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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Mahyari, Arash Aliakbarpour, Hadi PensacolaFL United States University of West Florida Department of Intelligent Systems and Robotics PensacolaFL United States Saint Louis University Department of Computer Science St. LouisMO United States

ISBN: (纸本)9798350359527

Neuromorphic cameras, or event cameras, are biologically-inspired sensors that detect changes in illumination at a pixel level, different from traditional cameras where each pixel independently and asynchronously outputs when it senses illumination changes. The neuromorphic cameras exhibit high-Temporal resolution and dynamic range, useful in various applications. Spiking Neural Networks (SNNs), mimicking biological neurons, are efficient in processing neuromorphic images due to their event-driven, low-power nature. However, training SNNs is challenging because of the non-differentiability of neuron models which require different optimization techniques. Inspired by recent works on unsupervised deep clustering in conventional deep learning, we introduce a novel deep clustering algorithm that employs SNNs to extract and group visual features from neuromorphic images into clusters. It utilizes an SNN feature extractor for neuromorphic images, grouping the extracted features using K-means clustering. This algorithm, which doesn't require pre-Trained SNN weights, alternates between cluster updates and SNN weight adjustments. The extracted feature vectors in SNN are clustered and used as pseudo-labels for supervised training of the SNN extractor. The performance of the proposed algorithm is evaluated on two datasets of MNIST and NMNIST, and its effectiveness is assessed using the Normalized Mutual Information (NMI) metric. The performance of our SNN system with neuromorphic images, while marginally below its counterparts of ANNs with traditional images, demonstrates considerable potential. © 2023 IEEE.

关键词： Cameras

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BIRD: Battlefield-Integrated Reconnaissance Drone with VQA

BIRD: Battlefield-Integrated Reconnaissance Drone with VQA

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Rayon, Nathan Menser, Kobi Catalano, Louis Vega, Jonathan Najarro, Julio Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Mechanical Engineering University of West Florida FL32514 United States Catalano Aerospace LLC FL32502 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper introduces a new unmanned aerial system (UAS) design featuring coaxial rotors and tethered operation with a base station. The coaxial rotor configuration enhances stability and maneuverability, particularly in challenging environments. The tethered connection to a base station ensures prolonged flight duration and enables continuous power and data transmission. Moreover, the UAS incorporates a visual question answering (VQA) based target identification system. This system leverages advanced computer vision algorithms to analyze visual input from onboard cameras, coupled with natural language processing techniques to interpret and respond to queries regarding identified targets. The integration of VQA technology enables critical decision-making decisions through providing crucial information identify about targets in real-time scenarios. The new design has a significant potential for applications in surveillance, reconnaissance, search and rescue, and disaster response, as this design was conceptualized to provide persistent Intelligence, Reconnaissance & Surveillance (ISR) capabilities. Further analysis of the concept design was conducted to investigate the feasibility of novel design characteristics. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Reconnaissance aircraft

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Onboard Perception-Assisted High Fidelity Simulation Framework for Autonomous Planetary Soft-Landing

Onboard Perception-Assisted High Fidelity Simulation Framewo...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Alibekov, Ulugbek Banerjee, Avijit Satpute, Sumeet Gajanan Nikolakopoulos, George ERASMUS MUNDUS JOINT MASTER in Intelligent Field Robotic Systems Faculty of Informatics Eötvös Loránd University Budapest Hungary Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents an onboard perception-assisted high-fidelity simulation framework for autonomous planetary soft-landing, enabling visual information processing tightly integrated with advanced onboard guidance system in a realistic simulation environments. Utilizing the open source Unity game engine, a physics-based simulation toolbox integrated with the Robotic Operating System (ROS2), we effectively emulate the motion of spacecraft approaching planetary terrain. The simulation environment features a realistic 3D terrain model and a lander spacecraft equipped with onboard camera to effectively capture detailed local terrain images. The simulation engine accurately captures the spacecraft’s 6-degrees of freedom motion, seamlessly integrating space craft orientation with the associated camera feed while incorporating various illumination conditions using a light source representing the Sun. To demonstrate the efficacy of the framework, we implement a recently developed attitude-constrained minimum jerk guidance algorithm to emulate spacecraft motion. Concurrently, the visual feed from the onboard RGB camera is processed through a gradient extraction-based perception system, providing the visual odometer. This system identifies provably safe landing sites, effectively avoiding potential risks of landing on uneven terrain. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Planetary landers

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Remote Aerial Vehicle Solutions for Weed Detection in Precision Agriculture

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IEEE Access 2025年 13卷 101440-101454页

作者： Shekhar S. Borah Aryan Anand Prabha Sundaravadivel Reginald Fletcher Krishna N. Reddy Department of Electrical and Computer Engineering Center for Robotics and Intelligent Systems The University of Texas at Tyler Tyler TX USA Crop Production Systems Research Unit USDA-Agricultural Research Service (ARS) Stoneville MS USA

This study presents a novel Remote Aerial Vehicle-based approach for detecting pigweeds in soybean (Glycine max) fields using a combination of deep learning and advanced image processing techniques. A custom, high-resolution dataset comprising RGB (red, green, and blue) and multispectral images was collected from USDA operated fields and manually annotated for target pigweed detection. Beyond incorporating YOLOv8 variants for real-time weed classification, this study integrates a comprehensive image processing pipeline, incorporating global thresholding, k-means clustering, 3D surface mapping, and spectral signature analysis to enhance interpretability and detection accuracy. A comparative evaluation of YOLOv8 nano, small, medium, and large models, along with Faster R-CNN, confirmed YOLOv8’s balance between detection accuracy and real-time performance for deployment in precision agriculture. The YOLOv8 nano model emerged as the most balanced in terms of precision (75.6%), recall (81.7%), and mAP@0.5 (81.5%), demonstrating effective weed detection performance under real field conditions. In addition, quadrant-level weed coverage and spatial heatmaps were generated to support targeted interventions. This study advances the current state of RAV based weed detection by providing a field-ready, explainable, and resource-efficient solution, contributing to sustainable farming and data-driven weed management practices.

关键词： Crops Deep learning Accuracy Autonomous aerial vehicles Agriculture Real-time systems Feature extraction Soil Precision agriculture Drones

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Advances in Wireless,Batteryless,Implantable Electronics for Real‑Time,Continuous Physiological Monitoring

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Nano-Micro Letters 2024年第3期16卷 254-302页

作者： Hyeonseok Kim Bruno Rigo Gabriella Wong Yoon Jae Lee Woon‑Hong Yeo IEN Center for Wearable Intelligent Systems and Healthcare Georgia Institute of TechnologyAtlantaGA 30332USA George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Tech and Emory University School of MedicineAtlantaGA 30332USA Parker H.Petit Institute for Bioengineering and Biosciences Institute for MaterialsInstitute for Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaGA 30332USA

This review summarizes recent progress in developing wireless,batteryless,fully implantable biomedical devices for real-time continuous physiological signal monitoring,focusing on advancing human health *** considerations,such as biological constraints,energy sourcing,and wireless communication,are discussed in achieving the desired performance of the devices and enhanced interface with human *** addition,we review the recent achievements in materials used for developing implantable systems,emphasizing their importance in achieving multi-functionalities,biocompatibility,and *** wireless,batteryless devices offer minimally invasive device insertion to the body,enabling portable health monitoring and advanced disease ***,we summarize the most recent practical applications of advanced implantable devices for human health care,highlighting their potential for immediate commercialization and clinical uses.

关键词： Implantable electronics Biomedical systems Batteryless devices Wireless electronics Physiological signal monitoring

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AR/VR Digital Twin for Simulation and Data Collection of Robotic Environments 25

AR/VR Digital Twin for Simulation and Data Collection of Rob...

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25th IEEE International Conference on Autonomous Robot systems and Competitions, ICARSC 2025

作者： Martins, João G. Nutonen, Karle Costa, Paulo Kuts, Vladimir Otto, Tauno Sousa, Armando Petry, Marcelo R. CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal Faculty of Engineering Porto4200-465 Portugal ViruTech - Virumaa innovation center of digitalization and green technologies Taltech Virumaa College Kohtla-Jarve30322 Estonia Taltech Department of Mechanical and Indusrial Tallinn19086 Estonia

ISBN: (纸本)9798331538606

Digital twins enable real-time modeling, simulation, and monitoring of complex systems, driving advancements in automation, robotics, and industrial applications. This study presents a large-scale digital twin-testing facility for evaluating mobile robots and pilot robotic systems in a research laboratory environment. The platform integrates high-fidelity physical and environmental models, providing a controlled yet dynamic setting for analyzing robotic behavior. A key feature of the system is its comprehensive data collection framework, capturing critical parameters such as position, orientation, and velocity, which can be leveraged for machine learning, performance optimization, and decision-making. The facility also supports the simulation of discrete operational systems, using predictive modeling to bridge informational gaps when real-time data updates are unavailable. The digital twin was validated through a matrix manufacturing system simulation, with an Augmented Reality (AR) interface on the HoloLens 2 to overlay digital information onto mobile platform controllers, enhancing situational awareness. The main contributions include a digital twin framework for deploying data-driven robotic systems and three key AR/VR integration optimization methods. Demonstrated in a laboratory setting, the system is a versatile tool for research and industrial applications, fostering insights into robotic automation and digital twin scalability while reducing costs and risks associated with real-world testing. © 2025 IEEE.

关键词： Augmented reality

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Holistic Construction Automation With Modular Robots: From High-Level Task Specification to Execution

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 16716-16727页

作者： Jonathan Külz Michael Terzer Marco Magri Andrea Giusti Matthias Althoff Department of Computer Engineering Technical University of Munich Munich Germany Munich Center for Machine Learning (MCML) Munich Germany Robotics and Intelligent Systems Engineering Unit Fraunhofer Italia Research Bolzano Italy

In situ robotic automation in construction is challenging due to constantly changing environments, a shortage of robotic experts, and a lack of standardized frameworks bridging robotics and construction practices. This work proposes a holistic framework for construction task specification, optimization of robot morphology, and mission execution using a mobile modular reconfigurable robot. Users can specify and monitor the desired robot behavior through a graphical interface. In contrast to existing, monolithic solutions, we automatically identify a new task-tailored robot for every task by integrating Building Information Modeling (BIM). Our framework leverages modular robot components that enable the fast adaption of robot hardware to the specific demands of the construction task. Other than previous works on modular robot optimization, we consider multiple competing objectives, which allow us to explicitly model the challenges of real-world transfer, such as calibration errors. We demonstrate our framework in simulation by optimizing robots for drilling and spray painting. Finally, experimental validation demonstrates that our approach robustly enables the autonomous execution of robotic drilling. Note to Practitioners—Construction sites are unpredictable and constantly changing, making automation difficult. To tackle this, we propose a modular robotic system that can adapt to specific tasks. In this work, we address the challenge of automating robotic drilling and introduce a general framework that also supports other construction tasks. We use Building Information Modelling (BIM) data to automatically determine the best robot configuration by optimizing factors such as setup time and robustness to positioning errors. Through an intuitive interface, workers can specify tasks, after which they assemble the robot following the provided instructions. The result of this work is a robotic system that can be deployed on diverse construction sites while requiring only r

关键词： Robots Optimization Service robots Drilling Automation Morphology Robustness Robot control Navigation Calibration

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Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detection 2

Applying Pre-trained Multilingual BERT in Embeddings for Imp...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Rahman, Md Abdur Shahriar, Hossain Wu, Fan Cuzzocrea, Alfredo University of West Florida Dept. of Intelligent Systems and Robotics PensacolaFL United States University of West Florida Center for Cybersecurity PensacolaFL United States Tuskegee University Department of Computer Science TuskegeeAL United States University of Calabria Idea Lab Rende Italy

ISBN: (纸本)9798331529529

Large language models (LLMs) are renowned for their exceptional capabilities, and applying to a wide range of applications. However, this widespread use brings significant vulnerabilities. Also, it is well observed that there are huge gap which lies in the need for effective detection and mitigation strategies against malicious prompt injection attacks in large language models, as current approaches may not adequately address the complexity and evolving nature of these vulnerabilities in real-world applications. Therefore, this work focuses the impact of malicious prompt injection attacks which is one of most dangerous vulnerability on real LLMs applications. It examines to apply various BERT (Bidirectional Encoder Representations from Transformers) like multilingual BERT, DistilBert for classifying malicious prompts from legitimate prompts. Also, we observed how tokenizing the prompt texts and generating embeddings using multilingual BERT contributes to improve the performance of various machine learning methods: Gaussian Naive Bayes, Random Forest, Support Vector Machine, and Logistic Regression. The performance of each model is rigorously analyzed with various parameters to improve the binary classification to discover malicious prompts. Multilingual BERT approach to embed the prompts significantly improved and outperformed the existing works and achieves an outstanding accuracy of 96.55% by Logistic regression. Additionally, we investigated the incorrect predictions of the model to gain insights into its limitations. The findings can guide researchers in tuning various BERT for finding the most suitable model for diverse LLMs vulnerabilities. © 2024 IEEE.

关键词： Embeddings

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Solving the Farmer's Dilemma with FlexBE Using GR(1) Synthesis

Solving the Farmer's Dilemma with FlexBE Using GR(1) Synthes...

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IEEE Southeastcon

作者： Josh Luzier David C. Conner Department of Physics Computer Science and Engineering Capable Humanitarian Robotics and Intelligent Systems Lab (CHRISLab) Christopher Newport University Newport News Virginia

This paper presents synthesis tools for the ROS 2 version of the Flexible Behavior Engine (FlexBE). Synthesis reduces the need for extensive testing and validation of hand crafted controllers by using mathematically precise specifications to generate “correct-by-construction” behavior controllers. Our approach builds upon work using the GR(1) (General Reactivity of rank 1) fragment of LTL to synthesize a reactive hierarchical finite state machine that can be directly executed in FlexBE. The presented work expands on previous ROS 1 tools, and extends them to ROS 2 in a way that supports more general specifications that incorporate environmental states. This paper presents an accessible open-source demonstration that serves as a general introduction to these powerful synthesis techniques.

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