bluuebunny/crossref_metadata_2025 huggingface dataset:
A Threshold Voltage Model for AOS TFTs Considering a Wide Range of Tail-State Density and Degeneration
Authors: Cai Minxi , Xu Piaorong , Liu Bei , Peng Ziqi , Cai Jianhua , Cao Jing
Published: October 2022

There have been significant differences in principle electrical parameters between amorphous oxide semiconductor (AOS) thin-film transistors (TFTs) and silicon-based devices for their distinct conduction mechanisms. Additionally, threshold voltage is one of the key parameters in device characterization and modeling. In this work, a threshold voltage model is developed for AOS TFTs considering the various density of exponential tail states below the conduction band, including degenerate conduction. The threshold condition is defined where the density ratio of free carriers to the trapped carriers reaches a critical value depending on the distribution parameters of tail states. The resulting threshold voltage expression is fully analytical and is of clear physical meaning, with simple parameter extraction methods. Numerical and experimental verifications show that this model provides appropriate values of threshold voltage for devices with different sub-gap tail states, which could be a useful method for identifying the threshold voltage of a large variety of AOS TFTs.
An Image Fusion Method Based on Special Residual Network and Efficient Channel Attention
Authors: Li Yang , Yang Haitao , Wang Jinyu , Zhang Changgong , Liu Zhengjun , Chen Hang
Published: October 2022

This paper presents an image fusion network based on a special residual network and attention mechanism. Compared with the traditional fusion network, the image fusion network has the advantages of an end-to-end network and integrates the feature extraction advantages of the attention mechanism residual network. It overcomes the shortcomings of the traditional network that need complex design rules and manual operation. In this method, hierarchical feature fusion is used to achieve effective fusion. A combined loss function is designed to optimize training results and improve image fusion quality. This paper uses many qualitative and quantitative experimental analyses on different data sets. The results show that, compared with the comparison algorithm, the method in this paper has a stronger retention ability of infrared and visible light information and better indexes. 72% of eleven indexes compared with some images in the public TNO data set are optimal or sub-optimal, and 80% are optimal or suboptimal in the RoadScene data set, which is much higher than other algorithms. The overall fusion effect is more in line with human visual perception.
Design of Anti-Swing PID Controller for Bridge Crane Based on PSO and SA Algorithm
Authors: Li Hui , Hui Yan-Bo , Wang Qiao , Wang Hong-Xiao , Wang Lin-Jun
Published: October 2022

Since the swing of the lifting load and the positioning of the trolley during the operation of a bridge crane seriously affect the safety and reliability of its work, we have not only designed Proportional Integral Derivative (PID) controllers for the anti-swing and positioning control but also proposed a hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm to optimize the gains of the controllers. In updating the PSO algorithm, a nonlinear adaptive method is utilized to update the inertia weight and learning coefficients, and the SA algorithm is also integrated when the PSO algorithm is searching for a global optimal solution, to reduce the probability of falling into the local optimal solution. The simulation results demonstrate that the PSO–SA algorithm proposed in this paper is prone to be a more effective method in searching for the optimal parameters for the controllers, compared with three other algorithms. As shown by the experimental results, the swing angle stabilization time of the novel algorithm is 6.9 s, while the values of the other algorithms range from 10.3 to 13.1 s under a common working condition. Simultaneously, the maximum swing angle of the novel algorithm is 7.8°, which is also better than the other algorithms.
Techno-Economic Optimization Study of Interconnected Heat and Power Multi-Microgrids with a Novel Nature-Inspired Evolutionary Method
Authors: Fracas Paolo , Zondervan Edwin , Franke Meik , Camarda Kyle , Valtchev Stanimir , Valtchev Svilen
Published: October 2022

The world is once again facing massive energy- and environmental challenges, caused by global warming. This time, the situation is complicated by the increase in energy demand after the pandemic years, and the dramatic lack of basic energy supply. The purely “green” energy is still not ready to substitute the fossil energy, but this year the fossil supplies are heavily questioned. Consequently, engineering must take flexible, adaptive, unexpected directions. For example, even the natural gas power plants are currently considered “green” by the European Union Taxonomy, joining the “green” hydrogen. Through a tight integration of highly intermittent renewable, or other distributed energy resources, the microgrid is the technology of choice to guarantee the expected impacts, making clean energy affordable. The focus of this work lies in the techno-economic optimization analysis of Combined Heat and Power (CHP) Multi-Micro Grids (MMG), a novel distribution system architecture comprising two interconnected hybrid microgrids. High computational resources are needed to investigate the CHP-MMG. To this aim, a novel nature-inspired two-layer optimization-simulation algorithm is discussed. The proposed algorithm is used to execute a techno-economic analysis and find the best settings while the energy balance is achieved at minimum operational costs and highest revenues. At a lower level, inside the algorithm, a Sequential Least Squares Programming (SLSQP) method ensures that the stochastic generation and consumption of energy deriving from CHP-MMG trial settings are balanced at each time-step. At the upper level, a novel multi-objective self-adaptive evolutionary algorithm is discussed. This upper level is searching for the best design, sizing, siting, and setting, which guarantees the highest internal rate of return (IRR) and the lowest Levelized Cost of Energy (LCOE). The Artificial Immune Evolutionary (AIE) algorithm imitates how the immune system fights harmful viruses that enter the body. The optimization method is used for sensitivity analysis of hydrogen costs in off-grid and on-grid highly perturbed contexts. It has been observed that the best CHP-MMG settings are those that promote a tight thermal and electrical energy balance between interconnected microgrids. The results demonstrate that such mechanism of energy swarm can keep the LCOE lower than 15 c€/kWh and IRR of over 55%.
Design of Irregularly Distributed Antenna Array towards Smart 6G Networks
Authors: Wang Kunye , Ma Zheng , Zhao Yao , Deng Yunkai , Luo Yunhua , He Mang , Xu Haitao
Published: October 2022

The integration of sensing, computing, storage, and communications is one of the main research directions of current network development. To achieve the integration, an innovative design in the antenna array is essential. This paper presents a novel design of an irregularly distributed antenna array for smart 6G networks. Firstly, to solve the problem of fast amplitude and phase distribution of conformal arrays, the fast electromagnetic code of the multilevel fast multipole (MLFMA) based on volume surface integral equations (VSIE) is used to simulate the radiation characteristics of the irregularly distributed antenna arrays. Secondly, another stochastic global optimization algorithm, Simulated Annealing (SA), has been widely used to solve multiscale nonlinear problems. Finally, the performance of the proposed antenna array is given by simulation results and tests, to prove the effectiveness and correctness.
Bee Sound Detector: An Easy-to-Install, Low-Power, Low-Cost Beehive Conditions Monitoring System
Authors: Kiromitis Dimitrios I. , Bellos Christos V. , Stefanou Konstantinos A. , Stergios Georgios S. , Katsantas Thomas , Kontogiannis Sotirios
Published: October 2022

One of the most significant agricultural tasks in beekeeping involves continually observing the conditions inside and outside the beehive. This is mainly performed for the early detection of some harmful events. There have been many studies on how to detect and prevent such occurrences by performing periodic interventions or, when the frequency of such actions is hard to enforce, by using sensory systems that record the temperature, humidity, and weight of the beehive. Nevertheless, such methods are inaccurate, and their delivered outcomes usually diverge from the actual event or false trigger and introduce more effort and damage. In this paper, the authors propose a new low-cost, low-power system called Bee Sound Detector (BeeSD). BeeSD is a low-cost, embedded solution for beehive quality control. It incorporates the sensors mentioned above as well as real-time sound monitoring. With the combination of temperature, humidity, and sound sensors, the BeeSD can spot Colony Collapse Disorder events due to famine and extreme weather events, queen loss, and swarming. Furthermore, as a system, the BeeSD uses cloud logging and an appropriate mobile phone application to push notifications of extreme measurements to the farmers. Based on achieved performance indicators, the authors present their BeeSD IoT device and system operation, focusing on its advantages of low-cost, low-power, and easy-to-install characteristics.
Sparse 3D Point Cloud Parallel Multi-Scale Feature Extraction and Dense Reconstruction with Multi-Headed Attentional Upsampling
Authors: Wu Meng , Jiao Hailong , Nan Junxiang
Published: October 2022

Three-dimensional (3D) point clouds have a wide range of applications in the field of 3D vision. The quality of the acquired point cloud data considerably impacts the subsequent work of point cloud processing. Due to the sparsity and irregularity of point cloud data, processing point cloud data has always been challenging. However, existing deep learning-based point cloud dense reconstruction methods suffer from excessive smoothing of reconstruction results and too many outliers. The reason for this is that it is not possible to extract features for local and global features at different scales and provide different levels of attention to different regions in order to obtain long-distance dependence for dense reconstruction. In this paper, we use a parallel multi-scale feature extraction module based on graph convolution and an upsampling method with an added multi-head attention mechanism to process sparse and irregular point cloud data to obtain extended point clouds. Specifically, a point cloud training patch with 256 points is inputted. The PMS module uses three residual connections in the multi-scale feature extraction stage. Each PMS module consists of three parallel DenseGCN modules with different size convolution kernels and different averaging pooling sizes. The local and global feature information of the augmented receptive field is extracted efficiently. The scale information is obtained by averaging the different pooled augmented receptive fields. The scale information was obtained using the different average pooled augmented receptive fields. The upsampling stage uses an upsampling rate of r=4, The self-attentive features with a different focus on different point cloud data regions obtained by fusing different weights make the feature representation more diverse. This operation avoids the bias of one attention, and each focuses on extracting valuable fine-grained feature information. Finally, the coordinate reconstruction module obtains 1024 dense point cloud data. Experiments show that the proposed method demonstrates good evaluation metrics and performance and is able to obtain better visual quality. The problems of over-smoothing and excessive outliers are effectively mitigated, and the obtained sparse point cloud is more dense.
Criminal Behavior Identification Using Social Media Forensics
Authors: Ashraf Noorulain , Mahmood Danish , Obaidat Muath A. , Ahmed Ghufran , Akhunzada Adnan
Published: October 2022

Human needs consist of five levels, which are: physiological needs, safety needs, love needs, esteem needs and self-actualization. All these needs lead to human behavior. If the environment of a person is positive, healthy behavior is developed. However, if the environment of the person is not healthy, it can be reflected in his/her behavior. Machines are intelligent enough to mimic human intelligence by using machine learning and artificial intelligence techniques. In the modern era, people tend to post their everyday life events on social media in the form of comments, pictures, videos, etc. Therefore, social media is a significant way of knowing certain behaviors of people such as abusive, aggressive, frustrated and offensive behaviors. Behavior detection by crawling the social media profile of a person is a crucial and important idea. The challenge of behavior detection can be sorted out by applying social media forensics on social media profiles, which involves NLP and deep learning techniques. This paper is based on the study of state of the art work on behavior detection, and based on the research, a model is proposed for behavior detection. The proposed model outperformed with an F1 score of 87% in the unigram + bigram class, and in the bigram + trigram class, it gave an F1 score of 88% when compared with models applied on state of the art work. This study is a great benefit to cybercrime and cyber-security agencies in shortlisting the profiles containing certain behaviors to prevent crimes in the future.
An Autonomous Vehicle Stability Control Using Active Fault-Tolerant Control Based on a Fuzzy Neural Network
Authors: Alsuwian Turki , Usman Mian Hamza , Amin Arslan Ahmed
Published: October 2022

Due to instability issues in autonomous vehicles, the risk of danger is increasing rapidly. These problems arise due to unwanted faults in the sensor or the actuator, which decrease vehicle efficiency. In this modern era of autonomous vehicles, the risk factor is also increased as the vehicles have become automatic, so there is a need for a fault-tolerant control system (FTCS) to avoid accidents and reduce the risk factors. This paper presents an active fault-tolerant control (AFTC) for autonomous vehicles with a fuzzy neural network that can autonomously identify any wheel speed problem to avoid instability issues in an autonomous vehicle. MATLAB/Simulink environment was used for simulation experiments and the results demonstrate the stable operation of the wheel speed sensors to avoid accidents in the event of faults in the sensor or actuator if the vehicle becomes unstable. The simulation results establish that the AFTC-based autonomous vehicle using a fuzzy neural network is a highly reliable solution to keep cars stable and avoid accidents. Active FTC and vehicle stability make the system more efficient and reliable, decreasing the chance of instability to a minimal point.
Spatial and Temporal Normalization for Multi-Variate Time Series Prediction Using Machine Learning Algorithms
Authors: Providence Alimasi Mongo , Yang Chaoyu , Orphe Tshinkobo Bukasa , Mabaire Anesu , Agordzo George K.
Published: October 2022

Multi-variable time series (MTS) information is a typical type of data inference in the real world. Every instance of MTS is produced via a hybrid dynamical scheme, the dynamics of which are often unknown. The hybrid species of this dynamical service are the outcome of high-frequency and low-frequency external impacts, as well as global and local spatial impacts. These influences impact MTS’s future growth; hence, they must be incorporated into time series forecasts. Two types of normalization modules, temporal and spatial normalization, are recommended to accomplish this. Each boosts the original data’s local and high-frequency processes distinctly. In addition, all components are easily incorporated into well-known deep learning techniques, such as Wavenet and Transformer. However, existing methodologies have inherent limitations when it comes to isolating the variables produced by each sort of influence from the real data. Consequently, the study encompasses conventional neural networks, such as the multi-layer perceptron (MLP), complex deep learning methods such as LSTM, two recurrent neural networks, support vector machines (SVM), and their application for regression, XGBoost, and others. Extensive experimental work on three datasets shows that the effectiveness of canonical frameworks could be greatly improved by adding more normalization components to how the MTS is used. This would make it as effective as the best MTS designs are currently available. Recurrent models, such as LSTM and RNN, attempt to recognize the temporal variability in the data; however, as a result, their effectiveness might soon decline. Last but not least, it is claimed that training a temporal framework that utilizes recurrence-based methods such as RNN and LSTM approaches is challenging and expensive, while the MLP network structure outperformed other models in terms of time series predictive performance.
A Review of Open-Circuit Switch Fault Diagnostic Methods for Neutral Point Clamped Inverter
Authors: Mullali Kunnontakath Puthiyapurayil Muhammed Ramees , Nadir Nasirudeen Mohamed , Saywan Yashkumar A. , Ahmad Md Waseem , Malik Hasmat
Published: October 2022

Due to numerous advantages, a neutral point clamped (NPC) inverter is a preferred choice for high-power applications and renewable technology. The reliability of the NPC inverter is a major concerning factor during the assessment of system performance as power semiconductor switches are vulnerable to abnormal conditions. Open-circuit (OC) switch faults are not as dangerous as short circuit (SC) faults but eventually have enough potential to cause cascaded failure to other components in the system and thus need to be supervised carefully. The OC faults result in a distortion of voltage and current signals in the NPC converter. Based on these signals, over the past few years, many efforts have been made to identify and localize the OC switch fault to the switch level in the NPC topology. In this paper, a review of different OC switch fault diagnostic methods is provided. Starting from the NPC inverter operation under healthy and faulty conditions, the various possible and unavailable switching states along with the deviation in pole voltage under different switch fault conditions is discussed. Then, based on the approach used for system-based fault detection, the OC fault detection methods are classified. The various OC methods are further discussed on the basis of signal, i.e., current, voltage or a combination of both signals used as a signature for fault detection. Emphasis is given to the principle involved, diagnostic variables utilized, the implementation approach and the diagnostic time required. Finally, the approaches are tabulated so as to provide a quick reference for NPC fault diagnostics.
Isogency Hosmer–Lemeshow Logistic Regression-Based Secured Information Sharing for Pharma Supply Chain
Authors: P Anitha , Chandrasekaran Srimathi
Published: October 2022

Counterfeit drugs are forgery-tagged medicines that are considered to be drugs without vigorous active pharmaceutical ingredients (API). India, being the world’s largest producer of drugs, faces a crucial issue of counterfeits. Moreover, counterfeits identify their path into the pharmaceutical supply chain (PSC) effortlessly owing to the dearth of security and traceability in the prevailing system. This is because the software applications currently in use stockpile the information about drugs on centralized servers and are accessed by manufacturers, distributors and retailers via the internet. The security of such systems is found to be weak. To address these issues, in this work, a novel method called Supersingular Isogeny and Hosmer–Lemeshow Logistic Regression-based (SI-HLLR) secured information sharing for the pharmaceutical supply chain is proposed. The SI-HLLR method is split into two sections, block validation and authentication. First, with the pharmaceutical sales data provided as input, the supersingular isogeny Diffie–Hellman key exchange model is applied for block validation and then is implemented using a blockchain. Next, with the validated blocks, the authentication mechanism is performed using Hosmer–Lemeshow logistic regression-based authentication that in turn eliminates the counterfeit drugs from the pharmaceutical supply chain. The hyperledger fabric blockchain solution using SI-HLLR leads to improved security ensuring data integrity and better authentication accuracy in the proposed method.
A Hybrid Asynchronous Brain–Computer Interface Based on SSVEP and Eye-Tracking for Threatening Pedestrian Identification in Driving
Authors: Sun Jianxiang , Liu Yadong
Published: October 2022

A brain–computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has achieved remarkable performance in the field of automatic driving. Prolonged SSVEP stimuli can cause driver fatigue and reduce the efficiency of interaction. In this paper, a multi-modal hybrid asynchronous BCI system combining eye-tracking and EEG signals is proposed for dynamic threatening pedestrian identification in driving. Stimuli arrows of different frequencies and directions are randomly superimposed on pedestrian targets. Subjects scan the stimuli according to the direction of arrows until the threatening pedestrian is selected. The thresholds determined by offline experiments are used to distinguish between working and idle states of the asynchronous online experiments. Subjects need to judge and select potentially threatening pedestrians in online experiments according to their own subjective experience. The three proposed decisions filter out the results with low confidence and effectively improve the selection accuracy of hybrid BCI. The experimental results of six subjects show that the proposed hybrid asynchronous BCI system achieves better performance compared with a single SSVEP-BCI, with an average selection time of 1.33 s, an average selection accuracy of 95.83%, and an average information transfer rate (ITR) of 67.50 bits/min. These results indicate that our hybrid asynchronous BCI has great application potential in dynamic threatening pedestrian identification in driving.
A State of Art Review on Methodologies of Occupancy Estimating in Buildings from 2011 to 2021
Authors: Zhao Liang , Li Yuxin , Liang Ruobing , Wang Peng
Published: October 2022

Occupancy information is important to building facility managers in terms of building energy efficiency, indoor environmental quality, comfort conditions, and safety management of buildings. When combing the distribution characteristics of the literature, it is found that the field of estimating occupancy counts is a very active area. Researchers from various countries have undertaken extensive explorations to obtain more research results. In this survey, the commonly used occupancy measurement systems and algorithms are described. Through the analysis and research of different occupancy measurement systems and algorithms, their advantages, disadvantages, and limitations are summarized, so that researchers can use them selectively. As for how to choose the method of estimating occupancy counts, suggestions are given in terms of the range of people, accuracy, cost, and privacy. There are still many pressing issues relating to high-density crowd occupancy counting, complex environmental impact, and system robustness. According to the current research progress and technology development trend, the possible future research directions are pointed out. The innovation of this review is the quantitative analysis of the selection of occupancy measurement systems for different ranges of people, and the occupancy counting accuracy situation of different measurement systems and algorithms. It provides more informed opinions on the selection of practical applications. It can be used by other researchers as a starting point for their research and/or project work.
Feature Selection Techniques for Big Data Analytics
Authors: Albattah Waleed , Khan Rehan Ullah , Alsharekh Mohammed F. , Khasawneh Samer F.
Published: October 2022

Big data applications have tremendously increased due to technological developments. However, processing such a large amount of data is challenging for machine learning algorithms and computing resources. This study aims to analyze a large amount of data with classical machine learning. The influence of different random sampling techniques on the model performance is investigated by combining the feature selection techniques and machine learning classifiers. The experiments used two feature selection techniques: random subset and random projection. Two machine learning classifiers were also used: Naïve Bayes and Bayesian Network. This study aims to maximize the model performance by reducing the data dimensionality. In the experiments, 400 runs were performed by reducing the data dimensionality of a video dataset that was more than 40 GB. The results show that the overall performance fluctuates between 70% accuracy to 74% for using sampled and non-sample (all the data), a slight difference in performance compared to the non-sampled dataset. With the overall view of the results, the best performance among all combinations of experiments is recorded for combination 3, where the random subset technique and the Bayesian network classifier were used. Except for the round where 10% of the dataset was used, combination 1 has the best performance among all combinations.
Applications of Multi-Agent Systems in Unmanned Surface Vessels
Authors: Males Lada , Sumic Dean , Rosic Marko
Published: October 2022

The comprehensive and safe application of unmanned surface vessels is certainly one of the biggest challenges currently facing maritime science. Such vessels can be implemented within a wide range of autonomy levels that goes from remote-controlled vessels to fully autonomous vessels in which intelligent vessel systems completely perform all necessary operations. One of the ways to achieve autonomous vessel systems is to implement multi-agent systems that take over all functions performed by the crew in classical manned crew vessels. A vessel is a complex system that conceptually can be considered as a set of interconnected subsystems. Theoretically, the functions of these subsystems could be performed using appropriate multi-agent systems. In this paper we analyzed 24 relevant papers. A review of the current state of implementation of multi-agent systems for performing the functions of unmanned surface vessels is presented.
Eye Aspect Ratio for Real-Time Drowsiness Detection to Improve Driver Safety
Authors: Dewi Christine , Chen Rung-Ching , Chang Chun-Wei , Wu Shih-Hung , Jiang Xiaoyi , Yu Hui
Published: October 2022

Drowsiness is a major risk factor for road safety, contributing to serious injury, death, and economic loss on the road. Driving performance decreases because of increased drowsiness. In several different applications, such as facial movement analysis and driver safety, blink detection is an essential requirement that is used. The extremely rapid blink rate, on the other hand, makes automatic blink detection an extremely challenging task. This research paper presents a technique for identifying eye blinks in a video series recorded by a car dashboard camera in real time. The suggested technique determines the facial landmark positions for each video frame and then extracts the vertical distance between the eyelids from the facial landmark positions. The algorithm that has been proposed estimates the facial landmark positions, extracts a single scalar quantity by making use of Eye Aspect Ratio (EAR), and identifies the eye closeness in each frame. In the end, blinks are recognized by employing the modified EAR threshold value in conjunction with a pattern of EAR values in a relatively short period of time. Experimental evidence indicates that the greater the EAR threshold, the worse the AUC’s accuracy and performance. Further, 0.18 was determined to be the optimum EAR threshold in our research.
The Effect of Data Augmentation Methods on Pedestrian Object Detection
Authors: Liu Bokun , Su Shaojing , Wei Junyu
Published: October 2022

Night landscapes are a key area of monitoring and security as information in pictures caught on camera is not comprehensive. Data augmentation gives these limited datasets the most value. Considering night driving and dangerous events, it is important to achieve the better detection of people at night. This paper studies the impact of different data augmentation methods on target detection. For the image data collected at night under limited conditions, three different types of enhancement methods are used to verify whether they can promote pedestrian detection. This paper mainly explores supervised and unsupervised data augmentation methods with certain improvements, including multi-sample augmentation, unsupervised Generative Adversarial Network (GAN) augmentation and single-sample augmentation. It is concluded that the dataset obtained by the heterogeneous multi-sample augmentation method can optimize the target detection model, which can allow the mean average precision (mAP) of a night image to reach 0.76, and the improved Residual Convolutional GAN network, the unsupervised training model, can generate new samples with the same style, thus greatly expanding the dataset, so that the mean average precision reaches 0.854, and the single-sample enhancement of the deillumination can greatly improve the image clarity, helping improve the precision value by 0.116.
Link-Aware Frame Selection for Efficient License Plate Recognition in Dynamic Edge Networks
Authors: Liu Jiaxin , Cong Rong , Wang Xiong , Zhou Yaxin
Published: October 2022

With the booming development of Internet of Things (IoT) and computer vision technology, running vision-based applications on IoT devices becomes an overwhelming tide. In vision-based applications, the Automatic License Plate Recognition (ALPR) is one of the fundamental services for smart-city applications such as traffic control, auto-drive and safety monitoring. However, existing works about ALPR usually assume that IoT devices have sufficient power to transmit the whole captured stream to edge servers via stable network links. Considering the limited resources of IoT devices and high-dynamic wireless links, this assumption is not suitable for realizing an efficient ALPR service on low-power IoT devices in real wireless edge networks. In this paper, we propose a link-aware frame selection scheme for ALPR service in dynamic edge networks aiming to reduce the transmission energy consumption of IoT devices. Specifically, we tend to select a few key frames instead of the whole stream and transmit them under good links. We propose a two-layer recognition frame selection algorithm to optimize the frame selection by exploiting both the video content variation and real-time link quality. The extensive results show that, by carefully selecting the offloaded frames to edge servers, our algorithm can significantly reduce the energy consumption of devices by 46.71% and achieve 97.95% recognition accuracy in the high-dynamic wireless link of the edge network.
Adaptive BIST for Concurrent On-Line Testing on Combinational Circuits
Authors: Chioktour Vasileios , Kakarountas Athanasios
Published: October 2022

Safety-critical systems embedding concurrent on-line testing techniques are vulnerable to design issues causing the degradation of totally self-checking (TSC) property, which is proved to be fatal for further operations (e.g., space electronics, medical devices). In addition to the exploration of the degradation of TSC property over time, a concurrent on-line testing architecture is offered that adjusts the input activity, addressing the absence of input values or the low frequency of their appearance (e.g., during sleep mode). During concurrent on-line testing, the inputs of the circuit under test (CUT) are, at the same time, its test vectors. This architecture tolerates possible degradation of the terms that contribute to the calculation of the totally self-checking goal (TSCG(t)). An adaptive built-in self-test (BIST) unit is proposed that dynamically applies test vector subsets when permitted, based on the frequency of appearance of the input values. The clustering of the inputs is based on the k-means algorithm and, in combination with the ordering of the test vectors to minimize the subsets, results in partitioning the test procedure in a significantly shorter time. The comparison to other solutions used for concurrent on-line testing showed that the proposed adaptive BIST has significant advantages. It can cope with rare occurrences, or even no occurrence, of input values by enabling the BIST mechanism appropriately. The results showed that it may increase the TSCG(t) up to almost 90% when applied during a low-power mode and present better concurrent test latency (CTL) when assumptions regarding the availability of all input values and the probability of occurrence are not realistic.
A Novel Tightly Coupled Information System for Research Data Management
Authors: Senagi Kennedy , Tonnang Henri E. Z.
Published: October 2022

Most research projects are data driven. However, many organizations lack proper information systems (IS) for managing data, that is, planning, collecting, analyzing, storing, archiving, and sharing for use and re-use. Many research institutions have disparate and fragmented data that make it difficult to uphold the FAIR (findable, accessible, interoperable, and reusable) data management principles. At the same time, there is minimal practice of open and reproducible science. To solve these challenges, we designed and implemented an IS architecture for research data management. Through it, we have a centralized platform for research data management. The IS has several software components that are configured and unified to communicate and share data. The software components are, namely, common ontology, data management plan, data collectors, and the data warehouse. Results show that the IS components have gained global traction, 56.3% of the total web hits came from news users, and 259 projects had metadata (and 17 of those also had data resources). Moreover, the IS aligned the institution’s scientific data resources to universal standards such as the FAIR principles of data management and at the same time showcased open data, open science, and reproducible science. Ultimately, the architecture can be adopted by other organizations to manage research data.
Integrating Teachers’ TPACK Levels and Students’ Learning Motivation, Technology Innovativeness, and Optimism in an IoT Acceptance Model
Authors: Almaiah Mohammed , Alfaisal Raghad , Salloum Said , Al-Otaibi Shaha , Shishakly Rima , Lutfi Abdalwali , Alrawad Mahmaod , Mulhem Ahmed , Awad Ali , Al-Maroof Rana
Published: October 2022

The growing use of the Internet of Things (IoT) around the world has encouraged researchers to investigate how and why the IoT is implemented in colleges and universities. Previous studies have focused on individual attitudes rather than the integration of attitudes from two different perspectives. Furthermore, other studies have investigated the use of the IoT in non-educational settings, ignoring the effect of the IoT related to the technology acceptance model (TAM) and technological pedagogical content knowledge (TPACK) model. The present work aims to address this research gap by determining the main factors that influence acceptance of the IoT, leading to increased awareness in collaborative learning, where technology forms the core tool in enhancing the use of the IoT. A questionnaire was used to collect data from teachers and students from colleges and universities in Oman and the United Arab Emirates (UAE). The data were analyzed through the structural equation modeling (SEM) method. The findings indicated that there are two levels of positive effects on the intention to use IoT. The first level is technology features, which are represented by technology optimism and technology innovation; these factors are crucial to using the IoT. The second level is learning motivation, which has a close relationship with teachers’ knowledge, and content pedagogy, which has a significant effect on the familiarity with IoT tools and applications. TAM constructs have a positive and direct impact on the intention to use IoT. The practical and managerial implications show that teachers, educators, and students can obtain benefits from these results to help IoT features to suit users’ needs.
A Novel Autocorrelation Combined MM-CDR Time-Interleaved ADC Timing Calibration in 28 nm CMOS Technology
Authors: Gu Youzhi , Feng Xinjie , Chi Runze , Wu Jiangfeng , Chen Yongzhen
Published: October 2022

With the great improvement in data transmission rate requirements, the analog-to-digital converter (ADC)-based wireline receiver has received more attention due to its flexible and powerful equalization capability. Time-interleaved ADC (TI-ADC) is the most commonly used architecture in high-speed ADC-based receivers. One of the major challenges in TI-ADC is the timing mismatch between the parallel sub-ADCs. The traditional skew detection and calibration circuits consume substantial power and area of the receiver system. In this article, we propose a novel calibration method using the autocorrelation principle combined with an existing Mueller–Müller clock and data recovery circuit (MM-CDR). This new method reuses the existing error-direction information of the MM-CDR in the ADC-based wireline receiver and combines the autocorrelation principle to obtain the timing mismatch information in the TI-ADC without adding an additional skew deviation extraction circuit, which greatly reduces the area and power consumption. In order to demonstrate the effectiveness and superiority of our skew calibration method, we designed a complete ADC-based wireline receiver circuit using the 28 nm CMOS technology. The simulation results show that our proposed calibration method could obtain 0.193 sensitivity per 1% skew, which was superior to traditional calibration methods. To verify the speed and accuracy of the convergence of our calibration method, the initial skews were set to +0.4 ps, +0.2 ps, −0.59 ps, and 0 ps for our 4 × 8 TI-ADC; the spurious free dynamic range (SFDR) and signal-to-noise and distortion ratio (SNDR) of the ADC were increased from 37.24 dB and 31.28 dB to 48.07 dB and 34.56 dB, respectively, after timing calibration with a 50 fs step. In order to compare the area and power consumption required by different skew calibration methods, we synthesized the expressions of various methods using the 28 nm CMOS technology, and the area and power consumption of our proposed skew calibration loop were 695 μm2 and 0.126 mW, respectively, which were the smallest among these methods.
Research on BeiDou B1C Signal Abnormal Monitoring Algorithm Based on Machine Learning
Authors: Liu Liang , Yu Baoguo , Yi Qingwu , Zhao Jingbo , Yang Jianglei , Guo Chengjun
Published: October 2022

High-precision systems such as civil aviation have put forward higher requirements for navigation systems, including indicators such as accuracy and integrity. Signal distortions and evil waveforms (EWF) generated by the signal-generating hardware on the satellite can severely affect the cross-correlation function of the signal, thereby affecting the integrity of the navigation system. With the further development of the BeiDou Navigation System (BDS), the types of signal distortion are subdivided into three types: analog distortion, subcarrier distortion, and PN code distortion. Traditional multi-correlator methods are no longer applicable under the requirements of modern navigation systems. In this paper, a machine learning-based BeiDou B1C signal anomaly monitoring algorithm is proposed. We detected and classified the signals using a quadratic discriminant analysis (QDA) method. The results show that our method can accurately classify the distortion types under the condition that the accuracy of distortion detection can be greatly improved. Meanwhile, our method is also highly effective and robust.
High Sound-Contrast Inverse Scattering by MR-MF-DBIM Scheme
Authors: Theu Luong Thi , Quang-Huy Tran , Duc-Nghia Tran , Solanki Vijender Kumar , Duc-Tan Tran , Tavares João Manuel R. S.
Published: October 2022

In ultrasound tomography, cross-sectional images represent the spatial distribution of the physical parameters of a target of interest, which can be obtained based on scattered ultrasound measurements. These measurements can be obtained from dense datasets collected at different transmitter and receiver locations, and using multiple frequencies. The Born approximation method, which provides a simple linear relationship between the objective function and the scattering field, has been adopted to resolve the inverse scattering problem. The distorted Born iterative method (DBIM), which utilizes the first-order Born approximation, is a productive diffraction tomography scheme. In this article, the range of interpolation applications is extended at the multilayer level, taking into account the advantages of integrating this multilayer level with multiple frequencies for the DBIM. Specifically, we consider: (a) a multi-resolution technique, i.e., a multi-step interpolation for the DBIM: MR-DBIM, with the advantage that the normalized absolute error is reduced by 18.67% and 37.21% in comparison with one-step interpolation DBIM and typical DBIM, respectively; (b) the integration of multi-resolution and multi-frequency techniques with the DBIM: MR-MF-DBIM, which is applied to image targets with high sound contrast in a strongly scattering medium. Relative to MR-DBIM, this integration offers a 44.01% reduction in the normalized absolute error.
A Whale Optimization Algorithm Based Resource Allocation Scheme for Cloud-Fog Based IoT Applications
Authors: Sing Ranumayee , Bhoi Sourav Kumar , Panigrahi Niranjan , Sahoo Kshira Sagar , Jhanjhi Nz , AlZain Mohammed A.
Published: October 2022

Fog computing has been prioritized over cloud computing in terms of latency-sensitive Internet of Things (IoT) based services. We consider a limited resource-based fog system where real-time tasks with heterogeneous resource configurations are required to allocate within the execution deadline. Two modules are designed to handle the real-time continuous streaming tasks. The first module is task classification and buffering (TCB), which classifies the task heterogeneity using dynamic fuzzy c-means clustering and buffers into parallel virtual queues according to enhanced least laxity time. The second module is task offloading and optimal resource allocation (TOORA), which decides to offload the task either to cloud or fog and also optimally assigns the resources of fog nodes using the whale optimization algorithm, which provides high throughput. The simulation results of our proposed algorithm, called whale optimized resource allocation (WORA), is compared with results of other models, such as shortest job first (SJF), multi-objective monotone increasing sorting-based (MOMIS) algorithm, and Fuzzy Logic based Real-time Task Scheduling (FLRTS) algorithm. When 100 to 700 tasks are executed in 15 fog nodes, the results show that the WORA algorithm saves 10.3% of the average cost of MOMIS and 21.9% of the average cost of FLRTS. When comparing the energy consumption, WORA consumes 18.5% less than MOMIS and 30.8% less than FLRTS. The WORA also performed 6.4% better than MOMIS and 12.9% better than FLRTS in terms of makespan and 2.6% better than MOMIS and 4.3% better than FLRTS in terms of successful completion of tasks.
Dual Reversible Data Hiding Based on AMBTC Using Hamming Code and LSB Replacement
Authors: Kim Cheonshik
Published: October 2022

The existing data hiding schemes conceal the data in the cover image and then communicate secretly on the channel. The weakness of these methods is that the security aspect is somewhat lacking, and there is a limit to hiding enough data. In this paper, we propose a reversible data hiding method based on dual AMBTC images. It improves security, which is a weakness of data hiding. AMBTC has strengths in low-bandwidth channel environments with simple calculations and efficient data performance. HC(7,4) and LSB replacement methods are applied to each block of AMBTC to hide secret data. After the embedding process, the two AMBTC-marked images are obtained, and these images are sent to different recipients. The recipients can extract hidden messages and restore the cover AMBTC image by using the proposed method and two marked images. Our proposed data hiding method guarantees sufficient data hiding, proper cover image quality, and restoration of the original cover image. Experimental results show that our method is efficient in terms of image quality and embedding ratio.
Early Warning of High-Voltage Reactor Defects Based on Acoustic–Electric Correlation
Authors: Gao Shuguo , Xing Chao , Zhang Zhigang , Xiang Chenmeng , Liu Haoyu , Liu Hongliang , Shi Rongbin , Wang Sihan , Ma Guoming
Published: October 2022

Traditional high-voltage reactor monitoring and diagnosis research has problems such as high sampling demand, difficulty in noise reduction on site, many false alarms, and lack of on-site data. In order to solve the above problems, this paper proposes an acoustic–electric fusion high-voltage reactor acquisition system and defect diagnosis method based on reactor pulse current and ultrasonic detection signal. Using the envelope peak signal as the basic detection data, the sampling requirement of the system is reduced. To fill the missing data with partial discharge (PD) information, a method based on k-nearest neighbor (KNN) is proposed. An adaptive noise reduction method is carried out, and a noise threshold calculation method is given for the field sensors. A joint analysis method of acoustic and electrical signals based on correlation significance is established to determine whether a discharge event has occurred based on correlation significance. Finally, the method is applied to a UHV reactor on the spot, which proves the effectiveness of the method proposed in this paper.
A General Equivalent Modeling Method for DFIG Wind Farms Based on Data-Driven Modeling
Authors: Zhu Qianlong , Tao Jun , Deng Tianbai , Zhu Mingxing
Published: October 2022

To enhance the stable performance of wind farm (WF) equivalent models in uncertain operating scenarios, a model-data-driven equivalent modeling method for doubly-fed induction generator (DFIG)-based WFs is proposed. Firstly, the aggregation-based WF equivalent models and the equivalent methods for aggregated parameters are analyzed and compared. Two mechanism models are selected from the perspective of practicality and complementarity of simulation accuracy. Secondly, the simulation parameters are set through two sampling methods to construct a training database. Next, the whole fault process is divided into five phases, and the weight coefficient optimization model is established according to the data-driven idea to achieve the adaptive configuration of the weight. Finally, the electromechanical transient simulations of the power systems with a DFIG-based WF is carried out by using the MATLAB/Simulink platform. Compared with the detailed WF model, the simulation time of the WF equivalent proposed in this paper can be significantly reduced by about 87%, and simulation results show that the proposed method can effectively improve the adaptability of the WF equivalent model in different wind scenarios and voltage dips.