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Resource allocation in visual sensor networks using a reinforcement learning framework.

, , , and . DSP, page 1-6. IEEE, (2013)

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Geometric Bargaining Approach for Optimizing Resource Allocation in Wireless Visual Sensor Networks., , and . IEEE Trans. Circuits Syst. Video Techn., 23 (8): 1388-1401 (2013)On the improvement of no-reference mean opinion score estimation accuracy by following a frame-level regression approach., , , and . ICIP, page 1850-1854. IEEE, (2015)Fairness issues in resource allocation schemes for wireless visual sensor networks., , and . Visual Information Processing and Communication, volume 8666 of SPIE Proceedings, page 866601. SPIE, (2013)A novel cumulative distortion metric and a no-reference sparse prediction model for packet prioritization in encoded video transmission., , , and . ICIP, page 2097-2101. IEEE, (2016)Quality Assessment of Single-Channel EEG for Wearable Devices., , , , , , and . Sensors, 19 (3): 601 (2019)Optimal power allocation and joint source-channel coding for wireless DS-CDMA visual sensor networks using the Nash Bargaining Solution., , and . ICASSP, page 2340-2343. IEEE, (2011)Game-theoretic solutions through intelligent optimization for efficient resource management in wireless visual sensor networks., , , and . Sig. Proc.: Image Comm., 29 (4): 472-493 (2014)Optimal power allocation and joint source-channel coding for wireless DS-CDMA visual sensor networks., , and . Visual Information Processing and Communication, volume 7882 of SPIE Proceedings, page 788206. SPIE, (2011)Resource allocation in visual sensor networks using a reinforcement learning framework., , , and . DSP, page 1-6. IEEE, (2013)A study on visual sensor network cross-layer resource allocation using quality-based criteria and metaheuristic optimization algorithms., , and . Appl. Soft Comput., (2015)