Publications
(*Bold
indicates Dr. Havens and his students)
Refereed
Publications/Proceedings
Peer-Reviewed
Journal Articles
1.
S.K. Kakula, A.J. Pinar, M.A. Islam, D.T.
Anderson, and T.C. Havens. Novel regularization for learning the
fuzzy Choquet integral with limited training data. Accepted,
IEEE Trans. Fuzzy Systems.
2.
S. Yazdanparast, T.C. Havens, and M. Jamalabdollahi. Linear time community detection by a novel
modularity gain acceleration in label propagation. Accepted, IEEE Trans. Big
Data.
3.
B.
Murray, M.A. Islam, A.J. Pinar, D.T. Anderson, G. Scott, T.C. Havens, and J.M. Keller.
Explainable AI for the Choquet integral. Accepted, IEEE
Trans. Emerging Topics Comp. Intell.
4.
S. Yazdanparast, T.C. Havens, and M. Jamalabdollahi. Soft overlapping community detection in
large-scale networks via fast fuzzy modularity maximization. Accepted, IEEE
Trans. Fuzzy Systems.
5.
S. Kabir,
C. Wagner, T.C. Havens, and D.T. Anderson. A similarity measure based on
bidirectional subsethood for intervals. Accepted, IEEE Trans. Fuzzy Systems.
6.
M.A.
Islam, D.T. Anderson, A.J. Pinar, T.C. Havens, G. Scott, and J.M. Keller (July, 2020). Enabling explainable fusion in deep learning
with fuzzy integral neural networks. IEEE
Trans. Fuzzy Systems, 28(7),
1291-1300.
7.
I.T. Cummings, T.J. Schulz, J.P. Doane,
and T.C. Havens (Dec, 2020). Aperture-level simultaneous
transmit and receive with digital phased arrays. IEEE Trans. Signal Processing, 68(1), 1243-1258.
8.
J. Bialas,
T. Oommen, and T.C. Havens (Oct,
2019). Optimal segmentation for building class in high spatial resolution
images using random forests. Int. J. App.
Earth Obs. Geoinf. 82, 101895.
9.
C.D.
Demars, M.C. Roggemann, A.J. Webb, and T.C. Havens. (Oct,
2018) Target localization and tracking by fusing Doppler differentials from cellular
emanations with a multi-spectral video tracker. Sensors, 18(11), 3687.
10.
A.J. Webb, T.C. Havens, and T.J. Schulz (Sept,
2018). Fast image reconstruction in forward looking GPR using dual l1
regularization. IEEE Trans. Computational
Imaging, 4(3), 470-478.
11. M.A. Islam, D.T.
Anderson, A.J. Pinar, and T.C. Havens (Aug, 2018). Data-driven compression and
efficient learning of the Choquet integral. IEEE Trans. Fuzzy Systems, 26(4), 1908-1922.
12.
H. Deilamsalehy and T.C. Havens (Apr, 2018).
Fuzzy adaptive extended Kalman filter for robust 3D pose estimation. Int. J. Intelligent Unmanned Systems, 6(2), 50-68.
13. H.I. Sweidan and T.C. Havens (Apr, 2018).
Sensor relocation for improved target tracking. IET Wireless Sensor Systems, 8(2), 76-86.
14. A.J. Pinar,
D.T. Anderson, A. Zare, T.C. Havens, and T. Adeyeba (Dec, 2017). Measures of the Shapley index for learning lower
complexity fuzzy integrals. Granular
Computing, 2(4), 303-319.
15. A.J. Pinar, J. Rice, T.C. Havens, D.T. Anderson, and L. Hu (Dec, 2017). Efficient multiple kernel classification using
feature and decision level fusion. IEEE Trans. Fuzzy Systems, 25(6), 1403-1416.
16. J. Frank, U. Rebbapragada, J. Bialas,
T. Oommen, and T.C. Havens (Aug,
2017). Effect of label noise on the machine-learned classification of
earthquake damage. Remote Sensing, 9(8),
803.
17. H. Deilamsalehy, T.C. Havens,
P. Lautala, E. Medici, and J. Davis (July, 2017). An automatic train car wheel flat spot
detection method using thermal camera imagery. J. Rail and Rapid Transit, 231(6), 690-700.
18. H. Deilamsalehy, T.C.
Havens, J. Manela (Apr, 2017).
Heterogeneous multi-sensor fusion for mobile platform 3D pose estimation. J. Dynamic Systems, Measurement, and
Control, 139(7), 071002.
19. S. Yazdanparast and T.C. Havens (Apr, 2017). Modularity maximization using
completely positive programming. Physica A: Statistical
Mechanics and its Applications, 471(1), 20-32.
20. D.T. Anderson, P.
Elmore, F. Petry, and T.C. Havens (Oct, 2016).
Fuzzy Choquet integration of homogeneous possibility
and probability distributions. Info
Sciences, 363, 24-39.
21. D.
Kumar, J.C. Bezdek, M. Palaniswami,
S. Rajasegarar, C. Leckie, and T.C. Havens. A hybrid approach to clustering in big data. Accepted,
IEEE Trans. Systems, Man, and
Cybernetics, 46(10), 2372-2385.
22. S. Nuchitprasitchai, M. Roggemann, and T.C. Havens (Sept,
2016). An algorithm for reconstructing three dimensional images from
overlapping two dimensional intensity measurements
with relaxed camera positioning requirements. Int. J. Modern Engineering Research, 6(9), 69-81.
23. C.D.
Demars, M. Roggemann, and T.C. Havens (Dec,
2015), Multi-spectral detection and
tracking of multiple moving targets in cluttered urban environments. Optical Engineering, 54(12), 123106.
24. T.C. Havens,
D.T. Anderson, and C. Wagner. Data-informed fuzzy measures for fuzzy
integration of intervals and fuzzy numbers. IEEE
Trans. Fuzzy Systems, 23(5),
1861-1875.
25. J. Su
and T.C. Havens. Quadratic
program-based modularity maximization for fuzzy community detection in social
networks. IEEE Trans. Fuzzy Systems, 23(5), 1356-1371.
26. A.J. Pinar,
B. Wijnen, G.C. Anzalone, T.C. Havens, P.G. Sanders, and J.M. Pearce (2015). Low-cost
open-source voltage and current monitor for gas metal arc weld 3-D printing. J. Sensors, 2015, paper ID 876714, 8
pages.
27. C.
Wagner, S. Miller, J.M. Garibaldi, D.T. Anderson, and T.C. Havens (2015). From interval-valued data to general type-2
fuzzy sets. IEEE Trans. Fuzzy Systems, 23(2),
248-269.
28. D.T.
Anderson, T.C. Havens, C. Wagner,
J.M. Keller, M.F. Anderson, and D.J. Wescott. Extension of the fuzzy integral
for general fuzzy set-valued information (2014). IEEE Trans. Fuzzy Systems, 22(6), 1625-1639.
29. M.
Moshtaghi, J.C. Bezdek, T.C. Havens, C. Leckie, S. Karunasekera, S. Rajasegarar, and
M. Palaniswami (2014). Streaming analysis in wireless
sensor networks. Wireless Communications
and Mobile Computing, 14(9), 905-921.
30. S.
Rajasegarar, T.C.
Havens, S. Karunasekera, C. Leckie, J.C. Bezdek, M. Jamriska, A. Gunatilaka, A. Skvortsov, and M. Palaniswami (2014). High resolution monitoring of
atmospheric pollutants using a system of low-cost sensors. IEEE Trans. Geoscience and Remote Sensing 52(7), 3823-3832.
31. T.C. Havens,
J.C. Bezdek, C. Leckie, K. Romamohanarao,
and M. Palaniswami (2013). A soft modularity function
for detecting fuzzy communities in social networks. IEEE Trans. Fuzzy Systems, 21(6), 1170-1175.
32. M.
Popescu, T.C. Havens, J.C. Bezdek, and J.M. Keller (2013). A cluster validity
framework based on induced partition dissimilarity. IEEE Trans. Cybernetics 43(1), 308-320.
33.
T.C.
Havens, J.C. Bezdek,
C. Leckie, L.O. Hall, and M. Palaniswami (2012).
Fuzzy c-means algorithms for very large data. IEEE Trans. Fuzzy Systems, 20(6),
1130-1146. CIS Publication Spotlight
34.
T.C. Havens and J.C. Bezdek
(2012).A new formulation of the coVAT
algorithm for visual assessment of clustering tendency in rectangular data. Int. J. Intelligent Systems 27(6),
590-612.
35.
T.C. Havens and J.C. Bezdek
(2012). An efficient formulation of the improved visual assessment of tendency
(iVAT) algorithm. IEEE
Trans. Knowledge and Data Engineering 24(5),
813-822.
36. J.C.
Bezdek, S. Rajasegarar, M. Moshtaghi, T.C.
Havens, C. Leckie, and M. Palaniswami. (2011)
Anomaly detection in environmental monitoring networks. Computational Intelligence Magazine. 6(2), 52-58.
37. M.
Moshtaghi, T.C.
Havens, J.C. Bezdek, L. Park, C. Leckie, S. Rajasegarar, J.M. Keller, and M. Palaniswami
(2011). Clustering ellipses for anomaly detection. Pattern Recognition, 44(1),
55-69.
38. I.J.
Sledge, T.C. Havens, J.C. Bezdek, and J.M. Keller (2010). Relational duals of cluster
validity functions for the c-means family. IEEE
Trans. Fuzzy Systems, 18(6), 1160-1170. CIS Publication
Spotlight
39. T.C. Havens,
J.M. Keller, and M. Popescu (2010). Computing with words with the ontological
self organizing map. IEEE Trans. Fuzzy
Systems, 18(3), 473-485.
40. Sledge, I.J., T.C. Havens, J.C. Bezdek,
and J.M. Keller (2010). Relational generalizations of validity indexes. IEEE
Trans. Fuzzy Systems, 18(4).
771-786.
41. T.C. Havens,
G.L. Alexander, C. Abbott, J.M. Keller, M. Skubic,
and M. Rantz (2010). Tracking exercise motions of
older adults using contours. J. Applied
Computer Science Methods, 1(2), 21-42.
42. Alexander,
G.L., T.C. Havens, M. Rantz, J.M. Keller, and C.C. Abbott (2010). An analysis of
human motion detection systems use during elder
exercise routines. Western J. of Nursing
Research, 32(2), 233-249. MNRS Best Paper Award
43. T.C. Havens,
J.C. Bezdek, J.M. Keller, M. Popescu, and J.M. Huband (2009). Is VAT really single linkage in disguise? Ann. Mathematics and Artificial
Intelligence, 55(3), 237-251.
44. Sledge,
I.J., T.C. Havens, J.M. Huband, J.C. Bezdek, and J.M.
Keller (2009). Finding the number of clusters in ordered dissimilarities. Soft Computing, 13(12), 1125-1142.
45. T.C. Havens,
J.C. Bezdek, J.M. Keller, and M. Popescu (2009).
Clustering in ordered dissimilarity data.
Int. J. Intelligent Systems, 24(5),
504-528.
46. Beyer,
J.T., M.C. Roggemann, L.J. Otten, T.J. Schulz, T.C. Havens, and W.W. Brown (2003).
Experimental estimation of the spatial statistics of turbulence-induced index
of refraction fluctuations in the upper atmosphere. Applied
Optics, 42, 908-921.
47. T.C. Havens,
M.C. Roggemann, T.J. Schulz, W.W. Brown, J.T. Beyer,
and L.J. Otten (2002). Measurement and data-processing approach for detecting
anisotropic spatial statistics of turbulence-induced index of refraction
fluctuations in the upper atmosphere. Applied
Optics, 41, 2800-2808.
48. Brown,
W.W., M.C. Roggemann, T.J. Schulz, T.C. Havens, J.T. Beyer, and L.J. Otten
(2001). Measurement and data-processing approach for estimating the spatial
statistics of turbulence-induced index of refraction fluctuations in the upper
atmosphere. Applied Optics, 40, 1863-1871.
Peer-Reviewed
Proceedings
1. S.K. Kakula, A.J. Pinar, D.T.
Anderson, and T.C. Havens (Oct., 2020). Online
learning of the fuzzy Choquet integral. IEEE Int.
Conf. Systems, Man, and Cybernetics.
2. S.K. Kakula, A.J. Pinar, T.C.
Havens, and D.T. Anderson (July, 2020). Extended
linear order statistic (ELOS) aggregation and regression. IEEE Int. Conf.
Fuzzy Systems.
3. A. Wilbik,
T.C. Havens, and T. Wilkin (July, 2020). On a
paradox of extended linguistic summaries. IEEE Int. Conf. Fuzzy Systems.
4. S.K. Kakula, A.J. Pinar, T.C.
Havens, and D.T. Anderson (July, 2020). Choquet integral ridge regression. IEEE Int. Conf. Fuzzy
Systems.
5. T.C. Havens and D.T. Anderson (June, 2019). Machine learning of Choquet
integral regression with respect to a bounded capacity (or non-monotonic fuzzy
measure). IEEE Int. Conf. Fuzzy Systems.
6. C. Veal, A. Yang, A.
Hurt, M. Islam, D.T. Anderson, G. Scott, T.C. Havens, J.M. Keller and B.
Tang (June, 2019). Linear order statistic neuron. IEEE Int. Conf. Fuzzy Systems.
7. B. Murray, M. Islam, A.J.
Pinar, D.T. Anderson, G. Scott, T.C. Havens, F. Petry and P. Elmore
(June, 2019). Transfer learning for the Choquet integral. IEEE
Int. Conf. Fuzzy Systems.
8. S. Kabir, C. Wagner, T.C.
Havens, and D.T. Anderson (June, 2019). Measuring similarity
between discontinuous intervals – challenges and solutions. IEEE Int. Conf. Fuzzy Systems.
9. I.T. Cummings, T.J. Schulz, J.P. Doane, S.A. Zekavat, and T.C.
Havens (Oct, 2018). Information-theoretic
optimization of full-duplex communication between digital phased arrays. Allerton Conf. Comm., Control, and Comp. 373-377.
10. T.C. Havens, A.J. Pinar, D.T.
Anderson, and C. Wagner (July, 2018). SPFI:
shape-preserving Choquet fuzzy integral for
non-normal fuzzy set-valued evidence. IEEE
Int. Conf. Fuzzy Systems.
11. B. Murray, M. Aminul Islam, A.J. Pinar, T.C. Havens, D.T.
Anderson, and G. Scott (July, 2018). Explainable AI
for understanding decision and data-driven optimization of the Choquet integral. IEEE
Int. Conf. Fuzzy Systems. Best Student Paper Award Finalist
12. S. Kabir, C. Wagner, T.C.
Havens, and D.T. Anderson (July, 2018). A
bi-directional subsethood based similarity measure
for fuzzy sets. IEEE Int. Conf. Fuzzy
Systems.
13. I.T. Cummings, T.J. Schulz, J.P. Doane, and T.C. Havens (July,
2018). Optimizing the information-theoretic partitioning of simultaneous
transmit and receive phased arrays. IEEE
Int. Symp. Antennas and Propagation.
14. U. Ahrawal,
A.J. Pinar, C. Wagner, T.C. Havens, D. Soria, and J. Garibaldi (June, 2018). Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with state-of-the-art
ensemble algorithms. Int. Conf. Info.
Process. and Management of Uncertainty, 329-341.
15. M.A. Islam, D.T.
Anderson, X. Du, T.C. Havens, and C. Wagner (June,
2018). Efficient binary fuzzy measure representation and Choquet
integral learning. Int. Conf. Info.
Process. and Management of Uncertainty, 115-126.
16. I.T. Cummings, T.J. Schulz, J.P. Doane, and T.C. Havens (April,
2018). An information-theoretic approach to partitioning simultaneous transmit
and receive digital phased arrays. IEEE
Radar Conf.
17. T.C. Havens and A.J.
Pinar (Dec, 2017). Generating random fuzzy
(capacity) measures for data fusion simulations. IEEE Symp. Series Comp. Intell.
18. A.J. Pinar, T.C.
Havens, D.T. Anderson, and M.A. Islam (June,
2017). Visualization and learning of the Choquet
integral with limited training data. IEEE
Int. Conf. Fuzzy Systems.
19. C. Wagner, T.C. Havens, and D.T. Anderson (June, 2017). The arithmetic recursive average as an instance
of the recursive weighted power mean. IEEE
Int. Conf. Fuzzy Systems.
20. T.C. Havens, C. Wagner, and D.T. Anderson (June, 2017). Efficient modeling and representation of
agreement in interval-valued data. IEEE
Int. Conf. Fuzzy Systems.
21. S. Kabir, C. Wagner, U. Aickelin, D.T.
Anderson, and T.C. Havens (June, 2017). Novel similarity measure for interval-valued
data based on their overlapping ratio. IEEE
Int. Conf. Fuzzy Systems.
22. H. Deilamsalehy, T.C. Havens, and P. Lautala (Apr, 2017). Sensor fusion of wayside visible and thermal
imagery for rail car wheel and bearing damage detection. Proc. Joint Rail Conference, no. JRC2017-2284.
23. A.J. Pinar, J. Rice,
T.C. Havens, M. Masarik,
J. Burns, and D.T. Anderson (Dec, 2016). Explosive
hazard detection with feature and decision level fusion, multiple kernel
learning, and fuzzy integrals. IEEE CISDA,
doi: 10.1109/SSCI.2016.7850069.
24. H. Deilamsalehy and T.C. Havens (Oct, 2016). Sensor-fused
three-dimensional localization using IMU, camera and lidar. IEEE SENSORS, 1-3.
25. H. Sweidan and T.C. Havens (July, 2016). Coverage
optimization in a terrain-aware wireless sensor network. IEEE Cong. Evolutionary Computation, 3687-3694.
26. J. Manela and T.C. Havens (July, 2016). Histogram
particle swarm optimization (HistPSO): evolving
non-parametric acceleration distributions. IEEE
Cong. Evolutionary Computation, 2071-2076.
27. L. Tomlin, D.T. Anderson,
C. Wagner, T.C. Havens, and J.M.
Keller (June, 2016). Fuzzy integral for rule
aggregation in fuzzy inference systems. Proc.
Int. Conf. Info. Proc. and Management of
Uncertainty, 78-90.
28. H. Deilamsalehy, T.C. Havens, and P. Lautala (Apr, 2016). Detection of sliding wheels and hot bearings
using wayside thermal cameras. Proc.
Joint Rail Conference, no. JRC2016-5711.
29. M.A. Islam, D.T.
Anderson, and T.C. Havens (Aug, 2015). Multi-criteria based
learning of the Choquet integral using goal
programming. Proc. NAFIPS, 1-6.
30. T. Adeyeba,
D.T. Anderson, and T.C. Havens (Aug, 2015). Insights and characterizations of l1-norm based
sparsity learning of a lexicographically encoded capacity vector for the Choquet integral. Proc.
IEEE Int. Conf. Fuzzy Systems, 1-7.
31. A. Pinar, T.C. Havens, D.T.
Anderson, and L. Hu (Aug, 2015). Feature and decision
level fusion using multiple kernel learning and fuzzy integrals. Proc. IEEE Int. Conf. Fuzzy Systems,
1-7.
32. H. Deilamsalehy, T.C. Havens, and P. Lautala (2015). Automatic
method for detecting and categorizing train car wheel and bearing defects. Proc. Joint Rail Conference, no.
JRC2015-5741.
33. L. Hu, D.T. Anderson, T.C. Havens, and J.M. Keller (2014). Efficient
and scalable nonlinear multiple kernel aggregation using the Choquet integral. CCIS,
vol. 442: Proc. Int. Conf. Info. Processing and Management of Uncertainty in
Knowledge-Based Systems, 206-215.
34. D.T. Anderson, S. Price,
and T.C. Havens (2014). Regularization-based
learning of the Choquet integral. Proc. IEEE Int. Conf. Fuzzy Systems,
2519-2526.
35. P. Bhatkhande and T.C. Havens (2014). Real time fuzzy controller for quadrotor
stability control. Proc. IEEE Int. Conf.
Fuzzy Systems, 913-919.
36. V. Navale and T.C. Havens (2014). Fuzzy logic controller for energy management of
power split hybrid electric vehicle transmission. Proc. IEEE Int. Conf. Fuzzy Systems, 940-947.
37. J. Su and T.C. Havens
(2014). Fuzzy community detection in social networks using a genetic algorithm.
Proc. IEEE Int. Conf. Fuzzy Systems, 2039-2046.
38. S. Price, D.T. Anderson,
C. Wagner, T.C. Havens, and J.M.
Keller (2014). Indices for introspection of the Choquet
integral. Studies in Fuzziness and Soft
Computing, vol. 312: Proc. World Conf. Soft Computing, 261-271.
39. J. Su and T.C. Havens
(2014). A generalized fuzzy t-norm formulation of fuzzy modularity for
community detection in social networks. Studies
in Fuzziness and Soft Computing, vol. 312: Proc. World Conf. Soft Computing, 65-76.
40. Z. Zhang and T.C. Havens
(2013). Scalable approximation of kernel fuzzy c-means. Proc. IEEE Int. Conf. Big Data, 161-168. (20% accept rate)
41. D. Kumar, M. Palaniswami, S. Rajasegarar, C.
Leckie, J.C. Bezdek, and T.C. Havens (2013). clusiVAT: a mixed
visual/numerical clustering algorithm for big data. Proc. IEEE Int. Conf. Big Data, 112-117. (20% accept rate)
42. C. Wagner, D.T. Anderson,
and T.C. Havens (2013).
Generalization of the fuzzy integral for discontinuous interval- and non-convex
interval fuzzy set-valued inputs. Proc.
IEEE Int. Conf. Fuzzy Systems, 1-8.
43. L. Hu, D.T. Anderson, and
T.C. Havens (2013). Multiple kernel
aggregation using fuzzy integrals. Proc.
IEEE Int. Conf. Fuzzy Systems, 1-7.
44. T.C. Havens, D.T. Anderson, C. Wagner, H. Deilamsalehy, and D. Wonnacott (2013). Fuzzy integrals of
intervals using a measure of generalized accord. Proc. IEEE Int. Conf. Fuzzy Systems, 1-8.
45. T.C. Havens, J.C. Bezdek, C.
Leckie, and M. Palaniswami (2013). Extension of iVAT to asymmetric matrices. Proc. IEEE Int. Conf. Fuzzy Systems, 1-6.
46. T.C. Havens, J.C. Bezdek, C.
Leckie, J. Chan, W. Liu, J. Bailey, K. Romamohanarao,
and M. Palaniswami (2013). Clustering and
visualization of fuzzy communities in social networks. Proc. IEEE Int. Conf. Fuzzy Systems, 1-7.
47. T.C. Havens,
J.C. Bezdek, and M. Palaniswami
(2013). Scalable single-linkage hierarchical clustering for big data. Proc. IEEE ISSNIP, 396-401.
48. T.C. Havens
(2012). Approximation of kernel k-means for streaming data. Int. Conf. Pattern Recognition, 509-512.
(Tier II Computer Science Conference, 15% oral accept rate)
49. T.C. Havens,
J.C. Bezdek, and M. Palaniswami
(2012). Cluster validity for kernel fuzzy clustering. Proc. IEEE Int. Conf. Fuzzy Systems, 1-8. Best Paper Finalist
50. D.T.
Anderson, T.C. Havens, C. Wagner,
J.M. Keller, M. Anderson, and D. Wescott (2012). Sugeno
fuzzy integral generalizations for sub-normal fuzzy set-valued inputs. Proc. IEEE Int. Conf. Fuzzy Systems, 1-8. Best
Paper Award
51. S.
Rajasegarar, J.C. Bezdek,
M. Moshtaghi, C. Leckie, T.C. Havens, and M. Palaniswami (2012).
Measures for clustering and anomaly detection in sets of higher dimensional
ellipsoids. IEEE Int. Joint Conf. Neural
Networks, 1-8. (Tier II Computer Science Conference)
52. M.
Popescu, J.M. Keller, J.C. Bezdek, and T.C. Havens (2011). Correlation cluster
validity. IEEE Systems, Man, Cybernetics, 2531-2536. IEEE Franklin V. Taylor Memorial
Best Paper Award
53. T.C. Havens,
R. Chitta, A.K. Jain, and R. Jin (2011). Speedup of
fuzzy and possibilistic c-means for large-scale clustering. Proc. IEEE Int. Conf. Fuzzy Systems,
463-470.
54. R.
Chitta, R. Jin, T.C.
Havens, and A.K. Jain (2011). Approximate kernel k-means: solution to large
scale kernel clustering. Proc. ACM SIGKDD
Conf. Knowledge Discovery and Data Mining, 895-903. (Tier I Computer Science
Conference, 17.6% overall accept rate)
55. T.C. Havens,
D.T. Anderson, and J.M. Keller (2010). A fuzzy Choquet
integral with an interval type-2 fuzzy-valued integrand. Proc. IEEE Int. Conf. Fuzzy Systems, 1-8.
56. Bezdek,
J.C., T.C. Havens, J.M. Keller, C.A.
Leckie, L. Park, and M. Palaniswami (2010).
Clustering elliptical anomalies in sensor networks. Proc. IEEE Int. Conf. Fuzzy Systems, 1-8.
57. Sledge,
I.J., T.C. Havens, J.C. Bezdek, and J.M. Keller (2010). Relational cluster
validity. Proc. IEEE Int. Conf. Fuzzy
Systems, 1-9.
58. Sledge,
I.J., J.C. Bezdek, T.C. Havens, and J.M. Keller (2010). A relational dual of the fuzzy
possibilistic c-means algorithm. Proc.
IEEE Int. Conf. Fuzzy Systems, 1-9.
59. D.T.
Anderson, J.M. Keller, and T.C. Havens
(2010). Learning fuzzy-valued fuzzy measures for the fuzzy-valued Sugeno fuzzy integral. Proc.
Int. Conf. Information Processing and Management of Uncertainty in
Knowledge-Based Systems, 502-511.
60. T.C. Havens,
J.C. Bezdek, and J.M. Keller (2010). A new implementation
of the co-VAT algorithm for visual assessment of clusters in rectangular
relational data. Artificial Intelligence
and Soft Computing, Part I, 363-371.
61. T.C. Havens,
J.M. Keller, G.L. Alexander, M. Skubic, and M. Rantz (2009). Fuzzy contour tracking of human silhouettes. Proc. IEEE Int. Conf. Fuzzy Systems,
951-956.
62. T.C. Havens,
G.L. Alexander, C. Abbott, J.M. Keller, M. Skubic,
and M. Rantz (2009). Contour tracking of human
exercises. Proc. IEEE Workshop on
Computational Intelligence and Computer Vision, 22-28.
63. T.C. Havens,
J.C. Bezdek, J.M. Keller, and M. Popescu (2008). Dunnճ
cluster validity index as a contrast measure of VAT images. Proc. Int. Conf. Pattern Recognition,
1-4. (38% poster accept rate)
64. Sledge,
I.J., J.M. Keller, T.C. Havens, G.L.
Alexander, and M. Skubic (2008). Temporal activity
analysis. Proc. AAAI Symposium on AI in
Eldercare, 101-108.
65. T.C. Havens,
C.J. Spain, N.G. Salmon, and J.M. Keller (2008). Roach infestation
optimization. Proc. IEEE Swarm
Intelligence Symposium, 1-7. (~40% oral accept rate)
66. T.C. Havens,
J.M. Keller, M. Popescu, and J.C. Bezdek (2008).
Ontological self-organizing maps for cluster visualization and functional
summarization of gene products using Gene Ontology similarity measures. Proc. IEEE Int. Conf. Fuzzy Systems,
104-109.
67. Popescu,
M., J.C. Bezdek, J.M. Keller, T.C. Havens, and J.M. Huband (2008). A
new cluster validity measure for bioinformatics relational datasets. Proc. IEEE Int. Conf. Fuzzy Systems,
726-731.
68. T.C. Havens,
J.M. Keller, E. MacNeal Rehrig,
H.M. Appel, M. Popescu, J.C. Schultz, and J.C. Bezdek
(2008). Fuzzy cluster analysis of bioinformatics data composed of microarray
expression data and Gene Ontology annotations. Proc. North American Fuzzy Information Processing Society, 1-6.
Other
Publications
Peer-Reviewed
Abstracts / Full Proceedings Publication
1. A. Webb, T.C. Havens, and T.J. Schulz (May, 2017). GPR imaging with mutual intensity. Proc. SPIE DSS, 10182, 101821B.
2. A.J. Pinar, T.C.
Havens,
and A. Webb (May,
2017). Multisensor fusion of FLGPR and thermal and
visible-spectrum cameras for standoff detection of buried objects. Proc. SPIE DSS, 10182, 101821A.
3. A. Webb, T.C. Havens,
and T.J. Schulz. Iterative image formation for forward looking GPR. MSS Battlefield, Survivability, and Discrimination.
4. A. Pinar, T.C. Havens, J.
Rice, M. Masarik, J. Burns, and B. Thelen (May,
2016). A comparison of robust
principal component analysis techniques for buried object detection in downward
looking GPR and EMI sensor data. Proc. SPIE DSS, 9823, 98230T.
5. J. Rice, A. Pinar, T.C.
Havens, and T.J. Schulz (May,
2016). Multiple instance
learning for buried hazard detection. Proc. SPIE DSS, 9823, 98231N.
6. A. Webb, T.C. Havens, and
T.J. Schulz (May, 2016). Spectral diversity for ground clutter mitigation in
forward-looking GPR. Proc. SPIE DSS, 9823, 98231M.
7. M.P. Masarik,
J. Burns, B.T. Thelen, J. Kelly, and T.C. Havens
(May, 2016). Enhanced buried UXO detection via
GPR/EMI data fusion. Proc. SPIE DSS,
9823, 98230R.
8. S.R.
Price, B. Murray, L. Hu, D.T. Anderson, T.C.
Havens, R.H. Luke, and J.M. Keller (May, 2016).
Multiple kernel based feature and decision level
fusion of iECO individuals for explosive hazard
detection in FLIR imagery. Proc. SPIE DSS, 9823, 98231G.
9. J.L.
Dowdy, D.T. Anderson, R.H. Luke, J.E. Ball, T.C. Havens, and J.M. Keller (May, 2016). Comparison of spatial frequency domain
features for the detection of side attack explosive ballistics in synthetic
aperture acoustics. Proc. SPIE DSS, 9823, 98231R.
10. S.R.
Price, D.T. Anderson, and T.C. Havens
(2015). Fusion of iECO image descriptors for buried
explosive hazard detection in forward-looking infrared imagery. Proc. SPIE, 9454, 945405.
11. J. Becker,
T.C. Havens, A. Pinar, and T.J. Schulz (2015). Deep belief networks for false
alarm rejection in forward-looking ground-penetrating radar. Proc. SPIE, 9454, 94540W.
12. M.P.
Masarik, J. Burns, B.T. Thelen,
and T.C. Havens (2015). GPR anomaly
detection with robust principal component analysis. Proc. SPIE, 9454, 945414.
13. A. Webb,
T.C. Havens, and T.J. Schulz (2015).
An apodization approach for processing forward-looking GPR for explosive hazard
detection. Proc. SPIE, 9454, 94540X.
14. A. Pinar,
M. Masarik, J. Kelly, T.C. Havens, J. Burns, B. Thelen, and J. Becker (2015). Approach to explosive
hazard detection using sensor fusion and multiple kernel learning with
downward-looking GPR and EMI sensor data. Proc.
SPIE, 9454, 94540B.
15. T.C. Havens,
J. Becker, A. Pinar, and T.J. Schulz (2014). Multi-band sensor-fused explosive
hazard detection in forward-looking ground-penetrating radar. Proc. SPIE, 9072, 90720T.
16. T.C. Havens,
J.M. Keller, K. Stone, K.C. Ho, T.T. Ton, D.C. Wong, and M. Soumekh
(2012). Multiple kernel learning for explosive hazards detection in
forward-looking ground-penetrating radar. Proc.
SPIE, 8357, 83571D.
17. J.
Farrell, T.C. Havens, K.C. Ho, J.M.
Keller, T.T. Ton, D.C. Wong, and M. Soumekh (2012).
Evaluation and improvement of spectral features for the detection of buried
explosive hazards using forward-looking ground-penetrating radar. Proc. SPIE, 8357, 8357C.
18. T.C. Havens,
J.M. Keller, K.C. Ho, T.T. Ton, D.C. Wong, and M. Soumekh
(2011). Narrow band processing and fusion approach for explosive hazard
detection in FLGPR. Proc. SPIE, 8017(1),
8017F.
19. J.
Farrell, T.C. Havens, K.C. Ho, J.M.
Keller, T.T. Ton, D.C. Wong, and M. Soumekh (2011).
Detection of explosive hazards using spectrum features from forward-looking
ground penetrating radar imagery. To appear, Proc. SPIE, 8017(1), 8017E.
20. T.C. Havens,
C.J. Spain, K.C. Ho, J.M. Keller, T.T. Ton, D.C. Wong, and M. Soumekh (2010). Improved detection and false alarm
rejection using ground-penetrating radar and color imagery in a forward-looking
system. Proc. SPIE, 7664(1), 76641U.
21. T.C. Havens,
K.C. Ho, J.M. Keller, M. Popescu, T.T. Ton, D.C. Wong, and M. Soumekh (2010). Locally adaptive detection algorithm for
forward-looking ground-penetrating radar. Proc.
SPIE, 7664(1), 76642E.
22. Popescu,
M., K. Stone, T.C. Havens, K.C. Ho,
and J.M. Keller (2010). Anomaly detection in forward-looking infrared imaging
using one class classifiers. Proc. SPIE,
7664(1), 76642B.
23. Stone,
K., J.M. Keller, M. Popescu, T.C. Havens,
and K.C. Ho (2010). Forward-looking anomaly detection via fusion of infrared
and color imagery. Proc. SPIE, 7664(1),
766425.
24. T.C. Havens,
K. Stone, J.M. Keller, and K.C. Ho (2009). Sensor-fused detection of explosive
hazards. Proc. SPIE, 7303(1), 73032A.
Peer-Reviewed
Abstracts / Abstract Publication
25. T.C. Havens, H. Deilamsalehy, and P. Lautala (June, 2017). Sensor
fusion of wayside visible and thermal imagery for rail car wheel and bearing
damage detection. Rail Infrastructure and
Vehicle Inspection Technology Conference.
26. C.N. Brooks, R.J. Dobson,
D.B. Dean, T. Oommen, T.C. Havens, T.M. Ahlborn, S.J. Cook, and A. Clover (2014).
Evaluating the use of unmanned aerial vehicles for transportation purposes: a
Michigan demonstration. Proc. 21st
World Congress: Intelligent Transportation Systems.
27. J.E.
Summers, T.C. Havens, and T.K. Meyer
(2014). Learning environmentally dependent feature representations for
classification of objects on or buried in the seafloor. J. Acoustical Society of America 135(4), 2296.
Reports
1. C.
Brooks, R.J. Dobson, D.M. Banach, D. Dean, T. Oommen,
R.E. Wolf, T.C. Havens, T.M.
Ahlborn, B. Hart (April, 2015). Evaluating the use of
unmanned aerial vehicles for transportation purposes. Technical Report RC-1616, Michigan Dept. of Transportation.
2. C. Brooks, R.J. Dobson, T. Oommen,
T.C. Havens, T.M. Ahlborn, D. Dean, D. Banach, N. Jessee,
R.E. Wolf (October, 2013). State of practice for
remote sensing of transportation infrastructure using unmanned aerial vehicles
(UAV). Technical Report, Michigan
Technological University.
Book
Chapters
1.
T.C. Havens, D.T. Anderson, K. Stone, J. Becker, and A.J.
Pinar (2016). Computational Intelligence in Forward Looking Explosive
Hazard Detection. In R. Abielmona et al. (Eds.), Recent Advances in Computational
Intelligence in Defense and Security (pp. 13-44). Berlin: Springer.
2.
T.C. Havens, J.C. Bezdek, and M. Palaniswami (2012). Incremental Kernel Fuzzy c-Means. In K.
Madani et al. (Eds.), Computational Intelligence,
Revised and Selected Papers of the International Joint Conference, IJCCI 2010 (pp. 3-18). Berlin: Springer.
3.
Popescu, M., T.C.
Havens, J.M. Keller, and J.C. Bezdek (2009).
Clustering with Ontologies. In M. Popescu and D. Xu (Eds.), Data Mining in Biomedicine Using Ontologies
(pp. 45-62). Boston, MA: Artech House.