Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Piechocki, Robert J"'
Feedback holds a pivotal role in practical communication schemes, even though it does not enhance channel capacity. Its main attribute includes adaptability in transmission that allows for a higher rate of convergence of the error probability to zero
Externí odkaz:
http://arxiv.org/abs/2403.14360
Autor:
Mavromatis, Ioannis, Spyridopoulos, Theodoros, Carnelli, Pietro, Chin, Woon Hau, Khalil, Ahmed, Chakravarty, Jennifer, Kun, Lucia Cipolina, Piechocki, Robert J., Robbins, Colin, Cunnington, Daniel, Chase, Leigh, Chiazor, Lamogha, Preston, Chris, Rahul, Khan, Aftab
The way we travel is changing rapidly, and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduces new risks and challenges, making cybersecurity a top priority for en
Externí odkaz:
http://arxiv.org/abs/2312.14687
The Open Radio Access Network (O-RAN) is a burgeoning market with projected growth in the upcoming years. RAN has the highest CAPEX impact on the network and, most importantly, consumes 73% of its total energy. That makes it an ideal target for optim
Externí odkaz:
http://arxiv.org/abs/2310.11131
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 an achievability bound that evaluates the exact probability of error of an ensemble of random codes that are decoded by a minimum distance decoder. Compared to the state-of-the-art which demands exponential computation time, this
Externí odkaz:
http://arxiv.org/abs/2305.09450
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
Consider n nodes communicating over an unreliable broadcast channel. Each node has a single packet that needs to be communicated to all other nodes. Time is slotted, and a time slot is long enough for each node to broadcast one packet. Each broadcast
Externí odkaz:
http://arxiv.org/abs/2107.10695
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