Lightweight and Cost-Effective Spectrum Analyser Based on Software Defined Radio and Raspberry Pi

2017 ◽  
Author(s):  
David Ball ◽  
Nitin Naik ◽  
Paul Jenkins
Keyword(s):  
Software Defined Radio ◽  
Cost Effective ◽  
Raspberry Pi ◽  
Spectrum Analyser

scholarly journals Design approach for wideband FM receiver using RTL-SDR and raspberry PI

2018 ◽  
Vol 7 (2.31) ◽  
pp. 9 ◽  
Author(s):  
P Satya Narayana ◽  
M N.V.S. Syam Kumar ◽  
A Keerthi Kishan ◽  
K V.R.K. Suraj
Keyword(s):  
Communication System ◽  
Software Defined Radio ◽  
Low Cost ◽  
Wide Band ◽  
Cost Effective ◽  
Raspberry Pi ◽  
Processing Unit ◽  
Capture Process ◽  
Band Frequency ◽  
Wide Range

Software defined radio replaced majority of hardware modules like mixers, filters, modulators and demodulators etc., with Software blocks in the field of radio electronics and communication. In this some or all the functionalities are Configurable using this software implemented on technologies like FPGAs, DSPs etc. Owing to lack of ease in implementing and reconfiguring huge hardware modules, we move on to implement an adaptable communication system with the help of SDR, as it can be easily configured to work with wide range of frequencies. We find various SDR transceiver modules which can be interfaced with digital computer and aided with firmware like GNU radio, SDR shark, etc., allowing us to construct blocks with the help of built in components that decode and process the received data and produce required output. In requirement of implementing a cost-effective, compact sized and portable system, we use a processing unit providing enough computational power to perform signal processing tasks which is Raspberry pi. Here we are going to implement a low cost SDR communication system that capture, process and visualize the Wide Band Frequency signal. 

IoT Based Bus Tracking System

2021 ◽  
Vol 9 (VI) ◽  
pp. 2889-2893
Author(s):  
Miss Payal W. Paratpure
Keyword(s):  
Tracking System ◽  
Cost Effective ◽  
Raspberry Pi ◽  
Accurate Estimation ◽  
Time Of Arrival ◽  
Test Results ◽  
Cloud Server ◽  
Bus Operator ◽  
Gps Device

Tracking of public bus location requires a GPS device to be installed, and lots of bus operators in developing countries don't have such an answer in situ to supply an accurate estimation of bus time of arrival (ETA). Without ETA information, it's very difficult for the overall public to plan their journey effectively. In this paper, implementation of an innovative IOT solution to trace the real time location of buses without requiring the deployment of a GPS device is discussed. It uses Bluetooth Low Energy (BLE) proximity beacon to trace the journey of a bus by deploying an Estimate location beacon on the bus. BLE detection devices (Raspberry Pi 4) are installed at selected bus stops along the path to detect the arrival of buses. Once detected, the situation of the bus is submitted to a cloud server to compute the bus ETAs. A field trial is currently being conducted in Johor, Malaysia together with an area bus operator on one single path. Our test results showed that the detection of BLE beacons is extremely accurate and it's feasible to trace the situation of buses without employing a GPS device during a cost-effective way.

scholarly journals MEDIA PEMBELAJARAN PEMANCAR WIRELESS FM MENGGUNAKAN RASPBERRY PI

2020 ◽  
Vol 11 (1) ◽  
pp. 257-262
Author(s):  
Solekhan Solekhan ◽  
Mohammad Iqbal
Keyword(s):  
Software Defined Radio ◽  
Raspberry Pi ◽  
Single Board Computer

Media pembelajaran merupakan alat bantu yang diperlukan untuk menambah pemahaman dalam pembelajaran. Pemancaran gelombang FM, yang merupakan sebagian dalam Pengolahan Sinyal tanpa kabel, dibutuhkan dalam pemahaman pemancaran gelombang Radio. Raspberry Pi yang merupakan Single Board Computer, dapat diprogram dengan mudah dan memiliki keluwesan dalam penggunaannya. Dalam penelitian ini Raspberry dimanfaatkan penggunaannya sebagai pemancar FM. Proses pemancaran dilakukan dengan memanfaatkan Software Defined Radio. Dari hasil pengujian, pengubahan variasi frekuensi pemancaran FM dapat dilakukan dengan mudah, dan mampu memancarkan gelombang FM dengan baik.  

scholarly journals Implementasi Multiple Honeypot pada Raspberry Pi dan Visualisasi Log Honeypot Menggunakan ELK Stack

Repositor ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 475
Author(s):  
Ilfan Arif Romadhan ◽  
Syaifudin Syaifudin ◽  
Denar Regata Akbi
Keyword(s):  
Network Security ◽  
Cost Effective ◽  
Raspberry Pi ◽  
Log Analysis ◽  
Security Threats ◽  
Multiple Sources ◽  
Important Data ◽  
Desktop Computers ◽  
Monitoring Result

ABSTRAKPerlindungan terhadap keamanan jaringan merupakan hal yang sangat penting untuk dilakukan. Mengingat kemudahan dalam mengakses jaringan memungkinkan adanya gangguan dari pihak yang ingin menyerang, merusak, bahkan mengambil data penting. Honeypot memang tidak menyelesaikan masalah pada keamanan jaringan, namun honeypot membuat penelitian tentang serangan menjadi lebih sederhana dengan konsep yang mudah untuk dimengerti dan dimplementasikan. Penelitian ini menerapkan beberapa honeypot menggunakan Raspberry pi dan ELK stack untuk monitoring hasil yang didapatkan oleh honeypot. Tujuan dari penelitian ini untuk merancang sistem yang mampu mendeteksi serangan pada jaringan menggunakan honeypot. Raspberry pi digunakan sebagai sensor honeypot untuk pemantauan ancaman keamanan terbukti hemat biaya dan efektif menggantikan komputer desktop. ELK stack memudahkan pemusatan data dari berbagai sumber dan membuat analisis log yang awalnya rumit untuk dianalisis menjadi lebih menarik.ABSTRACTProtection of network security is very important to do. Given the ease in accessing the network allows for interference from parties who want to attack, destroy, and even retrieve important data. Honeypot does not solve the problem on network security, but the honeypot makes research about attacks become simpler with concepts that are easy to understand and implement. This research applies some honeypot using Raspberry pi and ELK stack for monitoring result obtained by honeypot. The purpose of this research is to design a system capable of detecting attacks on a network using a honeypot. Raspberry pi is used as a honeypot sensor for monitoring proven cost-effective and cost-effective security threats to replace desktop computers. The ELK stack facilitates the convergence of data from multiple sources and makes log analysis initially complex for analysis to be more interesting.

scholarly journals UAV Landing Using Computer Vision Techniques for Human Detection

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 613
Author(s):  
David Safadinho ◽  
João Ramos ◽  
Roberto Ribeiro ◽  
Vítor Filipe ◽  
João Barroso ◽  
...  
Keyword(s):  
Computer Vision ◽  
Tall Buildings ◽  
Landing Site ◽  
Cost Effective ◽  
Human Detection ◽  
Raspberry Pi ◽  
Single Shot ◽  
Irregular Terrain ◽  
Effective System ◽  
Satellite Signal

The capability of drones to perform autonomous missions has led retail companies to use them for deliveries, saving time and human resources. In these services, the delivery depends on the Global Positioning System (GPS) to define an approximate landing point. However, the landscape can interfere with the satellite signal (e.g., tall buildings), reducing the accuracy of this approach. Changes in the environment can also invalidate the security of a previously defined landing site (e.g., irregular terrain, swimming pool). Therefore, the main goal of this work is to improve the process of goods delivery using drones, focusing on the detection of the potential receiver. We developed a solution that has been improved along its iterative assessment composed of five test scenarios. The built prototype complements the GPS through Computer Vision (CV) algorithms, based on Convolutional Neural Networks (CNN), running in a Raspberry Pi 3 with a Pi NoIR Camera (i.e., No InfraRed—without infrared filter). The experiments were performed with the models Single Shot Detector (SSD) MobileNet-V2, and SSDLite-MobileNet-V2. The best results were obtained in the afternoon, with the SSDLite architecture, for distances and heights between 2.5–10 m, with recalls from 59%–76%. The results confirm that a low computing power and cost-effective system can perform aerial human detection, estimating the landing position without an additional visual marker.

Implementation of Multi-Hop Cognitive Radio Testbed using Raspberry Pi and USRP

2017 ◽  
Vol 9 (4) ◽  
pp. 37-48 ◽  
Author(s):  
Hyun Jae Park ◽  
Gyu-min Lee ◽  
Seung-Hun Shin ◽  
Byeong-hee Roh ◽  
Ji Myeong Oh
Keyword(s):  
Cognitive Radio ◽  
Wireless Communication ◽  
Spectrum Sensing ◽  
Software Defined Radio ◽  
Software Radio ◽  
Energy Detection ◽  
Raspberry Pi ◽  
Public Attention ◽  
Test Environment ◽  
Cr System

The increased usage of wireless communication has created a wireless frequency shortage problem. Cognitive Radio (CR) has attracted public attention, as one of the solutions that can resolve this issue. In this paper, the authors built an actual CR system testbed using the SDR (Software Defined Radio) platform, USRP (Universal Software Radio Peripheral) board, the SDR development toolkit, GNU Radio, and Raspberry Pi3, which is a single board computer. They configured Secondary User (SU)s with Raspberry Pi3 for straightforward and portable test environment. The authors' testbed performs spectrum sensing based on energy detection and determines whether the channel is occupied or not. Experimental results not only show performance but also provide their testbed that works well in multi-hop environments.

scholarly journals Real time Implementation of Face Recognition System on Raspberry Pi

2018 ◽  
Vol 7 (2.17) ◽  
pp. 85
Author(s):  
K Raju ◽  
Dr Y.Srinivasa Rao
Keyword(s):  
Face Recognition ◽  
High Performance ◽  
Recognition Rate ◽  
Cost Effective ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Recognition System ◽  
Effective System ◽  
Face Detection And Recognition ◽  
Improved Accuracy

Face Recognition is the ability to find and detect a person by their facial attributes. Face is a multi dimensional and thus requires a considerable measure of scientific calculations. Face recognition system is very useful and important for security, law authorization applications, client confirmation and so forth. Hence there is a need for an efficient and cost effective system. There are numerous techniques that are as of now proposed with low Recognition rate and high false alarm rate. Hence the major task of the research is to develop face recognition system with improved accuracy and improved recognition time. Our objective is to implementing Raspberry Pi based face recognition system using conventional face detection and recognition techniques such as A Haar cascade classifier is trained for detection and Local Binary Pattern (LBP) as a feature extraction technique. With the use of the Raspberry Pi kit, we go for influencing the framework with less cost and simple to use, with high performance. 

Sign in / Sign up

Export Citation Format

Share Document