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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Ullah, Hameed Soto, Santos Miguel Orozco Mazhar, Naveed Ahmad, Irfan Lippiello, Vincenzo Ruggiero, Fabio University of Naples Federico II Department of Electrical Engineering and Information Technology Naples Italy Science and Technology College Autonomous University of Mexico City Mexico City Mexico National University of Sciences and Technology Unmanned Vehicles Research Lab DEE CEME Islamabad Pakistan Engineering School of Bilbao University of the Basque Country Department of Automatic Control and Systems Bilbao Spain

ISBN: (纸本)9798350355703

This paper presents the pushing and rotation of a heavy rigid body using an omnidirectional actively tilted aerial manipulator, controlled via a hybrid force/position controller. Unlike conventional unmanned aerial vehicles, the proposed system enables precise horizontal force application, making it ideal for aerial physical interaction tasks. The hybrid controller is implemented in the Robot Operating System and validated through extensive simulations in a Gazebo physics-engine-based environment. The manipulator's performance was evaluated in two scenarios: circular trajectory tracking and the rotation of a heavy rigid mass. Results demonstrated its capability to follow complex trajectories with high accuracy and exert sustained forces for object manipulation. The system exhibited excellent stability and precision, underscoring its potential for real-world applications in challenging environments. © 2024 IEEE.

关键词： Omnidirectional antennas

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Max Flow Vulnerability of Undirected Planar Networks 32

Max Flow Vulnerability of Undirected Planar Networks

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32nd Italian Symposium on Advanced Database Systems, SEBD 2024

作者： Balzotti, Lorenzo Franciosa, Paolo G. Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Statistical Science Sapienza University of Rome p.le Aldo Moro 5 Rome00185 Italy

In the real world, planar networks commonly model utility distribution networks such as gas pipelines, water distribution, city's electric grid, and transportation networks like railways and highways. Evaluating the vulnerability of these networks is crucial on several point of view: in a military context in order to prevent terrorist attack, or for maintenance to prevent wear and tear or natural disasters from inflicting extremely severe damage. This work aims to evaluate the vulnerability of undirected planar network studying the decrease of maximum st-flow when an edge or a vertex is removed, that is, we propose algorithms for computing the max flow vitality of edges and vertices. Our algorithms are applied to real world scenarios, and we prove that high vitality values are well approximated in time close to the time currently required to compute st-max flow O(n log log n). All our algorithms work in O(n) space. © 2024 Copyright for this paper by its authors.

关键词： Natural gas pipelines

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automatic Liver Segmentation from Multiphase CT Using Modified SegNet and ASPP Module

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SN computer science 2024年第4期5卷 377页

作者： Nayantara, P. Vaidehi Kamath, Surekha Kadavigere, Rajagopal Manjunath, Kanabagatte Nanjundappa Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Radiodiagnosis and Imaging Kasturba Medical College Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal 576104 India

Liver cancer is one of the dominant causes of cancer death worldwide. Computed Tomography (CT) is the commonly used imaging modality for diagnosing it. computer-based liver cancer diagnosis systems can assist radiologists in image interpretation and improve diagnosis speed and accuracy. Since liver segmentation is crucial to such systems, researchers are relentlessly pursuing various segmentation approaches. A clinically viable computer-aided system requires examining multiphase CT images. However, most of the research focuses only on the portal venous phase. In this work, we developed an automatic and efficient Deep Learning (DL) method using SegNet, atrous spatial pyramid pooling module and leaky ReLU layers for liver segmentation from quadriphasic abdominal CT volumes. The proposed method was validated on two datasets, an internal institutional dataset consisting of multiphase CT and a public dataset of portal venous phase CT volumes. The Dice Coefficients (DC) obtained were greater than 96% for the latter dataset and the portal venous phase of the former. For arterial, delayed and plain CT phases of the former dataset, the DC achieved were 94.61%, 95.01% and 93.23%, respectively. Experiments showed that our model performed better than the other state-of-the-art DL models. Ablation studies have revealed that the proposed model leverages the strengths of all the three components that make it up. The promising performance of the proposed method suggests that it is appropriate for incorporation in hepatic cancer diagnosis systems. © The Author(s) 2024.

关键词： Atrous spatial pyramid pooling Computed tomography computer-aided diagnosis Liver cancer Liver segmentation SegNet

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Evaluating the Reliability of Supervised Compression for Split Computing

Evaluating the Reliability of Supervised Compression for Spl...

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VLSI Test Symposium

作者： Juan-David Guerrero-Balaguera Josie E. Rodriguez Condia Marco Levorato Matteo Sonza Reorda Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy Computer Science Department University of California Irvine US

ISBN: (数字)9798350363784

ISBN: (纸本)9798350363791

Recent advances in Internet-of-things (IoT) and 5G infrastructures promote new computational paradigms such as Split Computing (SC) for deploying Deep Neural Networks (DNNs) on mobile applications. In SC, DNNs are partitioned into head and tail sub-models that are executed on the mobile device and cloud/edge servers, respectively. Modern SC models resort to head compression techniques to balance energy consumption, transmission data, and model size while preserving the outstanding accuracy of large state-of-the-art DNNs. These features make SC DNNs suitable for mobile applications, including safety-critical systems (e.g., self-driving vehicles, autonomous robots, and healthcare equipment), where reliability is a paramount factor mandated by strict safety standards. Despite there are many studies available about the reliability of DNNs, the SC models are still unexplored, especially when hardware faults threaten the operation of a mobile device. In this work, we present for the first time $i)$ an application-level fault injection strategy for modeling hardware faults on mobile GPUs executing SC DNNs and ii) an evaluation of the resilience of supervised compression methods utilized by SC systems. The preliminary results gathered on some representative benchmark networks and configurations show the feasibility and effectiveness of the approach. They also demonstrate that aggressive compression strategies lead to high accuracy degradation $(\approx$ 40%), increasing the overall vulnerability of the DNN and the system.

关键词： Computational modeling Artificial neural networks Tail Very large scale integration Mobile handsets Hardware Data models

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FPGA-based Real-Time High-resolution Image Processing for rocket on-board processing 17

FPGA-based Real-Time High-resolution Image Processing for ro...

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17th IEEE International Conference on computer Research and Development, ICCRD 2025

作者： Zhou, Hui Wang, Xiaofeng Xie, Yujia Cao, Yongxiang Tang, Yu Huo, Yongchun Wen, Huan Zhao, Xiongbo Li, Chaoran National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing Aerospace Automatic Control Institute Beijing China Beihang University Computer Science and Engineering Beijing China Beihang University National Superior College for Engineers Beijing China

ISBN: (纸本)9798331531881

The structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-Time computing on an embedded platform poses significant challenges due to strictly limited resources, power consumption constraints, and size limitations. Our review of rocket-borne applications reveals considerable variability in the design resources of different devices, indicating a need for expanded design approaches. Upon evaluating existing methods, we identified two primary drawbacks. First, certain operators within the high-resolution target detection model are difficult to parallelize, resulting in significant inference delays that hinder the ability to meet task requirements. Although existing methods have been extended, there remains significant potential for performance enhancement in core scheduling for poor acceleration. This paper proposes an optimized architecture for the target detection algorithm accelerator designed for high-resolution images, along with a novel highly parallel data pre-processing and post-processing module implemented on FPGA to address these issues. Compared to the ARM implementation, this architecture demonstrates an improved performance of 24.64x. Furthermore, to ensure flexible application across various rocket launch scenarios, we introduce an optimization structure for convolution, pooling, and fusion operators and a multi-core expansion optimization method. This approach yields a 1.29x improvement in computing unit utilization compared to state-of-The-Art multi-core scaling efforts. Finally, we assessed the accelerator architecture across multiple FPGA platforms, achieving a peak processing element utilization rate of 99.71% for a single core and layer. The overall computing efficiency, excluding the first layer, exceeded 90%. The peak computing power for the four cores reached 1638.4 GOPS, and the end-To-end computation time for

关键词： Image enhancement

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Reactive Synthesis for Expected Impacts

arXiv

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arXiv 2024年

作者： Chini, Emanuele Sala, Pietro Simonetti, Andrea Zare, Omid Department of Computer Control and Management Engineering University La Sapienza Rome Italy Department of Computer Science University of Verona Verona Italy

As business processes become increasingly complex, effectively modeling decision points, their likelihood, and resource consumption is crucial for optimizing operations. To address this challenge, this paper introduces a formal extension of the Business Process Model and Notation (BPMN) that incorporates choices, probabilities, and impacts, referred to as BPMN+CPI. This extension is motivated by the growing emphasis on precise control within business process management, where carefully selecting decision pathways in repeated instances is crucial for conforming to certain standards of multiple resource consumption and environmental impacts. In this context we deal with the problem of synthesizing a strategy (if any) that guarantees that the expected impacts on repeated execution of the input process are below a given threshold. We show that this problem belongs to PSPACE complexity class;moreover we provide an effective procedure for computing a strategy (if present). © 2024, CC BY.

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FPGA-based Real-time High-resolution Image Processing for rocket on-board processing

FPGA-based Real-time High-resolution Image Processing for ro...

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International Conference on computer Research and Development (ICCRD)

作者： Hui Zhou Xiaofeng Wang YuJia Xie Yongxiang Cao Yu Tang Yongchun Huo Huan Wen Xiongbo Zhao Chaoran Li Beijing Aerospace Automatic Control Institute National key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing China Computer Science and Engineering Beihang University Beijing China National Superior College for Engineers Beihang University Beijing China

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

The structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-time computing on an embedded platform poses significant challenges due to strictly limited resources, power consumption constraints, and size limitations. Our review of rocket-borne applications reveals considerable variability in the design resources of different devices, indicating a need for expanded design approaches. Upon evaluating existing methods, we identified two primary drawbacks. First, certain operators within the high-resolution target detection model are difficult to parallelize, resulting in significant inference delays that hinder the ability to meet task requirements. Although existing methods have been extended, there remains significant potential for performance enhancement in core scheduling for poor acceleration. This paper proposes an optimized architecture for the target detection algorithm accelerator designed for high-resolution images, along with a novel highly parallel data pre-processing and post-processing module implemented on FPGA to address these issues. Compared to the ARM implementation, this architecture demonstrates an improved performance of 24.64x. Furthermore, to ensure flexible application across various rocket launch scenarios, we introduce an optimization structure for convolution, pooling, and fusion operators and a multi-core expansion optimization method. This approach yields a 1.29x improvement in computing unit utilization compared to state-of-the-art multi-core scaling efforts. Finally, we assessed the accelerator architecture across multiple FPGA platforms, achieving a peak processing element utilization rate of 99.71% for a single core and layer. The overall computing efficiency, excluding the first layer, exceeded 90%. The peak computing power for the four cores reached 1638.4 GOPS, and the end-to-end computation time for

关键词： YOLO Rockets Power demand Convolution Multicore processing Reviews Scalability Accelerator architectures Real-time systems Field programmable gate arrays

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Ranking of attributes-comparative study based on data from stylometric domain 26

Ranking of attributes-comparative study based on data from s...

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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Zielosko, Beata Stanczyk, Urszula Zabinski, Krzysztof Institute of Computer Science University of Silesia in Katowice B edzinska 39 Sosnowiec41-200 Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The area of feature selection methods constantly expands along with the development of artificial intelligence domain, and has great impact on almost every field, whenever data is processed and explored. The paper presents research where a ranking method was proposed, inspired by an approach which comes from an algorithm for induction of decision rules. The ranking procedure was based on calculation of standard deviation for attributes, taking into account assigned class labels. This method was compared with another ranking mechanism, a modified version of popular Relief algorithm, with incorporating characteristics of variables by supervised discretisation. Comparison of obtained results included the aspect of knowledge representation as well as the perspective of the accuracy for constructed rule-based classifiers. The experiments were performed on datasets from stylometry domain, where authorship attribution was considered as a classification task, and stylometric descriptors as characteristic features defining writing styles of authors. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & engineering Systems (KES 2022)

关键词： Knowledge representation

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Effective Application-level Error Modeling of Permanent Faults on AI Accelerators

Effective Application-level Error Modeling of Permanent Faul...

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IEEE Symposium on On-Line Testing (IOLTS)

作者： Francesco Pessia Juan-David Guerrero-Balaguera Robert Limas Sierra Josie E. Rodriguez Condia Marco Levorato Matteo Sonza Reorda Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy Computer Science Department University of California Irvine US

ISBN: (数字)9798350370553

ISBN: (纸本)9798350370560

The deployment of Machine Learning (ML) applications extensively leverages Matrix Multiplication (MM) operations on modern and advanced accelerators, like Graphic Processing Units (GPUs), which employ Tensor Core Units (TCUs) to optimize MM’s execution efficiently. However, reliability concerns arise in devices with cutting-edge semiconductor technologies (7 nm or less), as faults can compromise some structures (e.g., TCUs) during their operation. In safety-critical applications, this can lead to wrong DNN outcomes and cause unpredictable and unacceptable actions. Thus, the impact evaluation of such faults is crucial to ensure that TCUs and GPUs meet the safety standard requirements (e.g., ISO26262). Currently, the reliability assessment of complex applications concerning hardware faults involves fault injection (FI) campaigns. Unfortunately, low-level FI campaigns might be computationally prohibitive for GPUs when these execute massive applications like DNNs. In this work, we propose an error modeling approach to accurately describe corruptions from permanent faults on TCUs, during the operation of MMs. This approach enables realistic reliability evaluations of computationally expensive MM-based workloads, resulting in a huge acceleration (up to 225X) compared with hardware-level FIs. Our experimental results show a very good accuracy (up to $93 \%$ correlation between our error modeling approach and FI campaigns conducted on TCUs).

关键词： Tensors Computational modeling Machine learning Reliability engineering Safety System analysis and design Standards

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Enhancing the Reliability of Split Computing Deep Neural Networks

Enhancing the Reliability of Split Computing Deep Neural Net...

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IEEE Symposium on On-Line Testing (IOLTS)

作者： Giuseppe Esposito Juan-David Guerrero-Balaguera Josie E. Rodriguez Condia Marco Levorato Matteo Sonza Reorda Politecnico di Torino - Department of Control and Computer Engineering (DAUIN) Turin Italy Computer Science Department University of California Irvine US

ISBN: (数字)9798350370553

ISBN: (纸本)9798350370560

Artificial intelligence is becoming increasingly popular for IoT applications in safety-critical fields (e.g., autonomous systems and biomedical, robots). Unfortunately, the inference’s workload process alone increases as the model size grows. To meet the computational power limitations of mobile devices running IoT applications, modern services sometimes resort to the Split Computing paradigm. Split Computing divides the inference process of a Neural Network into Head and Tail for their execution in a mobile device and a server, respectively, which also allows the reduction of the overall IoT device’s computational cost. Nonetheless, Split Computing can be used in safety-critical fields where reliability is crucial, especially when mobile devices have computational and cost restrictions. This paper introduces hardening techniques acting on the software to mitigate the effects of hardware faults on Split Computing models. The proposed hardening techniques consist of i) a bounded activation function whose thresholds are refined by training, and ii) a per-channel bounding of the bottleneck quantization of the split points. To quantitatively assess their effectiveness, we resorted to two different split configurations of a model for image classification. In addition, we considered a Split Computing model for object detection. Our findings indicate that the proposed approaches effectively reduces fault effects by $\mathbf{3. 5 \%}$ for image classifiers and $5.73 \%$ for object detectors when compared with other hardening approaches for general DNNs.

关键词： Training Quantization (signal) Head Computational modeling Object detection Artificial neural networks Mobile handsets

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