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Electrical & Computer Engineering

Electrical and Computer Engineering
3108 Engineering Hall
1701D Platt St.
Manhattan, KS 66506
Fax: 785-532-1188

Hours: Monday - Friday
8 a.m.-noon, 1-5p.m.

Biomedical Systems

Medical Component Design Laboratory (MCDL) 

Dr. Steve Warren directs the KSU ECE Medical Component Design Laboratory (MCDL), housed in Rathbone Hall. The primary mission of the MCDL is to support work in interoperable component design for medical systems: plug-and-play hardware/software elements that can be assembled rapidly to create care systems matched to patient needs. Interoperability standards, wireless devices, wearable sensors, and light-based devices play important roles in this research, which targets physiologic monitoring for humans and animals. Quality of life issues (e.g., successful aging and technology applications for the disabled) are important drivers for the pervasive care environments addressed by these projects. This laboratory also plays an important role in engineering education via the delivery of research products into the classroom and grant-sponsored research that focuses on how students learn and how students transfer and retain knowledge over multiple semesters. Primary collaborators in 2010 included Heartspring (Wichita, Kan.), East Carolina University, the KSU Department of Computing & Information Sciences, the KSU Department of Anatomy & Physiology, the KSU Electronics Design Laboratory, the KSU Mathematics Department, the KSU Physics Department, the KSU Kinesiology Department, the U.S. Food and Drug Administration, and the University of Pennsylvania. Project funding was received from the National Science Foundation (CCLI/TUES, CNS, CRI, & REESE), NASA and the KSU Targeted Excellence program.

Biomedical Computing and Devices Lab

We are interested in developing devices and systems capable of controlled energy delivery for targeted thermal therapy of cancer and benign disease. Energy sources of interest include RF currents, microwaves, and ultrasound. Intense heat may be used to ablate (i.e. destroy) tissue e.g. for minimally invasive treatment of tumors or cardiac arrhythmias. Moderate heat may be used to trigger drug release from nanoparticles or to augment radio/chemotherapy. Some examples of research areas include:

  • Device development and evaluation We design and build systems (energy sources, applicators consisting of antennas/electrodes/transducers, feedback control algorithms) for targeted energy delivery to the body. Our goal is to design devices capable of adequately heating targeted tissue with minimal damage to surrounding healthy tissue. We fabricate prototypes and evaluate them on the electrical lab bench and in appropriate tissue models.
  • Computer modeling We develop computer models of energy propagation through tissue and bioheat transfer to design devices and control algorithms for specific applications. We perform experiments to validate computer models and measure physical properties of tissue.
  • Optimizing treatment delivery on patient-specific anatomies Treatment plans employ computer models and optimization techniques to determine suitable device insertion paths and positions, optimal energy levels, and heating patterns. We are interested in techniques for rapid computation and 3D visualization of treatment plans to aid physicians in customizing therapy of patient-specific anatomies.
  • Thermally triggered targeted drug delivery We are interested in designing methods and systems for targeted heating with nanoparticles, which preferentially migrate into tumors and may be used to deliver therapeutic drugs via a thermal trigger.

We are also interested in applying these techniques to the design of other therapeutic medical devices.

Kansas State Epicenter
Kansas State University’s EPICENTER – Center for Complex Network Approach to Epidemiological, Biological, and Sociological Modeling and Simulation - is directed by Dr. Caterina Scoglio, associate professor of electrical and computer engineering, and Dr. Morgan Scott, professor of epidemiology in veterinary medicine. One of the main goals of EPICENTER is to provide policymakers with real-time, flexible modeling tools to curtail epidemiological outbreaks, whether it occurs in humans, animals, plants or computers. The most important aspect is use of a complex networks approach for the analysis of problems relating to multiple disciplines such as agriculture, veterinary science, biology, medicine, social sciences and engineering.
Highlights of the key areas under K-State EPICENTER are as follows:
  • Network-based modeling for epidemics. These projects are concerned with the study and implementation of mathematical models of epidemic spreading in a realistic environment with individual-based models and meta-population models. Work on models for specific contagious diseases such as foot and mouth disease and Rift Valley fever are in progress.
  • Agent-based epidemiological simulator for rural communities. The aim of this project is to design agent-based simulation software for a set of representative infectious diseases in a rural community to detect the conditions under which an epidemic would spread or die out, as well as to determine the direction and speed if it spreads. These results will be used to derive and analyze optimized policies and guidelines for containment and prevention of infectious diseases.
  • Modeling of interconnections among human behavior and epidemic spreading. Human behaviors play a crucial role in how an epidemic spreads in a social society. Despite extensive studies on how human beings percept a disease and the behavior they show in response, not many results have been reported on how human behavior would actually affect the epidemic spread. The goal in this study is to provide interconnected models for epidemic spread and individual behaviors, followed by simulation and analysis of the models.
  • Network partitioning for mitigation of epidemics. One of the considered mitigation strategies to control and reduce epidemic spreading is quarantine. When contacts are represented by a  network, quarantine can be determined using network partitioning algorithms. We are developing network partitioning algorithms, designed to be a simple, efficient method to partition a network into possible quarantine sections. Our algorithm, called Bloom, grows partitions and then allows the individuals to decide which partition they feel most comfortable in. We have implemented the first algorithm and done some initial testing on classical clustering graphs.