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International Conference on Mobile Computing and Ubiquitous Networking (ICMU)

作者： T. N. Gia L. Qingqing J. Peña Queralta H. Tenhunen Z. Zou T. Westerlund Turku Intelligent Embedded and Robotic Systems University of Turku Finland School of Information Science and Technology Fudan Universtiy China

ISBN: (数字)9784907626419

ISBN: (纸本)9781728142265

The need of data compression at smart Edge/Fog-based gateways is undeniable as data compression can significantly reduce the amount of data that has to be transmitted over a network. This, in turn, has a direct impact on reducing transmission latency and increasing network bandwidth. In time-critical and data sensitive IoT applications such as healthcare, lossless data compression is preferable as compressed data can be recovered without losing any information. However, it is not an easy task to choose a proper lossless data compression algorithm for IoT applications as each lossless data compression method has its own advantages and disadvantages. This paper focuses on the analysis of lossless data compression algorithms run at smart Edge/Fog gateways. Widely used lossless data compression are run at different hardware which is often used as smart Fog/Edge gateways. The latency of data compression and compression rate in different cases of input data sizes are analyzed. The paper provides guidelines for choosing a proper lossless data compression algorithm for time-critical IoT applications.

关键词： Logic gates Hardware Huffman coding Monitoring Medical services Cloud computing

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Edge Based Skin Cancer Decision Support System Using Machine Learning Algorithms

Edge Based Skin Cancer Decision Support System Using Machine...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Anum Nawaz Muhammad Irfan Hafza Ayesha Sadiqa Tomi Westerlund School of Information Science and Technology Fudan University Shanghai China Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology University of Turku Turku Finland

Skin lesions has grown rapidly in recent years. Detecting and treating skin lesions in its early stages can be extremely beneficial to its recovery. Using Digital image processing (DIP) to extract skin diseases from lesions emerged rapidly due to in accessibility of dermatologist and laboratory facilities in remote areas specifically in under developed countries. Out of many deep learning based feature extraction algorithms, convolutional neural networks (CNN) outperform along with different classifiers. In this study, we focused on edge based classifiers for robust skin lesions classification. To choose the best combination of classifier with existing CNN based feature extraction methods we used HAM-10000 dataset, supervised by ISIC. Different mobile based classifiers such as QDA, KNN and SVM, along with their different hybrid versions were tested in same conditions. Experimental results shows that the cubic kernel function used in the cubic SVM approach allows for more complex decision boundaries to be created for melanoma detection, as the characteristics of melanoma lesions can vary greatly. The cubic kernel function enables the SVM model to capture these complex patterns and make accurate predictions using less computational resources as compared to other mobile based classifiers. Cubic SVM shows the accuracy of 98% while using only 20 seconds of time.

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Enhancing Autonomy with Blockchain and Multi-Access Edge Computing in Distributed robotic systems

arXiv

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arXiv 2020年

作者： Queralta, Jorge Peña Qingqing, Li Zou, Zhuo Westerlund, Tomi Turku Intelligent Embedded and Robotic Systems Lab Faculty of Sciece and Engineering University of Turku Finland School of Information Science and Technolo Fudan Universit China

This conceptual paper discusses how different aspects involving the autonomous operation of robots and vehicles will change when they have access to next-generation mobile networks. 5G and beyond connectivity is bringing together a myriad of technologies and industries under its umbrella. High-bandwidth, low-latency edge computing services through network slicing have the potential to support novel application scenarios in different domains including robotics, autonomous vehicles, and the Internet of Things. In particular, multi-tenant applications at the edge of the network will boost the development of autonomous robots and vehicles offering computational resources and intelligence through reliable offloading services. The integration of more distributed network architectures with distributed robotic systems can increase the degree of intelligence and level of autonomy of connected units. We argue that the last piece to put together a services framework with third-party integration will be next-generation low-latency blockchain networks. Blockchains will enable a transparent and secure way of providing services and managing resources at the Multi-Access Edge Computing (MEC) layer. We overview the state-of-the-art in MEC slicing, distributed robotic systems and blockchain technology to define a framework for services the MEC layer that will enhance the autonomous operations of connected robots and vehicles. Copyright © 2020, The Authors. All rights reserved.

关键词： Edge computing

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Enhancing Autonomy with Blockchain and Multi-Access Edge Computing in Distributed robotic systems

Enhancing Autonomy with Blockchain and Multi-Access Edge Com...

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International Conference on Fog and Mobile Edge Computing (FMEC)

作者： Jorge Peña Queralta Li Qingqing Zhuo Zou Tomi Westerlund Turku Intelligent Embedded and Robotic Systems Lab Faculty of Sciece and Engineering University of Turku Finland School of Information Science and Technology Fudan University China

ISBN: (数字)9781728172163

ISBN: (纸本)9781728172170

关键词： Robots Edge computing 5G mobile communication Autonomous vehicles Network slicing Smart cities

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Optical Character Recognition Using Optimized Convolutional Networks*

Optical Character Recognition Using Optimized Convolutional ...

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International Conference on Fog and Mobile Edge Computing (FMEC)

作者： Anum Nawaz Muhammad Irfan Tomi Westerlund Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan University Shanghai China Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology University of Turku Turku Finland

Optical Character Recognition (OCR) has been a prominent area of research in pattern recognition for several decades, owing to its broad application potential in smart living. To improve offline OCR on mobile devices with limited computing resource, we have optimized Convolutional Neural Networks (CNNs) to efficiently detect text using minimal resources. To achieve this, we employed two distinct pretrained CNN models, namely AlexNet and Inception-V3, for feature extraction. Leveraging these models' unique characteristics and capabilities to extract diverse features, we aimed to enhance the classifier's accuracy. This, in turn, facilitates the development of an efficient edge-device application for faster and higher-quality OCR. Experimental results demonstrate that our proposed optimized algorithm outperforms existing CNN-based methods in the field of OCR, particularly in the categorization and detection of handwritten digits and character recognition. The conducted research yielded impressive accuracy results, with up to 97% accuracy on the MNIST dataset and 95.5% accuracy on the NIST dataset.

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MSS U-Net: 3D segmentation of kidneys and tumors from CT images with a multi-scale supervised U-Net

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Informatics in Medicine Unlocked 2020年 19卷

作者： Zhao, Wenshuai Jiang, Dihong Peña Queralta, Jorge Westerlund, Tomi School of Information Science and Technology Fudan University China Turku Intelligent Embedded and Robotic Systems Lab University of Turku Finland

Accurate segmentation of kidneys and kidney tumors is an essential step for radiomic analysis as well as developing advanced surgical planning techniques. In clinical analysis, the segmentation is currently performed by clinicians from the visual inspection of images gathered through a computed tomography (CT) scan. This process is laborious and its success significantly depends on previous experience. We present a multi-scale supervised 3D U-Net, MSS U-Net to segment kidneys and kidney tumors from CT images. Our architecture combines deep supervision with exponential logarithmic loss to increase the 3D U-Net training efficiency. Furthermore, we introduce a connected-component based post processing method to enhance the performance of the overall process. This architecture shows superior performance compared to state-of-the-art works, with the Dice coefficient of kidney and tumor up to 0.969 and 0.805 respectively. We tested MSS U-Net in the KiTS19 challenge with its corresponding dataset. © 2020 The Authors

关键词： 3D U-Net Exponential logarithmic loss Kidney segmentation Kidney tumor segmentation Multi-scale supervision

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Towards Active Vision with UAVs in Marine Search and Rescue: Analyzing Human Detection at Variable Altitudes

Towards Active Vision with UAVs in Marine Search and Rescue:...

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Li Qingqing Jussi Taipalmaa Jorge Peña Queralta Tuan Nguyen Gia Moncef Gabbouj Hannu Tenhunen Jenni Raitoharju Tomi Westerlund Turku Intelligent Embedded and Robotic Systems Lab University of Turku Finland Tampere University Finland Programme for Environmental Information Finnish Environment Institute Jyväskylä Finland

ISBN: (数字)9781665403900

ISBN: (纸本)9781665403917

Unmanned Aerial Vehicles (UAVs) have been playing an increasingly active role in supporting search and rescue (SAR) operations in recent years. The benefits are multiple such as enhanced situational awareness, status assessment, or mapping of the operational area through aerial imagery. Most of these application scenarios require the UAVs to cover a certain area. If the objective is to detect people or other objects, or analyze in detail the area, then there is a trade-off between speed (higher altitude coverage) and perception accuracy (lower altitude). An optimal point in between requires active perception on-board the UAV to dynamically adjust the flight altitude and path planning. As an initial step towards active vision in UAV search in maritime SAR scenarios, in this paper we focus on analyzing how the flight altitude affects the accuracy of object detection algorithms. In particular, we quantify what are the probabilities for false negatives and false positives in human detection at different altitudes. Our results define the correlation between the altitude and the ability of UAVs to effectively detect people in the water.

关键词： Robots Training Object detection Unmanned aerial vehicles Search problems Cameras Task analysis

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Towards Robust UAV Tracking in GNSS-Denied Environments: A Multi-LiDAR Multi-UAV Dataset

Towards Robust UAV Tracking in GNSS-Denied Environments: A M...

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IEEE International Conference on robotics and Biomimetics

作者： Iacopo Catalano Xianjia Yu Jorge Peña Queralta Turku Intelligent Embedded and Robotic Systems (TIERS) Lab University of Turku Finland Swiss Federal School of Technology in Zurich - ETH Zurich SCAI Laboratory at SPZ Switzerland

With the increasing prevalence of drones in various industries, the navigation and tracking of unmanned aerial vehicles (UAVs) in challenging environments, particularly GNSS-denied areas, have become crucial concerns. To address this need, we present a novel multi-LiDAR dataset specifically designed for UAV tracking. Our dataset includes data from a spinning LiDAR, two solid-state LiDARs with different Field of View (FoV) and scan patterns, and an RGB-D camera. This diverse sensor suite allows for research on new challenges in the field, including limited FoV adaptability and multi-modality data *** dataset facilitates the evaluation of existing algorithms and the development of new ones, paving the way for advances in UAV tracking techniques. Notably, we provide data in both indoor and outdoor environments. We also consider variable UAV sizes, from micro-aerial vehicles to more standard commercial UAV platforms. The outdoor trajectories are selected with close proximity to buildings, targeting research in UAV detection in urban areas, e.g., within counter-UAV systems or docking for UAV *** addition to the dataset, we provide a baseline comparison with recent LiDAR-based UAV tracking algorithms, benchmarking the performance with different sensors, UAVs, and algorithms. Importantly, our dataset shows that current methods have shortcomings and are unable to track UAVs consistently across different *** dataset is available on GitHub 1 .

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UAV Tracking with Solid-State Lidars: Dynamic Multi-Frequency Scan Integration

UAV Tracking with Solid-State Lidars: Dynamic Multi-Frequenc...

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International Conference on Advanced robotics (ICAR)

作者： Iacopo Catalano Ha Sier Xianjia Yu Tomi Westerlund Jorge Peña Queralta Turku Intelligent Embedded and Robotic Systems (TIERS) Lab University of Turku Finland SCAI Laboratory SPZ Swiss Federal School of Technology in Zurich - ETH Zurich Switzerland

With the increasing use of drones across various industries, the navigation and tracking of these unmanned aerial vehicles (UAVs) in challenging environments, namely GNSS-denied environments, have become critical issues. In this paper, we propose a novel method for a ground-based UAV tracking system using a solid-state LiDAR, which dynamically adjusts the LiDAR frame integration time based on the distance to the UAV and its speed. Our method fuses two simultaneous scan integration frequencies for high accuracy and persistent tracking, enabling reliable state estimation of the UAV even in challenging scenarios. The application of the Inverse Covariance Intersection method and Kalman filters allows for better tracking accuracy and can handle challenging tracking scenarios. Compared to previous works in solid-state lidar tracking, this paper presents a more complete and robust solution. We have performed a number of experiments to evaluate the performance of the proposed tracking system and identify its limitations. Our experimental results demonstrate that the proposed method clearly outperforms the baseline method and ensures tracking is more robust across different types of trajectories. The code is open-source and available at ***/TIERS/dynamic_scan_tracking.

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Vision-based Safe Autonomous UAV Docking with Panoramic Sensors

arXiv

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arXiv 2023年

作者： Thuan, Phuoc Nguyen Westerlund, Tomi Queralta, Jorge Peña Turku Intelligent Embedded and Robotic Systems University of Turku Finland Computing Sciences Faculty of Information Technology and Communication Sciences Tampere University Finland

The remarkable growth of unmanned aerial vehicles (UAVs) has also sparked concerns about safety measures during their missions. To advance towards safer autonomous aerial robots, this work presents a vision-based solution to ensuring safe autonomous UAV landings with minimal infrastructure. During docking maneuvers, UAVs pose a hazard to people in the vicinity. In this paper, we propose the use of a single omnidirectional panoramic camera pointing upwards from a landing pad to detect and estimate the position of people around the landing area. The images are processed in real-time in an embedded computer, which communicates with the onboard computer of approaching UAVs to transition between landing, hovering or emergency landing states. While landing, the ground camera also aids in finding an optimal position, which can be required in case of low-battery or when hovering is no longer possible. We use a YOLOv7-based object detection model and a XGBooxt model for localizing nearby people, and the open-source ROS and PX4 frameworks for communication, interfacing, and control of the UAV. We present both simulation and real-world indoor experimental results to show the efficiency of our methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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