scholarly journals VOICE COMMAND SYSTEM USING RASPBERRY PI

2016 ◽  
Vol 3 (3) ◽  
pp. 43-49 ◽  
Author(s):  
Surinder Kaur ◽  
Sanchit Sharma ◽  
Utkarsh Jain ◽  
Arpit Raj
Keyword(s):  
Raspberry Pi ◽  
Voice Command

scholarly journals A Human Safety Device - An Attaching Clip Using Internet of Things

2020 ◽  
pp. 126-131
Author(s):  
Shabdita Sharma ◽  
Smita Satav ◽  
Pratiksha Palkar ◽  
Shubham Ramdharne ◽  
Kiran Kolhar
Keyword(s):  
Internet Of Things ◽  
Old Age ◽  
Crime Rates ◽  
Raspberry Pi ◽  
Safety Device ◽  
Voice Command ◽  
Human Safety ◽  
Current Location ◽  
Day By Day

Nowadays Human safety has become a major concern, as crime rates are increasing day by day. Out of which most cases are reported against women. As not only women but many other people also need help when it comes to safety, this may include old age people, children, and anyone who is in danger. We are proposing a human safety device that takes input in the form of a voice command or by pressing the panic button. Once the device gets activated, a message and call is sent to the nearby police stations and preferred contacts with some details of the victim and current location of the victim. with this, the buzzers which are placed on the streets on a particular distance also start buzzing. As these buzzers make some amount of noise the nearby people will come to know about the victim and they can also come for help, till the police reach out to the victim.we will be using raspberry pi in the device, NodeMCU in the buzzer and GPS for the location.

scholarly journals Experimenting with Hotword Detection: The Pao-Pal

2020 ◽  
Vol 4 (5) ◽  
Author(s):  
Kuo-Pao Yang ◽  
Alexander Jee ◽  
Dane Leblanc ◽  
Jeremiah Weaver ◽  
Zachary Armand
Keyword(s):  
Raspberry Pi ◽  
Voice Command

Hotword detection and voice command are implemented in many of today’s current technological devices. In this paper, the implementations using hotword detection as well as the improvements in hotword technology will be discussed including the Pao-Pal. The Pao-Pal was made using Google’s AIY Voice Kit which contains a Raspberry Pi Zero WH, voice bonnet, and a speaker that allows it to playback pre-recorded voice lines. These components allow the Pao-Pal to act as a switch for a Keurig machine to make coffee when certain phrases are said. To allow the Pao-Pal to always listen for hotwords, it uses a hotword detection engine called Snowboy, which also allows it to playback the pre-recorded voice lines. Snowboy allows users to create and customize their own special hotword. Other projects concerning voice commands will be discussed along with the application of that kind of technology.

scholarly journals Sistem Navigasi Kursi Roda Elektrik untuk Pasien Penyandang Cacat Fisik Menggunakan Metode Convolutional Neural Network

2021 ◽  
Vol 17 (1) ◽  
pp. 29-35
Author(s):  
Sutikno Sutikno ◽  
Khairul Anam ◽  
Azmi Shaleh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Navigation System ◽  
Physical Disabilities ◽  
Experimental Results ◽  
Raspberry Pi ◽  
Main Method ◽  
Electric Wheelchair ◽  
Voice Command ◽  
Voice Data

Patients with physical disabilities, such as losing a leg or experiencing paralysis, will have difficulty moving from one place to another. As a result, they need someone or a device that can help them to move. One that is often used by patients is a wheelchair. This study proposes an electric wheelchair navigation system that can be controlled by voice commands using the Convolutional Neural Network (CNN) method. CNN is used as the main method for identifying commands embedded on the Raspberry Pi microcontroller. The recorded voice data is then converted to spectrogram images before being fed to CNN. This method is proven to be better in voice command recognition with an accuracy of above 90%. There are five different voice commands: forward, backward, left, right, and stop. Preliminary experimental results indicate that the electric wheelchair is able to move according to the commands given.

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.

Implementasi Voip Server Berbasis IPV6 Dengan Raspberry PI

2019 ◽  
Vol 9 (01) ◽  
pp. 47-54
Author(s):  
Rabbai San Arif ◽  
Yuli Fitrisia ◽  
Agus Urip Ari Wibowo
Keyword(s):  
Internet Protocol ◽  
Wireless Transmission ◽  
Raspberry Pi ◽  
Test Results ◽  
Average Delay ◽  
Ipv6 Network ◽  
Ip Network ◽  
Selection Of ◽  
Better Than

Voice over Internet Protocol (VoIP) is a telecommunications technology that is able to pass the communication service in Internet Protocol networks so as to allow communicating between users in an IP network. However VoIP technology still has weakness in the Quality of Service (QoS). VOPI weaknesses is affected by the selection of the physical servers used. In this research, VoIP is configured on Linux operating system with Asterisk as VoIP application server and integrated on a Raspberry Pi by using wired and wireless network as the transmission medium. Because of depletion of IPv4 capacity that can be used on the network, it needs to be applied to VoIP system using the IPv6 network protocol with supports devices. The test results by using a wired transmission medium that has obtained are the average delay is 117.851 ms, jitter is 5.796 ms, packet loss is 0.38%, throughput is 962.861 kbps, 8.33% of CPU usage and 59.33% of memory usage. The analysis shows that the wired transmission media is better than the wireless transmission media and wireless-wired.

Lampu Sein Helm Sepeda Berbasis Voice Recognition

2019 ◽  
Vol 11 (01) ◽  
pp. 20-25
Author(s):  
Indra Saputra ◽  
Parulian Silalahi ◽  
Bayu Cahyawan ◽  
Imam Akbar
Keyword(s):  
Voice Recognition ◽  
Driving Safety ◽  
Bicycle Helmet ◽  
Turn Signal ◽  
Voice Command ◽  
The Voice ◽  
Indicator Signal

Bicycles are not equipped with the turn signal. For driving safety, a bicycle helmet with a turn signal is designed with voice rrecognition. It is using the Arduino Nano as a controller to control the ON and OFF of turn signal lights with voice commands. This device uses a Voice Recognition sensor and microphone that placed on a bicycle helmet. When the voice command is mentioned in the microphone, the Voice Recognition sensor will detect the command specified, the sensor will automatically read and send a signal to Arduino, then the turn signal will light up as instructed, the Arduino on the helmet will send an indicator signal via the Bluetooth Module. The device is able to detect sound with a percentage of 80%. The tool can work with a distance of <2 meters with noise <71 db.

Conception and implementation of a course for professional training and education in the field of IoT and smart home security

2020 ◽  
Vol 2020 (3) ◽  
pp. 277-1-277-8
Author(s):  
Michael Pilgermann ◽  
Thomas Bocklisch ◽  
Reiner Creutzburg
Keyword(s):  
Smart Home ◽  
Professional Training ◽  
Raspberry Pi ◽  
Science Students ◽  
Training And Education ◽  
Home Security ◽  
Master Level ◽  
University Course ◽  
Home Technology ◽  
Smart Home Technology

The aim of this paper is to describe the new concept of a Master level university course for computer science students to address the issues of IoT and Smart Home Security. This concept is well suited for professional training for interested customers and allows the creation of practical exercises. The modular structure of the course contains lectures and exercises on the following topics: 1. Introduction - IoT and Smart Home Technology and Impact 2. Homematic Technology and Smart Home Applications 3. Loxone Technology and Smart Home Applications 4. Raspberry Pi and Smart Home Applications 5. Security of IoT and Smart Home Systems and contains laboratory exercises of diverse complexities.

An Adaptive Binarization Method for Cost-efficient Document Image System in Wavelet Domain

2020 ◽  
Vol 64 (3) ◽  
pp. 30401-1-30401-14 ◽  
Author(s):  
Chih-Hsien Hsia ◽  
Ting-Yu Lin ◽  
Jen-Shiun Chiang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Document Image ◽  
Background Information ◽  
Raspberry Pi ◽  
Discrete Wavelet ◽  
Image System ◽  
Low Frequencies ◽  
Cost Efficient ◽  
Binarization Method

Abstract In recent years, the preservation of handwritten historical documents and scripts archived by digitized images has been gradually emphasized. However, the selection of different thicknesses of the paper for printing or writing is likely to make the content of the back page seep into the front page. In order to solve this, a cost-efficient document image system is proposed. In this system, the authors use Adaptive Directional Lifting-Based Discrete Wavelet Transform to transform image data from spatial domain to frequency domain and perform on high and low frequencies, respectively. For low frequencies, the authors use local threshold to remove most background information. For high frequencies, they use modified Least Mean Square training algorithm to produce a unique weighted mask and perform convolution on original frequency, respectively. Afterward, Inverse Adaptive Directional Lifting-Based Discrete Wavelet Transform is performed to reconstruct the four subband images to a resulting image with original size. Finally, a global binarization method, Otsu’s method, is applied to transform a gray scale image to a binary image as the output result. The results show that the difference in operation time of this work between a personal computer (PC) and Raspberry Pi is little. Therefore, the proposed cost-efficient document image system which performed on Raspberry Pi embedded platform has the same performance and obtains the same results as those performed on a PC.

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