scholarly journals Rfid Based Cash Wallet for Parking Garages using Timestamp

2020 ◽  
Vol 9 (1) ◽  
pp. 1932-1936
Keyword(s):  
Human Activity ◽  
Raspberry Pi ◽  
Time Duration ◽  
Rfid Tag ◽  
Vehicle Identification ◽  
Public Areas ◽  
Parking Garages ◽  
Empty Slot ◽  
Parking Place

With the increase in usage and availability of vehicles, finding an empty parking slot becoming very difficult in many public areas. So there is a requirement for a device that identifies empty slots in a parking place and also keeps the track of vehicles which are already parked very expertly. This task reduces human activity on the parking location in searching of empty slots and scheming the payment based on the timestamp i.e. how much time the vehicle is parked at the particular slot based on the use of parking place with the resource of money pockets. Some of the steps included in this functioning are vehicle identification, detection of empty slot and scheming of amount based on the time duration of parking. Vehicle identification is achieved with the use of RFID Tag, slot detection is also shown and amount calculation is done on the duration of parking. Here we are connecting all of the sensors to Raspberry pi to stumble on RFID Tag and deduct the charge from the cash wallet.

Passive UHF RFID Technology Applied to Automatic Vehicle Identification

2012 ◽  
pp. 188-220 ◽  
Author(s):  
Salvador Ricardo Meneses González ◽  
Roberto Linares y Miranda
Keyword(s):  
Electromagnetic Compatibility ◽  
Rfid Technology ◽  
Uhf Rfid ◽  
Propagation Channel ◽  
Rfid Tag ◽  
Passive Uhf Rfid ◽  
Rfid Systems ◽  
Vehicle Identification ◽  
Automatic Vehicle Identification ◽  
Interference Problems

In this chapter, propagation channel aspects in current passive UHF RFID systems applied for automatic vehicular identification (AVI) are presented, considering the antennas design for passive UHF RFID tag and some problems relative to the electromagnetic compatibility. These issues are focused on RFID link, reader-tag-reader, and the channel modelling that is supported with measurements, and reader-reader interference problems are analysed.

scholarly journals Iot Based Smart Wallet Security and Fake Currency Detection System

2019 ◽  
Vol 8 (9S2) ◽  
pp. 30-34
Keyword(s):  
Detection System ◽  
Mobile App ◽  
Security System ◽  
Raspberry Pi ◽  
Rfid Technology ◽  
Rfid Tag ◽  
Rfid Reader ◽  
The Status

The main objective is to create a security system for wallet based on RFID technology and also keep an account of how much money is coming inside and going out of the wallet which is done using tcs3200 colour sensor by which we can have an account of the amount of money spent and update the same on the mobile app. So, this project basically alerts the person if the wallet is missing from his/her pocket and also shares the location of the same and also gives the information of how much he/she has spent. The major components used in this paper include Raspberry PI, RFID Reader, RFID Tag, GPS Module, and TCS3200 Colour Sensor. Whenever the RFID card is placed near to the reader, the RFID reader obtains the UID (unique key) information about the card. The location of the wallet is obtained using the GPS Module. This detail is notified to user when the wallet is not connected. The status obtained by the RFID reader and the GPS module is collected by Raspberry PI. Using the PI’s WIFI, the details are posted onto the cloud. All the details posted onto the cloud are accessed via the APP and also through a website portal in case of any emergency

scholarly journals Long-Range Monitoring System with PDMS Material

2018 ◽  
Vol 10 (3) ◽  
pp. 974
Author(s):  
Norsaidah Muhamad Nadzir ◽  
M. K. A. Rahim ◽  
F. Zubir ◽  
H. A. Majid
Keyword(s):  
Monitoring System ◽  
Long Range ◽  
Raspberry Pi ◽  
Uhf Rfid ◽  
Long Distance ◽  
Rfid Tag ◽  
Rfid Reader

This paper describes the development of a long range monitoring system that integrates Cottonwood: UHF Long Distance RFID reader module with Raspberry Pi 3. When a UHF RFID tag is within the UHF RFID reader antenna’s range, the unique ID of the tag will be transferred to the Raspberry Pi 3 to be processed. Then, the data will be sent over to the database wirelessly to be managed, stored, and displayed. The paper also describes the measurement done to determine the most suitable thickness of PDMS material so that it could be incorporated as a wearable transponder. After the result is calculated and tabulated, it can be concluded that the most suitable thickness of PDMS material for the transponder is 8 mm.

scholarly journals Energy Efficacious IoT Based Nifty Parking Information System

2020 ◽  
Vol 9 (3) ◽  
pp. 3151-3156
Keyword(s):  
Information System ◽  
Traffic Management ◽  
Recognition Algorithm ◽  
Energy Utilization ◽  
Raspberry Pi ◽  
License Plate ◽  
Parking Space ◽  
Rfid Tag ◽  
Camera Module ◽  
Mode Of Transportation

Urbanization has inflated populace. This has upsurged traffic and pollution turning traffic management into a tangible reality. Gazillions of people around the globe prefer ownership of private vehicles over public mode of transportation. There is an imbalance between the available parking space and demand. The proposed Internet-of-Things (IoT) based nifty parking information system (IPIS) module is deployed on-site to monitor vehicles, signal the availability of parking space to the user, facilitate reservation of the parking slot and thereby reduce the time in finding the parking slot. MIT App Inventor creates applications on Android operating system to facilitate slot reservation for authenticated users. IPIS integrates IoT based Raspberry Pi module with the mobile Application to design an eased parking system operable with minimal energy. The user details are recorded in a server database. Based on this, an RFID tag permits user entry and exit into the parking slot. A Raspberry-Pi(R-Pi) camera module captures the license plate image and uses image recognition algorithm to match the license plate of the vehicle with the database, authenticates and then allows the member to park his vehicle in the respective slot. IPIS provides highly secured, double verified user vehicle authentication. The Raspberry- Pi also adjusts the intensity of the lights using machine learning based on the density of the traffic recorded by the camera module. This research focuses on slot reservation for authenticated users, providing map guidance to the booked slot, maximizing slot utilization, facilitating with vehicle and user timestamp transit details in real time for surveillance, conserving parking slot light energy utilization while regulating the cars through parking spaces and also performs predictive analysis on evaluating the optimum distance between the camera and number plate for recognition and power dissipation.

scholarly journals Implementing Smart Attendance system using IoT

2020 ◽  
Vol 9 (3) ◽  
pp. 1635-1637
Keyword(s):  
Radio Frequency ◽  
Integrated Circuit ◽  
Raspberry Pi ◽  
Rfid Tag ◽  
Rfid Reader ◽  
Radio Frequency Signals

In schools and colleges, lot of time is wasted for manual attendance procedures, in such cases our system provides an automated attendance marking system. Every student will be provided with a RFID tag/card with his/her details fed in it and everyone’s tag is unique to others. This data is stored in the tag by modulating and demodulating transmitted radio frequency signals with the help of a built in integrated circuit. As soon as a student places his/her card in front of the RFID reader, the data in it is read and attendance for that student will be registered. This is done with the help of a raspberry pi interfaced with the reader. We can view the attendance status for every student obtained from the excel sheet that is generated. Thus lot of time is saved in providing attendance.

scholarly journals RFID Based Authentication for Non-contacting Attendance Management during Covid19 Pandemic using Raspberry Pi

2021 ◽  
pp. 14-20
Author(s):  
Raj Gajjar ◽  
Bhargav Patel ◽  
Shailesh Khant
Keyword(s):  
Direct Contact ◽  
Data File ◽  
Raspberry Pi ◽  
Card Reader ◽  
Rfid Tag ◽  
Finger Print ◽  
Corona Virus ◽  
Authentication System ◽  
Fingerprint Sensor

Authentication based attendance is very crucial in this COVID Pandemic situation for employers. Finger print based authentication for attendance system is not preferable because it is based on touching or contacting the finger print sensor. Multiple persons use the same sensor for incoming and outgoing authentication which becomes carrier for spreading the corona virus as another person would be in direct contact of the fingerprint sensor. To avoid this situation RFID based authentication system is proposed and initial stages of it are implemented in this paper. Each employee has given an RFID tag with numbers and names assigned to it. Raspberry Pi module is used which is interfaced with other modules like RFID card reader, LCD interface and GSM module. The contact details authenticated by the system can be saved in data file for further analysis of employees’ attendance. This small innovative step can enhance the efforts made to prevent every user from COVID19.

scholarly journals Modelling Human Activity using Smartphone Data

2021 ◽  
Vol 15 (1) ◽  
pp. 58-70
Author(s):  
Suriya Badrinath ◽  
Raja Muthalagu
Keyword(s):  
Real Time ◽  
Human Activity ◽  
Human Activities ◽  
Time Series Data ◽  
Sensor Data ◽  
Series Data ◽  
Raspberry Pi ◽  
Healthy Human ◽  
Deep Cnn ◽  
Rate Sensor

Background: Over time, multichannel time series data were utilized for the purpose of modeling human activity. Instruments such as an accelerometer and gyroscope which had sensors embedded in them, recorded sensor data which were then utilized to record 6-axes, single dimensional convolution for the purpose of formulating a deep CNN. The resultant network achieved 94.79% activity recognition accuracy on raw sensor data, and 95.57% accuracy when Fast Fourier Transform (FFT) knowledge was added to the sensor data. Objective: This study helps to achieve an orderly report of daily Human activities for the overall balanced lifestyle of a healthy human being. Methods: Interfacing is done using Arduino Uno, Raspberry-Pi 3, heart rate sensor and accelerometer ADXL345 to generate real time values of day-to-day human activities such as walking, sleeping, climbing upstairs/downstairs and so on. Initially, the heart pulse of our four tested individuals is recorded and tabulated to depict and draw conclusions all the way from “Low BP” to “Heavy Exercise”. The convolution neural network is initially trained with an online human activity dataset and tested using our real time generated values which are sent to the MAC OS using a Bluetooth interface. Results: We obtain graphical representations of the amount of each activity performed by the test set of individuals, and in turn conclusions which suggest increase or decrease in the consistency of certain activities to the users, depicted through our developed iOS application, “Fitnesse”. Conclusion: The result of this works is used to improve the daily health routines and the overall lifestyle of distressed patients.

scholarly journals ExerTrack—Towards Smart Surfaces to Track Exercises

Technologies ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 17 ◽  
Author(s):  
Biying Fu ◽  
Lennart Jarms ◽  
Florian Kirchbuchner ◽  
Arjan Kuijper
Keyword(s):  
Optical Sensors ◽  
Data Augmentation ◽  
Recognition Accuracy ◽  
Image Data ◽  
Whole Body ◽  
Raspberry Pi ◽  
Smart Surfaces ◽  
Test Study ◽  
Public Areas ◽  
High Recognition Accuracy

The concept of the quantified self has gained popularity in recent years with the hype of miniaturized gadgets to monitor vital fitness levels. Smartwatches or smartphone apps and other fitness trackers are overwhelming the market. Most aerobic exercises such as walking, running, or cycling can be accurately recognized using wearable devices. However whole-body exercises such as push-ups, bridges, and sit-ups are performed on the ground and thus cannot be precisely recognized by wearing only one accelerometer. Thus, a floor-based approach is preferred for recognizing whole-body activities. Computer vision techniques on image data also report high recognition accuracy; however, the presence of a camera tends to raise privacy issues in public areas. Therefore, we focus on combining the advantages of ubiquitous proximity-sensing with non-optical sensors to preserve privacy in public areas and maintain low computation cost with a sparse sensor implementation. Our solution is the ExerTrack, an off-the-shelf sports mat equipped with eight sparsely distributed capacitive proximity sensors to recognize eight whole-body fitness exercises with a user-independent recognition accuracy of 93.5% and a user-dependent recognition accuracy of 95.1% based on a test study with 9 participants each performing 2 full sessions. We adopt a template-based approach to count repetitions and reach a user-independent counting accuracy of 93.6%. The final model can run on a Raspberry Pi 3 in real time. This work includes data-processing of our proposed system and model selection to improve the recognition accuracy and data augmentation technique to regularize the network.

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