IEEE Sensors 2021
Two Early Stage Researchers (ESR) of the project MENELAOSNT represented ZESS at the IEEE SENSORS 2021 conference.
MENELAOSNT is an EU H2020 Marie Skłodowska-Curie Innovative Training Network (ITN) coordinated by the University of Siegen, hosting 15 ESRs, and spanning over top research institutes in five European countries. The presence of two ESRs from Siegen at IEEE SENSORS 2021 further consolidates the privileged position of ZESS as a world-class player in the sensors’ community.

Faisal Ahmed (left) and Alvaro Lopez Paredes (right) will represent ZESS at IEEE SENSORS 2021.
The first research outputs of an ambitious project
Faisal Ahmed and Alvaro Lopez Paredes started their PhD
studies at the Center of Sensor Systems (ZESS) of the
University of Siegen in November 2020. They were selected
within the Marie Sklodowska-Curie Innovative Training Network
Project MENELAOSNT, an ambitious project which involves several
universities, research institutions, and industrial partners
from Romania, Spain, Germany, Switzerland and Turkey, and aims
at applying Novel Technologies to realize multimodal-multi
sensor data fusion to optimally combine the information
delivered by different sensors (in-situ/remote, optical/non
optical) on different scales, with different resolutions, and
with different reliability.
Over the past few months, both were focused on the study of
Time-of-Flight (ToF) systems and Compressive Sensing (CS)
techniques from different perspectives. ToF systems allow
sensing the world in 3D, while CS techniques allow for
capturing the relevant information with minimal data flow, that
is, in an efficient manner. As a result of their respective
researches, they will take part in the IEEE SENSORS 2021
conference, the flagship conference of the IEEE SENSORS
Council, to be held virtually in Sydney, Australia between 29th
of October and 4th of November 2021. This is an important
milestone for both of them in their respective research careers
and represents not only a step forwards in their PhD studies
but a recognition of the valuable work performed in the
scientific context.
From lamps to sources of internet and 3D images
Faisal Ahmed received his MEng in Telecommunication
Engineering from the Mehran University of Engineering and
Technology (MUET), Pakistan in 2019. In 2020, he joined the
ZESS as ESR-3 under the umbrella of Marie Skłodowska-Curie
Innovative Training Network Project MENELAOSNT. Currently, he
is working on "Pseudo-Passive Indoor 3D ToF sensing exploiting
Visible Light Communication infrastructure". This is a novel
ToF sensing scheme which replaces the active illumination unit
present in ToF cameras with the inherent modulation present in
Visible Light Communication (VLC) infrastructure, turning
background indoor light into a useful optical signal for the
ToF camera for thefirst time.
This is an interesting and captivating topic that enables
multiple functionalities to be used simultaneously. ToF
3D-sensing has become a hot subject of research over the past
few years, and this trend will be further accelerated by the
application of VLC, which bundles together emerging services
such as intelligent lightning, light-based communications, and
3D imaging.

The research project of Faisal Ahmed proposes using the VLC infrastructure as conceptual evidence for a passive ToF sensing configuration.
State-of-the-art ToF cameras suffer from high power
consumption and may rise safety concerns due to the dedicated
high-power illumination units. Faisal Ahmed's research focuses
on the investigation of a novel passive 3D modality which will
eventually relegate to oblivion the illumination units in
existing ToF systems. This ambitious task has become feasible
thanks to rapid advances in semiconductor technologies that
have transformed lighting infrastructure. As a result,
illumination devices are currently migrating from conventional
lamps to light-emitting-diodes (LEDs). LEDs bring smarter
capabilities into the lighting infrastructure and enable one of
the most prominent emerging communication technologies for the
indoor environments, known as VLC.
Within this framework, the lighting infrastructure does not
only satisfy illumination purposes, but also provides
high-speed communication. This yields the pervasive presence of
modulated light signals in the indoor infrastructure. The
research work of Faisal Ahmed takes this a step further by
tapping VLC infrastructure as illumination source for Passive
3D ToF sensing.
This novel idea has a great potential in the field of
3D-sensing and, thus, is expected to awake industrial interest
in the near future. The significant reduction of cost and power
consumption allows the entry into new application fields, such
as smart homes, office environments, industries and vehicles,
where the VLC infrastructure and ToF cameras are valuable
assets.
In his paper, Faisal Ahmed studies the feasibility of this new
concept by using an off-the-shelf VLC module which follows the
four-phases algorithm, a standard tool for phase retrieval in
Continuous-Wave (CW) ToF systems. CW-ToF cameras make use of a
light source which illuminates the scene in connection with an
array of ToF pixels. This allows to recover the phase shifts
from the backscattered optical signals that return from the
scene. Hence, depth information can readily be obtained.
Moreover, the VLC module provides two dominant frequencies
arising from the underlying clock signal and coding scheme.
Consequently, he adopts the CW-ToF pipeline at both dominant
frequencies to carry out his simulations. The simulation
results show that accurate depth estimation is attainable both,
in short and long ranges. For further read, see:
F. Ahmed, M. Heredia Conde, and O. Loffeld, "Pseudo-Passive Indoor ToF Sensing exploiting Visible Light Communication Sources", 2021 IEEE SENSORS (to appear in)
3D-mapping the world in an effective
manner
Alvaro Lopez Paredes received his MEng in Aerospace
Engineering by the Technical University of Madrid (UPM) in
2012. In addition, he completed a Master's Degree in Numerical
Simulation majoring in Solid Mechanics in 2016, and a
Specialist's Degree in Numerical Simulation majoring in
Computational Fluid Dynamics in 2020, both by the UPM in
collaboration with ANSYS. He is the ESR-4 in the Marie
Skłodowska-Curie Innovative Training Network Project MENELAOSNT
and he will present the research paper titled "Effective
very-wide-area ToF sensing" at IEEE SENSORS 2021. This
study discusses the reconstruction of high-angular resolution
and long-range 3D scenarios using 3D ToF imaging systems by
making use of Compressive Sensing (CS) and sparsity-aware
techniques.
The objects that surround us-vehicles, houses, trees,
pedestrians-may be seen as discrete, sparse, points in the
space and can be expressed by a few coefficients if they are
represented in the appropriate basis, as a sinusoidal function
can be represented by its frequency and amplitude coefficients
in the Fourier domain. This property is called
sparsity and is a fundamental concept in the 3D
imaging theory. However, these discrete points can be located
anywhere in the space, as the frequency can take any arbitrary
positive value. This leads to the second and third fundamental
concepts for recovering sparse signals: randomness and
incoherence. The sensing scheme must efficiently
scatter the spatial domain in order to capture these unknown
locations. The randomness during the sensing processes
avoids any possible bias on the scattering of the scene. The
incoherence is a measurement of the degree of
dissimilarity between the columns of the sensing matrices. This
dissimilarity is good as the measurements arising from targets
at different locations can be seen and explained in a unique
way by the corresponding column of the matrix and this
significantly facilitates the recovery of the signals by using
greedy algorithms.

The research project of Álvaro López Paredes investigates novel ways of acquiring data with a ToF camera for efficiently sensing wide scenes exploiting its sparsity in 3D space.
The research focuses on pulsed ToF systems, which measure
samples of the correlation between time-delayed pulses
representing the response of the scene under study, and a
number of predefined sequences. The assembly of these
predefined sequences yields sensing matrices and the retrieval
of the depth and intensity values may be seen as an
under-determined optimization problem, such as the recovery of
a sparse signal from few measurements. Specifically, his paper
proposes a methodology capable of reconstructing an array of
highly sparse signals based on a preliminary slicing of the
spatial domain into several partitions and a posterior
localization of the signals within them. This permits to
neglect large empty areas and lower the size of the problem
with the consequent decrease of retrieval errors and processing
times. Then, the minimization problem is solved by applying a
greedy algorithm. The main objective of this study is to
develop a robust sensing scheme capable of working in nearly
real time at a relatively low computational cost or, in other
words, a 3D imaging system capable of seeing as far, wide, and
fast as possible, taking advantage of the sparse nature of the
signals under study in pulsed ToF imaging systems. The research
investigates these theoretical concepts, studies the
feasibility, and demonstrates the applicability of the sensing
approach by numerical simulations on synthetic datasets with
the ultimate scope of implementing such a scheme on simple
illumination systems and state-of-the-art ToF sensors. On the
hardware front, a rotary ToF system is proposed along with a
drastic reduction of the exposure times in order to increase
the lateral resolution and avoid the introduction of motion
artifacts into the view. This paper represents the first step
to build a fully-operational ToF camera prototype at the ZESS
facilities, a challenging and breath-taking objective to be
fulfilled over the next two years. For further read, see:
A. Lopez Paredes, M. Heredia Conde, and O. Loffeld. "Effective Very-wide-area 3D ToF Imaging" IEEE SENSORS 2021 (to appear in)
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