Eleni Aloupogianni 교수

To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process.

Abstract With the increase of IoT devices generating large amounts of user-sensitive data, improper firmware harms users’ security and privacy. Latest home appliances are integrated with features to assure compatibility with smart home IoT. However, applying complex security mechanisms to IoT is limited by device hardware capabilities, making them vulnerable to attacks. Such attacks have recently become frequent. To address this issue, we developed a secure verification mechanism for firmware re

SignificanceMalignant skin tumors, which include melanoma and nonmelanoma skin cancers, are the most prevalent type of malignant tumor. Gross pathology of pigmented skin lesions (PSL) remains manual, time-consuming, and heavily dependent on the expertise of the medical personnel. Hyperspectral imaging (HSI) can assist in the detection of tumors and evaluate the status of tumor margins by their spectral signatures.AimTumor segmentation of medical HSI data is a research field. The goal of this stu

Pigmented skin lesions (PSL) are prevalent in Asian populations and their gross pathology remains a manual, tedious task. Hyper-spectral imaging (HSI) is a non-invasive non-ionizing acquisition technique, allowing malignant tissue to be identified by its spectral signature. We set up a hyper-spectral imaging (HSI) system targeting cancer margin detection of PSL. Because classification among PSL is achieved via comparison of spectral signatures, appropriate calibration is necessary to ensure suff

Urban centers worldwide grapple with the intricate challenge of traffic congestion, necessitating sophisticated solutions grounded in real-time data analytics. This paper presents a cutting-edge Digital Twin (DT) framework tailored for urban traffic management, with a focus on the context of Singapore's technologically advanced landscape. By seamlessly integrating live weather data and on-road camera information, the proposed framework offers insights into traffic dynamics, enabling adaptive dec

The focus of this study is to examine how Artificial Intelligence (AI) influences the deployment of resources in the context of Beyond 5G (B5G) communications for modern applications. Employing a range of machine learning techniques, including neural networks and graph-based approaches, the investigation utilizes a small cell open dataset from the United States. The results highlight the exceptional performance of neural network models in streamlining small cell deployment, a pivotal aspect for

Investigation of more complex reduction and segmentation schemes with emphasis on the nature of HSI and optical properties of the skin is necessary. Insights on dimension reduction for skin tissue could facilitate the development of HSI-based systems for cancer margin detection at gross level.

This paper explores the application of machine learning for practical applications in the context of Beyond 5G (B5G) communications. A variety of machine learning techniques, including neural networks, was applied on a labeled dataset about network slicing. Neural network models demonstrate superior performance in optimizing virtual network slices, crucial for enhancing Internet of Things (IoT) connectivity and efficiency. The findings can assist telecommunications professionals and policymakers

This work presents a concept for an innovative Digital Twin (DT) framework for urban traffic monitoring and management, tailored for the city of Singapore. The proposed architecture leverages real-time traffic and weather data integration, AI processing, and modular design to offer adaptive and versatile traffic insights. By incorporating live information from various sources and integrating real-time weather data, the framework enables proactive traffic management and enhances safety during adv

Evaluation of tissue margins and hemodynamics is necessary during macropathology of skin lesions. This study aims to produce saliency maps of skin chromophores from ex-vivo specimens and observe the effect of formalin fixation on the maps. We used a multi-spectral imaging system with narrow-band illumination to capture various skin lesions. Saliency maps were produced with three different methods adapted from the literature by utilizing spectral absorption and absorption slope. Saliency maps der

Accurate prediction of road surface conditions can help authorities manage vehicular transportation effectively in large cities by helping to reduce congestion and the risk of accidents due to adverse weather. Image-based classification, using Convolutional Neural Networks (CNN) in combination with Transfer Learning (TL), can provide a real-time, data-driven and cost-effective solution for classifying road surfaces under varying weather, traffic and image recording conditions. This paper propose