Hardware Implementation of a Visual Image Watermarking Scheme Using Qubit/Quantum Computation Through Reversible Methodology

In this chapter, hardware implementation of an LSB replacement-based digital image watermarking algorithm is introduced. The proposed scheme is developed in spatial domain. In this watermarking process, data or watermark is implanted into the cover image pixels through an adaptive last significant bit (LSB) replacement technique. The real-time execution of the watermarking logic is developed here using reversible logic. Utilization of reversible logic reduces the power dissipation by means of no information loss. The lesser power dissipation enables a faster operation as well as holds up Moore's law. The experimental results confirm that the proposed scheme offers high imperceptibility with a justified robustness.

Landcover Change Detection Using PSO-Evaluated Quantum CA Approach on Multi-Temporal Remote-Sensing Watershed Images

Computer science plays a major role in image segmentation and image processing applications. Despite the computational cost, PSO evaluated QCA approaches perform comparable to or better than their crisp counterparts. This novel approach, proposed in this chapter, has been found to enhance the functionality of the CA rule base and thus enhance the established potentiality of the fuzzy-based segmentation domain with the help of quantum cellular automata. This new unsupervised method is able to detect clusters using 2-dimensional quantum cellular automata model based on PSO evaluation. As a discrete, dynamical system, cellular automaton explores uniformly interconnected cells with states. In the second phase, it utilizes a 2-dimensional cellular automata to prioritize allocations of mixed pixels among overlapping land cover areas. The authors experiment on Tilaya Reservoir Catchment on Barakar River. The clustered regions are compared with well-known PSO, FCM, and k-means methods and also with the ground truth knowledge. The results show the superiority of the new method.

A Quantum NeuroIS Data Analytics Architecture for the Usability Evaluation of Learning Management Systems

NeuroIS uses tools such as electroencephalogram (EEG) that can be used to measure high brainwave frequencies that can be linked to human anxiety. Past research showed that computer anxiety influences how users perceive ease of use of a learning management system (LMS). Although computer anxiety has been used successfully to evaluate the usability of LMS, the main data collection mechanisms proposed for its evaluation have been questionnaires. Questionnaires suffer from possible problems such as being inadequate to understand some forms of information such as emotions and honesty in the responses. Quantum-based approaches to consciousness have been very popular in the last years including the quantum model reduction in microtubules of Penrose and Hameroff (1995). The objective of the chapter is to propose an architecture based on a NeuroIS that collects data by using EEG from users and then use the collected data to perform analytics by using a quantum consciousness model proposed for computer anxiety measurements for the usability testing of a LMS.

True Color Image Segmentation by MUSIG Activation Function Using Self-Supervised QMLSONN Architecture With Context-Sensitive Thresholding

In this chapter, the authors propose the true color image segmentation in real-life images as well as synthetic images by means of thresholded MUSIG function, which is learnt by quantum-formulated self-supervised neural network according to change of phase. In the initial phase, the true color image is segregated in the source module to fragment three different components—red, green, and blue colors—for three parallel layers of QMLSONN architecture. This information is fused in the sink module of QPSONN to get the preferred output. Each pixel of the input image is converted to the corresponding qubit neurons according to the phase manner. The interconnection weights between the layers are represented by qubit rotation gates. The quantum measurement at the output layer destroys the quantum states and gets the output for the processed information by means of quantum backpropagation algorithm using fuzziness measure.

True Color Image Segmentation Using Quantum-Induced Modified-Genetic-Algorithm-Based FCM Algorithm

In this chapter, a quantum-induced modified-genetic-algorithm-based FCM clustering approach is proposed for true color image segmentation. This approach brings down the early convergence problem of FCM to local minima point, increases efficacy of conventional genetic algorithm, and decreases the computational cost and execution time. Effectiveness of genetic algorithm is tumid by modifying some features in population initialization and crossover section. To speed up the execution time as well as make it cost effective and also to get more optimized class levels some quantum computing phenomena like qubit, superposition, entanglement, quantum rotation gate are induced to modified genetic algorithm. Class levels which are yield now fed to FCM as initial input class levels; thus, the ultimate segmented results are formed. Efficiency of proposed method are compared with classical modified-genetic-algorithm-based FCM and conventional FCM based on some standard statistical measures.

Quantum-Inspired Automatic Clustering Technique Using Ant Colony Optimization Algorithm

Quantum computing has emerged as the most challenging field of research in efficient computation. This chapter introduces a novel quantum-inspired ant colony optimization technique for automatic clustering. This chapter presents an application of this proposed technique to the automatic clustering of real-life gray-scale image data sets. In contrary to the other techniques, the proposed one requires no previous knowledge of the data to be classified. It finds the optimal number of clusters of the data by itself. The Xie-Beni cluster validity measure has been employed as the objective function for clustering purpose. Effectiveness of the proposed technique is exhibited on four real-life gray-scale images. Superiority of the proposed technique is established over its counterpart with respect to various aspects, which include accuracy, stability, computational time and standard errors. Finally, a statistical supremacy test, called unpaired two-tailed t-test, is conducted between them. It shows that superiority in favor of the proposed technique is established.

Quantum-Computing-Inspired Algorithms in Machine Learning

In this chapter, the authors discuss the use of quantum computing concepts to optimize the decision-making capability of classical machine learning algorithms. Machine learning, a subfield of artificial intelligence, implements various techniques to train a computer to learn and adapt to various real-time tasks. With the volume of data exponentially increasing, solving the same problems using classical algorithms becomes more tedious and time consuming. Quantum computing has varied applications in many areas of computer science. One such area which has been transformed a lot through the introduction of quantum computing is machine learning. Quantum computing, with its ability to perform tasks in logarithmic time, aids in overcoming the limitations of classical machine learning algorithms.

An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture

This chapter proposes a quantum multi-layer neural network (QMLNN) architecture suitable for handwritten character recognition in real time, assisted by quantum backpropagation of errors calculated from the quantum-inspired fuzziness measure of network output states. It is composed of three second-order neighborhood-topology-based inter-connected layers of neurons represented by qubits known as input, hidden, and output layers. The QMLNN architecture is a feed forward network with standard quantum backpropagation algorithm for the adjustment of its weighted interconnection. QMLNN self-organizes the quantum fuzzy input image information by means of the quantum backpropagating errors at the intermediate and output layers of the architecture. The interconnection weights are described using rotation gates. After the network is stabilized, a quantum observation at the output layer destroys the superposition of quantum states in order to obtain true binary outputs.

Grayscale Image Segmentation With Quantum-Inspired Multilayer Self-Organizing Neural Network Architecture Endorsed by Context Sensitive Thresholding

A method for grayscale image segmentation is presented using a quantum-inspired self-organizing neural network architecture by proper selection of the threshold values of the multilevel sigmoidal activation function (MUSIG). The context-sensitive threshold values in the different positions of the image are measured based on the homogeneity of the image content and used to extract the object by means of effective thresholding of the multilevel sigmoidal activation function guided by the quantum superposition principle. The neural network architecture uses fuzzy theoretic concepts to assist in the segmentation process. The authors propose a grayscale image segmentation method endorsed by context-sensitive thresholding technique. This quantum-inspired multilayer neural network is adapted with self-organization. The architecture ensures the segmentation process for the real-life images as well as synthetic images by selecting intensity parameter as the threshold value.