Electronics

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15 pages, 2610 KiB

Open AccessArticle

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

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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

Open AccessArticle

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

Viewed by 333

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

Open AccessArticle

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 466

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

Open AccessArticle

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

Viewed by 719

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

Open AccessArticle

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 653

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

Open AccessArticle

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

Viewed by 1975

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

Open AccessArticle

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 931

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

Open AccessArticle

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 875

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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