Relevant bibliographies by topics / Optimum decision fusion rule

Academic literature on the topic 'Optimum decision fusion rule'

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Published: 14 March 2023

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Journal articles on the topic "Optimum decision fusion rule"

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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.

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The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance.
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Rauniyar, 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.

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Saleh, 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.

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<p class="Default">The distributed object decision (DOD) was applied to choose a single solution for problem among many complexes solutions. Most of DOD systems depend on traditional technique like small form factor optical (SFFO) method and scalable and oriented fast-based local features (SOFF) method. These two methods were statistically complex and depended to an initial value. In this paper proposed new optimal technical called gray wolf optimization (GWO) which is used to determine threshold of sensor decision rules from fusion center. The new algorithm gave better performance for fusion rule than numerical results. The results are providing to demonstrate of fusion system reduced of bayes risk by a high rate of 15%-20%. This algorithm also does not depend on the initial values and shows the degree of complexity is better than other algorithms.</p>
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Khan, 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.

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Internet of Things (IoT) is a new challenging paradigm for connecting a variety of heterogeneous networks. Since its introduction, many researchers have been studying how to efficiently exploit and manage spectrum resource for IoT applications. An explosive increase in the number of IoT devices accelerates towards the future-connected society but yields a high system complexity. Cognitive radio (CR) technology is also a promising candidate for future wireless communications. CR via dynamic spectrum access provides opportunities to secondary users (SUs) to access licensed spectrum bands without interfering primary users by performing spectrum sensing before accessing available spectrum bands. However, multipath effects can degrade the sensing capability of an individual SU. Therefore, for more precise sensing, it is helpful to exploit multiple collaborative sensing users. The main problem in cooperative spectrum sensing is the presence of inaccurate sensing information received from the multipath-affected SUs and malicious users at a fusion center (FC). In this paper, we propose a genetic algorithm-based soft decision fusion scheme to determine the optimum weighting coefficient vector against SUs’ sensing information. The weighting coefficient vector is further utilized in a soft decision rule at FC in order to make a global decision. Through extensive simulations, the effectiveness of the proposed scheme is evaluated compared with other conventional schemes.
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Liao, 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.

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Usman, 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.

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Both reliable detection of the primary signal in a noisy and fading environment and nullifying the effect of unauthorized users are important tasks in cognitive radio networks. To address these issues, we consider a cooperative spectrum sensing approach where each user is assigned nonuniform reliability based on the sensing performance. Users with poor channel or faulty sensor are assigned low reliability. The nonuniform reliabilities serve as identification tags and are used to isolate users with malicious behavior. We consider a link layer attack similar to the Byzantine attack, which falsifies the spectrum sensing data. Three different strategies are presented in this paper to ignore unreliable and malicious users in the network. Considering only reliable users for global decision improves sensing time and decreases collisions in the control channel. The fusion center uses the degree of reliability as a weighting factor to determine the global decision in scheme I. Schemes II and III consider the unreliability of users, which makes the computations even simpler. The proposed schemes reduce the number of sensing reports and increase the inference accuracy. The advantages of our proposed schemes over conventional cooperative spectrum sensing and the Chair-Varshney optimum rule are demonstrated through simulations.
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Zhu, 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.

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Yuan, 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.

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In order to solve the decision information fusion issues of resource-constrained wireless sensor network, several decision information fusion rules under exponential distribution fading channel are investigated in this paper. At first, optimal likelihood ratio rule is given. The detection performance of this fusion rule is best, however, this rule acquires channel information which is too costly for resource constrained sensor networks. To solve this problem, suboptimal likelihood ratio fusion rule is proposed which requires only the knowledge of channel statistics. In addition, the reduced forms of the suboptimal are also derived, in the case of extreme channel signal-to-noise ratio (SNR). Theoretical analysis and simulations show that suboptimal fusion rule needs much less computation and information, yet exhibits only slight performance degradation. Suboptimal fusion rules are practicable for resource constrained wireless sensor networks decision information fusion system working in ocean environment.
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Zhu, 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.

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Liu, 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.

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In this paper, we consider the problem of decision fusion for noncoherent detection in a wireless sensor network. Novel to the current work is the integration of the hybrid multi-access channel (MAC) in the fusion rule design. We assume that sensors transmit their local binary decisions over a hybrid MAC which is a composite of conventional orthogonal and nonorthogonal MACs. Under Rayleigh fading scenario, we present a likelihood ratio (LR)-based fusion rule, which has been shown to be optimal through theoretical analysis and simulation. However, it requires a large amount of computation, which is not easily implemented in resource-constrained sensor networks. Therefore, three sub-optimal alternatives with low-complexity are proposed, namely the weighed energy detector (WED), the deflection-coefficient-maximization (DCM), and the two-step (TS) rules. We show that when the channel signal-to-noise ratio (SNR) is low, the LR-based fusion rule reduces to the WED rule; at high-channel SNR, it is equivalent to the TS rule; and at moderate-channel SNR, it can be approached closely by the DCM rule. Compared with the conventional orthogonal and nonorthogonal MACs, numerical results show that the hybrid MAC with the proposed fusion rules can improve the detection performance when the channel SNR is medium.
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Dissertations / Theses on the topic "Optimum decision fusion rule"

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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.

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Every day we share our personal information through digital systems which are constantly exposed to threats. For this reason, security-oriented disciplines of signal processing have received increasing attention in the last decades: multimedia forensics, digital watermarking, biometrics, network monitoring, steganography and steganalysis are just a few examples. Even though each of these fields has its own peculiarities, they all have to deal with a common problem: the presence of one or more adversaries aiming at making the system fail. Adversarial Signal Processing lays the basis of a general theory that takes into account the impact that the presence of an adversary has on the design of effective signal processing tools. By focusing on the application side of Adversarial Signal Processing, namely adversarial information fusion in distributed sensor networks, and adopting a game-theoretic approach, this thesis contributes to the above mission by addressing four issues. First, we address decision fusion in distributed sensor networks by developing a novel soft isolation defense scheme that protects the network from adversaries, specifically, Byzantines. Second, we develop an optimum decision fusion strategy in the presence of Byzantines. In the next step, we propose a technique to reduce the complexity of the optimum fusion by relying on a novel nearly-optimum message passing algorithm based on factor graphs. Finally, we introduce a defense mechanism to protect decentralized networks running consensus algorithm against data falsification attacks.
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Qua, 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.

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Thesis (M.S. in Management)--Naval Postgraduate School, December 1990.
Thesis 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.
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Gutiérrez, Celaya Jorge Arturo. "Fusion d'informations en identification automatique des langues." Toulouse 3, 2005. http://www.theses.fr/2005TOU30098.

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En identification automatique des langues nous nous intéressons au problème de fusion des informations de décision issues de différents experts. Pour explorer et comparer des stratégies de fusion, nous les modélisons au moyen de méthodes formelles de classification provenant soit de la théorie statistique, comme les mélanges de lois gaussiennes, les réseaux de neurones et le classificateur discriminant, soit des travaux de recherche récents pour les théories des possibilités et des fonctions de croyance. Nous proposons une méthodologie formelle de fusion dans le paradigme bayésien : l'évaluation de la performance des experts par l'analyse factorielle discriminante fournit des indices de confiance, l'agrégation des décisions privilégie les méthodes donnant, directement ou par transformation, des valeurs de probabilité ou de vraisemblance pour les langues et la pondération de nouvelles fonctions de coût avec les indices de confiance conduit à la prise de décision par minimum de risque
Fusing 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
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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.

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Today, decision support systems based on predictive modeling are becoming more common, since organizations often collectmore data than decision makers can handle manually. Predictive models are used to find potentially valuable patterns in the data, or to predict the outcome of some event. There are numerous predictive techniques, ranging from simple techniques such as linear regression,to complex powerful ones like artificial neural networks. Complexmodels usually obtain better predictive performance, but are opaque and thus cannot be used to explain predictions or discovered patterns.The design choice of which predictive technique to use becomes even harder since no technique outperforms all others over a large set of problems. It is even difficult to find the best parameter values for aspecific technique, since these settings also are problem dependent.One way to simplify this vital decision is to combine several models, possibly created with different settings and techniques, into an ensemble. Ensembles are known to be more robust and powerful than individual models, and ensemble diversity can be used to estimate the uncertainty associated with each prediction.In real-world data mining projects, data is often imprecise, contain uncertainties or is missing important values, making it impossible to create models with sufficient performance for fully automated systems.In these cases, predictions need to be manually analyzed and adjusted.Here, opaque models like ensembles have a disadvantage, since theanalysis requires understandable models. To overcome this deficiencyof opaque models, researchers have developed rule extractiontechniques that try to extract comprehensible rules from opaquemodels, while retaining sufficient accuracy.This thesis suggests a straightforward but comprehensive method forpredictive modeling in situations with poor data quality. First,ensembles are used for the actual modeling, since they are powerful,robust and require few design choices. Next, ensemble uncertaintyestimations pinpoint predictions that need special attention from adecision maker. Finally, rule extraction is performed to support theanalysis of uncertain predictions. Using this method, ensembles can beused for predictive modeling, in spite of their opacity and sometimesinsufficient global performance, while the involvement of a decisionmaker is minimized.The main contributions of this thesis are three novel techniques that enhance the performance of the purposed method. The first technique deals with ensemble uncertainty estimation and is based on a successful approach often used in weather forecasting. The other twoare improvements of a rule extraction technique, resulting in increased comprehensibility and more accurate uncertainty estimations.

Sponsorship:

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.

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陳宏期. "Cognitive Radio Power Control With Optimum Channel Gain Decision Rule." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/48058472726314329596.

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碩士
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.
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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.

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碩士
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.
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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.

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碩士
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.
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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.

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碩士
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.
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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.

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碩士
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.
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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.

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碩士
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.
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Book chapters on the topic "Optimum decision fusion rule"

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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.

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Abrardo, 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.

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Solovjev, 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.

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Elhassouny, 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.

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Yang, 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.

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Wang, 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.

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Venkataramani, 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.

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Liao, 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.

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Since there has been an increasing interest in the areas of Internet of Things (IoT) and artificial intelligence that often deals with a large number of sensors, this chapter investigates the decision fusion problem for large-scale sensor networks. Due to unavoidable transmission channel interference, we consider sensor networks with nonideal channels that are prone to errors. When the fusion rule is fixed, we present the necessary condition for the optimal sensor rules that minimize the Monte Carlo cost function. For the K-out-of-L fusion rule chosen very often in practice, we analytically derive the optimal sensor rules. For general fusion rules, a Monte Carlo Gauss-Seidel optimization algorithm is developed to search for the optimal sensor rules. The complexity of the new algorithm is of the order of OLN compared with OLNL of the previous algorithm that was based on Riemann sum approximation, where L is the number of sensors and N is the number of samples. Thus, the proposed method allows us to design the decision fusion rule for large-scale sensor networks. Moreover, the algorithm is generalized to simultaneously search for the optimal sensor rules and the optimal fusion rule. Finally, numerical examples show the effectiveness of the new algorithms for large-scale sensor networks with nonideal channels.
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Berber, 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.

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In this chapter, based on definitions of signal synthesizers and analysers, a general and generic scheme of a discrete communication system is developed in order to deduce practical systems as its special cases. The synthesizer is transferred into a discrete transmitter, and the analyser is used as a correlation receiver followed by an optimum detector. The system structure is presented in terms of mathematical operators and supported by exact mathematical expressions based on the theory of discrete-time stochastic processes. The likelihood function is derived, and the maximum likelihood rule is applied to specify the decision process and construct the optimum detector. A multilevel system and a quadrature phase-shift keying system are deduced as special cases, and the bit error probability expression is derived. For the sake of continuity and completeness in presenting communication systems theory, a generic digital communication system is developed and related to its discrete counterpart.
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Mankad, 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.

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Intelligent System (IS) can be defined as the system that incorporates intelligence into applications being handled by machines. The chapter extensively discusses the role of Genetic Algorithm (GA) in the search and optimization process along with discussing applications developed so far. A very detailed discussion on the Fuzzy Rule-Based System is presented along with major applications developed in different domains. The chapter presents algorithm of implementing intelligent procedure to decide whether a patient is prone to heart disease or not. The procedure evolves solutions using genetic operators and provides its decision automatically. The chapter presents discussion on the results achieved as a result of prototypical implementation of the evolutionary fuzzy hybrid model. The significant advantage of the presented research work is that applications that do not have any mathematical formulation and still demand optimization can be easily solved using the designed approach.
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Conference papers on the topic "Optimum decision fusion rule"

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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.

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Songya 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.

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Mathew, 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.

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Capitaine, 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.

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Yunmin 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.

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Wang, 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.

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Mashreghi, 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.

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Minor, 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.

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Ren, 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.

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Yuan, 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.

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