bluuebunny/crossref_metadata_2025 huggingface dataset:
An LC Wireless Passive Pressure Sensor Based on Single-Crystal MgO MEMS Processing Technique for High Temperature Applications
Authors: Jia Pinggang , Liu Jia , Qian Jiang , Ren Qianyu , An Guowen , Xiong Jijun
Published: October 2021

An LC wireless passive pressure sensor based on a single-crystalline magnesium oxide (MgO) MEMS processing technique is proposed and experimentally demonstrated for applications in environmental conditions of 900 °C. Compared to other high-temperature resistant materials, MgO was selected as the sensor substrate material for the first time in the field of wireless passive sensing because of its ultra-high melting point (2800 °C) and excellent mechanical properties at elevated temperatures. The sensor mainly consists of inductance coils and an embedded sealed cavity. The cavity length decreases with the applied pressure, leading to a monotonic variation in the resonant frequency of the sensor, which can be retrieved wirelessly via a readout antenna. The capacitor cavity was fabricated using a MgO MEMS technique. This MEMS processing technique, including the wet chemical etching and direct bonding process, can improve the operating temperature of the sensor. The experimental results indicate that the proposed sensor can stably operate at an ambient environment of 22–900 °C and 0–700 kPa, and the pressure sensitivity of this sensor at room temperature is 14.52 kHz/kPa. In addition, the sensor with a simple fabrication process shows high potential for practical engineering applications in harsh environments.
Relay Selection for Capacity Increase in Underwater Acoustic Sensor Network
Authors: Narmeen Ramsha , Chung Jaehak
Published: October 2021

In long distance sensor nodes, propagation delay is the most crucial factor for the successful transmission of data packets in underwater acoustic sensors networks (UWAs). Therefore, to cope with the problem of propagation delay, we propose examining and selecting the best relay node (EBRN) technique based on checking the eligibility and compatibility of RN and selecting the best RN for UWAs. In the EBRN technique, the source node (S) creates a list of the best RNs, based on the minimum propagation delay to the midpoint of a direct link between S and the destination node (D). After that, the S attaches the list of selected RNs and transmit to the D along with data packets. Finally, from the list of selected RNs, the process of retransmission is performed. To avoid collision among control packets, we use a backoff timer that is calculated from the received signal strength indicator (RSSI), propagation delay and transmission time, whereas the collision among data packets is avoided by involving single RN in a particular time. The performance of the proposed EBRN technique is analyzed and evaluated based on throughput, packet loss rate (LR), packet delivery ratio (PDR), energy efficiency, and latency. The simulation results validate the effectiveness of the proposed EBRN technique. Compared with the existing schemes such as underwater cooperative medium access control (UCMAC) and shortest path first (SPF), the proposed EBRN technique performs remarkably well by increasing the throughput, PDR, and energy efficiency while decreasing the latency and LR in UWAs.
Fostering Cyber-Physical Social Systems through an Ontological Approach to Personality Classification Based on Social Media Posts
Authors: Cernian Alexandra , Vasile Nicoleta , Sacala Ioan Stefan
Published: October 2021

The exponential increase in social networks has led to emergent convergence of cyber-physical systems (CPS) and social computing, accelerating the creation of smart communities and smart organizations and enabling the concept of cyber-physical social systems. Social media platforms have made a significant contribution to what we call human behavior modeling. This paper presents a novel approach to developing a users’ segmentation tool for the Romanian language, based on the four DISC personality types, based on social media statement analysis. We propose and design the ontological modeling approach of the specific vocabulary for each personality and its mapping with text from posts on social networks. This research proposal adds significant value both in terms of scientific and technological contributions (by developing semantic technologies and tools), as well as in terms of business, social and economic impact (by supporting the investigation of smart communities in the context of cyber-physical social systems). For the validation of the model developed we used a dataset of almost 2000 posts retrieved from 10 social medial accounts (Facebook and Twitter) and we have obtained an accuracy of over 90% in identifying the personality profile of the users.
A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory
Authors: Gao Yangde , Kim Cheol Hong , Kim Jong-Myon
Published: October 2021

Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.
Association between the Degree of Pre-Synaptic Dopaminergic Pathway Degeneration and Motor Unit Firing Behavior in Parkinson’s Disease Patients
Authors: Nishikawa Yuichi , Watanabe Kohei , Holobar Aleš , Takahashi Tetsuya , Maeda Noriaki , Maruyama Hirofumi , Tanaka Shinobu , Hyngstrom Allison S
Published: October 2021

The relationship between motor unit (MU) firing behavior and the severity of neurodegeneration in Parkinson’s disease (PD) is not clear. This study aimed to elucidate the association between degeneration with dopaminergic pathways and MU firing behavior in people with PD. Fourteen females with PD (age, 72.6 ± 7.2 years, disease duration, 3.5 ± 2.1 years) were enrolled in this study. All participants performed a submaximal, isometric knee extension ramp-up contraction from 0% to 80% of their maximal voluntary contraction strength. We used high-density surface electromyography with 64 electrodes to record the muscle activity of the vastus lateralis muscle and decomposed the signals with the convolution kernel compensation technique to extract the signals of individual MUs. We calculated the degree of degeneration of the central lesion-specific binding ratio by dopamine transporter single-photon emission computed tomography. The primary, novel results were as follows: (1) moderate-to-strong correlations were observed between the degree of degeneration of the central lesion and MU firing behavior; (2) a moderate correlation was observed between clinical measures of disease severity and MU firing behavior; and (3) the methods of predicting central nervous system degeneration from MU firing behavior abnormalities had a high detection accuracy with an area under the curve >0.83. These findings suggest that abnormalities in MU activity can be used to predict central nervous system degeneration following PD.
Head Trajectory Diagrams for Gait Symmetry Analysis Using a Single Head-Worn IMU
Authors: Hwang Tong-Hun , Effenberg Alfred O.
Published: October 2021

Gait symmetry analysis plays an important role in the diagnosis and rehabilitation of pathological gait. Recently, wearable devices have also been developed for simple gait analysis solutions. However, measurement in clinical settings can differ from gait in daily life, and simple wearable devices are restricted to a few parameters, providing one-sided trajectories of one arm or leg. Therefore, head-worn devices with sensors (e.g., earbuds) should be considered to analyze gait symmetry because the head sways towards the left and right side depending on steps. This paper proposed new visualization methods using head-worn sensors, able to facilitate gait symmetry analysis outside as well as inside. Data were collected with an inertial measurement unit (IMU) based motion capture system when twelve participants walked on the 400-m running track. From head trajectories on the transverse and frontal plane, three types of diagrams were displayed, and five concepts of parameters were measured for gait symmetry analysis. The mean absolute percentage error (MAPE) of step counting was lower than 0.65%, representing the reliability of measured parameters. The methods enable also left-right step recognition (MAPE ≤ 2.13%). This study can support maintenance and relearning of a balanced healthy gait in various areas with simple and easy-to-use devices.
MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy
Authors: Zhu Xiaoliang , Ye Yuanxin , Zhao Liang , Shen Chen
Published: October 2021

In recent years, massive open online courses (MOOCs) have received widespread attention owing to their flexibility and free access, which has attracted millions of online learners to participate in courses. With the wide application of MOOCs in educational institutions, a large amount of learners’ log data exist in the MOOCs platform, and this lays a solid data foundation for exploring learners’ online learning behaviors. Using data mining techniques to process these log data and then analyze the relationship between learner behavior and academic performance has become a hot topic of research. Firstly, this paper summarizes the commonly used predictive models in the relevant research fields. Based on the behavior log data of learners participating in 12 courses in MOOCs, an entropy-based indicator quantifying behavior change trends is proposed, which explores the relationships between behavior change trends and learners’ academic performance. Next, we build a set of behavioral features, which further analyze the relationships between behaviors and academic performance. The results demonstrate that entropy has a certain correlation with the corresponding behavior, which can effectively represent the change trends of behavior. Finally, to verify the effectiveness and importance of the predictive features, we choose four benchmark models to predict learners’ academic performance and compare them with the previous relevant research results. The results show that the proposed feature selection-based model can effectively identify the key features and obtain good prediction performance. Furthermore, our prediction results are better than the related studies in the performance prediction based on the same Xuetang MOOC platform, which demonstrates that the combination of the selected learner-related features (behavioral features + behavior entropy) can lead to a much better prediction performance.
Effects and Mechanism of Surface Water Wettability and Operating Frequency on Response Linearity of Flexible IDE Capacitive Humidity Sensor
Authors: Yang Woo Seok , Han Seungoh , Lim Gyu-Ri , Kim Hyun You , Hong Sung-Hoon
Published: October 2021

Flexible capacitive humidity sensors are promising for low-cost, wearable, and radio frequency identification sensors, but their nonlinear response is an important issue for practical applications. Herein, the linearity of humidity response was controlled by surface water wettability and operating frequency of sensor, and the mechanism was explained in detail by surface water condensation. For a sensor with a Ag interdigitated electrode (IDE) on a poly(ethylene terephthalate) substrate, the capacitance showed a small linear increase with humidity up to 70% RH but a large nonlinear increase in the higher range. The response linearity was increased by a hydrophobic surface treatment of self-assembled monolayer coating while it was decreased by an ultraviolet/ozone irradiation for hydrophilicity. It was also increased by increasing the frequency in the range of 1–100 kHz, more prominently on a more hydrophilic surface. Based on experiment and simulation, the increase in sensor capacitance was greatly dependent on the geometric pattern (e.g., size, number, and contact angle) and electrical permittivity of surface water droplets. A larger and more nonlinear humidity response resulted from a larger increase in the number of droplets with a smaller contact angle on a sensor surface with higher water wettability and also from a higher permittivity of water at a lower frequency.
Efficient Design for Integrated Photonic Waveguides with Agile Dispersion
Authors: Wang Zhaonian , Du Jiangbing , Shen Weihong , Liu Jiacheng , He Zuyuan
Published: October 2021

Chromatic dispersion engineering of photonic waveguide is of great importance for Photonic Integrated Circuit in broad applications, including on-chip CD compensation, supercontinuum generation, Kerr-comb generation, micro resonator and mode-locked laser. Linear propagation behavior and nonlinear effects of the light wave can be manipulated by engineering CD, in order to manipulate the temporal shape and frequency spectrum. Therefore, agile shapes of dispersion profiles, including typically wideband flat dispersion, are highly desired among various applications. In this study, we demonstrate a novel method for agile dispersion engineering of integrated photonic waveguide. Based on a horizontal double-slot structure, we obtained agile dispersion shapes, including broadband low dispersion, constant dispersion and slope-maintained linear dispersion. The proposed inverse design method is objectively-motivated and automation-supported. Dispersion in the range of 0–1.5 ps/(nm·km) for 861-nm bandwidth has been achieved, which shows superior performance for broadband low dispersion. Numerical simulation of the Kerr frequency comb was carried out utilizing the obtained dispersion shapes and a comb spectrum for 1068-nm bandwidth with a 20-dB power variation was generated. Significant potential for integrated photonic design automation can be expected.
Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design
Authors: Basha Jameer , Bacanin Nebojsa , Vukobrat Nikola , Zivkovic Miodrag , Venkatachalam K. , Hubálovský Stepan , Trojovský Pavel
Published: October 2021

The research presented in this manuscript proposes a novel Harris Hawks optimization algorithm with practical application for evolving convolutional neural network architecture to classify various grades of brain tumor using magnetic resonance imaging. The proposed improved Harris Hawks optimization method, which belongs to the group of swarm intelligence metaheuristics, further improves the exploration and exploitation abilities of the basic algorithm by incorporating a chaotic population initialization and local search, along with a replacement strategy based on the quasi-reflection-based learning procedure. The proposed method was first evaluated on 10 recent CEC2019 benchmarks and the achieved results are compared with the ones generated by the basic algorithm, as well as with results of other state-of-the-art approaches that were tested under the same experimental conditions. In subsequent empirical research, the proposed method was adapted and applied for a practical challenge of convolutional neural network design. The evolved network structures were validated against two datasets that contain images of a healthy brain and brain with tumors. The first dataset comprises well-known IXI and cancer imagining archive images, while the second dataset consists of axial T1-weighted brain tumor images, as proposed in one recently published study in the Q1 journal. After performing data augmentation, the first dataset encompasses 8.000 healthy and 8.000 brain tumor images with grades I, II, III, and IV and the second dataset includes 4.908 images with Glioma, Meningioma, and Pituitary, with 1.636 images belonging to each tumor class. The swarm intelligence-driven convolutional neural network approach was evaluated and compared to other, similar methods and achieved a superior performance. The obtained accuracy was over 95% in all conducted experiments. Based on the established results, it is reasonable to conclude that the proposed approach could be used to develop networks that can assist doctors in diagnostics and help in the early detection of brain tumors.
Plant Leaf Detection and Counting in a Greenhouse during Day and Nighttime Using a Raspberry Pi NoIR Camera
Authors: Soetedjo Aryuanto , Hendriarianti Evy
Published: October 2021

A non-destructive method using machine vision is an effective way to monitor plant growth. However, due to the lighting changes and complicated backgrounds in outdoor environments, this becomes a challenging task. In this paper, a low-cost camera system using an NoIR (no infrared filter) camera and a Raspberry Pi module is employed to detect and count the leaves of Ramie plants in a greenhouse. An infrared camera captures the images of leaves during the day and nighttime for a precise evaluation. The infrared images allow Otsu thresholding to be used for efficient leaf detection. A combination of numbers of thresholds is introduced to increase the detection performance. Two approaches, consisting of static images and image sequence methods are proposed. A watershed algorithm is then employed to separate the leaves of a plant. The experimental results show that the proposed leaf detection using static images achieves high recall, precision, and F1 score of 0.9310, 0.9053, and 0.9167, respectively, with an execution time of 551 ms. The strategy of using sequences of images increases the performances to 0.9619, 0.9505, and 0.9530, respectively, with an execution time of 516.30 ms. The proposed leaf counting achieves a difference in count (DiC) and absolute DiC (ABS_DiC) of 2.02 and 2.23, respectively, with an execution time of 545.41 ms. Moreover, the proposed method is evaluated using the benchmark image datasets, and shows that the foreground–background dice (FBD), DiC, and ABS_DIC are all within the average values of the existing techniques. The results suggest that the proposed system provides a promising method for real-time implementation.
Bistatic Forward ISAR with DVB-T Transmitter of Opportunity
Authors: Lazarov Andon Dimitrov , Kostadinov Todor Pavlov
Published: October 2021

The radar geometry defined by a spatially separated transmitter and receiver with a moving object crossing the baseline is considered as a Bistatic Forward Inverse Synthetic Aperture Radar (BFISAR). As a transmitter of opportunity, a Digital Video Broadcast-Terrestrial (DVB-T) television station emitting DVB-T waveforms was used. A system of vector equations describing the kinematics of the object was derived. A mathematical model of a BFISAR signal received after the reflection of DVB-T waveforms from the moving object was described. An algorithm for extraction of the object’s image including phase correction and two Fourier transformations applied over the received BFISAR signal—in the range and azimuth directions—was created. To prove the correctness of mathematical models of the object geometry, waveforms and signals, and the image extraction procedure, graphical results of simulation numerical experiments were provided.
Enhanced Soft 3D Reconstruction Method with an Iterative Matching Cost Update Using Object Surface Consensus
Authors: Lee Min-Jae , Um Gi-Mun , Yun Joungil , Cheong Won-Sik , Park Soon-Yong
Published: October 2021

In this paper, we propose a multi-view stereo matching method, EnSoft3D (Enhanced Soft 3D Reconstruction) to obtain dense and high-quality depth images. Multi-view stereo is one of the high-interest research areas and has wide applications. Motivated by the Soft3D reconstruction method, we introduce a new multi-view stereo matching scheme. The original Soft3D method is introduced for novel view synthesis, while occlusion-aware depth is also reconstructed by integrating the matching costs of the Plane Sweep Stereo (PSS) and soft visibility volumes. However, the Soft3D method has an inherent limitation because the erroneous PSS matching costs are not updated. To overcome this limitation, the proposed scheme introduces an update process of the PSS matching costs. From the object surface consensus volume, an inverse consensus kernel is derived, and the PSS matching costs are iteratively updated using the kernel. The proposed EnSoft3D method reconstructs a highly accurate 3D depth image because both the multi-view matching cost and soft visibility are updated simultaneously. The performance of the proposed method is evaluated by using structured and unstructured benchmark datasets. Disparity error is measured to verify 3D reconstruction accuracy, and both PSNR and SSIM are measured to verify the simultaneous enhancement of view synthesis.
Building an IoT Platform Based on Service Containerisation
Authors: Antunes Mário , Santiago Ana Rita , Manso Sérgio , Regateiro Diogo , Barraca João Paulo , Gomes Diogo , Aguiar Rui L.
Published: October 2021

IoT platforms have become quite complex from a technical viewpoint, becoming the cornerstone for information sharing, storing, and indexing given the unprecedented scale of smart services being available by massive deployments of a large set of data-enabled devices. These platforms rely on structured formats that exploit standard technologies to deal with the gathered data, thus creating the need for carefully designed customised systems that can handle thousands of heterogeneous data sensors/actuators, multiple processing frameworks, and storage solutions. We present the SCoT2.0 platform, a generic-purpose IoT Platform that can acquire, process, and visualise data using methods adequate for both real-time processing and long-term Machine Learning (ML)-based analysis. Our goal is to develop a large-scale system that can be applied to multiple real-world scenarios and is potentially deployable on private clouds for multiple verticals. Our approach relies on extensive service containerisation, and we present the different design choices, technical challenges, and solutions found while building our own IoT platform. We validate this platform supporting two very distinct IoT projects (750 physical devices), and we analyse scaling issues within the platform components.
Automatic Prediction of Ischemia-Reperfusion Injury of Small Intestine Using Convolutional Neural Networks: A Pilot Study
Authors: Hou Jie , Strand-Amundsen Runar , Tronstad Christian , Høgetveit Jan Olav , Martinsen Ørjan Grøttem , Tønnessen Tor Inge
Published: October 2021

Acute intestinal ischemia is a life-threatening condition. The current gold standard, with evaluation based on visual and tactile sensation, has low specificity. In this study, we explore the feasibility of using machine learning models on images of the intestine, to assess small intestinal viability. A digital microscope was used to acquire images of the jejunum in 10 pigs. Ischemic segments were created by local clamping (approximately 30 cm in width) of small arteries and veins in the mesentery and reperfusion was initiated by releasing the clamps. A series of images were acquired once an hour on the surface of each of the segments. The convolutional neural network (CNN) has previously been used to classify medical images, while knowledge is lacking whether CNNs have potential to classify ischemia-reperfusion injury on the small intestine. We compared how different deep learning models perform for this task. Moreover, the Shapley additive explanations (SHAP) method within explainable artificial intelligence (AI) was used to identify features that the model utilizes as important in classification of different ischemic injury degrees. To be able to assess to what extent we can trust our deep learning model decisions is critical in a clinical setting. A probabilistic model Bayesian CNN was implemented to estimate the model uncertainty which provides a confidence measure of our model decisions.
A Novel Capacitive Measurement Device for Longitudinal Monitoring of Bone Fracture Healing
Authors: Sorriento Angela , Chiurazzi Marcello , Fabbri Luca , Scaglione Michelangelo , Dario Paolo , Ciuti Gastone
Published: October 2021

The healing process of surgically-stabilised long bone fractures depends on two main factors: (a) the assessment of implant stability, and (b) the knowledge of bone callus stiffness. Currently, X-rays are the main diagnostic tool used for the assessment of bone fractures. However, they are considered unsafe, and the interpretation of the clinical results is highly subjective, depending on the clinician’s experience. Hence, there is the need for objective, non-invasive and repeatable methods to allow a longitudinal assessment of implant stability and bone callus stiffness. In this work, we propose a compact and scalable system, based on capacitive sensor technology, able to measure, quantitatively, the relative pins displacements in bone fractures treated with external fixators. The measurement device proved to be easily integrable with the external fixator pins. Smart arrangements of the sensor units were exploited to discriminate relative movements of the external pins in the 3D space with a resolution of 0.5 mm and 0.5°. The proposed capacitive technology was able to detect all of the expected movements of the external pins in the 3D space, providing information on implant stability and bone callus stiffness.
Monitoring Flexions and Torsions of the Trunk via Gyroscope-Calibrated Capacitive Elastomeric Wearable Sensors
Authors: Frediani Gabriele , Vannetti Federica , Bocchi Leonardo , Zonfrillo Giovanni , Carpi Federico
Published: October 2021

Reliable, easy-to-use, and cost-effective wearable sensors are desirable for continuous measurements of flexions and torsions of the trunk, in order to assess risks and prevent injuries related to body movements in various contexts. Piezo-capacitive stretch sensors, made of dielectric elastomer membranes coated with compliant electrodes, have recently been described as a wearable, lightweight and low-cost technology to monitor body kinematics. An increase of their capacitance upon stretching can be used to sense angular movements. Here, we report on a wearable wireless system that, using two sensing stripes arranged on shoulder straps, can detect flexions and torsions of the trunk, following a simple and fast calibration with a conventional tri-axial gyroscope on board. The piezo-capacitive sensors avoid the errors that would be introduced by continuous sensing with a gyroscope, due to its typical drift. Relative to stereophotogrammetry (non-wearable standard system for motion capture), pure flexions and pure torsions could be detected by the piezo-capacitive sensors with a root mean square error of ~8° and ~12°, respectively, whilst for flexion and torsion components in compound movements, the error was ~13° and ~15°, respectively.
High Dynamic Weak Signal Tracking Algorithm of a Beidou Vector Receiver Based on an Adaptive Square Root Cubature Kalman Filter
Authors: Li Na , Zhang Shufang , Jiang Yi
Published: October 2021

Compared with a scalar tracking receiver, the Beidou vector tracking receiver has the advantages of smaller tracking errors, fast loss-of-lock reacquisition, and high stability. However, in extremely challenging conditions, such as highly dynamic and weak signals, the loop will exhibit a high degree of nonlinearity, and observations with gross errors and large deviations will reduce the positioning accuracy and stability. In view of this situation, based on the concepts of cubature Kalman filtering and square root filtering, a square root cubature Kalman filtering (SRCKF) algorithm is given. Then, combining this algorithm with the concept of covariance matching based on an innovation sequence, an adaptive square root cubature Kalman filter (ASRCKF) algorithm is proposed. The algorithm was verified, and the tracking performance of the vector locking loop (VLL) realized by the algorithm was compared with the SRCKF VLL and the ASRCKF scalar locking loop (SLL). The simulation results show that, regardless of whether in a highly dynamic weak signal environment or in a general situation where the signal-to-noise ratio is higher than the tracking threshold, the tracking accuracy and stability of the ASRCKF VLL are higher than those of the SRCKF VLL and the ASRCKF SLL, the three-dimensional position error of the ASRCKF VLL does not exceed 36 m, and the three-dimensional velocity error does not exceed 3.5 m/s.
3D Object Detection with SLS-Fusion Network in Foggy Weather Conditions
Authors: Mai Nguyen Anh Minh , Duthon Pierre , Khoudour Louahdi , Crouzil Alain , Velastin Sergio A.
Published: October 2021

The role of sensors such as cameras or LiDAR (Light Detection and Ranging) is crucial for the environmental awareness of self-driving cars. However, the data collected from these sensors are subject to distortions in extreme weather conditions such as fog, rain, and snow. This issue could lead to many safety problems while operating a self-driving vehicle. The purpose of this study is to analyze the effects of fog on the detection of objects in driving scenes and then to propose methods for improvement. Collecting and processing data in adverse weather conditions is often more difficult than data in good weather conditions. Hence, a synthetic dataset that can simulate bad weather conditions is a good choice to validate a method, as it is simpler and more economical, before working with a real dataset. In this paper, we apply fog synthesis on the public KITTI dataset to generate the Multifog KITTI dataset for both images and point clouds. In terms of processing tasks, we test our previous 3D object detector based on LiDAR and camera, named the Spare LiDAR Stereo Fusion Network (SLS-Fusion), to see how it is affected by foggy weather conditions. We propose to train using both the original dataset and the augmented dataset to improve performance in foggy weather conditions while keeping good performance under normal conditions. We conducted experiments on the KITTI and the proposed Multifog KITTI datasets which show that, before any improvement, performance is reduced by 42.67% in 3D object detection for Moderate objects in foggy weather conditions. By using a specific strategy of training, the results significantly improved by 26.72% and keep performing quite well on the original dataset with a drop only of 8.23%. In summary, fog often causes the failure of 3D detection on driving scenes. By additional training with the augmented dataset, we significantly improve the performance of the proposed 3D object detection algorithm for self-driving cars in foggy weather conditions.
Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data
Authors: Zhang Yuequn , Luo Lei , Ji Xu , Dai Yiyang
Published: October 2021

Fault detection and diagnosis (FDD) has received considerable attention with the advent of big data. Many data-driven FDD procedures have been proposed, but most of them may not be accurate when data missing occurs. Therefore, this paper proposes an improved random forest (RF) based on decision paths, named DPRF, utilizing correction coefficients to compensate for the influence of incomplete data. In this DPRF model, intact training samples are firstly used to grow all the decision trees in the RF. Then, for each test sample that possibly contains missing values, the decision paths and the corresponding nodes importance scores are obtained, so that for each tree in the RF, the reliability score for the sample can be inferred. Thus, the prediction results of each decision tree for the sample will be assigned to certain reliability scores. The final prediction result is obtained according to the majority voting law, combining both the predicting results and the corresponding reliability scores. To prove the feasibility and effectiveness of the proposed method, the Tennessee Eastman (TE) process is tested. Compared with other FDD methods, the proposed DPRF model shows better performance on incomplete data.
On-Site Calibration Method for Line-Structured Light Sensor-Based Railway Wheel Size Measurement System
Authors: Ran Yunfeng , He Qixin , Feng Qibo , Cui Jianying
Published: October 2021

Line-structured light has been widely used in the field of railway measurement, owing to its high capability of anti-interference, fast scanning speed and high accuracy. Traditional calibration methods of line-structured light sensors have the disadvantages of long calibration time and complicated calibration process, which is not suitable for railway field application. In this paper, a fast calibration method based on a self-developed calibration device was proposed. Compared with traditional methods, the calibration process is simplified and the calibration time is greatly shortened. This method does not need to extract light strips; thus, the influence of ambient light on the measurement is reduced. In addition, the calibration error resulting from the misalignment was corrected by epipolar constraint, and the calibration accuracy was improved. Calibration experiments in laboratory and field tests were conducted to verify the effectiveness of this method, and the results showed that the proposed method can achieve a better calibration accuracy compared to a traditional calibration method based on Zhang’s method.
Optical Fiber Ball Resonator Sensor Spectral Interrogation through Undersampled KLT: Application to Refractive Index Sensing and Cancer Biomarker Biosensing
Authors: Tosi Daniele , Ashikbayeva Zhannat , Bekmurzayeva Aliya , Myrkhiyeva Zhuldyz , Rakhimbekova Aida , Ayupova Takhmina , Shaimerdenova Madina
Published: October 2021

Optical fiber ball resonators based on single-mode fibers in the infrared range are an emerging technology for refractive index sensing and biosensing. These devices are easy and rapid to fabricate using a CO2 laser splicer and yield a very low finesse reflection spectrum with a quasi-random pattern. In addition, they can be functionalized for biosensing by using a thin-film sputtering method. A common problem of this type of device is that the spectral response is substantially unknown, and poorly correlated with the size and shape of the spherical device. In this work, we propose a detection method based on Karhunen−Loeve transform (KLT), applied to the undersampled spectrum measured by an optical backscatter reflectometer. We show that this method correctly detects the response of the ball resonator in any working condition, without prior knowledge of the sensor under interrogation. First, this method for refractive index sensing of a gold-coated resonator is applied, showing 1594 RIU−1 sensitivity; then, this concept is extended to a biofunctionalized ball resonator, detecting CD44 cancer biomarker concentration with a picomolar-level limit of detection (19.7 pM) and high specificity (30–41%).
Research on the Principle and Cooperative Processing Method of MRS Multisystem Joint Detection
Authors: Li Cong , Zeng Zhaofa , Wang Zhuo , Yi Xiaofeng
Published: October 2021

Magnetic resonance sounding (MRS) technology is the only geophysical means to directly and quantitatively detect groundwater and has achieved good results in hydrogeological prospecting applications. In recent years, researchers have conducted considerable research on the efficiency of a single instrument, yielding certain results. However, the overall work efficiency of this method has not been effectively determined in its application to a large-scale survey. Hence, we propose both a joint detection method for MRS that determines the minimum working distance when multiple systems operate simultaneously and a collaborative measurement method of dual systems operating simultaneously in a fixed range of work areas. The cooperative working mode of the instruments is tested in the detection area, and the working mode proposed in this paper is shown to effectively avoid measurement interference between systems. Compared with the working mode of a single set of instruments, the measurement efficiency is more than doubled. Through this research, the feasibility of multiple MRS instruments working together in the same work area is verified, which provides effective technical support for the rapid and high-efficiency utilization of MRS over a wide range of measurement areas.
Open Platform Cameras Based Bio-Imaging Evaluation System
Authors: Baek Ji-Yeon , Kim Jong-Dae , Kim Yu-Seop , Park Chan-Young , Hwang Ji-Soo
Published: October 2021

With the active development of mobile devices, a variety of ultra-small, high-definition, and open platform-based cameras are being mass-produced. In this paper, we established an emulation system to verify the bio-imaging performance of the bulky and expensive high-performance cameras and various smartphone cameras that have been used in bio-imaging devices. In the proposed system, the linearity of the brightness gradient change of four types of cameras was compared and analyzed. Based on these results, three cameras were selected in order of excellent linearity, and gel image analysis results were compared.
Magnetoimpedance of CoFeCrSiB Ribbon-Based Sensitive Element with FeNi Covering: Experiment and Modeling
Authors: Volchkov Stanislav O. , Pasynkova Anna A. , Derevyanko Michael S. , Bukreev Dmitry A. , Kozlov Nikita V. , Svalov Andrey V. , Semirov Alexander V.
Published: October 2021

Soft magnetic materials are widely requested in electronic and biomedical applications. Co-based amorphous ribbons are materials which combine high value of the magnetoimpedance effect (MI), high sensitivity with respect to the applied magnetic field, good corrosion stability in aggressive environments, and reasonably low price. Functional properties of ribbon-based sensitive elements can be modified by deposition of additional magnetic and non-ferromagnetic layers with required conductivity. Such layers can play different roles. In the case of magnetic biosensors for magnetic label detection, they can provide the best conditions for self-assembling processes in biological experiments. In this work, magnetic properties and MI effect were studied for the cases of rapidly quenched Co67Fe3Cr3Si15B12 amorphous ribbons and magnetic Fe20Ni80/Co67Fe3Cr3Si15B12/Fe20Ni80 composites obtained by deposition of Fe20Ni80 1 μm thick films onto both sides of the ribbons by magnetron sputtering technique. Their comparative analysis was used for finite element computer simulations of MI responses with different types of magnetic and conductive coatings. The obtained results can be useful for the design of MI sensor development, including MI biosensors for magnetic label detection.
Recent Advances in Rice Varietal Development for Durable Resistance to Biotic and Abiotic Stresses through Marker-Assisted Gene Pyramiding
Authors: Haque Md Azadul , Rafii Mohd Y. , Yusoff Martini Mohammad , Ali Nusaibah Syd , Yusuff Oladosu , Datta Debi Rani , Anisuzzaman Mohammad , Ikbal Mohammad Ferdous
Published: October 2021

Abiotic and biotic stresses adversely affect rice growth, development and grain yield. Traditional rice breeding techniques are insufficient in modern agriculture to meet the growing population’s food needs on a long-term basis. The development of DNA markers closely linked to target genes or QTLs on rice chromosomes, and advanced molecular techniques, such as marker-assisted selection (MAS), have encouraged the evolution of contemporary techniques in rice genetics and breeding, such as gene pyramiding. Gene pyramiding refers to the act of combining two or more genes from multiple parents into a single genotype, which allows the overexpression of more than one gene for broad-spectrum abiotic and biotic stress resistance. Marker-assisted pedigree, backcrossing and pseudo-backcrossing methods can increase the conventional breeding speed by reducing the number of breeding generations in order to enhance the pyramiding process. Pyramiding is affected by several factors: the number of transferred genes; the range within gene and flanking markers; the number of chosen populations in every breeding generation; the features of genes and germplasms; and the potentiality of breeders to identify the target genes. Modern breeding methods, such as the marker-assisted backcrossing approach, have made gene pyramiding more precise and reliable for the development of stress-tolerant rice varieties in the coming decades. This review presents up-to-date knowledge on gene pyramiding schemes, marker-assisted gene pyramiding techniques, the efficiency of marker-assisted gene pyramiding and the advantages and limitations of gene pyramiding methods. This review also reports on the potential application of marker-assisted selection breeding to develop stress-tolerant rice varieties that stabilize abiotic and biotic stresses. This review will help rice breeders to improve yields by increasing rice productivity under abiotic and biotic stress conditions.
Advancing Environmental Education for Sustainable Development in Higher Education in Nigeria: Current Challenges and Future Directions
Authors: Babalola Afe , Olawuyi Damilola S.
Published: October 2021

The important roles of environmental education (EE) as a tool for advancing the United Nations Sustainable Development Goals (SDGs) have been clearly identified in national policy visions and guidelines in Nigeria. Despite this increasing recognition however, the implementation and delivery of EE programs remain adversely impacted by a wide range of practical implementation challenges. While a number of existing studies have compiled the importance of EE for advancing the SDGs, a detailed examination of the law and governance challenges that limit the implementation of EE programs in Nigeria has remained absent. This article fills a gap in this regard. Various legal and institutional challenges that arise with the design and implementation of EE programs in Nigeria are examined in order to identify the ways in which an integrative governance framework on EE can help close these gaps. The study suggests that an elaboration of coherent national strategy on EE; a dedicated budgetary allocation for EE programs; reform of the existing laws to ensure coherent implementation of EE programs; and the designation of focal EE units at higher education institution are significant steps towards improving the development and implementation of EE programs in Nigeria.
Improving Governance Systems of National Parks: How the Instrument of a ‘Governance Scan’ Can Contribute
Authors: Heslinga Jasper Hessel , Hartman Stefan
Published: October 2021

This paper presents a diagnostics tool that we refer to as a ‘governance scan’ and discusses how this tool can contribute to improving governance systems of National Parks. This governance scan combines an analytical framework and an approach to have better understanding of these governance systems. Understanding how National Parks are managed is crucial to achieve improvements and steer towards more sustainable future situations. Governance systems are a fundamental aspect of this, being understood as “associational networks of public, private, civil society actors and how they engage in the making, setting and implementation of rules at various geographical scales”. How these systems are organized and function in practice can greatly shape conservation and development outcomes and hence future states of National Parks. The purpose of this paper is to; (1) elaborate on how this scan is rooted in the literature to explain its theoretical foundation and (2) step-by-step instruct how it is made applicable to use in practice. As an illustrative example, we discuss lessons learned from the application of the governance scan in the real-life context of the recently established ‘New Land’ National Park, located in The Netherlands. We conclude that the scan works as a diagnostics tool, to provide an overview of governance systems in place, facilitate knowledge transfer and discussions among different stakeholders, and set priorities in decision-making processes.
Measuring Learnability through Virtual Reality Laboratory Application: A User Study
Authors: Rasheed Ghazala , Khan Muzafar , Malik Noman , Akhunzada Adnan
Published: October 2021

The cutting-edge technology of virtual reality has changed almost every aspect of life in e-commerce, engineering, medicine, and entertainment. This technology has also made its way to the field of education in the form of virtual laboratories. A lack of student engagement and interest towards STEM subjects is reported in the literature. Several studies have been conducted to evaluate virtual reality in education, but these studies are limited in terms of participants and subject coverage. This study aimed to assess the effectiveness of virtual laboratories to develop student’s practical learning skills for secondary school physics. For this purpose, a desktop-based virtual laboratory application was developed based on the guidelines extracted from the literature. A user study was adopted as the main research method, and it was conducted with 184 students of 4 different schools. In each school, students were divided into two groups: experimental (used the virtual laboratory application) and control (used a physical laboratory). The data were collected through an academic quiz conducted at the end of the study. The mean score of the experimental group was 7.16, compared with 5.87 for the control group. The results revealed that the students’ learning using the virtual laboratory application was better compared with the control group. Interestingly, there was no significant difference in the performance of boys and girls in both groups. The usability questionnaire was also completed by 92 students of the experimental group to assess the application interface. The mean score was 73.5 (above average) with an internal consistency of 0.76. The participants found the virtual laboratory application to be user-friendly, easy to use, and supportive in learning.
Coffee (Coffea arabica L.): Methods, Objectives, and Future Strategies of Breeding in Ethiopia—Review
Authors: Melese Yebirzaf Yeshiwas , Kolech Semagn Asredie
Published: October 2021

Coffea arabica L. belongs to the Rubiaceae family, and the genus Coffea is believed to have a primary center of origin and genetic variability in the highlands of southwestern Ethiopia. It is a vital beverage commodity across the world and a valuable export product, ranking second in international trade after petroleum. Ethiopia is among the top five major coffee-producing countries and is Africa’s leading producer. However, its full production capacity has not yet been exploited, and research efforts to reduce biotic and abiotic factors through reproduction have been extremely limited. Hence, improvement through different breeding methods is essential to overcome the constraints in its production. Thus, the objective of this study is to review the different breeding methods applied for different traits in Ethiopia. Breeding methods depend on the type and the source of traits and the final breeding objectives. The main breeding objectives are production, resistance/tolerance to diseases, and cup quality. The commonly applied breeding methods are selected and intra-specific hybridization, germplasm enhancement, and the development of improved varieties with wider adaptability. There is also a practice of crossing parental lines selected for certain desirable traits for the development of hybrid varieties. Accordingly, some promising success has been obtained. Forty-one coffee varieties have been released so far. Because conventional breeding methods are time-consuming, integrating conventional breeding methods with biotechnological techniques could have an instrumental role in the rapid development of suitable varieties for the changing climate.