scholarly journals Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3087
Author(s):  
Sandi Ljubic ◽  
Franko Hržić ◽  
Alen Salkanovic ◽  
Ivan Štajduhar
Keyword(s):  
Magnetic Field ◽  
Neural Networks ◽  
Curve Fitting ◽  
Absolute Error ◽  
Interaction Space ◽  
Text Entry ◽  
Proof Of Concept ◽  
Field Sensor ◽  
Device Interaction ◽  
Input Techniques

In this paper, we investigate the possibilities for augmenting interaction around the mobile device, with the aim of enabling input techniques that do not rely on typical touch-based gestures. The presented research focuses on utilizing a built-in magnetic field sensor, whose readouts are intentionally affected by moving a strong permanent magnet around a smartphone device. Different approaches for supporting magnet-based Around-Device Interaction are applied, including magnetic field fingerprinting, curve-fitting modeling, and machine learning. We implemented the corresponding proof-of-concept applications that incorporate magnet-based interaction. Namely, text entry is achieved by discrete positioning of the magnet within a keyboard mockup, and free-move pointing is enabled by monitoring the magnet’s continuous movement in real-time. The related solutions successfully expand both the interaction language and the interaction space in front of the device without altering its hardware or involving sophisticated peripherals. A controlled experiment was conducted to evaluate the provided text entry method initially. The obtained results were promising (text entry speed of nine words per minute) and served as a motivation for implementing new interaction modalities. The use of neural networks has shown to be a better approach than curve fitting to support free-move pointing. We demonstrate how neural networks with a very small number of input parameters can be used to provide highly usable pointing with an acceptable level of error (mean absolute error of 3 mm for pointer position on the smartphone display).

Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

2020 ◽  
Author(s):  
Jingbai Li ◽  
Patrick Reiser ◽  
André Eberhard ◽  
Pascal Friederich ◽  
Steven Lopez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Excited State ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Ground Truth ◽  
Absolute Error ◽  
Photochemical Reactions ◽  
Computational Techniques ◽  
Full Potential

<p>Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr <i>et al.</i> demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH<sub>2</sub>NH<sub>2</sub><sup>+</sup>).</p><p>We have developed a Python-based code, Python Rapid Artificial Intelligence <i>Ab Initio</i> Molecular Dynamics (PyRAI<sup>2</sup>MD), to accomplish the unprecedented 10 ns <i>cis-trans</i> photodynamics of <i>trans</i>-hexafluoro-2-butene (CF<sub>3</sub>–CH=CH–CF<sub>3</sub>) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S<sub>1</sub> half-life (60.5 fs), the photochemical product ratio (<i>trans</i>: <i>cis</i> = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (<i>trans</i> → <i>cis</i> but not <i>cis</i> → <i>trans</i> pathways) that may offer future automated photochemical reaction discoveries.</p>

Magnetic Field Sensor Based on a Single Josephson Junction With a Multilayer Ferromagnet/Normal Metal Barrier

2021 ◽  
Vol 31 (5) ◽  
pp. 1-5
Author(s):  
Ivan P. Nevirkovets ◽  
Mikhail A. Belogolovskii ◽  
Oleg A. Mukhanov ◽  
John B. Ketterson
Keyword(s):  
Magnetic Field ◽  
Josephson Junction ◽  
Normal Metal ◽  
Magnetic Field Sensor ◽  
Field Sensor ◽  
Metal Barrier

Test bench for calibration of magnetic field sensor prototypes for COMPASS-U tokamak

2021 ◽  
Vol 168 ◽  
pp. 112467
Author(s):  
Andre Torres ◽  
Karel Kovarik ◽  
Tomas Markovic ◽  
Jiri Adamek ◽  
Ivan Duran ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Test Bench ◽  
Magnetic Field Sensor ◽  
Field Sensor

scholarly journals Sensuator: A Hybrid Sensor–Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 30
Author(s):  
Pornthep Preechayasomboon ◽  
Eric Rombokas
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Models ◽  
Open Loop ◽  
Proof Of Concept ◽  
State Estimator ◽  
Loop Control ◽  
Practical Applications ◽  
Soft Actuator ◽  
The Cost

Soft robotic actuators are now being used in practical applications; however, they are often limited to open-loop control that relies on the inherent compliance of the actuator. Achieving human-like manipulation and grasping with soft robotic actuators requires at least some form of sensing, which often comes at the cost of complex fabrication and purposefully built sensor structures. In this paper, we utilize the actuating fluid itself as a sensing medium to achieve high-fidelity proprioception in a soft actuator. As our sensors are somewhat unstructured, their readings are difficult to interpret using linear models. We therefore present a proof of concept of a method for deriving the pose of the soft actuator using recurrent neural networks. We present the experimental setup and our learned state estimator to show that our method is viable for achieving proprioception and is also robust to common sensor failures.

High-sensitivity fiber optic magnetic field sensor based on lossy mode resonance and hollow core-offset structure

2021 ◽  
pp. 1-12
Author(s):  
Xue-Peng Jin ◽  
Hong-Zhi Sun ◽  
Shuo-Wei Jin ◽  
Wan-Ming Zhao ◽  
Jing-Ren Tang ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Fiber Optic ◽  
High Sensitivity ◽  
Magnetic Field Sensor ◽  
Hollow Core ◽  
Field Sensor

An integrated electro-optic magnetic field sensor based on reflected Mach-Zehnder interferometer

Optik ◽  
2018 ◽  
Vol 157 ◽  
pp. 315-318 ◽  
Author(s):  
Jiahong Zhang ◽  
Xiaorong Wan ◽  
Yingna Li ◽  
Zhengang Zhao ◽  
Chuan Li
Keyword(s):  
Magnetic Field ◽  
Magnetic Field Sensor ◽  
Zehnder Interferometer ◽  
Electro Optic ◽  
Field Sensor ◽  
Mach Zehnder Interferometer

scholarly journals Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 135-149
Author(s):  
James Flynn ◽  
Cinzia Giannetti
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Urban Areas ◽  
Proof Of Concept ◽  
Major Step ◽  
Dominant Form ◽  
Residential Properties ◽  
The Uk ◽  
Ev Charging ◽  
Unique Dataset

With Electric Vehicles (EV) emerging as the dominant form of green transport in the UK, it is critical that we better understand existing infrastructures in place to support the uptake of these vehicles. In this multi-disciplinary paper, we demonstrate a novel end-to-end workflow using deep learning to perform automated surveys of urban areas to identify residential properties suitable for EV charging. A unique dataset comprised of open source Google Street View images was used to train and compare three deep neural networks and represents the first attempt to classify residential driveways from streetscape imagery. We demonstrate the full system workflow on two urban areas and achieve accuracies of 87.2% and 89.3% respectively. This proof of concept demonstrates a promising new application of deep learning in the field of remote sensing, geospatial analysis, and urban planning, as well as a major step towards fully autonomous artificially intelligent surveying techniques of the built environment.

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