bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatiotemporal Variation in Vegetation Growth Status and Its Response to Climate in the Three-River Headwaters Region, China
Authors: He Chenyang , Yan Feng , Wang Yanjiao , Lu Qi
Published: October 2022

The Three-River Headwaters Region (TRHR), located in the hinterland of the Qinghai–Tibet Plateau (QTP), is an important water-conservation and ecological-function reserve in China. Studies of the growth of vegetation in the TRHR and its response to climate under the background of global warming are of great relevance for ecological protection of the QTP. In this study, based on MOD13Q1 Enhanced Vegetation Index (EVI) data and ERA5-Land climate data, the ensemble empirical mode decomposition method, random forest algorithm, and Hurst exponent were used to detect the spatiotemporal dynamics and response to climate change in TRHR vegetation during 2000–2021. The results indicated the following. (1) Comparatively, the condition of vegetation growth was better in 2021, 2010, and 2018 and poorer in 2015, 2003, and 2008. The EVI gradually decreased from the southeast to the northwest, and the area of improved vegetation growth was larger than the area of degraded vegetation growth. (2) The area of zones with either monotonous greening or monotonous browning of vegetation was 30.30% and 6.30%, respectively, and the trend of reversed vegetation change occurred in 63.40% of the areas. The area of future degradation of vegetation in the TRHR was larger than the area of future improvement, and the risk of vegetation degradation was higher. (3) Precipitation and soil temperature are the main and secondary driving factors of vegetation change in the TRHR, respectively. Warming and humidification of the QTP climate play major roles in the improvement of vegetation growth in the TRHR.
Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method
Authors: Liu Yuanyuan , Wang Shaoqiang , Chen Jinghua , Chen Bin , Wang Xiaobo , Hao Dongze , Sun Leigang
Published: October 2022

As the second largest rice producer, India contributes about 20% of the world’s rice production. Timely, accurate, and reliable rice yield prediction in India is crucial for global food security and health issues. Deep learning models have achieved excellent performances in predicting crop yield. However, the interpretation of deep learning models is still rare. In this study, we proposed a transformer-based model, Informer, to predict rice yield across the Indian Indo-Gangetic Plains by integrating time-series satellite data, environmental variables, and rice yield records from 2001 to 2016. The results showed that Informer had better performance (R2 = 0.81, RMSE = 0.41 t/ha) than four other machine learning and deep learning models for end-of-season prediction. For within-season prediction, the Informer model could achieve stable performances (R2 ≈ 0.78) after late September, which indicated that the optimal prediction could be achieved 2 months before rice maturity. In addition, we interpreted the prediction models by evaluating the input feature importance and analyzing hidden features. The evaluation of feature importance indicated that NIRV was the most critical factor, while intervals 6 (mid-August) and 12 (mid-November) were the key periods for rice yield prediction. The hidden feature analysis demonstrated that the attention-based long short-term memory (AtLSTM) model accumulated the information of each growth period, while the Informer model focused on the information around intervals 5 to 6 (August) and 11 to 12 (November). Our findings provided a reliable and simple framework for crop yield prediction and a new perspective for explaining the internal mechanism of deep learning models.
An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy
Authors: Fathololoumi Solmaz , Karimi Firozjaei Mohammad , Biswas Asim
Published: October 2022

The accuracy of land crop maps obtained from satellite images depends on the type of feature selection algorithm and classifier. Each of these algorithms have different efficiency in different conditions; therefore, developing a suitable strategy for combining the capabilities of different algorithms in preparing a land crop map with higher accuracy can be very useful. The objective of this study was to develop a fusion-based framework for improving land crop mapping accuracy. First, the features were retrieved using the Sentinel 1, Sentinel 2, and Landsat-8 imagery. Then, training data and various feature selection algorithms including recursive feature elimination (RFE), random forest (RF), and Boruta were used for optimal feature selection. Various classifiers, including artificial neural network (ANN), support vector machine (SVM), and RF, were implemented to create maps of land crops relying on optimal features and training data. After that, in order to increase the result accuracy, maps of land crops derived from several scenarios were fused using a fusion-based voting strategy at the level of decision, and new maps of land crops and classification uncertainty maps were prepared. Subsequently, the performance of different scenarios was evaluated and compared. Among the feature selection algorithms, RF accuracy was higher than RFE and Boruta. Moreover, the efficiency of RF was higher than SVM and ANN. The overall accuracy of the voting scenario was higher than all other scenarios. The finding of this research demonstrated that combining the features’ capabilities extracted from sensors in different spectral ranges, different feature selection algorithms, and classifiers improved the land crop classification accuracy.
Pedestrian Origin–Destination Estimation Based on Multi-Camera Person Re-Identification
Authors: Li Yan , Sarvi Majid , Khoshelham Kourosh , Zhang Yuyang , Jiang Yazhen
Published: October 2022

Pedestrian origin–destination (O–D) estimates that record traffic flows between origins and destinations, are essential for the management of pedestrian facilities including pedestrian flow simulation in the planning phase and crowd control in the operation phase. However, current O–D data collection techniques such as surveys, mobile sensing using GPS, Wi-Fi, and Bluetooth, and smart card data have the disadvantage that they are either time consuming and costly, or cannot provide complete O–D information for pedestrian facilities without entrances and exits or pedestrian flow inside the facilities. Due to the full coverage of CCTV cameras and the huge potential of image processing techniques, we address the challenges of pedestrian O–D estimation and propose an image-based O–D estimation framework. By identifying the same person in disjoint camera views, the O–D trajectory of each identity can be accurately generated. Then, state-of-the-art deep neural networks (DNNs) for person re-ID at different congestion levels were compared and improved. Finally, an O–D matrix based on trajectories was generated and the resident time was calculated, which provides recommendations for pedestrian facility improvement. The factors that affect the accuracy of the framework are discussed in this paper, which we believe could provide new insights and stimulate further research into the application of the Internet of cameras to intelligent transport infrastructure management.
An Improved WMS-2f/1f Spectral Fitting Method Using Orthogonal Test in Initial Parameters Selection
Authors: Luo Liezhao , Li Ting , Deng Jiangge , Zhao Runzhou , Wang Jinkui
Published: October 2022

This paper proposes an improved wavelength modulation spectroscopy with the 2nd harmonics normalized by the 1st harmonics (WMS-2f/1f) spectral fitting method using the orthogonal test in selection of the initial parameters. The method is implemented and validated experimentally in measurement of the temperature of diluted H2O in air (1 atm, 291K, 0.7%) by the WMS-2f/1f technique. The transition center wavelength targets near 1344 nm. Results demonstrate that the sum-square-error (SSE) between the calculated and measured WMS-2f/1f spectral profiles decreases significantly within given updating times when the optimized initial parameters are used. Compared to the conventional method, the optimized initial parameters can make the fitting routine converge more efficiently. The temperature of the vapor inferred from the proposed spectral fitting method are in good agreement with the true values.
A Review on Human Comfort Factors, Measurements, and Improvements in Human–Robot Collaboration
Authors: Yan Yuchen , Jia Yunyi
Published: October 2022

As the development of robotics technologies for collaborative robots (COBOTs), the applications of human–robot collaboration (HRC) have been growing in the past decade. Despite the tremendous efforts from both academia and industry, the overall usage and acceptance of COBOTs are still not so high as expected. One of the major affecting factors is the comfort of humans in HRC, which is usually less emphasized in COBOT development; however, it is critical to the user acceptance during HRC. Therefore, this paper gives a review of human comfort in HRC including the influential factors of human comfort, measurement of human comfort in terms of subjective and objective manners, and human comfort improvement approaches in the context of HRC. Discussions on each topic are also conducted based on the review and analysis.
Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data
Authors: Al-Ramini Ali , Hassan Mahdi , Fallahtafti Farahnaz , Takallou Mohammad Ali , Rahman Hafizur , Qolomany Basheer , Pipinos Iraklis I. , Alsaleem Fadi , Myers Sara A.
Published: October 2022

Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, and hip joint angles, torques, and powers, as well as ground reaction forces (GRF). ML was able to classify those with and without PAD using Neural Networks or Random Forest algorithms with 89% accuracy (0.64 Matthew’s Correlation Coefficient) using all laboratory-based gait variables. Moreover, models using only GRF variables provided up to 87% accuracy (0.64 Matthew’s Correlation Coefficient). These results indicate that ML models can classify those with and without PAD using gait signatures with acceptable performance. Results also show that an ML gait signature model that uses GRF features delivers the most informative data for PAD classification.
Internet of Things: Security and Solutions Survey
Authors: Sadhu Pintu Kumar , Yanambaka Venkata P. , Abdelgawad Ahmed
Published: October 2022

The overwhelming acceptance and growing need for Internet of Things (IoT) products in each aspect of everyday living is creating a promising prospect for the involvement of humans, data, and procedures. The vast areas create opportunities from home to industry to make an automated lifecycle. Human life is involved in enormous applications such as intelligent transportation, intelligent healthcare, smart grid, smart city, etc. A thriving surface is created that can affect society, the economy, the environment, politics, and health through diverse security threats. Generally, IoT devices are susceptible to security breaches, and the development of industrial systems could pose devastating security vulnerabilities. To build a reliable security shield, the challenges encountered must be embraced. Therefore, this survey paper is primarily aimed to assist researchers by classifying attacks/vulnerabilities based on objects. The method of attacks and relevant countermeasures are provided for each kind of attack in this work. Case studies of the most important applications of the IoT are highlighted concerning security solutions. The survey of security solutions is not limited to traditional secret key-based cryptographic solutions, moreover physical unclonable functions (PUF)-based solutions and blockchain are illustrated. The pros and cons of each security solution are also discussed here. Furthermore, challenges and recommendations are presented in this work.
Novel Planar Strain Sensor Design for Capturing 3-Dimensional Fingertip Forces from Patients Affected by Hand Paralysis
Authors: Carducci Jacob , Olds Kevin , Krakauer John W. , Xu Jing , Brown Jeremy D.
Published: October 2022

Assessment and therapy for individuals who have hand paresis requires force sensing approaches that can measure a wide range of finger forces in multiple dimensions. Here we present a novel strain-gauge force sensor with 3 degrees of freedom (DOF) designed for use in a hand assessment and rehabilitation device. The sensor features a fiberglass printed circuit board substrate to which eight strain gauges are bonded. All circuity for the sensor is routed directly through the board, which is secured to a larger rehabilitative device via an aluminum frame. After design, the sensing package was characterized for weight, capacity, and resolution requirements. Furthermore, a test sensor was calibrated in a three-axis configuration and validated in the larger spherical workspace to understand how accurate and precise the sensor is, while the sensor has slight shortcomings with validation error, it does satisfy the precision, calibration accuracy, and fine sensing requirements in orthogonal loading, and all structural specifications are met. The sensor is therefore a great candidate for sensing technology in rehabilitation devices that assess dexterity in patients with impaired hand function.
Threshold-Based User-Assisted Cooperative Relaying in Beamspace Massive MIMO NOMA Systems
Authors: Alimo David , Hamamura Masanori , Sabuj Saifur Rahman
Published: October 2022

The incorporation of user-assisted cooperative relaying into beamspace massive multiple-input multiple-output (mMIMO) non-orthogonal multiple access (NOMA) system can extend the coverage area and improve the spectral and energy efficiency for millimeter wave (mmWave) communications when a dynamic cluster of mobile user terminals (MUTs) is formed within a beam. We propose threshold-based user-assisted cooperative relaying into a beamspace mMIMO NOMA system in a downlink scenario. Specifically, the intermediate MUTs between the next-generation base station (gNB) and the cell-edge MUT become relaying MUTs after the successful decoding of the signal of the cell-edge MUT only when they meet the predetermined signal-to-interference plus noise ratio (SINR) threshold. A zero forcing (ZF) precoder and iterative power allocation are used to minimize both inter- and intra-beam interferences to maximize the system sum rate. We then evaluate the performance of this system in a delay-intolerant cell-edge MUT scenario. Moreover, the outage probability of the cell-edge MUT of the proposed scheme is investigated and an analytic expression is derived. Simulation results confirm that the proposed threshold-based user-assisted cooperative relaying beamspace mMIMO NOMA system outperforms the user-assisted cooperative relaying in beamspace mMIMO NOMA, beamspace MIMO-NOMA, and beamspace MIMO orthogonal multiple access (OMA) systems in terms of spectrum efficiency, energy efficiency, and outage probability.
A Novel Segmentation Scheme with Multi-Probability Threshold for Human Activity Recognition Using Wearable Sensors
Authors: Zhou Bangwen , Wang Cheng , Huan Zhan , Li Zhixin , Chen Ying , Gao Ge , Li Huahao , Dong Chenhui , Liang Jiuzhen
Published: October 2022

In recent years, much research has been conducted on time series based human activity recognition (HAR) using wearable sensors. Most existing work for HAR is based on the manual labeling. However, the complete time serial signals not only contain different types of activities, but also include many transition and atypical ones. Thus, effectively filtering out these activities has become a significant problem. In this paper, a novel machine learning based segmentation scheme with a multi-probability threshold is proposed for HAR. Threshold segmentation (TS) and slope-area (SA) approaches are employed according to the characteristics of small fluctuation of static activity signals and typical peaks and troughs of periodic-like ones. In addition, a multi-label weighted probability (MLWP) model is proposed to estimate the probability of each activity. The HAR error can be significantly decreased, as the proposed model can solve the problem that the fixed window usually contains multiple kinds of activities, while the unknown activities can be accurately rejected to reduce their impacts. Compared with other existing schemes, computer simulation reveals that the proposed model maintains high performance using the UCI and PAMAP2 datasets. The average HAR accuracies are able to reach 97.71% and 95.93%, respectively.
Towards Lightweight Neural Networks for Garbage Object Detection
Authors: Cai Xinchen , Shuang Feng , Sun Xiangming , Duan Yanhui , Cheng Guanyuan
Published: October 2022

In recent years, garbage classification has become a hot topic in China, and legislation on garbage classification has been proposed. Proper garbage classification and improving the recycling rate of garbage can protect the environment and save resources. In order to effectively achieve garbage classification, a lightweight garbage object detection model based on deep learning techniques was designed and developed in this study, which can locate and classify garbage objects in real-time using embedded devices. Focusing on the problems of low accuracy and poor real-time performances in garbage classification, we proposed a lightweight garbage object detection model, YOLOG (YOLO for garbage detection), which is based on accurate local receptive field dilation and can run on embedded devices at high speed and with high performance. YOLOG improves on YOLOv4 in three key ways, including the design of DCSPResNet with accurate local receptive field expansion based on dilated–deformable convolution, network structure simplification, and the use of new activation functions. We collected the domestic garbage image dataset, then trained and tested the model on it. Finally, in order to compare the performance difference between YOLOG and existing state-of-the-art algorithms, we conducted comparison experiments using a uniform data set training model. The experimental results showed that YOLOG achieved AP0.5 of 94.58% and computation of 6.05 Gflops, thus outperformed YOLOv3, YOLOv4, YOLOv4-Tiny, and YOLOv5s in terms of comprehensive performance indicators. The network proposed in this paper can detect domestic garbage accurately and rapidly, provide a foundation for future academic research and engineering applications.
A Comparative Analysis between Efficient Attention Mechanisms for Traffic Forecasting without Structural Priors
Authors: Rad Andrei-Cristian , Lemnaru Camelia , Munteanu Adrian
Published: October 2022

Dot-product attention is a powerful mechanism for capturing contextual information. Models that build on top of it have acclaimed state-of-the-art performance in various domains, ranging from sequence modelling to visual tasks. However, the main bottleneck is the construction of the attention map, which is quadratic with respect to the number of tokens in the sequence. Consequently, efficient alternatives have been developed in parallel, but it was only recently that their performances were compared and contrasted. This study performs a comparative analysis between some efficient attention mechanisms in the context of a purely attention-based spatio-temporal forecasting model used for traffic prediction. Experiments show that these methods can reduce the training times by up to 28% and the inference times by up to 31%, while the performance remains on par with the baseline.
An Analysis of Noise in Multi-Bit ΣΔ Modulators with Low-Frequency Input Signals
Authors: Vera Pablo , Wiesbauer Andreas , Paton Susana
Published: October 2022

Digital and smart sensors are commonly implemented using multi-bit ΣΔ Modulators. Undesired signals can be present at the ADC input, such as low-frequency signals with medium or high amplitude, as a consequence of mechanical artifacts in the MEMS and/or temporary signal overload. Simulations and measurements of those sensors with such signals show temporary increments of in-band noise power. This paper investigates the factors that produce this transient performance loss. Interestingly, noise increments happen when the modulator is forced to toggle between three adjacent levels and is not correlated with the typical tonal behavior of ΣΔ Modulators. Hence, the sensor performance is sensitive to some specific input patterns even if tonal behavior is decreased by dithering the input of the ADC. Different error sources, such as the mismatch between DAC cells, loop filter linearity error, and quantization error, contribute to the observed noise increments. Our aim is to analyze each of these error sources to understand and quantify in-band noise power increments, and to desensitize the ADC from the undesired input patterns. Some estimation equations are proposed and verified through extensive simulations, by means of deterministic and stochastic methods. These equations are influenced by some modulator parameters and can be used to optimize them in order to reduce such in-band noise power increments.
Accelerated Quantitative 3D UTE-Cones Imaging Using Compressed Sensing
Authors: Athertya Jiyo S. , Ma Yajun , Masoud Afsahi Amir , Lombardi Alecio F. , Moazamian Dina , Jerban Saeed , Sedaghat Sam , Jang Hyungseok
Published: October 2022

In this study, the feasibility of accelerated quantitative Ultrashort Echo Time Cones (qUTE-Cones) imaging with compressed sensing (CS) reconstruction is investigated. qUTE-Cones sequences for variable flip angle-based UTE T1 mapping, UTE adiabatic T1ρ mapping, and UTE quantitative magnetization transfer modeling of macromolecular fraction (MMF) were implemented on a clinical 3T MR system. Twenty healthy volunteers were recruited and underwent whole-knee MRI using qUTE-Cones sequences. The k-space data were retrospectively undersampled with different undersampling rates. The undersampled qUTE-Cones data were reconstructed using both zero-filling and CS reconstruction. Using CS-reconstructed UTE images, various parameters were estimated in 10 different regions of interests (ROIs) in tendons, ligaments, menisci, and cartilage. Structural similarity, percentage error, and Pearson’s correlation were calculated to assess the performance. Dramatically reduced streaking artifacts and improved SSIM were observed in UTE images from CS reconstruction. A mean SSIM of ~0.90 was achieved for all CS-reconstructed images. Percentage errors between fully sampled and undersampled CS-reconstructed images were below 5% for up to 50% undersampling (i.e., 2× acceleration). High linear correlation was observed (>0.95) for all qUTE parameters estimated in all subjects. CS-based reconstruction combined with efficient Cones trajectory is expected to achieve a clinically feasible scan time for qUTE imaging.
Development of a Conductometric Sensor Based on Al,Ca-Doped ZnO for the Detection of Formaldehyde
Authors: Crispi Simona , Neri Giovanni
Published: October 2022

In the present study, the development of a conductometric gas sensor based on Al,Ca-doped zinc oxide composite which is finalized to the detection of formaldehyde (HCHO) at a low concentration in air is investigated. The electrical and sensing properties of the composite based on ZnO doped with different loadings of Al and/or Ca (from 0 up to 5 at%) were evaluated. The gas-sensing mechanism of Al,Ca-doped zinc oxide nanocomposite-based sensors was also discussed. The optimized 3%Al,3%Ca-ZnO sensor displayed a formaldehyde response of 3.5 (@ 4 ppm HCHO/air) and an experimental low detection limit of 125 ppb HCHO/air, at the operating temperature of 400 °C. The sensor was also shown to be selective to HCHO with respect to many interferent indoor gases, but NO2 changed the baseline resistance in an opposite way compared to the target gas. The developed device for monitoring HCHO in indoor and workplace environments has the advantage of a simple planar structure and can be easily fabricated for mass production by using low-cost materials and easy fabrication methods.
Experimental Analysis of Handcart Pushing and Pulling Safety in an Industrial Environment by Using IoT Force and EMG Sensors: Relationship with Operators’ Psychological Status and Pain Syndromes
Authors: Petrovic Milos , Vukicevic Arso M. , Djapan Marko , Peulic Aleksandar , Jovicic Milos , Mijailovic Nikola , Milovanovic Petar , Grajic Mirko , Savkovic Marija , Caiazzo Carlo , Isailovic Velibor , Macuzic Ivan , Jovanovic Kosta
Published: October 2022

Non-ergonomic execution of repetitive physical tasks represents a major cause of work-related musculoskeletal disorders (WMSD). This study was focused on the pushing and pulling (P&P) of an industrial handcart (which is a generic physical task present across many industries), with the aim to investigate the dependence of P&P execution on the operators’ psychological status and the presence of pain syndromes of the upper limbs and spine. The developed acquisition system integrated two three-axis force sensors (placed on the left and right arm) and six electromyography (EMG) electrodes (placed on the chest, back, and hand flexor muscles). The conducted experiment involved two groups of participants (with and without increased psychological scores and pain syndromes). Ten force parameters (for both left and right side), one EMG parameter (for three different muscles, both left and right side), and two time-domain parameters were extracted from the acquired signals. Data analysis showed intergroup differences in the examined parameters, especially in force integral values and EMG mean absolute values. To the best of our knowledge, this is the first study that evaluated the composite effects of pain syndromes, spine mobility, and psychological status of the participants on the execution of P&P tasks—concluding that they have a significant impact on the P&P task execution and potentially on the risk of WMSD. The future work will be directed towards the development of a personalized risk assessment system by considering more muscle groups, supplementary data derived from operators’ poses (extracted with computer vision algorithms), and cognitive parameters (extracted with EEG sensors).
Globally Optimal Distributed Fusion Filter for Descriptor Systems with Time-Correlated Measurement Noises
Authors: Ma Jing , Xu Liling
Published: October 2022

This paper concerns the distributed fusion filtering problem for descriptor systems with time-correlated measurement noises. The original descriptor is transformed into two reduced-order subsystems (ROSs) based on singular value decomposition. For the first ROS, a new measurement is obtained using measurement difference technology. Each sensor produces a local filter based on the fusion predictor from the fusion center and its own new measurement and then sends it to the fusion center. In the fusion center, based on local filters, a distributed fusion filter with feedback (DFFWF) in the linear minimum variance (LMV) sense is proposed by applying an innovative approach. The DFFWF for the second ROS is also obtained based on the DFFWF for the first ROS. Then, the DFFWF for the original descriptor is obtained. The proposed DFFWF can achieve the same estimation accuracy as the centralized fusion filter (CFF) under the condition that all local filter gain matrices are of full column rank. Its optimality is strictly proved. Moreover, it has robustness and reliability due to the parallel processing of local filters. Two simulation examples demonstrate the effectiveness of the developed fusion algorithm.
Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review
Authors: Moshawrab Mohammad , Adda Mehdi , Bouzouane Abdenour , Ibrahim Hussein , Raad Ali
Published: October 2022

Today’s world is changing dramatically due to the influence of various factors. Whether due to the rapid development of technological tools, advances in telecommunication methods, global economic and social events, or other reasons, almost everything is changing. As a result, the concepts of a “job” or work have changed as well, with new work shifts being introduced and the office no longer being the only place where work is done. In addition, our non-stop active society has increased the stress and pressure at work, causing fatigue to spread worldwide and becoming a global problem. Moreover, it is medically proven that persistent fatigue is a cause of serious diseases and health problems. Therefore, monitoring and detecting fatigue in the workplace is essential to improve worker safety in the long term. In this paper, we provide an overview of the use of smart wearable devices to monitor and detect occupational physical fatigue. In addition, we present and discuss the challenges that hinder this field and highlight what can be done to advance the use of smart wearables in workplace fatigue detection.
Muti-Frame Point Cloud Feature Fusion Based on Attention Mechanisms for 3D Object Detection
Authors: Zhai Zhenyu , Wang Qiantong , Pan Zongxu , Gao Zhentong , Hu Wenlong
Published: October 2022

Continuous frames of point-cloud-based object detection is a new research direction. Currently, most research studies fuse multi-frame point clouds using concatenation-based methods. The method aligns different frames by using information on GPS, IMU, etc. However, this fusion method can only align static objects and not moving objects. In this paper, we proposed a non-local-based multi-scale feature fusion method, which can handle both moving and static objects without GPS- and IMU-based registrations. Considering that non-local methods are resource-consuming, we proposed a novel simplified non-local block based on the sparsity of the point cloud. By filtering out empty units, memory consumption decreased by 99.93%. In addition, triple attention is adopted to enhance the key information on the object and suppresses background noise, further benefiting non-local-based feature fusion methods. Finally, we verify the method based on PointPillars and CenterPoint. Experimental results show that the mAP of the proposed method improved by 3.9% and 4.1% in mAP compared with concatenation-based fusion modules, PointPillars-2 and CenterPoint-2, respectively. In addition, the proposed network outperforms powerful 3D-VID by 1.2% in mAP.
An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-ray Scans
Authors: Batra Shivani , Sharma Harsh , Boulila Wadii , Arya Vaishali , Srivastava Prakash , Khan Mohammad , Krichen Moez
Published: October 2022

Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.
Pedestrian Flow Prediction and Route Recommendation with Business Events
Authors: Gu Jiqing , Song Chao , Ren Zheng , Lu Li , Jiang Wenjun , Liu Ming
Published: October 2022

Due to the potential economic benefits, pedestrian flow is considered an essential indication of public spaces. Pedestrian flow prediction is designed to assist operators in making decisions (such as shopping center owners). Operators hold certain events, such as sales promotions, to attract surrounding pedestrians; we refer to this type of event as a business event. Business events attract pedestrian flows, which means business opportunities for the merchants. Moreover, their placement will affect the distributions of the pedestrian flows. However, deciding which route is chosen for a specified event is difficult. To the best of our knowledge, we are the first to consider business events when predicting pedestrian flow. In this paper, we investigate two problems: one is pedestrian flow prediction with business events, and the other is route recommendation for business events. First, we propose an Attraction-Based Matrix Factorization model (ABMF) to efficiently predict the pedestrian flow with business events, which introduces the attraction index of different categories to pedestrians in matrix factorization. Second, we leverage the Skip-gram mode to learn the latent representations and improve the pair-wise ranking loss to a flow-aware-based method (SG-FWARP), which aims to learn events’ latent representations for route recommendation. Compared with other state-of-the-art methods, the experimental results show ABMF can predict pedestrian flow matrix with a similarity of over 0.9 compared with the ground truth, and SG-FWARP can recommend routes for business events with high accuracy.
Characterization of the Workspace and Limits of Operation of Laser Treatments for Vascular Lesions of the Lower Limbs
Authors: Oliveira Bruno , Morais Pedro , Torres Helena R. , Baptista António L. , Fonseca Jaime C. , Vilaça João L.
Published: October 2022

The increase of the aging population brings numerous challenges to health and aesthetic segments. Here, the use of laser therapy for dermatology is expected to increase since it allows for non-invasive and infection-free treatments. However, existing laser devices require doctors’ manually handling and visually inspecting the skin. As such, the treatment outcome is dependent on the user’s expertise, which frequently results in ineffective treatments and side effects. This study aims to determine the workspace and limits of operation of laser treatments for vascular lesions of the lower limbs. The results of this study can be used to develop a robotic-guided technology to help address the aforementioned problems. Specifically, workspace and limits of operation were studied in eight vascular laser treatments. For it, an electromagnetic tracking system was used to collect the real-time positioning of the laser during the treatments. The computed average workspace length, height, and width were 0.84 ± 0.15, 0.41 ± 0.06, and 0.78 ± 0.16 m, respectively. This corresponds to an average volume of treatment of 0.277 ± 0.093 m3. The average treatment time was 23.2 ± 10.2 min, with an average laser orientation of 40.6 ± 5.6 degrees. Additionally, the average velocities of 0.124 ± 0.103 m/s and 31.5 + 25.4 deg/s were measured. This knowledge characterizes the vascular laser treatment workspace and limits of operation, which may ease the understanding for future robotic system development.
IoTMapper: A Metrics Aggregation System Architecture in Support of Smart City Solutions
Authors: Vitorino João Pedro , Cruz Nuno
Published: October 2022

Smart cities are, nowadays, an unavoidable and growing reality, supported on software platforms that support city management, through the processing and presentation of a large number of data, obtained from sensors used throughout the cities. Low-power wide area networks (LPWAN) leverage the sensorization process; however, urban landscape, in turn, induces a high probability of change in the propagation conditions of the LPWAN network, thus requiring active monitoring solutions for assessing the city LPWAN network condition. Currently existing solutions usually consider the existence of only one type of LPWAN network to be monitored. In this paper, an architecture for aggregation of metrics from heterogeneous LPWAN networks is presented. The architecture, named IoTMapper, combines purpose build components with existing components from the FIWARE and Apache Kafka ecosystems. Implementation details for the LPWAN networks are abstracted by adapters so that new networks may be easily added. The validation was carried out using real data collected for long-range wide-area network (LoRaWAN) in Lisbon, and a simulated data set extrapolated from the collected data. The results indicate that the presented architecture is a viable solution for metrics aggregation that may be expanded to support multiple networks. However, some of the considered FIWARE components present performance bottlenecks that may hinder the scaling of the architecture while processing new message arrivals.
Multilayer Plasmonic Nanostructures for Improved Sensing Activities Using a FEM and Neurocomputing-Based Approach
Authors: Lo Sciuto Grazia , Napoli Christian , Kowol Paweł , Capizzi Giacomo , Brociek Rafał , Wajda Agata , Słota Damian
Published: October 2022

In order to obtain optimized elementary devices (photovoltaic modules, power transistors for energy efficiency, high-efficiency sensors) it is necessary to increase the energy conversion efficiency of these devices. A very effective approach to achieving this goal is to increase the absorption of incident radiation. A promising strategy to increase this absorption is to use very thin regions of active material and trap photons near these surfaces. The most effective and cost-effective method of achieving such optical entrapment is the Raman scattering from excited nanoparticles at the plasmonic resonance. The field of plasmonics is the study of the exploitation of appropriate layers of metal nanoparticles to increase the intensity of radiation in the semiconductor by means of near-field effects produced by nanoparticles. In this paper, we focus on the use of metal nanoparticles as plasmonic nanosensors with extremely high sensitivity, even reaching single-molecule detection. The study conducted in this paper was used to optimize the performance of a prototype of a plasmonic photovoltaic cell made at the Institute for Microelectronics and Microsystems IMM of Catania, Italy. This prototype was based on a multilayer structure composed of the following layers: glass, AZO, metal and dielectric. In order to obtain good results, it is necessary to use geometries that orthogonalize the absorption of light, allowing better transport of the photocarriers—and therefore greater efficiency—or the use of less pure materials. For this reason, this study is focused on optimizing the geometries of these multilayer plasmonic structures. More specifically, in this paper, by means of a neurocomputing procedure and an electromagnetic fields analysis performed by the finite elements method (FEM), we established the relationship between the thicknesses of Aluminum-doped Zinc oxide (AZO), metal, dielectric and their main properties, characterizing the plasmonic propagation phenomena as the optimal wavelengths values at the main interfaces AZO/METAL and METAL/DIELECTRIC.
Experimental and Numerical Assessment of Iso-Flux Cooling with Low Reynolds Pulsating Water Flow
Authors: Szodrai Ferenc
Published: October 2022

Almost every scale in thermal engineering requires performance optimization to lessen energy demand. The possibility of using pulsating flow for water cooling was investigated both experimentally and numerically. The experiments were conducted below a 60 mL∙min−1 flow rate and frequencies of 3.3, 4, 5, 6.6, and 10 Hz. The flow rate and temperatures were monitored while the solenoid valve was actuated and cooled with thermoelectric coolers. The measurements were replicated by using commercially available software capable of doing large-eddy simulations with coupled thermal modelling. Thermal boundaries were created by using steady inflow temperature and iso-flux conditions. The experimental and numerical results were compared and evaluated. The results show that the Nusselt number of the examined pulsating flow was lower when compared to constant flow scenarios at the corresponding averaged flow rate.
Scale-Mark-Based Gauge Reading for Gauge Sensors in Real Environments with Light and Perspective Distortions
Authors: Wang Chia-Hui , Huang Ke-Kai , Chang Ray-I , Huang Chien-Kang
Published: October 2022

Nowadays, many old analog gauges still require the use of manual gauge reading. It is a time-consuming, expensive, and error-prone process. A cost-effective solution for automatic gauge reading has become a very important research topic. Traditionally, different types of gauges have their own specific methods for gauge reading. This paper presents a systematized solution called SGR (Scale-mark-based Gauge Reading) to automatically read gauge values from different types of gauges. Since most gauges have scale marks (circular or in an arc), our SGR algorithm utilizes PCA (principal components analysis) to find the primary eigenvector of each scale mark. The intersection of these eigenvectors is extracted as the gauge center to ascertain the scale marks. Then, the endpoint of the gauge pointer is found to calculate the corresponding angles to the gauge’s center. Using OCR (optical character recognition), the corresponding dial values can be extracted to match with their scale marks. Finally, the gauge reading value is obtained by using the linear interpolation of these angles. Our experiments use four videos in real environments with light and perspective distortions. The gauges in the video are first detected by YOLOv4 and the detected regions are clipped as the input images. The obtained results show that SGR can automatically and successfully read gauge values. The average error of SGR is nearly 0.1% for the normal environment. When the environment becomes abnormal with respect to light and perspective distortions, the average error of SGR is still less than 0.5%.
A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors
Authors: Shi Zhiyuan , Xu Weiming , Meng Hao
Published: October 2022

Conventional point cloud simplification algorithms have problems including nonuniform simplification, a deficient reflection of point cloud characteristics, unreasonable weight distribution, and high computational complexity. A simplification algorithm, namely, the multi-index weighting simplification algorithm (MIWSA), is proposed in this paper. First, the point cloud is organized with a bounding box and kd-trees to find the neighborhood of each point, and the points are divided into small segments. Second, the feature index of each point is calculated to indicate the characteristics of the points. Third, the analytic hierarchy process (AHP) and criteria importance through intercriteria correlation (CRITIC) are applied to weight these indexes to determine whether each point is a feature point. Fourth, non-feature points are judged as saved or abandoned according to their spatial relationship with the feature points. To verify the effect of the MIWSA, 3D model scanning datasets are calculated and analyzed, as well as field area scanning datasets. The accuracy for the 3D model scanning datasets is assessed by the surface area and patch numbers of the encapsulated surfaces, and that for field area scanning datasets is evaluated by the DEM error statistics. Compared with existing algorithms, the overall accuracy of the MIWSA is 5% to 15% better. Additionally, the running time is shorter than most. The experimental results illustrate that the MIWSA can simplify point clouds more precisely and uniformly.
Research on the Influence of Coil LC Parallel Resonance on Detection Effect of Inductive Wear Debris Sensor
Authors: Huang Heng , He Shizhong , Xie Xiaopeng , Feng Wei , Zhen Huanyi
Published: October 2022

The coil structure of the inductive wear debris sensor plays a significant role in the effect of wear debris detection. According to the characteristics of LC parallel resonance, the capacitor and coil are connected in parallel to make sensor coils in the LC parallel resonance state, which is beneficial to improve the ability to detect wear particles. In this paper, the mathematical model of output-induced electromotance of the detection coil is established to analyze the influence of the structure on the detection sensitivity and enhance the sensor’s current rate of change to the disturbance magnetic field, which is essential to resist noise interference. Based on the coherent demodulation principle, the AD630 lock-in amplifier is applied to the test platform to amplify and identify weak signals. In addition, experiments are designed to test the output signals of debris under the condition of different original output voltages of the sensor with a parallel structure. Meanwhile, the near-resonance state of the detection coil with LC parallel circuit is tested by output signal information. Results show that the sensor detection sensitivity will be effectively improved with the LC parallel coil structure. For the sensor structure parameters designed in this paper, the optimal raw output amplification voltage for abrasive particle detection is 4.49 V. The detection performance of ferromagnetic particles and non-ferromagnetic particles is tested under this condition, realizing the detection ability of 103.33 μm ferromagnetic abrasive particles and 320.74 μm non-ferromagnetic abrasive particles.
Night Vision Anti-Halation Method Based on Infrared and Visible Video Fusion
Authors: Guo Quanmin , Wang Hanlei , Yang Jianhua
Published: October 2022

In order to address the discontinuity caused by the direct application of the infrared and visible image fusion anti-halation method to a video, an efficient night vision anti-halation method based on video fusion is proposed. The designed frame selection based on inter-frame difference determines the optimal cosine angle threshold by analyzing the relation of cosine angle threshold with nonlinear correlation information entropy and de-frame rate. The proposed time-mark-based adaptive motion compensation constructs the same number of interpolation frames as the redundant frames by taking the retained frame number as a time stamp. At the same time, considering the motion vector of two adjacent retained frames as the benchmark, the adaptive weights are constructed according to the interframe differences between the interpolated frame and the last retained frame, then the motion vector of the interpolated frame is estimated. The experimental results show that the proposed frame selection strategy ensures the maximum safe frame removal under the premise of continuous video content at different vehicle speeds in various halation scenes. The frame numbers and playing duration of the fused video are consistent with that of the original video, and the content of the interpolated frame is highly synchronized with that of the corresponding original frames. The average FPS of video fusion in this work is about six times that in the frame-by-frame fusion, which effectively improves the anti-halation processing efficiency of video fusion.