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The Design and Realization of a Self-Hosted and Open-Source Agile Internal Development Platform

IEEE Access 2025年 13卷 79516-79533页

作者： Ciancarini, Paolo Giancarlo, Raffaele Grimaudo, Gennaro Missiroli, Marcello Xia, Tian Cheng University of Bologna Department of Computer Science -Science and Engineering Bologna40126 Italy University of Palermo Department of Mathematics and Computer Science Palermo90123 Italy University of Palermo Department of Engineering Palermo90128 Italy University of Bologna Department of Computer Science and Engineering Bologna40126 Italy

Platform engineering (PE) is a growing area of Software engineering, with many facets, including the elusive concept of Internal Development Platforms (IDPs). They integrate various technologies and tools to support the realization of software projects, especially their managing and developing roles, fostering Agile collaboration and enhancing team productivity. In particular for the new trends of Agile remote working and for Agile applied to the Digital Transformation in the Public Sector. While proprietary IDPs abound, academic research that could naturally lead to open-source IDPs remain scarce. In particular for Scrum, a fundamental Agile methodology in the strategic area of Digital Transformation in the Public Sector, where IDP self-hosting is also appropriate. This paper addresses this gap by proposing the design and realization of the first, to the best of our knowledge, IDP supporting Scrum, being open-source and with self-hosting capabilities. The end-product of our research is the Compositional Agile System (CAS), an extendable microservice architecture-based IDP. Its design, as well as CAS, makes full use of hands-on previous successful experiences of IDP development by members of this team, specifically non-trivial tasks in Italian Public Administration and to foster remote working in University Software engineering classes, a practice started during the pandemic but nowa *** offer several architectural and example scenarios where CAS can be of use, concentrating on the Public Administration. Particularly relevant is the use of CAS in analyzing a pillar of Italian Digital Administration available to its citizens, i.e., AppIo. The software is publicly available. © 2013 IEEE.

关键词： Open source software

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Signal Alignment in Frequency-hopped IoT Networks

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IEEE Transactions on Wireless Communications 2025年第5期24卷 4131-4145页

作者： Peppas, Spyridon Karakasis, Paris A. Sidiropoulos, Nicholas D. Cabric, Danijela University of Virginia Department of Electrical and Computer Engineering CharlottesvilleVA22904 United States University of California Department of Electrical and Computer Engineering Los AngelesCA90095 United States

This paper introduces a novel method for decoding uplink messages in Internet of Things (IoT) networks that utilize packet repetition, such as Sigfox and LoRa. These protocols use packet repetition at different pseudo-random frequency hops to avoid collisions and channel fades, but they do not coherently combine signals across these random (a priori unknown) hops. This paper introduces a novel frequency alignment strategy that leverages the frequency-hopped repetition structure to decode signals at lower transmission power levels without requiring channel state information, providing a significant advantage over techniques that rely on channel estimates. The proposed approach is validated through extensive simulations and real-world experiments examining its effectiveness in both single-user and multi-user environments under conditions of high system load and interference. The results demonstrate that the proposed approach not only enhances the performance and reliability of IoT networks but also contributes to more energy-efficient communication for IoT Low Power Wide Area Networks (LPWANs), thus offering a substantial advancement in dependable IoT communications. © 2002-2012 IEEE.

关键词： Frequency hopping

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Ergodic Capacity of Passive and Active IRS-aided MIMO Systems

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IEEE Sensors Journal 2025年第10期25卷 17283-17292页

作者： Gharibi, Hadi Rabiei, Amir Masoud Olfat, Ali University of Tehran College of Engineering Department of Electrical and Computer Engineering Tehran143995713 Iran

Intelligent reflecting surface (IRS) has emerged as a promising solution to address the ever-increasing demand for reliable and high-speed communication in next generation wireless communication systems. In this paper, the ergodic capacity (EC) of IRS-aided multiple-input multiple-output (MIMO) wireless systems is investigated. Analytical approximations for the ECs of active and passive IRS-aided MIMO systems are derived. As an application of the established theoretical approximations, an optimization problem is formulated to maximize the system’s energy efficiency by determining the optimal number of IRS elements and the optimal transmission power. It is shown that this problem can be readily solved using an alternative optimization technique applicable to both active and passive IRS configurations. Numerical results demonstrate the accuracy of the EC approximations. The results also clarify the impact of operational and environmental parameters on the system’s EC. © 2001-2012 IEEE.

关键词： Vectors Transmission line matrix methods Rician channels Optimization Energy efficiency Array signal processing Intelligent sensors Transmitting antennas Spectral efficiency Receiving antennas

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Halo reduction multi-exposure image fusion technique

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Multimedia Tools and Applications 2025年第13期84卷 12347-12370页

作者： Sharif, Rizwan Amin, Benish Sukhia, Komal Nain Department of Electrical Engineering Institute of Space Technology Islamabad Pakistan Department of Computer Science Institute of Space Technology Islamabad Pakistan

Multi-exposure image fusion (MEF) involves combining images captured at different exposure levels to create a single, well-exposed fused image. MEF has a wide range of applications, including low light, low contrast, night photography, medical imaging, and remote sensing. However, MEF methods often face issues like artifacts, halos around edges, color inconsistencies, noise amplification, and difficulty in preserving fine details. Moreover, assessing the quality of fused images objectively is complex due to the subjective nature of human perception. Solving the challenges is essential to developing efficient MEF techniques that produce high-quality results across various scenarios. The proposed technique introduces an approach to handling halo artifacts and implementing MEF. The Dense Scale-Invariant Feature Transform (DSIFT) is used to capture vital information about image brightness, texture, and edges from source images. Three weight maps are computed from the local mean, signal strength, and the global gradient for initial weight estimation. The local mean represents the brightness of specific image areas, signal strength preserves essential details like textures and edges while reducing image noise, and global gradient helps identify regions with significant pixel value shifts across multiple exposure images. The weight maps are then combined using a weighted average and a Gaussian smoothing filter is applied to reduce inherent noise and discontinuities in the original weights. Subsequently, pyramid decomposition is performed to generate a fused image. The efficiency of the proposed method is extensively tested on challenging multi-exposure image sequences. The results of the proposed approach demonstrate its superiority in both subjective evaluation and objective metrics like the MEF-Structural Similarity Index (MEF-SSIM), Natural Image Quality Evaluator (NIQE) and Gradient based performance measure (QAB/f). © The Author(s), under exclusive licence to Springer

关键词： Image fusion

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CNN-Transformer Based Cooperative Spectrum Sensing in Cognitive Radio Networks

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IEEE Wireless Communications Letters 2025年第5期14卷 1576-1580页

作者： Fang, Xiangqian Jin, Ming Guo, Qinghua Jiang, Tao Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia

Recently, deep learning (DL)-based data-driven spectrum sensing has attracted much attention due to its powerful capabilities to exploit underlying signal features. Existing DL-based cooperative spectrum sensing (CSS) detectors usually extract the signal features from each secondary user (SU) individually, followed by feature fusion at a central fusion center (FC). However, this cannot extract correlation features across SUs. To address this limitation, this work proposes a CNN-Transformer neural network to exploit the correlations among the received signals across and within SUs. Leveraging the CNN-Transformer, we propose a posteriori probability ratio detector, which is optimal for the binary hypothesis testing problem under the Neyman-Pearson criterion. Moreover, unlike most DL-based CSS detectors, the proposed detector considers imperfect and independent reporting channels between SUs and the FC, and exploits the temporal correlation of signals among neighboring samples. Numerical results are provided to demonstrate the superior performance of the proposed detector, showing an improvement of approximately 6 dB over traditional model-driven energy detectors. Moreover, the proposed detector improves the detection probability to 0.99 from 0.88 of data-driven detectors under Rician fading channels. © 2012 IEEE.

关键词： Fading channels

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A Dynamic Characteristic Aware Index Structure Optimized for Real-world Datasets

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ACM Transactions on Storage 2025年第2期21卷 1-32页

作者： Yang, Jin Yoon, Heejin Yun, Gyeongchan Noh, Sam H. Choi, Young-Ri Computer Science and Engineering Unist Ulsan Korea Republic of Department of Computer Science Virginia Tech Blacksburg United States

Many datasets in real life are complex and dynamic, that is, their key densities are varied over the whole key space and their key distributions change over time. It is challenging for an index structure to efficiently support all key operations for data management, in particular, search, insert, and scan, for such dynamic datasets. In this article, we present DyTIS (Dynamic dataset Targeted Index Structure), an index that targets dynamic datasets. DyTIS, although based on the structure of Extendible hashing, leverages the CDF of the key distribution of a dataset, and learns and adjusts its structure as the dataset grows. The key novelty behind DyTIS is to group keys by the natural key order and maintain keys in sorted order in each bucket to support scan operations within a hash index. We also define what we refer to as a dynamic dataset and propose a means to quantify its dynamic characteristics. Our experimental results show that DyTIS provides higher performance than the state-of-the-art learned index for the dynamic datasets considered. We also analyze the effects of the dynamic characteristics of datasets, including sequential datasets, as well as the effect of multiple threads on the performance of the indexes. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Dynamic datasets index structure key distribution

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A community-based simulated annealing approach with a new structure-based neighborhood search to identify influential nodes in social networks

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Soft Computing 2025年第3期29卷 1567-1585页

作者： Abyaneh, Farzaneh Rajaee Charkari, Nasrollah Moghadam Roayaei, Mehdy Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Faculty of Electrical and Computer Engineering Tarbiat Modares University Shahid Chamran Highway Tehran Iran

Identifying influential nodes has attracted the attention of many researchers in recent years. Because of the weak tradeoff between accuracy and running time, and ignoring the community structure by the proposed algorithms in the past research studies, further studies in this area are required. In this paper, we consider communities and also use a novel structure-based neighborhood search to improve exploration strategy of the simulated annealing (SA) algorithm. Moreover, we use the k-shell method for generating a better initial solution instead of random generation. In the proposed algorithm called Ckshell-SA, first, the communities are detected, then the k-shell method is used in each community to find initial candidate nodes locally. Finally, SA algorithm is applied with a neighborhood search that considers the structural properties of the network, and three centralities to find the influential nodes globally. A derivative of the Ckshell-SA method called kshell-SA is also introduced in this paper to examine the impact of considering communities. Unlike the Ckshell-SA, the community structure is neglected, and the k-shell is performed on the whole network in kshell-SA algorithm. Extensive experiments are conducted on eight real-world networks under Independent Cascade Model (IC) and Weighted Independent Cascade Model (WC). The results show that the Ckshell-SA and kshell-SA algorithms outperform the state-of-the-art algorithms concerning influence spread. Furthermore, the results show that Ckshell-SA is more efficient in networks like Facebook with a high Power Law exponent and higher modularity. On the contrary, kshell-SA is more successful in networks like Slashdot or Epinions with lower modularity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Simulated annealing

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A stabilizing solution for the control system parameters tuning problem

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ISA Transactions 2025年 161卷 261-275页

作者： Rehimi, Sharara Bevrani, Hassan Urabe, Chiyori T. Kato, Takeyoshi Department of Electrical Engineering Nagoya University Nagoya Japan Department of Electrical & Computer Engineering University of Kurdistan Kurdistan Sanandaj Iran

Dynamic control systems present significant challenges in parameter tuning due to their complex behaviors, various performance requirements, and wide application domains. Existing strategies often rely on restrictive assumptions or are obtained sequentially, resulting in poor stability and performance. To overcome these limitations, this study introduces a developed static output feedback framework that stabilizes and simultaneously tunes multiple parameters in various dynamic control systems. The proposed methodology combines a systematic analytical procedure with a software-based implementation, enabling proper performance under varying system types, orders, and parameter sets. Indeed, the introduced tuning approach enables the simultaneous adjustment of all control system parameters, accommodating the designer's preferences regarding the number of parameters to be tuned and the control objectives. Its effectiveness and efficiency are demonstrated through extensive simulations and real-time experiments in aerospace, robotics, and electric systems. Results indicate that this approach covers significant system variations without compromising stability or performance, distinguishing it from the conventional tuning methods. These outcomes offer new insights into the importance of simultaneously retuning control system parameters due to technical and economic issues. It is worth emphasizing that the proposed approach provides a comprehensive plan for designing or tuning controller parameters simultaneously and extends the applicability of static output feedback control theory to a wider range of dynamic control systems. © 2025 The Authors

关键词： Feedback control

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Improving fish image detection speed with hybrid VGG16 and darknet

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Multimedia Tools and Applications 2025年第12期84卷 10551-10566页

作者： Sirigineedi, Manikanta Jagan Mohan, R.N.V. Sahu, Bandita Department of Computer Science and Engineering GIET University Odisha Gunupur765022 India Department of Computer Science and Engineering SRKR Engineering College Andhra Pradesh Bhimavaram534201 India

Fish classification and object detection are crucial tasks in the fishery industry. The use of computer vision and deep learning techniques can help automate these tasks and improve the efficiency of the fishery industry. In this paper, we propose a hybrid model of VGG16 and Darknet for fish classification and object detection. The VGG16 model is a popular deep learning model for image classification, and the Darknet model is a state-of-the-art object detection model. The hybrid model combines the strengths of both models to achieve better performance in fish classification and object detection. The proposed model is trained on a dataset of fish images, which is collected from various sources. The dataset includes images of different fish species, sizes, and orientations. The model is trained to classify fish species and detect fish objects in the images. The results of the experiments show that the proposed model outperforms the individual VGG16 and Darknet models in terms of accuracy and speed. The model achieves the training time for 500 sessions with all 951 images was only 1 h 12 min, which is very fast. Additionally, the size of the model weight file is 13 MB, which is relatively small. This suggests that the model is lightweight and efficient, which is desirable in many applications. In conclusion, the proposed hybrid model of VGG16 and Darknet is an effective solution for fish classification and object detection. The model can be applied in real-world fishery scenarios to improve the efficiency and accuracy of fishery tasks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fish

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M-Bi-GRU-CNN: a hybrid deep learning model with optimized feature selection for enhanced crop yield prediction

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Multimedia Tools and Applications 2025年 1-25页

作者： J, Madhuri M, Indiramma N, Nagarathna Department of Computer Science and Engineering Bangalore Institute of Technology Karnataka Bengaluru560004 India Department of Computer Science and Engineering BMS College of Engineering Karnataka Bengaluru560019 India

Weather variability significantly impacts crop yield, posing challenges for large-scale agricultural operations. This study introduces a deep learning-based approach to enhance crop yield prediction accuracy. A Multi-Head Attention-based Bi-Directional Gated Recurrent Unit (M-Bi-GRU) integrated with a Convolutional Neural Network (CNN) is proposed. The publicly available Crop Recommendation dataset is pre-processed using data cleaning and Min–Max normalization to eliminate inconsistencies. Then, the Adaptive Reptile Search Optimization (ARSO) algorithm is used to identify the most relevant features for the prediction task. The M-Bi-GRU-CNN model uses these selected features to predict crop yields. The implementation is carried out in Python, and the performance is measured using a variety of metrics. Experimental results demonstrate that the proposed model outperforms existing methods, achieving a Mean Absolute Error (MAE) of 0.45, Mean Absolute Percentage Error (MAPE) of 0.07, and Root Mean Square Error (RMSE) of 0.28. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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