bluuebunny/crossref_metadata_2025 huggingface dataset:
Joint Design of Polar Coding and Physical Network Coding for Two−User Downlink Non−Orthogonal Multiple Access
Authors: Xie Zhaopeng , Chen Pingping , Li Yong
Published: January 2023

In this paper, we propose a joint polar coding and physical network coding (PNC) for two−user downlink non−orthogonal multiple access (PN−DNOMA) channels, since the successive–interference–cancellation–aided polar decoding cannot be optimal for finite blocklength transmissions. In the proposed scheme, we first constructed the XORed message of two user messages. Then, the XORed message was superimposed with the message of the weak User 2 for broadcast. By doing so, we can utilize the PNC mapping rule and polar decoding to directly recover the message of User 1, while at User 2, we equivalently constructed a long−length polar decoder to obtain its user message. The channel polarization and decoding performance can be greatly improved for both users. Moreover, we optimized the power allocation of the two users with their channel conditions by considering the user fairness and the performance. The simulation results showed that the proposed PN−DNOMA can achieve performance gains of about 0.4−0.7 dB over the conventional schemes in two−user downlink NOMA systems.
Homogeneous Adaboost Ensemble Machine Learning Algorithms with Reduced Entropy on Balanced Data
Authors: Ramakrishna Mahesh Thyluru , Venkatesan Vinoth Kumar , Izonin Ivan , Havryliuk Myroslav , Bhat Chandrasekhar Rohith
Published: January 2023

Today’s world faces a serious public health problem with cancer. One type of cancer that begins in the breast and spreads to other body areas is breast cancer (BC). Breast cancer is one of the most prevalent cancers that claim the lives of women. It is also becoming clearer that most cases of breast cancer are already advanced when they are brought to the doctor’s attention by the patient. The patient may have the evident lesion removed, but the seeds have reached an advanced stage of development or the body’s ability to resist them has weakened considerably, rendering them ineffective. Although it is still much more common in more developed nations, it is also quickly spreading to less developed countries. The motivation behind this study is to use an ensemble method for the prediction of BC, as an ensemble model aims to automatically manage the strengths and weaknesses of each of its separate models, resulting in the best decision being made overall. The main objective of this paper is to predict and classify breast cancer using Adaboost ensemble techniques. The weighted entropy is computed for the target column. Taking each attribute’s weights results in the weighted entropy. Each class’s likelihood is represented by the weights. The amount of information gained increases with a decrease in entropy. Both individual and homogeneous ensemble classifiers, created by mixing Adaboost with different single classifiers, have been used in this work. In order to deal with the class imbalance issue as well as noise, the synthetic minority over-sampling technique (SMOTE) was used as part of the data mining pre-processing. The suggested approach uses a decision tree (DT) and naive Bayes (NB), with Adaboost ensemble techniques. The experimental findings shown 97.95% accuracy for prediction using the Adaboost-random forest classifier.
Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction
Authors: Jin Xue-Bo , Wang Zhong-Yao , Kong Jian-Lei , Bai Yu-Ting , Su Ting-Li , Ma Hui-Jun , Chakrabarti Prasun
Published: January 2023

The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A large number of air pollutant predictions have tried to predict pollutants by revealing their evolution patterns, emphasizing the fitting analysis of time series but ignoring the spatial transmission effect of adjacent areas, leading to low prediction accuracy. To solve this problem, we propose a time series prediction network with the self-optimization ability of a spatio-temporal graph neural network (BGGRU) to mine the changing pattern of the time series and the spatial propagation effect. The proposed network includes spatial and temporal modules. The spatial module uses a graph sampling and aggregation network (GraphSAGE) in order to extract the spatial information of the data. The temporal module uses a Bayesian graph gated recurrent unit (BGraphGRU), which applies a graph network to the gated recurrent unit (GRU) so as to fit the data’s temporal information. In addition, this study used Bayesian optimization to solve the problem of the model’s inaccuracy caused by inappropriate hyperparameters of the model. The high accuracy of the proposed method was verified by the actual PM2.5 data of Beijing, China, which provided an effective method for predicting the PM2.5 concentration.
Robust Variable Selection with Exponential Squared Loss for the Spatial Durbin Model
Authors: Liu Zhongyang , Song Yunquan , Cheng Yi
Published: January 2023

With the continuous application of spatial dependent data in various fields, spatial econometric models have attracted more and more attention. In this paper, a robust variable selection method based on exponential squared loss and adaptive lasso is proposed for the spatial Durbin model. Under mild conditions, we establish the asymptotic and “Oracle” properties of the proposed estimator. However, in model solving, nonconvex and nondifferentiable programming problems bring challenges to solving algorithms. To solve this problem effectively, we design a BCD algorithm and give a DC decomposition of the exponential squared loss. Numerical simulation results show that the method is more robust and accurate than existing variable selection methods when noise is present. In addition, we also apply the model to the 1978 housing price dataset in the Baltimore area.
Lossy P-LDPC Codes for Compressing General Sources Using Neural Networks
Authors: Ren Jinkai , Song Dan , Wu Huihui , Wang Lin
Published: January 2023

It is challenging to design an efficient lossy compression scheme for complicated sources based on block codes, especially to approach the theoretical distortion-rate limit. In this paper, a lossy compression scheme is proposed for Gaussian and Laplacian sources. In this scheme, a new route using “transformation-quantization” was designed to replace the conventional “quantization-compression”. The proposed scheme utilizes neural networks for transformation and lossy protograph low-density parity-check codes for quantization. To ensure the system’s feasibility, some problems existing in the neural networks were resolved, including parameter updating and the propagation optimization. Simulation results demonstrated good distortion-rate performance.
The Effect of Robotics Education on Gender Differences in STEM Attitudes among Dutch 7th and 8th Grade Students
Authors: van Wassenaer Nora , Tolboom Jos , van Beekum Olivier
Published: January 2023

Because of its hands-on and integrative approach to STEM, educational robotics has become increasingly popular in recent years. Yet, a gender gap still exists in attitudes towards STEM studies and careers, especially among middle and high school students, potentially resulting in a lack of women in the STEM workforce. This study explores the effect of a robotics curriculum on Dutch 7th- and 8th-grade students’ attitude towards STEM subjects and careers, as assessed by the S-STEM survey. The results revealed no difference between the pre-test and post-test in attitudes toward STEM for both boys and girls. However, boys scored significantly higher than girls on attitude towards technology, engineering and future STEM studies on the post-test. A post hoc analysis revealed a significant difference between boys and girls on their attitude towards engineering and technology during the pre-test. These results demonstrate the difference between boys and girls in their attitudes towards STEM subjects and careers within the context of robotics education. Considering the lack of research on educational robotics among young teenagers, this field needs to be further studied to assess its effect on gender differences within attitudes towards STEM.
Detecting Data Anomalies from Their Formal Specifications: A Case Study in IoT Systems
Authors: Aziz Benjamin
Published: January 2023

We present in this paper a new method in detecting anomalies in datasets representing systems behaviour, which is based on comparing a dataset to the data blueprint of the system representing its normal behaviour. This method removes some of the need for applying complex machine learning algorithms that aim at detecting abnormalities in such datasets and gives a more assured outcome of the presence of abnormalities. Our method first models a system using the formal language of the π-calculus, and then applies an abstract interpretation that ultimately generates an abstract multiset representing the messages exchanged in the system model. We term this multiset as the data blueprint of the system, and it represents the normal behaviour expected. We apply this method to the case of a recent study in literature, which attempts to analyse normal and abnormal behaviour in datasets representing runs of the MQTT protocol, both under attack and no attack conditions. We show that our method is able to detect these conditions in an easier and more straightforward manner than the original case study attempts to.
Onboard Centralized ISL-Building Planning for LEO Satellite Constellation Networks
Authors: Qiao Liang , Yan Hongcheng , Zhou Xiaoyi , Xu Yong , Wang Luyuan , Wen Xin
Published: January 2023

Large-scale low earth orbit (LEO) satellite constellation projects are increasingly adopting inter-satellite links (ISLs) to enable their autonomous and collaborative operation. Due to the large number of satellite constellation network nodes and their continuous movement in orbit, the network nodes may not remain visible to each other at all times, so the ISL-building choices among satellites are diverse and vary with time. As a result, maintaining a network topology requires onboard planning and management. In this paper, we creatively propose an onboard centralized ISL-building planning scheme with the goal of autonomous topology management. A multi-antenna visibility calculation method that takes the antenna installation angle and the turntable rotation threshold into account is provided for the visibility calculation procedure. Additionally, the link-building planning process is modeled using integer linear programming (ILP); however, to tackle the computational complexity problem of ILP, a link-building planning method based on topology stability optimization is presented. The simulation results show that the proposed onboard centralized ISL-building planning scheme can operate among satellites to successfully realize network status collection, visibility calculation, link-building planning, and planning result distribution, as conducted by the dynamic primary satellite. Moreover, the inter-plane link-building planning method based on topology stability optimization improves the network topology stability on the basis of reducing the network delay.
A Kind of Optoelectronic Memristor Model and Its Applications in Multi-Valued Logic
Authors: Wang Jiayang , Lin Yuzhe , Hu Chenhao , Zhou Shiqi , Gu Shenyu , Yang Mengjie , Ma Guojin , Yan Yunfeng
Published: January 2023

Memristors have been proved effective in intelligent computing systems owing to the advantages of non-volatility, nanometer size, low power consumption, compatibility with traditional CMOS technology, and rapid resistance transformation. In recent years, considerable work has been devoted to the question of how to design and optimize memristor models with different structures and physical mechanisms. Despite the fact that the optoelectronic effect inevitably makes the modelling process more complex and challenging, relatively few research works are dedicated to optoelectronic memristor modelling. Based on this, this paper develops an optoelectronic memristor model (containing mathematical model and circuit model). Moreover, the composite memristor circuit (series- and parallel-connected configuration) with a rotation mechanism is discussed. Further, a multi-valued logic circuit is designed, which is capable of performing multiple logic functions from 0–1, verifying the validity and effectiveness of the established memristor model, as well as opening up a new path for the circuit implementation of fuzzy logic.
A Hybrid Edge-Cloud System for Networking Service Components Optimization Using the Internet of Things
Authors: Pal Souvik , Jhanjhi N. Z. , Abdulbaqi Azmi Shawkat , Akila D. , Almazroi Abdulaleem Ali , Alsubaei Faisal S.
Published: January 2023

The need for data is growing steadily due to big data technologies and the Internet’s quick expansion, and the volume of data being generated is creating a significant need for data analysis. The Internet of Things (IoT) model has appeared as a crucial element for edge platforms. An IoT system has serious performance issues due to the enormous volume of data that many connected devices produce. Potential methods to increase resource consumption and responsive services’ adaptability in an IoT system include edge-cloud computation and networking function virtualization (NFV) techniques. In the edge environment, there is a service combination of many IoT applications. The significant transmission latency impacts the functionality of the entire network in the IoT communication procedure because of the data communication among various service components. As a result, this research proposes a new optimization technique for IoT service element installation in edge-cloud-hybrid systems, namely the IoT-based Service Components Optimization Model (IoT-SCOM), with the decrease of transmission latency as the optimization aim. Additionally, this research creates the IoT-SCOM model and optimizes it to choose the best deployment option with the least assured delay. The experimental findings demonstrate that the IoT-SCOM approach has greater accuracy and effectiveness for the difficulty of data-intensive service element installation in the edge-cloud environment compared to the existing methods and the stochastic optimization technique.
Differential Privacy-Enabled Multi-Party Learning with Dynamic Privacy Budget Allocating Strategy
Authors: Pan Ke , Feng Kaiyuan
Published: January 2023

As one of the promising paradigms of decentralized machine learning, multi-party learning has attracted increasing attention, owing to its capability of preventing the privacy of participants from being directly exposed to adversaries. Multi-party learning enables participants to train their model locally without uploading private data to a server. However, recent studies have shown that adversaries may launch a series of attacks on learning models and extract private information about participants by analyzing the shared parameters. Moreover, existing privacy-preserving multi-party learning approaches consume higher total privacy budgets, which poses a considerable challenge to the compromise between privacy guarantees and model utility. To address this issue, this paper explores an adaptive differentially private multi-party learning framework, which incorporates zero-concentrated differential privacy technique into multi-party learning to get rid of privacy threats, and offers sharper quantitative results. We further design a dynamic privacy budget allocating strategy to alleviate the high accumulation of total privacy budgets and provide better privacy guarantees, without compromising the model’s utility. We inject more noise into model parameters in the early stages of model training and gradually reduce the volume of noise as the direction of gradient descent becomes more accurate. Theoretical analysis and extensive experiments on benchmark datasets validated that our approach could effectively improve the model’s performance with less privacy loss.
Digital Service Platform and Innovation in Healthcare: Measuring Users’ Satisfaction and Implications
Authors: Kitsios Fotis , Stefanakakis Stavros , Kamariotou Maria , Dermentzoglou Lambros
Published: January 2023

When it comes to scheduling health consultations, e-appointment systems are helpful for patients. Non-attendance is a common obstacle that many medical practitioners must endure when it comes to the management of appointments in healthcare facilities and outpatient health settings. Prior surveys have found that many users are open to use such mechanisms and that patients would be likely to schedule an online appointment with their doctor if such a system was made accessible. Few studies have sought to determine how well e-appointment systems work, how well they are received by their users, and whether or not they increase the number of appointments booked. The purpose of this research was to collect information that would help executives of a state hospital in Thessaloniki, Greece, to improve their electronic appointment system by measuring the level of satisfaction their patients have with it. The results show that the level of service provided by the electronic appointment system is not satisfactory. The quality of the website is another significant factor that does not contribute to the level of satisfaction experienced by patients.
Toward Building Smart Contract-Based Higher Education Systems Using Zero-Knowledge Ethereum Virtual Machine
Authors: Fekete Dénes László , Kiss Attila
Published: January 2023

The issuing and verification of higher education certificates, including all higher education documents, still functions in a costly and inappropriately bureaucratic manner. Blockchain technology provides a more secure and consistent way to revolutionize the widely used generalized mechanisms and system concepts. In this paper, the most necessary requirements are examined regarding a blockchain-based higher education system, based on the most well-known research papers. Moreover, the opportunities of working on an education system by maintaining a decentralized structure organization are recommended as well. This paper recommends the most suitable blockchain scaling solution for the architecture of an education system which uses the most state-of-the-art EVM (Ethereum virtual machine) compatible approach to implement the higher education system with all the predefined requirements. It is proven that the explained smart contract-based higher education system, which uses zkEVM (zero-knowledge Ethereum virtual machine), consists of all necessary functionalities and satisfies all predefined requirements. In fact, the recommended system, by using a modular blockchain structure, implements all the functionality and capability of the examined related works in one system, namely GDPR (General Data Protection Regulation), which is compatible and more secure.
A Novel Classification Model of Date Fruit Dataset Using Deep Transfer Learning
Authors: Alsirhani Amjad , Siddiqi Muhammad Hameed , Mostafa Ayman Mohamed , Ezz Mohamed , Mahmoud Alshimaa Abdelraof
Published: January 2023

Date fruits are the most common fruit in the Middle East and North Africa. There are a wide variety of dates with different types, colors, shapes, tastes, and nutritional values. Classifying, identifying, and recognizing dates would play a crucial role in the agriculture, commercial, food, and health sectors. Nevertheless, there is no or limited work to collect a reliable dataset for many classes. In this paper, we collected the dataset of date fruits by picturing dates from primary environments: farms and shops (e.g., online or local markets). The combined dataset is unique due to the multiplicity of items. To our knowledge, no dataset contains the same number of classes from natural environments. The collected dataset has 27 classes with 3228 images. The experimental results presented are based on five stages. The first stage applied traditional machine learning algorithms for measuring the accuracy of features based on pixel intensity and color distribution. The second stage applied a deep transfer learning (TL) model to select the best model accuracy of date classification. In the third stage, the feature extraction part of the model was fine-tuned by applying different retrained points to select the best retraining point. In the fourth stage, the fully connected layer of the model was fine-tuned to achieve the best classification configurations of the model. In the fifth stage, regularization was applied to the classification layer of the best-selected model from the fourth stage, where the validation accuracy reached 97.21% and the best test accuracy was 95.21%.
Comparison of Deep Learning Models for Automatic Detection of Sarcasm Context on the MUStARD Dataset
Authors: Băroiu Alexandru-Costin , Trăușan-Matu Ștefan
Published: January 2023

Sentiment analysis is a major area of natural language processing (NLP) research, and its sub-area of sarcasm detection has received growing interest in the past decade. Many approaches have been proposed, from basic machine learning to multi-modal deep learning solutions, and progress has been made. Context has proven to be instrumental for sarcasm and many techniques that use context to identify sarcasm have emerged. However, no NLP research has focused on sarcasm-context detection as the main topic. Therefore, this paper proposes an approach for the automatic detection of sarcasm context, aiming to develop models that can correctly identify the contexts in which sarcasm may occur or is appropriate. Using an established dataset, MUStARD, multiple models are trained and benchmarked to find the best performer for sarcasm-context detection. This performer is proven to be an attention-based long short-term memory architecture that achieves an F1 score of 60.1. Furthermore, we tested the performance of this model on the SARC dataset and compared it with other results reported in the literature to better assess the effectiveness of this approach. Future directions of study are opened, with the prospect of developing a conversational agent that could identify and even respond to sarcasm.
Base Station MIMO Antenna in 1 × 6 Array Configurations with Reflector Design for Sub-6 GHz 5G Applications
Authors: Wasim Mohd. , Khera Shelej , Malik Praveen Kumar , Kumari Samudrala Vara , Das Sudipta , El-Shafai Walid , Aly Moustafa H.
Published: January 2023

In this article, a base station array antenna in 1 × 6 configuration is proposed for sub-6 GHz 5G applications. Analyses have been performed on two orthogonally arranged dipole strips, a balun with various feeding schemes, and a reflector with different side walls. At the balanced feed position, aluminum is used to connect the feeding balun and the dipole through a hole. A single crossed antenna element of size 66 × 66 × 78 mm3 is fabricated using an FR-4 substrate with a dielectric constant of 4.4, 1.6 mm thickness, and an operating frequency band from 3.2 to 5.22 GHz. The radiating element provides a stable and high gain of 11–18 dB using reflectors with sidewalls. The proposed element is simulated, and its electrical downward tilt is investigated for a 1 × 6 array arrangement with dimensions of 642 mm × 112 mm × 90 mm. Various radiation performance parameters are measured, such as gain, FBR (>26 dB), HPBW, and XPD (>11.5 dB) at 60° in the H-plane. A reflection coefficient of less than −15 dB and port-to-port isolation of greater than 27 dB are achieved. Simulation and measurement of radiation patterns are performed for the operating frequencies of 3.2, 4.2, and 5.2 GHz.
Table Structure Recognition Method Based on Lightweight Network and Channel Attention
Authors: Zhang Tao , Sui Yi , Wu Shunyao , Shao Fengjing , Sun Rencheng
Published: January 2023

The table recognition model rows and columns aggregated network (RCANet) uses a semantic segmentation approach to recognize table structure, and achieves better performance in table row and column segmentation. However, this model uses ResNet18 as the backbone network, and the model has 11.35 million parameters and a volume of 45.5 M, which is inconvenient to deploy to lightweight servers or mobile terminals. Therefore, from the perspective of model compression, this paper proposes the lightweight rows and columns attention aggregated network (LRCAANet), which uses the lightweight network ShuffleNetv2 to replace the original RCANet backbone network ResNet18 to simplify the model size. Considering that the lightweight network reduces the number of feature channels, it has a certain impact on the performance of the model. In order to strengthen the learning between feature channels, the rows attention aggregated (RAA) module and the columns attention aggregated (CAA) module are proposed. The RAA module and the CAA module add the squeeze and excitation (SE) module to the original row and column aggregated modules, respectively. Adding the SE module means the model can learn the correlation between channels and improve the prediction effect of the lightweight model. The experimental results show that our method greatly reduces the model parameters and model volume while ensuring low-performance loss. In the end, the average F1 score of our model is only 1.77% lower than the original model, the parameters are only 0.17 million, and the volume is only 0.8 M. Compared with the original model, the parameter amount and volume are reduced by more than 95%.
Hierarchical Queue Management Priority and Balancing Based Method under the Interaction Prediction Principle
Authors: Lemeshko Oleksandr , Yeremenko Oleksandra , Titarenko Larysa , Barkalov Alexander
Published: January 2023

This work is devoted to improving a two-level hierarchical queue management method based on priority and balancing under the interaction prediction principle. The lower level of calculations was connected with the problem optimization solution and was responsible for two tasks. Firstly, the packet flow aggregation and distribution among the macro-queues and sub-queues organized on the router interface must solve the congestion management problem. Secondly, the resource allocation problem solution was related to the balanced allocation of interface bandwidth among the sub-queues, which were weighted relative to their priorities under the traffic-engineering queues. The method’s lower-level functions were recommended to be placed on a set of processors of a routing device responsible for servicing the packets of individual macro-queues. At the same time, the processor coordinator could perform the functions of the upper-level calculations, providing interface bandwidth allocation among the macro-queues. The numerical research results of the proposed two-level hierarchical queue management method confirmed its effectiveness in ensuring high scalability. Balanced, priority-based packet flow distribution and interface bandwidth allocation among the macro-queues and sub-queues were implemented. In addition, the time was reduced for solving tasks related to queue management. The method demonstrated high convergence of the coordination procedure and the quality of the centralized calculations. The proposed approach can be used in various embedded systems.
An Approach for Classification of Alzheimer’s Disease Using Deep Neural Network and Brain Magnetic Resonance Imaging (MRI)
Authors: Hazarika Ruhul Amin , Maji Arnab Kumar , Kandar Debdatta , Jasinska Elzbieta , Krejci Petr , Leonowicz Zbigniew , Jasinski Michal
Published: January 2023

Alzheimer’s disease (AD) is a deadly cognitive condition in which people develop severe dementia symptoms. Neurologists commonly use a series of physical and mental tests to diagnose AD that may not always be effective. Damage to brain cells is the most significant physical change in AD. Proper analysis of brain images may assist in the identification of crucial bio-markers for the disease. Because the development of brain cells is so intricate, traditional image processing algorithms sometimes fail to perceive important bio-markers. The deep neural network (DNN) is a machine learning technique that helps specialists in making appropriate decisions. In this work, we used brain magnetic resonance scans to implement some commonly used DNN models for AD classification. According to the classification results, where the average of multiple metrics is observed, which includes accuracy, precision, recall, and an F1 score, it is found that the DenseNet-121 model achieved the best performance (86.55%). Since DenseNet-121 is a computationally expensive model, we proposed a hybrid technique incorporating LeNet and AlexNet that is light weight and also capable of outperforming DenseNet. To extract important features, we replaced the traditional convolution Layers with three parallel small filters (1×1,3×3, and 5×5). The model functions effectively, with an overall performance rate of 93.58%. Mathematically, it is observed that the proposed model generates significantly fewer convolutional parameters, resulting in a lightweight model that is computationally effective.
Design of Synaptic Driving Circuit for TFT eFlash-Based Processing-In-Memory Hardware Using Hybrid Bonding
Authors: Kim Younghee , Jin Hongzhou , Kim Dohoon , Ha Panbong , Park Min-Kyu , Hwang Joon , Lee Jongho , Woo Jeong-Min , Choi Jiyeon , Lee Changhyuk , Kwak Joon Young , Son Hyunwoo
Published: January 2023

This paper presents a synaptic driving circuit design for processing in-memory (PIM) hardware with a thin-film transistor (TFT) embedded flash (eFlash) for a binary/ternary-weight neural network (NN). An eFlash-based synaptic cell capable of programming negative weight values to store binary/ternary weight values (i.e., ±1, 0) and synaptic driving circuits for erase, program, and read operations of synaptic arrays have been proposed. The proposed synaptic driving circuits improve the calculation accuracy of PIM operation by precisely programming the sensing current of the eFlash synaptic cell to the target current (50 nA ± 0.5 nA) using a pulse train. In addition, during PIM operation, the pulse-width modulation (PWM) conversion circuit converts 8-bit input data into one continuous PWM pulse to minimize non-linearity in the synaptic sensing current integration step of the neuron circuit. The prototype chip, including the proposed synaptic driving circuit, PWM conversion circuit, neuron circuit, and digital blocks, is designed and laid out as the accelerator for binary/ternary weighted NN with a size of 324 × 80 × 10 using a 0.35 μm CMOS process. Hybrid bonding technology using bump bonding and wire bonding is used to package the designed CMOS accelerator die and TFT eFlash-based synapse array dies into a single chip package.
Thorough Study of Multi-Switching-Frequency-Based Spread-Spectrum Technique for Suppression of Conducted Emissions from Wireless Battery Chargers
Authors: Stepins Deniss , Sokolovs Aleksandrs , Zakis Janis
Published: January 2023

The multi-switching-frequency technique is one of the spread-spectrum techniques for suppression of conducted emissions generated by wireless battery chargers. Its advantage is a relatively easy implementation with a microcontroller. In this paper, an original thorough experimental study of the effect of the multi-switching-frequency-based spread spectrum technique parameters (e.g., combinations of number of pulses, frequency order, etc.) on the performance characteristics (conducted emissions levels, efficiency, etc.) of an inductive-resonant wireless battery charger with a closed loop control is presented. It is shown that combinations of a number of pulses and frequency order have a noticeable impact on the performance characteristics of the wireless chargers. The suppression of the conducted emissions can be improved significantly by using optimized parameters of the technique. Moreover, it is proved experimentally that a relatively inexpensive microcontroller with a transceiver can be used to implement both closed-loop control of the wireless charger and the multi-switching-frequency technique.
High-Performance Amorphous InGaSnO Thin-Film Transistor with ZrAlOx Gate Insulator by Spray Pyrolysis
Authors: Chang Yeoungjin , Bukke Ravindra Naik , Kim Youngoo , Ahn Kiwan , Bae Jinbaek , Jang Jin
Published: January 2023

Here, we report the high-performance amorphous gallium indium tin oxide (a-IGTO) thin-film transistor (TFT) with zirconium aluminum oxide (ZAO) gate insulator by spray pyrolysis. The Ga ratio in the IGTO precursor solution varied up to 20%. The spray pyrolyzed a-IGTO with a high-k ZAO gate insulator (GI) exhibits the field-effect mobility (μFE) of 16 cm2V−1s−1, threshold voltage (VTH) of −0.45 V subthreshold swing (SS) of 133 mV/dec., and ON/OFF current ratio of ~108. The optimal a-IGTO TFT shows excellent stability under positive-bias-temperature stress (PBTS) with a small ΔVTH shift of 0.35 V. The enhancements are due to the high film quality and fewer interfacial traps at the a-IGTO/ZAO interface. Therefore, the spray pyrolyzed a-IGTO TFT can be a promising candidate for flexible TFT in the next-generation display.
Design of High-Order Resonator HTS Diplexer with Very Different FBW
Authors: Zhou Liguo , Zhou Weikang , Sun Yuehang , Han Yu , Jiang Jiang , Zhang Dongwei
Published: January 2023

Adopting the deformed stepped impedance micro-strip line structure resonators (SIRs), the external weak coupling hairpin SIR (HSIR) and external strong coupling opening SIR (OSIR) resonators are designed, respectively. These two types of resonators are convenient for creating an ultra-narrow band and broadband filters, respectively, and can broaden the second harmonic passband. A high isolation diplexer without an impedance matching structure is realized by cascading 12-order and 14-order HTS filters composed of OSIR and HSIR resonators with a high isolation T-shaped structure. The diplexer is fabricated on a thin YBCO/MgO/YBCO (a MgO wafer with YBa2Cu3O7 thin film deposited on both sides) film with a dimension of 31.85 mm × 17.28 mm, a thickness of 0.5 mm, and a dielectric constant of 9.8. At 77 K, the measured central frequency of the diplexer is 2395 MHz and 3300 MHz, the fractional bandwidths (FBW) are 1.25% and 24.24%, the out-of-band rejections are greater than 60 dB/MHz, the 2f0 are located at 5.1 GHz and 6.6 GHz, the insertion loss is less than 0.20 dB, and the return losses are better than 16 dB and 15 dB. The diplexer has the advantages of a simple design method, compact structure, low insertion loss, high sideband rejection, and high isolation.
Changes in Slope Stability over the Growth and Decay of Japanese Cedar Tree Roots
Authors: Okada Yasuhiko , Cai Fei , Kurokawa Ushio
Published: January 2023

In Japan, repeated driftwood landslide disasters have become a major issue; thus, studies are required to better understand forest function to implement appropriate forest management and prevent such disasters. We investigated the effect of Japanese cedar tree roots on shallow landslide initiation. To incorporate the effect of roots on the two side-flanks of the shallow landslide, we propose a new slope-stability analysis method in which the sliding block is simplified as a three-prism model. The root reinforcement was approximated by the sum of the root pullout forces over a unit area, incorporating changes in the root strength with the growth of the trees after planting and the decay of the stumps after cutting. The reinforced root strength after the stump-cutting decreased linearly with time, with no strength remaining at 9 years. In contrast, the reinforced root strength of the new plants increased according to a logistic curve with time; thus, the root strength increased only slightly up to 9 years after planting, and the minimum total reinforced root strength was observed at this time. The safety factor of the slopes in a forest basin in Ibaraki Prefecture was calculated using the proposed three-prism method at intervals of 5 years on a 1-metre-resolution digital elevation model. The number of unstable grids peaked at 10 years, and a higher risk of slope instability was observed at 5–15 years. Therefore, implementing forest operations for lowering slope instability during this period should be important to prevent landslide disasters.
The Energy Efficiency of Heterogeneous Cellular Networks Based on the Poisson Hole Process
Authors: Chen Yonghong , Xun Lei , Zhang Shibing
Published: January 2023

In order to decrease energy consumption caused by the dense deployment of pico base stations (PBSs) in heterogeneous cellular networks (HetNets), this paper first analyzes the energy efficiency (EE) of two-tier HetNets and then proposes a method to maximize the network EE by adjusting the PBS transmit power. The two-tier HetNets are modeled by the Poisson point process (PPP) and the Poisson hole process (PHP), and then the coverage probability of the macro base station (MBS) and the PBS in the two-tier HetNets is derived based on the mean interference to signal ratio (MISR). According to the user association probability, the coverage probability of the PPP-PHP HetNets is obtained. Then, the tractable expression of the average achievable rate is deduced on the basis of the relationship between the coverage probability and the average achievable rate. Finally, the expression of EE is derived and the EE optimization algorithm is proposed based on the PBS transmit power. The simulation results show that the PPP-PHP network is superior to the PPP-PPP network in terms of coverage probability and EE, and the network EE can be effectively improved by setting an appropriate PBS transmit power.
Cascade Control for Two-Axis Position Mechatronic Systems
Authors: Morar Dora , Mihaly Vlad , Şuşcă Mircea , Dobra Petru
Published: January 2023

The current paper proposes an extension for two controller design procedures for a two-axis positioning mechatronic system, followed by a comparison between them. As such, the first method consists in formulating an optimization problem in terms of linear matrix inequalities (LMIs) in order to impose the location of the closed-loop poles, considering an uncertain model of such a system. The uncertain model is treated using various forms of linear differential inclusions (LDIs), namely, polytopic LDI (PLDI) and diagonal norm-bound LDI (DNLDI). Additionally, the problem regarding the command signal constraints is characterized in terms of LMIs. The imposed structure of the controller is a cascade one, with a PI controller for the position loop and a P controller for the velocity loop, having an additional feedforward term. On the other hand, the second method consists in designing a cascade controller with an inner P controller, as in the previous method, the outer controller being a fractional-order IλIDλD (FO–ID) controller. In terms of degrees of freedom, both methods present four degrees of freedom for each axis. The presented controller design procedures will be applied for a numerical example of such a positioning system, and a comparison of the obtained performance metrics will be performed.
Higher-Order Dispersive and Nonlinearity Modulations on the Propagating Optical Solitary Breather and Super Huge Waves
Authors: Abdelwahed H. G. , Alsarhana A. F. , El-Shewy E. K. , Abdelrahman Mahmoud A. E.
Published: January 2023

The nonlinearity form of the Schrödinger equation (NLSE) gives a sterling account for energy and solitary transmission properties in modern communications with optical-fiber energ- reinforcement actions. The solitary representation during fiber transmissions was regulated by NLSE coefficients such as nonlinear Kerr, evolutions, and dispersions, which controlled the energy changes through the model. Sometimes, the energy values predicted from the NLSEs computations may diverge due to variations in the amplitude and width caused by scattering, dispersive, and dissipative features of fiber materials. Higher-order nonlinear Schrödinger equations (HONLSEs) should be explored to alleviate these implications in energy and wave features. The unified solver approach is employed in this work to evaluate the HONLSEs. Steepness, HO dispersions, and nonlinearity self-frequency influences have been taken into consideration. The energy and solitary features were altered by higher-order actions. The unified solver approach is employed in this work to reform the HONLSE solutions and its energy properties. The steepness, HO dispersions, and nonlinearity self-frequency influences have been taken into consideration. The energy and soliton features in the investigated model were altered by the higher-order impacts. Furthermore, the new HONLSE solutions explain a wide range of important complex phenomena in wave energy and its applications.
Existence Results for a Differential Equation Involving the Right Caputo Fractional Derivative and Mixed Nonlinearities with Nonlocal Closed Boundary Conditions
Authors: Ahmad Bashir , Alnahdi Manal , Ntouyas Sotiris K.
Published: January 2023

In this study, we present a new notion of nonlocal closed boundary conditions. Equipped with these conditions, we discuss the existence of solutions for a mixed nonlinear differential equation involving a right Caputo fractional derivative operator, and left and right Riemann–Liouville fractional integral operators of different orders. We apply a decent and fruitful approach of fixed point theory to establish the desired results. Examples are given for illustration of the main results. The paper concludes with some interesting observations.
Robust Data Sampling in Machine Learning: A Game-Theoretic Framework for Training and Validation Data Selection
Authors: Mo Zhaobin , Di Xuan , Shi Rongye
Published: January 2023

How to sample training/validation data is an important question for machine learning models, especially when the dataset is heterogeneous and skewed. In this paper, we propose a data sampling method that robustly selects training/validation data. We formulate the training/validation data sampling process as a two-player game: a trainer aims to sample training data so as to minimize the test error, while a validator adversarially samples validation data that can increase the test error. Robust sampling is achieved at the game equilibrium. To accelerate the searching process, we adopt reinforcement learning aided Monte Carlo trees search (MCTS). We apply our method to a car-following modeling problem, a complicated scenario with heterogeneous and random human driving behavior. Real-world data, the Next Generation SIMulation (NGSIM), is used to validate this method, and experiment results demonstrate the sampling robustness and thereby the model out-of-sample performance.
Differential Game-Theoretic Models of Cournot Oligopoly with Consideration of the Green Effect
Authors: Ougolnitsky Guennady , Usov Anatoly
Published: January 2023

We built and investigated analytically and numerically a differential game model of Cournot oligopoly with consideration of pollution for the general case and the case of symmetrical agents. We conducted a comparative analysis of selfish agents’ behavior (a differential game in normal form), their hierarchical organization (differential Stackelberg games), and cooperation (optimal control problem) using individual and collective indices of relative efficiency. The same analysis wasperformed for the models with the green effect when players chose both output volumes and environmental protection efforts. We used the Pontryagin maximum principle for analytical investigation and the method of qualitatively representative scenarios in simulation modeling for numerical calculations. This method allows for reducing the number of computer simulations, providing sufficient precision. As a result of the comparative analysis, systems of collective and individual preferences were obtained.
Super-Hydrophobicity of Polyester Fabrics Driven by Functional Sustainable Fluorine-Free Silane-Based Coatings
Authors: Sfameni Silvia , Lawnick Tim , Rando Giulia , Visco Annamaria , Textor Torsten , Plutino Maria Rosaria
Published: January 2023

Polyester fibers are widely employed in a multitude of sectors and applications from the technical textiles to everyday life thanks to their durability, strength, and flexibility. Despite these advantages, polyester lacks in dyeability, adhesion of coating, hydrophilicity, and it is characterized by a low wettability respect to natural fibers. On this regard, beyond the harmful hydrophobic textile finishings of polyester fabrics containing fluorine-compounds, and in order to avoid pre-treatments, such as laser irradiation to improve their surface properties, research is moving towards the development of fluorine-free and safer coatings. In this work, the (3-glycidyloxypropyl)trimethoxysilane (GPTMS) and various long alkyl-chain alkoxysilanes were employed for the fabrication in the presence of a catalyst of a water-based superhydrophobic finishing for polyester fabrics with a simple sol-gel, non-fluorinated, sustainable approach and the dip-pad-dry-cure method. The finished polyester fabrics surface properties were investigated by static and dynamic water repellency tests. Additionally, the resistance to common water-based liquids, abrasion resistance, moisture adsorption, and air permeability measurements were performed. Scanning electron microscopy was employed to examine the micro- and nano-morphology of the functionalized polyester fabrics surfaces. The obtained superhydrophobic finishings displayed high water-based stain resistance as well as good hydrophobicity after different cycles of abrasion.
On the Sorbent Ability and Reusability of Graphene-Oxide–Chitosan Aerogels for the Removal of Dyes from Wastewater
Authors: Pinelli Filippo , Piras Chiara , Nogueira Liebert Parreiras , Rossi Filippo
Published: January 2023

One of the most persistent issues affecting people worldwide is water contamination due to the indiscriminate disposal of pollutants, causing severe environmental problems. Dyes are among the most harmful contaminants because of their high chemical stability and consequently difficult degradation. To remove contaminants from water, adsorption is the most widely used and effective method. In this work, we recall the results already published about the synthesis, the characterization and the use of porous graphene-oxide–chitosan aerogels as a sorbent material. Those systems, prepared by mixing GO sheets and CS chains, using APS as a cross-linking agent, and by further lyophilization, were further characterized using nano-computed tomography, supplying more understanding about their micro and nano-structure. Their sorbent ability has been investigated also by the study of their isotherm of adsorption of two different anionic dyes: Indigo Carmine and Cibacron Brilliant Yellow. Those analyses confirmed the potentialities of the aerogels and their affinity for those anionic dyes. Moreover, the possibility of regenerating and reusing the material was evaluated as a key aspect for applications of this kind. The treatment with NaOH, to promote the desorption of adsorbed dyes, and subsequent washing with HCl, to re-protonate the system, ensured the regeneration of the gels and their use in multiple cycles of adsorption with the selected water contaminants.