bluuebunny/crossref_metadata_2025 huggingface dataset:
Super-Resolution and Feature Extraction for Ocean Bathymetric Maps Using Sparse Coding
Authors: Yutani Taku , Yono Oak , Kuwatani Tatsu , Matsuoka Daisuke , Kaneko Junji , Hidaka Mitsuko , Kasaya Takafumi , Kido Yukari , Ishikawa Yoichi , Ueki Toshiaki , Kikawa Eiichi
Published: April 2022

The comprehensive production of detailed bathymetric maps is important for disaster prevention, resource exploration, safe navigation, marine salvage, and monitoring of marine organisms. However, owing to observation difficulties, the amount of data on the world’s seabed topography is scarce. Therefore, it is essential to develop methods that effectively use the limited data. In this study, based on dictionary learning and sparse coding, we modified the super-resolution technique and applied it to seafloor topographical maps. Improving on the conventional method, before dictionary learning, we performed pre-processing to separate the teacher image into a low-frequency component that has a general structure and a high-frequency component that captures the detailed topographical features. We learn the topographical features by training the dictionary. As a result, the root-mean-square error (RMSE) was reduced by 30% compared with bicubic interpolation and accuracy was improved, especially in the rugged part of the terrain. The proposed method, which learns a dictionary to capture topographical features and reconstructs them using a dictionary, produces super-resolution with high interpretability.
SERS and Indicator Paper Sensing of Hydrogen Peroxide Using Au@Ag Nanorods
Authors: Khlebtsov Boris N. , Burov Andrey M. , Zakharevich Andrey M. , Khlebtsov Nikolai G.
Published: April 2022

The detection of hydrogen peroxide and the control of its concentration are important tasks in the biological and chemical sciences. In this paper, we developed a simple and quantitative method for the non-enzymatic detection of H2O2 based on the selective etching of Au@Ag nanorods with embedded Raman active molecules. The transfer of electrons between silver atoms and hydrogen peroxide enhances the oxidation reaction, and the Ag shell around the Au nanorod gradually dissolves. This leads to a change in the color of the nanoparticle colloid, a shift in LSPR, and a decrease in the SERS response from molecules embedded between the Au core and Ag shell. In our study, we compared the sensitivity of these readouts for nanoparticles with different Ag shell morphology. We found that triangle core–shell nanoparticles exhibited the highest sensitivity, with a detection limit of 10−4 M, and the SERS detection range of 1 × 10−4 to 2 × 10−2 M. In addition, a colorimetric strategy was applied to fabricate a simple indicator paper sensor for fast detection of hydrogen peroxide in liquids. In this case, the concentration of hydrogen peroxide was qualitatively determined by the change in the color of the nanoparticles deposited on the nitrocellulose membrane.
Detection and Parameter Estimation Analysis of Binary Shift Keying Signals in High Noise Environments
Authors: Duong Van Minh , Vesely Jiri , Hubacek Petr , Janu Premysl , Phan Nhat Giang
Published: April 2022

In this paper, a new method for detecting and estimating the parameters of a binary phase shift keying (BPSK) signal, based on a cross-correlation function, is proposed. The proposed method consists of two stages. The first stage is used to detect or estimate a signal carrier frequency, and the second stage is used to estimate its pulse width or symbol rate. Firstly, the proposed method is investigated by use of a simulated BPSK signal in the form of Barker Codes 7, 11, and 13 in the MATLAB environment. Based on the simulation results, the functionality of this method is verified using a real-time BPSK signal generated by an E8267C generator. This is described in the second part of this paper. The experimental test results confirm that the proposed method is able to detect and estimate the parameters of all BPSK signals with SNR≥−21 dB.
Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFi
Authors: Kia Ghazaleh , Ruotsalainen Laura , Talvitie Jukka
Published: April 2022

A wide variety of sensors and devices are used in indoor positioning scenarios to improve localization accuracy and overcome harsh radio propagation conditions. The availability of these individual sensors suggests the idea of sensor fusion to achieve a more accurate solution. This work aims to address, with the goal of improving localization accuracy, the fusion of two conventional candidates for indoor positioning scenarios: Ultra Wide Band (UWB) and Wireless Fidelity (WiFi). The proposed method consists of a Machine Learning (ML)-based enhancement of WiFi measurements, environment observation, and sensor fusion. In particular, the proposed algorithm takes advantage of Received Signal Strength (RSS) values to fuse range measurements utilizing a Gaussian Process (GP). The range values are calculated using the WiFi Round Trip Time (RTT) and UWB Two Way Ranging (TWR) methods. To evaluate the performance of the proposed method, trilateration is used for positioning. Furthermore, empirical range measurements are obtained to investigate and validate the proposed approach. The results prove that UWB and WiFi, working together, can compensate for each other’s limitations and, consequently, provide a more accurate position solution.
Contact Compliance Based Visual Feedback for Tool Alignment in Robot Assisted Bone Drilling
Authors: Yen Ping-Lang , Chen Yu-Jui
Published: April 2022

In recent decades, robot-assisted surgery has been proven superior at providing more accurate outcomes than the conventional one, particularly in minimally invasive procedures. However, there are still limitations to these kinds of surgical robots. Accurate bone drilling on the steep and hard surface of cortical bone is still challenging. The issues of slipping away from the target entry point on the bone surface and subsequently deviating from the desired path are still not completely solved. Therefore, in this paper, a force control is proposed to accompany the resolved motion rate controller in a handheld orthopedic robot system. The force control makes it possible to adjust the contact compliance of the drill to the bone surface. With the proper contact compliance, the drill can be prevented from deflecting in contact with the bone surface, and will eventually be directed to the target entry point. The experiments on test jig and vertebra phantom also show that the robot under the proposed contact compliance visual feedback control structure could produce better usability positioning accuracy under various contact disturbances than its counterpart.
A Snapshot Imaging Spectrometer Based on Uniformly Distributed-Slit Array (UDA)
Authors: Xu Yan , Li Chunlai , Liu Shijie , Tang Guoliang , Xie Jianan , Wang Jianyu
Published: April 2022

Herein, we propose a system for a snapshot video hyperspectral imaging method based on a uniformly distributed-slit array (UDA) coding plate that not only effectively improves the scanning speed of spectrometers but also achieves a high spectral fidelity of snapshot videos. A mathematical model and optical link simulation of the new system are established. The analysis results show that the proposed method can more efficiently collect information and restore the spectral data cube, and the spectral smile of the system is less than 4.86 μm. The results of the spectral performance and external imaging tests of the system show that the system has the ability to collect spatial spectrum video information with a frame rate of 10 Hz and identify dynamic targets, laying a foundation for the design of a system with a higher frame rate and resolution.
An Integrated Success Model of Internet of Things (IoT)-Based Services in Facilities Management for Public Sector
Authors: Sidek Norliza , Ali Nor’ashikin , Alkawsi Gamal
Published: April 2022

The rapid growth of the Internet of Things (IoT) has vigorously affected government by enhancing quality and efficiency of public services. However, the application of IoT-based services in public sectors is slow, despite its benefits to citizens. Research is needed to deepen understanding of the factors that influence the successful implementation of facilities management as the Internet-of-Things-based services in public sectors. An integrated model is developed and validated to extend the DeLone and McLean IS success model by including technology readiness and other identified factors which impact the use of facilities management of IoT-based services in public sectors from the perspective of employees. An online questionnaire was developed and distributed to employees from all local authorities throughout Malaysia, and 187 usable responses were collected. The partial least squares structural equation modelling approach was used to test the model, with 90.8% of the variance in IoT-based services, suggesting an acceptable model fit with seven out of nine hypotheses were supported. Thus, the empirical evidence exerts significant effects of technology readiness towards the success of IoT-based facility management in the public sector.
Real-Time Fault Detection and Condition Monitoring for Industrial Autonomous Transfer Vehicles Utilizing Edge Artificial Intelligence
Authors: Gültekin Özgür , Cinar Eyup , Özkan Kemal , Yazıcı Ahmet
Published: April 2022

Early fault detection and real-time condition monitoring systems have become quite significant for today’s modern industrial systems. In a high volume of manufacturing facilities, fleets of equipment are expected to operate uninterrupted for days or weeks. Any unplanned interruptions to equipment uptime could jeopardize manufacturers’ cycle time, capacity, and, most significantly, credibility for their customers. With the help of smart manufacturing technologies, companies have started to develop and integrate fault detection and classification systems where end-to-end constant monitoring of equipment is facilitated, and smart algorithms are adapted for the early generation of fault alarms and classification. This paper proposes a generic real-time fault diagnosis and condition monitoring system utilizing edge artificial intelligence (edge AI) and a data distributor open source middleware platform called FIWARE. The implemented system architecture is flexible and includes interfaces that can be easily expanded for various devices. This work demonstrates it for condition monitoring of autonomous transfer vehicle (ATV) equipment targeting a smart factory use case. The system is verified in a designated industrial model environment in a lab with a single ATV operation. The anomaly conditions of the ATV are diagnosed by a deep learning-based fault diagnosis method performed in the Edge AI unit, and the results are transferred to the data storage via a data pipeline setup. The proposed system’s Edge AI solution for the ATV use case provides significant real-time performance. The network bandwidth requirement and total elapsed data transfer time have been reduced by 43 and 37 times, respectively. The proposed system successfully enables real-time monitoring of ATV fault conditions and expands to a fleet of equipment in a real manufacturing facility.
Deep Neural Network for Point Sets Based on Local Feature Integration
Authors: Chu Hao , He Zhenquan , Liu Shangdong , Liu Chuanwen , Yang Jiyuan , Wang Fei
Published: April 2022

The research of object classification and part segmentation is a hot topic in computer vision, robotics, and virtual reality. With the emergence of depth cameras, point clouds have become easier to collect and increasingly important because of their simple and unified structures. Recently, a considerable number of studies have been carried out about deep learning on 3D point clouds. However, data captured directly by sensors from the real-world often encounters severe incomplete sampling problems. The classical network is able to learn deep point set features efficiently, but it is not robust enough when the method suffers from the lack of point clouds. In this work, a novel and general network was proposed, whose effect does not depend on a large amount of point cloud input data. The mutual learning of neighboring points and the fusion between high and low feature layers can better promote the integration of local features so that the network can be more robust. The specific experiments were conducted on the ScanNet and Modelnet40 datasets with 84.5% and 92.8% accuracy, respectively, which proved that our model is comparable or even better than most existing methods for classification and segmentation tasks, and has good local feature integration ability. Particularly, it can still maintain 87.4% accuracy when the number of input points is further reduced to 128. The model proposed has bridged the gap between classical networks and point cloud processing.
Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
Authors: Giroto de Oliveira Lucas , Antes Theresa , Nuss Benjamin , Bekker Elizabeth , Bhutani Akanksha , Diewald Axel , Alabd Mohamad Basim , Li Yueheng , Pauli Mario , Zwick Thomas
Published: April 2022

In the context of all-digital radar systems, phase-modulated continuous wave (PMCW) based on pseudorandom binary sequences (PRBSs) appears to be a prominent candidate modulation scheme for applications such as autonomous driving. Among the reasons for its candidacy are its simplified transmitter architecture and lower linearity requirements (e.g., compared to orthogonal-frequency division multiplexing radars), as well as its high velocity unambiguity and multiple-input multiple-output operation capability, all of which are characteristic of digital radars. For appropriate operation of a PMCW radar, choosing a PRBS whose periodic autocorrelation function (PACF) has low sidelobes and high robustness to Doppler shifts is paramount. In this sense, this article performs an analysis of Doppler shift tolerance of the PACFs of typically adopted PRBSs in PMCW radar systems supported by simulation and measurement results. To accurately measure the Doppler-shift-induced degradation of PACFs, peak power loss ratio (PPLR), peak sidelobe level ratio (PSLR), and integrated-sidelobe level ratio (ISLR) were used as metrics. Furthermore, to account for effects on targets whose ranges are not multiples of the range resolution, oversampled PACFs are analyzed.
Guidelines for Installation of Sensors in Smart Sensing Platforms in Underground Spaces
Authors: Shen Zhenjiang , Teng Xiao , Zhang Yuntian , Fang Guoan , Xu Wei
Published: April 2022

The purpose of this study is to propose guidelines for sensor installation in different types of underground space smart sensing platforms. Firstly, we classify the underground space, analyze the scene requirements according to the classification of underground space, and sort out the requirements for sensors in various types of underground space. Secondly, according to the requirements of underground space scenes for sensors, the types of sensors and corresponding parameters are clarified. After that, the system design and sensor installation guidelines of the underground space smart sensing platform are proposed by sorting out the data acquired by the sensor.
Joint Optimization for Mobile Edge Computing-Enabled Blockchain Systems: A Deep Reinforcement Learning Approach
Authors: Hu Zhuoer , Gao Hui , Wang Taotao , Han Daoqi , Lu Yueming
Published: April 2022

A mobile edge computing (MEC)-enabled blockchain system is proposed in this study for secure data storage and sharing in internet of things (IoT) networks, with the MEC acting as an overlay system to provide dynamic computation offloading services. Considering latency-critical, resource-limited, and dynamic IoT scenarios, an adaptive system resource allocation and computation offloading scheme is designed to optimize the scalability performance for MEC-enabled blockchain systems, wherein the scalability is quantified as MEC computational efficiency and blockchain system throughput. Specifically, we jointly optimize the computation offloading policy and block generation strategy to maximize the scalability of MEC-enabled blockchain systems and meanwhile guarantee data security and system efficiency. In contrast to existing works that ignore frequent user movement and dynamic task requirements in IoT networks, the joint performance optimization scheme is formulated as a Markov decision process (MDP). Furthermore, we design a deep deterministic policy gradient (DDPG)-based algorithm to solve the MDP problem and define the multiple and variable number of consecutive time slots as a decision epoch to conduct model training. Specifically, DDPG can solve an MDP problem with a continuous action space and it only requires a straightforward actor–critic architecture, making it suitable for tackling the dynamics and complexity of the MEC-enabled blockchain system. As demonstrated by simulations, the proposed scheme can achieve performance improvements over the deep Q network (DQN)-based scheme and some other greedy schemes in terms of long-term transactional throughput.
Research on Performance Optimization and Application in Smart Home for Hyperledger Fabric
Authors: Ren Lanfang , Zhou Huachun , Hang Xiaoyong , Yang Bo , Su Li
Published: April 2022

With the popularity of smart home services, smart home devices are also increasing significantly. At the same time, security problems of smart home services are becoming more and more important. With the characteristics of non-tampering and multi-party consensus mechanism, Blockchain technology provides powerful capabilities in security protection. In this paper, we introduce the widely used permission Blockchain Fabric for smart home services. As the high requirements of performance, we firstly study the methods of performance optimization for Fabric. Then, a Fabric-based smart home security control system is designed. Based on this system, the smart home system is able to provide access control and security control for smart home devices. The experimental results show that the system works best when the number of concurrent registrations of smart home device is under 6000.
Lead-Free Sodium Potassium Niobate-Based Multilayer Structures for Ultrasound Transducer Applications
Authors: Kuscer Danjela , Kmet Brigita , Drnovšek Silvo , Bustillo Julien , Levassort Franck
Published: April 2022

Thick films with nominal composition (K0.5Na0.5)0.99Sr0.005NbO3 (KNNSr) on porous ceramics with identical nominal composition were investigated as potential candidates for environmentally benign ultrasonic transducers composed entirely of inorganic materials. In this paper, the processing of the multilayer structure, namely, the thick film by screen printing and the porous ceramic by sacrificial template method, is related to their phase composition, microstructure, electromechanical, and acoustic properties to understand the performance of the devices. The ceramic with a homogeneous distribution of 8 μm pores had a sufficiently high attenuation coefficient of 0.5 dB/mm/MHz and served as an effective backing. The KNNSr thick films sintered at 1100 °C exhibited a homogeneous microstructure and a relative density of 97%, contributing to a large dielectric permittivity and elastic constant and yielding a thickness coupling factor kt of ~30%. The electroacoustic response of the multilayer structure in water provides a centre frequency of 15 MHz and a very large fractional bandwidth (BW) of 127% at −6 dB. The multilayer structure is a candidate for imaging applications operating above 15 MHz, especially by realising focused-beam structure through lenses to further increase the sensitivity in the focal zone.
Ground Control System for UAS Safe Landing Area Determination (SLAD) in Urban Air Mobility Operations
Authors: Ariante Gennaro , Ponte Salvatore , Papa Umberto , Greco Alberto , Del Core Giuseppe
Published: April 2022

The use of the Unmanned Aerial Vehicles (UAV) and Unmanned Aircraft System (UAS) for civil, scientific, and military operations, is constantly increasing, particularly in environments very dangerous or impossible for human actions. Many tasks are currently carried out in metropolitan areas, such as urban traffic monitoring, pollution and land monitoring, security surveillance, delivery of small packages, etc. Estimation of features around the flight path and surveillance of crowded areas, where there is a high number of vehicles and/or obstacles, are of extreme importance for typical UAS missions. Ensuring safety and efficiency during air traffic operations in a metropolitan area is one of the conditions for Urban Air Mobility (UAM) operations. This paper focuses on the development of a ground control system capable of monitoring crowded areas or impervious sites, identifying the UAV position and a safety area for vertical landing or take-off maneuvers (VTOL), ensuring a high level of accuracy and robustness, even without using GNSS-derived navigation information, and with on-board terrain hazard detection and avoidance (DAA) capabilities, in particular during operations conducted in BVLOS (Beyond Visual Line Of Sight). The system is composed by a mechanically rotating real-time LiDAR (Light Detection and Ranging) sensor, linked to a Raspberry Pi 3 as SBC (Session Board Controller), and interfaced to a GCS (Ground Control Station) by wireless connection for data management and 3-D information transfer.
Sparse-View CT Reconstruction Based on a Hybrid Domain Model with Multi-Level Wavelet Transform
Authors: Bai Jielin , Liu Yitong , Yang Hongwen
Published: April 2022

The reconstruction of sparsely sampled projection data will generate obvious streaking artifacts, resulting in image quality degradation and affecting medical diagnosis results. Wavelet transform can effectively decompose directional components of image, so the artifact features and edge details with high directionality can be better detected in the wavelet domain. Therefore, a hybrid domain method based on wavelet transform is proposed in this paper for the sparse-view CT reconstruction. The reconstruction model combines wavelet, spatial, and radon domains to restore the projection consistency and enhance image details. In addition, the global distribution of artifacts requires the network to have a large receptive field, so that a multi-level wavelet transform network (MWCNN) is applied to the hybrid domain model. Wavelet transform is used in the encoding part of the network to reduce the size of feature maps instead of pooling operation and inverse wavelet transform is deployed in the decoding part to recover image details. The proposed method can achieve PSNR of 41.049 dB and SSIM of 0.958 with 120 projections of three angular intervals, and obtain the highest values in this paper. Through the results of numerical analysis and reconstructed images, it shows that the hybrid domain method is superior to the single-domain methods. At the same time, the multi-level wavelet transform model is more suitable for CT reconstruction than the single-level wavelet transform.
Robust Spatial–Spectral Squeeze–Excitation AdaBound Dense Network (SE-AB-Densenet) for Hyperspectral Image Classification
Authors: Munishamaiaha Kavitha , Rajagopal Gayathri , Venkatesan Dhilip Kumar , Arif Muhammad , Vicoveanu Dragos , Chiuchisan Iuliana , Izdrui Diana , Geman Oana
Published: April 2022

Increasing importance in the field of artificial intelligence has led to huge progress in remote sensing. Deep learning approaches have made tremendous progress in hyperspectral image (HSI) classification. However, the complexity in classifying the HSI data using a common convolutional neural network is still a challenge. Further, the network architecture becomes more complex when different spatial–spectral feature information is extracted. Usually, CNN has a large number of trainable parameters, which increases the computational complexity of HSI data. In this paper, an optimized squeeze–excitation AdaBound dense network (SE-AB-DenseNet) is designed to emphasize the significant spatial–spectral features of HSI data. The dense network is combined with the AdaBound and squeeze–excitation modules to give lower computation costs and better classification performance. The AdaBound optimizer gives the proposed model the ability to improve its stability and enhance its classification accuracy by approximately 2%. Additionally, the cutout regularization technique is used for HSI spatial–spectral classification to overcome the problem of overfitting. The experiments were carried out on two commonly used hyperspectral datasets (Indian Pines and Salinas). The experiment results on the datasets show a competitive classification accuracy when compared with state-of-the-art methods with limited training samples. From the SE-AB-DenseNet with the cutout model, the overall accuracies for the Indian Pines and Salinas datasets were observed to be 99.37 and 99.78, respectively.
Displacement Sensing of an Active String Actuator Using a Step-Index Multimode Optical Fiber Sensor
Authors: Tian Weihang , Wakimoto Shuichi , Kanda Takefumi , Yamaguchi Daisuke
Published: April 2022

A thin McKibben artificial muscle is a pneumatic actuator with an outer diameter of only 1.8 mm. We fabricated a string-shaped actuator called an “active string actuator,” which achieves a high contractile displacement by accumulating thin McKibben artificial muscles. To control the displacement, the length of the active string actuator should be estimated. However, this is difficult because bulky and rigid sensors are unsuitable for the sensor element of the active string actuator. Therefore, in this study, we propose a new sensing method for estimating the length of an active string actuator. The proposed sensing system is simple and comprises only three components: a step-index multimode optical fiber, a light emitter, and a light receiver. A step-index multimode optical fiber was combined with the active string actuator, and the length was estimated from the change in the amount of light propagating in the optical fiber when the active string actuator was driven. Fundamental experiments were conducted in this study, and the results demonstrated that the optical fiber sensor value changed with the actuator length. This suggests that it is possible to estimate the displacement of an active string actuator using an optical fiber sensor.
An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
Authors: Yang Xinwei , Yu Tianqi , Chen Zhongyue , Yang Jianfeng , Hu Jianling , Wu Yingrui
Published: April 2022

Flying ad hoc networks (FANETs) have been gradually deployed in diverse application scenarios, ranging from civilian to military. However, the high-speed mobility of unmanned aerial vehicles (UAVs) and dynamically changing topology has led to critical challenges for the stability of communications in FANETs. To overcome the technical challenges, an Improved Weighted and Location-based Clustering (IWLC) scheme is proposed for FANET performance enhancement, under the constraints of network resources. Specifically, a location-based K-means++ clustering algorithm is first developed to set up the initial UAV clusters. Subsequently, a weighted summation-based cluster head selection algorithm is proposed. In the algorithm, the remaining energy ratio, adaptive node degree, relative mobility, and average distance are adopted as the selection criteria, considering the influence of different physical factors. Moreover, an efficient cluster maintenance algorithm is proposed to keep updating the UAV clusters. The simulation results indicate that the proposed IWLC scheme significantly enhances the performance of the packet delivery ratio, network lifetime, cluster head changing ratio, and energy consumption, compared to the benchmark clustering methods in the literature.
Calibration Alignment Sensitivity in Corneal Terahertz Imaging
Authors: Zarrinkhat Faezeh , Baggio Mariangela , Lamberg Joel , Tamminen Aleksi , Nefedova Irina , Ala-Laurinaho Juha , Khaled Elsayed E. M. , Rius Juan M. , Romeu Jordi , Taylor Zachary
Published: April 2022

Improving the longitudinal modes coupling in layered spherical structure contributes significantly to corneal terahertz sensing, which plays a crucial role in the early diagnosis of cornea dystrophies. Using a steel sphere to calibrate reflection from the cornea sample assists in enhancing the resolution of longitudinal modes. The requirement and challenges toward applying the calibration sphere are introduced and addressed. Six corneas with different properties are spotted to study the effect of perturbations in the calibration sphere in a frequency range from 100 GHz to 600 GHz. A particle-swarm optimization algorithm is employed to quantify corneal characteristics considering cases of accurately calibrated and perturbed calibrated scenarios. For the first case, the study is carried out with signal-to-noise values of 40 dB, 50 dB and 60 dB at waveguide bands WR-5.1, WR-3.4, and WR-2.2. As expected, better estimation is achieved in high-SNR cases. Furthermore, the lower waveguide band is revealed as the most proper band for the assessment of corneal features. For perturbed cases, the analysis is continued for the noise level of 60 dB in the three waveguide bands. Consequently, the error in the estimation of corneal properties rises significantly (around 30%).
Evaluation of Antioxidants Using Electrochemical Sensors: A Bibliometric Analysis
Authors: Zheng Yuhong , Karimi-Maleh Hassan , Fu Li
Published: April 2022

The imbalance of oxidation and antioxidant systems in the biological system can lead to oxidative stress, which is closely related to the pathogenesis of many diseases. Substances with antioxidant capacity can effectively resist the harmful damage of oxidative stress. How to measure the antioxidant capacity of antioxidants has essential application value in medicine and food. Techniques such as DPPH radical scavenging have been developed to measure antioxidant capacity. However, these traditional analytical techniques take time and require large instruments. It is a more convenient method to evaluate the antioxidant capacity of antioxidants based on their electrochemical oxidation and reduction behaviors. This review summarizes the evaluation of antioxidants using electrochemical sensors by bibliometrics. The development of this topic was described, and the research priorities at different stages were discussed. The topic was investigated in 1999 and became popular after 2010 and has remained popular ever since. A total of 758 papers were published during this period. In the early stages, electrochemical techniques were used only as quantitative techniques and other analytical techniques. Subsequently, cyclic voltammetry was used to directly study the electrochemical behavior of different antioxidants and evaluate antioxidant capacity. With methodological innovations and assistance from materials science, advanced electrochemical sensors have been fabricated to serve this purpose. In this review, we also cluster the keywords to analyze different investigation directions under the topic. Through co-citation of papers, important papers were analyzed as were how they have influenced the topic. In addition, the author’s country distribution and category distribution were also interpreted in detail. In the end, we also proposed perspectives for the future development of this topic.
Angular-Resolved Thomson Parabola Spectrometer for Laser-Driven Ion Accelerators
Authors: Salgado-López Carlos , Apiñaniz Jon Imanol , Henares José Luis , Pérez-Hernández José Antonio , de Luis Diego , Volpe Luca , Gatti Giancarlo
Published: April 2022

This article reports the development, construction, and experimental test of an angle-resolved Thomson parabola (TP) spectrometer for laser-accelerated multi-MeV ion beams in order to distinguish between ionic species with different charge-to-mass ratio. High repetition rate (HHR) compatibility is guaranteed by the use of a microchannel plate (MCP) as active particle detector. The angular resolving power, which is achieved due to an array of entrance pinholes, can be simply adjusted by modifying the geometry of the experiment and/or the pinhole array itself. The analysis procedure allows for different ion traces to cross on the detector plane, which greatly enhances the flexibility and capabilities of the detector. A full characterization of the TP magnetic field is implemented into a relativistic code developed for the trajectory calculation of each pinhole beamlet. We describe the first test of the spectrometer at the 1PW VEGA 3 laser facility at CLPU, Salamanca (Spain), where up to 15MeV protons and carbon ions from a 3μm laser-irradiated Al foil are detected.
Low-Cost Microplate Reader with 3D Printed Parts for under 500 USD
Authors: Thompson Jonathan E.
Published: April 2022

A 96-well microplate reader for absorption spectroscopy was designed, constructed, and tested at a total cost of ca. 500 USD. The reduced cost of the device represents the major technical contribution of this manuscript, as costs were reduced 7 fold from previous reports. The device was able to achieve 3σ limits of detection of ca. 0.01 absorbance units (AU) over a 60 second measurement for the mid-visible wavelength range. Component parts are either commercially available, or 3D printed from plans. Analysis wavelength can be altered throughout the visible region through use of various photographic or theatrical filters. This feature allows the well plate reader to be used for typical laboratory assays such as cell population estimation by optical density (OD) at 600 nm, or enzyme-linked immunosorbent assays (ELISA) at 450 nm. This manuscript reports on the motivation and process of constructing the device, lists required parts, presents data demonstrating device function, and provides the community of scholars with plans to reproduce the work. The device can be reproduced in laboratories lacking sufficient resources to purchase commercially available options and this outcome contributes towards empowerment of individuals and equity of scientific enquiry.
Comprehensive Performance Analysis of Zigbee Communication: An Experimental Approach with XBee S2C Module
Authors: Haque Khandaker Foysal , Abdelgawad Ahmed , Yelamarthi Kumar
Published: April 2022

The recent development of wireless communications has prompted many diversified applications in both industrial and medical sectors. Zigbee is a short-range wireless communication standard that is based on IEEE 802.15.4 and is vastly used in both indoor and outdoor applications. Its performance depends on networking parameters, such as baud rates, transmission power, data encryption, hopping, deployment environment, and transmission distances. For optimized network deployment, an extensive performance analysis is necessary. This would facilitate a clear understanding of the trade-offs of the network performance metrics, such as the packet delivery ratio (PDR), power consumption, network life, link quality, latency, and throughput. This work presents an extensive performance analysis of both the encrypted and unencrypted Zigbee with the stated metrics in a real-world testbed, deployed in both indoor and outdoor scenarios. The major contributions of this work include (i) evaluating the most optimized transmission power level of Zigbee, considering packet delivery ratio and network lifetime; (ii) formulating an algorithm to find the network lifetime from the measured current consumption of packet transmission; and (iii) identifying and quantizing the trade-offs of the multi-hop communication and data encryption with latency, transmission range, and throughput.
Tomek Link and SMOTE Approaches for Machine Fault Classification with an Imbalanced Dataset
Authors: Swana Elsie Fezeka , Doorsamy Wesley , Bokoro Pitshou
Published: April 2022

Data-driven methods have prominently featured in the progressive research and development of modern condition monitoring systems for electrical machines. These methods have the advantage of simplicity when it comes to the implementation of effective fault detection and diagnostic systems. Despite their many advantages, the practical implementation of data-driven approaches still faces challenges such as data imbalance. The lack of sufficient and reliable labeled fault data from machines in the field often poses a challenge in developing accurate supervised learning-based condition monitoring systems. This research investigates the use of a Naïve Bayes classifier, support vector machine, and k-nearest neighbors together with synthetic minority oversampling technique, Tomek link, and the combination of these two resampling techniques for fault classification with simulation and experimental imbalanced data. A comparative analysis of these techniques is conducted for different imbalanced data cases to determine the suitability thereof for condition monitoring on a wound-rotor induction generator. The precision, recall, and f1-score matrices are applied for performance evaluation. The results indicate that the technique combining the synthetic minority oversampling technique with the Tomek link provides the best performance across all tested classifiers. The k-nearest neighbors, together with this combination resampling technique yielded the most accurate classification results. This research is of interest to researchers and practitioners working in the area of condition monitoring in electrical machines, and the findings and presented approach of the comparative analysis will assist with the selection of the most suitable technique for handling imbalanced fault data. This is especially important in the practice of condition monitoring on electrical rotating machines, where fault data are very limited.
Emotion Recognition Using a Reduced Set of EEG Channels Based on Holographic Feature Maps
Authors: Topic Ante , Russo Mladen , Stella Maja , Saric Matko
Published: April 2022

An important function of the construction of the Brain-Computer Interface (BCI) device is the development of a model that is able to recognize emotions from electroencephalogram (EEG) signals. Research in this area is very challenging because the EEG signal is non-stationary, non-linear, and contains a lot of noise due to artifacts caused by muscle activity and poor electrode contact. EEG signals are recorded with non-invasive wearable devices using a large number of electrodes, which increase the dimensionality and, thereby, also the computational complexity of EEG data. It also reduces the level of comfort of the subjects. This paper implements our holographic features, investigates electrode selection, and uses the most relevant channels to maximize model accuracy. The ReliefF and Neighborhood Component Analysis (NCA) methods were used to select the optimal electrodes. Verification was performed on four publicly available datasets. Our holographic feature maps were constructed using computer-generated holography (CGH) based on the values of signal characteristics displayed in space. The resulting 2D maps are the input to the Convolutional Neural Network (CNN), which serves as a feature extraction method. This methodology uses a reduced set of electrodes, which are different between men and women, and obtains state-of-the-art results in a three-dimensional emotional space. The experimental results show that the channel selection methods improve emotion recognition rates significantly with an accuracy of 90.76% for valence, 92.92% for arousal, and 92.97% for dominance.
All Optical Speckle Contrast-Based Vibration Sensor for Photoacoustic Signal Detection
Authors: Benyamin Matan , Zalevsky Zeev
Published: April 2022

Remote detection of photoacoustic signals is a well desired ability, enabling to perform advanced imaging in scenarios where contact is not possible. Various unique solutions have been suggested, including a camera-based speckle contrast photoacoustic detection. In this manuscript, a significant upgrade to the camera-based speckle contrast approach is presented and experimentally demonstrated. This solution is based on all-optical vibration sensing setup. The technique is based on spectral estimation of speckle pattern contrast and relies on several pre-developed works. First, it relies on the suggested application of speckle contrast to vibration sensing, and then on the realization of intensity pattern spectral manipulation, using a shearing interferometer. The method is evaluated and compared to traditional contrast estimation, and demonstrated in several applications in various vibration frequency band such as photoacoustic signal analysis and phonocardiographic heart sounds. The method is also applicable to measuring contrast changes due to a general speckle changing behavior, rather than surface vibration alone.
Building a UAV Based System to Acquire High Spatial Resolution Thermal Imagery for Energy Balance Modelling
Authors: Pintér Krisztina , Nagy Zoltán
Published: April 2022

High spatial resolution and geolocation accuracy canopy evapotranspiration (ET) maps are well suited tools for evaluation of small plot field trials. While creating such a map by use of an energy balance model is routinely performed, the acquisition of the necessary imagery at a suitable quality is still challenging. An UAV based thermal/RGB integrated imaging system was built using the RaspberryPi (RPi) microcomputer as a central unit. The imagery served as input to the two-source energy balance model pyTSEB to derive the ET map. The setup’s flexibility and modularity are based on the multiple interfaces provided by the RPi and the software development kit (SDK) provided for the thermal camera. The SDK was installed on the RPi and used to trigger cameras, retrieve and store images and geolocation information from an onboard GNSS rover for PPK processing. The system allows acquisition of 8 cm spatial resolution thermal imagery from a 60 m height of flight and less than 7 cm geolocation accuracy of the mosaicked RGB imagery. Modelled latent heat flux data have been validated against latent heat fluxes measured by eddy covariance stations at two locations with RMSE of 75 W/m2 over a two-year study period.
Fluorescence DNA Switch for Highly Sensitive Detection of miRNA Amplified by Duplex-Specific Nuclease
Authors: Li Xiaoqiang , Guo Zhenzhen , Luo Gangyin , Miao Peng
Published: April 2022

DNA is a type of promising material for the construction of sensors owing to its sequence programmability to control the formation of certain structures. MicroRNA (miRNA) can be applied as promising biomarkers for the diagnosis of a range of diseases. Herein, a novel fluorescent sensing strategy for miRNA is proposed combining duplex-specific nuclease (DSN)-mediated amplification and dumbbell DNA structural switch. Gold nanoparticles (AuNPs) are employed, which provide a 3D reaction interface. They also act as effective fluorescence quenchers. The proposed sensor exhibits high sensitivity (sub-femtomolar level) with a wide dynamic range. In addition, excellent selectivity to distinguish homology sequences is achieved. It also performs satisfactorily in biological samples. Overall, this fluorescent sensor provides a powerful tool for the analysis of miRNA levels and can be applied for related biological studies and clinical diagnosis.
Spatial Alignment for Unsupervised Domain Adaptive Single-Stage Object Detection
Authors: Liang Hong , Tong Yanqi , Zhang Qian
Published: April 2022

Domain adaptation methods are proposed to improve the performance of object detection in new domains without additional annotation costs. Recently, domain adaptation methods based on adversarial learning to align source and target domain image distributions are effective. However, for object detection tasks, image-level alignment enforces the alignment of non-transferable background regions, which affects the performance of important target regions. Therefore, how to balance the alignment of background and target remains a challenge. In addition, the current research with good effect is based on two-stage detectors, and there are relatively few studies on single-stage detectors. To address these issues, in this paper, we propose a selective domain adaptation framework for the spatial alignment of a single-stage detector. The framework can identify the background and target and pay different attention to them. On the premise that the single-stage detector does not generate region suggestions, it can achieve domain feature alignment and reduce the influence of the background, enabling transfer between different domains. We validate the effectiveness of our method for weather discrepancy, camera angles, synthetic to real-world, and real images to artistic images. Extensive experiments on four representative adaptation tasks show that the method effectively improves the performance of single-stage object detectors in different domains while maintaining good scalability.
Wake-Up Receiver-Based Routing for Clustered Multihop Wireless Sensor Networks
Authors: Weber Maximilian , Fersi Ghofrane , Fromm Robert , Derbel Faouzi
Published: April 2022

The Wireless Sensor Network (WSN) is one of the most promising solutions for the supervision of multiple phenomena and for the digitisation of the Internet of Things (IoT). The Wake-up Receiver (WuRx) is one of the most trivial and effective solutions for energy-constrained networks. This technology allows energy-autonomous on-demand communication for continuous monitoring instead of the conventional radio. The routing process is one of the most energy and time-consuming processes in WSNs. It is, hence, crucial to conceive an energy-efficient routing process. In this paper, we propose a novel Wake-up Receiver-based routing protocol called Clustered WuRx based on Multicast wake-up (CWM), which ensures energy optimisation and time-efficiency at the same time for indoor scenarios. In our proposed approach, the network is divided into clusters. Each Fog Node maintains the routes from each node in its cluster to it. When a sink requires information from a given node, it’s corresponding Fog Node uses a multicast wake-up mechanism to wake up the intended node and all the intermediate nodes that will be used in the routing process simultaneously. Measurement results demonstrate that our proposed approach exhibits higher energy efficiency and has drastic performance improvements in the delivery delay compared with other routing protocols.
Simu2VITA: A General Purpose Underwater Vehicle Simulator
Authors: de Cerqueira Gava Pedro Daniel , Nascimento Júnior Cairo Lúcio , Belchior de França Silva Juan Ramón , Adabo Geraldo José
Published: April 2022

This article presents an Unmanned Underwater Vehicle simulator named Simu2VITA, which was designed to be rapid to set up, easy to use, and simple to modify the vehicle’s parameters. Simulation of the vehicle dynamics is divided into three main Modules: the Actuator Module, the Allocation Module and the Dynamics Model. The Actuator Module is responsible for the simulation of actuators such as propellers and fins, the Allocation Module translates the action of the actuators into forces and torques acting on the vehicle and the Dynamics Module implements the dynamics equations of the vehicle. Simu2VITA implements the dynamics of the actuators and of the rigid body of the vehicle using the MATLAB/Simulink® framework. To show the usefulness of the Simu2VITA simulator, simulation results are presented for an unmanned underwater vehicle navigating inside a fully flooded tunnel and then compared with sensor data collected when the real vehicle performed the same mission using the controllers designed employing the simulator.