Current Research Projects
NSF GCR project
This grant aims to foster convergent approaches from mechanical and biomedical engineers, nonscientist, and neuroscientists to overcome intrinsic trial-and-error approaches for non-invasive brain therapeutics. The key is to understand how magnetic nanorobots communicate and interact inside the neuron networks, so that we can efficiently program them. The obtained understanding is important to translate non-invasive brain therapeutics into practice. Additionally, the proposed research activities will promote interdisciplinary integration crossing nanoscience, mechanical engineering, biomedical engineering, neuroscience and experimental therapeutics. This project will provide rigorous training opportunities for high school, undergraduate, and graduate students pursuing careers in STEM fields, in particular, under-represented students including women and first-generation college students. The proposed research will also provide practicum experiences for students pursuing careers as direct support professionals working with people with disabilities.
The knowledge generated by the convergent effort from the multidisciplinary team will: (i) form novel frameworks to catalyze scientific discovery and innovation in brain tissue regeneration and repair, and will provide a powerful, scalable and controllable technology of self-driving nanorobots transporting and functioning inside the brain environment. It will also, (ii) enhance our fundamental understanding of how long-term changes in the activity of specific neural circuits in the degenerative neurons. The explainable artificial intelligence framework will serve as additional quantitative assays to complement a biologically plausible continuously remodeling analytical microvascular networks model.
The total amount is $2.85M Texas A&M University (TAMU) and $0.75M Stony Brook University (SBU) for 5 years (2020-2025)
TAMU PI: Ya Wang Mechanical Engineering, Electrical and Computer Engineering, and Biomedical Engineering
Ying Li, Mechanical Engineering
Rahul Srinivasan, Neuroscience and Experimental Therapeutics
Shoshana Eitan, Phycology and Brain Science
SBU PI: Yi-xian Qin, Biomedical Engineering
ARPA-E SENSOR project
The goal of this project is to develop an advanced, low-cost occupancy sensor for residential homes, named SLEEPIR – Synchronized Low-Energy Electronically-chopped PIR, which builds upon commercially available pyroelectric infrared (PIR) sensor technology to detect human presence.
This innovation relies on the use of an “optical chopper” which periodically interrupts the incident infrared (IR) radiation to the sensor and allows the device to detect both stationary and moving individuals. The electronic chopper operates in the 8-12 micrometer long wave IR region, where human skin radiates the most.
The team will evaluate several approaches for the chopper, such as new low-power liquid crystal technology with no moving parts. The team will apply new signal processing techniques and machine learning to the infrared data, enabling differentiation between pets and people, and potentially sleep vs. active states.
The Texas A&M team received this competitive award from ARPA-E’s Saving Energy Nationwide in Structures with Occupancy Recognition (SENSOR) program, which supports innovative and highly accurate presence sensors and occupant counters that optimize heating, cooling, and ventilation (HVAC) of buildings while reducing cost and slashing energy use. SENSOR project teams can take advantage of existing low cost wireless and electronic communication technologies and could reduce HVAC energy usage by 30% while simultaneously addressing user requirements for cost, privacy, and usability.
Our project is one of 15 ARPA-E projects that will develop a new class of sensor systems to enable significant energy savings via reduced demand for heating and cooling in residential and commercial buildings.
Further details on the SENSOR program can be found HERE, and details on the 15 projects can be found HERE.
ARPA-E DELTA project
ARGUS (Activity Recognizer Guided by a Unobtrusive Sensor)
Zhangjie Chen, Hanwei Liu
In this project, a Activity Recognizer Guided by a Unobtrusive Sensor (ARGUS) is developed for activity tracking. By using a Support vector machine (SVM) method as the classifier, and a pre-trained convolutional neural network (CNN) model for feature extraction, a reliable and steady activity recognition is reached up to 1.8m. This proposed solution leads to many advantages, no privacy invasion, low-cost, and a potentially large market for human computer interaction in smart home appliances control and computer games.
ARGUS tracking demo is HERE. ARGUS+RoCo tracking video is HERE.
Ro-PIR (Rotationally-Chopped Pyroelectric)
Libo Wu, Haili Liu
Pyroelectric infrared sensors (PIR) are popularly used for occupancy detection, indoor light control, security detection. But PIR sensors are motion detectors by nature, and only respond to heat fluctuation. In this project, a rotationally-chopped PIR (Ro-PIR) sensor is developed for motion-independent occupancy detection. Ro-PIR also explores new functionalities of localization, tracking, and facing direction detection beyond presence detection by introducing a semi-transparent chopper and a smart detection algorithm. More specifically, by analyzing polarity-phase, and peak-peak voltage signal shaped by the chopper, Ro-PIR is capable of zone-level localization of stationary occupants. By further analyzing the duty cycle of the output signal, occupancy facing direction can also be predicted.
See details about Ro-PIR detector HERE.
Magnetic-plasmonic nano particles for advanced theranostics
Muzhaozi Yuan, Grace Hu
This project is focused on the synthesis of surface functionalized magnetic-plasmonic nanoparticles and the application in biomedical imaging, drug delivery, biosensing and phototherapy. We have synthesized uniform magnetic core-gold shell nanoparticles with different shell thickness (10nm-20nm) and tunable plasmonic properties. These nanoparticles are designed for further conjugation with biomolecules such as DNA, RNA, peptide, protein and antibodies.
Multifunctional iron oxide core-porous silica shell nanoparticles
Phil Smith, Brooke Ferber, Grace Hu
This project is to develop a core-shell, iron oxide core mesoporous silica shell, nanoparticle for brain cancer treatment. The mesoporous silica shell with controllable thickness is able to house and unload therapeutic drug molecules. The iron oxide core allows for targeted delivery with the use of an external magnetic field. The proposed nanoparticles also act as better contrast agents for MRI compared to conventional agents.
Lead-free piezoelectric BaTiO3 nanowires for sensing and energy harvesting
Wei Deng, Mieng Wah Ng
Barium titanate (BaTiO3) nanowires have been attracting considerable research interest due to their lead-free composition and strong energy conversion efficiency. Here we use a two-steps hydrothermal method to grow the BaTiO3 nanowire arrays vertically aligned on a transparent conductive substrate. The experiment parameters are studied to control the morphology and density of the nanowire arrays. And their influence to the piezoelectric property will then be investigated. The as-synthesized nanowire arrays could be fabricated to MEMS devices for sensing and energy harvesting.
Piezoaeroelastic Energy Harvesting from Airflow
Wusi Chen, Chung Ming Leung
This project is to develop an improved piezoaeroelastic energy harvester in order to power sensors and other on-board electronics. The developed energy harvester consists of an airfoil-shaped object, two supporting beam, two micro fiber composite (MFC) patches and a hinge with two torsional springs. The synergistic effect of different design parameters on the cut-in wind speed has been studied to enhance the power output efficiency. Experimental results show that the improved energy harvesting prototype has a cut-in speed of 4.2 m/s and a RMS power output of 58 mW at the wind speed of 8 m/s.
Footstep Energy Harvesting
Wusi Chen, Chung Ming Leung
The goal of this project is to harvest mechanical energy from pedestrians’ walking force using piezoelectric multilayer stacks. Due to their high stiffness, a low amount energy can be harvested through piezoelectric stacks. optimzed design factors have been applied to reduce the system stiffness while improve the harvesting efficiency. Experimental result shows that the piezoelectric stack improved design frames, is able to produce 4.2 mJ and 2.4 mJ respectively, under the simulated footstep force with the amplitude of 100 N and the time duration of 0.85 s.
Vibration-based Energy Harvesting using the inherent non-linearity property of the magnetic levitation
Rajesh Devaguptapu
This project presents a vibration-based energy harvester, which consists of a set of magnets stacked inside a tube levitated with the help of two fixed end magnets. The key feature being that the nonlinear restoring force is an integral property of the mechanism which allows the tuning of the resonant frequency. This project assesses the energy harvesting potential of the setup on low-frequency low amplitude vibration. A parametric study has been performed to determine the best possible magnetic stack arrangement to generate the highest power. A dynamic model is being created to analyze the general properties of the harvester by making analogy between mechanical systems and electrical circuits. Experiments are being carried out to verify the modeling and the advantages.
Tunable Broadband Magnetoelectric Energy Harvesting
Dacheng Zhao, Wei Deng, Chung Ming Leung
This project is to develop an advanced magnetoelectric energy harvester to be able to operate in a wide bandwidth while maintaining its high convering efficiency.
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Associate Professor
Leland T. Jordan Career Development Professorship
Electrical and Computer Engineering
Biomedical Engineering
Texas A&M University
MEOB 227
College Station, TX 77843-3123
Phone: 979-458-8123
Email: ya.wang@tamu.edu