Rancang Bangun Pembuatan Sistem Pengiriman Sensor Secara Real Time Menggunakan Python dan Raspberry Pi

2019 ◽  
Vol 3 (2) ◽  
pp. 43-46
Author(s):  
Destyara Zanneta Hidayatullifa
Keyword(s):  
Real Time ◽  
Raspberry Pi

PROTOTYPE ALAT PEMANTAU SALURAN AIR BAWAH TANAH DENGAN MENGGUNAKAN WEBCAM C170 BERBASIS RASPBERRY PI

2015 ◽  
Vol 1 (1) ◽  
pp. 37-45
Author(s):  
Irwansyah Irwansyah ◽  
Hendra Kusumah ◽  
Muhammad Syarif
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Public Awareness ◽  
Underground Water ◽  
Raspberry Pi ◽  
Laptop Computer ◽  
Web Browser ◽  
Disaster Monitoring ◽  
The Times ◽  
Human Desire

Along with the times, recently there have been found tool to facilitate human’s work. Electronics is one of technology to facilitate human’s work. One of human desire is being safe, so that people think to make a tool which can monitor the surrounding condition without being monitored with people’s own eyes. Public awareness of the underground water channels currently felt still very little so frequent floods. To avoid the flood disaster monitoring needs to be done to underground water channels.This tool is controlled via a web browser. for the components used in this monitoring system is the Raspberry Pi technology where the system can take pictures in real time with the help of Logitech C170 webcam camera. web browser and Raspberry Pi make everyone can control the devices around with using smartphone, laptop, computer and ipad. This research is expected to be able to help the users in knowing the blockage on water flow and monitored around in realtime.

scholarly journals Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

Inventions ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 42
Author(s):  
Worasit Sangjan ◽  
Arron H. Carter ◽  
Michael O. Pumphrey ◽  
Vadim Jitkov ◽  
Sindhuja Sankaran
Keyword(s):  
Real Time ◽  
Imaging System ◽  
Crop Growth ◽  
Sensor System ◽  
Plant Phenotyping ◽  
Raspberry Pi ◽  
The Internet ◽  
Growth Stages ◽  
Crop Breeding ◽  
Breeding Programs

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.

scholarly journals Real-time Air Quality Index monitoring experiments using SDS011 sensors and raspberry pi

2021 ◽  
Vol 1098 (4) ◽  
pp. 042090
Author(s):  
D Kurnia ◽  
F S Hadisantoso ◽  
A A Suprianto ◽  
E A Nugroho ◽  
J Janizal
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Quality Index ◽  
Air Quality Index ◽  
Raspberry Pi

RANCANG BANGUN TRACKING ARAH TEMBAKAN MENGGUNAKAN SENSOR POSISI BERBASIS PID

2021 ◽  
Vol 7 (1) ◽  
pp. 43-48
Author(s):  
Agung Raharjo ◽  
Eko Kuncoro ◽  
Imam Azhar
Keyword(s):  
Real Time ◽  
Data Communication ◽  
Raspberry Pi ◽  
Long Distance ◽  
Visualization System ◽  
Control Command ◽  
Combat Unit ◽  
Army Personnel ◽  
Automation Technology ◽  
Research Show

Seiring dengan perkembangan teknologi komunikasi dan otomasi, pelaksanaan tugas militer dapat dibantu dengan mengembangkan alutsista militer. Salah satunya pengembangan robot tempur yang akan digunakan sebagai alat untuk membantu tugas operasi jarak jauh pada satuan tempur TNI AD. Pada robot tempur tersebut akan ditanamkan sistem komunikasi data berupa perintah kendali laju robot, perintah kendali senjata serang, dan sistem visualisasi yang dapat digunakan untuk mendukung pergerakan robot hingga mencapai sasaran yang ditentukan, serta sebagai sistem penginderaan jarak jauh robot tempur untuk memonitor area musuh yang akan ditinjau. Operator menggunakan sebuah joystick untuk mengendalikan robot tempur dan tablet Android untuk memantau dan mendeteksi arah sasaran. Penelitian ini membahas tentang perancangan pendeteksian sasaran tembak yang dapat dikendalikan dari jarak jauh. Metode yang digunakan adalah metode eksperimen berbasis PID. Penelitian ini berfokus pada pendeteksian sasaran tembak yang nantinya akan terhubung dengan Raspberry Pi 3, sehingga senjata dapat mendeteksi adanya sasaran tembak yang ada di dalam jangkauan sensor posisi. Hasil dari penelitian menunjukkan bahwa robot dapat dikendalikan dengan mudah menggunakan joystick dan secara real-time terlihat pada layar Android yang terpasang pada kontrol joystick tersebut. Selain itu, sistem juga dapat membedakan antara sasaran tembak dan objek yang bukan sasaran tembak. Penelitian ini diharapkan dapat mendukung tugas operasi personel TNI dalam menjalankan misinya dengan memanfaatkan robot tempur. Along with the development of communication and automation technology, the implementation of military duties can be assisted by developing military defense equipment. One of them is the development of a combat robot that will be used as a tool to assist the task of long-distance operations on the Army combat unit. In the combat robot, a data communication system will be implanted in the form of a robot rate control command, an attack weapon control command, and a visualization system that is used to support the robot's movement to reach the target specified as a combat robot's remote sensing system for monitoring enemy areas to be reviewed. The operator has used a joystick to control the combat robot and to detect the direction of the target can be monitored with an android tablet. This research discusses the design of the detection of target fire that can be controlled remotely. The method used is experimental based on PID. This research focused on detecting firing targets that will be connected with Raspberry Pi 3 so that the weapon can detect the presence of firing targets within the position sensor. The results of the research show that the robot can be easily controlled using a Joystick and in real-time visible on the Android screen mounted on the Joystick control, the system can distinguish between target shooting and non-target objects. This research is expected to support the operational duties of army personnel in carrying out their missions by utilizing combat robots.

scholarly journals Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi

2021 ◽  
Vol 11 (22) ◽  
pp. 10540
Author(s):  
Navjot Rathour ◽  
Zeba Khanam ◽  
Anita Gehlot ◽  
Rajesh Singh ◽  
Mamoon Rashid ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Real Life ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Raspberry Pi ◽  
Input Device ◽  
Facial Emotions ◽  
Deep Network

There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system.

scholarly journals CONTROLLING AND MONITORING OF AUTOMATION WATER SUPPLY SYSTEM BASED ON IOT WITH THEFT IDENTIFICATION

2017 ◽  
Vol 5 (5) ◽  
pp. 320-325
Author(s):  
Ahmad T. Jaiad ◽  
Hamzah Sabr Ghayyib
Keyword(s):  
Water Supply ◽  
Real Time ◽  
Water Conservation ◽  
Water Distribution ◽  
Network Connectivity ◽  
Raspberry Pi ◽  
Human Beings ◽  
Residential Areas ◽  
Time Operation ◽  
Real Time Operation

Water is the most precious and valuable because it’s a basic need of all the human beings but, now a day water supply department are facing problem in real time operation this is because less amount of water in resources due to less rain fall. With increase in Population, urban residential areas have increased because of this reasons water has become a crucial problem which affects the problem of water distribution, interrupted water supply, water conservation, water consumption and also the water quality so, to overcome water supply related problems and make system efficient there is need of proper monitoring and controlling system. In this project, we are focusing on continuous and real time monitoring of water supply in IOT platform. Water supply with continuous monitoring makes a proper distribution so that, we can have a record of available amount of water in tanks, flow rate, abnormality in distribution line. Internet of things is nothing but the network of physical objects embedded with electronics, sensors, software, and network connectivity. Monitoring can be done from anywhere as central office. Using Adafruit as free sever data continuously pushed on cloud so we can see data in real time operation. Using different sensors with controller and raspberry pi as Mini computer can monitor data and also control operation from cloud with efficient client server communication.

scholarly journals Sistem Anti Maling Untuk Rumah Tinggal Menggunakan IoT Bluemix

2018 ◽  
Vol 3 (1) ◽  
pp. 55
Author(s):  
Griffani Megiyanto Rahmatullah ◽  
Muhammad Ayat ◽  
Wirmanto Suteddy
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Raspberry Pi ◽  
Home Security ◽  
Object Storage ◽  
Processing And Storage ◽  
Security Surveillance ◽  
Cloud Applications ◽  
Application Access ◽  
And Storage

Sistem keamanan rumah merupakan implementasi yang harus dilakukan untuk meningkatkan keamanan dari kejadian yang tidak diinginkan. Beberapa implementasi hanya memberikan notifikasi sederhana berupa alarm dan tidak menjadi bukti yang kuat apabila terjadi pencurian. Salah satu solusi yang dilakukan adalah penempatan kamera untuk memantau keamanan rumah secara real time diintegrasikan dengan penyimpanan cloud. Bluemix merupakan salah satu provider untuk aplikasi cloud yang memiliki layanan pengolahan dan penyimpanan data, akses aplikasi mobile, pengawasan serta Internet of Things (IoT). Sistem yang diimplementasikan adalah integrasi Raspberry Pi dengan layanan Bluemix untuk melakukan pengawasan keamanan rumah dan memberikan notifikasi kepada pengguna. Sistem mendeteksi jarak menggunakan sensor HC-SR04 terhadap objek dan apabila jarak melewati acuan, hal tersebut adalah indikasi terjadinya pencurian. Berikutnya sistem akan menyalakan buzzer sebagai keluaran suara dan mengaktifkan kamera untuk mengambil gambar lalu diunggah ke object storage Bluemix. Langkah berikutnya yaitu layanan IBM push notification memberikan notifikasi ke perangkat Android pengguna. Pengujian dilakukan dengan menghalangi pembacaan sensor sehingga terjadi indikasi pencurian. Hasilnya adalah sistem berhasil menyalakan buzzer, mengambil gambar lalu diunggah ke Bluemix, dan notifikasi berhasil masuk pada Android. Notifikasi diterima oleh file browser pada perangkat Android dan dilakukan sinkronisasi dengan object storage untuk melakukan pengunduhan berkas gambar yang telah diunggah sebelumnya.Kata kunci: Bluemix, Raspberry Pi, object sorage, IBM push notification Home security system is an implementation that needs to be done to improve the security of unwanted events. Some implementations only provide a simple notification such as alarm and cannot become strong evidence in case of theft. One of the solutions is camera placement to monitor home security in real time integrated with cloud storage. Bluemix is a provider for cloud applications that have data processing and storage services, mobile application access, monitoring and Internet of Things (IoT). System implemented was integration of Raspberry Pi with Bluemix services to conduct home security surveillance and provide notification to user. System detected distance using HC-SR04 sensor to object and if distance passes the reference, it was an indication of theft. Next, system will turned on buzzer as a sound output and activating the camera to take picture and uploaded to Bluemix Object Storage. Next step was IBM push notification service giving notification to user's Android device. The testing was done by blocking the sensor readings so that there was an indication of theft. The result was system succeeded in turning on the buzzer, taking pictures, uploading pictures to Bluemix, and notification successfully logged on Android. Notifications are received by the file browser on Android device and synchronized with object storage to download image files that have been uploaded previously.Keywords: Bluemix, Raspberry Pi, object storage, IBM push notification 

scholarly journals A Real Time Image Processing Bird Repellent System Using Raspberry Pi

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Oluwole Arowolo ◽  
Adefemi A Adekunle ◽  
Joshua A Ade-Omowaye
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Moving Objects ◽  
Detection System ◽  
Local Binary Patterns ◽  
Raspberry Pi ◽  
Feature Identification ◽  
Data Set ◽  
Haar Features ◽  
Haar Feature

Rice is one of the most consumed foods in Nigeria, therefore it’s production should be on the high as to meet the demand for it. Unfortunately, the quantity of rice produced is being affected by pests such as birds on fields and sometimes in storage. Due to the activities of birds, an effective repellent system is required on rice fields. The proposed effective repellent system is made up of hardware components which are the raspberry pi for image processing, the servo motors for rotation of camera for better field of view controlled by Arduino connected to the raspberry pi, a speaker for generating predator sounds to scare birds away and software component consisting of python and Open Cv library for bird feature identification. The model was trained separately using haar features, HOG (Histogram of Oriented Gradients) and LBP (Local Binary Patterns).Haar features resulted in the highest accuracy of 76% while HOG and LBP were, 27% and 72% respectively. Haar trained model was tested with two recorded real time videos with birds, the false positives were fairly low, about 41%. This haar feature trained model can distinguish between birds and other moving objects unlike a motion detection system which detects all moving objects. This proposed system can be improved to have a higher accuracy with a larger data set of positive and negative images. Keywords—Electronic pest repeller Haar cascade classifier, ultrasonic

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