Prasanta K. Panigrahi

Siddharth Patwardhan, Utso Majumder, Aditya Das Sarma et al.

The percolation threshold is an important measure to determine the inherent rigidity of large networks. Predictors of the percolation threshold for large networks are computationally intense to run, hence it is a necessity to develop predictors of the percolation threshold of networks, that do not rely on numerical simulations. We demonstrate the efficacy of five machine learning-based regression techniques for the accurate prediction of the percolation threshold. The dataset generated to train the machine learning models contains a total of 777 real and synthetic networks. It consists of 5 statistical and structural properties of networks as features and the numerically computed percolation threshold as the output attribute. We establish that the machine learning models outperform three existing empirical estimators of bond percolation threshold, and extend this experiment to predict site and explosive percolation. Further, we compared the performance of our models in predicting the percolation threshold using RMSE values. The gradient boosting regressor, multilayer perceptron and random forests regression models achieve the least RMSE values among considered models.

Debashis Saikia, Bikash K. Behera, Mayukha Pal et al.

We propose a quantum measurement-based framework for probabilistic transformation of grayscale images using adaptive positive operator-valued measures (POVMs). In contrast, to existing approaches that are largely centered around segmentation or thresholding, the transformation is formulated here as a measurement-induced process acting directly on pixel intensities. The intensity values are embedded in a finite-dimensional Hilbert space, which allows the construction of data-adaptive measurement operators derived from Gaussian models of the image histogram. These operators naturally define an unsharp measurement of the intensity observable, with the reconstructed image obtained through expectation values of the measurement outcomes. To control the degree of measurement localization, we introduce a nonlinear sharpening transformation with a sharpening parameter, $γ$, that induces a continuous transition from unsharp measurements to projective measurements. This transition reflects an inherent trade-off between probabilistic smoothing and localization of intensity structures. In addition to the nonlinear sharpening parameter, we introduce another parameter $k$ (number of gaussian centers) which controls the resolution of the image during the transformation. Experimental results on standard benchmark images show that the proposed method gives effective data-adaptive transformations while preserving structural information.

Prasanta K. Panigrahi

We study the thermal quantum correlations in tripartite atomic system under the existence of Herring-Flicker (HF) coupling among the atoms. We explore two topologically distinct configurations of three coupled two-level atoms, viz., loop and line, differing in their coupling pattern. Further, the systems having asymmetric arrangement of atoms are quantum mechanically correlated more strongly than systems having symmetric arrangements. The variable nature of HF coupling leads to the increase of both concurrence and discord from zero to a saturation value from where they decrease to zero as a function of inter-atomic distance. Further separation leads to both the quantities attaining another saturation value. This controlled correlations play an important role in the design of quantum data buses that can transfer quantum states to establish quantum communication. The systems coupled via HF coupling will be efficient for this task as they are maximally entangled in parametrically controlled manner. Thus, these systems will be propitious for various quantum protocols such as secure communication, quantum cryptography, quantum key distribution etc.

Siddharth Patwardhan, Subhayan Roy Moulick, Prasanta K. Panigrahi

We study controlled quantum secure direct communication (CQSDC), a cryptographic scheme where a sender can send a secret bit-string to an intended recipient, without any secure classical channel, who can obtain the complete bit-string only with the permission of a controller. We report an efficient protocol to realize CQSDC using Cluster state and then go on to construct a (2-3)-CQSDC using Brown state, where a coalition of any two of the three controllers is required to retrieve the complete message. We argue both protocols to be unconditionally secure and analyze the efficiency of the protocols to show it to outperform the existing schemes while maintaining the same security specifications.

Subhayan Roy Moulick, Prasanta K. Panigrahi

We propose the idea of a Quantum Cheque Scheme, a cryptographic protocol in which any legitimate client of a trusted bank can issue a cheque, that cannot be counterfeited or altered in anyway, and can be verified by a bank or any of its branches. We formally define a Quantum Cheque and present the first Unconditionally Secure Quantum Cheque Scheme and show it to be secure against any no-signaling adversary. The proposed Quantum Cheque Scheme can been perceived as the quantum analog of Electronic Data Interchange, as an alternate for current e-Payment Gateways.

Sabyasachi Mukhopadhyay, Soham Mandal, Sawon Pratiher et al.

Our proposed hyper kurtosis based histogram equalized DIC images enhances the contrast by preserving the brightness. The evolution and development of precancerous activity among tissues are studied through S-transform (ST). The significant variations of amplitude spectra can be observed due to increased medium roughness from normal tissue were observed in time-frequency domain. The randomness and inhomogeneity of the tissue structures among human normal and different grades of DIC tissues is recognized by ST based timefrequency analysis. This study offers a simpler and better way to recognize the substantial changes among different stages of DIC tissues, which are reflected by spatial information containing within the inhomogeneity structures of different types of tissue.

Sabyasachi Mukhopadhyay, Soham Mandal, Sawon Pratiher et al.

In this paper, a comparative study between proposed hyper kurtosis based modified duo-histogram equalization (HKMDHE) algorithm and contrast limited adaptive histogram enhancement (CLAHE) has been presented for the implementation of contrast enhancement and brightness preservation of low contrast human brain CT scan images. In HKMDHE algorithm, contrast enhancement is done on the hyper-kurtosis based application. The results are very promising of proposed HKMDHE technique with improved PSNR values and lesser AMMBE values than CLAHE technique.

Sabyasachi Mukhopadhyay, Nandan K. Das, Soham Mandal et al.

In this paper, we have carried out the detail studies of pre-cancer by wavelet coherency and multifractal based detrended fluctuation analysis (MFDFA) on differential interference contrast (DIC) images of stromal region among different grades of pre-cancer tissues. Discrete wavelet transform (DWT) through Daubechies basis has been performed for identifying fluctuations over polynomial trends for clear characterization and differentiation of tissues. Wavelet coherence plots are performed for identifying the level of correlation in time scale plane between normal and various grades of DIC samples. Applying MFDFA on refractive index variations of cervical tissues, we have observed that the values of Hurst exponent (correlation) decreases from healthy (normal) to pre-cancer tissues. The width of singularity spectrum has a sudden degradation at grade-I in comparison of healthy (normal) tissue but later on it increases as cancer progresses from grade-II to grade-III.

Subhayan Roy Moulick, Siddharth Arora, Chirag Jain et al.

A semi-blind watermarking scheme is presented based on Singular Value Decomposition (SVD), which makes essential use of the fact that, the SVD subspace preserves significant amount of information of an image and is a one way decomposition. The principal components are used, along with the corresponding singular vectors of the watermark image to watermark the target image. For further security, the semi-blind scheme is extended to an invisible hash based watermarking scheme. The hash based scheme commits a watermark with a key such that, it is incoherent with the actual watermark, and can only be extracted using the key. Its security is analyzed in the random oracle model and shown to be unforgeable, invisible and satisfying the property of non-repudiation.

Madhur Srivastava, Subhayan R. Moulick, Prasanta K. Panigrahi

We propose a novel method for image representation in quantum computers, which uses the two-dimensional (2-D) quantum states to locate each pixel in an image through row-location and column-location vectors for identifying each pixel location. The quantum state of an image is the linear superposition of the tensor product of the m-qubits row-location vector and the n-qubits column-location vector of each pixel. It enables the natural quantum representation of rectangular images that other methods lack. The amplitude/intensity of each pixel is incorporated into the coefficient values of the pixel's quantum state, without using any qubits. Due to the fact that linear superposition, tensor product and qubits form the fundamental basis of quantum computing, the proposed method presents the machine level representation of images on quantum computers. Unlike other methods, this method is a pure quantum representation without any classical components.

Madhur Srivastava, Satish K. Singh, Prasanta K. Panigrahi

The paper presents a non-uniform quantization method for the Detail components in the JPEG2000 standard. Incorporating the fact that the coefficients lying towards the ends of the histogram plot of each Detail component represent the structural information of an image, the quantization step sizes become smaller at they approach the ends of the histogram plot. The variable quantization step sizes are determined by the actual statistics of the wavelet coefficients. Mean and standard deviation are the two statistical parameters used iteratively to obtain the variable step sizes. Moreover, the mean of the coefficients lying within the step size is chosen as the quantized value, contrary to the deadzone uniform quantizer which selects the midpoint of the quantization step size as the quantized value. The experimental results of the deadzone uniform quantizer and the proposed non-uniform quantizer are objectively compared by using Mean-Squared Error (MSE) and Mean Structural Similarity Index Measure (MSSIM), to evaluate the quantization error and reconstructed image quality, respectively. Subjective analysis of the reconstructed images is also carried out. Through the objective and subjective assessments, it is shown that the non-uniform quantizer performs better than the deadzone uniform quantizer in the perceptual quality of the reconstructed image, especially at low bitrates. More importantly, unlike the deadzone uniform quantizer, the non-uniform quantizer accomplishes better visual quality with a few quantized values.

Madhur Srivastava, Satish K. Singh, Prasanta K. Panigrahi

We explicate a semi-automated statistical algorithm for object identification and segregation in both gray scale and color images. The algorithm makes optimal use of the observation that definite objects in an image are typically represented by pixel values having narrow Gaussian distributions about characteristic mean values. Furthermore, for visually distinct objects, the corresponding Gaussian distributions have negligible overlap with each other and hence the Mahalanobis distance between these distributions are large. These statistical facts enable one to sub-divide images into multiple thresholds of variable sizes, each segregating similar objects. The procedure incorporates the sensitivity of human eye to the gray pixel values into the variable threshold size, while mapping the Gaussian distributions into localized δ-functions, for object separation. The effectiveness of this recursive statistical algorithm is demonstrated using a wide variety of images.

Madhur Srivastava, Prasanta K. Panigrahi

The paper introduces the idea of non-uniform quantization in the detail components of wavelet transformed image. It argues that most of the coefficients of horizontal, vertical and diagonal components lie near to zeros and the coefficients representing large differences are few at the extreme ends of histogram. Therefore, this paper advocates need for variable step size quantization scheme which preserves the edge information at the edge of histogram and removes redundancy with the minimal number of quantized values. To support the idea, preliminary results are provided using a non-uniform quantization algorithm. We believe that successful implementation of non-uniform quantization in detail components in JPEG-2000 still image standard will improve image quality and compression efficiency with lesser number of quantized values.

Madhur Srivastava, Yashwant Yashu, Satish K. Singh et al.

In this paper, we carry out a comparative study of the efficacy of wavelets belonging to Daubechies and Coiflet family in achieving image segmentation through a fast statistical algorithm.The fact that wavelets belonging to Daubechies family optimally capture the polynomial trends and those of Coiflet family satisfy mini-max condition, makes this comparison interesting. In the context of the present algorithm, it is found that the performance of Coiflet wavelets is better, as compared to Daubechies wavelet.