scholarly journals A RASPBERRY PI SELF-DRIVING CART BASED ON OPENCV AND DEEP LEARNING .

2021 ◽  
Vol 10 (3) ◽  
pp. 61-67
Keyword(s):  
Deep Learning ◽  
Obstacle Avoidance ◽  
Target Recognition ◽  
Recognition Algorithm ◽  
Traffic Signal ◽  
The Self ◽  
Raspberry Pi ◽  
Signal Recognition ◽  
Ultrasonic Sensors ◽  
Better Than

The self-driving trolley created in this thesis uses cameras and ultrasonic sensors to obtain roadway information, and a deep learning based target recognition algorithm to find out which are the targets in the data obtained, so that the trolley can drive itself on a simulated roadway with functions such as obstacle avoidance and traffic signal recognition. Originally the car used a Raspberry Pi 3b+, but here the jetson nano, which is better than the Raspberry Pi 3b+, is used to implement it.

scholarly journals A Traffic Signal Recognition Algorithm Based on Self-paced Learning and Deep Learning

2020 ◽  
Vol 25 (2) ◽  
pp. 239-244
Author(s):  
Tingmei Wang ◽  
Haiwei Shen ◽  
Yuanjie Xue ◽  
Zhengkun Hu
Keyword(s):  
Deep Learning ◽  
Recognition Algorithm ◽  
Traffic Signal ◽  
Signal Recognition

scholarly journals IoT programming to Develop Self Driving Robotics Car using OpenCV and Other Emerging Technologies

2020 ◽  
Author(s):  
Mohammed Maaz ◽  
Sabah Mohammed
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
The Self ◽  
Reverse Direction ◽  
Raspberry Pi ◽  
Specific Direction ◽  
Different Types ◽  
Practical Model ◽  
Individual Effort

<p>The advancement of Artificial Intelligence & Deep Learning has catalyzed the field of technology. The progression in these fields is exponentially increasing, and the discoveries which were once just an imagination are now changed into reality. The evolution of cars each year has made a lot of difference in people travelling from one place to another. One such reform involving Artificial Intelligence & Deep Learning is the birth of a self-driving car. The future is here where one can reach their destination hassle-free safely without the fear of accidents. This paper introduces a practical model of the self-driving robotics car, which can travel from one position to another on different types of tracks. A Pi-camera module is attached with the help of Raspberry Pi, which sends series of image frames to the Convolutional neural network, which then foretells the car to move in a specific direction, i.e. right, left, forward and reverse direction. The outcome is the robotics car, which travels in the desired direction without any individual effort.<br></p>

scholarly journals Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas

2020 ◽  
Vol 10 (21) ◽  
pp. 7448
Author(s):  
Jorge Felipe Gaviria ◽  
Alejandra Escalante-Perez ◽  
Juan Camilo Castiblanco ◽  
Nicolas Vergara ◽  
Valentina Parra-Garces ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Urban Areas ◽  
Low Cost ◽  
Raspberry Pi ◽  
Automatic Identification ◽  
Signal Recognition ◽  
Main Challenge

Real-time automatic identification of audio distress signals in urban areas is a task that in a smart city can improve response times in emergency alert systems. The main challenge in this problem lies in finding a model that is able to accurately recognize these type of signals in the presence of background noise and allows for real-time processing. In this paper, we present the design of a portable and low-cost device for accurate audio distress signal recognition in real urban scenarios based on deep learning models. As real audio distress recordings in urban areas have not been collected and made publicly available so far, we first constructed a database where audios were recorded in urban areas using a low-cost microphone. Using this database, we trained a deep multi-headed 2D convolutional neural network that processed temporal and frequency features to accurately recognize audio distress signals in noisy environments with a significant performance improvement to other methods from the literature. Then, we deployed and assessed the trained convolutional neural network model on a Raspberry Pi that, along with the low-cost microphone, constituted a device for accurate real-time audio recognition. Source code and database are publicly available.

scholarly journals A Deep Neural Network-Based Target Recognition Algorithm for Robot Scenes

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Lijing Liu
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Target Identification ◽  
Target Recognition ◽  
Recognition Algorithm ◽  
Accuracy Rate ◽  
Intelligent Robots ◽  
Vision Sensors ◽  
Pyramid Model ◽  
High Level

Intelligent robots are a key vehicle for artificial intelligence and are widely employed in all aspects of everyday life and work, not just in the industry. One of the talents required for intelligent robots to complete their jobs is the capacity to identify their environment, which is a crucial obstacle to be overcome. Deep learning-based target identification algorithms currently do not fully leverage the link between high-level semantic and low-level detail information in the prediction step and hence are less successful in recognizing tiny target objects. Target recognition via vision sensors has also improved in accuracy and efficiency because of the development of deep learning. However, due to the insufficient usage of semantic information and precise texture information of underlying characteristics, tiny target recognition remains a difficulty. To address the aforementioned issues, we propose a target detection method based on a jump-connected pyramid model to improve the target detection performance of robots in complex scenarios. In order to verify the effectiveness of the algorithm, we designed and implemented a software system for target detection of intelligent robots and performed software integration of the proposed algorithm model with excellent experimental results. These experiments reveal that, when compared to other algorithms, our suggested algorithm’s characteristics have higher flexibility and robustness and can deliver a higher scene classification accuracy rate.

scholarly journals IoT programming to Develop Self Driving Robotics Car using OpenCV and Other Emerging Technologies

2020 ◽  
Author(s):  
Mohammed Maaz ◽  
Sabah Mohammed
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
The Self ◽  
Reverse Direction ◽  
Raspberry Pi ◽  
Specific Direction ◽  
Different Types ◽  
Practical Model ◽  
Individual Effort

<p>The advancement of Artificial Intelligence & Deep Learning has catalyzed the field of technology. The progression in these fields is exponentially increasing, and the discoveries which were once just an imagination are now changed into reality. The evolution of cars each year has made a lot of difference in people travelling from one place to another. One such reform involving Artificial Intelligence & Deep Learning is the birth of a self-driving car. The future is here where one can reach their destination hassle-free safely without the fear of accidents. This paper introduces a practical model of the self-driving robotics car, which can travel from one position to another on different types of tracks. A Pi-camera module is attached with the help of Raspberry Pi, which sends series of image frames to the Convolutional neural network, which then foretells the car to move in a specific direction, i.e. right, left, forward and reverse direction. The outcome is the robotics car, which travels in the desired direction without any individual effort.<br></p>

Experiments on Design of Obstacle Avoiding Robots Based on Sensors, Bluetooth, and IoT

2020 ◽  
pp. 125-147
Author(s):  
Shirshak Kumar ◽  
Suraj ◽  
Sahil Sandhu ◽  
Narinder Singh Jassal ◽  
Jitendra Virmani ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Mobile Robotics ◽  
Raspberry Pi ◽  
Ultrasonic Sensors ◽  
The Past ◽  
Obstacle Avoiding ◽  
Physical Environments ◽  
On Chip

The mobile robotics industry is related to creating mobile robots that can move around in physical environments. Different types of mobile robot designs for obstacle avoidance have been experimented in the past based on different sensors, trajectory algorithms, etc. The chapter presents implementation details of different obstacle avoiding robots (OARs) using sensors, Bluetooth module, and IoT modules. The sensor-based obstacle-avoiding robots are designed using ultrasonic sensors and Arduino microcontrollers. Bluetooth-based obstacle-avoiding robots have been designed using Arduino mega and Bluetooth module and an Android application. IoT-based obstacle-avoiding robots can be designed in three different ways, using ethernet shield, node MCU, or Raspberry Pi. The IoT-based obstacle-avoiding robot using Raspberry Pi is the most popular mobile robot model that uses maximum on-chip modules in comparison to other designs, and also, the design can be extended by using cameras to use images for sensing the objects in order to avoid collisions.

scholarly journals Radiant Source Signal Recognition Algorithm based on SAE

2018 ◽  
Vol 173 ◽  
pp. 02018
Author(s):  
Ye Wen-qiang ◽  
Yu Zhi-fu ◽  
Zhang Kui ◽  
Wang Hu-bang
Keyword(s):  
Recognition Rate ◽  
Training Model ◽  
Recognition Algorithm ◽  
Identification Algorithm ◽  
Signal Recognition ◽  
Softmax Classifier ◽  
Time Frequency ◽  
Reduction Methods ◽  
Source Signal ◽  
Better Than

Aiming at the shortcomings of traditional radar identification based on artificial judgment and module matching, this paper proposes an intelligent identification algorithm based on joint time-frequency. The radar radiation source signal is transformed by time-frequency, and the processed signal is input into the automatic encoder through different kinds of dimensionality reduction methods, and the pre-training adjustment depth learning model is adopted, and the commonly used softmax classifier is adopted to the pre-training model. Oversee fine school and identification, and finally complete the identification task. The simulation results show that high recognition rate can be achieved by this algorithm, and the joint dimension reduction is better than other methods.

SVM-based global vision system of sports competition and action recognition

2020 ◽  
pp. 1-12
Author(s):  
Changxin Sun ◽  
Di Ma
Keyword(s):  
Target Recognition ◽  
Vision System ◽  
Task Assignment ◽  
Recognition Algorithm ◽  
Control Factor ◽  
Training Time ◽  
Adaboost Algorithm ◽  
Advantages And Disadvantages ◽  
Time Control ◽  
Correlation Degree

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Implementasi Voip Server Berbasis IPV6 Dengan Raspberry PI

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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