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MikroSystemTechnik Kongress 2023: Mikroelektronik, Mikrosystemtechnik und ihre Anwendungen - Nachhaltigkeit und TechnologiesouveranitatMicrosystems Technology Congress 2023: Microelectronics, Microsystems Technology and their Applications - Sustainability and Technology Sovereignty

作者： Rossbach, Daniel Konegen, Daniel Rüb, Marcus Willmann, Alexander Kuderer, Markus Rietsche, Hansjörg Amft, Oliver Hahn-Schickard Villingen-Schwenningen Germany Intelligent Embedded Systems Lab Department of Computer Science University of Freiburg Germany

ISBN: (纸本)9783800762040

The aim of the project presented in this paper was to embed high-resolution stress sensors in the gripping surfaces of a robot gripper to derive information about the quality/stability and property of the grip using the data thus obtained during the gripping process. Based on the sensor information, the robot's control software can decide whether and how the position of the gripper relative to the object to be gripped needs to be adapted. ASICs for surface stress measurement were embedded in an elastic silicone mold and mounted on a commercial robot gripper. The ASIC readout was realized using an FPGA development board, for which software was developed to configure the system and display the recorded data. To interpret sensor data, gripping recordings were used to design and train a neural network for the classification of gripping states. Although the neural network was quantized to 8-bit and compressed by more than 75% to fit on the FPGA platform, it achieved a classification accuracy of 93% for recognising three different grasping classes. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Field programmable gate arrays (FPGA)

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Scalable and Low-Cost Remote lab Platforms: Teaching Industrial Robotics Using Open-Source Tools and Understanding Its Social Implications 16th

Scalable and Low-Cost Remote Lab Platforms: Teaching Indust...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Kumar, Amit Jose, Jaison Jain, Archit Kulkarni, Siddharth Arya, Kavi Centre for Systems and Control Indian Institute of Technology Bombay Mumbai India Embedded Real-Time Systems / e-Yantra Lab Indian Institute of Technology Bombay Mumbai India Computer Science and Engineering Department Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9789819635214

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cost platforms built using open-source tools like Robot Operating System (ROS) and its latest version ROS 2 to help students learn and test algorithms on remotely connected industrial robots. Universal Robotics (UR5) arm and a custom mobile rover were deployed in different life-size testbeds, a greenhouse, and a warehouse to create an Autonomous Agricultural Harvester System (AAHS) and an Autonomous Warehouse Management System (AWMS). These platforms were deployed for a period of 7 months and were tested for their efficacy with 1,433 and 1,312 students, respectively. The hardware used in AAHS and AWMS was controlled remotely for 160 and 355 hours, respectively, by students over a period of 3 months. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Costs

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STM32 miner : A lightweight blockchain mining system 1

STM32 miner : A lightweight blockchain mining system

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1st IEEE International Conference on Design, Test and Technology of Integrated systems, DTTIS 2023

作者： Ktari, Jalel Choura, Hedi Frikha, Tarek Baklouti, Mouna Mejdi, Dhia Kotti, Molka Zribi, Ahmed Touil, Hassine Oueslati, Ghaith National Engineering School of Sfax Computer Embedded Systems Lab Sfax Tunisia National Engineering School of Sfax Data Engineering and Semantics Unit Sfax Tunisia National Engineering School of Sfax Computer Sciences and Applied Mathematiques Departement Sfax Tunisia

ISBN: (纸本)9798350316957

Despite being a promising area of exploration, the application of embedded systems in blockchain technology, particularly with FFGA or Raspberry technology, remains relatively limited. Despite the advantages offered by FPGAs, such as high-speed memory, parallel computing blocks, and a flexible architecture, their use in blockchain has primarily been focused on Proof of Work (PoW) consensus *** this paper, our goal is to address this gap by presenting the implementation of a blockchain application specifically tailored for the STM32 platform. By leveraging the capabilities of the STM32 microcontroller, we aim to demonstrate the practicality and effectiveness of embedded systems in blockchain technology, overcoming the limitations observed with previous platforms. © 2023 IEEE.

关键词： Blockchain

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Quantum Calculus-Based Two-Dimensional Least Mean Square Algorithm 58

Quantum Calculus-Based Two-Dimensional Least Mean Square Alg...

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58th Asilomar Conference on Signals, systems and computers, ACSSC 2024

作者： Moinuddin, Muhammad Zerguine, Azzedine Al-Saggaf, Ubaid M. Arif, Muhammad King Abdulaziz University Electrical and Computer Engineering Department The Centre of Excellence in Intelligent Engineering Systems Jeddah Saudi Arabia Kfupm Electrical Engineering Department The Interdisciplinary Research Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria The Ubiquitous Sensing Systems Lab University of Klagenfurt-Silicon Austria Labs Klagenfurt Austria

ISBN: (纸本)9798350354058

Recent work of the one-dimensional q-LMS algorithm give insight that the Quantum Calculus based gradient called the q-gradient is capable of improving the convergence behavior in contrast to the standard LMS algorithm. The reason for this is the fact that the q-derivative takes larger steps in minimizing a cost function as it evaluates the secant in contrast to the tangent of the cost function. Motivated by this, we propose a two-dimensional version of the q-LMS algorithm. More precisely, we employ Quantum Calculus based q-gradient in steepest descent optimization of mean-square-error (MSE) cost function in 2D adaptive filtering scenario to obtain two-dimensional q-LMS (2D q-LMS) algorithm. A thorough analytical investigation of the proposed 2D q-LMS algorithm in both mean and mean-square sense is provided. Both the transient and steady-state convergence behaviours are examined. Consequently, excess MSE (EMSE) learning curve and its steady-state expression are evaluated in closed form. Simulation results are presented for the application of noise cancelation in images to show the superiority of the proposed 2D q-LMS algorithm. © 2024 IEEE.

关键词： Cost functions

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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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Artificial Intelligence for securing SDIoT: Challenges and countermeasures 20

Artificial Intelligence for securing SDIoT: Challenges and c...

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20th International Multi-Conference on systems, Signals and Devices, SSD 2023

作者： Zaidoun, Ameur Salem Baccouch, Hajer Guermazi, Abderrahmen Lachiri, Zied National Engineering School of Tunis Signal Images and Information Technologies Lab Tunisia Higher Institute of Technological Studies of Siliana Tunisia National Engineering School of Sfax Computer and Embedded Systems Lab Tunisia Higher Institute of Technological Studies of Sfax Tunisia

ISBN: (纸本)9798350332568

SDIoT (Software Defined IoT) is an emerging IoT technology, deployed using SDN architecture;nevertheless, it presents several weaknesses, which can be raised in different layers. To mitigate SDIoT intrusions and attacks, security as used in a common network cannot provide a complete solution and must be enhanced with AI (Artificial Intelligence) based countermeasures tools, mainly machine and deep learning. The paper identifies the salient features of SDIoT technology. Afterwards, it describes the most common attacks against SDIoT networks and possible countermeasures using several deep learning algorithms. In addition, the paper describes datasets used for learning steps. At the end, it discusses the suitability of AI based security tools to some attacks. © 2023 IEEE.

关键词： Deep learning

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Deep learning based cyber bullying early detection using distributed denial of service flow

Deep learning based cyber bullying early detection using dis...

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作者： Zaib, Muhammad Hassan Bashir, Faisal Qureshi, Kashif Naseer Kausar, Sumaira Rizwan, Muhammad Jeon, Gwanggil Department of Computer Science Bahria University Islamabad Pakistan Lab Bahria University Islamabad Pakistan Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

Cyber-bullying has been on the rise especially after the explosive widespread of various cyber-attacks. Various types of techniques have been used to tackle cyber-bullying. These techniques focused primarily on data traffic for monitoring malicious activities. This research proposes a methodology where we can detect early Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks. First, we formulate the problem in a practical scenario by comparing flow and non-flow-based datasets using Mann Whitney U statistical test. Flow and non-flow-based datasets and Artificial Neural Network (ANN) and Support Vector Machine (SVM) is used for classification. To keep original features, we use variance, correlation, ½ quartile method to eliminate the unimportant features. The forward selection wrapper method for feature selection is used to find out the best features. To validate the proposed methodology, we take multiple DoS and DDoS single flow and validate it on 10%, 20%, 30%, 40%, and 50%. For validation, the experimental results show + 90% accuracy on the early 10% flow. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Network security

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Near-Optimal Coverage Path Planning of Distributed Regions for Aerial Robots with Energy Constraint

Near-Optimal Coverage Path Planning of Distributed Regions f...

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2021 International Conference on Unmanned Aircraft systems, ICUAS 2021

作者： Khanam, Zeba McDonald-Maier, Klaus Ehsan, Shoaib Embedded and Intelligent Systems Lab School of Computer Science and Electronics Engineering University of Essex United Kingdom

ISBN: (纸本)9780738131153

Unmanned Aircraft Vehicles (UAVs) have gained immense popularity for area coverage having applications such as environmental monitoring, demining, search and rescue, among others. Most of the existing studies exploring area coverage have considered only a single region, however, few recent studies have considered coverage of multiple distributed regions. One of the limitations which UAV suffers while covering distributed multiple regions is energy constraints where complete area coverage is not possible. From a strategical point of view, we propose a novel algorithm which solves a variant of area coverage problem where the UAV aims to achieve near-optimal area coverage due to path length limitation caused by the energy constraint. In this paper, a preliminary study is conducted by first formulating the problem and later on presenting a solution. The solution has been partitioned into two inter-dependent sub-problems: i) inter-region coverage, ii) intra-region coverage. The performance of the algorithm has been evaluated by analysing its properties over an exhaustive set of test case scenarios and comparing it against two state-of-the-art area coverage approaches. © 2021 IEEE.

关键词： Motion planning

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Catalyzing EEG signal analysis: unveiling the potential of machine learning-enabled smart K nearest neighbor outlier detection

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International Journal of Information Technology (Singapore) 2024年 1-12页

作者： Aymen, Abid Khediri, Salim El Thaljaoui, Adel Miladi, Moahmed Kachouri, Abdennaceur Computer & amp Embedded Systems Lab National Engineering School of Sfax University of Sfax Sfax Tunisia Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science Faculty of Science of Gafsa University of Gafsa Gafsa Tunisia Preparatory Institute for Engineering Studies of Gafsa Gafsa Tunisia Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Sfax Tunisia Advanced Fluid Dynamics Energetics and Environment Lab National Engineering School of Sfax University of Sfax Sfax Tunisia

Electroencephalogram (EEG) data are susceptible to artifacts, such as lapses in concentration or poor imagination, which can significantly impact the accuracy of disease diagnosis in e-health applications. To mitigate this issue, the use of machine learning (ML) and potentially artificial intelligence (AI) solutions to accurately identify outliers becomes crucial. Unlike many AI methods that incorporate unnecessary or redundant input variables, our study focuses on detecting anomalous values in EEG data through the K nearest neighbor (KNN) process and Euclidean distance metric. Our proposed unsupervised non-parametric algorithm, known as the smart KNN outlier detector (SKOD), eliminates the need for initial parameter configurations such as the number of neighbors (K), while achieving high performance. Evaluation of SKOD using real EEG data from 140 trials demonstrated sensitivity and specificity exceeding 60%, with nearly perfect accuracy in detecting outliers reaching close to 100%. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Data analysis EEG signal Healthcare Knowledge-based systems Machine learning Outlier detection

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A New Approach for the Analysis of Resistance to Change in the Digital Transformation Context 11th

A New Approach for the Analysis of Resistance to Change in t...

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10th International Conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th International Conference on Innovation and New Trends in Information Technology, INTIS 2023

作者： El Karam, Bachira Abou Marghoubi, Rabia Khanboubi, Fadoua Allaki, Driss InnovatiVE REsearch on Software Systems and data INPT Rabat Morocco LIM Lab. IOS team Department of Computer Science FSTM Casablanca Morocco Smart Embedded Enterprise and Distributed Systems INPT Rabat Morocco

ISBN: (纸本)9783031473654

In a context marked by the rapid evolution of technologies and the omnipresence of IT at the heart of all activities, the digital transformation of companies is no longer a luxury but an absolute necessity. This technological transformation can lead to resistance from the employees affected by the change. In order to properly conduct the change management process, it is necessary to ensure the involvement of employees from the beginning of this process, which is composed of three phases: the change management strategy, the diagnosis, and the actions implementation. This paper focuses on the diagnostic phase and more specifically on the evaluation stage. This evaluation measures the employees’ hesitation to express their degree of resistance toward the digital initiatives launched in the company. However, this assessment is usually done subjectively, and employees express their hesitation in non-uniform manners. In this work, we propose a weighted generalized hesitating fuzzy approach based on the fusion of theoretical concepts of generalized hesitant fuzzy sets and formal concept analysis (FCA), to formalize the measurement of degree hesitation in change resistance. This approach is flexible and allows the top management of each organizational unit to correctly measure the resistance to change of their teams and then evaluate that measure at the global organizational level. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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