bluuebunny/crossref_metadata_2025 huggingface dataset:
Chaotic Mapping-Based Anti-Sorting Radio Frequency Stealth Signals and Compressed Sensing-Based Echo Signal Processing Technology
Authors: Jia Jinwei , Liu Limin , Liang Yuying , Han Zhuangzhi , Wang Xuetian
Published: October 2022

Radio frequency (RF) stealth anti-sorting technology can improve the battlefield survival rate of radar and is one of the research hotspots in the radar field. In this study, the signal design principle of anti-sequential difference histogram (SDIF) sorting was explored for the main sorting algorithm of the SDIF. Furthermore, we designed a piecewise linear chaotic system with interval number parameterization based on random disturbance and proposed a method to modulate the repetition period of widely spaced signal pulses using a chaotic system. Then, considering the difficulty of the traditional signal processing method to measure the velocity of the highly random anti-sorting signals designed in this paper, we used compressed sensing (CS) technology to process the echoes of the signals to solve the velocity and distance of the detection targets. Finally, simulation verification was performed from the correctness of the signal design principle, the performance of the chaotic system, the anti-sorting performance of the designed signals and the recovery and reconstruction performance of the signals by CS. The results show that: (a) the signal design principle presented in this paper can guide the signal design correctly; (b) the performance of the piecewise linear chaotic system with interval number parameterization is better than that of the classical one-dimensional chaotic system; (c) the anti-sorting signal modulated by the chaotic system can achieve anti-SDIF sorting, and the anti-sorting signals designed in this paper can be processed to obtain the velocity and distance of the targets.
Entropy Measurements for Leukocytes’ Surrounding Informativeness Evaluation for Acute Lymphoblastic Leukemia Classification
Authors: Pałczyński Krzysztof , Ledziński Damian , Andrysiak Tomasz
Published: October 2022

The study of leukemia classification using deep learning techniques has been conducted by multiple research teams worldwide. Although deep convolutional neural networks achieved high quality of sick vs. healthy patient discrimination, their inherent lack of human interpretability of the decision-making process hinders the adoption of deep learning techniques in medicine. Research involving deep learning proved that distinguishing between healthy and sick patients using microscopic images of lymphocytes is possible. However, it could not provide information on the intermediate steps in the diagnosis process. As a result, despite numerous examinations, it is still unclear whether the lymphocyte is the only object in the microscopic picture containing leukemia-related information or if the leukocyte’s surroundings also contain the desired information. In this work, entropy measures and machine learning models were applied to study the informativeness of both whole images and lymphocytes’ surroundings alone for Leukemia classification. This work aims to provide human-interpretable features marking the probability of sickness occurrence. The research stated that the hue distribution of images with lymphocytes obfuscated alone is informative enough to facilitate 93.0% accuracy in healthy vs. sick classification. The research was conducted on the ALL-IDB2 dataset.
Non-Hermitian Generalization of Rényi Entropy
Authors: Li Daili , Zheng Chao
Published: October 2022

From their conception to present times, different concepts and definitions of entropy take key roles in a variety of areas from thermodynamics to information science, and they can be applied to both classical and quantum systems. Among them is the Rényi entropy. It is able to characterize various properties of classical information with a unified concise form. We focus on the quantum counterpart, which unifies the von Neumann entropy, max- and min-entropy, collision entropy, etc. It can only be directly applied to Hermitian systems because it usually requires that the density matrices is normalized. For a non-Hermitian system, the evolved density matrix may not be normalized; i.e., the trace can be larger or less than one as the time evolution. However, it is not well-defined for the Rényi entropy with a non-normalized probability distribution relevant to the density matrix of a non-Hermitian system, especially when the trace of the non-normalized density matrix is larger than one. In this work, we investigate how to describe the Rényi entropy for non-Hermitian systems more appropriately. We obtain a concisely and generalized form of α-Rényi entropy, which we extend the unified order-α from finite positive real numbers to zero and infinity. Our generalized α-Rényi entropy can be directly calculated using both of the normalized and non-normalized density matrices so that it is able to describe non-Hermitian entropy dynamics. We illustrate the necessity of our generalization by showing the differences between ours and the conventional Rényi entropy for non-Hermitian detuning two-level systems.
Swimming Mode of Two Interacting Squirmers under Gravity in a Narrow Vertical Channel
Authors: Guan Geng , Lin Jianzhong , Nie Deming
Published: October 2022

The swimming mode of two interacting squirmers under gravity in a narrow vertical channel is simulated numerically using the lattice Boltzmann method (LBM) in the range of self-propelling strength 0.1 ≤ α ≤ 1.1 and swimming type −5 ≤ β ≤ 5. The results showed that there exist five typical swimming patterns for individual squirmers, i.e., steady upward rising (SUR), oscillation across the channel (OAC), oscillation near the wall (ONW), steady upward rising with small-amplitude oscillation (SURO), and vertical motion along the sidewall (VMS). The parametric space (α, β) illustrated the interactions on each pattern. In particular, the range of oscillation angle for ONW is from 19.8° to 32.4° as α varies from 0.3 to 0.7. Moreover, the swimming modes of two interacting squirmers combine the two squirmers’ independent swimming patterns. On the other hand, the pullers (β < 0) attract with each other at the initial stage, resulting in a low-pressure region between them and making the two pullers gradually move closer and finally make contact, while the result for the pushers (β > 0) is the opposite. After the squirmers’ interaction, the squirmer orientation and pressure distribution determine subsequent squirmer swimming patterns. Two pushers separate quickly, while there will be a more extended interaction period before the two pullers are entirely separated.
Dynamic Linkage between Bitcoin and Traditional Financial Assets: A Comparative Analysis of Different Time Frequencies
Authors: Wang Panpan , Liu Xiaoxing , Wu Sixu
Published: October 2022

This study employs the ADCC-GARCH approach to investigate the dynamic correlation between bitcoin and 14 major financial assets in different time-frequency dimensions over the period 2013–2021, for which the risk diversification, hedging and safe-haven properties of bitcoin for those traditional assets are further examined. The results show that, first, bitcoin is positively linked to risk assets, including stock, bond and commodity, and negatively linked to the U.S. dollar, which is a safe-haven asset, so bitcoin is closer in nature to a risk asset than a safe-haven asset. Second, the high short-term volatility and speculative nature of the bitcoin market makes its long-term correlation with other assets stronger than the short-term. Third, the positive linkage between the prices of bitcoin and risk assets increases sharply under extreme shocks (e.g., the outbreak of COVID-19 in early 2020). Fourth, bitcoin can hedge against the U.S. dollar, and in the long term, bitcoin can hedge against the Chinese stock market and act as a safe haven for the U.S. stock market and crude oil. However, for most other traditional assets, bitcoin is only an effective diversifier.
Ecosystem Services, Poverty Alleviation and Land Productivity: A Critical Survey of a Complex “Ménage à Trois”
Authors: Onofri Laura
Published: October 2022

Ecosystem services, like water provision or pollination, may increase both agricultural productivity (that is, the capability of the sector to increase the output in volume and value) and alleviate poverty (for instance, through food provision). In addition, increased agricultural productivity can help alleviate poverty by increasing the profitability of the sector, the income of the farmers, and the rates of return on (natural and other) capital investments. However, those beneficial effects come at the cost of (possible) deterioration of existing ESs, that, prima facie, represented the main driver for the generation of benefits. This paper, therefore, identifies and discusses the implications (and possible remedies) of a critical issue that, to our knowledge, is under-studied in an integrated context and methodological approach.
“I Find It Really Difficult to Control Myself Too”: A Qualitative Study of the Effects on the Family Dynamic When Parent and Child Have ADHD
Authors: Carr-Fanning Kate , McGuckin Conor
Published: October 2022

If having one child with Attention Deficit Hyperactivity Disorder (ADHD) contributes to family stress and dysfunction, then what happens when more than one family member has ADHD? This paper explores this question by drawing on findings from a multi-case study that explored the voices of stakeholders (child and parent/carer) affected by ADHD in Ireland. There were eight case studies (families) included. Each case had one parent/carer with ADHD and a child with ADHD. Eight children (aged 7–17 years; Mean = 12.6; S.D. = 3.4) and ten parents (2 males) participated. Four parentw/carers reported a diagnosis of ADHD themselves (2 females) and 6 mothers participated who had a spouse with ADHD. Triangulation was achieved using multiple interviews (parent/carers and child), a demographic survey, and creative methods with the children to contribute to a highly contextualised understanding of stakeholders’ experiences. Research findings demonstrated that there may be positive and negative consequences when both parent and child have ADHD. On the one hand, it may contribute to greater dysfunction, when parents with ADHD struggle to stick to routines and remain calm and organised. On the other hand, children with ADHD may feel a sense of belonging and less different, parents believe they have greater understanding, and shared interactional preferences may have benefits. Findings will be discussed in terms of their implications for practice with families and future research.
Design Principles for Considering the Participatory Relationship of Students, Teachers, Curriculum, and Place in Project-Based STEM Units
Authors: Stephenson Reaves Jessica R. , Likely Rasheda , Arias Anna Maria
Published: October 2022

Historically, STEM learning spaces and curriculum have overlooked the strengths and agency of students, teachers, and their communities. Project-based STEM units about environmental issues like water quality offer the possibility to create more expansive, equitable learning experiences. These units can leverage local problems and resources while also including the global dimensions of the issue to provide meaningful opportunities for diverse student sensemaking. However, even project-based STEM learning requires explicit attention to the agency of teachers, students, and place. In order to identify a set of design principles for supporting equitable learning in a project-based STEM curriculum, this manuscript brings together a set of empirical and theoretical frameworks including teacher participatory relationship with the curriculum, culturally sustaining pedagogy, critical pedagogy of place, and equitable sensemaking. The authors use these frameworks to describe a conceptual model of Participatory Relationship of Students, Communities, Teachers, Curriculum and Place. Then, the manuscript outlines a set of seven design principles that connect to the theoretical frameworks to the conceptual model and provide implementation strategies with examples of how we apply these design principles to a project-based STEM unit for 4–8 grade students. The design principles have implications for design of future project-based units and learning opportunities for teachers and students.
Chinese Students’ Perception and Expectation of Online and Post-Pandemic Teaching and Learning Approaches in a UK Transnational Program
Authors: Clerkin Caoimhe , Hatahet Taher , Malekigorji Maryam , Andrews Gavin P.
Published: October 2022

A cloud of uncertainty around how to deliver teaching on transnational education programs (TNE) in the new post pandemic era has been created due to little evidence of investigation available. This study for the first time explored the needs and expectations of students (324) enrolled on BSc Pharmaceutical Science and Pharmaceutical Biotechnology degrees at a China UK Joint College, to understand their preferences for the delivery of such programs. Surveys were circulated amongst students to collect qualitative (open questions) and quantitative (Likert-scaling) data around the infrastructure for online learning (internet, IT device, learning platforms and study place) as well as the challenges and expectations of online learning, including student preference towards the delivery of materials, in a post-pandemic era. Focus groups, facilitated by the delivery team, were organized to collect further qualitative data and to explore common themes arising from the surveys and to understand student requirements more thoroughly for a successful TNE program. The data gathered determined that the transition to online learning has been challenging for students, and that they significantly (p < 0.001) prefer a hybrid model of teaching, consisting of a mixture of digital and on-campus activities. For the first time, this study demonstrates strategies to ensure that the needs and expectations of students in a TNE program are reached in post-pandemic era and the quality of teaching and learning are enhanced.
Reality vs. Expectations of Assessment in STEM Education: An Exploratory Case Study of STEM Schools in Egypt
Authors: El Nagdi Mohamed Ali , Roehrig Gillian H.
Published: October 2022

In this exploratory case study, the assessment methods planned and used in Egyptian STEM schools were explored. The purpose of the study was to explore the relationship between the ideals provided in STEM education both from research and policy documents and the actual assessment strategies used both at the classroom and state level in order to understand the alignment between the proposed lofty goals of STEM and the modes of assessment actually used. Teachers in Egyptian STEM schools were surveyed and interviewed to explore this relationship. Samples of their assessments were also examined. Teachers were found to have been using two mutually exclusive models of assessment; a set of assessments at the disciplinary level and another set at multidisciplinary level including, but not restricted to, project and problem learning, inquiry, and reflective journaling. The study revealed partial alignment between expectations and reality of assessment in Egyptian STEM schools.
A Study of Primary Students’ Technology Acceptance and Flow State When Using a Technology-Enhanced Board Game in Mathematics Education
Authors: Lin Yen-Ting , Wang Tz-Chi
Published: October 2022

Mathematics is a major subject for primary students. However, in mathematics education, many abstract concepts may lead students to lose learning motivation and further affect their learning performance. Therefore, several papers have proposed various board games to support traditional mathematics education. However, teachers have applied traditional board games to support mathematics education in a way that makes it difficult to monitor or capture each student’s learning status and problems during game processes. As mentioned above, this study proposed an educational mathematics board game with mobile and sensor technologies to assist students in learning the concept of prime factorization. The proposed board game can guide students to conduct the game and support teachers to capture the students’ learning status during the game process. To evaluate students’ acceptance and flow state regarding the proposed board game, thirty-nine primary students were invited to participate in an experiment. The experimental results indicated that the students had a high technology acceptance with regard to the proposed board game and high flow state when playing the proposed board game. For high-achieving students, the proposed board game was more entertaining, while low-achieving students might feel a little challenged. In addition, boys were more engaged than girls when playing this board game.
Cultivating Global Citizenship through Higher Education: A Reflection on the Development from Civic to Global Engagement
Authors: Grad Johanna M. , van der Zande Indira S.E.
Published: October 2022

Due to globalization, society underwent changes towards multiculturalism and vast internationalization of the labor market. Those changes make it necessary to adapt the education of future citizens and equip them with tools to persist in an internationalized and globalized world. Contrary to popular concerns, service and engagement can be connected to the goal of internationalization in higher education. The aim of this theoretical paper is to provide new perspectives on the topic of internationalization in higher education and the development of Global Citizenship. We analyze the existing literature to explore the development and interconnections between different types of engagement, specifically the concepts of Civic Engagement, Service Learning, Global Engagement and Global Service Learning. Subsequently, we discuss the role and relevance of higher education institutions and partnerships in achieving Global Citizenship by equipping students with the cross-cultural competencies they need to be successful in a global society. Finally, we discuss the merit of Global Learning and the role engagement can play in the future of internationalized and engaged education.
Students’ Technology Preference and Computer Technology Applications in the Teaching and Learning of Physics Modules at the University Undergraduate Level in South Africa during the COVID-19 Pandemic
Authors: Mukumba Patrick , Shambira Ngwarai
Published: October 2022

This research aims to establish students’ technology preferences and computer technology applications in the teaching and learning of university physics modules during the COVID-19 pandemic. The proposed objectives were (a) to establish students’ technology preferences in physics modules for 2nd and 3rd-year undergraduate level students; (b) to establish students’ hardware technology preferences and hardware technology they own; (c) to determine relationships between technology preferences using Pearson’s correlation coefficient and (d) to determine the effectiveness of the integration of computer technology applications in the lecturer’s teaching and learning of physics modules. Forty-one students (58.5% male and 41.5% female) participated. The questionnaire data was examined utilising descriptive and inferential statistics. Based on the findings of this study, integrating technology in tertiary physics education is recommended as it may enhance the comprehension of abstract and difficult physics concepts.
The Field Skills’ Development through Teaching Environmental Interactions in High School: Draa-Tafilalet Region, Morocco
Authors: Akdim Mariam , Alami Anouar , Selmaoui Sabah , Sabiri Aboubakr , Akdim Hamid
Published: October 2022

The study apprehends the educational impacts of fieldwork in earth sciences teaching at high school. We assess its role in students’ skills improvement, based on a trip to Ait Idir (Ait Sedrate) in southeastern Morocco. The adopted approach consists of comparing skills acquired by a group of 26 high school students before and after the fieldwork. The area was chosen based on its scientific relevance, mainly in terms of morphologic diversity and accessibility. The students were engaged in the fieldwork on 4 December 2021 after doing a classroom course on introductive geology, granulometry, and sands’ morphoscopy. In the field, students were asked to observe and discuss chosen landforms and deposits that may facilitate their skills development. Several technic tools were used such as maps, satellite images, and other geotechnical and mechanical tools. The results show the important positive impact of the fieldwork in teaching earth sciences at high school. It offered observable elements that students enjoy describing and discussing. The students engage their critical thinking to assess and discuss the landscape structure, the geomorphic forms, and their genetic processes and perceive the importance of the scale concepts for example. The post-test confirms that 53% of the students gave the right answers to asked general questions on topography, deposits, landforms, contextualization, and human–nature interactions for example. A total of 75% of asked questions were correctly answered by students concerning the site location, its physical framework, and its sedimentologic impacts. The research results are important in terms of soft skills development, and regarding their scientific, didactic, and cognitive impacts.
Adoption of Digital Business Solutions: Designing and Monitoring Critical Success Factors
Authors: Doneva Rositsa , Gaftandzhieva Silvia
Published: October 2022

The success of a business organization on its path to digital transformation depends on the success of the various stages of the project in order to adopt the selected digital business solutions. The success of this project is determined to a large extent by identifying and monitoring critical success factors (CSFs) for these stages. Based on the studies in the field of CSFs and consultations with experts from the ICT sector and academia, the paper presents a comprehensive framework for the overall management of CSFs (from CSFs’ design to their monitoring). The framework helps business organizations conceptualize CSFs for different stages of a project for the adoption of a chosen digital business solution. Furthermore, the framework provides practical guidance in the form of a framework, a methodology on what organizations should do to identify, monitor and manage the proper CSFs so that they can take the most advantage of them. The proposed framework has broad practical application and can be used by companies implementing projects for the digitalization of their activity to improve the digital services offered and advance the organization’s efficiency, etc. Currently, aiming to validate the presented framework, it will be applied for the adoption of a customer relationship management (CRM) digitalization solution, based on a cooperation agreement with a national company from the internet and TV service delivery industry. The framework can be further developed and applied to other project types.
Online Reinforcement-Learning-Based Adaptive Terminal Sliding Mode Control for Disturbed Bicycle Robots on a Curved Pavement
Authors: Zhu Xianjin , Deng Yang , Zheng Xudong , Zheng Qingyuan , Liang Bin , Liu Yu
Published: October 2022

The reaction wheel is able to help improve the balancing ability of a bicycle robot on curved pavement. However, preserving good control performances for such a robot that is driving on unstructured surfaces under matched and mismatched disturbances is challenging due to the underactuated characteristic and the nonlinearity of the robot. In this paper, a controller combining proximal policy optimization algorithms with terminal sliding mode controls is developed for controlling the balance of the robot. Online reinforcement-learning-based adaptive terminal sliding mode control is proposed to attenuate the influence of the matched and mismatched disturbance by adjusting parameters of the controller online. Different from several existing adaptive sliding mode approaches that only tune parameters of the reaching controller, the proposed method also considers the online adjustment of the sliding surface to provide adequate robustness against mismatched disturbances. The co-simulation experimental results in MSC Adams illustrate that the proposed controller can achieve better control performances than four existing methods for a reaction wheel bicycle robot moving on curved pavement, which verifies the robustness and applicability of the method.
Out-of-Distribution (OOD) Detection Based on Deep Learning: A Review
Authors: Cui Peng , Wang Jinjia
Published: October 2022

Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from input data through a model. This problem has attracted increasing attention in the area of machine learning. OOD detection has achieved good intrusion detection, fraud detection, system health monitoring, sensor network event detection, and ecosystem interference detection. The method based on deep learning is the most studied in OOD detection. In this paper, related basic information on OOD detection based on deep learning is described, and we categorize methods according to the training data. OOD detection is divided into supervised, semisupervised, and unsupervised. Where supervised data are used, the methods are categorized according to technical means: model-based, distance-based, and density-based. Each classification is introduced with background, examples, and applications. In addition, we present the latest applications of OOD detection based on deep learning and the problems and expectations in this field.
Electricity Consumption Prediction in an Electronic System Using Artificial Neural Networks
Authors: Stošović Miona Andrejević , Radivojević Novak , Ivanova Malinka
Published: October 2022

The tremendous rise of electrical energy demand worldwide has led to many problems related to efficient use of electrical energy, consequently posing difficult challenges to electricity consumers of all levels—from households to large companies’ facilities. Most of these challenges could be overcome by the accurate prediction of electricity demand. Additionally, balance responsibility includes the penalty-based financial mechanism causing extra expense for badly estimated consumption, above the allowed imbalance limits. In this paper, a method for electricity consumption prediction based on artificial neural networks is proposed. The electricity consumption dataset is obtained from a cold storage facility, which generates data in hourly intervals. The data obtained are measured for a period of over 2 years and then separated to four seasons, so different models are developed for each season. Five different network structures (ordinary RNN, LSTM, GRU, bidirectional LSTM, bidirectional GRU) for five different values of horizon, i.e., input data (one day, two days, four days, one week, two weeks) are examined. Performance indices, such as mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE) and mean square error (MSE), are used in order to obtain qualitative and quantitative comparisons among the obtained models. The results show that the modifications of recurrent neural networks perform much better than ordinary recurrent neural networks. GRU and LSTMB structures with horizons of 168h and 336h are found to have the best performances.
An Optimized Architecture for CGA Operations and Its Application to a Simulated Robotic Arm
Authors: Vitabile Salvatore , Franchini Silvia , Vassallo Giorgio
Published: October 2022

Conformal geometric algebra (CGA) is a new geometric computation tool that is attracting growing attention in many research fields, such as computer graphics, robotics, and computer vision. Regarding the robotic applications, new approaches based on CGA have been proposed to efficiently solve problems as the inverse kinematics and grasping of a robotic arm. The hardware acceleration of CGA operations is required to meet real-time performance requirements in embedded robotic platforms. In this paper, we present a novel embedded coprocessor for accelerating CGA operations in robotic tasks. Two robotic algorithms, namely, inverse kinematics and grasping of a human-arm-like kinematics chain, are used to prove the effectiveness of the proposed approach. The coprocessor natively supports the entire set of CGA operations including both basic operations (products, sums/differences, and unary operations) and complex operations as rigid body motion operations (reflections, rotations, translations, and dilations). The coprocessor prototype is implemented on the Xilinx ML510 development platform as a complete system-on-chip (SoC), integrating both a PowerPC processing core and a CGA coprocessing core on the same Xilinx Virtex-5 FPGA chip. Experimental results show speedups of 78× and 246× for inverse kinematics and grasping algorithms, respectively, with respect to the execution on the PowerPC processor.
An Efficient Hybrid QHCP-ABE Model to Improve Cloud Data Integrity and Confidentiality
Authors: Singamaneni Kranthi Kumar , Nauman Ali , Juneja Sapna , Dhiman Gaurav , Viriyasitavat Wattana , Hamid Yasir , Anajemba Joseph Henry
Published: October 2022

Cloud computational service is one of the renowned services utilized by employees, employers, and organizations collaboratively. It is accountable for data management and processing through virtual machines and is independent of end users’ system configurations. The usage of cloud systems is very simple and easy to organize. They can easily be integrated into various storages of the cloud and incorporated into almost all available software tools such as Hadoop, Informatica, DataStage, and OBIEE for the purpose of Extraction-Transform-Load (ETL), data processing, data reporting, and other related computations. Because of this low-cost-based cloud computational service model, cloud users can utilize the software and services, the implementation environment, storage, and other on-demand resources with a pay-per-use model. Cloud contributors across this world move all these cloud-based apps, software, and large volumes of data in the form of files and databases into enormous data centers. However, the main challenge is that cloud users cannot have direct control over the data stored at these data centers. They do not even know the integrity, confidentiality, level of security, and privacy of their sensitive data. This exceptional cloud property creates several different security disputes and challenges. To address these security challenges, we propose a novel Quantum Hash-centric Cipher Policy-Attribute-based Encipherment (QH-CPABE) framework to improve the security and privacy of the cloud user’s sensitive data. In our proposed model, we used both structured and unstructured big cloud clinical data as input so that the simulated experimental results conclude that the proposal has precise, resulting in approximately 92% correctness of bit hash change and approximately 96% correctness of chaotic dynamic key production, enciphered and deciphered time as compared with conventional standards from the literature.
Detection of COVID-19 Cases Based on Deep Learning with X-ray Images
Authors: Wang Zhiqiang , Zhang Ke , Wang Bingyan
Published: October 2022

Since the outbreak of COVID-19, the coronavirus has caused a massive threat to people’s lives. With the development of artificial intelligence technology, identifying key features in medical images through deep learning, infection cases can be screened quickly and accurately. This paper uses deep-learning-based approaches to classify COVID-19 and normal (healthy) chest X-ray images. To effectively extract medical X-ray image features and improve the detection accuracy of COVID-19 images, this paper extracts the texture features of X-ray images based on the gray level co-occurrence matrix and then realizes feature selection by principal components analysis (PCA) and t-distributed stochastic neighbor embedding (T-SNE) algorithms. To improve the accuracy of X-ray image detection, this paper designs a COVID-19 X-ray image detection model based on the multi-head self-attention mechanism and residual neural network. It applies the multi-head self-attention mechanism to the residual network bottleneck layer. The experimental results show that the multi-head self-attention residual network (MHSA-ResNet) detection model has an accuracy of 95.52% and a precision of 96.02%. It has a good detection effect and can realize the three classifications of COVID-19 pneumonia, common pneumonia, and normal lungs, proving the method’s effectiveness and practicability in this paper.
Using Deep Learning Techniques in Forecasting Stock Markets by Hybrid Data with Multilingual Sentiment Analysis
Authors: Lin Ying-Lei , Lai Chi-Ju , Pai Ping-Feng
Published: October 2022

Electronic word-of-mouth data on social media influences stock trading and the confidence of stock markets. Thus, sentiment analysis of comments related to stock markets becomes crucial in forecasting stock markets. However, current sentiment analysis is mainly in English. Therefore, this study performs multilingual sentiment analysis by translating non-native English-speaking countries’ texts into English. This study used unstructured data from social media and structured data, including trading data and technical indicators, to forecast stock markets. Deep learning techniques and machine learning models have emerged as powerful ways of coping with forecasting problems, and parameter determination greatly influences forecasting models’ performance. This study used Long Short-Term Memory (LSTM) models employing the genetic algorithm (GA) to select parameters for predicting stock market indices and prices of company stocks by hybrid data in non-native English-speaking regions. Numerical results revealed that the developed LSTMGA model with hybrid multilingual sentiment data generates more accurate forecasting than the other machine learning models with various data types. Thus, the proposed LSTMGA model with hybrid multilingual sentiment analysis is a feasible and promising way of forecasting the stock market.
Design of Ultra High Aperture Efficiency Surface Wave Antenna Array Based on the Three-Dimensional Aperture Principle
Authors: Pang Ke , Xie Yongjun , Dai Legen , Wu Peiyu
Published: October 2022

To overcome the conflict between limited space and the requirement for high gain antennas, a novel high aperture efficiency antenna array consisting of surface wave end-fire antennas is proposed based on the three-dimensional aperture principle. By introducing longitudinal dimension into the calculation of the regular aperture antenna gain, the two-dimensional aperture principle is expanded into the three-dimensional aperture principle. Meanwhile, the directivity and gain of the array could increase without increasing the aperture area. The aperture efficiency of the array could be more than 100% in this situation. The gain of the surface wave end-fire element antenna is 9.5 dBi. The proposed 3 × 3 square antenna array with the size of 1.5λ × 1.5λ × 2.24λ obtains a gain of 15.3 dBi and its aperture efficiency reaches 120%.
Segmentation of Retinal Blood Vessels Using U-Net++ Architecture and Disease Prediction
Authors: Gargari Manizheh Safarkhani , Seyedi Mir Hojjat , Alilou Mehdi
Published: October 2022

This study presents a segmentation method for the blood vessels and provides a method for disease diagnosis in individuals based on retinal images. Blood vessel segmentation in images of the retina is very challenging in medical analysis and diagnosis. It is an essential tool for a wide range of medical diagnoses. After segmentation and binary image improvement operations, the resulting binary images are processed and the features in the blood vessels are used as feature vectors to categorize retinal images and diagnose the type of disease available. To carry out the segmentation task and disease diagnosis, we used a deep learning approach involving a convolutional neural network (CNN) and U-Net++ architecture. A multi-stage method is used in this study to better diagnose the disease using retinal images. Our proposed method includes improving the color image of the retina, applying the Gabor filter to produce images derived from the green channel, segmenting the green channel by receiving images produced from the Gabor filter using U-Net++, extracting HOG and LBP features from binary images, and finally disease diagnosis using a one-dimensional convolutional neural network. The DRIVE and MESSIDOR image banks have been used to segment the image, determine the areas related to blood vessels in the retinal image, and evaluate the proposed method for retinal disease diagnosis. The achieved results for accuracy, sensitivity, specificity, and F1-score are 98.9, 94.1, 98.8, 85.26, and, 98.14, respectively, in the DRIVE dataset and the obtained results for accuracy, sensitivity, and specificity are 98.6, 99, 98, respectively, in MESSIDOR dataset. Hence, the presented system outperforms the manual approach applied by skilled ophthalmologists.
BTM: Boundary Trimming Module for Temporal Action Detection
Authors: Hamdi Maher , Wen Shiping , Yang Yin
Published: October 2022

Temporal action detection (TAD) aims to recognize actions as well as their corresponding time spans from an input video. While techniques exist that accurately recognize actions from manually trimmed videos, current TAD solutions often struggle to identify the precise temporal boundaries of each action, which are required in many real applications. This paper addresses this problem with a novel Boundary Trimming Module (BTM), a post-processing method that adjusts the temporal boundaries of the detected actions from existing TAD solutions. Specifically, BTM operates based on the classification of frames in the input video, aiming to detect the action more accurately by adjusting the surrounding frames of the start and end frames of the original detection results. Experimental results on the THUMOS14 benchmark data set demonstrate that the BTM significantly improves the performance of several existing TAD methods. Meanwhile, we establish a new state of the art for temporal action detection through the combination of BTM and the previous best TAD solution.
A Novel Approach to Improve the Adaptive-Data-Rate Scheme for IoT LoRaWAN
Authors: Al-Gumaei Yousef A. , Aslam Nauman , Aljaidi Mohammad , Al-Saman Ahmed , Alsarhan Ayoub , Ashyap Adel Y.
Published: October 2022

The long-range wide-area network (LoRaWAN) uses the adaptive-data-rate (ADR) algorithm to control the data rate and transmission power. The LoRaWAN ADR algorithm adjusts the spreading factor (SF) to allocate the appropriate transmission rate and transmission power to reduce power consumption.The updating SF and transmission power of the standard ADR algorithm are based on the channel state, but it does not guarantee efficient energy consumption among all the nodes in complex environments with high-varying channel conditions. Therefore, this article proposes a new enhancement approach to the ADR+ algorithm at the network server, which only depends on the average signal-to-noise ratio (SNR). The enhancement ADR algorithm ADR++ introduces an energy-efficiency controller α related to the total energy consumption of all nodes, to use it for adjusting the average SNR of the last records. We implement our new enhanced ADR at the network server (NS) using the FLoRa module in OMNET++. The simulation results demonstrate that our proposed ADR++ algorithm leads to a significant improvement in terms of the network delivery ratio and energy efficiency that reduces the network energy consumption up to 17.5% and improves the packet success rate up to 31.55% over the existing ADR+ algorithm.
Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory
Authors: Orlowska-Kowalska Teresa , Wolkiewicz Marcin
Published: October 2022

In the original article [...]
EMC Impact of Disturbances Generated by Multiple Sources
Authors: Karami Hamidreza , Rubinstein Marcos , Rachidi Farhad , Perrenoud Christophe , de Raemy Emmanuel , Kraehenbuehl Pascal , Mediano Arturo
Published: October 2022

In this paper, the impact of an increasing number of arbitrary electrical/electronic devices on the overall radiated emissions is investigated. Understanding and quantifying such an impact are prerequisites to the proper evaluation of electromagnetic compatibility (EMC) of various electronic systems and devices and, if needed, to revisiting the international standards. To evaluate the radiated emissions from multiple electronic devices, each arbitrary electronic device is characterized using an equivalent Huygens’s surface, in which the tangential components of electric and magnetic near fields are calculated (or measured). The radiated emission from the arbitrary electronic device can be calculated using the electric and magnetic near fields for an arbitrary phase (correlated or uncorrelated), position, and orientation. The influence of several parameters affecting the radiated emissions from multiple arbitrary electronic devices, including the number of disturbance sources, the polarization of each device, the radiation pattern of each device, the location and orientation of each device, and the phase shifts between devices, are analyzed. The numerical results show that the mentioned parameters have a significant effect on the radiated emissions, and cannot be neglected in EMC considerations. In general, increasing the number of electronic devices leads to an increase in the level of radiated emissions. However, the increase depends on other parameters such as the arrangement (the radiation pattern for each device, the distance between the devices, and the orientation and/or polarization of each device). The proposed method can be straightforwardly applied to devices characterized by near-field measurements or multimodular large equipment with long cables.
A Method Noise-Based Convolutional Neural Network Technique for CT Image Denoising
Authors: Singh Prabhishek , Diwakar Manoj , Gupta Reena , Kumar Sarvesh , Chakraborty Alakananda , Bajal Eshan , Jindal Muskan , Shetty Dasharathraj K. , Sharma Jayant , Dayal Harshit , Naik Nithesh , Paul Rahul
Published: October 2022

Medical imaging is a complex process that capitulates images created by X-rays, ultrasound imaging, angiography, etc. During the imaging process, it also captures image noise during image acquisition, some of which are extremely corrosive, creating a disturbance that results in image degradation. The proposed work addresses the challenge to eliminate the corrosive Gaussian additive white noise from computed tomography (CT) images while preserving the fine details. The proposed approach is synthesized by amalgamating the concept of method noise with a deep learning-based framework of a convolutional neural network (CNN). The corrupted images are obtained by explicit addition of Gaussian additive white noise at multiple noise variance levels (σ = 10, 15, 20, 25). The denoised images obtained are then evaluated according to their visual quality and quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These metrics for denoised CT images are then compared with their respective values for the reference CT image. The average PSNR value of the proposed method is 25.82, the average SSIM value is 0.85, and the average computational time is 2.8760. To better understand the proposed approach’s effectiveness, an intensity profile of denoised and original medical images is plotted and compared. To further test the performance of the proposed methodology, the results obtained are also compared with that of other non-traditional methods. The critical analysis of the results shows the commendable efficiency of the proposed methodology in denoising the medical CT images corrupted by Gaussian noise. This approach can be utilized in multiple pragmatic areas of application in the field of medical image processing.
Design of VGSOT-MTJ-Based Logic Locking for High-Speed Digital Circuits
Authors: Divyanshu Divyanshu , Kumar Rajat , Khan Danial , Amara Selma , Massoud Yehia
Published: October 2022

Emerging spintronics devices in recent research have received much interest in various fields. Their unique physical aspects are being explored to keep Moore’s law alive. Therefore, the hardware security aspects of system-on-a-chip (SoC) designs using spintronics devices becomes important. Magnetic tunnel junctions (MTJ) are a potential candidate in spintronics-based devices for beyond-CMOS applications. This work uses voltage-gated spin-orbit torque-assisted magnetic tunnel junction (VGSOT-MTJ) based on the Verilog-A behavioral model to design a possible logic-locking system for hardware security. Compared with the SOT MTJ, which uses a heavy metal strip below the MTJ stack, VGSOT-MTJ has an antiferromagnetic (AFM) strip that utilizes the voltage-controlled magnetic anisotropy (VCMA) effect to significantly reduce the JSOT,critical. To design the logic-locking block, we performed a Monte Carlo analysis to account for the effect of process variation (PV) on critical MTJ parameters. Eye diagram tests and mask designing were performed, which included the effect of thermal noise and PV for high-speed digital circuit operations. Finally, transient performance was analyzed to demonstrate the VGSOT-MTJ’s ability to design logic-locking blocks from the circuit operation perspective.
Framework for Structuring Big Data Projects
Authors: Grander Gustavo , Da Silva Luciano Ferreira , Gonzalez Ernesto Del Rosário Santibañez , Penha Renato
Published: October 2022

This article aims to present a framework for structuring Big Data projects. The methodological procedures were divided into two phases: in-depth interview and focus group. The first phase embraces 12 in-depth individual interviews. In the second phase, three sessions of interviews with focus groups were applied. Both phases had as research subjects professionals with experience in Big Data projects. The analysis process was based on categorization through theory-driven and data-driven codes. Based on our analysis, it was possible to present a definition of a Big Data project and explore the beginning and its phases. We also identified 17 different critical factors in Big Data projects and proposed a discussion on technical and behavioral skills and decision-making issues in Big Data projects. We also developed and validated a framework to help structure a Big Data project. As the main theoretical contribution, our study aligns with a growing body of researchers who are proposing to debate Big Data projects, and with that increasing maturity on the topic. As a practical contribution, we present a framework that we hope will contribute to professionals working in the area, helping to conduct a Big Data project through a systemic view.