scholarly journals A Comparison of Java, Flutter and Kotlin/Native Technologies for Sensor Data-Driven Applications

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3324
Author(s):  
Kamil Wasilewski ◽  
Wojciech Zabierowski
Keyword(s):  
Resource Utilization ◽  
Mobile Applications ◽  
Mobile Technologies ◽  
Data Driven ◽  
Sensor Data ◽  
Fast Growing ◽  
Android Os ◽  
Continuous Progress ◽  
Choice Of Technology

As a result of the continuous progress and fast-growing popularity of mobile technologies in recent years, the demand for mobile applications has increased rapidly. One of the most important decisions that its developers have to make is the choice of technology on which their application will be based. This article is devoted to the comparison of Java, Flutter, and Kotlin/Native technologies for applications based on processing and analyzing data from sensors. The main elements of the comparison are the efficiency and resource utilization of mobile applications for Android OS implemented in each of the aforementioned technologies.

Developing and Validating a M-Governance Framework Adoption Model of a University for Communication Services to Its Affiliated Institutions

2017 ◽  
Vol 7 (7) ◽  
pp. 484
Author(s):  
E. Ramganesh ◽  
E. Kirubakaran ◽  
D. Ravindran ◽  
R. Gobi
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Public Service ◽  
Mobile Applications ◽  
Communication Technologies ◽  
Mobile Technologies ◽  
Communication Services ◽  
Governance Framework ◽  
Social And Economic Development ◽  
Acceptance Model

The m-Governance framework of auniversity aims to utilize the massive reach of mobile phones and harness the potential of mobile applications to enable easy and round the-clock access to the services of its affiliated institutions.  In the current mobile age there is need for transforming e-governance services to m-Governance as m-Governance is not a replacement for e-Governance rather it complements e-Governance. With this unparalleled advancement of mobile communication technologies, universities are turning to m-governance to realize the value of mobile technologies for responsive governance and measurable improvements to academic, social and economic development, public service delivery, operational efficiencies and active stakeholder engagement. In this context the present study, aims to develop and validate a m-governance framework of a university by extending Technology Acceptance Model (TAM) with its prime stakeholders so called the Heads of the affiliated institutions. A survey instrument was developed based on the framework and it was administered with 20 Heads of the affiliated Institutions. The results also showed that the Heads of the affiliated Institutions expressed their favorableness towards m-governance adoption.

scholarly journals Gamification-less: may gamification really foster civic participation? A controlled field experiment

2021 ◽  
Author(s):  
Marco Romano ◽  
Paloma Díaz ◽  
Ignacio Aedo
Keyword(s):  
Crisis Management ◽  
Mobile Applications ◽  
Civic Participation ◽  
Mobile Technologies ◽  
Design Experiment ◽  
Decision Making Process ◽  
Group Design ◽  
In The Wild ◽  
Open Issue ◽  
Smart Communities

AbstractIn the context of smart communities, it is essential an active and continuous collaboration between citizens, organizations and institutions. There are several cases where citizens may be asked to participate such as in public decision-making process by informing, voting or proposing projects or in crisis management by sharing precise and timely information with other citizens and emergency organizations. However, these opportunities do not automatically result in participatory practices sustained over time. Mobile technologies and social networks provide the substratum for supporting formal empowerment, but citizen engagement in participation processes is still an open issue. One of the techniques used to improve engagement is gamification based on the humans’ predisposition to games. So far, we still lack studies that can prove the advantage of gamified systems respect to non-gamified ones in civic participation context. In this work, we present a between-group design experiment performed in the wild using two mobile applications enabling civic participation, one gamified and the other not. Our results highlight that the gamified application generates a better user experience and civic engagement.

Extracting useful signals from flawed sensor data: Developing hybrid data-driven approaches with physical factors

2020 ◽  
Vol 185 ◽  
pp. 116282
Author(s):  
Cheng Yang ◽  
Glen T. Daigger ◽  
Evangelia Belia ◽  
Branko Kerkez
Keyword(s):  
Data Driven ◽  
Sensor Data ◽  
Physical Factors ◽  
Hybrid Data

Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems

2020 ◽  
pp. 1-1
Author(s):  
Naipeng Li ◽  
Yaguo Lei ◽  
Nagi Gebraeel ◽  
Zhijian Wang ◽  
Xiao Cai ◽  
...  
Keyword(s):  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Sensor Data ◽  
Useful Life

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

scholarly journals Teaching and Learning Physics with Smartphones

2015 ◽  
Vol 17 (1) ◽  
pp. 31-50 ◽  
Author(s):  
M. Á. González ◽  
Manuel Á. González ◽  
M. Esther Martín ◽  
César Llamas ◽  
Óscar Martínez ◽  
...  
Keyword(s):  
Mobile Applications ◽  
Teaching And Learning ◽  
Low Cost ◽  
Positive Influence ◽  
Mobile Technologies ◽  
Traditional Learning ◽  
Know How ◽  
Measurement Devices ◽  
The One ◽  
Physics Experiments

The use of mobile technologies is reshaping how to teach and learn. In this paper the authors describe their research on the use of these technologies to teach physics. On the one hand they develop mobile applications to complement the traditional learning and to help students learn anytime and anywhere. The use of this applications has proved to have very positive influence on the students' engagement. On the other hand, they use smartphones as measurement devices in physics experiments. This opens the possibility of designing and developing low cost laboratories where expensive material can be substituted by smartphones. The smartphones' sensors are reliable and accurate enough to permit good measurements. However, as it is shown with some examples, special care must be taken here if one does not know how these apps used to access the sensors' data are programmed.

scholarly journals A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

2020 ◽  
Author(s):  
Ahlam Mallak ◽  
Madjid Fathi
Keyword(s):  
Random Forest ◽  
Random Search ◽  
Hybrid Approach ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Sensor Data ◽  
Sensor Systems ◽  
Hydraulic Test ◽  
Model Based ◽  
Detection And Diagnosis

In this work, A hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, spotting the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using 3-fold cross-validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. Followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data, in order to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, as well as the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time series multivariate sensor readings.

scholarly journals Using M-Learning Technology in Teaching Foreign Languages: A Panacea during COVID-19 Pandemic Era

2021 ◽  
Vol 15 (15) ◽  
pp. 20
Author(s):  
Elena Dolzhich ◽  
Svetlana Dmitrichenkova ◽  
Mona Kamal Ibrahim
Keyword(s):  
Higher Education ◽  
Distance Learning ◽  
Foreign Language ◽  
Mobile Learning ◽  
Mobile Applications ◽  
Online Survey ◽  
Learning Technologies ◽  
Mobile Technologies ◽  
Foreign Languages ◽  
Learning Technology

<p class="0abstract">The higher education system around the world is being rapidly developed towards digitalization – from computers to laptops, from laptops to tablets and smartphones. Accordingly, traditional delivery of instruction is being shifted towards blended learning that is being gradually replaced with distance learning, i.e. higher education is moving forward with mobile learning (m-learning) technologies. The introduction of mobile learning became the most topical event in 2020 in the context of the COVID-19 pandemic, due to which many countries had to completely move to distance learning in higher education. The purpose of the study is to analyze the prospects for the widespread use of mobile applications in teaching English as a foreign language (EFL) in Russia to Russian and Arab learners. In the course of the study, an online survey based on a questionnaire consisting of four open and closed questions was conducted. An empirical method was applied to collect the research data.  The survey was conducted at the Department of Foreign Languages of the Engineering Academy of the Peoples' Friendship University of Russia (EA PFUR). The total research sample included 200 participants and consisted of: 50 potential employers, 50 Russian and Arab students of the Peoples' Friendship University of Russia studying Linguistics (training program code 035700), 50 faculty members, namely teachers of the Peoples' Friendship University of Russia, the Institute of Foreign Languages of the Moscow State Pedagogical University and the Moscow Institute of Physics and Technology, as well as 50 administrative staff of the Peoples' Friendship University of Russia. The purpose of the survey was to collect information about the use of mobile applications (Smartphone Apps) and the introduction of mobile learning technology (m-learning) in the process of teaching EFL to students. According to the results of the survey, instructors are actively using mobile technologies in their professional activities and all participants in the learning process are receptive to their introduction in education. At the same time, respondents believe that technical challenges are the major obstacle to the adoption of mobile applications; these problems must be overcome in order to enable more productive use of mobile applications. In this regard, the study of mobile applications that are suitable for specific aspects of learning a foreign language, such as spoken language, reading comprehension, listening or writing, can be considered a promising research area.</p>

Integrating Rule-Based Systems and Data Analytics Tools Using Open Standard PMML

2015 ◽  
Author(s):  
Yunpeng Li ◽  
Utpal Roy ◽  
Y. Tina Lee ◽  
Sudarsan Rachuri
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Analytics ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Data Driven ◽  
Sensor Data ◽  
Formal Representation ◽  
Rule Based ◽  
Open Standard

Rule-based expert systems such as CLIPS (C Language Integrated Production System) are 1) based on inductive (if-then) rules to elicit domain knowledge and 2) designed to reason new knowledge based on existing knowledge and given inputs. Recently, data mining techniques have been advocated for discovering knowledge from massive historical or real-time sensor data. Combining top-down expert-driven rule models with bottom-up data-driven prediction models facilitates enrichment and improvement of the predefined knowledge in an expert system with data-driven insights. However, combining is possible only if there is a common and formal representation of these models so that they are capable of being exchanged, reused, and orchestrated among different authoring tools. This paper investigates the open standard PMML (Predictive Model Mockup Language) in integrating rule-based expert systems with data analytics tools, so that a decision maker would have access to powerful tools in dealing with both reasoning-intensive tasks and data-intensive tasks. We present a process planning use case in the manufacturing domain, which is originally implemented as a CLIPS-based expert system. Different paradigms in interpreting expert system facts and rules as PMML models (and vice versa), as well as challenges in representing and composing these models, have been explored. They will be discussed in detail.

Sensor Data-Driven UAV Anomaly Detection using Deep Learning Approach

2021 ◽  
Author(s):  
Julio Galvan ◽  
Ashok Raja ◽  
Yanyan Li ◽  
Jiawei Yuan
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Data Driven ◽  
Sensor Data ◽  
Learning Approach
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