Joint downlink user association and interference avoidance with a load balancing approach in backhaul-constrained HetNets.

In heterogeneous networks (HetNets), different lower-power base stations are added in a typically unplanned manner to the well-planned macro-only network, bringing new challenges to the network functions. Small cells experience limited backhaul capacity since cost-effective backhaul is not easily accessible to them. This study focuses on the issue of user association in backhaul-constrained HetNets.

Green internet of things and solar energy.

The Internet of Things (IoT) stands out as one of the most captivating technologies of the current decade. Its ability to connect people and things anytime and anywhere has led to its rapid expansion and numerous impactful applications that enhance human life. With billions of connected devices and substantial power and infrastructure requirements, the IoT system can pose a threat to the environment.

Analytical study of two feature extraction methods in comparison with deep learning methods for classification of small metal objects.

This paper addresses the efficiency of two feature extraction methods for classifying small metal objects including screws, nuts, keys, and coins: the histogram of oriented gradients (HOG) and local binary pattern (LBP). The desired features for the labeled images are first extracted and saved in the form of a feature matrix. Using three different classification methods (non-parametric K-nearest neighbors algorithm, support vector machine, and naïve Bayesian method), the images are classified into four different classes.