Fundamentals Of Neural Networks Architectures Algorithms And Applications

Explorations in Quantum Computing.

Computed Tomography: Physical Principles, Clinical Applications, and Quality Control

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Handbook Of Medical Image Processing And Analysis

Grover’s search algorithm gives a quantum attack against block ciphers by searching for a key that matches a small number of plaintext-ciphertext pairs. This attack uses $O(\sqrt{N})$ calls to the cipher to search a key space of size N. Previous work in the specific case of AES derived the full gate cost by analyzing quantum circuits for the cipher, but focused on minimizing the number of qubits.

Implementing Grover Oracles for Quantum Key Search on AES and LowMC

An explicit quantum design of AES-128 is presented in this paper. The design is structured to utilize the lowest number of qubits. First, the main components of AES-128 are designed as quantum circuits and then combined to construct the quantum version of AES-128. Some of the most efficient approaches in classical hardware implementations are adopted to construct the circuits of the multiplier and multiplicative inverse in $${\mathbb {F}}_{2}[x]/(x^8+x^4+x^3+x+1)$$F2[x]/(x8+x4+x3+x+1). The results show that 928 qubits are sufficient to implement AES-128 as a quantum circuit. Moreover, to maintain the key uniqueness when the quantum AES-128 is employed as a Boolean function within a Black-box in other key searching quantum algorithms, a method with a cost of 930 qubits is also proposed.

Quantum reversible circuit of AES-128

Brain tumors have been created by abnormal and uncontrolled cell division inside the brain. A crucial and lengthy task is the segmentation of brain tumors, which can be gained manually with the help of Computed Tomography (CT). Treatment, diagnosis, signs and symptoms of the brain tumors mainly depend on the volume, shapes and location of the tumors. The accuracy and time of detecting brain tumor are vital contributions in the successful diagnosis and treatment of tumors. Therefore, the detection of brain tumor needs to be fast and accurate. Brain tumor segmentation and volume estimation have been considered a challenge mission in medical image processing. The main aim of this paper is that with the help of hidden Markov random field- Expectation Maximization (HMRF-EM) and threshold method, a novel approach of improving the segmentation of brain tumors from CT scan images is produced. The segmentation and volume estimation images are obtained by the study of 2D images. We calculate the volume of tumor using a new approach based on 2D images estimations and voxel space. In order to validate the proposed approach a comparison is carried out with a manual method using Mango software which, the noise or impurities are less than Mango software in measurement of tumor volume.

Segmentation and estimation of brain tumor volume in computed tomography scan images using hidden Markov random field Expectation Maximization algorithm

The construction and working principles of a plastic fiber sensor for examining the level of turbidity is studied in this paper. This work focuses on designing an inexpensive turbidity sensor that incorporates a pair of multimode fibers (MMF) that are attached side by side and their beveled tips are mounted vertically. The efficiency of different beveled angles is evaluated by simulation with Tracepro software. The reflected signal is collected by immersing the sensor head into a water mixture and analyzed for various concentration. It was found that there is a linear increment of output intensity when concentration of mixture is increased. The turbidity sensor is tested with real samples that are collected from lake, river and coastal areas to demonstrate its consistency with commercial apparatus in natural compounds. The results showed that the proposed sensor structure is able to produce reliable results in a dynamic range of detection from 0 to 1000 Nephelometric Turbidity Unit (NTU) to facilitate practical field measurements.

Plastic fiber evanescent sensor in measurement of turbidity

In this work, a quantum design for the Simplified-Advanced Encryption Standard (S-AES) algorithm is presented. Also, a quantum Grover attack is modeled on the proposed quantum S-AES. First, quantum circuits for the main components of S-AES in the finite field F2[x]/(x4 + x + 1), are constructed. Then, the constructed circuits are put together to form a quantum version of S-AES. A C-NOT synthesis is used to decompose some of the functions to reduce the number of the needed qubits. The quantum S-AES is integrated into a black-box queried by Grover's algorithm. A new approach is proposed to uniquely recover the secret key when Grover attack is applied. The entire work is simulated and tested on a quantum mechanics simulator. The complexity analysis shows that a block cipher can be designed as a quantum circuit with a polynomial cost. In addition, the secret key is recovered in quadratic speedup as promised by Grover's algorithm.

Quantum Grover Attack on the Simplified-AES

In this work, a method for applying Hopfield Neural Network (HNN) with gray images is presented. Hopfield networks are iterative Auto-Associative networks consisting of a single layer of fully connected processing elements thus categorizes as an associative memory. Associative memories provide one approach to the computer-engineering problem of storing and retrieving data which is based on content rather than storage address. HNN deals with the bipolar system (i.e. -1 and +1) for direct input data, however it is useful for binary images, but unuseful for gray-level or color images unless we suppose another way for input data of such images. To overcome this obstacle, one can suppose for 8-bit gray-level image that consists of 8-layers (bitplanes) of binaries can be represented as bipolar data. In this way it is possible to express each bitplane as single binary image for HNN. The experimental results showed the usefulness of using HNN in gray-level images recognition with good results. Furthermore, there are no limitations to the number of 8-bit gray level images that can be stored in the net memory with the same efficient results.

Kussay N. Mutter

Papers

Quantum reversible circuit of AES-128

Segmentation and estimation of brain tumor volume in computed tomography scan images using hidden Markov random field Expectation Maximization algorithm

Plastic fiber evanescent sensor in measurement of turbidity

Quantum Grover Attack on the Simplified-AES

Gray Image Recognition Using Hopfield Neural Network With Multi-Bitplane and Multi-Connect Architecture