Search

Increasing spectrum efficiency in new-generation communication networks is possible by raising the operating frequencies or increasing the number of serving cells. Both of the solutions indirectly lead to a decrease in the cell size. By shrinking the coverage area of the cells, the mobility management becomes a critical issue in order to provide seamless connectivity. The main tool for mobility management in connected mode is the handover procedure, which conventionally is triggered based on the user equipment's (UE's) measurements of serving and neighboring cells' signal quality. The performance of the methods provided in the 3GPP standard for setting up the handover is based on a comparison between the quality of the received signal from the serving cell and the neighbor cells, which is severely sensitive to the appropriate setting of threshold values and waiting time for setting up the handover. The remarkable methods in the literature to improve the handover performance in the 3GPP standard either utilize positional information or are based on complex algorithms. Positional information parameters such as speed, location, direction of movement, etc., need extra measurement modules. Also, complex algorithms like deep learning are not suitable for a wide range of active devices in 5 G networks with limited resources. In this paper, a novel fuzzy logic-based method is proposed to trigger the handover procedure based on estimated serving cell's and neighbor cells' radio link quality (RLQ) values. The proposed system consists of two stages. A second-order regressor beside a simple fuzzy logic system is introduced to predict the serving cell's and neighbor cells' RLQ. The final handover trigger decision is made with the help of another cascade fuzzy logic system, which is responsible for eliminating too-early, too-late, and ping-pong handovers. Considering the uncertainty handling feature of the interval type II fuzzy logic systems versus type I, we implement both methods and compare their results. Finally, we simulate the proposed algorithm using the ns-3 LTE module with a very tight setting for detecting radio link failure to meet 5 G strict standards. Proposed method succeeded in improving the performance of the handover process at high-speed scenarios by 50%, only with the help of radio link quality information. The main advantage of the proposed method is the proper management of the handover procedure, independent of UE's velocity, as well as its simple structure with few rules, which makes it suitable for use in UAV and IoT devices. [ABSTRACT FROM AUTHOR]

Parkinson's disease is one of the most common degenerative conditions that affect people aged 60 and older. The illness is normally diagnosed by clinical indicators that develop as a variety of movement symptoms and medical observations. Conventional diagnostic methods, on the other hand, rely on the detection of small motions, which are notoriously difficult to pin down with absolute precision. This makes it possible for these methods to lend themselves to subjective interpretations. This is because traditional diagnostic methods rely on the interpretation of motions. Image categorization is performed using an altered version of the ResNeXt model in the proposed model. The proposed ResNeXt extends version of ResNet architecture by introducing a new block called the "cardinality block." The cardinality block consists of multiple parallel branches, each with its own set of convolutional layers. These branches are then combined by concatenation before being passed to the next layer. The key idea behind the cardinality block is to increase the capacity of the network without significantly increasing the number of parameters. By using parallel branches with different filter sizes, ResNeXt is able to capture a wider range of features in the input image, leading to better performance on image classification tasks. In order to enhance the performance of the standard ResNeXt model, certain extra dense and dropout layers have been included. The size of the final model is reduced by pruning the model in order to improve the efficiency of the network connections and minimize the overall size of the model. The proposed method is compared to a number of deep learning models that are already in use, and it is shown that the acquired results are superior to those of the deep learning models that are already in use. [ABSTRACT FROM AUTHOR]