scholarly journals Penerapan Internet Of Things (IoT) Untuk Kontrol Lampu Menggunakan Arduino Berbasis Web

2019 ◽  
Vol 5 (1) ◽  
pp. 9-16
Author(s):  
Budi Artono ◽  
Rakhmad Gusta Putra
Keyword(s):  
Internet Of Things ◽  
Mobile Phone ◽  
Open Source ◽  
User Friendly

Keberadaan internet di tengah manusia, telah banyak memberikan perubahan, baik dalam kehidupan untuk berinteraksi, berkomunikasi, bersosial dan berbudaya. Internet sanggup mengubah dunia. Kita sudah mengenal dan menikmati kemudahan - kemudahan yang bermula dari internet. Kehadiran internet sebagai penghubung komunikasi antar manusia telah mengubah dunia. Internet tidak hanya untuk menghubungkan komunikasi antar manusia, tetapi sudah menghubungkan banyak peralatan bahkan apapun yang dapat terhubung, Internet of Things atau disingkat IoT. Internet of Things adalah sebuah implementasi komunikasi jaringan dari benda yang saling terkait, terhubung satu dengan yang lain dan saling berkomunikasi. Smartphone yang sudah bertebaran saat ini bisa dikatakan merupakan sebuah perangkat yang mampu untuk mengembangkan IoT. Smartphone memiliki kemampuan untuk terkoneksi dengan internet dan dilengkapi dengan fitur sensor. Untuk mengembangkan IoT, maka sensor tersebut harus bisa berkomunikasi dengan perangkat lain dengan kita memasangkan sebuah aplikasi yang membuatnya mampu melakukan hal tersebut. Pada penelitian berikut dikembangkan sebuah rancangan sistem menggunakan mikrokontroler Arduino dan dengan data dari sensor LDR yang dikomunikasikan secara nirkabel dengan menghubungkan ke Internet via WiFi dengan mengirimkan data ke situs open source (cayenne.mydevices.com). Cayenne adalah salah satu platform IoT (Internet of Things) yang  berfungsi sebagai server yang menyimpan project kontrol dan memonitoring sebuah alat serta mendukung untuk koneksi dengan berbagai jenis mikrokontroler, platform ini sangat user-friendly serta memiliki berbagai macam tipe koneksi dalam menghubungkan antara mikrokontroler dengan platform internet. Hasil dari perancangan adalah sistem control otomatis lampu melalui platform IoT Cayenne di web dan mobile phone dari aplikasinya. Desain project ini diharapkan mampu dikembangkan bagi penelitian IoT (Internet of Things).

MackDroid - An Android based Application to monitor devices

2016 ◽  
Vol 9 ◽  
pp. 1
Author(s):  
Maaz Sirkhot ◽  
Ekta Sirwani ◽  
Aishwarya Kourani ◽  
Akshit Batheja ◽  
Kajal Jethanand Jewani
Keyword(s):  
Operating System ◽  
Open Source ◽  
Vital Role ◽  
Mobile Users ◽  
Android Application ◽  
Web Page ◽  
Technological World ◽  
Monitoring Service ◽  
User Friendly

In this technological world, smartphones can be considered as one of the most far-reaching inventions. It plays a vital role in connecting people socially. The number of mobile users using an Android based smartphone has increased rapidly since last few years resulting in organizations, cyber cell departments, government authorities feeling the need to monitor the activities on certain targeted devices in order to maintain proper functionality of their respective jobs. Also with the advent of smartphones, Android became one of the most popular and widely used Operating System. Its highlighting features are that it is user friendly, smartly designed, flexible, highly customizable and supports latest technologies like IoT. One of the features that makes it exclusive is that it is based on Linux and is Open Source for all the developers. This is the reason why our project Mackdroid is an Android based application that collects data from the remote device, stores it and displays on a PHP based web page. It is primarily a monitoring service that analyzes the contents and distributes it in various categories like Call Logs, Chats, Key logs, etc. Our project aims at developing an Android application that can be used to track, monitor, store and grab data from the device and store it on a server which can be accessed by the handler of the application.

scholarly journals An Interactive WebGIS Framework for Coastal Erosion Risk Management

2021 ◽  
Vol 9 (6) ◽  
pp. 567
Author(s):  
Alessandra Capolupo ◽  
Cristina Monterisi ◽  
Alessandra Saponieri ◽  
Fabio Addona ◽  
Leonardo Damiani ◽  
...  
Keyword(s):  
Open Source ◽  
3D Visualization ◽  
Erosion Risk ◽  
Innovative Strategies ◽  
Web Mapping ◽  
Natural Processes ◽  
Multi Scale ◽  
Interactive Interface ◽  
Multi Temporal ◽  
User Friendly

The Italian coastline stretches over about 8350 km, with 3600 km of beaches, representing a significant resource for the country. Natural processes and anthropic interventions keep threatening its morphology, moulding its shape and triggering soil erosion phenomena. Thus, many scholars have been focusing their work on investigating and monitoring shoreline instability. Outcomes of such activities can be largely widespread and shared with expert and non-expert users through Web mapping. This paper describes the performances of a WebGIS prototype designed to disseminate the results of the Italian project Innovative Strategies for the Monitoring and Analysis of Erosion Risk, known as the STIMARE project. While aiming to include the entire national coastline, three study areas along the regional coasts of Puglia and Emilia Romagna have already been implemented as pilot cases. This WebGIS was generated using Free and Open-Source Software for Geographic information systems (FOSS4G). The platform was designed by combining Apache http server, Geoserver, as open-source server and PostgreSQL (with PostGIS extension) as database. Pure javascript libraries OpenLayers and Cesium were implemented to obtain a hybrid 2D and 3D visualization. A user-friendly interactive interface was programmed to help users visualize and download geospatial data in several formats (pdf, kml and shp), in accordance with the European INSPIRE directives, satisfying both multi-temporal and multi-scale perspectives.

scholarly journals Microvessel Chaste: An Open Library for Spatial Modelling of Vascularized Tissues

2017 ◽  
Author(s):  
J.A. Grogan ◽  
A.J. Connor ◽  
B. Markelc ◽  
R.J. Muschel ◽  
P.K. Maini ◽  
...  
Keyword(s):  
Open Source ◽  
Tumour Growth ◽  
Spatial Models ◽  
3D Models ◽  
Tissue Growth ◽  
Coronary Perfusion ◽  
Analysis Model ◽  
User Friendly

AbstractSpatial models of vascularized tissues are widely used in computational physiology, to study for example, tumour growth, angiogenesis, osteogenesis, coronary perfusion and oxygen delivery. Composition of such models is time-consuming, with many researchers writing custom software for this purpose. Recent advances in imaging have produced detailed three-dimensional (3D) datasets of vascularized tissues at the scale of individual cells. To fully exploit such data there is an increasing need for software that allows user-friendly composition of efficient, 3D models of vascularized tissue growth, and comparison of predictions with in vivo or in vitro experiments and other models. Microvessel Chaste is a new open-source library for building spatial models of vascularized tissue growth. It can be used to simulate vessel growth and adaptation in response to mechanical and chemical stimuli, intra- and extra-vascular transport of nutrient, growth factor and drugs, and cell proliferation in complex 3D geometries. The library provides a comprehensive Python interface to solvers implemented in C++, allowing user-friendly model composition, and integration with experimental data. Such integration is facilitated by interoperability with a growing collection of scientific Python software for image processing, statistical analysis, model annotation and visualization. The library is available under an open-source Berkeley Software Distribution (BSD) licence at https://jmsgrogan.github.io/MicrovesselChaste. This article links to two reproducible example problems, showing how the library can be used to model tumour growth and angiogenesis with realistic vessel networks.

scholarly journals RICOCHET: A quick-and-dirty approach to analyze μ-XRF synchrotron data using open-source utilities

2019 ◽  
Vol 23 ◽  
pp. 162
Author(s):  
E. Mitsi ◽  
T. J. Mertzimekis
Keyword(s):  
Open Source ◽  
Online Analysis ◽  
Synchrotron Facility ◽  
User Friendly

A semi-automated procedure based on open-source utilities was designed and built to analyze spectra collected at a synchrotron accelerator using the μ-XRF technique. The software (RICOCHET) has a fast, efficient and user-friendly design aimed at performing online analysis. A few examples of its application using recent μ-XRF data from the SUL-X beamline at ANKA Synchrotron Facility (KIT) are presented.

scholarly journals Covid19Risk.ai: An open source repository and online calculator of prediction models for early diagnosis and prognosis of Covid-19

2021 ◽  
Author(s):  
Iva Halilaj ◽  
Avishek Chatterjee ◽  
Yvonka van Wijk ◽  
Guangyao Wu ◽  
Brice van Eeckhout ◽  
...  
Keyword(s):  
Open Source ◽  
Predictive Models ◽  
Prediction Models ◽  
Patient Specific ◽  
Diagnosis And Prognosis ◽  
Online Calculator ◽  
User Friendly ◽  
Type 3 ◽  
Proof Of Principle

AbstractObjectiveThe current pandemic has led to a proliferation of predictive models being developed to address various aspects of COVID-19 patient care. We aimed to develop an online platform that would serve as an open source repository for a curated subset of such models, and provide a simple interface for included models to allow for online calculation. This platform would support doctors during decision-making regarding diagnoses, prognoses, and follow-up of COVID-19 patients, expediting the models’ transition from research to clinical practice.MethodsIn this proof-of-principle study, we performed a literature search in PubMed and WHO database to find suitable models for implementation on our platform. All selected models were publicly available (peer reviewed publications or open source repository) and had been validated (TRIPOD type 3 or 2b). We created a method for obtaining the regression coefficients if only the nomogram was available in the original publication. All predictive models were transcribed on a practical graphical user interface using PHP 8.0.0, and published online together with supporting documentation and links to the associated articles.ResultsThe open source website https://covid19risk.ai/ currently incorporates nine models from six different research groups, evaluated on datasets from different countries. The website will continue to be populated with other models related to COVID-19 prediction as these become available. This dynamic platform allows COVID-19 researchers to contact us to have their model curated and included on our website, thereby increasing the reach and real-world impact of their work.ConclusionWe have successfully demonstrated in this proof-of-principle study that our website provides an inclusive platform for predictive models related to COVID-19. It enables doctors to supplement their judgment with patient-specific predictions from externally-validated models in a user-friendly format. Additionally, this platform supports researchers in showcasing their work, which will increase the visibility and use of their models.

OpenIoT: Open Source Internet-of-Things in the Cloud

2015 ◽  
pp. 13-25 ◽  
Author(s):  
John Soldatos ◽  
Nikos Kefalakis ◽  
Manfred Hauswirth ◽  
Martin Serrano ◽  
Jean-Paul Calbimonte ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Open Source

scholarly journals Smart Door / COVID-19 Face Mask Detection

2021 ◽  
Vol 10 (9) ◽  
pp. 87-92
Author(s):  
Pavan Narayana A ◽  
◽  
Janardhan Guptha S ◽  
Deepak S ◽  
Pujith Sai P ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Open Source ◽  
Face Mask ◽  
Young Lady ◽  
Grayscale Image ◽  
Human Face ◽  
Happy Life ◽  
Public Areas ◽  
The Face ◽  
Human Faces

January 27 2020, a day that will be remembered by the Indian people for a few decades, where a deadly virus peeped into a life of a young lady and till now it has been so threatening as it took up the life of 3.26 lakh people just in India. With the start of the virus government has made mandatory to wear masks when we go out in to crowded or public areas such as markets, malls, private gatherings and etc. So, it will be difficult for a person in the entrance to check whether everyone one are entering with a mask, in this paper we have designed a smart door face mask detection to check whether who are wearing or not wearing mask. By using different technologies such as Open CV, MTCNN, CNN, IFTTT, ThingSpeak we have designed this face mask detection. We use python to program the code. MTCNN using Viola- Jones algorithm detects the human faces present in the screen The Viola-Jones algorithm first detects the face on the grayscale image and then finds the location on the colored image. In this algorithm MTCNN first detects the face in grayscale image locates it and then finds this location on colored image. CNN for detecting masks in the human face is constructed using sample datasets and MobileNetV2 which acts as an object detector in our case the object is mask. ThingSpeak is an open-source Internet of things application used to display the information we get form the smart door. This deployed application can also detect when people are moving. So, with this face mask detection, as a part to stop the spread of the virus, we ensure that with this smart door we can prevent the virus from spreading and can regain our happy life.

Open source column: Deep learning in the browser

2019 ◽  
Vol 11 (1) ◽  
pp. 1-1
Author(s):  
Sabrina Kletz ◽  
Marco Bertini ◽  
Mathias Lux
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mobile Phone ◽  
Open Source ◽  
Web Pages ◽  
Web Browsers ◽  
Open Source Framework ◽  
Interesting Approach ◽  
Cross Platform ◽  
Client Device

Having already discussed MatConvNet and Keras, let us continue with an open source framework for deep learning, which takes a new and interesting approach. TensorFlow.js is not only providing deep learning for JavaScript developers, but it's also making applications of deep learning available in the WebGL enabled web browsers, or more specifically, Chrome, Chromium-based browsers, Safari and Firefox. Recently node.js support has been added, so TensorFlow.js can be used to directly control TensorFlow without the browser. TensorFlow.js is easy to install. As soon as a browser is installed one is ready to go. Browser based, cross platform applications, e.g. running with Electron, can also make use of TensorFlow.js without an additional install. The performance, however, depends on the browser the client is running, and memory and GPU on the client device. More specifically, one cannot expect to analyze 4K videos on a mobile phone in real time. While it's easy to install, and it's easy to develop based on TensorFlow.js, there are drawbacks: (i) developers have less control over where the machine learning actually takes place (e.g. on CPU or GPU), that it is running in the same sandbox as all web pages in the browser do, and (ii) that in the current release it still has rough edges and is not considered stable enough to use in production.

Secure Network Solutions for Enterprise Cloud Services

2015 ◽  
pp. 1464-1486
Author(s):  
Chengcheng Huang ◽  
Phil Smith ◽  
Zhaohao Sun
Keyword(s):  
Cloud Computing ◽  
Experimental Evidence ◽  
Open Source ◽  
Network Protocols ◽  
Cloud Services ◽  
Important Challenge ◽  
Secure Network ◽  
Cloud Network ◽  
User Friendly ◽  
Implementation Stage

Securing a cloud network is an important challenge for delivering cloud services to enterprise clouds. There are a number of secure network protocols, such as VPN protocols, currently available, to provide different secure network solutions for enterprise clouds. For example, PPTP, IPSec, and SSL/TLS are the most widely used VPN protocols in today's securing network solutions. However, there are some significant challenges in the implementation stage. For example, which VPN solution is easy to deploy in delivering cloud services? Which VPN solution is most user-friendly in enterprise clouds? This chapter explores these issues by implementing different VPNs in a virtual cloud network environment using open source software and tools. This chapter also reviews cloud computing and cloud services and looks at their relationships. The results not only provide experimental evidence but also facilitate the network implementers in deployment of secure network solutions for enterprise cloud services.

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