Relevant bibliographies by topics / Iterative detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Iterative detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Iterative detection"

1

Mejri, Rafika, and Taoufik Aguili. "Modeling of Radiating Aperture Using the Iterative Method." Detection 09, no. 03 (2022): 29–36. http://dx.doi.org/10.4236/detection.2022.93003.

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Damnjanovic, Aleksandar D., and Branimir R. Vojcic. "Iterative multiuser detection." Journal of Communications and Networks 3, no. 3 (September 2001): 1–8. http://dx.doi.org/10.1109/jcn.2001.6596792.

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Poor, H. V. "Iterative multiuser detection." IEEE Signal Processing Magazine 21, no. 1 (January 2004): 81–88. http://dx.doi.org/10.1109/msp.2004.1267051.

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Zhang, Jicun, Jiyou Fei, Xueping Song, and Jiawei Feng. "An Improved Louvain Algorithm for Community Detection." Mathematical Problems in Engineering 2021 (November 23, 2021): 1–14. http://dx.doi.org/10.1155/2021/1485592.

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Social network analysis has important research significance in sociology, business analysis, public security, and other fields. The traditional Louvain algorithm is a fast community detection algorithm with reliable results. The scale of complex networks is expanding larger all the time, and the efficiency of the Louvain algorithm will become lower. To improve the detection efficiency of large-scale networks, an improved Fast Louvain algorithm is proposed. The algorithm optimizes the iterative logic from the cyclic iteration to dynamic iteration, which speeds up the convergence speed and split
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Tang, Chuan, Cang Liu, Luechao Yuan, and Zuocheng Xing. "Approximate iteration detection with iterative refinement in massive MIMO systems." IET Communications 11, no. 7 (May 11, 2017): 1152–57. http://dx.doi.org/10.1049/iet-com.2016.0826.

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Delgado Castro, Alejandro, and John E. Szymanski. "Multipitch estimation based on the iterative detection and separation of note events from single-channel polyphonic recordings." Journal of the Acoustical Society of America 154, no. 4 (October 1, 2023): 2625–41. http://dx.doi.org/10.1121/10.0021886.

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Multiple fundamental frequency estimation has been extensively used in applications such as melody extraction, music transcription, instrument identification, and source separation. This paper presents an approach based on the iterative detection and extraction of note events, which are considered to be harmonic sounds characterised by a continuous pitch trajectory. Note events are assumed to be associated with musical notes being played by a single instrument, and their pitch trajectories are iteratively estimated. In every iteration, the pitch contour of the predominant note event is selecte
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Herschel, Melanie, Felix Naumann, Sascha Szott, and Maik Taubert. "Scalable Iterative Graph Duplicate Detection." IEEE Transactions on Knowledge and Data Engineering 24, no. 11 (November 2012): 2094–108. http://dx.doi.org/10.1109/tkde.2011.99.

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Deng, Yuhao, Chengliang Chai, Lei Cao, Nan Tang, Jiayi Wang, Ju Fan, Ye Yuan, and Guoren Wang. "MisDetect: Iterative Mislabel Detection using Early Loss." Proceedings of the VLDB Endowment 17, no. 6 (February 2024): 1159–72. http://dx.doi.org/10.14778/3648160.3648161.

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Supervised machine learning (ML) models trained on data with mislabeled instances often produce inaccurate results due to label errors. Traditional methods of detecting mislabeled instances rely on data proximity, where an instance is considered mislabeled if its label is inconsistent with its neighbors. However, it often performs poorly, because an instance does not always share the same label with its neighbors. ML-based methods instead utilize trained models to differentiate between mislabeled and clean instances. However, these methods struggle to achieve high accuracy, since the models ma
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Yang, Sen, Zerun Li, Jinhui Wei, and Zuocheng Xing. "Deep learning-aided high-precision data detection for massive MU-MIMO systems." MATEC Web of Conferences 336 (2021): 04007. http://dx.doi.org/10.1051/matecconf/202133604007.

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The data detector for future wireless system needs to achieve high throughput and low bit error rate (BER) with low computational complexity. In this paper, we propose a deep neural networks (DNNs) learning aided iterative detection algorithm. We first propose a convex optimization-based method for calculating the efficient detection of iterative soft output data, and then propose a method for adjusting the iteration parameters using the powerful data driven by DNNs, which achieves fast convergence and strong robustness. The results show that the proposed method can achieve the same performanc
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Wang, Zhicheng, Rong Li, Zhihao Shao, Mengxin Ma, Jianhui Liang, Weizhao Liu, Jie Wang, and Yongli Liu. "Adaptive Harris corner detection algorithm based on iterative threshold." Modern Physics Letters B 31, no. 15 (May 26, 2017): 1750181. http://dx.doi.org/10.1142/s0217984917501810.

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An adaptive Harris corner detection algorithm based on the iterative threshold is proposed for the problem that the corner detection algorithm must be given a proper threshold when the corner detection algorithm is extracted. In order to avoid the phenomenon of clustering and restrain the pseudo corner, this algorithm realizes the adaptive threshold selection by iteration instead of the threshold value of the Harris corner detection algorithm. Simulation results show that the proposed method achieves good results in terms of threshold setting and feature extraction.
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Dissertations / Theses on the topic "Iterative detection"

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Shaheem, Asri. "Iterative detection for wireless communications." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0223.

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[Truncated abstract] The transmission of digital information over a wireless communication channel gives rise to a number of issues which can detract from the system performance. Propagation effects such as multipath fading and intersymbol interference (ISI) can result in significant performance degradation. Recent developments in the field of iterative detection have led to a number of powerful strategies that can be effective in mitigating the detrimental effects of wireless channels. In this thesis, iterative detection is considered for use in two distinct areas of wireless communications.
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Moher, Michael L. "Cross-entropy and iterative detection." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22171.pdf.

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Moher, Michael L. Carleton University Dissertation Engineering Systems and Computer. "Cross-entropy and iterative detection." Ottawa, 1997.

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El-Hajjar, Mohammed H. "Near-capacity MIMOs using iterative detection." Thesis, University of Southampton, 2008. https://eprints.soton.ac.uk/64487/.

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In this thesis, Multiple-Input Multiple-Output (MIMO) techniques designed for transmission over narrowband Rayleigh fading channels are investigated. Specifically, in order to provide a diversity gain while eliminating the complexity of MIMO channel estimation, a Differential Space-Time Spreading (DSTS) scheme is designed that employs non-coherent detection. Additionally, in order to maximise the coding advantage of DSTS, it is combined with Sphere Packing (SP) modulation. The related capacity analysis shows that the DSTS-SP scheme exhibits a higher capacity than its counterpart dispensing wit
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Xu, Danfeng. "Iterative coded multiuser detection using LDPC codes." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27939.

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Multiuser detection (MUD) has been regarded as an effective technique for combating cochannel interference (CCI) in time-division multiple access (TDMA) systems and multiple access interference (MAI) in code-division multiple access (CDMA) systems. An optimal multiuser detector for coded multiuser systems is usually practically infeasible due to the associated complexity. An iterative receiver consisting of a soft-input soft-output (SISO) multiuser detector and a bank of SISO single user decoders can provide a system performance which approaches to that of single user system after a few itera
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Valenti, Matthew C. "Iterative Detection and Decoding for Wireless Communications." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/28290.

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Turbo codes are a class of forward error correction (FEC) codes that offer energy efficiencies close to the limits predicted by information theory. The features of turbo codes include parallel code concatenation, recursive convolutional encoding, nonuniform interleaving, and an associated iterative decoding algorithm. Although the iterative decoding algorithm has been primarily used for the decoding of turbo codes, it represents a solution to a more general class of estimation problems that can be described as follows: a data set directly or indirectly drives the state transitions of two or
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Marsland, Ian D. "Iterative noncoherent detection of differentially encoded M-PSK." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0016/NQ46386.pdf.

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Wong, Eddy. "Iterative decoding of coded GMSK with discriminator detection." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ63037.pdf.

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Wu, Zining. "Coding and iterative detection for magnetic recording channels /." Boston, Mass. [u.a.] : Kluwer Academic Publ, 2000. http://www.loc.gov/catdir/enhancements/fy0820/99049501-d.html.

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Balasubramanyam, Ramkumar. "Adaptive iterative multiuser detection for wireless communication systems." Thesis, University of Greenwich, 2008. http://gala.gre.ac.uk/8203/.

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Wireless multi-user communication systems that operate in a low signal to interference noise ratio (SINR) region are studied in this thesis. This thesis examines a class of wireless communication systems that employs an adaptive receiver for multi-user symbol detection that operates in a low SINR (< 5 dB) region. Since the knowledge of channel-parameter estimates is unavailable at the receiver, a pilot (training) sequence is applied in the communication system, to learn the channel state information (CSI) at the receiver. In studying the classical view of a DFE, the mean square error (MSE) beh
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Books on the topic "Iterative detection"

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. Iterative Detection. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6.

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Chugg, Keith M. Iterative detection: Adaptivity, complexity reduction, and applications. Boston, Mass: Kluwer Academic Publishers, 2001.

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Achilleas, Anastasopoulos, and Chen Xiaopeng, eds. Iterative detection: Adaptivity, complexity reduction, and applications. Boston, Mass: Kluwer Academic Publishers, 2001.

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Chugg, Keith M. Iterative Detection: Adaptivity, Complexity Reduction, and Applications. Boston, MA: Springer US, 2001.

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Wong, Eddy. Iterative decoding of coded GMSK with discriminator detection. Ottawa: National Library of Canada, 2001.

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Wu, Zining. Coding and Iterative Detection for Magnetic Recording Channels. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4565-1.

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Wu, Zining. Coding and iterative detection for magnetic recording channels. Boston: Kluwer Academic, 2000.

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Wu, Zining. Coding and Iterative Detection for Magnetic Recording Channels. Boston, MA: Springer US, 2000.

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1952-, Hanzo Lajos, ed. Near-capacity multi functional MIMO systems: Sphere-packing, iterative detection, and cooperation. Chichester, West Sussex, U.K: Wiley, 2009.

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Coding and Iterative Detection for Magnetic Recording Channels. Springer, 2011.

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Book chapters on the topic "Iterative detection"

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Overview of Non-Iterative Detection." In Iterative Detection, 1–76. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_1.

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Principles of Iterative Detection." In Iterative Detection, 77–191. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_2.

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Iterative Detection for Complexity Reduction." In Iterative Detection, 193–238. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_3.

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Adaptive Iterative Detection." In Iterative Detection, 239–71. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_4.

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Applications in Two Dimensional Systems." In Iterative Detection, 273–313. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_5.

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Beerel, Peter A. "Implementation Issues: A Turbo Decoder Design Case Study." In Iterative Detection, 315–40. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_6.

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Lehti, Patrick, and Peter Fankhauser. "Probabilistic Iterative Duplicate Detection." In Lecture Notes in Computer Science, 1225–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11575801_19.

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Dörpinghaus, Meik. "Iterative Code-Aided Synchronized Detection." In On the Achievable Rate of Stationary Fading Channels, 101–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19780-2_6.

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Toyoda, Kenta, and Kazuhiro Hotta. "Abnormal Detection by Iterative Reconstruction." In Advances in Visual Computing, 443–53. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50832-0_43.

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Bai, Lin, Jinho Choi, and Quan Yu. "Iterative Channel Estimation and Detection." In Low Complexity MIMO Receivers, 215–31. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04984-7_9.

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Conference papers on the topic "Iterative detection"

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Hao, Dapeng, and Peter Adam Hoeher. "Superposition modulation with reliability-based hybrid detection." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613858.

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Vehkapera, Mikko, Keigo Takeuchi, Ralf R. Muller, and Toshiyuki Tanaka. "Iterative channel estimation, detection, and decoding in large CDMA systems." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613863.

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Li, Bing, Baoming Bai, and Mengyu Huang. "A robust noncoherent iterative detection algorithm for serially concatenated CPM." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613893.

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Qi, Wei, Wei Li, and Qian Chen. "Iterative Saliency Detection." In 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). IEEE, 2020. http://dx.doi.org/10.1109/aemcse50948.2020.00078.

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Wang, Yulei, Bai Xue, Lin Wang, Hsiao-Chi Li, Li-Chien Lee, Chunyan Yu, Meiping Song, Sen Li, and Chein-I. Chang. "Iterative anomaly detection." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127021.

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Raouafi, Fathi, Taoufik Majoul, and Meriem Jaidane. "Turbo codes behavior over near impulsive noisy channels: Audio watermark detection case." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613830.

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Chen, Danshan, and Alister G. Burr. "Adaptive linear precoding for iterative maximum likelihood detection in multi-antenna systems." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613850.

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Senanayake, Bathiya, and C. Reed Mark. "Multi-dimensional EXIT analysis for iterative multi-user detection with unequal power allocation." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613811.

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Yang, Jianxiao, Charbel Abdel Nour, and Charlotte Langlais. "Joint factor graph detection for LDPC and STBC coded MIMO systems: A new framework." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613820.

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Mishne, Gal, and Israel Cohen. "Iterative diffusion-based anomaly detection." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952443.

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Reports on the topic "Iterative detection"

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Song, H. C., Karim Sabra, W. A. Kuperman, and W. S. Hodgkiss. Multi-Static Detection and Localization of Buried Targets using Synthetic Aperture Iterative Time-Reversal Processing. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada494990.

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Kuperman, W. A., Karim Sabra, and Philippe Roux. Multi-Static Detection and Localization of Buried Targets Using Synthetic Aperture Iterative Time-Reversal Processing. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada612231.

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Kuperman, W. A., and Karim Sabra. Multi-Static Detection and Localization of Buried Targets using Synthetic Aperture Iterative Time-Reversal Processing. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada541152.

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Wen, Qingsong, Minzhen Ren, and Xiaoli Ma. Fixed-point Design of the Lattice-reduction-aided Iterative Detection and Decoding Receiver for Coded MIMO Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada586964.

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Hamlet, Benjamin, Ryan Prescott, John Burns, and Steven Kubica. IDC Re-Engineering Phase 2 Iteration E2 Draft Component Interface Specification: Signal Detection Control. Office of Scientific and Technical Information (OSTI), May 2016. http://dx.doi.org/10.2172/1761997.

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Miles, Gaines E., Yael Edan, F. Tom Turpin, Avshalom Grinstein, Thomas N. Jordan, Amots Hetzroni, Stephen C. Weller, Marvin M. Schreiber, and Okan K. Ersoy. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, August 1995. http://dx.doi.org/10.32747/1995.7570567.bard.

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In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were st
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