Publications (97)
As of 05/2024, in reverse temporal order. Most have full-text access via NCBI. Links to most of the full text copies are also available on http://sce.uhcl.edu/sakoglu under “Publications”.
J: Journal, P: Patent, C: Conference, BC: Book Chapter, O: Other.
Google Scholar Link: https://scholar.google.com/citations?hl=en&user=Btc4-gcAAAAJ.
Patents (1):
[P01] “Detector with Tunable Spectral Response”, Krishna S, Tyo JS, Hayat MM, Raghavan S, Sakoglu U, US Patent #7,217,951, issued in 2007 (2007). [Active & licensed patent, licensed in 2010]
Book Chapters (Peer-Reviewed) (1):
Peer-reviewed/Refereed Published Journal Papers (23):[J23] Cetin O, Canel B, Dogali G, Sakoglu U, Enhancing Precision in Multiple Sclerosis Lesion Segmentation: A U-Net Based Machine Learning Approach with Data Augmentation, preprint, SSRN, 2024.
[J22] Londhe K, Dharmadhikari N, Zaveri P, Sakoglu U, "Enhanced Travel Experience using Artificial Intelligence: A Data-driven Approach," Procedia Computer Science, Vol 235, Pages 1920-1928 (2024). https://www.sciencedirect.com/science/article/pii/S1877050924008585
[J21] Subasi A, Tuncer T, Dogan S, Tanko D, Sakoglu U, "EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier," Biomedical Signal Processing and Control Journal, Vol 68, 102648, July 2021.
https://doi.org/10.1016/j.bspc.2021.102648 [landmark paper on ML application to emotion recognition, #citations=110+]
[J20] Cetin O., Seymen V., Sakoglu U, "Multiple sclerosis lesion detection in multimodal MRI using simple clustering-based segmentation and classification," Informatics in Medicine Unlocked Vol .20, 100409 1-10, Elsevier (2020). https://doi.org/10.1016/j.imu.2020.100409
Peer-reviewed, Presented and Published Conference Proceeding Papers (14) and Abstracts (56):
[C64] Sakoglu U, Minton M, “Development of an algebraic nonuniformity correction algorithm for hexagonally-sampled infrared imagery,“ Proc. SPIE 11503, Infrared Sensors, Devices, and Applications X, 115030A (August 2020); https://doi.org/10.1117/12.2568156
Other(2):
[J19] Sakoglu U, Mete M, Esquivel J, Rubia K, Briggs R, Adinoff B, “Classification of Cocaine Dependent Subjects with Dynamic Functional Connectivity from Functional Magnetic Resonance Imaging Data,” Journal of Neuroscience Research, Wiley, Vol. 97(7), pp.790-803 (2019). [pdf, suppl.]
[J18] Gopinath K, Sakoglu U, Crosson B, Haley RW, “Exploring Brain Mechanisms Underlying Gulf War Illness with Group ICA based Analysis of fMRI Resting State Network,” Neuroscience Letters, Elsevier, Vol. 710, pp. 136-141 (2019). [pdf, suppl.]
[J17] Mete M, Sakoglu U, Spence JS, Devous MD, Harris TS, Adinoff B, “Successful Classification of Cocaine Dependence Using SPECT Imaging: A Machine Learning Approach,” BMC Bioinformatics, Vol. 17 (Suppl. 13), pp. 367-379 (2016). [pdf]
[J16] Kacar S and Sakoglu U, “Design of a Novel Biomedical Signal Processing and Analysis Tool for Functional Neuroimaging,” Computer Methods and Programs in Biomedicine, Elsevier, Vol. 125, pp. 46-57 (2016). [pdf]
[J15] Hempelmann CF, Sakoglu U, Gurupur VP, Jampana S, “An entropy-based evaluation method for knowledge bases of medical information systems,” Expert Systems with Applications, Elsevier, Vol. 46, pp. 272-263 (2016). [pdf] [seminal paper on applying information-theory/entropy-based method to medical IS, #citations=60+]
[J14] Akgun D, Sakoglu U, Esquivel J, Adinoff B, Mete M, “GPU accelerated dynamic functional connectivity analysis for functional MRI data,” Computerized Medical Imaging and Graphics, Elsevier, Vol. 43, pp. 53-63 (2015). [pdf]
[J13] Gurupur VP, Sakoglu U, Jain GP, Tanik UJ, “Semantic requirements sharing approach to develop software systems using concept maps and information entropy: A Personal Health Information System example,” Advances in Engineering Software Journal, Elsevier, Vol. 70 pp. 25-35 (2014). [pdf]
[J12] Sakoglu U, Upadhyay J, Chin C-L, Chandran P, Baker SJ, Cole T, Fox G, Day M, Luo F, “Paradigm shift in translational neuroimaging of CNS disorders,” Biochemical Pharmacology, Translational Medicine Special Issue, Elsevier, Vol. 81 Iss. 12, pp. 1374-1387, (2012). [pdf]
[J11] Upadhyay, Baker SJ, Chandran P, Miller L, Lee Y, Marek GJ, Sakoglu U, Chin C-L, Luo F, Fox G, Day M, “Default-Mode-Like Network Activation in Awake Rodents,” PLoS One, Vol. 6 Iss. 11, pp. e27839 (2011). [pdf] [seminal paper on discovering the default-mode-like network in rodent brain, #citations=100+]
[J10] Michael AM, King MD, Ehrlich S, Pearlson GD, White T, Holt DJ, Andreasen N, Sakoglu U, Ho B-C, Schulz SC, Calhoun VD, “A data-driven investigation of gray matter-function correlations in schizophrenia during a working memory task,” Frontiers in Human Neuroscience Vol. 5 No. 71 pp. 1-13 (2011). [pdf]
[J09] Sakoglu U, Pearlson GD, Kiehl KA, Wang YM, Michael AM, Calhoun VD, “A Method for Evaluating Dynamic Functional Network Connectivity and Task-Modulation: Application to Schizophrenia,” Magnetic Resonance Materials in Physics, Biology and Medicine (MAGMA), Special Issue on MR Imaging of Brain Networks, Vol. 23, pp. 351-366 (2010). [pdf] [seminal paper on brain’s dynamic (windowing) functional connectivity analyses for functional MRI data, #citations=620+]
[J08] Sakoglu U, Huisa-Garate B, Rosenberg G, Sood R, “Application of FT-based MMSE Deconvolution Method for Cerebral Blood Flow Measurement in Patients with Leukoaraiosis,” Magnetic Resonance Imaging, Elsevier, Vol. 27, pp. 625-630 (2009). [pdf]
[J07] Pai MP, Sakoglu U, Peterson S, Lyons CR, Sood R, “Characterization of BBB permeability in a preclinical model of cryptococcal meningoencephalitis using magnetic resonance imaging,” Journal of Cerebral Blood Flow and Metabolism, Vol. 29, pp. 545-553 (2009). [pdf]
[J06] Liu W, Sood R, Chen Q, Sakoglu U, Hendren J, Cetin O, Miyake M, Liu KJ, “Normobaric hyperoxia inhibits NADPH oxidase-mediated metalloproteinase-9 induction in cerebral microvessels in experimental stroke,” Journal of Neurochemistry, Vol. 107, Iss.5, pp. 1196-1205 (2008). [pdf] [developed and applied the permeability MRI data analysis to MRI data from rats, #citations=100+]
[J05] Sakoglu U, Sood R, “Cerebral blood flow estimation from perfusion-weighted MRI using FT-based MMSE filtering method,” Magnetic Resonance Imaging, Elsevier, Vol.26, Iss. 3, pp. 313-322 (2008). [pdf]
[J04] Paskaleva B, Sakoglu U, Wang Z, Hayat M, Tyo JS, Krishna S, “Algorithmic tunability of quantum-dot infrared detectors,” IEEE LEOS Newsletter, Vol. 20, No. 5, pp. 33-36 (2006). [pdf]
[J03] Sakoglu U, Dowd P, Hayat MM, Annamalai S, Posani KT, Tyo JS and Krishna S, “Statistical adaptive sensing using detectors with spectrally overlapping bands,” Applied Optics, Vol. 45, Iss. 28, pp. 7224-7234 (2006). [pdf]
[J02] Sakoglu U, Tyo JS, Hayat MM, Raghavan S, and Krishna S, “Spectrally adaptive infrared photodetectors with bias-tunable quantum dots,” Journal of the Optical Society of America B, Vol. 21, pp. 7-17 (2004). [pdf] [seminal paper on development of spectrally-tunable infrared detectors, PhD work, patent obtained and licensed; #citations=95+]
[J01] Hayat MM, Sakoglu U, Kwon O-H, Wang S, Campbell JC, Saleh BEA, Teich MC, “Breakdown probabilities for thin heterostructure avalanche photodiodes,” IEEE Journal of Quantum Electronics, Vol. 39, pp. 179-185 (2003).[pdf]
Peer-reviewed, Presented and Published Conference Proceeding Papers (14) and Abstracts (56):
[C70] Londhe K, Dharmadhikari N, Zaveri P, Sakoglu U, "Enhanced Travel Experience using Artificial Intelligence: A Data-driven Approach, International Conference on Machine Learning and Data Engineering (ICMLDE), UPES, Dehradun, India, November 2023 (2023).
[C69] Sakoglu U, Mishra A, Gopinath K, Crosson B, Haley RW, "Classification of Gulf War Illness Patients vs Control Veterans Using fMRI Dynamic Functional Connectivity," Proc. 30th Annual Meeting of International Society of Magnetic Resonance in Medicine (ISMRM), May 2022: https://index.mirasmart.com/ISMRM2022/PDFfiles/2567.html .
[C68] Sakoglu U, "An Algebraic Nonuniformity Correction Algorithm for Hexagonally-Sampled Infrared Images: A Simulation Study" was presented at the IEEE Annual International Conference on Image Processing (ICIP), September 2021: https://doi.org/10.1109/ICIP42928.2021.9506284 .
[C67] LeVan P and Sakoglu U, "Methane gas leak quantification employing infrared sensing at suspected leak sites" was presented at the Society for Optical Engineering's (SPIE) Annual Infrared Sensors, Devices and Applications Conference as part of the SPIE Annual International Optics+Photonics Meeting, San Diego, CA, August 2021: https://doi.org/10.1117/12.2595775 .
[C66] Sakoglu U, "Algebraic nonuniformity correction for infrared imagery using a hexagonal coordinate scheme" was presented at the Society for Optical Engineering's (SPIE) Annual Infrared Sensors, Devices and Applications Conference as part of the SPIE Annual International Optics+Photonics Meeting, San Diego, CA, August 2021: https://doi.org/10.1117/12.2596384 .
[C65] Sakoglu U, Bhupati L, Petrenko O, Calhoun VD, "Adaptive space-filling curve for improved feature selection from fMRI brain activation maps: application to schizophrenia classification," Proc. 29th Annual Meeting of International Society of Magnetic Resonance in Medicine (ISMRM), May 2021: https://index.mirasmart.com/ISMRM2021/PDFfiles/1522.html .
[C64] Sakoglu U, Minton M, “Development of an algebraic nonuniformity correction algorithm for hexagonally-sampled infrared imagery,“ Proc. SPIE 11503, Infrared Sensors, Devices, and Applications X, 115030A (August 2020); https://doi.org/10.1117/12.2568156
[C63] LeVan P, Sakoglu U, “Infrared sensing technologies assisting environmental monitoring,“ Proc. SPIE 11503, Infrared Sensors, Devices, and Applications X, 115030B (August 2020); https://doi.org/10.1117/12.2567769
[C60] Sakoglu U, Bhupati L, Beheshti N, Tsekos N, Johnsson L, "An Adaptive Space-Filling Curve Trajectory for Ordering 3D Datasets to 1D: Application to Brain MRI Data for Classification," International Conference on Computational Science 2020, Amsterdam, NL.
[C59] Reveriano F, Sakoglu U, Liu J, “Facial Expression Recognition: Utilizing Digital Image Processing, Deep Learning, and High Performance Computing,” peer-reviewed conference paper and presentation, ACM Practice and Experience in Advanced Research Computing (PEARC), July 28th - August 1, 2019, Chicago, IL. [paper pdf]
[C59] Reveriano F, Sakoglu U, Liu J, “Facial Expression Recognition: Utilizing Digital Image Processing, Deep Learning, and High Performance Computing,” peer-reviewed conference paper and presentation, ACM Practice and Experience in Advanced Research Computing (PEARC), July 28th - August 1, 2019, Chicago, IL. [paper pdf]
[C58] Gopinath K, Sakoglu U, Crosson B, Haley R, “Connectomics signatures of Gulf War Illness reveal brain mechanisms underlying the disorder,” 25th Annual Meeting of the Organization for Human Brain Mapping (OHBM), #1815, Th215, June 2019, Rome. [abstract pdf]
[C57] Gopinath K, Sakoglu U, Crosson B, Haley R, “Brain mechanisms underlying Gulf War Illness revealed by connectomics signatures of the disease,” 27th Annual Meeting of International Society of Magnetic Resonance in Medicine (ISMRM), #3887, May 2019, Montreal, QC. [abstract pdf]
[C56] Pannell R, Sanchez M, Ozdemir O, Sakoglu U, “Identification of Lung Opacities on Chest Radiographs Using Supervised Deep Learning Techniques,” IEEE DUAL Innovation/Automation Conference, November 2018, NASA JSC, Houston, TX. (Best Student Poster Presentation Award). [abstract pdf]
[C55] Sakoglu U, Galla M, Bhamidipati S, Gopinath K, Crosson B, Haley R, “Classification of Gulf War Illness Patients vs Control Veterans Using fMRI Functional Connectivity,” 24th Annual Meeting of the Organization for Human Brain Mapping (OHBM), #3441, June 2018, Singapore. [abstract pdf]
[C54] Sakoglu U, “Computing Dynamic Functional Connectivity and Task-Modulation Based Features from fMRI Data for Classification,” invited tutorial presentation at the 8th International Workshop on Pattern Recognition in Neuroimaging (PRNI), June 2018, Singapore. (invited) [abstract pdf]
[C53] Sakoglu U, Galla M, Gopinath K, Crosson B, Haley R, “Functional Connectivity-Based Classification of Gulf War Illness Patients vs Control Veterans,” 26th Annual Meeting of International Society of Magnetic Resonance in Medicine (ISMRM), #5542, June 2018, Paris, France. [abstract pdf]
[C52] Gopinath K, Sakoglu U, Crosson B, Haley R, “Exploratory Group Independent Components Analysis of resting state fMRI data reveals widespread brain function impairments in Gulf War Illness,” 26th Annual Meeting of International Society of Magnetic Resonance in Medicine (ISMRM), #3604, June 2018, Paris, France. [abstract pdf]
[C51] Gopinath K, Sakoglu U, Crosson B, Haley R, “Connectomics Correlates of Neurocognitive Deficits in Gulf War Illness Patients: A Resting State fMRI Study,” 26th Annual Meeting of International Society of Magnetic Resonance in Medicine (ISMRM), #0979, June 2018, Paris, France. [power pitch presentation]
[C50] Sakoglu U, De Leon J, Huerta C, Galla M, Mete M, Adinoff B, “Classification of Cocaine Addiction Using Hilbert-Curve Ordering of fMRI Activations,” International Society of Magnetic Resonance in Medicine (ISMRM) Machine Learning Workshop, March 2018, Pacific Grove, CA. [abstract pdf]
[C49] Sakoglu U, Galla M, Bhamidipadi S, Gopinath K, Crosson B, Haley R, “Classification of Gulf War Illness Using Machine Learning in fMRI,” International Society of Magnetic Resonance in Medicine (ISMRM) Machine Learning Workshop, March 2018, Pacific Grove, CA. [abstract pdf]
[C48] Gopinath K, Sakoglu U, Crosson B, Haley R, “Brain function network impairments and abnormal processing at rest in Gulf War Illness: a resting state fMRI study,” Annual International Meeting of the Society for Neuroscience (SfN), 456.09, November 2017, Washington, DC. [abstract pdf] [presentation pdf]
[C47] De Leon J, Huerta C, Sakoglu U, “Classification of fMRI Data Using Hilbert-Curve Ordering and Neural Networks,” IEEE DUAL Innovation/Automation Conference, October 2017, NASA JSC, Houston, TX. (2nd Best Student Poster Presentation Award) [abstract pdf]
[C46] Bhamidipati S, Pradeep C, Sakoglu U, “Classification of positive and negative stimuli using EEG data, functional connectivity and machine learning,” IEEE DUAL Innovation/Automation Conference, October 2017, NASA JSC, Houston, TX. [abstract pdf]
[C45] Galla M, Tippa R, He H, Sakoglu U, “Functional Connectivity–Based Classification of Brain Disorder Patients vs Healthy Participants Using Functional MRI,” IEEE DUAL Innovation/Automation Conference, October 2017, NASA JSC, Houston, TX. [abstract pdf]
[C44] Gopinath K, Thapa-Chetry B, Ouyang L, Krishnamurthy L, Krishnamurthy V, Goyal A, Gandhi P, Fang, Y, Sakoglu U, Crosson B, Haley R, “Gulf War Illness patients exhibit impaired/abnormal connectivity in multiple brain rsFMRI networks,” Annual International Conference of the Organization for Human Brain Mapping (OHBM), June 2017, Vancouver, BC. [abstract pdf]
[C43] Gopinath K, Thapa-Chetry B, Ouyang L, Krishnamurthy L, Krishnamurthy V, Goyal A, Gandhi P, Fang, Y, Sakoglu U, Haley R, “Gulf War Illness Patients Exhibit Impaired Connectivity in Multiple Brain Function Networks Consistent with Chronic Multi-Symptom Illness: A resting State fMRI Study,” Proc. 25th Annual Meeting of International Society of Magnetic Resonance in Medicine (ISMRM), #2258, April 2017, Honolulu, HI. [abstract pdf] [presentation pdf]
[C42] Sakoglu U, “Dynamic Functional Connectivity-based Classification of Healthy vs Cocaine-Addicted Brains from Functional Magnetic Resonance Imaging Data,” poster, Annual Gulf Coast Consortium Conference on Theoretical and Computational Neuroscience, January 27th, 2017, Rice University, Houston, TX. [abstract pdf]
[C41] Sakoglu U, “Dynamic Functional Connectivity Analysis For fMRI Data: An Application To Classification Of Cocaine Addicted Patients,” 19th International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI), Satellite Workshop on Advances in fMRI, October 2016, Athens, Greece. [abstract pdf]
[C40] LeVan PD, Sakoglu U, “LWIR pupil imaging and long-term calibration stability,” Proc. SPIE Optics and Photonics Conference, Infrared Sensors, Devices and Applications VI, Vol. 9974, pp. 99740S1-S10, August/September 2016, San Diego, CA. [paper pdf]
[C39] Mete M, Sakoglu U, Spence JS, Devous MD, Harris TS, Adinoff B, “Successful Classification of Cocaine Dependence Using Brain Imaging: A Machine Learning Approach,” Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS), March 2016, Memphis, TN. [presentation pdf]
[C38] LeVan PD, Sakoglu U, Stegall M, Pierce G, “LWIR pupil imaging for long-term infrared background compensation for faint objects,” Proc. SPIE Optics and Photonics Conference, Infrared Sensors, Devices and Applications V, Vol. 9609, pp. 96090U1–96090U9, August 9-13, 2015, San Diego, CA. [paper pdf]
[C37] Hempelmann C, Gurupur V, Sakoglu U, “Patient Centered Ontology Development for a Personal Health Information System,” American Medical Informatics Association (AMIA), 2015 Joint Summits on Translational Science, pp. 330-331, March 23-27, 2015, San Francisco, CA. [abstract pdf]
[C36] Akgün D, Sakoglu U, Mete M, Esquivel J, Adinoff B, “GPU-Accelerated Dynamic Functional Connectivity Analysis for Functional MRI Data Using OpenCL,” Proceedings of the IEEE International Conference on Electro/Information Technology (EIT), pp. 479 – 484, June 2014, Milwaukee, WI. [paper pdf]
[C35] Sakoglu U, Arslan A, Bohra K, Flores H, “In Search of Optimal Space-Filling Curves for 3-D to 1-D Mapping: Application to 3-D Brain MRI Data,” Proceedings of ISCA’s 6th International Conference on Bioinformatics and Computational Biology (BICOB), pp. 61-66, March 2014, Las Vegas, NV. [paper pdf]
[C34] Flores H, Bohra K, Arslan AN, Sakoglu U, “Finding Optimal Brain Mappings Using Integer Linear Programming Solvers,” Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS), March 2014, Stillwater, OK. [abstract pdf]
[C33] Esquivel J, Mete M, Sakoglu U, “DynaConn: A Software for Analyzing Brain’s Dynamic Functional Connectivity from fMRI Data,” Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS), March 2014, Stillwater, OK. [abstract pdf]
[C32] Bohra K, Sakoglu U, Mete M, “Software Toolbox for Multivariate Pattern Analysis of Different Brain States from Functional Magnetic Resonance Imaging Data,” Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS), March 2014, Stillwater, OK. (2nd best poster award) [abstract pdf]
[C31] Ankam H, Mete M, Sakoglu U, “A Compact Independent Component Analysis Implementation with Graphical Processing,” Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS), March 2014, Stillwater, OK. [abstract pdf]
[C30] Akgun D, Esquivel J, Mete M, Sakoglu U, “OpenMP-Accelerated Dynamic Functional Connectivity Analysis on Multicore Computer,” Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS), March 2014, Stillwater, OK. [abstract pdf]
[C29] Bohra K, Sakoglu U, “A Software for Multivoxel Pattern Analysis of Functional Magnetic Resonance Imaging Data,” Proc. IEEE EMBS Annual Medical Device Symposium, November 2013, Dallas, TX. [abstract pdf]
[C28] Esquivel J, Mete M, Sakoglu U, “Software for Analyzing Brain’s Dynamic Functional Connectivity from fMRI,” Proc. IEEE EMBS Annual Medical Device Symposium, November 2013, Dallas, TX. [abstract pdf]
[C27] Ankam H, Mete M, Sakoglu U, “A Compact Independent Component Analysis Implementation with Graphical Processing Unit,” Proc. IEEE EMBS Annual Medical Device Symposium, November 2013, Dallas, TX. [abstract pdf]
[C25] Wang TH, Johnson JJ, Sakoglu U, Rugg MD, “Content-selective cortical reinstatement effects in older and younger adults,” Cognitive Neuroscience Annual Meeting, April 2013, San Francisco, CA. [abstract pdf]
[C24] Mete M, Ankam H, Sakoglu U, “A Graphical Processing Unit Supported Neuroimaging Software in JAVA,” Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS), April 2013, Columbia, MO. [abstract pdf]
[C22] Wang TH, Sakoglu U, Rugg MD, “Differential sensitivity of GLM and MVPA approaches to the identification of cortical reinstatement effects in memory retrieval,” Society for Neuroscience Annual Meeting, October 2012, New Orleans, LA. [abstract pdf]
[C20] Wang TH, Johnson JJ, Sakoglu U, Gottlieb Lauren, Rugg MD, “Cortical reinstatement of auditory and visually presented contextual information as indexed by multi-voxel pattern classification,” Cognitive Neuroscience Annual Meeting, April 2011, San Francisco, CA. [poster pdf]
[C19] Kim D-I and Sakoglu U, “Brain Networks Toolbox: Software for functional connectivity analysis based on Small World Network Theory”, 26th Annual Meeting of the European Society for Magnetic Resonance in Medicine and Biology, Vol. 22 Suppl. 1, October 2009, Antalya, Turkey. [abstract pdf]
[C18] Sakoglu U, Gasparovic C, Bockholt HJ, Qualls CR, Sharrar J, Sibbitt W, Roldan C, “Comparison of two deconvolution methods in a CBF study of Lupus patients vs. healthy controls with perfusion MRI,” 24th International Symposium on Cerebral Blood Flow, Metabolism and Function, 2009 (29), S568–S580; July 2009, Chicago, IL. [abstract pdf]
[C16] Sakoglu U, Michael AM, Calhoun VD, “Classification of schizophrenia patients vs healthy controls with dynamic functional network connectivity,” Proc. 15th Annual Meeting of the Organization for Human Brain Mapping, Vol. 47, Suppl. 1, pp. S57, June 2009, San Francisco, CA. [abstract pdf]
[C14] Sakoglu U, Calhoun VD, “Dynamic windowing reveals task-modulation of functional connectivity in schizophrenia patients vs healthy controls,” Proc. 17th Annual Meeting of the International Society for Magnetic Resonance in Medicine, #3676, April 2009, Honolulu, HI. [abstract pdf]
[C13] Caprihan A, Sakoglu U, Pfeuffer J, Rael J, Stephen J, Lowe J, Duvall SW, Gasparovic C, Ohls RK, and Phillips JP, “Differences in Blood Perfusion between Extremely Low Birth Weight (ELBW) Pre-term Infants and Control Term Infants,” Proc. 17th Annual Meeting of the International Society for Magnetic Resonance in Medicine, #0491, April 2009, Honolulu, HI. [abstract pdf]
[C12] Pai M, Sood R, Sakoglu U, “Non-invasive Measurement of Blood-Brain-Barrier Disruption in Cryptococcal Meningoencephalitis (CNME)” 46th Annual Meeting of the Infectious Diseases Society of America (IDSA), October 2008, Washington, D.C. [abstract pdf]
[C12] Pai M, Sood R, Sakoglu U, “Non-invasive Measurement of Blood-Brain-Barrier Disruption in Cryptococcal Meningoencephalitis (CNME)” 46th Annual Meeting of the Infectious Diseases Society of America (IDSA), October 2008, Washington, D.C. [abstract pdf]
[C11] Cetin O, Sakoglu U, Sood R, “Software package to calculate permeability based on Patlak method,” 25th Annual Meeting of the European Society for Magnetic Resonance in Medicine and Biology, #988, Vol. 21, Suppl. 1, October 2008, Valencia, Spain. [abstract pdf]
[C10] Sakoglu U, Sood R, Pai M, “Application of MRI in the Development of a Preclinical Model of Cryptococcal Meningoencephalitis (CNME),” 16th Annual Meeting of International Society of Magnetic Resonance in Medicine, May 2008, Toronto, ON, Canada. [abstract pdf]
[C09] Sakoglu U, Sood R, Huisa-Garate B, Rosenberg G, “Comparison of an FT-based MMSE Method with oSVD Method for CBF Estimation in Patients with VCI,” 16th Annual Meeting of International Society of Magnetic Resonance in Medicine, #1904, May 2008, Toronto, ON, Canada. [abstract pdf]
[C08] Huisa-Garate B, Gasparovic C, Sakoglu U, Sood R, Thompson J, Prestopnik J, Rosenberg G, “Brain Perfusion MRI and Proton Magnetic Resonance Spectroscopy Aid in Identifying Ischemic White Matter in Patients with Vascular Cognitive Impairment,” 60th Annual Meeting of American Academy of Neurology, presentation #08126, 12-April 2008, Chicago, IL, USA.[abstract pdf]
[C07] Sakoglu U, Sood R, “Effect of AIF distortion and delay on CBF estimation using FT-based MMSE deconvolution technique,” Joint Annual Meeting of International Society for Magnetic Resonance in Medicine / European Society for Magnetic Resonance in Medicine and Biology, presentation #3497, May 2007, Berlin, Germany. [abstract pdf]
[C05] Hayat MM, Sakoglu U, Wang Z, Paskaleva B, Tyo JS, and Krishna S, “Algorithmic Tunability of Quantum-Dot Infrared Detectors,” Proceedings of IEEE/LEOS Summer Topical Meetings,pp. 34-35, July, 2006, Quebec City, QC, Canada. (invited) [paper pdf]
[C04] Sakoglu U, Wang Z, Hayat MM, Tyo JS, Senthil Annamalai, Philip Dowd, and Krishna S, “Quantum-dot detectors for mid-infrared sensing: Bias-controlled spectral tuning and matched filtering,” Proceedings for the SPIE: Nanosensing - Materials and Devices, M. Saif Islam, Achyut K. Dutta, eds., (SPIE, Bellingham, WA, 2004), Vol. 5593, pp. 396-407. (invited) [paper pdf]
[C03] Sakoglu U, Hardie RC, Hayat MM, Ratliff BM, and Tyo JS, “An algebraic restoration method for estimating fixed-pattern noise in infrared imagery from a video sequence,” Proceedings of the SPIE: Applications of Digital Image Processing XXVII, Andrew G. Tescher ed., (SPIE, Bellingham, WA, 2004), Vol. 5558, pp. 69-79. [paper pdf]
[C02] Wang Z, Sakoglu U, Annamalai S, Weisse-Bernstein NR, Dowd P, Tyo JS, Hayat MM and Krishna S, “Real-time implementation of spectral matched filtering algorithms using adaptive focal plane array technology,” Proc. SPIE: Imaging Spectrometry X, (SPIE, Bellingham, WA, 2004) Vol. 5546, pp. 73-83. [paper pdf]
[C01] Scaglione A and Sakoglu U, “Asymptotic Capacity of Space-Time Coding for Arbitrary Fading: A Closed Form Expression Using Girko’s Law,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (IEEE, New York, NY, 2001) Vol. 4, pp. 2509-2511. [paper pdf]
Other(2):
[O02] Sakoglu U “Signal-processing strategies for spectral tuning and chromatic nonuniformity correction for quantum-dot infrared sensors,” Ph.D. Dissertation, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA (2006). [pdf1] [pdf2]
[O01] Sakoglu U and Scaglione A “High Bit Rate Wireless Area Networks with Transmit and Receive Diversity in Arbitrary Fading: Asymptotic Closed Form Performance Expressions Using the Distribution of Random Eigenvalues“ unpublished master's research work (2001). [pdf]
[O01] Sakoglu U and Scaglione A “High Bit Rate Wireless Area Networks with Transmit and Receive Diversity in Arbitrary Fading: Asymptotic Closed Form Performance Expressions Using the Distribution of Random Eigenvalues“ unpublished master's research work (2001). [pdf]
