scholarly journals Inherence of Hard Decision Fusion in Soft Decision Fusion and a Generalized Radix-2 Multistage Decision Fusion Strategy

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 55701-55711
Author(s):  
Kamlesh Gupta ◽  
Shabbir N. Merchant ◽  
Uday B. Desai
Keyword(s):  
Decision Fusion ◽  
Soft Decision ◽  
Fusion Strategy ◽  
Hard Decision

scholarly journals A Soft–Hard Combination Decision Fusion Scheme for a Clustered Distributed Detection System with Multiple Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4370 ◽  
Author(s):  
Junhai Luo ◽  
Xiaoting He
Keyword(s):  
Detection System ◽  
Decision Fusion ◽  
Detection Performance ◽  
Distributed Detection ◽  
Fusion Center ◽  
Soft Decision ◽  
Multiple Sensors ◽  
Fusion Scheme ◽  
Hard Decision ◽  
Centralized System

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.

scholarly journals Robust Spectrum Sensing via Double-Sided Neighbor Distance Based on Genetic Algorithm in Cognitive Radio Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Noor Gul ◽  
Muhammad Sajjad Khan ◽  
Junsu Kim ◽  
Su Min Kim
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Decision Fusion ◽  
Radio Networks ◽  
Majority Voting ◽  
Soft Decision ◽  
Neighbor Distance ◽  
Hard Decision

In cognitive radio networks (CRNs), secondary users (SUs) can access vacant spectrum licensed to a primary user (PU). Therefore, accurate and timely spectrum sensing is vital for efficient utilization of available spectrum. The sensing result at each SU is unauthentic due to fading, shadowing, and receiver uncertainty problems. Cooperative spectrum sensing (CSS) provides a solution to these problems. In CSS, false sensing reports at the fusion center (FC) received from malicious users (MUs) drastically degrade the performance of cooperation in PU detection. In this paper, we propose a robust spectrum sensing scheme to minimize the effects of false sensing reports by MUs. The proposed scheme focuses on double-sided neighbor distance (DSND) based on genetic algorithm (GA) in order to filter out the MU sensing reports in CSS. The simulation results show that the sensing results are more accurate and reliable for the proposed GA majority-voting hard decision fusion (GAMV-HDF) and GA weighted soft decision fusion (GAW-SDF) compared to conventional equal gain combination soft decision fusion (EGC-SDF), maximum gain combination soft decision fusion (MGC-SDF), and majority-voting hard decision fusion (MV-HDF) schemes in the presence of MUs.

An Application of Soft Decision for Context Recognition on Reading

2013 ◽  
Vol 791-793 ◽  
pp. 1945-1948
Author(s):  
Yung Fa Huang ◽  
Bang Han Tsai ◽  
Ching Mu Chen
Keyword(s):  
External Noise ◽  
Context Aware ◽  
Soft Decision ◽  
Accuracy Rate ◽  
Context Recognition ◽  
Moving Windows ◽  
Context Aware System ◽  
Hard Decision

In this paper, a context aware system is investigated for reading. The experiments are performed by two ultrasound sensors to obtain the users three scenarios of study, relax and nap. In this paper, we proposed soft decision (SD) method for three contexts to improve the accuracy rate of contexts recognition to 93% comparing with the 78% of hard decision (HD) method. In addition, to remove the external noise or interference, the moving windows (MW) are proposed to further improve the accuracy rate to 98%.

DMMs-Based Multiple Features Fusion for Human Action Recognition

2015 ◽  
Vol 6 (4) ◽  
pp. 23-39 ◽  
Author(s):  
Mohammad Farhad Bulbul ◽  
Yunsheng Jiang ◽  
Jinwen Ma
Keyword(s):  
Action Recognition ◽  
Recognition Performance ◽  
Recognition Task ◽  
Human Action Recognition ◽  
Fusion Rule ◽  
Local Binary Patterns ◽  
Human Action ◽  
Decision Fusion ◽  
Soft Decision ◽  
Depth Sensors

The emerging cost-effective depth sensors have facilitated the action recognition task significantly. In this paper, the authors address the action recognition problem using depth video sequences combining three discriminative features. More specifically, the authors generate three Depth Motion Maps (DMMs) over the entire video sequence corresponding to the front, side, and top projection views. Contourlet-based Histogram of Oriented Gradients (CT-HOG), Local Binary Patterns (LBP), and Edge Oriented Histograms (EOH) are then computed from the DMMs. To merge these features, the authors consider decision-level fusion, where a soft decision-fusion rule, Logarithmic Opinion Pool (LOGP), is used to combine the classification outcomes from multiple classifiers each with an individual set of features. Experimental results on two datasets reveal that the fusion scheme achieves superior action recognition performance over the situations when using each feature individually.

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