AN EFFECTIVE APPROACH TO FACE RECOGNITION WITH ARTIFICIAL INTELLIGENCE AND THE INTERNET OF THINGS USING NVIDIA JETSON NANO by Ngoc Giau Pham, Nhien Loc Bui, Quang Hien Tran, Le Thanh Hai Tong, Hong Ngoc Tran

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Energy Efficiency of Kernel and User Space Level VPN Solutions in AIoT Networks by ALEKSANDAR JEVREMOVIC, Zona Kostic, Ivan Chorbev, Dragan Perakovic, Andrii Shalaginov, Ivan Cvitic

“…These systems are evaluated on a range of hardware platforms, including Raspberry Pi 3, Nvidia Jetson NANO, Nvidia Jetson TX2, and Nvidia Jetson AGX Xavier. …”
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Research on machine vision online monitoring system for egg production and quality in cage environment by Zhenlong Wu, Hengyuan Zhang, Cheng Fang

“…This refined model was implemented on Jetson AGX Orin industrial computer to facilitate real-world applications. …”
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GGSYOLOv5: Flame recognition method in complex scenes based on deep learning. by Fucai Sun, Liping Du, Yantao Dai

“…Moreover, the algorithm is deployed in the Jetson Nano embedded development board to build the flame detection system.…”
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Edge computing for detection of ship and ship port from remote sensing images using YOLO by Vasavi Sanikommu, Sai Pravallika Marripudi, Harini Reddy Yekkanti, Revanth Divi, R. Chandrakanth, P. Mahindra

“…The proposed system delivers a precision of 86% compared to existing methods; this approach is designed to allow for real-time deployment in the context of resource-constrained environments, especially with a Jetson Nano edge device. This deployment will ensure scalability, efficient processing, and reduced reliance on central computing resources, making it especially suitable for maritime settings in which real-time monitoring is vital. …”
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Aprofundamento da precarização da educação e da docência em tempos pandêmicos by Jetson Lourenço Lopes da Silva

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UAV target tracking method based on global feature interaction and anchor-frame-free perceptual feature modulation. by Yuanhong Dan, Jinyan Li, Yu Jin, Yong Ji, Zhihao Wang, Dong Cheng

“…In order to verify the reliability of the algorithm, we built a physical experimental environment on the Jetson Orin Nano platform. We realized a real-time processing speed of 30 frames per second.…”
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Knowledge Distillation in Object Detection for Resource-Constrained Edge Computing by Arief Setyanto, Theopilus Bayu Sasongko, Muhammad Ainul Fikri, Dhani Ariatmanto, I. Made Artha Agastya, Rakandhiya Daanii Rachmanto, Affan Ardana, In Kee Kim

“…Given their limited memory and computational power, edge devices like the Jetson Nano (J. Nano), Jetson Orin Nano (Orin Nano), and Raspberry Pi 4B (Raspi4B) require model optimization and compression techniques in order to deploy large OD models such as YOLO. …”
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SqueezeMaskNet: Real-Time Mask-Wearing Recognition for Edge Devices by Gibran Benitez-Garcia, Lidia Prudente-Tixteco, Jesus Olivares-Mercado, Hiroki Takahashi

“…SqueezeMaskNet achieved 96.7% accuracy on the challenging FineFM test set and ran at 297 FPS on a GPU and up to 96 FPS on edge devices like a Jetson Orin NX. We also introduced ImproperTFM, a subset of real-world images focusing on improper mask usage, which enhanced the model accuracy when combined with FineFM data. …”
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Advanced sensors network in a centralized IoT system using low-cost microcontrollers and automatic configuration by Andrei-Mihai VĂDAN, Liviu-Cristian MICLEA

“…Folosește hardware popular, precum Raspberry Pi, Raspberry Pi Pico, Espressif, Banana Pi, Nvidia Jetson, iar comunicarea între microcontrolere se face într-o rețea Ethernet, cu dispozitive conectate prin cablu sau wirelles. …”
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Space mission as a service (SMaaS): General-purpose computing on space by Leonardo Camargo-Forero, Julián G. Rodríguez-Ferreira, Carlos J. Barrios-Hernández

“…Such strategies will include evaluating standard operating systems and embedded companion computers, such as the NVIDIA® Jetson Series, under space conditions, common AI frameworks, High-Performance Embedded Computing, and cloud computing as an integrator between space computing devices and earth ground stations. …”
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OBTPN: A Vision-Based Network for UAV Geo-Localization in Multi-Altitude Environments by Nanxing Chen, Jiqi Fan, Jiayu Yuan, Enhui Zheng

“…OBTPN was successfully deployed on an NVIDIA Jetson TX2 onboard computer. This paper also proposes a high-altitude complex environment dataset, Crossview9, which addresses a research gap in the field of high-altitude visual navigation. …”
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Comparative analysis of neural network models performance on low-power devices for a real-time object detection task by A. Zagitov, E. Chebotareva, A. Toschev, E. Magid

“…The results of experiments provide insights into trade-offs between accuracy, speed, and computational efficiency of MobileNetV2 SSD, CenterNet MobileNetV2 FPN, EfficientDet, YoloV5, YoloV7, YoloV7 Tiny and YoloV8 neural network models on Raspberry Pi 4B, Raspberry Pi 3B and NVIDIA Jetson Nano with TensorFlow Lite. We fine-tuned the models on our custom dataset prior to benchmarking and used post-training quantization (PTQ) and quantization-aware training (QAT) to optimize the models’ size and speed. …”
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O Estado no Plano Político Estratégico do MST: breves considerações sobre a aproximação com o executivo do Estado durante o Governo Lula by Jetson Lourenço Lopes da Silva

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Pengaruh Luas Lubang Bukaan Dinding Terhadap Energi Disipasi Portal Beton Bertulang by Raihan Jetson Marsa, Jati Sunaryati, Jafril Tanjung

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Realtime Multispectral Pedestrian Detection With Visible and Far-Infrared Under Ambient Temperature Changing by Masato Okuda, Kota Yoshida, Takeshi Fujino

“…Moreover, our YOLOv8s-2stream has improved by 3.9 points of accuracy (AP@0.5:0.95) compared to YOLOv8s-4ch, and achieved 73 FPS inference speed on Jetson.…”
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Localization of mobile robot in prior 3D LiDAR maps using stereo image sequence by I.V. Belkin, A.A. Abramenko, V.D. Bezuglyi, D.A. Yudin

“…The method’s performance is demonstrated on different hardware platforms, including energy-efficient Nvidia Jetson Xavier AGX. With parallel code implementation, we achieve an input stereo image processing speed of 14 frames per second on Xavier AGX.…”
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LEAF-YOLO: Lightweight Edge-Real-Time Small Object Detection on Aerial Imagery by Van Quang Nghiem, Huy Hoang Nguyen, Minh Son Hoang

“…Experimental results show that both LEAF-YOLO and LEAF-YOLO-N outperform models with fewer than 20 million parameters in accuracy and efficiency on the Visdrone2019-DET-val dataset, running in real-time (>30 FPS) on the Jetson AGX Xavier. LEAF-YOLO-N achieves 21.9% AP.50:.95 and 39.7% AP.50 with only 1.2M parameters. …”
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Railway Foreign Object Intrusion Detection Using UAV Images and YOLO-UAT by Yang Yang, Zhanhao Liu, Junming Chen, Haiming Gao, Tao Wang

“…YOLO-UAT reduces the number of parameters by 36% compared to the original YOLOv5s and mAP increased by 6.1% to 91.5%. Implemented on a Jetson Nano, it achieves a detection rate of 26.4 FPS. …”
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Development of a handheld GPU-assisted DSC-TransNet model for the real-time classification of plant leaf disease using deep learning approach by Midhun P. Mathew, Sudheep Elayidom, V. P. Jagathy Raj, K. M. Abubeker

“…The developed DSC-TransNet model is deployed in NVIDIA Jetson Nano single board computer. This research contributes to advancing the field of automated plant disease classification, addressing critical challenges in modern agriculture and promoting more efficient and sustainable farming practices.…”
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