bluuebunny/crossref_metadata_2025 huggingface dataset:
LMDFS: A Lightweight Model for Detecting Forest Fire Smoke in UAV Images Based on YOLOv7
Authors: Chen Gong , Cheng Renxi , Lin Xufeng , Jiao Wanguo , Bai Di , Lin Haifeng
Published: July 2023

Forest fires pose significant hazards to ecological environments and economic society. The detection of forest fire smoke can provide crucial information for the suppression of early fires. Previous detection models based on deep learning have been limited in detecting small smoke and smoke with smoke-like interference. In this paper, we propose a lightweight model for forest fire smoke detection that is suitable for UAVs. Firstly, a smoke dataset is created from a combination of forest smoke photos obtained through web crawling and enhanced photos generated by using the method of synthesizing smoke. Secondly, the GSELAN and GSSPPFCSPC modules are built based on Ghost Shuffle Convolution (GSConv), which efficiently reduces the number of parameters in the model and accelerates its convergence speed. Next, to address the problem of indistinguishable feature boundaries between clouds and smoke, we integrate coordinate attention (CA) into the YOLO feature extraction network to strengthen the extraction of smoke features and attenuate the background information. Additionally, we use Content-Aware Reassembly of FEatures (CARAFE) upsampling to expand the receptive field in the feature fusion network and fully exploit the semantic information. Finally, we adopt SCYLLA-Intersection over Union (SIoU) loss as a replacement for the original loss function in the prediction phase. This substitution leads to improved convergence efficiency and faster convergence. The experimental results demonstrate that the LMDFS model proposed for smoke detection achieves an accuracy of 80.2% with a 5.9% improvement compared to the baseline and a high number of Frames Per Second (FPS)—63.4. The model also reduces the parameter count by 14% and Giga FLoating-point Operations Per second (GFLOPs) by 6%. These results suggest that the proposed model can achieve a high accuracy while requiring fewer computational resources, making it a promising approach for practical deployment in applications for detecting smoke.
Neural-Network-Based Equalization and Detection for Underwater Acoustic Orthogonal Frequency Division Multiplexing Communications: A Low-Complexity Approach
Authors: Zhou Mingzhang , Wang Junfeng , Feng Xiao , Sun Haixin , Qi Jie , Lin Rongbin
Published: July 2023

The performance of the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system is often restrained by time-varying channels with large delays. The existing frequency domain equalizers do not work well because of the high complexity and difficulty of finding the real-time signal-to-noise ratio. To solve these problems, we propose a low-complexity neural network (NN)-based scheme for joint equalization and detection. A simple NN structure is built to yield the detected symbols with the joint input of the segmented channel response and received symbol. The coherence bandwidth is investigated to find the optimal hyperparameters. By being completely trained offline with real channels, the proposed detector is applied independently in both simulations and sea trials. The results show that the proposed detector outperforms the ZF and MMSE equalizers and extreme learning machine (ELM)-based detectors in both the strongly reflected channels of the pool and time-variant channels of the shallow sea. The complexity of the proposed network is lower than the MMSE and ELM-based receiver.
Identification and Measurement of Shrinking Cities Based on Integrated Time-Series Nighttime Light Data: An Example of the Yangtze River Economic Belt
Authors: Tan Zhixiong , Xiang Siman , Wang Jiayi , Chen Siying
Published: July 2023

Urban shrinkage has gradually become an issue of world-concerning social matter. As urbanization progresses, some Chinese cities are experiencing population loss and economic decline. Our study attempts to correct and integrate DMSP/OLS and NPP/VIIRS data to complete the identification and measurement of shrinking cities in China’s Yangtze River Economic Belt (YREB). We identified 36 shrinking cities and 644 shrinking counties on the municipal and county scales. Based on this approach, we established the average urban shrinkage intensity index and the urban shrinkage frequency index, attempting to find out the causes of shrinking cities for different shrinkage characteristics, city types and shrinkage frequencies. The results show that (1) the shrinking cities are mainly concentrated in the Yangtze River Delta city cluster, the midstream city cluster and the Chengdu–Chongqing economic circle. (2) Most shrinking cities have a moderate frequency of shrinking, dominated by low–low clusters. Resource-based, heavy industrial, small and medium-sized cities are more inclined to shrink. (3) The single economic structure, the difficulty of industrial transformation and the lack of linkage among county-level cities are possible reasons for the urban shrinkage in the YREB. Exploring the causes of urban shrinkage from a more micro perspective will be an inevitable task for sustainable development in YREB and even in China.
Lessons for Sustainable Urban Development: Interplay of Construction, Groundwater Withdrawal, and Land Subsidence at Battersea, London
Authors: Agarwal Vivek , Kumar Amit , Qin Zhengyuan , Gomes Rachel L. , Marsh Stuart
Published: July 2023

The capacity of aquifers to store water and the stability of infrastructure can each be adversely influenced by variations in groundwater levels and subsequent land subsidence. Along the south bank of the River Thames, the Battersea neighbourhood of London is renovating a vast 42-acre (over 8 million sq ft) former industrial brownfield site to become host to a community of homes, shops, bars, restaurants, cafes, offices, and over 19 acres of public space. For this renovation, between 2016 and 2020, a significant number of bearing piles and secant wall piles, with diameters ranging from 450 mm to 2000 mm and depths of up to 60 m, were erected inside the Battersea Power Station. Additionally, there was considerable groundwater removal that caused the water level to drop by 2.55 ± 0.4 m/year between 2016 and 2020, as shown by Environment Agency data. The study reported here used Sentinel-1 C-band radar images and the persistent scatterer interferometric synthetic aperture radar (PSInSAR) methodology to analyse the associated land movement for Battersea, London, during this period. The average land subsidence was found to occur at the rate of −6.8 ± 1.6 mm/year, which was attributed to large groundwater withdrawals and underground pile construction for the renovation work. Thus, this study underscores the critical interdependence between civil engineering construction, groundwater management, and land subsidence. It emphasises the need for holistic planning and sustainable development practices to mitigate the adverse effects of construction on groundwater resources and land stability. By considering the Sustainable Development Goals (SDGs) outlined by the United Nations, particularly Goal 11 (Sustainable Cities and Communities) and Goal 6 (Clean Water and Sanitation), city planners and stakeholders can proactively address these interrelated challenges.
Multi-Difference Image Fusion Change Detection Using a Visual Attention Model on VHR Satellite Data
Authors: Luo Jianhui , Chen Qiang , Wang Lei , Huang Yixiao
Published: July 2023

For very-high-resolution (VHR) remote sensing images with complex objects and rich textural information, multi-difference image fusion has been proven as an effective method to improve the performance of change detection. However, errors are superimposed during this process and a single spectral feature cannot fully utilize the correlation between pixels, resulting in low robustness. To overcome these problems and optimize the performance of multi-difference image fusion in change detection, we propose a novel multi-difference image fusion change detection method based on a visual attention model (VA-MDCD). First, we construct difference images using change vector analysis (CVA) and spectral gradient difference (SGD). Second, we use the visual attention model to calculate multiple color, intensity and orientation features of the difference images to obtain the difference saliency images. Third, we use the wavelet transform fusion algorithm to fuse two saliency images. Finally, we execute the OTSU threshold segmentation algorithm (OTSU) to obtain the final change detection map. To validate the effectiveness of VA-MDCD on VHR images, two datasets of Jilin 1 and Beijing 2 are selected for experiments. Compared with classical methods, the proposed method has a better performance with fewer missed alarms (MA) and false alarms (FA), which proves that the method has a strong robustness and generalization ability. The F-measure of the two datasets is 0.6671 and 0.7313, respectively. In addition, the results of ablation experiments confirm that the three feature extraction modules of the model all play a positive role.
Multi-UAV Mapping and Target Finding in Large, Complex, Partially Observable Environments
Authors: Walker Violet , Vanegas Fernando , Gonzalez Felipe
Published: July 2023

Coordinating multiple unmanned aerial vehicles (UAVs) for the purposes of target finding or surveying points of interest in large, complex, and partially observable environments remains an area of exploration. This work proposes a modeling approach and software framework for multi-UAV search and target finding within large, complex, and partially observable environments. Mapping and path-solving is carried out by an extended NanoMap library; the global planning problem is defined as a decentralized partially observable Markov decision process and solved using an online model-based solver, and the local control problem is defined as two separate partially observable Markov decision processes that are solved using deep reinforcement learning. Simulated testing demonstrates that the proposed framework enables multiple UAVs to search and target-find within large, complex, and partially observable environments.
Object-Oriented Remote Sensing Approaches for the Detection of Terrestrial Impact Craters as a Reconnaissance Survey
Authors: Emmanuel Habimana , Yu Jaehyung , Wang Lei , Choi Sung Hi , Rwatangabo Digne Edmond Rwabuhungu
Published: July 2023

The purpose of this study is to employ a remote sensing reconnaissance survey based on optimal segmentation parameters and an object-oriented random forest approach to the identification of possible terrestrial impact craters from the global 30-m resolution SRTM DEM. A dataset consisting of 94 confirmed and well-preserved terrestrial impact craters, 104 volcanic calderas, and 124 valleys were extracted from real-world surface features. For craters with different sizes, eight optimal scale parameters from 80 to 3000 have been identified using multi-resolution segmentation, where the scale parameters have a positive correlation (R2 = 0.78) with the diameters of craters. The object-oriented random forest approach classified the tested impact craters, volcanic calderas, and valleys with an overall accuracy of 88.4% and a Kappa coefficient of 0.8. The investigated terrestrial impact craters, in general, have relatively lower rim circularity, higher length-to-width ratio, and lower relief, slope, and elevation than volcanic calderas. The topographic characteristics can be explained by geological processes associated with the formation and post-deformation of impact craters. The excavation and ejection by initial impact and rebound of excavated materials contribute to low elevation. The post-impact deformation, including inward collapse and slump of unstable rims, weathering, erosion, and sediment deposition, further reduces elevation and relief and modifies shapes resulting in lower circularity and higher length-to-width ratio. Due to the resolution limitation of the source DEM data and the number of real-world samples, the model has only been validated for craters of 0.88 to 100 km in diameter, which can be generalized to explore undiscovered terrestrial impact craters using cloud computing with global datasets provided by platforms such as Google Earth Engine and Microsoft Planetary Computer.
Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning
Authors: Lee Jae-Hyeon , Son Chang-Hwan
Published: July 2023

Pest counting, which predicts the number of pests in the early stage, is very important because it enables rapid pest control, reduces damage to crops, and improves productivity. In recent years, light traps have been increasingly used to lure and photograph pests for pest counting. However, pest images have a wide range of variability in pest appearance owing to severe occlusion, wide pose variation, and even scale variation. This makes pest counting more challenging. To address these issues, this study proposes a new pest counting model referred to as multiscale and deformable attention CenterNet (Mada-CenterNet) for internal low-resolution (LR) and high-resolution (HR) joint feature learning. Compared with the conventional CenterNet, the proposed Mada-CenterNet adopts a multiscale heatmap generation approach in a two-step fashion to predict LR and HR heatmaps adaptively learned to scale variations, that is, changes in the number of pests. In addition, to overcome the pose and occlusion problems, a new between-hourglass skip connection based on deformable and multiscale attention is designed to ensure internal LR and HR joint feature learning and incorporate geometric deformation, thereby resulting in improved pest counting accuracy. Through experiments, the proposed Mada-CenterNet is verified to generate the HR heatmap more accurately and improve pest counting accuracy owing to multiscale heatmap generation, joint internal feature learning, and deformable and multiscale attention. In addition, the proposed model is confirmed to be effective in overcoming severe occlusions and variations in pose and scale. The experimental results show that the proposed model outperforms state-of-the-art crowd counting and object detection models.
AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
Authors: Jiang Yi , Sun Hui , Yang Muyan
Published: July 2023

The Ad Hoc On-demand Distance Vector (AODV) is a routing protocol for mobile ad hoc networks (MANETs) and other wireless ad hoc networks. The vanilla AODV protocol is simple and easy to implement because it only uses the hop count as a routing metric. Single-metric route determination also causes problems, such as network congestion and energy exhaustion, which limit the usage of AODV in resource-limited applications. To solve these problems, the authors propose a new routing protocol that combines the analytic hierarchy process (AHP), the entropy weight method (EWM), and AODV. The proposed protocol uses energy, congestion, and the hop count as metrics and weights these three metrics using AHP and EWM. To address the importance of energy in applications, such as drones, the proposed protocol chooses different comparison matrices for AHP at different node residual energy levels. Finally, the node chooses the best route link according to the score (sum of weighted metrics). It is also suitable for wireless sensor networks because the proposed protocol considers the residual energy of the node. The simulation results show that the improved routing protocol can effectively reduce the average end-to-end delay and energy consumption and prolong the lifetime of the whole network.
Unlocking Insights in IoT-Based Patient Monitoring: Methods for Encompassing Large-Data Challenges
Authors: Waleed Muhammad , Kamal Tariq , Um Tai-Won , Hafeez Abdul , Habib Bilal , Skouby Knud Erik
Published: July 2023

The remote monitoring of patients using the internet of things (IoT) is essential for ensuring continuous observation, improving healthcare, and decreasing the associated costs (i.e., reducing hospital admissions and emergency visits). There has been much emphasis on developing methods and approaches for remote patient monitoring using IoT. Most existing frameworks cover parts or sub-parts of the overall system but fail to provide a detailed and well-integrated model that covers different layers. The leverage of remote monitoring tools and their coupling with health services requires an architecture that handles data flow and enables significant interventions. This paper proposes a cloud-based patient monitoring model that enables IoT-generated data collection, storage, processing, and visualization. The system has three main parts: sensing (IoT-enabled data collection), network (processing functions and storage), and application (interface for health workers and caretakers). In order to handle the large IoT data, the sensing module employs filtering and variable sampling. This pre-processing helps reduce the data received from IoT devices and enables the observation of four times more patients compared to not using edge processing. We also discuss the flow of data and processing, thus enabling the deployment of data visualization services and intelligent applications.
Variations in Concurrent Validity of Two Independent Inertial Measurement Units Compared to Gold Standard for Upper Body Posture during Computerised Device Use
Authors: Lee Roger , Akhundov Riad , James Carole , Edwards Suzi , Snodgrass Suzanne J.
Published: July 2023

Inertial measurement units (IMUs) may provide an objective method for measuring posture during computer use, but research is needed to validate IMUs’ accuracy. We examine the concurrent validity of two different IMU systems in measuring three-dimensional (3D) upper body posture relative to a motion capture system (Mocap) as a potential device to assess postures outside a laboratory environment. We used 3D Mocap and two IMU systems (Wi-Fi and Bluetooth) to capture the upper body posture of twenty-six individuals during three physical computer working conditions (monitor correct, monitor raised, and laptop). Coefficient of determination (R2) and root-mean-square error (RMSE) compared IMUs to Mocap. Head/neck segment [HN], upper trunk segment [UTS], and joint angle [HN-UTS] were the primary variables. Wi-Fi IMUs demonstrated high validity for HN and UTS (sagittal plane) and HN-UTS (frontal plane) for all conditions, and for HN rotation movements (both for the monitor correct and monitor raised conditions), others moderate to poor. Bluetooth IMUs for HN, and UTS (sagittal plane) for the monitor correct, laptop, and monitor raised conditions were moderate. Frontal plane movements except UTS (monitor correct and laptop) and all rotation had poor validity. Both IMU systems were affected by gyroscopic drift with sporadic data loss in Bluetooth IMUs. Wi-Fi IMUs had more acceptable accuracy when measuring upper body posture during computer use compared to Mocap, except for trunk rotations. Variation in IMU systems’ performance suggests validation in the task-specific movement(s) is essential.
HealthLock: Blockchain-Based Privacy Preservation Using Homomorphic Encryption in Internet of Things Healthcare Applications
Authors: Ali Aitizaz , Al-rimy Bander Ali Saleh , Alsubaei Faisal S. , Almazroi Abdulwahab Ali , Almazroi Abdulaleem Ali
Published: July 2023

The swift advancement of the Internet of Things (IoT), coupled with the growing application of healthcare software in this area, has given rise to significant worries about the protection and confidentiality of critical health data. To address these challenges, blockchain technology has emerged as a promising solution, providing decentralized and immutable data storage and transparent transaction records. However, traditional blockchain systems still face limitations in terms of preserving data privacy. This paper proposes a novel approach to enhancing privacy preservation in IoT-based healthcare applications using homomorphic encryption techniques combined with blockchain technology. Homomorphic encryption facilitates the performance of calculations on encrypted data without requiring decryption, thus safeguarding the data’s privacy throughout the computational process. The encrypted data can be processed and analyzed by authorized parties without revealing the actual contents, thereby protecting patient privacy. Furthermore, our approach incorporates smart contracts within the blockchain network to enforce access control and to define data-sharing policies. These smart contracts provide fine-grained permission settings, which ensure that only authorized entities can access and utilize the encrypted data. These settings protect the data from being viewed by unauthorized parties. In addition, our system generates an audit record of all data transactions, which improves both accountability and transparency. We have provided a comparative evaluation with the standard models, taking into account factors such as communication expense, transaction volume, and security. The findings of our experiments suggest that our strategy protects the confidentiality of the data while at the same time enabling effective data processing and analysis. In conclusion, the combination of homomorphic encryption and blockchain technology presents a solution that is both resilient and protective of users’ privacy for healthcare applications integrated with IoT. This strategy offers a safe and open setting for the management and exchange of sensitive patient medical data, while simultaneously preserving the confidentiality of the patients involved.
Conditional Generative Models for Dynamic Trajectory Generation and Urban Driving
Authors: Paz David , Zhang Hengyuan , Xiang Hao , Liang Andrew , Christensen Henrik I.
Published: July 2023

This work explores methodologies for dynamic trajectory generation for urban driving environments by utilizing coarse global plan representations. In contrast to state-of-the-art architectures for autonomous driving that often leverage lane-level high-definition (HD) maps, we focus on minimizing required map priors that are needed to navigate in dynamic environments that may change over time. To incorporate high-level instructions (i.e., turn right vs. turn left at intersections), we compare various representations provided by lightweight and open-source OpenStreetMaps (OSM) and formulate a conditional generative model strategy to explicitly capture the multimodal characteristics of urban driving. To evaluate the performance of the models introduced, a data collection phase is performed using multiple full-scale vehicles with ground truth labels. Our results show potential use cases in dynamic urban driving scenarios with real-time constraints. The dataset is released publicly as part of this work in combination with code and benchmarks.
A Novel Closed-Loop Structure for Drag-Free Control Systems with ESKF and LQR
Authors: Ye Xiaorong , Lian Junxiang , Zhao Guoying , Zhang Dexuan
Published: July 2023

Space-borne gravitational wave detection satellite confronts many uncertain perturbations, such as solar pressure, dilute atmospheric drag, etc. To realize an ultra-static and ultra-stable inertial benchmark achieved by a test-mass (TM) being free to move inside a spacecraft (S/C), the drag-free control system of S/C requires super high steady-state accuracies and dynamic performances. The Active Disturbance Rejection Control (ADRC) technique has a certain capability in solving problems with common perturbations, while there is still room for optimization in dealing with the complicated drag-free control problem. When faced with complex noises, the steady-state accuracy of the traditional control method is not good enough and the convergence speed of regulating process is not fast enough. In this paper, the optimized Active Disturbance Rejection Control technique is applied. With the extended state Kalman filter (ESKF) estimating the states and disturbances in real time, a novel closed-loop control structure is designed by combining the linear quadratic regulator (LQR) and ESKF, which can satisfy the design targets competently. The comparative analysis and simulation results show that the LQR controller designed in this paper has a faster response and a higher accuracy compared with the traditional nonlinear state error feedback (NSEF), which uses a deformation of weighting components of classical PID. The new drag-free control structure proposed in the paper can be used in future gravitational wave detection satellites.
BSN-ESC: A Big–Small Network-Based Environmental Sound Classification Method for AIoT Applications
Authors: Peng Lujie , Yang Junyu , Yan Longke , Chen Zhiyi , Xiao Jianbiao , Zhou Liang , Zhou Jun
Published: July 2023

In recent years, environmental sound classification (ESC) has prevailed in many artificial intelligence Internet of Things (AIoT) applications, as environmental sound contains a wealth of information that can be used to detect particular events. However, existing ESC methods have high computational complexity and are not suitable for deployment on AIoT devices with constrained computing resources. Therefore, it is of great importance to propose a model with both high classification accuracy and low computational complexity. In this work, a new ESC method named BSN-ESC is proposed, including a big–small network-based ESC model that can assess the classification difficulty level and adaptively activate a big or small network for classification as well as a pre-classification processing technique with logmel spectrogram refining, which prevents distortion in the frequency-domain characteristics of the sound clip at the joint part of two adjacent sound clips. With the proposed methods, the computational complexity is significantly reduced, while the classification accuracy is still high. The proposed BSN-ESC model is implemented on both CPU and FPGA to evaluate its performance on both PC and embedded systems with the dataset ESC-50, which is the most commonly used dataset. The proposed BSN-ESC model achieves the lowest computational complexity with the number of floating-point operations (FLOPs) of only 0.123G, which represents a reduction of up to 2309 times in computational complexity compared with state-of-the-art methods while delivering a high classification accuracy of 89.25%. This work can achieve the realization of ESC being applied to AIoT devices with constrained computational resources.
Immunosensors for Autoimmune-Disease-Related Biomarkers: A Literature Review
Authors: Karachaliou Chrysoula-Evangelia , Livaniou Evangelia
Published: July 2023

Immunosensors are a special class of biosensors that employ specific antibodies for biorecognition of the target analyte. Immunosensors that target disease biomarkers may be exploited as tools for disease diagnosis and/or follow-up, offering several advantages over conventional analytical techniques, such as rapid and easy analysis of patients’ samples at the point-of-care. Autoimmune diseases have been increasingly prevalent worldwide in recent years, while the COVID-19 pandemic has also been associated with autoimmunity. Consequently, demand for tools enabling the early and reliable diagnosis of autoimmune diseases is expected to increase in the near future. To this end, interest in immunosensors targeting autoimmune disease biomarkers, mainly, various autoantibodies and specific pro-inflammatory proteins (e.g., specific cytokines), has been rekindled. This review article presents most of the immunosensors proposed to date as potential tools for the diagnosis of various autoimmune diseases, such as type 1 diabetes, rheumatoid arthritis, and multiple sclerosis. The signal transduction and the immunoassay principles of each immunosensor have been suitably classified and are briefly presented along with certain sensor elements, e.g., special nano-sized materials used in the construction of the immunosensing surface. The main concluding remarks are presented and future perspectives of the field are also briefly discussed.
Design and Verification of Deep Submergence Rescue Vehicle Motion Control System
Authors: Jiang Chunmeng , Zhang Hongrui , Wan Lei , Lv Jinhua , Wang Jianguo , Tang Jian , Wu Gongxing , He Bin
Published: July 2023

A six degree-of-freedom (DOF) motion control system for docking with a deep submergence rescue vehicle (DSRV) test platform was the focus of this study. The existing control methods can meet the general requirements of underwater operations, but the complex structures or multiple parameters of some methods have prevented them from widespread use. The majority of the existing methods assume the heeling effect to be negligible and ignore it, achieving motion control in only four or five DOFs. In view of the demanding requirements regarding positions and inclinations in six DOFs during the docking process, the software and hardware architectures of the DSRV platform were constructed, and then sparse filtering technology was introduced for data smoothing. Based on the adaptive control strategy and with a consideration of residual static loads, an improved S-plane control method was developed. By converting the force (moment) calculated by the controller to the body coordinate system, the complexity of thrust allocation was effectively reduced, and the challenge of thrust allocation in the case of a high inclination during dynamic positioning was solved accordingly. The automatic control of the trimming angle and heeling angle was realized with the linkage system of the ballast tank and pump valve. A PID method based on an intelligent integral was proposed, which not only dealt with the integral “saturation” problem, but also reduced the steady-state error and overshooting. Water pool experiments and sea trials were carried out in the presence of water currents for six-DOF motion control. The responsiveness and precision of the control system were verified by the pool experiment and sea trial results and could meet the control requirements in engineering practice. The reliability and operational stability of the proposed control system were also verified in a long-distance cruise.
Ammonia and Humidity Sensing by Phthalocyanine–Corrole Complex Heterostructure Devices
Authors: Di Zazzo Lorena , Ganesh Moorthy Sujithkumar , Meunier-Prest Rita , Lesniewska Eric , Di Natale Corrado , Paolesse Roberto , Bouvet Marcel
Published: July 2023

The versatility of metal complexes of corroles has raised interest in the use of these molecules as elements of chemical sensors. The tuning of the macrocycle properties via synthetic modification of the different components of the corrole ring, such as functional groups, the molecular skeleton, and coordinated metal, allows for the creation of a vast library of corrole-based sensors. However, the scarce conductivity of most of the aggregates of corroles limits the development of simple conductometric sensors and requires the use of optical or mass transducers that are rather more cumbersome and less prone to be integrated into microelectronics systems. To compensate for the scarce conductivity, corroles are often used to functionalize the surface of conductive materials such as graphene oxide, carbon nanotubes, or conductive polymers. Alternatively, they can be incorporated into heterojunction devices where they are interfaced with a conductive material such as a phthalocyanine. Herewith, we introduce two heterostructure sensors combining lutetium bisphthalocyanine (LuPc2) with either 5,10,15-tris(pentafluorophenyl) corrolato Cu (1) or 5,10,15-tris(4-methoxyphenyl)corrolato Cu (2). The optical spectra show that after deposition, corroles maintain their original structure. The conductivity of the devices reveals an energy barrier for interfacial charge transport for 1/LuPc2, which is a heterojunction device. On the contrary, only ohmic contacts are observed in the 2/LuPc2 device. These different electrical properties, which result from the different electron-withdrawing or -donating substituents on corrole rings, are also manifested by the opposite response with respect to ammonia (NH3), with 1/LuPc2 behaving as an n-type conductor and 2/LuPC2 behaving as a p-type conductor. Both devices are capable of detecting NH3 down to 10 ppm at room temperature. Furthermore, the sensors show high sensitivity with respect to relative humidity (RH) but with a reversible and fast response in the range of 30–60% RH.
Every Vessel Counts: Neural Network Based Maritime Traffic Counting System
Authors: Petković Miro , Vujović Igor , Kaštelan Nediljko , Šoda Joško
Published: July 2023

Monitoring and counting maritime traffic is important for efficient port operations and comprehensive maritime research. However, conventional systems such as the Automatic Identification System (AIS) and Vessel Traffic Services (VTS) often do not provide comprehensive data, especially for the diverse maritime traffic in Mediterranean ports. The paper proposes a real-time vessel counting system using land-based cameras is proposed for maritime traffic monitoring in ports, such as the Port of Split, Croatia. The system consists of a YOLOv4 Convolutional Neural Network (NN), trained and validated on the new SPSCD dataset, that classifies the vessels into 12 categories. Further, the Kalman tracker with Hungarian Assignment (HA) algorithm is used as a multi-target tracker. A stability assessment is proposed to complement the tracking algorithm to reduce false positives by unwanted objects (non-vessels). The evaluation results show that the system has an average counting accuracy of 97.76% and an average processing speed of 31.78 frames per second, highlighting its speed, robustness, and effectiveness. In addition, the proposed system captured 386% more maritime traffic data than conventional AIS systems, highlighting its immense potential for supporting comprehensive maritime research.
Research on the Prediction Method of Braking Rotation Angle for Remote-Controlled Excavator
Authors: Lin Yutong , Xiong Jingqi , Zhu Wenlian , Sun Rui
Published: July 2023

To calculate, analyze, and predict the rotation angle during the deceleration and braking process of large remote-controlled excavators, this article established a spatial coordinate system based on a simplified model of a hydraulic excavator’s upper structure. Using the D-H parameter method, a mathematical model of the working device’s center of gravity and its rotational inertia was established. Based on the characteristics of the excavator’s hydraulic system and the relationship between brake torque variations, a prediction model was developed to forecast the stopping position (brake rotary angle) of the excavator’s bucket after braking. Subsequently, the predicted results were validated using simulation and compared with existing experimental data to assess the accuracy of the model. The findings demonstrate that the predictive model exhibited high precision with minimal error. The utilization of this model enabled effective forecasting of the excavator’s braking position changes, providing a theoretical foundation for the intelligent remote control of excavators.
Weak Localization of Radiographic Manifestations in Pulmonary Tuberculosis from Chest X-ray: A Systematic Review
Authors: Feyisa Degaga Wolde , Ayano Yehualashet Megersa , Debelee Taye Girma , Schwenker Friedhelm
Published: July 2023

Pulmonary tuberculosis (PTB) is a bacterial infection that affects the lung. PTB remains one of the infectious diseases with the highest global mortalities. Chest radiography is a technique that is often employed in the diagnosis of PTB. Radiologists identify the severity and stage of PTB by inspecting radiographic features in the patient’s chest X-ray (CXR). The most common radiographic features seen on CXRs include cavitation, consolidation, masses, pleural effusion, calcification, and nodules. Identifying these CXR features will help physicians in diagnosing a patient. However, identifying these radiographic features for intricate disorders is challenging, and the accuracy depends on the radiologist’s experience and level of expertise. So, researchers have proposed deep learning (DL) techniques to detect and mark areas of tuberculosis infection in CXRs. DL models have been proposed in the literature because of their inherent capacity to detect diseases and segment the manifestation regions from medical images. However, fully supervised semantic segmentation requires several pixel-by-pixel labeled images. The annotation of such a large amount of data by trained physicians has some challenges. First, the annotation requires a significant amount of time. Second, the cost of hiring trained physicians is expensive. In addition, the subjectivity of medical data poses a difficulty in having standardized annotation. As a result, there is increasing interest in weak localization techniques. Therefore, in this review, we identify methods employed in the weakly supervised segmentation and localization of radiographic manifestations of pulmonary tuberculosis from chest X-rays. First, we identify the most commonly used public chest X-ray datasets for tuberculosis identification. Following that, we discuss the approaches for weakly localizing tuberculosis radiographic manifestations in chest X-rays. The weakly supervised localization of PTB can highlight the region of the chest X-ray image that contributed the most to the DL model’s classification output and help pinpoint the diseased area. Finally, we discuss the limitations and challenges of weakly supervised techniques in localizing TB manifestations regions in chest X-ray images.
Evaluating the Performance of Proposed Switched Beam Antenna Systems in Dynamic V2V Communication Networks
Authors: Ahmed Tahir H. , Tiang Jun Jiat , Mahmud Azwan , Gwo-Chin Chung , Do Dinh-Thuan
Published: July 2023

This paper develops a novel approach for reliable vehicle-to-vehicle (V2V) communication in various environments. A switched beam antenna is deployed at the transmitting and receiving points, with a beam management system that concentrates the power in each beam using a low-computation algorithm and a potential mathematical model. The algorithm is designed to be flexible for various environments faced by vehicles. Additionally, an anti-failure system is proposed in case the intelligent transportation system (ITS) system fails to retrieve real-time Packet Delivery Ratio (PDR) values related to traffic density. Performance metrics include the time to collision in seconds, the bit error rate (BER), the packet error rate (PER), the average throughput (Mbps), the beam selection probability, and computational complexity factors. The proposed system is compared with traditional systems. Extensive experiments, simulations, and comparisons show that the proposed approach is excellent and reliable for vehicular systems. The proposed study demonstrates an average throughput of 1.7 Mbps, surpassing conventional methods’ typical throughput of 1.35 Mbps. Moreover, the bit error rate (BER) of the proposed study is reduced by a factor of 0.1. Additionally, the proposed framework achieves a beam power efficiency of touching to 100% at computational factor of 34. These metrics indicate that the proposed method is both efficient and sufficiently robust.
Estimated Nucleotide Reconstruction Quality Symbols of Basecalling Tools for Oxford Nanopore Sequencing
Authors: Kuśmirek Wiktor
Published: July 2023

Currently, one of the fastest-growing DNA sequencing technologies is nanopore sequencing. One of the key stages involved in processing sequencer data is the basecalling process, where the input sequence of currents measured on the nanopores of the sequencer reproduces the DNA sequences, called DNA reads. Many of the applications dedicated to basecalling, together with the DNA sequence, provide the estimated quality of the reconstruction of a given nucleotide (quality symbols are contained on every fourth line of the FASTQ file; each nucleotide in the FASTQ file corresponds to exactly one estimated nucleotide reconstruction quality symbol). Herein, we compare the estimated nucleotide reconstruction quality symbols (signs from every fourth line of the FASTQ file) reported by other basecallers. The conducted experiments consisted of basecalling the same raw datasets from the nanopore device by other basecallers and comparing the provided quality symbols, denoting the estimated quality of the nucleotide reconstruction. The results show that the estimated quality reported by different basecallers may vary, depending on the tool used, particularly in terms of range and distribution. Moreover, we mapped basecalled DNA reads to reference genomes and calculated matched and mismatched rates for groups of nucleotides with the same quality symbol. Finally, the presented paper shows that the estimated nucleotide reconstruction quality reported in the basecalling process is not used in any investigated tool for processing nanopore DNA reads.
Robust Resource Control Based on AP Selection in 6G-Enabled IoT Networks
Authors: Taneja Ashu , Alqahtani Ali , Saluja Nitin , Alqahtani Nayef
Published: July 2023

The diverse application vertices of internet-of-things (IoT) including internet of vehicles (IoV), industrial IoT (IIoT) and internet of drones things (IoDT) involve intelligent communication between the massive number of objects around us. This digital transformation strives for seamless data flow, uninterrupted communication capabilities, low latency and ultra-high reliability. The limited capabilities of fifth generation (5G) technology have given way to sixth generation (6G) wireless technology. This paper presents a dynamic cell-free framework for a 6G-enabled IoT network. A number of access points (APs) are distributed over a given geographical area to serve a large number of user nodes. A pilot-based AP selection (PBAS) algorithm is proposed, which offers robust resource control through AP selection based on pilots. Selecting a subset of APs against all APs for each user node results in improved performance. In this paper, the performance of the proposed transmission model is evaluated for the achieved data rate and spectral efficiency using the proposed algorithm. It is shown that the proposed PBAS algorithm improves the spectral efficiency by 22% at the cell-edge and 1.5% at the cell-center. A comparison of the different combining techniques used at different user locations is also provided, along with the mathematical formulations. Finally, the proposed model is compared with two other transmission models for performance evaluation. It is observed that the spectral efficiency achieved by an edge node with the proposed scheme is 5.3676 bits/s/Hz, compared to 0.756 bits/s/Hz and 1.0501 bits/s/Hz, attained with transmission schemes 1 and 2, respectively.
Capture-Aware Dense Tag Identification Using RFID Systems in Vehicular Networks
Authors: Xu Weijian , Song Zhongzhe , Sun Yanglong , Wang Yang , Lai Lianyou
Published: July 2023

Passive radio-frequency identification (RFID) systems have been widely applied in different fields, including vehicle access control, industrial production, and logistics tracking, due to their ability to improve work quality and management efficiency at a low cost. However, in an intersection situation where tags are densely distributed with vehicle gathering, the wireless channel becomes extremely complex, and the readers on the roadside may only decode the information from the strongest tag due to the capture effect, resulting in tag misses and considerably reducing the performance of tag identification. Therefore, it is crucial to design an efficient and reliable tag-identification algorithm in order to obtain information from vehicle and cargo tags under adverse traffic conditions, ensuring the successful application of RFID technology. In this paper, we first establish a Nakagami-m distributed channel capture model for RFID systems and provide an expression for the capture probability, where each channel is modeled as any relevant Nakagami-m distribution. Secondly, an advanced capture-aware tag-estimation scheme is proposed. Finally, extensive Monte Carlo simulations show that the proposed algorithm has strong adaptability to circumstances for capturing under-fading channels and outperforms the existing algorithms in terms of complexity and reliability of tag identification.
Performance Evaluation for Clinical Stroke Rehabilitation via an Automatic Mobile Gait Trainer
Authors: Shih Chih-Jen , Li You-Chi , Yuan Wei , Chen Szu-Fu , Lin Ang-Chieh , Lin Tzu-Tung , Wang Fu-Cheng
Published: July 2023

This paper investigates the clinical efficacy of an automatic mobile trainer for gait training in stroke patients. Neuro-Developmental Treatment (NDT) is a rehabilitation method for stroke patients that enhances motor learning through repeated practice. Despite the proven effectiveness of therapist-assisted NDT, it is labor-intensive and demands health resources. Therefore, we developed automatic trainers based on NDT principles to perform gait training. This paper modifies the mobile trainer’s intervention patterns to improve the subject’s longitudinal gait symmetry, lateral pelvic displacement symmetry, and pelvic rotation. We first invited ten healthy subjects to test the modified trainer and then recruited 26 stroke patients to undergo the same gait training. Longitudinal symmetry, lateral symmetry, and pelvic rotation were assessed before, during, and after the intervention. Most subjects show improvements in longitudinal symmetry, lateral symmetry, and pelvic rotation after using the trainer. These results confirm the trainer’s effectiveness of the modified intervention schemes in helping clinical gait rehabilitation for stroke patients.
Determination of Greenhouse Gas Concentrations from the 16U CubeSat Spacecraft Using Fourier Transform Infrared Spectroscopy
Authors: Mayorova Vera , Morozov Andrey , Golyak Iliya , Golyak Igor , Lazarev Nikita , Melnikova Valeriia , Rachkin Dmitry , Svirin Victor , Tenenbaum Stepan , Vintaykin Ivan , Anfimov Dmitriy , Fufurin Igor
Published: July 2023

Greenhouse gases absorb the Earth’s thermal radiation and partially return it to the Earth’s surface. When accumulated in the atmosphere, greenhouse gases lead to an increase in the average global air temperature and, as a result, climate change. In this paper, an approach to measuring CO2 and CH4 concentrations using Fourier transform infrared spectroscopy (FTIR) is proposed. An FTIR spectrometer mockup, operating in the wavelength range from 1.0 to 1.7 μm with a spectral resolution of 10 cm−1, is described. The results of CO2 and CH4 observations throughout a day in urban conditions are presented. A low-resolution FTIR spectrometer for the 16U CubeSat spacecraft is described. The FTIR spectrometer has a 2.0–2.4 μm spectral range for CO2 and CH4 bands, a 0.75–0.80 μm range for reference O2 bands, an input field of view of 10−2 rad and a spectral resolution of 2 cm−1. The capabilities of the 16U CubeSat spacecraft for remote sensing of greenhouse gas emissions using a developed FTIR spectrometer are discussed. The design of a 16U CubeSat spacecraft equipped with a compact, low-resolution FTIR spectrometer is presented.