COVID'19 has caused the entire universe to be in existential health crisis by spreading globally in the year 2020. The lungs infection is detected in Computed Tomography (CT) images which provide the best way to increase the existing healthcare schemes in preventing the deadly virus. Nevertheless, separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes in the characteristics of the infection. To resolve these issues, a new inf-Net (Lung Infection Segmentation Deep Network) is designed for detecting the affected areas from the CT images automatically. For the worst segmentation results, the Edge-Attention Representation (EAR) is optimized using Adaptive Donkey and Smuggler Optimization (ADSO). The edges which are identified by the ADSO approach is utilized for calculating dissimilarities. An IFCM (Intuitionistic Fuzzy C-Means) clustering approach is applied for computing the similarity of the EA component among the generated edge maps and Ground-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation (SSS) structure is designed using the Randomly Selected Propagation (RP) technique and Inf-Net, which needs only less number of images and unlabelled data. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed using a Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all the advantages of the disease segmentation done using Semi Inf-Net and enhances the execution of multi-class disease labelling. The newly designed SSMCS approach is compared with existing U-Net++, MCS, and Semi-Inf-Net.
In LTE there is a logical grouping of cells called Tracking Area (TA) and TAs are further grouped into Tracking Area List (TAL). Signaling overhead is greatly affected by the size of the TA and TAL respectively. Designing an optimum TAL would greatly reduce signaling overhead resulting from Tracking Area Update (TAU) and Paging procedures, which in return maximizes the network performance. This paper adopts a 2D Markov model that can be used for design optimization of TAL in LTE system by estimating the number of users in a cell within a time slot and the probability of the next location they might move to, as users move from and into cells periodically. The model was simulated in Matlab simulation software. The 2D Markov model was used to calculate TAU overhead, paging overhead and the total signaling overheads. The numerical results show that our model probably reduces the signaling overhead by about an average of 56% than that of the conventional TA scheme.
The rapid technological developments in the modern era have led to increased electrical equipment in our daily lives, work, and homes. From this standpoint, the main objective of this study is to evaluate the potential relationship between the intensity of electromagnetic radiation and the total energy of household appliances in the living environment within the building by measuring and analyzing the strength of the electric field and the entire electromagnetic radiation flux density of electrical devices operating at frequencies (5 Hz to 1 kHz). The living room was chosen as a center for measurement at 15 homes in three different environmental regions (urban, suburbs, and open areas). The three measurement methods are (Mode 1: people in a sitting position with electrical appliances on. Mode 2: People in a standing position with electrical appliances on. Mode 3: People are in the upright position while turning off the electrical devices) in the living room. These measurement methods and their results reinforce the importance of this research. The results showed that the average electric field strength measured in Mode 2 is much greater than the two methods, and we also found less electromagnetic radiation in Mode 3 than in the two modes. All results remain within the recommended overall exposure developed by the International Committee for the Prevention of Non-Ionizing Radiation and the International Electrotechnical Commission.
The emergence of multimedia services has meant a substantial increase in the number of devices in mobile networks and driving the demand for higher data transmission rates. The result is that, cellular networks must technically evolve to support such higher rates, to be equipped with greater capacity, and to increase the spectral and energy efficiency. Compared with 4G technology, the 5G networks are being designed to transmit up to 100 times more data volume with devices whose battery life is 10 times longer. Therefore, this new generation of networks has adopted a heterogeneous and ultra-dense architecture, where different technological advances are combined such as device-to-device (D2D) communication, which is one of the key elements of 5G networks. It has immediate applications such as the distribution of traffic load (data offloading), communications for emergency services, and the extension of cellular coverage, etc. In this communication model, two devices can communicate directly if they are close to each other without using a base station or a remote access point. Thus, eliminating the interference between the D2D and cellular communication in the network. The interference management has become a hot issue in current research. In order to address this problem, this paper proposes a joint resource allocation algorithm based on the idea of mode selection and resource assignment. Simulation results show that the proposed algorithm effectively improves the system performance and reduces the interference as compared with existing algorithms.
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