Publications

a. Refereed Publications/Proceedings

Peer-Reviewed Journal Articles

1.      A.J. Pinar, T.C. Havens, D.T. Anderson, and L. Hu. Efficient multiple kernel classification using feature and decision level fusion. Accepted, IEEE Trans. Fuzzy Systems.

2.      H. Deilamsalehy, T.C. Havens, P. Lautala, E. Medici, and J. Davis. An automatic train car wheel flat spot detection method using thermal camera imagery. J. Rail and Rapid Transit. doi: 10.1177/0954409716638703.

3.      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.

4.      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.

5.      T.C. Havens, D.T. Anderson, and C. Wagner. Data-informed fuzzy measures for fuzzy integration of intervals and fuzzy numbers. Accepted, IEEE Trans. Fuzzy Systems, 23(5), 1861-1875.

6.      J. Su and T.C. Havens. Quadratic program-based modularity maximization for fuzzy community detection in social networks. Accepted, IEEE Trans. Fuzzy Systems, 23(5), 1356-1371.

7.      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.

8.      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.

9.      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.

10.   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.

11.   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.

12.   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.

13.   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.

14.   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

15.   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.

16.   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.

17.   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.

18.   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.

19.   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

20.   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.

21.   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.

22.   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.

23.   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

24.   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.

25.   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.

26.   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.

27.   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.

28.   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.

29.   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.      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. Accepted, Proc. Joint Rail Conference.

2.      A.J. Pinar, J. Rice, T.C. Havens, M. Masarik, J. Burns, and D.T. Anderson. Explosive hazard detection with feature and decision level fusion, multiple kernel learning, and fuzzy integrals. Accepted, IEEE CISDA.

3.      H. Deilamsalehy and T.C. Havens (Oct, 2016). Sensor-fused three-dimensional localization using IMU, camera and lidar. IEEE SENSORS.

4.      H. Sweidan and T.C. Havens (July, 2016). Coverage optimization in a terrain-aware wireless sensor network. IEEE Cong. Evolutionary Computation.

5.      J. Manela and T.C. Havens (July, 2016). Histogram particle swarm optimization (HistPSO): evolving non-parametric acceleration distributions. IEEE Cong. Evolutionary Computation.

6.      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.

7.      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.

8.      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.

9.      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.

10.   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.

11.   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.

12.   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.

13.   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.

14.   P. Bhatkhande and T.C. Havens (2014). Real time fuzzy controller for quadrotor stability control. Proc. IEEE Int. Conf. Fuzzy Systems, 913-919.

15.   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.

16.   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.

17.   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.

18.   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.

19.   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)

20.   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)

21.   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.

22.   L. Hu, D.T. Anderson, and T.C. Havens (2013). Multiple kernel aggregation using fuzzy integrals. Proc. IEEE Int. Conf. Fuzzy Systems, 1-7.

23.   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.

24.   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.

25.   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.

26.   T.C. Havens, J.C. Bezdek, and M. Palaniswami (2013). Scalable single-linkage hierarchical clustering for big data. Proc. IEEE ISSNIP, 396-401.

27.   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)

28.   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

29.   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

30.   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)

31.   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

32.   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. 

33.   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)

34.   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.  

35.   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.

36.   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.

37.   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.

38.   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.

39.   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.

40.   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.

41.   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.

42.   T.C. Havens, J.C. Bezdek, J.M. Keller, and M. Popescu (2008). DunnŐs cluster validity index as a contrast measure of VAT images. Proc. Int. Conf. Pattern Recognition, 1-4. (38% poster accept rate)

43.   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.

44.   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) 

45.   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. 

46.   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.

47.   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.

 

b. Other Publications

Peer-Reviewed Abstracts / Full Proceedings Publication

1.      A. Webb, T.C. Havens, and T.J. Schulz. Iterative image formation for forward looking GPR. Accepted, MSS Battlefield, Survivability, and Discrimination.

2.      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.

3.      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.

4.      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.

5.      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.

6.      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.

7.      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.

8.      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.

9.      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.

10.   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.

11.   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.

12.   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.

13.   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.

14.   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.

15.   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.

16.   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.

17.   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.

18.   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.

19.   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.

20.   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.

21.   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.

22.   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

23.   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.

24.   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.

 

c. 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.

 

d. Books Authored/Edited

 

e. 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.