This paper presents a new methodology based on texture and color for the detection and monitoring of different sources of forest fire smoke using unmanned aerial vehicles (UAVs). A novel dataset has been gathered comprised of thin smoke and dense smoke generated from the dry leaves on the floor of the forest, which is a source of igniting forest fires. A classification task has been done by training a feature extractor to check the feasibility of the proposed dataset. A meta-architecture is trained above the feature extractor to check the dataset viability for smoke detection and tracking. Results have been obtained by implementing the proposed methodology on forest fire smoke images, smoke videos taken on a stand by the camera, and real-time UAV footages. A microaverage F1-score of 0.865 has been achieved with different test videos. An F1-score of 0.870 has been achieved on real UAV footage of wildfire smoke. The structural similarity index has been used to show some of the difficulties encountered in smoke detection, along with examples.

In recent times, multi-phase induction motor drives have attracted attention where high overall device performance and a decrease in total power per phase are desired. The extra phases in conjunction with normal 3-phase drive ensure that the mechanism continues to function without torque ripple, disturbance and vibration even under unstable circumstances. To overcome this problem, a suitable terminal sliding mode control system may achieve a ripple-less high torque in a five-phase induction motor. The current control loop offers significant benefits to enhance the stability of the drive system. With outstanding transient responses, this device can conduct strong dynamic current management. The Terminal Sliding Mode Control based on MPCC is used to optimize the motor control system. Whereas, HOSM is introduced to eliminate the chattering phenomenon.