Coverage Area Research Articles

Coverage hetnet based picocell and femtocell for uplink condition around building environment with single knife edge method

The development of HetNet (Heterogeneous Network) radio base stations has experienced many developments. This is indicated by the existence of microcells, macrocells, picocells, femtocells, and so on. In this research, the research is aimed at the propagation of user equipment communication systems in uplink conditions with HetNet picocells and femtocells. UE propagation is on a straight path between the building environment. The communication frequency used is 10 GHz. The communication between Tx and Rx is modeled as a diffraction mechanism, AWGN channel, and atmospheric attenuation. The Single Knife Edge (SKE) method is used to model the mechanism. The propagation channel is faced with AWGN (Additive White Gaussian Noise). The analysis of this research includes the SNR value, Adaptive Modulation and Coding (AMC) level, and percentage of communication coverage area. The AMC is based on the use of MCS (Modulation and Code Scheme). Some of the MCS modulations used QPSK, 16 QAM, and 64 QAM. As research results show that the percentage of communication coverage area obtained was gNB1 80.59 percent, gNB2 65.67%, and selection combining HetNet 95.52%.

Improving quality of service for Internet of Things(IoT) in real life application: A novel adaptation based Hybrid Evolutionary Algorithm

In this paper, we address critical challenges in IoT sensor lifespan, service latency, and coverage area, all impacting energy consumption in smart agriculture applications. To enhance the quality of service (QoS) while prolonging the energy efficiency of smart sensors, a novel optimization algorithm is introduced. Referred to as the ”Adaptation-Based Hybrid Evolutionary Algorithm,” this innovative approach combines the strengths of Grey Wolf Optimizers (GWO) and Differential Evolution (DE) algorithms. The methodology involves a new adaptation-based strategy and incorporates a hybrid algorithm that synergizes the exploratory and exploitative capabilities of both GWO and DE algorithms. This hybrid approach is leveraged to meticulously select optimal mutation new adaptation services, drawing from the GWO and DE algorithm frameworks. Notably, the algorithm’s control parameters autonomously adjust through insights gained from prior evolutionary searches. Furthermore, we enhance the DE-based crossover technique by integrating the proficient search capabilities of the GWO algorithm, renowned for tackling continuous global optimization problems. To validate our approach, we apply it to IoT scenarios and optimize QoS through a fitness function that comprehensively accounts for energy consumption, coverage rate, lifespan, and latency. Comparative evaluations against standard algorithms underscore the superior performance of our proposed methodology, particularly evident in its application to IoT-smart agriculture settings.

Direct Solution Deposition of Large-Area Non-Solvated Fullerene Single-Crystal Films for High-Performance n-Type Field-Effect Transistors.

Fullerene (C60) crystals have attracted considerable attention in the field of optoelectronic devices owing to their excellent performance as n-type semiconductor material. However, a challenge still remains unbeaten as to the continuous crystallization of non-solvated C60 single-crystal films with high coverage and uniform alignment using low-cost solution techniques. Here, a facile bar coating method is used to prepare ribbon-shaped non-solvated C60 crystals with a large area (up to centimeters) and high coverage (>95%) by precisely controlling the crystallization process from specific solvents. Benefiting from the non-solvated crystalline structure, well-distributed thickness, uniform morphological alignment, and crystallographic orientation, organic field-effect transistors fabricated from the C60 single-crystal films exhibit a high average electron mobility of 2.28cm2V-1s-1, along with the coefficient of variance (CV) as small as 13.6%. This efficient manufacturing method will lay a strong foundation for C60 single-crystal films to fit into the future high-performance integrated optoelectronic application.

Kualitas Jaringan Operator Selular Coverage Indoor dan Outdoor di Lingkungan Universitas Warmadewa

Telecommunications technology is growing rapidly, and telecommunications providers are now in the 5G technology phase. Providing very fast speed and data transfer. However, in the development of cellular technology in Indonesia, especially in Bali, the distribution of provider signal coverage areas is not evenly distributed. Although many BTS (Base Transceiver Station) have been scattered and built in the Denpasar city area, there are still locations that are not well covered. In this study the location chosen was the campus area at Warmadewa University. The method used was survey and sample interviews among students, lecturers, and staff. The number of data samples is 100 people. The survey results obtained are the findings of the signal coverage of Telkomsel, Indosat, XL-Axiata providers including coverage ranging from very poor to very good, while the Smartfren and 3 providers include good and very good signal coverage received in the Warmadewa University area. The use of phone calls (pulses) is rarely used because data packages are more flexible and efficient, the selection of calls (pulses) is usually used when urgent conditions.

A City Image Study by Kevin Lynch’s Theoretical Approach: A Case Study of Pontianak City, West Kalimantan Province of Indonesia

Pontianak City is the capital city of West Kalimantan Province reaches area coverage of 118.31 km2.This city has multiple functions as the government and economic centrals of the West Kalimantan Province of Indonesia. It is also known as the Equatorial city because it is located on the equator line and by the end of 2023, its population will reach 679.818 people and will become the 26th most populous city in Indonesia. The City of Pontianak located on the equator line with altitude height ranging from 0.1 to 1.5 meters above sea level, and from administrative divisions, Pontianak City consists of several district areas: (a) North Pontianak District, (b) East Pontianak District, (c) South Pontianak District, (d) Southeast Pontianak District, (e) the Pontianak City District, and (f) West Pontianak District, whether from geographical perspective, City of Pontianak is traversed by the longest river in Indonesia, the Kapuas River which has become a life support for the surrounding community. The analytical method applied in this research to study the city image of Pontianak is the triangulation method where it is used to compare information obtained through field observations with theoretical perspectives that have been studied previously. The result found in this article is the City of Pontianak has fulfilled the requirement of 5 elements of city image according to Kevin Lynch’s theory and it has Tugu Khatulistiwa or Equator Monument as a symbol or the icon of Pontianak City.

Multi-Unmanned Aerial Vehicle-Assisted Flood Navigation of Waterborne Vehicles Using Deep Reinforcement Learning

Abstract During disasters, such as floods, it is crucial to get real-time ground information for planning rescue and response operations. With the advent of technology, unmanned aerial vehicles (UAVs) are being deployed for real-time path planning to provide support to evacuation teams. However, their dependency on expert human pilots for command and control limits their operational capacity to the line-of-sight range. In this article, we utilize a deep reinforcement learning algorithm to autonomously control multiple UAVs for area coverage. The objective is to identify serviceable paths for safe navigation of waterborne evacuation vehicles (WBVs) to reach critical location(s) during floods. The UAVs are tasked to capture the obstacle-related data and identify shallow water regions for unrestricted motion of the WBV(s). The data gathered by UAVs is used by the minimum expansion A* (MEA*) algorithm for path planning to assist WBV(s). MEA* addresses the node expansion issue with the standard A* algorithm, by pruning the unserviceable nodes/locations based on the captured information, hence expediting the path planning process. The proposed approach, MEA*MADDPG, is compared with other prevalent techniques from the literature over simulated flood environments with moving obstacles. The results highlight the significance of the proposed model as it outperforms other techniques when compared over various performance metrics.

Overlap between priority conservation areas and natural assets appeals to a shared responsibility for global primate conservation

Expanding protected areas (PAs) coverage to match countries’ conservation responsibility is vital to stemming global biodiversity loss. Uneven biodiversity distribution and conservation responsibility among countries pose challenges to the implementation of conservation targets, and currently diminish conservation effectiveness. Using primates as a global model, we evaluated the conservation responsibility of countries to assess if their PA coverage adequately meets their conservation responsibility. We quantified spatial national responsibility for primate species conservation in each country from their range size of priority conservation areas (PCAs) and protected areas coverage on PCAs. We identified the high-responsibility countries for global primate conservation as countries that held > 1 % of global PCAs. We paid particular attention to the circumstances associated with PCAs in the least developed countries, especially the overlap between PCAs and the areas containing natural assets (natural assets areas). Seventeen countries were identified as high-responsibility countries that in total covered 84 % area of global PCAs. Current protected areas only covered less than 50 % of PCAs for fifteen of these high-responsibility countries. Among these fifteen countries, four countries (DR Congo, PA coverage: 19 %; Myanmar, 9 %; Madagascar, 29 %; Laos, 22 %) were the least developed countries. Most existing PCAs (95 %; SD 6 %) in these 17 countries are located within natural assets areas, which creates tension between primate conservation and local development. Global primate conservation is a shared responsibility among the international community. Developing countries have greater responsibility than most because of the overlapping of their PCAs with natural assets areas. For developing countries to fulfill their conservation responsibilities requires international conservation resources (funding support and cooperation). Protected areas expansion has to be matched with a realizable target for each country to avoid crippling conflicts between conservation and local development. Community-based management models, including other effective area-based conservation measures, may reduce potential conflict and improve conservation effectiveness.

Learning continuous multi-UAV controls with directed explorations for flood area coverage

Real time on-ground information is of critical value during any natural disaster such as floods. The disaster response teams require the latest ground information of the flooded areas to effectively plan and execute rescue operations. Unmanned Aerial Vehicles (UAVs) are increasingly becoming a tool to perform quick surveys of larger areas such as flood disasters. In this paper, we propose a method to perform critical area coverage of flood-struck regions using multiple autonomous UAVs. A Deep Reinforcement Learning algorithm is proposed to learn continuous multi-UAV controls, incorporating a directed exploration strategy for the DDPG’s target actor, which relies on the D-infinity (DINF) algorithm. The DINF water flow estimation technique utilizes surface elevation data to understand and predict the directed discharge of floodwater. Further, we introduce a Path scatter strategy for the multi-UAV system that inhibits the clustered formation of the UAVs over low-elevated regions. The performance of the proposed D3S (DDPG+DINF+Path Scatter) algorithm is evaluated using various performance metrics, such as average cumulative rewards, number of collisions, and UAVs’ spread observed over the environment. In comparison to the baseline algorithms and other prevalent approaches in the literature, the proposed method is found to be better placed as the results highlight a significantly improved performance by D3S across different metrics.

A spatial decision support framework for equitable sensor network distribution in the smart city

AbstractThis paper introduces a proof‐of‐concept spatial decision support system (SDSS) that assists decision‐makers to generate equitably distributed sensor networks and evaluate their placement with reference to specific population‐based coverage criteria. Our approach centres equity in infrastructure distribution; we focus on the decision‐making process required to achieve the best possible sensor coverage of the geographical area for selected vulnerable populations and visualise trade‐offs in coverage inherent in infrastructure distribution. The development of the tool brings together expertise from quantitative geography, urban planning, data science and software engineering, and its technical development is underpinned and shaped by interviews with decision‐makers and their iterative feedback. Through this, we ask: how can decision support tools help with the work of building equitable infrastructure? As well as a technical application, our approach develops a conceptual framework for evaluating sensor network purpose and distribution before actual placement. The unique combination of distribution algorithms, user interface and decision‐maker input, developed by an interdisciplinary team, offers a novel approach to sensor network conceptualisation and generation. Our research contributes to the understanding of the distribution of essential infrastructure and can be repurposed for any sensor type and geographical location to promote equity in infrastructure distribution.

Enhanced Non-Orthogonal Multiple Access Performance Using Channel Coding for High-Altitude Platform System (HAPS)

The high-altitude platform system (HAPS) is a promising technology for providing high-speed data transmission and coverage for remote areas. Still, it suffers from unpredictable channel transmission, especially in the Rician channel. To overcome the issue in transmission, we used one channel coding strategy, namely the quasi-cyclic low-density parity-check (QC-LDPC). This study aims to evaluate the performance of power domain non-orthogonal multiple access (PD-NOMA), which allows multiple users to share the same spectrum in a downlink communication system from HAPS to three ground stations. We simulated the system using MATLAB simulation with different power allocation coefficients for every ground station. The simulation results showed that QC-LDPC codes can significantly lower the signal-to-noise ratio (SNR) required to achieve the same bit error rate (BER) as uncoded NOMA, especially in scenarios with uneven power allocation. The average improvement of QC-LDPC over uncoded NOMA was 8.4% in SNR. However, we also observed that NOMA is prone to domino errors, where decoding errors in one ground station can propagate and degrade the performance of the subsequent ground station. Furthermore, we found that power allocation significantly impacts the system performance, while the location and power allocation of the second ground station have a minor effect. Based on these findings, we conclude that QC-LDPC can enhance system performance in scenarios with low received power and that power allocation is a crucial factor for NOMA systems with multiple ground stations.

Header Height Detection and Terrain-Adaptive Control Strategy Using Area Array LiDAR

During the operation of combine harvesters, the cutting platform height is typically controlled using manual valve hydraulic systems, which can result in issues such as delays in adjustment and high labor intensity, affecting both the quality and efficiency of the operation. There is an urgent need to enhance the automation level. Conventional methods frequently employ single-point measurements and lack extensive area coverage, which means their results do not fully represent the terrain’s variations in the area and are prone to local anomalies. Given the inherently undulating terrain of farmland during harvesting, a control strategy that does not adjust for minor undulations but only for significant ones proves to be more rational. To this end, a sine wave superposition model was established to simulate three-dimensional ground elevation changes, and an area array LiDAR was used to collect 8 × 8 data for the header height. The effects of mounds and stubble on the measurement results were analyzed, and a dynamic process simulation model for the solenoid valve core was developed to analyze the on/off delay characteristics of a three-position four-way electromagnetic directional valve. Moreover, a physical model of the hydraulic system was constructed based on the Simscape module in Simulink, and the Bang Bang switch predictive control system based on position threshold was introduced to achieve early switching of the electromagnetic directional valve circuit. In addition, an automatic control system for cutting platform height was designed based on an STM32 microcontroller. The control system was tested on the hydraulic automatic control test rig developed by Shanxi Agricultural University. The simulation and experimental results demonstrated that the control system and strategy were robust to output disturbances, effectively enhancing the intelligence and environmental adaptability of agricultural machinery operations.

A Novel SAR Imaging Method for GEO Satellite–Ground Bistatic SAR System with Severe Azimuth Spectrum Aliasing and 2-D Spatial Variability

The satellite–ground bistatic configuration, which uses geosynchronous synthetic aperture radar (GEO SAR) for illumination and ground equipment for reception, can achieve wide coverage, high revisit, and continuous illumination of interest areas. Based on the analysis of the signal characteristics of GEO satellite–ground bistatic SAR (GEO SG-BiSAR), it is found that the bistatic echo signal has problems of azimuth spectrum aliasing and 2-D spatial variability. Therefore, to overcome those problems, a novel SAR imaging method for a GEO SG-BiSAR system with severe azimuth spectrum aliasing and 2-D spatial variability is proposed. Firstly, based on the geometric configuration of the GEO SG-BiSAR system, the time-domain and frequency-domain expressions of the signal are derived in detail. Secondly, in order to avoid the increasing cost caused by traditional multi-channel reception technology and the processing burden caused by inter-channel errors, the azimuth deramping is executed to solve the azimuth spectrum aliasing of the signal under the special geometric structure of GEO SG-BiSAR. Thirdly, based on the investigation of azimuth and range spatial variability characteristics of GEO SG-BiSAR in the Range Doppler (RD) domain, the azimuth spatial variability correction strategy is proposed. The signal corrected by the correction strategy has the same migration characteristics as monostatic radar. Therefore, the traditional chirp scaling function (CSF) is also modified to solve the range spatial variability of the signal. Finally, the two-dimensional spectrum of GEO SG-BiSAR with modified chirp scaling processing is derived, followed by the SPECAN operation to obtain the focused SAR image. Furthermore, the completed flowchart is also given to display the main composed parts for GEO SG-BiSAR imaging. Both azimuth spectrum aliasing and 2-D spatial variability are taken into account in the imaging method. The simulated data and the real data obtained by the Beidou navigation satellite are used to verify the effectiveness of the proposed method.

Optimizing drone-assisted victim localization and identification in mass-disaster management: a study on feasible flying patterns and technical specifications

The prompt emphasizes the importance of identifying victims in a disaster area within 48 hours and highlights the potential benefits of using drones in search and rescue missions. However, the use of drones is limited by factors such as battery life, processing speed, and communication range. To address these limitations, the paper presents a detailed research study on the most effective flying pattern for drones during search and rescue missions. The study utilized energy consumption and coverage area as performance metrics and collected precise images that could be analyzed by the forensic team. The research was conducted using OMNET++ and fieldwork at Pulau Sebang, Melaka, in collaboration with search and rescue agencies in Malaysia. The results suggest that the square flying pattern is the most effective, as it provides the highest coverage area with reasonable energy utilization. Both simulation and fieldwork results showed coverage of 100% and 97.96%, respectively, for this pattern. Additionally, the paper provides technical specifications for rescue teams to use when deploying drones during search and rescue missions.

A seamless bus network without external transfers using autonomous modular vehicles

Recently, autonomous modular vehicles (AMVs) capable of enabling in-motion transfer (IMT), i.e. passengers moving between vehicles while the vehicles are traveling, have been developed. IMT allows new paradigms for public transport operations that can enhance the speed, flexibility, and reliability of the system. We leverage IMT technology to propose the first network-wide seamless, stop-less bus service concept. We recently introduced the concept of a Stop-Less Autonomous Modular (SLAM) bus service, which eliminates the need for passengers to stop at each bus stop while traveling along a bus line. The current work extends this paradigm to the network level by developing a bus service framework in which multiple SLAM bus lines operating on main arterials interact by exchanging pods among each other to provide passengers with a travel experience that not only eliminates most extra passenger stops within a single line, but also eliminates external transfers, i.e. it removes the need to disembark at a bus stop to change buses when transferring from one bus line to another. The result is a high-speed service that provides a completely seamless journey from the passenger’s origin stop to destination stop anywhere in the coverage area, with integrated (i.e. internal) transfers that take place inside the vehicles themselves while they are in-motion. Our results show that while the proposed service has more stringent operating requirements than a comparable conventional bus service, it provides significantly superior performance, reducing passengers’ travel cost by up to 35% under realistic system settings.

Deep Reinforcement Learning-based Collaborative Multi-UAV Coverage Path Planning

Abstract The coverage path planning problem has gained significant attention in research due to its wide applicability and practical value in various fields such as logistics and distribution, smart homes, and unmanned vehicles. This paper focuses on studying the coverage path planning problem under multi-UAV collaboration to maximize the coverage of the mission area within a given time. To address this problem, we propose a multi-objective optimization model and reformulate it with the framework of Decentralized Partially Observable Markov Decision Process (Dec-POMDP). We then employ a multi-agent deep reinforcement learning (MADRL) method to solve the problem. Specifically, we introduce the ε—Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (ε—MADT3), which incorporates an exploration coefficient based on MATD3. This coefficient gradually decays with the number of iterations, allowing for a balance between exploration and exploitation. Numerous simulation results demonstrate that ε—MADT3 outperforms the baseline algorithm in terms of coverage rate and number of collisions.

Inferring the extinction risk of marine fish to inform global conservation priorities.

While extinction risk categorization is fundamental for building robust conservation planning for marine fishes, empirical data on occurrence and vulnerability to disturbances are still lacking for most marine teleost fish species, preventing the assessment of their International Union for the Conservation of Nature (IUCN) status. In this article, we predicted the IUCN status of marine fishes based on two machine learning algorithms, trained with available species occurrences, biological traits, taxonomy, and human uses. We found that extinction risk for marine fish species is higher than initially estimated by the IUCN, increasing from 2.5% to 12.7%. Species predicted as Threatened were mainly characterized by a small geographic range, a relatively large body size, and a low growth rate. Hotspots of predicted Threatened species peaked mainly in the South China Sea, the Philippine Sea, the Celebes Sea, the west coast Australia and North America. We also explored the consequences of including these predicted species' IUCN status in the prioritization of marine protected areas through conservation planning. We found a marked increase in prioritization ranks for subpolar and polar regions despite their low species richness. We suggest to integrate multifactorial ensemble learning to assess species extinction risk and offer a more complete view of endangered taxonomic groups to ultimately reach global conservation targets like the extending coverage of protected areas where species are the most vulnerable.

RSU-Net: A new method for fine classification of corn residue coverage in black soil area using Chinese GF-1B PMS image

Corn residue coverage (CRC) plays an important role in soil protection, with respect to both the restoration of soil fertility and the reduction of soil erosion, particularly in black soil are-as. Fine classification of areas with high and low coverage of crop residue is essential for accu-rately quantifying the soil protection level; it is insufficient to focus solely on whether the crop residue is covered. Remote sensing images from Chinese GaoFen-1 (GF-1) B, which can be acquired with a high spatial resolution of 2 m and a revisit period of 4d, have potential for the fine classification of high and low coverage of corn residues (HLCCR). To improve the efficiency and accuracy of the fine classification of HLCCR, a deep neural network RSU-Net was proposed in this study. RSU-Net combines residual units with spatial pyramid pooling technology to enhance the ability to extract the features of different coverage types from GF-1 B images. Comparison ex-periments were conducted to validate the effectiveness of RSU-Net and compare it with the classical U-Net. The results show that the overall accuracy, kappa, and mean intersection over union of RSU-Net in fine classification of HLCCR were 86.29%, 0.75, and 0.6844, respectively, and those of U-Net were 84.61%, 0.72, and 0.6474, respectively. Compared with the classical U-Net, the classification accuracy of RSU-Net was improved. These results suggest that RSU-Net has great po-tential for fine classification of HLCCR.

Virtual Backhaul Connectivity for Enhanced Coverage in Fiber-Less Areas

Virtual Backhaul Connectivity for Enhanced Coverage in Fiber-Less Areas

Spatial clusters and social inequities in COVID-19 vaccine coverage among children in Brazil.

This study examined the spatial distribution and social inequalities in COVID-19 vaccine coverage among children aged 5-11 in Brazil. First and second dose vaccine coverage was calculated for all Brazilian municipalities and analyzed by geographic region and deciles based on human development index (HDI-M) and expected years of schooling at 18 years of age. Multilevel models were used to determine the variance partition coefficient, and bivariate local Moran's I statistic was used to assess spatial association. Results showed significant differences in vaccine coverage rates among Brazilian municipalities, with lower coverage in the North and Midwest regions. Municipalities with lower HDI and expected years of schooling had consistently lower vaccine coverage rates. Bivariate clustering analysis identified extensive concentrations of municipalities in the Northern and Northeastern regions with low vaccine coverage and low human development, while some clusters of municipalities in the Southeast and South regions with low coverage were located in areas with high HDI-M. These findings highlight the persistent municipal-level inequalities in vaccine coverage among children in Brazil and the need for targeted interventions to improve vaccine access and coverage in underserved areas.

Automatic detection and prediction of signal strength degradation in urban areas using data-driven machine learning

Signal strength degradation represents a variation in the coverage area for radio networks. Building maps that represent this degradation requires collecting information about signal coverage in scattered locations, which can be done conventionally by measurement methods such as the manual drive test. Nevertheless, as this process is large-scale, time-consuming, and costly, several methods for the minimization of drive tests have been introduced. In this study, our methodology first consisted of dividing the study area into several zones, and each zone into several sectors without considering the position of the existing broadcast base station. Then, we developed a custom mobile application to collect the signal strength data and the location coordinates of the concerned area. For data collection, we deployed the mobile application on more than 10 users' phones, who navigated in different areas using their cars. We applied the gradient-boosted trees algorithm to predict signal strength degradation in different areas. Our model has shown some interesting results after studying and analyzing the collected data, based on data mining algorithms. We also evaluated our model's ability to predict the zone's structure according to the strength of the degradation signal.