Complex Wavelet-Based Face Recognition Using Independent Component Analysis

Chai, Zhi; Ma, Kai-Kuang; Liu, Zhengguang

doi:10.1109/iih-msp.2009.325

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…To overcome this issue, vectorial features can be represented more precisely using a complex representation [28], [29]. Since, in our case, vectorial features m = [x, y] are the primary source of information, a complex representation of these features allows better correlation between them [28]- [30].…”

Section: B Pre-processing and Feature Selection Modulesmentioning

confidence: 99%

Operational Data Based Intrusion Detection System for Smart Grid

Efstathopoulos¹,

Radoglou-Grammatikis

Sarigiannidis

et al. 2019

2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

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With the rapid progression of Information and Communication Technology (ICT) and especially of Internet of Things (IoT), the conventional electrical grid is transformed into a new intelligent paradigm, known as Smart Grid (SG). SG provides significant benefits both for utility companies and energy consumers such as the two-way communication (both electricity and information), distributed generation, remote monitoring, selfhealing and pervasive control. However, at the same time, this dependence introduces new security challenges, since SG inherits the vulnerabilities of multiple heterogeneous, co-existing legacy and smart technologies, such as IoT and Industrial Control Systems (ICS). An effective countermeasure against the various cyberthreats in SG is the Intrusion Detection System (IDS), informing the operator timely about the possible cyberattacks and anomalies. In this paper, we provide an anomaly-based IDS especially designed for SG utilising operational data from a real power plant. In particular, many machine learning and deep learning models were deployed, introducing novel parameters and feature representations in a comparative study. The evaluation analysis demonstrated the efficacy of the proposed IDS and the improvement due to the suggested complex data representation.

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Section: B Pre-processing and Feature Selection Modulesmentioning

confidence: 99%

Operational Data Based Intrusion Detection System for Smart Grid

Efstathopoulos¹,

Radoglou-Grammatikis

Sarigiannidis

et al. 2019

2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

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show abstract

“…Since, in our case and many similar scenarios, vectorial features such as a location, speed, gradients or angles, are the primary source of information, a hyper-complex representation of these features is more efficient allowing better correlation between these channels [1,14,5]. The proposed method exploits the hypercomplex (quaternion) representation capturing the dependencies within the EEG sensors located on the sides of the head and the ones over the eyes, [13,4].…”

Section: Quaternion Principal Component Analysismentioning

confidence: 99%

Gaze estimation using EEG signals for HCI in augmented and virtual reality headsets

Montenegro

Argyriou

2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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“…Since, in our case and many similar scenarios, vectorial features such as a location, speed, gradients or angles, are the primary source of information, a hyper-complex representation of these features is more efficient allowing better correlation between these channels [47][48][49]. The proposed method exploits the hyper-complex (quaternion) representation capturing the dependencies within the EEG sensors located on the sides of the head and the ones over the eyes, [51,50].…”

Section: Emotion Recognition Using Fiducial Points and Eeg Quaternionmentioning

confidence: 99%

Emotion Understanding Using Multimodal Information Based on Autobiographical Memories for Alzheimer’s Patients

Montenegro¹,

Gkelias

Argyriou³

2017

Lecture Notes in Computer Science

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Abstract. Alzheimer Disease (AD) early detection is considered of high importance for improving the quality of life of patients and their families. Amongst all the different approaches for AD detection, significant work has been focused on emotion analysis through facial expressions, body language or speech. Many studies also use the electroencephalogram in order to capture emotions that patients cannot physically express. Our work introduces an emotion recognition approach using facial expression and EEG signal analysis. A novel dataset was created specifically to remark the autobiographical memory deficits of AD patients. This work uses novel EEG features based on quaternions, facial landmarks and the combination of them. Their performance was evaluated in a comparative study with a state of the art methods that demonstrates the proposed approach.

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Complex Wavelet-Based Face Recognition Using Independent Component Analysis

Cited by 4 publications

References 6 publications

Operational Data Based Intrusion Detection System for Smart Grid

Operational Data Based Intrusion Detection System for Smart Grid

Gaze estimation using EEG signals for HCI in augmented and virtual reality headsets

Emotion Understanding Using Multimodal Information Based on Autobiographical Memories for Alzheimer’s Patients

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