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IEEE Signal Processing Magazine

EDITOR-IN-CHIEF

Christian Jutten—Université Grenoble Alpes, France

AREA EDITORS

Feature Articles

Laure Blanc-Féraud—Université Côte d’Azur, France

Special Issues

Xiaoxiang Zhu—German Aerospace Center, Germany

Columns and Forum

Rodrigo Capobianco Guido—São Paulo State University (UNESP), Brazil

H. Vicky Zhao—Tsinghua University, R.P. China

e-Newsletter

Hamid Palangi—Microsoft Research Lab (AI), USA

Social Media and Outreach

Emil Björnson—KTH Royal Institute of Technology, Sweden

EDITORIAL BOARD

Massoud Babaie-Zadeh—Sharif University of Technology, Iran

Waheed U. Bajwa—Rutgers University, USA

Caroline Chaux—French Center of National Research, France

Mark Coates—McGill University, Canada

Laura Cottatellucci—Friedrich-Alexander University of Erlangen-Nuremberg, Germany

Davide Dardari—University of Bologna, Italy

Mario Figueiredo—Instituto Superior Técnico, University of Lisbon, Portugal

Sharon Gannot—Bar-Ilan University, Israel

Yifan Gong—Microsoft Corporation, USA

Rémi Gribonval—Inria Lyon, France

Joseph Guerci—Information Systems Laboratories, Inc., USA

Ian Jermyn—Durham University, U.K.

Ulugbek S. Kamilov—Washington University, USA Patrick Le Callet—University of Nantes, France

Sanghoon Lee—Yonsei University, Korea

Danilo Mandic—Imperial College London, U.K.

Michalis Matthaiou—Queen’s University Belfast, U.K.

Phillip A. Regalia—U.S. National Science Foundation, USA

Gaël Richard—Télécom Paris, Institut Polytechnique de Paris, France

Reza Sameni—Emory University, USA

Ervin Sejdic—University of Pittsburgh, USA

Dimitri Van De Ville—Ecole Polytechnique Fédérale de Lausanne, Switzerland

Henk Wymeersch—Chalmers University of Technology, Sweden

ASSOCIATE EDITORS—COLUMNS AND FORUM

Ulisses Braga-Neto—Texas A&M University, USA

Cagatay Candan—Middle East Technical University, Turkey

Wei Hu—Peking University, China

Andres Kwasinski—Rochester Institute of Technology, USA

Xingyu Li—University of Alberta, Edmonton, Alberta, Canada

Xin Liao—Hunan University, China

Piya Pal—University of California San Diego, USA

Hemant Patil—Dhirubhai Ambani Institute of Information and Communication Technology, India

Christian Ritz—University of Wollongong, Australia

ASSOCIATE EDITORS—e-NEWSLETTER

Abhishek Appaji—College of Engineering, India

Subhro Das—MIT-IBM Watson AI Lab, IBM Research, USA

Behnaz Ghoraani—Florida Atlantic University, USA

Panagiotis Markopoulos—The University of Texas at San Antonio, USA

IEEE SIGNAL PROCESSING SOCIETY

Athina Petropulu—President

Min Wu—President-Elect

Ana Isabel Pérez-Neira—Vice President, Conferences

Roxana Saint-Nom—VP Education

Kenneth K.M. Lam—Vice President, Membership

Marc Moonen—Vice President, Publications

Alle-Jan van der Veen—Vice President, Technical Directions

IEEE SIGNAL PROCESSING SOCIETY STAFF

Richard J. Baseil—Society Executive Director

William Colacchio—Senior Manager, Publications and Education Strategy and Services

Rebecca Wollman—Publications Administrator

IEEE PERIODICALS MAGAZINES DEPARTMENT

Sharon Turk, Journals Production Manager

Katie Sullivan, Senior Manager, Journals Production

Janet Dudar, Senior Art Director

Gail A. Schnitzer, Associate Art Director

Theresa L. Smith, Production Coordinator

Mark David, Director, Business Development - Media & Advertising

Felicia Spagnoli, Advertising Production Manager

Peter M. Tuohy, Production Director

Kevin Lisankie, Editorial Services Director

Dawn M. Melley, Senior Director, Publishing Operations


SCOPE: IEEE Signal Processing Magazine publishes tutorial-style articles on signal processing research and applications as well as columns and forums on issues of interest. Its coverage ranges from fundamental principles to practical implementation, reflecting the multidimensional facets of interests and concerns of the community. Its mission is to bring up-to-date, emerging, and active technical developments, issues, and events to the research, educational, and professional communities. It is also the main Society communication platform addressing important issues concerning all members.


Digital Object Identifier 10.1109/MSP.2023.3262437

IEEE Signal Processing Magazine   |  June 2023

from the guest editors

IEEE Signal Processing Society: Celebrating 75 Years of Remarkable Achievements

Rodrigo Capobianco Guido, Tulay Adali, Emil Björnson, Laure Blanc-Féraud, Ulisses Braga-Neto, Behnaz Ghoraani, Christian Jutten, Alle-Jan Van Der Veen, Hong Vicky Zhao, Xiaoxing Zhu

It is our great pleasure to introduce the first part of this special issue to you! The IEEE Signal Processing Society (SPS) has completed 75 years of remarkable service to the signal processing community. When the Society was founded in 1948, we couldn’t imagine, for instance, how wireless networks of smartphones would be able to connect us easily at all times, or that an image processing algorithm would be able to detect cancer in a few seconds. Those are just simple examples of the immense technological progress over the past 75 years, which became possible thanks in great part to the dedicated work of professional members of the SPS.

Celebrating 75 years

A special issue of IEEE Signal Processing Magazine was published 25 years ago to celebrate the 50th anniversary of the SPS. To celebrate the 75th anniversary, we have focused on what has happened during the previous 25 years in the field of signal processing, in addition to the main perspectives considering both societal and technical aspects in different domains covered by our Society. In response to an open call for papers, we received 41 white paper submissions. Among those, 18 were selected and invited to be considered for publication upon submission of a full version. Finally, 11 were accepted for inclusion in this first part of the special issue, while the remaining ones will appear in the upcoming second part.

The first three articles in this first part of the special issue focus on the history of the SPS. The article by Petropulu (SPS president) et al. [A1] describes the extraordinary growth we have witnessed in the field of digital signal processing (DSP) since 1998, where the SPS played a fundamental role in promoting cross-disciplinary collaboration and knowledge sharing. Then, the article by Ward (former SPS President) [A2] focuses on women researchers and volunteers and their active role within the SPS. Finally, Pérez-Neira (SPS vice president, conferences) et al. [A3] present an article that comments on the most prominent SPS conferences and their evolution. These articles also discuss the main challenges and opportunities for the SPS.

Next, we have a powerful testimony by Edwards [A4], who has contributed significantly to our magazine and Society over the years. He begins by recalling a very special occasion: the day he was 14 years old and visited the 1969 IEEE International Convention & Exhibition and decided on his future career. Then, using his unique journalistic skills, he narrates lots of interesting events with significant value to our DSP community.

As signal processing can be classified along techniques and methods such as sampling, transforms, statistical techniques including machine learning, and so on, it can also be partitioned into major application areas, such as speech and audio, image processing and multimedia, communication and sensor array processing. Our technical committees (TCs) and unified Editors Information Classification Scheme (EDICS) reflect these dual partitionings. The selected feature articles included in this special issue provide a cross section of those fields. Particularly, in this first issue, we present seven of these feature articles. The first one, authored by Leus et al. [A5] describes the role of graph signal processing for signal analysis over the recent decades in a variety of applications, including image and video processing; social, transportation, communication, and brain networks; recommender systems; financial engineering; distributed control; and learning. The second feature article is by Aviyente et al. [A6]. In it, the authors offer a brief history of the IEEE Bioimaging and Signal Processing TC, providing an overview of the main technological and methodological contributions and highlight promising new directions. Then, Bajic´ et al. [A7] review both the history of multimedia signal processing as well as the IEEE Multimedia Signal Processing TC, with a focus on the last three decades.

The fourth feature article we present in this special issue is authored by Liu et al. [A8], where an overview of the IEEE Sensor Array and Multichannel TC and its activities are introduced, followed by the main technological advances and new developments in the area along with promising future research directions. The fifth feature article, authored by Pesavento et al. [A9], presents an overview and advances in multiple-input, multiple-output systems, including details on direction of arrival, direction of departure, time delay of arrival, and Doppler mechanisms. The sixth feature article is authored by Björnson et al. [A10] and presents the story of wireless communication technologies over the past 25 years, including the advances in air interface, channel coding, source compression, connection protocols, and related areas, covering from 2G to 5G technologies. Finally, the seventh feature article, authored by Elbir et al. [A11], describes relevant details on the development of beamformers, emphasizing minimum-variance distortionless response strategies and the corresponding major breakthroughs over the past decades.

This concludes the first part of this special issue. In the second part, to be published in the magazine’s July issue, another set of relevant articles will appear, concluding our efforts to group together the most significant contributions received to celebrate the 75th anniversary of the SPS. We would like to specially express our gratitude to all our contributing authors and reviewers, in addition to Rebecca Wollman, who efficiently helped us with all the administrative details, and the entire team, led by Sharon Turk, who brilliantly promoted and supervised the editorial process.

We sincerely hope that you enjoy reading the first part of this special issue.

Acknowledgment

Rodrigo Capobianco Guido is the lead guest editor of this special issue.

Guest Editors

Rodrigo Capobianco Guido (guido@ieee.org) received his Ph.D. degree in computational applied physics from the University of São Paulo (USP), Brazil, in 2003. Following two postdoctoral programs in signal processing at USP, he obtained the title of associate professor in signal processing, also from USP, in 2008. Currently, he is an associate professor at São Paulo State University, São José do Rio Preto, São Paulo, 15054-000, Brazil. He has been an area editor of IEEE Signal Processing Magazine and was recently included in Stanford University’s rankings of the world’s top 2% scientists. His research interests include signal and speech processing based on wavelets and machine learning. He is a Senior Member of IEEE.

Tulay Adali (adali@umbc.edu) received her Ph.D. degree in electrical engineering from North Carolina State University. She is a distinguished university professor at the University of Maryland, Baltimore County, Baltimore, MD 21250 USA. She is chair of IEEE Brain and past vice president of technical directions for the IEEE Signal Processing Society (SPS). She is a Fulbright Scholar and an SPS Distinguished Lecturer. She received a Humboldt Research Award, an IEEE SPS Best Paper Award, the University System of Maryland Regents’ Award for Research, and a National Science Foundation CAREER Award. Her research interests include statistical signal processing and machine learning and their applications, with an emphasis on applications in medical image analysis and fusion. She is a Fellow of IEEE and a fellow of the American Institute for Medical and Biological Engineering.

Emil Björnson (emilbjo@kth.se) is a full (tenured) professor of wireless communication at the KTH Royal Institute of Technology, Stockholm, 100 44, Sweden. He received the 2018 and 2022 IEEE Marconi Prize Paper Awards in Wireless Communications, the 2019 EURASIP Early Career Award, the 2019 IEEE Communications Society Fred W. Ellersick Prize, the 2019 IEEE Signal Processing Magazine Best Column Award, the 2020 Pierre-Simon Laplace Early Career Technical Achievement Award, the 2020 Communication Theory Technical Committee Early Achievement Award, the 2021 IEEE Communications Society Radio Communications Committee Early Achievement Award, and the 2023 IEEE Communications Society Outstanding Paper Award. His work has also received six Best Paper Awards at conferences. He is a Fellow of IEEE, and a Digital Futures and Wallenberg Academy fellow.

Laure Blanc-Féraud (laure.blanc-feraud@univ-cotedazur.fr) received her Ph.D. degree and habilitation to conduct research in inverse problems in image processing from University Côte d’Azur in 1989 and 2000, respectively. She is a researcher with Informatique Signaux et Systèmes at Sophia Antipolis (I3S) Lab, the University Côte d’Azur, Centre national de la recherche scientifique (CNRS), Sophia Antipolis, 06900 France. She served/serves on the IEEE Biomedical Image and Signal Processing Technical Committee (2007–2015; 2019–) and has been general technical chair (2014) and general chair (2021) of the IEEE International Symposium on Biomedical Imaging. She has been an associate editor of SIAM Imaging Science (2013–2018) and is currently an area editor of IEEE Signal Processing Magazine. She headed the French national research group GDR Groupement de recherche–Information, Signal, Image et ViSion (ISIS) of CNRS on Information, Signal Image and Vision (2021–2018). Her research interests include inverse problems in image processing using partial differential equation and optimization. She is a Fellow of IEEE.

Ulisses Braga-Neto (ulisses@tamu.edu) received his Ph.D. degree in electrical and computer engineering from Johns Hopkins University in 2002. He is a professor in the Electrical and Computer Engineering Department, Texas A&M University, College Station TX 77843 USA. He is founding director of the Scientific Machine Learning Lab at the Texas A&M Institute of Data Science. He is an associate editor of IEEE Signal Processing Magazine and a former elected member of the IEEE Signal Processing Society Machine Learning for Signal Processing Technical Committee and the IEEE Biomedical Imaging and Signal Processing Technical Committee. He has published two textbooks and more than 150 peer-reviewed journal articles and conference papers. He received the 2009 National Science Foundation CAREER Award. His research focuses on machine learning and statistical signal processing. He is a Senior Member of IEEE.

Behnaz Ghoraani (bghoraani@fau.edu) received her Ph.D. from the Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada, followed by a Postdoctoral Fellow period with the Faculty of Medicine, University of Toronto, Toronto, Canada. She is an associate professor of electrical engineering and computer science at Florida Atlantic University, Boca Raton FL 33431 USA, with a specialization in biomedical signal analysis, machine learning, wearable and assistive devices for rehabilitation, and remote home monitoring. She is an associate editor of IEEE Journal of Biomedical and Health Informatics and BioMedical Engineering OnLine Journal. Her research has received recognition through multiple best paper awards and the Gordon K. Moe Young Investigator Award. Her research has been funded by grants from the National Institutes of Health, the National Science Foundation (including a CAREER Award), and the Florida Department of Health. She is an esteemed member of the Board of Scientific Counselors of National Library of Medicine, as well as the IEEE SPS Biomedical Signal and Image Professional Technical Committee. She has also taken on the role of the IEEE Women in Signal Processing Committee Chair and an Area Editor for the IEEE SPM eNewsletter.

Christian Jutten (christian.jutten@grenoble-inp.fr) received his Ph.D. and Doctor es Sciences degrees from Grenoble Polytechnic Institute, France, in 1981 and 1987, respectively. He was an associate professor (1982–1989) and a professor (1989–2019), and has been a professor emeritus since September 2019 at University Grenoble Alpes, Saint-Martin-d’Hères 38400. He was an organizer or program chair of many international conferences, including the first Independent Component Analysis Conference in 1999 (ICA’99) and the 2009 IEEE International Workshop on Machine Learning for Signal Processing. He was the technical program cochair of ICASSP 2020. Since 2021, he has been editor-in-chief of IEEE Signal Processing Magazine. Since the 1980s, his research interests have been in machine learning and source separation, including theory and applications (brain and hyperspectral imaging, chemical sensing, and speech). He is a Fellow of IEEE and a fellow of the European Association for Signal Processing.

Alle-Jan Van Der Veen (a.j.vanderveen@tudelft.nl) received his Ph.D. in system theory at the Circuits and Systems Group, Department of Electrical Engineering, TU Delft, The Netherlands, with a postdoctoral research position at Stanford University, USA. He is a professor and chair of the Signal Processing Systems group at Delft University of Technology, Delft, 2628, The Netherlands. He was editor-in-chief of IEEE Transactions on Signal Processing and IEEE Signal Processing Letters. He was an elected member of the IEEE Signal Processing Society (SPS) Board of Governors. He was chair of the IEEE SPS Fellow Reference Committee, chair of the IEEE SPS Signal Processing for Communications Technical Committee, and technical cochair of ICASSP 2011 (Prague). He is currently the IEEE SPS vice president of technical directions (2022–2024). His research interests are in the areas of array signal processing and signal processing for communication, with applications to radio astronomy and sensor network localization. He is a Fellow of IEEE and a fellow of the European Association for Signal Processing.

Hong Vicky Zhao (vzhao@tsinghua.edu.cn) received her Ph.D. degree in electrical engineering from the University of Maryland, College Park, in 2004. Since May 2016, she has been an associate professor with the Department of Automation, Tsinghua University, Beijing, 100084, China. She received the IEEE Signal Processing Society 2008 Young Author Best Paper Award. She is the coauthor of “Multimedia Fingerprinting Forensics for Traitor Tracing” (Hindawi, 2005), “Behavior Dynamics in Media-Sharing Social Networks” (Cambridge University Press, 2011), and “Behavior and Evolutionary Dynamics in Crowd Networks” (Springer, 2020). She was a member of the IEEE Signal Processing Society Information Forensics and Security Technical Committee and the Multimedia Signal Processing Technical Committee. She is the senior area editor, area editor, and associate editor of IEEE Signal Processing Letters, IEEE Signal Processing Magazine, IEEE Transactions on Information Forensics and Security, and IEEE Open Journal of Signal Processing. Her research interests include media-sharing social networks, information security and forensics, digital communications, and signal processing.

Xiaoxing Zhu (xiaoxiang.zhu@tum.de) received her Dr.-Ing. degree and her “Habilitation” in signal processing from the Technical University of Munich (TUM), in 2011 and 2013, respectively. She is the chair professor for data science in Earth observation at TUM, Munich, 80333, Germany. She was founding head of the “EO Data Science” Department at the Remote Sensing Technology Institute, German Aerospace Center. Since October 2020, she has served as a director of the TUM Munich Data Science Institute. She is currently a visiting artificial intelligence professor at the European Space Agency’s Phi Lab. Her research interests include remote sensing and Earth observation, signal processing, machine learning, and data science, with their applications to tackling societal grand challenges, e.g., global urbanization, the United Nations’ sustainable development goals, and climate change. She is a Fellow of IEEE.

Appendix: Related Articles

[A1] A. Petropulu, J. M. F. Moura, R. K. Ward, and T. Argiropoulos, “Empowering the growth of signal processing,” IEEE Signal Process. Mag., vol. 40, no. 4, pp. 14–22, Jul. 2023, doi: 10.1109/MSP.2023.3262905.

[A2] R. K. Ward, “The evolution of women in signal processing and science, technology, engineering, and mathematics,” IEEE Signal Process. Mag., vol. 40, no. 4, pp. 23–35, Jul. 2023, doi: 10.1109/MSP.2023.3236475.

[A3] A. I. Perez-Neira, F. Pereira, C. Regazzoni, and C. Johnson, “IEEE signal processing society flagship conferences over the past 10 years,” IEEE Signal Process. Mag., vol. 40, no. 4, pp. 36–45, Jul. 2023, doi: 10.1109/MSP.2023.3240852.

[A4] J. Edwards, “How the 1969 IEEE convention and exhibition changed my life forever,” IEEE Signal Process. Mag., vol. 40, no. 4, pp. 46–48, Jul. 2023, doi: 10.1109/MSP.2023.3253254.

[A5] G. Leus, A. G. Marques, J. M. F. Moura, A. Ortega, and D. I. Shuman, “Graph signal processing,” IEEE Signal Process. Mag., vol. 40, no. 4, pp. 49–60, Jul. 2023, doi: 10.1109/MSP.2023.3262906.

[A6] S. Aviyente et al., “From nano to macro,” IEEE Signal Process. Mag., vol. 40, no. 4, pp. 61–71, Jul. 2023, doi: 10.1109/MSP.2023.3242833.

[A7] I. V. Bajic´, M. Mrak, F. Dufaux, E. Magli, and T. Chen, “Multimedia signal processing,” IEEE Signal Process. Mag., vol. 40, no. 4, pp. 72–79, Jul. 2023, doi: 10.1109/MSP.2023.3260989.

[A8] W. Liu, M. Haardt, M. S. Greco, C. F. Mecklenbräuker, and P. Willett, “Twenty-five years of sensor array and multichannel signal processing,” IEEE Signal Process. Mag., vol. 40, no. 4, pp. 80–91, Jul. 2023, doi: 10.1109/MSP.2023.3258060.

[A9] M. Pesavento, M. Trinh-Hoang, and M. Viberg, “Three more decades in array signal processing research,” IEEE Signal Process. Mag., vol. 40, no. 4, pp. 92–106, Jul. 2023, doi: 10.1109/MSP.2023.3255558.

[A10] E. Björnson, Y. C. Eldar, E. G. Larsson, A. Lozano, and H. V. Poor, “Twenty-five years of signal processing advances for multiantenna communications,” IEEE Signal Process. Mag., vol. 40, no. 4, pp. 107–117, Jul. 2023, doi: 10.1109/MSP.2023.3261505.

[A11] A. M. Elbir, K. V. Mishra, S. A. Vorobyov, and R. W. Heath Jr., “Twenty-five years of advances in beamforming,” IEEE Signal Process. Mag., vol. 40, no. 4, pp. 118–131, Jul. 2023, doi: 10.1109/MSP.2023.3262366.

Digital Object Identifier 10.1109/MSP.2023.3269591


IEEE Signal Processing Magazine   |  June 2023

from the editor

Celebrating Technological Breakthroughs and Navigating the Future With Care

Christian Jutten, Christian Jutten, Athina Petropulu

Athina Petropulu
IEEE Signal Processing Society President
a.petropulu@ieee.org

Christian Jutten
Editor-in-Chief
christian.jutten@grenoble-inp.fr

The 75th anniversary of the IEEE Signal Processing Society (SPS) is an ideal time to look at the rapid advances in our field and the many ways that these increasingly powerful technologies have transformed our professions and the world. This is not just a time to celebrate past achievements and pat ourselves on the back, but also to educate young students and innovators about the history of our profession, the challenges we have overcome, and the breakthroughs that have led to the incredible growth of Signal Processing (SP). More importantly, this reflection will help shape our path forward, by inspiring new innovations, and also bringing awareness of the ethical issues associated with evolving and emerging technologies. This awareness will help us to develop meaningful safeguards and ensure responsible use of these technologies.

The 75th anniversary of the SPS coincides with another important 75th anniversary, that of a tiny yet mighty device: the transistor. This is not a mere coincidence. From their birth, signal and image processing (SIP) has been strongly associated with technological advances, especially in electronics and computers. In fact, SIP requires both sensors, for recording signals and images and computers, for implementing smart and efficient processing.

Fantastic growth during the last decades

For the sake of our younger SPS scientists, let’s start with a few milestones in the common history of SIP and hardware. The first computer was very big. ENIAC, built in 1943, was about 170 m2 and 27 tons, with a power consumption of 150 kW. It had a very limited computational capacity: approximately 0.2 ms for addition or subtraction, 2 ms for multiplication, and up to 65 ms for performing a division or a square root! ENIAC was a decimal machine, but, a few years later, in 1946, in the framework of the Electronic Discrete Variable Automatic Computer (EDVAC) project, the concept of the von Neumann machine appeared, using binary coding and computing. The basic components of these machines were electronic tubes.

By the 1950s and up to end of the 1960s, a few computers were built with transistors as discrete components. In 1958, Kilby (a Nobel Prize winner in 2000) invented the first integrated circuit, which was patented in 1964 by Texas Instruments. This discovery had an incredible impact on the development of the digital world.

The first microprocessor appeared in 1971: the Intel 4004, a 4-bit microprocessor with 2,300 transistors and a clock frequency of about 100 kHz. This 16-pin integrated circuit (about 3.8 × 2.8 cm) had a computational power similar to that of ENIAC! Of course, advances in microelectronics provided increasingly powerful integrated circuits and microprocessors. Here are just a few milestones, to show this impressive growth:

  • 1972: Intel 8080: 8 bits; 3,500 transistors; and clock of 200 kHz
  • 1979: Intel 8088: 16 bits; 29,000 transistors; and clock of 5 MHz
  • 1989: Intel 80486: 32 bits; 1,200,000 transistors; and clock of 16–100 MHz.

Today, microprocessors are 64 bits and multicore, with more than 2 million transistors and a clock of about 5 GHz!

The microprocessor Intel 8088 was the basic component of the first IBM personal computer built in 1981. With 16 kB of random-access memory (RAM), extensible to 256 kB, and a floppy disk of 160 kB, its price was quite high, and it was primarily used by companies and, later, by some laboratories.

Until the 1980s, images were recorded using a Vidicon camera, based on a cathodic ray tube, which provides an image by the scanning of an electron beam. At that time, it was impossible to store such images in computer memory because the time access of the memory was not compatible with the speed of the scanning (30 frames/s and about 500 lines), and the capacity of the memory (even dynamic RAM) was too small—fewer than 256 kB [1]. In 1970, Boyle and Smith (Nobel Prize winners in 2009) published a paper on charge-coupled semiconductor devices (CCDs) [2], which could be used as image sensors. The first commercial image CCD sensors were proposed by Fairchild in 1974, with 100 × 100 pixels. Then, in 1983, Sony developed the first mass-produced consumer video camera based on a CCD sensor (CCD-G5) with 384 × 491 pixels. Now, the size of CCD or CMOS image sensors in a camera is about 8,000 × 6,000 pixels or better!

Advances in technology have had a strong impact in many domains for the development of other electronic devices and sensors, especially in medicine, remote sensing, transportation, and telecommunications.

Christian’s experiences as a researcher in his university lab in France provide some important perspectives on the impact of increasingly powerful technologies. “In 1980, about 20 researchers in three labs shared access to two 16-bit computers: HP 1000 and T1600 (from the French company Télémécanique). On average, we could use one machine for about 1 h per day, with a personal partition of 24 kB of memory—for both the program and the data! Programs were written in Fortran, and there was no graphical output: we had to manually draw curves from the numerical results.” One of Christian’s friends designed methods for doing handwritten character recognition: despite small images of 128 × 32 pixels, computations had to be done using integers since coding and computing in floating point were impossible using 24-kB memory.

Later, in 1985, Christian’s lab got its first PC, and it was possible to use other languages, like Pascal and Basic. “But the performance was still very limited,” he notes. “A very simple program of source separation required about one hour to converge. In the lab, we did some simulations on computers, but, typically, Ph.D. students also built dedicated machines.” To overcome the computer’s slowness, Christian implemented the source separation algorithm with operational amplifiers, field effect transistors, and other discrete components (Figure 1). The convergence of this analog implementation required only a few milliseconds, and he added a low-pass RC circuit to slow down the convergence speed so that it became observable!

Figure 1. This analog electronic implementation of a source separation algorithm was about 1 million times faster than the simulation on a PC available in 1985.

Christian’s team worked on artificial neural networks (ANNs), and, by the end of the 1980s, a few Ph.D. students had designed new systolic and parallel architectures with the related software for overcoming the limitations of classic computers for simulating ANNs.

Currently, e-mail and Internet access are essential tools in our lives, both personal and professional. We’ve become so used to fast, reliable, 24-h connectivity that we see it as a crisis if our web server is down for more than a few minutes. Young people wonder how it was possible to get work done, locate journal articles, do comprehensive research, share ideas, and communicate with each other without e-mail and the Internet. Christian remembers that one of the first e-mails that he sent in 1985 came back three weeks later, with an error message and the list of servers through which it had passed! Before reliable Internet and e-mail connectivity became available at the beginning of the 1990s, we had access to some printed journals in the lab or university library, and, when we had to share documents with collaborators outside our workplace, we did so by fax.

In that era, writing articles and papers for journals and conferences was also much more tricky. “We used an electric typewriter,” says Christian. “If we needed to change the font, such as when typing equations or Greek characters, we had to replace the typeball.” When Christian’s lab acquired its first LaserJet in 1989, it became so easy to print a text with different fonts in one step using PostScript. It was also possible to design figures on a computer and to add them to the text, and later it became easy to include photos and images, too.

Now, tablets, laptops, PCs, and even smartphones are so powerful and fast, with huge memories, tens of gigabytes, and hard disks of a few terabytes. For very complex simulations and computations, researchers can share university-based and national computing centers with incredibly powerful machines. All of these means of high-performance computations seem commonplace today, but it’s important to be mindful that the growth of these tools has been extraordinarily fast over the last decades. The growth in SIP followed a similar trajectory, and it is easy to understand why image processing, computational imaging, wireless communications, and forensics, to name a few, didn’t appear until the 1990s since they required devices, sensors, and computers that didn’t exist or were not powerful enough.

Challenges for the future: Growth versus ethics and ecology

In the 2020s, the developments in integrated circuits have led to GPUs whose highly parallel architectures are well-suited for efficiently performing a large number of operations. Multicore computers and GPUs provide researchers with the tools to train deep neural networks more quickly and efficiently than was previously possible. These tools enabled the development of large-scale deep learning frameworks, such as TensorFlow and PyTorch, making it easier for researchers and engineers to experiment with artificial intelligence (AI) models. These tools also supported the development of large-scale language models, such as the ChatGPT, developed by OpenAI, enabling language translation, chatbots, and content generation.

With all of the computational power available today, AI can analyze large amounts of data and identify patterns and insights that might be difficult for humans to detect. AI is now capable of performing a wide range of tasks, including image recognition, natural language processing, decision making, and even creative tasks, such as music composition and art generation.

While AI can accelerate the pace of scientific discovery, it also poses several concerns. AI systems may perpetuate and amplify biases that exist in society, such as racial or gender bias. This can happen when the AI system is trained on biased data, or if the algorithm itself is designed in a way that perpetuates bias. Another problem with AI methods is that they operate as “black boxes,” meaning that their inner workings are not transparent or easily understandable by humans. This can make it difficult to explain how the AI system arrived at a particular decision or prediction and can also make it challenging to identify and correct errors or biases in the system. When it comes to AI language models, such as the ChatGPT, there are serious concerns stemming from the kind of information it is accessed and potential violations of data privacy and intellectual rights. ChatGPT designs its answers by utilizing various resources available on the web and other servers, but the accuracy of these sources cannot always be guaranteed. Additionally, it is important to consider whether such AI tools respect academic integrity. When scientists or students write a paper or report, they must carefully cite all sources used; otherwise, the work may be considered plagiarism. Unfortunately, in ChatGPT’s answers, sources are not always accurately referenced.

More research is needed to make AI systems more explainable and trustworthy by using interpretable or explainable machine learning algorithms. Such algorithms should produce results that can be easily understood by humans and provide insights into how the AI system arrived at its predictions or decisions. Additionally, it is crucial for such systems to provide a measure of uncertainty in their answers, such as confidence intervals or standard deviations, similar to scientific practices, where results are often reported with a range of values that account for possible variations in the data or measurement errors.

Another significant concern with AI methods, particularly deep learning algorithms, is that they consume a lot of power. This is because these algorithms require large amounts of computational resources to train and run. They use huge servers and high-performance GPUs, which require high power, large amounts of memory, and also communications between servers and GPUs. The energy consumption is only going to increase as the use of AI continues to grow, and more powerful AI systems are developed. In addition to the environmental impact of energy consumption, high levels of power consumption can also result in higher operating costs and can limit the scalability and accessibility of AI systems.

There is increasing research and development focused on developing more energy-efficient AI systems. This involves a range of techniques and strategies, including the use of specialized hardware, such as tensor processing units; the development of more efficient algorithms and architectures; and the use of techniques, such as model compression and pruning, to reduce the computational requirements of AI systems. In addition to these technical approaches, there is also a need for broader policy and regulatory measures to encourage the development and adoption of energy-efficient AI systems. This could include incentives for energy-efficient design, regulations on the energy consumption of AI systems, and the development of standards and benchmarks to encourage the use of more energy-efficient AI technologies [4], [5], [6]. It is also important to consider whether AI is necessary to solve the problem at hand or whether simpler and less costly solutions exist. Furthermore, it’s crucial to evaluate the impact of any proposed AI solution on both humans and the environment. In evaluating and comparing AI systems, one should use metrics that take into account both performance and complexity or power consumption, such as the Akaike criterion [7] or similar ones.

The rapid evolution of technology has opened the doors to many extraordinary breakthroughs that have had an incredible impact on our field and will continue to transform the world. Let’s celebrate these achievements, but let’s also be mindful that, with these promising technologies, there can also be significant peril—to scientific progress, to society and human well-being, and to the ecological environment. Innovation comes with great responsibility. Let us all do our best to be smart and thoughtful as we navigate the future.

In this IEEE Signal Processing Magazine special issue celebrating the 75th anniversary of the SPS, you will find additional insights into the history of SPS during the last decades and more technical articles about the evolution, breakthroughs, and discoveries in different domains in SIP.

References

[1] S. Matsue et al., “A 256 K dynamic RAM,” in Proc. IEEE Int. Solid-State Circuits Conf. Dig. Tech. Papers, San Francisco, CA, USA, 1980, pp. 232–233, doi: 10.1109/ISSCC.1980.1156048.

[2] W. S. Boyle and G. E. Smith, “Charge coupled semiconductor devices,” Bell Syst. Tech. J., vol. 49, no. 4, pp. 587–593, Apr. 1970, doi: 10.1002/j.1538-7305.1970.tb01790.x.

[3] C. Jutten and J. Hérault, “Analog implementation of a permanent unsupervised learning algorithm,” in Proc. NATO Workshop Neurocomputing, Les Arcs, France, 1989, pp. 145–152, doi: 10.1007/978-3-642-76153-9_18.

[4] E. Azarkhish, D. Rossi, I. Loi, and L. Benini, “Neurostream: Scalable and energy efficient deep learning with smart memory cubes,” IEEE Trans. Parallel Distrib. Syst., vol. 29, no. 2, pp. 420–434, Feb. 2018, doi: 10.1109/TPDS.2017.2752706.

[5] W. J. McKibbin and A. C. Morris, Policy Challenges for the Global Transition to a Low-Carbon Economy. Washington, DC, USA: Brookings Institution, 2019.

[6] V. Galaz et al., “Artificial intelligence, systemic risks, and sustainability,” Technol. Soc., vol. 67, Nov. 2021, Art. no. 101741, doi: 10.1016/j.techsoc.2021.101741.

[7] H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Autom. Control, vol. 19, no. 6, pp. 716–723, Dec. 1974, doi: 10.1109/TAC.1974.1100705.

Digital Object Identifier 10.1109/MSP.2023.3266472


IEEE Signal Processing Magazine   |  June 2023