Debanjan Konar

An improved Hybrid Quantum-Inspired Genetic Algorithm (HQIGA) for scheduling of real-time task in multiprocessor system

Graphical abstractDisplay Omitted HighlightsAn efficient real-time task scheduling assisted by Hybrid Quantum-Inspired Genetic Algorithm (HQIGA) in multiprocessor environment is presented.It relies on the concepts and the principles of quantum mechanics.To drive schedule toward better convergence, HQIGA operates using rotation gate for exploration of variable chromosomes described by qubits in Hilbert hyperspace.A fitness function associated with popularly known heuristic Earliest Deadline First (EDF) is employed and random key distribution is adopted to convert the qubits chromosomes to valid schedule solutions.It outperforms the classical genetic algorithm (CGA) in terms of obtaining better fitness value using less number of generations and also it improves the scheduling time than CGA. This article concerns an efficient real-time task scheduling assisted by Hybrid Quantum-Inspired Genetic Algorithm (HQIGA) in multiprocessor environment. Relying on concepts and the principles of quantum mechanics, HQIGA explores the computing power of quantum computation. To drive schedule toward better convergence, HQIGA operates using rotation gate for exploration of variable chromosomes described by qubits in Hilbert hyperspace. A fitness function associated with popularly known heuristic earliest deadline first (EDF) is employed and random key distribution is adopted to convert the qubits chromosomes to valid schedule solutions. In addition to this, permutation based trimming technique is applied to diversify the population which yields good quality schedules. To establish the effectiveness of the suggested HQIGA, it demonstrates using various number of real-time tasks and processors along with arbitrary processing time. Simulation result shows that HQIGA outperforms the classical genetic algorithm (CGA) and Hybrid Particle Swarm Optimization (HPSO) in terms of fitness values obtained using less number of generations and also it improves the scheduling time significantly. HQIGA is also tested separately with the heuristic Shortest Computation Time First (SCTF) technique to show the superiority of EDF over SCTF.

A quantum bi-directional self-organizing neural network (QBDSONN) architecture for binary object extraction from a noisy perspective

Graphical abstractDisplay Omitted This article proposes an efficient technique for binary object extraction in real time from noisy background using quantum bi-directional self-organizing neural network (QBDSONN) architecture. QBDSONN exploits the power of quantum computation. It is composed of three second order neighborhood topology based inter-connected layers of neurons (represented by qubits) arranged as input, intermediate and output layers. In the suggested network architecture, the inter-connection weights and activation values are represented by rotation gates. A self-supervised learning algorithm, suggested in this proposed architecture, relies on the steepest descent algorithm. The quantum neurons enjoy full-connectivity in each layer of the network architecture. The image pixels in terms of qubits are self-organized in between the intermediate or hidden and output layers of the QBDSONN architecture using counter-propagation of the quantum states to obviate time consuming quantum back propagation algorithm. In the final phase, quantum measurement is carried out at the output layer to eliminate superposition of the quantum states of the outputs. In order to establish the result, the proposed QBDSONN architecture is applied on an artificial synthetic and on a real life spanner image with different degrees of uniform and Gaussian noises. Experimental results show that QBDSONN outperforms both its classical counterpart and the supervised auto-associative Hopfield network as far as extraction time is concerned and it retains the shapes of the extracted images with great precision. Experiments are also carried out using a linear method named local statistics (Wiener filter) and a nonlinear technique named median filter with adaptive discrete wavelet transformations (DWT) for binary object extraction to show the dominance of the proposed QBDSONN with respect to the quality of extracted images. Finally, a statistical significance of the proposed QBDSONN is reported by applying 2 sample one sided Kolmogorov-Smirnov test with the existing methods.

An Efficient Dynamic Scheduling Algorithm for Soft Real-Time Tasks in Multiprocessor System Using Hybrid Quantum-Inspired Genetic Algorithm

This paper proposes a hybrid approach for dynamic scheduling of soft real-time tasks in multiprocessor environment using hybrid quantum-inspired genetic algorithm (HQIGA) combined with well-known heuristic earlier-deadline-first (EDF) algorithm. HQIGA exploits the power of quantum computation which relies on the concepts and principles of quantum mechanics. The HQIGA comprises variable size chromosomes represented as qubits for exploration in the Hilbert space 0–1 using the updating operator rotation gate. Earlier-deadline-first algorithm is employed in the proposed work for finding fitness values. In order to establish the comparison with the classical genetic algorithm-based approach, this paper demonstrates the usage of various numbers of processors and tasks along with arbitrary processing time. Simulation results show that quantum-inspired genetic algorithm-based approach outperforms the classical counterpart in finding better fitness values using same number of generations.

An efficient pure color image denoising using quantum parallel bidirectional self-organizing neural network architecture

Abstract This chapter presents a self-supervised learning network in a quantum environment, named a “quantum parallel bidirectional self-organizing neural network (QPBDSONN) architecture” appropriate for pure color image denoising. The suggested QPBDSONN architecture mimics the classical parallel bidirectional self-organizing neural network architecture by embedding quantum computation which invokes the fundamental concepts and principles of quantum mechanics. The QPBDSONN architecture comprises three quantum bidirectional self-organizing neural networks (QBDSONNs) in the input layer to process three distinct color components (red, green, blue) separately used in the color noisy images. The principle of the operation of this QPBDSONN architecture is posed as a cluster of three parallel QBDSONNs after isolation of these three distinct basic color components from the pure noisy color images in the initial phase. Afterward, the basic color components obtained from the input noisy color images are fed simultaneously thorough three parallel different basic color component QBDSONNs for subsequent processing. Each of the three constituent QBDSONNs in the proposed network architecture comprises input, hidden or intermediate, and output layers of neurons. Each neuron of these three layers is intraconnected by means of an eight-connected neighborhood topology designated as qubits . The current parallel network architecture does not rely on a quantum back-propagation algorithm to adjust interconnection weights (described by rotation gates). Instead it uses qubit layers of neurons between the hidden layer and the output layer in a counterpropagation sense. Finally, quantum measurement is done with the aim of diminishing superposition of qubits in the output layer followed by a fusion operation to merge processed color image components in the sink layer and produce the true color output image. The superiority of the proposed QPBDSONN architecture is established by its application on synthetic and real-life wrench pure color images with different degrees of uniform noise and Gaussian noise by comparison with the classical parallel bidirectional self-organizing neural network, a supervised autoassociative Hopfield network, and a nonlinear technique named the “median filter with adaptive Discrete Wavelet Transformation (DWT).” The QPBDSONN architecture outperforms its classical counterpart and the threefold parallel Hopfield network as far as timing efficiency is concerned and it also restores the shapes of extracted images with greater accuracy than with the median filter with adaptive discrete wavelet transformations. Finally, the statistical significance of the proposed QPBDSONN architecture is determined by a two-sample Kolmogorov-Smirnov test.

A Quantum Bi-Directional Self-Organizing Neural Network (QBDSONN) for binary image denoising

A Quantum Bi-directional Self-Organizing Neural Network (QBDSONN) architecture suitable for binary image denoising in real time is proposed in this article. It is composed of three second order neighborhood topology based interconnected layers of neurons (represented by qubits) known as input, intermediate and output layers. Moreover, it does not use any quantum back-propagation algorithm for the adjustment of its interconnection weights. Instead, it resorts to a counter-propagation of quantum states of the intermediate layer and the output layer. In the proposed architecture, the inter-connection weights and activation values are represented by rotation gates. The quantum neurons of each network layer follow a cellular network pattern and are fully intra-connected to each other. QBDSONN self-organizes the quantized input image information by means of the counter-propagating fashion of the quantum network states of the intermediate and output layers of the architecture. A quantum measurement at the output layer collapses superposition of quantum states of the processed information thereby yielding the desired outputs once the network attains stability. Applications of QBDSONN are demonstrated on the denoising of a synthetic and real life spanner image with different degrees of uniform noise and Gaussian noise. Comparative results indicate that QBDSONN outperforms its classical counterpart in terms of time and also it retains the shapes of the denoised images with great precision.

A quantum parallel bi-directional self-organizing neural network (QPBDSONN) architecture for extraction of pure color objects from noisy background

This paper is aimed to propose a suitable real-time pure color image denoising procedure using a self-supervised network referred to as Quantum Parallel Bi-directional Self-Organizing Neural Network (QPBDSONN) architecture. The proposed QPBDSONN replicates the Parallel Bi-directional Self-Organizing Neural Network (PBDSONN) architecture and exploits by the power of quantum computation. To process three distinct basic color components (Red, Green and Blue) of noisy color image, QPBDSONN uses trinity of Quantum Bi-directional Self-organizing Neural Network (QBDSONN) architecture at the source layer in parallel mode. Each constituent QBDSONN comprises input, intermediate or hidden and output layers interconnected by 8-connected neighborhood topology based layer of neurons represented by qubits. Each constituent QBDSONN updates weighted interconnections in the form of quantum states through counter-propagation between hidden and output layer to obviate quantum back propagation. Rotation gates are introduced to represent weighted inter-links and values of activation. Finally, a quantum measurement operation is performed at the output layer of each constituent QBDSONN followed by fusion operation at the sink layer of QPBDSONN to concatenate the processed color image components resulting in the true output. The superiority of the proposed network architecture over the classical PBDSONN can be established using a real-life spanner pure color image and synthetic pure color image corrupted with various intensity of uniform and Gaussian noise in terms of extraction time and shapes.

Rough Set based keyword selection and weighing for textual answer evaluation

Automatic assessment of learners responses has gained wider acceptance and popularity in recent times. Due to associated complexities of free text evaluation, the trend has gradually shifted towards close ended question which have their limitations. The current work proposes a rough set based strategy to augment automated free text evaluation system(s) using keyword and associated expression based technique. The proposed method uses human evaluated answers as training data and using Rough Set Theory, extracts information from them to be used in the shortlisting and weighing of keywords which are to be used in assessment. The results of the proposed technique outperforms the manual keyword selection and weight association and also higher correlation with human evaluators.