Automatic target-reading device based on image recognition and Raspberry Pi

This study is about the manufacturing of a personified automatic robotic lawn mower with image recognition. The system structure is that the platform above the crawler tracks is combined with the lawn mower, steering motor, slide rail, and webcam to achieve the purpose of personification. Crawler tracks with a strong grip and good ability to adapt to terrain are selected as a moving vehicle to simulate human feet. In addition, a lawn mower mechanism is designed to simulate the left and right swing of human mowing to promote efficiency and innovation, and then human eyes are replaced by Webcam to identify obstacles. A human-machine interface is added so that through the mobile phone remote operation, users can choose a slow mode, inching mode, and obstacle avoidance mode on the human-machine interface. When the length of both sides of the rectangular area is input to the program, the automatic robotic lawn mower will complete the instruction according to the specified path. The chip of a Digital Signal Processor (DSP) TMS320F2808 is used as the core controller, and Raspberry Pi is used as image recognition and human-machine interface design. This robot can reduce labor costs and improve the efficiency of mowing by remote control. In addition to the use as an automatic mower on farms, this study concept can also be used in the lawn maintenance of golf courses and school playgrounds.

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A Low-cost Artificial Neural Network Model for Raspberry Pi

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.3357 ◽

2020 ◽

Vol 10 (2) ◽

pp. 5466-5469 ◽

Cited By ~ 1

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.

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Image recognition based billing system for fruit shop using raspberry PI

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1055/1/012030 ◽

2021 ◽

Vol 1055 (1) ◽

pp. 012030

Author(s):

A Vennila ◽

S Balambigai ◽

G Deepa ◽

M Ilakkia ◽

P Harshikaa Devi ◽

...

Keyword(s):

Image Recognition ◽

Raspberry Pi ◽

Billing System

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Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189048 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5699-5711

Author(s):

Shirong Long ◽

Xuekong Zhao

Keyword(s):

Feature Extraction ◽

Particle Swarm Optimization ◽

Deep Learning ◽

Real Time ◽

Image Recognition ◽

Particle Swarm ◽

Learning Technology ◽

Search Performance ◽

Swarm Optimization ◽

Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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Implementasi Raspberry Pi Sebagai IP Kamera untuk Memonitoring Studio Bioskop

KOPERTIP Jurnal Ilmiah Manajemen Informatika dan Komputer ◽

10.32485/kopertip.v1i1.4 ◽

2017 ◽

Vol 1 (1) ◽

pp. 5-10

Author(s):

Sugiyatno Sugiyatno

Keyword(s):

Raspberry Pi

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PROTOTYPE ALAT PEMANTAU SALURAN AIR BAWAH TANAH DENGAN MENGGUNAKAN WEBCAM C170 BERBASIS RASPBERRY PI

Journal CERITA ◽

10.33050/cerita.v1i1.192 ◽

2015 ◽

Vol 1 (1) ◽

pp. 37-45

Author(s):

Irwansyah Irwansyah ◽

Hendra Kusumah ◽

Muhammad Syarif

Keyword(s):

Real Time ◽

Monitoring System ◽

Public Awareness ◽

Underground Water ◽

Raspberry Pi ◽

Laptop Computer ◽

Web Browser ◽

Disaster Monitoring ◽

The Times ◽

Human Desire

Along with the times, recently there have been found tool to facilitate human’s work. Electronics is one of technology to facilitate human’s work. One of human desire is being safe, so that people think to make a tool which can monitor the surrounding condition without being monitored with people’s own eyes. Public awareness of the underground water channels currently felt still very little so frequent floods. To avoid the flood disaster monitoring needs to be done to underground water channels.This tool is controlled via a web browser. for the components used in this monitoring system is the Raspberry Pi technology where the system can take pictures in real time with the help of Logitech C170 webcam camera. web browser and Raspberry Pi make everyone can control the devices around with using smartphone, laptop, computer and ipad. This research is expected to be able to help the users in knowing the blockage on water flow and monitored around in realtime.

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Implementasi Voip Server Berbasis IPV6 Dengan Raspberry PI

Manutech : Jurnal Teknologi Manufaktur ◽

10.33504/manutech.v9i01.32 ◽

2019 ◽

Vol 9 (01) ◽

pp. 47-54

Author(s):

Rabbai San Arif ◽

Yuli Fitrisia ◽

Agus Urip Ari Wibowo

Keyword(s):

Internet Protocol ◽

Wireless Transmission ◽

Raspberry Pi ◽

Test Results ◽

Average Delay ◽

Ipv6 Network ◽

Ip Network ◽

Selection Of ◽

Better Than

Voice over Internet Protocol (VoIP) is a telecommunications technology that is able to pass the communication service in Internet Protocol networks so as to allow communicating between users in an IP network. However VoIP technology still has weakness in the Quality of Service (QoS). VOPI weaknesses is affected by the selection of the physical servers used. In this research, VoIP is configured on Linux operating system with Asterisk as VoIP application server and integrated on a Raspberry Pi by using wired and wireless network as the transmission medium. Because of depletion of IPv4 capacity that can be used on the network, it needs to be applied to VoIP system using the IPv6 network protocol with supports devices. The test results by using a wired transmission medium that has obtained are the average delay is 117.851 ms, jitter is 5.796 ms, packet loss is 0.38%, throughput is 962.861 kbps, 8.33% of CPU usage and 59.33% of memory usage. The analysis shows that the wired transmission media is better than the wireless transmission media and wireless-wired.

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