Laboratory for High Performance DSP & Data Engineering Research (HPDER)

Advances in high performance digital signal and data processing, network computing, low-latency internetworking, storage of data with real-time access and supported by advanced database systems have been transforming R&D activities of almost every discipline. IT centric and data intensive scientific discovery offers unmatched opportunities with potential impact on complex systems, and to infer intelligence and actionable information from bigdata. It demands cutting edge High Performance Computing, Internet and Data Engineering expertise strongly coupled with mathematics. Many industries and businesses have already built state of the art data and information processing infrastructure for their operations.

HPDER is a Research & Development laboratory to advance theory and implementation of analytically oriented high performance DSP & data engineering solutions for new technology development to address bigdata and signal processing problems.

HPDER activities are highlighted below.

GRADUATE COURSES

• ECE 641 Laboratory for High Performance Digital Signal Processing
  
  This course first introduces today's FPGA and GPU technologies, VHDL and CUDA coding, the design tools for the state-of-the-art DSP algorithms and systems. It focuses on computer arithmetic including possible number representations for DSP with FPGA like distributed arithmetic (DA) and CORDIC algorithm. Then, it introduces CUDA development tools for GPUs. Finally, there is a set of high performance DSP implementations spanning from finite impulse response and infinite impulse response filters to wavelet processors with two-channel filter banks and others. Each student is assigned a term project for the course to be implemented on FPGA or GPU from scratch.


• ECE 640 Digital Signal Processing
  
  
• ECE 740 Advanced Digital Signal Processing
  
  
• ECE 747 Signal Decomposition Techniques: Transforms, Subbands and Wavelets
  

  TECHNOLOGY PARTNERS

• ALTERA
• JUNIPER NETWORKS
• NVIDIA
• TEXAS INSTRUMENTS

  HPDER ALUMNI

• C. Benar, On Explainability of Neural Networks. Ph.D. Thesis. Completed in 2023.
• I. Lateef, Machine Learning Techniques for Network Analysis. Ph.D. Thesis. Completed in 2021.
• Anqi Xiong, Subspace Portfolios: Design and Performance Comparison. Ph.D. Thesis. Completed in 2020.
• Onur Yilmaz, Subspace Methods for Portfolio Design. Ph.D. Thesis. Completed in 2016.
• Yuewen Wang, Generalized DFT: Extensions in Communications. Ph.D. Thesis. Completed in 2016.
• Yanjia Sun, Verification of Emotion Recognition From Facial Expression. Ph.D. Thesis. Completed in 2016.
• Mustafa U. Torun, High Performance Signal Processing: Theory, Design, and Applications in Finance. Ph.D. Thesis. Completed in 2013.
• Boyan Zhang, GPU Implementation of Block Transforms. M.S. Thesis. Completed in 2012.
• Di Mu, Single-Carrier Frequency Domain Equalization Using Subband Decomposition For Optical Wireless Communications. M.S. Thesis. Completed in 2011. (Co-Advisor: Y. Bar-Ness)
• H.A. Siddiquee, High Speed Perceptron Implementation Using VHDL and System Verilog. M.S. Project, Completed in 2020.
• J. Dougherty, A Method to Compute the Fisher Information Distance. M.S. Project. Completed in 2018. (Co-Advisor: S.M. Taylor)
• Kyle Marshall, On Options Pricing. M.S. Project. Completed in 2015.
• Smitri Sharma, Test Codes for Performance Monitoring of Field Deployed LTE RRHs. M.S. Project. Completed in 2014.
• Ziyan Wu, Digital Audio Processing. M.S. Project. Completed in 2014.
• Jiakai Wu, The Algorithm Analysis for FPGA Based DCT and Hardware Implementation with Varied Image Block Size. M.S. Project. Completed in 2014.
• Omar A. Elrafei, HMM Based Speech Recognition System. M.S. Project. Completed in 2012.
• Christopher N. Njoku, A Simple Trading Algorithm. M.S. Project. Completed in 2011.
• Berker Pekoz, B.S. Student, Summer Intern, 2014.
• Arda Yalcin, B.S. Student, Summer Intern, 2014.
• Alexander Leventhal, B.S. Student, 2014-2015.

TALKS & PROFESSIONAL ACTIVITY

• A.N. Akansu, Invited Talk, Cluster Portfolios, The 8th Big Data Finance Conference, (Virtual) New York, NY, June 4, 2020.
• A.N. Akansu (Session Organizer and Chair), A.G. Constantinides, D.P. Mandic, and D.P. Palomar, Co-Organizers, Special Session, Signal Processing Methods for Finance Applications, IEEE ICASSP, (Virtual) Barcelona, Spain, May 4-8, 2020.
• A.N. Akansu, Invited Talk, Subspace Methods and Finance Applications, Conference on Innovations in Intelligent Systems and Applications (ASYU), Izmir, Turkey, Oct. 31-Nov. 2, 2019.
• A.N. Akansu, Panelist, Capital Markets: Technology - Risk, Turkish Capital Markets Conference, Istanbul, Turkey, November 14-15, 2017.
• A.N. Akansu, Invited Talk, Financial Signal Processing and High Frequency Trading, IEEE 25th Signal Processing and Communications Applications Conference (SIU), Antalya, Turkey, May 15-18, 2017.
• A.N. Akansu, Invited Talk, Flash Crash of May 6, 2010: Why Did It Happen? Can It Happen Again?, Algorithmic Trading: Theory and Practice, Turkish Capital Markets Association (Turkiye Sermaye Piyasalari Birligi), Istanbul, Turkey, May 13, 2016.
• A.N. Akansu, D. Malioutov, and D.P. Palomar, Organizers, Special Session, Financial Signal Processing and Machine Learning for Electronic Trading, IEEE ICASSP, Shanghai, China, March 20-25, 2016.
• A.N. Akansu, E. Jay, D. Malioutov, D.P. Mandic, and D.P. Palomar, Eds., Special Issue on Financial Signal Processing and Machine Learning for Electronic Trading, IEEE Journal of Selected Topics in Signal Processing, September 2016.
• A.N. Akansu and D. Malioutov, Tutorial, Covariance Analysis and Machine Learning Methods for Electronic Trading, IEEE ICASSP, Brisbane, Australia, April 19-24, 2015.
• A.N. Akansu, Plenary Talk, Eigen Subspaces: From Eigenfaces to Eigenportfolios in Finance, 80th Anniversary Celebration of Faculty of Electrical and Electronic Engineering, Technical University of Istanbul, Turkey, Oct. 24, 2014.
• A.N. Akansu and I. Pollak, Tutorial, High Frequency Trading and Signal Processing Models for the Microstructure of Financial Markets, IEEE ICASSP, Vancouver, Canada, May 26-31, 2013.
• A.N. Akansu and I. Pollak, Organizers, Special Session, Financial Signal Processing and Electronic Trading, IEEE ICASSP, Vancouver, Canada, May 26-31, 2013.
• A.N. Akansu, Invited Talk, Generalized DFT Waveforms for MIMO Radar Systems, 9th Military Antennas, Washington DC, Sept. 24-27, 2012.
• A.N. Akansu, S.R. Kulkarni, M.M. Avellaneda, and A.R. Barron, Eds., Special Issue on Signal Processing Methods in Finance and Electronic Trading, IEEE Journal of Selected Topics in Signal Processing, Aug. 2012.
• A.N. Akansu, Invited Talk, A Vision for Future of the Global Village: An Electrical Engineer's Perspective, MASFOR, Centennial Celebration of Electrical and Electronic Engineering Department, Bogazici University, Istanbul, Turkey, June 23, 2012.
• A.N. Akansu, Plenary Talk, Financial Signal Processing and High Frequency Trading: A Killer App for Smart Clouds, The 13th IEEE International Workshop on Signal Processing Advances in Wireless Communications, Cesme, Turkey, June 19, 2012.
• A.N. Akansu, Organizer, Special Session, Financial Signal Processing, The 13th IEEE International Workshop on Signal Processing Advances in Wireless Communications, Cesme, Turkey, June 2012.
• A.N. Akansu, Plenary Talk, Electronic Trading: A Data Intensive Discovery, International Conference on Mathematical Finance and Economics, Istanbul, Turkey, July 6, 2011.
• A.N. Akansu, I. Pollak, and F. Rubio, Tutorial, Electronic Trading and Portfolio Optimization: A Signal Processing Perspective, EUSIPCO European Signal Processing Conference, Barcelona, Spain, Aug. 29, 2011.
• A.N. Akansu and M. Avellaneda, Organizers, Special Session, Signal Processing Methods for Finance Applications, IEEE ICASSP, Prague, Czech Republic, May 24, 2011.

BOOKS

• A.N. Akansu, S.R. Kulkarni and D. Malioutov, Eds., Financial Signal Processing and Machine Learning. Wiley-IEEE Press, 2016.


• A.N. Akansu and M.U. Torun, A Primer for Financial Engineering: Financial Signal Processing and Electronic Trading. Elsevier, 2015.



BOOK CHAPTERS

• M.U. Torun, O. Yilmaz and A.N. Akansu, Explicit Kernel and Sparsity of Eigen Subspace for AR(1) Process. A Chapter in Financial Signal Processing and Machine Learning. A.N. Akansu, S.R. Kulkarni and D. Malioutov, Eds., Wiley-IEEE Press, 2016.

PAPERS

• C. Benar and A.N. Akansu, ``Correlation Based Node Partitioning to Sparse Multilayer Perceptron," TechRxiv. Preprint. June 2023. https://doi.org/10.36227/techrxiv.23713962.v1


• C. Benar and A.N. Akansu, On Explainability of A Simple Classifier for AR(1) Source, IEEE 56th Annual Conference on Information Sciences and Systems (CISS), March 2022.


• A.N. Akansu, M. Avellaneda and A. Xiong, Quant Investing in Cluster Portfolios, Journal of Investment Strategies (Risk Journals), vol. 9, no. 4, pp. 61-78, April 2021.


• A.N. Akansu and A. Xiong, Eigenportfolios of US Equities for the Exponential Correlation Model, Journal of Investment Strategies (Risk Journals), vol. 9, no. 1, pp. 55-77, March 2020.


• Y. Sun, H. Ayaz and A.N. Akansu, Multimodal Affective State Assessment using fNIRS + EEG and Spontaneous Facial Expression, Brain Sciences, 10, no. 2: 85, Feb. 2020.


• A. Xiong and A.N. Akansu, ``On Sparsity of Eigenportfolios to Reduce Transaction Cost," Journal of Capital Markets Studies, vol. 3 no. 1, pp. 82-90, 2019.


• A. Xiong and A.N. Akansu, Performance Comparison of Minimum Variance, Market and Eigen Portfolios for US Equities, IEEE 53rd Annual Conference on Information Sciences and Systems (CISS), March 2019.


• A.N. Akansu and A. Xiong, Design of Eigenportfolios for US Equities Using Exponential Correlation Model, IEEE 53rd Annual Conference on Information Sciences and Systems (CISS), March 2019.


• A.N. Akansu, The Flash Crash: A Review, Journal of Capital Markets Studies (Invited Paper), vol. 1, no. 1, pp. 89-100, 2017.


• A.N. Akansu, J. Cicon, S.P. Ferris, and Y. Sun, Firm Performance in the Face of Fear: How CEO Moods Affect Firm Performance," Journal of Behavioral Finance, vol. 18, issue 4, pp. 373-389, 2017.


• I. Lateef and A.N. Akansu, Link-Level Interpretation of Eigenanalysis for Network Traffic Flows, Proc. IEEE 51st Annual Conference on Information Sciences and Systems (CISS), March 2017.


• M.U. Torun, O. Yilmaz and A.N. Akansu, FPGA, GPU, and CPU Implementations of Jacobi Algorithm for Eigenanalysis, Journal of Parallel and Distributed Computing (Elsevier), vol. 96, pp. 172-180, Oct. 2016.


• O. Yilmaz and A.N. Akansu, Performance Analysis of Eigenportfolios for AR(1) Process, Proc. IEEE 50th Annual Conference on Information Sciences and Systems (CISS), March 2016.


• Y. Wang and A.N. Akansu, Symbol Alphabet Modifier for PAPR Reduction in OFDM Communications, Proc. IEEE 50th Annual Conference on Information Sciences and Systems (CISS), March 2016.


• Y. Sun, H. Ayaz and A.N. Akansu, Neural Correlates of Affective Context in Facial Expression Analysis: A Simultaneous EEG-fNIRS Study, Proc. 3rd IEEE GlobalSIP Conference, Symposium on Signal Processing Challenges in Human Brain Connectomics, Dec. 2015.


• Y. Wang, A.N. Akansu, K. Belfield, B. Hubbi, and X. Liu, Robust Motion Tracking Based on Adaptive Speckle Decorrelation Analysis of OCT Signal, Biomedical Optics Express, vol. 9, no. 11, pp. 4302-4316, Nov. 2015.


• Y. Wang and A.N. Akansu, A Low-Complexity Peak-to-Average Power Ratio Reduction Method for OFDM Communications, IET Communications, vol. 9, issue 17, pp. 2153-2159, Nov. 2015.


• O. Yilmaz and A.N. Akansu, Quantization of Eigen Subspace for Sparse Representation, IEEE Trans. on Signal Processing, vol. 63, no. 14, pp. 3616-3625, July 2015.


• O. Yilmaz and A.N. Akansu, A Method to Sparse Eigen Subspace and Eigenportfolios, Proc. The International Conference on Information Fusion, July 2015.


• Y. Sun, A.N. Akansu and J. Cicon, The Power of Fear: Facial Emotion Analysis of CEOs to Forecast Firm Performance, Proc. 15th IEEE International Conference on Information Reuse and Integration, Aug. 2014.


• Y. Sun and A.N. Akansu, Facial Expression Recognition with Regional Hidden Markov Models, IEE Electronics Letters, vol. 50, issue 9, pp. 671-673, April 2014.


• Y. Sun and A.N. Akansu, Automatic Inference of Mental States from Spontaneous Facial Expressions, Proc. IEEE ICASSP, May 2014.


• M.U. Torun and A.N. Akansu, An Efficient Method to Derive Explicit KLT Kernel for First-Order Autoregressive Discrete Process, IEEE Trans. on Signal Processing, vol. 61, no. 15, pp. 3944-3953, Aug. 2013.


• O. Yilmaz, M.U. Torun and A.N. Akansu, A Fast Derivation of Karhunen-Loeve Transform Kernel for First-Order Autoregressive Discrete Process, Proc. Big Data Analytics Workshop at SIGMETRICS, June 2013.


• M.U. Torun and A.N. Akansu, A Novel Method to Derive Explicit KLT Kernel for AR(1) Process, Proc. IEEE ICASSP, May 2013.


• M.U. Torun, O. Yilmaz and A.N. Akansu, FPGA Based Eigenfiltering for Real-Time Portfolio Risk Analysis, Proc. IEEE ICASSP, May 2013.


• A.N. Akansu and M.U. Torun, Toeplitz Approximation to Empirical Correlation Matrix of Asset Returns: A Signal Processing Perspective, IEEE Journal of Selected Topics in Signal Processing, vol. 6, no. 4, pp. 319-326, Aug. 2012.


• M.U. Torun and A.N. Akansu, A Novel GPU Implementation of Eigen Analysis for Risk Management, Proc. The 13th IEEE International Workshop on Signal Processing Advances in Wireless Communications, June 2012.


• Y. Wang, A.N. Akansu and A. Haimovich, Generalized DFT Waveforms for MIMO Radar, Proc. The Seventh IEEE Sensor Array and Multichannel Signal Porcessing Workshop, June 2012.


• M.U. Torun, O. Yilmaz and A.N. Akansu, Novel GPU Implementation of Jacobi Algorithm for Karhunen-Loeve Transform of Dense Matrices, Proc. IEEE 46th Annual Conference on Information Sciences and Systems (CISS), March 2012.


• A.N. Akansu and M.U. Torun, On Toeplitz Approximation to Empirical Correlation Matrix of Financial Asset Returns, Proc. IEEE 46th Annual Conference on Information Sciences and Systems (CISS), March 2012.


• Y. Wang and A.N. Akansu, Generalized DFT Based Partial Matched Filter Bank for Doppler Estimation, Proc. IEEE 46th Annual Conference on Information Sciences and Systems (CISS), March 2012.


• W.P. Weydig, M.U. Torun and A.N. Akansu, Implementation of Generalized DFT on Field Programmable Gate Array, Proc. IEEE ICASSP, March 2012.


• M.U. Torun and A.N. Akansu, On Epps Effect and Rebalancing of Hedged Portfolio in Multiple Frequencies, Proc. The Fourth International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, San Juan, Puerto Rico, Dec. 2011.


• M.U. Torun, A.N. Akansu and M. Avellaneda, Portfolio Risk in Multiple Frequencies, IEEE Signal Processing Magazine, vol. 28, no. 5, pp. 61-71, Sept. 2011.


• M.U. Torun and A.N. Akansu, On Basic Price Model and Volatility in Multiple Frequencies, Proc. IEEE Statistical Signal Processing Workshop, Nice, France, June 2011.


• M.U. Torun, A.N. Akansu and M. Avellaneda, Risk Management for Trading in Multiple Frequencies, Proc. IEEE ICASSP, Prague, Czech Republic, May 2011.



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