scholarly journals Speaker Recognition System for Home Security using Raspberry Pi and Python

2018 ◽  
Vol 7 (4.5) ◽  
pp. 95
Author(s):  
Dr Bageshree Pathak ◽  
Shriyanti Kulkarni
Keyword(s):  
Speaker Recognition ◽  
Recognition System ◽  
Raspberry Pi ◽  
Biometric Identification ◽  
Home Automation ◽  
Security Systems ◽  
Biometric Security ◽  
Home Security ◽  
Python Language ◽  
Environment Control

The transfer of manual controls to machine controls is automation. Automation is the need of the hour. Home automation is automation of home systems to create smart homes. It includes security systems, appliance control and environment control. The increasing need for safety and security has brought biometric security systems to the forefront. Speech being unique and individualistic can be used for biometric identification. The proposed system is a prototype which can be fitted for speaker recognition for home security. The system will identify the registered speakers and will allow access to the recognized speaker. The system is implemented on Raspberry pi platform using Python language.  

scholarly journals Voice Controlled Vehicle Dashboard

2020 ◽  
Vol 9 (1) ◽  
pp. 1022-1027
Keyword(s):  
Speech Recognition ◽  
Speaker Recognition ◽  
Feature Matching ◽  
Heavy Traffic ◽  
Recognition Rate ◽  
Recognition System ◽  
Low Noise ◽  
Raspberry Pi ◽  
Mel Frequency Cepstral Coefficients ◽  
Power Spectral

Driving a vehicle or a car has become tedious job nowadays due to heavy traffic so focus on driving is utmost important. This makes a scope for automation in Automobiles in minimizing human intervention in controlling the dashboard functions such as Headlamps, Indicators, Power window, Wiper System, and to make it possible this is a small effort from this paper to make driving distraction free using Voice controlled dashboard. and system proposed in this paper works on speech commands from the user (Driver or Passenger). As Speech Recognition system acts Human machine Interface (HMI) in this system hence this system makes use of Speaker recognition and Speech recognition for recognizing the command and recognize whether the command is coming from authenticated user(Driver or Passenger). System performs Feature Extraction and extracts speech features such Mel Frequency Cepstral Coefficients(MFCC),Power Spectral Density(PSD),Pitch, Spectrogram. Then further for Feature matching system uses Vector Quantization Linde Buzo Gray(VQLBG) algorithm. This algorithm makes use of Euclidean distance for calculating the distance between test feature and codebook feature. Then based on speech command recognized controller (Raspberry Pi-3b) activates the device driver for motor, Solenoid valve depending on function. This system is mainly aimed to work in low noise environment as most speech recognition systems suffer when noise is introduced. When it comes to speech recognition acoustics of the room matters a lot as recognition rate differs depending on acoustics. when several testing and simulation trials were taken for testing, system has speech recognition rate of 76.13%. This system encourages Automation of vehicle dashboard and hence making driving Distraction Free.

An Architectural Framework for Facebook Messenger Chatbot Enabled Home Appliance Control System

2022 ◽  
pp. 842-858
Author(s):  
Segun Aina ◽  
Samuel Dayo Okegbile ◽  
Perfect Makanju ◽  
Adeniran Ishola Oluwaranti
Keyword(s):  
Control System ◽  
Raspberry Pi ◽  
Home Automation ◽  
Correct Classification ◽  
Home Appliances ◽  
Python Language ◽  
Control Precision ◽  
Development Level ◽  
Architectural Framework ◽  
Home Appliance

The need to remotely control home appliances is an important aspect of home automation and is now receiving lot of attentions in the literature. The works so far are still at a development level making further research necessary. This article presents a framework for chatbot-controlled home appliance control system and was implemented by programming a Raspberry Pi using the Python language while the chatbot server was also implemented using a Node.js on JavaScript. The Raspberry Pi was connected to the chatbot server via Wi-Fi using a websockets protocol. The chatbot server is linked to Facebook Messenger using the Messenger Application Protocol Interface. Messages received at the chatbot server are analyzed with RasaNLU to classify the user's intention and extract necessary information which are sent over websocket to the connected Raspberry pi. The system was evaluated using control precision and percentage correct classification with both producing a significant level of acceptance. This work produced a Facebook Messenger chatbot-based framework capable of controlling Home Appliances remotely.

An Architectural Framework for Facebook Messenger Chatbot Enabled Home Appliance Control System

2019 ◽  
Vol 10 (2) ◽  
pp. 18-33 ◽  
Author(s):  
Segun Aina ◽  
Samuel Dayo Okegbile ◽  
Perfect Makanju ◽  
Adeniran Ishola Oluwaranti
Keyword(s):  
Control System ◽  
Raspberry Pi ◽  
Home Automation ◽  
Home Appliances ◽  
Python Language ◽  
Control Precision ◽  
Development Level ◽  
Architectural Framework ◽  
Home Appliance ◽  
Percentage Correct

The need to remotely control home appliances is an important aspect of home automation and is now receiving lot of attentions in the literature. The works so far are still at a development level making further research necessary. This article presents a framework for chatbot-controlled home appliance control system and was implemented by programming a Raspberry Pi using the Python language while the chatbot server was also implemented using a Node.js on JavaScript. The Raspberry Pi was connected to the chatbot server via Wi-Fi using a websockets protocol. The chatbot server is linked to Facebook Messenger using the Messenger Application Protocol Interface. Messages received at the chatbot server are analyzed with RasaNLU to classify the user's intention and extract necessary information which are sent over websocket to the connected Raspberry pi. The system was evaluated using control precision and percentage correct classification with both producing a significant level of acceptance. This work produced a Facebook Messenger chatbot-based framework capable of controlling Home Appliances remotely.

scholarly journals Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi

2020 ◽  
Vol 8 (3) ◽  
pp. 210-216
Author(s):  
Subiyanto Subiyanto ◽  
Dina Priliyana ◽  
Moh. Eki Riyadani ◽  
Nur Iksan ◽  
Hari Wibawanto
Keyword(s):  
Face Recognition ◽  
Smart Home ◽  
Principal Component ◽  
Recognition System ◽  
Classification Performance ◽  
Security System ◽  
Raspberry Pi ◽  
Home Security ◽  
Ga Algorithm ◽  
The Face

Genetic algorithm (GA) can improve the classification of the face recognition process in the principal component analysis (PCA). However, the accuracy of this algorithm for the smart home security system has not been further analyzed. This paper presents the accuracy of face recognition using PCA-GA for the smart home security system on Raspberry Pi. PCA was used as the face recognition algorithm, while GA to improve the classification performance of face image search. The PCA-GA algorithm was implemented on the Raspberry Pi. If an authorized person accesses the door of the house, the relay circuit will unlock the door. The accuracy of the system was compared to other face recognition algorithms, namely LBPH-GA and PCA. The results show that PCA-GA face recognition has an accuracy of 90 %, while PCA and LBPH-GA have 80 % and 90 %, respectively.

Application of Face Recognition Techniques in Video for Biometric Security

2016 ◽  
pp. 460-478
Author(s):  
Bijuphukan Bhagabati ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Ease Of Use ◽  
Surveillance Systems ◽  
Security Systems ◽  
Biometric Security ◽  
Emerging Trends ◽  
Face Recognition System ◽  
Video Based Face Recognition ◽  
Primary Focus

Biometric based attributes are the latest additions to the existing mechanisms used for security of information system and for access control. Among a host of others, face recognition is the most effective biometric system for identification and verification of persons. Face recognition from video has gained attention due to its popularity and ease of use with security systems based on vision and surveillance systems. The automated video based face recognition system provides a huge assortment of challenges as it is necessary to perform facial verification under different viewing conditions. Face recognition in video continues to attract lot of attention from researchers world over hence considerable advances are being recorded in this area. The aim of this chapter is to perform a review of the basic methods used for such techniques and finding the emerging trends of the research in this area. The primary focus is to summarize some well-known methods of face recognition in video sequences for application in biometric security and enumerate the emerging trends.

scholarly journals Internet of Things : Sistem Keamanan Rumah berbasis Raspberry Pi dan Telegram Messenger

2018 ◽  
Vol 6 (1) ◽  
pp. 1 ◽  
Author(s):  
MUHAMAD IRFAN KURNIAWAN ◽  
UNANG SUNARYA ◽  
ROHMAT TULLOH
Keyword(s):  
Internet Of Things ◽  
Surveillance System ◽  
Performance Test ◽  
Video Recording ◽  
Raspberry Pi ◽  
Human Being ◽  
Security Systems ◽  
Home Security ◽  
Research Designs ◽  
Infra Red

ABSTRAKBanyak orang memasang kamera pengawas di rumah untuk memantau rumah ketika  dalam keadaan kosong. Namun tidak ada pemberitahuan secara langsung kepada pemilik rumah ketika ada orang yang tidak dikehendaki terdeteksi oleh sistem kamera pengawas. Kekurangan lainnya adalah kamera tetap merekam video meskipun tidak ada aktifitas yang terdeteksi. Penelitian ini merancang sistem keamanan rumah berbasis Internet of Things (IoT) memanfaatkan Telegram Messenger. Ketika sensor PIR (Passive Infra Red) mendeteksi gerak manusia, maka kamera Raspberry Pi akan mengambil foto dan mengirimkan hasilnya kepada pengguna melalui Telegram Messenger. Bot pada Telegram Messenger akan menawarkan 2 fitur yang dapat dipilih oleh pemilik rumah, yaitu mengambil foto atau video. Dari hasil pengujian yang dilakukan, didapatkan hasil berupa jarak maksimum deteksi obyek terhadap sensor adalah 6 meter. Dari Pengujian yang dilakukan terbukti sistem mampu bekerja mendeteksi, merekam dan mengirim hasilnya ke pengguna. Waktu yang dibutuhkan untuk pegiriman pesan deteksi obyek sebesar 4.73 detik. Untuk request foto sampai dengan foto diterima membutuhkan waktu 5.73 detik dan untuk video membutuhkan waktu 14.86 detik.Kata kunci: Keamanan rumah, Raspberry Pi, IoT, Sensor PIR, Telegram MessengerABSTRACTMany people install surveillance system at home to monitor when the house is empty. But there is no direct notification to the homeowner when unwanted person is detected by the surveillance system. Another drawback is that the camera remains a video recording even though no activity is detected. These research designs home security systems based on the IoT using Telegram Messenger. The way the system works is when the PIR sensor detects the presence of human being objects, the Raspberry Pi camera will take photos and send the results to the user via the Telegram Messenger. The bot on the Telegram Messenger will offer 2 features that can be selected by users, which are taking photos or videos. The results of performance test of the system, show that the maximum distance of the object against the sensor that can be detected is  6 meters. The system proved able to work to detect, record and send the results to the user. Average time for the delivery of alert messages is 4.73 seconds. Time needed to process photo request until received by users are 5.73 seconds and 14.86 seconds respectively.Keywords: Home security, Raspberry Pi, IoT, PIR Sensor, Telegram Messenger

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.

Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

2020 ◽  
Vol 8 (1) ◽  
pp. 57-73
Author(s):  
Lery Sakti Ramba
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Background Noise ◽  
Electronic Devices ◽  
Recognition System ◽  
Background Intensity ◽  
Automation System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Home Automation System

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.

Sign in / Sign up

Export Citation Format

Share Document