Mobile Device Applications Research Articles

Design of a Wireless, Inexpensive Ocean of Things System for Volunteer Bathymetry

Although much progress has been made in recent years to fully map the world ocean, only approximately 20% is adequately mapped to modern standards. Filling in the remainder must by necessity be a multi-modal effort, with traditional ocean mapping technologies such as crewed survey ships with multibeam echosounders being mixed with newer systems such as uncrewed, sail-powered mapping systems. Volunteer data from any ship with an echosounder can also be used, but although there have been commercial efforts in this field, most of these systems do not contribute data into the public arena and public entities have largely avoided this field due to complexities of costs, data processing, and uncertainty on how to handle the effort. This paper describes the design of an end-to-end system for managed volunteer bathymetric collection consisting of an inexpensive (~$20) wireless “ocean of things” data logger for NMEA0183 and NMEA2000 data, associated firmware to manage the collection, a mobile device application to off-load, aggregate, and transfer the data into the cloud, and a cloud segment to process the data and submit it to an international data repository. All of the design has been released under an Open Hardware or Open Source license, allowing independent organizations to initiate data collection efforts without having to do any of the design work themselves. The goal is to provide a simple, approachable implementation, encouraging greater adoption of these ideas in hard-to-reach areas of the world with minimal effort on the part of the host organization.

A real-time system to report abnormal events involving staff in a nuclear medicine therapy unit.

A system for internal and voluntary reporting of abnormal events in a Nuclear Medicine Therapy Unit is described. This system is based on the Internet of Things and is composed of an application for mobile devices and a wireless network of detectors. The application is addressed to healthcare professionals and is intended to be a user-friendly tool to make the reporting procedure little laborious. The network of detectors allows for a real-time measurement of the dose distribution in the patient's room. The staff was involved in all stages, from the design of the dosimetry system and mobile application up to their final testing. Face-to-face interviews were carried out with 24 operators in different roles in the Unit (radiation protection experts, physicians, physicists, nuclear medicine technicians and nurses). The preliminary results of the interviews and the current state of development of the application and the detection network will be described.

A Lightweight Attentional Shift Graph Convolutional Network for Skeleton-Based Action Recognition

In the field of skeleton-based human behavior recognition, graph convolutional neural networks have made remarkable achievements. However, high precision networks are often accompanied by numerous parameters and computational cost, and their application in mobile devices has considerable limitations. Aiming at the problem of excessive spatiotemporal complexity of high-accuracy methods, this paper further analyzes the lightweight human action recognition model and proposes a lightweight architecture attentional shift graph convolutional network. There are three main improvements in this model. Firstly, shift convolution is a lightweight convolution method that can be combined with graph convolution to effectively reduce its complexity. At the same time, a shallow architecture for multi-stream early fusion is designed to reduce the network scale by merging multi-stream networks and reducing the number of network layers. In addition, the efficient channel attention module is introduced into the model to capture the underlying characteristic information in the channel domain. Experiments are conducted on the three existing skeleton datasets, NTU RGB+D, NTU-120 RGB+D, and Northwestern-UCLA. Results demonstrate that the proposed model is not only competitive in accuracy, but also outperforms current mainstream methods in parameter count and computational cost, and supports running in some devices with limited computing and storage resources.

Gamification in the process of cognitive stimulation in children with Down syndrome

This research studies the use of new information and communication technologies for the benefit of children with Down syndrome, it is about the design and programming of an application for mobile devices that stimulates playful interactions with the resource of gamification that takes advantage of the technological potential of video games in the educational field. Its interface was designed using Adobe Animate software with Action script programming language, designing two-dimensional animated graphics that encourage affective, cognitive and behavioral processes such as animals, their habitat and food; it also incorporates scenarios and sounds of easy grouping to encourage their academic activity in attention, visual retention and cognitive stimulation. The methodology used to design the first prototype was a survey of  teachers, psychologists and language specialists of the Foundation for children with special abilities "San Miguel de Salcedo" FUNESAMI, who actively contributed in the development, structure, content and implementation of the application, which was evaluated with the active participation of 14 children with Down syndrome in average age of 6 to 12 years, The application was evaluated with the active participation of 14 children with Down syndrome with an average age of 6 to 12 years, finding remarkable improvements in the attention and retention that the child directs to the game, achieving an interaction that facilitates the teaching-learning process compared to the traditional method, thus contributing to the improvement of the quality of life and development of the child patient, thus complying with what is stated in the Constitution of Ecuador contributing to the good living.

Adaptation to the Context in M-Learning Application Using Mobile Design Patterns

The fast progress of mobile devices and the popularity of mobile applications in the market requires complex software to be run on mobile devices without issues. Mobile devices are known as handhelds and they have different characteristics such as size, resolution, battery life and processing power. These are limitations to consider when developing an M-learning application, which can make it difficult to design and develop mobile applications for different mobile devices. Design Patterns, a set of tried and tested solutions for common problems were originally designed for desktop computers, so application of these patterns in M-learning applications might not meet its requirements such as adaptation of the content to the context of the device mobile used by learners. However, mobile design patterns that have been adapted or designed for such an environment can be used to overcome the challenges and limitations of M-learning application development. In this paper we try to propose an approach to engineering design patterns allowing the development of M-learning applications to provide an adapted content taking into account the context of device mobile used by learners. We named these patterns: “Mobile design patterns”.

.NET Tools for Software Development: Tool Selection, Key Benefits of .NET Web Applications

Aims: This report analysis the top 5 .NET tools that can be used to develop website and software.
 Study Design: We have shortlisted the 5 .NET tools that can be used to design and implement website and software.
 Methodology: According to Microsoft, .NET is a software-based approach for integrating information, users, technologies, and devices, and it encompasses everything that an organization requires to build and deploy an integrated data architecture: servers used to host services; development tools; and apps that utilize them. We have selected Web essential for Visual studio, Resharper, Ncrunch, NuGet and Elmah tools.
 Results: The exposed qualities connect to modern emerging techniques such as components, allocation, software and services, and online redirection. As a result of the available options, .NET is recognized as one of the most extensively used software development platforms. The framework .NET's of devices and technologies as well as their optimal usage in practice, need knowledge with its tool set.
 Conclusion: The NET-framework offers a variety of software types, with a focus on producing apps for Windows, mobile devices, and the Internet. Windows applications can be terminal (text-based) or graphical, including drivers, libraries, and services. Applications for mobile devices are created for smartphones and tablets. ASP.NET web services and web applications written for the Internet.

Improvement of GHz Range Electromagnetic Properties of Ferrite Thin Film Magnetic Core Integrated RF Inductor

Ferrite thin film magnetic core integrated radio frequency inductors (RFIs) were investigated in order to realize RFIs with a smaller size, higher driving frequency, and higher quality factor (Q). The ferrite thin films were fabricated by low-temperature spin-spray deposition assuming adaptation to the photolithography process for inductor fabrication with two different compositions: Ni0.16Zn0.20Fe2.64O4+δ (NZFO) and Co0.11Fe2.89O4+δ (CFO). The RFI with CFO had a 34% higher inductance and 43% Q improvement compared with the air core RFI at a frequency of 1 GHz. It was confirmed that the complex permeability of the films was correlated with the frequency characteristics of the RFIs with the ferrite thin films. The results suggest that integrating ferrite thin film in RFIs is effective for future mobile device applications.

A framework for designing interactive mobile training course content using augmented reality.

Because mobile technology and the widespread usage of mobile devices have swiftly and radically evolved, several training centers have started to offer mobile training (m-training) via mobile devices. Thus, designing suitable m-training course content for training employees via mobile device applications has become an important professional development issue to allow employees to obtain knowledge and improve their skills in the rapidly changing mobile environment. Previous studies have identified challenges in this domain. One important challenge is that no solid theoretical framework serves as a foundation to provide instructional design guidelines for interactive m-training course content that motivates and attracts trainees to the training process via mobile devices. This study proposes a framework for designing interactive m-training course content using mobile augmented reality (MAR). A mixed-methods approach was adopted. Key elements were extracted from the literature to create an initial framework. Then, the framework was validated by interviewing experts, and it was tested by trainees. This integration led us to evaluate and prove the validity of the proposed framework. The framework follows a systematic approach guided by six key elements and offers a clear instructional design guideline checklist to ensure the design quality of interactive m-training course content. This study contributes to the knowledge by establishing a framework as a theoretical foundation for designing interactive m-training course content. Additionally, it supports the m-training domain by assisting trainers and designers in creating interactive m-training courses to train employees, thus increasing their engagement in m-training. Recommendations for future studies are proposed.

From Risk Assessment on Site to How to Improve Safety: An Easy “App” to Control Construction Site Conditions

A construction site has specific risks and organizational conditions requiring on-site safety inspections. Paperwork inspections have important limitations that can be overcome by substituting paper records with digital registers and using new information and communication technologies. Although academic literature has provided several tools to carry out on-site safety inspections adopting new technologies, most construction sites are not currently ready to adopt them. This paper covers this need of on-site control by providing an application that uses a simple technology accessible to most construction companies. The main objective and contribution of this paper is to design, develop, and implement a mobile device application (App), named "RisGES". It is based on the model of risk that grounds the Construction Site Risk Assessment Tool (CONSRAT) and on the related models that connect risk with specific organizational and safety resources. This proposed application is aimed to assess the on-site risk and organizational structure by using new technologies and considering all relevant resources and material safety conditions. The paper includes practical examples of how to use RisGES in real settings. Evidence for the discriminant validity of CONSRAT is also provided. The RisGES tool is at once preventive and predictive since it yields a specific set of criteria for interventions intended to decrease the levels of risks on-site, as well as to detect improvement needs in the site structure and resources for increasing the safety levels.

Higher education peer mentoring programme to promote student community using Mobile Device Applications

This paper presents an Italian peer mentoring programme implemented by second year undergraduate students (peer mentors) to support first years (mentees). The aim of the peer mentoring was to promote student community building and endorse active learning. We recruited 19 mentors and 32 mentees into the programme. Data was extracted through the use of semi-structed questionnaires, which were exposed to qualitative analysis. Peer mentors decided to implement their peer mentoring activities using Mobile Device Applications [MDA]. A qualitative content analysis was employed to evaluate student’s perception. Our findings suggest that students were satisfied with the adoption of MDA to foster active learning for, and community building between, undergraduate students. However, participants have highlighted criticisms and improvements.

Assuring quality in assisted reproduction laboratories: assessing the performance of ART Compass - a digital art staff management platform.

Staff management is the most cited ART/IVF laboratory inspection deficiency. Small ART/IVF clinics may be challenged to perform these activities by low staff volume; similarly, large ART/IVF networks may be challenged by high staff volume and large datasets. Here, we sought to investigate the performance of an automated, digital platform solution to manage this necessary task. The ART Compass (ARTC) digital staff management platform was used to assess the clinical decision-making of ART laboratory staff. The survey modules presented standardized instructions to technologists and measured inter- and intra-technologist variability for subjective "clinical decision-making" type questions. Internal and external comparisons were achieved by providing technologists two answers: (1) a comparison to their own lab director and (2) to the most popular response collectively provided by all lab director level accounts. The platform is hosted on HIPAA compliant Amazon web servers, accessible via web browser and mobile applications for iOS (Apple) and Android mobile devices. Here, we investigated the performance of a digital staff management platform for single embryologist IVF practices and for three IVF lab networks (sites A, B, C) from 2020 to 2022. Embryology dish preparation survey results show variance among respondents in the following: PPE use, media volume, timing of oil overlay, and timing of moving prepared dishes to incubators. Surveying the perceived Gardner score and terms in use for early blastocysts reveals a lack of standardization of terminology and fair to poor agreement. We observed moderate inter-technologist agreement for ICM and TE grade (0.47 and 0.52, respectively). Lastly, the clinical decision of choice to freeze or discard an embryo revealed that agreement to freeze was highest for the top-quality embryos, and that some embryos can be highly contested, evenly split between choice to freeze or discard. We conclude that a digital platform is a novel and effective tool to automate, routinely monitor, and assure quality for staff-related parameters in ART and IVF laboratories. Use of a digital platform can increase regulatory compliance and provide actionable insight for quality assurance in both single embryologist practices and for large networks. Furthermore, clinical decision-making can be augmented with artificial intelligence integration.

ConvNeXtPose: A Fast Accurate Method for 3D Human Pose Estimation and Its AR Fitness Application in Mobile Devices

ConvNeXtPose: A Fast Accurate Method for 3D Human Pose Estimation and Its AR Fitness Application in Mobile Devices

Youth Vote, applications in mobile devices and Independent Candidacy ́s on Presidential Elections of 2018

El proceso electoral de 2018 se distinguió por la integración de candidatos independientes a la presidencia de la república, así como la implementación por parte del Instituto Nacional Electoral (INE) de una aplicación móvil para registrar firmas de apoyo a dichos candidatos. El objetivo de este estudio fue conocer la información que disponían los jóvenes acerca del apoyo a los candidatos independientes que participaron en el proceso electoral 2018. Para ello se realizó una investigación cuantitativa, contando con una muestra no probabilística y de conveniencia de 264 alumnos de licenciatura. En este estudio se logró identificar el evidente desconocimiento sobre las candidaturas independientes y la App del INE. Para ello, se realizaron algunas pruebas de contraste como el análisis de regresión múltiple lineal y de regresión logística multinomial.

DeepEdge: A Deep Reinforcement Learning Based Task Orchestrator for Edge Computing

The improvements in the edge computing technology pave the road for diversified applications that demand real-time interaction. However, due to the mobility of the end-users and the dynamic edge environment, it becomes challenging to handle the task offloading with high performance. Moreover, since each application in mobile devices has different characteristics, a task orchestrator must be adaptive and have the ability to learn the dynamics of the environment. For this purpose, we develop a deep reinforcement learning based task orchestrator, DeepEdge, which learns to meet different task requirements without needing human interaction even under the heavily-loaded stochastic network conditions in terms of mobile users and applications. Given the dynamic offloading requests and time-varying communication conditions, we successfully model the problem as a Markov process and then apply the Double Deep Q-Network (DDQN) algorithm to implement DeepEdge. To evaluate the robustness of DeepEdge, we experiment with four different applications including image rendering, infotainment, pervasive health, and augmented reality in the network under various loads. Furthermore, we compare the performance of our agent with the four different task offloading approaches in the literature. Our results show that DeepEdge outperforms its competitors in terms of the percentage of satisfactorily completed tasks.

Solid state lithium-ion rechargeable batteries: An overview

Rechargeable solid-state Li-ion batteries have potential for applications in mobile devices and electric vehicles in the near future to meet the growing demand for high energy storage. Research on rechargeable solid-state Li-ion batteries has a long history and has been accelerating recently. Solid electrolytes are the most important component in the all-solid-state batteries. Solid electrolytes can be divided into the following groups: oxide groups (Perovskite Li3.3La0.56TiO3, NASICON LiTi2(PO4)3, LISICON Li14Zn(GeO4)4, and Li7La3Zr2O12 garnets), sulfide groups (Li2S – P2S5 and Li2S – P2S5 – MxS), hydride group (LiBH4, LiBH4–LiX (X=Cl, Br or I), LiBH4–LiNH2, LiNH2, Li3AlH6 and Li2NH), halogen group (LiI, spinel Li2ZnI4 and anti-perovskite Li3OCl), and polymer group (mainly polyethylene). Although electrolytes with good ionic conductivity have been used, the performance of solid-state rechargeable Li-ion batteries is still far behind that of the ones using liquid electrolytes. Along with the development of science and technology, many scientific and technical problems in solid-state rechargeable Li-ion batteries have been discovered. In this review, the major issues of solid-state rechargeable Li-ion batteries will be breifly documented: the interface between the active material (AM) and the solid electrolyte (SE), aging of the solid-solid interface, electrode structure, and fabrication methods.

Citizen science data validates aerial imagery to track the ‘rise and fall’ of woody vegetation through extremes of climate

Context Ground truthing remotely sensed imagery for detecting changes in wetland vegetation can be time-consuming and costly for monitoring. Harnessing the resources of citizen scientists (CS) using mobile devices has been under utilised in Australia. Aims The project aimed to test the feasibility and practicality of using CS to collect data using mobile devices to ground truth remotely sensed imagery. Methods Using high-resolution aerial imagery, we detected the establishment of woody vegetation over a 20-year dry phase from 2000 to 2020 in Thirlmere Lakes National Park, NSW, Australia. To ground truth these woody species, we engaged with a local community group using a customised, freely available mobile device application. Key results During the dry event of 2020, CS documented well-established woody species, such as Melaleuca linariifolia (flax-leaved paperbark), amongst the Lepironia articulata grey rush. With the La Niña wet events in early 2020–22 and subsequent higher water levels, the CS documented the survival of M. linariifolia but the dieback of eucalypts, and other woody species. Conclusions Observations at higher temporal frequencies by CS using mobile devices, augmented with researchers’ observations, proved to be a valuable, quality-controlled method to ground truth high-resolution aerial imagery. Implications This case study showed that monitoring the phenology of vegetation in a peat wetland can be supplemented by the inclusion of a CS programme. This under-utilised resource can increase coverage and frequency of data observations, lower costs as well as create community awareness, capability and engagement in scientific research.

Exploring the relationship between social media knowledge platform (SMKP) using smart devices and the impact on students’ academic performances

This research study is an attempt to investigate and recommend the applications and usefulness of social media as a knowledge sharing platform using smart mobile devices and students’ academic performances. Research literature reveals that further examination of this idea is a must as it is increasingly becoming an integral part and a current mandate for digital-era learners. This study is exploratory based on learners’ data which includes students from foundation, junior and senior courses at a university. The objective was to cognize learners’ perceptions on collaborative learning while using applications of social media knowledge platform and smart mobile devices. Structural Model approach is followed to validate the data and undertake the required analysis. The resulting analysis shows that a significantly positive relation exists between the available platforms of knowledge applications using smart devices and its substantial impact on students’ overall performances. In addition, it could be observed that generally students have positive and valuable perception on the use of social media knowledge platform and smart mobile devices in collaborative learning settings. Moreover, such settings encourage students-students’ interactions, studentsteachers’ interactions, and the growing trends of students to online experts’ interactions. Furthermore, it was noticed that these interactions improve students’ engagement with such platforms using smart devices, which consequently has significant positive impact on students’ academic performances. The findings propose the inculcation in students the positive role of the use of social media knowledge platforms using smart devices so to encourage collaborative learning, to enhance active learning, to be more dynamic & engaging, to broaden their domain-knowledge, and to promote academic researchoriented learning, which are all crucial for graduate employability.

Beamwidth Enhancement of Microstrip Antennas Using Capacitive Via-Fence Loading

In this paper, a general approach to enhance the beamwidth of microstrip antennas is proposed for wide beam coverage in both E- and H-planes. A microstrip antenna loaded with two arrays of capacitive via fences is propounded and systematically studied. By introducing the vertical currents brought by the capacitive metalized vias, the half-power beamwidth (HPBW) is effectively broadened compared with the regular microstrip antennas. In addition, by utilizing the air medium with low loss, the microstrip antenna can be supported by the two arrays of via fences, maintaining a high radiation efficiency. To validate the proposed design, a prototype is fabricated and tested. The measurement results agree well with the simulated ones, with enhanced HPBWs of 100° and 90° in E- and H-planes, respectively. Compared with the existing 2.4-GHz antennas, the propounded antenna is with the advantages of wide beamwidth and high radiation efficiency, exhibiting the potential applications for space-limited mobile devices with wide coverage requirement.

A Security Application for Smart Phone and Mobile Device

A Security Application for Smart Phone and Mobile Device

Predicting and Visualizing Citrus Color Transformation Using a Deep Mask-Guided Generative Network

Citrus rind color is a good indicator of fruit development, and methods to monitor and predict color transformation therefore help the decisions of crop management practices and harvest schedules. This work presents the complete workflow to predict and visualize citrus color transformation in the orchard featuring high accuracy and fidelity. A total of 107 sample Navel oranges were observed during the color transformation period, resulting in a dataset containing 7,535 citrus images. A framework is proposed that integrates visual saliency into deep learning, and it consists of a segmentation network, a deep mask-guided generative network, and a loss network with manually designed loss functions. Moreover, the fusion of image features and temporal information enables one single model to predict the rind color at different time intervals, thus effectively shrinking the number of model parameters. The semantic segmentation network of the framework achieves the mean intersection over a union score of 0.9694, and the generative network obtains a peak signal-to-noise ratio of 30.01 and a mean local style loss score of 2.710, which indicate both high quality and similarity of the generated images and are also consistent with human perception. To ease the applications in the real world, the model is ported to an Android-based application for mobile devices. The methods can be readily expanded to other fruit crops with a color transformation period. The dataset and the source code are publicly available at GitHub.