scholarly journals Review Paper on Real Time Application of Embedded System for Driver Safety by Using Raspberry Pi

2021 ◽  
Author(s):  
Yogesh Shankar Ghodake ◽  
Sushil S Kulkarni ◽  
Nikhil Deshpande
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Review Paper ◽  
Power Source ◽  
Raspberry Pi ◽  
Driver Safety ◽  
Vehicle Accidents ◽  
Driver Drowsiness ◽  
Frequent Monitoring ◽  
Real Time Application

This project gives approach for real time detection of car driver drowsiness and alcoholic intoxication. There are various reasons behind the increasing number of vehicle accidents which are becoming a very serious issue but the ones that can be prevented are the mental and physical condition of the driver while driving and frequent monitoring of it can be achieved with the help of technology. In this project, we have developed an automatic system with will perform some task like issuing the alarm notification and switching off the car power source to stop the car upon receiving the positive detection message from Raspberry-pi.

scholarly journals Artificial vision system for object classification in real time using Raspberry Pi and a Web camera

2021 ◽  
pp. 20-25
Author(s):  
Tomás Serrano-Ramírez ◽  
Ninfa del Carmen Lozano-Rincón ◽  
Arturo Mandujano-Nava ◽  
Yosafat Jetsemaní Sámano-Flores
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Vision System ◽  
Low Cost ◽  
Artificial Vision ◽  
Maintenance Cost ◽  
Raspberry Pi ◽  
Smart Cameras ◽  
Suggested Technique ◽  
Web Camera

Computer vision systems are an essential part in industrial automation tasks such as: identification, selection, measurement, defect detection and quality control in parts and components. There are smart cameras used to perform tasks, however, their high acquisition and maintenance cost is restrictive. In this work, a novel low-cost artificial vision system is proposed for classifying objects in real time, using the Raspberry Pi 3B + embedded system, a Web camera and the Open CV artificial vision library. The suggested technique comprises the training of a supervised classification system of the Haar Cascade type, with image banks of the object to be recognized, subsequently generating a predictive model which is put to the test with real-time detection, as well as the calculation for the prediction error. This seeks to build a powerful vision system, affordable and also developed using free software.

scholarly journals A Low-cost Artificial Neural Network Model for Raspberry Pi

2020 ◽  
Vol 10 (2) ◽  
pp. 5466-5469 ◽  
Author(s):  
S. N. Truong
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Embedded System ◽  
Real Time ◽  
Image Recognition ◽  
Low Cost ◽  
Recognition Rate ◽  
Raspberry Pi ◽  
The Neural Network ◽  
Binary Array

In this paper, a ternary neural network with complementary binary arrays is proposed for representing the signed synaptic weights. The proposed ternary neural network is deployed on a low-cost Raspberry Pi board embedded system for the application of speech and image recognition. In conventional neural networks, the signed synaptic weights of –1, 0, and 1 are represented by 8-bit integers. To reduce the amount of required memory for signed synaptic weights, the signed values were represented by a complementary binary array. For the binary inputs, the multiplication of two binary numbers is replaced by the bit-wise AND operation to speed up the performance of the neural network. Regarding image recognition, the MINST dataset was used for training and testing of the proposed neural network. The recognition rate was as high as 94%. The proposed ternary neural network was applied to real-time object recognition. The recognition rate for recognizing 10 simple objects captured from the camera was 89%. The proposed ternary neural network with the complementary binary array for representing the signed synaptic weights can reduce the required memory for storing the model’s parameters and internal parameters by 75%. The proposed ternary neural network is 4.2, 2.7, and 2.4 times faster than the conventional ternary neural network for MNIST image recognition, speech commands recognition, and real-time object recognition respectively.

scholarly journals Design of Watchdog Timer for Real Time Applications

2019 ◽  
Vol 8 (9S2) ◽  
pp. 695-697
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Operation Time ◽  
Critical Conditions ◽  
System Cost ◽  
Design Structure ◽  
Real Time Applications ◽  
The Embedded System ◽  
Real Time Application ◽  
Automatic Systems

The Embedded system is employ in safety and critical application, which is greater reliability. The watchdog timers are used in automatic systems to handle the operation time for secure the timer failure. Majority of the watchdog timers used an additional circuit to adjust their timeout position and it will provide limited services in terms of working. This paper presents the architecture of a watchdog timer and also gives the design structure, it will working in safety and critical conditions. The operations are general and it can be used to monitor the working of any processor in real-time application. This paper discussed the implementation of the proposed timer in a FPGA. This will helps to design easily in different applications, it will gives reduces the overall system cost. The watchdog timers is to detect and give response very effectively and also gives the responses of faults by analyzing the simulations

scholarly journals Design and Analysis of IOT Based Real Time System for Door Locking/Unlocking using Face Identification

2020 ◽  
Vol 8 (5) ◽  
pp. 2093-2095
Keyword(s):  
Real Time ◽  
Recognition System ◽  
Raspberry Pi ◽  
Real Time System ◽  
Security Issues ◽  
Door Opening ◽  
The Face ◽  
Face Detection And Recognition ◽  
Real Time Application ◽  
Detection And Recognition

In this era we are facing security issues in every aspect. So for resolving this issue we are proposes a real time application controlled door locking/unlocking mechanism which harnesses the power of IOT and machine learning for smooth functionality. The door unlocking system proposed here uses a Raspberry Pi 3 model B for computation along with a Pi Camera to take face as an input of the user. Also in order to make door unlocking fail proof, fingerprint sensor is used. Scenarios like bad lighting and camera failure can be easily dealt using this sensor. The face detection and recognition system used for door opening will be able to learn user’s faces from time to time and update its dataset. So any subtle changes in the face of user like addition of spectacles or removal of beard can be easily dealt with.

scholarly journals Assessing the Best Gap-Filling Technique for River Stage Data Suitable for Low Capacity Processors and Real-Time Application Using IoT

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6354
Author(s):  
Antonio Madueño Luna ◽  
Miriam López Lineros ◽  
Javier Estévez Gualda ◽  
Juan Vicente Giráldez Cervera ◽  
José Miguel Madueño Luna
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Raspberry Pi ◽  
Data Sets ◽  
Analysis Method ◽  
Gap Filling ◽  
Remote Communication ◽  
Catchment Basin ◽  
River Stage ◽  
Real Time Application

Hydrometeorological data sets are usually incomplete due to different reasons (malfunctioning sensors, collected data storage problems, etc.). Missing data do not only affect the resulting decision-making process, but also the choice of a particular analysis method. Given the increase of extreme events due to climate change, it is necessary to improve the management of water resources. Due to the solution of this problem requires the development of accurate estimations and its application in real time, this work present two contributions. Firstly, different gap-filling techniques have been evaluated in order to select the most adequate one for river stage series: (i) cubic splines (CS), (ii) radial basis function (RBF) and (iii) multilayer perceptron (MLP) suitable for small processors like Arduino or Raspberry Pi. The results obtained confirmed that splines and monolayer perceptrons had the best performances. Secondly, a pre-validating Internet of Things (IoT) device was developed using a dynamic seed non-linear autoregressive neural network (NARNN). This automatic pre-validation in real time was tested satisfactorily, sending the data to the catchment basin process center (CPC) by using remote communication based on 4G technology.

scholarly journals Foot Print Health Monitoring System based on Open Cv

2019 ◽  
Vol 8 (6) ◽  
pp. 699-701
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Raspberry Pi ◽  
Time Base ◽  
Base System ◽  
Computing Platform ◽  
Human Footprint ◽  
Key Points ◽  
The Embedded System ◽  
Data Requirements

Human footprint is considered has the latest traits that could be used to detect an individual’s identity computes parameters. The main objective is to establish the ability of image processing algorithms on a small computing platform. We designed the embedded system which reads and recognizes a person their identity. The major aim of the paper briefs the characteristics of Patient’s data, requirements and Report behind implementing a real-time base system. The person’s foot image is segmented and its key points are located. The foot is aligned and edited, cropped as per the key points and is developed and resized. These methods are used for recognizing and subdividing. Color place a major role in multiple application for footprint detection. This project is focused on lightweight technique were mainly used due to the drawback of real time based applications and Raspberry Pi capabilities

scholarly journals A Novel Approach to Detect Driver Drowsiness and Alcohol Intoxication using Haar Algorithm with Raspberry Pi

2020 ◽  
Vol 3 (9) ◽  
pp. 48-51
Author(s):  
M. Agna Manu ◽  
Dayana Jaijan ◽  
S. N. Nissa ◽  
S. Jesna ◽  
Abin Shukoor ◽  
...  
Keyword(s):  
Real Time ◽  
Detection System ◽  
Alcohol Intoxication ◽  
Drunk Driving ◽  
Research Area ◽  
Processing Technique ◽  
Road Accident ◽  
Image Processing Technique ◽  
Raspberry Pi ◽  
Driver Drowsiness

Drowsiness in driver and alcohol consumption are the critical cause of road accident and death. Lives of pedestrian and passengers are put to risk as drivers tend to fall asleep and also when the driver is in his drunken state. Detection of driver drowsiness and its indication is an active research area now. There are 3 methods for detection of driver fatigue which includes vehicle-based method, behavioural method, and physiological based method. We adopt behavioural method. This project is aimed towards developing a prototype of drowsiness and alcohol detection system using Haar algorithm with raspberry pi. This project proposes a real time detection of driver’s drowsiness as well as alcohol intoxication and subsequently alerting them. The primary purpose of this drowsiness and alcohol detection system is to develop a system that can reduce the number of accidents from drowsiness and drunk driving of vehicle. It consists of camera which is placed in front of the driver to detect the face. An alcohol sensor which is a gas sensor used to sense the drinking state of driver. Haar algorithm is used for face detection. The results demonstrate the accuracy and robustness of the hybridized of image processing technique. Thus, it can be concluded the proposed approach is an effective solution for a real-time of driver drowsiness and alcohol detection.

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