This paper examines the properties of Number Theoretic Transforms over FFT. The aim of this study is to show that Number Theoretic Transforms (NTTs) can be really beneficial in terms of error free and faster computation. One and two dimensional NTTs are implemented in MATLAB and properties are verified and as an example convolution is implemented using Fermat number transform(FNT) which is a variant of NTT.A study of comparison of NTT to FFT proves that NTTs are really beneficial in terms of computational complexity and error free computation.
In this paper face recognition system for the purpose of security is explored whose design is based on Raspberry Pi system. An add on temperature detection system also gives detection of COVID affected people. With the COVID-19 pandemic, temperature checking before you enter a premise is a necessary step to ensure you are well and help keep others safe. One of the most common ways is to use a handheld digital thermometer to find out your own temperature and recording it on some kind of logbook placed near the front door. For a workplace, employees are encouraged to record their temperature twice a day
In this paper properties of number theoretic transforms are investigated and it is found that they can be used to compress the regular image effectively. NTT variants namely Fermat and Mersenne transforms are applied on test images of size 16x16 and the results are analyzed and a compression scheme is developed. The algorithm is implemented in MATLAB and results are analyzed and compared with DCT in terms of total number of zero coefficients and total number of pixels in error when inverse transform is applied. The study shows that the transform is error free and can compress regular data effectively. Further investigations on these transforms are to be carried out and algorithms need to be developed to compress other images as well in order to achieve lossless compression.
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