The Raspberry Pi auto-aligner: Machine learning for automated alignment of laser beams

This research presents an automation process of controlling room temperature based on the number of people detected in a room. The system consists of a single board raspberry pi computer, esp8266 micro controller, pi camera, and an infrared module. This research is divided into two parts, namely object detection using Raspbery Pi and Tensorflow and Open CV libraries and controlling air cooling system (ACS) using esp8266 and infrared modules by transmitting hexadecimal AC control codes. The ACS temperature is divided into four levels with a minimum value at 18o C and a maximum at 24o C. System testings were carried out in an empty room and a room with a capacity of 50 people that is fully occupied. The results show that the system is able to detect the number of people in the room and control the ACS, but under certain conditions some objects are not detected because the position and camera tilt is not optimal.

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A Research Paper on Traffic Sign Recognition with Machine Learning and IOT

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35309 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 1774-1778

Author(s):

Mahesh Singh

Keyword(s):

Machine Learning ◽

Object Detection ◽

Real World ◽

Research Paper ◽

Raspberry Pi ◽

Detection Accuracy ◽

Great Success ◽

The Real ◽

Road Traffic Safety ◽

Execution Speed

This paper will help to bring out some amazing findings about autonomous prediction and performing action by establishing a connection between the real world with machine learning and Internet Of thing. The purpose of this research paper is to perform our machine to analyze different signs in the real world and act accordingly. We have explored and found detection of several features in our model which helped us to establish a better interaction of our model with the surroundings. Our algorithms give very optimized predictions performing the right action .Nowadays, autonomous vehicles are a great area of research where we can make it more optimized and more multi - performing .This paper contributes to a huge survey of varied object detection and feature extraction techniques. At the moment, there are loads of object classification and recognition techniques and algorithms found and developed around the world. TSD research is of great significance for improving road traffic safety. In recent years, CNN (Convolutional Neural Networks) have achieved great success in object detection tasks. It shows better accuracy or faster execution speed than traditional methods. However, the execution speed and the detection accuracy of the existing CNN methods cannot be obtained at the same time. What's more, the hardware requirements are also higher than before, resulting in a larger detection cost. In order to solve these problems, this paper proposes an improved algorithm based on convolutional model A classic robot which uses this algorithm which is installed through raspberry pi and performs dedicated action.

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Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features

Electronics ◽

10.3390/electronics9010144 ◽

2020 ◽

Vol 9 (1) ◽

pp. 144 ◽

Cited By ~ 6

Author(s):

Yan Naung Soe ◽

Yaokai Feng ◽

Paulus Insap Santosa ◽

Rudy Hartanto ◽

Kouichi Sakurai

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Detection System ◽

Detection Performance ◽

Cyber Attacks ◽

Raspberry Pi ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

New Feature ◽

Iot Devices

The application of a large number of Internet of Things (IoT) devices makes our life more convenient and industries more efficient. However, it also makes cyber-attacks much easier to occur because so many IoT devices are deployed and most of them do not have enough resources (i.e., computation and storage capacity) to carry out ordinary intrusion detection systems (IDSs). In this study, a lightweight machine learning-based IDS using a new feature selection algorithm is designed and implemented on Raspberry Pi, and its performance is verified using a public dataset collected from an IoT environment. To make the system lightweight, we propose a new algorithm for feature selection, called the correlated-set thresholding on gain-ratio (CST-GR) algorithm, to select really necessary features. Because the feature selection is conducted on three specific kinds of cyber-attacks, the number of selected features can be significantly reduced, which makes the classifiers very small and fast. Thus, our detection system is lightweight enough to be implemented and carried out in a Raspberry Pi system. More importantly, as the really necessary features corresponding to each kind of attack are exploited, good detection performance can be expected. The performance of our proposal is examined in detail with different machine learning algorithms, in order to learn which of them is the best option for our system. The experiment results indicate that the new feature selection algorithm can select only very few features for each kind of attack. Thus, the detection system is lightweight enough to be implemented in the Raspberry Pi environment with almost no sacrifice on detection performance.

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Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

10.1201/9781351006668 ◽

2020 ◽

Author(s):

Shrirang Ambaji Kulkarni ◽

Varadraj P. Gurupur ◽

Steven L. Fernandes

Keyword(s):

Machine Learning ◽

Image Processing ◽

Raspberry Pi

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A Proposal of Implementation of Sitting Posture Monitoring System for Wheelchair Utilizing Machine Learning Methods

Sensors ◽

10.3390/s21196349 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6349

Author(s):

Jawad Ahmad ◽

Johan Sidén ◽

Henrik Andersson

Keyword(s):

Machine Learning ◽

Pressure Distribution ◽

Real Time ◽

Monitoring System ◽

Pressure Ulcers ◽

Machine Learning Algorithms ◽

Raspberry Pi ◽

Support Vector ◽

Processing Unit ◽

Posture Recognition

This paper presents a posture recognition system aimed at detecting sitting postures of a wheelchair user. The main goals of the proposed system are to identify and inform irregular and improper posture to prevent sitting-related health issues such as pressure ulcers, with the potential that it could also be used for individuals without mobility issues. In the proposed monitoring system, an array of 16 screen printed pressure sensor units was employed to obtain pressure data, which are sampled and processed in real-time using read-out electronics. The posture recognition was performed for four sitting positions: right-, left-, forward- and backward leaning based on k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), decision tree (DT) and LightGBM machine learning algorithms. As a result, a posture classification accuracy of up to 99.03 percent can be achieved. Experimental studies illustrate that the system can provide real-time pressure distribution value in the form of a pressure map on a standard PC and also on a raspberry pi system equipped with a touchscreen monitor. The stored pressure distribution data can later be shared with healthcare professionals so that abnormalities in sitting patterns can be identified by employing a post-processing unit. The proposed system could be used for risk assessments related to pressure ulcers. It may be served as a benchmark by recording and identifying individuals’ sitting patterns and the possibility of being realized as a lightweight portable health monitoring device.

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SmartScope: An AI-Powered Digital Auscultation Device To Detect Cardiopulmonary Diseases

10.36227/techrxiv.14921268 ◽

2021 ◽

Author(s):

Aadhav Prabu

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Critical Factor ◽

Raspberry Pi ◽

Band Pass Filter ◽

Learning Models ◽

Pass Filter ◽

Delayed Treatment ◽

Learning Agent ◽

Machine Learning Models

<p>Cardiopulmonary diseases are leading causes of death worldwide, accounting for nearly 15 million deaths annually. Accurate diagnosis and routine monitoring of these diseases by auscultation are crucial for early intervention and treatment. However, auscultation using a conventional stethoscope is low in amplitude and subjective, leading to possible missed or delayed treatment. My research aimed to develop a stethoscope called SmartScope powered by machine-learning to aid physicians in rapid analysis, confirmation, and augmentation of cardiopulmonary auscultation. Additionally, SmartScope helps patients take personalized auscultation readings at home effectively as it performs an intelligent selection of auscultation points interactively and quickly using the reinforcement learning agent: Deep Q-Network. SmartScope consists of a Raspberry Pi-enabled device, machine-learning models, and an iOS app. Users initiate the auscultation process through the app. The app communicates with the device using MQTT messaging to record the auscultation, which is augmented by an active band-pass filter and an amplifier. Additionally, the auscultation readings are refined by a Gaussian-shaped frequency filter and segmented by a Long Short-Term Memory Network. The readings are then classified using two Convolutional Recurrent Neural Networks. The results are displayed within the app and LCD. After the machine-learning models were trained, 90% accuracy for cardiopulmonary diseases was achieved, and the number of auscultation points was reduced threefold. SmartScope is an affordable, comprehensive, and user-friendly device that patients and physicians can widely use to monitor and accurately diagnose diseases like COPD, COVID-19, Asthma, and Heart Murmur instantaneously, as time is a critical factor in saving lives.</p>

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RANCANG BANGUN APLIKASI NEW NORMAL COVID-19 DETEKSI PENGGUNAAN MASKER MENGGUNAKAN HAAR CASCADE CLASSIFIER

Jurnal Teknologi Informasi Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika ◽

10.47111/jti.v15i2.3291 ◽

2021 ◽

Vol 15 (2) ◽

pp. 199-209

Author(s):

Ade chandra Saputra ◽

Ahmadi Ahmadi ◽

Ariesta Lestari

Keyword(s):

Machine Learning ◽

Image Processing ◽

Programming Language ◽

Surveillance System ◽

Raspberry Pi ◽

Cascade Classifier ◽

Public Places ◽

The Face ◽

Haar Cascade ◽

Analysis Design

During the COVID-19 pandemic, when in public places, it is required to apply the 4M health protocol, namely wearing masks, washing hands, maintaining distance, and avoiding crowds. In its implementation, there are officers who always maintain and remind people not to violate health protocols. Like remembering to wear a mask. The mask detection application is made as a computerized surveillance system that can store images of violations of the use of masks and provide warning sounds. Observations, discussions and literature studies are sources of data in this empirical research. Using Python as a programming language assisted with OpenCV for image processing. After passing through the 4 stages of Waterfall, namely Analysis, Design, Manufacturing and Development and Testing, an application is produced where the Raspberry Pi is a processing tool and images are captured from the camera module with a resolution of 1080x1024 px. This application can detect the use of masks with an accuracy of 90.5% using the Machine Learning Haar Cascade Classifier method. Where the condition of the face is a maximum of 30 degrees turned to the side and looked up

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Real Time IoT Application for Classification of Crop Diseases using Machine Learning in Cloud Environment

Regular Issue - International Journal of Innovative Science and Modern Engineering ◽

10.35940/ijitee.d1186.016420 ◽

2020 ◽

Vol 6 (4) ◽

pp. 1-4

Keyword(s):

Machine Learning ◽

Image Processing ◽

Real Time ◽

Clustering Algorithm ◽

Agricultural Land ◽

Image Processing Technique ◽

Raspberry Pi ◽

Crop Diseases ◽

Internet Of Things Technology

India is an agricultural country. A total of 61.5% of the people cultivate in India. Due to lack of agricultural land and change of weather, manytypes of diseases occur on crops and insects are born.Therefore, the production of crops is coming down. To reduce this problem, Internet of Things technology will prove to be an important role. In this system, a sensor network will be created on agricultural land using Raspberry Pi 3 model. The images of the crops will be taken by sensor cameras and these images will be sent to the cloud server via Raspberry Pi 3 model. In this proposed methodology, various image processing techniques willbe apply on acquired images for classification of crop diseases using k-means clustering algorithm with unsupervised machine learning. This paper will also shows the method of image processing technique such as image acquisition, image pre-processing, image segmentation and feature extraction for classification of crop diseases.In bad natural environment, the farmers can produce quality crops and people will get healthy foodby this proposed methodologyand make more profit.In real time treatme

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