Every year, a large amount of population reconciles gun-related violence all over the world. In this work, we develop a computer-based fully automated system to identify basic armaments, particularly handguns and rifles. Recent work in the field of deep learning and transfer learning has demonstrated significant progress in the areas of object detection and recognition. We have implemented YOLO V3 “You Only Look Once” object detection model by training it on our customized dataset. The training results confirm that YOLO V3 outperforms YOLO V2 and traditional convolutional neural network (CNN). Additionally, intensive GPUs or high computation resources were not required in our approach as we used transfer learning for training our model. Applying this model in our surveillance system, we can attempt to save human life and accomplish reduction in the rate of manslaughter or mass killing. Additionally, our proposed system can also be implemented in high-end surveillance and security robots to detect a weapon or unsafe assets to avoid any kind of assault or risk to human life.

Around 300 remote communities in Canada rely on diesel for their energy needs, a situation associated with high costs, high emissions, and accessibility problems. Various studies have addressed this problem by proposing renewable energy microgrids, which have a lot of potential due to the abundance and availability of renewable sources. However, there is a lack of studies regarding remote communities in Newfoundland and combined heat and power microgrids. This study chose Cartwright remote community based on consumption and available resources. Both distributed and centralized microgrid components were designed using MS Excel, Polysun, HOMER, and BEOPT. The final system included solar thermal, PV, wind energy, hydroelectric energy and fuel cells for energy generation, and hydrogen as an energy carrier for storage. The solar thermal distributed system reduced the thermal load by 35%. The microgrid reduced diesel consumption by 71% and CO ₂ emissions by 9000 tons. Renewable sources provided 100% of the electric load and 63.5% of the thermal load. The microgrid achieved a Levelized cost of −0.0245 $ \$ $ /kWh, which is only possible for combined heat and power systems.

In everyday life, electricity is necessary, and proper use is critical. To strengthen home electricity control, the existing systems have been examined over the years. However, the existing PMAS method’s error ratio is higher and does not allow for a remote monitoring system. Therefore, this study proposes a smart monitoring and control system (SMACS) for household appliances. The application’s significance is to monitor household appliances’ electricity usage using hardware and the Internet of Things (IoT) methods. The prototype of the proposed system is designed and developed considering Arduino UNO, a liquid crystal display (LCD), an ACS712 current sensor module, relays, and AC sources. The components are selected from the software library, and the simulation results are found the same as the prototype. WiFi module ESP8266 is not included in the design because it is not provided in the system. The data is recorded in cloud storage using Thing‐speak. A mobile application (Virtuino) also accesses the data to visualize it through the graphical and numerical display. This study provides users with an easy system to monitor and control household appliances’ power consumption using mobile applications. Results show that the proposed system provides 0.6% current errors for the hairdryer appliance, whereas the existing Power Monitoring and Switching (PMAS) system provides 7.8% current errors.