A location-aware framework for intelligent real-time mobile applications

2011 ◽  
Vol 10 (3) ◽  
pp. 58-67 ◽  
Author(s):  
Sean J. Barbeau ◽  
Rafael A. Perez ◽  
Miguel A. Labrador ◽  
Alfredo J. Perez ◽  
Philip L. Winters ◽  
...  
Keyword(s):  
Real Time ◽  
Mobile Applications ◽  
Location Aware

scholarly journals Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps

2019 ◽  
Vol 1 (1) ◽  
pp. 450-465 ◽  
Author(s):  
Abhishek Sehgal ◽  
Nasser Kehtarnavaz
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Open Source Software ◽  
Mobile Applications ◽  
Network Models ◽  
Software Tools ◽  
Learning Tools ◽  
Smartphone Apps ◽  
Learning Models ◽  
Neural Network Models

Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools toward real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A uniform flow of implementation is devised for both Android and iOS smartphones. The advantage of using multi-threading to achieve or improve real-time throughputs is also showcased. A benchmarking framework consisting of accuracy, CPU/GPU consumption, and real-time throughput is considered for validation purposes. The developed deployment approach allows deep learning models to be turned into real-time smartphone apps with ease based on publicly available deep learning and smartphone software tools. This approach is applied to six popular or representative convolutional neural network models, and the validation results based on the benchmarking metrics are reported.

A Collaborative Augmented Campus Based on Location-Aware Mobile Technology

2012 ◽  
Vol 10 (1) ◽  
pp. 55-73 ◽  
Author(s):  
A. De Lucia ◽  
R. Francese ◽  
I. Passero ◽  
G. Tortora
Keyword(s):  
Augmented Reality ◽  
Mobile Applications ◽  
Collaborative Environments ◽  
Content Areas ◽  
Student Collaboration ◽  
Wall Area ◽  
Content Sharing ◽  
Augmented Reality System ◽  
Location Aware ◽  
Or Groups

Mobile devices are changing the way people work and communicate. Most of the innovative devices offer the opportunity to integrate augmented reality in mobile applications, permitting the combination of the real world with virtual information. This feature can be particularly useful to enhance informal and formal didactic actions based on student collaboration. This paper describes a “collaborative campus”, originated in the physical architectural space, but exposing learning contents and social information structured as augmented virtual areas. ACCampus, a mobile augmented reality system, supporting the sharing of contextualized information is proposed. This system combines the world perceived by the phone camera with information concerning student location and community, enabling users to share multimedia information in location-based content areas. User localization is initially detected through QR codes. The successive positions of the user are determined using the mobile device sensors. Each augmented area is univocally spatially associated to a representative real wall area. Selective content sharing and collaboration are supported, enabling a user to distribute his/her augmented contents to specific users or groups. An evaluation of the proposed environment is also conducted, which considers that learning in collaborative environments is related to perceived member contribution, enjoinment, motivation, and student participation.

Digital health application for real-time patient-reported outcomes during prostate radiotherapy.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 157-157 ◽  
Author(s):  
Daniel Xiao Yang ◽  
Jackson Thea ◽  
Yi An ◽  
James B. Yu
Keyword(s):  
Quality Of Life ◽  
Real Time ◽  
Mobile Applications ◽  
Mobile Application ◽  
Patient Reported Outcomes ◽  
Digital Health ◽  
Prostate Radiotherapy ◽  
Patient Reported

157 Background: The use of digital health technology, including mobile applications, in the clinical setting is becoming increasingly more prevalent. Such technology is currently being explored as clinical research tools. While the side effects of prostate radiotherapy are well documented after treatment, there remains a paucity of data on patient-reported outcomes and changes in quality of life (QOL) during the treatment period. Therefore, mobile applications represent a practical platform to enable patient reporting in real-time during prostate radiotherapy. Methods: Using an existing open source code framework (Apple ResearchKit), we developed a novel mobile application that enables prostate cancer patients to report, either during or immediately following daily radiation treatment, changes in urinary, bowel, sexual, and hormonal QOL domains. The mobile application utilizes validated questions from the Expanded Prostate Index Composite for Clinical Practice (EPIC-CP) Survey, and allows for survey responses to be tracked over time throughout the treatment period and at routine follow up. Results: For the initial phase of our study, we are currently piloting the mobile application at a single institution with a goal of accruing 50 patients. Study results will be compared to data from traditional surveys, which are available at follow-up but impracticable for real-time symptom reporting. By ASCO 2016 Genitourinary Cancers Symposium, we plan to begin the second phase of our study where any patient can enroll online through a mobile software distribution platform (Apple App Store). Conclusions: We demonstrate the feasibility of using a mobile application to enable patients to report quality of life changes in real-time during prostate radiotherapy. Moreover, our application facilitates clinical trials where patient data collection can be automated and completed at scale. Future prospective studies are planned to evaluate validity of clinical trial data gathered through such methodology.

scholarly journals Effective Schemes for Place Name Annotations with Mobile Crowd

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Yao-Chung Fan ◽  
Hsueh-Wen Tseng
Keyword(s):  
Information Systems ◽  
Information Sharing ◽  
Real Time ◽  
Mobile Devices ◽  
Mobile Applications ◽  
Access Point ◽  
Research Community ◽  
Geospatial Information ◽  
Mobile Crowd ◽  
Geospatial Information Systems

With the popularity of mobile devices, numerous mobile applications have been and will continue to be developed for various interesting usage scenarios. Riding this trend, recent research community envisions a novel information retrieving and information-sharing platform, which views the users with mobile devices, being willing to accept crowdsourcing tasks ascrowd sensors. With the neat idea, a set of crowd sensors applications have emerged. Among the applications, the geospatial information systems based on crowd sensors show significant potentials beyond traditional ones by providing real-time geospatial information. In the applications, user positioning is of great importance. However, existing positioning techniques have their own disadvantages. In this paper, we study using pervasive Wi-Fi access point as user position indicators. The major challenge for using Wi-Fi access point is that there is no mechanism for mapping observed Wi-Fi signals to human-defined places. To this end, our idea is to employ crowdsourcing model to perform place name annotations by mobile participants to bridge the gap between signals and human-defined places. In this paper, we propose schemes for effectively enabling crowdsourcing-based place name annotation, and conduct real trials with recruited participants to study the effectiveness of the proposed schemes. The experiment results demonstrate the effectiveness of the proposed schemes over existing solutions.

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