Enhancing Near-Field Wireless Localization with LiDAR-Assisted RIS in Multipath Environments

In Next-Generation Wireless Networks that Adopt Millimeter-Waves and Large RIS, the User is Expected to Be in the Near-Field Region, Where the Widely Adopted Far-Field Algorithms based on Far-Field Can Yield Low Positioning Accuracy. Also, the Localization of UE Becomes More Challenging in Multipath Environments. in This Paper, We Propose a Localization Algorithm for a UE in the Near-Field of a RIS in Multipath Environments. the Proposed Scheme Utilizes a LiDAR to Assist the UE Positioning by Providing Geometric Information About Some of the Scatterers in the Environment. This Information is Fed to a Sparse Recovery Algorithm to Improve the Localization Accuracy of the UE by Reducing the Number of Variables (I.e., Angle of Arrivals and Distances) to Be Estimated. the Numerical Results Show that the Proposed Scheme Can Improve the Localization Accuracy by 65% Compared to the Standard CS Scheme

Single-Snapshot Localization for Near-Field Ris Model using Atomic Norm Minimization

Reconfigurable intelligent surfaces (RISs) are expected to play a significant role in the next generation of wireless cellular technology. This paper proposes an uplink localization scheme using a single-snapshot solution for user equipment (UE) that is located in the near-field of the RIS. We propose utilizing the atomic norm minimization method to achieve super-resolution localization accuracy. We formulate an optimization problem to estimate the UE location parameters (i.e., angles and distances) by minimizing the atomic norm. Then, we propose to exploit strong duality to solve the atomic norm problem using the dual problem and semidefinite programming (SDP). The RIS is controlled and designed using estimated parameters to enhance the beamforming capabilities. Finally, we compare the localization performance of the proposed atomic norm minimization with compressed sensing (CS) in terms of the localization error. The numerical results show a superior performance of the proposed atomic norm method over the CS where a sub-cm level of accuracy can be achieved under some of the system configuration conditions using the proposed atomic norm method

Patients Arms Segmentation And Gesture Identification Using Standalone 3D LiDAR Sensors

The intelligent and autonomous learning of patients\u27 activities will lead to an incredible progression toward future smart e-health systems. With the recent advances in artificial intelligence, signal processing, and computational capabilities; light detection and ranging (LiDAR) technology can play a significant role in enhancing the current patients\u27 activity recognition (PAR) systems. In this paper, we propose confidential and accurate patient arms behavior monitoring using a standalone three-dimensional (3D) LiDAR sensor. Due to the unavailability of LiDAR data, we use a computer-programmed 3D simulator to generate virtual-LiDAR (V-LiDAR) 3D point cloud data that simulates real patient movements. These virtual data are used to train a multi-layer-perception (MLP) model to segment the data points of the patient\u27s body into arms versus not arms. We further propose a sub-segmentation technique to segment patient\u27s arms point cloud data into upper or lower arms. Finally, we demonstrate uses of arms gesture identification using the proposed scheme. The numerical results show that the proposed MLP model achieves a test accuracy of $90.8\%$ and a cross-validation accuracy of $87.4\%$

Unmanned-Aircraft-System-Assisted Early Wildfire Detection with Air Quality Sensors †

Numerous Hectares of Land Are Destroyed by Wildfires Every Year, Causing Harm to the Environment, the Economy, and the Ecology. More Than Fifty Million Acres Have Burned in Several States as a Result of Recent Forest Fires in the Western United States and Australia. According to Scientific Predictions, as the Climate Warms and Dries, Wildfires Will Become More Intense and Frequent, as Well as More Dangerous. These Unavoidable Catastrophes Emphasize How Important Early Wildfire Detection and Prevention Are. the Energy Management System Described in This Paper Uses an Unmanned Aircraft System (UAS) with Air Quality Sensors (AQSs) to Monitor Spot Fires Before They Spread. the Goal Was to Develop an Efficient Autonomous Patrolling System that Detects Early Wildfires While Maximizing the Battery Life of the UAS to Cover Broad Areas. the UAS Will Send Real-Time Data (Sensor Readings, Thermal Imaging, Etc.) to a Nearby Base Station (BS) When a Wildfire is Discovered. an Optimization Model Was Developed to Minimize the Total Amount of Energy Used by the UAS While Maintaining the Required Levels of Data Quality. Finally, the Simulations Showed the Performance of the Proposed Solution under Different Stability Conditions and for Different Minimum Data Rate Types