bluuebunny/crossref_metadata_2025 huggingface dataset:
An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment
Authors: Lei Lifeng , Kou Liang , Zhan Xianghao , Zhang Jilin , Ren Yongjian
Published: October 2022

With the advent of the digital information age, new data services such as virtual reality, industrial Internet, and cloud computing have proliferated in recent years. As a result, it increases operator demand for 5G bearer networks by providing features such as high transmission capacity, ultra-long transmission distance, network slicing, and intelligent management and control. Software-defined networking, as a new network architecture, intends to increase network flexibility and agility and can better satisfy the demands of 5G networks for network slicing. Nevertheless, software-defined networking still faces the challenge of network intrusion. We propose an abnormal traffic detection method based on the stacking method and self-attention mechanism, which makes up for the shortcoming of the inability to track long-term dependencies between data samples in ensemble learning. Our method utilizes a self-attention mechanism and a convolutional network to automatically learn long-term associations between traffic samples and provide them to downstream tasks in sample embedding. In addition, we design a novel stacking ensemble method, which computes the sample embedding and the predicted values of the heterogeneous base learner through the fusion module to obtain the final outlier results. This paper conducts experiments on abnormal traffic datasets in the software-defined network environment, calculates precision, recall and F1-score, and compares and analyzes them with other algorithms. The experimental results show that the method designed in this paper achieves 0.9972, 0.9996, and 0.9984 in multiple indicators of precision, recall, and F1-score, respectively, which are better than the comparison methods.
SoftMatch: Comparing Scanpaths Using Combinatorial Spatio-Temporal Sequences with Fractal Curves
Authors: Newport Robert Ahadizad , Russo Carlo , Liu Sidong , Suman Abdulla Al , Di Ieva Antonio
Published: October 2022

Recent studies matching eye gaze patterns with those of others contain research that is heavily reliant on string editing methods borrowed from early work in bioinformatics. Previous studies have shown string editing methods to be susceptible to false negative results when matching mutated genes or unordered regions of interest in scanpaths. Even as new methods have emerged for matching amino acids using novel combinatorial techniques, scanpath matching is still limited by a traditional collinear approach. This approach reduces the ability to discriminate between free viewing scanpaths of two people looking at the same stimulus due to the heavy weight placed on linearity. To overcome this limitation, we here introduce a new method called SoftMatch to compare pairs of scanpaths. SoftMatch diverges from traditional scanpath matching in two different ways: firstly, by preserving locality using fractal curves to reduce dimensionality from 2D Cartesian (x,y) coordinates into 1D (h) Hilbert distances, and secondly by taking a combinatorial approach to fixation matching using discrete Fréchet distance measurements between segments of scanpath fixation sequences. These matching “sequences of fixations over time” are a loose acronym for SoftMatch. Results indicate high degrees of statistical and substantive significance when scoring matches between scanpaths made during free-form viewing of unfamiliar stimuli. Applications of this method can be used to better understand bottom up perceptual processes extending to scanpath outlier detection, expertise analysis, pathological screening, and salience prediction.
Design and Analysis of Small Size Eddy Current Displacement Sensor
Authors: Wang Sheng-Ching , Xie Bo-Ren , Huang San-Ming
Published: October 2022

A systematic method is employed for the design and analysis of a small size eddy current (EC) displacement sensor. Simulations are first performed to determine the optimal winding structure and dimensions of the sensor. A linear-fitting approach is then developed for converting the AC displacement signal of the sensor to a DC signal. Finally, a compensation method is proposed for mitigating the temperature drift of the EC sensor under different working temperatures. The experimental results show that the proposed sensor has a sensitivity of approximately 3 μm, a working temperature range of 25–55 °C, and a linearity of ±1.025%.
A Low-Cost, Low-Power Water Velocity Sensor Utilizing Acoustic Doppler Measurement
Authors: Catsamas Stephen , Shi Baiqian , Deletic Boris , Wang Miao , McCarthy David T.
Published: October 2022

Current commercial sensors to monitor water flow velocities are expensive, bulky, and require significant effort to install. Low-cost sensors open the possibility of monitoring storm and waste water systems at a much greater spatial and temporal resolution without prohibitive costs and resource investment. To aid in this, this work developed a low-cost, low-power velocity sensor based on acoustic Doppler velocimetry. The sensor, costing less than 50 USD is open-source, open-hardware, compact, and easily interfaceable to a wide range of data-logging systems. A freely available sensor design at this price point does not currently exist, and its novelty is in enabling high-resolution real-time monitoring schemes. The design is capable of measuring water velocities up to 1200 mm/s. The sensor is characterised and then verified in an in-field long-term test. Finally, the data from this test are then used to evaluate the performance of the sensor in a real-world scenario. The analysis concludes that the sensor is capable of effectively measuring water velocity.
The Clutter Simulation of a Known Terrain by the 3D Parabolic Equation and RCS Computation
Authors: Wang Zhiyi , Ding Jieru , Wang Min , Yang Shuyuan
Published: October 2022

Ground clutter data are usually generated using a statistical model, but they cannot effectively reflect the spatial distribution characteristics of ground objects. It is important in practical projects to effectively predict ground clutter when terrain data are known. In this paper, a scheme of ground clutter simulation based on the three-dimensional parabolic equation (3DPE) model is proposed. Radio wave propagation was modeled by the PE model and the spatial field distribution was solved. After the radar cross section (RCS) calculation based on space cells, the ground clutter information data were obtained. Then the radar echo was obtained and the clutter map was simulated. The simulation experimental results show that the ground clutter map simulated by the proposed method has a good reference value, meets the demand of strong clutter area prediction under known terrain conditions, and provides a theoretical basis for radar location and optimal deployment.
A Single-Antenna Method and Post-Processing Strategy for Radar Cross-Section Measurements at Near-Field Ranges
Authors: Mihai Ilie Valentin , Constantin Andreea , Bucuci Stefania , Tamas Razvan D.
Published: October 2022

The single-antenna technique proposed in this paper was developed for measuring the radar cross-section at near-field distances in a real environment, from reflection coefficient measurements on the antenna. The near-field radar cross-section is corrected with an analytical factor calculated as a ratio between the radar cross-section computed at far-field and near-field. The analytical correction factor takes into account the effects of the diffraction at the edges of the target at incidence angles higher than 20°. An improved, distance averaging technique is proposed to reduce the multipath propagation effects. A time-gating procedure is additionally used in order to better isolate the reflection from the target and to remove the real environment contributions. The method was successfully tested on a rectangular metallic plate as a target over a wide frequency band, at normal and oblique incidence angles; however, it might also work for arbitrarily shaped targets, because they can actually be divided into small rectangular patches.
YOLO Algorithm for Long-Term Tracking and Detection of Escherichia Coli at Different Depths of Microchannels Based on Microsphere Positioning Assistance
Authors: Sun Lesheng , Xu Ying , Rao Zhikang , Chen Juntao , Liu Zhe , Lu Ning
Published: October 2022

The effect evaluation of the antibiotic susceptibility test based on bacterial solution is of great significance for clinical diagnosis and prevention of antibiotic abuse. Applying a microfluidic chip as the detection platform, the detection method of using microscopic images to observe bacteria under antibiotic can greatly speed up the detection time, which is more suitable for high-throughput detection. However, due to the influence of the depth of the microchannel, there are multiple layers of bacteria under the focal depth of the microscope, which greatly affects the counting and recognition accuracy and increases the difficulty of relocation of the target bacteria, as well as extracting the characteristics of bacterial liquid changes under the action of antibiotics. After the focal depth of the target bacteria is determined, although the z-axis can be controlled with the help of a three-dimensional micro-operator, the equipment is difficult to operate and the long-term changes of the target bacteria cannot be tracked quickly and accurately. In this paper, the YOLOv5 algorithm is adopted to accurately identify bacteria with different focusing states of multi-layer bacteria at the z-axis with any focal depth. In the meantime, a certain amount of microspheres were mixed into bacteria to assist in locating bacteria, which was convenient for tracking the growth state of bacteria over a long period, and the recognition rates of both bacteria and microspheres were high. The recognition accuracy and counting accuracy of bacteria are 0.734 and 0.714, and the two recognition rates of microspheres are 0.910 and 0.927, respectively, which are much higher than the counting accuracy of 0.142 for bacteria and 0.781 for microspheres with the method of enhanced depth of field (EDF method). Moreover, during long-term bacterial tracking and detection, target bacteria at multiple z-axis focal depth positions can be recorded by the aid of microspheres as a positioning aid for 3D reconstruction, and the focal depth positions can be repositioned within 3–10 h. The structural similarity (SSIM) of microscopic image structure differences at the same focal depth fluctuates between 0.960 and 0.975 at different times, and the root-mean-square error (RMSE) fluctuates between 8 and 12, which indicates that the method also has good relocation accuracy. Thus, this method provides the basis for rapid, high-throughput, and long-term analysis of microscopic changes (e.g., morphology, size) of bacteria detection under the addition of antibiotics with different concentrations based on microfluidic channels in the future.
Position Measurements Using Magnetic Sensors for a Shape Memory Alloy Linear Actuator
Authors: Cortez Vega Ricardo , Cubas Gabriel , Sandoval-Chileño Marco Antonio , Castañeda Briones Luis Ángel , Lozada-Castillo Norma Beatriz , Luviano-Juárez Alberto
Published: October 2022

This article presents the design and implementation of a linear actuator based on NiTi Shape Memory Alloys with temperature and position measurements based on a magnetic sensor array and a set of thermistors. The position instrumentation is contact free to avoid friction perturbations; the position signal conditioning is carried out through the calculation of the response of each magnetic sensor, selecting the closest sensor to ensure accurate results on the full range of movement. Experimental results validate the accuracy of the position sensing with a competitive behaviour.
A Comparison of Surrogate Behavioral Models for Power Amplifier Linearization under High Sparse Data
Authors: Galaviz-Aguilar Jose Alejandro , Vargas-Rosales Cesar , Cárdenas-Valdez José Ricardo , Aguila-Torres Daniel Santiago , Flores-Hernández Leonardo
Published: October 2022

A good approximation to power amplifier (PA) behavioral modeling requires precise baseband models to mitigate nonlinearities. Since digital predistortion (DPD) is used to provide the PA linearization, a framework is necessary to validate the modeling figures of merit support under signal conditioning and transmission restrictions. A field-programmable gate array (FPGA)-based testbed is developed to measure the wide-band PA behavior using a single-carrier 64-quadrature amplitude modulation (QAM) multiplexed by orthogonal frequency-division multiplexing (OFDM) based on long-term evolution (LTE) as a stimulus, with different bandwidths signals. In the search to provide a heuristic target approach modeling, this paper introduces a feature extraction concept to find an appropriate complexity solution considering the high sparse data issue in amplitude to amplitude (AM-AM) and amplitude to phase AM-PM models extraction, whose penalties are associated with overfitting and hardware complexity in resulting functions. Thus, experimental results highlight the model performance for a high sparse data regime and are compared with a regression tree (RT), random forest (RF), and cubic-spline (CS) model accuracy capabilities for the signal conditioning to show a reliable validation, low-complexity, according to the peak-to-average power ratio (PAPR), complementary cumulative distribution function (CCDF), coefficients extraction, normalized mean square error (NMSE), and execution time figures of merit. The presented models provide a comparison with original data that aid to compare the dimension and robustness for each surrogate model where (i) machine learning (ML)-based and (ii) CS interpolate-based where high sparse data are present, NMSE between the CS interpolated based are also compared to demonstrate the efficacy in the prediction methods with lower convergence times and complexities.
Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy
Authors: Alharbi Nouf , Mackenzie Lewis , Pezaros Dimitrios
Published: October 2022

The emergence of the Industrial Internet of Things (IIoT) has accelerated the adoption of Industrial Wireless Sensor Networks (IWSNs) for numerous applications. Effective communication in such applications requires reduced end-to-end transmission time, balanced energy consumption and increased communication reliability. Graph routing, the main routing method in IWSNs, has a significant impact on achieving effective communication in terms of satisfying these requirements. Graph routing algorithms involve applying the first-path available approach and using path redundancy to transmit data packets from a source sensor node to the gateway. However, this approach can affect end-to-end transmission time by creating conflicts among transmissions involving a common sensor node and promoting imbalanced energy consumption due to centralised management. The characteristics and requirements of these networks encounter further complications due to the need to find the best path on the basis of the requirements of IWSNs to overcome these challenges rather than using the available first-path. Such a requirement affects the network performance and prolongs the network lifetime. To address this problem, we adopt a Covariance-Matrix Adaptation Evolution Strategy (CMA-ES) to create and select the graph paths. Firstly, this article proposes three best single-objective graph routing paths according to the IWSN requirements that this research focused on. The sensor nodes select best paths based on three objective functions of CMA-ES: the best Path based on Distance (PODis), the best Path based on residual Energy (POEng) and the best Path based on End-to-End transmission time (POE2E). Secondly, to enhance energy consumption balance and achieve a balance among IWSN requirements, we adapt the CMA-ES to select the best path with multiple-objectives, otherwise known as the Best Path of Graph Routing with a CMA-ES (BPGR-ES). A simulation using MATALB with different configurations and parameters is applied to evaluate the enhanced graph routing algorithms. Furthermore, the performance of PODis, POEng, POE2E and BPGR-ES is compared with existing state-of-the-art graph routing algorithms. The simulation results reveal that the BPGR-ES algorithm achieved 87.53% more balanced energy consumption among sensor nodes in the network compared to other algorithms, and the delivery of data packets of BPGR-ES reached 99.86%, indicating more reliable communication.
Piezoelectric Micromachined Ultrasonic Transducer-Integrated Helmholtz Resonator with Microliter-Sized Volume-Tunable Cavity
Authors: Feng Guo-Hua , Chen Wen-Sheng
Published: October 2022

In this study, a piezoelectric micromachined ultrasonic transducer (PMUT) is integrated with a microliter-sized volume-tunable Helmholtz resonator. The passive Helmholtz resonator is constructed using an SU8 photolithography-defined square opening plate as the neck portion, a 3D-printed hollow structure with a threaded insert nut, and a precision set screw to form the volume-controllable cavity of the Helmholtz resonator. The fabricated piezoelectric films acted as ultrasonic actuators attached to the surface of the neck SU8 plate. Experimental results show that the sound pressure level (SPL) and operation bandwidth could be effectively tuned, and a 200% SPL increase and twofold bandwidth enhancement are achieved when setting the cavity length to 0.75 mm compared with the open-cavity case. A modified Helmholtz resonator model is proposed to explain the experimental results. The adjusting factors of the effective mass and viscous damper are created to modify the existing parameters in the conventional Helmholtz resonator model. The relationship between the adjusting factors and cavity length can be described well using a two-term power series curve. This modified Helmholtz resonator model not only provides insight into this active-type Helmholtz resonator operation but also provides a useful estimation for its optimal design and fabrication.
Smartphone Sensor-Based Human Motion Characterization with Neural Stochastic Differential Equations and Transformer Model
Authors: Lee Juwon , Kim Taehwan , Park Jeongho , Park Jooyoung
Published: October 2022

With many conveniences afforded by advances in smartphone technology, developing advanced data analysis methods for health-related information from smartphone users has become a fast-growing research topic in the healthcare field. Along these lines, this paper addresses smartphone sensor-based characterization of human motions with neural stochastic differential equations (NSDEs) and a Transformer model. NSDEs and modeling via Transformer networks are two of the most prominent deep learning-based modeling approaches, with significant performance yields in many applications. For the problem of modeling dynamical features, stochastic differential equations and deep neural networks are frequently used paradigms in science and engineering, respectively. Combining these two paradigms in one unified framework has drawn significant interest in the deep learning community, and NSDEs are among the leading technologies for combining these efforts. The use of attention has also become a widely adopted strategy in many deep learning applications, and a Transformer is a deep learning model that uses the mechanism of self-attention. This concept of a self-attention based Transformer was originally introduced for tasks of natural language processing (NLP), and due to its excellent performance and versatility, the scope of its applications is rapidly expanding. By utilizing the techniques of neural stochastic differential equations and a Transformer model along with data obtained from smartphone sensors, we present a deep learning method capable of efficiently characterizing human motions. For characterizing human motions, we encode the high-dimensional sequential data from smartphone sensors into latent variables in a low-dimensional latent space. The concept of the latent variable is particularly useful because it can not only carry condensed information concerning motion data, but also learn their low-dimensional representations. More precisely, we use neural stochastic differential equations for modeling transitions of human motion in a latent space, and rely on a Generative Pre-trained Transformer 2 (GPT2)-based Transformer model for approximating the intractable posterior of conditional latent variables. Our experiments show that the proposed method can yield promising results for the problem of characterizing human motion patterns and some related tasks including user identification.
Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning
Authors: Nasir Muhammad Umar , Zubair Muhammad , Ghazal Taher M. , Khan Muhammad Farhan , Ahmad Munir , Rahman Atta-ur , Hamadi Hussam Al , Khan Muhammad Adnan , Mansoor Wathiq
Published: October 2022

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient’s data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.
Automatic Modulation Recognition Based on the Optimized Linear Combination of Higher-Order Cumulants
Authors: Hussain Asad , Alam Sheraz , Ghauri Sajjad A. , Ali Mubashir , Sherazi Husnain Raza , Akhunzada Adnan , Bibi Iram , Gani Abdullah
Published: October 2022

Automatic modulation recognition (AMR) is used in various domains—from general-purpose communication to many military applications—thanks to the growing popularity of the Internet of Things (IoT) and related communication technologies. In this research article, we propose an innovative idea of combining the classical mathematical technique of computing linear combinations (LCs) of cumulants with a genetic algorithm (GA) to create super-cumulants. These super-cumulants are further used to classify five digital modulation schemes on fading channels using the K-nearest neighbor (KNN). Our proposed classifier significantly improves the percentage recognition accuracy at lower SNRs when using smaller sample sizes. A comparison with existing techniques manifests the supremacy of our proposed classifier.
Sensor Effects in LCL-Type Grid-Connected Shunt Active Filters Control Using Higher-Order Sliding Mode Control Techniques
Authors: Alali Mohamad Alaa Eddin , Shtessel Yuri B , Barbot Jean-Pierre , Di Gennaro Stefano
Published: October 2022

The effects of measuring devices/sensors on improving the power quality (PQ) of electric networks are studied in this paper. In this context, improving the performance of an LCL-type grid connected to a three-phase three-wire shunt active filter (SAF) in the presence of voltage perturbations is studied. In order to ensure the high-quality performance of LCL-SAF in the presence of voltage perturbations, the robust continuous second-order sliding mode controller (2-SMC), including twisting and super-twisting controllers, and continuous higher-order sliding mode controller (C-HOSMC)-based approaches are employed. These controllers, whose outputs are processed by pulse-width modulation (PWM), allow minimization of the phase shift and prevent the generation of discontinuous chattering commands, which can severely damage the VSI components. Moreover, an integration of a generalized instantaneous power identification algorithm with an advanced phase locked loop (PLL) was proposed and experimentally tested to validate the effective performances of SAF under severe perturbations. Additionally, the studied approaches were tested via simulations taking into account a conventional nonlinear industrial load in a real textile factory environment, using measurements provided by power quality analyzers. Finally, the effects of the measuring devices, including the current and voltage sensors, on the accuracy and reliability of the SAF and, consequently, on the PQ of the electric power grid were studied via simulations and experimentally. The results of this study support the validity of the recently published patent.
LiDAR Intensity Completion: Fully Exploiting the Message from LiDAR Sensors
Authors: Dai Weichen , Chen Shenzhou , Huang Zhaoyang , Xu Yan , Kong Da
Published: October 2022

Light Detection and Ranging (LiDAR) systems are novel sensors that provide robust distance and reflection strength by active pulsed laser beams. They have significant advantages over visual cameras by providing active depth and intensity measurements that are robust to ambient illumination. However, the systemsstill pay limited attention to intensity measurements since the output intensity maps of LiDAR sensors are different from conventional cameras and are too sparse. In this work, we propose exploiting the information from both intensity and depth measurements simultaneously to complete the LiDAR intensity maps. With the completed intensity maps, mature computer vision techniques can work well on the LiDAR data without any specific adjustment. We propose an end-to-end convolutional neural network named LiDAR-Net to jointly complete the sparse intensity and depth measurements by exploiting their correlations. For network training, an intensity fusion method is proposed to generate the ground truth. Experiment results indicate that intensity–depth fusion can benefit the task and improve performance. We further apply an off-the-shelf object (lane) segmentation algorithm to the completed intensity maps, which delivers consistent robust to ambient illumination performance. We believe that the intensity completion method allows LiDAR sensors to cope with a broader range of practice applications.
EEG-Validated Photobiomodulation Treatment of Dementia—Case Study
Authors: Vrankic Miroslav , Vlahinić Saša , Šverko Zoran , Markovinović Ivan
Published: October 2022

In this article, we perform a case study of the impact of photobiomodulation (PBM) on brain power spectrum and connectivity in an elderly person with a Self Administered Gerocognitive Exam (SAGE) score indicating probable memory and thinking disorder. First, we designed and realized the prototype of a near-infrared (NIR) device for PBM. Analysing the alpha band of the power spectrum, we found a positive long-term effect in nine out of sixteen electrodes in the eyes-open condition (OE) and in twelve out of sixteen electrodes in the eyes-closed condition (CE), while in the theta band, a positive long-term effect was found in nine out of sixteen electrodes for OE and seven out of sixteen electrodes for CE. When considering the theta-alpha ratio (TAR), the positive long-term effect is found on thirteen of sixteen electrodes for OE and on fourteen of sixteen electrodes for CE. A connectivity analysis using the imaginary component of the complex Pearson correlation coefficient (imCPCC) was also performed, and a global efficiency measure based on connectivity matrices with thresholds was calculated. The global efficiency calculated for the long-term effect was higher than before stimulation by a factor of 5.24 for the OE condition and by a factor of 1.25 for the CE condition. This case study suggests that PBM could have positive effects on improving desired brain activity, measured as improvement in power spectrum and connectivity measures in theta and alpha bands, for elderly people with memory and thinking disorders.
Leveraging Deep Learning for Practical DoA Estimation: Experiments with Real Data Collected via USRP
Authors: Chung Hyeonjin , Park Hyunwoo , Kim Sunwoo
Published: October 2022

This paper presents an experimental validation of deep learning-based direction-of-arrival (DoA) estimation by using realistic data collected via universal software radio peripheral (USRP). Deep neural network (DNN) and convolutional neural network (CNN) structures are designed to estimate the DoA. Two types of data are used for training networks. One is the data synthesized by the signal model, and the other is the data collected by USRP. Here, the signal model considers both mutual coupling and multipath signals. Experimental results show that the estimation performance is most accurate when training DNN and CNN with the collected data. Furthermore, the estimation tends to be poor in the indoor environment, which suffers from the strong non-line-of-sight (NLoS) signals.
Three-Phase Handover Management and Access Point Transition Scheme for Dynamic Load Balancing in Hybrid LiFi/WiFi Networks
Authors: Murad Sallar Salam , Yussof Salman , Hashim Wahidah , Badeel Rozin
Published: October 2022

Since LiFi and WiFi do not interfere with one another, a LiFi/WiFi hybrid network may provide superior performance to existing wireless options. With a large number of users and constant changes, a network can easily become overloaded, leading to slowdowns and fluctuations in data transfer speeds. Handover (HO) increases significantly with an increase in users, which can negatively impact system performance and quality of service (QoS) due to connection loss and/or delay. Innovative three-phase handover management and AP transition (TPHM-APT) is proposed with the goals of maintaining a steady link with reduced HOs for all connected users, meeting high per-user data rates, and having low outage performance. The proposed scheme primarily focuses on reducing the total number of HOs, which improves reliability and keeps user densities low on individual LiFi APs, which conserves bandwidth and energy. Conventional methods of HO management and user assignment, such as those based on signal strength strategy (SSS), involve reallocating users to a different AP the moment they encounter a HO. Our technique consists of three stages that focus on the optical gain, the incidence angle of the receiver FOV, and user mobility speed for decision-making. Specifically, a data rate threshold (DRT), which is equivalent to the data rate gained from the optical gain, is used to determine whether users must be served by a LiFi or a WiFi AP. In addition, an incidence angle threshold (IAT) is identified to manage the handover process and user AP transition with the consideration of the user mobility threshold (UMT). The proposed method considers load balancing (LB) among all connected users as well. This approach is evaluated using Monte Carlo simulations with MATLAB. Mathematical expressions are derived to analyze the performance of the proposed method. Different aspects, for example, Outage Probability, HO Overhead, User density, System Average Throughput (SAT), and Average Data Rate Requirement (ADRR), are studied. Analysis shows performance gains in overall system performance in terms of system data rates, fairness, and HO rates. Simulation results show that against the standard HO scheme and traditional HO skipping and APA methods, the proposed scheme can effectively decrease HO rates, save LiFi resources, and increase user throughput. It also shows good correspondence to the analysis and reveals the associated trade-offs that occur when moving between the span of narrow to wide FOVs and vice versa (HO rates and APS). The proposed scheme achieves almost identical results for low-density and high-density systems as well, with different ADRR and HO overhead values.
An Interoperable Digital Twin with the IEEE 1451 Standards
Authors: da Rocha Helbert , Pereira João , Abrishambaf Reza , Espirito Santo Antonio
Published: October 2022

The shop floor or factory floor is the area inside a factory where manufacturing production is executed. The digitalisation of this area has been increasing in the last few years, introducing the Digital Twin (DT) and the Industry 4.0 concepts. A DT is the digital representation of a real object or an entire system. A DT includes a high diversity of components from different vendors that need to interact with each other efficiently. In most cases, the development of standards and protocols does not consider the need to operate with other standards and protocols, causing interoperability issues. Transducers (sensors and actuators) use the communication layer to exchange information with digital contra parts, and for this reason, the communication layer is one of the most relevant aspects of development. This paper covers DT development, going from the physical to the visualisation layer. The reference architecture models, standards, and protocols focus on interoperability to reach a syntactic level of communication between the IEEE 1451 and the IEC 61499 standards. A semantic communication layer connects transducer devices to the digital representation, achieving a semantic level of interoperability. This communication layer adds semantics to the communication process, allowing the development of an interoperable DT based on the IEEE 1451 standards. The DT presented reaches the syntactic and semantic levels of interoperability, allowing the monitoring and visualisation of a prototype system.
Framework for Efficient Auto-Scaling of Virtual Network Functions in a Cloud Environment
Authors: Zafar Saima , Ayub Usman , Alkhammash Hend I. , Ullah Nasim
Published: October 2022

Network Function Virtualization (NFV) offers an alternate method to design, deploy and manage network services. The NFV decouples network functions from the dedicated hardware and moves them to the virtual servers so that they can run in the software. One of the major strengths of the NFV is its ability to dynamically extend or reduce resources allocated to Virtual Network Functions (VNF) as needed and at run-time. There is a need for a comprehensive metering component in the cloud to store and process the metrics/samples for efficient auto-scaling or load-management of the VNF. In this paper, we propose an integrating framework for efficient auto-scaling of VNF using Gnocchi; a time-series database that is integrated within the framework to store, handle and index the time-series data. The objective of this study is to validate the efficacy of employing Gnocchi for auto-scaling of VNF, in terms of aggregated data points, database size, data recovery speed, and memory consumption. The employed methodology is to perform a detailed empirical analysis of the proposed framework by deploying a fully functional cloud to implement NFV architecture using several OpenStack components including Gnocchi. Our results show a significant improvement over the legacy Ceilometer configuration in terms of lower metering storage size, less memory utilization in processing and management of metrics, and reduced time delay in retrieving the monitoring data to evaluate alarms for the auto-scaling of VNF.
Thermal Evaluation of Multi-Antenna Systems Proposed to Treat Bone Tumors: Finite Element Analysis
Authors: Trujillo-Romero Citlalli Jessica , Dionisio Merida Juan , Ramírez-Guzmán Texar Javier , Martínez-Valdez Raquel , Leija-Salas Lorenzo , Vera-Hernández Arturo , Rico-Martínez Genaro , Flores-Cuautle José Jesús Agustín , Gutiérrez-Martínez Josefina , Sacristán-Rock Emilio
Published: October 2022

Microwave ablation is commonly used in soft tissue tumors, but its application in bone tumors has been barely analyzed. Antennas to treat bone tissue (~3 cm2), has been lately designed. Bone tumors at pathological stage T1 can reach 8 cm wide. An antenna cannot cover it; therefore, our goal is to evaluate the thermal performance of multi-antenna arrays. Linear, triangular, and square configurations of double slot (DS) and monopole (MTM) antennas were evaluated. A parametric study (finite element method), with variations in distance between antennas (ad) and bone thickness (bt) was implemented. Array feasibility was evaluated by SWR, ablated tissue volume, etc. The linear configuration with DS and MTM antennas showed SWR ≤ 1.6 for ad = 1 mm–15 mm and bt = 20 mm–40 mm, and ad = 10 mm–15 mm and bt = 25 mm–40 mm, respectively; the triangular showed SWR ≤ 1.5 for ad = 5 mm–15 mm and bt = 20 mm–40 mm and ad = 10 mm–15 mm and bt = 25 mm–40 mm. The square configuration (DS) generated SWR ≤ 1.5 for ad = 5 mm–20 mm and bt = 20 mm–40 mm, and the MTM, SWR ≤ 1.5 with ad = 10 mm and bt = 25 mm–40 mm. Ablated tissue was 4.65 cm3–10.46 cm3 after 5 min. According to treatment time and array configuration, maximum temperature and ablated tissue is modified. Bone tumors >3 cm3 can be treated by these antenna-arrays.
A Secure Online Fingerprint Authentication System for Industrial IoT Devices over 5G Networks
Authors: Bedari Aseel , Wang Song , Yang Wencheng
Published: October 2022

The development of 5G networks has rapidly increased the use of Industrial Internet of Things (IIoT) devices for control, monitoring, and processing purposes. Biometric-based user authentication can prevent unauthorized access to IIoT devices, thereby safeguarding data security during production. However, most biometric authentication systems in the IIoT have no template protection, thus risking raw biometric data stored as templates in central databases or IIoT devices. Moreover, traditional biometric authentication faces slow, limited database holding capacity and data transmission problems. To address these issues, in this paper we propose a secure online fingerprint authentication system for IIoT devices over 5G networks. The core of the proposed system is the design of a cancelable fingerprint template, which protects original minutia features and provides privacy and security guarantee for both entity users and the message content transmitted between IIoT devices and the cloud server via 5G networks.Compared with state-of-the-art methods, the proposed authentication system shows competitive performance on six public fingerprint databases, while saving computational costs and achieving fast online matching.
RSU-Based Online Intrusion Detection and Mitigation for VANET
Authors: Haydari Ammar , Yilmaz Yasin
Published: October 2022

Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel machine learning techniques for intrusion detection and mitigation based on centralized communications through roadside units (RSU) are proposed for the considered attacks. The performance of the proposed methods is evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior detection and localization performance of the proposed methods by 78% in the best case and 27% in the worst case, while achieving the same level of false alarm probability.
A New Sliding Mode Control Algorithm of IGC System for Intercepting Great Maneuvering Target Based on EDO
Authors: Niu Kang , Chen Xi , Yang Di , Li Jiaxun , Yu Jianqiao
Published: October 2022

To intercept the great maneuvering target, combining with the sliding mode and the extended disturbance observer, a new control algorithm for integrated guidance and control (IGC) system is proposed in this paper. Firstly, the paper formulates the Missile–Target problem. Then the paper establishes an uncertain IGC dynamic model where the nonlinearities, the perturbations and the maneuvering of the target are regarded as disturbance. Secondly, a second-order disturbance observer is designed to estimate the disturbance and their derivatives.. After this, combining with the second-order disturbance observer, a modified sliding surface and the corresponding reaching law are designed to obtain the rudder deflection command directly. Thus, the real sense of IGC system is achieved. Next, the paper uses the Lyapunov stability theory to prove the stability of the system. Finally, the paper provides different simulation cases, which have different maneuver modes of the target, to demonstrate the superiority of the proposed method in reducing the response time, increasing the rudder response, and having a high interception probability.
Three-Dimensional Engine-Based Geometric Model Optimization Algorithm for BIM Visualization with Augmented Reality
Authors: Aung Pa Pa Win , Choi Woonggyu , Kulinan Almo Senja , Cha Gichun , Park Seunghee
Published: October 2022

Building information modeling (BIM), a common technology contributing to information processing, is extensively applied in construction fields. BIM integration with augmented reality (AR) is flourishing in the construction industry, as it provides an effective solution for the lifecycle of a project. However, when applying BIM to AR data transfer, large and complicated models require large storage spaces, increase the model transfer time and data processing workload during rendering, and reduce visualization efficiency when using AR devices. The geometric optimization of the model using mesh reconstruction is a potential solution that can reduce the required storage while maintaining the shape of the components. In this study, a 3D engine-based mesh reconstruction algorithm that can pre-process BIM shape data and implement an AR-based full-size model is proposed, which is likely to increase the efficiency of decision making and project processing for construction management. As shown in the experimental validation, the proposed algorithm significantly reduces the number of vertices, triangles, and storage for geometric models while maintaining the overall shape. Moreover, the model elements and components of the optimized model have the same visual quality as the original model; thus, a high performance can be expected for BIM visualization in AR devices.
Efficient Model Assisted Probability of Detection Estimations in Eddy Current NDT with ACA-SVD Based Forward Solver
Authors: Bao Yang , Xu Minxuan , Qiu Jiahao , Song Jiming
Published: October 2022

Model assisted probability of detection (MAPoD) is crucial for quantifying the inspection capability of a nondestructive testing (NDT) system which uses the coil or probe to sense the size and location of the cracks. Unfortunately, it may be computationally intensive for the simulation models. To improve the efficiency of the MAPoD, in this article, an efficient 3D eddy current nondestructive evaluation (ECNDE) forward solver is proposed to make estimations for PoD study. It is the first time that singular value decomposition (SVD) is used as the recompression technique to improve the overall performance of the adaptive cross approximation (ACA) algorithm-based boundary element method (BEM) ECNDE forward solver for implementation of PoD. Both the robustness and efficiency of the proposed solver are demonstrated and testified by comparing the predicted impedance variations of the coil with analytical, semi-analytical and experimental benchmarks. Calculation of PoD curves assisted by the proposed simulation model is performed on a finite thickness plate with a rectangular surface flaw. The features, which are the maximum impedance variations of the coil for various flaw lengths, are obtained entirely by the proposed model with selection of the liftoff distance as the uncertain parameter in a Gaussian distribution. The results show that the proposed ACA-SVD based BEM fast ECNDE forward solver is an excellent simulation model to make estimations for MAPoD study.
Optical Oxygen Sensing and Clark Electrode: Face-to-Face in a Biosensor Case Study
Authors: Melnikov Pavel V. , Alexandrovskaya Anastasia Yu. , Naumova Alina O. , Arlyapov Vyacheslav A. , Kamanina Olga A. , Popova Nadezhda M. , Zaitsev Nikolay K. , Yashtulov Nikolay A.
Published: October 2022

In the last decade, there has been continuous competition between two methods for detecting the concentration of dissolved oxygen: amerometric (Clark electrode) and optical (quenching of the phosphorescence of the porphyrin metal complex). Each of them has obvious advantages and disadvantages. This competition is especially acute in the development of biosensors, however, an unbiased comparison is extremely difficult to achieve, since only a single detection method is used in each particular study. In this work, a microfluidic system with synchronous detection of the oxygen concentration by two methods was created for the purpose of direct comparison. The receptor element is represented by Saccharomyces cerevisiae yeast cells adsorbed on a composite material, previously developed by our scientific group. To our knowledge, this is the first work of this kind in which the comparison of the oxygen detection methods is carried out directly.
Wearable Sensors for Learning Enhancement in Higher Education
Authors: Khosravi Sara , Bailey Stuart G. , Parvizi Hadi , Ghannam Rami
Published: October 2022

Wearable sensors have traditionally been used to measure and monitor vital human signs for well-being and healthcare applications. However, there is a growing interest in using and deploying these technologies to facilitate teaching and learning, particularly in a higher education environment. The aim of this paper is therefore to systematically review the range of wearable devices that have been used for enhancing the teaching and delivery of engineering curricula in higher education. Moreover, we compare the advantages and disadvantages of these devices according to the location in which they are worn on the human body. According to our survey, wearable devices for enhanced learning have mainly been worn on the head (e.g., eyeglasses), wrist (e.g., watches) and chest (e.g., electrocardiogram patch). In fact, among those locations, head-worn devices enable better student engagement with the learning materials, improved student attention as well as higher spatial and visual awareness. We identify the research questions and discuss the research inclusion and exclusion criteria to present the challenges faced by researchers in implementing learning technologies for enhanced engineering education. Furthermore, we provide recommendations on using wearable devices to improve the teaching and learning of engineering courses in higher education.