Mobile device ≈ Mobile device
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Open
We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks…
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Communication-Efficient Learning of Deep Networks from Decentralized Data Open
Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image …
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Federated Learning with Non-IID Data Open
Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provide…
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Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices Open
Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e…
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The effects of integrating mobile devices with teaching and learning on students' learning performance: A meta-analysis and research synthesis Open
Mobile devices such as laptops, personal digital assistants, and mobile phones have become a learning tool with great potential in both classrooms and outdoor learning. Although there have been qualitative analyses of the use of mobile dev…
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Federated Learning of Deep Networks using Model Averaging Open
Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image …
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Driver crash risk factors and prevalence evaluation using naturalistic driving data Open
Significance This paper presents findings about the riskiest factors faced by drivers as informed through the first large-scale, crash-only analysis of naturalistic driving data. Results indicate that many secondary tasks or activities, pa…
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ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices Open
We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, po…
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Mobile apps in trauma education Open
Mobile apps are becoming popular educational tools due to their accessibility, interactivity and ability to provide personalised learning. Numerous apps are now available for healthcare professionals, including in the area of medical educa…
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Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks Open
Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mech…
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Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning Open
Unmanned aerial vehicles (UAVs) have been recently considered as means to provide enhanced coverage or relaying services to mobile users (MUs) in wireless systems with limited or no infrastructure. In this paper, a UAV-based mobile cloud c…
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Delay-optimal computation task scheduling for mobile-edge computing systems Open
Mobile-edge computing (MEC) emerges as a promising paradigm to improve the quality of computation experience for mobile devices. Nevertheless, the design of computation task scheduling policies for MEC systems inevitably encounters a chall…
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A Survey of Wearable Devices and Challenges Open
As smartphone penetration saturates, we are witnessing a new trend in personal mobile devices-wearable mobile devices or simply wearables as it is often called. Wearables come in many different forms and flavors targeting different accesso…
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UAV-Based IoT Platform: A Crowd Surveillance Use Case Open
Unmanned aerial vehicles (UAVs) are used to provide diverse civilian, commercial, and governmental services. In addition to their original tasks, UAVs can also be used to offer numerous value-added internet of things services (VAIoTSs). Sh…
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Compression of Deep Convolutional Neural Networks for Fast and Low Power\n Mobile Applications Open
Although the latest high-end smartphone has powerful CPU and GPU, running\ndeeper convolutional neural networks (CNNs) for complex tasks such as ImageNet\nclassification on mobile devices is challenging. To deploy deep CNNs on mobile\ndevi…
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Once-for-All: Train One Network and Specialize it for Efficient\n Deployment Open
We address the challenging problem of efficient inference across many devices\nand resource constraints, especially on edge devices. Conventional approaches\neither manually design or use neural architecture search (NAS) to find a\nspecial…
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Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification Open
Federated Learning enables visual models to be trained in a privacy-preserving way using real-world data from mobile devices. Given their distributed nature, the statistics of the data across these devices is likely to differ significantly…
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A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects Open
The Augmented Reality (AR) technology enhances the human perception of the world by combining the real environment with the virtual space. With the explosive growth of powerful, less expensive mobile devices, and the emergence of sophistic…
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Mobile Health Apps on COVID-19 Launched in the Early Days of the Pandemic: Content Analysis and Review Open
Background Mobile health (mHealth) app use is a major concern because of the possible dissemination of misinformation that could harm the users. Particularly, it can be difficult for health care professionals to recommend a suitable app fo…
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Digital technology for treating and preventing mental disorders in low-income and middle-income countries: a narrative review of the literature Open
Few individuals living with mental disorders around the globe have access to mental health care, yet most have access to a mobile phone. Digital technology holds promise for improving access to, and quality of, mental health care. We revie…
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Behavioral Functionality of Mobile Apps in Health Interventions: A Systematic Review of the Literature Open
The lack of large sample studies using mobile phone apps may signal a need for additional studies on the potential use of mobile apps to assist individuals in changing their health behaviors. Of these studies, there is early evidence that …
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Blockchain for Secure EHRs Sharing of Mobile Cloud Based E-Health Systems Open
Recent years have witnessed a paradigm shift in the storage of Electronic Health Records (EHRs) on mobile cloud environments, where mobile devices are integrated with cloud computing to facilitate medical data exchanges among patients and …
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A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities Open
Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants …
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MediaPipe Hands: On-device Real-time Hand Tracking Open
We present a real-time on-device hand tracking pipeline that predicts hand skeleton from single RGB camera for AR/VR applications. The pipeline consists of two models: 1) a palm detector, 2) a hand landmark model. It's implemented via Medi…
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Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables Open
The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advan…
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The Use of Mobile Apps and SMS Messaging as Physical and Mental Health Interventions: Systematic Review Open
The review shows the promising and emerging efficacy of using mobile apps and SMS text messaging as mHealth interventions.
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A Systematic Review of Reviews Evaluating Technology-Enabled Diabetes Self-Management Education and Support Open
Technology-enabled diabetes self-management solutions significantly improve A1c. The most effective interventions incorporated all the components of a technology-enabled self-management feedback loop that connected people with diabetes and…
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Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing Open
Computation off-loading in mobile edge computing (MEC) systems constitutes an efficient paradigm of supporting resource-intensive applications on mobile devices. However, the benefit of MEC cannot be fully exploited, when the communication…
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Edge Assisted Real-time Object Detection for Mobile Augmented Reality Open
Most existing Augmented Reality (AR) and Mixed Reality (MR) systems are able to understand the 3D geometry of the surroundings but lack the ability to detect and classify complex objects in the real world. Such capabilities can be enabled …
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A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations Open
The commercial market for technologies to monitor and improve personal health and sports performance is ever expanding. A wide range of smart watches, bands, garments, and patches with embedded sensors, small portable devices and mobile ap…