bioMed-student  03UAB is home to a world-class medical center, and as a result, the Department of Electrical and Computer Engineering is uniquely positioned to play an integral role in biomedical research. With a diverse variety of specialization areas that spans microelectronics and embedded systems, signal and image processing, biotechnology, and nanotechnology, the department plays a critical role in developing novel solutions for health-care problems to benefit our society.

As medicine becomes increasingly integrated with technology, the demand for electrical engineers who can collaborate across disciplines will only increase.

Many electrical engineering undergraduates (such as UAB President Ray Watts, M.D.) go to medical school, while others may choose to pursue advanced degrees in electrical or other engineering disciplines.

UAB ECE alumni hold many leading and critical positions in industry, national laboratories, and faculty positions around the world. Our Vision is to become a top internationally recognized department of Electrical and Computer Engineering and a leading choice for undergraduate and graduate education as well as to play an integral role in critical interdisciplinary research efforts with worldwide societal impact.


 




Projects

inductive power dataImplantable micro devices for biomedical applications, such as neuromuscular stimulators, cochlear implants, and visual prostheses, allow medical practitioners to collect patient health information. These Implantable systems cannot use batteries due to the potential leakage of battery fluid or loss of functionality after a certain number of recharge cycles.

We are currently working on developing efficient techniques for providing power and communication links to implantable medical devices and sensors.

neural amplifierMulti-Electrode Neural Recording SystemThe human brain is a collection of millions of neurons that form the basis of perception, recognition and control by simultaneous neuronal activities. Neuronal activities are also associated with several brain related diseases such as epilepsy, perkinson’s, migraine, distonia, etc. To unveil the abnormal activities of neurons in real time fashion, a multielectrode neural recording system (Fig. 1) is essential where the bioamplifiers play a vital role of amplifying the neuronal signals extracted by the neural electrodes. The noise contribution of the bioamplifiers to the entire system must be minimized to improve the system performance.

In addition, for an implantable system, the power consumption by the large array of bioamplifiers must be monitored to prevent tissue damage by excessive heating. For reliable operation of an implantable neural recording system, our on-going research focuses on the design of an ultra-low-power low-noise bioamplifier (Fig. 2) structure using standard RF-CMOS technology. The self-biased structure obviates the biasing circuitry and an inverter based gain stage results in improved bioamplifier structure with a minimum number of transistors. We are at present working for the development of an efficient neural recording system for better diagnosis of the neuronal activities.

bioamplifierCircuit Schematic of the Ultra-Low Power, Low-Noise Bioamplifier

The ever-increasing demand for complex and miniaturized electronic systems, such as mobile electronic devices and medical implantable devices, has led to an increasing demand for robust and efficient DC-DC converters, which are used to change the power supply levels to the levels required by each of the system blocks. Our group is investigating efficient design techniques for on-chip DC-DC robust converters. We are currently working on improving the voltage regulation capability, which is important to provide a steady fluctuation-less output voltage, thus enhancing the robustness of the DC-DC converter. Our research considers the various aspects that govern the control mechanism and design a comprehensive control model for the DC-DC converter, which can improve its voltage regulation efficiency and thereby enhance its robustness. We perform our experiments on two different types of DC-DC converters, namely, buck converter and boost converter. While a buck converter is used to step-down (or reduce) the initial supply voltage, the boost converter is used to step-up (or increase) the initial supply voltage.

buckconverterboostconverter



The problem of liver fibrosis is widespread, and the need in objective tools for fibrosis detection and evaluation is obvious. In this research project, we are trying to provide tools for robust liver fibrosis staging, based on diffusion MRI image analysis.

The current practice of fibrosis assessment, which is based on painful liver biopsy, might be dangerous. Moreover, the decision of the histopathologist based on a biopsy is subjective, and depends on the sample, because the fibrosis level varies along the liver. No objective standard has been developed yet for histological fibrosis assessment.

Magnetic resonance volume data has much lower resolution than histological image data, but it includes the entire liver volume (Figure 1). Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool. Previously it has been hypothesized that the average brightness of Apparent Diffusion Coefficient (ADC) in diffusion MRI correlates with the fibrosis stage.

In our research, we have tested different ADC image texture features, and have found features (e.g., standard deviation of color distribution) that correlate with histological results much better than average brightness. We have developed algorithm for automatic hepatic MRI segmentation, and algorithm for automatic liver staging, based on optimal feature grouping.

LiverMRI


nanotrackerRobust general tracking tools are of major interest for applications ranging from surveillance and security to robotic navigation and nanotechnology. In these applications, the objects of interest may be translated, rotated, scaled, or non-rigidly deformed, and the goal is to find trajectories and features of those objects automatically. To solve the problem of tracking, one needs to incorporate theories of image processing, segmentation (separating objects from the background), registration (object matching), control, and estimation.

The proposed tools have been successfully applied to three different categories of targets: Objects filmed by a regular hand-held or stationary camera (including people and cars); deforming objects filmed by an infrared camera; nanofluids filmed by a camera attached to a microscope (Figure 1). The project on multi-target nanofluid tracking allowed modeling of nanoplatform dynamic behavior as a function of external magnetic field. Based on this work, it may be possible to develop nanoplatforms to be utilized for the simultaneous identification, mapping, targeting and destruction of cancer cells.