bluuebunny/crossref_metadata_2025 huggingface dataset:
Offset Compensation in Resistive Stretch Sensors Using Low-Frequency Feedback Topology
Authors: Drzazga Jakub , Cyganek Bogusław
Published: October 2022

Respiration monitoring systems play an important role in healthcare and fitness. For this purpose, resistive stretch sensors are frequently used, which are cheap and simple in operation. However, they are not free from drawbacks. Varying offset due to patient movement, low signal amplitude, as well as susceptibility to interference, can all pose serious challenges. In this paper, a novel signal conditioning circuit for a resistive respiration sensor is proposed that alleviates some of the above problems. Namely, the proposed low-frequency feedback topology improves the dynamic range by offset compensation, sustaining a high signal amplification. Further advantages of the new configuration are the phase shift of 0.5 degrees in the band of interest and higher gain for the respiration signal than for the offset. The topology was proved to correctly represent signal amplitude changes, as well as to be able to sample human respiration in the home environment. However, the circuit shows some nonlinear behavior around resistance discontinuity points–settling time after body position change of the patient, which can be as long as 40 s. The circuit was tested both in bench tests and in the prototype of a respiratory polygraphy device during actual sleep apnea examinations. The results indicate that resistive stretch sensors, along with low-frequency feedback topology, are a promising development path for future respiration monitoring devices.
Very Simple System for Walking-Speed Measurement in Geriatric Patients
Authors: Scandurra Graziella , Basile Giorgio , Ciofi Carmine
Published: October 2022

Walking speed in geriatric patients is an important index for inferring the patient’s state of health and estimating the success rate of some surgical procedures. Although different solutions for monitoring the gait of a subject exist in scientific literature and on the market, there is a need for a system that is very simple, especially to wear, considering that elderly subjects often have movement difficulties. For this reason, we investigated the possibility of using a standard miniaturized wireless microphone, that can be easily attached to patients’ clothes by means of a clip, as the sole sensing device to be worn by the test subject. A transceiver, a sound card and a PC complete the system, which turns out to be quite simple to be set up and use, thanks to a proper graphic user interface that controls its entire operation. The system essentially tracks the position of the test subject over time by measuring the propagation times of repeated sound pulses from the speaker to the microphone. To avoid hearing discomfort, the frequency of the pulses is chosen at the higher end of the audio spectrum, so that they are essentially undetectable by adults. The measurement range is in excess of 6 m, that is sufficient for the standard 4 m walking-speed test. Tests performed in a laboratory environment have confirmed the effectiveness of the approach we propose.
Global Adaptive Control for Uncertain Nonlinear Systems under Non-Lipschitz Condition with Quantized States
Authors: Sun Caihao , Liu Zongcheng , Chen Yong , Zhou Yang , Qu Qingyang
Published: October 2022

A novel adaptive control method is designed for the uncertain nonlinear systems with quantized states under the condition that system nonlinearities do not satisfy the Lipschitz condition. In this paper, the global control for an uncertain nonlinear system with quantized states in the case that the system nonlinearities do not satisfy the Lipschitz condition is first achieved. Separation theorem is used to model the uncertainties appropriately and then an adaptive term is designed to help attenuate the effects of system uncertainties. Furthermore, the global boundedness of all signals of the closed-loop system is proved based on the Lyapunov stability theorem. Finally, simulation results are given to demonstrate the effectiveness of the proposed methods.
Secure State Estimation of Cyber-Physical System under Cyber Attacks: Q-Learning vs. SARSA
Authors: Jin Zengwang , Ma Menglu , Zhang Shuting , Hu Yanyan , Zhang Yanning , Sun Changyin
Published: October 2022

This paper proposes a reinforcement learning (RL) algorithm for the security problem of state estimation of cyber-physical system (CPS) under denial-of-service (DoS) attacks. The security of CPS will inevitably decline when faced with malicious cyber attacks. In order to analyze the impact of cyber attacks on CPS performance, a Kalman filter, as an adaptive state estimation technology, is combined with an RL method to evaluate the issue of system security, where estimation performance is adopted as an evaluation criterion. Then, the transition of estimation error covariance under a DoS attack is described as a Markov decision process, and the RL algorithm could be applied to resolve the optimal countermeasures. Meanwhile, the interactive combat between defender and attacker could be regarded as a two-player zero-sum game, where the Nash equilibrium policy exists but needs to be solved. Considering the energy constraints, the action selection of both sides will be restricted by setting certain cost functions. The proposed RL approach is designed from three different perspectives, including the defender, the attacker and the interactive game of two opposite sides. In addition, the framework of Q-learning and state–action–reward–state–action (SARSA) methods are investigated separately in this paper to analyze the influence of different RL algorithms. The results show that both algorithms obtain the corresponding optimal policy and the Nash equilibrium policy of the zero-sum interactive game. Through comparative analysis of two algorithms, it is verified that the differences between Q-Learning and SARSA could be applied effectively into the secure state estimation in CPS.
Person Entity Alignment Method Based on Multimodal Information Aggregation
Authors: Wang Huansha , Huang Ruiyang , Zhang Jianpeng
Published: October 2022

Entity alignment is used to determine whether entities from different sources refer to the same object in the real world. It is one of the key technologies for constructing large-scale knowledge graphs and is widely used in the fields of knowledge graphs and knowledge complementation. Because of the lack of semantic connection between the visual modality face attribute of the person entity and the text modality attribute and relationship information, it is difficult to model the visual and text modality into the same semantic space, and, as a result, that the traditional multimodal entity alignment method cannot be applied. In view of the scarcity of multimodal person relation graphs datasets and the difficulty of the multimodal semantic modeling of person entities, this paper analyzes and crawls open-source semi-structured data from different sources to build a multimodal person entity alignment dataset and focuses on using the facial and semantic information of multimodal person entities to improve the similarity of entity structural features which are modeled using the graph convolution layer and the dynamic graph attention layer to calculate the similarity. Through verification on the self-made multimodal person entity alignment dataset, the method proposed in this paper is compared with other entity alignment models which have a similar structure. Compared with AliNet, the probability that the first item in the candidate pre-aligned entity set is correct is increased by 12.4% and average ranking of correctly aligned entities in the candidate pre-aligned entity set decreased by 32.8, which proves the positive effect of integrating multimodal facial information, applying dynamic graph attention and a layer-wise gated network to improve the alignment effect of person entities.
Blockchain-Enabled Decentralized Secure Big Data of Remote Sensing
Authors: Razzaq Abdul , Mohsan Syed Agha Hassnain , Ghayyur Shahbaz Ahmed Khan , Alsharif Mohammed H. , Alkahtani Hend Khalid , Karim Faten Khalid , Mostafa Samih M.
Published: October 2022

Blockchain technology has emerged as a promising candidate for space exploration and sustainable energy systems. This transformative technology offers secure and decentralized strategies to process and manipulate space resources. Remote sensing provides viable potential with the coexistence of open data from various sources, such as short-range sensors on unmanned aerial vehicles (UAVs) or Internet-of-Things (IoT) tags and far-range sensors incorporated on satellites. Open data resources have most recently emerged as attractive connecting parties where owners have shown consent to share data. However, most data owners are anonymous and untrustworthy, which makes shared data likely insecure and unreliable. At present, there are several tools that distribute open data, serving as an intermediate party to link users with data owners. However, these platforms are operated by central authorities who develop guidelines for data ownership, integrity, and access, consequently restricting both users and data owners. Therefore, the need and feasibility of a decentralized system arise for data sharing and retrieving without involving these intermediate limiting parties. This study proposes a blockchain-based system without any central authority to share and retrieve data. Our proposed system features (i) data sharing, (ii) maintaining the historical data, and (iii) retrieving and evaluation of data along with enhanced security. We have also discussed the use of blockchain algorithms based on smart contracts to track space transactions and communications in a secure, verifiable, and transparent manner. We tested the suggested framework in the Windows environment by writing smart contracts prototype on an Ethereum TESTNET blockchain. The results of the study showed that the suggested strategy is efficient, practicable, and free of common security attacks and vulnerabilities.
Research on Student Performance Prediction Based on Stacking Fusion Model
Authors: Yu Fuxing , Liu Xinran
Published: October 2022

Online learning is gradually becoming popular with the continuous development of Internet technology and the rapid development of educational informatization. It plays a key role in predicting students’ course performance based on their online learning behavior. It can optimize the effects of teaching and improve teaching strategies. Student performance prediction models that are built with a single algorithm currently have limited prediction accuracy. Meanwhile, model fusion improvement technology can combine many algorithms into a single model, thereby enhancing the overall effect of the model and providing better performance. In this paper, a stacking fusion model based on RF-CART–XGBoost–LightGBM is proposed. The first layer of the model uses a decision tree (CART), random forest, XGBoost and LightGBM as the base models. The second layer uses the LightGBM model. We used the Kalboard360 student achievement dataset, and features related to online learning behavior were selected as the model’s input for model training. Finally, we employed five-fold cross-validation to assess the model’s performance. In comparison with the four single models, the two fusion models based on the four single models both show significantly better performance. The prediction accuracies of the bagging fusion model and stacking fusion model are 83% and 84%, respectively. This proves that the proposed stacking fusion model has better performance, which helps to improve the accuracy of the performance prediction model further. It also provides an effective basis for optimizing the effects of teaching.
A Deep Learning-Based Approach for the Diagnosis of Acute Lymphoblastic Leukemia
Authors: Saeed Adnan , Shoukat Shifa , Shehzad Khurram , Ahmad Ijaz , Eshmawi Ala’ Abdulmajid , Amin Ali H. , Tag-Eldin Elsayed
Published: October 2022

Leukemia is a deadly disease caused by the overproduction of immature white blood cells (WBS) in the bone marrow. If leukemia is detected at the initial stages, the chances of recovery are better. Typically, morphological analysis for the identification of acute lymphoblastic leukemia (ALL) is performed manually on blood cells by skilled medical personnel, which has several disadvantages, including a lack of medical personnel, sluggish analysis, and prediction that is dependent on the medical personnel’s expertise. Therefore, we proposed the Multi-Attention EfficientNetV2S and EfficientNetB3 state-of-the-art deep learning architectures using transfer learning-based fine-tuning approach to distinguish the normal and blast cells from microscopic blood smear images that both are pretrained on large-scale ImageNet database. We simply modified the last block of both models and added additional layers to both models. After including this Multi-Attention Mechanism, it not only reduces the model’s complexities but also generalizes its network quite well. By using the proposed technique, the accuracy has improved and the overall loss is also minimized. Our Multi-Attention EfficientNetV2S and EfficientNetB3 models achieved 99.73% and 99.25% accuracy, respectively. We have further compared the proposed model’s performance to other individual and ensemble models. Upon comparison, the proposed model outclassed the existing literature and other benchmark models, thus proving its efficiency.
Behavior Analysis Using Enhanced Fuzzy Clustering and Deep Learning
Authors: Altameem Arwa A. , Hafez Alaaeldin M.
Published: October 2022

Companies aim to offer customized treatments, intelligent care, and a seamless experience to their customers. Interactions between a company and its customers largely depend on the company’s ability to learn, understand, and predict customer behaviors. Customer behavior prediction is a pivotal factor in improving a company’s quality of services and thus its growth. Different machine learning techniques have been applied to gather customer data to predict behavioral patterns. Traditional methods are unable to discover hidden patterns in ideal situations and need to be improved to produce more accurate predictions. This work proposes a novel hybrid model comprised of two modules: a novel clustering module on the basis of an optimized fuzzy deep belief network and a customer behavior prediction module on the basis of a deep recurrent neural network. Customers’ previous purchasing characteristics and portfolio details were analyzed by applying learning parameters. In this paper, the deep learning techniques were optimized by applying the butterfly optimization method, which minimizes the maximum error classification problem. The performance of the system was evaluated using experimental analysis. The proposed approach was compared to other single and hybrid-model-based approaches and attained the highest performance in the respective metrics.
Convolution Network Enlightened Transformer for Regional Crop Disease Classification
Authors: Wang Yawei , Chen Yifei , Wang Dongfeng
Published: October 2022

The overarching goal of smart farming is to propose pioneering solutions for future sustainability of humankind. It is important to recognize the image captured for monitoring the growth of plants and preventing diseases and pests. Currently, the task of automatic recognition of crop diseases is to research crop diseases based on deep learning, but the existing classifiers have problems regarding, for example, accurate identification of similar disease categories. Tomato is selected as the crop of this article, and the corresponding tomato disease is the main research point. The vision transformer (VIT) method has achieved good results on image tasks. Aiming at image recognition, tomato plant images serve as this article’s data source, and their structure is improved based on global ViT and local CNN (convolutional neural network) networks, which are built to diagnose disease images. Therefore, the features of plant images can be precisely and efficiently extracted, which is more convenient than traditional artificial recognition. The proposed architecture’s efficiency was evaluated by three image sets from three tomato-growing areas and acquired by drone and camera. The results show that this article method garners an average counting accuracy of 96.30%. It provides scientific support and a reference for the decision-making process of precision agriculture.
Analysis of Optimal Operation of Charging Stations Based on Dynamic Target Tracking of Electric Vehicles
Authors: Huang Kun , Zhao Jingtao , Sun Xiaohan , Li Wei , Zheng Shu
Published: October 2022

In view of the large impact of traditional charging stations on the power grid and the investment in the construction of charging stations for electric vehicle infrastructure services, this paper considers the configuration of optical storage equipment in charging stations from a practical point of view and proposes an economic operation strategy for charging stations to meet the economically optimal requirements of different scenarios. First, we analyze the behavioral characteristics of multiple types of electric vehicles, consider the influence of charging queues, and establish a daily load model of charging stations by taking into account the daily monitoring load and nighttime lighting load of charging stations. Then, considering the electric vehicle (EV) charging demand, photovoltaic (PV) output and energy storage charging and discharging power, the daily economic optimal operation problem based on the dynamic target tracking of charging stations is established; the objective is to maximize the daily operating revenue of charging stations under the condition of satisfying the EV charging demand and PV consumption. Secondly, the objective function is linearized, and the economic operation model is transformed into a mixed integer linear programming model for solving, and the simulation is verified under different scenarios. The results show that the economic optimal operation strategy can adapt to the economic operation requirements of charging stations in different scenarios and maximize the charging station revenue.
Experimental Study on Positronium Detection under Millimeter Waves Generated from Plasma Wakefield Acceleration
Authors: Min Sun-Hong , Park Chawon , Lee Kyo Chul , Lee Yong Jin , Sattorov Matlabjon , Kim Seonmyeong , Hong Dongpyo , Park Gun-Sik
Published: October 2022

Positronium (Ps) is an unstable system created by the temporary combination of electrons and negative electrons, and Ps generation technology under resonance conditions at millimeter waves is emerging as a new research topic. In general, Ps can be observed when an unstable separate state remains after electron and positron pair annihilation, as in positron emission tomography (PET). However, in this study, a plasma wakefield accelerator based on vacuum electronics devices (VEDs) was designed in the ponderomotive force generating electrons and positrons simultaneously using annular relativistic electron beams. It can induce Cherenkov radiation from beam–wave interaction by using dielectric materials. According to the size of dielectric materials, the frequency of oscillation is approximately 203 GHz at the range of millimeter waves. At this time, the output power is about 109 watts-levels. Meanwhile, modes of millimeter waves polarized by a three-stepped axicon lens are used to apply the photoconversion technology. Thus, it is possible to confirm light emission in the form of a light-converted Bessel beam.
Discovery of Business Process Models from Incomplete Logs
Authors: Wang Lili , Fang Xianwen , Shao Chifeng
Published: October 2022

The completeness of event logs and long-distance dependencies are two major challenges for process mining. Until now, most process mining methods have not been able to discover long-distance dependency and assume that the directly-follows relationship in the log is complete. However, due to the existence of high concurrency and the cycle, it is difficult to guarantee that the real-life log is complete regarding the directly-follows relationship. Therefore, process mining needs to be able to deal with incompleteness. In this paper, we propose a method for discovering process models including sequential, exclusive, concurrent, and cyclic structures from incomplete event logs. The method analyzes the co-occurrence class of the log and the model and then uses the technology of combining the behavior profile and co-occurrence class to obtain the communication behavior profile of the co-occurrence class. Furthermore, a method of constructing a substructure from the event log using the co-occurrence class is presented. Finally, the whole process model is built by combining those substructures. The experimental results show that the proposed method can discover process models with complex structures involving cycles from incomplete event logs and also can deal with long-distance dependency in the event log. Meanwhile, the discovered process model has a good degree of consistency with the original model.
Multiwalled Carbon Nanotubes Polylactide Composites for Electrical Engineering—Fabrication and Electrical Properties
Authors: Pietrzak Lukasz , Raniszewski Grzegorz , Szymanski Lukasz
Published: October 2022

In the article, both the processes of manufacturing highly homogeneous polylactide resin/multiwalled carbon nanotubes (CNTs) and their properties are described. Regarding the application of carbon nanotubes polymer composites, one of the most important problems to solve is obtaining good dispersion of the filler in the polymer matrix. Preparation of polylactide/multiwall CNTs composites by quick polymer solidification and freezing the state of the dispersion of the nanotubes in the polymer solution is described. The method we used employs an increase in viscosity (carried out rapidly) of the sonicated polymer solution containing the CNTs by spray deposition. Good dispersion of the nanotubes is confirmed by electron microscopy. The obtained nanocomposites exhibit a low percolation threshold for electrical conductivity (above 0.25% by weight). The described method leads to obtaining an electrical conductive surface on virtually any material and reduces the small amount of an expensive filler (CNTs) needed to achieve good electrical conductivity. Furthermore, the carbon nanotubes used in the fabrication process of the composites were obtained using the liquid-source chemical-vapor deposition (LSCVD) synthesis method.
Emerging Trends in Deep Learning for Credit Scoring: A Review
Authors: Hayashi Yoichi
Published: October 2022

This systematic review aims to provide deep insights on emerging trends in, and the potential of, advanced deep learning techniques, such as machine learning algorithms being partially replaced by deep learning (DL) algorithms for credit scoring owing to the higher accuracy of the latter. This review also seeks to explain the reasons that deep belief networks (DBNs) can achieve higher accuracy than shallower networks, discusses the potential classification capabilities of DL-based classifiers, and bridges DL and explainable credit scoring. The theoretical characteristics of DBNs are also presented along with the reasons for their higher accuracy compared to that of shallower networks. Studies published between 2019 and 2022 were analysed to review and compare the most recent DL techniques that have been found to achieve higher accuracies than ensemble classifiers, their hybrids, rule extraction methods, and rule-based classifiers. The models reviewed in this study were evaluated and compared according to their accuracy and area under the receiver operating characteristic curve for the Australian, German (categorical), German (numerical), Japanese, and Taiwanese datasets, which are commonly used in the credit scoring community. This review paper also explains how tabular datasets are converted into images for the application of a two-dimensional convolutional neural network (CNN) and how “black box” models using local and global rule extraction and rule-based methods are applied in credit scoring. Finally, a new insight on the design of DL-based classifiers for credit scoring datasets is provided, along with a discussion on promising future research directions.
A Physical-Layer Watermarking Scheme Based on 5G NR
Authors: Xie Xu , Chen Wan , Xu Zhengguang
Published: October 2022

Based on the existing 5G NR system, a physical-layer watermarking scheme is proposed to enhance the physical-layer security in 5G communication systems. A new scheme for watermark generation is proposed to improve the robustness of the authentication. The watermark signal is embedded in the phase of the demodulation reference signal (DMRS), and the influence of the watermark on the demodulation reference signal is reduced by designing the encoder of the watermark. Simulation results show that the watermarking scheme proposed in this paper has good anti-noise and anti-frequency-offset performance, and has good feasibility both in the Gaussian channel and Rayleigh fading channel.
Optimum of Grain Loss Sensors by Analyzing Effects of Grain Collision Attitude on Signal Characteristics
Authors: Li Jun , Liang Zhenwei , Zhu Fangyu , Liu Chuanchao
Published: October 2022

Grain loss rate is an important indicator to evaluate the performance of combine harvesters. It is indicted that signal voltage amplitude and signal frequency are the key factors for designing a grain loss sensor. In this work, the high-speed photography and signal high-speed acquisition technique were utilized to capture grain collision attitude and the corresponding collision signal characteristics and the effect of grain moisture content and collision angle on signal voltage amplitude and signal frequency was studied in detail, which lays a good foundation for optimizing grain loss signal processing circuit parameters. Then, monitoring resolution of the grain loss sensor was improved by adding constrained damping layer to the sensitive plate under the instruction of experimental modal analysis. At last, a field experiment was carried out; the field experiment results indicate that the monitoring performance improved.
The Effects of Total Ionizing Dose on the SEU Cross-Section of SOI SRAMs
Authors: Zhao Peixiong , Li Bo , Liu Hainan , Yang Jinhu , Jiao Yang , Chen Qiyu , Sun Youmei , Liu Jie
Published: October 2022

The total ionizing dose (TID) effects on single-event upset (SEU) hardness are investigated for two silicon-on-insulator (SOI) static random access memories (SRAMs) with different layout structures in this paper. The contrary changing trends of TID on SEU sensitivity for 6T and 7T SOI SRAMs are observed in our experiment. After 800 krad(Si) irradiation, the SEU cross-sections of 6T SRAMs increases by 15%, while 7T SRAMs decreases by 60%. Experimental results show that the SEU cross-sections are not only affected by TID irradiation, but also strongly correlate with the layout structure of the memory cells. Theoretical analysis shows that the decrease of SEU cross-section of 7T SRAM is caused by a raised OFF-state equivalent resistance of the delay transistor N5 after TID exposure, which is because the radiation-induced charges are trapped in the shallow trench, and isolation oxide (STI) and buried oxide (BOX) enhance the carrier scattering rate of delay transistor N5.
Research on Surface Defect Detection of Camera Module Lens Based on YOLOv5s-Small-Target
Authors: He Gang , Zhou Jianyun , Yang Hu , Ning Yuan , Zou Huatao
Published: October 2022

For the problem of low resolution of camera module lens surface defect image, small target and blurred defect details leading to low detection accuracy, a camera module lens surface defect detection algorithm YOLOv5s-Defect based on improved YOLOv5s is proposed. Firstly, to solve the problems arising from the anchor frame generated by the network through K-means clustering, the dynamic anchor frame structure DAFS is introduced in the input stage. Secondly, the SPP-D (Spatial Pyramid Pooling-Defect) improved from the SPP module is proposed. The SPP-D module is used to enhance the reuse rate of feature information in order to reduce the loss of feature information due to the maximum pooling of SPP modules. Then, the convolutional attention module is introduced to the network model of YOLOv5s, which is used to enhance the defective region features and suppress the background region features, thus improving the detection accuracy of small targets. Finally, the post-processing method of non-extreme value suppression is improved, and the improved method DIoU-NMS improves the detection accuracy of small targets in complex backgrounds. The experimental results show that the mean average precision mAP@0.5 of the YOLOv5s-Small-Target algorithm is 99.6%, 8.1% higher than that of the original YOLOv5s algorithm, the detection speed FPS is 80 f/s, and the model size is 18.7M. Compared with the traditional camera module lens surface defect detection methods, YOLOv5s-Small-Target can detect the type and location of lens surface defects more accurately and quickly, and has a smaller model volume, which is convenient for deployment in mobile terminals, meeting the demand for real-time and accuracy of camera module lens surface defect detection.
Throughput Optimized Reversible Cellular Automata Based Security Algorithm
Authors: Nanda Surendra Kumar , Mohanty Suneeta , Pattnaik Prasant Kumar , Sain Mangal
Published: October 2022

Reversible cellular automation is a highly parallel system and produces enhanced throughput in its cryptographic applications. The throughput optimized security algorithm based on reversible cellular automata produces a better result in high-performance systems with many cores of CPU or GPU. We designed a throughput optimized block encryption technique using reversible cellular automata and compared its performance with other cellular automata-based algorithms. We tested its performance in both 8 core and 64 core CPU systems and the results showed an enhancement in throughput. This encryption system produced plaintext blocks that are immune to other blocks during cryptanalysis because of segmentation and the use of the different random numbers as seeds. It was built with 128 bits block encryption, but it was easily scalable to a higher block size without changing the algorithm. Each block of encryption used a cipher block chaining mode and was hence more secure and effective.
Power Efficiency Characterization with Various Gate Oxide Thicknesses in Class DE Amplifiers for HIFU Applications
Authors: Choi Hyun-Sik , Ngo Thinh , Zhou Yushi
Published: October 2022

Skin and cancer cell treatments using high-intensity focused ultrasound (HIFU) have garnered considerable attention as a technology with fewer side effects. Hence, various schemes have been developed to operate ultrasound transducers with high efficiencies. Class DE power amplifiers operate in zero-voltage switching (ZVS) and zero-derivative switching (ZDS); therefore, high-efficiency operation is possible. However, during the CMOS process, a difference in efficiency arises depending on the gate oxide process, which has not yet been analyzed. In high-power devices, a thick oxide layer is primarily used to prevent breakdown. However, this can lead to a decrease in power efficiency. This study analyzes the overall power consumption for each oxide layer thickness during the AMS H35 CMOS process and compares its efficiency. The results confirm that an output power of approximately 1.8 W and a power efficiency of 94% can be obtained with just a relatively thin gate oxide thickness of approximately 10 nm. Furthermore, an additional power efficiency of approximately 3% can be obtained by reducing only the gate oxide thickness.
Optimized Torque Performance of a 7-Phase Outer-Rotor Surface-Mounted Permanent Magnet Synchronous Machine for In-Wheel E-Motorcycle Application
Authors: Ghorbani Hamidreza , Moradian Mohammadreza , Benbouzid Mohamed
Published: October 2022

Four outer rotor surface-mounted permanent magnet synchronous machines (SMPSM), supplied by a seven-phase drive system, are proposed in this study, considering different q (number of stator slot per phase per pole ratio) to achieve a satisfying value of electromagnetic torque and Back-Electromotive Force (Back-EMF) with lower torque pulsation. Accordingly, the proposed configurations are investigated, and results are comparatively reported. Thus based on the results, the best-performing configuration, the candidate model, which presents the lowest torque pulsation with a desirable value of Tavg and Back-EMF is selected. In order to demonstrate the advantages of this candidate model, an optimization analysis is performed using 2D Finite Element Analysis (FEA). The resultant values of the variables are applied, designing three optimized models. Performance results of the optimized models demonstrate that TCog reduced noticeably and TRipple declined below 5%. The Artemis Drive-Cycles analysis results are also included for the best-optimized model, considering E-Motorcycle requirements and properties for urban, rural, and motorway driving conditions. Accordingly, in terms of In-Wheel application of the optimized machine, high torque/power density along with high values of PF and efficient performance are provided for E-Motorcycle application.
Integrated Framework to Assess the Extent of the Pandemic Impact on the Size and Structure of the E-Commerce Retail Sales Sector and Forecast Retail Trade E-Commerce
Authors: Tudor Cristiana
Published: October 2022

With customers’ increasing reliance on e-commerce and multimedia content after the outbreak of COVID-19, it has become crucial for companies to digitize their business methods and models. Consequently, COVID-19 has highlighted the prominence of e-commerce and new business models while disrupting conventional business activities. Hence, assessing and forecasting e-commerce growth is currently paramount for e-market planners, market players, and policymakers alike. This study sources data for the global e-commerce market leader, the US, and proposes an integrated framework that encompasses automated algorithms able to estimate six statistical and machine-learning univariate methods in order to accomplish two main tasks: (i) to produce accurate forecasts for e-commerce retail sales (e-sale) and the share of e-commerce in total retail sales (e-share); and (ii) to assess in quantitative terms the pandemic impact on the size and structure of the e-commerce retail sales sector. The results confirm that COVID-19 has significantly impacted the trend and structure of the US retail sales sector, producing cumulative excess (or abnormal) retail e-sales of $227.820 billion and a cumulative additional e-share of 10.61 percent. Additionally, estimations indicate a continuation of the increasing trend, with point estimates of $378.691 billion for US e-commerce retail sales that are projected to account for 16.72 percent of total US retail sales by the end of 2025. Nonetheless, the current findings also document that the growth of e-commerce is not a consequence of the COVID-19 crisis, but that the pandemic has accelerated the evolution of the e-commerce sector by at least five years. Overall, the study concludes that the shift towards e-commerce is permanent and, thus, governments (especially in developing countries) should prioritize policies aimed at harnessing e-commerce for sustainable development. Furthermore, in light of the research findings, digital transformation should constitute a top management priority for retail businesses.
A Secure Personal Health Record Sharing System with Key Aggregate Dynamic Searchable Encryption
Authors: Oh Jihyeon , Lee JoonYoung , Kim MyeongHyun , Park Youngho , Park KiSung , Noh SungKee
Published: October 2022

Recently, as interest in individualized health has increased, the Personal Health Record (PHR) has attracted a lot of attention for prognosis predictions and accurate diagnoses. Cloud servers have been used to manage the PHR system, but privacy concerns are evident since cloud servers process the entire PHR, which contains the sensitive information of patients. In addition, cloud servers centrally manage the PHR system so patients lose direct control over their own PHR and cloud servers can be an attractive target for malicious users. Therefore, ensuring the integrity and privacy of the PHR and allocating authorization to users are important issues. In this paper, we propose a secure PHR sharing system using a blockchain, InterPlanetary File System (IPFS), and smart contract to ensure PHR integrity and secure verification. To guarantee the patient’s authority over the management of his/her own PHR, as well as provide convenient access, we suggest a key aggregate dynamic searchable encryption. We prove the security of the proposed scheme through informal and formal analyses including an Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation, Burrows–Abadi–Needham (BAN) logic, and security-model-based games. Furthermore, we estimate the computational costs of the proposed scheme using a Multiprecision Integer and Rational Arithmetic Cryptographic Library (MIRACL) and compare the results with those of previous works.
A Rumor Detection Method Based on Multimodal Feature Fusion by a Joining Aggregation Structure
Authors: Zhong Nanjiang , Zhou Guomin , Ding Weijie , Zhang Jiawen
Published: October 2022

Online rumors spread rapidly through social media, which is a great threat to public safety. Existing solutions are mainly based on content features or propagation structures for rumor detection. However, due to the variety of strategies in creating rumors, only considering certain features cannot achieve good enough detection results. In addition, existing works only consider the rumor propagation structure and ignore the aggregation structures of rumors, which cannot provide enough discriminative features (especially in the early days of rumors, when the structure of the propagation is incomplete). To solve these problems, this paper proposes a rumor detection method with multimodal feature fusion and enhances the feature representation of the rumor propagation network by adding aggregation features. More specifically, we built a graph model of the propagation structure as well as the aggregation structure. Next, by utilizing the BERT pre-training model and the bidirectional graph convolutional network, we captured the features of text content, propagation structure, and aggregation structure, respectively. Finally, the multimodal features were aggregated based on the attention mechanism, and the final result was obtained through the MLP classifier. Experiments on real-world datasets show that our model outperforms state-of-the-art approaches.
NeuralFMU: Presenting a Workflow for Integrating Hybrid NeuralODEs into Real-World Applications
Authors: Thummerer Tobias , Stoljar Johannes , Mikelsons Lars
Published: October 2022

The term NeuralODE describes the structural combination of an Artificial Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODE), the former acts as the right-hand side of the ODE to be solved. This concept was further extended by a black-box model in the form of a Functional Mock-up Unit (FMU) to obtain a subclass of NeuralODEs, named NeuralFMUs. The resulting structure features the advantages of the first-principle and data-driven modeling approaches in one single simulation model: a higher prediction accuracy compared to conventional First-Principle Models (FPMs) and also a lower training effort compared to purely data-driven models. We present an intuitive workflow to set up and use NeuralFMUs, enabling the encapsulation and reuse of existing conventional models exported from common modeling tools. Moreover, we exemplify this concept by deploying a NeuralFMU for a consumption simulation based on a Vehicle Longitudinal Dynamics Model (VLDM), which is a typical use case in the automotive industry. Related challenges that are often neglected in scientific use cases, such as real measurements (e.g., noise), an unknown system state or high-frequency discontinuities, are handled in this contribution. To build a hybrid model with a higher prediction quality than the original FPM, we briefly highlight two open-source libraries: FMI.jl, which allows for the import of FMUs into the Julia programming language, as well as the library FMIFlux.jl, which enables the integration of FMUs into neural network topologies to obtain a NeuralFMU.
Towards QoS-Based Embedded Machine Learning
Authors: Springer Tom , Linstead Erik , Zhao Peiyi , Parlett-Pelleriti Chelsea
Published: October 2022

Due to various breakthroughs and advancements in machine learning and computer architectures, machine learning models are beginning to proliferate through embedded platforms. Some of these machine learning models cover a range of applications including computer vision, speech recognition, healthcare efficiency, industrial IoT, robotics and many more. However, there is a critical limitation in implementing ML algorithms efficiently on embedded platforms: the computational and memory expense of many machine learning models can make them unsuitable in resource-constrained environments. Therefore, to efficiently implement these memory-intensive and computationally expensive algorithms in an embedded computing environment, innovative resource management techniques are required at the hardware, software and system levels. To this end, we present a novel quality-of-service based resource allocation scheme that uses feedback control to adjust compute resources dynamically to cope with the varying and unpredictable workloads of ML applications while still maintaining an acceptable level of service to the user. To evaluate the feasibility of our approach we implemented a feedback control scheduling simulator that was used to analyze our framework under various simulated workloads. We also implemented our framework as a Linux kernel module running on a virtual machine as well as a Raspberry Pi 4 single board computer. Results illustrate that our approach was able to maintain a sufficient level of service without overloading the processor as well as providing an energy savings of almost 20% as compared to the native resource management in Linux.
Deep Learning-Based Time-Series Analysis for Detecting Anomalies in Internet of Things
Authors: Gopali Saroj , Siami Namin Akbar
Published: October 2022

Anomaly detection in time-series data is an integral part in the context of the Internet of Things (IoT). In particular, with the advent of sophisticated deep and machine learning-based techniques, this line of research has attracted many researchers to develop more accurate anomaly detection algorithms. The problem itself has been a long-lasting challenging problem in security and especially in malware detection and data tampering. The advancement of the IoT paradigm as well as the increasing number of cyber attacks on the networks of the Internet of Things worldwide raises the concern of whether flexible and simple yet accurate anomaly detection techniques exist. In this paper, we investigate the performance of deep learning-based models including recurrent neural network-based Bidirectional LSTM (BI-LSTM), Long Short-Term Memory (LSTM), CNN-based Temporal Convolutional (TCN), and CuDNN-LSTM, which is a fast LSTM implementation supported by CuDNN. In particular, we assess the performance of these models with respect to accuracy and the training time needed to build such models. According to our experiment, using different timestamps (i.e., 15, 20, and 30 min), we observe that in terms of performance, the CuDNN-LSTM model outperforms other models, whereas in terms of training time, the TCN-based model is trained faster. We report the results of experiments in comparing these four models with various look-back values.
Design of Mutual-Information-Maximizing Quantized Shuffled Min-Sum Decoder for Rate-Compatible Quasi-Cyclic LDPC Codes
Authors: Kang Peng , Cai Kui , He Xuan
Published: October 2022

In this paper, we propose a finite alphabet iterative decoder (FAID) named rate-compatible mutual-information-maximizing quantized shuffled min-sum (RC-MIM-QSMS) decoder, for decoding quasi-cyclic low-density parity-check (QC-LDPC) codes with various code rates. Our proposed decoder exchanges the coarsely quantized messages represented by symbols from finite alphabets and adopts single-input lookup tables (LUTs) to implement the node updates. To construct the LUTs used for decoding, we first propose a modified density evolution by considering the shuffled schedule to generate the LUTs which vary with different layers and iterations. Furthermore, to reduce the memory requirement for storing the LUTs, we optimize the constructed LUTs into a unique set of LUTs that only change with different decoding iterations. To the best of our knowledge, the RC-MIM-QSMS decoder is the first one to integrate the rate compatibility of LDPC codes with the shuffled decoding schedule. Simulation results show that the proposed RC-MIM-QSMS decoder outperforms the floating-point shuffled belief propagation decoder in the high signal-to-noise region and achieves comparable convergence speed to other state-of-the-art FAIDs. Moreover, the RC-MIM-QSMS decoder is able to save up to 93.22% memory requirement compared to the benchmark MIM-FAIDs.
Sampled-Data Linear Parameter Variable Approach for Voltage Regulation of DC–DC Buck Converter
Authors: Hooshmandi Kaveh , Bayat Farhad , Bartoszewicz Andrzej
Published: October 2022

This paper addresses the new method for output voltage regulation of DC–DC buck converter nonlinear systems by a sampled-data linear parameter varying (LPV) controller. For this purpose, an output-error state-space affine LPV model is presented for DC–DC buck converter nonlinear systems. The sampled-data structure of the controller is considered as a time delay in the input, and stabilization conditions are obtained for LPV systems with affine dependence on the parameter by using a parameter-dependent Lyapunov–Krasovskii functional. Then, the design condition of the sampled-data LPV controller with an appropriate sampling period is derived to guarantee that the output voltage of the DC–DC buck converter can be adjusted to the desired voltage. Finally, simulation results are provided to show the validity of the presented approach in practical control applications where there are limitations on the value of the sampling period and the cost of the digital implementation.
Novel Highly Flexible PCB Design Based on a Via-Less Meander Ground Structure to Transmit mm-Wave RF Signals in 5G Foldable Mobile Products
Authors: Bae Bumhee , Yang Kwangmo , Kim Younho , Kim Minseok , Seong Younghun , Lee Jaehoon , Cheon Jeongnam
Published: October 2022

Recently, new form factors, such as foldable, have increased demand for mobile products.Moreover, mobile phones should support the RF signal frequency up to the mm-wave frequencydue to the expansion of 5G mobile products. Therefore, 5G foldable products require componentsthat facilitate both mm-wave RF transmission and ultra-high flexibility for interconnecting throughthe hinge structure of foldable products. To improve flexibility, a flexible PCB must be thin with noground vias in its bending section; in contrast, the low-loss flexible PCB for mm-wave transmissionmust be thick and have many ground vias, so there is a trade-off relationship between flexibilityand RF characteristics. This paper proposes a new flexible PCB structure that does not experienceproblems regarding signal transmission to the mm-wave band, even when folded 200,000 times.To overcome the physical limits of the trade-off relationship, an interlayer air-gap was formed; astructure with a via-less and meander ground shape is proposed. The simulated loss of the proposedstructure was 0.0254 dB/mm @ 10 GHz, and the isolation between signals ranged from 21.98 dBto 10 GHz. The simulated results of insertion loss and isolation were experimentally verified. Theproposed structure is currently being applied to the RF flexible PCB that interconnects through thehinge of a foldable phone, and is currently being mass-produced.