Computer Vision based Indoor Navigation Utilizing Information from Planar Surfaces

Traditional wireless signalling based outdoor navigation techniques generally result in unsatisfactory performance for indoor environments due to low signal strength and multipath distortions. Computer vision (CV) sensors, due to their low cost and high performance, have gained enormous interest in indoor navigation over the past years. CV based 6DOF trajectory estimation is understood to be a computationally intensive ill-posed problem. Drastic simplification and enhanced robustness are possible in scenarios where camera observed features are constrained to a plane, such as a floor surface. Furthermore, if the features have geometric patterns, such as a regularly tiled surface, significantly more powerful constraints can be implemented. Exploration of such constraints is the aim of this thesis. Experimental results show that centimeter level accuracy in trajectory estimation can be achieved for arbitrary camera motion spanning several meters. As shown in this thesis, this accuracy is a result of constraints due to planar features observed

Real-Time Continuous Action Detection And Recognition Using Depth Images And Inertial Signals

This paper presents an approach to detect and recognize actions of interest in real-time from a continuous stream of data that are captured simultaneously from a Kinect depth camera and a wearable inertial sensor. Actions of interest are considered to appear continuously and in a random order among actions of non-interest. Skeleton depth images are first used to separate actions of interest from actions of non-interest based on pause and motion segments. Inertial signals from a wearable inertial sensor are then used to improve the recognition outcome. A dataset consisting of simultaneous depth and inertial data for the smart TV actions of interest occurring continuously and in a random order among actions of non-interest is studied and made publicly available. The results obtained indicate the effectiveness of the developed approach in coping with actions that are performed realistically in a continuous manner

Multifunctional and multitargeted nanoparticles for drug delivery to overcome barriers of drug resistance in human cancers

The recurrence and metastatic spread of cancer are major drawbacks in cancer treatment. Although chemotherapy is one of the most effective methods for the treatment of metastatic cancers, it is nonspecific and causes significant toxic damage. The development of drug resistance to chemotherapeutic agents through various mechanisms also limits their therapeutic potential. However, as we discuss here, the use of nanodelivery systems that are a combination of diagnostics and therapeutics (theranostics) is as relatively novel concept in the treatment of cancer. Such systems are likely to improve the therapeutic benefits of encapsulated drugs and can transit to the desired site, maintaining their pharmaceutical properties. The specific targeting of malignant cells using multifunctional nanoparticles exploits theranostics as an improved agent for delivering anticancer drugs and as a new solution for overriding drug resistance