bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterizing Forest Cover and Landscape Pattern Using Multi-Source Remote Sensing Data with Ensemble Learning
Authors: Wang Yu , Liu Han , Sang Lingling , Wang Jun
Published: October 2022

Accurate information on forest distribution is an essential basis for the protection of forest resources. Recent advances in remote sensing and machine learning have contributed to the monitoring of forest-cover distribution cost-effectively, but reliable methods for rapid forest-cover mapping over mountainous areas are still lacking. In addition, the forest landscape pattern has proven to be closely related to the functioning of forest ecosystems, yet few studies have explicitly measured the forest landscape pattern or revealed its driving forces in mountainous areas. To address these challenges, we developed a framework for forest-cover mapping with multi-source remote sensing data (Sentinel-1, Sentinel-2) and an automated ensemble learning method. We also designed a scheme for forest landscape pattern evaluation and driver attribution based on landscape metrics and random forest regression. Results in the Qilian Mountains showed that the proposed framework and scheme could accurately depict the distribution and pattern of forest cover. The overall accuracy of the obtained level-1 and level-2 forest-cover maps reached 95.49% and 78.05%, respectively. The multi-classifier comparison revealed that for forest classification, the ensemble learning method outperformed base classifiers such as LightGBM, random forests, CatBoost, XGBoost, and neural networks. Integrating multi-dimensional features, including spectral, phenological, topographic, and geographic information, helped distinguish forest cover. Compared with other land-cover products, our mapping results demonstrated high quality and rich spatial details. Furthermore, we found that forest patches in the Qilian Mountains were concentrated in the eastern regions with low-to-medium elevations and shady aspects. We also identified that climate was the critical environmental determent of the forest landscape pattern in the Qilian Mountains. Overall, the proposed framework and scheme have strong application potential for characterizing forest cover and landscape patterns. The mapping and evaluation results can further support forest resource management, ecological assessment, and regional sustainable development.
Copper(II) Oxide Nanoparticles Embedded within a PEDOT Matrix for Hydrogen Peroxide Electrochemical Sensing
Authors: Lete Cecilia , Spinciu Adela-Maria , Alexandru Maria-Gabriela , Calderon Moreno Jose , Leau Sorina-Alexandra , Marin Mariana , Visinescu Diana
Published: October 2022

The aim of this study is the preparation of nanostructured copper(II) oxide-based materials (CuONPs) through a facile additive-free polyol procedure that consists of the hydrolysis of copper(II) acetate in 1,4-butane diol and its application in hydrogen peroxide sensing. The nonenzymatic electrochemical sensor for hydrogen peroxide determination was constructed by drop casting the CuONP sensing material on top of a glassy carbon electrode (GCE) modified by a layer of poly(3,4-ethylenedioxythiophene) conducting polymer (PEDOT). The PEDOT layer was prepared on GCE using the sinusoidal voltage method. The XRD pattern of the CuONPs reveals the formation of the monoclinic tenorite phase, CuO, with average crystallite sizes of 8.7 nm, while the estimated band gap from UV–vis spectroscopy is of 1.2 eV. The SEM, STEM, and BET analyses show the formation of quasi-prismatic microaggregates of nanoparticles, with dimensions ranging from 1 µm up to ca. 200 µm, with a mesoporous structure. The developed electrochemical sensor exhibited a linear response toward H2O2 in the concentration range from 0.04 to 10 mM, with a low detection limit of 8.5 μM of H2O2. Furthermore, the obtained sensor possessed an excellent anti-interference capability in H2O2 determination in the presence of interfering compounds such as KNO3 and KNO2.
Fast and Accurate Pose Estimation with Unknown Focal Length Using Line Correspondences
Authors: Guo Kai , Zhang Zhixiang , Zhang Zhongsen , Tian Ye , Chen Honglin
Published: October 2022

Estimating camera pose is one of the key steps in computer vison, photogrammetry and SLAM (Simultaneous Localization and Mapping). It is mainly calculated based on the 2D–3D correspondences of features, including 2D–3D point and line correspondences. If a zoom lens is equipped, the focal length needs to be estimated simultaneously. In this paper, a new method of fast and accurate pose estimation with unknown focal length using two 2D–3D line correspondences and the camera position is proposed. Our core contribution is to convert the PnL (perspective-n-line) problem with 2D–3D line correspondences into an estimation problem with 3D–3D point correspondences. One 3D line and the camera position in the world frame can define a plane, the 2D line projection of the 3D line and the camera position in the camera frame can define another plane, and actually the two planes are the same plane, which is the key geometric characteristic in this paper’s estimation of focal length and pose. We establish the transform between the normal vectors of the two planes with this characteristic, and this transform can be regarded as the camera projection of a 3D point. Then, the pose estimation using 2D–3D line correspondences is converted into pose estimation using 3D–3D point correspondences in intermediate frames, and, lastly, pose estimation can be finished quickly. In addition, using the property whereby the angle between two planes is invariant in both the camera frame and world frame, we can estimate the camera focal length quickly and accurately. Experimental results show that our proposed method has good performance in numerical stability, noise sensitivity and computational speed with synthetic data and real scenarios, and has strong robustness to camera position noise.
Livestock Identification Using Deep Learning for Traceability
Authors: Dac Hai Ho , Gonzalez Viejo Claudia , Lipovetzky Nir , Tongson Eden , Dunshea Frank R. , Fuentes Sigfredo
Published: October 2022

Farm livestock identification and welfare assessment using non-invasive digital technology have gained interest in agriculture in the last decade, especially for accurate traceability. This study aimed to develop a face recognition system for dairy farm cows using advanced deep-learning models and computer vision techniques. This approach is non-invasive and potentially applicable to other farm animals of importance for identification and welfare assessment. The video analysis pipeline follows standard human face recognition systems made of four significant steps: (i) face detection, (ii) face cropping, (iii) face encoding, and (iv) face lookup. Three deep learning (DL) models were used within the analysis pipeline: (i) face detector, (ii) landmark predictor, and (iii) face encoder. All DL models were finetuned through transfer learning on a dairy cow dataset collected from a robotic dairy farm located in the Dookie campus at The University of Melbourne, Australia. Results showed that the accuracy across videos from 89 different dairy cows achieved an overall accuracy of 84%. The computer program developed may be deployed on edge devices, and it was tested on NVIDIA Jetson Nano board with a camera stream. Furthermore, it could be integrated into welfare assessment previously developed by our research group.
Rapid Detection of Amitriptyline in Dried Blood and Dried Saliva Samples with Surface-Enhanced Raman Spectroscopy
Authors: Boroujerdi Ramin , Paul Richard , Abdelkader Amor
Published: October 2022

There is growing demand for rapid, nondestructive detection of trace-level bioactive molecules including medicines, toxins, biomolecules, and single cells, in a variety of disciplines. In recent years, surface-enhanced Raman scattering has been increasingly applied for such purposes, and this area of research is rapidly growing. Of particular interest is the detection of such compounds in dried saliva spots (DSS) and dried blood spots (DBS), often in medical scenarios, such as therapeutic drug monitoring (TDM) and disease diagnosis. Such samples are usually analyzed using hyphenated chromatography techniques, which are costly and time consuming. Here we present for the first time a surface-enhanced Raman spectroscopy protocol for the detection of the common antidepressant amitriptyline (AMT) on DBS and DSS using a test substrate modified with silver nanoparticles. The validated protocol is rapid and non-destructive, with a detection limit of 95 ppb, and linear range between 100 ppb and 1.75 ppm on the SERS substrate, which covers the therapeutic window of AMT in biological fluids.
Reduction of White Spot Defects in CMOS Image Sensors Fabricated Using Epitaxial Silicon Wafer with Proximity Gettering Sinks by CH2P Molecular Ion Implantation
Authors: Kadono Takeshi , Hirose Ryo , Onaka-Masada Ayumi , Kobayashi Koji , Suzuki Akihiro , Okuyama Ryosuke , Koga Yoshihiro , Fukuyama Atsuhiko , Kurita Kazunari
Published: October 2022

Using a new implantation technique with multielement molecular ions consisting of carbon, hydrogen, and phosphorus, namely, CH2P molecular ions, we developed an epitaxial silicon wafer with proximity gettering sinks under the epitaxial silicon layer to improve the gettering capability for metallic impurities. A complementary metal-oxide-semiconductor (CMOS) image sensor fabricated with this novel epitaxial silicon wafer has a markedly reduced number of white spot defects, as determined by dark current spectroscopy (DCS). In addition, the amount of nickel impurities gettered in the CH2P-molecular-ion-implanted region of this CMOS image sensor is higher than that gettered in the C3H5-molecular-ion-implanted region; and this implanted region is formed by high-density black pointed defects and deactivated phosphorus after epitaxial growth. From the obtained results, the CH2P-molecular-ion-implanted region has two types of complexes acting as gettering sinks. One includes carbon-related complexes such as aggregated C–I, and the other includes phosphorus-related complexes such as P4–V. These complexes have a high binding energy to metallic impurities. Therefore, CH2P-molecular-ion-implanted epitaxial silicon wafers have a high gettering capability for metallic impurities and contribute to improving the device performance of CMOS image sensors. (This manuscript is an extension from a paper presented at the 6th IEEE Electron Devices Technology & Manufacturing Conference (EDTM 2022)).
Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis
Authors: Kaupp Lukas , Humm Bernhard , Nazemi Kawa , Simons Stephan
Published: October 2022

Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal—always on—variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert.
Investigating the Impact of Synchrotron THz Radiation on the Corneal Hydration Using Synchrotron THz-Far Infrared Beamline
Authors: Foroughimehr Negin , Vilagosh Zoltan , Yavari Ali , Wood Andrew
Published: October 2022

Due to increasing interest in imaging, industrial, and the development of wireless communication operating at THz frequencies, it is crucial to ascertain possible health impacts arising from exposure to THz radiation. This paper reports on the pilot study of transmittance and absorbance spectra of the porcine cornea following THz frequency irradiation at a synchrotron THz/Far-IR beamline. The exposure period was 4 hours. One cornea was exposed to the radiation, with a second cornea acting as a control. An Attenuated Total Reflection (ATR) apparatus was used, and the frequency range of 2.4 to 8 THz was selected to evaluate any changes. It was found that the synchrotron THz radiation intensities are too low to produce induced corneal injury, but may lead to subtle changes in the state of water. Our results suggest that THz spectroscopy is a promising modality for corneal tissue hydration sensing.
Improvement of Temperature Performance of Singlemode-Multimode-Singlemode Fiber Structure
Authors: Zhang Rongxiang , Wang Weiyu , Zhang Jianfei , Han Yuhong , Liu Tao
Published: October 2022

A theoretical model for studying the temperature properties of singlemode-multimode-singlemode (SMS) fiber structure fabricated by absorptive multimode fiber (MMF) cladding is established. Moreover, an SMS-based temperature sensor is fabricated and experimentally demonstrated. Experimental results show that the dip wavelength of the transmission spectrum changes linearly with temperature, which is in good agreement with the simulated results obtained by using the model. Further, a comprehensive study of temperature characteristics affected by the thermo-optic effect, thermal expansion effect, and thermal effect of absorption characteristics is performed for SMS fiber optic structures with different refractive indexes, thermo-optic coefficients, and absorption properties of MMF cladding, MMF core diameters, and thermal expansion coefficients of packaging shell. According to the obtained rules, investigations are carried out into the thermal response of an SMS fiber structure resulting from combined thermal effects for temperature performance optimization. Excellent temperature stability with a temperature sensitivity of 0 pm/°C or good temperature sensitivity of −441.58 pm/°C is achieved accordingly.
Online Multivariate Anomaly Detection and Localization for High-Dimensional Settings
Authors: Mozaffari Mahsa , Doshi Keval , Yilmaz Yasin
Published: October 2022

This paper considers the real-time detection of abrupt and persistent anomalies in high-dimensional data streams. The goal is to detect anomalies quickly and accurately so that the appropriate countermeasures could be taken in time before the system possibly gets harmed. We propose a sequential and multivariate anomaly detection method that scales well to high-dimensional datasets. The proposed method follows a nonparametric, i.e., data-driven, and semi-supervised approach, i.e., trains only on nominal data. Thus, it is applicable to a wide range of applications and data types. Thanks to its multivariate nature, it can quickly and accurately detect challenging anomalies, such as changes in the correlation structure. Its asymptotic optimality and computational complexity are comprehensively analyzed. In conjunction with the detection method, an effective technique for localizing the anomalous data dimensions is also proposed. The practical use of proposed algorithms are demonstrated using synthetic and real data, and in variety of applications including seizure detection, DDoS attack detection, and video surveillance.
PLA Renewable Bio Polymer Based Solid-State Gamma Radiation Detector-Dosimeter for Biomedical and Nuclear Industry Applications
Authors: Jiang Wen , DiPrete David , Taleyarkhan Rusi P.
Published: October 2022

Polylactic acid (PLA) as a “green,” renewable corn-soy based polymer resin was assessed as a novel solid-state detector for rapid-turnaround gamma radiation dosimetry in the 1–100 kGy range–of significant interest in biomedical and general nuclear industry applications. Co-60 was used as the source of gamma photons. It was found that PLA resin responds well in terms of rheology and porosity metrics with an absorbed gamma dose (Dg). In this work, rheological changes were ascertained via measuring the differential mass loss ratio (MLR) of irradiated PLA placed within PTFE-framed (40 mm × 20 mm × 0.77 mm) cavities bearing ~0.9 g of PLA resin and pressed for 12–16 min in a controlled force hot press under ~6.6 kN loading and platens heated to 227 °C for the low Dg range: 0–11 kGy; and to 193 °C for the extended Dg range: 11–120 kGy. MLR varied quadratically from 0.05 to ~0.2 (1σ ~ 0.007) in the 0–11 kGy experiments, and from 0.05 to ~0.5 (1σ ~0.01) in the 0–120 kGy experiments. Rheological changes from gamma irradiation were modeled and simultaneously correlated with void-pocket formations, which increase with Dg. A single PLA resin bead (~0.04 g) was compressed 5 min at 216 °C in 0–16 kGy experiments, and compressed 2 min at 232 °C in the 16–110 kGy experiments, to form sturdy ~100 µm thick wafers in the same press. Aggregate coupon porosity was then readily measurable with conventional optical microscope imaging and analyzed with standard image processing; this provided complementary data to MLR. Average porosity vs. dose varied quadratically from ~0 to ~15% in the 0–16 kGy range and from ~0 to ~18% over the 16–114 kGy range. These results provide evidence for utilizing “green”/renewable (under $0.01) PLA resin beads for rapid and accurate (+/−5–10%) gamma dosimetry over a wide 0–120 kGy range, using simple to deploy mass and void measuring techniques using common laboratory equipment.
Identification and Localisation Algorithm for Sugarcane Stem Nodes by Combining YOLOv3 and Traditional Methods of Computer Vision
Authors: Zhou Deqiang , Zhao Wenbo , Chen Yanxiang , Zhang Qiuju , Deng Ganran , He Fengguang
Published: October 2022

Sugarcane stem node identification is the core technology required for the intelligence and mechanization of the sugarcane industry. However, detecting stem nodes quickly and accurately is still a significant challenge. In this paper, in order to solve this problem, a new algorithm combining YOLOv3 and traditional methods of computer vision is proposed, which can improve the identification rate during automated cutting. First, the input image is preprocessed, during which affine transformation is used to correct the posture of the sugarcane and a rotation matrix is established to obtain the region of interest of the sugarcane. Then, a dataset is built to train the YOLOv3 network model and the position of the stem nodes is initially determined using the YOLOv3 model. Finally, the position of the stem nodes is further located accurately. In this step, a new gradient operator is proposed to extract the edge of the image after YOLOv3 recognition. Then, a local threshold determination method is proposed, which is used to binarize the image after edge extraction. Finally, a localization algorithm for stem nodes is designed to accurately determine the number and location of the stem nodes. The experimental results show that the precision rate, recall rate, and harmonic mean of the stem node recognition algorithm in this paper are 99.68%, 100%, and 99.84%, respectively. Compared to the YOLOv3 network, the precision rate and the harmonic mean are improved by 2.28% and 1.13%, respectively. Compared to other methods introduced in this paper, this algorithm has the highest recognition rate.
Detection of Dihydrocoumarin in Coconut Juice via Photoelectric Detection System Based on Ultraviolet Absorption Spectrometry
Authors: Wang Xiaoyan , Xu Jiangyu , Ji Rendong , Bian Haiyi , Feng Xiaotao , Jiang Zhezhen , Guo Xinyue , Zhang Yuan , Abdalla Ahmed N.
Published: October 2022

Based on ultraviolet absorption spectroscopy technology combined with stoichiometry, a portable photoelectric detection system with wireless transmission was designed with the advantages of simple operation, low cost, and quick response to realize the non-destructive detection of dihydrocoumarin content in coconut juice. Through the detection of a sample solution, the light intensity through the solution is measured and converted into absorbance. Particle swarm optimization (PSO) is applied to optimize support vector regression (SVR) to establish a corresponding concentration prediction model. At the same time, in order to solve the shortcomings of the conventional portable photoelectric detection equipment in data storage, data transmission, and other aspects, based on the optimal PSO-SVR model, we used Python language to develop a friendly graphical user interface (GUI), integrating data collection, storage, analysis, and prediction modeling in one, greatly simplifying the operation process. The experimental results show that, compared with the traditional methods, the system achieves the purpose of rapid and non-destructive detection and has a small gap compared with the detection results of the ultraviolet spectrophotometer. It provides a good method for the determination of dihydrocoumarin in coconut juice.
Robust Secure Resource Allocation for RIS-Aided SWIPT Communication Systems
Authors: Yu Bencheng , Ren Zihui , Tang Shoufeng
Published: October 2022

Aiming at the influence of channel uncertainty, user information leakage and harvested energy improvement, this paper proposes a robust resource allocation algorithm for reconfigurable intelligent reflector (RIS) multiple-input single-output systems based on imperfect channel state information. First, considering the legal user minimum secret rate constraint, the base station maximum transmit power constraint and the RIS phase shift constraint with the bounded channel uncertainty, a joint optimization of the base station active beam, energy beam and RIS phase shift is established. A multivariate coupled nonlinear resource allocation problem for matrices is addressed. Then, using S-procedure and alternating optimization methods, the original non-convex problem is transformed into a deterministic convex optimization problem and an alternating optimization algorithm based on continuous convex approximation is proposed. The simulation results show that the proposed algorithm has better fairness harvested energy compared with the traditional robust algorithm.
A Complement Method for Magnetic Data Based on TCN-SE Model
Authors: Chen Wenqing , Zhang Rui , Shi Chenguang , Zhu Ye , Lin Xiaodong
Published: October 2022

The magnetometer is a vital measurement component for attitude measurement of near-Earth satellites and autonomous magnetic navigation, and monitoring health is significant. However, due to the compact structure of the microsatellites, the stray magnetic changes caused by the complex working conditions of each system will inevitably interfere with the magnetometer measurement. In addition, due to the limited capacity of the satellite–ground measurement channels and the telemetry errors caused by the harsh space environment, the magnetic data collected by the ground station are partially missing. Therefore, reconstructing the telemetry data on the ground has become one of the key technologies for establishing a high-precision magnetometer twin model. In this paper, firstly, the stray magnetic interference is eliminated by correcting the installation matrix for different working conditions. Then, the autocorrelation characteristics of the residuals are analyzed, and the TCN-SE (temporal convolutional network-squeeze and excitation) network with long-term memory is designed to model and extrapolate the historical residual data. In addition, MAE (mean absolute error) is used to analyze the data without missing at the corresponding time in the forecast period and decreases to 74.63 nT. The above steps realize the accurate mapping from the simulation values to the actual values, thereby achieving the reconstruction of missing data and establishing a solid foundation for the judgment of the health state of the magnetometer.
Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning
Authors: Naeem Muddasar , Coronato Antonio , Ullah Zaib , Bashir Sajid , Paragliola Giovanni
Published: October 2022

Multiple Input Multiple Output (MIMO) systems have been gaining significant attention from the research community due to their potential to improve data rates. However, a suitable scheduling mechanism is required to efficiently distribute available spectrum resources and enhance system capacity. This paper investigates the user selection problem in Multi-User MIMO (MU-MIMO) environment using the multi-agent Reinforcement learning (RL) methodology. Adopting multiple antennas’ spatial degrees of freedom, devices can serve to transmit simultaneously in every time slot. We aim to develop an optimal scheduling policy by optimally selecting a group of users to be scheduled for transmission, given the channel condition and resource blocks at the beginning of each time slot. We first formulate the MU-MIMO scheduling problem as a single-state Markov Decision Process (MDP). We achieve the optimal policy by solving the formulated MDP problem using RL. We use aggregated sum-rate of the group of users selected for transmission, and a 20% higher sum-rate performance over the conventional methods is reported.
A Permanent Magnet Ferromagnetic Wear Debris Sensor Based on Axisymmetric High-Gradient Magnetic Field
Authors: Fan Bin , Liu Yong , Zhang Peng , Wang Lianfu , Zhang Chao , Wang Jianguo
Published: October 2022

The detection of wear debris in lubricating oil is effective for determining current equipment operating conditions for fault diagnosis. In this paper, a permanent magnet ferromagnetic wear debris sensor is proposed that is composed of a compact structure and a detection coil that generates an induced voltage when wear debris passes through a magnetic field. A three-dimensional model of the sensor is established, the internal axisymmetric high-gradient magnetic field of the sensor is analyzed, and a mathematical model of the sensor signal is proposed. The effects of the air gap structure of the sensor and the relative permeability, velocity, and volume of the wear debris on the sensor performance are analyzed. The correctness of the theoretical results is proven by single particle experiments, and the sensor is calibrated to achieve quantitative analysis of the wear debris.
Brain Activity Associated with the Planning Process during the Long-Time Learning of the Tower of Hanoi (ToH) Task: A Pilot Study
Authors: Mitani Keita , Rathnayake Namal , Rathnayake Upaka , Dang Tuan Linh , Hoshino Yukinobu
Published: October 2022

Planning and decision-making are critical managerial functions involving the brain’s executive functions. However, little is known about the effect of cerebral activity during long-time learning while planning and decision-making. This study investigated the impact of planning and decision-making processes in long-time learning, focusing on a cerebral activity before and after learning. The methodology of this study involves the Tower of Hanoi (ToH) to investigate executive functions related to the learning process. Generally, ToH is used to measure baseline performance, learning rate, offline learning (following overnight retention), and transfer. However, this study performs experiments on long-time learning effects for ToH solving. The participants were involved in learning the task over seven weeks. Learning progress was evaluated based on improvement in performance and correlations with the learning curve. All participants showed a significant improvement in planning and decision-making over seven weeks of time duration. Brain activation results from fMRI showed a statistically significant decrease in the activation degree in the dorsolateral prefrontal cortex, parietal lobe, inferior frontal gyrus, and premotor cortex between before and after learning. Our pilot study showed that updating information and shifting issue rules were found in the frontal lobe. Through monitoring performance, we can describe the effect of long-time learning initiated at the frontal lobe and then convert it to a task execution function by analyzing the frontal lobe maps. This process can be observed by comparing the learning curve and the fMRI maps. It was also clear that the degree of activation tends to decrease with the number of tasks, such as through the mid-phase and the end-phase of training. The elucidation of this structure is closely related to decision-making in human behavior, where brain dynamics differ between “thinking and behavior” during complex thinking in the early stages of training and instantaneous “thinking and behavior” after sufficient training. Since this is related to human learning, elucidating these mechanisms will allow the construction of a brain function map model that can be used universally for all training tasks.
Measurement, Validation and Uncertainty of an Experimental Procedure to Characterize the Size-of-Source Effect of Radiation Thermometers, in the Framework of an Industrial Calibration Laboratory
Authors: de Lucas Javier
Published: October 2022

An experimental procedure for characterizing the size-of-source effect (SSE) is proposed. Such an effect is the cause of one of the main influence variables generating uncertainty in the measurement, both in calibration and use, of direct reading radiation thermometers (RT). The procedure and uncertainty calculation described in the paper are aligned in terms of metrological traceability, with the requirements generally imposed to ensure the accuracy of measurements in industry and science. Results of application and validation of this particular procedure with equipment, including black body (BB) sources normally used in radiation thermometry calibration laboratories in the industrial field, are shown.
A Pair of Coupled Waveguides as a Classical Analogue for a Solid-State Qubit
Authors: Schegolev Andrey E. , Klenov Nikolay V. , Bogatskaya Anna V. , Yusupov Rustam D. , Popov Alexander M.
Published: October 2022

We have determined conditions when a pair of coupled waveguides, a common element for integrated room-temperature photonics, can act as a qubit based on a system with a double-well potential. Moreover, we have used slow-varying amplitude approximation (SVA) for the “classical” wave equation to study the propagation of electromagnetic beams in a couple of dielectric waveguides both analytically and numerically. As a part of an extension of the optical-mechanical analogy, we have considered examples of “quantum operations” on the electromagnetic wave state in a pair of waveguides. Furthermore, we have provided examples of “quantum-mechanical” calculations of nonlinear transfer functions for the implementation of the considered element in optical neural networks.
SDN-Defend: A Lightweight Online Attack Detection and Mitigation System for DDoS Attacks in SDN
Authors: Wang Jin , Wang Liping
Published: October 2022

With the development of Software Defined Networking (SDN), its security is becoming increasingly important. Since SDN has the characteristics of centralized management and programmable, attackers can easily take advantage of the security vulnerabilities of SDN to carry out distributed denial of service (DDoS) attacks, which will cause the memory of controllers and switches to be occupied, network bandwidth and server resources to be exhausted, affecting the use of normal users. To solve this problem, this paper designs and implements an online attack detection and mitigation SDN defense system. The SDN defense system consists of two modules: anomaly detection module and mitigation module. The anomaly detection model uses a lightweight hybrid deep learning method—Convolutional Neural Network and Extreme Learning Machine (CNN-ELM) for anomaly detection of traffic. The mitigation model uses IP traceback to locate the attacker and effectively filters out abnormal traffic by sending flow rule commands from the controller. Finally, we evaluate the SDN defense system. The experimental results show that the SDN defense system can accurately identify and effectively mitigate DDoS attack flows in real-time.
Impact Velocity Measurement Method Based on Trajectory and Impact Position
Authors: Liu Hui , Wang Jingfan , Wu Yuantao
Published: October 2022

The impact velocity of falling weight is an instantaneous quantity. Currently, measurement of impact velocity relies on high-speed sensors to capture the moment of impact. The trajectory-position measurement method (TPMM) is proposed in this study. The main steps are: (1) The impact position is used to capture the impact time. It can be measured when the falling weight is stationary. (2) The discrete falling trajectory is measured and a new empirical regression algorithm is proposed to fit the expression of falling trajectory. (3) The impact velocity is obtained by taking the impact time into the first derivative of the trajectory expression. For 1–5 m falling height, the simulation shows that the relative maximum error and relative expanded uncertainty of the proposed method are less than 0.481% and 0.442%, respectively. Then, the actual experiment is carried out to verify the simulation. The proposed method has high accuracy and low uncertainty. The reasons are: (1) Only a low-speed displacement sensor is need for impact velocity measurement. It is easier to improve accuracy and stability of a low-speed sensor. (2) The empirical regression algorithm can improve the stability of falling trajectory fitting.
Detection of 2-Furaldehyde in Milk by MIP-Based POF Chips Combined with an SPR-POF Sensor
Authors: Alberti Giancarla , Arcadio Francesco , Pesavento Maria , Marzano Chiara , Zeni Luigi , Zeid Naji Abi , Cennamo Nunzio
Published: October 2022

An innovative optical-chemical sensor has been used to detect the 2-furaldehyde (2-FAL) in milk. The proposed sensing approach exploits the refractive index changing in a microstructured chip based on a plastic optical fiber (POF) with orthogonal micro-holes containing a specific molecularly imprinted polymer (MIP). This POF-MIP chemical chip modifies the surface plasmon resonance (SPR) phenomena excited in another sensor chip realized in POFs (SPR-POF) and connected in series. The proposed sensor configuration exploits MIP receptors avoiding any modification of the gold film of the SPR platform. This work reports the performance, particularly the high sensitivity and low detection limit, in complex matrices such as buffalo milk fortified with 2-FAL and in different commercial kinds of cow milk thermally treated for pasteurization. The measurements were carried out in about ten minutes by dropping the solution under-test on the planar D-shaped POF surface of the chemical chip. In contrast, on the gold surface of the SPR-POF platform, a water drop is always placed to excite the SPR phenomenon, which is modulated by the chemical chip via MIP-2-FAL binding. Furthermore, the experimental results demonstrated the pros and cons of the proposed sensor system. Thanks to the high sensitivity of the sensor system, the detection of 2-FAL in the diluted milk sample (1:50) was achieved. The dilution is required to reduce the interferent effect of the complex matrix.
Augmented Reality Based Interactive Cooking Guide
Authors: Majil Isaias , Yang Mau-Tsuen , Yang Sophia
Published: October 2022

Cooking at home is a critical survival skill. We propose a new cooking assistance system in which a user only needs to wear an all-in-one augmented reality (AR) headset without having to install any external sensors or devices in the kitchen. Utilizing the built-in camera and cutting-edge computer vision (CV) technology, the user can direct the AR headset to recognize available food ingredients by simply looking at them. Based on the types of the recognized food ingredients, suitable recipes are suggested accordingly. A step-by-step video tutorial providing details of the selected recipe is then displayed with the AR glasses. The user can conveniently interact with the proposed system using eight kinds of natural hand gestures without needing to touch any devices throughout the entire cooking process. Compared with the deep learning models ResNet and ResNeXt, experimental results show that the YOLOv5 achieves lower accuracy for ingredient recognition, but it can locate and classify multiple ingredients in one shot and make the scanning process easier for users. Twenty participants test the prototype system and provide feedback via two questionnaires. Based on the analysis results, 19 of the 20 participants would recommend others to use the proposed system, and all participants are overall satisfied with the prototype system.
Efficacy and Damage Diagnosis of Reinforced Concrete Columns and Joints Strengthened with FRP Ropes Using Piezoelectric Transducers
Authors: Karayannis Chris G. , Golias Emmanouil , Naoum Maria C. , Chalioris Constantin E.
Published: October 2022

Recent research has indicated that the implantation of a network of piezoelectric transducer patches in element regions of potential damage development, such as the beam–column joint (BCJ) area, substantially increases the efficacy and accuracy of the structural health monitoring (SHM) methods to identify damage level, providing a reliable diagnosis. The use of piezoelectric lead zirconate titanate (PZT) transducers for the examination of the efficiency of an innovative strengthening technique of reinforced concrete (RC) columns and BCJs is presented and commented on. Two real-scale RC BCJ subassemblages were constructed for this investigation. The columns and the joint panel of the second subassemblage were externally strengthened with carbon fiber-reinforced polymer (C-FRP) ropes. To examine the efficiency of this strengthening technique we used the following transducers: (a) PZT sensors on the ropes and the concrete; (b) tSring linear variable displacement transducers (SLVDTs), diagonally installed on the BCJ, to measure the shear deformations of the BCJ panel; (c) Strain gauges on the internal steel bars. From the experimental results, it became apparent that the PZT transducers successfully diagnosed the loading step at which the primary damage occurred in the first BCJ subassemblage and the damage state of the strengthened BCJ during the loading procedure. Further, data acquired from the diagonal SLVDTs and the strain gauges provided insight into the damage state of the two tested specimens at each step of the loading procedure and confirmed the diagnosis provided by the PZT transducers. Furthermore, data acquired by the PZT transducers, SLVDTs and strain gauges proved the effectiveness of the applied strengthening technique with C-FRP ropes externally mounted on the column and the conjunction area of the examined BCJ subassemblages.
Innovative IntraValvular Impedance Sensing Applied to Biological Heart Valve Prostheses: Design and In Vitro Evaluation
Authors: Gironi Camilla , Cercenelli Laura , Bortolani Barbara , Emiliani Nicolas , Tartarini Lorenzo , Marcelli Emanuela
Published: October 2022

Subclinical valve thrombosis in heart valve prostheses is characterized by the progressive reduction in leaflet motion detectable with advanced imaging diagnostics. However, without routine imaging surveillance, this subclinical thrombosis may be underdiagnosed. We recently proposed the novel concept of a sensorized heart valve prosthesis based on electrical impedance measurement (IntraValvular Impedance, IVI) using miniaturized electrodes embedded in the valve structure to generate a local electric field that is altered by the cyclic movement of the leaflets. In this study, we investigated the feasibility of the novel IVI-sensing concept applied to biological heart valves (BHVs). Three proof-of-concept prototypes of sensorized BHVs were assembled with different size, geometry and positioning of the electrodes to identify the optimal IVI-measurement configuration. Each prototype was tested in vitro on a hydrodynamic heart valve assessment platform. IVI signal was closely related to the electrodes’ positioning in the valve structure and showed greater sensitivity in the prototype with small electrodes embedded in the valve commissures. The novel concept of IVI sensing is feasible on BHVs and has great potential for monitoring the valve condition after implant, allowing for early detection of subclinical valve thrombosis and timely selection of an appropriate anticoagulation therapy.
Retrieving the Motion of Beaufort Sea Ice Using Brightness Temperature Data from FY-3D Microwave Radiometer Imager
Authors: Ni Kun , Chen Haihua , Li Lele , Meng Xin
Published: October 2022

Sea ice is an important marine phenomenon in the Arctic region, and it is of great importance to study the motion of Arctic sea ice in the present day when its melting is accelerated by global warming. This study proposes a method to retrieve the motion of sea ice based on the maximum cross-correlation (MCC) and the successive correction method (SCM). The proposed method can apply different scales of search ranges to template matching according to the location of sea ice in the Arctic area. In addition, the data assimilation method can assign different weights to different data. We used 36.5 GHz and 89 GHz brightness temperature (Tb) data from the microwave radiometer imager (MWRI) aboard the Fengyun-3D (FY-3D) satellite, for the first time in the literature, to retrieve the sea ice motion in the Beaufort Sea from January to April 2019. The retrieved sea ice motion results were in good agreement with those obtained from the motion of the buoys. Compared with the data from the buoys, the root mean-squared error (RMSE) of the sea ice motion retrieved from FY-3D/MWRI Tb data was 1.1418 cm/s in the zonal direction and 1.0481 cm/s in the meridional direction, and the mean absolute error (MAE) between them was 0.7166 cm/s in the zonal direction and 0.6777 cm/s in the meridional direction. The RMSE between the sea ice motion obtained from the National Snow and Ice Data Center (NSIDC) and the motion of the buoys was 0.9515 cm/s in the zonal direction and 0.67003 cm/s in the meridional direction, and the MAE between them was 0.6576 cm/s in the zonal direction and 0.4922 cm/s in the meridional direction. The RMSE of daily average velocity from the FY-3D/MWRI results and NSIDC data product was 2.2726 cm/s in zonal and 1.9270 cm/s in meridional, and the MAE was 1.5103 cm/s in zonal and 1.1071 cm/s in zonal. The density of the merged data was higher than that obtained from a single polarization or frequency in this paper. The results indicate that FY-3D/MWRI Tb data can retrieve the sea ice motion successfully.
Capacity Strengthening Undertaking—Farm Organized Response of Workers against Risk for Diabetes: (C.S.U.—F.O.R.W.A.R.D. with Cal Poly)—A Concept Approach to Tackling Diabetes in Vulnerable and Underserved Farmworkers in California
Authors: Sikalidis Angelos K. , Kristo Aleksandra S. , Reaves Scott K. , Kurfess Franz J. , DeLay Ann M. , Vasilaky Kathryn , Donegan Lorraine
Published: October 2022

In our project herein, we use the case of farmworkers, an underserved and understudied population at high risk for Type-2 Diabetes Mellitus (T2DM), as a paradigm of an integrated action-oriented research, education and extension approach involving the development of long-term equitable strategies providing empowerment and tailored-made solutions that support practical decision-making aiming to reduce risk of T2DM and ensuing cardiovascular disease (CVD). A Technology-based Empowerment Didactic module (TEDm) and an Informed Decision-Making enhancer (IDMe) coupled in a smart application (app) for farmworkers aiming to teach, set goals, monitor, and support in terms of nutrition, hydration, physical activity, sleep, and circadian rhythm towards lowering T2DM risk, is to be developed and implemented considering the particular characteristics of the population and setting. In parallel, anthropometric, biochemical, and clinical assessments will be utilized to monitor risk parameters for T2DM and compliance to dietary and wellness plans. The app incorporating anthropometric/clinical/biochemical parameters, dietary/lifestyle behavior, and extent of goal achievement can be continuously refined and improved through machine learning and re-programming. The app can function as a programmable tool constantly learning, adapting, and tailoring its services to user needs helping optimization of practical informed decision-making towards mitigating disease symptoms and associated risk factors. This work can benefit apart from the direct beneficiaries being farmworkers, the stakeholders who will be gaining a healthier, more vibrant workforce, and in turn the local communities.
Infrared Single-Frame Small Target Detection Based on Block-Matching
Authors: Man Yi , Yang Qingyun , Chen Tao
Published: October 2022

The robust detection of small targets is one of the crucial techniques in an infrared system. It is still a challenge to detect small targets under complex backgrounds. Aiming at the problem where infrared small target detection is easily disturbed by complex backgrounds, an infrared single frame detection method based on a block-matching approach is proposed in this paper. Firstly, the input infrared image is processed by extracting blocks from it. A new infrared model is constructed by finding blocks that are similar to each such block. Then, the small target detection based on the block-matching model is formulated as an optimization problem of recovering low-rank and sparse matrices, which are effectively solved using robust principal component analysis. Finally, the results of processing are reconstructed to obtain the target and background images. A simple segmentation method is used to segment the target image. The experimental results from the actual infrared sequences show that the proposed method has better background suppression ability under complex backgrounds and better detection performance than conventional baseline methods.
Compensation for Vanadium Oxide Temperature with Stereo Vision on Long-Wave Infrared Light Measurement
Authors: Lin Chun-Yi , Yao Wu-Sung
Published: October 2022

In this paper, using automated optical inspection equipment and a thermal imager, the position and the temperature of the heat source or measured object can effectively be grasped. The high-resolution depth camera is with the stereo vision distance measurement and the low-resolution thermal imager is with the long-wave infrared measurement. Based on Planck’s black body radiation law and Stefan–Boltzmann law, the binocular stereo calibration of the two cameras was calculated. In order to improve the measured temperature error at different distances, equipped with Intel Real Sense Depth Camera D435, a compensator is proposed to ensure that the measured temperature of the heat source is correct and accurate. From the results, it can be clearly seen that the actual measured temperature at each distance is proportional to the temperature of the thermal image vanadium oxide, while the actual measured temperature is inversely proportional to the distance of the test object. By the proposed compensation function, the compensation temperature at varying vanadium oxide temperatures can be obtained. The errors between the average temperature at each distance and the constant temperature of the test object at 39 °C are all less than 0.1%.