Over the last three decades the program transformation methodology has been proved to be a powerful technique for deriving programs from specifications, verifying program properties, specializing programs w.r.t. their context of use, and deriving more efficient program versions from less efficient ones. The transformation methodology has been first proposed in the area of functional programming...

This paper mainly focused on implementation of AES encryption and decryption standard AES-128. All the transformations of both Encryption and Decryption are simulated using an iterativedesign approach in order to minimize the hardware consumption. This method can make it avery low-complex architecture, especially in saving the hardware resource in implementing theAES InverseSub Bytes module and...

Background: Asymptomatic hemorrhagic transformation infarct (AHTI) is known as a complication of ischemic attack and maybe occurs in the entire stroke. However, the role of AHTI in the result of the treatment is still not clear, because it is based on the definition of an asymptomatic and not identifiable. The aim of this study was assessment and evaluation frequency of AHTI in acute ischemic s...

Over the last three decades the program transformation methodology has been proved to be a powerful technique for deriving programs from specifications, verifying program properties, specializing programs w.r.t. their context of use, and deriving more efficient program versions from less efficient ones. The transformation methodology has been first proposed in the area of functional programming...

cacl2 treatment followed by heat shock is the most common method for artificial transformation. here, the cells were transformed using cacl2 treatment either with heat shock (standard protocol) or without heat shock (lab protocol) to comprehend the difference in transformation efficiency. the bl21 strain of escherichia coli (e. coli) was being susceptible using cacl2 treatment. some cells were ...

The fine-grained image classification method does not have a very good training effect when performing random weight for neural networks. For the under condition of convolutional network, main reason is that its upgrading can better improve stability network. Fine-grained through bilinear networks to effectively merge several networks, so as obtain high-resolution advantage classification. With...