Related papers: Real-time motion amplification on mobile devices
In this article we propose the use of accelerometer embedded by default in smartphone as a cost-effective, reliable and efficient way to provide remote physical activity monitoring for the elderly and people requiring healthcare service.…
Real-time video inference on edge devices like mobile phones and drones is challenging due to the high computation cost of Deep Neural Networks. We present Adaptive Model Streaming (AMS), a new approach to improving performance of efficient…
Dynamic Mode Decomposition (DMD) is a numerical method that seeks to fit timeseries data to a linear dynamical system. In doing so, DMD decomposes dynamic data into spatially coherent modes that evolve in time according to exponential…
Video motion magnification is a technique to capture and amplify subtle motion in a video that is invisible to the naked eye. The deep learning-based prior work successfully demonstrates the modelling of the motion magnification problem…
The ability to amplify or reduce subtle image changes over time is useful in contexts such as video editing, medical video analysis, product quality control and sports. In these contexts there is often large motion present which severely…
Smartphones equipped with sensors such as accelerometers, gyroscopes, and magnetometers offer valuable opportunities for physics education, allowing students to measure motion using their own devices. However, commonly used applications…
Markerless Motion Capture (MoCap) using smartphone cameras is a promising approach to making exergames more accessible and cost-effective for health and rehabilitation. Unlike traditional systems requiring specialized hardware, recent…
Moire patterns appear frequently when taking photos of digital screens, drastically degrading the image quality. Despite the advance of CNNs in image demoireing, existing networks are with heavy design, causing redundant computation burden…
In order to mitigate the long processing delay and high energy consumption of mobile augmented reality (AR) applications, mobile edge computing (MEC) has been recently proposed and is envisioned as a promising means to deliver better…
Today very few deep learning-based mobile augmented reality (MAR) applications are applied in mobile devices because they are significantly energy-guzzling. In this paper, we design an edge-based energy-aware MAR system that enables MAR…
Modern smartphones contain motion sensors, such as accelerometers and gyroscopes. These sensors have many useful applications; however, they can also be used to uniquely identify a phone by measuring anomalies in the signals, which are a…
Detecting and magnifying imperceptible high-frequency motions in real-world scenarios has substantial implications for industrial and medical applications. These motions are characterized by small amplitudes and high frequencies.…
Fast and accurate video object recognition, which relies on frame-by-frame video analytics, remains a challenge for resource-constrained devices such as traffic cameras. Recent advances in mobile edge computing have made it possible to…
Real-time computational speed and a high degree of precision are requirements for computer-assisted interventions. Applying a segmentation network to a medical video processing task can introduce significant inter-frame prediction noise.…
Mobile smartphones along with embedded sensors have become an efficient enabler for various mobile applications including opportunistic sensing. The hi-tech advances in smartphones are opening up a world of possibilities. This paper…
This paper presents a motion data augmentation scheme incorporating motion synthesis encouraging diversity and motion correction imposing physical plausibility. This motion synthesis consists of our modified Variational AutoEncoder (VAE)…
Recently, two technologies: video analysis and mobile device sensors have considerable impacted Physics teaching. However, in general, these techniques are usually used independently. Here, we focus on a less-explored feature: the…
Recent advances in software-defined mobile networks (SDMNs), in-network caching, and mobile edge computing (MEC) can have great effects on video services in next generation mobile networks. In this paper, we jointly consider SDMNs,…
Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications, including intrusion detection, patient care, and smart home. Researchers have shown different…
Video generation is experiencing rapid growth, driven by advances in diffusion models and the development of better and larger datasets. However, producing high-quality videos remains challenging due to the high-dimensional data and the…