Considering the ever‐growing demand for network traffic, increasing the capacity of cellular networks has always been a necessity. Heterogeneous cellular networks using small‐scale base stations in addition to macro base stations (BSs) are a low cost and effective solution to this problem. The diversity of the BSs in heterogeneous networks, however, has raised new issues in terms of cell association and interference management compared with the traditional single‐tier networks. Devising novel and effective methods for cell association and resource allocation in these networks is currently an open and developing problem. This paper addresses cell and subband association in the downlink of orthogonal frequency‐division multiple‐access heterogeneous networks. Unlike the existing works in this field, our scheme maximizes network utility while preventing harmful interference for all connections in the network. Using the protocol interference model, necessary and sufficient constraints for preventing interference in cellular networks are presented. By choosing the appropriate interference constraint, the joint problem of cell and subband association is formulated as an integer optimization problem. After simplifying the optimization problem, an equivalent linear problem is obtained. Using one‐level dual decomposition, an iterative algorithm with a near‐optimal solution has been devised. A distributed protocol is then presented, in which each user and each BS requires only its own local data to determine its connection for service. The simulation results demonstrate the algorithm behavior and confirm the efficiency and near‐optimality of the proposed solution.
Handwriting analysis has been addressed by researchers for decades, and many advances were achieved in understanding handwritten texts so far. However, some applications have been rarely discussed. One of these applications that has received less attention is the understanding and analyzing of handwritten circuits. Today, with the widespread use of intelligent tools in engineering and educational processes, the need for new and accurate solutions for processing such handwritings is felt more than ever. This paper presents a new method to analyze handwritten logic circuits. In this method, circuit components are first identified using a deep neural network based on YOLO. Then, the connection among these components is recognized using a new simple boundary tracking method. Then, the binary function related to the handwritten circuit is obtained. Finally, the truth table of the logic circuit is generated. We have also created a set of various handwritten logic circuits called JSU-HWLC. The results of the experiments show the proper performance of the proposed method on the collected dataset. The experiments demonstrated that the YOLO algorithm achieved better results than other deep learning methods such as faster R-CNN, Detectron2, and RetinaNet. For this reason, YOLO has been used to identify logic gates in the proposed system.
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