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A Survey on Database Systems in the Big Data Era: Architectures, Performance, and Open Challenges

IEEE Access 2025年 13卷 95068-95084页

作者： Dritsas, Elias Trigka, Maria University of West Attica Department of Informatics and Computer Engineering Egaleo Park Campus Athens12243 Greece

The advent of Big Data has fundamentally transformed the field of database management systems (DBMS), necessitating the development of innovative paradigms, architectures, and technologies to address unprecedented challenges. Traditional systems, despite their historical dominance, falter under the high velocity, massive volume, and diverse variety of Big Data. These limitations have catalyzed the emergence of alternative solutions such as No (structured query language) SQL, NewSQL, and cloud-native databases, each offering unique approaches to scalability, flexibility, and performance optimization. This survey provides a comprehensive and systematic overview of the evolving database ecosystem in the Big Data era. It delves into the historical progression from traditional relational database management systems (RDBMS) to modern paradigms, emphasizing the motivations, trade-offs, and innovations inherent in these transformations. The classification of databases based on data models, deployment strategies, scalability mechanisms, and consistency models is explored in depth, providing a structured framework for understanding their diverse capabilities. Furthermore, critical performance characteristics, including throughput, latency, fault tolerance, and cost efficiency, are analyzed to assess their effectiveness in real-world applications. By highlighting persistent challenges such as data heterogeneity, security, and interoperability, this survey outlines key research directions, fostering a holistic understanding of the domain and inspiring future advancements in database technologies. © 2013 IEEE.

关键词： Structured Query Language

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Cavity-agnostic acoustofluidic manipulations enabled by guided flexural waves on a membrane acoustic waveguide actuator

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Microsystems & Nanoengineering 2024年第2期10卷 153-171页

作者： Philippe Vachon Srinivas Merugu Jaibir Sharma Amit Lal Eldwin J.Ng Yul Koh Joshua E.-Y.Lee Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Institute of Microelectronics Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore SonicMEMS Laboratory School of Electrical and Computer EngineeringCornell UniversityIthacaNYUSA School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip *** localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising *** traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic ***,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane *** demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS *** experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.

关键词： waveguide fluid cavity

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Alkali Metal Doping on Sol-Gel-Derived Copper Oxide for Performance Enhancement of Thin-Film Transistors

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第6期3卷 228-233页

作者： Baek, Seokhyeon Choi, Jun-Gyu Park, Sungjun Ajou University Department of Intelligence Semiconductor and Engineering Suwon16449 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16449 Korea Republic of Ajou University The Department of Electrical and Computer Engineering Suwon16449 Korea Republic of

In recent years, copper oxide (CuxO) has emerged as a promising p-type oxide semiconductor owing to its high Hall mobility. However, its inherent drawbacks, such as the substantial native defects and uncontrolled stoichiometry, limit its application in thin-film transistors (TFTs) for energy-efficient complementary devices. In this study, we employ the sol-gel synthetic approach for facile doping to investigate the doping effects of alkali metals (Li, Na, and K) on the electrical performance of CuxO TFTs. The results demonstrate that doping, particularly with lithium (Li), significantly improves the electrical performance of CuxO TFTs. The similarity in ionic radius facilitates efficient hole transport, which considerably enhances the field-effect mobility (4.7×10-3 cm2/Vs), subthreshold swing (SS) (8.3 V/dec), and on-off current ratio (∼104) with minimized hysteresis. The structural analysis of the Li-doped CuxO films using X-ray diffraction (XRD) does not exhibit any significant lattice distortion and an increase in the grain size implies the reduction of trap sites. Consequently, the successful fabrication of Li-doped CuxO TFTs on polyimide substrates, using ZrOxO as a gate dielectric layer, demonstrates its compatibility with flexible electronics. This approach enhances the electrical performance of p-type CuxO TFTs and presents a scalable and efficient pathway for the development of advanced TFT technology in flexible electronics. © 2022 IEEE.

关键词： Sol-gel process

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Hybrid two-level protection system for preserving pre-trained DNN models ownership

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Neural Computing and Applications 2024年第34期36卷 21415-21449页

作者： Fkirin, Alaa Moursi, Ahmed Samy Attiya, Gamal El-Sayed, Ayman Shouman, Marwa A. Department of Electrical Engineering Faculty of Engineering Fayoum University Fayoum Egypt Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia Governorate Menouf32952 Egypt

Recent advancements in deep neural networks (DNNs) have made them indispensable for numerous commercial applications. These include healthcare systems and self-driving cars. Training DNN models typically demands substantial time, vast datasets and high computational costs. However, these valuable models face significant risks. Attackers can steal and sell pre-trained DNN models for profit. Unauthorised sharing of these models poses a serious threat. Once sold, they can be easily copied and redistributed. Therefore, a well-built pre-trained DNN model is a valuable asset that requires protection. This paper introduces a robust hybrid two-level protection system for safeguarding the ownership of pre-trained DNN models. The first-level employs zero-bit watermarking. The second-level incorporates an adversarial attack as a watermark by using a perturbation technique to embed the watermark. The robustness of the proposed system is evaluated against seven types of attacks. These are Fast Gradient Method Attack, Auto Projected Gradient Descent Attack, Auto Conjugate Gradient Attack, Basic Iterative Method Attack, Momentum Iterative Method Attack, Square Attack and Auto Attack. The proposed two-level protection system withstands all seven attack types. It maintains accuracy and surpasses current state-of-the-art methods. © The Author(s) 2024.

关键词： Deep neural networks

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ViT-LSTM synergy: a multi-feature approach for speaker identification and mask detection

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Neural Computing and Applications 2024年第35期36卷 22569-22586页

作者： Nassif, Ali Bou Shahin, Ismail Bader, Mohamed Ahmed, Abdelfatah Werghi, Naoufel Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering and Computer Sciences Khalifa University Abu Dhabi United Arab Emirates

The global health crisis caused by the COVID-19 pandemic has brought new challenges to speaker identification systems, particularly due to the acoustic alterations caused by the widespread use of face masks. Aiming to mitigate these distortions and improve the accuracy of speaker identification, this study introduces a novel two-level classification system, leveraging a unique integration of Vision Transformers (ViT) and Long Short-Term Memory (LSTM). This ViT-LSTM model was trained and tested on an extensive dataset composed of diverse speakers, both masked and unmasked, allowing a comprehensive evaluation of its capabilities. Our experimental results demonstrate remarkable improvements in speaker identification, with an accuracy score of 95.67%, significantly surpassing traditional and other deep learning-based methods. Moreover, our framework also shows considerable strength in detecting the presence of a mask, achieving an accuracy of 91.15% and outperforming existing state-of-the-art models. This study provides the first-ever benchmark for mask detection in the context of speaker identification, opening new pathways for research in this emerging area and presenting a robust solution for speaker identification in the era of face masks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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Selection of Wind Turbine Systems for the Sultanate of Oman

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computer Systems Science & engineering 2023年第4期45卷 343-359页

作者： M.A.A.Younis Anas Quteishat Department of Electrical and Computer Engineering Sohar UniversitySultanate of Oman Electrical Engineering Department Faculty of Engineering TechnologyAl-Balqa Applied UniversityAl-SaltJordan

The Sultanate of Oman has been dealing with a severe renewable energy issue for the past few decades,and the government has struggled to find a *** addition,Oman’s strategy for converting power generation to sources of renewable energy includes a goal of 60 percent of national energy demands being met by renewables by 2040,including solar and wind ***,the use of small-scale energy from wind devices has been on the rise in recent *** upward trend is attributed to advancements in wind turbine technology,which have lowered the cost of energy from *** calculate the internal and external factors that affect the small-scale energy of wind technologies,the study used a fuzzy analytical hierarchy process technique for order of preference by similarity to an ideal *** a result,in the decision model,four criteria,seventeen sub-criteria,and three resources of renewable energy were calculated as options from the viewpoint of the Sultanate of *** research is based on an examination of statistics on energy produced by wind turbines at various locations in the Sultanate of ***,six distinct miniature wind turbines were investigated for four different *** outcomes of this study indicate that the tiny wind turbine has a lot of potential in the Sultanate of Oman for applications such as homes,schools,college campuses,irrigation,greenhouses,communities,and small *** government should also use renewable energy resources to help with the renewable energy issue and make sure that the country has enough renewable energy for its long-term growth.

关键词： Multi criteria decision making model fuzzy theory fuzzy sets renewable energy wind turbine supplier selection

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M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion

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IEEE Transactions on Communications 2024年第2期72卷 845-860页

作者： Liu, Yangyi Rini, Stefano Salehkalaibar, Sadaf Chen, Jun McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4L8 Canada Department of Electrical and Computer Engineering Hsinchu30010 Taiwan University of Toronto Department of Electrical and Computer Engineering TorontoONM5A 4T4 Canada

In federated learning (FL), the communication constraint between the remote clients and the Parameter Server (PS) is a crucial bottleneck. For this reason, model updates must be compressed so as to minimize the loss in accuracy resulting from the communication constraint. This paper proposes 'M-magnitude weighted L2 distortion + 2 degrees of freedom' (M22) algorithm, a rate-distortion inspired approach to gradient compression for federated training of deep neural networks (DNNs). In particular, we propose a family of distortion measures between the original gradient and the reconstruction we referred to as ' M-magnitude weighted L{2} ' distortion, and we assume that gradient updates follow an i.i.d. distribution-generalized normal or Weibull, which have two degrees of freedom. In both the distortion measure and the gradient distribution, there is one free parameter for each that can be fitted as a function of the iteration number. Given a choice of gradient distribution and distortion measure, we design the quantizer to minimize the expected distortion in gradient reconstruction. To measure the gradient compression performance under a communication constraint, we define the per-bit accuracy as the optimal improvement in accuracy that one bit of communication brings to the centralized model over the training period. Using this performance measure, we systematically benchmark the choice of gradient distribution and distortion measure. We provide substantial insights on the role of these choices and argue that significant performance improvements can be attained using such a rate-distortion inspired compressor. © 1972-2012 IEEE.

关键词： Deep neural networks

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A novel on–off linear quadratic regulator control approach for satellite rendezvous

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Aerospace Systems 2023年第4期6卷 613-620页

作者： Rouzegar, Hossein Ghanbarisabagh, Mohammad Department of Electrical and Computer Engineering Semnan University Semnan Iran Department of Electrical Engineering Faculty of Electrical Engineering and Computer Sciences Islamic Azad University North Tehran Branch Tehran Iran

In this paper, a novel on–off linear quadratic regulator (LQR) control for satellite rendezvous as an example of linear systems with on–off inputs has been proposed for the first time. It simultaneously benefits from unique potentials of LQR control method and the extensive applications of systems with on–off inputs in various areas. The on–off LQR control approach has been applied on the system of orbital rendezvous and docking of satellites equipped with thrusters which are appropriate samples of systems with on–off inputs. Because of the energy consumption significance in many practical applications, the proposed approach is designed to consume less energy as well. Simulation results show the energy consumption of the presented method has been reduced about 36% compared to the continuous LQR approach. © 2023, Shanghai Jiao Tong University.

关键词： Satellites

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Optimizing UPVC profile production using adaptive neuro-fuzzy inference system

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International Journal of Information Technology (Singapore) 2024年 1-18页

作者： Naghipour, Avaz Salehpour, Arash Iranag, Behnam Safiri Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

This paper explores the implementation of an Adaptive Neuro-Fuzzy Inference System to optimize Unplasticized Polyvinyl Chloride profile production. Given the intrinsic complexities of polymer extrusion, such as maintaining consistent quality amidst varying raw material properties and environmental conditions, traditional control methods like Proportional-Integral-Derivative (PID) controllers often need to catch up in adapting to non-linear production environments. This study proposes an ANFIS-based control system that leverages the adaptability of neural networks and the human-like reasoning of fuzzy logic to address these challenges effectively. The system uses real-world data from expert operators to refine its control parameters, ensuring accurate regulation of critical variables such as extrusion temperature, feed material composition, and ambient conditions. By testing the system against traditional control methods, the research demonstrates significant improvements in quality control, efficiency, and waste reduction. The ANFIS system’s adaptability allows it to minimize errors and adjust dynamically to variations in production conditions, outperforming traditional PID systems in maintaining consistent quality. Furthermore, the study outlines the implications of this research for intelligent manufacturing within Industry 4.0 frameworks, highlighting how ANFIS can integrate seamlessly with existing manufacturing systems to optimize production. This paper ultimately contributes to the growing literature advocating for intelligent control systems in polymer extrusion. It underscores their potential to transform manufacturing efficiency and quality in Unplasticized Polyvinyl Chloride production. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adaptive neuro-fuzzy inference system (ANFIS) Fuzzy logic controller Manufacturing optimization Neural networks Polymer extrusion process UPVC profile production

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Advancing Deepfake Detection Using Xception Architecture:A Robust Approach for Safeguarding against Fabricated News on Social Media

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computers, Materials & Continua 2024年第12期81卷 4285-4305页

作者： Dunya Ahmed Alkurdi Mesut Cevik Abdurrahim Akgundogdu College of Information Engineering Al-Nahrain UniversityBaghdad10011Iraq Department of Electrical and Computer Engineering Altinbas UniversityIstanbul34000Turkey Electrical and Electronics Engineering Department Istanbul University-CerrahpasaIstanbul34320Turkey

Deepfake has emerged as an obstinate challenge in a world dominated by ***,the authors introduce a new deepfake detection method based on Xception *** model is tested exhaustively with millions of frames and diverse video clips;accuracy levels as high as 99.65%are *** are the main reasons for such high efficacy:superior feature extraction capabilities and stable training mechanisms,such as early stopping,characterizing the Xception *** methodology applied is also more advanced when it comes to data preprocessing steps,making use of state-of-the-art techniques applied to ensure constant *** an ever-rising threat from fake media,this piece of research puts great emphasis on stringent memory testing to keep at bay the spread of manipulated *** also justifies better explanation methods to justify the reasoning done by the model for those decisions that build more trust and *** ensemble models being more accurate have been studied and examined for establishing a possibility of combining various detection frameworks that could together produce superior ***,the study underlines the need for real-time detection tools that can be effective on different social media sites and digital ***,protecting privacy,and public awareness in the fight against the proliferation of deepfakes are important *** significantly contributing to the advancements made in the technology that has actually advanced detection,it strengthens the safety and integrity of the cyber world with a robust defense against ever-evolving deepfake threats in ***,the findings generally go a long way to prove themselves as the crucial step forward to ensuring information authenticity and the trustworthiness of society in this digital world.

关键词： Deepfake Detection Xception architecture data processing image processing intelligent information systems social media security

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