IEEE Circuits and Systems Magazine - Q2 2018 - 2

Features (continued)

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90

Editor-in-Chief
Chai Wah Wu
T.J. Watson Research Center
IBM, USA
E-mail: cwwu@us.ibm.com

CNN Technology-Tools and Applications
Ákos Zarándy, András Horváth, and Péter Szolgay

cellular neural/nonlinear networks were invented almost 30 years ago. the cnn
research led to high performance computational architectures, neuromorphic models of
the visual and auditory pathways, stability results for arrays of coupled dynamic processors, and for practical image processing results. the main stages of the research and
the results are summarized in this paper.

CMOS Vision Sensors: Embedding Computer Vision
at Imaging Front-Ends
A. Rodríguez-Vázquez, J. Fernández-Berni, J.A. Leñero-Bardallo,
I. Vornicu, and R. Carmona-Galán

cmoS Image Sensors (cIS) are key for imaging technologies. these chips are conceived for capturing optical scenes focused on their surface, and for delivering electrical
images, commonly in digital format. the term cVISs (cmoS VIsion Sensors) defines
the other class of sensor front-ends which are aimed at performing vision tasks right at
the focal plane. cVIS and cISs are similar regarding physical implementation. however,
while inputs of both cIS and cVIS are images captured by photo-sensors placed at the
focal-plane, cVISs primary outputs may not be images but either image features or
even decisions based on the spatial-temporal analysis of the scenes. We may hence
state that cVISs are more "intelligent" than cISs as they focus on information instead
of on raw data.
one of the challenges of cVISs architects is incorporating computer vision concepts
into the design flow. the cellular nonlinear network universal machine (cnnum) paradigm, proposed by profs. chua and roska, defined an adequate framework for such
conciliation as it is particularly well suited for hardware-software co-design. this paper
overviews cVISs chips that were conceived and prototyped at ImSe Vision Lab over the
past twenty years. Some of them fit the cnnum paradigm while others are tangential
to it. all them employ per-pixel mixed-signal processing circuitry to achieve sensorprocessing concurrency in the quest of fast operation with reduced energy budget.

Deputy Editor-in-Chief
Alyssa B. Apsel
Cornell University, USA

Associate Editors
Pamela Abshire
University of Maryland, USA
Ming Cao
University of Groningen, The Netherlands
Herbert Iu
University of Western Australia, Australia
Ljupco Kocarev
University "Sv. Kiril i Metodij", Macedonia
University of California, San Diego, USA
Anthony Kopa
Charles Stark Draper Laboratory, USA
Henry Leung
University of Calgary, Canada
Jinhu Lü
Chinese Academy of Sciences, China
Marvin Onabajo
Northeastern University, USA
Sule Ozev
Arizona State University, USA
Xuan Zhang
Washington University in St. Louis, USA

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From the Editor

From the Guest Editors

Final materials for IEEE Circuits and Systems
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Scope: Insofar as the technical articles presented in the proposed magazine, the plan is to cover the subject areas represented by the
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