December 2, 2016
FTN1663 Opportunistic Piggyback Marking for IP Traceback - IEEE NS2 Project 2016 - 2017

FTN1663 Opportunistic Piggyback Marking for IP Traceback – IEEE NS2 Project 2016 – 2017

FTN1663 Opportunistic Piggyback Marking for IP Traceback –  IEEE NS2 Project 2016 – 2017 ABSTRACT: IP traceback is a solution for attributing cyber attacks, and it […]
December 2, 2016
FTM1624-Recognizing-Focal-Liver-Lesions-in-CEUS-With-Dynamically-Trained-Latent-Structured-Models-IEEE-MATLAB-Project-2016-2017

FTM1624 Recognizing Focal Liver Lesions in CEUS With Dynamically Trained Latent Structured Models -IEEE MATLAB Project 2016 – 2017

FTM1624 Recognizing Focal Liver Lesions in CEUS With Dynamically Trained Latent Structured Models -IEEE MATLAB Project 2016 – 2017  ABSTRACT: Work investigates how to automatically classify […]
December 1, 2016
FTN1661 PROVEST Provenance-based Trust Model for Delay Tolerant Networks - IEEE NS2 Project 2016 - 2017

FTN1661 PROVEST Provenance-based Trust Model for Delay Tolerant Networks – IEEE NS2 Project 2016 – 2017

FTN1661 PROVEST Provenance-based Trust Model for Delay Tolerant Networks –  IEEE NS2 Project 2016 – 2017 ABSTRACT: Delay tolerant networks (DTNs) are often encountered in military […]
December 1, 2016
FTM1623-Real-Time-Automatic-Artery-Segmentation-Reconstruction-and-Registration-for-Ultrasound-Guided-Regional-Anaesthesia-of-the-Femoral-Nerve-IEEE-MATLAB-Project-2016-2017

FTM1623 Real-Time Automatic Artery Segmentation, Reconstruction and Registration for Ultrasound-Guided Regional Anaesthesia of the Femoral Nerve – IEEE MATLAB Project 2016 – 2017

FTM1623 Real-Time Automatic Artery Segmentation, Reconstruction and Registration for Ultrasound-Guided Regional Anaesthesia of the Femoral Nerve – IEEE MATLAB Project 2016 – 2017 ABSTRACT: The goal is […]
December 1, 2016
FTM1622-On-Combining-Multiple-Instance-Learning-and-Active-Learning-for-Computer-Aided-Detection-of-Tuberculosis-IEEE-MATLAB-Project-2016-2017

FTM1622 On Combining Multiple-Instance Learning and Active Learning for Computer-Aided Detection of Tuberculosis – IEEE MATLAB Project 2016 – 2017

FTM1622 On Combining Multiple-Instance Learning and Active Learning for Computer-Aided Detection of Tuberculosis – IEEE MATLAB Project 2016 – 2017 ABSTRACT: The major advantage of multiple-instance learning […]