bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling Trajectories Obtained from External Sensors for Location Prediction via NLP Approaches
Authors: Cruz Lívia Almada , Coelho da Silva Ticiana Linhares , Magalhães Régis Pires , Melo Wilken Charles Dantas , Cordeiro Matheus , de Macedo José Antonio Fernandes , Zeitouni Karine
Published: October 2022

Representation learning seeks to extract useful and low-dimensional attributes from complex and high-dimensional data. Natural language processing (NLP) was used to investigate the representation learning models to extract words’ feature vectors using their sequential order in the text via word embeddings and language models that maintain their semantic meaning. Inspired by NLP, in this paper, we tackle the representation learning problem for trajectories, using NLP methods to encode external sensors positioned in the road network and generate the features’ space to predict the next vehicle movement. We evaluate the vector representations of on-road sensors and trajectories using extrinsic and intrinsic strategies. Our results have shown the potential of natural language models to describe the space of features on trajectory applications as the next location prediction.
Calibration of BRDF Based on the Field Goniometer System Using a UAV Multispectral Camera
Authors: Kim Minji , Jin Cheonggil , Lee Sejin , Kim Kyoung-Min , Lim Joongbin , Choi Chuluong
Published: October 2022

The bidirectional reflectance distribution function (BRDF) is important for estimating the physical properties of a surface in remote sensing. In the laboratory, the BRDF can be estimated quickly and accurately using a goniometer, but it is very difficult to operate in the field. The purpose of this study was to evaluate whether estimating the BRDF with reasonable accuracy using an unmanned aerial vehicle (UAV) with a multispectral camera is possible in the field. Hemispherical reflectance was created from images taken using an UAV multispectral camera. The ground targets were four calibrated reference tarps (CRTs) of different reflectance, and the UAV was operated five times. Down-welling irradiance for reflectance calculation was measured in two ways: a sunlight sensor was mounted on a UAV, and a spectroradiometer with a remote cosine receptor (RCR) was installed on the ground. The BRDF was assessed through the anisotropy factor (ANIF) of the CRT reflectance derived from the collected data. As a result, the irradiance data for the reflectance calculation were more effective from the spectroradiometer with RCR on the ground than from the sunlight sensor mounted on an UAV. Furthermore, the high reflectance CRTs, ANIF, and BRDF had similar results. Therefore, when analyzing the BRDF, the effectiveness can be guaranteed when the reflectance of the target is over 21~46%, because a low reflectance tendency differs due to the adjacency effect. In addition, weather affects irradiance, so it is more effective to conduct fieldwork in clear weather.
Fast Registration of Point Cloud Based on Custom Semantic Extraction
Authors: Wu Jianing , Xiao Zhang , Chen Fan , Peng Tianlin , Xiong Zhi , Yuan Fengwei
Published: October 2022

With the increase in the amount of 3D point cloud data and the wide application of point cloud registration in various fields, the question of whether it is possible to quickly extract the key points of registration and perform accurate coarse registration has become a question to be urgently answered. In this paper, we proposed a novel semantic segmentation algorithm that enables the extracted feature point cloud to have a clustering effect for fast registration. First of all, an adaptive technique was proposed to determine the domain radius of a local point. Secondly, the feature intensity of the point is scored through the regional fluctuation coefficient and stationary coefficient calculated by the normal vector, and the high feature region to be registered is preliminarily determined. In the end, FPFH is used to describe the geometric features of the extracted semantic feature point cloud, so as to realize the coarse registration from the local point cloud to the overall point cloud. The results show that the point cloud can be roughly segmented based on the uniqueness of semantic features. The use of a semantic feature point cloud can make the point cloud have a very fast response speed based on the accuracy of coarse registration, almost equal to that of using the original point cloud, which is conducive to the rapid determination of the initial attitude.
Significant Features for Human Activity Recognition Using Tri-Axial Accelerometers
Authors: Bennasar Mohamed , Price Blaine A. , Gooch Daniel , Bandara Arosha K. , Nuseibeh Bashar
Published: October 2022

Activity recognition using wearable sensors has become essential for a variety of applications. Tri-axial accelerometers are the most widely used sensor for activity recognition. Although various features have been used to capture patterns and classify the accelerometer signals to recognise activities, there is no consensus on the best features to choose. Reducing the number of features can reduce the computational cost and complexity and enhance the performance of the classifiers. This paper identifies the signal features that have significant discriminative power between different human activities. It also investigates the effect of sensor placement location, the sampling frequency, and activity complexity on the selected features. A comprehensive list of 193 signal features has been extracted from accelerometer signals of four publicly available datasets, including features that have never been used before for activity recognition. Feature significance was measured using the Joint Mutual Information Maximisation (JMIM) method. Common significant features among all the datasets were identified. The results show that the sensor placement location does not significantly affect recognition performance, nor does it affect the significant sub-set of features. The results also showed that with high sampling frequency, features related to signal repeatability and regularity show high discriminative power.
Highly Sensitive MEMS Sensor Using Bimetallic Pd–Ag Nanoparticles as Catalyst for Acetylene Detection
Authors: Tian Yuan , Qiao Hui , Yao Tao , Gao Shuguo , Dai Lujian , Zhao Jun , Chen Ying , Xu Pengcheng
Published: October 2022

Acetylene detection plays an important role in fault diagnosis of power transformers. However, the available dissolved gas analysis (DGA) techniques have always relied on bulky instruments and are time-consuming. Herein, a high-performance acetylene sensor was fabricated on a microhotplate chip using In2O3 as the sensing material. To achieve high sensing response to acetylene, Pd–Ag core-shell nanoparticles were synthesized and used as catalysts. The transmission electron microscopy (TEM) image clearly shows that the Ag shell is deposited on one face of the cubic Pd nanoseeds. By loading the Pd–Ag bimetallic catalyst onto the surface of In2O3 sensing material, the acetylene sensor has been fabricated for acetylene detection. Due to the high catalytic performance of Pd–Ag bimetallic nanoparticles, the microhotplate sensor has a high response to acetylene gas, with a limit of detection (LOD) of 10 ppb. In addition to high sensitivity, the fabricated microhotplate sensor exhibits satisfactory selectivity, good repeatability, and fast response to acetylene. The high performance of the microhotplate sensor for acetylene gas indicates the application potential of trace acetylene detection in power transformer fault diagnosis.
Target Enclosing and Coverage Control for Quadrotors with Constraints and Time-Varying Delays: A Neural Adaptive Fault-Tolerant Formation Control Approach
Authors: Zhao Ziqian , Zhu Ming , Zhang Xiaojun
Published: October 2022

This paper investigates the problem of formation fault-tolerant control of multiple quadrotors (QRs) for a mobile sensing oriented application. The QRs subject to faults, input saturation and time-varying delays can be controlled to perform a target-enclosing and covering task while guaranteeing the state constraints will not be exceeded. A distributed formation control scheme is proposed, using a radial basis function neural network (RBFNN)-based time-delay position controller and an adaptive fault-tolerant attitude controller. The Lyapunov–Krasovskii approach is used to analyze the time-varying delay. Barrier Lyapunov function is deployed to handle the prescribed constraints, and an auxiliary system combined with a command filter is designed to resolve the saturation problem. An RBFNN and adaptive estimators are deployed to provide estimates of disturbances, fault signals and uncertainties. It is proven that all the closed-loop signals are bounded under the proposed protocol, while the prescribed constraints will not be violated, which enhances the flight safety and QR formation’s applicability. Comparative simulations based on application scenarios further verify the effectiveness of the proposed method.
Caregivers’ Profiles Based on the Canadian Occupational Performance Measure for the Adoption of Assistive Technologies
Authors: Gatta Francesco Della , Fabrizi Elisa , Giubilei Franco , Grau María Dolores , Moret-Tatay Carmen
Published: October 2022

The COPM (Canadian Occupational Performance Measure) is a tool that is based on the identification of self-perceived performance and satisfaction problems in the performance of occupations, allowing the creation of a hierarchy in the order of the interventions to be carried out, and speeding up the identification of the necessary AT (Assistive Technologies). Given the importance of the caregiver’s perception about their own performance in the design of AT, this research examines the caregiver’s profile through the COPM. A sample of 40 caregivers volunteered to participate in the study. A cluster analysis was carried out on the COPM scores. Two caregiver profiles were found in relation to the COPM measure, one with low scores on performance and satisfaction and another with high scores on both of these two variables. The main predictor was found to be the self-perception of performance. The structure was replicated through a hierarchical cluster analysis, where the role of caregivers was of interest. These results are relevant on both a theoretical and practical level.
Multiple Fingerprinting Localization by an Artificial Neural Network
Authors: Yoo Jaehyun
Published: October 2022

Fingerprinting localization is a promising indoor positioning methods thanks to its advantage of using preinstalled infrastructure. For example, WiFi signal strength can be measured by pre-existing WiFi routers. In the offline phase, the fingerprinting localization method first stores of position and RSSI measurement pairs in a dataset. Second, it predicts a target’s location by comparing the stored fingerprint database to the current measurement. The database size is normally huge, and data patterns are complicated; thus, an artificial neural network is used to model the relationship of fingerprints and locations. The existing fingerprinting locations, however, have been developed to predict only single locations. In practice, many users may require positioning services, and as such, the core algorithm should be capable of multiple localizations, which is the main contribution of this paper. In this paper, multiple fingerprinting localization is developed based on an artificial neural network and an analysis of the number of targets that can be estimated without loss of accuracy is conducted by experiments.
Design and Shape Optimization of Strain Gauge Load Cell for Axial Force Measurement for Test Benches
Authors: Al-Dahiree Omar Sabah , Tokhi Mohammad Osman , Hadi Nabil Hassan , Hmoad Nassar Rasheid , Ghazilla Raja Ariffin Raja , Yap Hwa Jen , Albaadani Emad Abdullah
Published: October 2022

The load cell is an indispensable component of many engineering machinery and industrial automation for measuring and sensing force and torque. This paper describes the design and analysis of the strain gauge load cell, from the conceptional design stage to shape optimization (based on the finite element method (FEM) technique) and calibration, providing ample load capacity with low-cost material (aluminum 6061) and highly accurate force measurement. The amplifier circuit of the half Wheatstone bridge configuration with two strain gauges was implemented experimentally with an actual load cell prototype. The calibration test was conducted to evaluate the load cell characteristics and derive the governing equation for sensing the unknown load depending on the measured output voltage. The measured sensitivity of the load cell is approximately 15 mV/N and 446.8 µV/V at a maximum applied load of 30 kg. The findings are supported by FEM results and experiments with an acceptable percentage of errors, which revealed an overall error of 6% in the worst situation. Therefore, the proposed load cell meets the design considerations for axial force measurement for the laboratory test bench, which has a light weight of 20 g and a maximum axial force capacity of 300 N with good sensor characteristics.
Age-Related Differences in Intermuscular Coherence EMG-EMG of Ankle Joint Antagonist Muscle Activity during Maximal Leaning
Authors: Konieczny Mariusz , Domaszewski Przemysław , Skorupska Elżbieta , Borysiuk Zbigniew , Słomka Kajetan J.
Published: October 2022

Background: Intermuscular synchronization is one of the fundamental aspects of maintaining a stable posture and is of great importance in the aging process. This study aimed to assess muscle synchronization and postural stabilizer asymmetry during quiet standing and the limits of stability using wavelet analysis. Intermuscular synchrony and antagonistic sEMG-sEMG (surface electromyography) coherence asymmetry were evaluated in the tibialis anterior and soleus muscles. Methods: The study involved 20 elderly (aged 65 ± 3.6) and 20 young (aged 21 ± 1.3) subjects. The task was to perform a maximum forward bend in a standing position. The prone test was divided into three phases: quiet standing (10 s), dynamic learning, and maintenance of maximum leaning (20 s). Wavelet analysis of coherence was performed in the delta and beta bands. Results: Young subjects modulated interface coherences to a greater extent in the beta band. Analysis of postural stability during standing tasks showed that only the parameter R2b (the distance between the maximal and minimal position central of pressure), as an indicator for assessing the practical limits of stability, was found to be significantly associated with differences in aging. Conclusion: The results showed differences in the beta and delta band oscillations between young and older subjects in a postural task involving standing quietly and leaning forward.
Skin Lesion Classification on Imbalanced Data Using Deep Learning with Soft Attention
Authors: Nguyen Viet Dung , Bui Ngoc Dung , Do Hoang Khoi
Published: October 2022

Today, the rapid development of industrial zones leads to an increased incidence of skin diseases because of polluted air. According to a report by the American Cancer Society, it is estimated that in 2022 there will be about 100,000 people suffering from skin cancer and more than 7600 of these people will not survive. In the context that doctors at provincial hospitals and health facilities are overloaded, doctors at lower levels lack experience, and having a tool to support doctors in the process of diagnosing skin diseases quickly and accurately is essential. Along with the strong development of artificial intelligence technologies, many solutions to support the diagnosis of skin diseases have been researched and developed. In this paper, a combination of one Deep Learning model (DenseNet, InceptionNet, ResNet, etc) with Soft-Attention, which unsupervisedly extract a heat map of main skin lesions. Furthermore, personal information including age and gender are also used. It is worth noting that a new loss function that takes into account the data imbalance is also proposed. Experimental results on data set HAM10000 show that using InceptionResNetV2 with Soft-Attention and the new loss function gives 90 percent accuracy, mean of precision, F1-score, recall, and AUC of 0.81, 0.81, 0.82, and 0.99, respectively. Besides, using MobileNetV3Large combined with Soft-Attention and the new loss function, even though the number of parameters is 11 times less and the number of hidden layers is 4 times less, it achieves an accuracy of 0.86 and 30 times faster diagnosis than InceptionResNetV2.
Design, Fabrication, and Testing of a Fully 3D-Printed Pressure Sensor Using a Hybrid Printing Approach
Authors: Verma Akash , Goos Ruben , Weerdt Jurre De , Pelgrims Patrick , Ferraris Eleonora
Published: October 2022

Pressure sensing is not a new concept and can be applied by using different transduction mechanisms and manufacturing techniques, including printed electronics approaches. However, very limited efforts have been taken to realise pressure sensors fully using additive manufacturing techniques, especially for personalised guide prosthetics in biomedical applications. In this work, we present a novel, fully printed piezoresistive pressure sensor, which was realised by using Aerosol Jet® Printing (AJP) and Screen Printing. AJ®P was specifically chosen to print silver interconnects on a selective laser sintered (SLS) polyamide board as a customised substrate, while piezoresistive electrodes were manually screen-printed on the top of the interconnects as the sensing layer. The sensor was electromechanically tested, and its response was registered upon the application of given signals, in terms of sensitivity, hysteresis, reproducibility, and time drift. When applying a ramping pressure, the sensor showed two different sensitive regions: (i) a highly sensitive region in the range of 0 to 0.12 MPa with an average sensitivity of 106 Ω/MPa and a low sensitive zone within 0.12 to 1.25 MPa with an average sensitivity of 7.6 Ω/MPa with some indeterminate overlapping regions. Hysteresis was negligible and an electrical resistance deviation of about 14% was observed in time drift experiments. Such performances will satisfy the demands of our application in the biomedical field as a smart prosthetics guide.
Coulometer from a Digitally Controlled Galvanostat with Photometric Endpoint Detection
Authors: González-Arjona Domingo , Roldán González Emilio , López-Pérez Germán , Domínguez Pérez Manuel María , Calero-Castillo Marina
Published: October 2022

In this work, a coulometer was developed from a digitally controlled galvanostat. A simple colorimeter based on a RGB LED was used as a light emitter coupled to light detectors, while light dependent resistance (LDR) and photodiodes have been developed as endpoint detectors. Both hardware and software have been adapted from the original galvanostat design. Regarding the hardware, new electrical signal conditioners (filters and voltage dividers) were included to optimize the working system. The software was developed based on an open source Arduino UNO microcontroller. The different variables that control the titration process are managed by an add-in module for Excel data acquisition software that is freely available. A study of the possible variables that influence the titration process has been carried out. The system was tested with two classical coulometric titrations such as iodometry (thiosulfate, ascorbic acid) and acid/base (potassium acid phthalate as standard). The developed system is versatile as different endpoint color indicators can be employed (starch and phenolphthalein for the investigated reactions). Different experimental arrangements have been studied: the nature of the electrodes (Pt, Ag), type of cells (two separate compartments or a single compartment), and light detectors (LDR, photodiode). The influence of several experimental parameters (both electrical, light, and integration time) was studied and chosen to obtain the best performance of the complete system. Reproducibility results below 1% can be obtained under controlled conditions. In the case of acid/base titrations, the presence of atmospheric carbon dioxide was detected, whose interference was mainly affected by the stirring rate and the titration time.
Assessing Walking Stability Based on Whole-Body Movement Derived from a Depth-Sensing Camera
Authors: Promsri Arunee
Published: October 2022

Stability during walking is considered a crucial aspect of assessing gait ability. The current study aimed to assess walking stability by applying principal component analysis (PCA) to decompose three-dimensional (3D) whole-body kinematic data of 104 healthy young adults (21.9 ± 3.5 years, 54 females) derived from a depth-sensing camera into a set of movement components/synergies called “principal movements” (PMs), forming together to achieve the task goal. The effect of sex as the focus area was tested on three PCA-based variables computed for each PM: the relative explained variance (rVAR) as a measure of the composition of movement structures; the largest Lyapunov exponent (LyE) as a measure of variability; and the number of zero-crossings (N) as a measure of the tightness of neuromuscular control. The results show that the sex effects appear in the specific PMs. Specifically, in PM1, resembling the swing-phase movement, females have greater LyE (p = 0.013) and N (p = 0.017) values than males. Moreover, in PM3, representing the mid-stance-phase movement, females have smaller rVAR (p = 0.020) but greater N (p = 0.008) values than males. These empirical findings suggest that the inherent sex differences in walking stability should be considered in assessing and training locomotion.
Real-Time Non-Contact Millimeter Wave Radar-Based Vital Sign Detection
Authors: Gao Zhiqiang , Ali Luqman , Wang Cong , Liu Ruizhi , Wang Chunwei , Qian Cheng , Sung Hokun , Meng Fanyi
Published: October 2022

In this paper, the extraction of the life activity spectrum based on the millimeter (mm) wave radar is designed to realize the detection of target objects and the threshold trigger module. The maximum likelihood estimation method is selected to complete the design of the average early warning probability trigger function. The threshold trigger module is designed for the echo signal of static objects in the echo signal. It will interfere with the extraction of Doppler frequency shift results. The moving target detection method is selected, and the filter is designed. The static clutter interference is filtered without affecting the phase difference between the detection sequences, and the highlight target signal is improved. The frequency and displacement of thoracic movement are used as the detection data. Through the Fourier transform calculation of the sequence, the spectrum value is extracted within the estimated range of the heartbeat and respiration spectrum, and the heartbeat and respiration signals are picked up. The proposed design uses Modelsim and Quartus for CO-simulation to complete the simulation verification of the function, extract the number of logical units occupied by computing resources, and verify the algorithm with the vital signs experiment. The heartbeat and respiration were detected using the sports bracelet; the relative errors of heartbeat detection were 0–6.3%, the respiration detection was 0–9.5%, and the relative errors of heartbeat detection were overwhelmingly less than 5%.
A Review of Functional Encryption in IoT Applications
Authors: Shahzad Khurram , Zia Tanveer , Qazi Emad-ul-Haq
Published: October 2022

The Internet of Things (IoT) represents a growing aspect of how entities, including humans and organizations, are likely to connect with others in their public and private interactions. The exponential rise in the number of IoT devices, resulting from ever-growing IoT applications, also gives rise to new opportunities for exploiting potential security vulnerabilities. In contrast to conventional cryptosystems, frameworks that incorporate fine-grained access control offer better opportunities for protecting valuable assets, especially when the connectivity level is dense. Functional encryption is an exciting new paradigm of public-key encryption that supports fine-grained access control, generalizing a range of existing fine-grained access control mechanisms. This survey reviews the recent applications of functional encryption and the major cryptographic primitives that it covers, identifying areas where the adoption of these primitives has had the greatest impact. We first provide an overview of different application areas where these access control schemes have been applied. Then, an in-depth survey of how the schemes are used in a multitude of applications related to IoT is given, rendering a potential vision of security and integrity that this growing field promises. Towards the end, we identify some research trends and state the open challenges that current developments face for a secure IoT realization.
Using Shear-Wave Elastography to Assess Exercise-Induced Muscle Damage: A Review
Authors: Ličen Urška , Kozinc Žiga
Published: October 2022

Shear-wave elastography is a method that is increasingly used to assess muscle stiffness in clinical practice and human health research. Recently, shear-wave elastography has been suggested and used to assess exercise-induced muscle damage. This review aimed to summarize the current knowledge of the utility of shear-wave elastography for assessment of muscle damage. In general, the literature supports the shear-wave elastography as a promising method for assessment of muscle damage. Increases in shear modulus are reported immediately and up to several days after eccentric exercise, while studies using shear-wave elastography during and after endurance events are showing mixed results. Moreover, it seems that shear modulus increases are related to the decline in voluntary strength loss. We recommend that shear modulus is measured at multiple muscles within a muscle group and preferably at longer muscle lengths. While further studies are needed to confirm this, the disruption of calcium homeostasis seems to be the primary candidate for the underlying mechanism explaining the increases in shear modulus observed after eccentric exercise. It remains to be investigated how well the changes in shear modulus correlate with directly assessed amount of muscle damage (biopsy).
A Historical Twist on Long-Range Wireless: Building a 103 km Multi-Hop Network Replicating Claude Chappe’s Telegraph
Authors: Rady Mina , Muñoz Jonathan , Abu-Aisheh Razanne , Vučinić Mališa , Astorga Tobar José , Cortes Alfonso , Lampin Quentin , Barthel Dominique , Watteyne Thomas
Published: October 2022

In 1794, French Engineer Claude Chappe coordinated the deployment of a network of dozens of optical semaphores. These formed “strings” that were hundreds of kilometers long, allowing for nationwide telegraphy. The Chappe telegraph inspired future developments of long-range telecommunications using electrical telegraphs and, later, digital telecommunication. Long-range wireless networks are used today for the Internet of Things (IoT), including industrial, agricultural, and urban applications. The long-range radio technology used today offers approximately 10 km of range. Long-range IoT solutions use “star” topology: all devices need to be within range of a gateway device. This limits the area covered by one such network to roughly a disk of a 10 km radius. In this article, we demonstrate a 103 km low-power wireless multi-hop network by combining long-range IoT radio technology with Claude Chappe’s vision. We placed 11 battery-powered devices at the former locations of the Chappe telegraph towers, hanging under helium balloons. We ran a proprietary protocol stack on these devices so they formed a 10-hop multi-hop network: devices forwarded the frames from the “previous” device in the chain. This is, to our knowledge, the longest low power multi-hop wireless network built to date, demonstrating the potential of combining long-range radio technology with multi-hop technology.
Multi-Connectivity for 5G Networks and Beyond: A Survey
Authors: Sylla Tidiane , Mendiboure Leo , Maaloul Sassi , Aniss Hasnaa , Chalouf Mohamed Aymen , Delbruel Stéphane
Published: October 2022

To manage a growing number of users and an ever-increasing demand for bandwidth, future 5th Generation (5G) cellular networks will combine different radio access technologies (cellular, satellite, and WiFi, among others) and different types of equipment (pico-cells, femto-cells, small-cells, macro-cells, etc.). Multi-connectivity is an emerging paradigm aiming to leverage this heterogeneous architecture. To achieve this, multi-connectivity proposes to enable UE to simultaneously use component carriers from different and heterogeneous network nodes: base stations, WiFi access points, etc. This could offer many benefits in terms of quality of service, energy efficiency, fairness, mobility, and spectrum and interference management. Therefore, this survey aims to present an overview of multi-connectivity in 5G networks and beyond. To do so, a comprehensive review of existing standards and enabling technologies is proposed. Then, a taxonomy is defined to classify the different elements characterizing multi-connectivity in 5G and future networks. Thereafter, existing research works using multi-connectivity to improve the quality of service, energy efficiency, fairness, mobility management, and spectrum and interference management are analyzed and compared. In addition, lessons common to these different contexts are presented. Finally, open challenges for multi-connectivity in 5G networks and beyond are discussed.
Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis
Authors: Antony Mary Judith , Sankaralingam Baghavathi Priya , Mahendran Rakesh Kumar , Gardezi Akber Abid , Shafiq Muhammad , Choi Jin-Ghoo , Hamam Habib
Published: October 2022

An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronounced when CSP filtering is used. Furthermore, traditional CSP methods lack frequency domain information and require many input channels. Therefore, to overcome this shortcoming, a feature extraction method based on Online Recursive Independent Component Analysis (ORICA)-CSP is proposed. For EEG-based brain—computer interfaces (BCIs), especially online and real-time BCIs, the most widely used classifiers used to be linear discriminant analysis (LDA) and support vector machines (SVM). Previous evaluations clearly show that SVMs generally outperform other classifiers in terms of performance. In this case, Adaptive Support Vector Machine (A-SVM) is used for classification together with the ORICA-CSP method. The results are promising, and the experiments are performed on EEG data of 4 classes’ motor images, namely Dataset 2a of BCI Competition IV.
A Multi-Pass Optically Pumped Rubidium Atomic Magnetometer with Free Induction Decay
Authors: Zhang Lulu , Yang Yongbiao , Zhao Ni , He Jun , Wang Junmin
Published: October 2022

A free-induction-decay (FID) type optically-pumped rubidium atomic magnetometer driven by a radio-frequency (RF) magnetic field is presented in this paper. Influences of parameters, such as the temperature of rubidium vapor cell, the power of pump beam, and the strength of RF magnetic field and static magnetic field on the amplitude and the full width at half maximum (FWHM) of the FID signal, have been investigated in the time domain and frequency domain. At the same time, the sensitivities of the magnetometer for the single-pass and the triple-pass probe beam cases have been compared by changing the optical path of the interaction between probe beam and atomic ensemble. Compared with the sensitivity of ∼21.2 pT/Hz1/2 in the case of the single-pass probe beam, the amplitude of FID signal in the case of the triple-pass probe beam has been significantly enhanced, and the sensitivity has been improved to ∼13.4 pT/Hz1/2. The research in this paper provids a reference for the subsequent study of influence of different buffer gas pressure on the FWHM and also a foundation for further improving the sensitivity of FID rubidium atomic magnetometer by employing a polarization-squeezed light as probe beam, to achieve a sensitivity beyond the photo-shot-noise level.
Switchable Broadband-to-Tunable Narrowband Magnetic Probe for Near-Field Measurements
Authors: Abdulhameed Abdulghafor A. , Kubík Zdeněk
Published: October 2022

This paper presents a printed magnetic probe that can switch from broadband to tunable narrowband for near-field measurement. In the early design stage, we created a printed loop gap resonator as a magnetic reference sensor for the pre-compliance test in a band up to 6 GHz. Consequently, the results showed a good response in terms of the S11 and S21 parameters of the proposed probe compared with the commercial magnetic sensor XF-R 3-1. The source noise might spread among different frequency bands, making the broadband magnetic probe the closest choice for estimating the magnetic field in the near-field region. Unfortunately, broadband magnetic probes have lower sensitivity than narrowband ones. One of the solutions to get high sensitivity is to connect the LNA to the output of the passive magnetic sensor. This work proposes a novel method to solve this issue using a PIN diode to change the broadband status into a high sensitivity narrowband status and then tune this narrowband across the most critical applications such as 3.5 GHz, 3.75 GHz, 4.8 GHz, and 5.2 GHz with the help of a varactor diode. Compared to the broadband status, an improvement of more than 10 dB has been obtained across all these wireless bands. Furthermore, the proposed structure’s isolation between the electrical and magnetic fields is about 13 dB.
Memory Visualization-Based Malware Detection Technique
Authors: Shah Syed Shakir Hameed , Jamil Norziana , Khan Atta ur Rehman
Published: October 2022

Advanced Persistent Threat is an attack campaign in which an intruder or team of intruders establishes a long-term presence on a network to mine sensitive data, which becomes more dangerous when combined with polymorphic malware. This type of malware is not only undetectable, but it also generates multiple variants of the same type of malware in the network and remains in the system’s main memory to avoid detection. Few researchers employ a visualization approach based on a computer’s memory to detect and classify various classes of malware. However, a preprocessing step of denoising the malware images was not considered, which results in an overfitting problem and prevents us from perfectly generalizing a model. In this paper, we introduce a new data engineering approach comprising two main stages: Denoising and Re-Dimensioning. The first aims at reducing or ideally removing the noise in the malware’s memory-based dump files’ transformed images. The latter further processes the cleaned image by compressing them to reduce their dimensionality. This is to avoid the overfitting issue and lower the variance, computing cost, and memory utilization. We then built our machine learning model that implements the new data engineering approach and the result shows that the performance metrics of 97.82% for accuracy, 97.66% for precision, 97.25% for recall, and 97.57% for f1-score are obtained. Our new data engineering approach and machine learning model outperform existing solutions by 0.83% accuracy, 0.30% precision, 1.67% recall, and 1.25% f1-score. In addition to that, the computational time and memory usage have also reduced significantly.
EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning
Authors: Cao Jun , Garro Enara Martin , Zhao Yifan
Published: October 2022

There is high demand for techniques to estimate human mental workload during some activities for productivity enhancement or accident prevention. Most studies focus on a single physiological sensing modality and use univariate methods to analyse multi-channel electroencephalography (EEG) data. This paper proposes a new framework that relies on the features of hybrid EEG–functional near-infrared spectroscopy (EEG–fNIRS), supported by machine-learning features to deal with multi-level mental workload classification. Furthermore, instead of the well-used univariate power spectral density (PSD) for EEG recording, we propose using bivariate functional brain connectivity (FBC) features in the time and frequency domains of three bands: delta (0.5–4 Hz), theta (4–7 Hz) and alpha (8–15 Hz). With the assistance of the fNIRS oxyhemoglobin and deoxyhemoglobin (HbO and HbR) indicators, the FBC technique significantly improved classification performance at a 77% accuracy for 0-back vs. 2-back and 83% for 0-back vs. 3-back using a public dataset. Moreover, topographic and heat-map visualisation indicated that the distinguishing regions for EEG and fNIRS showed a difference among the 0-back, 2-back and 3-back test results. It was determined that the best region to assist the discrimination of the mental workload for EEG and fNIRS is different. Specifically, the posterior area performed the best for the posterior midline occipital (POz) EEG in the alpha band and fNIRS had superiority in the right frontal region (AF8).
A Residual-Inception U-Net (RIU-Net) Approach and Comparisons with U-Shaped CNN and Transformer Models for Building Segmentation from High-Resolution Satellite Images
Authors: Sariturk Batuhan , Seker Dursun Zafer
Published: October 2022

Building segmentation is crucial for applications extending from map production to urban planning. Nowadays, it is still a challenge due to CNNs’ inability to model global context and Transformers’ high memory need. In this study, 10 CNN and Transformer models were generated, and comparisons were realized. Alongside our proposed Residual-Inception U-Net (RIU-Net), U-Net, Residual U-Net, and Attention Residual U-Net, four CNN architectures (Inception, Inception-ResNet, Xception, and MobileNet) were implemented as encoders to U-Net-based models. Lastly, two Transformer-based approaches (Trans U-Net and Swin U-Net) were also used. Massachusetts Buildings Dataset and Inria Aerial Image Labeling Dataset were used for training and evaluation. On Inria dataset, RIU-Net achieved the highest IoU score, F1 score, and test accuracy, with 0.6736, 0.7868, and 92.23%, respectively. On Massachusetts Small dataset, Attention Residual U-Net achieved the highest IoU and F1 scores, with 0.6218 and 0.7606, and Trans U-Net reached the highest test accuracy, with 94.26%. On Massachusetts Large dataset, Residual U-Net accomplished the highest IoU and F1 scores, with 0.6165 and 0.7565, and Attention Residual U-Net attained the highest test accuracy, with 93.81%. The results showed that RIU-Net was significantly successful on Inria dataset. On Massachusetts datasets, Residual U-Net, Attention Residual U-Net, and Trans U-Net provided successful results.
Autonomous Mutual Authentication Protocol in the Edge Networks
Authors: Sheu Ruey-Kai , Pardeshi Mayuresh Sunil , Chen Lun-Chi
Published: October 2022

A distinct security protocol is necessary for the exponential growth in intelligent edge devices. In particular, the autonomous devices need to address significant security concern to function smoothly in the high market demand. Nevertheless, exponential increase in the connected devices has made cloud networks more complex and suffer from information processing delay. Therefore, the goal of this work is to design a novel server-less mutual authentication protocol for the edge networks. The aim is to demonstrate an autonomous mutual authentication amongst the connected smart devices within the edge networks. The solution addresses applications of autonomous cars, smart things, and Internet of Things (IoT) devices in the edge or wireless sensor networks (WSN), etc. In this paper, the design proposes use of a public-key system, octet-based balanced-tree transitions, challenge–response mechanism, device unique ID (UID), pseudo-random number generator (PRNG), time-stamps, and event specific session keys. Ultimately, server-less design requires less infrastructure and avoids several types of network-based communication attacks, e.g., impersonating, Man in the middle (MITM), IoT-DDOS, etc. Additionally, the system overhead is eliminated by no secret key requirements. The results provide sufficient evidence about the protocol market competitiveness and demonstrate better benchmark comparison results.
A Spatiotemporal Calibration Algorithm for IMU–LiDAR Navigation System Based on Similarity of Motion Trajectories
Authors: Li Yunhui , Yang Shize , Xiu Xianchao , Miao Zhonghua
Published: October 2022

The fusion of light detection and ranging (LiDAR) and inertial measurement unit (IMU) sensing information can effectively improve the environment modeling and localization accuracy of navigation systems. To realize the spatiotemporal unification of data collected by the IMU and the LiDAR, a two-step spatiotemporal calibration method combining coarse and fine is proposed. The method mainly includes two aspects: (1) Modeling continuous-time trajectories of IMU attitude motion using B-spline basis functions; the motion of the LiDAR is estimated by using the normal distributions transform (NDT) point cloud registration algorithm, taking the Hausdorff distance between the local trajectories as the cost function and combining it with the hand–eye calibration method to solve the initial value of the spatiotemporal relationship between the two sensors’ coordinate systems, and then using the measurement data of the IMU to correct the LiDAR distortion. (2) According to the IMU preintegration, and the point, line, and plane features of the lidar point cloud, the corresponding nonlinear optimization objective function is constructed. Combined with the corrected LiDAR data and the initial value of the spatiotemporal calibration of the coordinate systems, the target is optimized under the nonlinear graph optimization framework. The rationality, accuracy, and robustness of the proposed algorithm are verified by simulation analysis and actual test experiments. The results show that the accuracy of the proposed algorithm in the spatial coordinate system relationship calibration was better than 0.08° (3δ) and 5 mm (3δ), respectively, and the time deviation calibration accuracy was better than 0.1 ms and had strong environmental adaptability. This can meet the high-precision calibration requirements of multisensor spatiotemporal parameters of field robot navigation systems.
Path Loss Measurements and Model Analysis in an Indoor Corridor Environment at 28 GHz and 38 GHz
Authors: Oladimeji Tolulope T. , Kumar Pradeep , Elmezughi Mohamed K.
Published: October 2022

This paper examines the large-scale path loss models for an indoor corridor environment at frequencies of 28 and 38 GHz. The measurement environment consists of an indoor corridor with both line-of-sight (LOS) and non-line of sight (NLOS) scenarios using vertical–vertical (V–V) and vertical–horizontal (V–H) antenna polarizations. The single-frequency close-in (CI), floating intercept (FI), free space large-scale path loss models and measured data from the measurement campaign were used to evaluate the performance analysis. The paper also focuses on various parameters, such as standard deviation, path loss exponent (PLE), accuracy, simplicity, and stability of the models. The analysis focuses on the peculiarity of the effect of the wall proximity on the path loss parameters as well as comparisons with the parameters in some of the reviewed literature studies. The FI and CI models produce comparable results for both antenna polarizations and clearly fit with the measured path loss. The PLE, with the highest value of 3.33 at 38 GHz (V–H), is much higher in the NLOS scenario with V–H polarization due to the signal degradation along the path from the transmitter (Tx) to the receiver (Rx). This is because there is no direct LOS between the Tx and Rx antennas. The Rx only relies on signal diffractions and reflections from obstacles as it transmits through the path from the Tx antenna. The path loss measurements and model analysis presented here are useful in designing 5G wireless communication systems for indoor environments, particularly for power budget calculations.
A Novel Detection and Assessment Method for Operational Defects of Pipe Jacking Tunnel Based on 3D Longitudinal Deformation Curve: A Case Study
Authors: Lin Wei , Li Pan , Xie Xiongyao
Published: October 2022

Adjacent tunnel construction and environmental disturbances can lead to longitudinal deformation in pipe-jacking tunnels. The longitudinal deformation of the tunnel is closely related to the occurrence of joint dislocation, joint opening, and other defects. In view of the difficulty of obtaining 3D longitudinal deformation curves, a method is proposed to obtain 3D longitudinal deformation curves based on a large number of 3D point cloud data with high spatial resolution and large spatial dimensions. Combined with the mechanism of defects occurrence, a theoretical basis for tunnel defects assessment based on tunnel longitudinal deformation is proposed. Taking one pipe jacking tunnel as an example, the longitudinal settlement curve and the 3D longitudinal deformation curve are compared. The correlation between the 3D longitudinal deformation curve and defects such as mud leakage, cracks, and differential deformation is illustrated from the perspective of three indexes: deformation amount, bending deformation, and shearing deformation. The accuracy and reliability of the 3D longitudinal deformation curve in tunnel defects detection and assessment are verified.
A Framework for Inclusion of Unmodelled Contact Tasks Dynamics in Industrial Robotics
Authors: Gordić Zaviša , Jovanović Kosta
Published: October 2022

This paper presents a method to include unmodeled dynamics of load or a robot’s end-effector into algorithms for collision detection or general understanding of a robot’s operation context. The approach relies on the application of a previously developed modification of the Dynamic Time Warping algorithm, as well as a universally applicable algorithm for identifying kinematic parameters. The entire process can be applied to arbitrary robot configuration, and it does not require identification of dynamic parameters. The paper addresses the two main categories of contact tasks with unmodelled dynamics, which are determined based on whether the external contact force has a consistent profile in the end effector or base coordinate. Conclusions for representative examples analysed in the paper are applicable to tasks such as load manipulation, press bending, and crimping for the first type of forces and applications such as drilling, screwdriving, snap-fit, bolting, and riveting assembly for the latter category. The results presented in the paper are based on realistic testing with measurements obtained from an industrial robot.