no code implementations • 13 Jul 2023 • Haoyu Ren, Xue Li, Darko Anicic, Thomas A. Runkler
The field of Tiny Machine Learning (TinyML) has made substantial advancements in democratizing machine learning on low-footprint devices, such as microcontrollers.
no code implementations • 11 Apr 2023 • Haoyu Ren, Darko Anicic, Thomas A. Runkler
Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs).
1 code implementation • 18 Jul 2022 • Haoyu Ren, Kirill Dorofeev, Darko Anicic, Youssef Hammad, Roland Eckl, Thomas A. Runkler
Therefore, this paper presents a framework called Semantic Low-Code Engineering for ML Applications (SeLoC-ML), built on a low-code platform to support the rapid development of ML applications in IIoT by leveraging Semantic Web technologies.
1 code implementation • 18 Feb 2022 • Haoyu Ren, Darko Anicic, Thomas Runkler
Tiny machine learning (TinyML) has gained widespread popularity where machine learning (ML) is democratized on ubiquitous microcontrollers, processing sensor data everywhere in real-time.
no code implementations • 4 May 2021 • Haoyu Ren, Darko Anicic, Thomas Runkler
Focusing on comprehensive networking, big data, and artificial intelligence, the Industrial Internet-of-Things (IIoT) facilitates efficiency and robustness in factory operations.
no code implementations • 15 Mar 2021 • Haoyu Ren, Darko Anicic, Thomas Runkler
The neural network is first trained using a large amount of pre-collected data on a powerful machine and then flashed to MCUs.
5 code implementations • 5 May 2020 • Andreas Lugmayr, Martin Danelljan, Radu Timofte, Namhyuk Ahn, Dongwoon Bai, Jie Cai, Yun Cao, Junyang Chen, Kaihua Cheng, SeYoung Chun, Wei Deng, Mostafa El-Khamy, Chiu Man Ho, Xiaozhong Ji, Amin Kheradmand, Gwantae Kim, Hanseok Ko, Kanghyu Lee, Jungwon Lee, Hao Li, Ziluan Liu, Zhi-Song Liu, Shuai Liu, Yunhua Lu, Zibo Meng, Pablo Navarrete Michelini, Christian Micheloni, Kalpesh Prajapati, Haoyu Ren, Yong Hyeok Seo, Wan-Chi Siu, Kyung-Ah Sohn, Ying Tai, Rao Muhammad Umer, Shuangquan Wang, Huibing Wang, Timothy Haoning Wu, Hao-Ning Wu, Biao Yang, Fuzhi Yang, Jaejun Yoo, Tongtong Zhao, Yuanbo Zhou, Haijie Zhuo, Ziyao Zong, Xueyi Zou
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
no code implementations • 11 Jun 2019 • Mostafa El-Khamy, Haoyu Ren, Xianzhi Du, Jungwon Lee
In this paper, we introduce the problem of estimating the real world depth of elements in a scene captured by two cameras with different field of views, where the first field of view (FOV) is a Wide FOV (WFOV) captured by a wide angle lens, and the second FOV is contained in the first FOV and is captured by a tele zoom lens.
no code implementations • 7 Jun 2019 • Haoyu Ren, Mostafa El-Khamy, Jungwon Lee
We introduce two different scene understanding modules based on scene classification and coarse depth estimation respectively.
no code implementations • 16 Oct 2018 • Haoyu Ren, Mostafa El-Khamy, Jungwon Lee
The results show that DN-ResNets are more efficient, robust, and perform better denoising than current state of art deep learning methods, as well as the popular variants of the BM3D algorithm, in cases of blind and non-blind denoising of images corrupted with Poisson, Gaussian or Poisson-Gaussian noise.
no code implementations • 11 Nov 2017 • Haoyu Ren, Mostafa El-Khamy, Jungwon Lee
We propose methodologies to train highly accurate and efficient deep convolutional neural networks (CNNs) for image super resolution (SR).
no code implementations • ICCV 2015 • Haoyu Ren, Ze-Nian Li
In this paper, we propose a high-accuracy object detector based on co-occurrence features.
no code implementations • CVPR 2015 • Haoyu Ren, Ze-Nian Li
We show that the basis mapping based weak classifier is an approximation of kernel weak classifiers while keeping the same computation cost as linear weak classifiers.