Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Brosnan Yuen"'
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-16 (2024)
Abstract Training large neural networks on big datasets requires significant computational resources and time. Transfer learning reduces training time by pre-training a base model on one dataset and transferring the knowledge to a new model for anoth
Externí odkaz:
https://doaj.org/article/80c5b7bb13c348719e2f76215d75bb9a
Autor:
Brosnan Yuen, Yifeng Bie, Duncan Cairns, Geoffrey Harper, Jason Xu, Charles Chang, Xiaodai Dong, Tao Lu
Publikováno v:
IEEE Access, Vol 10, Pp 91027-91044 (2022)
Previous contact tracing systems required the users to perform many manual actions, such as installing smartphone applications, joining wireless networks, or carrying custom user devices. This increases the barrier to entry and lowers the user adopti
Externí odkaz:
https://doaj.org/article/9b1b49e3dea84216b00b2633371ed795
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
Abstract This article proposes a universal activation function (UAF) that achieves near optimal performance in quantification, classification, and reinforcement learning (RL) problems. For any given problem, the gradient descent algorithms are able t
Externí odkaz:
https://doaj.org/article/761a26ef959a4ff2b37e7710b3ce6b10
Publikováno v:
IEEE Access, Vol 8, Pp 143802-143817 (2020)
In this paper, we propose a novel machine learning pipeline to detect QRS complexes in very noisy wearable electrocardiogram (ECG) devices. The machine learning pipeline consists of a Butterworth filter, two wavelet convolutional neural networks (Wav
Externí odkaz:
https://doaj.org/article/b34577921bff4b958323a12902694134
Publikováno v:
IEEE Access, Vol 7, Pp 169359-169370 (2019)
In this paper, a convolutional neural network (CNN) with long short-term memory (LSTM) is designed to detect QRS complexes in noisy electrocardiogram (ECG) signals. The CNN performs feature extraction while the LSTM determines the QRS complex timings
Externí odkaz:
https://doaj.org/article/ef42c4b1412a4d66bf83477b403df109
Publikováno v:
IEEE Sensors Journal. 20:6160-6169
This paper proposes a semi-sequential probabilistic model (SSP) that applies an additional short term memory to enhance the performance of the probabilistic indoor localization. The conventional probabilistic methods normally treat the locations in t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3861c997303f60ffc6d61b9ed4bb64b6
https://doi.org/10.1109/jsen.2020.2972850
https://doi.org/10.1109/jsen.2020.2972850
Publikováno v:
IEEE Internet of Things Journal. 6:10639-10651
This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::298d673ed24dd2e6b972cfd0f34e5513
https://doi.org/10.1109/jiot.2019.2940368
https://doi.org/10.1109/jiot.2019.2940368
Autor:
Tyler Reese, Minh Tu Hoang, Yizhou Zhu, Tao Lu, Brosnan Yuen, Robert Westendorp, Xiaodai Dong, Michael Xie
Publikováno v:
IEEE Sensors Journal. 18:10208-10216
This paper proposes a soft range limited K nearest neighbours (SRL-KNN) localization fingerprinting algorithm. The conventional KNN determines the neighbours of a user by calculating and ranking the fingerprint distance measured at the unknown user l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a49b45834f4152239125804718276a1
https://doi.org/10.1109/jsen.2018.2874453
https://doi.org/10.1109/jsen.2018.2874453
Autor:
Mihai Sima, Brosnan Yuen
CubeSats are small satellites used for scientific experiments because they cost less than full sized satellites. Each CubeSat uses an on-board computer. The on-board computer performs sensor measurements, data processing, and CubeSat control. The cha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::545386606919b683c9365d9d84163ab8
http://arxiv.org/abs/1902.04117
http://arxiv.org/abs/1902.04117
