Magnetic resonance imaging (MRI) is the most powerful instrument for imaging anatomical structures. One of the most essential components of the MRI scanner is a radiofrequency (RF) coil. It induces resonant phenomena and receives the resonated RF signal from the body. Then, the signal is computed and reconstructed for MR images. Therefore, improving image quality by increasing the receiver's (Rx) efficiency is always remarkable. This research introduces a flexible and stretchable receive RF coil embedded in a dielectric-loaded material. Recent studies show that the adaptable coil can improve imaging quality by flexing and stretching to fit well with the sample's surface, reducing the spatial distance between the load and the coil. High permittivity dielectric material positioned between the coil and phantom was known to increase the RF field distribution's efficiency significantly. Recent studies integrating the high dielectric material with the coil show a significant improvement in signal-to-noise ratio (SNR), which can improve the overall efficiency of the coil. Previous research also introduced new elastic dielectric material, which shows improvement in uniformity when incorporated with an RF coil. Combining the adaptable RF coil with the elastic dielectric material has the potential to enhance the coil's performance further. The flexible dielectric material's limitations and unknown interaction with the coil pose a challenge. Thus, each component was integrated into a simple loop coil step-by-step, which allowed for experimentation and evaluation of the performance of each part. The mechanical performance was tested manually. The introduced coil is highly flexible and can stretch up to 20% of its original length in one direction. The electrical performance was evaluated in simulations and experiments on a 9.4T MRI scanner compared to conventional RF coils.

In convective heat transfer processes, heat transfer rate increases generally with a large fluid velocity, which leads to complex flow patterns. However, numerically analyzing the complex transport process and conjugated heat transfer requires extensive time and computing resources. Recently, data-driven approach has risen as an alternative method to solve physical problems in a computational efficient manner without necessitating the iterative computations of the governing physical equations. However, the research on data-driven approach for convective heat transfer is still in nascent stage. This study aims to introduce data-driven approaches for modeling heat and mass convection phenomena. As the first step, this research explores a deep learning approach for modeling the internal forced convection heat transfer problems. Conditional generative adversarial networks (cGAN) are trained to predict the solution based on a graphical input describing fluid channel geometries and initial flow conditions. A trained cGAN model rapidly approximates the flow temperature, Nusselt number (Nu) and friction factor (f) of a flow in a heated channel over Reynolds number (Re) ranging from 100 to 27750. The optimized cGAN model exhibited an accuracy up to 97.6% when predicting the local distributions of Nu and f. Next, this research introduces a deep learning based surrogate model for three-dimensional (3D) transient mixed convention in a horizontal channel with a heated bottom surface. Conditional generative adversarial networks (cGAN) are trained to approximate the temperature maps at arbitrary channel locations and time steps. The model is developed for a mixed convection occurring at the Re of 100, Rayleigh number of 3.9E6, and Richardson number of 88.8. The cGAN with the PatchGAN based classifier without the strided convolutions infers the temperature map with the best clarity and accuracy. Finally, this study investigates how machine learning analyzes the mass transfer in 3D printed fluidic devices. Random forests algorithm is hired to classify the flow images taken from semi-transparent 3D printed tubes. Particularly, this work focuses on laminar-turbulent transition process occurring in a 3D wavy tube and a straight tube visualized by dye injection. The machine learning model automatically classifies experimentally obtained flow images with an accuracy > 0.95.

BioMEMS has the potential to provide many future tools for life sciences, combined with microfabrication technologies and biomaterials. Especially due to the recent corona 19 epidemic, interest in BioMEMS technology has increased significantly, and the related research has also grown significantly. The field with the highest demand for BioMEMS devices is in the medical field. In particular, the implantable device field is the largest sector where cutting-edge BioMEMS technology is applied along with nanotechnology, artificial intelligence, genetic engineering, etc. However, implantable devices used for brain diseases are still very limited because unlike other parts of human organs, the brain is still unknow area which cannot be completely replaceable.To date, the most commercially used, almost only, implantable device for the brain is a shunt system for the treatment of hydrocephalus. The current cerebrospinal fluid (CSF) shunt treatment yields high failure rates: ~40% within first 2 years and 98% within 10 years. These failures lead to high hospital admission rates and repeated invasive surgical procedures, along with reduced quality of life. New treatments are needed to improve the disease burden associated with hydrocephalus. In this research, the proposed catheter-free, completely-passive miniaturized valve is designed to alleviate hydrocephalus at the originating site of the disorder and diminish failure mechanisms associated with current treatment methods. The valve is composed of hydrogel diaphragm structure and polymer or glass outer frame which are 100% bio-compatible material. The valve aims to be implanted between the sub-arachnoid space and the superior sagittal sinus to regulate the CSF flow substituting for the obstructed arachnoid granulations.
A cardiac pacemaker is one of the longest and most widely used implantable devices and the wireless technology is the most widely used with it for easy acquisition of vital signs and rapid disease diagnosis without clinical surgery. But the conventional pacemakers with some wireless technology face some essential complications associated with finite battery life, ultra-vein pacing leads, and risk of infection from device pockets and leads. To solve these problems, wireless cardiac pacemaker operating in fully-passive modality is proposed and demonstrates the promising potential by realizing a prototype and functional evaluating.

Electromagnetic fields (EMFs) generated by biologically active neural tissue are critical in the diagnosis and treatment of neurological diseases. Biological EMFs are characterized by electromagnetic properties such as electrical conductivity, permittivity and magnetic susceptibility. The electrical conductivity of active tissue has been shown to serve as a biomarker for the direct detection of neural activity, and the diagnosis, staging and prognosis of disease states such as cancer. Magnetic resonance electrical impedance tomography (MREIT) was developed to map the cross-sectional conductivity distribution of electrically conductive objects using externally applied electrical currents. Simulation and in vitro studies of invertebrate neural tissue complexes demonstrated the correlation of membrane conductivity variations with neural activation levels using the MREIT technique, therefore laying the foundation for functional MREIT (fMREIT) to detect neural activity, and future in vivo fMREIT studies.



The development of fMREIT for the direct detection of neural activity using conductivity contrast in in vivo settings has been the focus of the research work presented here. An in vivo animal model was developed to detect neural activity initiated changes in neuronal membrane conductivities under external electrical current stimulation. Neural activity was induced in somatosensory areas I (SAI) and II (SAII) by applying electrical currents between the second and fourth digits of the rodent forepaw. The in vivo animal model involved the use of forepaw stimulation to evoke somatosensory neural activations along with hippocampal fMREIT imaging currents contemporaneously applied under magnetic field strengths of 7 Tesla. Three distinct types of fMREIT current waveforms were applied as imaging currents under two inhalants – air and carbogen. Active regions in the somatosensory cortex showed significant apparent conductivity changes as variations in fMREIT phase (φ_d and ∇^2 φ_d) signals represented by fMREIT activation maps (F-tests, p <0.05). Consistent changes in the standard deviation of φ_d and ∇^2 φ_d in cortical voxels contralateral to forepaw stimulation were observed across imaging sessions. These preliminary findings show that fMREIT may have the potential to detect conductivity changes correlated with neural activity.