Reliable, smooth, and fault free speed control of a Permanent Magnet (PM) DC motor using an H-bridge is an important need for many industrial applications such as robotics, automotive, and process industry to improve the overall efficiency and productivity. The reliability of H-bridge depends on the semiconductor switches used. The faults in these components can lead to a complete failure of the system. This paper presents a dual redundancy-based fault-tolerant system with a Fault Detection and Isolation (FDI) unit that can detect, isolate, and replace the faulty switch with the standby to prevent the unwanted shut down of the system and support the process continuity thereby increasing reliability. MATLAB/Simulink environment was used for simulation experiments and the results demonstrate the stable operation of the motor in the events of faults while maintaining its speed. The presented work establishes that the dual redundancy-based fault-tolerant H-bridge with the FDI unit is a highly reliable solution for the speed control of a DC motor.

Weapon detection in CCTV camera surveillance videos is a challenging task and its importance is increasing because of the availability and easy access of weapons in the market. This becomes a big problem when weapons go into the wrong hands and are often misused. Advances in computer vision and object detection are enabling us to detect weapons in live videos without human intervention and, in turn, intelligent decisions can be made to protect people from dangerous situations. In this article, we have developed and presented an improved real-time weapon detection system that shows a higher mean average precision (mAP) score and better inference time performance compared to the previously proposed approaches in the literature. Using a custom weapons dataset, we implemented a state-of-the-art Scaled-YOLOv4 model that resulted in a 92.1 mAP score and frames per second (FPS) of 85.7 on a high-performance GPU (RTX 2080TI). Furthermore, to achieve the benefits of lower latency, higher throughput, and improved privacy, we optimized our model for implementation on a popular edge-computing device (Jetson Nano GPU) with the TensorRT network optimizer. We have also performed a comparative analysis of the previous weapon detector with our presented model using different CPU and GPU machines that fulfill the purpose of this work, making the selection of model and computing device easier for the users for deployment in a real-time scenario. The analysis shows that our presented models result in improved mAP scores on high-performance GPUs (such as RTX 2080TI), as well as on low-cost edge computing GPUs (such as Jetson Nano) for weapon detection in live CCTV camera surveillance videos.