Coverage Efficiency Research Articles

A Comprehensive Strategy for Implementing Adaptive Data Rate (ADR) in LoRaWAN Technology: Optimizing IoT Networks

The Internet of Things (IoT) is increasingly essential for creating smart environments by establishing long range connectivity among devices. LoRaWAN is a prominent technology choice for these applications due to its energy efficiency and extensive coverage. Central to LoRaWAN’s functionality is the Adaptive Data Rate (ADR) mechanism, which optimizes network performance by adjusting each device’s data rate and transmission power dynamically. This study delves into ADR’s operational principles, benefits, and implementation challenges in LoRaWAN networks. The proposed simulation model, featuring configurable parameters like the spreading distribution factor (SF), the adjustable number of devices, and the SNR threshold, denotes enhanced network performance after implementing the ADR algorithm. With an SNR threshold of 15 dB, ADR reduced the average transmission power of 200 devices from 7 dBm to 3 dBm after twenty iterations, underscoring ADR’s role in minimizing energy consumption while improving network efficiency.

UHPLC-QTOFMS Urine Drug Screening With Dilute-and-Shoot Sample Preparation and Vacuum-Insulated Probe-Heated Electrospray Ionization.

We developed a method for comprehensive urine drug screening by applying dilute-and-shoot extraction and vacuum-insulated probe-heated electrospray ionization with ultra-high performance liquid chromatography high-resolution quadrupole time-of-flight mass spectrometry (DS-UHPLC-VIP-HESI-QTOFMS). The method involved five-fold post-hydrolysis dilution of urine samples and chromatography on a C18 UHPLC column prior to QTOFMS analysis. The recently introduced VIP-HESI ion source was chosen due to its enhanced ionization efficiency and compatibility with UHPLC-QTOFMS. Extensive data was acquired in positive ion mode with a low collision energy (7 eV) and an elevated collision energy (30 eV), using the broadband collision-induced dissociation data acquisition scan mode that continuously generated high-resolution and accurate mass for parent and fragment qualifier ions, and parent ion isotopic patterns. Compound identification was performed against an in-house database with 1263 compound entries, using an automated post-run reverse target database search with preset identification criteria. Method validation with 56 different drugs showed acceptable results for the limit of identification (median 5 ng/mL), matrix effects (70-130%), repeatability of retention times (< 1%), mass accuracy (< 1 mDa), as well as for specificity and stability. As compared with an established UHPLC-QTOFMS method relying on solid-phase extraction and conventional electrospray ionization, DS-UHPLC-VIP-HESI-QTOFMS produced comparable results from authentic clinical urine samples for most drugs, but showed clearly improved detectability for pregabalin, gabapentin, and ritalinic acid. We anticipate that the new method will be a step forward for laboratories performing routine urine drug screening due to its fast turnaround time, reduced manual workload, cost efficiency, and broad substance coverage.

Combustion performance of an evaporative flameholder under subsonic-supersonic mixing inflow

The combustion process in rocket-assisted subsonic ramjet engines represents a key advancement in integrated aerospace propulsion, particularly for embedded rocket-based systems. These engines offer the potential to improve combustion performance at altitudes of 25–35 km. However, the significant temperature and velocity differentials between the rocket jet and the subsonic ramjet flow restrict heat and mass transfer. Investigating the relationship between combustion performance and inlet parameters under subsonic-supersonic mixing conditions offers a promising approach to enhancing thrust performance. This study introduces subsonic and supersonic airflow mixing via a flat-plate shear layer in a rectangular channel, with an evaporative flameholder placed centrally to assess combustion. Results reveal that combustion efficiency decreases as the equivalence ratio exceeds 0.2, while the static temperature ratio has minimal impact on efficiency but strongly influences the maximum flame stabilization limit. As the temperature ratio increases from 1.30 to 1.80, the flame limit narrows from 1.656 to 0.237. Higher pressure ratios initially enhance combustion efficiency and flame coverage but eventually cause a decrease. The flame limit broadens from 0.900 to 1.626 as the pressure ratio increases from 1.12 to 1.50. While Mach number changes have little effect on efficiency, the flame limit exhibits an initial rise followed by a drop. Novel findings include an asymmetrical flame pattern and a “Z” shaped outlet temperature distribution, contributing to optimized combustion strategies for combined-cycle engines.

Impact of Geometric Forms on the Effectiveness and Physical Features of POD-based Geometric Parameterization

Effective geometric parameterization is crucial in aerodynamic shape optimization for enabling flexible surface deformation while maximizing design space coverage. This paper studies the impact of different geometric forms (closed and open curves) on the effectiveness of Proper Orthogonal Decomposition (POD) geometric parameterization and explores the information of physical features contained in the POD bases. The efficiency and design space coverage which are the main indicators of effectiveness. These indicators of two POD-based parameterization methods are tested on a hybrid database and four typical airfoils. The convergence of aerodynamic properties is also investigated in typical airfoils. In addition, a modified application criterion for POD bases is established by the recursive feature elimination method. A feature importance-based explainable AI (xAI) method is also developed, combining Shapley additive explanations (SHAP) and modified application criterion to explore the physical information contained in POD bases. The results indicate that the POD-based parameterization method constructed from open curves can reconstruct database with better efficiency and design space coverage. Additionally, compared to geometric errors, more POD bases may be required to cover the airfoil design space in order to achieve superior aerodynamic accuracy. Significant differences exist in the importance and correlation of POD bases derived from different geometric forms, with those from closed curves providing better reconstruction efficiency for geometric parameters.

Distributed UAV swarms for 3D urban area coverage with incomplete information using event-triggered hierarchical reinforcement learning

This paper addresses the problem of distributed 3D area coverage with multiple UAVs under incomplete information. It proposes a leader-follower UAV framework that integrates pheromone-based coverage and reinforcement learning dispatching. The 3D space is segmented for dimensionality reduction, and pheromone matrices enable dynamic area coverage. To reduce redundant coverage and achieve multi-layered coordination, the leader UAV uses a virtual decision mechanism and a communication neural network to handle local observations and asynchronous scheduling challenges. Additionally, an event-triggered reinforcement learning is introduced to minimize communication costs and enhance system robustness. Pheromone-based implicit teamwork addresses sparse rewards and improves coverage efficiency. This modular framework enhances general applicability and significantly reduces overall coverage time.

Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (Brassica rapa subsp. Pekinensis) Plants

The exploration of the impact of different spatial scales on the low-altitude remote sensing identification of Chinese cabbage (Brassica rapa subsp. Pekinensis) plants offers important theoretical reference value in balancing the accuracy of plant identification with work efficiency. This study focuses on Chinese cabbage plants during the rosette stage; RGB images were obtained by drones at different flight heights (20 m, 30 m, 40 m, 50 m, 60 m, and 70 m). Spectral sampling analysis was conducted on different ground backgrounds to assess their separability. Based on the four commonly used vegetation indices for crop recognition, the Excess Green Index (ExG), Red Green Ratio Index (RGRI), Green Leaf Index (GLI), and Excess Green Minus Excess Red Index (ExG-ExR), the optimal index was selected for extraction. Image processing methods such as frequency domain filtering, threshold segmentation, and morphological filtering were used to reduce the impact of weed and mulch noise on recognition accuracy. The recognition results were vectorized and combined with field data for the statistical verification of accuracy. The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. The method proposed in this paper is beneficial for agricultural growers and managers in carrying out precision planting management and planting structure optimization analysis and can aid in the timely adjustment of planting density or layout to improve land use efficiency and optimize resource utilization.

Installation of Public Access Defibrillators for Out-of-Hospital Cardiac Arrests: Identifying Suitable Locations by Using a Geographic Information System.

Survival following an out-of-hospital cardiac arrest depends on prompt defibrillation. Despite the efforts made to install automated external defibrillators (AEDs) in crowded areas, their usage rate remains suboptimal. This study evaluated the efficiency of installing AEDs at key landmarks in Taoyuan City to enhance accessibility and usage. This retrospective cohort study analyzed nontraumatic public out-of-hospital cardiac arrest cases in Taoyuan City from 2017 to 2021, using data from the Taoyuan Fire Department and a regional registry. AED data were collected for 1163 devices. A geographic information system mapped target locations within the city, and real-world walking routes were examined to assess coverage. The primary outcome was actual coverage and the coverage efficiency ratio, calculated as the actual coverage divided by the number of facilities at a location. The coverage efficiency ratio compared the coverage efficiency of target locations with existing public access defibrillators (PADs). Top locations for superior coverage in both downtown and outside downtown areas were bus stops and convenience stores (7-Eleven and FamilyMart), which outperformed existing PADs. Convenience stores had a higher coverage efficiency ratio than the public service sector. Bus stops showed high AED usage rates before ambulance arrival. The current PAD locations in Taoyuan City offer limited coverage, which highlights the need for strategically installed AEDs, particularly in convenience stores. Policymakers should consider using the cultural relevance and accessibility of convenience stores, particularly 7-Eleven branches, to enhance AED usage rates.

Safety hazard inspection system during typhoon warning period to improve the disaster resistance ability of road and bridge construction site

Typhoons are frequent in coastal cities, and the vulnerability of road and bridge construction sites is high, but existing studies have not focused on the safety management of road and bridge construction sites under typhoon disasters. Traditional manual safety inspections suffer from problems such as low efficiency, high subjectivity, and incomplete coverage. Therefore, this study aims to develop an intelligent safety management system for road and bridge construction sites during the typhoon warning period, in order to achieve rapid and automated typhoon prevention and control. In this study, the automatic identification algorithm of potential safety hazards was developed based on the MATLAB platform, and then the system design of the MATLAB GUI was carried out, which ultimately forms the automatic safety hazard inspection and measure enquiry system during the typhoon warning period for road and bridge construction sites (RBS-TY). The system can realize the detection of vertical deformation, safety distance and safety height by using the point cloud data of the object to be measured. According to the identified safety hazards, it will automatically retrieve the database of typhoon safety measures in the road and bridge construction site and provide the countermeasures in the GUI interface. Experimentally verified results show that this system can assist safety management personnel to quickly guide typhoon prevention and control work during the 24-h typhoon warning period, improve the efficiency of safety management, and help to promote the development of intelligent typhoon emergency response technology at road and bridge construction sites.

Based on coverage evaluation design method study of factor low-orbit infrared remote sensing constellation configuration

Abstract To satisfy the infrared detection coverage demands of Low Earth Orbit (LEO) satellites in global missile surveillance, we have devised a comprehensive evaluation system for global missile infrared remote sensing coverage performance. By referencing Walker’s extensive and uninterrupted global coverage constellation layout, we conducted a thorough analysis of various constellation configuration parameters. This analysis aimed to assess the coverage efficiency of differently configured LEO networking satellites. Following a detailed comparative analysis, we settled on an LEO infrared remote sensing constellation configuration featuring an orbit height of 1150 km and an orbit inclination of 60°. After an in-depth examination of the capabilities of the infrared remote sensing constellation and the characteristics of the detection zone, we constructed a Blind Spot Compensation Constellation. This was achieved by integrating four additional LEO satellites with a 0° orbit inclination to bolster coverage in low-latitude regions. Using our comprehensive coverage evaluation system, we rigorously evaluated the ground coverage capabilities of multiple constellations. Notably, the Blind Spot Compensation Constellation achieved impressive coverage, reaching 100%, and excelled particularly in the latitude belt of 20° or less. Although the Original Constellation performed better in the 40° latitude belt compared to the Blind Spot Compensation Constellation, it showed some deficiencies in coverage at a 50 km altitude. Specifically, it averaged 18.417 gaps per day, with a coverage rate slightly below 100%. However, it achieved full coverage at other altitudes. To validate our findings, we conducted scenario simulations. For missile targets, the Original Constellation offered a maximum coverage multiplicity of 6, with a total access duration of 4539.1 seconds. On the other hand, the Blind Spot Compensation Constellation boasted a maximum coverage multiplicity of 8, with a total access duration of 5465.9 seconds. Importantly, both constellations fully covered the missile’s entire 1056-second lifecycle, showcasing commendable performance. In conclusion, the LEO infrared remote sensing constellation and its complementary counterpart, developed and rigorously tested in this study, not only meet the demands of continuous global detection but also cater to the priority detection needs of diverse geographical regions.

Design of railway simulation training system based on WebGL

Abstract In order to solve the problems of poor interaction with students and insufficient practicality in the previous railway freight station operation training, this article developed a training system based on WebGL technology that can interact with students and truly display the operation situation of the freight yard. This system is based on the Unity3D engine and WebGL technology. It imports 3D models to build large map scenes, and introduces functions such as cargo bundling and map roaming. The complete designed system is published to the Web through WebGL technology. After experimental analysis, the parking lot training system has solved the problems of poor practicality, little interaction, low efficiency, and narrow coverage in previous teaching. It has promoted the standardization and standardization of training students’ future work, and has effectively improved the professionalism of parking lot managers.

A Multi-Area Task Path-Planning Algorithm for Agricultural Drones Based on Improved Double Deep Q-Learning Net

With the global population growth and increasing food demand, the development of precision agriculture has become particularly critical. In precision agriculture, accurately identifying areas of nitrogen stress in crops and planning precise fertilization paths are crucial. However, traditional coverage path-planning (CPP) typically considers only single-area tasks and overlooks the multi-area tasks CPP. To address this problem, this study proposed a Regional Framework for Coverage Path-Planning for Precision Fertilization (RFCPPF) for crop protection UAVs in multi-area tasks. This framework includes three modules: nitrogen stress spatial distribution extraction, multi-area tasks environmental map construction, and coverage path-planning. Firstly, Sentinel-2 remote-sensing images are processed using the Google Earth Engine (GEE) platform, and the Green Normalized Difference Vegetation Index (GNDVI) is calculated to extract the spatial distribution of nitrogen stress. A multi-area tasks environmental map is constructed to guide multiple UAV agents. Subsequently, improvements based on the Double Deep Q Network (DDQN) are introduced, incorporating Long Short-Term Memory (LSTM) and dueling network structures. Additionally, a multi-objective reward function and a state and action selection strategy suitable for stress area plant protection operations are designed. Simulation experiments verify the superiority of the proposed method in reducing redundant paths and improving coverage efficiency. The proposed improved DDQN achieved an overall step count that is 60.71% of MLP-DDQN and 90.55% of Breadth-First Search–Boustrophedon Algorithm (BFS-BA). Additionally, the total repeated coverage rate was reduced by 7.06% compared to MLP-DDQN and by 8.82% compared to BFS-BA.

Methods and research for deformation monitoring of earth and rock dams based on close-range photogrammetry

Abstract Traditional means of monitoring deformation in earth and rock dams encounter challenges such as low monitoring efficiency and limited coverage. Despite the potential of emerging technologies such as GPS and three-dimensional laser scanning, their adoption is expensive and hard to promote. This paper presents a deformation monitoring method for earth and rock dams based on the close-range photogrammetry technique. The proposed approach focuses on analytical algorithm the design and deployment of monitoring points, photographic schemes, camera checking and calibration, as well as deformation analysis methods. Initially, based on the analysis of the parsing algorithms’ applicability, they are fused to address the shortcomings of common image parsing methods in meeting the requirements of high precision and multi-image processing for deformation monitoring of earth and rock dams. Subsequently, the fused algorithm is introduced to analyze the acquired image data for 3D reconstruction, and the deformation in earth and rock dams is assessed based on the generated dense point cloud model. The proposed deformation monitoring method is applied to Pine Bridge Reservoir Dam, and the results demonstrated its capacity to comprehensively analyze the deformation. Furthermore, the required equipment is simple and easy to operate, aligning with the requirements for deformation monitoring accuracy of earth and rock dams.&amp;#xD;

Facility Layout and Spatial Configuration Efficiency Assessment

AbstractWith rapid regional development and urbanization, many public and private facilities and infrastructures (e.g., sirens, cellphone base stations, bike sharing stations, wind turbines, etc.) require regular renovation or supplementation. Evaluating existing facility efficiency and expanding to new facility locations are of broad interest among stakeholders, including businesses, urban planners, government agencies, and the public more generally. Such evaluation can be used to improve overall social accessibility, equity and efficiency by reconfiguring or adding new facilities in the best way possible. A regularly distributed lattice is often viewed as an optimal configuration given important observed properties and characteristics. In this paper, we formulate a spatial optimization model to evaluate spatial coverage efficiency. Specifically, given two sets of points, the model seeks the optimal location and orientation of an idealized lattice to align with an existing facility configuration. The distance between existing facilities and the ideally configured lattice under the optimal alignment represents efficiency. An iterative heuristic based on gradient descent and spatial indexing is developed to solve this problem. Extensive computational experience demonstrates the importance of this problem and the effectiveness of the derived solution approach, as well as highlights assistance provided to decision makers in identifying inefficiencies as well as improving existing infrastructure service systems.

A water quality prediction approach for the Downstream and Delta of Dongjiang River Basin under the joint effects of water intakes, pollution sources, and climate change

Water quality prediction is an important means for scientific water environment management and early warning measures. However, its reliability is often restricted by the uncertainties of parameters and pollution sources. Previous methods for resolving these uncertainties still have shortcomings due to low computational efficiency and incomplete coverage of mechanisms. With insufficient data, they also overlooked revealing the comprehensive impact of water intakes, pollution sources, and climate change on water quality in river networks. Insight understanding migration law and the main driving mechanisms under these factors is crucial for water resources and sustainable socioeconomic development. Here, we developed an efficient multi-scenario ensemble water quality prediction (MEWQP) approach to address the deficiency. The Downstream and Delta of Dongjiang River Basin was the demonstrative case. Firstly, we developed multiple parameters and pollution sources inverse estimation method by incorporating the Water Quality Analysis Simulation Program (WASP), general likelihood uncertainty estimation, and inverse methods to improve accuracy. Secondly, the Markov-Chain was used to predict flows affected by climate change. Thirdly, 60 scenarios were analyzed to quantify the impacts of multiple factors on water quality including 5-day biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), and dissolved oxygen (DO), and identify an optimized scheme of water intakes. Results show that: (1) WASP has good performance for BOD5, NH3-N, and DO with Mean Absolute Relative Error of 5.87%, 9.19%, and 15.25%, respectively; (2) pollution sources have the greatest impact, followed by extreme climate and intakes. Their combined impacts are greater than individuals; (3) under non-water intakes DO can be improved by 31%, also offset the impact of climate change by 16%; (4) the optimized scheme of intakes could improve DO by 6%. Therefore, decision-makers should simultaneously adjust water intake and reduce pollution sources to obtain high-quality water resources.

Effective capacity of STAR-RIS assisted RSMA network with imperfect SIC

The proliferation of various Internet of Everything applications places urgent demand for high energy efficiency, massive connection and wide coverage for the future communication era. Motivated by this, rate-splitting multiple access (RSMA) and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has attracted wide publicity from industry and academia. In this paper, we consider a STAR-RIS assisted two-user RSMA network with imperfect successive interference cancellation, and all channels are modeled as Nakagami-m distribution. By adopting effective capacity (EC) as the metric, we analyze the system performance under certain quality-of-service constraint. Specifically, analytical expressions of EC for a pair of RSMA users are provided. To explore further insights, high signal-to-noise ratio (SNR) conditions are also considered. Furthermore, the effect of some specific elements on system EC performance are analyzed, and some significant conclusions are obtained as follows: (1) As SNR grows, the effective capacities of two users improve, but finally converge to constant at high SNR. (2) The increase in the amount of the STAR-RIS elements can obviously increase the effective capacities. (3) Compared to traditional STAR-RIS assisted non-orthogonal multiple access networks, STAR-RIS assisted RSMA network has distinct advantages to improve system EC performance.

Multi-agent cooperative area coverage: A two-stage planning approach based on reinforcement learning

Multi-agent area coverage aims to accomplish the complete traversal of the target area through cooperation between agents. Focusing on the problems of low coverage efficiency and weak practicability in the existing methods, we propose a two-stage area coverage method based on multi-agent deep reinforcement learning. In the first stage, we convert the coverage path planning problem into an optimal grid selection problem, and according to the equivalence of agents in cooperative tasks, we propose a distributed coverage path planning algorithm based on QMIX and a grid coverage map. The second stage is to realize the cooperative navigation control in a constrained environment with obstacles and non-ideal communication conditions. To implement the stage, we design a hybrid attention mechanism to adaptively aggregate important feature information of adjacent agents and obstacles, which efficiently exploits the limited local perception and communication capabilities of agents to perform cooperative control. The experimental results show that the proposed two-stage multi-agent area coverage method can accomplish the area coverage task in the environment with random obstacles, and the area coverage efficiency and robustness are significantly better than other reinforcement learning based or traditional coverage algorithms. In addition, the results also verify that the proposed method has the advantage of adapting to the dynamic changes in the number of agents and the communication range.

Research on the Reconstruction Method of Old Houses in Mindong, Republic of China, Based on a Wearable Mobile Scanning System

To address the complex structures of historical buildings in Mindong, lack of historical reference materials, and heavy workload of traditional surveying and mapping methods in the field, a 3D reconstruction method of historical building based on a wearable mobile scanning system for data acquisition was studied. First, a wearable mobile scanning system is used to scan historical buildings in three dimensions. The data are fused to obtain high-precision three-dimensional point cloud data of historical buildings. Second, based on the processed point cloud, a 2D map of historical buildings is accurately drawn using point cloud slicing. Finally, three-dimensional reconstruction of historical buildings is realized using a 2D atlas of historical buildings and modern modeling technology. The accuracy of the three-dimensional reconstruction model was analyzed using a fitting algorithm and structural geometric relationship to verify the effectiveness and accuracy of the method. An old house in Mindong, Republic of China, was taken as the research object. Experimental analysis revealed that (1) a wearable mobile scanning system is suitable for high-quality point cloud data acquisition of buildings with complex geometric features, solving the problems of low efficiency and poor coverage of traditional surveying and mapping results; and (2) the method realizes 2D results rendering and 3D reconstruction of historical buildings based on the wearable mobile scanning system data with good accuracy, providing a reference for the protection and repair of historical buildings and health assessment.

Coverage Path Planning with Adaptive Hyperbolic Grid for Step-Stare Imaging System

Step-stare imaging systems are widely used in aerospace optical remote sensing. In order to achieve fast scanning of the target region, efficient coverage path planning (CPP) is a key challenge. However, traditional CPP methods are mostly designed for fixed cameras and disregard the irregular shape of the sensor’s projection caused by the step-stare rotational motion. To address this problem, this paper proposes an efficient, seamless CPP method with an adaptive hyperbolic grid. First, we convert the coverage problem in Euclidean space to a tiling problem in spherical space. A spherical approximate tiling method based on a zonal isosceles trapezoid is developed to construct a seamless hyperbolic grid. Then, we present a dual-caliper optimization algorithm to further compress the grid and improve the coverage efficiency. Finally, both boustrophedon and branch-and-bound approaches are utilized to generate rotation paths for different scanning scenarios. Experiments were conducted on a custom dataset consisting of 800 diverse geometric regions (including 2 geometry types and 40 samples for 10 groups). The proposed method demonstrates comparable performance of closed-form path length relative to that of a heuristic optimization method while significantly improving real-time capabilities by a minimum factor of 2464. Furthermore, in comparison to traditional rule-based methods, our approach has been shown to reduce the rotational path length by at least 27.29% and 16.71% in circle and convex polygon groups, respectively, indicating a significant improvement in planning efficiency.

AI and game-based efficient resource allocation and interference mitigation scheme for D2D communication

Device-to-device (D2D) communication essentially means to communicate between two devices without the base station interfering. D2D communication is used in wireless 5G networks, the Internet of Things, Bluetooth and WiFi, and vehicular networks. D2D communication is useful as it increases spectral efficiency, network efficiency, user experience, and throughput, all of which are better than those available in cellular communication. Thus, D2D communication is a preferred mode of communication. However, D2D communication faces issues like mode selection, interference, resource allocation (RA), and security. The need for RA stands because multiple D2D users (DUs) compete for the same resources. Now, the goal should be to optimize RA to enhance the spectrum efficiency and network coverage. Still, the issues here are caused by eavesdroppers (ɛ), devices that can interfere with communication and encrypt/decrypt the messages. In our proposed scheme, we combined artificial intelligence (AI) and coalition game theory to resolve the issues of optimized RA and security. The coalition game is used for efficient RA, but it will take all the DUs that increase the overhead into the system. To mitigate this issue, we proposed an AI-based solution, which selects the best DUs based on their channel conditions. Our major evaluation parameters were accuracy, sum secrecy capacity and data rate.

75‐1: Invited Paper: Liquid Crystal Polarization Hologram for Eye Tracking Application

Eye‐tracking is vital in AR/VR/MR systems for display correction, rendering, accommodation, and more. However, existing eye‐tracking solutions fall short in accuracy, efficiency, form factor, and population coverage. Using Liquid crystal polarization hologram (LCPH) for eye‐tracking purposes offers a compact form‐factor with excellent performance.