and Computing, 24–35. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03302-6_3.
Full textPratapur, Satish, and D. C. Shubangi. "Detection of Forgery in the JPEG Images Using Forward Quantization Noise Method." In Algorithms for Intelligent Systems, 31–43. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6707-0_3.
Full textYadav, Gaurav Kumar, Tarun Kancharla, and Smita Nair. "Real Time Vehicle Detection for Rear and Forward Collision Warning Systems." In Advances in Computing and Communications, 368–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22726-4_39.
Full textOrtega, Juan Diego, Marcos Nieto, Andoni Cortes, and Julian Florez. "Perspective Multiscale Detection of Vehicles for Real-Time Forward Collision Avoidance Systems." In Advanced Concepts for Intelligent Vision Systems, 645–56. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02895-8_58.
Full textShrivastava, Amit. "Detection of Induction Motor Bearing Fault Using Time Domain Analysis and Feed-Forward Neural Network." In Algorithms for Intelligent Systems, 359–69. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1059-5_40.
Full textMartin, Don Joe, Aaditya Saraiya, V. Kalaichelvi, and R. Karthikeyan. "Vision-Based Forward Kinematics Using ANN for Weld Line Detection with a 5-DOF Robot Manipulator." In Advances in Intelligent Systems and Computing, 309–18. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8237-5_30.
Full textAhmed, Sajjad, Klestia Balla, Knut Hinkelmann, and Flavio Corradini. "Fact Checking: Detection of Check Worthy Statements Through Support Vector Machine and Feed Forward Neural Network." In Advances in Intelligent Systems and Computing, 520–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73103-8_37.
Full textKirubavathi Venkatesh, G., and R. Anitha Nadarajan. "HTTP Botnet Detection Using Adaptive Learning Rate Multilayer Feed-Forward Neural Network." In Information Security Theory and Practice. Security, Privacy and Trust in Computing Systems and Ambient Intelligent Ecosystems, 38–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30955-7_5.
Full textLee, Pei Shyuan, Majdi Owda, and Keeley Crockett. "The Detection of Fraud Activities on the Stock Market Through Forward Analysis Methodology of Financial Discussion Boards." In Advances in Intelligent Systems and Computing, 212–20. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03405-4_14.
Full textConference papers on the topic "Forward detection systems"
Baptista, Renan Martins, and Carlos Henrique Wildhagen Moura. "Leak Detection Systems for Multiphase Flow: Moving Forward." In 2002 4th International Pipeline Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/ipc2002-27283.
Full textPei, Shengyu, Lang Tong, Xia Li, Jin Jiang, and Jingyu Huang. "Feed-forward network for cancer detection." In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017. http://dx.doi.org/10.1109/fskd.2017.8393356.
Full textGkizeli, Maria, and George N. Karystinos. "Noncoherent Detection in Amplify-and-Forward Relay Systems." In IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference. IEEE, 2008. http://dx.doi.org/10.1109/glocom.2008.ecp.193.
Full textZhang, Keke, Xueyong Zheng, Xingkui Yan, Shizhe Chen, Xiaozheng Wan, Jiming Zhang, Qiang Zhao, Bo Wang, and Huanyu Zhao. "Research on visibility detection based on forward scattering technology." In Advanced Sensor Systems and Applications IX, edited by Tiegen Liu, Gang-Ding Peng, and Zuyuan He. SPIE, 2019. http://dx.doi.org/10.1117/12.2540728.
Full textHou, Weikun, Xianbin Wang, and Ahmed Refaey. "Misbehavior detection in amplify-and-forward cooperative OFDM systems." In ICC 2013 - 2013 IEEE International Conference on Communications. IEEE, 2013. http://dx.doi.org/10.1109/icc.2013.6655437.
Full textLingjia Gu, Shuxu Guo, Ruizhi Ren, and Shuang Zhang. "Iterative detection for differential decode-and-forward cooperative communication." In 2007 5th International Conference on Communications, Circuits and Systems. IEEE, 2007. http://dx.doi.org/10.1109/icccas.2007.4348160.
Full textNing Wei, Weixin Liu, Zhongpei Zhang, and Shaoqian Li. "Iterative detection for differential decode-and-forward cooperative communication." In 2007 5th International Conference on Communications, Circuits and Systems. IEEE, 2007. http://dx.doi.org/10.1109/icccas.2007.6250078.
Full textMostafa, H., M. Marey, M. Ahmed, and O. Dobre. "Detection Techniques for Two-Relays Decode and Forward Cooperative Systems." In 2011 IEEE Global Communications Conference (GLOBECOM 2011). IEEE, 2011. http://dx.doi.org/10.1109/glocom.2011.6133833.
Full textde Wit, J. J. M., and W. L. van Rossum. "Forward scatter radar for detection of moving people inside buildings." In International Conference on Radar Systems (Radar 2017). Institution of Engineering and Technology, 2017. http://dx.doi.org/10.1049/cp.2017.0399.
Full textTakizawa, Kenichi, Huan-Bang Li, and Ryuji Kohno. "Precise Leading Edge Detection using a Forward Error Correction Coding." In 2006 3rd International Symposium on Wireless Communication Systems. IEEE, 2006. http://dx.doi.org/10.1109/iswcs.2006.4362398.
Full textReports on the topic "Forward detection systems"
Schmitigal, Joel. Evaluation of Particle Counter Technology for Detection of Fuel Contamination Detection Utilizing Advanced Aviation Forward Area Refueling System. Fort Belvoir, VA: Defense Technical Information Center, January 2014. http://dx.doi.org/10.21236/ada597855.
Full textPatwa, Abid Mahmood. The Forward preshower system and a study of the $J/\psi$ trigger with the D0 detector. Office of Scientific and Technical Information (OSTI), January 2002. http://dx.doi.org/10.2172/1420962.
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