Background: Brain tumors are among the most difficult diseases to deal with in modern medicine due to the uncontrolled cell proliferation, which causes grave damage to the nervous system. Brain tumors can be broadly classified into two categories: primary tumors, which originate within the brain, and secondary tumors, which are metastatic in nature. Effective glioma, meningioma, and pituitary tumor diagnosis and treatment requires the precise differentiation of these tumors as well as non-tumors for improved clinical outcomes. Methods: Here, we present a new method to classify brain tumors based on the MobileNetV2 architecture with advanced preprocessing for high accuracy. We accessed an MRI image dataset from Kaggle that contained 1311 images in the test set. We split the data into 80% training and 20% testing. All images underwent extensive preprocessing, including grayscale conversion, noise removal, and contrast-limited-adaptive-histogram equalization (CLAHE). All images were resized to 224 × 224 pixels. Using transfer learning, the baseline frozen layers were kept intact while the top layers were trained with a learning rate of 0.0001, which was tuned to the model’s requirements using early stopping to avoid overfitting. Results: With the outlined methodology, we obtained an astounding accuracy of 99.16%, including strong performance in the no-tumor category, where recall rates were approaching 100% and false positive rates were minimized. Conclusions: These findings strongly indicate that the application of lightweight convolutional neural networks in diagnostic imaging can considerably expedite accurate brain tumor identification by radiologists.

Ultra-Reliable Low-Latency Communication (URLLC) is challenging due to its extremely higher reliability requirement with stringent short latency packet transmission. In order to overcome this reliability and latency bound, a communication access scheme needs to assure almost error-free and high speed packet transmission. In this paper, a new multiple-access scheme—Orthogonal Frequency-Subcarrier-based Multiple Access (OFSMA)—is proposed with URLLC’s high requirement adaptation. In this scheme, the packet diversity concept is incorporated to achieve the expected packet transmission reliability and a diverse number of the duplicated packet are processed with a set of operations and transmitted over randomly selected orthogonal subcarrier frequency channels. Performances of the OFSMA system are measured in terms of applying several numbers of frequency bands, a massive number of subcarrier channels, a different number of packet duplications, and a diverse rate of traffic arrival conditions. We determined the minimum number of subcarrier channels requirement to satisfy the reliability of 99.999% for different packet duplication in presence of different frequency bands. The reliability response for a fixed number of subcarrier channels is evaluated for different frequency band conditions. Finally, the air interface latency of the OFSMA system is measured for single packet uplink transmission and compared with that of a traditional OFDMA system. The performance results in terms of reliability and latency express that the OFSMA scheme can assure the expected reliability and latency defined by URLLC.

In data communication aspects, wireless medium is more popular than wired medium. The MAC protocol performs a vital role in regulating of message communication. Due to reduce number of collision and improving throughput a lot of wireless sensor network MAC protocol has been proposed. However, the main drawbacks of asynchronous MAC protocol are data collision, hidden terminal problem, energy consumption and idle listening. With a view to decreasing data collision and minimize hidden and exposed terminal problem, we propose an improvement of RI-MAC entitled as DURI-MAC. In DURI-MAC, we use dual channel in order to minimize number of hidden and exposed terminal that reduce number of collision and improve network throughput. Moreover, we use a unique CCA time generator that also enhances system throughput. We use OMNET++ as our simulation platform and compare our result with RI-MAC, RCMAC and IRI-MAC. The simulation result shows that DURI-MAC outperforms compare to other MAC in terms of throughput, reduced collision and successful transmission.