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Advanced Technologies and Applications in Computer Science and Engineering
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Advanced Technologies and Applications in Computer Science and Engineering

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 24449

Special Issue Editors

Dr. Nikolay Hinov

E-Mail Website
Guest Editor
Faculty of Computer Systems and Technologies, Department of Computer Systems, Technical University in Sofia, 8 Ohridski Blvd., 1000 Sofia, Bulgaria
Interests: artificial intelligence; mathematical modeling; control theory and applications; smart cities and smart grids
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ognyan Nakov

E-Mail Website
Guest Editor
Faculty of Computer Systems and Technologies, Technical University of Sofia, 1000 Sofia, Bulgaria
Interests: software engineering; software technologies and application systems; cloud technologies; internet of things; smart cities; cybersecurity; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Milena Lazarova

E-Mail Website
Guest Editor
Depаrtment “Computer systems”, Faculty of Computer Systems and Technologies, Technical Unversity of Sofia, Kliment Ohridski Blvd. 8, 1000 Sofia, Bulgaria
Interests: artificial intelligence; machine learning and deep learning; neural networks; pattern recognition; image analysis; optimization algorithms; metaheuristics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer science is one of the fastest-growing branches of engineering. This is due both to the increased capabilities and accessibility of the hardware, and to the successful implementation of modern information and communication technologies. Achievements in the fields of artificial intelligence, big data, cloud technologies, modeling and computational mathematics are notable. In this respect, there is no other field of science that contributes so directly to improving the quality of life of modern society. Intelligent medicine, virtual and augmented reality, smart networks and cities, autonomous cars, digital factories and productions and many other achievements of modern civilization are the product of progress in the field of computer science.

The 2023 11th International Scientific Conference COMPUTER SCIENCE (COMPSCI 2023) organized by the Faculty of Computer Systems and Technologies continues the tradition of a series of ten conferences organized between 2004 and 2022 as a scientific forum to present a discussion of innovative ideas, concepts and technologies in the field of computer science, computer and software engineering, information technology and their application. Participation in the conference of researchers from different countries stimulates the building of a scientific community and encourages interaction and international cooperation.

This Special Issue primarily represents a collection of extended versions of selected papers presented at the 2023 11th International Scientific Conference COMPUTER SCIENCE (COMPSCI 2023). However, papers not presented at the COMPSCI 2023 are also welcome. We invite you to contribute original research articles or comprehensive review papers to this Special Issue. The topics of interest include, but are not limited to, the following:

  • Artificial intelligence, robotics and control;
  • Cyber security and cyber protection;
  • Data structures and storage;
  • Cloud and blockchain technologies;
  • Electric and autonomous vehicles;
  • High technology management;
  • Human-centered computing;
  • Renewable and green energy;
  • Smart cities and smart society;
  • Software engineering;
  • Software technologies and applications systems;
  • Telecommunications engineering.

Dr. Nikolay Hinov
Prof. Dr. Ognyan Nakov
Prof. Dr. Milena Lazarova
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence, robotics and control
  • cyber security and cyber protection
  • data structures and storage
  • cloud and blockchain technologies
  • electric and autonomous vehicles
  • smart cities and smart society
  • software engineering
  • software technologies and applications systems
  • human-centered computing
  • renewable and green energy
  • telecommunications engineering

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Published Papers (12 papers)

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Editorial

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6 pages, 150 KiB  
Editorial
Advanced Technologies and Applications in Computer Science and Engineering
by Ognyan Nakov, Milena Lazarova and Nikolay Hinov
Electronics 2025, 14(4), 753; https://doi.org/10.3390/electronics14040753 - 14 Feb 2025
Viewed by 855
Abstract
This editorial article introduces the Special Issue entitled “Advanced Technologies and Applications in Computer Science and Engineering”, which highlights a diverse range of contemporary research and technological advancements in these fields. The issue features selected contributions that explore cutting-edge innovations, including artificial intelligence, [...] Read more.
This editorial article introduces the Special Issue entitled “Advanced Technologies and Applications in Computer Science and Engineering”, which highlights a diverse range of contemporary research and technological advancements in these fields. The issue features selected contributions that explore cutting-edge innovations, including artificial intelligence, machine learning, quantum computing, the Internet of Things, and big data analytics, emphasizing their transformative impact on industry, science, and society. The articles address both theoretical advancements and practical applications, showcasing novel approaches to solving complex computational and engineering problems. Special attention is given to the challenges associated with the integration and implementation of these emerging technologies, including scalability, security, and ethical considerations. The selected studies present innovative solutions in key areas such as robotics, network security, cloud computing, and data-driven decision-making, contributing to the development of efficient, intelligent, and sustainable technological solutions. By bringing together a multidisciplinary perspective, this Special Issue aims to foster collaboration among researchers, engineers, and industry professionals, ultimately advancing the frontiers of computer science and engineering. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications in Computer Science and Engineering)

Research

Jump to: Editorial

19 pages, 6954 KiB  
Article
Typhoon Trajectory Prediction by Three CNN+ Deep-Learning Approaches
by Gang Lin, Yanchun Liang, Adriano Tavares, Carlos Lima and Dong Xia
Electronics 2024, 13(19), 3851; https://doi.org/10.3390/electronics13193851 - 28 Sep 2024
Cited by 2 | Viewed by 1827
Abstract
The accuracy in predicting the typhoon track can be key to minimizing their frequent disastrous effects. This article aims to study the accuracy of typhoon trajectory prediction obtained by combining several algorithms based on current deep-learning techniques. The combination of a Convolutional Neural [...] Read more.
The accuracy in predicting the typhoon track can be key to minimizing their frequent disastrous effects. This article aims to study the accuracy of typhoon trajectory prediction obtained by combining several algorithms based on current deep-learning techniques. The combination of a Convolutional Neural Network with Long Short-Term Memory (CNN+LSTM), Patch Time-Series Transformer (CNN+PatchTST) and Transformer (CNN+Transformer) were the models chosen for this work. These algorithms were tested on the best typhoon track data from the China Meteorological Administration (CMA), ERA5 data from the European Centre for Medium-Range Weather Forecasts (ECMWF), and structured meteorological data from the Zhuhai Meteorological Bureau (ZMB) as an extension of existing studies that were based only on public data sources. The experimental results were obtained by testing two complete years of data (2021 and 2022), as an alternative to the frequent selection of a small number of typhoons in several years. Using the R-squared metric, results were obtained as significant as CNN+LSTM (0.991), CNN+PatchTST (0.989) and CNN+Transformer (0.969). CNN+LSTM without ZMB data can only obtain 0.987, i.e., 0.004 less than 0.991. Overall, our findings indicate that appropriately augmenting data near land and ocean boundaries around the coast improves typhoon track prediction. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications in Computer Science and Engineering)
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19 pages, 5200 KiB  
Article
Research on Multiplication Routine Based on Reconfigurable Four-Valued Logic Processor
by Shanchuan Liao, Shuang Li, Luqun Li, Xiaofeng Li, Xingquan Gu and Sulan Zhang
Electronics 2024, 13(18), 3720; https://doi.org/10.3390/electronics13183720 - 19 Sep 2024
Viewed by 1382
Abstract
Despite the indispensable role of traditional electronic computers in modern society, their limitations in parallel processing capabilities, bit-width constraints, and processor bit-width are becoming increasingly apparent, especially when handling large-scale datasets and complex computational tasks. Although hardware technology and algorithm optimization continue to [...] Read more.
Despite the indispensable role of traditional electronic computers in modern society, their limitations in parallel processing capabilities, bit-width constraints, and processor bit-width are becoming increasingly apparent, especially when handling large-scale datasets and complex computational tasks. Although hardware technology and algorithm optimization continue to advance, the arithmetic units of traditional computers—adders—remain constrained by carry delay and bit-width limitations. This bottleneck is particularly pronounced in multiplication operations, mainly when adders are used for partial product accumulation. However, since 2018, the emergence of a new type of Reconfigurable Four-Valued Logic Electronic Processor (RFLEP) has provided a potential solution to these traditional limitations. With its large processor bit-width, flexible bit grouping capabilities, and dynamic hardware function reconfiguration features, this processor has brought revolutionary changes to the field of computing. In this context, this paper proposes and implements a Reconfigurable Four-Valued Logic Multiplication Routine (RFLMR) tailored explicitly for the RFLEP. The RFLMR utilizes the Modified Signed-Digit (MSD) representation method in multi-valued logic combined with the M transformation in four-valued logic to generate partial products. These partial products are then efficiently summed in parallel using the JW-MSD parallel adder, achieving the rapid execution of multiplication operations. Experimental results demonstrate that the multiplication routine based on the RFLEP performs multiplication operations accurately and meets theoretical expectations regarding implementation efficiency and performance. This research not only provides new ideas for developing next-generation high-performance computing systems but also paves the way for exploring more efficient and powerful computing models, heralding a profound transformation in future computing technology. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications in Computer Science and Engineering)
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29 pages, 3538 KiB  
Article
FBLearn: Decentralized Platform for Federated Learning on Blockchain
by Daniel Djolev, Milena Lazarova and Ognyan Nakov
Electronics 2024, 13(18), 3672; https://doi.org/10.3390/electronics13183672 - 16 Sep 2024
Cited by 2 | Viewed by 3221
Abstract
In recent years, rapid technological advancements have propelled blockchain and artificial intelligence (AI) into prominent roles within the digital industry, each having unique applications. Blockchain, recognized for its secure and transparent data storage, and AI, a powerful tool for data analysis and decision [...] Read more.
In recent years, rapid technological advancements have propelled blockchain and artificial intelligence (AI) into prominent roles within the digital industry, each having unique applications. Blockchain, recognized for its secure and transparent data storage, and AI, a powerful tool for data analysis and decision making, exhibit common features that render them complementary. At the same time, machine learning has become a robust and influential technology, adopted by many companies to address non-trivial technical problems. This adoption is fueled by the vast amounts of data generated and utilized in daily operations. An intriguing intersection of blockchain and AI occurs in the realm of federated learning, a distributed approach allowing multiple parties to collaboratively train a shared model without centralizing data. This paper presents a decentralized platform FBLearn for the implementation of federated learning in blockchain, which enables us to harness the benefits of federated learning without the necessity of exchanging sensitive customer or product data, thereby fostering trustless collaboration. As the decentralized blockchain network is introduced in the distributed model training to replace the centralized server, global model aggregation approaches have to be utilized. This paper investigates several techniques for model aggregation based on the local model average and ensemble using either local or globally distributed validation data for model evaluation. The suggested aggregation approaches are experimentally evaluated based on two use cases of the FBLearn platform: credit risk scoring using a random forest classifier and credit card fraud detection using a logistic regression. The experimental results confirm that the suggested adaptive weight calculation and ensemble techniques based on the quality of local training data enhance the robustness of the global model. The performance evaluation metrics and ROC curves prove that the aggregation strategies successfully isolate the influence of the low-quality models on the final model. The proposed system’s ability to outperform models created with separate datasets underscores its potential to enhance collaborative efforts and to improve the accuracy of the final global model compared to each of the local models. Integrating blockchain and federated learning presents a forward-looking approach to data collaboration while addressing privacy concerns. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications in Computer Science and Engineering)
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15 pages, 2610 KiB  
Article
Incremental Placement Technology Based on Front-End Design
by Zihang Zhang and Gang Chen
Electronics 2024, 13(14), 2745; https://doi.org/10.3390/electronics13142745 - 12 Jul 2024
Viewed by 1036
Abstract
As the scale and complexity of chips continue to increase, chip design becomes increasingly challenging. Designers typically need multiple iterations to achieve satisfactory results, but the substantial time required for each modification exacerbates the time pressure in the chip design process. Incremental methods [...] Read more.
As the scale and complexity of chips continue to increase, chip design becomes increasingly challenging. Designers typically need multiple iterations to achieve satisfactory results, but the substantial time required for each modification exacerbates the time pressure in the chip design process. Incremental methods are an effective technique to shorten the development iteration time. Therefore, this paper proposes a module-based incremental layout technique utilizing the hierarchical structure of the unflattened netlist. We have developed an incremental EDA tool for mid-version evaluation, covering the process from RTL to placement DEF. This tool enables faster synthesis and layout, assisting designers in assessing the feasibility of the current RTL design, thereby accelerating the estimation of the PPA (Power, Performance, and Area) during version iterations. It aids in making better choices for RTL design and logic synthesis, consequently shortening the chip development iteration time. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications in Computer Science and Engineering)
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30 pages, 9822 KiB  
Article
A Fast Operation Method for Predicting Stress in Nonlinear Boom Structures Based on RS–XGBoost–RF Model
by Qing Dong, Youcheng Su, Gening Xu, Lingjuan She and Yibin Chang
Electronics 2024, 13(14), 2742; https://doi.org/10.3390/electronics13142742 - 12 Jul 2024
Cited by 2 | Viewed by 1164
Abstract
The expeditious and precise prediction of stress variations in nonlinear boom structures is paramount for ensuring the safe, dependable, and effective operation of pump trucks. Nonetheless, balancing prediction accuracy and efficiency by constructing a suitable machine-learning model remains a challenge in engineering practice. [...] Read more.
The expeditious and precise prediction of stress variations in nonlinear boom structures is paramount for ensuring the safe, dependable, and effective operation of pump trucks. Nonetheless, balancing prediction accuracy and efficiency by constructing a suitable machine-learning model remains a challenge in engineering practice. To this end, this paper introduces an interpretable fusion model named RS–XGBoost–RF (Random Search–Extreme Gradient Boosting Tree–Random Forest) and develops an intelligent algorithm for the stress prediction of the nonlinear boom structure of concrete pump trucks. Firstly, an information acquisition system is deployed to collect relevant data from the boom systems of ZLJ5440THBBF 56X-6RZ concrete pump trucks during its operational phase. Data pre-processing is conducted on the 2.4 million sets of acquired data. Then, a sample dataset of typical working conditions is obtained. Secondly, the RS algorithm, RF model, and XGBoost model are selected based on their complementary strengths to construct the fusion model. The model fusion condition is established with a focus on prediction efficiency. By leveraging the synergy between search and prediction mechanisms, the RS–XGBoost model is constructed for the prediction of the master hyperparameters of the RF model. This model uses the random search (RS) process to obtain the mapping between the loss function and the hyperparameters. This mapping relationship is then learned using the XGBoost model, and the hyperparameter value with the smallest loss value is predicted. Finally, the RS–XGBoost–RF model with optimized hyperparameters is employed to achieve rapid stress prediction at various detection points of the nonlinear boom structure. The findings demonstrate that, within the acceptable prediction efficiency for engineering practice, the fitting accuracy (R2) of the RS–XGBoost–RF model consistently exceeds 0.955 across all measurement points, with only a few exceptions. Concerning the stress magnitudes themselves, the mean absolute error (MAE) and root mean square error (RMSE) are maintained within the ranges of 2.22% to 3.91% and 4.79% to 7.85%, respectively. In comparison with RS–RF–RF, RS–RF–XGBoost, and RS–XGBoost–XGBoost, the proposed model exhibits the optimal prediction performance. The method delineated in this paper offers valuable insights for expeditious structural stress prediction in the realm of inherent safety within construction machinery. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications in Computer Science and Engineering)
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38 pages, 7363 KiB  
Article
CMAF: Context and Mobility-Aware Forwarding Model for V-NDN
by Elídio Tomás da Silva, Joaquim Macedo and António Costa
Electronics 2024, 13(12), 2394; https://doi.org/10.3390/electronics13122394 - 19 Jun 2024
Viewed by 1219
Abstract
Content dissemination in Vehicular Ad hoc Networks (VANET) is a challenging topic due to the high mobility of nodes, resulting in the difficulty of keeping routing tables updated. State-of-the-art proposals overcome this problem by avoiding the management of routing tables but resort to [...] Read more.
Content dissemination in Vehicular Ad hoc Networks (VANET) is a challenging topic due to the high mobility of nodes, resulting in the difficulty of keeping routing tables updated. State-of-the-art proposals overcome this problem by avoiding the management of routing tables but resort to the so-called table of neighbors (NT) from which a next-hop is selected. However, NTs also require updating. For this purpose, some solutions resort to broadcasting beacons. We propose a Context- and Mobility-Aware Forwarding (CMAF) strategy that resorts to a Short-Term Mobility Prediction—STMP—algorithm, for keeping the NT updated. CMAF is based in Named Data Networking (NDN) and works in two modes, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2V CMAF leverages the overheard packets to extract mobility information used to manage NT and feed the STMP algorithm. V2I CMAF also uses a controlled and less frequent beaconing, initially from the Road-Side Units (RSUs), for a further refinement of the predictions from STMP. Results from extensive simulations show that CMAF presents superior performance when compared to the state of the art. In both modes, V2V and V2I (with one beacon broadcast every 10 s) present 5–10% higher Interest Satisfaction Ratio (ISR) than those of CCLF for the same overhead, at a cost of 1 s of increased Interest Satisfaction Delay (ISD). Moreover, the number of retransmissions of CMAF is also comparatively low for relatively the same number of hops. Compared to VNDN and Multicast, CMAF presents fewer retransmissions and 10% to 45% higher ISR with an increased overhead of about 20%. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications in Computer Science and Engineering)
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15 pages, 3691 KiB  
Article
Data-Driven Prediction Model for Analysis of Sensor Data
by Ognyan Yotov and Adelina Aleksieva-Petrova
Electronics 2024, 13(10), 1799; https://doi.org/10.3390/electronics13101799 - 7 May 2024
Cited by 4 | Viewed by 2817
Abstract
In view of Industry 4.0, data generation and analysis are challenges. For example, machine health monitoring and remaining useful life prediction use sensor signals, which are difficult to analyze using traditional methods and mathematical techniques. Machine and deep learning algorithms have been used [...] Read more.
In view of Industry 4.0, data generation and analysis are challenges. For example, machine health monitoring and remaining useful life prediction use sensor signals, which are difficult to analyze using traditional methods and mathematical techniques. Machine and deep learning algorithms have been used extensively in Industry 4.0 to process sensor signals and improve the accuracy of predictions. Therefore, this paper proposes and validates the data-driven prediction model to analyze sensor data, including in the data transformation phase Principal Component Analysis tested by Fourier Transformation and Wavelet Transformation, and the modeling phase based on machine and deep learning algorithms. The machine learning algorithms used for tests in this research are Random Forest Regression (RFR), Multiple Linear Regression (MLR), and Decision Tree Regression (DTR). For the deep learning comparison, the algorithms are Deep Learning Regression and Convolutional network with LeNet-5 Architecture. The experimental results indicate that the models show promising results in predicting wear values and open the problem to further research, reaching peak values of 92.3% accuracy for the first dataset and 62.4% accuracy for the second dataset. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications in Computer Science and Engineering)
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35 pages, 961 KiB  
Article
Forester: Approximate Processing of an Imperative Procedure for Query-Time Exploratory Data Analysis in a Relational Database
by Md Arif Rahman and Young-Koo Lee
Electronics 2024, 13(4), 759; https://doi.org/10.3390/electronics13040759 - 14 Feb 2024
Viewed by 1148
Abstract
Query-time Exploratory Data Analysis (qEDA) is an increasingly demanding aspect of the data analysis process that entails visually and quantitatively summarizing, comprehending, and interpreting the primary characteristics of a dataset. Nowadays, an imperative procedure is popular in relational databases for EDA because it [...] Read more.
Query-time Exploratory Data Analysis (qEDA) is an increasingly demanding aspect of the data analysis process that entails visually and quantitatively summarizing, comprehending, and interpreting the primary characteristics of a dataset. Nowadays, an imperative procedure is popular in relational databases for EDA because it enables us to write multiple dependent declarative queries with imperative logic. As online analytical processing (OLAP) systems contain extremely large datasets, data scientists often need quick visualizations of data, using approximate processing of imperative procedures, before analyzing them in their entirety. We identify gaps in the existing techniques, in that they are unable to sample both declarative-dependent statements and control logic at the same time and perform multi-dependent sampling-based approximate processing within the permitted time in qEDA. Traditional approximate query processing (AQP) involves tuple sampling for a single query approximation and enables queries to be executed over arbitrary random samples of tables. However, available AQP methods cannot produce a further representative sample of the data distribution for the dependent statements to estimate accurately and quickly for multiple dependent statements. On the other hand, sampling control structures, like loops and conditional statements, are discussed separately, without regard to the imperative structure of statements in a procedure. In this study, we propose Forester, a novel agile approximate processing method for imperative procedures that performs imperative program-aware sampling, which includes both statements with control regions (i.e., branch and loop) and processes them approximately within the permitted time in qEDA. Our method produces more targeted samples for each relation, while maintaining the data and control flow of dependent queries and imperative logic and determining all the conditions for a relation across all the statements in the sample that guarantee the existence of relevant data for dependent data distribution. Utilizing a workload of multi-statement imperative procedures from the Transaction Processing Performance Council Decision Support (TPC-DS) database, our experiment demonstrates that Forester outperforms the existing system in sampling, producing minimum error, and improving response time. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications in Computer Science and Engineering)
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18 pages, 2254 KiB  
Article
Filtering and Detection of Real-Time Spam Mail Based on a Bayesian Approach in University Networks
by Maksim Sharabov, Georgi Tsochev, Veska Gancheva and Antoniya Tasheva
Electronics 2024, 13(2), 374; https://doi.org/10.3390/electronics13020374 - 16 Jan 2024
Cited by 2 | Viewed by 4790
Abstract
With the advent of digital technologies as an integral part of today’s everyday life, the risk of information security breaches is increasing. Email spam, commonly known as junk email, continues to pose a significant challenge in the digital realm, inundating inboxes with unsolicited [...] Read more.
With the advent of digital technologies as an integral part of today’s everyday life, the risk of information security breaches is increasing. Email spam, commonly known as junk email, continues to pose a significant challenge in the digital realm, inundating inboxes with unsolicited and often irrelevant messages. This relentless influx of spam not only disrupts user productivity but also raises security concerns, as it frequently serves as a vehicle for phishing attempts, malware distribution, and other cyber threats. The prevalence of spam is fueled by its low-cost dissemination and its ability to reach a wide audience, exploiting vulnerabilities in email systems. This paper marks the inception of an in-depth investigation into the viability and potential implementation of a robust spam filtering and prevention system tailored explicitly to university networks. With the escalating threat of email-based hacking attacks and the incessant deluge of spam, the need for a comprehensive and effective defense mechanism within academic institutions becomes increasingly imperative. In exploring potential solutions, this study delves into the applicability and efficacy of Bayesian filters, a class of probabilistic classifiers renowned for their aptitude in distinguishing between legitimate emails and spam messages. Bayesian filters utilize statistical algorithms to analyze email content, learning patterns and features to accurately categorize incoming emails. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications in Computer Science and Engineering)
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22 pages, 96717 KiB  
Article
RepRCNN: A Structural Reparameterisation Convolutional Neural Network Object Detection Algorithm Based on Branch Matching
by Xudong Li, Xinyao Lv, Linghui Sun, Jingzhi Zhang and Ruoming Lan
Electronics 2023, 12(19), 4180; https://doi.org/10.3390/electronics12194180 - 9 Oct 2023
Viewed by 1703
Abstract
A CNN object detection method based on the structural reparameterisation technique using branch matching is proposed to address the problem of balancing accuracy and speed in object detection techniques. By the structural reparameterisation of the convolutional layer in the object detection network, the [...] Read more.
A CNN object detection method based on the structural reparameterisation technique using branch matching is proposed to address the problem of balancing accuracy and speed in object detection techniques. By the structural reparameterisation of the convolutional layer in the object detection network, the amount of computation and the number of parameters in the network inference are reduced, the memory overhead is lowered, and the use of the branch-matching method to improve the structural reparameterisation model improves the computational efficiency and speed of the network while maintaining the detection accuracy. Optimisation is also carried out in terms of target screening and loss function, and a new CPC NMS screening strategy was introduced to further improve the performance of the model. The experimental results show that the proposed method achieves competitive results on the PASCAL VOC2012 and MS COCO2017 datasets compared to the traditional object detection methods and the current mainstream models, achieving a better balance between the detection accuracy and detection speed. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications in Computer Science and Engineering)
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20 pages, 661 KiB  
Article
Beamsteering-Aware Power Allocation for Cache-Assisted NOMA mmWave Vehicular Networks
by Wei Cao, Jinyuan Gu, Xiaohui Gu and Guoan Zhang
Electronics 2023, 12(12), 2653; https://doi.org/10.3390/electronics12122653 - 13 Jun 2023
Viewed by 1270
Abstract
Cache-enabled networks with multiple access (NOMA) integration have been shown to decrease wireless network traffic congestion and content delivery latency. This work investigates optimal power control in cache-assisted NOMA millimeter-wave (mmWave) vehicular networks, where mmWave channels experience double-Nakagami fading and the mmWave beamforming [...] Read more.
Cache-enabled networks with multiple access (NOMA) integration have been shown to decrease wireless network traffic congestion and content delivery latency. This work investigates optimal power control in cache-assisted NOMA millimeter-wave (mmWave) vehicular networks, where mmWave channels experience double-Nakagami fading and the mmWave beamforming is subjected to beamsteering errors. We aim to optimize vehicular quality of service while maintaining fairness among vehicles, through the maximization of successful signal decoding probability for paired vehicles. A comprehensive analysis is carried out to understand the decoding success probabilities under various caching scenarios, leading to the development of optimal power allocation strategies for diverse caching conditions. Moreover, an optimal power allocation is proposed for the single-antenna case, for exploiting the cached data as side information to cancel interference. The robustness of our proposed scheme against variations in beamforming orientation is assessed by studying the influence of beamsteering errors. Numerical results demonstrate the effectiveness of the proposed cache-assisted NOMA scheme in enhancing cache utility and NOMA efficiency, while underscoring the performance gains achievable with larger cache sizes. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications in Computer Science and Engineering)
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