The high voltage insulator requires continuous monitoring and inspection to prevent failures and emergencies. Manual inspections are costly as it requires covering a large geographical area where insulators are often subjected to harsh weather conditions. Automatic detection of insulators from aerial images is the first step towards performing real-time classification of insulator conditions using Unmanned Aerial Vehicle (UAV). In this paper, we provide a cost-effective solution for detecting insulators under the conditions of an uncluttered background, varied object resolution and illumination conditions using You Only Look Once (YOLO) deep learning neural network model from aerial images. We apply data augmentation to avoid overfitting with a training set size of 56000 image samples. It is demonstrated experimentally that this method can accurately locate insulator on UAV based real-time image data. The detected insulator images are then successfully subjected to insulator surface condition assessment for the presence of ice, snow and water using different classifiers.

The volume, veracity, variability, and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks, and open problems in the field of neuromemristive circuits for edge computing.

An important application in the growing field of unmanned aerial vehicles (UAVs) is in monitoring and inspection of high voltage power lines and electrical networks. The UAV-based monitoring method will save energy, simplify access to impassable or remote areas, reduce inspection cost, and automate the inspection process. However, the battery capacity of a medium scale drone limits their travel distance and mission duration. It is crucial to improve the energy feeding system of drones and overcome the battery capacity problem to foster the use of drones for routine monitoring operations. In this study, we focus on presenting wireless techniques available for drone mission duration improvement as well as discuss and practically examine the most feasible and reliable technique to charge UAV using power lines.

The on-chip implementation of learning algorithms would speed up the training of neural networks in crossbar arrays. The circuit level design and implementation of a back-propagation algorithm using gradient descent operation for neural network architectures is an open problem. In this paper, we propose analog backpropagation learning circuits for various memristive learning architectures, such as deep neural network, binary neural network, multiple neural network, hierarchical temporal memory, and long short-term memory. The circuit design and verification are done using TSMC 180-nm CMOS process models and TiO₂-based memristor models. The application level validations of the system are done using XOR problem, MNIST character, and Yale face image databases.