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检索条件"机构=Laboratory of Embedded and Distributed Systems - LEDS"

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TinyML Applied in Hyperspectral Image Classification on COTS Microcontroller 14

TinyML Applied in Hyperspectral Image Classification on COTS...

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14th Brazilian Symposium on Computing systems Engineering, SBESC 2024

作者： Hutner, Joao Victor Santos Viel, Felipe Zeferino, Cesar A. Bezerra, Eduardo Augusto University of Vale do Itajaí - UNIVALI Laboratory of Embedded and Distributed Systems - LEDS SC Itajaí Brazil Federal University of Santa Catarina - UFSC SpaceLab SC Florianópolis Brazil

ISBN: (纸本)9798331509002

Commercial off-the-shelf (COTS) microcontrollers used in remote sensing are increasingly being used because of the great versatility of application and development provided by this type of component. There has been a significant increase in missions using CubeSats, mainly due to the greater accessibility and flexibility of the COTS equipment used, as well as lower development costs related to this type of equipment. However, CubeSats have main constraints in power, size, and data transmission capacity. As a solution, numerous works use onboard data processing to compress or classify the captured data to reduce the use of storage memory and the amount of data sent to Earth stations. One of the types of data collected by remote sensing satellites are hyperspectral images, which despite being computationally challenging for classification in microcontrollers. Several algorithms are capable of doing hyperspectral image classification even that constrained by low-power microcontrollers with the advances in deep learning. In this sense, the aim and contribution of this study was to demonstrate and explore the feasibility and performance of implementing a deep learning model on a COTS microcontroller for hyperspectral image classification. We developed a convolutional neural network (CNN) architecture adapted for microcontrollers using the TinyML concept with the TensorFlow Lite library to demonstrate the feasibility and performance of deep learning in COTS microcontroller. The results for the overall accuracy of the embedded model were from 82.57 % to 95.81 % for three different literature datasets. The dissipated power was 105 mW for all dataset samples, with the execution time per sample varying from 12.14 ms to 93.18 ms, and the total energy consumed varying from 4.09 mJ to 7.52 mJ for the entire dataset tested. © 2024 IEEE.

关键词： Convolutional neural networks

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TinyML Applied in Hyperspectral Image Classification on COTS Microcontroller

TinyML Applied in Hyperspectral Image Classification on COTS...

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Brazilian Symposium on Computing System Engineering (SBESC)

作者： João Victor Santos Hütner Felipe Viel Cesar A. Zeferino Eduardo Augusto Bezerra Laboratory of Embedded and Distributed Systems - LEDS University of Vale do Itajaí - UNIVALI Itajaí SC Brazil SpaceLab Federal University of Santa Catarina - UFSC Florianópolis SC Brazil

ISBN: (数字)9798331509002

ISBN: (纸本)9798331532857

关键词： Deep learning Adaptation models Satellites Microcontrollers Tiny machine learning Computational modeling Convolutional neural networks CubeSat Hyperspectral imaging Image classification

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Evaluating the CCSDS 123 Compressor Running on RISC-V and ARM Architectures 10

Evaluating the CCSDS 123 Compressor Running on RISC-V and AR...

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10th Brazilian Symposium on Computing systems Engineering, SBESC 2020

作者： Imianosky, Carolina Valim, Paulo R. O. Zeferino, Cesar A. Viel, Felipe Laboratory of Embedded and Distributed Systems-LEDS University of Vale Do Itajaí-UNIVALI ItajaíSC Brazil

ISBN: (纸本)9781728182865

Hyperspectral images are widely used in spatial applications and are three-dimensional data structures in which the x and y axes contain spatial information, and the z-Axis contains the spectral bands, which that can reach the order of hundreds of layers. Such images generate a large amount of data, and applying compression algorithms, such as the CCSDS 123 compressor, is highly necessary to deal with the communication and storage constraints of the platforms used to capture hyperspectral images (e.g., a spacecraft). Given that the CCSDS 123 algorithm has a high computational cost, it is necessary to evaluate which processor architectures can deal with it onboard. Given the context above, this work presents an evaluation of performance and power consumption of the CCSDS 123 algorithm running on RISC-V and ARM processors and an evaluation of using two real-Time operating systems (FreeRTOS and Zephyr). The experimental results show that, for the development kits used, the RISC-V processor dissipates less power than the ARM processor, which, in turn, offers much higher performance and lower energy consumption than RISC-V. Results also show that FreeRTOS adds a lower overhead to the algorithm execution in comparison to Zephyr when running over the RISC-V processor. © 2020 IEEE.

关键词： Hyperspectral imaging

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Evaluating the CCSDS 123 Compressor Running on RISC-V and ARM Architectures

Evaluating the CCSDS 123 Compressor Running on RISC-V and AR...

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Brazilian Symposium on Computing System Engineering (SBESC)

作者： Carolina Imianosky Paulo R. O. Valim Cesar A. Zeferino Felipe Viel Laboratory of Embedded and Distributed Systems - LEDS University of Vale do Itajaí - UNIVALI Itajaí SC Brazil

ISBN: (数字)9781728182865

ISBN: (纸本)9781728182872

Hyperspectral images are widely used in spatial applications and are three-dimensional data structures in which the x and y axes contain spatial information, and the z-axis contains the spectral bands, which that can reach the order of hundreds of layers. Such images generate a large amount of data, and applying compression algorithms, such as the CCSDS 123 compressor, is highly necessary to deal with the communication and storage constraints of the platforms used to capture hyperspectral images (e.g., a spacecraft). Given that the CCSDS 123 algorithm has a high computational cost, it is necessary to evaluate which processor architectures can deal with it onboard. Given the context above, this work presents an evaluation of performance and power consumption of the CCSDS 123 algorithm running on RISC- V and ARM processors and an evaluation of using two real-time operating systems (FreeRTOS and Zephyr). The experimental results show that, for the development kits used, the RISC- V processor dissipates less power than the ARM processor, which, in turn, offers much higher performance and lower energy consumption than RISC-V. Results also show that FreeRTOS adds a lower overhead to the algorithm execution in comparison to Zephyr when running over the RISC- V processor.

关键词： Image coding Hyperspectral imaging Metadata Computer architecture Sensors Microprocessors Prediction algorithms

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A Hardware Accelerator for the Segmentation of Hyperspectral Images

A Hardware Accelerator for the Segmentation of Hyperspectral...

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Symposium on Integrated Circuits and systems Design (SBCCI)

作者： Arthur Passos Felipe Viel Cesar A. Zeferino Laboratory of Embedded and Distributed Systems – LEDS University of Vale do Itajaí – UNIVALI Itajaí SC Brazil

ISBN: (数字)9781728196251

ISBN: (纸本)9781728196268

Hyperspectral images (HSIs) contain hundreds of spectral bands that combine spatial characteristics of a scene and are widely used in object identification and classification in various industry and science contexts. However, the processing of HSIs requires high processing power, and one of its compute-intensive tasks is the image segmentation. In this context, this paper presents the development of a first hardware accelerator proposed to speedup a multiresolution algorithm for the segmentation of HSIs. The most computationally expensive step of this algorithm was described in HDL and synthesized to FPGA. Results show that the performance of the hardware accelerator is close to that of a 3.2 GHz PC desktop, spending 20% of the energy consumed by the PC to process a single band.

关键词： Image segmentation Hardware Field programmable gate arrays Energy resolution Spatial resolution Standards

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A Low-Cost Fault-Tolerant RISC-V Processor for Space systems

A Low-Cost Fault-Tolerant RISC-V Processor for Space Systems

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International Conference on Design and Technology of Integrated systems in Nanoscale Era (DTIS)

作者： Douglas Almeida Santos Lucas Matana Luza Cesar Albenes Zeferino Luigi Dilillo Douglas Rossi Melo Laboratory of Embedded and Distributed Systems (LEDS) University of Vale do Itajaí Brazil Laboratoire d’Informatique de Robotique et de Micro´lectronique de Montpellier (LIRMM) France

ISBN: (数字)9781728154268

ISBN: (纸本)9781728154275

embedded processors have been used in a diversity of applications, such as consumer electronics, home appliances, and automation systems. Another area that embedded processors can be adopted is space systems, which demand faulttolerant components to deal with the environmental hostility. This work presents a low-cost fault-tolerant implementation of the RISC-V architecture, an emerging open industry standard for the building of embedded processors. The proposed implementation employs physical and information redundancy to reduce error propagation, with competitive silicon and power overheads when comparing with other RISC-V implementations.

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Privacy in the Internet of Things: A Study to Protect User's Data in LPR systems Using Blockchain

Privacy in the Internet of Things: A Study to Protect User's...

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Annual Conference on Privacy, Security and Trust, PST

作者： Iago Ochôa Leonardo Calbusch Karize Viecelli Juan de Paz Valderi Leithardt Cesar Zeferino Laboratory of Embedded and Distributed Systems - LEDS University of Vale do Itajaí Itajaí SC Brazil Departamento Informática y Automática Plaza de la Merced Salamanca Spain

ISBN: (纸本)9781728132662

Over the past decade, smart crime-fighting solutions have been adopted by the major cities around the world. In this context, license plate recognition (LPR) systems have been used by public safety forces to monitor vehicle movement. However, current systems store vehicle location data indistinctly, without differentiating vehicles that are under criminal investigation from those that are not. This monitoring may be used to infer personal data about the owner of the vehicle, resulting in a violation of privacy by disregarding data protection laws. This paper presents a study about the use of technologies to ensure privacy in the Internet of Things and proposes a model to protect data collected by LPR systems. Our solution uses private blockchains regulated by smart contracts to ensure that the storage of data complies with current data protection laws.

关键词： IoT Blockchain Privacy LPR Privacy Internet of things computer information security Data Security LOW PRESSURE RECIRCULATION SYSTEM vehicle Public safety Internets

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Internet of things: Concepts, architectures and technologies 13

Internet of things: Concepts, architectures and technologies

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13th IEEE International Conference on Industry Applications, INDUSCON 2018

作者： Viel, Felipe Silva, Luis A. Valderi Leithardt, R.Q. Zeferino, Cesar A. Laboratory of Embedded and Distributed Systems - LEDS University of Vale Do Itajaí Itajaí - SC Brazil

ISBN: (纸本)9781538679951

Internet of Things - IoT - has opened up opportunities for the development of new applications in various segments, being one of the drivers of the revolutions that are occurring in cities and industries. Smart cities and Industry 4.0 represent these revolutions. Given the breadth of concepts and technologies related to IoT, this work seeks to present an overview of these different aspects that support the development of solutions for IoT. In this work, we present a literature review and identify the primary hardware and communication technologies, protocols, algorithms, and security and privacy services for IoT. © 2018 IEEE.

关键词： Industry 4.0

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