Robert Jackson Marks II (born August 25, 1950) is an American electrical engineer, computer scientist and distinguished professor at Baylor University. His contributions include the Zhao-Atlas-Marks (ZAM) time-frequency distribution in the field of signal processing,Leon Cohen, Time Frequency Analysis: Theory and Applications, Prentice Hall, (1994) the Cheung–Marks theoremJ.L. Brown and S.D.Cabrera, "On well-posedness of the Papoulis generalized sampling expansion," IEEE Transactions on Circuits and Systems, May 1991 Volume: 38, Issue 5, pp. 554–556 in Shannon sampling theory and the Papoulis-Marks-Cheung (PMC) approach in multidimensional sampling.Matthew A. Prelee and David L. Neuhoff. "Multidimensional Manhattan Sampling and Reconstruction." IEEE Transactions on Information Theory 62, no. 5 (2016): 2772-2787. He was instrumental in the defining of the field of computational intelligence and co-edited the first book using computational intelligence in the title. A Christian and an old earth creationist, he is a subject of the 2008 pro-intelligent design motion picture, Expelled: No Intelligence Allowed.
Professional career Marks received his bachelor's and master's degrees from Rose–Hulman Institute of Technology in 1972 and 1973, respectively. During his doctoral studies at Texas Tech University, he was supervised by J.F. Walkup; his dissertation focused on optical signal processing. He obtained his Ph.D. degree in 1977.
Marks is a distinguished professor of electrical and computer engineering at Baylor University and serves as the director of the Walter Bradley Center for Natural and Artificial Intelligence.Bradley Center home page From 1977 to 2003, he was on the faculty of the University of Washington in Seattle. He was the first president of the Institute of Electrical and Electronics Engineers (IEEE) Neural Networks Council (now the IEEE Computational Intelligence Society). He is a Fellow of the IEEE"For leadership and contributions to the field of neural networks."(1994) and the Optical Society of America."For contributions to image recovery and synthesis, optical processing, and electro-optical neural networks." (1989)
Technical contributions Marks is a researcher in the area of electrical engineering. Marks's CV.
Treatment of prostate cancer. Marks and his colleagues developed algorithms for real time identification of placement of radioactive seeds in cancerous prostates. S. Narayanan, P.S. Cho and R.J. Marks II, "Fast Cross-Projection Algorithm for Reconstruction of Seeds in Prostate Brachytherapy", Med. Phys. 29 (7), July 2002, pp. 1572–1579. S. Narayanan, P.S. Cho and R.J. Marks II, "Three-dimensional seed reconstruction from an incomplete data set for prostate brachytherapy", Phys. Med. Biol., vol.49, pp. 3483–3494 (2004). For this work, he was a co-recipient of the Judith Stitt Best Abstract Award from the American Brachytherapy Society.American Brachytherapy Society homepage The algorithm is used clinically.D.R. Reed, K.E. Wallner, S.Narayanan, S.G. Sutlief, E.C. Ford, P.S. Cho, "Intraoperative fluoroscopic dose assessment in prostate brachytherapy patients," International Journal of Radiation Oncology, Biology, Physics, Vol 63, Issue 1, September, 2005, pp. 301–307 Optimal detection. In the field of detection theory, Marks and his colleagues developed the first closed form solution for the Neyman–Pearson optimal detection of signals in non-Gaussian noiseS. A. Kassam, Signal Detection in Non-Gaussian Noise. Springer Verlag, 1988.Detection in Laplace noise R.J. Marks II, G.L. Wise, D.G. Haldeman and J.L. Whited, IEEE Transactions on Aerospace and Electronic Systems, vol. AES-14, pp. 866–872 (1978).
Power load forecasting using neural networks. With his colleagues at the University of Washington, Marks was the firstA. Khotanzad, R. Afkhami-Rohani, Lu Tsun-Liang, A. Abaye, M. Davis, D.J. Maratukulam, "ANNSTLF-a neural-network-based electric load forecasting system," IEEE Transactions on Neural Networks, Volume 8, Issue 4, Jul 1997 pp. 835–846. to apply an artificial neural network to forecast power demands for utilities in 1991.D.C. Park, M.A. El-Sharkawi, R.J. Marks II, L.E. Atlas & M.J. Damborg, "Electric load forecasting using an artificial neural network", IEEE Transactions on Power Engineering, vol.6, pp. 442–449 (1991). Six years later neural networks were being used by 32 major North American utilities and remains in common use today. IEEE sponsors a MATLAB based webinar on use of neural networks in load forecasting. IEEE Spectrum Webinar, "Electricity Demand and Price Forecasting with MATLAB," A technique "similar to one already used to successfully forecast electrical load needs" has been used to forecast Dow Jones closing values using data from millions of Twitter messages. "Analyzing almost 10 million tweets, research finds public mood can predict Dow days in advance," Indiana University Press Release. The Smith Tube. Marks was a member of the Baylor research team that introduced the Smith Tube, a visualization tool useful in advanced microwave systems design.Fellows, Matthew, Matthew Flachsbart, Jennifer Barlow, Charles Baylis, and Robert J. Marks. "The Smith Tube: Selection of radar chirp waveform bandwidth and power amplifier load impedance using multiple-bandwidth load-pull measurements." WAMICON 2014, pp. 1-5. IEEE, 2014. A generalization of the Smith Chart, the Smith Tube is currently in Keysight's Advanced Design System (ADS) software package.Agilent's "3D Smith Chart for Broadband PA Design" announcement
Convolutional neural networks. With Homma and Atlas, Marks developed a temporal convolutional neural network used widely in Deep learning. Signal display in time and frequency. The Zhao-Atlas-Marks time-frequency distribution, (a.k.a. the ZAM distribution or ZAMD), was originally called the cone shaped time-frequency distribution. Y. Zhao, L. E. Atlas, and R. J. Marks, "The use of cone-shape kernels for generalized time-frequency representations of nonstationary signals," IEEE Trans. Acoustics, Speech, Signal Processing, vol. 38, no. 7, pp. 1084–1091, July 1990 The ZAMD is a special case of Cohen's class of time-frequency distributions. The ZAMD is currently in the MATLAB Time-Frequency Toolbox Time-Frequency Toolbox For Use with MATLAB and National Instruments' LabVIEW Tools for Time-Frequency, Time-Series, and Wavelet Analysis National Instruments. LabVIEW Tools for Time-Frequency, Time-Series, and Wavelet Analysis. TFA Cone-Shaped Distribution VI The ZAMD has been applied in numerous areas: Remote sensing. Marks and his colleagues L. Tsang, Z. Chen, S. Oh, R.J. Marks II and A.T.C. Chang, "Inversion of snow parameters from passive microwave remote sensing measurements by a neural network trained with a multiple scattering model" IEEE Transactions on Goescience and Remote Sensing, vol. 30, no.5, pp. 1015–1024 (1992).A. Ishimaru, R.J. Marks II, L. Tsang, C.M. Lam, D.C. Park and S. Kitamaru, "Particle size distribution using optical sensing and neural networks", Optics Letters, vol.15, pp. 1221–1223 (1990). were the first to use neural network inversion in remote sensing. They measured snow parameters from microwave measurements made by satellites. Their general approach is widely used today.Vladimir M. Krasnopolsky and Helmut Schillerb, "Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements," Neural Networks, Volume 16, Issues 3–4, April–May 2003, pp. 321–334F. Van der Meer, "Geophysical inversion of imaging spectrometer data for geologic modelling," International Journal of Remote Sensing, Volume 21, Issue 2, pp. 387–393 (2000) Wireless arrays. Marks is a co-recipient of a NASA Tech Brief for pioneering power efficient communication in wireless arrays.NASA Recognizes Baylor Engineer For Innovative Technology A.K. Das, R.J. Marks II, M.A. El-Sharkawi, Payman Arabshahi and Andrew Gray, "Minimum Power Broadcast Trees for Wireless Networks: Optimization Using the Viability Lemma", Proceedings of the NASA Earth Science Technology Conference, June 11–13, 2002, Pasadena, CA Power generation. Working with Southern California Edison, Marks and his colleagues pioneered computational intelligence based methods for early detection of intermittent shorted windings in multi ton electric generators while the rotors were still turning. M.A. El-Sharkawi, R.J. Marks II, S.Oh, S.J. Huang, I. Kerszenbaum and A. Rodriguez, "Localization of Winding Shorts Using Fuzzified Neural Networks", IEEE Transactions on Energy Conversion, vol. 10, no.1, March 1995, pp. 147–155.)S. Guttormsson, R.J. Marks II, M.A. El-Sharkawi and I. Kerszenbaum, "Elliptical novelty grouping for on-line short-turn detection of excited running rotors", IEEE Transactions on Energy Conversion, IEEE Transactions on Volume: 14 1, March 1999, pp. 16–22 Marks has made contributions to the sampling theorem including authoring the first book exclusively dedicated to the subject.R.J. Marks II, Introduction to Shannon Sampling and Interpolation Theory, Springer-Verlag, (1991). Restoration of lost samples. Using "sophisticated estimation of the missing samples using previous and future samples",Farokh A. Marvasti, Peter M. Clarkson, Miroslav V. Dokic, Ut Goenchanart, and Chuande Liu, "Reconstruction of Speech Signals with Lost Samples," IEEE Transactions on Signal Processing, Volume 40, Issue 12, pp. 2897–2903, December 1992. MarksR.J. Marks II, "Restoring lost samples from an oversampled bandlimited signal", IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-31, pp. 752–755 (1983). first showed that, when a signal is sampled above its Nyquist rate, lost samples "are redundant, in the sense that any finite number of them can be obtained from the remaining ones by solving a system of linear equations".P.J.S.G. Ferreira, Incomplete sampling series and the recovery of missing samples from oversampled bandlimited signals," IEEE Transactions on Signal Processing, (40) 1 pp. 225 227 (1992). Ill-posed sampling (The Cheung-Marks Theorem). The sampling theorem's Cheung–Marks theoremJohn L. Brown, Jr. and Sergio D. Cabrera, "On Well-Posedness of the Papoulis Generalized Sampling Expansion," IEEE Transactions on Circuits and Systems, pp.554-556, 1991. shows that samples taken from a signal at or above the Nyquist rate may prove incapable of restoring the signal in the presence of small amounts of noise. K.F. Cheung and R.J. Marks II, "Ill-posed sampling theorems", IEEE Transactions on Circuits and Systems, vol. CAS-32, pp. 829–835 (1985). Optimal image sampling. An image is said to be optimally sampled when the samples per unit area are minimized subject to no degradation of the interpolated image. Marks's contributions to optimal image sampling include: The Papoulis-Marks-Cheung Approach. Marks and CheungR. J. Marks, II, "Multidimensional-signal sample dependency at Nyquist densities," J. Opt. Soc. Amer. A, vol. 3, pp. 268–273, Feb. 1986. K. F. Cheung and R. J. Marks, II, "Imaging sampling below the Nyquist density without aliasing," J. Opt. Soc. Amer. A, vol. 7, no. 1, pp. 92–105, Jan. 1990. K. F. Cheung, "A multidimensional extension of Papoulis' generalized sampling expansion with the application in minimum density sampling," in Advanced Topics in Shannon Sampling and Interpolation Theory, Robert J. Marks II, Editor. New York, NY, USA: Springer-Verlag, 1993, pp. 85–119. extended the generalized sampling expansion of Athanasios PapoulisAthanasios Papoulis, "Generalized sampling expansion," IEEE Trans. Circuits Syst., vol. 24, no. 11, pp. 652–654, Nov. 1977. to higher dimensions. Sub-Nyquist Sampling. Cheung and Marks K.F. Cheung and R.J. Marks II, "Image sampling below the Nyquist density without aliasing", Journal of the Optical Society of America A, vol.7, pp. 92–105 (1990) showed that images could be sampled below their Nyquist rate and still be recovered without aliasing.