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Physical Beam Sharing for Communications With Multiple Low Earth Orbit Satellites

IEEE Transactions on Signal Processing 2024年 72卷 2783-2798页

作者： He, Yan-Yin Tsai, Shang-Ho Poor, H. Vincent Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

Physical (analog) beamforming is expected to become an important technique in Low Earth Orbit (LEO) satellite transmission in upcoming 6G communications. To build dense networks via LEO satellites and decrease deployment expenses, the corresponding satellites should have minimal hardware, low computational complexity, and limited power consumption. Additionally, issues such as different propagation delays, Doppler effects, long feedback delays, and serious attenuation caused by bad weather should also be addressed in LEO satellite communications. This work proposes a novel physical beam sharing scheme for multiple-satellite systems that enables a satellite to simultaneously serve multiple users using only one Radio-Frequency (RF) chain, consequently leading to a significant reduction in hardware complexity and power consumption. In this case, all users can achieve full multiplexing gain. Furthermore, we propose two ways of optimizing for this novel scheme. One is maximizing the throughput of all users, and the other is minimizing the LEO satellite system's power consumption. For the problems, we propose both solutions using instantaneous Channel State Information (iCSI) and long-term statistical Channel State Information (sCSI). Simulation results demonstrated that the proposals significantly outperform conventional schemes in terms of throughput and power consumption, and the performance of the proposed sCSI solutions is close to that of iCSI ones. © 1991-2012 IEEE.

关键词： Electric power utilization

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Toward a Realistic Benchmark for Out-of-Distribution Detection 10

Toward a Realistic Benchmark for Out-of-Distribution Detecti...

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10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023

作者： Recalcati, Pietro Garcea, Fabio Piano, Luca Lamberti, Fabrizio Morra, Lia Politecnico di Torino Department of Control and Computer Engineering Torino Italy

ISBN: (纸本)9798350345032

Deep neural networks are increasingly used in a wide range of technologies and services, but remain highly susceptible to out-of-distribution (OOD) samples, that is, drawn from a different distribution than the original training set. A common approach to address this issue is to endow deep neural networks with the ability to detect OOD samples. Several benchmarks have been proposed to design and validate OOD detection techniques. However, many of them are based on farOOD samples drawn from very different distributions, and thus lack the complexity needed to capture the nuances of real-world scenarios. In this work, we introduce a comprehensive benchmark for OOD detection, based on ImageNet and Places365, that assigns individual classes as in-distribution or out-of-distribution depending on the semantic similarity with the training set. Several techniques can be used to determine which classes should be considered in-distribution, yielding benchmarks with varying properties. Experimental results on different OOD detection techniques show how their measured efficacy depends on the selected benchmark and how confidence-based techniques may outperform classifier-based ones on near-OOD samples. © 2023 IEEE.

关键词： Deep neural networks

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Design of a garbage classification system based on deep transfer learning

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Journal of Ambient Intelligence and Humanized Computing 2025年第1期16卷 225-232页

作者： Zucai, Tang Luping, Wang Miaoyan, Qu Aitong, Sheng Nianwang, Huai Department of Automation University of Shenyang Aerospace Liaoning Shenyang China Engineering Training Center University of Shenyang Aerospace Liaoning Shenyang China Department of computer science and technology University of Shenyang Aerospace Liaoning Shenyang China

In light of the present challenges related to the insufficient autonomous identification and classification of garbage, along with the management and movement of garbage bins, a novel intelligent garbage classification system utilizing deep learning is suggested. This system is designed to autonomously identify types of garbage and to finalize the mobile garbage classification system for packaging. An analysis of the three-dimensional structure of the garbage bin and a dynamic examination of the motion system of the three-wheeled chassis are performed. The Raspberry Pi 3B and STM32 microcontrollers are employed for management, while the Inception v3 network structure algorithm is carefully adjusted and enhanced to facilitate garbage image recognition and classification. The outcomes of experimental research demonstrate that the fine-tuned and optimized Inception v3 network structure algorithm effectively facilitates the identification of garbage image recognition, and the model demonstrates superior performance in recognizing and categorizing images. This investigation offers a reliable approach for enhancing the efficiency and accuracy of garbage classification, thereby contributing to environmental protection and sustainable development. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Transfer learning

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Enhancing Scene Realism through Neural Radiance Fields and Monocular Depth Estimation 8

Enhancing Scene Realism through Neural Radiance Fields and M...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： De Magistris, Giorgio Rodriguez, Juan David Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Italy

This paper addresses the challenge of differentiable rendering, focusing on a novel implementation designed to integrate 3D objects seamlessly into reconstructed 3D environments, thereby creating entirely new perspectives of the scene. Our methodology leverages Neural Radiance Field (NeRF) models to reconstruct the 3D environments with high fidelity, alongside monocular depth estimation algorithms for deriving the 3D characteristics of objects from single images. The main goal of our approach lies in harmonizing the depth map output from the NeRF model with the depth data of the inserted object. This synergy enables the accurate and space-coherent placement of the object within the scene, ensuring a natural integration that enhances the overall realism of the virtual environment. © 2023 Copyright for this paper by its authors.

关键词： Image reconstruction

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Giobergia at Multi-Task Transformer Tuning for Joint Conspiracy Theory Detection and Classification 8

Giobergia at Multi-Task Transformer Tuning for Joint Conspir...

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8th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop, EVALITA 2023

作者： Giobergia, Flavio Department of Control and Computer Engineering Politecnico di Torino Turin Italy

Conspiracy theories, prevalent in contemporary society, often propagate misinformation and distrust, impacting public opinion and decision-making processes. In this paper, we present an automated approach to detect and classify conspiracy theories in Italian to address the Automatic Conspiracy Theory Identification (ACTI) task. Our methodology leverages a transformer-based architecture trained on a multi-task problem to tackle the challenging task of conspiracy theory identification. Through this multi-task learning framework, we aim to build a single model capable of addressing both the detection and the classification tasks simultaneously. We show that tackling both problems in a multi-task setting results in improved performance w.r.t. simple transformer-based solutions. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Decision making

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Barrier Function-Based Backstepping Fractional-Order Sliding Mode control for Quad-Rotor Unmanned Aerial Vehicle under External Disturbances

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IEEE Transactions on Aerospace and Electronic Systems 2024年第1期60卷 716-728页

作者： Liu, Bowen Wang, Yingxun Mofid, Omid Mobayen, Saleh Khooban, Mohammad Hassan Beihang University School of Automation Science and Electrical Engineering Beijing100191 China University of Zanjan Department of Electrical Engineering Zanjan45371-38791 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliou64002 Taiwan Aarhus University Department of Electrical and Computer Engineering Aarhus8200 Denmark

The main control objective for the quad-rotor system is the attitude and position tracking control which is accomplished in this article using the backstepping fractional-order sliding mode control approach combined with the adaptive tuning based barrier function theory. The quad-rotor's dynamical model is obtained at the appearance of the disturbance that is entered to the quad-rotor system by an exterior force. Hence, the attitude and position tracking errors between the quad-rotor's actual states and their desired trajectory are defined. Afterward, the fractional-order sliding surfaces are defined to guarantee finite-time reachability of the defined tracking error. The designed fractional-order surface eases the control process due to the removal of the high-derivative of virtual controllers. Then, the extended adaptive barrier function laws named fractional-order-based adaptive barrier function laws are employed to eliminate the knowledge of the upper bounds of external disturbances. The Lyapunov theory concept mixed with the backstepping control strategy is used to demonstrate that the fractional-order sliding surface reaches the neighborhood of origin in the finite time. Finally, simulation results in various scenarios on the MATLAB/ Simulink environment are provided to show that the designed control procedure in this article is an efficient control technique to assure that attitude and position's trajectories of the quad-rotor system track desired trajectories properly in the existence of the external disturbances. Moreover, the designed method is compared to the existing method to validate the efficiency of the proposed method. Lastly, experimental results using the Speedgoat real-time target machine is implemented to assure validity of the recommended method. © 1965-2011 IEEE.

关键词： Backstepping

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Research, Implementation and Analysis of Source Code Metrics in Rust-Code-Analysis 23

Research, Implementation and Analysis of Source Code Metrics...

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Ardito, Luca Ballario, Marco Valsesia, Michele Politecnico di Torino Department of Control and Computer Engineering Torino Italy

ISBN: (纸本)9798350319583

The software industry is proliferating at an unprecedented pace, with a massive volume of software being released every day. Among the manifold challenges faced by software engineering researchers, one of the most significant is maintaining and enhancing software quality. Software metrics, designed to quantify various aspects of software, are essential in achieving this goal. They provide developers with a comprehensive snapshot of a codebase's status throughout its evolution, thereby facilitating timely intervention and continual improvement. Tools like Rust-Code-Analysis (RCA), developed and maintained by Mozilla, serve as crucial aids in this endeavour. RCA is a static code analyser that scrutinises a source code without executing it and computes a series of source code metrics, which quantitatively assess code characteristics such as complexity, maintainability, and robustness. The present article seeks to contribute to this area by undertaking a threefold task. Firstly, we intend to explore new source code Java metrics that can be integrated into RCA. We have chosen Java language due to its not yet declined pervasiveness in many industrial software and world of smartphones. The metrics will be selected based on their potential to provide valuable insights into codebase status and facilitate optimisation. Once the new metrics have been identified, the second part of our task involves implementing these metrics within RCA's library and also accessed through its CLI. This involves the coding and integration of the metrics using the modern Rust language, taking advantage of its unique features like memory safety without garbage collection, and data concurrency. Finally, to ascertain the effectiveness and reliability of metrics, we conduct an evaluation using diverse Java repositories. This involves studying the values generated by these metrics across repositories of varying sizes and levels of activity. From the smallest library to large-scale applications, our an

关键词： Static analysis

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GINN: Towards Gender InclusioNeural Network

GINN: Towards Gender InclusioNeural Network

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Berta, Matteo Vacchetti, Bartolomeo Cerquitelli, Tania Politecnico di Torino Department of Control and Computer Engineering Turin Italy

ISBN: (纸本)9798350324457

Today's data-driven systems and official statistics often oversimplify the concept of gender, reducing it to binary data, with far-reaching implications for policy development and equitable access to services. This simplification can lead to misclassification and discrimination against individuals who identify as *** are working to advance our research in this area to develop new, more equitable approaches that can avoid discrimination based on gender identity. Within this research framework, our primary focus is on mitigating the problem of underrepresentation and, in some cases, the complete absence of non-binary individuals in data *** this goal in mind, we present the GINN Gender InclusioNeural Network. This is our first attempt to develop an equitable neural network that accurately identifies gender in a multiclass context and includes individuals whose gender identity does not fall on the binary spectrum. To achieve this goal, we conducted a comprehensive comparative analysis of several fine-tuned neural network models. Our goal was to gain a deep understanding of the crucial distinguishing features in gender identify classification and to highlight the limitation of current methods using explainable AI *** initial results are promising and demonstrate the effectiveness of a fine-tuned EfficientNetB0 model in accurately categorizing images of individuals into their self-reported gender, but we are skeptical about the application in a real-world scenario because of the amount of data available about non-binary people at the moment. © 2023 IEEE.

关键词： Automatic Gender Recognition Gender Equality Neural Network Non-Binary

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A Soft-Switching Type-3 Fuzzy control for Electrically Driven Nonholonomic Robots

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IEEE Access 2025年 13卷 99234-99251页

作者： Yuan, Xinhong Fekih, Afef Taghizadeh, Abouzar Zhang, Chunwei Mohammadzadeh, Ardashir Zhejiang Polytechnic University of Mechanical and Electrical Engineering School of Automation Hangzhou China University of Louisiana at Lafayette Department of Electrical and Computer Engineering Lafayette LA United States Shenyang University of Technology Multidisciplinary Center for Infrastructure Engineering Shenyang China Sakarya University Faculty of Engineering Department of Electrical and Electronics Engineering Sakarya Turkey

The wheeled mobile robots (WMRs) are used in extensive fields because of their simple structure and high efficiency. Due to the different applications of WMRs in different problems, the control problem of WMRs is challenging. Various frictions and disturbances complicate the dynamics of MRs. To cope with these problems, various control systems have been developed. But, in most of the existing methods, just the structural uncertainties are considered, and non-structural uncertainties such as disturbance and un-modeled dynamics are neglected. Although in some studies, the sliding mode controller (SMC) and its combination with different methods have been presented, however, in most of these controllers, to ensure stability, some coefficients are conservatively set with large values, and as a result, large control signals are generated. These signals are often not within the acceptable range, and their practical implementation is difficult. In this paper, an adaptive controller is designed that is robust under structural/un-structural uncertainties and unknown dynamics. In the designed controller, the coefficients and bounds of uncertainties are adjusted using an adaptive type-3 (T3) fuzzy system (FS). Based on back-stepping kinematic control, new adaptation laws, sufficient conditions for stability, and control signals are derived. Adaptive T3-FS is designed to create a soft-switching mechanism. New learning rules of T3-FS are derived to adjust the upper/lower bounds of structural and un-structural/uncertainties. The stability is mathematically analyzed and proved under uncertainties. Several simulations are given to prove the effectiveness. © 2013 IEEE.

关键词： adaptive control kinematic control Mobile robot sliding mode control type-3 fuzzy

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DDoS Attack Detection in Smart Grid Networks Using Visual Features 2

DDoS Attack Detection in Smart Grid Networks Using Visual Fe...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ali Naqvi, Sardar Shan Li, Yuancheng Uzair, Muhammad School Of Control And Computer Engineering North China Electric Power University Beijing102206 China Comsats University Islamabad Department Of Computer Engineering Wah cantt Pakistan

ISBN: (纸本)9798350394962

Smart grid networks play a key role in making electric power infrastructure more efficient and ensuring reliable energy distribution. However, the interconnected communication devices in these networks expose the system to several network threats, such as the distributed denial of service (DDoS) attacks. Timely DDoS attack detection and mitigation is an important but challenging task for maintaining service integrity. In this paper, we propose to leverage computer vision models to detect DDoS attacks in smart grid networks. After preprocessing the input network data and extracting features, we transform the features into two-dimensional images, employing them to train and fine-tune a vision MLP mixer model. Our approach offers the advantage of adaptability, as any pre-existing computer vision model can be utilized for fine-tuning purposes, providing flexibility in the absence of readily available pretrained models in the DDoS detection domain. Performance metrics, including accuracy, precision, recall, and F1 score obtained from our experiments, show promising results for DDoS attack detection in smart grid networks using the UNB-ISCX-IDS dataset, which contains real-world network traffic data containing different attack scenarios. Our approach will be beneficial for robust and secure smart grid operation in real-world scenarios. © 2024 IEEE.

关键词： Network security

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