Tag Archives: havens

Havens and Pinar Present in Naples and Attend Invited Workshop in UK

Timothy Havens
Timothy Havens

Tim Havens (ECE/CS) and Tony Pinar (ECE) presented several papers at the IEEE International Conference on Fuzzy Systems in Naples, Italy. Havens also chaired a session on Innovations in Fuzzy Inference.

Havens and Pinar also attend the Invited Workshop on the Future of Fuzzy Sets and Systems in Rothley, UK. This event invited leading researchers from around the globe for a two-day workshop to discuss future directions and strategies, in particular, to cybersecurity. The event was hosted by the University of Nottingham, UK, and sponsored by the National Cyber Security Centre, part of UK’s GCHQ.


MIT Lincoln Laboratory contract for Tim Havens

Timothy Havens
Timothy Havens

Associate Professor Tim Havens received a $15,000 contract from MIT Lincoln Laboratory. Tim and his team will investigate signal processing for active phased array systems with simultaneous transmit and receive capability. While this capability offers increased performance in communications, radar, and electronic warfare applications, the challenging aspect is that a high-level of isolation must be achieved between the transmit and receive antennas in order to mitigate self-interference in the array. This is a half-year project. Timothy Schulz at ECE is the co-PI of the project. Excellent work Tim!


Computational Intelligence Aids in Explosive Hazard Detection

To detect buried explosive hazards in places like Afghanistan, and to save the lives of civilians and US soldiers, Michigan Tech researcher Tim Havens realizes it requires a team—a team
of sensors.

This technology has the potential to not only save lives, but also to advance the basic science of how to combine sensors and information together to get a whole better than the sum of its parts.

A new $983,000 research project, “Heterogeneous Multisensor Buried Target Detection Using Spatiotemporal Feature Learning,” will look at how forward-looking ground-penetrating radar, LiDAR, and video sensors can be combined synergistically to see into the ground, capture high-quality images, and then automatically notify the operator of threats. With funding from the US Army Research Office, Havens and Tim Schulz, professor of electrical and computer engineering at Michigan Tech, will work with three PhD students to create a high probability-of-detection/low false-alarm rate solution.

“It’s a very difficult problem to solve because most of the radar energy bounces right off the surface of the earth,” says Havens, the William and Gloria Jackson Assistant Professor of Computer Systems at Michigan Tech. “This technology has the potential to not only save lives, but also to advance the basic science of how to combine sensors and information together to get a whole better than the sum of its parts.”

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This new project will advance additional sensor-related work Havens and collaborators completed between 2013–2015. The US Army-funded project studied signal processing and computer-aided detection and classification using forward-looking, ground-penetrating, vehicle-mounted radar.
The Army currently fields ground-penetrating radars in its fleet. The problem is they cannot detect hazards until they’re right above them, putting a multi-million dollar radar—and soldiers—directly in the path of danger.

“The big ideas here were to process data to obtain better images, see into the ground in a high-fidelity manner, and develop algorithms to automatically find buried threats—notifying operators of what the possible threats actually are,” Havens adds.

Havens has partnered with the Army since 2008 when he was a PhD student.


Associate Professor Timothy Havens received a research award

Timothy HavensAssociate Professor Timothy Havens received a DoD Army Research Office research award with a budget of $99,779 during the first year.

This is also a 3-year project with a total budget of $1,066,799. The project is titled “Multisensor Analysis and Algorithm Development for Detection and Classification of Buried and Obscured Targets.”

Tim and his students will develop new algorithms to detect and classify buried objects, one of the important research areas for ARO.


Three Faculty Receive External Funding

Dear All,

Please join me in congratulating Zhenlin, Tim, and Philart on receiving external research funding during the summer!

Zhenlin received an NSF research award with a total budget of $375,000. This is a 3-year project with a title of “CSR:Small: Effective Sampling-Based Miss Ratio Curves: Theory and Practice”. In this project, Zhenlin and his students will use miss ratio curves (MRCs), which relate cache miss ratio to cache size, to model working set and cache locality. The project develops a new cache locality theory to construct MRCs effectively and then applies it to several caching or memory management systems.

Tim received a DoD Army Research Office research award with a budget of $99,779 during the first year. This is also a 3-year project with a total budget of $1,066,799. The project is titled “Multisensor Analysis and Algorithm Development for Detection and Classification of Buried and Obscured Targets.” Tim and his students will develop new algorithms to detect and classify buried objects, one of the important research areas for ARO.

Philart received a research award from Hyundai Motor Company in the amount of $130,236. The project is entitled, “Novel In-vehicle Interaction Design and Evaluation”. Philart and his students will investigate the effectiveness of an in-vehicle control system and culture-specific sound preference.

Congratulations Zhenlin, Tim, and Philart! Thanks for the great job!

Best,
Min Song



DOD-ARO Funding for Tim Havens

Timothy Havens
Timothy Havens

Timothy Havens received a research grant of $285,900 for the first year of a potential three-year project totaling $983,124. The work is funded by the U.S. Department of Defense-Army Research Office. Timothy Schulz of Electrical and Computer Engineering (ECE) is the project Co-PI. Havens has a joint appointment in both Computer Science and ECE.

The project is entitled “Heterogeneous Multisensor Buried Target Detection Using Spatiotemporal Feature Learning.” The project will investigate theory and algorithms for multisensor buried target detection that achieve high probability of detection and classification with low false-alarm-rate. The primary sensors of interest are multisensor FLGPR (i.e., FLGPR plus other sensor modalities, such as thermal video or LIDAR) and acoustic/seismic systems, although the methods will be applicable to other modalities as well.


Tim Havens Presents on Fuzzy Systems

FUZZ IEEE 2015Tim Havens (ECE/CS) presented two papers at the IEEE Int. Conference on Fuzzy Systems in Istanbul, Turkey. The first paper was entitled, “Feature and Decision Level Fusion Using Multiple Kernel Learning and Fuzzy Integrals,” authored by ECE PhD student Anthony Pinar and coauthored by Havens and Derek Anderson and Lequn Hu from Mississippi State University. The second paper was authored by Titilope Adeyeba (Miss. State), Anderson and Havens, entitled, “Insights and Characterization of L1-Norm Based Sparsity Learning of a Lexicographically Encoded Capacity Vector for the Choquet Integral.” Havens also served as an Area Chair and Session Chair at the conference.



CS Department Seminar – Dr. Timothy Havens, Speaker

Department of Computer Science Seminar
February 27, 2012 – 4:04 PM – Room G005 – Rekhi Hall
Title: “Fuzzy Kernel Clustering of Large Scale Biomedical and Bioinformatics Data”

Dr. Timothy Havens

Abstract:
Since the early 1990’s, the ubiquity of personal computing technology has produced an abundance of staggeringly large data sets—it is estimated that Facebook alone logs over 25 terabytes of data per day and large bioinformatics data sets that integrate microarrays, sequences, and ontology annotations continue to grow. To compound this fact, these data sets are populated from disparate, often unknown, sources and are in a wide-range of formats. There is a great need for systems by which one can elucidate the similarity among and between groups in these data sets and produce easy-to-understand visualizations of the results. In this talk, I will discuss a method for efficiently and accurately approximating the solution of the kernel c-means clustering algorithm, specifically focusing on the fuzzy variant. Kernel clustering has been shown to be effective for data sets where the groups are not linearly separable in the input space or are high-dimensional. However, kernel fuzzy c-means (kFCM) presents computation and storage requirement challenges: clustering 500,000 objects requires 1 terabyte of main memory. I will show that on medium scale data (~50,000 objects) the approximate kFCM (akFCM) algorithm gives up to three orders of magnitude speed-up and a constant factor reduction in memory footprint with little-to-no degradation in performance, as compared to literal kFCM. I also demonstrate that akFCM performs well on large-scale data (>500,000 objects), including magnetic resonance imaging volumes. Last, I will apply the clustering method to bioinformatics data composed of genes described by Gene Ontology annotations to show how akFCM can be used for comparative genomics.