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2020 International Conference on Embedded Wireless Systems and networks, EWSN 2020

作者： Cao, Nam Meyer, Matthias Thiele, Lothar Saukh, Olga Institute of Technical Informatics Graz University of Technology Austria Computer Engineering and Networks Lab ETH Zurich Switzerland Complexity Science Hub Vienna Austria

ISBN: (纸本)9780994988645

Airborne pollen cause seasonal allergies and the number of people affected increases yearly due to global warming and urbanization. Governmental pollen sensing stations are sampling traps which require manual pollen identification and counting by trained personnel in the lab. In the past years, a number of researchers and startups started working towards automated pollen measurements by exploring a wide range of techniques. Many solutions reported in the literature are expensive or work for a limited number of pollen species. In this paper, we present the design of a prototype of an automated and affordable pollen detection device built from off-the-shelf components. The design consists of three subsystems operating in the field and communicating the data to the backend server: (1) a particle trap with automatic filtering, (2) a particle concentration subsystem, and (3) a digital transmitted light microscope with layer-wise focus. The prototype shows effective particle gathering, filtering and concentration in a tiny-sized area. As a result, we reduce particle loss and improve image quality taken by the optical system when searching and focusing on pollen grains. The test results show that our device achieves high efficiency with up to 150 l/min air flow rates, evaluates over 90% of captured pollen grains, and achieves 1 h measurement delay on average (2 h at maximum). The prototype collects raw time-stamped microscopic images of pollen with 5-60 depth layers per sample depending on the number of objects contained in one sample. All images are transmitted to the backend server where we run a pollen detection algorithm to extract individual pollen grains from every image. We achieve 0.90 average precision and F1-score of 0.88 when detecting pollen in the images of individual layers taken in the field. Our prototype successfully operated in the wild for 115 days between April and August 2019, and shows high stability under a wide range of varying weather conditions, l

关键词： Global warming

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Automated Pollen Detection with an Affordable Technology 20

Automated Pollen Detection with an Affordable Technology

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Proceedings of the 2020 International Conference on Embedded Wireless Systems and networks

作者： Nam Cao Matthias Meyer Lothar Thiele Olga Saukh Institute of Technical Informatics Graz University of Technology Austria Computer Engineering and Networks Lab ETH Zurich Switzerland

ISBN: (纸本)9780994988645

Airborne pollen cause seasonal allergies and the number of people affected increases yearly due to global warming and urbanization. Governmental pollen sensing stations are sampling traps which require manual pollen identification and counting by trained personnel in the lab. In the past years, a number of researchers and startups started working towards automated pollen measurements by exploring a wide range of techniques. Many solutions reported in the literature are expensive or work for a limited number of pollen species. In this paper, we present the design of a prototype of an automated and affordable pollen detection device built from off-the-shelf components. The design consists of three subsystems operating in the field and communicating the data to the backend server: (1) a particle trap with automatic filtering, (2) a particle concentration subsystem, and (3) a digital transmitted light microscope with layer-wise focus. The prototype shows effective particle gathering, filtering and concentration in a tiny-sized area. As a result, we reduce particle loss and improve image quality taken by the optical system when searching and focusing on pollen grains. The test results show that our device achieves high efficiency with up to 150 l/min air flow rates, evaluates over 90 % of captured pollen grains, and achieves 1 h measurement delay on average (2 h at maximum). The prototype collects raw time-stamped microscopic images of pollen with 5-60 depth layers per sample depending on the number of objects contained in one sample. All images are transmitted to the backend server where we run a pollen detection algorithm to extract individual pollen grains from every image. We achieve 0.90 average precision and F1-score of 0.88 when detecting pollen in the images of individual layers taken in the field. Our prototype successfully operated in the wild for 115 days between April and August 2019, and shows high stability under a wide range of varying weather conditions,

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Neural Gradient Regularizer

arXiv

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arXiv 2023年

作者： Xu, Shuang Wang, Yifan Zhao, Zixiang Peng, Jiangjun Cao, Xiangyong Meng, Deyu Zhang, Yulun Timofte, Radu Van Gool, Luc School of Mathematics and Statistics Northwestern Polytechnical University Xi’an710021 China Research and Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518063 China School of Electronic and Information Engineering The Key Laboratory for Intelligent Networks and Network Security Ministry of Education Xi’an Jiaotong University Xi’an710049 China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an710049 China Computer Vision Lab ETH Zurich Zürich8092 Switzerland

Owing to its significant success, the prior imposed on gradient maps has consistently been a subject of great interest in the field of image processing. Total variation (TV), one of the most representative regularizers, is known for its ability to capture the intrinsic sparsity prior underlying gradient maps. Nonetheless, TV and its variants often underestimate the gradient maps, leading to the weakening of edges and details whose gradients should not be zero in the original image (i.e., image structures is not describable by sparse priors of gradient maps). Recently, total deep variation (TDV) has been introduced, assuming the sparsity of feature maps, which provides a flexible regularization learned from large-scale datasets for a specific task. However, TDV requires to retrain the network with image/task variations, limiting its versatility. To alleviate this issue, in this paper, we propose a neural gradient regularizer (NGR) that expresses the gradient map as the output of a neural network. Unlike existing methods, NGR does not rely on any subjective sparsity or other prior assumptions on image gradient maps, thereby avoiding the underestimation of gradient maps. NGR is applicable to various image types and different image processing tasks, functioning in a zero-shot learning fashion, making it a versatile and plug-and-play regularizer. Extensive experimental results demonstrate the superior performance of NGR over state-of-the-art counterparts for a range of different tasks, further validating its effectiveness and versatility. © 2023, CC BY.

关键词： Large dataset

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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Interpretable and transferable models to understand the impact of lockdown measures on local air quality

arXiv

引用

arXiv 2020年

作者： Einsiedler, Johanna Cheng, Yun Papst, Franz Saukh, Olga Vienna University of Business Economics CSH Vienna Austria Graz University of Technology CSH Vienna Austria Computer Engineering and Networks Lab. ETH Zurich Switzerland

The COVID-19 related lockdown measures offer a unique opportunity to understand how changes in economic activity and traffic affect ambient air quality and how much pollution reduction potential can the society offer through digitalization and mobility-limiting policies. In this work, we estimate pollution reduction over the lockdown period by using the measurements from ground air pollution monitoring stations, training a long-term prediction model and comparing its predictions to measured values over the lockdown month. We show that our models achieve state-of-the-art performance on the data from air pollution measurement stations in Switzerland, Austria and in China: evaluate up to -29.4% / +9.8% and -22.6% / +11.5%, change in NO2 / PM10 in Eastern Switzerland and Lower Austria respectively;-28.1%, / -10.8 % and -52.8 % / -50.0 % in NO2 / PM2.5 in Beijing and Wuhan respectively. Our reduction estimates are consistent with recent publications, yet in contrast to prior works, our method takes local weather into account. What can we learn from pollution emissions during lockdown? The lockdown period was too short to train meaningful models from scratch. To tackle this problem, we use transfer learning to newly fit only traffic-dependent variables. We show that the resulting models are accurate, suitable for an analysis of the post-lockdown period and capable of estimating the future air pollution reduction potential. © 2020, CC BY.

关键词： Air quality

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Poster abstract: An automated real-time and affordable airborne pollen sensing system 18

Poster abstract: An automated real-time and affordable airbo...

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18th ACM/IEEE International Conference on Information Processing in Sensor networks, IPSN 2019

作者： Cao, Nam Saukh, Olga Thiele, Lothar Institute of Technical Informatics Graz University of Technology Austria Institute of Technical Informatics TU Graz / CSH Vienna Austria Computer Engineering and Networks Lab ETH Zurich Switzerland

ISBN: (纸本)9781450362849

In this paper, we present the design of our prototype of an automated real-time and affordable pollen sensing system. The design consists of three main subsystems: (1) a trap with automatic filtering, (2) a particle concentration system, and (3) a digital microscope with autofocus. The prototype shows effective particle gathering, filtering and concentration in a tiny sized area. As a result, we reduce particle loss and improve image quality taken by the optical system when searching and autofocusing on pollen grains. Our first prototype collects raw time-stamped data and transmits these to the backend server where we plan to run the detection and classification algorithms to extract accurate pollen counts from microscopic images. The key advantage of processing images at the backend is that we let the experts undertake corrective actions and help the system learn to detect and classify pollen using state-of-the-art interactive imitation learning algorithms. The final model can then run locally on embedded hardware. © 2019 Copyright held by the owner/author(s).

关键词： Image enhancement

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Poster Abstract: An Automated Real-time and Affordable Airborne Pollen Sensing System

Poster Abstract: An Automated Real-time and Affordable Airbo...

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International Symposium on Information Processing in Sensor networks (IPSN)

作者： Nam Cao Olga Saukh Lothar Thiele Institute of Technical Informatics Graz University of Technology ETH Zurich Computer Engineering and Networks Lab

ISBN: (纸本)9781728107271

关键词： Microscopy Prototypes Cyclones Sensors Real-time systems Filtering Image resolution

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Competition: Low-Power Wireless Bus Baseline 19

Competition: Low-Power Wireless Bus Baseline

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Proceedings of the 2019 International Conference on Embedded Wireless Systems and networks

作者： Fabian Mager Romain Jacob Reto Da Forno Marco Zimmerling Networked Embedded Systems Lab TU Dresden Germany Computer Engineering and Networks Laboratory ETH Zurich Switzerland

No abstract available.

ISBN: (纸本)9780994988638

No abstract available.

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Proceedings of the first workshop on weakly supervised learning (WeaSuL)

arXiv

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arXiv 2021年

作者： Hedderich, Michael A. Roth, Benjamin Kann, Katharina Plank, Barbara Ratner, Alex Klakow, Dietrich Madan, Vivek Khetan, Ashish Karnin, Zohar Wang, Cheng Kim, Sun Park, Taiwoo Choudhary, Sajal Park, Sunghyun Kim, Young-Bum Sarikaya, Ruhi Lee, Sungjin Huang, Xin Khetan, Ashish Cvitkovic, Milan Karnin, Zohar Cachay, Salva Rühling Boecking, Benedikt Dubrawski, Artur Tran, Linh Taghanaki, Saeid Asgari Khasahmadi, Amir Hosein Sanghi, Aditya Meyer, Matthias Thiele, Lothar Wenner, Michaela Walter, Fabian Hibert, Clément Saarland University Germany University of Vienna Austria University of Colorado Boulder United States IT University of Copenhagen Denmark University of Washington United States AWS AI Labs Amazon Alexa AI Amazon AI PostEra Technical University of Darmstadt Carnegie Mellon University Autodesk AI Lab Imperial College London Computer Engineering and Networks Laboratory ETH Zurich Zurich Switzerland Laboratory of Hydraulics Hydrology and Glaciology ETH Zurich WSL Birmensdorf Institut de Physique du Globe de Strasbourg University of Strasbourg

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Quantle: Fair and Honest Presentation Coach in Your pocket

Quantle: Fair and Honest Presentation Coach in Your pocket

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International Symposium on Information Processing in Sensor networks (IPSN)

作者： Olga Saukh Balz Maag TU Graz / CSH Vienna Institute of Technical Informatics Austria ETH Zurich Computer Engineering and Networks Lab Switzerland

ISBN: (纸本)9781728107271

Great public speakers are made, not born. Practicing a presentation in front of colleagues is common practice and results in a set of subjective judgements what could be improved. In this paper we describe the design and implementation of a mobile app which estimates the quality of speaker's delivery in real time in a fair, repeatable and privacy-preserving way. Quantle estimates the speaker's pace in terms of the number of syllables, words and clauses, computes pitch and duration of pauses. The basic parameters are then used to estimate the talk complexity based on readability scores from the literature to help the speaker adjust his delivery to the target audience. In contrast to speech-to-text-based methods used to implement a digital presentation coach, Quantle does processing locally in real time and works in the flight mode. This design has three implications: (1) Quantle does not interfere with the surrounding hardware, (2) it is power-aware, since 95.2 % of the energy used by the app on iPhone 6 is spent to operate the built-in microphone and the screen, and (3) audio data and processing results are not shared with a third party therewith preserving speaker's privacy. We evaluate Quantle on artificial, online and live data. We artificially modify an audio sample by changing the volume, speed, tempo, pitch and noise level to test robustness of Quantle and its performance limits. We then test Quantle on 1017 TED talks held in English and compare computed features to those extracted from the available transcript processed by online text evaluation services. Quantle estimates of syllable and word counts are 85.4 % and 82.8 % accurate, and pitch is over 90 % accurate. We use the outcome of this study to extract typical ranges for each vocal characteristic. We then use Quantle on live data at a social event, and as a tool for speakers to track their delivery when rehearsing a talk. Our results confirm that Quantle is robust to different noise levels, varying dista

关键词： Real-time systems Tools Feature extraction Estimation Complexity theory Microphones Information processing

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