Relay selection based clustering techniques for high density LTE networks

In very crowded areas, a large number of LTE users contained in a single cell will try to access services at the same time causing high load on the Base Station (BS). Some users may be blocked from getting their requested services due to this high load. Using a two-hop relay architecture can help in increasing the system capacity, increasing coverage area, decreasing energy consumption, and reducing the BS load. Clustering techniques can be used to configure the nodes in such two-layer topology. This paper proposes a new algorithm for relay selection based on the Basic Sequential Algorithmic Scheme (BSAS) along with power control protocol. Unlike other capacity improving techniques such as small cells and relay stations this approach does not require additional infrastructure. Instead, users themselves will act as a temporary relay stations. Modifications are implemented to the original BSAS to make it suitable for LTE environment and to improve its performance. The protocol for resource allocation and power control is implemented assuming a multi cell scenario. The algorithm is compared to other relaying and clustering schemes in addition to the conventional LTE. The simulation results show that the proposed algorithm has improved system capacity and energy consumption compared to other existing clustering/relaying schemes

Effectiveness of Machine Learning in Assessing the Diagnostic Quality of Bitewing Radiographs

Background: Identifying the diagnostic value of bitewing radiographs (BW) is highly dependent on the operator’s knowledge and experience. The aim of this study is to assess the effectiveness of machine learning (ML) to classify the BW according to their diagnostic quality. Methods: 864 BW radiographs from records of 100 patients presented at King Abdulaziz University Dental Hospital, Jeddah, Saudi Arabia were assessed. The radiographic errors in representing proximal contact areas (n = 1951) were categorized into diagnostic and non-diagnostic. Labeling and training of the BW were done using Roboflow. Data were divided into validation, training, and testing sets to train the pre-trained model Efficientdet-d0 using TensorFlow. The model’s performance was assessed by calculating recall, precision, F1 score, and log loss value. Results: The model excelled at detecting “overlap within enamel” and “overlap within restoration (clear margins) with F1 score of 0.89 and 0.76, respectively. The overall system errors made by the built model showed a log loss value of 0.15 indicating high accuracy of the model. Conclusions: The model is a “proof of concept” for the effectiveness of ML in diagnosing the quality of the BW radiographs based on the contact areas. More dataset specification and optimization are needed to overcome the class imbalance