Work with thought leaders and academic experts in Signal Processing

Companies can benefit from working with Signal Processing experts in various ways. These experts can provide innovative solutions to complex problems, optimize signal processing algorithms for better performance, develop advanced signal processing techniques for data analysis, and design efficient communication systems. They can also contribute to the development of cutting-edge technologies such as image and speech recognition, radar and sonar systems, and biomedical signal processing. By collaborating with Signal Processing researchers, companies can gain a competitive edge, improve product quality, enhance data processing capabilities, and accelerate technological advancements.

Researchers on NotedSource with backgrounds in Signal Processing include Aruna Ranaweera, Nicolangelo Iannella, Siddharth Maddali, Dmitry Batenkov, Ph.D., Edoardo Airoldi, Vladimir Shapiro, Ph.D., Tim Osswald, David J. Lilja, Lee Weinstein, Dhritiman Das, Ph.D., Dr. Mona Saleh, Athul Prasad, and Hussein Al-Hussein.

Aruna Ranaweera

Colombo
18 Years Experience
Professor at University of Kelaniya, PhD(Kyung Hee University, South Korea)
Education

Kyung Hee University - Global Campus

Doctor of Philosophy, Department of Electronics and Radio Engineering / February, 2017

Yongin

University of Kelaniya Faculty of Science

B.Sc., Department of Physics / March, 2006

Kelaniya
Experience

University of Kelaniya Faculty of Science

Professor / November, 2021 — Present

Lecturer / July, 2008 — Present

Assistant Lecturer / June, 2006 — February, 2008

Wayamba University of Sri Lanka

Lecturer / February, 2008 — June, 2008

Most Relevant Research Expertise
Signal Processing
Other Research Expertise (16)
Wireless Power Transfer
Metamaterials
Supercapacitor Assisted Power Electronics
Electronic, Optical and Magnetic Materials
Surfaces, Coatings and Films
And 11 more
About
I am dedicated and passionate about inspiring and engaging my students in an effective learning process to generate new knowledge, do innovations, engage in technology transfer, and enhance human capital through interdisciplinary and collaborative research for the well-being of academia, industry, and society.
Most Relevant Publications (1+)

30 total publications

Supercapacitor Assisted Hybrid PV System for Efficient Solar Energy Harnessing

Electronics / Oct 04, 2021

Piyumal, K., Ranaweera, A., Kalingamudali, S., & Kularatna, N. (2021). Supercapacitor Assisted Hybrid PV System for Efficient Solar Energy Harnessing. Electronics, 10(19), 2422. https://doi.org/10.3390/electronics10192422

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Nicolangelo Iannella

Oslo
6 Years Experience
Senior Research fellow, The University of Oslo, Faculty of Mathematics and Natural Sciences
Education

University of Adelaide

Graduate Certificate in Education (Higher Education) , School of Electrical & Electronic engineering / December, 2012

Adelaide, South Australia, Australia

Denki Tsushin Daigaku

PhD (Eng), Information and Communications Engineering / March, 2009

Chofu
Experience

University of Oslo

Postdoctoral Fellow / July, 2018 — Present

Most Relevant Research Expertise
Signal Processing
Other Research Expertise (18)
Neuromorphic circuits
Neural networks, Neural learning and applications
Theoretical and Mathematical neuroscience
Computational neuroscience
Artificial Intelligence
And 13 more
About
Following pre-doctoral studies in Mathematics and Theoretical Physics, I received a PhD in Computational Neuroscience from the University of Electro-Communications, Japan in 2009. From 2009, I was a Postdoctoral Researcher in RIKEN BSI. In 2010, I won the prestigious Australian Research Council (ARC) Australian Postdoctoral Award (APD) fellowship, based at the University of Adelaide from 2010–2014. In 2012 he completed a Graduate Certificate in Education (Higher Education) (GCEHE) from the University of Adelaide. From 2014–2017 he was an adjunct research fellow at the University of South Australia. From 2016–2018, he was a Cascade (Marie Curie) Research Fellow in Mathematical Sciences at the University of Nottingham. From 2018- a research fellow at the University of Oslo. His research interests include AI, Artificial and spiking neural networks and learning algorithms, synaptic plasticity, neuronal dynamics, and neuromorphic engineering. Dr. Iannella is a member of SFN and a Senior member of the IEEE.
Most Relevant Publications (1+)

47 total publications

Finding iterative roots with a spiking neural network

Information Processing Letters / Sep 01, 2005

Iannella, N., & Kindermann, L. (2005). Finding iterative roots with a spiking neural network. Information Processing Letters, 95(6), 545–551. https://doi.org/10.1016/j.ipl.2005.05.022

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Siddharth Maddali

Fremont, California, United States of America
8 Years Experience
Computational physicist with a specialization in X-ray and optical imaging and microscopy for condensed matter and materials systems.
Education

Carnegie Mellon University

PhD, Physics / May, 2016

Pittsburgh, Pennsylvania, United States of America

Carnegie Mellon University

MS, Physics / May, 2011

Pittsburgh, Pennsylvania, United States of America

Indian Institute of Technology Madras

M.Sc, Physics / May, 2009

Chennai
Experience

KLA (United States)

Research Scientist / November, 2022 — Present

Sensitivity enhancement of optical inspection of semiconductor wafers

Argonne National Laboratory

Staff Scientist / October, 2019 — September, 2022

(1) Imaging: Inverse problems for 3D nanoscale materials imaging using coherent X-ray probes. (2) Time-resolved studies: Signal processing methods for XPCS at free electron laser facilities. (3) Experiments: POCs & demonstrations for the above at APS/future APS-U instruments. (4) Fundraising: Research grants (LDRD, DoE), APS, ESRF user-time proposals. (5) Dissemination/Outreach: Publications, peer review, editorship, conferences, tech reports. (6) Mentoring/Organization: Postdocs, students (unofficial), workshop planning/chairing.

Post-doctoral researcher / January, 2017 — September, 2019

National Energy Technology Laboratory

Postdoctoral Fellow / May, 2016 — November, 2016

Machine learning -driven materials discovery of steel alloys for optimized power plant components

Most Relevant Research Expertise
signal processing
Other Research Expertise (21)
Computational microscopy
Fourier/physical optics
physics
HPC
Electrical and Electronic Engineering
And 16 more
About
Computational materials, imaging and microscopy scientist with **8 years combined experience** in industry and national laboratories. Expert in physics-based imaging and characterization with X-rays and optical probes, high-performance computing for light-matter interaction and materials data analysis. Experienced in machine learning for materials discovery. Previous experience at the National Energy Technology Laboratory, Argonne National Laboratory and KLA Corporation. <br>

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Dmitry Batenkov, Ph.D.

New York City, New York, United States of America
15 Years Experience
A highly experienced applied mathematician working in academia (faculty) and industry (consulting), with 15+ years of research and teaching expertise in inverse problems, signal processing, and data science.
Education

Weizmann Institute of Science

Ph.D., Applied Mathematics / January, 2014

Rehovot
Experience

Tel Aviv University

Assistant Professor / July, 2019 — Present

Producing high-impact research in inverse problems, super-resolution, numerical analysis, signal processing, physics-informed machine learning, computational harmonic analysis, optimization, atmospheric remote sensing • Advised 4 postdocs, 2 PhD, 4 M.Sc. students and 3 undergraduates • Developed and taught an advanced graduate class on Inverse Problems and Super-Resolution

Most Relevant Research Expertise
Signal Processing
Other Research Expertise (30)
Applied Harmonic Analysis
Sparse Representations
Numerical Analysis
Approximation Theory
Inverse Problems
And 25 more
About
I am passionate about solving big problems with scientific and computational tools. A highly experienced applied mathematician working in academia (faculty) and industry (consulting), with 15+ years of research and teaching expertise in inverse problems, signal processing, and data science. A highly-skilled software engineer and analyst/architect with 6+ years of experience as a technical lead in professional software development.
Most Relevant Publications (3+)

50 total publications

Moment inversion problem for piecewise D -finite functions

Inverse Problems / Sep 16, 2009

Batenkov, D. (2009). Moment inversion problem for piecewise D -finite functions. Inverse Problems, 25(10), 105001. https://doi.org/10.1088/0266-5611/25/10/105001

Decimated Prony's Method for Stable Super-Resolution

IEEE Signal Processing Letters / Jan 01, 2023

Katz, R., Diab, N., & Batenkov, D. (2023). Decimated Prony’s Method for Stable Super-Resolution. IEEE Signal Processing Letters, 30, 1467–1471. https://doi.org/10.1109/lsp.2023.3324553

Stable soft extrapolation of entire functions

Inverse Problems / Dec 07, 2018

Batenkov, D., Demanet, L., & Mhaskar, H. N. (2018). Stable soft extrapolation of entire functions. Inverse Problems, 35(1), 015011. https://doi.org/10.1088/1361-6420/aaedde

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Edoardo Airoldi

Professor of Statistics & Data Science Temple University & PI, Harvard University
Education

Università Bocconi

B.Sc., Institute for Quantitative Methods

Milano

Carnegie Mellon University

Ph.D., School of Computer Science

Pittsburgh, Pennsylvania, United States of America
Experience

Harvard University

Most Relevant Research Expertise
Signal Processing
Other Research Expertise (43)
Statistics
Causal Inference
Network Science
Cell Biology
Molecular Biology
And 38 more
About
Edoardo Airoldi is a Professor in the Department of Machine Learning at Temple University. He is also the Director of the Center for Machine Learning and Health. He is a world-renowned expert in the fields of machine learning and artificial intelligence, with a focus on applications to health. Airoldi is a member of the prestigious Association for the Advancement of Artificial Intelligence (AAAI) and the International Machine Learning Society (IMLS). He has published over 200 papers in leading journals and conferences, and his work has been covered by various media outlets including The New York Times, The Wall Street Journal, The Economist, and Wired.
Most Relevant Publications (1+)

106 total publications

SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series

IEEE Transactions on Signal Processing / Oct 01, 2017

Han, Q., Ding, J., Airoldi, E. M., & Tarokh, V. (2017). SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series. IEEE Transactions on Signal Processing, 65(19), 4994–5005. https://doi.org/10.1109/tsp.2017.2716898

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Vladimir Shapiro, Ph.D.

Boston, Massachusetts, United States of America
30 Years Experience
PRINCIPAL AI/COMPUTER VISION DATA SCIENTIST; EXPERIENCED SOFTWARE (PYTHON, C/C++, R) DEVELOPER; ADJUNCT UNIVERSITY PROFESSOR
Education

Technical University of Sofia

Electrical & Computer Engineering / January, 1991

Sofia
Experience

Northeastern University

Adjunct Professor / September, 2019 — Present

Developed and taught numerous courses in AI, Machine Learning, Statistics, Programming, etc., at the graduate and undergraduate levels

Avitas Systems (Baker Hughes from 2019, General Electric Venture from 2016),

Principal Computer Vision Data Scientist / November, 2016 — May, 2023

● Research, prototyping, designing, and efficiently implementing image and video analytics algorithms with Deep Learning frameworks, e.g., TensorFlow/Keras. ● Agile development in Python, C++, in Linux, including on Amazon AWS. ● Design of the Machine Learning pipelines and workflows, including the entire dataset and model lifecycle. Management of the data annotation and curation operation, including directing the team.

Most Relevant Research Expertise
Signal Processing
Other Research Expertise (14)
Computer Vision and Pattern Recognition
Hardware and Architecture
Computer Science Applications
Software
Artificial Intelligence
And 9 more
About
• Expertise in image and video processing, machine vision, machine learning, digital signal processing, deep learning and pattern recognition algorithm development. • Expertise of production quality C/C++, Python language implementation including for real-time and multiple including embedded platforms. • Experience of working for start-ups and global companies. • Over 50 scientific publications and patents. Specialties: AI, image/video processing, computer vision, machine vision, deep learning, pattern recognition, machine learning, data science, software engineering, embedded software, real-time systems, motor control, Python, C/C++, R and MATLAB programming, software development, object oriented, Linux, Windows, algorithms, Agile development.
Most Relevant Publications (4+)

37 total publications

Handwritten document image segmentation and analysis

Pattern Recognition Letters / Jan 01, 1993

Shapiro, V., Gluhchev, G., & Sgurev, V. (1993). Handwritten document image segmentation and analysis. Pattern Recognition Letters, 14(1), 71–78. https://doi.org/10.1016/0167-8655(93)90134-y

Accuracy of the straight line Hough Transform: The non-voting approach

Computer Vision and Image Understanding / Jul 01, 2006

Shapiro, V. (2006). Accuracy of the straight line Hough Transform: The non-voting approach. Computer Vision and Image Understanding, 103(1), 1–21. https://doi.org/10.1016/j.cviu.2006.02.001

On the hough transform of multi-level pictures

Pattern Recognition / Apr 01, 1996

A. Shapiro, V. (1996). On the hough transform of multi-level pictures. Pattern Recognition, 29(4), 589–602. https://doi.org/10.1016/0031-3203(95)00116-6

Motion analysis via interframe point correspondence establishment

Image and Vision Computing / Mar 01, 1995

Shapiro, V., Backalov, I., & Kavardjikov, V. (1995). Motion analysis via interframe point correspondence establishment. Image and Vision Computing, 13(2), 111–118. https://doi.org/10.1016/0262-8856(95)93152-i

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Tim Osswald

36 Years Experience
Polymers Professor - University of Wisconsin
Education

University of Illinois at Urbana-Champaign

PhD, Mechanical Engineering / January, 1987

Urbana, Illinois, United States of America

South Dakota School of Mines and Technology

M.S., Mechanical Engineering / May, 1982

Rapid City, South Dakota, United States of America

South Dakota School of Mines and Technology

B.S., Mechanical Engineering / May, 1981

Rapid City, South Dakota, United States of America
Experience

University of Wisconsin Madison

Professor / August, 1989 — Present

Rheinisch Westfalische Technische Hochschule Aachen

Humboldt Fellow / February, 1987 — June, 1989

Most Relevant Research Expertise
Signal Processing
Other Research Expertise (44)
Polymer Engineering
Advanced Manufacturing
Composites
Additive Manufacturing
Materials Chemistry
And 39 more
About
T. Osswald is Hoeganaes Professor of Materials at the University of Wisconsin-Madison, where he has been a faculty member since 1989. Osswald received the PhD in Mechanical Engineering from the University of Illinois at Urbana-Champaign in 1987, the MS in Mechanical Engineering from the South Dakota School of Mines and Technology in 1982, and the BS in Mechanical Engineering from the South Dakota School of Mines and Technology in 1981. Before joining the UW-Madison faculty, Osswald was a Humboldt Fellow at the Rheinisch Westfalische Technische Hochschule Aachen. Osswald’s research interests are in the areas of processing-structure-property relationships for metals and composites, with a focus on powder metallurgy and metal injection molding. His research has been supported by the National Science Foundation, the Department of Energy, the US Army Research Office, and industry. Osswald is a Fellow of ASM International and the American Academy of Mechanics, and he has received the Extrusion Division Award, the Powder Metallurgy Division Award, and the Distinguished Teaching Award from TMS.
Most Relevant Publications (1+)

117 total publications

Technical Development of Multi-Resin Three-Dimensional Printer Using Bottom-Up Method

International Journal of Automation and Smart Technology / Dec 01, 2018

Jiang, C.-P. (2018). Technical Development of Multi-Resin Three-Dimensional Printer Using Bottom-Up Method. International Journal of Automation and Smart Technology, 8(4), 173–178. https://doi.org/10.5875/ausmt.v8i4.1840

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David J. Lilja

Minneapolis, Minnesota, United States of America
40 Years Experience
Professor Emeritus of Electrical and Computer Engineering, University of Minnesota
Education

University of Illinois Urbana-Champaign

Ph.D., Electrical Engineering

Urbana, Illinois, United States of America

University of Illinois Urbana-Champaign

M.S., Electrical Engineering

Urbana, Illinois, United States of America

Iowa State University

B.S., Computer Engineering

Ames, Iowa, United States of America
Experience

University of Minnesota - Twin Cities

Professor

Department Head

University of Canterbury, Christchurch, New Zealand

Visiting Professor

University of Western Australia, Perth, Australia

Visiting Professor

Most Relevant Research Expertise
Signal Processing
Other Research Expertise (15)
Computer architecture
high-performance parallel processing
computer systems performance analysis
approximate computing
Hardware and Architecture
And 10 more
About
**Research Expertise** Computer architecture, high-performance parallel processing, computer systems performance analysis, approximate computing, computing with emerging technologies, and storage systems. **Biographical summary** David J. Lilja received a Ph.D. and an M.S., both in Electrical Engineering, from the [University of Illinois at Urbana-Champaign,](http://www.uiuc.edu/) and a B.S. in Computer Engineering from [Iowa State University](http://www.iastate.edu/) in Ames. He is Professor Emeritus of [Electrical and Computer Engineering](http://www.ee.umn.edu/) at the [University of Minnesota](http://www.umn.edu/) in Minneapolis. He previously served as a member of the graduate faculties in [Computer Science](http://www.cs.umn.edu/), [Scientific Computation](http://www.scicomp.umn.edu/), and [Data Science](http://datascience.umn.edu//).  He served ten years as the head of the ECE department at the University of Minnesota, worked as a research assistant at the Center for Supercomputing Research and Development at the [University of Illinois,](http://www.uiuc.edu/) and as a development engineer at [Tandem Computers Incorporated](http://www.tandem.com/) in Cupertino, California.  He received a [Fulbright](http://www.fulbright.org/) Senior Scholar Award to visit the University of Western Australia and was a visiting Professor at the University of Canterbury in Christchurch, New Zealand. He has chaired and served on the program committees of numerous conferences.  He was elected a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow of the American Association for the Advancement of Science (AAAS) for contributions to the statistical analysis of computer performance. He also is a registered Professional Engineer.
Most Relevant Publications (4+)

99 total publications

Dynamic task scheduling using online optimization

IEEE Transactions on Parallel and Distributed Systems / Jan 01, 2000

Lilja, D. J., Lau Ying Kit, & Hamidzadeh, B. (2000). Dynamic task scheduling using online optimization. IEEE Transactions on Parallel and Distributed Systems, 11(11), 1151–1163. https://doi.org/10.1109/71.888636

An effective processor allocation strategy for multiprogrammed shared-memory multiprocessors

IEEE Transactions on Parallel and Distributed Systems / Jan 01, 1997

Yue, K. K., & Lilja, D. J. (1997). An effective processor allocation strategy for multiprogrammed shared-memory multiprocessors. IEEE Transactions on Parallel and Distributed Systems, 8(12), 1246–1258. https://doi.org/10.1109/71.640017

The potential of compile-time analysis to adapt the cache coherence enforcement strategy to the data sharing characteristics

IEEE Transactions on Parallel and Distributed Systems / May 01, 1995

Mounes-Toussi, F., & Lilja, D. J. (1995). The potential of compile-time analysis to adapt the cache coherence enforcement strategy to the data sharing characteristics. IEEE Transactions on Parallel and Distributed Systems, 6(5), 470–481. https://doi.org/10.1109/71.382316

Coarse-grained thread pipelining: a speculative parallel execution model for shared-memory multiprocessors

IEEE Transactions on Parallel and Distributed Systems / Sep 01, 2001

Kazi, I. H., & Lilja, D. J. (2001). Coarse-grained thread pipelining: a speculative parallel execution model for shared-memory multiprocessors. IEEE Transactions on Parallel and Distributed Systems, 12(9), 952–966. https://doi.org/10.1109/71.954629

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Lee Weinstein

6 Years Experience
STEM Educator
Education

Massachusetts Institute of Technology

Ph.D., Mechanical Engineering

Cambridge, Massachusetts, United States of America

Massachusetts Institute of Technology

S.M., Mechanical Engineering / August, 2013

Cambridge, Massachusetts, United States of America

University of California Berkeley

B.S., Mechanical Engineering / May, 2011

Berkeley, California, United States of America
Experience

Brilliant.org

Principal Scientist / 2017 — Present

Most Relevant Research Expertise
Signal Processing
Other Research Expertise (21)
Energy conversion
solar energy
thermoelectrics
Energy Engineering and Power Technology
Fuel Technology
And 16 more
About
Weinstein is a Principal Scientist at Brilliant.org, where he focuses on engineering education. He holds a Ph.D. in Mechanical Engineering from the Massachusetts Institute of Technology, and his research interests include the design of educational technology and the development of educational content.
Most Relevant Publications (1+)

22 total publications

Vortex shedding induced energy harvesting from piezoelectric materials in heating, ventilation and air conditioning flows

Smart Materials and Structures / Mar 14, 2012

Weinstein, L. A., Cacan, M. R., So, P. M., & Wright, P. K. (2012). Vortex shedding induced energy harvesting from piezoelectric materials in heating, ventilation and air conditioning flows. Smart Materials and Structures, 21(4), 045003. https://doi.org/10.1088/0964-1726/21/4/045003

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Dhritiman Das, Ph.D.

10 Years Experience
Postdoctoral Researcher at Massachusetts Institute of Technology : AI | Computer Vision | Signal Processing | Healthcare
Education

Technical University of Munich

Ph.D., Computer Science

Munich

Arizona State University

M.S., Bioengineering

Tempe, Arizona, United States of America

Manipal Institute of Technology

B.E., Biomedical Engineering

Manipal
Experience

Massachusetts Institute of Technology

Postdoctoral Researcher

Technical University of Munich

Scientific Staff / 2015 — 2020

GE Healthcare

Early Stage Researcher / 2015 — 2019

Most Relevant Research Expertise
Signal Processing
Other Research Expertise (14)
Machine Learning
Medical Image Analysis
Computer Vision
Electronic, Optical and Magnetic Materials
Condensed Matter Physics
And 9 more
About
Dhritiman Das is a highly accomplished computer scientist with a strong background in bioengineering. He holds a Ph.D. in Computer Science from the Technical University of Munich, where he focused on developing innovative machine learning algorithms for medical imaging applications. More specifically, he developed applied machine learning and computer vision tools for accelerated processing and analysis of large-scale brain imaging (MRSI) data. Prior to his doctoral studies, Dhritiman earned a Master of Science in Bioengineering from Arizona State University and a Bachelor of Engineering in Biomedical Engineering from Manipal Institute of Technology. Throughout his academic career, Dhritiman has demonstrated a strong passion for research and has published several papers in top computer science and biomedical engineering journals. He has also presented his work at numerous international conferences and workshops, gaining recognition from the scientific community. In addition to his academic achievements, Dhritiman has gained valuable industry experience through various internships and research positions. He has worked as a Postdoctoral Researcher at the Massachusetts Institute of Technology, where he collaborated with leading researchers to develop cutting-edge technologies for healthcare applications. His work here focused on self-supervised learning, generative models and neuroinformatics. He has also held positions at GE Healthcare and Siemens Limited, where he applied his expertise in information theory, computer vision and machine learning to solve real-world challenges in the field of medical imaging. Dhritiman is a skilled researcher and problem-solver with a strong background in both computer science and bioengineering. He is dedicated to using his knowledge and expertise to make a positive impact in the field of healthcare and beyond.

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Dr. Mona Saleh

New Jersey
22 Years Experience
Associate Professor in Wireless Communications and DSP with publications on Physical Layer Technologies
Education

Ain Shams University

PhD, Electronics and Communications

Cairo

Ain Shams University

MSc, Electronics and Communications

Cairo

Ain Shams University

BSc, Electronics and Communications

Cairo
Experience

Ain Shams University

Associate Professor / 2019 — 2023

Assistant Professor / 2014 — 2019

Research Associate / 2008 — 2014

Teaching Assistant / Lecturer / 2002 — 2008

Most Relevant Research Expertise
Signal Processing
Other Research Expertise (6)
Wireless Communications
5G
6G
OFDM
DVB
And 1 more
About
Senior IEEE Member 2023, received her BSc. (with honor), M.Sc., and Ph.D. degrees in the electrical engineering field from the faculty of engineering at Ain Shams University in 2002, 2008, and 2013, respectively. In 2002, she joined the electronics and communications engineering department, Ain Shams University as a teaching assistant; in 2008, she became a teaching associate; in 2014, she was promoted to assistant professor; and since 2019, she works as an associate professor in the same department. Her current research interests include, but are not limited to, signal processing and wireless communication systems. She has participated in many research projects and has many conference and journal publications in these areas. She has been an organizing committee member at several scientific conferences, e.g., MMS’03, NRSC’07, and ESOLE since 2009. She has also been an executive office member of the ESOLE journal since April 2014.

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Athul Prasad

17 Years Experience
5G / 6G Technology and Ventures at Samsung; D.Sc. (Tech), MBA
Education

Massachusetts Institute of Technology

Master of Business Administration, Sloan School of Management / May, 2020

Cambridge, Massachusetts, United States of America

Aalto-yliopisto Sähkötekniikan korkeakoulu

Doctor of Science (Technology), Communication and Networking / December, 2015

Aalto

Aalto University

M.Sc. (Tech), Wireless Communications / March, 2011

Espoo
Experience

Nokia Solutions and Networks Oy

Senior Specialist, Radio Research / June, 2014 — January, 2023

Wireless research, scientific publications, patents

Samsung Electronics

Senior Manager / January, 2023 — Present

Technology partnership, strategy, business development

NEC Europe Ltd

Research Engineer / December, 2012 — May, 2014

Most Relevant Research Expertise
Signal Processing
Other Research Expertise (35)
Machine Learning
Mobility Management
5G / New Radio
Dynamic Resource Allocation
Electrical and Electronic Engineering
And 30 more
About
Dr. Athul Prasad received his MBA from MIT where he was a Sloan Fellow, M.Sc. (Tech.) (with distinction) and D.Sc. (Tech) from Aalto University, B.Tech (with distinction) from University of Kerala, and is also a graduate of the year-long executive management (LEAD) program from Stanford University's Graduate School of Business. He was with Nokia from 2014-2023 and is currently with Samsung based out of Mountain View, CA. He has coauthored over 40 peer reviewed scientific publications and has written a book on 5G "End-to-End Mobile Communications: Evolution to 5G," McGraw-Hill, Aug. 2020. He's also the co-inventor of over 90 patents.
Most Relevant Publications (1+)

75 total publications

Dynamic base station planning with power adaptation for green wireless cellular networks

EURASIP Journal on Wireless Communications and Networking / May 15, 2014

Yigitel, M. A., Incel, O. D., & Ersoy, C. (2014). Dynamic base station planning with power adaptation for green wireless cellular networks. EURASIP Journal on Wireless Communications and Networking, 2014(1). https://doi.org/10.1186/1687-1499-2014-77

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Hussein Al-Hussein

8 Years Experience
Adjunct Faculty, Mission College & De Anza College, California
Education

Stanford University

Electrical Enginering / March, 1990

Stanford, California, United States of America

Stanford University

MS, Electrical Engineering / January, 1981

Stanford, California, United States of America
Experience

Mission College

Adjunct Faculty / August, 2016 — Present

Teaching computer science courses like C++, data structures, and algorithms.

De Anza College

Adjunct Faculty / December, 2021 — Present

Teaching computer science courses like C++, data structures, and algorithms.

Most Relevant Research Expertise
Signal Processing
Other Research Expertise (28)
Image Processing
Computer Visions
OpenCV
Algorithms
OCR
And 23 more
About
Technical Skills: ·       **Programming Languages:** Python, OpenCV, C++ 11 and up, C, and Java. ·       **Image processing**: Image segmentation, alignment, registration, enhancement, filters, noise removal, binarization, object detection and identification, English & Arabic OCR, 2D barcode readers, watermarking, DCT (Discrete Cosine Transform), check cropping, industrial defect detection, and fault detection of assembled systems. ·       **Software Engineering Skills:** OOP, algorithms, data structures, SW processes, static and dynamic code analysis, algorithms performance, MISRA standards code compliance, Google C & C++ coding guidelines, git, and hg.   Cryptography
Most Relevant Publications (1+)

14 total publications

Machine-printed character recognition revisited: re-application of recent advances in handwritten character recognition research

Image and Vision Computing / Aug 01, 1998

Rahman, A. F. R., & Fairhurst, M. C. (1998). Machine-printed character recognition revisited: re-application of recent advances in handwritten character recognition research. Image and Vision Computing, 16(12–13), 819–842. https://doi.org/10.1016/s0262-8856(98)00056-0

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Example Signal Processing projects

How can companies collaborate more effectively with researchers, experts, and thought leaders to make progress on Signal Processing?

Optimizing Signal Processing Algorithms for Image Recognition

A company in the computer vision industry can collaborate with a Signal Processing expert to optimize their image recognition algorithms. By leveraging advanced signal processing techniques, the expert can improve the accuracy and speed of image recognition systems, enabling the company to develop more efficient and reliable computer vision solutions.

Developing Advanced Signal Processing Techniques for Data Analysis

A data analytics company can partner with a Signal Processing researcher to develop advanced signal processing techniques for data analysis. These techniques can help the company extract valuable insights from complex datasets, identify patterns and trends, and make data-driven decisions. By leveraging the expertise of the researcher, the company can enhance their data analysis capabilities and gain a competitive advantage in the market.

Designing Efficient Communication Systems

A telecommunications company can collaborate with a Signal Processing expert to design efficient communication systems. The expert can develop signal processing algorithms and protocols that optimize data transmission, reduce noise and interference, and improve overall system performance. By working with the researcher, the company can enhance the reliability and efficiency of their communication networks, leading to improved customer satisfaction and business growth.

Advancing Biomedical Signal Processing

A healthcare technology company can partner with a Signal Processing specialist to advance biomedical signal processing techniques. The researcher can develop algorithms and methods for analyzing physiological signals, such as ECG and EEG, to detect abnormalities, monitor patient health, and improve medical diagnosis. By collaborating with the expert, the company can enhance their healthcare solutions and contribute to the development of innovative medical technologies.

Improving Radar and Sonar Systems

A defense contractor can collaborate with a Signal Processing researcher to improve radar and sonar systems. The expert can develop signal processing algorithms that enhance target detection, tracking, and classification capabilities, improving the performance and accuracy of these systems. By leveraging the expertise of the researcher, the company can strengthen their defense technologies and gain a competitive edge in the market.