Voice recognition by Google Home and Raspberry Pi for smart socket control

Author(s):  
Chen-Yen Peng ◽  
Rung-Chin Chen
2019 ◽  
Vol 9 (3) ◽  
pp. 224 ◽  
Author(s):  
Dimitrios Loukatos ◽  
Konstantinos G. Arvanitis

Inspired by the mobile phone market boost, several low cost credit card-sized computers have made the scene, able to support educational applications with artificial intelligence features, intended for students of various levels. This paper describes the learning experience and highlights the technologies used to improve the function of DIY robots. The paper also reports on the students’ perceptions of this experience. The students participating in this problem based learning activity, despite having a weak programming background and a confined time schedule, tried to find efficient ways to improve the DIY robotic vehicle construction and better interact with it. Scenario cases under investigation, mainly via smart phones or tablets, involved from touch button to gesture and voice recognition methods exploiting modern AI techniques. The robotic platform used generic hardware, namely arduino and raspberry pi units, and incorporated basic automatic control functionality. Several programming environments, from MIT app inventor to C and python, were used. Apart from cloud based methods to tackle the voice recognition issues, locally running software alternatives were assessed to provide better autonomy. Typically, scenarios were performed through Wi-Fi interfaces, while the whole functionality was extended by using LoRa interfaces, to improve the robot’s controlling distance. Through experimentation, students were able to apply cutting edge technologies, to construct, integrate, evaluate and improve interaction with custom robotic vehicle solutions. The whole activity involved technologies similar to the ones making the scene in the modern agriculture era that students need to be familiar with, as future professionals.


In modern life, with the ability to perform tasks, the virtual assistant (VA) can make our lives easier and smart. The virtual assistant can perform as a librarian, very smartly, and effectively. We build our VA with Raspberry Pi and Alexa Voice Service. As a result, few discussions that occur in library environments such as find books, short review books, university notice are accurately performed. The common way of communication used by people in day to day life is through speech. If the assistant system can be heard to the customer for the handle of the day to day affairs, then grant the right reply, it will be much simple for customers to transmit with their assistant system, and the assistant will be much better “Smart” as a personal assistant. We heard a very old story “Ali Baba and the Forty Thieves”, where the mouth of a treasure cave secured by magic. It unrolls on the words "unroll sesame" and seals itself on the words "near sesame". The magic is a VA in the modern world. The VA system built on artificial intelligence (AI), machine learning, natural language processing, and voice recognition technology.


Author(s):  
Mohammad Shahrul Izham Sharifuddin ◽  
Sharifalillah Nordin ◽  
Azliza Mohd Ali

In this paper, we develop an intelligent wheelchair using CNNs and SVM voice recognition methods. The data is collected from Google and some of them are self-recorded. There are four types of data to be recognized which are go, left, right, and stop. Voice data are extracted using MFCC feature extraction technique. CNNs and SVM are then used to classify and recognize the voice data. The motor driver is embedded in Raspberry PI 3B+  to control the movement of the wheelchair prototype. CNNs produced higher accuracy i.e. 95.30% compared to SVM which is only 72.39%. On the other hand, SVM only took 8.21 seconds while CNNs took 250.03 seconds to execute. Therefore, CNNs produce better result because noise are filtered in the feature extraction layer before classified in the classification layer. However, CNNs took longer time due to the complexity of the networks and the less complexity implementation in SVM give shorter processing time.


2018 ◽  
Vol 4 (2) ◽  
pp. 180-189
Author(s):  
Diah Aryani ◽  
Dedy Iskandar ◽  
Fitri Indriyani

The server door is the main access to enter the server room. Currently the door lock on the server room is still done manually using the physical key as a tool to open or lock the door. Physical keys are easily lost or left behind which results in the officer not being able to enter the server room. This resulted in the officer can not access the server. Based on these reasons, the server door is integrated with a computer system that can unlock the door using voice recognition to unlock the server door. Voice recognition is able to identify a person through his voice. Voice recognition is divided into 2 parts namely speech recognition and speeker recognition. Meanwhile, the authors use the speech recognition section to open thedoor server door lock. Where, speech recognition can identify what is spoken by someone. The design of this tool is made using Raspberry pi 3 as the processing center and ULN2803 as ic to increase the voltage so that it can move the solenoid that serves to move the doorlock. Then raspberry gives command to the servo motor to open the door. Only staff who have id and password are only able to open the door lock on the server room using voice recognition. While those who do not have id and password can not unlock the door in the server room. So with the design of smart door lock tool using voice recognition raspberry-based pi 3 provides a level of security and access more computerized.


2020 ◽  
Vol 8 (2) ◽  
pp. 14
Author(s):  
J. MANIKANDAN ◽  
M. THANKAM ◽  
K. P. AISHWARYA ◽  
S. RADHA ◽  
◽  
...  

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 414 ◽  
Author(s):  
Eduardo Rodríguez-Orozco ◽  
Enrique García-Guerrero ◽  
Everardo Inzunza-Gonzalez ◽  
Oscar López-Bonilla ◽  
Abraham Flores-Vergara ◽  
...  

A new embedded chaotic cryptosystem is introduced herein with the aim to encrypt digital images and performing speech recognition as an external access key. The proposed cryptosystem consists of three technologies: (i) a Spartan 3E-1600 FPGA from Xilinx; (ii) a 64-bit Raspberry Pi 3 single board computer; and (iii) a voice recognition chip manufactured by Sunplus. The cryptosystem operates with four embedded algorithms: (1) a graphical user interface developed in Python language for the Raspberry Pi platform, which allows friendly management of the system; (2) an internal control entity that entails the start-up of the embedded system based on the identification of the key access, the pixels-entry of the image to the FPGA to be encrypted or unraveled from the Raspberry Pi, and the self-execution of the encryption/decryption of the information; (3) a chaotic pseudo-random binary generator whose decimal numerical values are converted to an 8-bit binary scale under the VHDL description of m o d ( 255 ) ; and (4) two UART communication algorithms by using the RS-232 protocol, all of them described in VHDL for the FPGA implementation. We provide a security analysis to demonstrate that the proposed cryptosystem is highly secure and robust against known attacks.


Author(s):  
B. Shoban Babu ◽  
V. Priyadharshini ◽  
Prince Patel

One of the most essential life skills is to be able to communicate easily. In order to produce greater comprehension, communication is described as transmitting knowledge. Communication and technologies are not mutually exclusive. Speech Recognition is a technique that facilitates the processing of voice information to text and is independent of the speaker. This enables it to be used in various applications, from digital assistants to machinery control. The aim of this paper is to study numerous robotic vehicles powered by human speech commands. To accomplish this functionality, most of these systems run with the use of an android smart phone that transmits voice commands to a raspberry pi. The voice-operated robot is used to build one moving object. It is moved as per the voice recognition module commands, and the robot obtains that command. The robot compares the command with the stored software and then sets the command using wireless communication as per voice. These suggested methods would be useful for devices such as assistive robotics for people with disabilities or automotive applications such as work robots.


Author(s):  
Disari Chattopadhyay

Abstract: This paper represents the development of an automated system based on IoT, which can mainly be used in the home and some features can also be implemented in offices, banks, or schools. The main purpose of this project is to save time and manpower along with security and convenience, using Raspberry pi. The salient features of this automated system are gas leakage detection for safety purposes, motion detection for security purposes, and controlling the home appliances as per the user’s need. The system takes command through voice as well as text as per the user’s requirements using google assistant, which further sends a response to Raspberry-Pi via Firebase for the required action. DHT22 sensor is used for the measurement of temperature and humidity, room temperature and Humidity will be displayed through Google assistant. This system consists of Python as the main programming language by default, provided by Raspberry Pi. The system will detect human presence with the help of a motion sensor i.e, whenever a person enters the room, the motion is detected and automatically an alert message will be sent to the user via Google assistant. Keywords: IoT, Raspberry-pi, Google assistant, Firebase, Python, Dialogflow, Voice Recognition.


Author(s):  
Dabiah Alboaneen ◽  
Dalia Alsaffar ◽  
Amani Alqahtani ◽  
Lama Alamri ◽  
Amjad Alfahhad ◽  
...  

This article aims to develop a smart information desk system through a smart mirror for universities. It is a mirror with extra capabilities of displaying answers for academic inquiries such as asking about the lecturers’ office numbers and hours, exams dates and times on the mirror surface. In addition, the voice recognition feature was used to answer spoken inquiries in audio responds to serve all types of users including disabled ones. Furthermore, the system showed general information such as date, weather, time and the university map. The smart mirror was connected to an outdoor camera to monitor the traffics at the university entrance gate. The system was implemented on a Raspberry Pi 4 model B connected to a two-way mirror and an infrared (IR) touch frame. The results of this study helped to overcome the problem of the information desk absence in the university. Therefore, it helped users to save their time and effort in making requests for important academic information.


Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 168
Author(s):  
Mohsen Bakouri ◽  
Mohammed Alsehaimi ◽  
Husham Farouk Ismail ◽  
Khaled Alshareef ◽  
Ali Ganoun ◽  
...  

Many wheelchair people depend on others to control the movement of their wheelchairs, which significantly influences their independence and quality of life. Smart wheelchairs offer a degree of self-dependence and freedom to drive their own vehicles. In this work, we designed and implemented a low-cost software and hardware method to steer a robotic wheelchair. Moreover, from our method, we developed our own Android mobile app based on Flutter software. A convolutional neural network (CNN)-based network-in-network (NIN) structure approach integrated with a voice recognition model was also developed and configured to build the mobile app. The technique was also implemented and configured using an offline Wi-Fi network hotspot between software and hardware components. Five voice commands (yes, no, left, right, and stop) guided and controlled the wheelchair through the Raspberry Pi and DC motor drives. The overall system was evaluated based on a trained and validated English speech corpus by Arabic native speakers for isolated words to assess the performance of the Android OS application. The maneuverability performance of indoor and outdoor navigation was also evaluated in terms of accuracy. The results indicated a degree of accuracy of approximately 87.2% of the accurate prediction of some of the five voice commands. Additionally, in the real-time performance test, the root-mean-square deviation (RMSD) values between the planned and actual nodes for indoor/outdoor maneuvering were 1.721 × 10−5 and 1.743 × 10−5, respectively.


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