Active cross-query learning: A reliable labeling mechanism via crowdsourcing for smart surveillance

2020 ◽  
Vol 152 ◽  
pp. 149-154
Author(s):  
Bohan Li ◽  
Anman Zhang ◽  
Weitong Chen ◽  
Hailian Yin ◽  
Ken Cai
Keyword(s):  
Query Learning ◽  
Smart Surveillance

SMART SURVEILLANCE USING ON CLOUD MACHINE LEARNING AND INTERNET CONTROLLED UAVS

2020 ◽  
Vol 9 (6) ◽  
pp. 3789-3795
Author(s):  
A. Ahlawat ◽  
Kabir ◽  
K. Sharma ◽  
K. Pathak ◽  
S. Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Surveillance

Smart Surveillance and Tracking System

2020 ◽  
Author(s):  
Syed Umaid Ahmed ◽  
Hamza Khalid ◽  
Muhammad Affan ◽  
Tauqeer Ali Khan ◽  
Marium Ahmad
Keyword(s):  
Tracking System ◽  
Smart Surveillance

WSN‐ and IoT‐Based Smart Surveillance Systems for Patients with Closed‐Loop Alarm

2022 ◽  
pp. 143-176
Author(s):  
Amit Sundas ◽  
Sumit Badotra ◽  
Shalli Rani ◽  
Chhabildas Madhukar Gajare
Keyword(s):  
Closed Loop ◽  
Surveillance Systems ◽  
Smart Surveillance

scholarly journals Remote Fault-Tolerant Control for Industrial Smart Surveillance System

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Atif Mahmood ◽  
Abdul Qayyum Khan ◽  
Ghulam Mustafa ◽  
Nasim Ullah ◽  
Muhammad Abid ◽  
...  
Keyword(s):  
Surveillance System ◽  
Fault Tolerant ◽  
Sufficient Conditions ◽  
Fault Tolerant Control ◽  
Linear Matrix ◽  
Control Law ◽  
Uncertain Network ◽  
Smart Surveillance ◽  
Communication Links ◽  
Smart Surveillance System

We design a remote fault-tolerant control for an industrial surveillance system. The designed controller simultaneously tolerates the effects of local faults of a node, the propagated undesired effects of neighboring connected nodes, and the effects of network-induced uncertainties from a remote location. The uncertain network-induced time delays of communication links from the sensor to the controller and from the controller to the actuator are modeled using two separate Markov chains and packet dropouts using the Bernoulli process. Based on linear matrix inequalities, we derive sufficient conditions for output feedback-based control law, such that the controller does not directly depend on output, for stochastic stability of the system. The simulation study shows the effectiveness of the proposed approach.

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