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Register a new userData Compression for Some Geometrical Shapes and Recognizing Them Using Haar wavelet
ضغط البيانات لبعض الأشكال الهندسية و التعرّف عليها باستخدام مويجات هآر
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برلنت مطيط( باحث )
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تعد مسألة ضغط البيانات من المسائل الهامة في وقتنا الحالي , لما لها من أهمية كبيرة في توفير كلفة تخزين البيانات , بالإضافة إلى تقليص الزمن اللازم لمعالجة البيانات , حيث تعطي البيانات المضغوطة نتائج البيانات الأصلية بوقت حساب أقل . و نقدم في هذه الورقة خوارزمية للتعرف على بعض الأشكال الهندسية المغلقة و غير المغلقة , و ذلك بضغط هذه الأشكال باستخدام المويجات هآر , و يعد التحويل المويجي من التحولات الحديثة و المهمة التي استخدمها الباحثون في كثير من التطبيقات لما له من مزايا و خواص في المعالجة الرقمية . وقد لاحظنا أن الصور المضغوطة حسب خوارزميتنا أعطت نتائج بوقت حساب أقل يصل إلى ربع الوقت اللازم لمعالجة الصور الأصلية . وتم انجاز الخوارزمية باستخدام برنامج Mathematica 8.0 , اذ يعد من أقوى لغات البرمجة الرياضية.
The compression data problem is one of most important problems nowadays, because it saves storage requirements, and reduces the time for the processing, The compressed data give the returns of main data in few times. In this article, we offer an algorithm for recognizing the closed and unclosed shapes, and that was done by compressing the images using haar wavelet, and we noted that the compressed images give the wanted returns in about quarter the time that main images take. This algorithm was done using the Mathematica 8.0 program as one of the most powerful programming languages.
References used
ANTONIADIS A., 2007- Wavelet methods in statistics: Some recent developments and their applications. Statistics Surveys, Vol.1,16-55
ANTOINE J., MURENZI R.,VANDERGHEYNST P., and TWAREQUE S., 2004- Two-Dimensional Wavelets and their Relatives. Cambridge University Press, USA
BENJAMIN J., BASKARAN R., 2011- Edge Preserved Image Compression Technique Using Wavelet and Edge Based Segmentation. International Journal of Computer Science & Informatics, Vol.2,80-85
BREMAUD P., 2002- Mathematical principles of signal processing. Springer-Verlag, Berlin
DAVID F., 2002- An Introduction to Wavelet Analysis. Birkhauser , Boston
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خلال العقد الأخير من القرن العشرين ظهرت مجموعة من المتغيرات التكنولوجية المتقدمة في مجالات نظم المعلومات المرتبطة بالحاسبات الآلية و وسائل الاتصال و ضغط البيانات و نقلها عبر شبكات الحاسب الآلي. حيث انتقلت نظم المعلومات من اعتمادها على النص و بعض الرس
ومات البيانية البسيطة إلى اعتمادها على استخدام الوسائط المتعددة التي تعمل على توصيل المعلومات في أشكال مختلفة من خلال ترابط و تكامل مجموعة متباينة من التكنولوجيات المختلفة (الصوت, الصور, النص, الفيديو, ..الخ).
و قد كان تطور تلك النظم في البداية مقصوراً على الاستخدام المنفرد, و لكن نظراً لأهمية نظم الاتصالات و تطور شبكة الانترنت و استخدام نظم الوسائط المتعددة من قبل مستخدمين متعددين في أماكن مختلفة من حيث الموقع الجغرافي, ظهرت أهمية المشاركة في بيانات الوسائط المتعددة, و بالتالي حتمية تداولها من خلال شبكات الحاسب الآلي. و من هنا ظهرت الحاجة إلى ظهور شبكات ذات مواصفات خاصة يمكنها التعامل مع عناصر الوسائط المتعددة بكفاءة عالية. و من جانب آخر ظهرت أهمية وجود نظم وسائط متعددة لديها القدرة على التعامل مع شبكات الحاسب الآلي.
من ذلك نرى بأن هذه النظم سوف تتسم بكبر حجم بياناتها إضافة إلى الصعوبة الحقيقية في نقل هذه البيانات و خاصة عبر شبكات الحاسب. لذلك فقد دعت مشاكل تخزين أحجام كبيرة من البيانات مقارنة مع صغر سعة الأجهزة التخزينية و مشاكل نقل كميات كبيرة منها عبر الشبكات إلى تطوير تقنيات لتخفيض (اختصار) أحجام البيانات قدر الإمكان مما يساعد على توفير في المساحات التخزينية من جهة و توفير الوقت عند إرسال البيانات من جهة ثانية
The built environment has an internal and surrounding effect to the health and life of organisms where it
spends most of its time in that environment. With the growing role of the built environment in everyday's
life, several negative aspects have
appeared and & have affected those organisms, these aspects have
represented main factors: From one hand is what affects those objects from diseases linked to buildings
while from the other hand is the close association with the growth and production rates.
From this point many ideas and architectural theories have appeared while it derived roots of ancient
civilizations and previous experience has been supported by modern thought, but these ideas and theories
failed to solve some problems of modern technology, such as the negative effects of electromagnetic fields
and radiative forcing on living organisms; all of what has been stated has forced the architects to have a
second thought concerning these theories by trying to find out several other solutions and concepts to the
design system' and find simple ways to help in the design and evaluation process while improving its
performance.
From this point the Biogeometry science "Vital Shapes Engineering Science" which describes the
geometric shapes that organizes the place's energy (Electromagnetic fields in the space) and re-enforce the
energy balance of the organisms contained in it or around it, this leads to raise the efficiency of the objects
on one hand and increases the growth rates and production at those objects on the other hand.
The search could define the concept of an energy surrounding with the concept of the Biogeometry
engineering from one hand, while the impact of these energy & the Biogeometry forms the plants from
another. hence we have to clarify the direct influence of the Biogeometry forms on the plants life,
eventually and after proving that effect, it is proved that different Beogaomitri forms leads to different
effects caused by those plants.
The principal objective of this research is an adoption of the Genetic
Algorithm (GA) for studying it firstly, and to stop over the operations which
are introduced from the genetic algorithm.The candidate field for applying
the operations of the g
enetic algorithm is the sound data compression field.
This research uses the operations of the genetic algorithm for the
enhancement of the performance of one of the popular compression method.
Vector Quantization (VQ) method is selected in this work. After studying
this method, new proposed algorithm for mixing the (GA) with this method
was constructed and then the required programs for testing this algorithm
was written. A good enhancement was recorded for the performance of the
(VQ) method when mixed with the (GA). The proposed algorithm was
tested by applying it on some sound data files. Some fidelity measures are
calculated to evaluate the performance of the new proposed algorithm.
In this project we study wavelet and wavelet transform, and the possibility of its employment in the processing and analysis of the speech signal in order to enhance the signal and remove noise of it. We will present different algorithms that depend
on the wavelet transform and the mechanism to apply them in order to get rid of noise in the speech, and compare the results of the application of these algorithms with some traditional algorithms that are used to enhance the speech.
Image compression is one of the most important branches of digital image
processing. It reduces the size of the captured images and minimizes the storage space on
the drivers to speed up the transferring and transmission.
In this paper we will pre
sent a new approach for compressing stereo images based on
three algorithms; the first one is comparing the two images that perform the stereoscopic
view by noticing the great similarities between them and encoding the difference between
the two images instead of encoding the whole image. The second one is reducing the
redundancy between the Pixels using a 2D Digital Curvelet Transformation so we can
utilize the great ability to represent the curves in the image with minimum number of
coefficients. Then quantize them and remove undesirable coefficient. The low number of
coefficient contains most of image data. Last one is using Huffman Encoding and take
advantage of the lossless property so we can encode image and reduce the size of data
without getting any image distortion or lose any part of this image.
The performance of the proposed algorithm evaluated using Compression Ratio
standard which is the number of the image bits after compression to the number of the
original image bits before compression. Also, Peak Signal to Noise Ratio standard (PSNR)
which represent the similarity between the restored image and the original image. In final,
the Mean Square Error standard (MSE) which represent the error between the restored
image and original image.
In conclusion, the main objective here is to get the lowest rate for image compression
ratio with the highest value for the image quality PSNR at the lowest value of the errors
MSE.
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