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Kim Bjerge
Kim Bjerge
Associate Professor, Aarhus University School of Engineering
ase.au.dk üzerinde doğrulanmış e-posta adresine sahip
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
Deep learning and computer vision will transform entomology
TT Høye, J Ärje, K Bjerge, OLP Hansen, A Iosifidis, F Leese, HMR Mann, ...
Proceedings of the National Academy of Sciences 118 (2), e2002545117, 2021
2762021
An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning
K Bjerge, JB Nielsen, MV Sepstrup, F Helsing-Nielsen, TT Høye
Sensors 21 (2), 343, 2021
832021
Towards the fully automated monitoring of ecological communities
M Besson, J Alison, K Bjerge, TE Gorochowski, TT Høye, T Jucker, ...
Ecology Letters 25 (12), 2753-2775, 2022
792022
A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony
K Bjerge, CE Frigaard, PH Mikkelsen, TH Nielsen, M Misbih, P Kryger
Computers and Electronics in Agriculture 164, 104898, 2019
672019
Real-time insect tracking and monitoring with computer vision and deep learning
K Bjerge, HMR Mann, TT Høye
Remote Sensing in Ecology and Conservation, 2021
652021
A living laboratory exploring mobile support for everyday life with diabetes
AM Kanstrup, K Bjerge, JE Kristensen
Wireless personal communications 53, 395-408, 2010
402010
Accurate detection and identification of insects from camera trap images with deep learning
K Bjerge, J Alison, M Dyrmann, CE Frigaard, HMR Mann, TT Høye
PLOS Sustainability and Transformation 2 (3), e0000051, 2023
242023
Enhancing non-technical skills by a multidisciplinary engineering summer school
PG Larsen, EL Kristiansen, J Bennedsen, K Bjerge
European Journal of Engineering Education 42 (6), 1076-1096, 2017
182017
A light trap and computer vision system to detect and classify live moths (Lepidoptera) using tracking and deep learning
K Bjerge, JB Nielsen, M Videbæk Sepstrup, F Helsing-Nielsen, TT Høye
bioRxiv, 2020.03. 18.996447, 2020
132020
A scalable and efficient convolutional neural network accelerator using HLS for a system-on-chip design
K Bjerge, JH Schougaard, DE Larsen
Microprocessors and microsystems 87, 104363, 2021
112021
Guide for getting started with SystemC development
K Bjerge
Danish Technological Institute, 2007
112007
Vibration signatures in ball bearings as a function of lubricant viscosity ratio κ, under alternating lubrication conditions
MO Jakobsen, ES Herskind, K Bjerge, P Ahrendt, CF Pedersen, ...
Tribology International 156, 106840, 2021
82021
Camera assisted roadside monitoring for invasive alien plant species using deep learning
M Dyrmann, AK Mortensen, L Linneberg, TT Høye, K Bjerge
Sensors 21 (18), 6126, 2021
72021
Supporting the Partitioning process in Hardware/Software Co-design with VDM-RT
JAE Isasa, PG Larsen, K Bjerge
Nico Plat, Claus Ballegaard Nielsen and Steve Riddle (Eds.), 5, 2012
72012
Accurate image-based identification of macroinvertebrate specimens using deep learning—How much training data is needed?
TT Høye, M Dyrmann, C Kjær, J Nielsen, M Bruus, CL Mielec, ...
PeerJ 10, e13837, 2022
62022
Hierarchical classification of insects with multitask learning and anomaly detection
K Bjerge, Q Geissmann, J Alison, HMR Mann, TT Høye, M Dyrmann, ...
Ecological Informatics 77, 102278, 2023
52023
Object Detection of Small Insects in Time-Lapse Camera Recordings
K Bjerge, CE Frigaard, H Karstoft
Sensors 23 (16), 7242, 2023
52023
A mobile observatory powered by sun and wind for near real time measurements of atmospheric, glacial, terrestrial, limnic and coastal oceanic conditions in remote off-grid areas
S Rysgaard, K Bjerge, W Boone, E Frandsen, M Graversen, TT Høye, ...
HardwareX 12, e00331, 2022
42022
Kamerabaseret overvågning af insekter på grønne bytage
TT Høye, HMR Mann, K Bjerge
Aarhus Universitet, DCE–Nationalt Center for Miljø og Energi©, 2020
32020
3D impurity inspection of cylindrical transparent containers
M Kragh, K Bjerge, P Ahrendt
Measurement Science and Technology 28 (1), 017002, 2016
32016
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Makaleler 1–20