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2025 Joint National Conference on Cybersecurity, ITASEC and SERICS 2025

作者： Musa, Alberto Parisi, Emanuele Barbierato, Luca Patti, Edoardo Acquaviva, Andrea Barchi, Francesco Università di Bologna Department of Electrical Electronic and Information Engineering "Guglielmo Marconi" Bologna Italy Politecnico di Torino Department of Control and Computer Engineering Turin Italy

RISC-V architectures are increasingly utilized in security-critical embedded systems, with OpenTitan standing out as a prominent open-source silicon Root-of-Trust. OpenTitan delivers essential functionalities, such as secure boot and execution integrity, but introduces notable area overhead. To mitigate this issue, we propose TitanSSL, a secure software stack that offloads cryptographic operations to OpenTitan. TitanSSL comprises an OpenSSL backend, a Linux driver for system communication, and a custom OpenTitan firmware. Communication between components is facilitated through a custom Application Binary Interface (ABI), ensuring the driver remains independent of the specific cryptographic operations executed by the OpenSSL engine. This design supports extensibility, enabling the integration of additional cryptographic primitives and enhancing the system flexibility. We evaluated TitanSSL on a System-on-Chip (SoC) featuring a CVA6 application core running Linux and OpenTitan, both clocked at 40 MHz on a Xilinx VCU118 FPGA. Our results reveal that, while there is communication overhead between system components, it is outweighed by substantial performance gains. Specifically, TitanSSL achieves speedups of 40x for SHA-256 and 82x for AES-256-CBC compared to a software-only implementation running on the CVA6 core. In addition, we provide design guidelines for future optimizations to improve system performance. © 2025 Copyright for this paper by its authors.

关键词： Firmware

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Video Polyp Segmentation: A Deep Learning Perspective

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Machine Intelligence Research 2022年第6期19卷 531-549页

作者： Ge-Peng Ji Guobao Xiao Yu-Cheng Chou Deng-Ping Fan Kai Zhao Geng Chen Luc Van Gool Research School of Engineering Australian National UniversityCanberra 2601Australia College of Computer and Control Engineering Minjiang UniversityFuzhou 350108China Department of Computer Science Johns Hopkins UniversityBaltimore 21218USA Computer Vision Laboratory ETH ZürichZürich 8092Switzerland Department of Radiological Sciences University of CaliforniaLos Angeles 90095USA School of Computer Science and Engineering Northwestern Polytechnical UniversityXi′an 710072China

We present the first comprehensive video polyp segmentation(VPS)study in the deep learning *** the years,developments in VPS are not moving forward with ease due to the lack of a large-scale dataset with fine-grained segmentation *** address this issue,we first introduce a high-quality frame-by-frame annotated VPS dataset,named SUN-SEG,which contains 158690colonoscopy video frames from the well-known *** provide additional annotation covering diverse types,i.e.,attribute,object mask,boundary,scribble,and ***,we design a simple but efficient baseline,named PNS+,which consists of a global encoder,a local encoder,and normalized self-attention(NS)*** global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations,which are then progressively refined by two NS *** experiments show that PNS+achieves the best performance and real-time inference speed(170 fps),making it a promising solution for the VPS ***,we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based ***,we discuss several open issues and suggest possible research directions for the VPS *** project and dataset are publicly available at https://***/GewelsJI/VPS.

关键词： Video polyp segmentation(VPS) dataset self-attention colonoscopy abdomen

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A Foreground Feature Embedding Network for Object Detection in Remote Sensing Images 18th

A Foreground Feature Embedding Network for Object Detection...

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Wu, Jiahui Cai, Yuanzheng Wang, Tao Luo, Zhiming Shan, Senhua Li, Zuoyong College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350108 China Department of Artificial Intelligence Xiamen University Xiamen361005 China Istrong Technology Limited Company Fuzhou350108 China

ISBN: (纸本)9789819996360

Compared with traditional natural images, remote sensing images (RSIs) typically have high resolution. The objects in the images are densely distributed, with heterogeneous orientation and large scale variation, even among objects of the same class. In recent years, object detection algorithms have made great strides in general images, but they are still difficult to meet the challenges that exist in RSIs. Therefore, we propose a foreground feature embedding network (FFE-Net) for object detection in RSIs. To better grasp the object features in RSIs, we design a foreground feature embedding module (FFEM) to learn the foreground features of the object. This is achieved by introducing an additional semantic segmentation branch and embedding the features in the classification and regression branches. Simultaneously, we propose a modified Gaussian function with focal loss (MGFFL) as a way to eliminate the extra background noise from soft labels, making the learned foreground features more robust. Our experimental results on two publicly available remote sensing image datasets, DOTA-v1.0 and HRSC2016, validate the effectiveness of FFE-Net. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Guidance of Agricultural Ground Robots Team with an Aerial Vehicle: A Cost-Effective Solution 56

Guidance of Agricultural Ground Robots Team with an Aerial V...

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56th International Symposium on Robotics, ISR Europe 2023

作者： Ugurlu, Halil Ibrahim Bardakci, Deniz Pham, Huy Xuan Kayacan, Erdal The Department of Electrical and Computer Engineering Aarhus University Aarhus C8000 Denmark Automatic Control Group Department of Electrical Engineering and Information Technology Paderborn University Paderborn Germany

ISBN: (纸本)9783800761418

Increasing the operational efficiency of agricultural machines is essential by the use of artificial intelligence (AI)-based navigation, planning, and control algorithms to handle the increasing demand for food production without compromising sustainability. In this study, an end-to-end path planning algorithm (AgroRL) is proposed for aerial-ground robots team collaboration. In the proposed solution, while main operations in the field are handled by the ground vehicle, the aerial robot is responsible for re-planning a collision-free trajectory for the ground robot when the robot faces an obstacle. Deep reinforcement learning is used for training the end-to-end policy for local re-planning of the aerial robot. The agent, informed by the global trajectory, generates local plans based on depth images. Variational autoencoders are also investigated for dimension reduction of the depth images in obstacle avoidance context to speed up deep reinforcement learning and alleviate the computational complexity of the policy network. The agriculture environment is developed in the Webots open-source robot simulator for training and testing purposes. The efficiency and efficacy of the end-to-end planner are evaluated over a number of cluttered field scenarios. The simulation experiments demonstrate a single aerial vehicle guiding multiple ground robots in agricultural operations. © VDE VERLAG GMBH Berlin Offenbach.

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MIRROR: MiRNA regulation-level differential network to study sex and ethnic disparities in cancer 46

MIRROR: MiRNA regulation-level differential network to study...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Alfano, Caterina Filetti, Marco Farina, Lorenzo Petti, Manuela Sapienza University of Rome Dept. of Experimental Medicine Rome Italy Fondazione Policlinico Universitario A. Gemelli Irccs Phase 1 Unit Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering PhD Program in Network Oncology and Precision Medicine Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350371499

Cancer disparities are adverse differences in cancer measures that exist among certain population groups. Given that the role they play not only in the disease prognosis but also in therapy response, there is an urgent need to understand what causes them. Most studies investigate these disparities by analyzing transcriptomic data and in particular miRNAs for their regulatory role, but only focusing on expression levels. To face this challenge we propose MIRROR, a new method which analyzes a differential co-expression network of miRNAs between patients' cohorts, to study the role they play at the target genes' level. Doing so, we can study the altered molecular mechanism that are linked to cancer disparities. The application of MIRROR to two different cases of cancer disparities has demonstrated its efficacy in identifying molecular players involved in the considered disparity, presenting itself as a viable option to approach this challenge. © 2024 IEEE.

关键词： differential network modeling disparities in cancer graph theory miRNA-target association transcriptomics

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Where to Turn: Road Fork Detection in Sparse 3D Point Cloud 3rd

Where to Turn: Road Fork Detection in Sparse 3D Point Clou...

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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Hu, Di Zhang, Kai Zhong, Yipan Xu, Jiachen Yuan, Xia Zhao, Chunxia School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Research Institute of Intelligence Southwest Research Institute of Information Control Chengdu611700 China

ISBN: (纸本)9789819711024

Intersection detection plays a crucial role in localizing and planning the path of autonomous vehicles in urban environments. This paper presents a novel approach, PVWO, for adaptive intersection detection in autonomous vehicles equipped with 3D LiDAR. Firstly, the algorithm removes ground and obstacles effectively, and eliminates noise points. Secondly, a polyarticular viewpoints beam model is applied to detect the type and location of intersections. Thirdly, a linear feature extraction approach in a rolling window is proposed to detect road width and optimize key parameters, enhancing model robustness across different scenes. Compared to parallel intersection detection algorithms, our method exhibits excellent performance under diverse road conditions. Experiments in sparse point clouds show an average precision exceeding 89% and an average processing time of approximately 88 ms/frame. These results demonstrate that our method can accurately detect intersections in real-time, across various road structures. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Extraction

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Unknown Input Observers Breaking Confidentiality of controller States 63

Unknown Input Observers Breaking Confidentiality of Controll...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Breukelman, Enno Sandberg, Henrik Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Division of Decision and Control Systems Malvinas väg 10 StockholmSE-100 44 Sweden

ISBN: (纸本)9798350316339

Driven by ubiquitous digitalization and cyberattacks on critical infrastructure, there is a high interest in research on the security of cyber-physical systems. If an attacker gains access to protected and sensitive information, such as the internal states of a control system, this is considered a breach of confidentiality. Access to sensitive information can be the first step in a larger cyber-attack scheme, such as a stealthy false data injection attack. Considering process and measurement noise in the plant, existing research investigated when an attacker equipped with a Kalman filter can perfectly estimate the internal controller states if the attacker has access to plant measurements and all model parameters. For this estimate to converge, the controller is required to have stable poles. In this paper, we show that if the attacker has access to the control inputs instead of the plant measurements, the controller needs to have stable zeros. Additionally, we demonstrate that an attacker equipped with an Unknown Input Observer, using tools from delayed system inversion, can get a delayed yet perfect estimate of the controller states from the control inputs without knowledge of the plant's parameters and noise characteristics. Lastly, we present simulation results from a three-tank system to showcase the differences in controller state estimation. © 2024 IEEE.

关键词： Delay control systems

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Fractional-order binary bat algorithm for feature selection on high-dimensional microarray data

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7453-7467页

作者： Esfandiari, Ahmad Farivar, Faezeh Khaloozadeh, Hamid Department of Computer and Mechatronics Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Systems and Control Faculty of Electrical Engineering K. N. Toosi University of Technology Seyed-Khandan Shariati Ave. Tehran16317-14191 Iran

High-dimensional microarray data suffer from the confounding effects of irrelevant, redundant and noisy genes on the scalability and efficiency of classification algorithms. In order for an effective dimensionality reduction and the selection of informative genes, this paper introduces a novel approach using fractional calculus concepts. This study proposes a modified version of binary the bat algorithm named fractional-order binary bat algorithm (FBBA) able to control the convergence process using more historical memory of bat behaviors. The gene selection technique contains a two-stage hybrid filter/wrapper method which employs a new correlation-based feature clustering (CFC) algorithm in the filter stage and the FBBA in the wrapper stage. The CFC-FBBA is evaluated on ten microarray gene expression datasets by employing the support vector machine classifier with a k-fold Monte Carlo cross validation data partitioning model. Furthermore, the results show that the CFC-FBBA, while minimizing the size of the gene subset, achieves the highest classification accuracy in most cases compared to several state-of-art hybrid techniques. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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Comparison of Two AC Current De-tumbling Methods for Uncooperative Space Target through Electromagnetic Characteristic Analysis 1

Comparison of Two AC Current De-tumbling Methods for Uncoope...

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1st Aerospace Frontiers Conference, AFC 2024

作者： Du, Lei Chen, Zhen Hu, Hengzai Liu, Xiangdong Zhang, Qiang Zhang, Kemo Liu, Hui School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Control Engineering Beijing100191 China Beijing Institute Computer for Application Beijing100089 China

ISBN: (纸本)9781510681613

A large number of space debris with rotational motion occupy scarce orbital resources, thus, a de-tumbling phase may be necessary prior to the capturing phase to reduce the risk of collision. A new contactless AC current de-tumbling method based on the principle of electromagnetic eddy current is proposed in this paper. Firstly, an analysis is conducted on external disturbances affecting the attitude of the debris and de-tumbling device, such as magnetic field perturbations. Compared to the electromagnetic forces acting on de-tumbling device, the influence of the geomagnetic force on detumbling device can be negligible. However, the geomagnetic torque acting on de-tumbling device cannot be ignored. Then, the advantages and disadvantages of the two AC current cases are compared and analyzed, and the method to increase the de-tumbling torque can be obtained by adjusting the frequency of the three phase AC current. Finally, the three-dimensional attitude dynamics of the uncooperative space target is established taking a spinning cylinder as the hypothetical target of de-tumbling. Simulation shows that the de-tumbling method is effective and the de-tumbling rate can be greatly improved by adjusting the frequency of the three phase AC current. © 2024 SPIE.

关键词： Space debris

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Autonomous Surveillance Breakthrough by Implementing Facial Recognition in Dog Robots

Autonomous Surveillance Breakthrough by Implementing Facial ...

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2024 Intermountain engineering, Technology and Computing, IETC 2024

作者： Hafezi, Amir Zibran, Minhaz Deemyad, Taher Idaho State University Department of Mechanical and Measurement & Control Engineering PocatelloID United States Idaho State University Department of Computer Science PocatelloID United States

ISBN: (纸本)9798350373073

As the importance of security in today's world continues to escalate, technology's role in its enhancement has evolved significantly. From the inception of fixed security cameras monitored by individuals, advancements have steered towards autonomous threat recognition systems and the deployment of mobile robots for proactive security measures. Despite these technological strides, the exploration of autonomous robots in security has been relatively scant. This study takes a novel approach by repurposing the commercial GO2 Edu dog robot for security purposes, equipping it with a thermal camera and custom software for robust face detection in varying lighting conditions, aimed at achieving safer and more reliable face recognition. Furthermore, the mapping and navigational capabilities are studied by conducting experiments with a Turtlebot outfitted with a 2D RPLIDAR and depth camera using SLAM, laying the groundwork for the GO2 robot's autonomous operations. The results of these enhancements have shown to be promising in the context of bolstering security measures. The paper concludes with a discussion on potential future enhancements, paving the way for more sophisticated and autonomous security robots. © 2024 IEEE.

关键词： Face recognition

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