Zobrazeno 1 - 10
of 334
pro vyhledávání: '"Fernandez Edgar"'
Autor:
Santos-Fernandez, Edgar, Hoef, Jay M. Ver, Peterson, Erin E., McGree, James, Villa, Cesar A., Leigh, Catherine, Turner, Ryan, Roberts, Cameron, Mengersen, Kerrie
The use of in-situ digital sensors for water quality monitoring is becoming increasingly common worldwide. While these sensors provide near real-time data for science, the data are prone to technical anomalies that can undermine the trustworthiness o
Externí odkaz:
http://arxiv.org/abs/2409.07667
This study analysed sprint kayak pacing profiles in order to categorise and compare an athlete's race profile throughout their career. We used functional principal component analysis of normalised velocity data for 500m and 1000m races to quantify pa
Externí odkaz:
http://arxiv.org/abs/2407.07120
Data collected from arrays of sensors are essential for informed decision-making in various systems. However, the presence of anomalies can compromise the accuracy and reliability of insights drawn from the collected data or information obtained via
Externí odkaz:
http://arxiv.org/abs/2403.10791
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 3, pp. 2596-2605, December 2021
Digital images and videos play a very important role in everyday life. Nowadays, people have access the affordable mobile devices equipped with advanced integrated cameras and powerful image processing applications. Technological development facilita
Externí odkaz:
http://arxiv.org/abs/2403.07891
Autor:
John Tabakwot Ayuba, Isaac Echoru, Fred Ssempijja, Monima Lemuel Ann, Fernandez Edgar, Mohammed Buhari
Publikováno v:
Forensic Science International: Reports, Vol 1, Iss , Pp - (2019)
Background: Lip prints are essential identification tools in forensics. Lip prints are individually unique and inheritable which makes them possible for personal identification. Aim: We studied sexual differences in print patterns and sizes among Uga
Externí odkaz:
https://doaj.org/article/ac875a94ffb243a299edf464203ce66d
Autor:
Gamakumara, Puwasala, Santos-Fernandez, Edgar, Talagala, Priyanga Dilini, Hyndman, Rob J., Mengersen, Kerrie, Leigh, Catherine
Time series often reflect variation associated with other related variables. Controlling for the effect of these variables is useful when modeling or analysing the time series. We introduce a novel approach to normalize time series data conditional o
Externí odkaz:
http://arxiv.org/abs/2305.12651
Autor:
Santos-Fernandez, Edgar, Vercelloni, Julie, Price, Aiden, Heron, Grace, Christensen, Bryce, Peterson, Erin E., Mengersen, Kerrie
Crowdsourcing methods facilitate the production of scientific information by non-experts. This form of citizen science (CS) is becoming a key source of complementary data in many fields to inform data-driven decisions and study challenging problems.
Externí odkaz:
http://arxiv.org/abs/2305.01144
Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems and human societies. Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology. Anomaly detection is
Externí odkaz:
http://arxiv.org/abs/2304.09367
Autor:
Bon, Joshua J., Bretherton, Adam, Buchhorn, Katie, Cramb, Susanna, Drovandi, Christopher, Hassan, Conor, Jenner, Adrianne L., Mayfield, Helen J., McGree, James M., Mengersen, Kerrie, Price, Aiden, Salomone, Robert, Santos-Fernandez, Edgar, Vercelloni, Julie, Wang, Xiaoyu
Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians o
Externí odkaz:
http://arxiv.org/abs/2211.10029
Autor:
Forbes, Owen, Santos-Fernandez, Edgar, Wu, Paul Pao-Yen, Xie, Hong-Bo, Schwenn, Paul E., Lagopoulos, Jim, Mills, Lia, Sacks, Dashiell D., Hermens, Daniel F., Mengersen, Kerrie
Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature. The approach of reporting results from one `best' model out of several candidate clusteri
Externí odkaz:
http://arxiv.org/abs/2209.04117