We will discuss two complementary mechanical models for DNA that deal with, respectively two problems: one, phase transitions in a DNA molecule, and two, allosteric interactions between two ligands bound […]
Cellulose nanofibrils (CNFs) are a nanomaterial derived from plants that have high specific stiffness and strength, can be made into optically transparent materials, and are biodegradable. These properties make CNFs […]
The importance of meaningful tactile experiences has become increasingly apparent in areas such as teleoperation, education, and gaming. As the applications of rendering haptic stimuli are growing, so are the […]
Committee Members: Sue Ann Bidstrup Allen, Advisor; Raymond Gorte, John Vohs and Mark Allen.
Committee: Sue Ann Bidstrup Allen, Advisor; Raymond Gorte, John Vohs and Mark Allen
This era of the internet of things is poised to experience explosive growth in the number of interconnected smart electronic devices. Machine learning algorithms, such as neural networks, are expected […]
Autonomous flight through unknown environments in the presence of obstacles is a challenging problem for micro aerial vehicles (MAVs). A majority of the current state-of-art research focuses on modeling obstacles […]
Committee: Robert Riggleman, PhD (co-advisor); Russell Composto, PhD (co-advisor); Ravi Radhakrishan, PhD; and Jeff Meth, PhD
Low temperature synthesis of high quality 2D materials directly on flexible substrates remains a fundamental limitation towards realization of robust, strainable electronics possessing the unique physical properties of atomically thin […]
A reception on inclusion will be held for new Penn Engineering doctoral students to connect them to their peers and supporting structures within the school. Please join the Dean, Deputy Dean, […]
Abstract: We aim to establish and experimentally test mathematical models of embryogenesis. While the foundation of this research is based on models of isolated developmental events, the ultimate challenge is […]
Compressive Sensing is an acquisition technique which relies on the sparsity of the underlying signals, to enable sampling below the classical Nyquist rate. To do so, the signals must be […]