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Register a new userCreating an OpenCV Application Raspberry Pi-Based Beowulf Cluster
معالجة الصورة باستخدام الحوسبة التفرعية مبني على نظام راسبيري باي 3
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Tartous University مشروع تخرج
Publication date
2018
fields
Informatics Engineering
and research's language is
العربية
Created by
حسام جنيدي
Ask ChatGPT about the research
فكرة المشروع الأساسية هي إنشاء دليل عربي متكامل فيما يخص بناء حاسوب تفرعي أو ما يسمى حاسوب خارق باستخدام عدة عقد (راسبيري باي)، مع الخطوات التفصيلية والروابط اللازمة للعمل، بالإضافة إلى المشكلات والحلول المطلوبة، مع تطبيق مفهوم الحوسبة التفرعية والبرمجة التفرعية كذلك، وترسيخ الفكرة بتطبيق معالجة صورة (عد النجوم في صورة لوكالة ناسا)،
This project is about an Arabic guide for building a supercomputer (cluster) based on raspberry pi nodes with a brief of the problems and the solutions needed, with an OpenCV application about counting stars in a Nasa Image
References used
Raspberrypi.org
https://help.ubuntu.com/community/MpichCluster
https://www.parallella.org/2015/05/25/how-the-do-i-program-the-parallella/
http://www.mathcs.emory.edu/~cheung/Courses/355/Syllabus/92-MPI/intro.html
https://wiki.archlinux.org/index.php/SSH_keys
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As we enter the age of artificial intelligence, the need for intelligent home appliances has become very important for what this smart equipment can provide in the provision of electrical energy and water resources that are treasures should human pre
servation, in addition to the contribution of this equipment to protect the environment from pollution, where we face the challenges next:
High prices of electrical equipment.
The number of hours of electricity supply in many areas is low
because of the current conditions in our country.
- Water shortage.
- The rise in prices of materials used in daily life in general and
household detergents in particular
- Great waste of electricity.
- Pollution of the environment and groundwater with detergents used in the laundry process.
Moreover, the unjust economic blockade imposed on our country is pushing us to work to produce low-cost national housing equipment that competes with foreign products in order to alleviate the material burden on the citizens and promote the national economy.
In order to accomplish this smart washing machine, we have written a code for f type-2 fuzzy microcontroller, using the Python programming language. This controller has received four entries, which are: The first income (clothing color), obtained by taking a picture of the clothes that we need to wash by a camera with a resolution of 8 megapixels, analyzed using OpenCV library, and the second income (clothing type), determined by the local binary pattern algorithm, which is common digital image processing algorithm that widely used to identify shapes that follow specific pattern and structure, the third income (degree of dirt), and was identified by taking a picture of the clothes after soaking them with water for two minutes. The image was then analyzed by the OpenCV library and the fourth (washing weight) that getting From the Load Cell, which measures the physical weights. The readings were converted to digital values via the HX711 digital analogue converter and then sent to Arduino UNO to determine the weight. The weight values were eventually sent to the Raspberry PI for use in the controller. The system generates three exits: washing time (the length of time the laundry was washed), the temperature required for washing, and the amount of detergent required.
After selecting all the previous values, we transferred to control Wattar washing machine model 402, where the water valve was controlled to allow the water to pass into the powder box and from it to the washing basin. The water heater was controlled, which heated the water to the temperature determined by the Fuzzy algorithm, The temperature was monitored by the DS18B20 temperature sensor, which gives a signal to the Raspberry PI at the arrival of the temperature to the required value, and the washing machine engine is controlled for a third of the time specified in the Fuzzy algorithm and we controlled the pump Water to empty basin Washing from water, the process repeated for three consecutive times, we control using a software interface designed using TKinter library
We have been able to design a smart Fuzzy logic type-2 controller with the following advantages:
o save electricity consumption o Provide quantity of detergents o Shortenwashingtime
We have been able to control the following physical components within the washing machine:
o Control the water pump
o control Water valve
o controlMotor
o controlTemperaturesensor o controlLCDscreen
We have built a smart washing machine with the following characteristics:
o Have the ability to recognize the condition of clothes o Identify the type of clothing
o Identify the color of clothes
o Dothewashingwithoutusingapredefinedprogram.
The controller we designed gives good results to calculate the following:
o Washingtime
o Quantity of detergents o Temperature
All diagrams appear in the case of the incremental gradient with an increased degree of dirt and as values correspond to each type of clothing.
Keywords: smart washing machine, saving electricity, saving detergent, shortening washing time, color and clothing distinction, artificial intelligence, fuzzy logic type-2, Raspberry PI, control, Python programming language, HX711.
Content based 2Dcerebral digital subtraction angiography(DSA) images retrieval
system has been built. The systemfinds and retrieves images fromcerebral DSA imagedatabase(
Cerebral Sacular Aneurysms) which have a similar content to a query image.
R
etrieval is done by extracting the visual shape features of cerebral saccular aneurysms
from a query image, formulating them in a feature vector, comparing feature vector
components with those of the cerebralDSA images in the database. Similarity measures
using Euclidian distanceare computed,based on the similarity measures, images which
have a similar content to the query image are retrieved. Resolution has been calculated by
finding the ratio between cerebral sacular aneurysm area in first retrieved image to cerebral
sacular aneurysm area in the query image for the eight query process which have been
done, average resolution was 98%. Results indicates that the designed content based image
retrieval could be used to calculate unknown cerebral saccular aneurysms area from a
cerebral saccular aneurysms database images whose areas are known.
In recent years, the problem of classifying objects in images has increased by using deep learning as a result of the industrial sector requirements. Despite of many algorithms used in this field, such as Deep Learning Neural Network DNN and Convolut
ional Neural Network CNN, the proposed systems to address this problem Lack of comprehensive solution to the difficulties of long training time and floating memory during the training process, low rating classification. Convolutional Neural Networks (CNNs), which are the most used algorithms for this task, were a mathematical pattern for analyzing images data. A new deep-traversal network pattern was proposed to solve the above problems. The aim of the research is to demonstrate the performance of the recognition system using CNNs networks on the available memory and training time by adapting appropriate variables for the bypass network. The database used in this research is CIFAR10, which consists of 60000 colorful images belonging to ten categories, as every 6,000 images are for a class of these items. Where there are 50,000 training images and 10,000 test tubes. When tested on a sample of selected images from the CIFAR10 database, the model achieved a rating classification of 98.87%.
The various types of radial distortions generated by digital cameras are presented in this paper, like Barrel Distortions and Pincushion Distortion.
Image processing techniques are used to correct the barrel distortion generated by wide-angle lenses
of digital cameras. A model for barrel distortions is founded. Moreover, an algorithm for correcting this distortion is developed. This algorithm depends on finding the right parameters of the model. The grid pattern is used to detect pixels that caused the distortion and reallocate these pixels back into their original locations, making the corrected photo as close as possible to the original.
This research serves the process of organizing the traffic by reducing traffic congestion,
especially at peak times to a minimum, presenting an effective method is not used locally to
automate traffic lights adoption concepts, image processing, con
trollers, communication. It is
organizing image capture processes on a regular basis in all the streets leading to the traffic
node, and then this image was taken to the central computer, which is processes each image by
comparing them with all the pictures taken at a certain moment and based on the comparison, the
Central Computer conclusion of the new chronological order to change traffic lights in all the
streets then sends the new update to the controllers in each signal light which leads to re-
Automatics these signals,by reducing bottlenecks to the minimum level and repeat imaging
processes and modernization of signals throughout the day we can have access to a system to
automate the traffic contract is close to the ideal note that by connecting traffic contract with
each other and with the main central computer we can solve other problems like emergency and
firefighting.
Key words: a traffic node, signal processing, camera, network, processor, microcontroller.
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