Future wireless networks are expected to be dense and employ a higher frequency spectrum such as millimeter wave (mmwave) to support higher data rates. In a dense urban environment, the presence of obstructions causes the transmissions between the user equipment and base stations to transit from line-of-sight (LOS) to non-LOS (NLOS). This transit hence emphasizes the significance of NLOS links for reliable mmwave communication. The work presented in this paper investigates the downlink performance of a mmwave cellular system by modeling the NLOS channel using stretched exponential path loss model (SEPLM) and employing a 3GPP distance-dependent LOS probability function. This path loss model has the inherent ability to define short ranges as well as obstructions in the environment as a function of its parameter resulting in a more realistic performance analysis. The path loss model is first validated for NLOS link using a data set from an open-source mmwave channel simulator. Then, a mathematical model incorporating LOS and NLOS transmissions is developed to study the impact of path loss on signal-to-interference-plus-noise (SINR) coverage probability and area spectral efficiency (ASE). The proposed framework can provide coverage performance indication over various blockage environments. Our results demonstrate that SINR coverage probability decreases exponentially with increasing base station density. Moreover, ASE initially increases with increasing BS density and is maximized for a particular density value, after which it converges to zero for higher densities. The results are also benchmarked with the existing path loss model of mmwave cellular system with different exponents for LOS and NLOS paths. It was observed that as the base station density increases, the SINR degrades more rapidly when using SEPLM as compared to the existing mmwave path loss model.

Cardiovascular diseases (CVDs) are a major cause of global mortality, underscoring the need for intelligent and accessible cardiac health monitoring. This paper proposes a non-wearable Internet-of-Medical-Things (IoMT) system combining real-time sensing, edge processing, and AI-driven diagnostics. Stationary sensors MAX30102 (heart rate, SpO₂) and AD8232 (ECG) interfaced with micro-controller (ESP8266), processes data locally and feeds into the machine learning models trained on UCI Cleveland dataset. Random Forest and XGBoost achieved over 80% accuracy in predicting early cardiac risk. A Flask-SQLite web application provides role-based doctor/patient access, and a Natural Language Processing (NLP)-based interactive chatbot offers personalized guidance. The system delivers scalable, real-time, edge-enabled cardiac diagnostics without relying on wearable devices.

Quadrupedal robots are confronted with the intricate challenge of navigating dynamic environments fraught with diverse and unpredictable scenarios. Effectively identifying and responding to obstacles is paramount for ensuring safe and reliable navigation. This paper introduces a pioneering method for 3D object detection, termed viewpoint feature histograms, which leverages the established paradigm of 2D detection in projection. By translating 2D bounding boxes into 3D object proposals, this approach not only enables the reuse of existing 2D detectors but also significantly increases the performance with less computation required, allowing for real-time detection. Our method is versatile, targeting both bird’s eye view objects (e.g., cars) and frontal view objects (e.g., pedestrians), accommodating various types of 2D object detectors. We showcase the efficacy of our approach through the integration of YOLO3D, utilizing LiDAR point clouds on the KITTI dataset, to achieve real-time efficiency aligned with the demands of autonomous vehicle navigation. Our model selection process, tailored to the specific needs of quadrupedal robots, emphasizes considerations such as model complexity, inference speed, and customization flexibility, achieving an accuracy of up to 99.93%. This research represents a significant advancement in enabling quadrupedal robots to navigate complex and dynamic environments with heightened precision and safety.

Future networks would be dense, reducing the link length between users and the base stations (BSs). Moreover, a higher frequency spectrum, such as millimeter wave (mmWave) will be employed for providing high data rates and capacity but at the cost of increased path loss and blockage. The challenges in a dense network are two-fold: firstly, small link lengths require taking into account the BS height for optimum coverage performance; secondly, to mitigate signal loss at high frequencies, the BS and users must be equipped with antenna arrays. In this work, we derive mathematical expressions for signal-to-interference-plus-noise (SINR) coverage probability for a three-dimensional mmWave network by considering the height of BS and the buildings’ blockage. Uniform cylindrical antenna arrays are employed at BS and user equipment. Results show that there exists a certain BS height for a particular BS density and cell radius at which the coverage probability could be maximized.

Millimeter wave (mmwave) is a potential technology to cater to the data requirements of future cellular networks through its wider spectrum and directional beamforming. With such directional communication, exact alignment of communicating beams is crucial. In this paper, the directivity gains with an antenna model having a Gaussian main lobe is used and the impact of beam alignment error due to random movement of the user are investigated on the uplink system performance of mmwave cellular network. Using stochastic geometry, we have derived Signal-to-Interference-plus-Noise Ratio (SINR) coverage probability. Numerical results show that an optimal number of base station beams exist that maximizes coverage probability at different user velocities and cell radii.