CTM - Indexed Articles in Journals

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Browsing CTM - Indexed Articles in Journals by Author "3889"
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    802.11 wireless simulation and anomaly detection using HMM and UBM
    ( 2020) Anisa Allahdadidastjerdi ; Ricardo Morla ; Jaime Cardoso ; 5587 ; 3645 ; 3889
    Despite the growing popularity of 802.11 wireless networks, users often suffer from connectivity problems and performance issues due to unstable radio conditions and dynamic user behavior, among other reasons. Anomaly detection and distinction are in the thick of major challenges that network managers encounter. The difficulty of monitoring broad and complex Wireless Local Area Networks, that often requires heavy instrumentation of the user devices, makes anomaly detection analysis even harder. In this paper we exploit 802.11 access point usage data and propose an anomaly detection technique based on Hidden Markov Model (HMM) and Universal Background Model (UBM) on data that is inexpensive to obtain. We then generate a number of network anomalous scenarios in OMNeT /INET network simulator and compare the detection outcomes with those in baseline approaches—RawData and Principal Component Analysis. The experimental results show the superiority of HMM and HMM-UBM models in detection precision and sensitivity.
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    Deep Anomaly Detection for In-Vehicle Monitoring—An Application-Oriented Review
    ( 2022) Caetano,F ; Pedro Miguel Carvalho ; Jaime Cardoso ; 3889 ; 4358
    Anomaly detection has been an active research area for decades, with high application potential. Recent work has explored deep learning approaches to the detection of abnormal behaviour and abandoned objects in outdoor video surveillance scenarios. The extension of this recent work to in-vehicle monitoring using solely visual data represents a relevant research opportunity that has been overlooked in the accessible literature. With the increasing importance of public and shared transportation for urban mobility, it becomes imperative to provide autonomous intelligent systems capable of detecting abnormal behaviour that threatens passenger safety. To investigate the applicability of current works to this scenario, a recapitulation of relevant state-of-the-art techniques and resources is presented, including available datasets for their training and benchmarking. The lack of public datasets dedicated to in-vehicle monitoring is addressed alongside other issues not considered in previous works, such as moving backgrounds and frequent illumination changes. Despite its relevance, similar surveys and reviews have disregarded this scenario and its specificities. This work initiates an important discussion on application-oriented issues, proposing solutions to be followed in future works, particularly synthetic data augmentation to achieve representative instances with the low amount of available sequences.
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    Evolution, Current Challenges, and Future Possibilities in ECG Biometrics
    ( 2018) João Tiago Pinto ; Jaime Cardoso ; Lourenco,A ; 7250 ; 3889
    Face and fingerprint are, currently, the most thoroughly explored biometric traits, promising reliable recognition in diverse applications. Commercial products using these traits for biometric identification or authentication are increasingly widespread, from smartphones to border control. However, increasingly smart techniques to counterfeit such traits raise the need for traits that are less vulnerable to stealthy trait measurement or spoofing attacks. This has sparked interest on the electrocardiogram (ECG), most commonly associated with medical diagnosis, whose hidden nature and inherent liveness information make it highly resistant to attacks. In the last years, the topic of ECG-based biometrics has quickly evolved toward the commercial applications, mainly by addressing the reduced acceptability and comfort by proposing new off-the-person, wearable, and seamless acquisition settings. Furthermore, researchers have recently started to address the issues of spoofing prevention and data security in ECG biometrics, as well as the potential of deep learning methodologies to enhance the recognition accuracy and robustness. In this paper, we conduct a deep review and discussion of 93 state-of-the-art publications on their proposed methods, signal datasets, and publicly available ECG collections. The extracted knowledge is used to present the fundamentals and the evolution of ECG biometrics, describe the current state of the art, and draw conclusions on prior art approaches and current challenges. With this paper, we aim to delve into the current opportunities as well as inspire and guide future research in ECG biometrics.
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    Secure Triplet Loss: Achieving Cancelability and Non-Linkability in End-to-End Deep Biometrics
    ( 2021) João Tiago Pinto ; Miguel Velhote Correia ; Jaime Cardoso ; 3889 ; 4996 ; 7250
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    Towards vehicle occupant-invariant models for activity characterisation
    ( 2022) Leonardo Gomes Capozzi ; Barbosa,V ; Pinto,C ; João Tiago Pinto ; Américo José Pereira ; Pedro Miguel Carvalho ; Jaime Cardoso ; 3889 ; 4358 ; 6078 ; 7250 ; 8288