Soli

Lien, Jaime; Gillian, Nicholas; Karagozler, Mustafa Emre; Amihood, Patrick M.; Schwesig, Carsten; Olson, Erik M.; Raja, Hakim; Poupyrev, Ivan

doi:10.1145/2897824.2925953

Cited by 774 publications

(167 citation statements)

References 74 publications

(60 reference statements)

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“…[4] We focus on optimally configuring dynamic metasurface hardware, a promising alternative to more traditional antenna arrays for beam-forming and wavefront shaping. Microwave sensors can operate through optically opaque materials such as clothing, are not impacted by external visible lighting and scene color, minimally infringe upon privacy (unlike visual cameras) and may eventually help sense through fog, smoke, and "around-the-corner."…”

Section: Lisp For Object Recognition With Dynamic Metasurface Aperturesmentioning

confidence: 99%

Learned Integrated Sensing Pipeline: Reconfigurable Metasurface Transceivers as Trainable Physical Layer in an Artificial Neural Network

et al. 2019

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The rapid proliferation of intelligent systems (e.g., fully autonomous vehicles) in today's society relies on sensors with low latency and computational effort. Yet current sensing systems ignore most available a priori knowledge, notably in the design of the hardware level, such that they fail to extract as much task‐relevant information per measurement as possible. Here, a “learned integrated sensing pipeline” (LISP), including in an end‐to‐end fashion both physical and processing layers, is shown to enable joint learning of optimal measurement strategies and a matching processing algorithm, making use of a priori knowledge on task, scene, and measurement constraints. Numerical results demonstrate accuracy improvements around 15% for object recognition tasks with limited numbers of measurements, using dynamic metasurface apertures capable of transceiving programmable microwave patterns. Moreover, it is concluded that the optimal learned microwave patterns are nonintuitive, underlining the importance of the LISP paradigm in current sensorization trends.

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Section: Lisp For Object Recognition With Dynamic Metasurface Aperturesmentioning

confidence: 99%

Learned Integrated Sensing Pipeline: Reconfigurable Metasurface Transceivers as Trainable Physical Layer in an Artificial Neural Network

et al. 2019

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“…However more recent devices offer higher resolution of gesturesensing based on radar [31] and sonar [49] technologies.…”

Section: Touchless Systemsmentioning

confidence: 99%

“…Recent advances in gesture recognition technologies [31,40] are driving a new class of interactive systems where a user is able to view, control and manipulate digital content without touching the interface. For example, touchless interactions are being explored as part of medical surgery [51], to design games that benefit children with Autism Spectrum Disorder (ASD) [4], and touchless controllers for car dashboards [2](see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Agency in Mid-air Interfaces

Cornelio

Pirro

Thanh

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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Touchless interfaces allow users to view, control and manipulate digital content without physically touching an interface. They are being explored in a wide range of application scenarios from medical surgery to car dashboard controllers. One aspect of touchless interaction that has not been explored to date is the Sense of Agency (SoA). The SoA refers to the subjective experience of voluntary control over actions in the external world. In this paper, we investigated the SoA in touchless systems using the intentional binding paradigm. We first compare touchless systems with physical interactions and then augmented different types of haptic feedback to explore how different outcome modalities influence users' SoA.From our experiments, we demonstrated that an intentional binding effect is observed in both physical and touchless interactions with no statistical difference. Additionally, we found that haptic and auditory feedback help to increase SoA compared with visual feedback in touchless interfaces. We discuss these findings and identify design opportunities that take agency into consideration.

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“…RadarCat uses a Google Soli [Lien et al 2016] and our so ware has two parts i) a GUI in C++ using Qt and ii) a classi er backend in Java using the Weka API. Employing machine learning techniques on the Soli signals, we demonstrate the reliable extraction of data about the target material or object that we use for novel interaction.…”

Section: Methodsmentioning

confidence: 99%

Tangible UI by object and material classification with radar

Yeo

Ens

Quigley

2017

SIGGRAPH Asia 2017 Emerging Technologies

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Radar signals penetrate, sca er, absorb and re ect energy into proximate objects and ground penetrating and aerial radar systems are well established. We describe a highly accurate system based on a combination of a monostatic radar (Google Soli), supervised machine learning to support object and material classi cation based UIs. Based on RadarCat techniques, we explore the development of tangible user interfaces without modi cation of the objects or complex infrastructures. is a ords new forms of interaction with digital devices, proximate objects and micro-gestures.

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Soli

Cited by 774 publications

References 74 publications

Learned Integrated Sensing Pipeline: Reconfigurable Metasurface Transceivers as Trainable Physical Layer in an Artificial Neural Network

Learned Integrated Sensing Pipeline: Reconfigurable Metasurface Transceivers as Trainable Physical Layer in an Artificial Neural Network

Agency in Mid-air Interfaces

Tangible UI by object and material classification with radar

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