662 research outputs found
Mobile Device Background Sensors: Authentication vs Privacy
The increasing number of mobile devices in recent years has caused the collection of a large amount of personal information that needs to be protected. To this aim, behavioural biometrics has become very popular. But, what is the discriminative power of mobile behavioural biometrics in real scenarios? With the success of Deep Learning (DL), architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM), have shown improvements compared to traditional machine learning methods. However, these DL architectures still have limitations that need to be addressed. In response, new DL architectures like Transformers have emerged. The question is, can these new Transformers outperform previous biometric approaches? To answers to these questions, this thesis focuses on behavioural biometric authentication with data acquired from mobile background sensors (i.e., accelerometers and gyroscopes). In addition, to the best of our knowledge, this is the first thesis that explores and proposes novel behavioural biometric systems based on Transformers, achieving state-of-the-art results in gait, swipe, and keystroke biometrics. The adoption of biometrics requires a balance between security and privacy. Biometric modalities provide a unique and inherently personal approach for authentication. Nevertheless, biometrics also give rise to concerns regarding the invasion of personal privacy. According to the General Data Protection Regulation (GDPR) introduced by the European Union, personal data such as biometric data are sensitive and must be used and protected properly. This thesis analyses the impact of sensitive data in the performance of biometric systems and proposes a novel unsupervised privacy-preserving approach. The research conducted in this thesis makes significant contributions, including: i) a comprehensive review of the privacy vulnerabilities of mobile device sensors, covering metrics for quantifying privacy in relation to sensitive data, along with protection methods for safeguarding sensitive information; ii) an analysis of authentication systems for behavioural biometrics on mobile devices (i.e., gait, swipe, and keystroke), being the first thesis that explores the potential of Transformers for behavioural biometrics, introducing novel architectures that outperform the state of the art; and iii) a novel privacy-preserving approach for mobile biometric gait verification using unsupervised learning techniques, ensuring the protection of sensitive data during the verification process
Broadband Coherent Anti-Stokes Raman Spectroscopy: A Comprehensive Approach to Analyzing Crystalline Materials
Broadband Coherent Anti-Stokes Raman scattering (B-CARS) is an advanced Raman spectroscopy technique used to investigate the vibrational properties of materials. B-CARS combines the spectral sensitivity of spontaneous Raman scattering with the enhanced signal intensity of coherent Raman techniques. While B-CARS has been successfully applied in biomedicine for ultra-fast imaging of biological tissue, its potential in solid-state physics remains largely unexplored. This work delves into the challenges and adaptations necessary to apply B-CARS to crystalline materials and shows its potential as a powerful tool for high-speed, hyperspectral investigations.
The theoretical part of this work covers inelastic light-matter scattering fundamentals and the signal generation process of B-CARS, with special attention given to the so-called Non-Resonant Background (NRB). This sample-unspecific signal amplifies the B-CARS intensity but also distorts the shape and position of the measured spectral peaks.
A reliable NRB correction becomes crucial to retrieve precise spectral parameters containing information on the investigated material's crystallographic structure, defect density, and stress distribution.
The first results chapter presents a practical guideline for an optimized workflow of sample preparation, measurement procedure, and data analysis. The influences of sample surfaces, focus positioning, and polarization sensitivity are discussed. The successful NRB removal is achieved by adapting an algorithm initially designed for biomedical purposes.
The second chapter involves a transnational Round Robin investigating the same set of materials using different experimental setups. The influences of laser source, detection range, and transmission vs. epi detection are explored to optimize the experimental parameters.
This work showcases applications such as high-speed, hyperspectral imaging of ferroelectric domain walls in LiNbO3, demonstrating the potential of B-CARS in the cutting-edge field of domain wall engineering.
Additionally, imaging and polarization-sensitive measurements are shown for MoO3 flakes, paving the way for B-CARS investigations of 2D materials.
The final chapter presents advanced techniques, such as Three-Color CARS and Time-Delay CARS, applied to crystalline materials. Three-Color CARS is especially promising, as it enhances the signal intensity for low-frequency Raman modes, which are particularly interesting for solid-state physics compared to the usual large-shift modes investigated in biomedical research. Meanwhile, Time-Delay CARS is sensitive to relaxation processes of vibrational and NRB states, enabling experimental NRB removal and lifetime measurements. Additionally, a neural network-based NRB removal method is presented, eliminating the need for a prior NRB spectrum and offering rapid computation.
In summary, this work demonstrates the successful implementation of B-CARS for crystalline materials and provides a comprehensive guideline for the optimal experimental setup, workflow, and data processing. The application of B-CARS for imaging bulk crystalline materials, ferroelectric domain walls, and 2D structures shows promising possibilities for future research
Raman Spectroscopy Techniques for the Detection and Management of Breast Cancer
Breast cancer has recently become the most common cancer worldwide, and with increased incidence, there is increased pressure on health services to diagnose and treat many more patients. Mortality and survival rates for this particular disease are better than other cancer types, and part of this is due to the facilitation of early diagnosis provided by screening programmes, including the National Health Service breast screening programme in the UK. Despite the benefits of the programme, some patients undergo negative experiences in the form of false negative mammograms, overdiagnosis and subsequent overtreatment, and even a small number of cancers are induced by the use of ionising radiation. In addition to this, false positive mammograms cause a large number of unnecessary biopsies, which means significant costs, both financially and in terms of clinicians' time, and discourages patients from attending further screening. Improvement in areas of the treatment pathway is also needed. Surgery is usually the first line of treatment for early breast cancer, with breast conserving surgery being the preferred option compared to mastectomy. This type of operation achieves the same outcome as mastectomy - removal of the tumour - while allowing the patient to retain the majority of their normal breast tissue for improved aesthetic and psychological results. Yet, re-excision operations are often required when clear margins are not achieved, i.e. not all of the tumour is removed. This again has implications on cost and time, and increases the risk to the patient through additional surgery.
Currently lacking in both the screening and surgical contexts is the ability to discern specific chemicals present in the breast tissue being assessed/removed. Specifically relevant to mammography is the presence of calcifications, the chemistry of which holds information indicative of pathology that cannot be accessed through x-rays. In addition, the chemical composition of breast tumour tissue has been shown to be different to normal tissue in a variety of ways, with one particular difference being a significant increase in water content. Raman spectroscopy is a rapid, non-ionising, non-destructive technique based on light scattering. It has been proven to discern between chemical types of calcification and subtleties within their spectra that indicate the malignancy status of the surrounding tissue, and differentiate between cancerous and normal breast tissue based on the relative water contents.
Furthermore, this thesis presents work aimed at exploring deep Raman techniques to probe breast calcifications at depth within tissue, and using a high wavenumber Raman probe to discriminate tumour from normal tissue predominantly via changes in tissue water content. The ability of transmission Raman spectroscopy to detect different masses and distributions of calcified powder inclusions within tissue phantoms was tested, as well as elucidating a signal profile of a similar inclusion through a tissue phantom of clinically relevant thickness. The technique was then applied to the measurement of clinically active samples of bulk breast tissue from informed and consented patients to try to measure calcifications. Ex vivo specimens were also measured with a high wavenumber Raman probe, which found significant differences between tumour and normal tissue, largely due to water content, resulting in a classification model that achieved 77.1% sensitivity and 90.8% specificity. While calcifications were harder to detect in the ex vivo specimens, promising results were still achieved, potentially indicating a much more widespread influence of calcification in breast tissue, and to obtain useful signal from bulk human tissue is encouraging in itself. Consequently, this work demonstrates the potential value of both deep Raman techniques and high wavenumber Raman for future breast screening and tumour margin assessment methods
Analysis and monitoring of single HaCaT cells using volumetric Raman mapping and machine learning
No explorer reached a pole without a map, no chef served a meal without tasting, and no surgeon implants untested devices. Higher accuracy maps, more sensitive taste buds, and more rigorous tests increase confidence in positive outcomes. Biomedical manufacturing necessitates rigour, whether developing drugs or creating bioengineered tissues [1]–[4]. By designing a dynamic environment that supports mammalian cells during experiments within a Raman spectroscope, this project provides a platform that more closely replicates in vivo conditions. The platform also adds the opportunity to automate the adaptation of the cell culture environment, alongside spectral monitoring of cells with machine learning and three-dimensional Raman mapping, called volumetric Raman mapping (VRM). Previous research highlighted key areas for refinement, like a structured approach for shading Raman maps [5], [6], and the collection of VRM [7]. Refining VRM shading and collection was the initial focus, k-means directed shading for vibrational spectroscopy map shading was developed in Chapter 3 and exploration of depth distortion and VRM calibration (Chapter 4). “Cage” scaffolds, designed using the findings from Chapter 4 were then utilised to influence cell behaviour by varying the number of cage beams to change the scaffold porosity. Altering the porosity facilitated spectroscopy investigation into previously observed changes in cell biology alteration in response to porous scaffolds [8]. VRM visualised changed single human keratinocyte (HaCaT) cell morphology, providing a complementary technique for machine learning classification. Increased technical rigour justified progression onto in-situ flow chamber for Raman spectroscopy development in Chapter 6, using a Psoriasis (dithranol-HaCaT) model on unfixed cells. K-means-directed shading and principal component analysis (PCA) revealed HaCaT cell adaptations aligning with previous publications [5] and earlier thesis sections. The k-means-directed Raman maps and PCA score plots verified the drug-supplying capacity of the flow chamber, justifying future investigation into VRM and machine learning for monitoring single cells within the flow chamber
Advances in computational and translational approaches for malignant glioma
Gliomas are the most common primary brain tumors in adults and carry a dismal prognosis for patients. Current standard-of-care for gliomas is comprised of maximal safe surgical resection following by a combination of chemotherapy and radiation therapy depending on the grade and type of tumor. Despite decades of research efforts directed towards identifying effective therapies, curative treatments have been largely elusive in the majority of cases. The development and refinement of novel methodologies over recent years that integrate computational techniques with translational paradigms have begun to shed light on features of glioma, previously difficult to study. These methodologies have enabled a number of point-of-care approaches that can provide real-time, patient-specific and tumor-specific diagnostics that may guide the selection and development of therapies including decision-making surrounding surgical resection. Novel methodologies have also demonstrated utility in characterizing glioma-brain network dynamics and in turn early investigations into glioma plasticity and influence on surgical planning at a systems level. Similarly, application of such techniques in the laboratory setting have enhanced the ability to accurately model glioma disease processes and interrogate mechanisms of resistance to therapy. In this review, we highlight representative trends in the integration of computational methodologies including artificial intelligence and modeling with translational approaches in the study and treatment of malignant gliomas both at the point-of-care and outside the operative theater in silico as well as in the laboratory setting
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging
No abstract available
ML-based Approaches for Wireless NLOS Localization: Input Representations and Uncertainty Estimation
The challenging problem of non-line-of-sight (NLOS) localization is critical
for many wireless networking applications. The lack of available datasets has
made NLOS localization difficult to tackle with ML-driven methods, but recent
developments in synthetic dataset generation have provided new opportunities
for research. This paper explores three different input representations: (i)
single wireless radio path features, (ii) wireless radio link features
(multi-path), and (iii) image-based representations. Inspired by the two latter
new representations, we design two convolutional neural networks (CNNs) and we
demonstrate that, although not significantly improving the NLOS localization
performance, they are able to support richer prediction outputs, thus allowing
deeper analysis of the predictions. In particular, the richer outputs enable
reliable identification of non-trustworthy predictions and support the
prediction of the top-K candidate locations for a given instance. We also
measure how the availability of various features (such as angles of signal
departure and arrival) affects the model's performance, providing insights
about the types of data that should be collected for enhanced NLOS
localization. Our insights motivate future work on building more efficient
neural architectures and input representations for improved NLOS localization
performance, along with additional useful application features.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible. Work partly supported by the RA Science Committee grant
No. 22rl-052 (DISTAL) and the EU under Italian National Recovery and
Resilience Plan of NextGenerationEU on "Telecommunications of the Future"
(PE00000001 - program "RESTART"
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
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