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CSI Symposium on Real-Time and embedded systems and Technologies (RTEST)

作者： Mahsa Farahani Ghazal Shenavar Ali Hosseinghorban Alireza Ejlali Department of Computer Engineering Embedded Systems Research Laboratory (ESRLab) Sharif University of Technology Tehran Iran

ISBN: (数字)9798331529284

ISBN: (纸本)9798331529291

Firmware serves as a foundational software layer in modern computers, initiating as the first code executed on platform hardware, similar in function to a minimal operating system. Defined as a software interface between an operating system and platform firmware, the Unified Extensible Firmware Interface (UEFI) standardizes system initialization and management. A prominent open-source implementation of UEFI, the EFI Development Kit II (EDK2), plays a crucial role in shaping firmware architecture. Despite its widespread adoption, the architecture faces challenges such as limited system resources at early stages and a lack of standard security features. Furthermore, the scarcity of open-source tools specifically designed for firmware analysis emphasizes the need for adaptable, innovative *** this paper, we explore the application of general code audit tools to firmware, with a particular focus on EDK2. Although these tools were not originally designed for firmware analysis, they have proven effective in identifying critical areas for enhancement in firmware security. Our findings, derived from deploying key audit tools on EDK2, categorize these tools based on their methodologies and illustrate their capability to uncover unique firmware attributes, significantly contributing to the understanding and improvement of firmware security.

关键词： Fault tolerance Codes Operating systems Refining computer architecture Software Hardware Security Standards Microprogramming

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Real-Time Multi-Object Tracking using YOLOv8 and SORT on a SoC FPGA

arXiv

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arXiv 2025年

作者： Danilowicz, Michal Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Krakow Poland

Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation on low-power and real-time embedded platforms is highly desirable. Modern MOT algorithms should be able to track objects of a given class (e.g. people or vehicles). In addition, the number of objects to be tracked is not known in advance, and they may appear and disappear at any time, as well as be obscured. For these reasons, the most popular and successful approaches have recently been based on the tracking paradigm. Therefore, the presence of a high quality object detector is essential, which in practice accounts for the vast majority of the computational and memory complexity of the whole MOT system. In this paper, we propose an FPGA (Field-Programmable Gate Array) implementation of an embedded MOT system based on a quantized YOLOv8 detector and the SORT (Simple Online Realtime Tracker) tracker. We use a modified version of the FINN framework to utilize external memory for model parameters and to support operations necessary required by YOLOv8. We discuss the evaluation of detection and tracking performance using the COCO and MOT15 datasets, where we achieve 0.21 mAP and 38.9 MOTA respectively. As the computational platform, we use an MPSoC system (Zynq UltraScale+ device from AMD/Xilinx) where the detector is deployed in reprogrammable logic and the tracking algorithm is implemented in the processor system. © 2025, CC BY.

关键词： System-on-chip

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Lightweight Disaster Semantic Segmentation for UAV On-Device Intelligence

Lightweight Disaster Semantic Segmentation for UAV On-Device...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Hanbin Lee Gihwan Kim Sangho Ha Hyungshin Kim Department of Computer Science and Engineering Embedded Systems Laboratory (ESLAB) Chungnam National University Daejeon Republic of Korea

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Unmanned aerial vehicles (UAVs) are being used for monitoring natural disasters. To promptly assess the severity of the disaster, it is advantageous to analyze the disaster scenes with the on-board computer in real time. Various AI segmentation models with high accuracy are developed mainly for offline processing. They require significant memory capacity and computational power. However, on-device AI has the challenge of compressing high-precision models due to the limited memory size and the small computation power. In this research, we develop a lightweight disaster semantic segmentation model for UAV on-device intelligence. From a simple FANet as our baseline, we apply various optimization, such as compact backbone, lightweight attention block, quantization, knowledge distillation, and post processing. With our optimized model, we can reduce 84.2% of the inference time while achieving 0.5% increase in accuracy compared to the baseline model.

关键词： Accuracy Quantization (signal) Disasters Computational modeling Semantic segmentation Autonomous aerial vehicles Real-time systems

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Reliability analysis in ARM *** heterogeneous multi-core processor for IoT

Reliability analysis in ARM *** heterogeneous multi-core pro...

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International Conference on Internet of Things and Applications (IoT)

作者： Mahin Moradiyan Yaser Sedaghat Computer Engineering Department Dependable Distributed Embedded Systems (DDEmS) Laboratory Ferdowsi University of Mashhad Mashhad Iran

ISBN: (数字)9798350374674

ISBN: (纸本)9798350374681

This study navigates the landscape of the Internet of Things (IoT), spotlighting the escalating demand for robust processing solutions, thereby propelling the pervasive adoption of heterogeneous multi-core processors. Within this sphere, the ARM *** architecture takes center stage, adeptly harmonizing high performance with energy efficiency. The investigation zooms in on the pivotal role of failure-sensitive applications in the IoT realm, critical domains like healthcare. The paper underscores the indispensable need for failure analysis concerning ARM *** processors, unravel the complexities of data flow errors (DFE) and control flow errors (CFE). By providing insights into potential pitfalls and strategies for fortifying reliability, the study contributes to the evolving discourse on the resilience of ARM *** processors in the context of failure-sensitive IoT applications. Also concludes by emphasizing the need for continued exploration of optimal task mapping and fault tolerance strategies, paving the way for future research to refine and extend the reliability analysis of ARM *** platforms in evolving IoT landscapes.

关键词： Program processors Multicore processing Smart cities Navigation Fault tolerant systems Medical services Propulsion

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Real-Time Implementation of Quadrotor UAV Control System Based on a Deep Reinforcement Learning Approach

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computers, Materials & Continua 2024年第12期81卷 4757-4786页

作者： Taha Yacine Trad Kheireddine Choutri Mohand Lagha Souham Meshoul Fouad Khenfri Raouf Fareh Hadil Shaiba Aeronautical Sciences Laboratory Aeronautical and Spatial Studies InstituteBlida 1 UniversityBlida0900Algeria Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Energy and Embedded Systems for Transportation Research Department ESTACA-LABMontigny-Le-Bretonneux78066France Department of Electrical Engineering University of SharjahSharjah27272United Arab Emirates Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in *** strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control *** paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental *** address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini *** results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.

关键词： Deep reinforcement learning hyper-parameters optimization path following quadrotor twin delayed deep deterministic policy gradient and simulated annealing

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How to Make Artificial Intelligence Self Manifest the Sense of Humour

How to Make Artificial Intelligence Self Manifest the Sense ...

引用

Future of Information and Communication Conference, FICC 2021

作者： Benazzouz, Yazid Boudour, Rachid Embedded Systems Laboratory Computer Science Department Badji Mokhtar University Annaba Algeria

ISBN: (纸本)9783030731021

In everyday life, sense of humor usually comes through the production of jokes. The ability to be funny or to be amused by a joke is not an easy task for human because of cultural background and their capacity to perceive the ludicrous. This makes it a big challenge for artificial intelligence to imitate this behaviour. It is an advanced capability to create artificial life and interactive robots in particular. This technology could be interesting for many applications such as aerospace. Astronauts are called to stay in space for long periods of time and their emotional state must be monitored through interactions in order to keep stress and anxiety levels very low. Laughter can be part of the solution, especially since it comes from a machine. In this paper, we propose a machine learning based architecture to allow a machine not only understand jokes but also to generate them automatically according to discourse context. Moreover, multiple scenarios are evaluated to test the feasibility of our approach: emotional response to jokes during human machine interaction and autonomic robot reactions when reading a book or watching funny TV series. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Speech recognition

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Using Sequence to Sequence Model to Transform Recognized ASL Words to Understandable Sentences

Using Sequence to Sequence Model to Transform Recognized AS...

引用

Future Technologies Conference, FTC 2020

作者： Benazzouz, Yazid Boudour, Rachid Embedded Systems Laboratory Computer Science Department Badji Mokhtar University Annaba Algeria

ISBN: (纸本)9783030631277

There was widespread agreement that Communication can be a struggle for some deaf and hearing impaired people. The big breakthrough is the development of sign language (SL) which has enabled this category of people to communicate. SL has enabled them to integrate into modern society, and access to education and work. Although sign language is very well documented and taught it does not allow them to interact with all people. It still hard for them to conduct a real life as the rest of the society, for example, participating in work meetings, reading any books, taking classes at the university, nor to use the available means of communication, such as video chatting and conferencing. With the recent development in artificial intelligence solutions began to appear on the horizon to accentuate the integration of deaf and dumb people. This paper attempts to answer a small corner of these needs. It presents a technique for transforming sign language into understandable sentences that can in turn be synthesized into speech or sent as it is by email or SMS. The aim is to allow the society understand SL without making a learning effort. In order to show the feasibility of this approach, a real-time prototype is developed by integrating an existing sign recognition model. © 2021, Springer Nature Switzerland AG.

关键词： Audition

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TariqAmn's Innovative Road Safety Paradigm through Smart Technology

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Periodica Polytechnica Transportation Engineering 2024年第4期52卷 372-381页

作者： Malek, Nada Ahmed Boudour, Rachid Embedded Systems Laboratory Computer Science Department Faculty of Technology BADJI Mokhtar- Annaba University P.O.B. 12 Annaba23000 Algeria

In response to escalating road safety challenges within Algeria, marked by rising traffic volumes and urban development, the "TariqAmn Algeria" initiative emerges as a groundbreaking approach in traffic management. This study delineates the development and deployment of this innovative system, which leverages intelligent traffic signs, LiDAR (Light Detection and Ranging, using laser light to measure distances) radar, and proactive brake control technologies, embedded within a robust traffic management framework. The system's dynamic adaptation to real-time traffic conditions and its enforcement of speed compliance are central to its design. Rigorous evaluations through SUMO (Simulation of Urban Mobility, a traffic simulation software) simulations and field tests in high-risk urban settings affirm its efficacy, demonstrating a 60% reduction in traffic incidents and a 50% reduction in speeding violations. By marrying local cultural insights with cutting-edge technology, "TariqAmn Algeria" not only enhances immediate road safety but also establishes a new paradigm in traffic management systems. The findings illuminate the significant scalability potential of the system, promising broader applications in enhancing road safety and adherence to traffic regulations across various regions, such as densely populated urban areas, high-traffic corridors, and accident-prone black spots. This initiative underscores the critical role of Intelligent Transportation systems (ITS) in modern road safety strategies and sets a precedent for future technological advancements in the field. © 2024 Budapest University of Technology and Economics. All rights reserved.

关键词： Traffic signs

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Traffic Sign Detection With Event Cameras and DCNN 25

Traffic Sign Detection With Event Cameras and DCNN

引用

25th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2022

作者： Wzorek, Piotr Kryjak, Tomasz Embedded Vision Systems Group Agh University of Science and Technology Computer Vision Laboratory Department of Automatic Control and Robotics Kraków Poland Silesian University of Technology Department of Digital Systems Gliwice Poland

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

In recent years, event cameras (DVS - Dynamic Vision Sensors) have been used in vision systems as an alternative or supplement to traditional cameras. They are characterised by high dynamic range, high temporal resolution, low latency, and reliable performance in limited lighting conditions - parameters that are particularly important in the context of advanced driver assistance systems (ADAS) and self-driving cars. In this work, we test whether these rather novel sensors can be applied to the popular task of traffic sign detection. To this end, we analyse different representations of the event data: event frame, event frequency, and the exponentially decaying time surface, and apply video frame reconstruction using a deep neural network called FireNet. We use the deep convolutional neural network YOLOv4 as a detector. For particular representations, we obtain a detection accuracy in the range of 86.9-88.9% mAP@0.5. The use of a fusion of the considered representations allows us to obtain a detector with higher accuracy of 89.9% mAP@0.5. In comparison, the detector for the frames reconstructed with FireNet is characterised by an accuracy of 72.67% mAP@0.5. The results obtained illustrate the potential of event cameras in automotive applications, either as standalone sensors or in close cooperation with typical frame-based cameras. © 2022 Division of Signal Processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Cameras

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FADE: Forecasting for Anomaly Detection on ECG

arXiv

引用

arXiv 2025年

作者： Ruiz-Barroso, Paula Castro, Francisco M. Miranda, José Constantinescu, Denisa-Andreea Atienza, David Guil, Nicolás Department of Computer Architecture University of Málaga Malaga29071 Spain Embedded Systems Laboratory the École Polytechnique Fédérale of Lausanne Lausanne Switzerland

Background and Objective: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multiple approaches have been proposed in the literature to address the challenge of detecting ECG anomalies. Typically, these methods are based on the manual interpretation of ECG signals, which is time consuming and depends on the expertise of healthcare professionals. The objective of this work is to propose a deep learning system, FADE, designed for normal ECG forecasting and anomaly detection, which reduces the need for extensive labeled datasets and manual interpretation. Methods: We propose FADE, a deep learning system designed for normal ECG forecasting, trained in a self-supervised manner with a novel morphological inspired loss function, that can be used for anomaly detection. Unlike conventional models that learn from labeled anomalous ECG waveforms, our approach predicts the future of normal ECG signals, thus avoiding the need for extensive labeled datasets. Using a novel distance function to compare forecasted ECG signals with actual sensor data, our method effectively identifies cardiac anomalies. Additionally, this approach can be adapted to new contexts (e.g., different sensors, patients, etc.) through domain adaptation techniques. To evaluate our proposal, we performed a set of experiments using two publicly available datasets: MIT-BIH NSR and MIT-BIH Arrythmia. Results: The results demonstrate that our system achieves an average accuracy of 83.84% in anomaly detection, while correctly classifying normal ECG signals with an accuracy of 85.46%. Conclusions: Our proposed approach exhibited superior performance in the early detection of cardiac anomalies in ECG signals, surpassing previous methods that predominantly identify a limited range of anomalies. FADE effectively detects both abnormal heartbeats and arrhy

关键词： Electrocardiography

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