bluuebunny/crossref_metadata_2025 huggingface dataset:
Capturing Features and Performing Human Detection from Human Gaits Using RFID
Authors: Zhang Yajun , Liu Xu , Yang Zhixiong , Li Zijian , Zhang Xinyue , Yuan Bo
Published: November 2022

Recently, radio frequency identification (RFID) sensing has attracted much attention due to its contact-free nature, low cost, light weight and other advantages. RFID-based person detection has also become a hot research topic, but there are still some problems in the existing research. First, most of the current studies cannot identify numerous people at a time well. Second, in order to detect more accurately, it is necessary to evaluate the whole-body activity of a person, which will consume a lot of time to process the data and cannot be applied in time. To solve these problems, in this paper we propose RF-Detection, a person detection system using RFID. First of all, RF-Detection takes step length as the standard for person detection, divides step length into specific sections according to the relationship between step length and height, and achieves high accuracy for new user detection through a large amount of training for a specific step length. Secondly, RF-Detection can better identify the number of people in the same space by segmenting continuous people. Finally, the data collection was reduced by expanding the data set, and the deep learning method was used to further improve the accuracy. The results show that the overall recognition accuracy of RF-Detection is 98.93%.
A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force Distribution
Authors: Chu Qunyi , Sun Wen , Zhang Yuanjian
Published: November 2022

In the study of braking force distribution of trucks, the accurate estimation of the state parameters of the vehicle is very critical. However, during the braking process, the state parameters of the vehicle present a highly nonlinear relationship that is difficult to estimate accurately and that seriously affects the accuracy of the braking force distribution strategy. To solve this problem, this paper proposes a machine-learning-based state-parameter estimation method to provide a solid data base for the braking force distribution strategy of the vehicle. Firstly, the actual collected complete vehicle information is processed for data; secondly, random forest is applied for the feature screening of data to reduce the data dimensionality; subsequently, the generalized regression neural network (GRNN) model is trained offline, and the vehicle state parameters are estimated online; the estimated parameters are used to implement the four-wheel braking force distribution strategy; finally, the effectiveness of the method is verified by joint simulation using MATLAB/Simulink and TruckSim.
A Review of Automated Bioacoustics and General Acoustics Classification Research
Authors: Mutanu Leah , Gohil Jeet , Gupta Khushi , Wagio Perpetua , Kotonya Gerald
Published: November 2022

Automated bioacoustics classification has received increasing attention from the research community in recent years due its cross-disciplinary nature and its diverse application. Applications in bioacoustics classification range from smart acoustic sensor networks that investigate the effects of acoustic vocalizations on species to context-aware edge devices that anticipate changes in their environment adapt their sensing and processing accordingly. The research described here is an in-depth survey of the current state of bioacoustics classification and monitoring. The survey examines bioacoustics classification alongside general acoustics to provide a representative picture of the research landscape. The survey reviewed 124 studies spanning eight years of research. The survey identifies the key application areas in bioacoustics research and the techniques used in audio transformation and feature extraction. The survey also examines the classification algorithms used in bioacoustics systems. Lastly, the survey examines current challenges, possible opportunities, and future directions in bioacoustics.
Digital Single-Image Smartphone Assessment of Total Body Fat and Abdominal Fat Using Machine Learning
Authors: Farina Gian Luca , Orlandi Carmine , Lukaski Henry , Nescolarde Lexa
Published: November 2022

Background: Obesity is chronic health problem. Screening for the obesity phenotype is limited by the availability of practical methods. Methods: We determined the reproducibility and accuracy of an automated machine-learning method using smartphone camera-enabled capture and analysis of single, two-dimensional (2D) standing lateral digital images to estimate fat mass (FM) compared to dual X-ray absorptiometry (DXA) in females and males. We also report the first model to predict abdominal FM using 2D digital images. Results: Gender-specific 2D estimates of FM were significantly correlated (p < 0.001) with DXA FM values and not different (p > 0.05). Reproducibility of FM estimates was very high (R2 = 0.99) with high concordance (R2 = 0.99) and low absolute pure error (0.114 to 0.116 kg) and percent error (1.3 and 3%). Bland–Altman plots revealed no proportional bias with limits of agreement of 4.9 to −4.3 kg and 3.9 to −4.9 kg for females and males, respectively. A novel 2D model to estimate abdominal (lumbar 2–5) FM produced high correlations (R2 = 0.99) and concordance (R2 = 0.99) compared to DXA abdominal FM values. Conclusions: A smartphone camera trained with machine learning and automated processing of 2D lateral standing digital images is an objective and valid method to estimate FM and, with proof of concept, to determine abdominal FM. It can facilitate practical identification of the obesity phenotype in adults.
Stress-Adaptive Training: An Adaptive Psychomotor Training According to Stress Measured by Grip Force
Authors: Sahar Yotam , Wagner Michael , Barel Ariel , Shoval Shraga
Published: November 2022

Current training methods show advances in simulation technologies; however, most of them fail to account for changes in the physical or mental state of the trainee. An innovative training method, adaptive to the trainee’s stress levels as measured by grip force, is described and inspected. It is compared with two standard training methods that ignore the trainee’s state, either leaving the task’s level of difficulty constant or increasing it over time. Fifty-two participants, divided into three test groups, performed a psychomotor training task. The performance level of the stress-adaptive group was higher than for both control groups, with a main effect of t = −2.12 (p = 0.039), while the training time was shorter than both control groups, with a main effect of t = 3.27 (p = 0.002). These results indicate that stress-adaptive training has the potential to improve training outcomes. Moreover, these results imply that grip force measurement has practical applications. Future studies may aid in the development of this training method and its outcomes.
3D Reconstruction Using 3D Registration-Based ToF-Stereo Fusion
Authors: Jung Sukwoo , Lee Youn-Sung , Lee Yunju , Lee KyungTaek
Published: November 2022

Depth sensing is an important issue in many applications, such as Augmented Reality (AR), eXtended Reality (XR), and Metaverse. For 3D reconstruction, a depth map can be acquired by a stereo camera and a Time-of-Flight (ToF) sensor. We used both sensors complementarily to improve the accuracy of 3D information of the data. First, we applied a generalized multi-camera calibration method that uses both color and depth information. Next, depth maps of two sensors were fused by 3D registration and reprojection approach. Then, hole-filling was applied to refine the new depth map from the ToF-stereo fused data. Finally, the surface reconstruction technique was used to generate mesh data from the ToF-stereo fused pointcloud data. The proposed procedure was implemented and tested with real-world data and compared with various algorithms to validate its efficiency.
Ubiquitous Computing in Sports and Physical Activity—Recent Trends and Developments
Authors: Baca Arnold , Dabnichki Peter , Hu Che-Wei , Kornfeind Philipp , Exel Juliana
Published: November 2022

The use of small, interconnected and intelligent tools within the broad framework of pervasive computing for analysis and assessments in sport and physical activity is not a trend in itself but defines a way for information to be handled, processed and utilised: everywhere, at any time. The demand for objective data to support decision making prompted the adoption of wearables that evolve to fulfil the aims of assessing athletes and practitioners as closely as possible with their performance environments. In the present paper, we mention and discuss the advancements in ubiquitous computing in sports and physical activity in the past 5 years. Thus, recent developments in wearable sensors, cloud computing and artificial intelligence tools have been the pillars for a major change in the ways sport-related analyses are performed. The focus of our analysis is wearable technology, computer vision solutions for markerless tracking and their major contribution to the process of acquiring more representative data from uninhibited actions in realistic ecological conditions. We selected relevant literature on the applications of such approaches in various areas of sports and physical activity while outlining some limitations of the present-day data acquisition and data processing practices and the resulting sensors’ functionalities, as well as the limitations to the data-driven informed decision making in the current technological and scientific framework. Finally, we hypothesise that a continuous merger of measurement, processing and analysis will lead to the development of more reliable models utilising the advantages of open computing and unrestricted data access and allow for the development of personalised-medicine-type approaches to sport training and performance.
Optimizing Face Recognition Inference with a Collaborative Edge–Cloud Network
Authors: Oroceo Paul P. , Kim Jeong-In , Caliwag Ej Miguel Francisco , Kim Sang-Ho , Lim Wansu
Published: November 2022

The rapid development of deep-learning-based edge artificial intelligence applications and their data-driven nature has led to several research issues. One key issue is the collaboration of the edge and cloud to optimize such applications by increasing inference speed and reducing latency. Some researchers have focused on simulations that verify that a collaborative edge–cloud network would be optimal, but the real-world implementation is not considered. Most researchers focus on the accuracy of the detection and recognition algorithm but not the inference speed in actual deployment. Others have implemented such networks with minimal pressure on the cloud node, thus defeating the purpose of an edge–cloud collaboration. In this study, we propose a method to increase inference speed and reduce latency by implementing a real-time face recognition system in which all face detection tasks are handled on the edge device and by forwarding cropped face images that are significantly smaller than the whole video frame, while face recognition tasks are processed at the cloud. In this system, both devices communicate using the TCP/IP protocol of wireless communication. Our experiment is executed using a Jetson Nano GPU board and a PC as the cloud. This framework is studied in terms of the frame-per-second (FPS) rate. We further compare our framework using two scenarios in which face detection and recognition tasks are deployed on the (1) edge and (2) cloud. The experimental results show that combining the edge and cloud is an effective way to accelerate the inferencing process because the maximum FPS achieved by the edge–cloud deployment was 1.91× more than the cloud deployment and 8.5× more than the edge deployment.
Photometric-Stereo-Based Defect Detection System for Metal Parts
Authors: Cao Yanlong , Ding Binjie , Chen Jingxi , Liu Wenyuan , Guo Pengning , Huang Liuyi , Yang Jiangxin
Published: November 2022

Automated inspection technology based on computer vision is now widely used in the manufacturing industry with high speed and accuracy. However, metal parts always appear in high gloss or shadow on the surface, resulting in the overexposure of the captured images. It is necessary to adjust the light direction and view to keep defects out of overexposure and shadow areas. However, it is too tedious to adjust the position of the light direction and view the variety of parts’ geometries. To address this problem, we design a photometric-stereo-based defect detection system (PSBDDS), which combines the photometric stereo with defect detection to eliminate the interference of highlights and shadows. Based on the PSBDDS, we introduce a photometric-stereo-based defect detection framework, which takes images captured in multiple directional lights as input and obtains the normal map through the photometric stereo model. Then, the detection model uses the normal map as input to locate and classify defects. Existing learning-based photometric stereo methods and defect detection methods have achieved good performance in their respective fields. However, photometric stereo datasets and defect detection datasets are not sufficient for training and testing photometric-stereo-based defect detection methods, thus we create a photometric stereo defect detection (PSDD) dataset using our PSBDDS to eliminate gaps between learning-based photometric stereo and defect detection methods. Furthermore, experimental results prove the effectiveness of the proposed PSBBD and PSDD dataset.
High-Speed Extraction of Regions of Interest in Optical Camera Communication Enabled by Grid Virtual Division
Authors: Hu Xin , Zhang Pinpin , Sun Yimao , Deng Xiong , Yang Yanbing , Chen Liangyin
Published: November 2022

Optical camera communication (OCC), enabled by light-emitting diodes (LEDs) and embedded cameras on smartphones, has drawn considerable attention thanks to the pervasive adoption of LED lighting and mobile devices. However, most existing studies do not consider the performance bottleneck of Region of Interest (RoI) extraction during decoding, making it challenging to improve communication capacity further. To this end, we propose a fast grid virtual division scheme based on pixel grayscale values, which extracts RoI quickly without sacrificing computational complexity, thereby reducing the decoding delay and improving the communication capacity of OCC. Essentially, the proposed scheme uses a grid division strategy to divide the received image into blocks and randomly sample several pixels within different blocks to quickly locate the RoI with high grayscale values in the original image. By implementing the lightweight RoI extraction algorithm, we experimentally verify its effectiveness in reducing decoding latency, demonstrating its superior performance in terms of communication capacity. The experimental results clearly show that the decoding delay of the proposed scheme is 70% lower than that provided by the Gaussian blur scheme for the iPhone receiver at a transmission frequency of 5 kHz.
Caps Captioning: A Modern Image Captioning Approach Based on Improved Capsule Network
Authors: Javanmardi Shima , Latif Ali , Sadeghi Mohammad , Jahanbanifard Mehrdad , Bonsangue Marcello , Verbeek Fons
Published: November 2022

In image captioning models, the main challenge in describing an image is identifying all the objects by precisely considering the relationships between the objects and producing various captions. Over the past few years, many methods have been proposed, from an attribute-to-attribute comparison approach to handling issues related to semantics and their relationships. Despite the improvements, the existing techniques suffer from inadequate positional and geometrical attributes concepts. The reason is that most of the abovementioned approaches depend on Convolutional Neural Networks (CNNs) for object detection. CNN is notorious for failing to detect equivariance and rotational invariance in objects. Moreover, the pooling layers in CNNs cause valuable information to be lost. Inspired by the recent successful approaches, this paper introduces a novel framework for extracting meaningful descriptions based on a parallelized capsule network that describes the content of images through a high level of understanding of the semantic contents of an image. The main contribution of this paper is proposing a new method that not only overrides the limitations of CNNs but also generates descriptions with a wide variety of words by using Wikipedia. In our framework, capsules focus on the generation of meaningful descriptions with more detailed spatial and geometrical attributes for a given set of images by considering the position of the entities as well as their relationships. Qualitative experiments on the benchmark dataset MS-COCO show that our framework outperforms state-of-the-art image captioning models when describing the semantic content of the images.
Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning
Authors: Sun Bingjian , Cheng Pengle , Huang Ying
Published: November 2022

To date, most existing forest fire smoke detection methods rely on coarse-grained identification, which only distinguishes between smoke and non-smoke. Thus, non-fire smoke and fire smoke are treated the same in these methods, resulting in false alarms within the smoke classes. The fine-grained identification of smoke which can identify differences between non-fire and fire smoke is of great significance for accurate forest fire monitoring; however, it requires a large database. In this paper, for the first time, we combine fine-grained smoke recognition with the few-shot technique using metric learning to identify fire smoke with the limited available database. The experimental comparison and analysis show that the new method developed has good performance in the structure of the feature extraction network and the training method, with an accuracy of 93.75% for fire smoke identification.
Cloud Servers: Resource Optimization Using Different Energy Saving Techniques
Authors: Hijji Mohammad , Ahmad Bilal , Alam Gulzar , Alwakeel Ahmed , Alwakeel Mohammed , Abdulaziz Alharbi Lubna , Aljarf Ahd , Khan Muhammad Umair
Published: November 2022

Currently, researchers are working to contribute to the emerging fields of cloud computing, edge computing, and distributed systems. The major area of interest is to examine and understand their performance. The major globally leading companies, such as Google, Amazon, ONLIVE, Giaki, and eBay, are truly concerned about the impact of energy consumption. These cloud computing companies use huge data centers, consisting of virtual computers that are positioned worldwide and necessitate exceptionally high-power costs to preserve. The increased requirement for energy consumption in IT firms has posed many challenges for cloud computing companies pertinent to power expenses. Energy utilization is reliant upon numerous aspects, for example, the service level agreement, techniques for choosing the virtual machine, the applied optimization strategies and policies, and kinds of workload. The present paper tries to provide an answer to challenges related to energy-saving through the assistance of both dynamic voltage and frequency scaling techniques for gaming data centers. Also, to evaluate both the dynamic voltage and frequency scaling techniques compared to non-power-aware and static threshold detection techniques. The findings will facilitate service suppliers in how to encounter the quality of service and experience limitations by fulfilling the service level agreements. For this purpose, the CloudSim platform is applied for the application of a situation in which game traces are employed as a workload for analyzing the procedure. The findings evidenced that an assortment of good quality techniques can benefit gaming servers to conserve energy expenditures and sustain the best quality of service for consumers located universally. The originality of this research presents a prospect to examine which procedure performs good (for example, dynamic, static, or non-power aware). The findings validate that less energy is utilized by applying a dynamic voltage and frequency method along with fewer service level agreement violations, and better quality of service and experience, in contrast with static threshold consolidation or non-power aware technique.
Development and Optimization of Silicon−Dioxide−Coated Capacitive Electrode for Ambulatory ECG Measurement System
Authors: Kang Younghwan , Choi Sangdong , Koo Chiwan , Joung Yeunho
Published: November 2022

This paper presents a silicon−dioxide−coated capacitive electrode system for an ambulatory electrocardiogram (ECG). The electrode was coated with a nano−leveled (287 nm) silicon dioxide layer which has a very high resistance of over 200 MΩ. Due to this high resistance, the electrode can be defined as only a capacitor without a resistive characteristic. This distinct capacitive characteristic of the electrode brings a simplified circuit analysis to achieve the development of a high−quality ambulatory ECG system. The 240 um thickness electrode was composed of a stainless−steel sheet layer for sensing, a polyimide electrical insulation layer, and a copper sheet connected with the ground to block any electrical noises generated from the back side of the structure. Six different diameter electrodes were prepared to optimize ECG signals in ambulatory environment, such as the amplitude of the QRS complex, amplitude of electromagnetic interference (EMI), and baseline wandering of the ECG signals. By combining the experimental results, optimal ambulatory ECG signals were obtained with electrodes that have a diameter from 1 to 3 cm. Moreover, we achieved high−quality ECG signals in a sweating simulation environment with 2 cm electrodes.
Design and Experimental Research of Robot Finger Sliding Tactile Sensor Based on FBG
Authors: Lu Guan , Fu Shiwen , Xu Yiming
Published: November 2022

Aiming at the problem of flexible sliding tactile sensing for the actual grasp of intelligent robot fingers, a double-layer sliding tactile sensor based on fiber Bragg grating (FBG) for robot fingers is proposed in this paper. Firstly, the optimal embedding depth range of FBG in the elastic matrix of polydimethylsiloxane (PDMS) was determined through finite element analysis and static detection experiments of finger tactile sensing. Secondly, the sensor structure is optimized and designed through the simulation and dynamic experiments of sliding sensing to determine the final array structure. Thirdly, the sensing array is actually pasted on the surface of the robot finger and the sensing characteristics testing platform is built to test and analyze the basic performance of the sliding tactile sensor. Then, the sensor array is actually attached to the finger surface of the robot and the sensing characteristics testing platform is built to experiment and analyze the basic performance of the sliding tactile sensor. Finally, a sliding tactile sensing experiment of robot finger grasping is conducted. The experimental results show that the sliding tactile sensor designed in this paper has good repeatability and creep resistance, with sensitivities of 12.4 pm/N, 11.6 pm/N, and 14.5 pm/N, respectively, and the overall deviation is controlled within 5 pm. Meanwhile, it can effectively sense the signals of the robot fingers during static contact and sliding. The sensor has a high degree of fit with the robot finger structure, and has certain application value for the perception of sliding tactile signals in the object grasping of intelligent robot objects.
A Compact Monopole Antenna for Underwater Acoustic Monitoring Beacons
Authors: Bucuci Stefania , Constantin Andreea , Paun Mirel , Pastorcici Marius N. , Tamas Razvan D. , Danisor Alin , Constantinescu Rodica
Published: November 2022

Protected wetlands such as deltas, lakes or rivers provide a sanctuary for many endangered species. In order to protect these areas from illegal human interventions, it is necessary to monitor the unauthorized entrance of motor boats. In order to mitigate such an impact, we have developed a network of floating beacons for underwater acoustic monitoring, using LoRa communication modules operating at 433 MHz. Such beacons should be equipped with compact antennas. In this paper, we use a genetic algorithm approach to design the compact, monopole antennas required for the beacons; size constraints would apply not only to the radiating element but also to the ground plane. Although the antenna input is unbalanced, such a small ground plane may yield common mode currents on the antenna feeder, which distort the radiation pattern of the antenna. In order to investigate the effect of the common mode currents, we developed a distance averaging method, while, for characterizing the antenna, we used a single-antenna method. For the experimental validation of the system in real conditions, a continuous monitoring of the lake was carried out. During the monitoring, multiple events generated by incursions of motor boats were successfully detected and recorded.
Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers—A Systematic Literature Review
Authors: Kuang Boyu , Gu Chengzhen , Rana Zeeshan A. , Zhao Yifan , Sun Shuang , Nnabuife Somtochukwu Godfrey
Published: November 2022

Background: The planetary rover is an essential platform for planetary exploration. Visual semantic segmentation is significant in the localization, perception, and path planning of the rover autonomy. Recent advances in computer vision and artificial intelligence brought about new opportunities. A systematic literature review (SLR) can help analyze existing solutions, discover available data, and identify potential gaps. Methods: A rigorous SLR has been conducted, and papers are selected from three databases (IEEE Xplore, Web of Science, and Scopus) from the start of records to May 2022. The 320 candidate studies were found by searching with keywords and bool operators, and they address the semantic terrain segmentation in the navigation vision of planetary rovers. Finally, after four rounds of screening, 30 papers were included with robust inclusion and exclusion criteria as well as quality assessment. Results: 30 studies were included for the review, and sub-research areas include navigation (16 studies), geological analysis (7 studies), exploration efficiency (10 studies), and others (3 studies) (overlaps exist). Five distributions are extendedly depicted (time, study type, geographical location, publisher, and experimental setting), which analyzes the included study from the view of community interests, development status, and reimplementation ability. One key research question and six sub-research questions are discussed to evaluate the current achievements and future gaps. Conclusions: Many promising achievements in accuracy, available data, and real-time performance have been promoted by computer vision and artificial intelligence. However, a solution that satisfies pixel-level segmentation, real-time inference time, and onboard hardware does not exist, and an open, pixel-level annotated, and the real-world data-based dataset is not found. As planetary exploration projects progress worldwide, more promising studies will be proposed, and deep learning will bring more opportunities and contributions to future studies. Contributions: This SLR identifies future gaps and challenges by proposing a methodical, replicable, and transparent survey, which is the first review (also the first SLR) for semantic terrain segmentation in the navigation vision of planetary rovers.
Oriented Vehicle Detection in Aerial Images Based on YOLOv4
Authors: Lin Tai-Hung , Su Chih-Wen
Published: November 2022

CNN-based object detectors have achieved great success in recent years. The available detectors adopted horizontal bounding boxes to locate various objects. However, in some unique scenarios, objects such as buildings and vehicles in aerial images may be densely arranged and have apparent orientations. Therefore, some approaches extend the horizontal bounding box to the oriented bounding box to better extract objects, usually carried out by directly regressing the angle or corners. However, this suffers from the discontinuous boundary problem caused by angular periodicity or corner order. In this paper, we propose a simple but efficient oriented object detector based on YOLOv4 architecture. We regress the offset of an object’s front point instead of its angle or corners to avoid the above mentioned problems. In addition, we introduce the intersection over union (IoU) correction factor to make the training process more stable. The experimental results on two public datasets, DOTA and HRSC2016, demonstrate that the proposed method significantly outperforms other methods in terms of detection speed while maintaining high accuracy. In DOTA, our proposed method achieved the highest mAP for the classes with prominent front-side appearances, such as small vehicles, large vehicles, and ships. The highly efficient architecture of YOLOv4 increases more than 25% detection speed compared to the other approaches.
Statistical Study on Human Temperature Measurement by Infrared Thermography
Authors: Švantner Michal , Lang Vladislav , Skála Jiří , Kohlschütter Tomáš , Honner Milan , Muzika Lukáš , Kosová Eliška
Published: November 2022

Increased temperature in humans is the symptom of many infectious diseases and it is thus an important diagnostic tool. Infrared temperature measurement methods have been developed and applied over long periods due to their advantage of non-contact and fast measurements. This study deals with a statistical evaluation of the possibilities and limitations of infrared/thermographic human temperature measurement. A short review of the use of infrared temperature measurement in medical applications is provided. Experiments and statistics-based evaluation to confirm the expected accuracy and limits of thermography-based human temperature measurement are introduced. The results presented in this study show that the standard deviation of the thermographic measurement of the eyes maximum temperature was 0.4–0.9 °C and the mean values differences from the armpit measurement were up to 0.5 °C, based on the used IR camera, even though near ideal measurement conditions and permanent blackbody correction were used. It was also shown that a certain number of outliers must be assumed in such measurements. Extended analyses including simulations of true negative/false positive, sensitivity/specificity and receiver operating characteristics (ROC) curves are presented. The statistical evaluation as well as the extended analyses show that maximum eyes temperature is more relevant than a forehead temperature examination.
Non-Local Temporal Difference Network for Temporal Action Detection
Authors: He Yilong , Han Xiao , Zhong Yong , Wang Lishun
Published: November 2022

As an important part of video understanding, temporal action detection (TAD) has wide application scenarios. It aims to simultaneously predict the boundary position and class label of every action instance in an untrimmed video. Most of the existing temporal action detection methods adopt a stacked convolutional block strategy to model long temporal structures. However, most of the information between adjacent frames is redundant, and distant information is weakened after multiple convolution operations. In addition, the durations of action instances vary widely, making it difficult for single-scale modeling to fit complex video structures. To address this issue, we propose a non-local temporal difference network (NTD), including a chunk convolution (CC) module, a multiple temporal coordination (MTC) module, and a temporal difference (TD) module. The TD module adaptively enhances the motion information and boundary features with temporal attention weights. The CC module evenly divides the input sequence into N chunks, using multiple independent convolution blocks to simultaneously extract features from neighboring chunks. Therefore, it realizes the information delivered from distant frames while avoiding trapping into the local convolution. The MTC module designs a cascade residual architecture, which realizes the multiscale temporal feature aggregation without introducing additional parameters. The NTD achieves a state-of-the-art performance on two large-scale datasets, 36.2% mAP@avg and 71.6% mAP@0.5 on ActivityNet-v1.3 and THUMOS-14, respectively.
Analog Sensor Interface for Field Mill Sensors in Atmospheric Applications
Authors: Agorastou Zoi , Noulis Thomas , Siskos Stylianos
Published: November 2022

An overview of the electric field mill sensor specifications in applications related to the measurement of the atmospheric electric field was conducted. The different design approaches of the field mill sensor interface are presented and analyzed, while the sensitivity-related parameters of a field mill are discussed. The design of a non-complex analog sensor interface that can be employed for the measurement of the electric field in both fair and foul weather conditions, such as thunderstorms, is implemented using discrete components for experimental validation and is optimized in an integrated version in terms of noise and power consumption. Advanced noise simulations are conducted in a 180 nm CMOS process (XH018 XFAB). The energy-autonomous operation of the sensor for extended periods of time is made feasible due to the low power consumption of the front-end circuitry (165 μW at 3 V) as well as the proposed intermittent style of operation of the motor. The total sensing system is low power, and its realization is simple and cost-effective, while also offering adequate sensitivity (45 mV/kV/m), making it comparable to the existing works.
Robust RFID Tag Identification
Authors: Benedetti David , Maselli Gaia
Published: November 2022

Fast and reliable identification of Radio Frequency Indentification (RFID) tags by means of anticollision (MAC) protocols has been a problem of substantial interest for more than a decade. However, improvements in identification rate have been slow, as most solutions rely on sequential approaches that try to avoid collisions, which have limited margin for performance improvement. Recently, there has been growing interest in concurrent techniques that exploit the structure of collisions to recover tag IDs. While these techniques promise substantial improvements in speed, a key question that remains unaddressed is how to deal with noise or interference that might introduce errors in the recovery process at the reader. Our goal in this paper is to consider a noisy wireless channel and add robustness to concurrent RFID identification techniques. We propose a new protocol, called CIRF (Concurrent Identification of RFids), which uses multiple antennas to add robustness to noise and leverages block sparsity-based optimization to recover EPC IDs of transmitting tags. We include fail-safe methods to handle errors that persist after the optimization stage. Extensive simulations show that CIRF achieves substantial resilience improvement in a range of very low to medium Signal-to-Noise (SNR) situations, being able to always correctly recover 99% of tags.
3D Metrology Using One Camera with Rotating Anamorphic Lenses
Authors: Chen Xiaobo , Zhang Jinkai , Xi Juntong
Published: November 2022

In this paper, a novel 3D metrology method using one camera with rotating anamorphic lenses is presented based on the characteristics of double optical centers for anamorphic imaging. When the anamorphic lens rotates −90° around its optical axis, the 3D data of the measured object can be reconstructed from the two anamorphic images captured before and after the anamorphic rotation. The anamorphic lens imaging model and a polynomial anamorphic distortion model are firstly proposed. Then, a 3D reconstruction model using one camera with rotating anamorphic lenses is presented. Experiments were carried out to validate the proposed method and evaluate its measurement accuracy. Compared with stereo vision, the main advantage of the proposed 3D metrology approach is the simplicity of point matching, which makes it suitable for developing compact sensors for fast 3D measurements, such as car navigation applications.
A Framework for Online Document Verification Using Self-Sovereign Identity Technology
Authors: Satybaldy Abylay , Subedi Anushka , Nowostawski Mariusz
Published: November 2022

As the world is gradually moving towards digitization, forgery of vital digital documents has become relatively easy. Therefore, the need for efficient and secure verification and authentication practices of digital documents is also increasing. Self-sovereign identity (SSI) is a set of technologies that build on core concepts in identity management, blockchain technology, and cryptography. SSI enables entities to create fraud-proof verifiable credentials and instantly verify the authenticity of a digital credential. The online document verification solutions must deal with a myriad of issues in regard to privacy and security. Moreover, various challenging and tedious processes have made document verification overly complex and time-consuming which motivated us to conduct this research. This work presents a novel framework for online document verification based on SSI technology. The solution address the complexity and interoperability issues that are present in the current digital document verification systems. We look at a particular use case, i.e., document verification in online loan processing and evaluate how this proposed approach can make an impact on the existing system. Our solution based on SSI standards replaces the intermediary and enables trust between players in the ecosystem. The technology also holds the potential to make the system more efficient, interoperable, and privacy-preserving.
A 3D Hand Attitude Estimation Method for Fixed Hand Posture Based on Dual-View RGB Images
Authors: Ji Peng , Wang Xianjian , Ma Fengying , Feng Jinxiang , Li Chenglong
Published: November 2022

This work provides a 3D hand attitude estimation approach for fixed hand posture based on a CNN and LightGBM for dual-view RGB images to facilitate the application of hand posture teleoperation. First, using dual-view cameras and an IMU sensor, we provide a simple method for building 3D hand posture datasets. This method can quickly acquire dual-view 2D hand image sets and automatically append the appropriate three-axis attitude angle labels. Then, combining ensemble learning, which has strong regression fitting capabilities, with deep learning, which has excellent automatic feature extraction capabilities, we present an integrated hand attitude CNN regression model. This model uses a Bayesian optimization based LightGBM in the ensemble learning algorithm to produce 3D hand attitude regression and two CNNs to extract dual-view hand image features. Finally, a mapping from dual-view 2D images to 3D hand attitude angles is established using a training approach for feature integration, and a comparative experiment is run on the test set. The results of the experiments demonstrate that the suggested method may successfully solve the hand self-occlusion issue and accomplish 3D hand attitude estimation using only two normal RGB cameras.
A Fault Tolerant Surveillance System for Fire Detection and Prevention Using LoRaWAN in Smart Buildings
Authors: Safi Abdullah , Ahmad Zulfiqar , Jehangiri Ali Imran , Latip Rohaya , Zaman Sardar Khaliq uz , Khan Muhammad Amir , Ghoniem Rania M.
Published: November 2022

In recent years, fire detection technologies have helped safeguard lives and property from hazards. Early fire warning methods, such as smoke or gas sensors, are ineffectual. Many fires have caused deaths and property damage. IoT is a fast-growing technology. It contains equipment, buildings, electrical systems, vehicles, and everyday things with computing and sensing capabilities. These objects can be managed and monitored remotely as they are connected to the Internet. In the Internet of Things concept, low-power devices like sensors and controllers are linked together using the concept of Low Power Wide Area Network (LPWAN). Long Range Wide Area Network (LoRaWAN) is an LPWAN product used on the Internet of Things (IoT). It is well suited for networks of things connected to the Internet, where terminals send a minute amount of sensor data over large distances, providing the end terminals with battery lifetimes of years. In this article, we design and implement a LoRaWAN-based system for smart building fire detection and prevention, not reliant upon Wireless Fidelity (Wi-Fi) connection. A LoRa node with a combination of sensors can detect smoke, gas, Liquefied Petroleum Gas (LPG), propane, methane, hydrogen, alcohol, temperature, and humidity. We developed the system in a real-world environment utilizing Wi-Fi Lora 32 boards. The performance is evaluated considering the response time and overall network delay. The tests are carried out in different lengths (0–600 m) and heights above the ground (0–2 m) in an open environment and indoor (1st Floor–3rd floor) environment. We observed that the proposed system outperformed in sensing and data transfer from sensing nodes to the controller boards.
Real-Time Condition Monitoring System for Electrode Alignment in Resistance Welding Electrodes
Authors: Ibáñez Daniel , García Eduardo , Soret Jesús , Martos Julio
Published: November 2022

Electrode misalignment, produced by mechanical fatigue or bad adjustments of the welding gun, leads to an increase in expulsions, deformations and quality problems of the welding joints. Different studies have focused on evaluations of the influence of a misalignment of the electrodes and the final quality of the weld nugget. However, few studies have focused on determining a misalignment of the electrodes to avoid problems caused by this defect, especially in industrial environments. In this paper, a method for performing the condition monitoring of electrode alignment degradation was developed following previous research, which has shown the relationship between the misalignment of short-circuited electrodes and the magnetic field generated by them. This method was carried out by means of a device capable of measuring the magnetic field. Finally, an integral system for the detection of misalignments in real production lines is presented. This system set behavior thresholds based on the experimentation, allowing the condition monitoring of the alignment after each welding cycle.
Optimal Water Level Management for Mitigating GHG Emissions through Water-Conserving Irrigation in An Giang Province, Vietnam
Authors: Ogawa Satoshi , Yamamoto Kyosuke , Uno Kenichi , Thuan Nguyen Cong , Togami Takashi , Shindo Soji
Published: November 2022

Rational water and fertilizer management approaches and technologies could improve water use efficiency and fertilizer use efficiency in paddy rice cultivation. A promising water-conserving technology for paddy rice farming is the alternate wetting and drying irrigation system, established by the International Rice Research Institute. However, the strategy has still not been widely adopted, because water level measurement is challenging work and sometimes leads to a decrease in the rice yield. For the easy implementation of alternate wetting and drying among farmers, we analyzed a dataset obtained from a farmer’s water management study carried out over a three-year period with three cropping seasons at six locations (n = 82) in An Giang Province, Southern Vietnam. We observed a significant relationship between specific water level management and the rice yield and greenhouse gas emissions during different growth periods. The average water level during the crop period was an important factor in increasing the rice yield and reducing greenhouse gas emissions. The average water level at 2 days after nitrogen fertilization also showed a potential to increase the rice yield. The greenhouse gas emissions were reduced when the number of days of non-flooded soil use was increased by 1 day during the crop period. The results offer insights demonstrating that farmers’ implementation of multiple drainage during whole crop period and nitrogen fertilization period has the potential to contribute to both the rice yield increase and reduction in greenhouse gas emissions from rice cultivation.
Performance Evaluation of GNSS Position Augmentation Methods for Autonomous Vehicles in Urban Environments
Authors: Swaminathan Harihara Bharathy , Sommer Aron , Becker Andreas , Atzmueller Martin
Published: November 2022

Global Navigation Satellite Systems provide autonomous vehicles with precise position information through the process of position augmentation. This paper presents a series of performance tests aimed to compare the position accuracy of augmentation techniques such as classical Differential Global Navigation Satellite System, Real-time Kinematic and Real-time eXtended. The aim is to understand the limitations and choose the best position augmentation technique in order to obtain accurate, trustworthy position estimates of a vehicle in urban environments. The tests are performed in and around the German cities of Wuppertal and Duesseldorf, using a vehicle fitted with the navigation system POS-LV 220, developed by Applanix Corporation. In order to evaluate the real-time performance of position augmentation techniques in a highly challenging environment, a total of four test regions are selected. The four test regions are characterized mainly by uneven terrain with tall buildings around the University of Wuppertal, flat terrain with roads of varying width in the city centre of Wuppertal and Duesseldorf and flat terrain in a tunnel section located in the city of Wuppertal. The performances of the different position augmentation are compared using a Root Mean Square (RMS) error estimate obtained as an output from the Applanix system. Furthermore, a High-Definition map of the environment is used for the purpose of model validation, which justifies the use of RMS error estimate as an evaluation metric for the performance analysis tests. According to the performance tests carried out as per the conditions specified in this paper, the Real-time eXtended (RTX) position augmentation method enables to obtain a more robust position information of the vehicle than Real-time Kinematic (RTK) method, with a typical accuracy of a few centimeter in an urban environment.
Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
Authors: Korde-Patel Asmita , Barry Richard K. , Mohsenin Tinoosh
Published: November 2022

Compressive sensing is a simultaneous data acquisition and compression technique, which can significantly reduce data bandwidth, data storage volume, and power. We apply this technique for transient photometric events. In this work, we analyze the effect of noise on the detection of these events using compressive sensing (CS). We show numerical results on the impact of source and measurement noise on the reconstruction of transient photometric curves, generated due to gravitational microlensing events. In our work, we define source noise as background noise, or any inherent noise present in the sampling region of interest. For our models, measurement noise is defined as the noise present during data acquisition. These results can be generalized for any transient photometric CS measurements with source noise and CS data acquisition measurement noise. Our results show that the CS measurement matrix properties have an effect on CS reconstruction in the presence of source noise and measurement noise. We provide potential solutions for improving the performance by tuning some of the properties of the measurement matrices. For source noise applications, we show that choosing a measurement matrix with low mutual coherence can lower the amount of error caused due to CS reconstruction. Similarly, for measurement noise addition, we show that by choosing a lower expected value of the binomial measurement matrix, we can lower the amount of error due to CS reconstruction.