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Displaying Results 1 - 25 of 29 on page 1 of 2
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A gate hit detection system for canoe slalom
(2015)
Ringwood, John; Qian, Wenkan; Conde Fernandez, Javier
A gate hit detection system for canoe slalom
(2015)
Ringwood, John; Qian, Wenkan; Conde Fernandez, Javier
Abstract:
Many sports now look to technology to resolve contentious decisions, for example in tennis, Gaelic games, snooker, association football, etc. However, the sport of canoe slalom, an Olympic sport, still relies completely on the subjective decision of a human judge, as to whether a competitor has touched a ‘gate’ or not. With the time difference between gold and bronze medals frequently being less than 2 secs, a single 2 second penalty has a significant impact and reliable, objective and repeatable judging is vital. This paper reports on a prototype slalom pole hitdetection system, which has been developed in consultation with the International Canoe Federation (ICF) Slalom Committee.
http://mural.maynoothuniversity.ie/6683/
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A user-oriented study of metadata in focal.ie
(2014)
de Barra-Cusack, Fionnuala
A user-oriented study of metadata in focal.ie
(2014)
de Barra-Cusack, Fionnuala
Abstract:
Subject-field classification systems are implemented in every major termbank to facilitate the internal management of terminographic work, and this study begins with an account of how the DANTERM subject-field classification system was selected to meet such needs in the bilingual Irish-English termbank focal.ie. Little is known, however, about how subject-field labels and other metadata are actually used by users of termbanks. The current study thus also sets out to investigate users’ beliefs and opinions about how the presentation of metadata, and especially subject-field labels, affects user behaviour and success in the context of English-Irish translation, including use of the bilingual Irish-English termbank focal.ie. Users’ opinions and beliefs are investigated in a series of five focus groups involving nineteen users of focal.ie. Actual use of the termbank is subsequently observed in a contextual inquiry, involving observation of and interviews with nine professional translato...
http://doras.dcu.ie/20237/
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Analysis of data warehouse architectures: modeling and classification
(2019)
Yang, Qishan; Ge, Mouzhi; Helfert, Markus
Analysis of data warehouse architectures: modeling and classification
(2019)
Yang, Qishan; Ge, Mouzhi; Helfert, Markus
Abstract:
With decades of development and innovation, data warehouses and their architectures have been extended to a variety of derivatives in various environments to achieve different organisations’ requirements. Although there are some ad-hoc studies on data warehouse architecture (DWHA) investigations and classifications, limited research is relevant to systematically model and classify DWHAs. Especially in the big data era, data is generated explosively. More emerging architectures and technologies are leveraged to manipulate and manage big data in this domain. It is therefore valuable to revisit and investigate DWHAs with new innovations. In this paper, we collect 116 publications and model 73 disparate DWHAs using Archimate, then 9 representativeDWHAs are identified and summarised into a ”big picture”. Furthermore, it proposes a new classification model sticking to state-of-the-art DWHAs. This model can guide researchers and practitioners to identify, analyse and compare differenc...
http://doras.dcu.ie/23520/
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Automatic detection of joints and quantification of knee osteoarthritis severity using convolutional neural networks
(2017)
Antony, Joseph; McGuinness, Kevin; Moran, Kieran; O'Connor, Noel E.
Automatic detection of joints and quantification of knee osteoarthritis severity using convolutional neural networks
(2017)
Antony, Joseph; McGuinness, Kevin; Moran, Kieran; O'Connor, Noel E.
Abstract:
This paper introduces a new approach to automatically quantify the severity of knee OA using X-ray images. Automatically quantifying knee OA severity involves two steps: first, automatically localizing the knee joints; next, classifying the localized knee joint images. We introduce a new approach to automatically detect the knee joints using a fully convolutional neural network (FCN). We train convolutional neural networks (CNN) from scratch to automatically quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross-entropy and mean-squared loss. This joint training further improves the overall quantification of knee OA severity, with the added benefit of naturally producing simultaneous multi-class classification and regression outputs. Two public datasets are used to evaluate our approach, the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST), with extremely promising results that outperform existing approaches.
http://doras.dcu.ie/21761/
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Classification of composite semantic relations by a distributional-relational model
(2018)
Barzegar, Siamak; Davis, Brian; Handschuh, Siegfried; Freitas, Andre
Classification of composite semantic relations by a distributional-relational model
(2018)
Barzegar, Siamak; Davis, Brian; Handschuh, Siegfried; Freitas, Andre
Abstract:
Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text. However, in most cases, it is not possible to assign a direct semantic relation between entities/terms. This paper proposes an approach for composite semantic relation classification using one or more relations between entities/term mentions, extending the traditional semantic relation classification task. The proposed model is different from existing approaches which typically use machine learning models built over lexical and distributional word vector features in that is uses a combination of a large commonsense knowledge base of binary relations, a distributional navigational algorithm and sequence classification to provide a solution for the composite semantic relation classification problem. The proposed approach outperformed existing baselines with regard to F1-score, Accuracy, Precision and Recall.
http://mural.maynoothuniversity.ie/13241/
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Classification of dual language audio-visual content: Introduction to the VideoCLEF 2008 pilot benchmark evaluation task
(2008)
Larson, Martha; Newman, Eamonn; Jones, Gareth J.F.
Classification of dual language audio-visual content: Introduction to the VideoCLEF 2008 pilot benchmark evaluation task
(2008)
Larson, Martha; Newman, Eamonn; Jones, Gareth J.F.
Abstract:
VideoCLEF is a new track for the CLEF 2008 campaign. This track aims to develop and evaluate tasks in analyzing multilingual video content. A pilot of a Vid2RSS task involving assigning thematic class labels to video kicks off the VideoCLEF track in 2008. Task participants deliver classification results in the form of a series of feeds, one for each thematic class. The data for the task are dual language television documentaries. Dutch is the dominant language and English-language content (mostly interviews) is embedded. Participants are provided with speech recognition transcripts of the data in both Dutch and English, and also with metadata generated by archivists. In addition to the classification task, participants can choose to participate in a translation task (translating the feed into a language of their choice) and a keyframe selection task (choosing a semantically appropriate keyframe for depiction of the videos in the feed).
http://doras.dcu.ie/16191/
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Classification of sporting activities using smartphone accelerometers
(2013)
Mitchell, Edmond; Monaghan, David; O'Connor, Noel E.
Classification of sporting activities using smartphone accelerometers
(2013)
Mitchell, Edmond; Monaghan, David; O'Connor, Noel E.
Abstract:
In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today’s society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT inp...
http://doras.dcu.ie/18074/
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Colour texture analysis: a comparative study paper
(2000)
Drimbarean, Alexandru; Whelan, Paul F.
Colour texture analysis: a comparative study paper
(2000)
Drimbarean, Alexandru; Whelan, Paul F.
http://doras.dcu.ie/18867/
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Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task
(2011)
Leamy, Darren J.; Collins, Ronan; Ward, Tomas E.
Combining fNIRS and EEG to improve motor cortex activity classification during an imagined movement-based task
(2011)
Leamy, Darren J.; Collins, Ronan; Ward, Tomas E.
Abstract:
This work serves as an initial investigation into improvements to classification accuracy of an imagined movement-based Brain Computer Interface (BCI) by combining the feature spaces of two unique measurement modalities: functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG). Our dual-modality system recorded concurrent and co-locational hemodynamic and electrical responses in the motor cortex during an imagined movement task, participated in by two subjects. Offline analysis and classification of fNIRS and EEG data was performed using leave-one-out cross-validation (LOOCV) and linear discriminant analysis (LDA). Classification of 2-dimensional fNIRS and EEG feature spaces was performed separately and then their feature spaces were combined for further classification. Results of our investigation indicate that by combining feature spaces, modest gains in classification accuracy of an imagined movement-based BCI can be achieved by employing a supplemental meas...
http://mural.maynoothuniversity.ie/4365/
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Composite Semantic Relation Classification
(2017)
Barzegar, Siamak; Freitas, Andre; Handschuh, Siegfried; Davis, Brian
Composite Semantic Relation Classification
(2017)
Barzegar, Siamak; Freitas, Andre; Handschuh, Siegfried; Davis, Brian
Abstract:
Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text. However, in most cases it is not possible to assign a direct semantic relation between entities/terms. This paper proposes an approach for composite semantic relation classification, extending the traditional semantic relation classification task. Different from existing approaches, which use machine learning models built over lexical and distributional word vector features, the proposed model uses the combination of a large commonsense knowledge base of binary relations, a distributional navigational algorithm and sequence classification to provide a solution for the composite semantic relation classification problem.
http://mural.maynoothuniversity.ie/11828/
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Computationally Tractable Location Estimation on WiFi Enabled Mobile Phones
(2009)
Kelly, Damian; Behan, Ross; Villing, Rudi; McLoone, Sean F.
Computationally Tractable Location Estimation on WiFi Enabled Mobile Phones
(2009)
Kelly, Damian; Behan, Ross; Villing, Rudi; McLoone, Sean F.
Abstract:
Enriching a mobile device with the ability to detect its location can enable the provision of a range of Location Based Services to its user. Outdoors, the location detection facility is sufficiently provided by GPS, however GPS is not suited to the challenge of non-line-of-sight indoor environments. In these environments smaller scale location estimation techniques must be employed. Due to their ubiquity, WiFi signals are a commonly employed indicator of location; knowledge of the identity and intensity of these signals throughout an environment can allow the estimation of the receiving device’s location. This paper outlines work towards the development of efficient, privacy conservative positioning algorithms suitable for deployment on commonly available mobile phones. For a number of algorithms, the frequency of correct location prediction is presented along with the execution time on a real mobile phone.
http://mural.maynoothuniversity.ie/2490/
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Conditional Visualization for Statistical Models: An Introduction to the condvis Package in R
(2017)
O'Connell, Mark; Hurley, Catherine B.; Domijan, Katarina
Conditional Visualization for Statistical Models: An Introduction to the condvis Package in R
(2017)
O'Connell, Mark; Hurley, Catherine B.; Domijan, Katarina
Abstract:
The condvis package is for interactive visualization of sections in data space, showing fitted models on the section, and observed data near the section. The primary goal is the interpretation of complex models, and showing how the observed data support the fitted model. There is a video accompaniment to this paper available at https: //www.youtube.com/watch?v=rKFq7xwgdX0.
http://mural.maynoothuniversity.ie/10012/
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Detection of semantic risk situations in lifelog data for improving life of frail people
(2020)
Yebda, Thinhinane; Benois-Pineau, Jenny; Pech, Marion; Amièva, Hélène; Gurrin, Cathal
Detection of semantic risk situations in lifelog data for improving life of frail people
(2020)
Yebda, Thinhinane; Benois-Pineau, Jenny; Pech, Marion; Amièva, Hélène; Gurrin, Cathal
Abstract:
The automatic recognition of risk situations for frail people is an urgent research topic for the interdisciplinary artificial intelligence and multimedia community. Risky situations can be recognized from lifelog data recorded with wearable devices. In this paper, we present a new approach for the detection of semantic risk situations for frail people in lifelog data. Concept matching between general lifelog and risk taxonomies was realized and tuned AlexNet was deployed for detection of two semantic risks situations such as risk of domestic accident and risk of fraud with promising results.
http://doras.dcu.ie/24629/
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Direct and Indirect Classification of High Frequency LNA Gain Performance - A Comparison Between SVMs and MLPs
(2009)
Hung, Peter C.; McLoone, Sean F.; Farrell, Ronan
Direct and Indirect Classification of High Frequency LNA Gain Performance - A Comparison Between SVMs and MLPs
(2009)
Hung, Peter C.; McLoone, Sean F.; Farrell, Ronan
Abstract:
The task of determining low noise amplifier (LNA) high-frequency performance in functional testing is as challenging as designing the circuit itself due to the difficulties associated with bringing high frequency signals offchip. One possible strategy for circumventing these difficulties is to inferentially estimate the high frequency performance measures from measurements taken at lower, more accessible, frequencies. This paper investigates the effectiveness of this strategy for classifying the high frequency gain of the amplifier, a key LNA performance parameter. An indirect Multilayer Perceptron (MLP) and direct support vector machine (SVM) classification strategy are considered. Extensive Monte-Carlo simulations show promising results with both methods, with the indirect MLP classifiers marginally outperforming SVMs.
http://mural.maynoothuniversity.ie/2721/
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Evaluation of local orientation for texture classification
(2008)
Ilea, Dana E.; Ghita, Ovidiu; Whelan, Paul F.
Evaluation of local orientation for texture classification
(2008)
Ilea, Dana E.; Ghita, Ovidiu; Whelan, Paul F.
Abstract:
The aim of this paper is to present a study where we evaluate the optimal inclusion of the texture orientation in the classification process. In this paper the orientation for each pixel in the image is extracted using the partial derivatives of the Gaussian function and the main focus of our work is centred on the evaluation of the local dominant orientation (which is calculated by combining the magnitude and local orientation) on the classification results. While the dominant orientation of the texture depends strongly on the observation scale, in this paper we propose to evaluate the macro-texture by calculating the distribution of the dominant orientations for all pixels in the image that sample the texture at micro-level. The experimental results were conducted on standard texture databases and the results indicate that the dominant orientation calculated at micro-level is an appropriate measure for texture description.
http://doras.dcu.ie/14817/
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Experiments in colour texture analysis
(2001)
Drimbarean, Alexandru; Whelan, Paul F.
Experiments in colour texture analysis
(2001)
Drimbarean, Alexandru; Whelan, Paul F.
http://doras.dcu.ie/18819/
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Investigating the relationship between classification quality and SMT performance in discriminative reordering models
(2017)
Kazemi, Arefeh; Toral, Antonio; Way, Andy; Monadjemi, Amirhassan
Investigating the relationship between classification quality and SMT performance in discriminative reordering models
(2017)
Kazemi, Arefeh; Toral, Antonio; Way, Andy; Monadjemi, Amirhassan
Abstract:
Reordering is one of the most important factors affecting the quality of the output in statistical machine translation (SMT). A considerable number of approaches that proposed addressing the reordering problem are discriminative reordering models (DRM). The core component of the DRMs is a classifier which tries to predict the correct word order of the sentence. Unfortunately, the relationship between classification quality and ultimate SMT performance has not been investigated to date. Understanding this relationship will allow researchers to select the classifier that results in the best possible MT quality. It might be assumed that there is a monotonic relationship between classification quality and SMT performance, i.e., any improvement in classification performance will be monotonically reflected in overall SMT quality. In this paper, we experimentally show that this assumption does not always hold, i.e., an improvement in classification performance might actually degrade the qu...
http://doras.dcu.ie/23311/
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Modelling, classification and synthesis of facial expressions
(2008)
Reilly, Jane; Ghent, John; McDonald, John
Modelling, classification and synthesis of facial expressions
(2008)
Reilly, Jane; Ghent, John; McDonald, John
Abstract:
The field of computer vision endeavours to develop automatic approaches to the interpretation of images from the real world. Over the past number of decades researchers within this field have created systems specifically for the automatic analysis of facial expression. The most successful of these approaches draw on the tools from behavioural science. In this chapter we examine facial expression analysis from both a behavioural science and a computer vision perspective. First we will provide details of the principal approach used in behavioural science to analyze facial expressions. This will include an overview of the evolution of facial expression analysis, where we introduce the field of facial expression analysis with Darwin’s initial findings (Darwin, 1872). We then go on to show how his findings were confirmed nearly 100 years later by Ekman et al. (Ekman et al., 1969). Following on from this we provide details of recent works investigating the appearance and dynamics of facia...
http://mural.maynoothuniversity.ie/8256/
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Online Trajectory Classification
(2003)
Sas, Corina; O'Hare, Gregory; Reilly, Ronan
Online Trajectory Classification
(2003)
Sas, Corina; O'Hare, Gregory; Reilly, Ronan
Abstract:
This study proposes a modular system for clustering on-line motion trajectories obtained while users navigate within a virtual environment. It presents a neural network simulation that gives a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organizing map algorithm was tested and improved to above 85% by using learning vector quantization. The benefits of this approach and the possibility of extending the methodology to the study of navigation in Human Computer Interaction are discussed.
http://mural.maynoothuniversity.ie/8213/
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Overview of VideoCLEF 2009: New perspectives on speech-based multimedia content enrichment
(2009)
Larson, Martha; Newman, Eamonn; Jones, Gareth J.F.
Overview of VideoCLEF 2009: New perspectives on speech-based multimedia content enrichment
(2009)
Larson, Martha; Newman, Eamonn; Jones, Gareth J.F.
Abstract:
VideoCLEF 2009 offered three tasks related to enriching video content for improved multimedia access in a multilingual environment. For each task, video data (Dutch-language television, predominantly documentaries) accompanied by speech recognition transcripts were provided. The Subject Classification Task involved automatic tagging of videos with subject theme labels. The best performance was achieved by approaching subject tagging as an information retrieval task and using both speech recognition transcripts and archival metadata. Alternatively, classifiers were trained using either the training data provided or data collected from Wikipedia or via general Web search. The Affect Task involved detecting narrative peaks, defined as points where viewers perceive heightened dramatic tension. The task was carried out on the “Beeldenstorm” collection containing 45 short-form documentaries on the visual arts. The best runs exploited affective vocabulary and audience directed speech. Othe...
http://doras.dcu.ie/16183/
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Predicting organic acid concentration from UV/vis spectrometry measurements – A comparison of machine learning techniques
(2011)
Wolf, Christian; Gaida, Daniel; Stuhlsatz, Andre; Ludwig, Thomas; McLoone, Sean F.; Bon...
Predicting organic acid concentration from UV/vis spectrometry measurements – A comparison of machine learning techniques
(2011)
Wolf, Christian; Gaida, Daniel; Stuhlsatz, Andre; Ludwig, Thomas; McLoone, Sean F.; Bongards, Michael
Abstract:
The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and...
http://mural.maynoothuniversity.ie/3868/
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Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks
(2016)
Antony, Joseph; McGuinness, Kevin; O'Connor, Noel E.; Moran, Kieran
Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks
(2016)
Antony, Joseph; McGuinness, Kevin; O'Connor, Noel E.; Moran, Kieran
Abstract:
This paper proposes a new approach to automatically quantify the severity of knee osteoarthritis (OA) from radiographs using deep convolutional neural networks (CNN). Clinically, knee OA severity is assessed using Kellgren & Lawrence (KL) grades, a five point scale. Previous work on automatically predicting KL grades from radiograph images were based on training shallow classifiers using a variety of hand engineered features. We demonstrate that classification accuracy can be significantly improved using deep convolutional neural network models pre-trained on ImageNet and fine-tuned on knee OA images. Furthermore, we argue that it is more appropriate to assess the accuracy of automatic knee OA severity predictions using a continuous distance-based evaluation metric like mean squared error than it is to use classification accuracy. This leads to the formulation of the prediction of KL grades as a regression problem and further improves accuracy. Results on a dataset of X-ray imag...
http://doras.dcu.ie/21355/
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Ridgelet-based signature for natural image classification
(2005)
Le Borgne, Hervé; O'Connor, Noel E.
Ridgelet-based signature for natural image classification
(2005)
Le Borgne, Hervé; O'Connor, Noel E.
Abstract:
This paper presents an approach to grouping natural scenes into (semantically) meaningful categories. The proposed approach exploits the statistics of natural scenes to define relevant image categories. A ridgelet-based signature is used to represent images. This signature is used by a support vector classifier that is well designed to support high dimensional features, resulting in an effective recognition system. As an illustration of the potential of the approach several experiments of binary classifications (e.g. city/landscape or indoor/outdoor) are conducted on databases of natural scenes.
http://doras.dcu.ie/317/
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Speech-music discrimination from MPEG-1 bitstream
(2001)
Jarina, Roman; Murphy, Noel; O'Connor, Noel E.; Marlow, Seán
Speech-music discrimination from MPEG-1 bitstream
(2001)
Jarina, Roman; Murphy, Noel; O'Connor, Noel E.; Marlow, Seán
Abstract:
This paper describes a proposed algorithm for speech/music discrimination, which works on data directly taken from MPEG encoded bitstream thus avoiding the computationally difficult decoding-encoding process. The method is based on thresholding of features derived from the modulation envelope of the frequency-limited audio signal. The discriminator is tested on more than 2 hours of audio data, which contain clean and noisy speech from several speakers and a variety of music content. The discriminator is able to work in real time and despite its simplicity, results are very promising.
http://doras.dcu.ie/332/
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Steady State RF Fingerprinting for Identity Verification: One Class Classifier Versus Customized Ensemble
(2010)
Kroon, Bernard; Bergin, Susan; Kennedy, Irwin O.; O'Mahony Zamora, Georgina
Steady State RF Fingerprinting for Identity Verification: One Class Classifier Versus Customized Ensemble
(2010)
Kroon, Bernard; Bergin, Susan; Kennedy, Irwin O.; O'Mahony Zamora, Georgina
Abstract:
Mobile phone proliferation and increasing broadband penetration presents the possibility of placing small cellular base stations within homes to act as local access points. This can potentially lead to a very large increase in authentication requests hitting the centralized authentication infrastructure unless access is mediated at a lower protocol level. A study was carried out to examine the effectiveness of using Support Vector Machines to accurately identify if a mobile phone should be allowed access to a local cellular base station using differences imbued upon the signal as it passes through the analogue stages of its radio transmitter. Whilst allowing prohibited transmitters to gain access at the local level is undesirable and costly, denying service to a permitted transmitter is simply unacceptable. Two different learning approaches were employed, the first using One Class Classifiers (OCCs) and the second using customized ensemble classifiers. OCCs were found to perform poo...
http://mural.maynoothuniversity.ie/8684/
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