bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method
Authors: Song Ze , Xu Wenxin , Dong Huilin , Wang Xiaowei , Cao Yuqi , Huang Pingjie , Hou Dibo , Wu Zhengfang , Wang Zhongyi
Published: June 2022

Frequent outbreaks of cyanobacterial blooms have become one of the most challenging water ecosystem issues and a critical concern in environmental protection. To overcome the poor stability of traditional detection algorithms, this paper proposes a method for detecting cyanobacterial blooms based on a deep-learning algorithm. An improved vegetation-index method based on a multispectral image taken by an Unmanned Aerial Vehicle (UAV) was adopted to extract inconspicuous spectral features of cyanobacterial blooms. To enhance the recognition accuracy of cyanobacterial blooms in complex scenes with noise such as reflections and shadows, an improved transformer model based on a feature-enhancement module and pixel-correction fusion was employed. The algorithm proposed in this paper was implemented in several rivers in China, achieving a detection accuracy of cyanobacterial blooms of more than 85%. The estimate of the proportion of the algae bloom contamination area and the severity of pollution were basically accurate. This paper can lay a foundation for ecological and environmental departments for the effective prevention and control of cyanobacterial blooms.
Image Edge Detection Methods in Perimeter Security Systems Using Distributed Fiber Optical Sensing
Authors: Dejdar Petr , Záviška Pavel , Valach Soběslav , Münster Petr , Horváth Tomáš
Published: June 2022

This paper aims to evaluate detection algorithms for perimeter security systems based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). Our own designed and developed sensor system was used for the measurement. The main application of the system is in the area the perimeter fencing intrusion detection. The system is unique thanks to the developed motherboard, which contains a field-programmable gate array (FPGA) that takes care of signal processing. This allows the entire system to be integrated into a 1U rack chassis. A polygon containing two different fence types and also cable laid underground in a plastic tube was used for testing. Edge detection algorithms using the Sobel and Prewitt operators are considered for post-processing. The comparison is made based on the signal-to-noise ratio (SNR) values calculated for each event. Results of algorithms based on edge detection methods are compared with the conventional differential method commonly used in Φ-OTDR systems.
Automatically Controlled Dust Generation System Using Arduino
Authors: Hofstetter Dan , Fabian Eileen , Dominguez Dorian , Lorenzoni A. Gino
Published: June 2022

A dust generator was developed to disperse and maintain a desired concentration of airborne dust in a controlled environment chamber to study poultry physiological response to sustained elevated levels of particulate matter. The goal was to maintain an indicated PM10 concentration of 50 µg/m3 of airborne dust in a 3.7 m × 4.3 m × 2.4 m (12 ft × 14 ft × 8 ft) controlled environment chamber. The chamber had a 1.5 m3/s (3200 cfm) filtered recirculation air handling system that regulated indoor temperature levels and a 0.06 m3/s (130 cfm) exhaust fan that exchanged indoor air for fresh outdoor air. Dry powdered red oak wood dust that passed through an 80-mesh screen cloth was used for the experiment. The dust generator metered dust from a rectangular feed hopper with a flat bottom belt to a 0.02 m3/s (46 cfm) centrifugal blower. A vibratory motor attached to the hopper ran only when the belt was operated to prevent bridging of powdered materials and to provide an even material feed rate. A laser particle counter was used to measure the concentration of airborne dust and provided feedback to an Arduino-based control system that operated the dust generator. The dust generator was operated using a duty cycle of one second on for every five seconds off to allow time for dispersed dust to mix with chamber air and reach the laser particle counter. The control system maintained an airborne PM10 dust concentration of 54.92 ± 6.42 µg/m3 in the controlled environment chamber during six weeks of continuous operation using red oak wood dust. An advantage of the automatically controlled dust generator was that it continued to operate to reach the setpoint concentration in response to changes in material flow due to humidity, partial blockages, and non-uniform composition of the material being dispersed. Challenges included dust being trapped by the recirculation filter and the exhaust fan removing airborne dust from the environmental chamber.
Open Source Assessment of Deep Learning Visual Object Detection
Authors: Paniego Sergio , Sharma Vinay , Cañas José María
Published: June 2022

This paper introduces Detection Metrics, an open-source scientific software for the assessment of deep learning neural network models for visual object detection. This software provides objective performance metrics such as mean average precision and mean inference time. The most relevant international object detection datasets are supported along with the most widely used deep learning frameworks. Different network models, even those built from different frameworks, can be fairly compared in this way. This is very useful when developing deep learning applications or research. A set of tools is provided to manage and work with different datasets and models, including visualization and conversion into several common formats. Detection Metrics may also be used in automatic batch processing for large experimental tests, saving researchers time, and new domain-specific datasets can be easily created from videos or webcams. It is open-source, can be audited, extended, and adapted to particular requirements. It has been experimentally validated. The performance of the most relevant state-of-the-art neural models for object detection has been experimentally compared. In addition, it has been used in several research projects, guiding in selecting the most suitable network model architectures and training procedures. The performance of the different models and training alternatives can be easily measured, even on large datasets.
Advancing Digital Medicine with Wearables in the Wild
Authors: McGinnis Ryan S. , McGinnis Ellen W.
Published: June 2022

This editorial provides a concise overview of the use and importance of wearables in the emerging field of digital medicine [...]
Sensors Special Issue: “Vibration Energy Harvesting for Wireless Sensors”
Authors: Hadas Zdenek , Zelenika Saša , Pakrashi Vikram
Published: June 2022

Mechanical vibrations occur in the operation of most technical systems [...]
Design Methodology of Automotive Time-Sensitive Network System Based on OMNeT++ Simulation System
Authors: Luo Feng , Wang Bowen , Yang Zhenyu , Zhang Ping , Ma Yifei , Fang Zihao , Wu Mingzhi , Sun Zhipeng
Published: June 2022

Advances in automotive technology require networks to support a variety of communication requirements, such as reliability, real-time performance, low jitter, and strict delay limits. Time-Sensitive Network (TSN) is a keyframe transmission delay-guaranteed solution based on the IEEE 802 architecture of the automotive Ethernet. However, most of the existing studies on automotive TSN performance are based on a single mechanism, lacking a complete and systematic research tool. At the same time, the design method should be considered from a global perspective when designing an automotive TSN system, rather than only considering a single mechanism that TSN applies to. This paper discusses the correspondence between traffic types and automotive scenarios and proposes a methodology to target the delay constraint of traffic types as the design goal of automotive TSN networks. To study the performance of automotive TSN under different mechanisms such as time-aware shaper (TAS), credit-based shaper (CBS), cyclic queuing and forwarding (CQF), etc., this paper also develops a systematic automotive TSN simulation system based on OMNeT++. The simulation system plays a crucial role in the whole methodology, including all applicable TSN standards for the automotive field. Lastly, a complex automotive scenario based on zonal architecture provided by a major motor company in Shanghai is analyzed in the simulated system; verifying TSN can guarantee real-time performance and reliability of the in-vehicle network.
Road Pavement Structural Health Monitoring by Embedded Fiber-Bragg-Grating-Based Optical Sensors
Authors: Braunfelds Janis , Senkans Ugis , Skels Peteris , Janeliukstis Rims , Porins Jurgis , Spolitis Sandis , Bobrovs Vjaceslavs
Published: June 2022

Fiber Bragg grating (FBG) optical sensors are state-of-the-art technology that can be integrated into the road structure, providing real-time traffic-induced strain readings and ensuring the monitoring of the road’s structural health. By implementing specific FBG sensors, it is possible to detect each vehicle’s axle count and the induced strain changes in the road structure. In this study, FBG sensors are embedded at the top of the 240-mm-thick cement-treated reclaimed asphalt pavement mixture layer of the road (specifically, 25 mm deep within the road). Optical sensors’ signal interrogation units are used to measure the strain and temperature and collect data of the road’s passing vehicles, starting from passenger cars that have two axles and up to heavy trucks that have six axles. Passenger cars with 2 axles generate a typical (90% events) strain of 0.8–4.1 μm/m, the 2-axle minibus 5.5–8.5 μm/m, 2–3-axle trucks 11–26 μm/m, but 4–6-axle trucks 14–36 μm/m per each axle. A large number of influencing parameters determine the pavement design leading to the great uncertainty in the prediction of the strain at the boundary between the asphalt surface and cement-treated base layers. Real-time strain and temperature measurements help to understand the actual behavior of the pavement structure under an applied load, thus assisting in validating the proposed pavement design.
Visual-SLAM Classical Framework and Key Techniques: A Review
Authors: Jia Guanwei , Li Xiaoying , Zhang Dongming , Xu Weiqing , Lv Haojie , Shi Yan , Cai Maolin
Published: June 2022

With the significant increase in demand for artificial intelligence, environmental map reconstruction has become a research hotspot for obstacle avoidance navigation, unmanned operations, and virtual reality. The quality of the map plays a vital role in positioning, path planning, and obstacle avoidance. This review starts with the development of SLAM (Simultaneous Localization and Mapping) and proceeds to a review of V-SLAM (Visual-SLAM) from its proposal to the present, with a summary of its historical milestones. In this context, the five parts of the classic V-SLAM framework—visual sensor, visual odometer, backend optimization, loop detection, and mapping—are explained separately. Meanwhile, the details of the latest methods are shown; VI-SLAM (Visual inertial SLAM) is reviewed and extended. The four critical techniques of V-SLAM and its technical difficulties are summarized as feature detection and matching, selection of keyframes, uncertainty technology, and expression of maps. Finally, the development direction and needs of the V-SLAM field are proposed.
Distributed Camera Subsystem for Obstacle Detection
Authors: Oščádal Petr , Spurný Tomáš , Kot Tomáš , Grushko Stefan , Suder Jiří , Heczko Dominik , Novák Petr , Bobovský Zdenko
Published: June 2022

This work focuses on improving a camera system for sensing a workspace in which dynamic obstacles need to be detected. The currently available state-of-the-art solution (MoveIt!) processes data in a centralized manner from cameras that have to be registered before the system starts. Our solution enables distributed data processing and dynamic change in the number of sensors at runtime. The distributed camera data processing is implemented using a dedicated control unit on which the filtering is performed by comparing the real and expected depth images. Measurements of the processing speed of all sensor data into a global voxel map were compared between the centralized system (MoveIt!) and the new distributed system as part of a performance benchmark. The distributed system is more flexible in terms of sensitivity to a number of cameras, better framerate stability and the possibility of changing the camera number on the go. The effects of voxel grid size and camera resolution were also compared during the benchmark, where the distributed system showed better results. Finally, the overhead of data transmission in the network was discussed where the distributed system is considerably more efficient. The decentralized system proves to be faster by 38.7% with one camera and 71.5% with four cameras.
Quantifying the Surface Strain Field Induced by Active Sources with Distributed Acoustic Sensing: Theory and Practice
Authors: Hubbard Peter G. , Vantassel Joseph P. , Cox Brady R. , Rector James W. , Yust Michael B. S. , Soga Kenichi
Published: June 2022

Quantitative dynamic strain measurements of the ground would be useful for engineering scale problems such as monitoring for natural hazards, soil-structure interaction studies, and non-invasive site investigation using full waveform inversion (FWI). Distributed acoustic sensing (DAS), a promising technology for these purposes, needs to be better understood in terms of its directional sensitivity, spatial position, and amplitude for application to engineering-scale problems. This study investigates whether the physical measurements made using DAS are consistent with the theoretical transfer function, reception patterns, and experimental measurements of ground strain made by geophones. Results show that DAS and geophone measurements are consistent in both phase and amplitude for broadband (10 s of Hz), high amplitude (10 s of microstrain), and complex wavefields originating from different positions around the array when: (1) the DAS channels and geophone locations are properly aligned, (2) the DAS cable provides good deformation coupling to the internal optical fiber, (3) the cable is coupled to the ground through direct burial and compaction, and (4) laser frequency drift is mitigated in the DAS measurements. The transfer function of DAS arrays is presented considering the gauge length, pulse shape, and cable design. The theoretical relationship between DAS-measured and pointwise strain for vertical and horizontal active sources is introduced using 3D elastic finite-difference simulations. The implications of using DAS strain measurements are discussed including directionality and magnitude differences between the actual and DAS-measured strain fields. Estimating measurement quality based on the wavelength-to-gauge length ratio for field data is demonstrated. A method for spatially aligning the DAS channels with the geophone locations at tolerances less than the spatial resolution of a DAS system is proposed.
Optimization of PBFT Algorithm Based on QoS-Aware Trust Service Evaluation
Authors: Liu Wei , Zhang Xuhao , Feng Wenlong , Huang Mengxing , Xu Yun
Published: June 2022

In service-transaction scenarios, blockchain technology is widely used as an effective tool for establishing trust between service providers and consumers. The consensus algorithm is the core technology of blockchain. However, existing consensus algorithms, such as the practical Byzantine fault tolerance (PBFT) algorithm, still suffer from high resource consumption and latency. To solve this problem, in this study, we propose an improved PBFT blockchain consensus algorithm based on quality of service (QoS)-aware trust service evaluation for secure and efficient service transactions. The proposed algorithm, called the QoS-aware trust practical Byzantine fault tolerance (QTPBFT) algorithm, efficiently achieves consensus, significantly reduces resource consumption, and enhances consensus efficiency. QTPBFT incorporates a QoS-aware trust service global evaluation mechanism that implements service reliability ranking by conducting a dynamic evaluation according to the real-time performance of the services. To reduce the traffic of the blockchain, it uses a mechanism that selects nodes with higher values to form a consensus group that votes for consensus according to the global evaluation result of the trust service. A practical protocol is also constructed for the proposed algorithm. The results of extensive simulations and comparison with other schemes verify the efficacy and efficiency of the proposed scheme.
Soil Moisture a Posteriori Measurements Enhancement Using Ensemble Learning
Authors: Ruszczak Bogdan , Boguszewska-Mańkowska Dominika
Published: June 2022

This work aimed to assess the recalibration and accurate characterization of commonly used smart soil-moisture sensors using computational methods. The paper describes an ensemble learning algorithm that boosts the performance of potato root moisture estimation and increases the simple moisture sensors’ performance. It was prepared using several month-long everyday actual outdoor data and validated on the separated part of that dataset. To obtain conclusive results, two different potato varieties were grown on 24 separate plots on two distinct soil profiles and, besides natural precipitation, several different watering strategies were applied, and the experiment was monitored during the whole season. The acquisitions on every plot were performed using simple moisture sensors and were supplemented with reference manual gravimetric measurements and meteorological data. Next, a group of machine learning algorithms was tested to extract the information from this measurements dataset. The study showed the possibility of decreasing the median moisture estimation error from 2.035% for the baseline model to 0.808%, which was achieved using the Extra Trees algorithm.
MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN
Authors: Jiang Yun , Liang Jing , Cheng Tongtong , Lin Xin , Zhang Yuan , Dong Jinkun
Published: June 2022

Retinal vessel segmentation is extremely important for risk prediction and treatment of many major diseases. Therefore, accurate segmentation of blood vessel features from retinal images can help assist physicians in diagnosis and treatment. Convolutional neural networks are good at extracting local feature information, but the convolutional block receptive field is limited. Transformer, on the other hand, performs well in modeling long-distance dependencies. Therefore, in this paper, a new network model MTPA_Unet is designed from the perspective of extracting connections between local detailed features and making complements using long-distance dependency information, which is applied to the retinal vessel segmentation task. MTPA_Unet uses multi-resolution image input to enable the network to extract information at different levels. The proposed TPA module not only captures long-distance dependencies, but also focuses on the location information of the vessel pixels to facilitate capillary segmentation. The Transformer is combined with the convolutional neural network in a serial approach, and the original MSA module is replaced by the TPA module to achieve finer segmentation. Finally, the network model is evaluated and analyzed on three recognized retinal image datasets DRIVE, CHASE DB1, and STARE. The evaluation metrics were 0.9718, 0.9762, and 0.9773 for accuracy; 0.8410, 0.8437, and 0.8938 for sensitivity; and 0.8318, 0.8164, and 0.8557 for Dice coefficient. Compared with existing retinal image segmentation methods, the proposed method in this paper achieved better vessel segmentation in all of the publicly available fundus datasets tested performance and results.
Comparative Study on Recognition Models of Black-Odorous Water in Hangzhou Based on GF-2 Satellite Data
Authors: Yu Zhifeng , Huang Qiyu , Peng Xiaoxue , Liu Haijian , Ai Qin , Zhou Bin , Yuan Xiaohong , Fang Meihong , Wang Ben
Published: June 2022

To improve the ability of remote sensing technology in recognizing black-odorous water bodies in Hangzhou, this study analyzed the typical spectral characteristics of black-odorous water in Hangzhou based on measured spectral data and water quality parameters, including the transparency, dissolved oxygen, oxidation reduction potential, and ammonia nitrogen. The single-band threshold method, the normalized difference black-odorous water index (NDBWI) model, the black-odorous water index (BOI) model, and the color purity on a Commission Internationale de L’Eclairage (CIE) model were compared to analyze the spatial and temporal distribution characteristics of the black-odorous water in Hangzhou. The results showed that: (1) The remote sensing reflectance of black-odorous water was lower than that of ordinary water, the spectral curve was gentle, and the wave peak shifted toward the near-infrared direction in the wavelength range of 650–850 nm; (2) Among the aforementioned models, the normalized and improved normalized black-odorous water index methods had a higher accuracy, reaching 87.5%, and the threshold values for black-odorous water identification were 0.14 and 0.1, respectively; (3) From 2015 to 2018, the quantity of black-odorous water in the main urban area of Hangzhou showed a decreasing trend, and black-odorous water was mainly distributed in the Gongshu District and tended to appear in narrow rivers, densely populated areas, and factory construction sites. This study is expected to be of great practical value for the rapid tracking and monitoring of urban black-odorous water by using remote sensing technology for future work.
SiO2 Microsphere Array Coated by Ag Nanoparticles as Raman Enhancement Sensor with High Sensitivity and High Stability
Authors: Sha Haiyang , Wang Zhengkun , Zhang Jie
Published: June 2022

In this paper, a monolayer SiO2 microsphere (MS) array was self-assembled on a silicon substrate, and monolayer dense silver nanoparticles (AgNPs) with different particle sizes were transferred onto the single-layer SiO2 MS array using a liquid–liquid interface method. A double monolayer “Ag@SiO2” with high sensitivity and high uniformity was prepared as a surface-enhanced Raman scattering (SERS) substrate. The electromagnetic distribution on the Ag@SiO2 substrate was analyzed using the Lumerical FDTD (finite difference time domain) Solutions software and the corresponding theoretical enhancement factors were calculated. The experimental results show that a Ag@SiO2 sample with a AgNPs diameter of 30 nm has the maximal electric field value at the AgNPs gap. The limit of detection (LOD) is 10−16 mol/L for Rhodamine 6G (R6G) analytes and the analytical enhancement factor (AEF) can reach ~2.3 × 1013. Our sample also shows high uniformity, with the calculated relative standard deviation (RSD) of ~5.78%.
Sensing Range Extension for Short-Baseline Stereo Camera Using Monocular Depth Estimation
Authors: Seo Beom-Su , Park Byungjae , Choi Hoon
Published: June 2022

This paper proposes a method to extend a sensing range of a short-baseline stereo camera (SBSC). The proposed method combines a stereo depth and a monocular depth estimated by a convolutional neural network-based monocular depth estimation (MDE). To combine a stereo depth and a monocular depth, the proposed method estimates a scale factor of a monocular depth using stereo depth–mono depth pairs and then combines the two depths. Another advantage of the proposed method is that the trained MDE model may be utilized for different environments without retraining. The performance of the proposed method is verified qualitatively and quantitatively using the directly collected and open datasets.
Early-Stage Alzheimer’s Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains
Authors: Tufail Ahsan Bin , Anwar Nazish , Othman Mohamed Tahar Ben , Ullah Inam , Khan Rehan Ali , Ma Yong-Kui , Adhikari Deepak , Rehman Ateeq Ur , Shafiq Muhammad , Hamam Habib
Published: June 2022

Alzheimer’s Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and amyloid plaques in the early as well as later stages of the disease. Neuroimaging modalities are routinely used in clinical practice to capture brain alterations associated with AD. On the other hand, deep learning methods are routinely used to recognize patterns in underlying data distributions effectively. This work uses Convolutional Neural Network (CNN) architectures in both 2D and 3D domains to classify the initial stages of AD into AD, Mild Cognitive Impairment (MCI) and Normal Control (NC) classes using the positron emission tomography neuroimaging modality deploying data augmentation in a random zoomed in/out scheme. We used novel concepts such as the blurring before subsampling principle and distant domain transfer learning to build 2D CNN architectures. We performed three binaries, that is, AD/NC, AD/MCI, MCI/NC and one multiclass classification task AD/NC/MCI. The statistical comparison revealed that 3D-CNN architecture performed the best achieving an accuracy of 89.21% on AD/NC, 71.70% on AD/MCI, 62.25% on NC/MCI and 59.73% on AD/NC/MCI classification tasks using a five-fold cross-validation hyperparameter selection approach. Data augmentation helps in achieving superior performance on the multiclass classification task. The obtained results support the application of deep learning models towards early recognition of AD.
Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson’s Disease Hand Tremor
Authors: Khwaounjoo Prashanna , Singh Gurleen , Grenfell Sophie , Özsoy Burak , MacAskill Michael R. , Anderson Tim J. , Çakmak Yusuf O.
Published: June 2022

Parkinson’s disease affects millions worldwide with a large rise in expected burden over the coming decades. More easily accessible tools and techniques to diagnose and monitor Parkinson’s disease can improve the quality of life of patients. With the advent of new wearable technologies such as smart rings and watches, this is within reach. However, it is unclear what method for these new technologies may provide the best opportunity to capture the patient-specific severity. This study investigates which locations on the hand can be used to capture and monitor maximal movement/tremor severity. Using a Leap Motion device and custom-made software the volume, velocity, acceleration, and frequency of Parkinson’s (n = 55, all right-handed, majority right-sided onset) patients’ hand locations (25 joints inclusive of all fingers/thumb and the wrist) were captured simultaneously. Distal locations of the right hand, i.e., the ends of fingers and the wrist showed significant trends (p < 0.05) towards having the largest movement velocities and accelerations. The right hand, compared with the left hand, showed significantly greater volumes, velocities, and accelerations (p < 0.01). Supplementary analysis showed that the volumes, acceleration, and velocities had significant correlations (p < 0.001) with clinical MDS-UPDRS scores, indicating the potential suitability of using these metrics for monitoring disease progression. Maximal movements at the distal hand and wrist area indicate that these locations are best suited to capture hand tremor movements and monitor Parkinson’s disease.
Accuracy Assessment of Percutaneous Pedicle Screw Placement Using Cone Beam Computed Tomography with Metal Artifact Reduction
Authors: Charles Yann Philippe , Al Ansari Rawan , Collinet Arnaud , De Marini Pierre , Schwartz Jean , Nachabe Rami , Schäfer Dirk , Brendel Bernhard , Gangi Afshin , Cazzato Roberto Luigi
Published: June 2022

Metal artifact reduction (MAR) algorithms are used with cone beam computed tomography (CBCT) during augmented reality surgical navigation for minimally invasive pedicle screw instrumentation. The aim of this study was to assess intra- and inter-observer reliability of pedicle screw placement and to compare the perception of baseline image quality (NoMAR) with optimized image quality (MAR). CBCT images of 24 patients operated on for degenerative spondylolisthesis using minimally invasive lumbar fusion were analyzed retrospectively. Images were treated using NoMAR and MAR by an engineer, thus creating 48 randomized files, which were then independently analyzed by 3 spine surgeons and 3 radiologists. The Gertzbein and Robins classification was used for screw accuracy rating, and an image quality scale rated the clarity of pedicle screw and bony landmark depiction. Intra-class correlation coefficients (ICC) were calculated. NoMAR and MAR led to similarly good intra-observer (ICC > 0.6) and excellent inter-observer (ICC > 0.8) assessment reliability of pedicle screw placement accuracy. The image quality scale showed more variability in individual image perception between spine surgeons and radiologists (ICC range 0.51–0.91). This study indicates that intraoperative screw positioning can be reliably assessed on CBCT for augmented reality surgical navigation when using optimized image quality. Subjective image quality was rated slightly superior for MAR compared to NoMAR.
Lightweight Hot-Spot Fault Detection Model of Photovoltaic Panels in UAV Remote-Sensing Image
Authors: Zheng Qiuping , Ma Jinming , Liu Minghui , Liu Yuchen , Li Yanxiang , Shi Gang
Published: June 2022

Photovoltaic panels exposed to harsh environments such as mountains and deserts (e.g., the Gobi desert) for a long time are prone to hot-spot failures, which can affect power generation efficiency and even cause fires. The existing hot-spot fault detection methods of photovoltaic panels cannot adequately complete the real-time detection task; hence, a detection model considering both detection accuracy and speed is proposed. In this paper, the feature extraction part of YOLOv5 is replaced by the more lightweight Focus structure and the basic unit of ShuffleNetv2, and then the original feature fusion method is simplified. Considering that there is no publicly available infrared photovoltaic panel image dataset, this paper generates an infrared photovoltaic image dataset through frame extraction processing and manual annotation of a publicly available video. Consequently, the number of parameters of the model was 3.71 M, mAP was 98.1%, and detection speed was 49 f/s. A comprehensive comparison of the accuracy, detection speed, and model parameters of each model showed that the indicators of the new model are superior to other detection models; thus, the new model is more suitable to be deployed on the UAV platform for real-time photovoltaic panel hot-spot fault detection.
A Novel Method for Baroreflex Sensitivity Estimation Using Modulated Gaussian Filter
Authors: Ku Tienhsiung , Zida Serge Ismael , Harfiya Latifa Nabila , Li Yung-Hui , Lin Yue-Der
Published: June 2022

The evaluation of baroreflex sensitivity (BRS) has proven to be critical for medical applications. The use of α indices by spectral methods has been the most popular approach to BRS estimation. Recently, an algorithm termed Gaussian average filtering decomposition (GAFD) has been proposed to serve the same purpose. GAFD adopts a three-layer tree structure similar to wavelet decomposition but is only constructed by Gaussian windows in different cutoff frequency. Its computation is more efficient than that of conventional spectral methods, and there is no need to specify any parameter. This research presents a novel approach, referred to as modulated Gaussian filter (modGauss) for BRS estimation. It has a more simplified structure than GAFD using only two bandpass filters of dedicated passbands, so that the three-level structure in GAFD is avoided. This strategy makes modGauss more efficient than GAFD in computation, while the advantages of GAFD are preserved. Both GAFD and modGauss are conducted extensively in the time domain, yet can achieve similar results to conventional spectral methods. In computational simulations, the EuroBavar dataset was used to assess the performance of the novel algorithm. The BRS values were calculated by four other methods (three spectral approaches and GAFD) for performance comparison. From a comparison using the Wilcoxon rank sum test, it was found that there was no statistically significant dissimilarity; instead, very good agreement using the intraclass correlation coefficient (ICC) was observed. The modGauss algorithm was also found to be the fastest in computation time and suitable for the long-term estimation of BRS. The novel algorithm, as described in this report, can be applied in medical equipment for real-time estimation of BRS in clinical settings.
SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection
Authors: Zhang Lejun , Li Yuan , Jin Tianxing , Wang Weizheng , Jin Zilong , Zhao Chunhui , Cai Zhennao , Chen Huiling
Published: June 2022

With countless devices connected to the Internet of Things, trust mechanisms are especially important. IoT devices are more deeply embedded in the privacy of people’s lives, and their security issues cannot be ignored. Smart contracts backed by blockchain technology have the potential to solve these problems. Therefore, the security of smart contracts cannot be ignored. We propose a flexible and systematic hybrid model, which we call the Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC). The model showed excellent performance benefits in smart contract vulnerability detection. In addition, we propose a serial-parallel convolution (SPCNN) suitable for our hybrid model. It can extract features from the input sequence for multivariate combinations while retaining temporal structure and location information. The Ensemble Classifier is used in the classification phase of the model to enhance its robustness. In addition, we focused on six typical smart contract vulnerabilities and constructed two datasets, CESC and UCESC, for multi-task vulnerability detection in our experiments. Numerous experiments showed that SPCBIG-EC is better than most existing methods. It is worth mentioning that SPCBIG-EC can achieve F1-scores of 96.74%, 91.62%, and 95.00% for reentrancy, timestamp dependency, and infinite loop vulnerability detection.
Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review
Authors: Bellavista-Parent Vladimir , Torres-Sospedra Joaquín , Pérez-Navarro Antoni
Published: June 2022

Nowadays, there are a multitude of solutions for indoor positioning, as opposed to standards for outdoor positioning such as GPS. Among the different existing studies on indoor positioning, the use of Wi-Fi signals together with Machine Learning algorithms is one of the most important, as it takes advantage of the current deployment of Wi-Fi networks and the increase in the computing power of computers. Thanks to this, the number of articles published in recent years has been increasing. This fact makes a review necessary in order to understand the current state of this field and to classify different parameters that are very useful for future studies. What are the most widely used machine learning techniques? In what situations have they been tested? How accurate are they? Have datasets been properly used? What type of Wi-Fi signals have been used? These and other questions are answered in this analysis, in which 119 papers are analyzed in depth following PRISMA guidelines.
Data Collection Framework for Context-Aware Virtual Reality Application Development in Unity: Case of Avatar Embodiment
Authors: Moon Jiyoung , Jeong Minho , Oh Sangmin , Laine Teemu H. , Seo Jungryul
Published: June 2022

Virtual Reality (VR) has been adopted as a leading technology for the metaverse, yet most previous VR systems provide one-size-fits-all experiences to users. Context-awareness in VR enables personalized experiences in the metaverse, such as improved embodiment and deeper integration of the real world and virtual worlds. Personalization requires context data from diverse sources. We proposed a reusable and extensible context data collection framework, ManySense VR, which unifies data collection from diverse sources for VR applications. ManySense VR was implemented in Unity based on extensible context data managers collecting data from data sources such as an eye tracker, electroencephalogram, pulse, respiration, galvanic skin response, facial tracker, and Open Weather Map. We used ManySense VR to build a context-aware embodiment VR scene where the user’s avatar is synchronized with their bodily actions. The performance evaluation of ManySense VR showed good performance in processor usage, frame rate, and memory footprint. Additionally, we conducted a qualitative formative evaluation by interviewing five developers (two males and three females; mean age: 22) after they used and extended ManySense VR. The participants expressed advantages (e.g., ease-of-use, learnability, familiarity, quickness, and extensibility), disadvantages (e.g., inconvenient/error-prone data query method and lack of diversity in callback methods), future application ideas, and improvement suggestions that indicate potential and can guide future development. In conclusion, ManySense VR is an efficient tool for researchers and developers to easily integrate context data into their Unity-based VR applications for the metaverse.
InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback
Authors: Ud Din Muhammad Munir , Alshammari Nasser , Alanazi Saad Awadh , Ahmad Fahad , Naseem Shahid , Khan Muhammad Saleem , Haider Hafiz Syed Imran
Published: June 2022

Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user’s requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users’ feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud services using parameters such as availability, security, reliability, and cost. Here, the Quality of Web Service (QWS) dataset is used, which has seven major cloud services categories, ranked from 0–6, to extract the required persuasive features through Sequential Minimal Optimization Regression (SMOreg). The classification outcomes through SMOreg are capable and demonstrate a general accuracy of around 98.71% in identifying optimum cloud services through the identified parameters. The main advantage of SMOreg is that the amount of memory required for SMO is linear. The findings show that our improved model in terms of precision outperforms prevailing techniques such as Multilayer Perceptron (MLP) and Linear Regression (LR).
LiDAR Echo Gaussian Decomposition Algorithm for FPGA Implementation
Authors: Zhou Guoqing , Zhou Xiang , Chen Jinlong , Jia Guoshuai , Zhu Qiang
Published: June 2022

As the existing processing algorithms for LiDAR echo decomposition are time-consuming, this paper proposes an FPGA-based improved Gaussian full-waveform decomposition method. The proposed FPGA architecture consists of three modules: (i) a pre-processing module, which is used to pipeline data reading and Gaussian filtering, (ii) the inflection point coordinate solution module, applied to the second-order differential operation and to calculate inflection point coordinates, and (iii) the Gaussian component parameter solution and echo component positioning module, which is utilized to calculate the Gaussian component and echo time parameters. Finally, two LiDAR datasets, covering the Congo and Antarctic regions, are used to verify the accuracy and speed of the proposed method. The experimental results show that (i) the accuracy of the FPGA-based processing is equivalent to that of PC-based processing, and (ii) the processing speed of the FPGA-based processing is 292 times faster than that of PC-based processing.
Hybrid Technique for Cyber-Physical Security in Cloud-Based Smart Industries
Authors: Garg Deepak , Rani Shalli , Herencsar Norbert , Verma Sahil , Wozniak Marcin , Ijaz Muhammad Fazal
Published: June 2022

New technologies and trends in industries have opened up ways for distributed establishment of Cyber-Physical Systems (CPSs) for smart industries. CPSs are largely based upon Internet of Things (IoT) because of data storage on cloud servers which poses many constraints due to the heterogeneous nature of devices involved in communication. Among other challenges, security is the most daunting challenge that contributes, at least in part, to the impeded momentum of the CPS realization. Designers assume that CPSs are themselves protected as they cannot be accessed from external networks. However, these days, CPSs have combined parts of the cyber world and also the physical layer. Therefore, cyber security problems are large for commercial CPSs because the systems move with one another and conjointly with physical surroundings, i.e., Complex Industrial Applications (CIA). Therefore, in this paper, a novel data security algorithm Dynamic Hybrid Secured Encryption Technique (DHSE) is proposed based on the hybrid encryption scheme of Advanced Encryption Standard (AES), Identity-Based Encryption (IBE) and Attribute-Based Encryption (ABE). The proposed algorithm divides the data into three categories, i.e., less sensitive, mid-sensitive and high sensitive. The data is distributed by forming the named-data packets (NDPs) via labelling the names. One can choose the number of rounds depending on the actual size of a key; it is necessary to perform a minimum of 10 rounds for 128-bit keys in DHSE. The average encryption time taken by AES (Advanced Encryption Standard), IBE (Identity-based encryption) and ABE (Attribute-Based Encryption) is 3.25 ms, 2.18 ms and 2.39 ms, respectively. Whereas the average time taken by the DHSE encryption algorithm is 2.07 ms which is very much less when compared to other algorithms. Similarly, the average decryption times taken by AES, IBE and ABE are 1.77 ms, 1.09 ms and 1.20 ms and the average times taken by the DHSE decryption algorithms are 1.07 ms, which is very much less when compared to other algorithms. The analysis shows that the framework is well designed and provides confidentiality of data with minimum encryption and decryption time. Therefore, the proposed approach is well suited for CPS-IoT.
Voltammetric Behaviour of Rhodamine B at a Screen-Printed Carbon Electrode and Its Trace Determination in Environmental Water Samples
Authors: Honeychurch Kevin C.
Published: June 2022

The voltammetric behaviour of Rhodamine B was studied at a screen-printed carbon electrode (SPCE), by cyclic and differential pulse voltammetry. Cyclic voltammograms exhibited two reduction peaks (designated R1 and R2) generated from the reduction of the parent compound through, first, one electron reduction (R1) to give a radical species, and then a further one-electron, one-proton reduction to give a neutral molecule (R2). On the reverse positive-going scan, two oxidation peaks were observed. The first, O1, resulted from the oxidation of the species generated at R2, and the second, O2, through the one-electron oxidation of the amine group. The nature of the redox reactions was further investigated by observing the effect of scan rate and pH on the voltammetric behaviour. The developed SPCE method was evaluated by carrying out Rhodamine B determinations on a spiked and unspiked environmental water sample. A mean recovery of 94.3% with an associated coefficient of variation of 2.9% was obtained. The performance characteristics indicated that reliable data may be obtained for Rhodamine B measurements in environmental water samples using this approach.
Micro-Expression-Based Emotion Recognition Using Waterfall Atrous Spatial Pyramid Pooling Networks
Authors: Stofa Marzuraikah Mohd , Zulkifley Mohd Asyraf , Zainuri Muhammad Ammirrul Atiqi Mohd
Published: June 2022

Understanding a person’s attitude or sentiment from their facial expressions has long been a straightforward task for humans. Numerous methods and techniques have been used to classify and interpret human emotions that are commonly communicated through facial expressions, with either macro- or micro-expressions. However, performing this task using computer-based techniques or algorithms has been proven to be extremely difficult, whereby it is a time-consuming task to annotate it manually. Compared to macro-expressions, micro-expressions manifest the real emotional cues of a human, which they try to suppress and hide. Different methods and algorithms for recognizing emotions using micro-expressions are examined in this research, and the results are presented in a comparative approach. The proposed technique is based on a multi-scale deep learning approach that aims to extract facial cues of various subjects under various conditions. Then, two popular multi-scale approaches are explored, Spatial Pyramid Pooling (SPP) and Atrous Spatial Pyramid Pooling (ASPP), which are then optimized to suit the purpose of emotion recognition using micro-expression cues. There are four new architectures introduced in this paper based on multi-layer multi-scale convolutional networks using both direct and waterfall network flows. The experimental results show that the ASPP module with waterfall network flow, which we coined as WASPP-Net, outperforms the state-of-the-art benchmark techniques with an accuracy of 80.5%. For future work, a high-resolution approach to multi-scale approaches can be explored to further improve the recognition performance.
Fixed Amidst Change: 20 Years of Media Coverage on Carbon Capture and Storage in Germany
Authors: Otto Danny , Pfeiffer Maria , de Brito Mariana Madruga , Gross Matthias
Published: June 2022

Carbon capture and storage (CCS) technologies are controversially discussed worldwide. Germany is no exception. Here, CO2 storage is banned, although successful pilot plants were installed in the late 2000s. However, the recent burgeoning political interest in this technology prompts us to investigate why and how the (public) image of CCS technologies has changed over time and with regard to different CCS applications. For this purpose, we examine the coverage of CCS in German newspapers over the last 20 years on the basis of a quantitative analysis of about 4000 newspaper articles. A sample of 571 articles with different political orientations was studied qualitatively to analyse reporting on different CCS frames and actors. We find evidence that the media debate is shifting towards the application of CCS for negative emissions technologies and carbon removal. However, the negative image of CCS connected to coal fired power plants persists, suggesting that public and political support remain a problem for a technology fixed in binary negotiations for or against it.