# Towards Immersive Generosity: The Need for a Novel Framework to Explore
Large Audiovisual Archives through Embodied Experiences in Immersive
Environments

Giacomo Alliata  Laboratory of Experimental Museology, EPFL  Corresponding
author<EMAIL_ADDRESS>- Rue des Jordils 41, St-Sulpice, Switzerland
Sarah Kenderdine  Laboratory of Experimental Museology, EPFL  Lily Hibberd
Laboratory of Experimental Museology, EPFL  UNSW Sydney  Ingrid Mason
Australian National University

###### Abstract

This article proposes an innovative framework to explore large audiovisual
archives using Immersive Environments to place users inside a dataset and
create an embodied experience. It starts by outlining the need for such a
novel interface to meet the needs of archival scholars and the GLAM sector,
and discusses issues in the current modes of access, mostly restrained to
traditional information retrieval systems based on metadata. The paper
presents the concept of “generous interfaces” as a preliminary approach to
address these issues, and argues some of the key reasons why employing
Immersive Visual Storytelling might benefit such frameworks. The theory of
embodiment is leveraged to justify this claim, showing how a more embodied
understanding of a collection can result in a stronger engagement for the
public. By placing users as actors in the experience rather than mere
spectators, the emergence of narrative is driven by their interactions, with
benefits in terms of engagement with the public and understanding of the
cultural component. The framework we propose is applied to two existing
installations to analyze them in-depth and critique them, highlighting the key
directions to pursue for further development.

##  1 Introduction

At the beginning of his book _Zen and the Art of Motorcycle Maintenance_ ,
Robert [49] presents the difference felt while travelling by car compared to
on a motorbike. In the first case, travelers are watching the environment
through their car windows, while in the second, they are “feeling”, “living”
that same environment. The humidity in the air, the wind on their faces, the
vivid sound of the motor, the unconstrained visual field, all contribute to
create a more embodied and immersive experience for the motorcyclists. A
comparable experience is described by [59], in which one of the authors, who,
after several years of looking through the windows of a computer room in
passing, is one day obliged to enter and is confronted with the shock of
suddenly going “inside” what had only ever been seen from the “outside”. This
short story illustrates what Immersive Environments (IEs) offer to their
users: the possibility to be “inside” and therefore to feel as if you were
“there”, a more fully embodied experience, (more or less) completely
surrounded by a virtual world. Often described as the sense of “being there”,
this psychological state is sometimes also referred to as “presence” and is a
concept that is one of the central features of IEs [62]. “Immersion” is
arguably the second central feature of IEs: the extent to which the system can
deliver “an inclusive, extensive, surrounding and vivid illusion of reality to
the senses of a human participant” [[]p. 1]slater_framework_1997. Since the
display size remains a critical limitation in the presentation and
understanding of data visualizations [28], many large display systems (more or
less immersive) have been developed in the last decades, such as wide
gigapixel screens, dome-like structures, CAVEs and half-CAVEs or panoramic
screens (see [24] for an historical perspective on IEs).

Despite this progress, the exploration of cultural collections in IEs faces a
range of limitations and dilemmas, for which a new framework is required. This
article proposes that such a new paradigm is possible and that, within the
project _Narratives from the Long Tail: Transforming Access to Audiovisual
Archives_ , the foundations for this innovative framework are being laid and
will be applied to important audiovisual collections to create new ways to
explore them111Details on the archives explored can be found at
https://www.futurecinema.live/archives-and-collections/.. _Narratives_ ’s goal
is to produce a “groundbreaking visualization framework for interactively
(re)discovering hundreds of thousands of hours of audiovisual materials” [32].
The remainder of this introduction offers a summary of the key concepts and
technologies to be discussed.

As the foremost mnemonic records of the 21st century, audiovisual recordings
are omnipresent in our daily lives. From the second half of the 20th century,
starting with the introduction of television, to today’s online sharing
platforms, such as Youtube or TikTok, audiovisual recordings play an important
role in the way we document, disseminate and preserve knowledge and culture.
Broadcasting institutions are digitizing their collections, with examples such
as the Radio Télévision Suisse (RTS) with 200,000 hours of footage [54], or
the British Broadcasting Corporation (BBC) with more than a million recorded
hours [66]. Furthermore, cultural video collections are useful to preserve
Intangible Cultural Heritage (ICH), which has been defined as “the culture
that people practise as part of their daily lives” [35] and “all [the]
immaterial manifestations of culture [that] represent the variety of living
heritage of humanity as well as the most important vehicle of cultural
diversity” [37]. One can think of events like rituals, dance performances or
festivals that audiovisual collections, due to their temporal value as well as
the combination of video and audio channels, are more apt to represent than
images or textual elements ever could. Audiovisual archives by themselves,
however, can only offer a mostly linear, single-channel, non-interactive
narrative. They need to be augmented by some sort of interface to be explored
in a meaningful way.

In the field of cultural heritage, the need for innovative ways to present
such collections to the public is evident. A 2014 survey conducted on 1200
cultural institutions in Europe established that around 80% have digitized
their collections [20], and this number has certainly increased since then.
The Europeana platform222https://www.europeana.eu/en is, for instance, a key
example, with more than 30 million items accessible online through a category-
based interface. The majority of these institutions provide online access to
(at least part of) their collections, and even though innovative forms of
access can be noted [65], one can argue that these largely remain constrained
to the web. Web-based systems, such as Europeana’s, are obviously powerful
tools to enhance access to these collections and democratisation of culture,
however they lack the immersion that IEs installations can offer. These are
especially relevant when showcasing collections in public spaces such as
museums or cultural institutions venues. An innovative framework to explore
large digital collections is therefore necessary, in particular, to present
audiovisual archives to a larger public.

Within IE theory, one key aspect to consider is the way narrative can emerge
in an embodied experience, what can be referred to as Immersive Visual
Storytelling (IVS). This term comprises the ensemble of processes and
approaches that are employed to generate narratives in immersive experiences
(see [38, 36], for some examples). It is on this basis that we claim that,
through this emergence of narrative, users can better understand and explore
large cultural collections, by being placed “inside” the dataset [57] and
being offered levels of interactivity to freely browse the collection, as well
as making them actors in the experience rather than mere spectators, resulting
in a more “embodied understanding” of the content displayed [27].

The next part of this paper justifies the need for a new framework to explore
large audiovisual collections (and cultural collections, in general), drawing
on Whitelaw’s “generous interface” theory [64] and outlining the benefits
immersion can bring to the exploration of such archives. The relevant theories
of embodiment will then be presented and discussed in relations to IEs and IVS
with specific examples, in order to understand how “embodied understanding”
works and therefore how it can be leveraged to enhance the experience.
Finally, two previous works will be analyzed in-depth and critiqued in light
of the proposed framework: _T_Visionarium II_ , developed at UNSW’s iCinema
Research Centre in 2006, and _Jazz Luminaries_ , created in 2019 at EPFL’s
Laboratory of Experimental Museology. In each case, users are immersed within
large audiovisual datasets, and this results in an embodied understanding of
the archives, from which narrative can emerge.

##  2 The Need for a New Framework: Towards Immersive Generosity

From the perspective of scholarly archival community, it is clear that large
audiovisual archives are currently lacking proper frameworks to explore them.
These collections remain mostly inaccessible because cultural institutions are
constrained to screening sessions where only a handful of videos can be shown
at one time, without revealing the dataset in its entirety. The Digital
Humanities and GLAM sector (galleries, libraries, archives, and museums) have
also called for innovative forms of engagement through compelling frameworks
to explore this kind of collections [21].

For one, the classical information retrieval system restrains users to
classifications and tagging prior interpretations provided by curators and
data managers. Furthermore, the metadata compiled prevents users from
accessing visual features because images are by nature harder to verbally
capture [40]. A “simple search” interface is thus insufficient to provide
compelling public engagement with the diverse array of media formats held in
cultural institutions [53]. The framework proposed in this article intends to
solve this problem, as it provides users new ways to access large collections,
thus dispensing with the need to rely on metadata and traditional cataloguing
forms of description.

A catalogue-like database description of an audiovisual collection cannot
fully describe its content because discrete categories do not encompass the
visual and temporal complexity of videos. More continuous analytics can in
theory characterize visual features of a video but in practice these seem to
be harder to capture as they require some sort of interpretation. One could
imagine a scale from 0 to 1 measuring the visual complexity of a shot, for
instance. However, a catalogue of entries between 0 and 1 would be much harder
to interpret than actually watching the related videos and visually compare
them, as in the SEMIA333https://bertspaan.nl/semia/#/ project. This recent
initiative undertaken at the University of Amsterdam is a key example of
modern web-based approaches to the exploration of audiovisual archives [40].
Figure 1 presents the main view of the application, in which 103,273 shots
from 6,969 videos are spatially distributed in 2-D based on similarities on
visual features: color, shape, movement or visual complexity (as in the
example above). With this interface, one can easily appreciate comparable
shots and grasp the full collection at a glance, an impossible task with just
a list of catalogue entries.

Figure 1: Main view of the SEMIA application. Shots are spatially distributed
by color using a t-SNE algorithm of the similarity between all shots (credits:
Bert Spaan).

Furthermore, traditional access to cultural collections shapes the memories we
create of the past. These memories “are not inherent in the archival stock,
but are created in the context of reception, through processes of remediation
and recontextualisation” [9]. This relates to the idea that traditional
metadata rely on prior assumptions of their authors, as well as on the initial
goal of these descriptions. For an archival entity, a catalogue is first and
foremost a way to document their collections, usually connected to a database
system with an information retrieval tool to (at least internally) find the
relevant items. This purpose is quite different than browsing through a
collection without any prior knowledge (a goal that seems to be relevant for
casual users, [39] and therefore solely relying on these metadata might not be
enough. Browsing is indeed “a rich and fundamental human information
behaviour” [10], an iterative process based on scanning [51] or glimpsing [3]
a collection of items. These processes also depend on the questions one might
ask, and it has been shown that revealing a collection in its entirety through
the use of spatial distributions for instance can prompt innovative questions
[47]. People “browse with or without a goal in mind, and goals may change as
the process unfolds” [64].

Visualization is therefore at the center of an innovative framework to explore
large audiovisual collections. Visualization, as “a medium for communication
(or persuasion, or engagement)” and a tool for “understanding (or problem
solving, planning, orienting)” [[]pp. 1-2]scagnetti2011visual, can reveal
patterns, structures, relationships in the data and prompt new enquiries. Much
like Moretti’s “distant reading” approach to literature, which can disclose
hidden meanings in the text [45], visualization can expose new knowledge. For
design and humanities scholar Johanna Drucker, visualization “produces”
knowledge through “graphical forms expressing interpretation”, and that
because of the “fundamentally interpreted condition on which data is
constructed” visualizations are a feature of both “knowledge production and
[its] presentation” [19].

Shneiderman’s “visual information seeking” approach entails a taxonomy of
tasks users might want to perform while exploring a collection: overview,
zoom, filter, details-on-demand, relate, history, and extract [58]. This
interaction paradigm requires surrogates in the form of previews for single
items and overviews for groups of items [23], to represent the collection
objects while exploring it along its latent dimensions. According to [18],
however, Shneiderman’s information visualization “mantra” is “pragmatic, but
highly mechanistic” and supposes users with clear goals in mind, something
that might not actually hold true for casual visitors in a museum space. This
ubiquitous task-based approach, widespread in the fields of human-computer
interaction and information retrieval, does not meet the criteria of a more
humanistic approach. It is furthermore essential to recognize the inherent
reward component and creativity aspect in the action of browsing a collection
[51]. While browsing, a user is the sole director of the experience (although
they might be more or less consciously guided towards a certain path) and is
therefore creating their own narrative. It is a very different thing to go
through a curated list of items and discover the same items while autonomously
exploring the collection. In the latter case, serendipity, “the fact of
finding interesting or valuable things by chance” [50], plays a major role.
The feeling of finding new items by chance, of “serendipitous” discoveries
[63], entails a procedural emergence of narrative, driven by the user-agent of
the installation.

All these concepts are summarized by Whitelaw’s innovative notion of “generous
interfaces” [64]. As he argues, searching requires “rich, browsable interfaces
that reveal the scale and complexity of digital heritage collections”. It
should be a “humanistic model of interface and interaction that emphasises
exploration and interpretation over task and information retrieval”. The modes
of visual storytelling offered by these generous interfaces are simultaneously
“horizontal” (through the browsing features) and “vertical” (through the
details-on-demand functionalities), more or less completely driven by the
user. Whitelaw’s interfaces empower users to generate, to craft new knowledge,
through the narrative they are creating. Psychologist Jerome Bruner discusses
the importance of narrative for its fundamental role in creating and
interpreting human culture [8]. He states that human beings are natural
storytellers: they make sense of the world and themselves through narrative.
From the time they are very young, children learn that the way to integrate
their own desires with their family’s norms and rules is to construct a story
about their actions. This push to construct narrative, Bruner maintains,
shapes how children acquire language, and the habit persists into adulthood as
a primary instrument for making meaning. These storytelling skills insure our
place within human society. This point is sustained by constructionist
theories [14, 48], according to which individuals do not learn by passively
perceiving content but rather by actively crafting, manipulating and therefore
creating new knowledge.

Through their rich browsing features and interactivity, generous interfaces
evidently offer ideal modes of access to large digital collections, which are
far more interesting than traditional information retrieval systems. These
interfaces are however mainly web-based and therefore restrained to single
users in front of small and flat screens. One could argue the immersive
component of IVS is completely lacking here, as well as the multi-user aspect.
The framework we suggest would solve these issues through the use of Immersive
Environments, relying on the concept of an immersive generosity.

By transposing the generous interface concept to IEs, one must however
consider how it will affect the narrative. Indeed, a story is closely
correlated with the medium used to convey it. This is perfectly illustrated
with cinematic adaptations of books: the overall story being told is perhaps
the same, but the way it is told, its content and its intensity can greatly
differ. Applying this idea to IVS, it is clear that the active role a user has
in an immersive and embodied application vastly influences the way narrative
can emerge, requiring a distinction between purely authorial storytelling and
interactive approaches. [2] propose a useful model for this with four
dimensions to characterize the narrative component of different mediums:
“Contingency” (the contingency of time and space of the story being told with
respect to the real time and space of the user); “Presence” (how much the user
feels present in the story); “Interactivity” (the degree of controls they have
on the narrative) and “Narrative Representation” (the form narrative adopts,
be it through mental models for literature for instance or purely visual and
aural for cinema). They argue that, when compared to the most common cases of
narrative mediums, namely literature, cinema and theatre, virtual reality
offers the greater contingency in time and space, the strongest feeling of
“being there” and the highest degree of interactivity.

Combined, these considerations imply that IVS is a form of narrative that
moves beyond the Platonic concepts of “diegesis” and “mimesis” (based on an
authorial view of storytelling) to the idea of “experiencing” and “creating” a
story. When focusing on multi-user experiences, typically offered by large
interactive systems, the concept of a user-led narrative has even greater
implications for the way the other users in the interactive space experience
the story being created. In Geert Mul’s work, users can simultaneously be seen
as “highly productive, in that the appearance of the works changes based on
their input” and “merely one in a much larger series of variables that
determine the outcome of the calculation” [46]. Although these views might
seem at odds, they both imply that, when IVS is user-led, it requires an
external public (other users) to interpret it and appreciate it fully, through
a “third-person’s perspective”. One must also remember that museums are
historically social venues, and the relationships between visitors are
intrinsic to the experience of exploring their collections, meaning that these
principles must be applied to to the exploration of large datasets in multi-
user immersive installations.

In conclusion, Whitelaw’s “generous interfaces” have been highly influential
as a first attempt to solve the issues outlined by archival and digital
humanities scholars in the access to large audiovisual collections.
Nonetheless, we argue that these interfaces would benefit from a further
component of immersion, through the use of IEs and thus IVS, to create a truly
embodied exploration of a cultural archive. To conceptually frame this idea,
philosopher Mark Johnson’s theory of the body is leveraged in the next section
to better frame how narrative can emerge from such installations through
“embodied understanding” [27].

##  3 Embodied Understanding in Immersive Environments

For [27], the importance of “embodied understanding”, based on the 20th
century findings of cognitive science, challenges centuries during which the
body was considered less important than the mind. He provides the counter-
argument that “understanding is profoundly embodied, insofar as our
conceptualization and reasoning recruit sensory, motor, and affective patterns
and processes to structure our understanding of, and engagement with, our
world”. It is therefore clear that IVS, through its embodied approach, can
indeed be beneficial to cultural institutions aiming to give the wider public
meaningful access to their collections.

To put these ideas in practice in IEs-based installations, one must however
first appreciate how our understanding of the world is embodied. According to
the field of embodied cognition, organism-environment interactions are the
sources of all our human perception and understanding of the world [15]. To
really understand something, we must first experience it, a complex process
based on Damasio’s “homeostatis” balanced state between organism and
environment that comprises both how the organism is feeling and acting as well
as how the environment is structured [13]. This equilibrium is dynamic,
because it evolves as the organism and the environment evolve, and is also
related to the quality, the value of the experience and overall our well-
being. This is why emotions are such an important element of our “embodied
understanding” of the world. Neuroscientists have further shown how organisms,
through the detection of “emotionally competent stimulus”, move their body-
states to favorable positions for their survival and well-being [12]. Emotions
are therefore at the core of understanding, something that seems quite obvious
in storytelling, as any story plays with our emotions to convey its narrative.

Going one step further, [26] talks of the “bodily sources of meaning”. Humans
have indeed always used their bodies to express themselves, from spontaneous
gestures while talking to more elaborate performances such as dance or ritual
practices [25, 67]. It is thus necessary to define this “body” of ours, and
relate its dimensions to IVS in the frame of the exploration of large cultural
collections. Drawing on Merleau-Pointy’s _Phenomelogy of Perception_ and John
Dewey’s somatic naturalism, Johnson explains how our bodies are not just
“objects interacting with other objects” but are “lived”, “phenomenal
bod[ies]” [43], and require at least five intertwined dimensions to be fully
comprehended: the biological, ecological, phenomenological, social and
cultural body.

Going back to Damasio’s homeostatis state, our bodies are first and foremost
“flesh-and-blood”, “functioning biological organism[s] that can perceive, move
within, respond to, and transform [their] environments” [15]. Our “biological
bodies” are in a continuous exchange with their environments, continuously
evolving the aforementioned equilibrium, and are the locus of feelings and
emotions that push us towards our physical and social well-being [12]. When
being confronted with an immersive narrative experience, users are therefore
first and foremost a biological body, with all their individualities and body
specificities. In her interactive and immersive experience _Osmose_
444http://www.immersence.com/osmose/ (1995), media artist Char Davies empowers
visitors to automatically drive the narrative through their breathing and
balance, two fundamentals and unconscious human activities. These are part of
the preconscious activities [22] identifies as the “body schema” that govern
our interactions with the environment.

Once users start to actively engage and interact with an installation, the
second dimension of the body emerges: the “phenomenological body”. Our
“tactile-kinaesthetic body” [56] depends on proprioception (our feeling of our
bodily posture and orientation), our kinaesthetic sensations of bodily
movement and our awareness of our internal body states through our emotions
and feelings [12]. In contrast to the body schema, [22] associates our more
conscious activities to the “body image”: activities that comprehend the
affordances of the system to explore the collection, Drucker’s “conventions of
the diagrammatic knowledge form” [19]. Furthermore, this phenomenological
paradigm can result in the enhancement of a cognitive operation through a
shift in the nature of the task itself, where abstract operations (such as
finding all the items that correspond to a certain query) can be mapped to
more natural actions, the so-called “tangialities” [44].

Dario Rodighiero and his colleagues at metaLab at Harvard have pushed this
dimension to the extreme of posing the entire body as the “interaction
device”, a sort of “choreographic interface”. In _Surprise Machines_
555https://dariorodighiero.com/Surprise-Machines-for-Harvard-Art-Museums,
visitors explore Harvard Art Museum collections through the use of precise and
choreographed gestures [52], each one mapped to a specific task that remind us
of Shneiderman’s visualization mantra: “overview first, zoom and filter, then
details-on-demand” [58]. Figure 2 shows the digital collection spatially
distributed according to visual similarity of the different items and the
gesture vocabulary defined to explore this latent space.

Figure 2: _Surprise Machines_ media installation with the museum’s collection
spatially distributed on the left and the gesture vocabulary on the right
(credits: metaLAB (at) Harvard).

In this installation, the digital collection is shaped to create a virtual
environment that users can freely explore through their bodies. This intrinsic
relationship to the environment is elucidated by a third dimension: the
“ecological body”, which can be defined as the continuous process between our
bodies and the environment we evolve within [15, 43]. The twofold phenomena of
“embodied understanding’ and the “ecological body” is pushed even further in
IEs such as the EPFL Laboratory of Experimental Museology’s Panorama+ or its
predecessor, the UNSW’s iCinema Research Centre’s landmark system Advanced
Interaction and Visualization Environment (AVIE). The Panorama+ is a
360-degree stereoscopic, interactive environment of ten meters diameter and
four meters high, with five projectors and surround sound audio system,
controlled by a cluster of six computers (see [42] for a more in-depth
technical description of the AVIE). Its omnidirectional nature recreates a
fully immersive data space that allows for both allocentric (relationships
between objects) and egocentric (personal relationships to objects) cognition
and spatial perspectives simultaneously [5], cited in [30]. Visitors can
indeed physically represent themselves with respect to digital objects in the
virtual landscape and appreciate relationships between these digital items,
blurring the line between what is real and what is virtual. This phenomenon
increases their sense of presence and thus enhancing the narrative component
of the exploratory experience. The digital collection is no longer an ascetic
database or list of catalogue entries but a fully-fledged environment that
users can explore on their own terms and to which they can individually or
collectively relate with other visitors, in the case of multi-user systems.

It is in these multi-user experiences that the fourth dimension of the body
Johnson outlines, the “social body”, is revealed. Indeed, our environment is
not just physical or biological but also composed of human relationships and
interactions with our peers. In the field of developmental psychology, the
effect (and importance) of other people during our childhood is well-known
[60], and this continues as adults, through interactions with our colleagues
and friends. This is especially true in social spaces such as museums, where
users are generally not alone but rather continuously confronted with the
presence of others, usually strangers, who become the “spectators” in the
trichotomy “system-user-spectators” [17]. Embodiment, on this basis, can thus
be argued to have a “participatory” status where the user driving the
experience becomes the author of narrative for a larger public. Here again,
the “third-person’s perspective” Geert Mul mentioned in his work, as well as
the dual view of the role of users in generative interactive pieces are
crucial [46]. From the perspective of performance studies theory, “it is the
ways in which the user perceives and experiences the act of interacting with
the system under the potential scrutiny of spectators that greatly influences
the interaction as a whole … it is precisely this awareness of the
(potentiality of a) spectator that transforms the user into a performer” [11].
Similarly, the latest theoretical frameworks on creativity highlight the
importance of the “public” in the creative act [61]: the interaction with a
given system in this case. This creative act of interacting with an
installation also has an important effect on the “embodied understanding” of
the collection, as put forward by constructionist theories on learning [14,
48]. Individuals actively engaged with the knowledge they are being presented
will learn more than those passively witnessing it. Bloom’s famous taxonomy of
educational objectives supports this claim, since its main categories include
Application, Analysis, Synthesis and Evaluation (activities that require the
manipulation and creation of knowledge), in addition to Knowledge and
Comprehension [6]. One of the more modern revisions of this taxonomy puts
emphasis on the notion of creating new knowledge by splitting the original
classification into two dimensions: the first on the actual knowledge being
addressed, and the second on the cognitive processes applied to this knowledge
[34]. Furthermore, empirical evidence suggests the hierarchical characteristic
of these taxonomies, placing the category of Create at the top [1].

The fifth and last dimension of the body Johnson describes is the cultural
one. He argues that various cultural aspects contribute to the shaping of our
bodies and the way we see and relate to them. This explains why gestures and
postures vary across the world, as well as our attitudes towards our
environment. Cultures are enacted through rituals, practices, customs
performed by humans as inherently embodied beings [26]. Therefore, in IVS, the
emergent narrative will be enacted by the embodied users, attended by their
prior knowledge and specific cultures. The interpretative action of the
visitors mentioned before depends on the individual preconceptions people
bring to the experience, and consequently the emergent narrative that results
from their interactions can greatly differ. The dialogue between system and
users is driven by these interpretations, users with specific backgrounds will
draw connections that might appear rather strange to other visitors but
nonetheless “make sense” in their specific dialogue, in their specific
narrative. This fundamentally individual interpretative action reverts back to
the social body and Mul’s “third-person’s perspective”, where the individual
differences between users spark seemingly infinite combinations and unfolding
of different narratives. Viewers’ own cultures and previous knowledge become
new variables in the process of generating these narratives, both for the user
actually driving the experience and for the public interpreting it.
Furthermore, the cultural aspect is particularly relevant when exploring
audiovisual archives, because the “immaterial manifestations of culture” (what
we refer to as Intangible Cultural Heritage) can be captured and documented
through videos, so that exploring such a collection amounts to exploring
culture itself (or at least an aspect or portion of it). The power of IEs to
“plac[e] users inside the dataset” [57] can thus immerse them in a cultural
setting, with the various benefits for their experience, both for their
engagement and for their learning. This full immersion in a collection creates
an “embodied theatre of participation” that “permits an unprecedented level of
viewer co-presence in a narrative-discovery of a cultural landscape”,
facilitating “dynamic inter-actor participation and cultural learning” [29].

The need for an innovative framework having been outlined and said framework
being situated within embodiment theory, this thinking will now be applied to
analyze and critique two previously-built installations, to highlight the
particularities and advantages of our proposition.

##  4 Critique of Interactive Installations in Light of the Proposed
Framework: _T_Visionarium II_ and _Jazz Luminaries_

The need for an immersive generosity having been justified and the concept of
embodied understanding explained through Johnson’s theory of the body, we now
illustrate how this innovative framework we propose can be applied to
previously built installations created to explore large video collections.
These use cases will first be presented and then critiqued in light of the
proposed framework, in an attempt to formalize it and draw conclusions on what
the next stage of immersive interfaces for exploring large video collections
should aim for. The two installations discussed here are _T_Visionarium II_
and _Jazz Luminaries_.

_T_Visionarium II_ is part of the _T_Visionarium_ project, developed at the
UNSW’s iCinema Research Centre between 2004 and 2017, resulting in three
iterations of the work666See
http://www.icinema.unsw.edu.au/projects/t_visionarium/project-overview/ for a
project overview.. The first version was developed for an inflatable dome
structure [4], while the more advanced second and third versions use the AVIE
system, as it can be seen in Figure 3. The interactive and immersive
installation explores 24 hours of television footage, segmented, manually
annotated (based on a thesaurus) and transformed in a database of more than
20,000 clips of a few seconds each. The system is meant for a single user who
navigates it with a touch tablet and with the larger audience witnessing the
emergent narrative. Hundreds of clips are simultaneously playing on the
360-degree screen, and when the user-agent selects one, the digital landscape
rearranges itself, mapping semantically similar clips closer to the selected
item (based on the annotated metadata). The selected clips can be recombined
together, rewriting the linear narrative of the original footage (already
broken down by the initial segmentation) and resulting in a “recombinatory” or
“transcriptive” narrative [7].

Figure 3: View of the _T_Visionarium II_ installation (credits: Sarah
Kenderdine)

_Jazz Luminaries_ , on the other hand, is a much more recent project. Part of
the _Infinity Room II_ exhibition, the application was developed by EPFL’s
Laboratory of Experimental Museology in 2019, in a full dome structure of six
meters diameter. 13,000 videos of the Montreux Jazz Archive are arranged in a
network where nodes represent artists and links their collaborations during
the festival. The network can be navigated with a spherical controller,
mimicking the structure of the dome, and when passing over a certain node, its
corresponding sound excerpts rapidly plays, resulting in an acoustic search
akin to radio channel surfing. Lying down under the dome, users thus explore
the archive and when an artist is selected, they can choose a specific
performance to finally reach a fractal view of the corresponding video
(allowing users to appreciate it in spite of their relative position under the
dome). As in _T_Visionarium II_ , one spectator drives the experience with the
spherical controller, as shown in Figure 4, while the others appreciate the
unfolding of narrative, reclined under the dome [31].

Figure 4: View of the _Jazz Luminaries_ installation (credits: Sarah
Kenderdine)

Both installations evidently pivot on a browsing experience browsing
experience, where the user-agent explores hundreds of videos simultaneously,
gradually revealing details of the collection. The omnidirectional immersion
offered by the AVIE system relates to Johnson’s idea of the ecological body,
as users are “inside” the dataset, affected and affecting it through their
interactions. Similarly, the dome structure recalls a rich history of
planetarium structures with the obvious metaphor of the sky vault, a history
rich of hundreds of years where the goal is to cover the human field of view.
By completely covering the surface of the screen with videos or images, users
are naturally inclined to turn their head and look around, as if they were
physically exploring a real room filled with archival content, echoing the
ecological and phenomenological body through this kinaesthetic paradigm. The
first iteration of the _T_Visionarium_ project put even a stronger emphasis on
this phenomenological aspect, since the visitor wore a head-tracking system
enabling the projected portion of the dome surface, calibrated to the
orientation of their head. While turning around, they would therefore visually
unveil the extent of the database [4]. The phenomenon of wandering around in
the archive, without a clear path to follow, calls up the model of the
“information flaneur” [16]. Curious, creative and critical, the goal of this
information-seeker modeled on the 1840s Paris urban flaneur is not to find
something in particular but rather to appreciate the collection as a whole,
and to be surprised by what they have stumbled upon and ultimately simply be
immersed in the archive. Furthermore, this serendipitous search paradigm
relates to the idea that “information is organic” [41], and hence should be
explored in an organic way.

In _Jazz Luminaries_ , such a natural aspect of the data is also suggested
through the acoustic search, that draws on the biological body and references
the concept of “tangialities”, that are not just related to the touch but
include all the five senses [44]. The social interactions that might arise
from a single user driving the experience for a larger public recall the
social body as well as Mul’s “third-person’s perspective”, since one could
argue it is the public that is experiencing the full performance, defined by
the emergent narrative that the user-agent is creating. There is, as such, no
predefined narrative but an infinite combination of items sequences only
bounded by viewers’ interpretative power. While browsing the archive, it is as
if users were “sculpting” its contents [33], shaping the experience the way
they wish, while being guided by the relationships between elements based on
the metadata without being constrained by them. It is clear that such a
paradigm could be augmented with modern computational approaches, allowing to
generate much more intricacy and thus possibilities in the database, in
particular if relying also on more visual features, such as in the SEMIA
project illustrated previously [40]. Finally, the cultural body is inherent in
the exploration of a cultural collection, and entails that prior individual
knowledge as well as different cultures are yet another variable in the
process of generating narrative. Suffice to say, that a visitor actually
having attended one of the Montreux Jazz Festival concerts and then re-
experiencing it reclined under the dome will have a much different experience
than those discovering the Festival for the first time.

Recalling Aylett and Louchart’s narrative theory, _T_Visionarium II_ and _Jazz
Luminaries_ have a strong contingency in time and space: first, because the
narrative is completely generative and determined by users’ interactions;
secondly due to the important feeling of “being there” due to the full
immersion in the archive and the idea of “sculpting” it; third, thanks to the
high level of interactivity (at least for the user-agent driving the
experience). In this way, IVS adopts a form of narrative based on the concept
of creating a story through the millions of paths embedded in the latent
structures of the collections. Through interactivity and the freedom to
explore the horizontal axes of the archive, visitors obtain a strong authorial
power on the narrative. In Geert Mul’s words, visitors are “highly productive,
in that the appearance of the works changes based on their input” [46], and
this entails the clear benefits in the learning experience maintained by
constructionist theories. At the same time, it is important to ensure
intuitive interaction frameworks are enabled to minimize the learning curve to
adopt the system as well as not relying on users having clear goals in mind,
something that cannot reliably be expected from casual visitors in a museum
setting. In addition, as previously stated, museums are social venues, and
interactions between visitors are welcomed and encouraged, such as viewers
passing around the spherical controller in _Jazz Luminaries_. Johson’s concept
of the social body is one of the reasons why multi-user shared spaces in IEs
are arguably more interesting than traditional Head-Mounted Displays (HMDs)
for IVS in museums spaces. Indeed, even though from a technical point of view,
immersion might be higher while using HMDs, the stronger grounding in the
physical reality offered by large display screen systems such as the Panorama+
/ AVIE allows for a more humanistic approach to the collection, based on
social proximity with other visitors.

The two installations discussed, _T_Visionarium II_ (as well as its
predecessor) and _Jazz Luminaries_ , have provided clear use cases to
illustrate the innovative framework proposed. These projects have their
limitations, however, often because they are based on traditional metadata
rather than more intrinsic relationships related to strictly visual features
(such as colors, visual complexity, movement…) that modern computer vision
approaches can unveil. These restrictions are amongst the issues that the next
generation of audiovisual archives browsers and projects like _Narratives from
the Long Tail: Transforming Access to Audiovisual Archives_ are endeavouring
to overcome.

##  5 Conclusion

In this article, we present an innovative framework to explore large
audiovisual collections through embodied experiences in immersive
environments, drawing on Immersive Visual Storytelling theory, Whitelaw’s
concept of generous interfaces, and Johnson’s theory of the body. The need for
such a framework is highlighted by archival and humanities scholars as well as
the GLAM sector. Two use cases have been analyzed in-depth and critiqued in
light of our proposition, supporting our claim that a move towards a more
immersive generosity will enhance the experience of visitors engaging with
large cultural collections in museum settings through embodied understanding.

The physicality of moving through the collection within which users are
immersed, and modifying the way the system presents itself after each
interaction, entails the metaphor of sculpting the data, as if it were a raw
block of material offered to the viewer to create their own narrative.
Furthermore, the social relationships that arise in such shared immersive
spaces are key to obtaining a full picture of the embodied understanding of
the kinds of cultural collections that people can experience in a museum.
Nonetheless, work remains to be done in the field to improve access to further
democratize these collections, defining the research that the _Narratives from
the Long Tail: Transforming Access to Audiovisual Archives_ project intends to
undertake as it maps such possibilities.

##  6 Acknowledgements

This research is supported by the Swiss National Science Foundation through a
Sinergia grant for the interdisciplinary project _Narratives from the Long
Tail: Transforming Access to Audiovisual Archives_ , lead by co-author Prof.
Sarah Kenderdine (grant number CRSII5_198632, see
https://www.futurecinema.live/project/ for a project description).

## References

  * [1] Lorin W Anderson and David R Krathwohl  “A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives”  Longman, 2001
  * [2] R. Aylett and S. Louchart  “Towards a Narrative Theory of Virtual Reality”  In _Virtual Reality_ 7.1, 2003
  * [3] Marcia J Bates  “What is Browsing Really? A Model Drawing from Behavioural Science Research”  In _Information Research - an International Electronic Journal_ 12.4 University of Sheffield, 2007
  * [4] Jill Bennett  “T_Visionarium : A user’s guide”  University of New South Wales Press Ltd, 2008
  * [5] Barry Blesser and Linda-Ruth Salter  “Spaces Speak, Are You Listening? Experiencing Aural Architecture”  MIT press, 2009
  * [6] Benjamin Samuel Bloom  “Taxonomy of Educational Objectives: The Classification of Educational Goals”  In _Cognitive domain_ Longman, 1956
  * [7] Neil C.M. Brown, Timothy S. Barker and Dennis Del Favero  “Performing Digital Aesthetics: The Framework for a Theory of the Formation of Interactive Narratives”  In _Leonardo_ 44.3, 2011, pp. 212–219
  * [8] Jerome Bruner  “Acts of Meaning”  Harvard University Press, 1990
  * [9] Dagmar Brunow  “Curating Access to Audiovisual Heritage: Cultural Memory and Diversity in European Film Archives”  In _Image [ &] Narrative_ 18.1 Open Humanities Press, 2017, pp. 97–110
  * [10] Shan-Ju Chang and Ronald E Rice  “Browsing: A Multidimensional Framework”  In _Annual Review of Information Science and Technology (ARIST)_ 28 ERIC, 1993, pp. 231–76
  * [11] Peter Dalsgaard and Lone Koefoed Hansen  “Performing Perception—Staging Aesthetics of Interaction”  In _ACM Transactions on Computer-Human Interaction (TOCHI)_ 15.3 ACM New York, NY, USA, 2008, pp. 1–33
  * [12] Antonio R. Damasio  “Looking for Spinoza: Joy, Sorrow, and the Feeling Brain”  Houghton Mifflin Harcourt, 2003
  * [13] Antonio R. Damasio  “Self Comes to Mind: Constructing the Conscious”  In _Brain_ , 2010
  * [14] John Dewey  “Democracy and Education: An Introduction to the Philosophy of Education”  LondonNew York, Free Press Macmillan, 1966
  * [15] John Dewey  “The Later Works of John Dewey, Volume 1, 1925-1953: 1925, Experience and Nature”  Carbondale: Southern Illinois University Press, 1981
  * [16] Marian Dörk, Sheelagh Carpendale and Carey Williamson  “The Information Flaneur: A Fresh Look at Information Seeking”  In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems_ , 2011, pp. 1215–1224
  * [17] Paul Dourish  “Seeking a Foundation for Context-Aware Computing”  In _Human–Computer Interaction_ 16.2-4 Taylor & Francis, 2001, pp. 229–241
  * [18] Johanna Drucker  “Performative Materiality and Theoretical Approaches to Interface”  In _DHQ: Digital Humanities Quarterly_ 007.1, 2013
  * [19] Johanna Drucker  “Graphesis: Visual Forms of Knowledge Production”  Harvard University Press Cambridge, MA, 2014
  * [20] ENUMERATE  “Survey Report on Digitisation in European Cultural Heritage Institutions 2014”, 2014  URL: http://www.enumerate.eu/fileadmin/ENUMERATE/documents/ENUMERATE-Digitisation-Survey-2014.pdf
  * [21] Giovanna Fossati  “Found Footage Filmmaking, Film Archiving and New Participatory Platforms”  In _Found Footage. Cinema Exposed. Amsterdam: Amsterdam University Press/EYE Film Institute Netherlands_ , 2012, pp. 177–184
  * [22] Shaun Gallagher  “How the Body Shapes the Mind”  Clarendon Press, 2006
  * [23] Stephan Greene, Gary Marchionini, Catherine Plaisant and Ben Shneiderman  “Previews and Overviews in Digital Libraries: Designing Surrogates to Support Visual Information Seeking”  In _Journal of the American Society for Information Science_ 51.4, 2000, pp. 380–393
  * [24] Erkki Huhtamo  “Illusions in Motion: Media Archaeology of the Moving Panorama and Related Spectacles”  MIT Press, 2013
  * [25] Mark Johnson  “The Meaning of the Body: Aesthetics of Human Understanding”  University of Chicago Press, 2007
  * [26] Mark Johnson  “What Makes a Body?”  In _The Journal of Speculative Philosophy_ 22.3, 2008, pp. 159–169
  * [27] Mark Johnson  “Embodied Understanding”  In _Frontiers in Psychology_ 6 Frontiers, 2015, pp. 875
  * [28] David J Kasik et al.  “Data Transformations and Representations for Computation and Visualization”  In _Information Visualization_ 8.4 SAGE Publications Sage UK: London, England, 2009, pp. 275–285
  * [29] Sarah Kenderdine  “Somatic Solidarity, Magical Realism and Animating Popular Gods: Place-Hampi ”where intensities are felt””  In _2007 11th International Conference Information Visualization (IV ’07)_ , 2007, pp. 402–408  DOI: 10.1109/IV.2007.103
  * [30] Sarah Kenderdine  “Embodiment, Entanglement, and Immersion in Digital Cultural Heritage”  In _A New Companion to Digital Humanities_ John Wiley & Sons, Ltd, 2015, pp. 22–41  DOI: 10.1002/9781118680605.ch2
  * [31] Sarah Kenderdine  “Experimental Museology: Immersive Visualisation and Cultural (Big) Data”  In _Experimental Museology_ , 2021, pp. 15
  * [32] Sarah Kenderdine, Ingrid Mason and Lily Hibberd  “Computational Archives for Experimental Museology”  In _International Conference on Emerging Technologies and the Digital Transformation of Museums and Heritage Sites_ , 2021, pp. 3–18  Springer
  * [33] Sarah Kenderdine, Jeffrey Shaw and Tobias Gremmler  “Cultural Data Sculpting: Omnidirectional Visualization for Cultural Datasets”  In _Knowledge Visualization Currents: From Text to Art to Culture_ London: Springer, 2013, pp. 199–220
  * [34] David R Krathwohl  “A Revision of Bloom’s Taxonomy: An Overview”  In _Theory into practice_ 41.4 Taylor & Francis, 2002, pp. 212–218
  * [35] Richard Kurin  “Safeguarding Intangible Cultural Heritage in the 2003 UNESCO Convention: a critical appraisal”  In _Museum International_ 56.1-2, 2004, pp. 66–77
  * [36] Kris Layng et al.  “CAVE: Making Collective Virtual Narrative: Best Paper Award”  In _Leonardo_ 52.4 MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info …, 2019, pp. 349–356
  * [37] Federico Lenzerini  “Intangible Cultural Heritage: The Living Culture of Peoples”  In _European Journal of International Law_ 22.1, 2011, pp. 101–120  DOI: 10.1093/ejil/chr006
  * [38] Joan Llobera, Kristopher J Blom and Mel Slater  “Telling Stories within Immersive Virtual Environments”  In _Leonardo_ 46.5 MIT Press, 2013, pp. 471–476
  * [39] Irene Lopatovska, Iris Bierlein, Heather Lember and Eleanor Meyer  “Exploring Requirements for Online Art Collections”  In _Proceedings of the American Society for Information Science and Technology_ 50.1 Wiley Online Library, 2013, pp. 1–4
  * [40] Eef Masson, Christian Gosvig Olesen, Nanne Noord and Giovanna Fossati  “Exploring Digitised Moving Image Collections: The SEMIA Project, Visual Analysis and the Turn to Abstraction”  In _DHQ: Digital Humanities Quarterly_ , 2020
  * [41] Helen McCorry  “Museums, the Web and the Serendipity Facilitator”  In _MDA Information_ , 2001, pp. 133–135
  * [42] Matthew McGinity et al.  “AVIE: a Versatile Multi-User Stereo 360° Interactive VR Theatre”  Association for Computing Machinery, 2007
  * [43] Maurice Merleau-Ponty  “Phenomenology of Perception”  Routledge, 2013
  * [44] Slavko Milekic  “Towards Tangible Virtualities: Tangialities”  ERIC, 2002
  * [45] Franco Moretti  “Distant Reading”  Verso Books, 2013
  * [46] Geert Mul and Eef Masson  “Data-Based Art, Algorithmic Poetry: Geert Mul in Conversation with Eef Masson”  In _TMG Journal for Media History_ 21.2 Netherlands Institute for SoundVision, 2018
  * [47] Christian Gosvig Olesen et al.  “Data-driven Research for Film History: Exploring the Jean Desmet Collection”  In _Moving Image: The Journal of the Association of Moving Image Archivists_ 16.1 JSTOR, 2016, pp. 82–105
  * [48] Jean Piaget  “To Understand is to Invent: The Future of Education”, 1973
  * [49] Robert M. Pirsig  “Zen and the Art of Motorcycle Maintenance”  William MorrowCompany, 1974
  * [50] Cambridge University Press  “Definition of serendipity from the Cambridge Advanced Learner’s Dictionary and Thesaurus”, 2021  URL: https://dictionary.cambridge.org/dictionary/english/serendipity
  * [51] Ronald E Rice, Maureen McCreadie and Shan-Ju L Chang  “Accessing and Browsing Information and Communication”  MIT Press, 2001
  * [52] Dario Rodighiero et al.  “Surprise Machines: Revealing Harvard Art Museums’ Image Collection”  In _Information Design Journal_ , forthcoming
  * [53] Katja Rogers, Uta Hinrichs and Aaron Quigley  “It doesn’t Compare to Being There: In-situ vs. Remote Exploration of Museum Collections”, 2014
  * [54] RTSArchives  “Le nouveau site RTSarchives”, 2018  URL: https://www.rts.ch/archives/5919889-le-nouveau-site-rtsarchives.html
  * [55] G Scagnetti  “Visual Epistemology for Design in Complex Systems, lecture at Tecnológica de Monterrey: Academic leaders’ program”, 2011
  * [56] Maxine Sheets-Johnstone  “The primacy of Movement”  John Benjamins Publishing, 2011
  * [57] Haifeng Shen et al.  “Information Visualisation Methods and Techniques: State-of-the-art and Future Directions”  In _Journal of Industrial Information Integration_ 16 Elsevier, 2019, pp. 100–102
  * [58] Ben Shneiderman  “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations”  In _The Craft of Information Visualization_ Morgan Kaufmann, 2003, pp. 364–371
  * [59] Mel Slater and Sylvia Wilbur  “A Framework for Immersive Virtual Environments (FIVE): Speculations on the Role of Presence in Virtual Environments”  In _Presence: Teleoperators and Virtual Environments_ 6.6, 1997, pp. 603–616  DOI: 10.1162/pres.1997.6.6.603
  * [60] Daniel N Stern  “The Interpersonal World of the Infant: A View from Psychoanalysis and Developmental Psychology”  Routledge, 2018
  * [61] Robert J Sternberg and Sareh Karami  “An 8P Theoretical Framework for Understanding Creativity and Theories of Creativity”  In _The Journal of Creative Behavior_ 56.1 Wiley Online Library, 2022, pp. 55–78
  * [62] Jonathan Steuer  “Defining Virtual Reality: Dimensions Determining Telepresence”  In _Journal of communication_ 42.4 Wiley Online Library, 1992, pp. 73–93
  * [63] Elaine G Toms  “Serendipitous Information Retrieval”  In _DELOS_ , 2000
  * [64] Mitchell Whitelaw  “Generous Interfaces for Digital Cultural Collections”  In _DHQ: Digital Humanities Quarterly_ , 2015
  * [65] Florian Windhager et al.  “Visualization of Cultural Heritage Collection Data: State of the Art and Future Challenges”  In _IEEE Transactions on Visualization and Computer Graphics_ 25.6, 2019, pp. 2311–2330  DOI: 10.1109/TVCG.2018.2830759
  * [66] R. Wright  “The Future of Television Archives - Digital Preservation Coalition”, 2017  URL: https://www.dpconline.org/blog/wdpd/the-future-of-television-archives
  * [67] Tom Ziemke, Jordan Zlatev and Roslyn M Frank  “Body, Language, and Mind”  Walter de Gruyter, 2007


Remotely Operating a Single-Point LDV System to Acquire 2D Measurements Alec
Ikei, Dr. Kaushik Sampath Acoustic Signal Processing and Systems Branch
Acoustics Division January 27, 2021 Distribution A: Approved for public
release: distribution unlimited NRL Memorandum Report01/01/2019 - 01/07/2021
Alec K. Ikei, Dr. Kaushik Sampath U.S Naval Research Laboratory
4555 Overlook Ave SW
Washington, DC 20375 U U U UU Alec K. Ikei (202) - 404 - 4816

# Remote Operation of a Single-Point LDV System to Acquire 2D Measurements

Alec K. Ikei  Dr. Kaushik Sampath

Due to the unprecedented increase in telework requirements, the motivation to
further automate and remotely control experiments has become apparent. This
work documents the technical development of creating a two-dimensional (2D)
Laser Doppler Vibrometry (LDV) measurement using a single-point LDV system
through an automated and remotely controllable process. This report aims to
assist in rapid development of setups for similar use cases. The setup
described is also modular, and has been used to analyze the modal response of
samples actuated through air-based acoustic signals as well as those
mechanically induced.

###### Contents

  1. 1 Introduction
    1. 1.1 Motivation
    2. 1.2 Background
    3. 1.3 A Brief Overview of This Work
      1. 1.3.1 Input Excitation
      2. 1.3.2 Output Measurement
      3. 1.3.3 Data Analysis
  2. 2 Equipment and Connections
  3. 3 Data Acquisition Using LabVIEW
    1. 3.1 FPGA
    2. 3.2 RT Controller
    3. 3.3 Desktop PC
  4. 4 Data Analysis in MATLAB
    1. 4.1 Loading and Processing Data
    2. 4.2 Using FFT Data to Perform Modal Analysis
  5. 5 Discussion
  6. 6 Conclusion
  7. A LabVIEW Data Acquisition Code
  8. B MATLAB Parallel Processing 2D LDV Code
  9. C MATLAB Custom Function: func_SSA
  10. D MATLAB Custom Function: importVelocity

Due to the unprecedented increase in telework requirements, the motivation to
further automate and remotely control experiments has become apparent. This
work documents the technical development of creating a two-dimensional (2D)
Laser Doppler Vibrometry (LDV) measurement using a single-point LDV system
through an automated and remotely controllable process. This report aims to
assist in rapid development of setups for similar use cases. A key achievement
of this work is the complete control integration of various types of hardware
and communication protocols, i.e. Field-Programmable Gate Array (FPGA),
function generator, translation stage, LDV and accelerometer. While several
experiments may be ’executed’ remotely once the equipment parameters have been
optimized in person, in this work, each tunable parameter of all the hardware
can be adjusted remotely as well, therefore, eliminating that extra
requirement. The setup described is also modular, and has been used to analyze
the modal response of samples actuated through air-based acoustic signals as
well as those induced mechanically.
In the setup described in this work, an arbitrary waveform is set in a
function generator, which is then amplified and played through a speaker in an
opened transmission loss tube. The acoustic signal from the speaker travels
through the air in the tube to vibrate the sample surface. The LDV is mounted
on a 2D motorized platform, which is controlled through LabVIEW on a desktop
computer. The LDV continuously sends velocity and signal strength data to a
Compact Rio (cRio) data acquisition device, which records data upon trigger
from the the function generator. The recorded data is then streamed and saved
on the desktop computer. The Fourier transform of each measurement location is
calculated in MATLAB, generating a 3D data array, consisting of two spatial
dimensions and one dimension in frequency. A single frequency slice of data is
taken from this array, which gives the amplitude of a 2D surface at a single
frequency, also called a mode shape. These mode shapes can then be compared to
expected results from COMSOL simulations.

##  Chapter 1 Introduction

###  1.1 Motivation

The current COVID-19 pandemic has required adaptation to a rapidly changing
situation and long-term telework. To continue to do bench work, it makes sense
to automate processes that are able to be automated, and allow for remote
access and control of the testing environment. For example, one of our
experiments required finely spaced vibration measurements across the surface
of a sample, with various input excitations, measurement sampling rates and
spacing. With only a single-point LDV system and a data acquisition system
(DAQ), it would take many hours to take the measurements while also increasing
the chance of human error. Instead of the experiment operator manually
changing the excitation and measurement parameters, it is more efficient and
convenient to automate, remotely control and monitor the experiment.
Therefore, the function generator parameter setup was automated and integrated
with the pre-pandemic point-scanning setup.

An LDV system is widely used to measure the response of a system/sample to an
excitation. Such experiments involve three broad stages: (1) sample
fabrication and mounting, (2) excitation and (3) measurement scans. The sample
preparation is entirely problem-specific and is usually not integrable with
the latter two. A variety of different excitation mechanisms/inputs maybe
employed for experiments. In our lab, we perform vibrational or acoustic
excitations. Typically, once the sample is fabricated and mounted to the
excitation mechanism in a location accessible to the LDV for scanning, it
still requires a significant (at-work) time commitment to synchronize and
optimize the excitation and LDV settings before the experiment can begin.

Here, our remote operation of the LDV system integrates all the components of
the excitation mechanism as well, allowing the user to remotely control
excitation and LDV settings to optimize them and run the measurement scan.
This includes control of the function generator, thereby adjusting the output
of a vibrational exciter or sound tube.

Commercial solutions to just the scanning stage of the problem exist for some
specific applications, such as the 2D and 3D LDV systems sold by various
companies. However, higher dimensional systems can cost on the order of 10
times more than a single-point system for each added dimension of measurement
capability. For many lab setups, this may not be feasible, and in many
situations do not allow for enough adaptability and control of the scan
parameters. The methods described here can be reproduced at lower cost and
less administrative time used to purchase a 2D LDV while still producing high
quality results, in addition to customization and integration with the testing
environment. Another crucial advantage of the current approach is that a
vendor-built custom 2D scanning system is wedded to just the LDV measurement,
and has no use during the machine’s down-time. Whereas, as a consequence of
the present work, the 2D motorized stage can be used modularly for a wide
range of other experiments, and the automation/remote-control components carry
forward to several other case scenarios. For example, the motorized stage and
function generator can be placed in an acoustic (hydrophone) scan experiment
and moved back to the LDV setup as needed.

###  1.2 Background

Historically, in the Acoustic Signal Processing and Systems Branch (Code
7160), we performed experiments using scanning-type measurements for
underwater acoustic tests, using an automated system to move and acquire data
from a hydrophone in 2D and 3D scans. The code used to run the underwater
tests was modified to run on different hardware and connect to different
systems, but the idea behind the measurement remains the same. By moving the
data acquisition location around, the data collected represents the same data
that would have been collected if using a large array of measurement devices.
Conversely, the excitation source can be moved around with a constant data
acquisition location, which would emulate having an array of sources.

The data collected at each measurement point is usually then processed in
MATLAB to extract binned time series data or frequency domain data. These
analyses can then be represented as a propagating wave or modal shape,
respectively. As additive manufacturing and featuring capabilities achieve
finer resolutions, the demand for higher frequency scans has only been
increasing. Moreover, an interest in studying non-linear acoustic or transient
phenomena also requires large, dense collections of data points and high-
frequency scans. These result in a substantial increase in the amount of data
acquired per scan, necessitating parallel processing of the data processing
step.

###  1.3 A Brief Overview of This Work

####  1.3.1 Input Excitation

The testing setup described in this work sets an arbitrary waveform in a
function generator, which is then amplified and played through a speaker in an
opened transmission loss tube. The acoustic signal from the speaker travels
through the air in the tube to vibrate the sample surface. The transmission
tube can be replaced by a vibration exciter, if the desired actuation is
mechanical rather than pressure based.

####  1.3.2 Output Measurement

The LDV is mounted on a 2D motorized platform, which is controlled through
LabVIEW on a desktop computer. The LDV continuously sends velocity and signal
strength data to a cRio data acquisition device, which records data upon
trigger from the the function generator. At the same time, the data from the
accelerometer is also collected, so that the output can be normalized for each
measurement location. The recorded data is then streamed and saved on the
desktop computer. Since the controls for this setup are all on the desktop
personal computer (PC), this allows remote desktop users to modify the input
excitation, linear stages and DAQ parameters while teleworking.

####  1.3.3 Data Analysis

The Fourier transform of each measurement location is calculated in MATLAB,
generating a 3D data array. A single frequency slice of data is taken from
this array, which gives the amplitude of a 2D surface at a single frequency,
also called a mode shape. These mode shapes can then be further analyzed and
compared to expected results in COMSOL simulations, as seen in Fig. 1.1.

Figure 1.1: Vibrational modes of a circular elastomeric plate, produced using
the setup described in this work and compared with a COMSOL model. Figure
reused with permission wissman2019soft wissman2019liquid.

##  Chapter 2 Equipment and Connections

The desktop PC was connected through a Universal Serial Bus (USB) cable to the
function generator. The signal output of the function generator was connected
to the input of the amplifier through a Bayonet Neill-Concelman (BNC) cable.
The output of the amplifier was connected to the input of the tube through
banana cables. The impedance/sound tube kit can be substituted with a
vibrational exciter when mechanical actuation is required instead.

The PC was connected through RS-232 to the 2D stage controller. The LDV was
mounted on a custom mounting bracket to the stage. The PC was connected to the
DAQ through an Ethernet cable, and the DAQ modules were inserted into the DAQ
chassis. The signal strength and velocity outputs of the LDV were connected to
the analog input of the DAQ.

The trigger signal from the function generator was connected through BNC to
the digital module on the DAQ as well as the anti-drift input on the LDV
controller. This causes the voltage output of the LDV to be set to 0V at each
trigger, which helps to avoid the signal exceeding the maximum allowed by the
LDV controller (10V). The trigger is also used by the DAQ to determine when to
start recording the signal for each measurement location. The equipment and
connection schematics are illustrated in Fig. 2.1, and their make and model
are listed in Table 2.1.

Equipment | Model | Manufacturer
---|---|---
Function Generator | 33500B | Agilent Technologies
Power Amplifier | Type 2718 | Brüel & Kjær (B&K)
Transmission Loss Tube Kit | Type 4206 | Brüel & Kjær (B&K)
2D Linear Stage | Bi-Slide | Velmex Inc.
Single-Point LDV | CLV-2534 | Polytec Gmbh
DAQ Chassis | cRio-9035 | National Instruments
Analog Input Module | NI-9223 | National Instruments
Digital I/O Module | NI-9402 | National Instruments
Table 2.1: List of equipment and their respective makes and models, used to
take a 2D LDV scan of the elastomeric plate seen in Fig 1.1. Figure 2.1:
Equipment connections used to perform 2D LDV scan of the elastomeric plate
seen in Fig 1.1.

##  Chapter 3 Data Acquisition Using LabVIEW

The description here assumes basic familiarity with LabVIEW. The code is run
from several systems: the desktop PC, the Real-Time (RT) controller, and the
FPGA inside of the controller chassis. The LabView code uses the FPGA and the
C-series DAQ modules on the cRio to take data, passes it along through the RT
controller and then the PC. In addition to the base LabVIEW program, the
Embedded Control Suite is required to operate the FPGA. A flow chart showing
the communication logic is displayed in Fig. A.1. LabVIEW block diagrams are
shown in Appendix A.

###  3.1 FPGA

In the FPGA code, the digital input is constantly read until it detects a
rising edge from the function generator trigger. It then takes the analog
voltage time series through a for-loop, and passes the values into a Direct
Memory Access (DMA) first-in first-out (FIFO) channel. DMA FIFO channels are a
way to communicate data at high sampling rates between the FPGA and the RT
controller. The number of iterations of the for-loop and the timing between
iterations is set by a control. Control values are modifiable from the RT
code. The LDV signal strength is passed into an indicator. The DMA FIFO is
also checked if it overflowed, and this value is passed onto an indicator. The
overflow should be incorporated with a feedback node, so that successive reads
of the overflow indicator do not reset the value. The FPGA code can be seen in
Figure A.3.

###  3.2 RT Controller

The RT controller opens the FPGA program, and sets the FPGA control values
using the “Read/Write Control” function. The RT controller waits until the 2D
stage has stopped, which is determined by a Boolean shared variable. Before
and after data acquisition, the RT controller code checks if the 2D stage has
stopped moving, and tells the PC that it is done acquiring data for that
sampling location through Boolean shared variables. The FPGA code is then
started, and the data is read from the DMA FIFO through a while loop. The
while loop queries the DMA FIFO to check if there are elements stored on it.
If there are, it reads the elements and places it in an array. The array is
appended to previous iterations of the while loop. When the while loop is
done, the array contains data from a single average. The for loop that
surrounds the while loop repeats the single average process until it contains
the data from the number of averages requested, as seen in Fig. A.4.

###  3.3 Desktop PC

The desktop PC controls the 2D stage and the function generator, and
coordinates with the RT controller to acquire data between movements of the
LDV. In the first section of the PC code, the 2D linear stage is initialized,
which gives the stage controller an origin to reference. The initialization
sub-function of the LabVIEW Virtual Instrument (sub VI) is custom made, based
on communication protocols supplied by the manufacturer. The function
generator parameters and the arbitrary waveform file are read into a custom
made sub VI, which utilizes drivers from the manufacturer. This is shown in
Fig. A.5.

In the next selected snippet, the PC code creates a raster grid based on user
input in Fig. A.6. The raster grid is iterated through nested for loops,
reading and saving data each time to the hard drive, as seen in Fig. A.7. The
PC tells the RT controller when the movement is done through a Boolean shared
variable, and waits for the data acquisition to finish before reading the
data. Once it finishes saving, it moves on to the next iteration of the nested
for loop. When the nested for loop is done, it tells the cRio that the scan is
done, and closes the communication port for the function generator. Failure to
close ports properly as mentioned here, will lead to communication issues in
subsequent operation, which can usually be remedied by resetting the device.

Some of the default settings on the function generator are not suited for our
application. For example, the function generator applies a low pass filter at
17kHz, which limits the types of response that can be induced in the sample.
By including code that can modify parameters on the function generator, these
settings can be turned off, modified and automated, allowing for different
parameter sets to be used when acquiring multiple data sets.

##  Chapter 4 Data Analysis in MATLAB

###  4.1 Loading and Processing Data

The data taken during the experiment was saved in a folder as separate comma
delimited text files. In MATLAB, the working directory was changed to the
folder containing the data files. Since the script described here used
parallel processing to increase the processing speed, the MATLAB parallel
processing toolbox is required to run it. In the script, the file names in the
directory are read into an array, and the X and Y values are extracted from
their names into two 1D arrays. The frequency array is calculated based on the
sampling rate and the amount of zero padding that is desired. Zero padding
refers to a signal processing technique in which zeros are added to the
original time series data, which decreases the discrete frequency step size in
the corresponding fast Fourier Transform (FFT). While increasing the zero
padding of a data set provides an increasingly small frequency step size, the
resulting FFT calculations taking increasingly more time to calculate, and
therefore there is an inherent trade-off between the acceptable processing
time and the minimum frequency step size achievable. A parallelized for
(parfor) loop is then used to read the data and perform an FFT on all the
files in the directory, resulting in a 2D array (each column is the FFT of a
different position, and each row represents a different frequency).

###  4.2 Using FFT Data to Perform Modal Analysis

In the next section of the script, a new directory is created, which is used
later to save the figure files in. The frequency limit of the mode shapes to
be displayed are set here, because the frequency range of interest is usually
less than what the entire FFT contains. Using a parfor loop again, the images
are generated from the FFT data. The X and Y arrays are used to index the FFT
array, and the iteration counter of the parfor loop is used to select the row,
which generates a 2D array of amplitudes that represent a single frequency.
The 2D array is then plotted as a color map or a 3D mesh figure. Using this
code, 500 images containing 143 by 136 spatial points (representing 19,448
sampling locations) were created in about 2 minutes on a computer with a 16
core processor. Further optimization can be carried out by doing parallel
analysis on a Graphics Processing Unit (GPU) rather than on a Central
Processing Unit (CPU) as was done here, which would allow many additional
parallel processes, resulting in lower computation time.

In comparison, without a parallel processing approach the code would have to
run on a single processor, which would likely take approximately 16 times as
long, saving half an hour for a relatively small subset of the data. With
larger datasets or more detailed binning of the Fourier transform, the time
saved would likely increase linearly, with a similar factor of performance
improvement. The MATLAB code is included in Appendix B, and the custom
functions used are included in Appendix C and D.

##  Chapter 5 Discussion

The method described here shows that a 2D LDV measurement can be done well
with a single-point LDV and easily obtained components. The modular nature of
the setup allows it to be used for different applications; to actuate a thin
elastomeric plate as seen here through pressure waves in air, or through
mechanical means to vibrate more rigid samples, using a vibration exciter and
a stinger. In addition, some components can be swapped out for cheaper parts,
especially the DAQ. A simple FPGA based DAQ card can be obtained on the order
of $100, which is two orders of magnitude cheaper than a cRio. Uniquely for
NRL, and perhaps many other research laboratories, FPGA DAQs are usually
present with a lot of down time. The modularity of this solution can be
extended to various existing hardware.

Conversely, to acquire data at faster speeds, better damping on the linear
stages and increasing its rigidity can be done to decrease the time needed for
each sampling location. The number of averages and the number of time samples
can also be modified to increase acquisition speed. However, this can affect
the quality of the data produced, since noise generally decreases with the
square root of the number of averages taken.

Another important factor to consider is the sample mounting. Poorly mounted
samples can lead to poor actuation of the sample. The sample shown in Fig. 1.1
has the elastomer bonded with an acrylic frame, which helps to ensure more
uniform actuation. Especially in mechanical actuation (e.g. actuation of more
solid objects, like plastic or metal plates), the contact between the
vibration exciter, the stinger, and the sample must be snug.

The signal strength value from the LDV system can also be used to filter out
low signal sampling locations. In many cases, signal strength varies
arbitrarily due to surface roughness effects, and more often a region of good
signal strength maybe found infinitesimally close to a spot with poor signal
strength. Therefore, for these scenarios, the LabVIEW code could also be
modified to step a small amount in the X or Y direction upon measurement of a
poor signal location. If these approaches are not feasible for a particular
use case, then the data can also be spatially filtered to smooth out the
figure surface.

##  Chapter 6 Conclusion

To summarize the process described in this work, a sample is vibrated using an
arbitrary waveform played through a speaker. A 2D linear stage is used to move
a single-point LDV, which takes the velocity time series of the sample’s
surface. The time series is saved to individual text files, and the FFT is
taken of each file. For each frequency bin in the FFT, the sampling position
is used to assign its position in a 2D mesh. The mesh is then plotted either
as a surface plot or as a colorplot, resulting in a mode shape plot. By using
parallel processing, this process is sped up significantly, resulting in large
time savings which increase with the complexity and size of the analysis. The
modular nature of this setup also allows for modification and therefore
versatility for different use cases and budgets.

In addition to the time savings and low cost, the automation and remote
operation of its sub components makes this setup even more appealing in times
where lab access is restricted. As such, this report serves to document the
technical process used to develop the measurement system to accelerate the
development of similar setups and preserve institutional knowledge.

The extent of remote operation can be further improved from this setup by
employing a third-axis translation stage (in the direction of the LDV scanning
beam). For optimal LDV signal strength, it requires the sample to be separated
at discretized distances from the beam output location. The third stage can
help achieve this remotely, and eliminate the need for in-person adjustment of
the sample and/or LDV. Furthermore, when samples have a curvature, thereby
varying that distance during a scan, the third axis translation stage can be
used to dynamically adjust the LDV separation based on each sampling location
to provide the best signal strength throughout the sample.

The high level of detail and discussion of the code used are included to help
unfamiliar engineers to be brought up to speed quickly. The LabVIEW and MATLAB
codes used are shown in the appendices below, to give further guidance.

###### Acknowledgements.

The LabVIEW code displayed here was built upon work performed by Dr. Michael
Nicholas (NRL, Code 7165). We thank Wissman et. al. for permission to reuse
their figure. We also thank Dr. Matthew Guild (NRL, Code 7165) and Dr. David
Calvo (NRL, Code 7165) for their time spent reviewing this manuscript.

## References

  * Wissman et al. (2019a) J. Wissman, K. Sampath, A. Ikei, K. B. Özütemiz, C. Majidi, and C. A. Rohde,  “Soft-matter pressure sensors for turbulence detection,”  Proceedings of the Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019, volume 10970 (International Society for Optics and Photonics), 2019a, p. 109702D.
  * Wissman et al. (2019b) J. P. Wissman, K. Sampath, A. Ikei, K. B. Özütemiz, M. Carmel, and C. Rohde,  “Liquid metal-based resistive membranes for flow acoustics detection,”  _The Journal of the Acoustical Society of America_ 146(4), 2997–2997 (2019b).

##  Appendix A LabVIEW Data Acquisition Code

Figure A.1: Communication flow chart between systems running LabVIEW code.
Figure A.2: Organization of LabVIEW files. Opening the ”.lvproj” file opens
this LabVIEW project window. Figure A.3: FPGA code, located under cRio
$\rightarrow$ Chassis $\rightarrow$ FPGA Target. This code reads the digital
input and moves to the second panel after receiving a rising edge. The second
panel contains a for loop that collects the analog voltage signal and passes
the value into the DMA FIFO at specifically timed intervals. If the FIFO
overflows, it sets a boolean indicator to true. The signal strength of the LDV
is also recorded as a single value. The controls can be adjusted from the RT
controller code. Figure A.4: Selected portion of the Real-Time Controller
Code, located under cRIO. This code runs the FPGA code, then reads the LDV
strength into a shared variable. The DMA FIFO for each channel is queried. If
there are elements remaining, they are read and passed out of the while loop
into the for loop. The for loop continues until the number of averages taken
is complete. The data is then averaged and passed into shared variables and
the front panel display. Figure A.5: First part of the PC code, located under
My Computer. This section initializes the 2D stage, so that it has an origin
to reference. The waveform generator is also loaded with the arbitrary
waveform. Figure A.6: Second part of the PC code, located under My Computer.
This section generates the raster grid based on user input, and then moves the
stage, iterating through nested for loops. Figure A.7: Next important part of
the PC code, located under My Computer. This section reads the data stored int
he shared variables, and saves them to text files. The precision is set by the
format string ”%.6f”.

##  Appendix B MATLAB Parallel Processing 2D LDV Code

    %%Attribution
    %Code written by Alec Ikei and Kaushik Sampath, November 2020
    %Included in 2D LDV US Naval Research Laboratory Memorandum Report

    %% Read Data and Parallel Calculate FFT
    % get directory list
    clc; clear; close all;
    folder = uigetdir(); %prompts user to select folder containing data
    fileList = dir(fullfile(folder, ’*.tsv’)); %only lists .tsv files
    fileNames=strcat(folder,’\’,{fileList.name}); %extract the names of the files and
    %put them into a n x 1 cell array

    %User inputs
    samplingRate=100000; %sampling rate used in Hz
    padMultiplier=1; %number of TS lengths of 0s to pad
    minFreq=100; maxFreq=20000; %set frequency limits for slicing
    signalCutoff=0.05;
    %end of user inputs

    x=zeros(length(fileNames),1); y=x;z=x; badpxList=x; %length(a)=number of files
    badpx=0; %# of low signal str pixels

    %calculate the frequency vector for just one timeseries
    velocityArray=importVelocity(fileNames{1}); %get LDV data for particular
    %spatial location
    localfft=func_SSA(velocityArray,padMultiplier); %calculate FFT vector
    numofBins=length(velocityArray)/2*padMultiplier; %calculate # frequency bins
    localfreqBinSize=samplingRate*0.5/numofBins; %calculate bin width
    freqArray=0:localfreqBinSize:samplingRate/2; %generate frequency array

    %locate index of max frequency
        dist = abs(freqArray - maxFreq);
        minDist = min(dist);
        maxFreqidx = find(dist == minDist);

    %locate index of min frequency
        dist = abs(freqArray - minFreq);
        minDist = min(dist);
        minFreqidx = find(dist == minDist);

    %Pre-allocate variable for FFT array
        fftArray=zeros(length(localfft(minFreqidx:maxFreqidx)),length(fileNames));
    Ψ%for FFT in columns

    parfor m=1:length(fileNames) %parallel computation of FFTs
        m %shows current iteration in command window
        indexX=regexp(fileNames{m},’X_’);
        indexY=regexp(fileNames{m},’Y2_’);
        indexS=regexp(fileNames{m},’Strength’);
        indexP=regexp(fileNames{m},’.tsv’);%location from filename
        if str2double(fileNames{m}(indexS+8:indexP-1))>signalCutoff
    Ψ% only use data if signal strength is good
            x(m)=str2double(fileNames{m}(indexX+2:indexY-1));
    ΨΨ% X coordinate from filename
            y(m)=str2double(fileNames{m}(indexY+3:indexS-1));
    ΨΨ% Y2 coordinate
            velocityArray=importVelocity(fileNames{m});
    ΨΨ%LDV data for particular spatial location
            localfft=func_SSA(velocityArray,padMultiplier);
    ΨΨ%calculate FFT
            fftArray(:,m)=localfft(minFreqidx:maxFreqidx);
    ΨΨ%place relevant FFT data in columns
        else
            badpx=badpx+1; %count number of excluded pixels
            badpxList(m)=1; %list bad pixels
        end
    end

    x(badpxList>0)=[];y(badpxList>0)=[];fftArray(:,badpxList>0)=[]; %remove bad pixels
    fftArrayMax=max(max(abs(fftArray))); %global maximum amplitude

    disp(’done reading data’);
    %% Create color plot of each slice and save as .png
    pngfolder=strcat(folder,’\pngs’);figfolder=strcat(folder,’\figs’);
    %folders for images
    mkdir(pngfolder); mkdir(figfolder);

    xx=unique(x,’rows’);yy=unique(y,’rows’); %remove duplicate coordinates
    %creates a 2d set of points to assign the sampNormPlt values to
    [X,Y]=meshgrid(min(xx):xx(2)-xx(1):max(xx),min(yy):yy(2)-yy(1):max(yy));

    %pre-allocate space to save all surface data
    surfaceData=zeros(length(minFreqidx:maxFreqidx),length(yy),length(xx));

    currentFreq=minFreqidx:maxFreqidx;
    parfor k=1:length(currentFreq) %must start at 1, since parfor
        f=scatteredInterpolant(x,y,fftArray(currentFreq(k),:)’);
    Ψ%create function from data
        Z=f(X,Y)-mean(f(X,Y)); %create surface values from function, minus DC offset
        surf(X,Y,real(Z),’LineStyle’,’none’); %create surface plot
        view(2); %flat view
        imageName=strcat(num2str(freqArray(currentFreq(k))), ’ Hz’);
        print(’-dpng’,’-r75’,strcat(pngfolder,’\’,imageName));
    Ψ%convert current fig to png
        saveas(gcf, strcat(figfolder,’\’,imageName));
    Ψ%saves the current figure as Matlab file
    end

##  Appendix C MATLAB Custom Function: func_SSA

This function takes the single sided amplitude complex Fourier transform. It
also can zero pad the timeseries input, to change the frequency binning. Used
in the parallel processing 2D LDV code.

    function [SSA,Phase] =  func_SSA(X,k)
    %k=1; changed from kaushiks code
    % Sampling Length
    L = length(X);
    % Pad with a length of k times L
    Lpad = k*L;X1 = zeros(Lpad,1);
    X1(Lpad/2-L/2+1:Lpad/2+L/2)=X;
    X=X1;

    % Compute FFT
    FFT1 = fft(X);
    Phase=imag(FFT1);
    % Single Sided Amplitude Spectrum
    FFT2 = FFT1/Lpad;
    SSA = FFT2(1:Lpad/2+1,:);
    SSA(2:end-1,:) = 2*SSA(2:end-1,:);
    SSA = SSA/(L/Lpad);

##  Appendix D MATLAB Custom Function: importVelocity

This function reads the text data file and converts it into a vector. Used in
the parallel processing 2D LDV code.

    function velocityArray = importVelocity(filename, dataLines)
    if nargin < 2
        dataLines = [1, Inf];
    end

    %% Setup the Import Options
    opts = delimitedTextImportOptions("NumVariables", 2);

    % Specify range and delimiter
    opts.DataLines = dataLines;
    opts.Delimiter = "\t";

    % Specify column names and types
    opts.VariableNames = ["VarName1", "Var2"];
    opts.SelectedVariableNames = "VarName1";
    opts.VariableTypes = ["double", "string"];
    opts = setvaropts(opts, 2, "WhitespaceRule", "preserve");
    opts = setvaropts(opts, 2, "EmptyFieldRule", "auto");
    opts.ExtraColumnsRule = "ignore";
    opts.EmptyLineRule = "read";

    % Import the data
    tbl = readtable(filename, opts);

    %% Convert to output type
    velocityArray = tbl.VarName1;
    end