bluuebunny/crossref_metadata_2025 huggingface dataset:
BRISK: Dynamic Encryption Based Cipher for Long Term Security
Authors: Dwivedi Ashutosh Dhar
Published: September 2021

Several emerging areas like the Internet of Things, sensor networks, healthcare and distributed networks feature resource-constrained devices that share secure and privacy-preserving data to accomplish some goal. The majority of standard cryptographic algorithms do not fit with these constrained devices due to heavy cryptographic components. In this paper, a new block cipher, BRISK, is proposed with a block size of 32-bit. The cipher design is straightforward due to simple round operations, and these operations can be efficiently run in hardware and suitable for software. Another major concept used with this cipher is dynamism during encryption for each session; that is, instead of using the same encryption algorithm, participants use different ciphers for each session. Professor Lars R. Knudsen initially proposed dynamic encryption in 2015, where the sender picks a cipher from a large pool of ciphers to encrypt the data and send it along with the encrypted message. The receiver does not know about the encryption technique used before receiving the cipher along with the message. However, in the proposed algorithm, instead of choosing a new cipher, the process uses the same cipher for each session, but varies the cipher specifications from a given small pool, e.g., the number of rounds, cipher components, etc. Therefore, the dynamism concept is used here in a different way.
Delay-Tolerant Distributed Inference in Tracking Networks
Authors: Alimadadi Mohammadreza , Stojanovic Milica , Closas Pau
Published: September 2021

This paper discusses asynchronous distributed inference in object tracking. Unlike many studies, which assume that the delay in communication between partial estimators and the central station is negligible, our study focuses on the problem of asynchronous distributed inference in the presence of delays. We introduce an efficient data fusion method for combining the distributed estimates, where delay in communications is not negligible. To overcome the delay, predictions are made for the state of the system based on the most current available information from partial estimators. Simulation results show the efficacy of the methods proposed.
Gait Phase Estimation by Using LSTM in IMU-Based Gait Analysis—Proof of Concept
Authors: Sarshar Mustafa , Polturi Sasanka , Schega Lutz
Published: September 2021

Gait phase detection in IMU-based gait analysis has some limitations due to walking style variations and physical impairments of individuals. Therefore, available algorithms may not work properly when the gait data is noisy, or the person rarely reaches a steady state of walking. The aim of this work was to employ Artificial Intelligence (AI), specifically a long short-term memory (LSTM) algorithm, to overcome these weaknesses. Three supervised LSTM-based models were designed to estimate the expected gait phases, including foot-off (FO), mid-swing (MidS) and foot-contact (FC). For collecting gait data two tri-axial inertial sensors were located above each ankle. The angular velocity magnitude, rotation matrix magnitude and free acceleration magnitude were captured for data labeling and turning detection and to strengthen the model, respectively. To do so, a train dataset based on a novel movement protocol was acquired. A validation dataset similar to a train dataset was generated as well. Five test datasets from already existing data were also created to independently evaluate the models. After testing the models on validation and test datasets, all three models demonstrated promising performance in estimating desired gait phases. The proposed approach proves the possibility of employing AI-based algorithms to predict labeled gait phases from a time series of gait data.
State Estimation of Axisymmetric Target Based on Beacon Linear Features and View Relation
Authors: Cao Xiaohua , Peng Shuaiyu , Liu Daofan
Published: September 2021

In order to realize state estimation for axisymmetric targets and improve the accuracy and robustness of state estimation, a state estimation method for axisymmetric targets based on beacon linear features and view relation is proposed in this paper. The depth camera is used to collect the image and depth information of the object, and the features of the beacon line are extracted by the thinning process and Hough transform. Then, the rotation matrix model based on view relation is constructed to solve the target state. Finally, an axisymmetric shore power plug is taken as the experimental object and the L–V (linear features and view relation) state estimation method is compared with the C–H and C–IPPE state estimation methods. The experimental results show that the L–V state estimation method has higher accuracy and robustness.
Homography Ranking Based on Multiple Groups of Point Correspondences
Authors: Ondrašovič Milan , Tarábek Peter
Published: September 2021

Homography mapping is often exploited to remove perspective distortion in images and can be estimated using point correspondences of a known object (marker). We focus on scenarios with multiple markers placed on the same plane if their relative positions in the world are unknown, causing an indeterminate point correspondence. Existing approaches may only estimate an isolated homography for each marker and cannot determine which homography achieves the best reprojection over the entire image. We thus propose a method to rank isolated homographies obtained from multiple distinct markers to select the best homography. This method extends existing approaches in the post-processing stage, provided that the point correspondences are available and that the markers differ only by similarity transformation after rectification. We demonstrate the robustness of our method using a synthetic dataset and show an approximately 60% relative improvement over the random selection strategy based on the homography estimation from the OpenCV library.
A Novel Method for Online Extraction of Small-Angle Scattering Pulse Signals from Particles Based on Variable Forgetting Factor RLS Algorithm
Authors: Zhang Rongrui , Zhao Heng
Published: September 2021

The small-angle optical particle counter (OPC) can detect particles with strong light absorption. At the same time, it can ignore the properties of the detected particles and detect the particle size singly and more accurately. Reasonably improving the resolution of the low pulse signal of fine particles is key to improving the detection accuracy of the small-angle OPC. In this paper, a new adaptive filtering method for the small-angle scattering signals of particles is proposed based on the recursive least squares (RLS) algorithm. By analyzing the characteristics of the small-angle scattering signals, a variable forgetting factor (VFF) strategy is introduced to optimize the forgetting factor in the traditional RLS algorithm. It can distinguish the scattering signal from the stray light signal and dynamically adapt to the change in pulse amplitude according to different light absorptions and different particle sizes. To verify the filtering effect, small-angle scattering pulse extraction experiments were carried out in a simulated smoke box with different particle properties. The experiments show that the proposed VFF-RLS algorithm can effectively suppress system stray light and background noise. When the particle detection signal appears, the algorithm has fast convergence and tracking speed and highlights the particle pulse signal well. Compared with that of the traditional scattering pulse extraction method, the resolution of the processed scattering pulse signal of particles is greatly improved, and the extraction of weak particle scattering pulses at a small angle has a greater advantage. Finally, the effect of filter order in the algorithm on the results of extracting scattering pulses is discussed.
Fluidic Oscillators, Feedback Channel Effect under Compressible Flow Conditions
Authors: Bergadà Josep M. , Baghaei Masoud , Prakash Bhanu , Mellibovsky Fernando
Published: September 2021

Fluidic oscillators are often used to modify the forces fluid generates on any given bluff body; they can also be used as flow, pressure or acoustic sensors, with each application requiring a particular oscillator configuration. Regarding the fluidic oscillators’ main performance, a problem which is not yet clarified is the understanding of the feedback channel effect on the oscillator outlet mass flow frequency and amplitude, especially under compressible flow conditions. In order to bring light to this point, a set of three-dimensional Direct Numerical Simulations under compressible flow conditions are introduced in the present paper; four different feedback channel lengths and two inlet Reynolds numbers Re = 12,410 and Re = 18,617 are considered. From the results obtained, it is observed that as the inlet velocity increases, the fluidic oscillator outlet mass flow frequency and amplitude increase. An increase of the feedback channel length decreases the outlet mass flow oscillating frequency. At large feedback channel lengths, the former main oscillation tends to disappear, the jet inside the mixing chamber simply fluctuates at high frequencies. Once the Feedback Channel (FC) length exceeds a certain threshold, the oscillation stops. Under all conditions studied, pressure waves are observed to be traveling along the feedback channels, their origin and interaction with the jet entering the mixing chamber are thoroughly evaluated. The paper proves that jet oscillations are pressure-driven.
Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis
Authors: Gagliardi Valerio , Bianchini Ciampoli Luca , Trevisani Sebastiano , D’Amico Fabrizio , Alani Amir M. , Benedetto Andrea , Tosti Fabio
Published: September 2021

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques are gaining momentum in the assessment and health monitoring of infrastructure assets. Amongst others, the Persistent Scatterers Interferometry (PSI) technique has proven to be viable for the long-term evaluation of ground scatterers. However, its effectiveness as a routine tool for certain critical application areas, such as the assessment of millimetre-scale differential displacements in airport runways, is still debated. This research aims to demonstrate the viability of using medium-resolution Copernicus ESA Sentinel-1A (C-Band) SAR products and their contribution to improve current maintenance strategies in case of localised foundation settlements in airport runways. To this purpose, “Runway n.3” of the “Leonardo Da Vinci International Airport” in Fiumicino, Rome, Italy was investigated as an explanatory case study, in view of historical geotechnical settlements affecting the runway area. In this context, a geostatistical study is developed for the exploratory spatial data analysis and the interpolation of the Sentinel-1A SAR data. The geostatistical analysis provided ample information on the spatial continuity of the Sentinel 1 data in comparison with the high-resolution COSMO-SkyMed data and the ground-based topographic levelling data. Furthermore, a comparison between the PSI outcomes from the Sentinel-1A SAR data—interpolated through Ordinary Kriging—and the ground-truth topographic levelling data demonstrated the high accuracy of the Sentinel 1 data. This is proven by the high values of the correlation coefficient (r = 0.94), the multiple R-squared coefficient (R2 = 0.88) and the Slope value (0.96). The results of this study clearly support the effectiveness of using Sentinel-1A SAR data as a continuous and long-term routine monitoring tool for millimetre-scale displacements in airport runways, paving the way for the development of more efficient and sustainable maintenance strategies for inclusion in next generation Airport Pavement Management Systems (APMSs).
Experimental Validation and Deployment of Observability Applications for Monitoring of Low-Voltage Distribution Grids
Authors: Nainar Karthikeyan , Ciontea Catalin Iosif , Shahid Kamal , Iov Florin , Olsen Rasmus Løvenstein , Schäler Christine , Schwefel Hans-Peter Christian
Published: September 2021

Future distribution grids will be subjected to fluctuations in voltages and power flows due to the presence of renewable sources with intermittent power generation. The advanced smart metering infrastructure (AMI) enables the distribution system operators (DSOs) to measure and analyze electrical quantities such as voltages, currents and power at each customer connection point. Various smart grid applications can make use of the AMI data either in offline or close to real-time mode to assess the grid voltage conditions and estimate losses in the lines/cables. The outputs of these applications can enable DSOs to take corrective action and make a proper plan for grid upgrades. In this paper, the process of development and deployment of applications for improving the observability of distributions grids is described, which consists of the novel deployment framework that encompasses the proposition of data collection, communication to the servers, data storage, and data visualization. This paper discussed the development of two observability applications for grid monitoring and loss calculation, their validation in a laboratory setup, and their field deployment. A representative distribution grid in Denmark is chosen for the study using an OPAL-RT real-time simulator. The results of the experimental studies show that the proposed applications have high accuracy in estimating grid voltage magnitudes and active energy losses. Further, the field deployment of the applications prove that DSOs can gain insightful information about their grids and use them for planning purposes.
No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing
Authors: Attygalle Nuwan T. , Leiva Luis A. , Kljun Matjaž , Sandor Christian , Plopski Alexander , Kato Hirokazu , Čopič Pucihar Klen
Published: September 2021

Physical objects are usually not designed with interaction capabilities to control digital content. Nevertheless, they provide an untapped source for interactions since every object could be used to control our digital lives. We call this the missing interface problem: Instead of embedding computational capacity into objects, we can simply detect users’ gestures on them. However, gesture detection on such unmodified objects has to date been limited in the spatial resolution and detection fidelity. To address this gap, we conducted research on micro-gesture detection on physical objects based on Google Soli’s radar sensor. We introduced two novel deep learning architectures to process range Doppler images, namely a three-dimensional convolutional neural network (Conv3D) and a spectrogram-based ConvNet. The results show that our architectures enable robust on-object gesture detection, achieving an accuracy of approximately 94% for a five-gesture set, surpassing previous state-of-the-art performance results by up to 39%. We also showed that the decibel (dB) Doppler range setting has a significant effect on system performance, as accuracy can vary up to 20% across the dB range. As a result, we provide guidelines on how to best calibrate the radar sensor.
Validity of a Magnet-Based Timing System Using the Magnetometer Built into an IMU
Authors: Pérez-Chirinos Buxadé Carla , Fernández-Valdés Bruno , Morral-Yepes Mónica , Tuyà Viñas Sílvia , Padullés Riu Josep Maria , Moras Feliu Gerard
Published: September 2021

Inertial measurement units (IMUs) represent a technology that is booming in sports right now. The aim of this study was to evaluate the validity of a new application on the use of these wearable sensors, specifically to evaluate a magnet-based timing system (M-BTS) for timing short-duration sports actions using the magnetometer built into an IMU in different sporting contexts. Forty-eight athletes (22.7 ± 3.3 years, 72.2 ± 10.3 kg, 176.9 ± 8.5 cm) and eight skiers (17.4 ± 0.8 years, 176.4 ± 4.9 cm, 67.7 ± 2.0 kg) performed a 60-m linear sprint running test and a ski slalom, respectively. The M-BTS consisted of placing several magnets along the course in both contexts. The magnetometer built into the IMU detected the peak-shaped magnetic field when passing near the magnets at a certain speed. The time between peaks was calculated. The system was validated with photocells. The 95% error intervals for the total times were less than 0.077 s for the running test and 0.050 s for the ski slalom. With the M-BTS, future studies could select and cut the signals belonging to the other sensors that are integrated in the IMU, such as the accelerometer and the gyroscope.
Solution-Processed Chloroaluminum Phthalocyanine (ClAlPc) Ammonia Gas Sensor with Vertical Organic Porous Diodes
Authors: Madhaiyan Govindsamy , Sun An-Ting , Zan Hsiao-Wen , Meng Hsin-Fei , Horng Sheng-Fu , Chen Li-Yin , Hung Hsiao-Wen
Published: September 2021

In this research work, the gas sensing properties of halogenated chloroaluminum phthalocyanine (ClAlPc) thin films were studied at room temperature. We fabricated an air-stable ClAlPc gas sensor based on a vertical organic diode (VOD) with a porous top electrode by the solution process method. The surface morphology of the solution-processed ClAlPc thin film was examined by field emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM). The proposed ClAlPc-based VOD sensor can detect ammonia (NH3) gas at the ppb level (100~1000 ppb) at room temperature. Additionally, the ClAlPc sensor was highly selective towards NH3 gas compared to other interfering gases (NO2, ACE, NO, H2S, and CO). In addition, the device lifetime was tested by storing the device at ambient conditions. The effect of relative humidity (RH) on the ClAlPc NH3 gas sensor was also explored. The aim of this study is to extend these findings on halogenated phthalocyanine-based materials to practical electronic nose applications in the future.
The Rise of Wearable Devices during the COVID-19 Pandemic: A Systematic Review
Authors: Channa Asma , Popescu Nirvana , Skibinska Justyna , Burget Radim
Published: September 2021

The COVID-19 pandemic has wreaked havoc globally and still persists even after a year of its initial outbreak. Several reasons can be considered: people are in close contact with each other, i.e., at a short range (1 m), and the healthcare system is not sufficiently developed or does not have enough facilities to manage and fight the pandemic, even in developed countries such as the USA and the U.K. and countries in Europe. There is a great need in healthcare for remote monitoring of COVID-19 symptoms. In the past year, a number of IoT-based devices and wearables have been introduced by researchers, providing good results in terms of high accuracy in diagnosing patients in the prodromal phase and in monitoring the symptoms of patients, i.e., respiratory rate, heart rate, temperature, etc. In this systematic review, we analyzed these wearables and their need in the healthcare system. The research was conducted using three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between December 2019 and June 2021. This article was based on the PRISMA guidelines. Initially, 1100 articles were identified while searching the scientific literature regarding this topic. After screening, ultimately, 70 articles were fully evaluated and included in this review. These articles were divided into two categories. The first one belongs to the on-body sensors (wearables), their types and positions, and the use of AI technology with ehealth wearables in different scenarios from screening to contact tracing. In the second category, we discuss the problems and solutions with respect to utilizing these wearables globally. This systematic review provides an extensive overview of wearable systems for the remote management and automated assessment of COVID-19, taking into account the reliability and acceptability of the implemented technologies.
Enabling Next-Generation Public Safety Operations with Mission-Critical Networks and Wearable Applications
Authors: Saafi Salwa , Hosek Jiri , Kolackova Aneta
Published: September 2021

Public safety agencies have been working on the modernization of their communication networks and the enhancement of their mission-critical capabilities with novel technologies and applications. As part of these efforts, migrating from traditional land mobile radio (LMR) systems toward cellular-enabled, next-generation, mission-critical networks is at the top of these agencies’ agendas. In this paper, we provide an overview of cellular technologies ratified by the 3rd Generation Partnership Project (3GPP) to enable next-generation public safety networks. On top of using wireless communication technologies, emergency first responders need to be equipped with advanced devices to develop situational awareness. Therefore, we introduce the concept of the Internet of Life-Saving Things (IoLST) and focus on the role of wearable devices—more precisely, cellular-enabled wearables, in creating new solutions for enhanced public safety operations. Finally, we conduct a performance evaluation of wearable-based, mission-critical applications. So far, most of the mission-critical service evaluations target latency performance without taking into account reliability requirements. In our evaluation, we examine the impact of device- and application-related parameters on the latency and the reliability performance. We also identify major future considerations for better support of the studied requirements in next-generation public safety networks.
QoS-Based Service-Time Scheduling in the IoT-Edge Cloud
Authors: Mutichiro Briytone , Tran Minh-Ngoc , Kim Young-Han
Published: September 2021

In edge computing, scheduling heterogeneous workloads with diverse resource requirements is challenging. Besides limited resources, the servers may be overwhelmed with computational tasks, resulting in lengthy task queues and congestion occasioned by unusual network traffic patterns. Additionally, Internet of Things (IoT)/Edge applications have different characteristics coupled with performance requirements, which become determinants if most edge applications can both satisfy deadlines and each user’s QoS requirements. This study aims to address these restrictions by proposing a mechanism that improves the cluster resource utilization and Quality of Service (QoS) in an edge cloud cluster in terms of service time. Containerization can provide a way to improve the performance of the IoT-Edge cloud by factoring in task dependencies and heterogeneous application resource demands. In this paper, we propose STaSA, a service time aware scheduler for the edge environment. The algorithm automatically assigns requests onto different processing nodes and then schedules their execution under real-time constraints, thus minimizing the number of QoS violations. The effectiveness of our scheduling model is demonstrated through implementation on KubeEdge, a container orchestration platform based on Kubernetes. Experimental results show significantly fewer violations in QoS during scheduling and improved performance compared to the state of the art.
Channel Exchanging for RGB-T Tracking
Authors: Zhao Long , Zhu Meng , Ren Honge , Xue Lingjixuan
Published: September 2021

It is difficult to achieve all-weather visual object tracking in an open environment only utilizing single modality data input. Due to the complementarity of RGB and thermal infrared (TIR) data in various complex environments, a more robust object tracking framework can be obtained using video data of these two modalities. The fusion methods of RGB and TIR data are the core elements to determine the performance of the RGB-T object tracking method, and the existing RGB-T trackers have not solved this problem well. In order to solve the current low utilization of information intra single modality in aggregation-based methods and between two modalities in alignment-based methods, we used DiMP as the baseline tracker to design an RGB-T object tracking framework channel exchanging DiMP (CEDiMP) based on channel exchanging. CEDiMP achieves dynamic channel exchanging between sub-networks of different modes hardly adding any parameters during the feature fusion process. The expression ability of the deep features generated by our data fusion method based on channel exchanging is stronger. At the same time, in order to solve the poor generalization ability of the existing RGB-T object tracking methods and the poor ability in the long-term object tracking, more training of CEDiMP on the synthetic dataset LaSOT-RGBT is added. A large number of experiments demonstrate the effectiveness of the proposed model. CEDiMP achieves the best performance on two RGB-T object tracking benchmark datasets, GTOT and RGBT234, and performs outstandingly in the generalization testing.
Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment
Authors: Zhang Feng , Li Jiang , Wang Ye , Guo Lihong , Wu Dongyan , Wu Hao , Zhao Hongwei
Published: September 2021

Capability assessment plays a crucial role in the demonstration and construction of equipment. To improve the accuracy and stability of capability assessment, we study the neural network learning algorithms in the field of capability assessment and index sensitivity. Aiming at the problem of overfitting and parameter optimization in neural network learning, the paper proposes an improved machine learning algorithm—the Ensemble Learning Based on Policy Optimization Neural Networks (ELPONN) with the policy optimization and ensemble learning. This algorithm presents an optimized neural network learning algorithm through different strategies evolution, and builds an ensemble learning model of multi-intelligent algorithms to assess the capability and analyze the sensitivity of the indexes. Through the assessment of capabilities, the algorithm effectively avoids parameter optimization from entering the minimum point in performance to improve the accuracy of equipment capability assessment, which is significantly better than previous neural network assessment methods. The experimental results show that the mean relative error is 4.10%, which is better than BP, GABP, and early stopping. The ELPONN algorithm has better accuracy and stability performance, and meets the requirements of capability assessment.
Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis
Authors: Wang Xuemeng , Zhang Han , Song Rui , He Xin , Mao Peisheng , Jia Shangang
Published: September 2021

Seed aging detection and viable seed prediction are of great significance in alfalfa seed production, but traditional methods are disposable and destructive. Therefore, the establishment of a rapid and non-destructive seed screening method is necessary in seed industry and research. In this study, we used multispectral imaging technology to collect morphological features and spectral traits of aging alfalfa seeds with different storage years. Then, we employed five multivariate analysis methods, i.e., principal component analysis (PCA), linear discrimination analysis (LDA), support vector machines (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) to predict aged and viable seeds. The results revealed that the mean light reflectance was significantly different at 450~690 nm between non-aged and aged seeds. LDA model held high accuracy (99.8~100.0%) in distinguishing aged seeds from non-aged seeds, higher than those of SVM (87.4~99.3%) and RF (84.6~99.3%). Furthermore, dead seeds could be distinguished from the aged seeds, with accuracies of 69.7%, 72.0% and 97.6% in RF, SVM and LDA, respectively. The accuracy of nCDA in predicting the germination of aged seeds ranged from 75.0% to 100.0%. In summary, we described a nondestructive, rapid and high-throughput approach to screen aged seeds with various viabilities in alfalfa.
One Metre Plus (1M+): A Multifunctional Open-Source Sensor for Bicycles Based on Raspberry Pi
Authors: Henao Andres , Apparicio Philippe , Maignan David
Published: September 2021

During the last decade, bicycles equipped with sensors became an essential tool for research, particularly for studies analyzing the lateral passing distance between motorized vehicles and bicycles. The objective of this article is to describe a low-cost open-source sensor called one metre plus (1m+) capable of measuring lateral passing distance, registering the geographical position of the cyclist, and video-recording the trip. The plans, codes, and schematic design are open and therefore easily accessible for the scientific community. This study describes in detail the conceptualization process, the characteristics of the device, and the materials from which they are made. The study also provides an evaluation of the product and describes the sensor’s functionalities and its field of application. The objective of this project is to democratize research and develop a platform/participative project that offers tools to researchers worldwide, in order to standardize knowledge sharing and facilitate the comparability of results in various contexts.
Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset
Authors: Umair Muhammad , Khan Muhammad Shahbaz , Ahmed Fawad , Baothman Fatmah , Alqahtani Fehaid , Alian Muhammad , Ahmad Jawad
Published: September 2021

The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.
Microseismic P-Wave Travel Time Computation and 3D Localization Based on a 3D High-Order Fast Marching Method
Authors: Li Yijia , Wang Jing , Wang Zhengfang , Sui Qingmei , Xiong Ziming
Published: September 2021

The travel time computation of microseismic waves in different directions (particularly, the diagonal direction) in three-dimensional space has been found to be inaccurate, which seriously affects the localization accuracy of three-dimensional microseismic sources. In order to solve this problem, this research study developed a method of calculating the P-wave travel time based on a 3D high-order fast marching method (3D_H_FMM). This study focused on designing a high-order finite-difference operator in order to realize the accurate calculation of the P-wave travel time in three-dimensional space. The method was validated using homogeneous velocity models and inhomogeneous layered media velocity models of different scales. The results showed that the overall mean absolute error (MAE) of the two homogenous models using 3D_H_FMM had been reduced by 88.335%, and 90.593% compared with the traditional 3D_FMM. On that basis, the three-dimensional localization of microseismic sources was carried out using a particle swarm optimization algorithm. The developed 3D_H_FMM was used to calculate the travel time, then to conduct the localization of the microseismic source in inhomogeneous models. The mean error of the localization results of the different positions in the three-dimensional space was determined to be 1.901 m, and the localization accuracy was found to be superior to that of the traditional 3D_FMM method (mean absolute localization error: 3.447 m) with the small-scaled inhomogeneous model.
An Ultrahigh Sensitive Microwave Microfluidic System for Fast and Continuous Measurements of Liquid Solution Concentrations
Authors: Słobodzian Piotr , Szostak Krzysztof , Skowronek Katarzyna , Jasińska Laura , Malecha Karol
Published: September 2021

In this paper, we describe a low-cost microwave microfluidic system of ultrahigh sensitivity for detecting small changes in the concentration of polar solutions (liquid dielectrics) in the 2.4 GHz ISM band. Its principle of operation is based on microwave interferometry, which is implemented using planar microstrip lines and integrated microwave components. The key features of this system include small solution intake (<200 µL per measurement), short time of measurement (ca. 20 ms), ultrahigh sensitivity of concentration changes (up to 55 dB/%), and low error of measurement (below 0.1%). The ultrahigh sensitivity was proven experimentally by measurements of the fat content of milk. In addition, it is a user-friendly system due to an effortless and fast calibration procedure. Moreover, it can be made relatively compact (<20 cm2) and features low power consumption (200 mW). Thus, the proposed system is perfect for industrial applications, especially for highly integrated lab-on-chip devices.
Energy Trading among Power Grid and Renewable Energy Sources: A Dynamic Pricing and Demand Scheme for Profit Maximization
Authors: Yoo Yoon-Sik , Jeon Seung Hyun , Newaz S. H. Shah , Lee Il-Woo , Choi Jun Kyun
Published: September 2021

With the technical growth and the reduction of deployment cost for distributed energy resources (DERs), such as solar photovoltaic (PV), energy trading has been recently encouraged to energy consumers, which can sell energy from their own energy storage system (ESS). Meanwhile, due to the unprecedented rise of greenhouse gas (GHG) emissions, some countries (e.g., Republic of Korea and India) have mandated using a renewable energy certificate (REC) in energy trading markets. In this paper, we propose an energy broker model to boost energy trading between the existing power grid and energy consumers. In particular, to maximize the profits of energy consumers and the energy provider, the proposed energy broker is in charge of deciding the optimal demand and dynamic price of energy in an REC-based energy trading market. In this solution, the smart agents (e.g., IoT intelligent devices) of consumers exchange energy trading associated information, including the amount of energy generation, price and REC. For deciding the optimal demand and dynamic pricing, we formulate convex optimization problems using dual decomposition. Through a numerical simulation analysis, we compare the performance of the proposed dynamic pricing strategy with the conventional pricing strategies. Results show that the proposed dynamic pricing and demand control strategies can encourage energy trading by allowing RECs trading of the conventional power grid.
Tunable Optical Diffusers Based on the UV/Ozone-Assisted Self-Wrinkling of Thermal-Cured Polymer Films
Authors: Jiang Shulan , Tan Yong , Peng Yong , Zhao Jiang
Published: September 2021

Tunable optical diffusers have attracted attention because of the rapid development of next generation stretchable optoelectronics and optomechanics applications. Flexible wrinkle structures have the potential to change the light path and tune the diffusion capability, which is beneficial to fabricate optical diffusers. The generation of wrinkles usually depends on an external stimulus, thus resulting in complicated fabricating equipment and processes. In this study, a facile and low-cost method is proposed to fabricate wrinkle structures by the self-wrinkling of thermal-cured polymer for tunable optical diffusers. The uncured polydimethylsiloxane (PDMS) precursors were exposed to UV/ozone to obtain hard silica layers and then crosslinked via heating to induce the wrinkle patterns. The wrinkle structures were demonstrated as strain-dependent tunable optical diffusers and the optical diffusion of transmitted light via the deformable wrinkle structures was studied and adjusted. The incident light isotropically diffused through the sample at the initial state. When the wrinkle structures deformed, it showed a more pronounced isotropic optical diffusion with uniaxial tensile strain. The optical diffusion is anisotropical with a further increase in uniaxial tensile strain. The proposed method of fabricating wrinkles by UV/ozone-assisted self-wrinkling of thermal-cured polymer films is simple and cost-effective, and the obtained structures have potential applications in tunable optical diffusers.
The Application of Hough Transform and Canny Edge Detector Methods for the Visual Detection of Cumuliform Clouds
Authors: Lapušinskij Aleksandr , Suzdalev Ivan , Goranin Nikolaj , Janulevičius Justinas , Ramanauskaitė Simona , Stankūnavičius Gintautas
Published: September 2021

The increase in flying time of unmanned aerial vehicles (UAV) is a relevant and difficult task for UAV designers. It is especially important in such tasks as monitoring, mapping, or signal retranslation. While the majority of research is concentrated on increasing the battery capacity, it is also important to utilize natural renewable energy sources, such as solar energy, thermals, etc. This article proposed a method for the automatic recognition of cumuliform clouds. Practical application of this method allows diverting of an unmanned aerial vehicle towards the identified cumuliform cloud and improving its probability of flying into a thermal flow, thus increasing the flight time of the UAV, as is performed by glider and paraglider pilots. The proposed method is based on the application of Hough transform and Canny edge detector methods, which have not been used for such a task before. For testing the proposed method a dataset of different clouds was generated and marked by experts. The achieved average accuracy of 87% on the unbalanced dataset demonstrates the practical applicability of the proposed method for detecting thermals related to cumuliform clouds. The article also provides the concept of VilniusTech developed UAV, implementing the proposed method.
Survey: Vulnerability Analysis of Low-Cost ECC-Based RFID Protocols against Wireless and Side-Channel Attacks
Authors: Gabsi Souhir , Beroulle Vincent , Kieffer Yann , Dao Hiep Manh , Kortli Yassin , Hamdi Belgacem
Published: September 2021

The radio frequency identification (RFID) system is one of the most important technologies of the Internet of Things (IoT) that tracks single or multiple objects. This technology is extensively used and attracts the attention of many researchers in various fields, including healthcare, supply chains, logistics, asset tracking, and so on. To reach the required security and confidentiality requirements for data transfer, elliptic curve cryptography (ECC) is a powerful solution, which ensures a tag/reader mutual authentication and guarantees data integrity. In this paper, we first review the most relevant ECC-based RFID authentication protocols, focusing on their security analysis and operational performances. We compare the various lightweight ECC primitive implementations designed for RFID applications in terms of occupied area and power consumption. Then, we highlight the security threats that can be encountered considering both network attacks and side-channel attacks and analyze the security effectiveness of RFID authentication protocols against such types of attacks. For this purpose, we classify the different threats that can target an ECC-based RFID system. After that, we present the most promising ECC-based protocols released during 2014–2021 by underlining their advantages and disadvantages. Finally, we perform a comparative study between the different protocols mentioned regarding network and side-channel attacks, as well as their implementation costs to find the optimal one to use in future works.
Damage Localization in Composite Plates Using Wavelet Transform and 2-D Convolutional Neural Networks
Authors: Azuara Guillermo , Ruiz Mariano , Barrera Eduardo
Published: September 2021

Nondestructive evaluation of carbon fiber reinforced material structures has received special attention in the last decades. Usage of Ultrasonic Guided Waves (UGW), particularly Lamb waves, has become one of the most popular techniques for damage location, due to their sensitivity to defects, large range of inspection, and good propagation in several material types. However, extracting meaningful physical features from the response signals is challenging due to several factors, such as the multimodal nature of UGW, boundary conditions and the geometric shape of the structure, possible material anisotropies, and their environmental dependency. Neural networks (NN) are becoming a practical and accurate approach to analyzing the acquired data using data-driven methods. In this paper, a Convolutional-Neural-Network (CNN) is proposed to predict the distance-to-damage values from the signals corresponding to a transmitter-receiver path of transducers. The NN input is a 2D image (time-frequency) obtained as the Wavelet transform of the acquired experimental signals. The distances obtained with the NN are the input of a novel damage location algorithm which outputs a bidimensional image of the structure’s surface showing the estimated damage locations with a deviation of the actual position lower than 15 mm.
An Optimized Self-Compensated Solution for Temperature and Strain Cross-Sensitivity in FBG Interrogators Based on Edge Filter
Authors: Silveira Mariana L. , Rocha Helder R. O. , Antunes Paulo F. C. , André Paulo S. B. , Segatto Marcelo E. V. , Frizera Anselmo , Díaz Camilo A. R.
Published: September 2021

Optical fiber sensors based on fiber Bragg gratings (FBGs) are prone to measurement errors if the cross-sensitivity between temperature and strain is not properly considered. This paper describes a self-compensated technique for canceling the undesired influence of temperature in strain measurement. An edge-filter-based interrogator is proposed and the central peaks of two FBGs (sensor and reference) are matched with the positive and negative slopes of a Fabry–Perot interferometer that acts as an optical filter. A tuning process performed by the grey wolf optimizer (GWO) algorithm is required to determine the optimal spectral characteristics of each FBG. The interrogation range is not compromised by the proposed technique, being determined by the spectral characteristics of the optical filter in accordance with the traditional edge-filtering interrogation. Simulations show that, by employing FBGs with optimal characteristics, temperature variations of 30 °C led to an average relative error of 3.4% for strain measurements up to 700μϵ. The proposed technique was experimentally tested under non-ideal conditions: two FBGs with spectral characteristics different from the optimized results were used. The temperature sensibility decreased by 50.8% as compared to a temperature uncompensated interrogation system based on an edge filter. The non-ideal experimental conditions were simulated and the maximum error between theoretical and experimental data was 5.79%, proving that the results from simulation and experimentation are compatible.
Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems
Authors: Arellano-Espitia Francisco , Delgado-Prieto Miguel , Gonzalez-Abreu Artvin-Darien , Saucedo-Dorantes Juan Jose , Osornio-Rios Roque Alfredo
Published: September 2021

The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater challenge, since uncharacterized catastrophic faults can occur. However, the existing methods for anomaly detection present limitations when dealing with highly complex industrial systems. For that purpose, a novel fault diagnosis methodology is developed to face the anomaly detection. An unsupervised anomaly detection framework named deep-autoencoder-compact-clustering one-class support-vector machine (DAECC-OC-SVM) is presented, which aims to incorporate the advantages of automatically learnt representation by deep neural network to improved anomaly detection performance. The method combines the training of a deep-autoencoder with clustering compact model and a one-class support-vector-machine function-based outlier detection method. The addressed methodology is applied on a public rolling bearing faults experimental test bench and on multi-fault experimental test bench. The results show that the proposed methodology it is able to accurately to detect unknown defects, outperforming other state-of-the-art methods.
Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings
Authors: Saucedo-Dorantes Juan Jose , Arellano-Espitia Francisco , Delgado-Prieto Miguel , Osornio-Rios Roque Alfredo
Published: September 2021

Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid or ceramic bearings, are of high interest towards high-performance rotary electromechanical actuators. Therefore, most of the diagnosis approaches for bearing fault detection are highly dependent of the bearing technology, commonly focused on the metallic bearings. Although the mechanical principles remain as the basis to analyze the characteristic patterns and effects related to the fault appearance, the quantitative response of the vibration pattern considering different bearing technology varies. In this regard, in this work a novel data-driven diagnosis methodology is proposed based on deep feature learning applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology consists of three main stages: first, a deep learning-based model, supported by stacked autoencoder structures, is designed with the ability of self-adapting to the extraction of characteristic fault-related features from different signals that are processed in different domains. Second, in a feature fusion stage, information from different domains is integrated to increase the posterior discrimination capabilities during the condition assessment. Third, the bearing assessment is achieved by a simple softmax layer to compute the final classification results. The achieved results show that the proposed diagnosis methodology based on deep feature learning can be effectively applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology is validated in front of two different electromechanical systems and the obtained results validate the adaptability and performance of the proposed approach to be considered as a part of the condition-monitoring strategies where different bearing technologies are involved.