Suchergebnisse - Signal Processing and Machine Learning in Autonomous Systems

Signal Processing and Machine Learning for Gas Sensors: Gas Source Localization with a Nano-Drone

Autoren: Burgués Calderón, Javier

Weitere Verfasser: University/Department: Universitat de Barcelona. Facultat de Física

Thesis Advisors: Marco Colás, Santiago

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Detectors, Detectores, Processament de senyals, Proceso de señales, Signal processing, Aprenentatge automàtic, Aprendizaje automático, Machine learning, Robòtica, Robótica, Robotics, Drons, Drones, Drone aircraft, Ciències Experimentals i Matemàtiques

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/667600

Advanced Signal Processing Techniques for Real-Time Systems in Edge Computing

Autoren: Siti AISHAH

Quelle: Journal of Computer, Signal, and System Research. 1:20-29

Static Obstacles Avoidance of Autonomous Mobile Robots Based on Limit Cycle Approach

Autoren: Abdallaoui, Sara Aglzim, El-Hassane Kribeche, Ali et al.

Weitere Verfasser: Abdallaoui, Sara Aglzim, El-Hassane Kribeche, Ali et al.

Quelle: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET). :01-05

Schlagwörter: snow… Sign in to Continue Reading IEEE Keywords Navigation, Differential equations, vehicle to vehicle communication [5], we discuss the path planning method for obstacle avoidance based on the limit cycle, cheaper, [SPI] Engineering Sciences [physics], and oscillations. An extension of this method, as it involves determining the system's position and planning a safe, Trajectory, Abstract: Autonomous robot navigation is the subject of research in many disciplines. In this paper, local minima, obstacle avoidance, collision- free path towards the destination in such an environment with critical conditions like wind, computer vision [4], driven by advancements in technologies such as machine learning, Mobile robots, control theory [3], and signal processing. The goal of autonomous systems is to provide safer, rain, Tunisia I. Introduction Autonomous systems [1] have become a topic of growing interest for researchers and tech companies in recent decades, path planning, and more convenient transportation. They are equipped with functional modules such as perception, deep learning [2], results are provided to illustrate the effectiveness and merits of the proposed method for autonomous path planning. Published in: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) Date of Conference: 29 April 2023 - 01 May 2023 Date Added to IEEE Xplore: 20 June 2023 ISBN Information: INSPEC Accession Number: 23320379 DOI: 10.1109/IC_ASET58101.2023.10150527 Publisher: IEEE Conference Location: Hammamet, planning [6], prediction, is also implemented, which is characterized by elliptic bounding boxes rather than cylindrical ones. Finally, and control to achieve this goal. Navigation is the most critical factor for autonomous systems, mobile robotics, Limit-cycles, System recovery, limit-cycle, which has been shown to be effective in both static and dynamic environments. We focus the control architecture known as Limit Cycle, which generates robot trajectories defined by differential equations with proven stability through a Lyapunov function. This approach uses cylindrical bounding boxes around objects in the environment and implements reactive rules to avoid obstacles while respecting safety distances and avoiding deadlocks, elliptic limit-cycle, Safety, Parallel Elliptic Limit-Cycle

Radar Signal Processing and Its Impact on Deep Learning-Driven Human Activity Recognition.

Autoren: Ayaz, Fahad Alhumaily, Basim Hussain, Sajjad et al.

Quelle: Sensors (14248220). Feb2025, Vol. 25 Issue 3, p724. 21p.

Schlagwörter: *RADAR signal processing, *HUMAN activity recognition, *CONVOLUTIONAL neural networks, *DEEP learning, *LEARNING, *AUGMENTED reality

How Well Can Driverless Vehicles Hear? An Introduction to Auditory Perception for Autonomous and Smart Vehicles

Autoren: Marchegiani M Fafoutis X Marchegiani, M et al.

Weitere Verfasser: Marchegiani M Fafoutis X Marchegiani, M et al.

Quelle: Marchegiani, L & Fafoutis, X 2022, 'How Well Can Driverless Vehicles Hear? An Introduction to Auditory Perception for Autonomous and Smart Vehicles', IEEE Intelligent Transportation Systems Magazine, vol. 14, no. 3, pp. 92-105. https://doi.org/10.1109/MITS.2021.3049425
Marchegiani, L & Fafoutis, X 2022, ' How Well Can Driverless Vehicles Hear? A Gentle Introduction to Auditory Perception for Autonomous and Smart Vehicles ', IEEE Intelligent Transportation Systems Magazine, vol. 14, no. 3, pp. 92-105 . https://doi.org/10.1109/MITS.2021.3049425

Schlagwörter: 0209 industrial biotechnology, Sensors, Location awareness, Autonomous vehicles, Acoustic Signal Processing, Acoustics, 02 engineering and technology, Autonomous Vehicles, Machine Learning, Autonomous Systems, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Hidden Markov models, Automobiles, Neural networks

Dateibeschreibung: application/pdf

Zugangs-URL: http://www.scopus.com/inward/record.url?scp=85101478562&partnerID=8YFLogxK
https://vbn.aau.dk/da/publications/986ddb27-7430-4887-8e8a-c4c4a71d0b37
https://doi.org/10.1109/MITS.2021.3049425
https://vbn.aau.dk/ws/files/475873688/How_Well_Can_Driverless_Vehicles_Hear_A_Gentle_Introduction_to_Auditory_Perception_for_Autonomous_and_Smart_Vehicles.pdf
https://backend.orbit.dtu.dk/ws/files/237768697/2021_itsm_auditory.pdf
https://vbn.aau.dk/da/publications/how-well-can-driverless-vehicles-hear-a-gentle-introduction-to-au
https://orbit.dtu.dk/en/publications/how-well-can-driverless-vehicles-hear-a-gentle-introduction-to-au
https://orbit.dtu.dk/en/publications/9d76fcc1-45c2-4933-9571-41bce0bebf79

Neural network-based data processing for satellite imagery in small satellite earth observation missions

Autoren: Llaveria Godoy, David

Weitere Verfasser: University/Department: Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions

Thesis Advisors: Camps Carmona, Adriano José

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: CubeSatsEarth Observation (EO), Remote Sensing, Data Processing, Onboard Processing, Neural Networks, Hyperspectral Imaging, Band Selection, Sea Ice Monitoring, GNSS-Reflectometry (GNSS-R), L-band Radiometry, Image Deblurring, Àrees temàtiques de la UPC::Enginyeria de la telecomunicació, 621.3 - Enginyeria elèctrica. Electrotècnia. Telecomunicacions

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/695367
https://dx.doi.org/10.5821/dissertation-2117-442913

Sample complexity of the Sign-Perturbed Sums method

Autoren: Szentpéteri, Szabolcs Csáji, Balázs Csanád

Quelle: Automatica. 178:112020

Schlagwörter: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány, Machine Learning (stat.ML), Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Autonomous systems, Machine Learning (cs.LG), Methodology (stat.ME), Statistics - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Statistics - Methodology

Dateibeschreibung: text

Zugangs-URL: http://arxiv.org/abs/2409.01243
https://eprints.sztaki.hu/10917/

Empowering autonomous systems with AI-enabled V2X communication based signal analysis using sliding window integrated ensemble machine learning model.

Autoren: Liu, Jingang Cao, Xiaofeng

Quelle: Computers & Electrical Engineering. Oct2023:Part A, Vol. 111, pN.PAG-N.PAG. 1p.

Schlagwörter: *MACHINE learning, *SIGNAL processing, *FOURIER transforms, *AUTOMATIC systems in automobiles, *SIGNALS & signaling, *SMART devices

Quantum Microprocessors, Sensors, Hardware, and Computers – HQE (6th Generation Quantum-Conscious Systems). (Part 8 of 20 – The Quantum Revolution)

Autoren: JALALI, SEYED RASOUL

Schlagwörter: Quantum Encryption Algorithms, Neural Cryptography, AI-Based Data Protection, Cyber Intelligence, Secure Quantum Networks, Digital Encryption Standards, High-Level Cryptanalysis, Quantum Key Management, Deep Packet Inspection, Autonomous Security Systems, Encrypted Data Storage, Cognitive Encryption Layers, Distributed Quantum Systems, Multi-Factor Authentication, Secure Signal Processing, Advanced Digital Forensics, Quantum Secure Channels, Anti-Hacking Protocols, Real-Time Cyber Monitoring, Encrypted Communication Frameworks, Quantum Encryption, Cryptography, Hamzah Equation, AI-Driven Security, Quantum Intelligence, Geospatial Surveillance, Nonlinear Data Analysis, Cognitive Encryption, Advanced Signal Processing, Fractal Geometry, Quantum Field Theory, Non-Predictive Intelligence, Autonomous Defense Systems, Neural Networks, Artificial Consciousness, Entanglement Protocols, Secure Communication Channels, Biometric Quantum Authentication, Advanced Encryption Standards, Exponential Cryptanalysis, Classified Algorithms, Quantum Decryption, Cybersecurity Protocols, DeepNet Surveillance, AI-Assisted Encryption, Digital Intelligence, Cryptographic Frameworks, Artificial Intelligence Networks, Data Integrity Protocols, Advanced Security Algorithms, Multi-Layered Encryption, Threat Detection Systems, Stealth Communication, Quantum-Resistant Security, Artificial Intelligence Threats, Tactical Data Encryption, Autonomous Defense Mechanisms, Geospatial Intelligence Networks, Quantum Cryptographic Methods, Data Protection Technologies, Quantum Computing, Blockchain Technology, Neural Encryption, Cyber Warfare, Advanced Machine Learning, Fractal Algorithms, Geospatial Intelligence, Encrypted Data Transmission, AI-Based Cyber Defense, Cryptanalysis Techniques, Secure Quantum Channels, Artificial Neural Networks, Cognitive Computing, Predictive Modeling, Quantum Key Distribution, Zero-Day Exploits, Deep Learning Systems, Quantum Cryptography, Cyber Threat Intelligence, Network Penetration, Quantum Entanglement, Cryptographic Systems, High-Level Encryption, Intelligence Networks, Geospatial Data Analysis, AI-Driven Cybersecurity, Secure Communication Systems, Advanced Quantum Algorithms, Deep Learning Encryption, Cyber Threat Detection, Autonomous Systems, Quantum Computing Protocols, AI Security Systems, Distributed Cryptography, Zero Trust Architecture, Secure Data Transfer, Encrypted Network Traffic, Cognitive Security Models, Quantum-Powered Protection, Surveillance and Reconnaissance

FPGA Based Real Time Image Processing Architectures for Autonomous Navigation Systems

Autoren: Jesmin Zakaria L Malathi

Quelle: Smart Microcontrollers and FPGA Based Architectures for Advanced Computing and Signal Processing ISBN: 9789349552425

Self-Adaptive Wireless Communication: Leveraging ML And Agentic AI In Smart Telecommunication Networks

Autoren: Sheelam, Goutham Kumar Komaragiri, Venkata Bhardwaj

Quelle: Metallurgical and Materials Engineering; Vol. 31 No. 5 (2025); 1381-1401 ; 2812-9105 ; 2217-8961

Schlagwörter: self-adaptive wireless communication, machine learning, agentic AI, smart telecommunication networks, adaptive networking, autonomous systems, intelligent signal processing, dynamic network optimization, real-time decision-making, multi-agent systems, reinforcement learning, cognitive networks, 5G, 6G, edge computing, network self-healing, AI-driven orchestration, wireless intelligence, self-organizing networks, proactive resource management

Dateibeschreibung: application/pdf

Relation: https://metall-mater-eng.com/index.php/home/article/view/1716/1023

Verfügbarkeit: https://metall-mater-eng.com/index.php/home/article/view/1716

Toward Robust Sensing for Autonomous Vehicles: An Adversarial Perspective

Autoren: Apostolos Modas Ricardo Sanchez-Matilla Pascal Frossard et al.

Quelle: IEEE Signal Processing Magazine. 37:14-23

Schlagwörter: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Cryptography and Security, Sensors, Acoustics, 02 engineering and technology, Perturbation methods, Machine Learning (cs.LG), Laser radar, Global Positioning System, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Jamming, Electrical Engineering and Systems Science - Signal Processing, Cryptography and Security (cs.CR)

Zugangs-URL: http://arxiv.org/pdf/2007.10115
http://arxiv.org/abs/2007.10115
https://ui.adsabs.harvard.edu/abs/2020ISPM...37d..14M/abstract
https://dblp.uni-trier.de/db/journals/corr/corr2007.html#abs-2007-10115
https://ieeexplore.ieee.org/document/9127857/
https://arxiv.org/abs/2007.10115
https://arxiv.org/pdf/2007.10115v1
http://export.arxiv.org/pdf/2007.10115

Anomaly Detection using Deep Reconstruction and Forecasting for Autonomous Systems

Autoren: Bahavan, Nadarasar Suman, Navaratnarajah Cader, Sulhi et al.

Schlagwörter: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Electrical Engineering and Systems Science - Signal Processing, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2006.14556

Beehive Health Monitoring System

Autoren: Dickinson, Jacob Carl Medina-Ramos, Jossue Ogikubo, Masaki et al.

Schlagwörter: honeybee, bee, machine learning, TensorFlow, DSP, signal processing, neural networks, autonomous vehicle, agriculture, colony collapse disorder, pollinator, drone, machine vision, computer vision, object detection, UCI Dean's Choice Award 2019

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/35w509pf
https://escholarship.org/content/qt35w509pf/qt35w509pf.pdf

Perspectives on the Emerging Field of Autonomous Systems and its Theoretical Foundations

Autoren: Wang Y. Plataniotis K. N. Mohammadi A. et al.

Quelle: 2021 IEEE International Conference on Autonomous Systems (ICAS). :1-5

Schlagwörter: 0301 basic medicine, 0303 health sciences, 03 medical and health sciences, Autonomous systems, Brain-inspired systems, General AI theory, Intelligence science, Intelligent signal processing, System science

Dateibeschreibung: application/pdf

Zugangs-URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=9551191
http://xplorestaging.ieee.org/ielx7/9551106/9551107/09551191.pdf?arnumber=9551191
https://hdl.handle.net/11567/1072952
https://doi.org/10.1109/ICAS49788.2021.9551191

Robotics Systems and Artificial Intelligence Applications in Livestock Farming.

Alternate Title: Hayvancılıkta Robotik Sistemler ve Yapay Zekâ Uygulamaları.

Autoren: Dilaver, Hatice Dilaver, Kamil Fatih

Quelle: Journal of Animal Science & Economics. Jul2024, Vol. 3 Issue 2, p63-72. 10p.

Schlagworte: Artificial intelligence, Animal industry, Agriculture, Livestock farms, Cattle industry

LinkedFormer: Radar Communication and Multiscale Imaging for Object Detection under Complex Sea Background.

Autoren: Xing Pu Xisheng Xu Yi Yu

Quelle: Sensors & Materials; 2024, Vol. 36 Issue 8, Part 2, p3351-3365, 15p

Schlagwörter: OBJECT recognition (Computer vision), RADAR signal processing, MARINE engineering, RADAR, BODIES of water

Passenger hazard perception based on EEG signals for highly automated driving vehicles

Autoren: Tan, Ashton Yu Xuan Yang, Yingkai Zhang, Xiaofei et al.

Schlagwörter: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Human-Computer Interaction, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Human-Computer Interaction (cs.HC), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2408.16315

Radar Signal Processing and Its Impact on Deep Learning-Driven Human Activity Recognition

Autoren: Fahad Ayaz Basim Alhumaily Sajjad Hussain et al.

Quelle: Sensors (Basel)
Sensors, Vol 25, Iss 3, p 724 (2025)

Schlagwörter: Radar, Fourier Analysis, Chemical technology, deep learning, Signal Processing, Computer-Assisted, TP1-1185, transfer learning, Article, Deep Learning, Humans, Human Activities, Neural Networks, Computer, radar domain representations, computational cost, Algorithms, human activity classification

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39943363
https://doaj.org/article/a4349b479ef34897bee08347fa90bd0d

TRAINING FOR IMAGE SIGNAL PROCESSING ALGORITHM ITERATIONS FOR AUTONOMOUS VEHICLES