Flexible Coverage Research Articles

Advanced IoT-Based Wireless Sensors for Remote Geotechnical Monitoring and Structural Diagnostics

This paper presents an innovative design and implementation of a wireless sensor module using low-power Internet of Things (IoT) technologies for applications in geotechnical engineering and structural diagnostics, with a main focus on monitoring rock formations below the water surface in areas without access to electrical power. Designed for continuous monitoring of linear displacements and other critical parameters in structural engineering, geotechnical engineering and engineering diagnostics, this sensor module is particularly useful in remote locations. Utilizing wireless data transmission and advanced IoT technologies such as LoRa, IQRF and NB-IoT, this system allows for flexible coverage options and efficient remote management. The system allows users to remotely change measurement parameters, the period of messaging and set thresholds for alarm activation. The study highlights the importance of innovative IoT solutions to improve accuracy and efficiency in monitoring geotechnical and civil engineering structures, especially in challenging sub-surface environments, opening new opportunities for predictive maintenance and risk management in these specific applications.

Exploring Multiple-Objective Optimization for Efficient and Effective Test Paper Design with Dynamic Programming Guided Genetic Algorithm.

Automatic test paper design is critical in education to reduce workloads for educators and facilitate an efficient teaching process. However, current designs fail to satisfy the realistic teaching requirements of educators, including the consideration of both test quality and efficiency. This is the main reason why teachers still manually construct tests in most teaching environments. In this paper, the quality of tests is quantitatively defined while considering multiple objectives, including a flexible coverage of knowledge points, cognitive levels, and question difficulty. Then, a model based on the technique of linear programming is delicately designed to explore the optimal results for this newly defined problem. However, this technique is not efficient enough, which cannot obtain results in polynomial time. With the consideration of both test quality and generation efficiency, this paper proposes a genetic algorithm (GA) based method, named dynamic programming guided genetic algorithm with adaptive selection (DPGA-AS). In this method, a dynamic programming method is proposed in the population initialization part to improve the efficiency of the genetic algorithm. An adaptive selection method for the GA is designed to avoid prematurely falling into the local optimal for better test quality. The question bank used in our experiments is assembled based on college-level calculus questions from well-known textbooks. The experimental results show that the proposed techniques can construct test papers with both high effectiveness and efficiency. The computation time of the test assembly problem is reduced from 3 hours to 2 seconds for a 5000-size question bank as compared to a linear programming model with similar test quality. The test quality of the proposed method is better than the other baselines.

Hyperspectral image restoration based on color superpixel segmentation

Hyperspectral images (HSI) are often degraded by various types of noise during the acquisition process, such as Gaussian noise, impulse noise, dead lines and stripes, etc. Recently, there exists a growing attenrion on low-rank matrix/tensor-based methods for HSI data restoration, assuming that the overall data is low-rank. However, the assumption of overall low-rankness often proves inaccurate due to the spatially heterogeneous local similarity characteristics of HSI. Traditional cube-based methods involve dividing the HSI into fixed-size cubes. However, using fixed-size cubes does not provide flexible coverage of locally similar regions at varying scales. Inspired by superpixel segmentation, this paper proposes the Shrink Low-rank Super-tensor (SLRST) approach for HSI recovery. Instead of using fixed-size cubes, SLRST employs a size-adaptive super-tensor. The proposed approach is effectively solved using the Alternating Direction Method of Multipliers (ADMM). Numerical experiments on HSI data verify that the proposed method outperforms other competing methods.

Modeling and Analysis of Air-Ground Integrated Networks With Flexible Beam Coverage

Air platforms, such as unmanned aerial vehicles, airships, and balloons are expected to complement traditional ground networks to provide flexible coverage solutions. However, most existing models for air-ground integrated networks (AGINs) neglect the spatial dependence caused by the complementary deployment of the aerial and ground nodes. Accordingly, in this paper, we propose two AGIN models with horizontal dependence that differ in the vertical dimension, namely uniformly independent altitudes and location-dependent altitudes. The air platforms serve as aerial base stations, distributed as a marked Poisson hole process, and provide flexible beam coverage through varying altitudes. Under this setup, we propose a region-based user association scheme and derive the association probabilities as well as the serving distance distributions of an arbitrarily located user. Considering Nakagami fading and air-to-ground propagation properties, we characterize the signal-to-interference ratio and area spectral efficiency for each model. Using the proposed analytical framework, we demonstrate the importance of deploying the air platforms more sensibly to provide targeted services and flexible beam coverage in reducing the load of base stations and improving the user coverage and network capacity performance.

Intelligent Resource Allocation Using an Artificial Ecosystem Optimizer with Deep Learning on UAV Networks

An Unmanned Aerial Vehicle (UAV)-based cellular network over a millimeter wave (mmWave) frequency band addresses the necessities of flexible coverage and high data rate in the next-generation network. But, the use of a wide range of antennas and higher propagation loss in mmWave networks results in high power utilization and UAVs are limited by low-capacity onboard batteries. To cut down the energy cost of UAV-aided mmWave networks, Energy Harvesting (EH) is a promising solution. But, it is a challenge to sustain strong connectivity in UAV-based terrestrial cellular networks due to the random nature of renewable energy. With this motivation, this article introduces an intelligent resource allocation using an artificial ecosystem optimizer with a deep learning (IRA-AEODL) technique on UAV networks. The presented IRA-AEODL technique aims to effectually allot the resources in wireless UAV networks. In this case, the IRA-AEODL technique focuses on the maximization of system utility over all users, combined user association, energy scheduling, and trajectory design. To optimally allocate the UAV policies, the stacked sparse autoencoder (SSAE) model is used in the UAV networks. For the hyperparameter tuning process, the AEO algorithm is used for enhancing the performance of the SSAE model. The experimental results of the IRA-AEODL technique are examined under different aspects and the outcomes stated the improved performance of the IRA-AEODL approach over recent state of art approaches.

Optimal coverage-based placement of static leak detection devices for pipeline water supply networks

In this paper, we provide a mathematical optimization-based framework to determine the location of leak detection devices along a network. Assuming that the devices are endowed with a known coverage area, we analyze two different models. The first model aims to minimize the number of devices to be located in order to (fully or partially) cover the volume of the network. In the second model, the number of devices is given, and the goal is to locate them to provide a coverage volume as broad as possible. Unlike other approaches in the literature, in our models, it is not assumed that the devices are located on the network (nodes or edges) but in the whole space and that the different segments in the networks may be partially covered, which allows for more flexible coverage. We also derive a method to construct initial solutions as well as a math-heuristic approach for solving the problem for larger instances. We report the results of a series of experiments on real-world water supply pipeline networks, supporting the validity of our models.

Geospatial Assessment to Improve Time to Treatment (GAITT)

IntroductionGeographic information systems (GIS) can optimize trauma systems by identifying ways to reduce time to treatment. Using GIS, this study analyzed a system in Maryland served by Johns Hopkins Suburban Hospital and the University of Maryland Capital Region Medical Center. It was hypothesized that including Walter Reed National Military Medical Center (WRNMMC) in the Maryland trauma system in an access simulation would provide increased timely access for a portion of the local population. Materials and methodsUsing ArcGIS Online, catchment areas with and without WRNMMC were built. Catchment areas captured Johns Hopkins Suburban Hospital, University of Maryland Capital Region Medical Center, and WRNMMC at 5-, 10-, 15-, 20-, 25-, 30-, 45-, and 60-min. Various time conditions were simulated (12 am, 8 am, 12 pm, and 5 pm) on a weekday and weekend day. Data was enriched with 19 variables addressing population size, socioeconomic status, and diversity. ResultsAll catchment areas benefited on at least one time-day simulation, but the largest increases in mean population coverage were in the 0-5 (10.5%), 5-10 (12.3%), and 10-15 min (5.7%) catchment areas. These areas benefited regardless of time-day simulation. The lowest increase in mean population coverage was seen in the 20-25-min catchment area (0.1%). Subgroup analysis revealed that all socioeconomic status and diversity groups gained coverage. ConclusionsThis study suggests that incorporating WRNMMC into the Maryland trauma system might yield increased population coverage for timely trauma access. If incorporated, WRNMMC may provide nonstop or flexible coverage, possibly in different traffic scenarios or while civilian centers are on diversion status.

Deep-reinforcement-learning-based range-adaptive distributed power control for cellular-V2X

A distributed congestion control must be adaptable to varying target communication ranges as cellular V2X (C-V2X) is evolving to support flexible coverage suitable for various service scenarios. This study proposes range-adaptive distributed power control (Ra-DPC) based on deep reinforcement learning (DRL) with the Monte Carlo policy gradient algorithm. A key finding is that the agents learn Ra-DPC more effectively when the cumulative interference power of the subchannels is adopted as the state of the DRL model, rather than the channel busy ratio. The proposed Ra-DPC algorithm performs better in energy efficiency and packet delivery ratio than the existing technologies.

A Study of the Active Access-Point Configuration Algorithm under Channel Bonding to Dual IEEE 802.11n and 11ac Interfaces in an Elastic WLAN System for IoT Applications

Currently, Internet of Things (IoT) has become common in various applications, including smart factories, smart cities, and smart homes. In them, wireless local-area networks (WLANs) are widely used due to their high-speed data transfer, flexible coverage ranges, and low costs. To enhance the performance, the WLAN configuration should be optimized in dense WLAN environments where multiple access points (APs) and hosts exist. Previously, we have studied the active AP configuration algorithm for dual interfaces using IEEE802.11n and 11ac protocols at each AP under non-channel bonding (non-CB). In this paper, we study the algorithm considering the channel bonding (CB) to enhance its capacity by bonding two channels together. To improve the throughput estimation accuracy of the algorithm, the reduction factor is introduced at contending hosts for the same AP. For evaluations, we conducted extensive experiments using the WIMENT simulator and the testbed system using Raspberry Pi 4B APs. The results show that the estimated throughput is well matched with the measured one, and the proposal achieves the higher throughput with a smaller number of active APs than the previous configurations.

Selective classification considering time series characteristics for spiking neural networks

In this paper, we propose new methods for estimating the relative reliability of prediction and rejection methods for selective classification for spiking neural networks (SNNs). We also optimize and improve the efficiency of the RC curve, which represents the relationship between risk and coverage in selective classification. Efficiency here means greater coverage for risk and less risk for coverage in the RC curve. We use the model internal representation when time series data is input to SNN, rank the prediction results that are the output, and reject them at an arbitrary rate. We propose multiple methods based on the characteristics of datasets and the architecture of models. Multiple methods, such as a simple method with discrete coverage and a method with continuous and flexible coverage, yielded results that exceeded the performance of the existing method, softmax response.

Cloud-Edge-End Collaboration in Air–Ground Integrated Power IoT: A Semidistributed Learning Approach

The combination of air–ground integrated power Internet of Things (AGI-PIoT) and cloud-edge-end collaboration enables flexible coverage and real-time data processing. However, how to achieve intelligent cloud-edge-end collaboration in AGI-PIoT faces several challenges such as dynamics of aerial networks, coupling of resource allocation in multiple layers, timescales, and dimensions, incomplete information, and dimensionality curse. In this article, we propose a FEderated Deep rEinforcement leaRning-based multi-lAyer multi-Timescale multi-dImensional resOurce allocatioN algorithm (FEDERATION). The multilayer multitimescale multidimensional resource allocation problem is decomposed into three subproblems based on Lyapunov optimization. For the subproblem of joint task offloading and power control, a federated deep actor-critic-based semidistributed algorithm is developed. The subproblem of admission control is solved by quadratic programming. The third subproblem is addressed through smooth approximation and Lagrange dual decomposition. Simulation results indicate that FEDERATION outperforms existing algorithms in queuing delay, energy consumption, and convergence.

Additive manufacturing for capacitive liquid level sensors

A new manufacturing wave concerning the Additive Manufacturing of sensors is spreading: several benefits, such as cost, time, and manual task reduction, can be achieved. The aim of the present research is the one-shot Additive Manufacturing of a low-cost capacitive sensor for liquid level sensing. The Material extrusion (MeX) technology was used to fabricate the proposed sensors (composed of a flexible substrate, two conductive electrodes, and a top flexible coverage), and a Design for Additive Manufacturing (DfAM) approach in conjunction with the 3D printing force analysis was performed. Very thin conductive tracks (0.5 mm) were manufactured to obtain a sensor having a final capacitance value of 125 pF, readable by common laboratory instrumentation. The sensor has been tested for the liquid level sensing using two different liquids, i.e., sunflower oil and distilled water, exhibiting very good sensitivity of 0.078 frac{pF}{mm} and 0.79 frac{pF}{mm}, respectively, with high repeatability, thus obtaining sensing performances comparable with that of more expensive sensors found in literature. Moreover, the proposed sensor showed high linearity (R2 ≥ 0.997), which resulted in a maximum propagated level error of 1.4 mm. The present research proves that the inexpensive MeX technology can be successfully employed for the fabrication of high-performance capacitive sensors: the sensor manufacturing cost (related to raw materials) is 0.38 €, and no manual assembly tasks were performed. This study lays the foundation for the one-shot fabrication of smart structures with capacitive sensors on board, saving manufacturing time and cost.

The state of the art in beyond 5G distributed massive multiple-input multiple-output communication system solutions

Beyond fifth generation (5G) communication systems aim towards data rates in the tera bits per second range, with improved and flexible coverage options, introducing many new technological challenges in the fields of network architecture, signal processing, and radio frequency front-ends. One option is to move towards cell-free, or distributed massive Multiple-Input Multiple-Output (MIMO) network architectures and highly integrated front-end solutions. This paper presents an outlook on beyond 5G distributed massive MIMO communication systems, the signal processing, characterisation and simulation challenges, and an overview of the state of the art in millimetre wave antennas and electronics.

Aesthetic Reconstruction of a Facial Defect After Subtotal Resection of the Parotid Gland Using a Superficial Circumflex Iliac Artery Pure Skin Perforator Flap.

In the reconstruction of facial defects, thin and flexible coverage of the defect is desirable for cosmetic reasons. A free flap is 1 possible surgical option; however, a flap containing a fatty layer is bulky. in this study, the authors describe the reconstruction of a facial defect after subtotal parotidectomy for right parotid carcinoma using a superficial circumflex iliac artery pure skin perforator flap, with consideration given to facial contour. The superficial circumflex iliac artery pure skin perforator flap is a viable option to achieve the desired clinical and aesthetic outcome.

Multi-UAV Content Caching Strategy and Cooperative, Complementary Content Transmission Based on Coalition Formation Game.

The transmission of a large amount of video and picture content brings more challenges to wireless communication networks. Unmanned aerial vehicle (UAV)-aided small cells with active content caching deployed on cellular networks are recognized as a promising way to alleviate wireless backhaul and support flexible coverage. However, a UAV cannot operate for a long time due to limited battery life, and its caching capacity is also limited. For this, a multi-UAV content-caching strategy and cooperative, complementary content transmission among UAVs are jointly studied in this paper. Firstly, a user-clustering-based caching strategy is designed, where user clustering is based on user similarity, concurrently taking into consideration similarities in content preference and location. Then, cooperative, complementary content transmission between multiple UAVs is modeled as a coalition formation game (CFG) to maximize the utility of the whole network. Finally, the effectiveness of the proposed algorithms is demonstrated through numerical simulations.

Blockchain and Semi-Distributed Learning-Based Secure and Low-Latency Computation Offloading in Space-Air-Ground-Integrated Power IoT

Power systems impose stringent security and delay requirements on computation offloading, which cannot be satisfied by existing power Internet of Things (PIoT) networks. In this paper, we tackle this challenge by combining blockchain, space-air-ground integrated PIoT (SAG-PIoT) and machine learning. Low earth orbit (LEO) satellites assist in broadcasting a consensus message to reduce the block creation delay, and unmanned aerial vehicles (UAVs) provide flexible coverage enhancement. Specifically, we propose a Blockchain and semi-distributed leaRning-based secure and low-latency electromAgnetic interferenCe-awarE computation offloading algorithm (BRACE) to minimize the total queuing delay under the long-term security constraint. First, the task offloading is decoupled from the computational resource allocation by Lyapunov optimization. Second, the task offloading problem is solved by the proposed federated deep actor-critic-based electromagnetic interference-aware task offloading algorithm (FDAC-EMI). Finally, the resource allocation problem is solved by smooth approximation and Lagrange optimization. Simulation results verify that BRACE achieves superior delay and security performance.

BamToCov: an efficient toolkit for sequence coverage calculations.

MotivationMany genomics applications require the computation of nucleotide coverage of a reference genome or the ability to determine how many reads map to a reference region.ResultsBamToCov is a toolkit for rapid and flexible coverage computation that relies on the most memory efficient algorithm and is designed for integration in pipelines, given its ability to read alignment files from streams. The tools in the suite can process sorted BAM or CRAM files, allowing the user to extract coverage information via different filtering approaches and to save the output in different formats (BED, Wig or counts). The BamToCov algorithm can also handle strand-specific and/or physical coverage analyses.Availability and implementationThis program, accessory utilities and their documentation are freely available at https://github.com/telatin/BamToCov.Supplementary information Supplementary data are available at Bioinformatics online.

Evaluation of Using Dermis-Fat Graft in Oro-Nasal Fistula Repair

ABSTRACT Background One of the most devastating complication that follows cleft palate surgery is oro- nasal fistula. It can lead to many problems like fluid and food regurgitation, poor oral hygiene and retropharyngeal incompetence. The repair of palatal fistula had long presented a remarkable challenge to the plastic surgeon. Repair of these fistulae required the use of more than the usual two-layered closure procedures; demanding the introduction of a ‘Third layer’ of tissue to enhance smooth healing, and thereby reduce the chance of closure failure which predispose to fistulization. In this study we used an autogenous graft of ‘’ dermis and fat (dermis-fat) graft ‘’ to serve as the third protective, supportive and enhancive layer. Aim of the study: To evaluate the use of dermis-fat graft as autogenous tissue to achieve three layers closure of the palatal fistula. PATIENT AND METHOD The study included 22 patients ( 13 males, 9 females ), ranging in the ages between 2 to 40 years (13 males versus 9 females). The fistula size varies from 4 mm to 25 mm in diameter. Timing of fistula repair ranged between 6 months up to more than 4 years, Fistula assessment was done focusing on size, site, number and shape. Then assessment of surrounding tissue viability and pliability Results, operated upon, having 30 oro-nasal, We used minimal thickness of the graft ( 4-6 mm ) in 23 fistulae, and 7-8 mm thick graft in 7 ones. 24 cases was having full healing while in 2 fistulae partial wound dehiscence occurred. In another 2 fistulae there was obliteration of the buccal sulcus. While partial graft exposure in 2 other fistulae. We experienced no hematoma, or infection or airway obstruction. conclusion Using of interpositional dermis-fat graft had shown to have promising result in closure of oro-nasal fistula that followed cleft palate surgery. It provided durable, stable, and flexible coverage with minimal donor site morbidity. It can be used for fistula of less than 1.5 cm of various locations.

Partially Overlapping Channel Assignment Using Bonded and Non-Bonded Channels in IEEE 802.11n WLAN

Nowadays, wireless local area network (WLAN) has become prevalent Internet access due to its low-cost gadgets, flexible coverage and hassle-free simple wireless installation. WLAN facilitates wireless Internet services to users with mobile devices like smart phones, tablets, and laptops through deployment of multiple access points (APs) in a network field. Every AP operates on a frequency band called channel. Popular wireless standard such as IEEE 802.11n has a limited number of channels where frequency spectrum of adjacent channels overlaps partially with each other. In a crowded environment, users may experience poor Internet services due to channel collision i.e., interference from surrounding APs that affects the performance of the WLAN system. Therefore, it becomes a challenge to maintain expected performance in a crowded environment. A mathematical model of throughput considering interferences from surrounding APs can play an important role to set up a WLAN system properly. While set up, assignment of channels considering interference can maximize network performance. In this paper, we investigate the signal propagation of APs under interference of partially overlapping channels for both bonded and non-bonded channels. Then, a throughput estimation model is proposed using difference of operating channels and received signal strength indicator (RSSI). Then, a channel assignment algorithm is introduced using proposed throughput estimation model. Finally, the efficiency of the proposal is verified by numerical experiments using simulator. The results show that the proposal selects the best channel combination of bonded and non-bonded channels that maximize the performance.

Power Allocation and Energy Cooperation for UAV-Enabled MmWave Networks: A Multi-Agent Deep Reinforcement Learning Approach.

Unmanned Aerial Vehicle (UAV)-assisted cellular networks over the millimeter-wave (mmWave) frequency band can meet the requirements of a high data rate and flexible coverage in next-generation communication networks. However, higher propagation loss and the use of a large number of antennas in mmWave networks give rise to high energy consumption and UAVs are constrained by their low-capacity onboard battery. Energy harvesting (EH) is a viable solution to reduce the energy cost of UAV-enabled mmWave networks. However, the random nature of renewable energy makes it challenging to maintain robust connectivity in UAV-assisted terrestrial cellular networks. Energy cooperation allows UAVs to send their excessive energy to other UAVs with reduced energy. In this paper, we propose a power allocation algorithm based on energy harvesting and energy cooperation to maximize the throughput of a UAV-assisted mmWave cellular network. Since there is channel-state uncertainty and the amount of harvested energy can be treated as a stochastic process, we propose an optimal multi-agent deep reinforcement learning algorithm (DRL) named Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to solve the renewable energy resource allocation problem for throughput maximization. The simulation results show that the proposed algorithm outperforms the Random Power (RP), Maximal Power (MP) and value-based Deep Q-Learning (DQL) algorithms in terms of network throughput.