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Discrete Fourier Transform-Based Point Cloud Compression for Efficient SLAM in Featureless Terrain

Discrete Fourier Transform-Based Point Cloud Compression for...

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international conference on Autonomous Robots and Agents, ICARA

作者： Riku Suzuki Ayumi Umemura Shreya Santra Kentaro Uno Kazuya Yoshida Department of Aerospace Engineering Space Robotics Lab. (SRL) Tohoku University Sendai Japan

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Simultaneous Localization and Mapping (SLAM) is an essential technology for the efficiency and reliability of unmanned robotic exploration missions. While the onboard computational capability and communication bandwidth are critically limited, the point cloud data handled by SLAM is large in size, attracting attention to data compression methods. To address such a problem, in this paper, we propose a new method for compressing point cloud maps by exploiting the Discrete Fourier Transform (DFT). The proposed technique converts the Digital Elevation Model (DEM) to the frequency-domain 2D image and omits its high-frequency components, focusing on the exploration of gradual terrains such as planets and deserts. Unlike terrains with detailed structures such as artificial environments, high-frequency components contribute little to the representation of gradual terrains. Thus, this method is effective in compressing data size without significant degradation of the point cloud. We evaluated the method in terms of compression rate and accuracy using camera sequences of two terrains with different elevation profiles.

关键词： Point cloud compression Simultaneous localization and mapping Image coding Discrete Fourier transforms Moon Focusing Real-time systems Deserts Reliability Mobile robots

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MADiff: Text-Guided Fashion Image Editing with Mask Prediction and Attention-Enhanced Diffusion

MADiff: Text-Guided Fashion Image Editing with Mask Predicti...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhan, Zechao Gao, Dehong Zhang, Jinxia Huang, Jiale Hu, Yang Wang, Xin Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China Advanced Ocean Institute Southeast University Nantong226010 China Northwestern Polytechnical University School of Cybersecurity Xi'an China Binjiang Institute of Artificial Intelligence ZJUT Hangzhou China Alibaba Group Hangzhou China

ISBN: (纸本)9798350368741

Text-guided image editing model has achieved great success in general domain. However, directly applying these models to the fashion domain may encounter two issues: (1) Inaccurate localization of editing region;(2) Weak editing magnitude. To address these issues, the MADiff model is proposed. Specifically, to more accurately identify editing region, the MaskNet is proposed, in which the foreground region, densepose and mask prompts from large language model are fed into a lightweight UNet to predict the mask for editing region. To strengthen the editing magnitude, the Attention-Enhanced Diffusion Model is proposed, where the noise map, attention map, and the mask from MaskNet are fed into the proposed Attention Processor to produce a refined noise map. By integrating the refined noise map into the diffusion model, the edited image can better align with the target prompt. Given the absence of benchmarks in fashion image editing, we constructed a dataset named Fashion-E, comprising 28390 image-text pairs in the training set, and 2639 image-text pairs for four types of fashion tasks in the evaluation set. Extensive experiments on Fashion-E demonstrate that our proposed method can accurately predict the mask of editing region and significantly enhance editing magnitude in fashion image editing compared to the state-of-the-art methods. © 2025 IEEE.

关键词： Diffusion Model Fashion Domain Text-guided Image Editing

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Deep Learning-Based Implementation of Telerobotic Control with Gesture Recognition from EMG Signals

Deep Learning-Based Implementation of Telerobotic Control wi...

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international conference on Autonomous Robots and Agents, ICARA

作者： Péter Zsoldos Balázs Nagy Feki Pangestu Wijaya Department of Artificial Intelligence Eö tvö s Loránd University Budapest Hungary

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

This research explores the field of telerobotics, focusing on the deep learning based remote control of a robot using Electromyography (EMG) and an Inertial Measurement Unit (IMU). The aim is to enable control of a robot arm by capturing muscle responses via electrical activity using the Myo armband. The captured signals representing hand gestures are classified using a four layer wide fully connected neural network, allowing for seamless translation of gestures into robot commands. Training the model with online and self-collected datasets demonstrates the effectiveness of the proposed method. The model has an accuracy of 82.92% on previously unseen data. Successful implementation of telerobotics relies on the ability of the Myo armband to capture EMG signals and IMU data, facilitating control over the end effector of the robot and movement through gesture recognition and orientation conversion.

关键词： Training Accuracy Translation Neural networks Gesture recognition Robot sensing systems Electromyography Robustness Data models Telerobotics

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Minimization of Nonlinear Energies in Python Using FEM and Automatic Differentiation Tools 15th

Minimization of Nonlinear Energies in Python Using FEM and...

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15th international conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Béreš, Michal Valdman, Jan Institute of Geonics of the Czech Academy of Sciences Studentská 1768 Ostrava708 00 Czech Republic Department of Applied Mathematics VSB - Technical University of Ostrava 17. listopadu 15/2172 Ostrava708 00 Czech Republic Institute of Information Theory and Automation of the Czech Academy of Sciences Pod vodárenskou věží 4 Prague18208 Czech Republic Department of Computer Science Faculty of Science University of South Bohemia Branišovská 31 České Budějovice37005 Czech Republic

ISBN: (纸本)9783031857027

This contribution examines the capabilities of the Python ecosystem to solve nonlinear energy minimization problems, with a particular focus on transitioning from traditional MATLAB methods to Python’s advanced computational tools, such as automatic differentiation. We demonstrate Python’s streamlined approach to minimizing nonlinear energies by analyzing three problem benchmarks - the p-Laplacian, the Ginzburg-Landau model, and the Neo-Hookean hyperelasticity. This approach merely requires the provision of the energy functional itself, making it a simple and efficient way to solve this category of problems. The results show that the implementation is about ten times faster than the MATLAB implementation for large-scale problems. Our findings highlight Python’s efficiency and ease of use in scientific computing, establishing it as a preferable choice for implementing sophisticated mathematical models and accelerating the development of numerical simulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Python

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Towards Accurate Diabetes Prediction: A Synergistic Approach Using Adaptive Deep Learning Techniques

Towards Accurate Diabetes Prediction: A Synergistic Approach...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： B.Shuriya V. Balajishanmugam P.Sivaprakash Umamaheswari Department of CSE PPG Institute of Technology Coimbatore India Department of Computer Science and Business Systems Dr. NGP Institute of Technology Coimbatore India Department of ECE Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Diabetes is a burgeoning international health issue, requiring effective and precise predictive models. Here, we compare Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for predicting diabetes using the Pima Indians Diabetes Dataset (containing 768 samples with 8 clinical features). For improving model performance, we utilized data normalization, removal of outliers, and feature scaling in preprocessing. A 5-fold cross-validation approach was used to maintain robustness. The best CNN model had an accuracy of 85.20%, which was better than the RNN model (83.50%) and current methods like CNN-RNN hybrids, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models. Hyper parameters like learning rate, batch size, and neurons were optimized to prevent over fitting and gain maximum performance. Comparative analysis depicts that deep learning models, i.e., CNNs, can effectively distinguish diabetic and non-diabetic cases. In future work, emphasis will also be given to blending other datasets and optimization techniques for greater generalizability.

关键词： Deep learning Adaptation models Accuracy Predictive models Data models Diabetes Telecommunication computing Convolutional neural networks Optimization Long short term memory

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A Forefront System for Sugarcane Leaf Disease Classification Using Deep Learning

A Forefront System for Sugarcane Leaf Disease Classification...

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international conference on robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Abu Noman Md Sakib Sajal Basak Partha Mohammad Anik Islam Shojib MD Mahamudul Hasan Badiuzzaman Shuvo Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

The agriculture sector has already seen a favorable advancement because of the involvement of machine learning (ML) methods. Sugarcane industry faces a gruesome drawback because of several types of leaf diseases. Nowadays, ML methods have come handy also to detect the diseases at the very early stage. Our work represents a cutting-edge deep learning method with a goal to classify five popular sugarcane leaf diseases: Bacterial Blight, Mosaic, RedRot, Rust, Yellow, and can also identify which one is Healthy. This study also conducts a comparative analysis of various state-of-the-art models, evaluating their training time, parameters, accuracy and model size using the same dataset. Based on this evaluation, the optimal model was selected for final evaluation. Several pre-trained models were modified to enhance speed and reduce storage requirements. The system can identify the disease category for every given sugarcane leaf image using the EfficientNetB0 model, which demonstrated the best performance, and also provides the probability of correct identification. Several preprocessing steps are involved such as removing noise, addressing missing data, and standardizing images to ensure maximum accuracy. The models were trained, validated, and tested on a dataset of 4,800 sugarcane leaf images from the Sugarcane Leaf Disease Dataset. Among all the models, EfficienNetB0, InceptionV3 and ResNet50 achieved high accuracy (98.75%, 98.54% and 98.54%, respectively). However, EfficienNetB0 was ultimately chosen due to its faster training, smaller size, and fewer parameters, making it ideal for this study.

关键词： Training Deep learning Analytical models Accuracy Computational modeling Transfer learning Signal processing algorithms Agriculture Diseases Residual neural networks

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Probabilistic Graph models: A Key to Boosting Deep Reinforcement Learning in Urban Traffic Networks

Probabilistic Graph Models: A Key to Boosting Deep Reinforce...

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international conference on Computer and automation Engineering, ICCAE

作者： Ali Reza Sattarzadeh Pubudu N. Pathirana School of Engineering Deakin University Geelong Victoria Australia

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Intelligent traffic signal control is a critical solution for optimizing urban transportation networks by reducing congestion and enhancing traffic flow efficiency. This study introduces a deep reinforcement learning (DRL) framework integrated with probabilistic graph models (PGMs) to address the challenges of adaptability and interpretability in traffic management. The PGMs enable a structured representation of complex dependencies within traffic systems, allowing for interpretable insights into dynamic traffic conditions. The proposed model is trained and validated in the SUMO simulation environment, accurately reflecting real-world traffic scenarios. Results demonstrate substantial improvements in throughput, reduced travel times, and minimized intersection delays compared to traditional control methods. The integration of PGMs with DRL not only enhances decision-making processes but also improves scalability and robustness in real-time applications. This innovative approach lays a solid foundation for implementing adaptive and intelligent traffic management systems, paving the way for smarter urban infrastructures capable of handling evolving transportation demands.

关键词： Adaptation models Smart cities Computational modeling Scalability Transportation Probabilistic logic Deep reinforcement learning Throughput Solids Robustness

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A Survey on Fast Task Adaptation Techniques in Machine Learning

A Survey on Fast Task Adaptation Techniques in Machine Learn...

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international conference on Inventive Computation Technologies (ICICT)

作者： Ramesh G Kiran Raj K M Shantveer Kesti Charandeep B S Manvith Suvarna Akash Nayak Department of ISE NMAM Institute of Technology Nitte(Deemed to be University Udupi Karnataka India Department of Artificial Intelligence and Machine Learning Alva's Institute of Engineering and Technology Mangalore Karnataka India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Meta-reinforcement learning is a cutting-edge approach that facilitates swift and adaptable responses to new tasks in dynamic environments with significant resource limitations. Utilizing meta-learning principles offers substantial benefits, enabling Meta-RL frameworks to effectively learn from minimal data samples while improving their adaptability and resilience in various domains, including IoT, robotics, autonomous vehicles, and energy management. This paper examines different Meta-Rreinforcement learning approaches and frameworks, such as actor-critic models, Bayesian techniques, attention-driven networks, and adversarial learning methods. The adoption of these frameworks highlights their capabilities.

关键词： Metalearning Training Surveys Adaptation models Scalability Robustness Bayes methods Internet of Things Vehicle dynamics Robots

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Cyber Threat Intelligence Extraction Using Text Mining Techniques for Proactive Security

Cyber Threat Intelligence Extraction Using Text Mining Techn...

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automation and Computation (AUTOCOM), international conference on

作者： Suresh Kumar S C.Rohith Bhat Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science (SIMATS) Saveetha University Chennai India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Cyber Threat Intelligence (CTI) is valuable in gaining insight into current tactics, techniques and procedures used by the adversaries in a cyber attack. In this work, a new framework applying GAN for classifying tactics and techniques from unstructured CTI data is presented. The described approach combines two models by addressing the link between high-level tactics and low-level middle-level technologies by using a hierarchical method and CNNs based on attention mechanisms. Encoders from the specific subject fields as well as high-quality preprocessing methods are used to transform the initial information from the CTI reports into the set of features which could be easily utilized. Thus, the adversarial training mechanism ensures that the generated data are reasonable and semantically correct to promote enhanced classification. The proposed framework was tested on several datasets, and the experimental results demonstrated higher accuracy, precision, recall, F1 score than other relevant approaches. The imposed hierarchy on GAN improves system's realism in feature representation, supporting CTI analysis and cyberattack behavior comprehension. This approach establishes the groundwork for the implementation of large-scale, preventive attitude into the cybersecurity models, and arms analysts with information obtained from difficult and chaotic data feedstocks.

关键词： Training Text mining automation Attention mechanisms Accuracy Transforms Generative adversarial networks Data models Cyber threat intelligence Cyberattack

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HypCAD: Geometry-Enhanced Hyperbolic Contrastive Learning for CAD Model Retrieval

HypCAD: Geometry-Enhanced Hyperbolic Contrastive Learning fo...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Adam Misik Driton Salihu Xin Su Heike Brock Eckehard Steinbach Siemens Technology Munich Germany School of Computation Information and Technology Chair of Media Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Munich Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Retrieving CAD models for real-world object scans enhances object-level mapping, providing a nuanced spatial understanding crucial for precise interactions in robotics or mixed reality. Commonly, CAD model retrieval is performed by matching features learned in Euclidean space. However, learning discriminative features in Euclidean space faces significant challenges, primarily due to its flat nature and the wide variety of CAD models with different levels of detail. To address the limitations of Euclidean space and improve CAD model retrieval, this paper introduces HypCAD, a contrastive learning framework in hyperbolic space. We present a novel geometry-enhanced hyperbolic distance and utilize a three-component contrastive learning loss to learn hyperbolic feature representations for the CAD model retrieval task. We demonstrate HypCAD’s superior retrieval accuracy through comparisons with baseline contrastive learning methods on both the synthetic ShapeNet dataset and the real-world Scan2CAD dataset.

关键词： Solid modeling Accuracy Mixed reality Contrastive learning Signal processing Feature extraction Acoustics Speech processing Robots Faces

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