Engineering Commons^™

Unconventional Computation Including Quantum Computation, Bruce J. Maclennan

Faculty Publications and Other Works -- EECS

Unconventional computation (or non-standard computation) refers to the use of non-traditional technologies and computing paradigms. As we approach the limits of Moore’s Law, progress in computation will depend on going beyond binary electronics and on exploring new paradigms and technologies for information processing and control. This book surveys some topics relevant to unconventional computation, including the definition of unconventional computations, the physics of computation, quantum computation, DNA and molecular computation, and analog computation. This book is the content of a course taught at UTK.

Non-Contact Heart Rate Estimation In Low Snr Environments Using Mmwave Radar, Chandler J. Bauder, Aly E. Fathy

Faculty Publications and Other Works -- EECS

Extracting accurate heart rate estimates of human subjects from a distance in high-noise scenarios using radar is a common problem. Often, frequency components from sources such as movement and vital signs from other subjects can overpower the weak reflected signal of the heart. In this study, we propose a signal processing scheme using a state-of-the-art Adaptive Multi-Trace Carving algorithm (AMTC) to accurately detect the heart rate signal over time in non-ideal scenarios. In our initial proof-of-concept results, we show a low heart rate estimation mean absolute error (MAE) of 3bpm for a single subject marching in place and less than …

Design And Optimization Of Printed Circuit Board Inductors For Wireless Power Transfer System, Ashraf B. Islam, Syed K. Islam, Fahmida S. Tulip

Faculty Publications and Other Works -- EECS

Wireless power transfer via inductive link is becoming a popular choice as an alternate powering scheme for biomedical sensor electronics. Spiral printed circuit board (PCB) inductors are gaining attractions for wireless power transfer applications due to their various advantages over conventional inductors such as low-cost, batch fabrication, durability, manufacturability on flexible substrates, etc. In this work, design of a multi-spiral stacked solenoidal inductor for biomedical application in 13.56 MHz band is presented. Proposed stacking method enhances the inductance density of the inductor for a given area. This paper reports an optimization technique for design and implementation of the PCB inductors. …

Building Better Interdisciplinary Scientists: Creating Graduate Level Courses To Address The Communication Gap In Interdisciplinary Research, Denise R. Koessler, Elizabeth G. Johnson, Jordan M. Utley, Harry A. Richards, Cynthia B. Peterson

Faculty Publications and Other Works -- EECS

Background

The SCALE-IT (Scalable Computing and Leading Edge Innovative Technologies) program at the University of Tennessee-Knoxville is one of an increasing number of programs at institutions across the country that relies on the success of interdisciplinary research. To prepare students for interdisciplinary problem solving, universities typically offer advanced courses or seminars in interdisciplinary topics. While courses like this are ideal for advanced students who have extensive backgrounds in both computational science and domain sciences, most graduate students lack core competency in fields outside of their own disciplines and are thus unprepared to …

Highly Interconnected Genes In Disease-Specific Networks Are Enriched For Disease-Associated Polymorphisms, Fredrik Barrenas, Sreenivas Chavali, Alexessander C. Alves, Lachlan Coin, Marjo-Riitta Jarvelin, Rebecka Jornsten, Michael A. Langston, Adaikalavan Ramasamy, Gary Rogers, Hui Wang, Michael Benson

Faculty Publications and Other Works -- EECS

Background

Complex diseases are associated with altered interactions between thousands of genes. We developed a novel method to identify and prioritize disease genes, which was generally applicable to complex diseases.

Results

We identified modules of highly interconnected genes in disease-specific networks derived from integrating gene-expression and protein interaction data. We examined if those modules were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies. First, we analyzed publicly available gene expression microarray and genome-wide association study (GWAS) data from 13, highly diverse, complex diseases. In each disease, highly interconnected genes formed modules, which were …

Latent Semantic Indexing Of Pubmed Abstracts For Identification Of Transcription Factor Candidates From Microarray Derived Gene Sets, Sujoy Roy, Kevin Heinrich, Vinhthuy Phan, Michael W. Berry, Ramin Homayouni

Faculty Publications and Other Works -- EECS

Background

Identification of transcription factors (TFs) responsible for modulation of differentially expressed genes is a key step in deducing gene regulatory pathways. Most current methods identify TFs by searching for presence of DNA binding motifs in the promoter regions of co-regulated genes. However, this strategy may not always be useful as presence of a motif does not necessarily imply a regulatory role. Conversely, motif presence may not be required for a TF to regulate a set of genes. Therefore, it is imperative to include functional (biochemical and molecular) associations, such as those found in the biomedical literature, into algorithms for …

Comparative Studies Of High-Throughput Biological Graphs, Jonathan Reyles, Charles Phillips

Faculty Publications and Other Works -- EECS

Background

The exponential growth of biological data has given rise to new and difficult challenges. Because large data is often dealt with, it is inefficient to infer from each individual characteristics of a given dataset. Bioinformaticists are developing quantitative techniques to analyze and interpret key data properties. Graph algorithms can provide powerful and intuitive insight on such properties [1]. Using this approach, we collect biological data from transcriptomic and protein-protein interaction (PPI) sources. These data can be represented as a correlation matrix, where the rows are the vertices and the columns are the edges. We will analyze these …

Decentralized Turbo Bayesian Ompressed Sensing With Application To Uwb Systems, Depeng Yang, Husheng Li, Gregory D. Peterson

Faculty Publications and Other Works -- EECS

In many situations, there exist plenty of spatial and temporal redundancies in original signals. Based on this observation, a novel Turbo Bayesian Compressed Sensing (TBCS) algorithm is proposed to provide an efficient approach to transfer and incorporate this redundant information for joint sparse signal reconstruction. As a case study, the TBCS algorithm is applied in Ultra-Wideband (UWB) systems. A space-time TBCS structure is developed for exploiting and incorporating the spatial and temporal a priori information for space-time signal reconstruction. Simulation results demonstrate that the proposed TBCS algorithm achieves much better performance with only a few measurements in the presence of …

Discovering Gene Functional Relationships Using Faun (Feature Annotation Using Nonnegative Matrix Factorization), Elina Tjioe, Michael W. Berry, Ramin Homayouni

Faculty Publications and Other Works -- EECS

Background

Searching the enormous amount of information available in biomedical literature to extract novel functional relationships among genes remains a challenge in the field of bioinformatics. While numerous (software) tools have been developed to extract and identify gene relationships from biological databases, few effectively deal with extracting new (or implied) gene relationships, a process which is useful in interpretation of discovery-oriented genome-wide experiments.

Results

In this study, we develop a Web-based bioinformatics software environment called FAUN or Feature Annotation Using Nonnegative matrix factorization (NMF) to facilitate both the discovery and classification of functional relationships among genes. Both the computational complexity …

Graph Algorithms For Machine Learning: A Case-Control Study Based On Prostate Cancer Populations And High Throughput Transcriptomic Data, Gary L. Rogers, Pablo Moscato, Michael A. Langston

Faculty Publications and Other Works -- EECS

Background

The continuing proliferation of high-throughput biological data promises to revolutionize personalized medicine. Confirming the presence or absence of disease is an important goal. In this study, we seek to identify genes, gene products and biological pathways that are crucial to human health, with prostate cancer chosen as the target disease.

Materials and methods

Using case-control transcriptomic data, we devise a graph theoretical toolkit for this task. It employs both innovative algorithms and novel two-way correlations to pinpoint putative biomarkers that classify unknown samples as cancerous or normal.

Results and conclusion

Observed accuracy on real data suggests that we are …

Inferring Gene Coexpression Networks For Low Dose Ionizing Radiation Using Graph Theoretical Algorithms And Systems Genetics, Sudhir Naswa, Gary L. Rogers, Rachel M. Lynch, Stephen A. Kania, Suchita Das, Elissa J. Chesler, Arnold M. Saxton, Brynn H. Voy, Michael A. Langston

Faculty Publications and Other Works -- EECS

Background

Biological data generated through large scale -omics technologies have resulted in a new paradigm in the study of biological systems. Instead of focusing on individual genes or proteins these technologies enable us to extract biological networks using powerful computing and statistical algorithms that are scalable to very large datasets.

Materials and methods

We have developed a tool chain using novel graph algorithms to extract gene coexpression networks from microarray data. We highlight implementation of our tool chain to investigate the effects of in vivo low dose ionizing radiation treatments on mice. We are using systems genetics approach to investigate …

Development Of Tools For The Automated Analysis Of Spectra Generated By Tandem Mass Spectrometry, Sally R. Ellingson, Joe Hughes, Dylan Storey, Rick Weber, Nathan Verberkmoes

Faculty Publications and Other Works -- EECS

Background

While multiple tools exist for the analysis and identification of spectra generated in shotgun proteomics experiments, few easily implemented tools exist that allow for the automated analysis of the quality of spectra. A researcher’s knowledge of the quality of a spectra from an experiment can be helpful in determining possible reasons for misidentification or lack of identification of spectra in a sample.

Materials and methods

We are developing a automated high throughput method that analyses spectra from 2d-LC-MS/MS datasets to determine their quality and overall determines the quality of the run. We will then compare our programs to existing …

Developing Measures For Microbial Genome Assembly Quality Control, Rachel M. Adams, Jason B. Harris, Jeremy J. Jay, Beth G. Johnson, Miriam L. Land, Loren J. Hauser

Faculty Publications and Other Works -- EECS

Background

Advances in sequencing technologies are outpacing the rate at which genomes can be thoroughly finished and analyzed. Over the next year, genome sequencing will increase many-fold, but high quality and high-throughput annotation methods have yet to be developed to handle the need. As more microbial genomes are sequenced, whole-genome annotation methods identify many putative genes which need further verification. By analyzing a broad range of annotated genomes we can identify patterns and statistics useful in determining the annotation quality and spurious gene outliers. Our work is attempting to identify quality control measures based on a full inter-genomic comparison instead …

Threshold Selection In Gene Co-Expression Networks Using Spectral Graph Theory Techniques, Andy D. Perkins, Michael A. Langston

Faculty Publications and Other Works -- EECS

Abstract

Background

Gene co-expression networks are often constructed by computing some measure of similarity between expression levels of gene transcripts and subsequently applying a high-pass filter to remove all but the most likely biologically-significant relationships. The selection of this expression threshold necessarily has a significant effect on any conclusions derived from the resulting network. Many approaches have been taken to choose an appropriate threshold, among them computing levels of statistical significance, accepting only the top one percent of relationships, and selecting an arbitrary expression cutoff.

Results

We apply spectral graph theory methods to develop a systematic method for threshold selection. …

A Module-Based Analytical Strategy To Identify Novel Disease-Associated Genes Shows An Inhibitory Role For Interleukin 7 Receptor In Allergic Inflammation, Reza Mobini, Bengt A. Andersson, Jonas Erjefält, Mirjana Hahn-Zoric, Michael A. Langston, Andy D. Perkins, Lars O. Cardell, Mikael Benson

Faculty Publications and Other Works -- EECS

Background

The identification of novel genes by high-throughput studies of complex diseases is complicated by the large number of potential genes. However, since disease-associated genes tend to interact, one solution is to arrange them in modules based on co-expression data and known gene interactions. The hypothesis of this study was that such a module could be a) found and validated in allergic disease and b) used to find and validate one ore more novel disease-associated genes.

Results

To test these hypotheses integrated analysis of a large number of gene expression microarray experiments from different forms of allergy was performed. This …

Statistical Analysis Of Multipath Fading Channels Using Generalizations Of Shot-Noise, Charalambos Charalambous, Seddik Djouadi, Christos Kourtellaris

Faculty Publications and Other Works -- EECS

This paper provides a connection between the shot-noise analysis of Rice and the statistical analysis of multipath fading wireless channels when the received signals are a low-pass signal and a bandpass signal. Under certain conditions, explicit expressions are obtained for autocorrelation functions, power spectral densities, and moment-generating functions. In addition, a central limit theorem is derived identifying the mean and covariance of the received signals, which is a generalization of Campbell_s theorem. The results are easily applicable to transmitted signals which are random and to CDMA signals.