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Machine Learning at the Network Edge: A Survey [article]

M.G. Sarwar Murshed, Christopher Murphy, Daqing Hou, Nazar Khan, Ganesh Ananthanarayanan, Faraz Hussain
2021 arXiv   pre-print
This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems.  ...  This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools  ...  The Raspberry Pi, a single-board computer developed by the Raspberry Pi Foundation, is one of the most common devices used for edge computing.  ... 
arXiv:1908.00080v4 fatcat:mw4lwwvzf5gupjr6pgdgnabeuu

IoT for Smart Environment Monitoring Based on Python: A Review

Saad Hikmat Haji, Amira B. Sallow
2021 Asian Journal of Research in Computer Science  
The review is divided based on the objectives of applying SEM methods, analyzing each objective about the sensors used, machine learning, and classification methods.  ...  Moreover, the authors have thoroughly examined how advancements in sensor technology, the Internet of Things, and machine learning methods have made environmental monitoring into a truly smart monitoring  ...  deep neural network learning method.  ... 
doi:10.9734/ajrcos/2021/v9i130215 fatcat:zyxpyyor3jeldfk4d65ct27fv4

Robust, real-time and autonomous monitoring of ecosystems with an open, low-cost, networked device

Sarab S. Sethi, Robert M. Ewers, Nick S. Jones, C. David L. Orme, Lorenzo Picinali
2018 Methods in Ecology and Evolution  
Raspberry Pi.  ...  In this study 79 we outline the design for an inexpensive autonomous ecosystem monitoring device 80 based around a low-power Raspberry Pi, costing under £250 ($331 USD) per unit, 81 which will enable continuous  ... 
doi:10.1111/2041-210x.13089 fatcat:okyx4zpi4vabdl2nawleppi7bm

The Design and Deployment of an End-to-end IoT Infrastructure for the Natural Environment [article]

Vatsala Nundloll, Barry Porter, Gordon Blair, Jack Cosby, Bridget Emmett, Ben Winterbourn, Graham Dean, Philip Beattie, Rory Shaw, Davey Jones, Dave Chadwick, Mike Brown, Wayne Shelley (+1 others)
2019 arXiv   pre-print
We report on the design, deployment and use of IoT infrastructure for environmental monitoring and management.  ...  Based on this experience we discuss key future work for the IoT community when working in these kinds of environmental deployments.  ...  wildlife, particularly zebras, for biology researchers [18] .  ... 
arXiv:1901.06270v1 fatcat:x2cnsf5uvjau5kkcrof7l5fray

Program

2020 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)  
This paper presents a deep learning-based method for effective image companding.  ...  Because of the diversity of image tampering, it's difficult to collect a sufficient amount of tampered images for supervised learning.  ...  We used Raspberry Pi 3 and ReSpeaker 2-Mics Pi HAT to achieve our goal.  ... 
doi:10.1109/icce-taiwan49838.2020.9258230 fatcat:g25vw7mzvradxna2grlzp6kgiq

A Collaborative Pilot Platform for Data Annotation and Enrichment in Viticulture

Phivos Mylonas, Yorghos Voutos, Anastasia Sofou
2019 Information  
focusing on improving the quality of digital agricultural information analysis and its presentation, thus establishing new ways for its efficient exploitation in a larger scale with benefits both for  ...  In particular, by using image and free text analysis methodologies for automatic metadata enrichment, in accordance to the human expertise for enrichment, it offers a cornerstone for future researchers  ...  The hardware part of the project was implemented through an array of sink nodes based on Raspberry Pi model 2, and the software related to the development of a prediction model was implemented using the  ... 
doi:10.3390/info10040149 fatcat:6eptpgrdxbdkln6e2ut4y53cxi

Technology innovation: advancing capacities for the early detection of and rapid response to invasive species

Barbara Martinez, Jamie K. Reaser, Alex Dehgan, Brad Zamft, David Baisch, Colin McCormick, Anthony J. Giordano, Rebecca Aicher, Shah Selbe
2019 Biological Invasions  
We provide an assessment of federal government capacities for the early detection of and rapid response to invasive species (EDRR) through advances in technology application; examples of emerging technologies  ...  This paper complements and draws on an Innovation Summit, review of advanced biotechnologies applicable to invasive species management, and a survey of federal agencies that respond to these high-level  ...  Acknowledgements This paper advances action 5.1.6 of the  ... 
doi:10.1007/s10530-019-02146-y fatcat:v5ximqh33bezrav2qnodo42jgi

Citizen Science SDG Conference Abstract volume - CS-SDG project [article]

Silke Voigt-Heucke, Claudia Fabó Cartas, Kim Mortega
2021 Zenodo  
This is the abstract volume of the Citizen Science SDG Conference titled "Knowledge for Change: A decade of Citizen Science (2020-2030) in support of the SDGs" that took place on 14.-15.  ...  The conference took place as part of the CS-SDG project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 101000014.  ...  multiple classifications per image.  ... 
doi:10.5281/zenodo.4473071 fatcat:wze3nrqrhzckxbhd2uym3rgovm

A variable passive low‐frequency absorber

Niels Werner Larsen, Eric R. Thompson, Anders Christian Gade
2005 Journal of the Acoustical Society of America  
The survey suggested an unfavorable acoustic environment for language learning. 10:50 1aAA9. Classroom noise and children learning in a second language.  ...  ., Tsukuba, Ibaraki 305-8566, Japan͒ Assessing sound environment of classrooms for the aged is a very important issue, because classrooms can be used by the aged for their lifelong learning, especially  ...  Learning on the five tasks followed one of two general patterns. For ITD and intensity discrimination, multiple-hour practice did not lead to greater learning than that seen in untrained listeners.  ... 
doi:10.1121/1.4785850 fatcat:fjm27kdi7vf5jmboifxktuegba

Temporal and spectral resolution of hearing in patients with precipitous hearing loss: Gap release of masking (GRM) and the role of cognitive function

Martin D. Vestergaard
2005 Journal of the Acoustical Society of America  
The survey suggested an unfavorable acoustic environment for language learning. 10:50 1aAA9. Classroom noise and children learning in a second language.  ...  ., Tsukuba, Ibaraki 305-8566, Japan͒ Assessing sound environment of classrooms for the aged is a very important issue, because classrooms can be used by the aged for their lifelong learning, especially  ...  Learning on the five tasks followed one of two general patterns. For ITD and intensity discrimination, multiple-hour practice did not lead to greater learning than that seen in untrained listeners.  ... 
doi:10.1121/1.4785958 fatcat:srovwo2u65dsrl7vcobmwfm3pe

Annals of the University of Oradea, Fascicle: Ecotoxicology, Animal Husbandry and Food Science and Technology, Vol. XVIII/B 2019

University of Oradea, Faculty of Environmental Protection
2020 Zenodo  
Annals of the University of Oradea, Fascicle: Ecotoxicology, Animal Husbandry and Food Science and Technology, Vol. XVIII/B 2019  ...  The present work was funded by the project "Study of synergic bioactivity of some antioxidant mixes fortification with the role to fortify patients with Parkinson's disease", No 47/12.11.2015 and Additional  ...  Electrical conductivity (EC) -Currently the measurement of electrical conductivity is the most useful quality parameter for the classification of honeys which can be determined by relatively inexpensive  ... 
doi:10.5281/zenodo.4090941 fatcat:f5emwzinqfaozdfdi5gno4p5ym

Anthropogenic marine debris and its dynamics across peri-urban and urban mangroves on Penang Island, Malaysia [article]

Su Yin Chee, John Barry Gallagher, Jean Chai Yee, Danielle Carey, Yusri Yusup
2019 biorxiv/medrxiv   pre-print
For Penang Island, the study highlights the areas in need of attention and prioritization, lists the types of debris needing proper management, and will aid in the future monitoring, mitigation and/or  ...  closer to the edge is increasingly sorted and lost to the water body in favour of smaller plastic items, for a constant wind field and irrespective of neap-spring phases.  ...  Raspberry-Pi.  ... 
doi:10.1101/756106 fatcat:m6oy4besircffmdhmffppw6yra

Automation of Feature Selection and Generation of Optimal Feature Subsets for Beehive Audio Sample Classification

Aditya Bhouraskar
2020
Selecting good features forms the basis for machine learning models to further classify these audio samples.  ...  The purpose of finding the best features is to get a better audio classification which helps beekeepers know about the health of beehives and address problems such as bee immunity, effects of pesticides  ...  file every 15 minutes on a USB storage device connected to the Raspberry Pi [14] .  ... 
doi:10.26076/ba69-74be fatcat:ee3gnjk6wbccxa2dvf3rnwbti4

Automated acoustic monitoring of ecosystems

Sarab Singh Sethi, Nicholas Jones, Robert Ewers, Lorenzo Picinali, Natural Environment Research Council (Great Britain)
2020
Similar studies inspecting the dynamics of data from large-scale ecological monitoring networks may provide a fruitful avenue for further explorations.  ...  We then move to the automated analysis of eco-acoustic data, and exploit a learned acoustic feature embedding to achieve accurate monitoring of ecosystem health across multiple spatial and temporal scales  ...  Here we present an inexpensive autonomous ecosystem monitoring device based around a Raspberry Pi, which uses mobile data networks to implement continuous data collection from the field.  ... 
doi:10.25560/82261 fatcat:xgr4grr4azfxdfe3f2ukur2zgm

Low-Power Smart Devices for the IoT Revolution [article]

Nardello Matteo
2020
layer of data processing directly in-situ where the data is acquired, providing a higher quality of service to the implemented application.  ...  Figure 4 . 3 . 43 It is based on a Raspberry PI 3 2 that provides the environment for acquiring and processing the captured pictures.  ...  However, enabling deep learning on the device side is still very challenging.  ... 
doi:10.15168/11572_274371 fatcat:q3357yjxvzfv7hcuvr4cgzramm
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