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Статті в журналах з теми "Optimum decision fusion rule"
Arshad, Kamran, Muhammad Ali Imran, and Klaus Moessner. "Collaborative Spectrum Sensing Optimisation Algorithms for Cognitive Radio Networks." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–20. http://dx.doi.org/10.1155/2010/424036.
Повний текст джерелаRauniyar, Ashish, Jae Min Jang, and Soo Young Shin. "Optimal Hard Decision Fusion Rule for Centralized and Decentralized Cooperative Spectrum Sensing in Cognitive Radio Networks." Journal of Advances in Computer Networks 3, no. 3 (2015): 207–12. http://dx.doi.org/10.7763/jacn.2015.v3.168.
Повний текст джерелаSaleh, Ibrahim Ahmed, Omar Ibrahim Alsaif, and Maan A. Yahya. "Optimal distributed decision in wireless sensor network using gray wolf optimization." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (December 1, 2020): 646. http://dx.doi.org/10.11591/ijai.v9.i4.pp646-654.
Повний текст джерелаKhan, Muhammad Sajjad, Noor Gul, Junsu Kim, Ijaz Mansoor Qureshi, and Su Min Kim. "A Genetic Algorithm-Based Soft Decision Fusion Scheme in Cognitive IoT Networks with Malicious Users." Wireless Communications and Mobile Computing 2020 (January 31, 2020): 1–10. http://dx.doi.org/10.1155/2020/2509081.
Повний текст джерелаLiao, Yiwei, Xiaojing Shen, and Hang Rao. "Analytic Sensor Rules for Optimal Distributed Decision Given $K$-Out-of-$L$ Fusion Rule Under Monte Carlo Approximation." IEEE Transactions on Automatic Control 65, no. 12 (December 2020): 5488–95. http://dx.doi.org/10.1109/tac.2020.2977890.
Повний текст джерелаUsman, Muhammad, and Insoo Koo. "Secure Cooperative Spectrum Sensing for the Cognitive Radio Network Using Nonuniform Reliability." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/101809.
Повний текст джерелаZhu, Yunmin, and Xiaorong Li. "Optimal decision fusion given sensor rules." Journal of Control Theory and Applications 3, no. 1 (February 2005): 47–54. http://dx.doi.org/10.1007/s11768-005-0060-z.
Повний текст джерелаYuan, Xiao Guang, Dong Zhu Feng, Jian Deng, and Yuan Jie Bai. "Resource-Constrained Wireless Sensor Network Information Decision Fusion in Ocean Environment." Applied Mechanics and Materials 433-435 (October 2013): 229–32. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.229.
Повний текст джерелаZhu, Yunmin. "Optimum Fusion in Distributed Multisensor Network Decision Systems." IFAC Proceedings Volumes 32, no. 2 (July 1999): 8321–26. http://dx.doi.org/10.1016/s1474-6670(17)57419-4.
Повний текст джерелаLiu, Shoujun, Kehao Wang, Kezhong Liu, and Wei Chen. "Noncoherent Decision Fusion over Fading Hybrid MACs in Wireless Sensor Networks." Sensors 19, no. 1 (January 1, 2019): 120. http://dx.doi.org/10.3390/s19010120.
Повний текст джерелаДисертації з теми "Optimum decision fusion rule"
KALLAS, KASSEM. "A Game-Theoretic Approach for Adversarial Information Fusion in Distributed Sensor Networks." Doctoral thesis, Università di Siena, 2017. http://hdl.handle.net/11365/1005735.
Повний текст джерелаQua, John F. "Optimum levels of work in process (WIP) for navy field contracting organizations a decision rule /." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA241831.
Повний текст джерелаThesis Advisor: Lamm, David V. Second Reader: Caldwell, William J. "December 1990." Description based on title screen as viewed on April 1, 2010. DTIC Identifier(s): Contract administration, decision making, navy, WIP(work in process), backlogs, regression analysis, small purchase actions, procurement, theses. Author(s) subject terms:Decision rule, contracting, small purchase, backlog. Includes bibliographical references (p. 63-64). Also available in print.
Gutiérrez, Celaya Jorge Arturo. "Fusion d'informations en identification automatique des langues." Toulouse 3, 2005. http://www.theses.fr/2005TOU30098.
Повний текст джерелаFusing decision information coming out of different experts is an important issue in Automatic Language Identification. In order to explore and compare different fusion strategies, the information behaviour is modelled by means of formal classification methods provided either by the Statistics Theory, such as the Gaussian Mixture Model, the Neural Networks and the Discriminant Classifier, or by recent research advances in Possibility and Evidential Theories. As an alternative to empirical procedures, a formal fusion methodology within the Bayesian paradigm is proposed: evaluating expert performance by means of the Discriminant Factor Analysis provides us with confidence indices, aggregating expert decisions takes us to choose those fusion methods that provide us directly, or after transformation, with probability or likelihood values of languages, and building and weighting new loss functions with confidence indices lead us to make unique decisions by minimum risk
König, Rikard. "Predictive Techniques and Methods for Decision Support in Situations with Poor Data Quality." Licentiate thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-3517.
Повний текст джерелаSponsorship:
This work was supported by the Information Fusion Research
Program (www.infofusion.se) at the University of Skövde, Sweden, in
partnership with the Swedish Knowledge Foundation under grant
2003/0104.
陳宏期. "Cognitive Radio Power Control With Optimum Channel Gain Decision Rule." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/48058472726314329596.
Повний текст джерела國立臺灣海洋大學
通訊與導航工程學系
99
In this thesis, we proposed an optimum transmitter power-control scheme to make the primary system approaching the performance requirements of spectrum efficiency and signal-to-noise-interference power ratio (SINR) under the multi-path fading channel condition. The optimum transmitter power-control parameter, which is generated via the specific value of optimum decision from Bayes decision rule, could be effectively controlled the cognitive transmitter power and could not influence the primary system performance. Analysis and simulation results in terms of spectrum efficiency and signal-to-noise-interference power ratio demonstrate that the proposed optimum transmitter power-control scheme significantly outperforms the general method.
Liu, Han-Yi, and 劉涵一. "Optimum Decision Rule applied to STBC 4x1 MIMO Wireless Receiver Design." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/18722825809166040304.
Повний текст джерела國立臺灣海洋大學
通訊與導航工程系
97
This thesis mainly investigates the system performance of MIMO 4-by-1, incorporated with a Bayes decision rule. Apart from using conventional Space-Time Block Code and Multiple-Input Multiple-Output (STBC-MIMO) detection, this proposed scheme provides relatively contribution to the sub-channel enhancement and signal robustness in the spatially correlated multipath fading channel. To validate our proposed scheme, the channel simulation using Jakes model for generating independently and identically distributed (i.i.d.) multipath fading channel and Kronecker channel matrix for spatially correlated antenna structure in the transmitter end are adopted to mimic the realistic MIMO channel. The STBC applied for this analysis includes the half-rate full-diversity and full-rate half-diversity schemes. The Bayes decision rule is to generate the optimum weights which maximizes the most likely “closest” transmitted signal power to the received vector with a minimum ‘Risk’ criterion. The simulation results demonstrate remark performance improvement in the Bit-Error-Rate and cumulative eigen-distribution using this optimum weights.
Tai, Yung-Jing, and 戴雍政. "Distributed Decision Fusion Rule using Multi-level Censoring Scheme with Soft-Decision Combining Rule in Wireless Sensor Network." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/01452278452223533004.
Повний текст джерела國立暨南國際大學
資訊工程學系
99
The battery-powered wireless sensor network(WSN) is an emerging technology with a wide variety of applications. One of the major energy consumptions in WSN is spent on the data communication. Hence, the lifetime of a sensor is limited by the power constraint. Therefore, the issue of power saving in WSN should be probed into. A highly energy-efficient algorithm to reduce error rate and energy consumption in the wireless transmission has been proposed in the research. In this study, a multi-level censoring scheme with two soft-decision combining rules, MAP and EGC, is used to design a mechanism to achieve the goal of energy saving in WSN. Each sensor makes a quantitatively criterion on the reliability of its observation, which decides the SNR of the signal transmitted to the fusion center(FC). The difference between the two fusion rules is the complexity in the final judgement at the FC. The results show that the proposed mechanism outperforms the conventional WSN system in both energy efficiency and error performance.
Hsieh, Ming-Yu, and 謝銘祐. "Distributed Decision Fusion Rule using Multi-level Censoring Scheme with Hard-Decision Combining Rule in Wireless Sensor Network." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/13428798362330684713.
Повний текст джерела國立暨南國際大學
資訊工程學系
99
Nowadays, the battery-powered wireless sensor network (WSN) has been widely used in detecting various environments. The working time of a sensor depends on the amount of energy. One of the major energy consumptions in WSN is spent on data communication. Hence, the issue of energy saving in WSN should be probed into. In this study, a multilevel censoring scheme is used in WSN to design two mechanisms for reducing error probability and energy expenditure. A quantitatively defined criterion on the reliability of the observation detected by a censor is made, which controls the SNR of the transmitted signal to the Fusion Center (FC). One mechanism is to quantify the signal the FC receives with the information of the priori-probabilities of the multilevel allocation, and the other is a similar quantization without the information of the probabilities. The results show that the proposed multilevel censoring mechanism outperforms the conventional WSN system in both energy efficiency and error performance.
Liu, Ming-Chen, and 劉明臻. "Distributed Decision Fusion Rule using Multi-level Censoring Scheme in Wireless Sensor Network." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/94002295454644956306.
Повний текст джерела國立暨南國際大學
資訊工程學系
97
Nowadays, the battery-powered wireless sensor network(WSN) is an emerging technology with a wide variety of applications. The lifetime of a sensor is limited by the power constraint. One of the major energy consumptions in WSN is spent on the data communication. Hence, the issue of power saving in WSN should be probed into. In this study, binary codes are used individually to design a mechanism to save power in WSN. A highly energy-efficient algorithm to save the energy consumption on the wireless transmission has been proposed. Each sensor transmits data to the fusion center only when the reliability of its observation is beyond a threshold, or the sensor remains in a latent status defined "censoring", and hence the goal of power saving is achieved. Furthermore, a mechanism with a multi-level censoring rule on the reliability of the observation made by a sensor is also examined. The SNR of the transmitted signal to the FC is adjusted according to the reliability. The goal of power saving is achieved while the proposed multi-level censoring mechanism outperforms the conventional WSN system.
Chiou, Yung-Shiuan, and 邱詠瑄. "Distributed Decision Fusion Rule using M-ary Source Coding Scheme in Wireless Sensor Network." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/51480226998028682411.
Повний текст джерела國立暨南國際大學
資訊工程學系
100
The wireless sensor network(WSN) structured by the battery-powered sensor has been widely used in detecting a variety of environments. The sensor consumes energy while transmitting signals and hence the life of battery directly limits the working hours of the sensor. Therefore, reducing the power consumption of wireless transmission is an issue worth exploring. This study focuses on the design of energy-efficient algorithm to reduce energy consumption while the goal of better error performance is also achieved. In the conventional WSN systems, 1 bit is used to record the observation of the environment. In this thesis, we use M-ary source coding scheme to divide and to encode the observation to expand the binary algorithm to an M-ary algorithm. Soft-decision and hard-decision rules are both adopted at the fusion center. The results show that lower error rates and energy consumption are achieved via the proposed M-ary source coding scheme.
Частини книг з теми "Optimum decision fusion rule"
Abrardo, Andrea, Mauro Barni, Kassem Kallas, and Benedetta Tondi. "An Efficient Nearly-Optimum Decision Fusion Technique Based on Message Passing." In Information Fusion in Distributed Sensor Networks with Byzantines, 83–101. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-32-9001-3_6.
Повний текст джерелаAbrardo, Andrea, Mauro Barni, Kassem Kallas, and Benedetta Tondi. "A Game-Theoretic Framework for Optimum Decision Fusion in the Presence of Byzantines." In Information Fusion in Distributed Sensor Networks with Byzantines, 57–81. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-32-9001-3_5.
Повний текст джерелаSolovjev, Denis, Alexander Arzamastsev, Inna Solovjeva, Yuri Litovka, Alexey L’vov, and Nina Melnikova. "Search of Optimum Conditions of Plating Using a Fuzzy Rule-Based Knowledge Model." In Recent Research in Control Engineering and Decision Making, 563–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12072-6_46.
Повний текст джерелаElhassouny, Azeddine, Soufiane Idbraim, Aissam Bekkarri, Driss Mammass, and Danielle Ducrot. "Multisource Fusion/Classification Using ICM and DSmT with New Decision Rule." In Lecture Notes in Computer Science, 56–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31254-0_7.
Повний текст джерелаYang, Fucheng, Jie Song, Yilin Si, and Lixin Li. "Equal Gain Combining Based Sub-optimum Posterior Noncoherent Fusion Rule for Wireless Sensor Networks." In Intelligent Robotics and Applications, 63–72. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27538-9_6.
Повний текст джерелаWang, Tsang-Yi, Po-Ning Chen, Yunghsiang S. Han, and Yung-Ti Wang. "On the Design of Soft-Decision Fusion Rule for Coding Approach in Wireless Sensor Networks." In Wireless Algorithms, Systems, and Applications, 140–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11814856_15.
Повний текст джерелаVenkataramani, Krithika, and B. V. K. Vijaya Kumar. "Role of Statistical Dependence Between Classifier Scores in Determining the Best Decision Fusion Rule for Improved Biometric Verification." In Multimedia Content Representation, Classification and Security, 489–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11848035_65.
Повний текст джерелаLiao, Yiwei, Xiaojing Shen, Junfeng Wang, and Yunmin Zhu. "Decision Fusion for Large-Scale Sensor Networks with Nonideal Channels." In Functional Calculus - Recent Advances and Development [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.106075.
Повний текст джерелаBerber, Stevan. "Operation of a Discrete Communication System." In Discrete Communication Systems, 172–214. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198860792.003.0005.
Повний текст джерелаMankad, Kunjal Bharatkumar. "An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic." In Handbook of Research on Artificial Intelligence Techniques and Algorithms, 44–81. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7258-1.ch002.
Повний текст джерелаТези доповідей конференцій з теми "Optimum decision fusion rule"
Qiang Cheng and T. S. Huang. "Optimum detection of multiplicative watermarks using locally optimum decision rule." In IEEE International Conference on Multimedia and Expo, 2001. ICME 2001. IEEE, 2001. http://dx.doi.org/10.1109/icme.2001.1237718.
Повний текст джерелаSongya Pan, Baro Hyun, Pierre Kabamba, and Anouck Girard. "Optimal fusion rules in team classification under three decision structures." In 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6580425.
Повний текст джерелаMathew, Bini, and Ebin M. Manuel. "Cooperative discriminant analysis based spectrum sensing using optimum fusion rule." In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2014. http://dx.doi.org/10.1109/icacci.2014.6968194.
Повний текст джерелаCapitaine, Hoel Le, and Carl Frelicot. "An Optimum Class-Rejective Decision Rule and Its Evaluation." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.810.
Повний текст джерелаYunmin Zhu, X. R. Li, and Zhisheng You. "Unified fusion rule in multisensor network decision systems." In Proceedings of the Third International Conference on Information Fusion. IEEE, 2000. http://dx.doi.org/10.1109/ific.2000.859882.
Повний текст джерелаWang, Yongwei, Yunan Liu, Wei Qiu, Shuai Liu, and Cheng Si. "Situation Assessment Based on Group Decision and Rule Fusion." In 2013 6th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2013. http://dx.doi.org/10.1109/iscid.2013.122.
Повний текст джерелаMashreghi, Mehran, and Bahman Abolhassani. "Optimum number of secondary users and optimum fusion rule in cooperative spectrum sensing to maximize channel throughput." In 2010 5th International Symposium on Telecommunications (IST). IEEE, 2010. http://dx.doi.org/10.1109/istel.2010.5733988.
Повний текст джерелаMinor, Christian, and Kevin Johnson. "Multisensor fusion with non-optimal decision rules: the challenges of open world sensing." In SPIE Sensing Technology + Applications, edited by Jerome J. Braun. SPIE, 2014. http://dx.doi.org/10.1117/12.2053298.
Повний текст джерелаRen, Qing'an, Yunmin Zhu, and Yifan Xia. "Optimal Sensor Rules and Unified Fusion Rules for Multisensor Multi-hypothesis Network Decision Systems with Fading Channels." In 2008 IEEE Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 2008. http://dx.doi.org/10.1109/ramech.2008.4681429.
Повний текст джерелаYuan, Xiaoguang, Dongzhu Feng, and Xinhuai Wang. "Decision fusion rule for dynamic large-scale wireless sensor networks." In International Conference on Information Engineering. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/icie130071.
Повний текст джерела