Journal articles: '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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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.

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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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Han, Weijia, Jiandong Li, Zan Li, Jiangbo Si, and Yan Zhang. "Efficient Soft Decision Fusion Rule in Cooperative Spectrum Sensing." IEEE Transactions on Signal Processing 61, no. 8 (April 2013): 1931–43. http://dx.doi.org/10.1109/tsp.2013.2245659.

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Yousefli, Amir, and Mehdi Ghazanfari. "A Stochastic Decision Support System for Economic Order Quantity Problem." Advances in Fuzzy Systems 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/650419.

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Improving decisions efficiency is one of the major concerns of the decision support systems. Specially in the uncertain environment, decision support systems could be implemented efficiently to simplify decision making process. In this paper stochastic economic order quantity (EOQ) problem is investigated in which decision variables and objective function are uncertain in nature and optimum probability distribution functions of them are calculated through a geometric programming model. Obtained probability distribution functions of the decision variables and the objective function are used as optimum knowledge to design a new probabilistic rule base (PRB) as a decision support system for EOQ model. The developed PRB is a new type of the stochastic rule bases that can be used to infer optimum or near optimum values of the decision variables and the objective function of the EOQ model without solving the geometric programming problem directly. Comparison between the results of the developed PRB and the optimum solutions which is provided in the numerical example illustrates the efficiency of the developed PRB.

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Parfenov, V. I., and V. D. Le. "Distributed detection basis on using soft decision scheme both in local sensors and in fusion center." Issues of radio electronics, no. 3 (June 25, 2021): 49–56. http://dx.doi.org/10.21778/2218-5453-2021-3-49-56.

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The paper considers distributed detection problem basis on using soft decision scheme both in the local sensors and in the fusion center (FC). The algorithm for making soft decisions when receiving data from local sensors in the fusion center and its performance characteristics, which are necessary for the formation decision fusion rule, are presented. The dependencies of the total error probability on the energy parameter taking into account signal-to-noise ratio at the level of local sensors and the channel’s signal-to-noise ratio are given. The gain of the fusion rule basis on the aggregation of soft decisions in the FC when receiving data about soft local decisions, in efficiency compared to hard fusion rule.

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Elkosantini, Sabeur, and Ahmed Frikha. "Decision fusion for signalized intersection control." Kybernetes 44, no. 1 (January 12, 2015): 57–76. http://dx.doi.org/10.1108/k-08-2013-0185.

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Purpose – Traffic congestion is becoming a serious problem that has adverse consequences on the socio-economy, environment, and public health of various cities worldwide. The purpose of this paper is to contribute to the continuous search for new alternative solutions to prevent or alleviate these concerns. It particularly deals with the development of decision support system based on a data fusion for the management and control of traffic at signalized intersections. The role of such systems is to manage the existing infrastructure to ease congestion and respond to crises. The proposed system is based on multi-detector data fusion, a data processing function that combines imperfect information collected from systems involving several detectors. The developed system is then tested on a virtual junction, and the results obtained are reported and discussed. Design/methodology/approach – This paper presents a new traffic light control based on multi-detectors data fusion theory. The system uses a new multi-detectors data fusion method for traffic data analysis. Moreover, the system integrates a method for the estimation of the reliability degree of different detectors taking into account their imperfection and the conflict between them. These estimated reliability degrees are combined using Dempster’s rule of combination. Findings – The paper provides a decision support system for traffic regulation at intersection based on multi-sensors. It suggests the fusion of captured data by many sensors for measuring information. The system use the Belief Functions Theory for information fusion and decision making using combination and decision rules. Originality/value – The paper proposes a new Adaptive Traffic Control System based on a new data fusion approach that include a method for the estimation of the reliability degree of different detectors taking into account their imperfection and the conflict between them. These estimated reliability degrees are combined using Dempster’s rule of combination.

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Guan, Xudong, Chong Huang, and Rui Zhang. "Integrating MODIS and Landsat Data for Land Cover Classification by Multilevel Decision Rule." Land 10, no. 2 (February 19, 2021): 208. http://dx.doi.org/10.3390/land10020208.

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In some cloudy and rainy regions, the cloud cover is high in moderate-high resolution remote sensing images collected by satellites with a low revisit cycle, such as Landsat. This presents an obstacle for classifying land cover in cloud-covered parts of the image. A decision fusion scheme is proposed for improving land cover classification accuracy by integrating the complementary information of MODIS (Moderate-resolution Imaging Spectroradiometer) time series data with Landsat moderate-high spatial resolution data. The multilevel decision fusion method includes two processes. First, MODIS and Landsat data are pre-classified by fuzzy classifiers. Second, the pre-classified results are assembled according to their assessed performance. Thus, better pre-classified results are retained and worse pre-classified results are restrained. For the purpose of solving the resolution difference between MODIS and Landsat data, the proposed fusion scheme employs an object-oriented weight assignment method. A decision rule based on a compromise operator is applied to assemble pre-classified results. Three levels of data containing different types of information are combined, namely the MODIS pixel-level and object-level data, and the Landsat pixel-level data. The multilevel decision fusion scheme was tested on a site in northeast Thailand. The fusion results were compared with the single data source classification results, showing that the multilevel decision fusion results had a higher overall accuracy. The overall accuracy is improved by more than 5 percent. The method was also compared to the two-level combination results and a weighted sum decision rule-based approach. A comparison experiment showed that the multilevel decision fusion rule had a higher overall accuracy than the weighted sum decision rule-based approach and the low-level combination approach. A major limitation of the method is that the accuracy of some of the land covers, where areas are small, are not as improved as the overall accuracy.

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Pradhan, Biswabrata. "An Application of Multivariate Statistical Techniques for Cost Effective Decision Making A Case Study." Calcutta Statistical Association Bulletin 57, no. 1-2 (March 2005): 109–20. http://dx.doi.org/10.1177/0008068320050109.

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This paper focuses on the application of multivariate statical techniques for making a cost effective decision in an industrial set up. The objective of this study is to take a decision with respect to several parameters whether a particular product can be sent to the customer or not. The techniques like MANOVA, discriminant and classification function analysis have been used to fulfil the objectives. An optimum classification rule has been established for making the decision. A cost benefit analysis has also been done after iniplementing the proposed optimum decisionmaking rule.

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Ren, Qing’an, Yunmin Zhu, Xiaojing Shen, and Enbin Song. "Optimal sensor rules and unified fusion rules for multisensor multi-hypothesis network decision systems with channel errors." Automatica 45, no. 7 (July 2009): 1694–702. http://dx.doi.org/10.1016/j.automatica.2009.02.032.

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Warreir, P. Nandakumar. "Optimum Commodity Taxation When Employment Matters." International Review of Business and Economics 5, no. 2 (2021): 73–85. http://dx.doi.org/10.56902/irbe.2021.5.2.4.

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Ramsey's inelasticity rule for optimum commodity taxation has been criticized for indicating tax rates for basic necessities which have lower elasticities of demand than luxury consumption items. However, it is noted that skillful marketing techniques tend to lower the demand elasticities of luxury items so that it is not evident that the Ramsey rule is inequitable. In this paper equity concerns from a macroeconomic viewpoint are introduced, modifying the Ramsey rule approach by including total employment in the decision-maker's utility function. Then it is seen that the relative tax rate of the product which is more labour-intensive in production is lower than under the original Ramsey rule. The weight given by the government to an additional unit of employment relative to additional tax revenue now enters the tax rule specification. It is also observed that when the employment objective is included, the relative tax rates of products such as furniture that are labour-intensive in production will be lower than under the pure Ramsey inelasticity rule.

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Benaliouche, Houda, and Mohamed Touahria. "Comparative Study of Multimodal Biometric Recognition by Fusion of Iris and Fingerprint." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/829369.

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This research investigates the comparative performance from three different approaches for multimodal recognition of combined iris and fingerprints: classical sum rule, weighted sum rule, and fuzzy logic method. The scores from the different biometric traits of iris and fingerprint are fused at the matching score and the decision levels. The scores combination approach is used after normalization of both scores using the min-max rule. Our experimental results suggest that the fuzzy logic method for the matching scores combinations at the decision level is the best followed by the classical weighted sum rule and the classical sum rule in order. The performance evaluation of each method is reported in terms of matching time, error rates, and accuracy after doing exhaustive tests on the public CASIA-Iris databases V1 and V2 and the FVC 2004 fingerprint database. Experimental results prior to fusion and after fusion are presented followed by their comparison with related works in the current literature. The fusion by fuzzy logic decision mimics the human reasoning in a soft and simple way and gives enhanced results.

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Rajavel, R., and P. S. Sathidevi. "Optimum integration weight for decision fusion audio-visual speech recognition." International Journal of Computational Science and Engineering 10, no. 1/2 (2015): 145. http://dx.doi.org/10.1504/ijcse.2015.067044.

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Maserati, Matthew B., Bradley Stephens, Zohny Zohny, Joon Y. Lee, Adam S. Kanter, Richard M. Spiro, and David O. Okonkwo. "Occipital condyle fractures: clinical decision rule and surgical management." Journal of Neurosurgery: Spine 11, no. 4 (October 2009): 388–95. http://dx.doi.org/10.3171/2009.5.spine08866.

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Object Occipital condyle fractures (OCFs) are rare injuries and their treatment remains controversial. Several classification systems have been proposed, first by Anderson and Montesano and more recently by Tuli and colleagues and Hanson and associates, who sought to stratify these fractures in a manner that would guide treatment that has typically ranged from semirigid collar immobilization to halo fixation or occipitocervical fusion. It has been the authors' impression, based on experience with OCFs at their institution, that classification is cumbersome and contributes little to the clinical decision-making process, while the identification of craniocervical misalignment and neural element compromise is paramount, and sufficient, for the planning of treatment. Methods The authors performed a retrospective review of 24,745 consecutive trauma presentations to a single Level I trauma center (UPMC Presbyterian Hospital) over a 6-year period, identifying 100 patients with 106 OCFs. All patients were evaluated by the spine trauma service and underwent imaging of the craniocervical junction using reconstructed CT scans. Patient characteristics, fracture characteristics (including fracture classification according to the 2 major classification systems), initial management, and status at follow-up were recorded. Results The incidence of OCF in this trauma population was 0.4%. Two patients had evidence of craniocervical misalignment on reconstructed CT imaging at the time of admission; both patients underwent occipitocervical fusion. One patient underwent occipitocervical fusion for unrelated C1–2 fractures. The remainder of those surviving to discharge, whose fractures represented all fracture subtypes, received treatment with a rigid cervical collar or counseling alone. No patients, including 4 patients with bilateral OCFs, were found to have developed delayed craniocervical instability or misalignment on follow-up, or to require further neurosurgical intervention for an OCF. Neural element compression was not identified in any of the patients, and there were no cases of delayed cranial neuropathy. Conclusions Beyond the identification of craniocervical misalignment on reconstructed CT scans at admission, further classification of OCFs is unnecessary. Management should consist of up-front occipitocervical fusion or halo fixation in cases demonstrating occipitocervical misalignment, or of immobilization in a rigid cervical collar followed by delayed clinical and radiographic evaluation in a spine trauma clinic if misalignment is not present.

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Jiang, Sisi, Zhiwen Zhao, Sheng Mou, Zushun Wu, and Yi Luo. "Linear Decision Fusion under the Control of Constrained PSO for WSNs." International Journal of Distributed Sensor Networks 8, no. 1 (January 1, 2012): 871596. http://dx.doi.org/10.1155/2012/871596.

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A major application of a distributed WSN (wireless sensor network) is to monitor a specific area for detecting some events such as disasters and enemies. In order to achieve this objective, each sensor in the network is required to collect local observations which are probably corrupted by noise, make a local decision regarding the presence or absence of an event, and then send its local decision to a fusion center. After that, the fusion center makes the final decision depending on these local decisions and a decision fusion rule, so an efficient decision fusion rule is extremely critical. It is obvious that the decision-making capability of each node is different owing to the dissimilar signal noise ratios and some other factors, so it is easy to understand that a specific sensor contribution to the global decision should be constrained by this sensor decision-making capability, and, based on this idea, we establish a novel linear decision fusion model for WSNs. Moreover, the constrained particle swarm optimization (constrained PSO) algorithm is creatively employed to control the parameters of this model in this paper and we also apply the typical penalty function to solve the constrained PSO problem. The emulation results indicate that our design is capable of achieving very high accuracy.

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Fatemipour, F., and M. R. Akbarzadeh-T. "Dynamic Fuzzy Rule-based Source Selection in Distributed Decision Fusion Systems." Fuzzy Information and Engineering 10, no. 1 (January 2, 2018): 107–27. http://dx.doi.org/10.1080/16168658.2018.1509524.

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Umebayashi, Kenta, Janne J. Lehtomaki, Takanao Yazawa, and Yasuo Suzuki. "Efficient Decision Fusion for Cooperative Spectrum Sensing Based on OR-rule." IEEE Transactions on Wireless Communications 11, no. 7 (July 2012): 2585–95. http://dx.doi.org/10.1109/twc.2012.052412.111727.

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Chow, J. C., Q. Zhu, R. Fischl, and M. Kam. "Design of a decision fusion rule for power system security assessment." IEEE Transactions on Power Systems 8, no. 3 (1993): 858–64. http://dx.doi.org/10.1109/59.260918.

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Sanjekar, Priti Shivaji, and J. B. Patil. "Multimodal biometrics with serial, parallel and hierarchical mode at decision level fusion." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (December 1, 2019): 1303. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1303-1310.

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Biometric based personal authentication is playing a vital role in various security based applications. This paper presents the effective fusion of fingerprint, palmprint and iris traits at decision level. Combining different traits at the decision level is a challenging task due to less information available at this level. The focus of the work is to examine the performance of multimodal biometrics at decision level fusion in three different i.e. serial, parallel and hierarchical modes of operation. Serial mode is performed by taking unimodals serially while parallel mode of operation is carried out by processing all modals simulatenously using Majority Voting Rule and the hierarchical mode of operation is performed with proper combination of traits in parallel and serial mode using AND and OR rule. The experiments are performed on 100 different users from publically available FVC2006 fingerprint database, CASIA V1 palmprint database and IITD iris database. The experimental results suggest that proper fusion of different traits in hierarchical way can give best performance even at decision level fusion as compared to serial and parallel mode of operation.

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Chatterjee, Shoutir Kishore, and gaurangadeb Chattopadhyay. "Detailed Statistical Inference-multiple Decision Problem." Calcutta Statistical Association Bulletin 43, no. 3-4 (September 1993): 155–80. http://dx.doi.org/10.1177/0008068319930302.

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The procedure for detailed statistical inference developed by the authors in an earlier paper (1992) for the two-decision case, is extended here to the case of several decisions. Alongwith the rule for choosing the decision, the problem of stating data dependent measures of confidence in terms of betting odds, is considered. The extension involves generalization oftlie coneept of legitimacy of betting odds introduced in the earlier paper and the choice of a suitable utility function for bets. The actual solution is worked out in the case of logarithmic utility. A rather intricate mathematical result requires to be established to prove the existence of an optimum rule in this case. Application of the procedure is illustrated through some numerical examples.

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Shen, Xiaojing, Yunmin Zhu, Lamei He, and Zhisheng You. "A Near-Optimal Iterative Algorithm via Alternately Optimizing Sensor and Fusion Rules in Distributed Decision Systems." IEEE Transactions on Aerospace and Electronic Systems 47, no. 4 (2011): 2514–29. http://dx.doi.org/10.1109/taes.2011.6034648.

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Xu, Shi Jun, Li Hong, and Yong Hong Hu. "A Distributed Bayesian Fusion Algorithm Research." Advanced Materials Research 181-182 (January 2011): 1006–12. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.1006.

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In this paper, the signal detection problem when distributed sensors are used a global decision is desired is considered. Local decisions from the sensors are fed to the data fusion center which then yields a global decision based on a fusion rule. Based on The data fusion theories of Bayesian criterion used for a distributed parallel structure, fusion rules at the fusion center、 the decision rules of sensors and the results of the computer simulation for two identical sensors, two different sensors and three identical sensors are presented. The results of the computer simulation show that the performance of the fusion system, compared with the sensor, has been improved. For the case there are three identical sensors in the fusion system, Bayesian risk is reduced by 26.5%, compared with the sensor.

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Giompapa, S., A. Farina, F. Gini, A. Graziano, R. Croci, and R. Di Stefano. "Naval Target Classification by Fusion of Multiple Imaging Sensors Based on the Confusion Matrix." International Journal of Navigation and Observation 2009 (March 3, 2009): 1–15. http://dx.doi.org/10.1155/2009/714508.

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This paper presents an algorithm for the classification of targets based on the fusion of the class information provided by different imaging sensors. The outputs of the different sensors are combined to obtain an accurate estimate of the target class. The performance of each imaging sensor is modelled by means of its confusion matrix (CM), whose elements are the conditional error probabilities in the classification and the conditional correct classification probabilities. These probabilities are used by each sensor to make a decision on the target class. Then, a final decision on the class is made using a suitable fusion rule in order to combine the local decisions provided by the sensors. The overall performance of the classification process is evaluated by means of the “fused” confusion matrix, i.e. the CM pertinent to the final decision on the target class. Two fusion rules are considered: a majority voting (MV) rule and a maximum likelihood (ML) rule. A case study is then presented, where the developed algorithm is applied to three imaging sensors located on a generic air platform: a video camera, an infrared camera (IR), and a spotlight Synthetic Aperture Radar (SAR).

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Souihli, Oussama, and Tomoaki Ohtsuki. "Typical Set Cognitive Sensing." ISRN Communications and Networking 2011 (August 29, 2011): 1–10. http://dx.doi.org/10.5402/2011/709091.

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In cognitive radio (CR) cooperative sensing schemes, wireless sensor nodes deployed in the network sense the licensed spectrum and send their local sensing decisions to a fusion center (FC) that makes a global decision on whether to allow the unlicensed user transmit on the licensed spectrum, based on a decision (fusion) rule. k-out-of-N is widely used in the literature owing to its practical simplicity. Regrettably, it exhibits a tradeoff between the achievable probabilities of false alarm and miss detection, which could have consequent effects on the performance of CR. In this paper, based on the notion of typical sequences, we propose a novel fusion rule in which the false alarm and miss detection probabilities can be simultaneously made as small as desired (asymptotically zero as the number of sensors goes to infinity).

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Zheng, Hong, and Kai Zhang. "Decision Fusion Gait Recognition Based on Bayesian Rule and Support Vector Machine." Applied Mechanics and Materials 411-414 (September 2013): 1287–90. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1287.

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To distinguish people’s identities, the information is normally included in one gait periodic sequence image. First, the gait energy image for feature extraction of wavelet moments was constructed. After boundary unwrapping, the gait silhouette boundary was extracted and principal component analysis (PCA) was use to obtain its compressed contour features. Then nearest neighbor classifier and support vector machines were applied for classification of these two features. Finally, support vector machine (SVM) on Bayesian rule were used to complete gait recognition with information fusion of different features. The method is evaluated on the National Laboratory of Pattern Recognition (NLPR) gait database and the correct recognition rate is relatively high. The experimental results show that the proposed method has good recognition performance.

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Alkheir, Ala Abu, and Mohamed Ibnkahla. "A selective decision-fusion rule for cooperative spectrum sensing using energy detection." Wireless Communications and Mobile Computing 16, no. 12 (September 16, 2015): 1603–11. http://dx.doi.org/10.1002/wcm.2615.

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Chen, You Ling, Ya Xuan Ma, Dou Zhang, and Juan Tang. "Research on Modeling Method for Equipment Maintenance Strategy Based on Reliability and Residual Life." Applied Mechanics and Materials 215-216 (November 2012): 817–25. http://dx.doi.org/10.4028/www.scientific.net/amm.215-216.817.

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This paper deals with the research on method for the optimum equipment preventive maintenance strategy based on equipment reliability and equipment residual life in indirect condition monitoring. We use the Markov chain to express the equipment deterioration condition and propose the equipment reliability function; besides, study on the equipment condition probability distribution and establish the equipment residual life model by using the Bayes rule and the hidden Markov model, based on the above research background, we obtain the optimum maintenance decision criteria by analyzing the equipment maintenance strategy based on equipment reliability and equipment residual life in indirect monitoring. Finally, a case study is given to illustrate the implementation of the optimum maintenance decision criteria in indirect monitoring.

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35

Teoh, A., S. A. Samad, and A. Hussain. "FUSION DECISION FOR A BIMODAL BIOMETRIC VERIFICATION SYSTEM USING SUPPORT VECTOR MACHINE AND ITS VARIATIONS." ASEAN Journal on Science and Technology for Development 19, no. 1 (December 10, 2017): 1–16. http://dx.doi.org/10.29037/ajstd.326.

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This paw presents fusion detection technique comparisons based on support vector machine and its variations for a bimodal biometric verification system that makes use of face images and speech utterances. The system is essentially constructed by a face expert, a speech expert and a fusion decision module. Each individual expert has been optimized to operate in automatic mode and designed for security access application. Fusion decision schemes considered are linear, weighted Support Vector Machine (SVM) and linear SVM with quadratic transformation. The conditions tested include the balanced and unbalanced conditions between the two experts in order to obtain the optimum fusion module from these techniques best suited to the target application.

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Ren, Minglun, Pei He, and Junjie Zhou. "Decision fusion of two sensors object classification based on the evidential reasoning rule." Expert Systems with Applications 210 (December 2022): 118620. http://dx.doi.org/10.1016/j.eswa.2022.118620.

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Geng, Xiaojiao, Yan Liang, and Lianmeng Jiao. "Multi-frame decision fusion based on evidential association rule mining for target identification." Applied Soft Computing 94 (September 2020): 106460. http://dx.doi.org/10.1016/j.asoc.2020.106460.

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Mi, Aizhong, Lei Wang, and Junyan Qi. "A Multiple Classifier Fusion Algorithm Using Weighted Decision Templates." Scientific Programming 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/3943859.

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Fusing classifiers’ decisions can improve the performance of a pattern recognition system. Many applications areas have adopted the methods of multiple classifier fusion to increase the classification accuracy in the recognition process. From fully considering the classifier performance differences and the training sample information, a multiple classifier fusion algorithm using weighted decision templates is proposed in this paper. The algorithm uses a statistical vector to measure the classifier’s performance and makes a weighed transform on each classifier according to the reliability of its output. To make a decision, the information in the training samples around an input sample is used by thek-nearest-neighbor rule if the algorithm evaluates the sample as being highly likely to be misclassified. An experimental comparison was performed on 15 data sets from the KDD’99, UCI, and ELENA databases. The experimental results indicate that the algorithm can achieve better classification performance. Next, the algorithm was applied to cataract grading in the cataract ultrasonic phacoemulsification operation. The application result indicates that the proposed algorithm is effective and can meet the practical requirements of the operation.

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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." IEEE Transactions on Information Forensics and Security 11, no. 6 (June 2016): 1333–45. http://dx.doi.org/10.1109/tifs.2016.2526963.

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Al-Rawi, M. "Performance measurement of one-bit hard decision fusion scheme for cooperative spectrum sensing in CR." International Review of Applied Sciences and Engineering 8, no. 1 (June 2017): 9–16. http://dx.doi.org/10.1556/1848.2017.8.1.3.

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This paper measures the performance of cooperative spectrum sensing, over Rayleigh-fading channel and additive white Gaussian noise, based on one-bit hard decision scheme for both AND and OR rules. Three measures based on energy detection are considered including effect of false alarm probability, effect of number of users, and effect of number of samples. Simulation results show that the detection probability increases with increasing false alarm probability, number of users, and number of samples for both AND and OR rules. Also, the performance of OR rule is better than the performance of AND rule.

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Cherrat, El mehdi, Rachid Alaoui, and Hassane Bouzahir. "A multimodal biometric identification system based on cascade advanced of fingerprint, fingervein and face images." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (March 1, 2020): 1562. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1562-1570.

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In this paper, we present a multimodal biometric recognition system that combines fingerprint, fingervein and face images based on cascade advanced and decision level fusion. First, in fingerprint recognition system, the images are enhanced using gabor filter, binarized and passed to thinning method. Then, the minutiae points are extracted to identify that an individual is genuine or impostor. In fingervein recognition system, image processing is required using Linear Regression Line, Canny and local histogram equalization technique to improve better the quality of images. Next, the features are obtained using Histogram of Oriented Gradient (HOG). Moreover, the Convolutional Neural Networks (CNN) and the Local Binary Pattern (LBP) are applied to detect and extract the features of the face images, respectively. In addition, we proposed three different modes in our work. At the first, the person is identified when the recognition system of one single biometric modality is matched. At the second, the fusion is achieved at cascade decision level method based on AND rule when the recognition system of both biometric traits is validated. At the last mode, the fusion is accomplished at decision level method based on AND rule using three types of biometric. The simulation results have demonstrated that the proposed fusion algorithm increases the accuracy to 99,43% than the other system based on unimodal or bimodal characteristics.

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Luo, Jin, and Qi Bin Deng. "Application of Uncertain Information Fusion in Diagnosis Decision of Electronic Equipment." Applied Mechanics and Materials 341-342 (July 2013): 715–18. http://dx.doi.org/10.4028/www.scientific.net/amm.341-342.715.

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Focuses on how to dispose the multi-source uncertain information and promote the testability evaluation and fault diagnosis capability of the electronic equipment, this paper uses fuzzy theory in the uncertain information description and modeling. Based on the fuzzy set description of fuzzy target, new method is proposed to obtain fuzzy evidences from fuzzy fault features, and then, Dempster-Shafer combination rule are used to fuse multi-source fuzzy evidence to get diagnosis results. The proposed method of fuzzy evidence extraction can reduces uncertainties in fusion makings and improves fault identifications, and the fusion diagnosis method based on multi fuzzy evidence matching enhances the precision and reliability of the system fault diagnosis decision furthermore.

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Klein, Lawrence A., Ping Yi, and Hualiang Teng. "Decision Support System for Advanced Traffic Management Through Data Fusion." Transportation Research Record: Journal of the Transportation Research Board 1804, no. 1 (January 2002): 173–78. http://dx.doi.org/10.3141/1804-23.

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The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

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Amin, Md, Md Rahman, Mohammad Hossain, Md Islam, Kazi Ahmed, and Bikash Miah. "Unscented Kalman Filter Based on Spectrum Sensing in a Cognitive Radio Network Using an Adaptive Fuzzy System." Big Data and Cognitive Computing 2, no. 4 (December 17, 2018): 39. http://dx.doi.org/10.3390/bdcc2040039.

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In this paper, we proposed the unscented Kalman filter (UKF) based on cooperative spectrum sensing (CSS) scheme in a cognitive radio network (CRN) using an adaptive fuzzy system—in this proposed scheme, firstly, the UKF to apply the nonlinear system which is used to minimize the mean square estimation error; secondly, an adaptive fuzzy logic rule based on an inference engine to estimate the local decisions to detect a licensed primary user (PU) that is applied at the fusion center (FC). After that, the FC makes a global decision by using a defuzzification procedure based on a proposed algorithm. Simulation results show that the proposed scheme achieved better detection gain than the conventional schemes like an equal gain combining (EGC) based soft fusion rule and a Kalman filter (KL) based soft fusion rule under any conditions. Moreover, the proposed scheme achieved the lowest global probability of error compared to both the conventional EGC and KF schemes.

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Luo, Junhai, and Xiaoting He. "A Soft–Hard Combination Decision Fusion Scheme for a Clustered Distributed Detection System with Multiple Sensors." Sensors 18, no. 12 (December 10, 2018): 4370. http://dx.doi.org/10.3390/s18124370.

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In the distributed detection system with multiple sensors, there are two ways for local sensors to deliver their local decisions to the fusion center (FC): a one-bit hard decision and a multiple-bit soft decision. Compared with the soft decision, the hard decision has worse detection performance due to the loss of sensing information but has the main advantage of smaller communication costs. To get a tradeoff between communication costs and detection performance, we propose a soft–hard combination decision fusion scheme for the clustered distributed detection system with multiple sensors and non-ideal communication channels. A clustered distributed detection system is configured by a fuzzy logic system and a fuzzy c-means clustering algorithm. In clusters, each local sensor transmits its local multiple-bit soft decision to its corresponding cluster head (CH) under the non-ideal channel, in which a simple and efficient soft decision fusion method is used. Between clusters, the fusion center combines all cluster heads’ one-bit hard decisions into a final global decision by using an optimal fusion rule. We show that the clustered distributed system with the proposed scheme has a good performance that is close to that of the centralized system, but it consumes much less energy than the centralized system at the same time. In addition, the system with the proposed scheme significantly outperforms the conventional distributed detection system that only uses a hard decision fusion. Using simulation results, we also show that the detection performance increases when more bits are delivered in the soft decision in the distributed detection system.

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Wang, Haibin, Xin Guan, Xiao Yi, Ying Liu, and Guidong Sun. "A Fusion Recognition Method Based on Temporal Evidence Reasoning." Mathematical Problems in Engineering 2023 (February 15, 2023): 1–14. http://dx.doi.org/10.1155/2023/5873034.

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In order to improve the effectiveness of system decision-making, the use of the evidence theory to identify target intentions has always been a research hotspot. In information fusion using the evidence theory, there are relatively few research studies on temporal domain evidence information fusion. Due to the obvious dynamic, sequential, and real-time characteristics of temporal domain information fusion, traditional spatial domain information fusion methods are not suitable. Therefore, it is very necessary to study new methods for the temporal evidence fusion problem. In this article, a temporal evidence fusion method under the framework of the evidence reasoning rule (the ER rule) is proposed. The method uses complementary reliability integration rules and the time-series evidence distance function to obtain the reliability of evidence at adjacent moments. According to the temporal domain evidence credibility decay model, the evidence weight of the temporal domain evidence is determined. Then, through the integration of the ER rule, the temporal domain evidence reliability and evidence weight are used to combine the evidence. The capability of this method is verified by numerical experiments and compared with other methods. The results show that the proposed method can effectively deal with the temporal domain evidence combination problem, has strong anti-interference ability, and can support target intent recognition.

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Kaliappan, Jayakumar, Revathi Thiagarajan, and Karpagam Sundararajan. "Fusion of Heterogeneous Intrusion Detection Systems for Network Attack Detection." Scientific World Journal 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/314601.

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An intrusion detection system (IDS) helps to identify different types of attacks in general, and the detection rate will be higher for some specific category of attacks. This paper is designed on the idea that each IDS is efficient in detecting a specific type of attack. In proposed Multiple IDS Unit (MIU), there are five IDS units, and each IDS follows a unique algorithm to detect attacks. The feature selection is done with the help of genetic algorithm. The selected features of the input traffic are passed on to the MIU for processing. The decision from each IDS is termed as local decision. The fusion unit inside the MIU processes all the local decisions with the help of majority voting rule and makes the final decision. The proposed system shows a very good improvement in detection rate and reduces the false alarm rate.

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Gao, Xiue, Panling Jiang, Wenxue Xie, Yufeng Chen, Shengbin Zhou, and Bo Chen. "Decision fusion method for fault diagnosis based on closeness and Dempster-Shafer theory." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 12185–94. http://dx.doi.org/10.3233/jifs-210283.

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Decision fusion is an effective way to resolve the conflict of diagnosis results. Aiming at the problem that Dempster-Shafer (DS) theory deals with the high conflict of evidence and produces wrong results, a decision fusion algorithm for fault diagnosis based on closeness and DS theory is proposed. Firstly, the relevant concepts of DS theory are introduced, and the normal distribution membership function is used as the evidence closeness. Secondly, the harmonic average is introduced, and the weight of each evidence is established according to the product of closeness of each evidence and its harmonic average. Thirdly, the weight of conflicting evidence is regularized, and the final decision fusion result is obtained by using the Dempster’s rule. Lastly, the simulation and application examples are designed. Simulation and application results show that the method can effectively reduce the impact of diagnostic information conflicts and improve the accuracy of decision fusion. What’s more, the method considers the overall average distribution of evidence in the identification framework, it can reduce evidence conflicts while preserving important diagnostic information.

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Khan, Md Nazmuzzaman, and Sohel Anwar. "Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion." Sensors 19, no. 21 (November 5, 2019): 4810. http://dx.doi.org/10.3390/s19214810.

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Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster–Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but original DS theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. An effective algorithm offering fusion of highly conflicting information in spatial domain is not widely reported in the literature. In this paper, a successful fusion algorithm is proposed which addresses these limitations of the original Dempster–Shafer (DS) framework. A novel entropy function is proposed based on Shannon entropy, which is better at capturing uncertainties compared to Shannon and Deng entropy. An 8-step algorithm has been developed which can eliminate the inherent paradoxes of classical DS theory. Multiple examples are presented to show that the proposed method is effective in handling conflicting information in spatial domain. Simulation results showed that the proposed algorithm has competitive convergence rate and accuracy compared to other methods presented in the literature.

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Rajavel, R., and P. S. Sathidevi. "Adaptive Reliability Measure and Optimum Integration Weight for Decision Fusion Audio-visual Speech Recognition." Journal of Signal Processing Systems 68, no. 1 (February 2, 2011): 83–93. http://dx.doi.org/10.1007/s11265-011-0578-x.

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Journal articles on the topic 'Optimum decision fusion rule'

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