Parkinson’s Disease (PD) is a neural system disorder that disturbs the mental activities and physical activities of human beings. Analyzing the symptoms and biosignal data of PD is crucially focused in medical research fields. The existing PD diagnosis models are limited to real-time issues, insufficient deep data extraction, and early monitoring problems. On the scope, the proposed Optimal Health Support and PD Analysis System (OHPAS) analyses the symptoms of PD using a deeply trained biosensors network environment. The novel system trains the biosensor network using complex Machine Learning (ML) and Deep Learning (DL) approaches. The environment of OHPAS sets up acoustic sensors (UT-PF), microphones (MC-1500 unit), and multimodal sensor units (MC-10 sensor). MC-10 is the sensor suite that has an accelerometer sensor, gyro sensor, and Electro Cardio Gram (ECG) sensor to observe the biosignals. For establishing the biodata analysis framework, OHPAS initiates the fusion of Variable Auto Encoder (VAER) and K-Means clustering techniques. This phase comprises dataset feature reduction, data regularization, and clustering operations to make the dataset effective for the training process. Finally, the Long Short Term Memory network (LSTM) uses the preprocessed dataset for computing the training dataset. The proposed OHPAS contributes novel features such as a real-time patient monitoring environment, effective sensor data reduction, distributed sensor data analysis, day-wise PD symptom prediction, reactive PD alerts, and accurate early detection solutions. Considering effective medical data analysis with minimal response time, the proposed model creates reactive body sensor network. Under this sensor platform, sensor modules contain proposed DL procedures in its internal memory for initiating data analysis practices. Consequently, the symptoms of PD are commendably detected and predicted with minimal response time. The experimental results indicate the proposed PD system outperforms the existing systems with 8% to 10% of better results.

With the increasing importance of reduced-energy wireless communication in 5G networks, maximizing the energy efficiency of device-to-device (D2D) communication has gained a lot of research interest. But so far, the methods have only been able to address the single-cell scenario. This paper proposes an algorithm to improve the non-convex problem in underlaid D2D communication for multiple band scenarios. In 5G wireless communications, device to device is one of the methods used for achieving better energy efficiency. It also reduces the throughput latency. The optimization problem is formulated with a derivative algorithm and proposed modified derivative algorithm. Both the algorithms are compared, and this comparison shows that the modified derivative algorithm is more efficient than the derivative algorithm.

Generalized frequency division multiplexing (GFDM) has emerged as a quintessential waveform candidate poised to meet the stringent requirements of next-generation wireless communication frameworks, addressing pivotal 5G physical layer challenges through its flexible, adaptive structure and spectral efficiency. Despite its numerous advantages, GFDM, akin to other multi-carrier schemes, is afflicted by an inherent high peak-to-average power ratio (PAPR), which can degrade power efficiency and impair system performance under high-power amplifier constraints. In this paper, we rigorously derive the complementary cumulative distribution function (CCDF) for GFDM systems, elucidating its intricate dependence on the design and characteristics of pulse-shaping filters, which are instrumental in managing spectral and temporal properties. Furthermore, we introduce an optimized non-linear companding scheme, specifically calibrated to enhance PAPR performance in GFDM systems by tailoring key parameters to achieve a balance between signal integrity and power efficiency. Extensive simulations demonstrate that the proposed approach, in conjunction with refined pulse-shaping filter configurations, yields substantial enhancements in PAPR reduction, outperforming conventional methods within the GFDM framework. To substantiate the theoretical and simulated outcomes, we have developed an experimental testbed leveraging the universal software radio peripheral (USRP) and LabVIEW environment, providing empirical validation and underscoring the real-world applicability of our proposed technique.

Videonystagmography (VNG) is a diagnostic technique that utilizes infrared video goggles to record eye movements and assess the vestibulo-ocular reflex (VOR) by tracking involuntary eye movements such as nystagmus and saccades. In recent years, eye-tracking technologies have gained attention in educational research for understanding learner engagement and cognitive processing. The novelty of this study lies in the application of clinical-grade VNG technology to quantitatively correlate saccadic eye movement parameters with academic performance in engineering students, an area that remains largely unexplored. An investigation was carried out to analyze the relationship between VNG parameters and the academic performance of undergraduate engineering students. A total of 36 students (18 males and 18 females), aged 19–20 years, participated in the study. Performance scores were computed as the average of six theory subjects. The VNG parameters analyzed included velocity, precision, and latency of horizontal and vertical saccades for both eyes. Statistical analysis revealed that saccadic velocity, precision, and latency are mutually independent parameters, while strong correlations exist between the left and right eyes. Regression analysis showed that vertical saccadic latencies of both eyes significantly predict academic performance, while horizontal latency of the right eye also showed statistical significance. Gender was found to significantly influence most eye-tracking parameters. Although conducted in a controlled laboratory environment with a limited sample size, this study demonstrates the potential of VNG-based eye-tracking metrics as objective indicators of cognitive processing in engineering education, offering new directions for technology-enabled educational assessment.

Among IoT networks, Wireless Sensor Networks (WSNs) play a very important role. The wireless WSN has a crucial energy efficiency vs security (reliability) tradeoff when routing data; The protocols usually don't analyze data importance vs reliable behavior of a node and would cause early collapse of a WSN and security attacks. This article presents SAGT (Semantic-Aware Graph Transformer) in an end-to-end framework that aims to perform data routing and trust estimation at the same time. By injecting the data-oriented semantic contexts, e.g., data urgency and node role in the network, into the multi-head graph attention, SAGT could select better nodes and bypass the untrustworthy nodes/resource exhausted nodes. Experimental simulations confirmed the framework's higher performance; the introduced model attains a 94.8% PDR and a 15% reduction in the average energy consumption per packet against state-of-the-art ones. Furthermore, the framework reaches 91.4% accuracy on the detection of the adversarial nodes, effectively isolating the Byzantine attacks from the graph network. This demonstrates that semantic-aware attention significantly overcomes semantic-insensitive routing with respect to performance and efficiency, as it guarantees a flexible, adaptable, yet reliable method that fully supports the deployment of future intelligent and mission-critical networked applications by narrowing the communication-efficiency semantics gap. This approach to cross-layer optimization represents a landmark for future self-healing wireless networks and ultra-reliable networks.