Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Bocus, Mohammud J."'
Autor:
Iacob, Alex, Gusmão, Pedro P. B., Lane, Nicholas D., Koupai, Armand K., Bocus, Mohammud J., Santos-Rodríguez, Raúl, Piechocki, Robert J., McConville, Ryan
Human Activity Recognition (HAR) training data is often privacy-sensitive or held by non-cooperative entities. Federated Learning (FL) addresses such concerns by training ML models on edge clients. This work studies the impact of privacy in federated
Externí odkaz:
http://arxiv.org/abs/2305.12134
This paper presents a novel approach for multimodal data fusion based on the Vector-Quantized Variational Autoencoder (VQVAE) architecture. The proposed method is simple yet effective in achieving excellent reconstruction performance on paired MNIST-
Externí odkaz:
http://arxiv.org/abs/2302.12636
Autor:
Koupai, Armand K., Bocus, Mohammud J., Santos-Rodriguez, Raul, Piechocki, Robert J., McConville, Ryan
The pervasiveness of Wi-Fi signals provides significant opportunities for human sensing and activity recognition in fields such as healthcare. The sensors most commonly used for passive Wi-Fi sensing are based on passive Wi-Fi radar (PWR) and channel
Externí odkaz:
http://arxiv.org/abs/2209.03765
A new method for multimodal sensor fusion is introduced. The technique relies on a two-stage process. In the first stage, a multimodal generative model is constructed from unlabelled training data. In the second stage, the generative model serves as
Externí odkaz:
http://arxiv.org/abs/2208.02183
Autor:
Fan, Jiahe, Bocus, Mohammud J., Hosking, Brett, Wu, Rigen, Liu, Yanan, Vityazev, Sergey, Fan, Rui
This paper presents a novel pothole detection approach based on single-modal semantic segmentation. It first extracts visual features from input images using a convolutional neural network. A channel attention module then reweighs the channel feature
Externí odkaz:
http://arxiv.org/abs/2112.13082
Autor:
Bocus, Mohammud J., Li, Wenda, Vishwakarma, Shelly, Kou, Roget, Tang, Chong, Woodbridge, Karl, Craddock, Ian, McConville, Ryan, Santos-Rodriguez, Raul, Chetty, Kevin, Piechocki, Robert
This paper presents a comprehensive dataset intended to evaluate passive Human Activity Recognition (HAR) and localization techniques with measurements obtained from synchronized Radio-Frequency (RF) devices and vision-based sensors. The dataset cons
Externí odkaz:
http://arxiv.org/abs/2110.04239
Autor:
Lau, Hok-Shing, McConville, Ryan, Bocus, Mohammud J., Piechocki, Robert J., Santos-Rodriguez, Raul
Traditional approaches to activity recognition involve the use of wearable sensors or cameras in order to recognise human activities. In this work, we extract fine-grained physical layer information from WiFi devices for the purpose of passive activi
Externí odkaz:
http://arxiv.org/abs/2104.09072
Autor:
Wang, Hengli, Liu, Yuxuan, Huang, Huaiyang, Pan, Yuheng, Yu, Wenbin, Jiang, Jialin, Lyu, Dianbin, Bocus, Mohammud J., Liu, Ming, Pitas, Ioannis, Fan, Rui
In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD). Our proposed framework consists of: 1) SwiftFlow, an efficient and accurate convolutional
Externí odkaz:
http://arxiv.org/abs/2008.09305
Manual visual inspection performed by certified inspectors is still the main form of road pothole detection. This process is, however, not only tedious, time-consuming and costly, but also dangerous for the inspectors. Furthermore, the road pothole d
Externí odkaz:
http://arxiv.org/abs/2008.06840
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