arXiv subjects

Sabyasachi Mukhopadhyay

Publications and source records attributed to Sabyasachi Mukhopadhyay.

At least 19 recordsLinked to original sources

arXiv2025

Satellites promise global-scale quantum networks

Academia, governments, and industry around the world are on a quest to build long-distance quantum communication networks for a future quantum internet. Using air and fiber channels, quantum communication quickly faced the daunting challenge of exponential photon loss with distance. Quantum repeaters were invented to solve the loss problem by probabilistically establishing entanglement over short distances and using quantum memories to synchronize the teleportation of such entanglement to long distances. However, due to imperfections and complexities of quantum memories, ground-based proof-of-concept repeater demonstrations have been restricted to metropolitan-scale distances. In contrast, direct photon transmission from satellites through empty space faces almost no exponential absorption loss and only quadratic beam divergence loss. A single satellite successfully distributed entanglement over more than 1,200 km. It is becoming increasingly clear that quantum communication over large intercontinental distances (e.g. 4,000-20,000 km) will likely employ a satellite-based architecture. This could involve quantum memories and repeater protocols in satellites, or memory-less satellite-chains through which photons are simply reflected, or some combination thereof. Rapid advancements in the space launch and classical satellite communications industry provide a strong tailwind for satellite quantum communication, promising economical and easier deployment of quantum communication satellites.

quant-ph↗

arXiv2024

A Survey of using Large Language Models for Generating Infrastructure as Code

Infrastructure as Code (IaC) is a revolutionary approach which has gained significant prominence in the Industry. IaC manages and provisions IT infrastructure using machine-readable code by enabling automation, consistency across the environments, reproducibility, version control, error reduction and enhancement in scalability. However, IaC orchestration is often a painstaking effort which requires specialised skills as well as a lot of manual effort. Automation of IaC is a necessity in the present conditions of the Industry and in this survey, we study the feasibility of applying Large Language Models (LLM) to address this problem. LLMs are large neural network-based models which have demonstrated significant language processing abilities and shown to be capable of following a range of instructions within a broad scope. Recently, they have also been adapted for code understanding and generation tasks successfully, which makes them a promising choice for the automatic generation of IaC configurations. In this survey, we delve into the details of IaC, usage of IaC in different platforms, their challenges, LLMs in terms of code-generation aspects and the importance of LLMs in IaC along with our own experiments. Finally, we conclude by presenting the challenges in this area and highlighting the scope for future research.

cs.SE↗

arXiv2023

Resource Management in Quantum Virtual Private Networks

In this study, we develop a resource management framework for a quantum virtual private network (qVPN), which involves the sharing of an underlying public quantum network by multiple organizations for quantum entanglement distribution. Our approach involves resolving the issue of link entanglement resource allocation in a qVPN by utilizing a centralized optimization framework. We provide insights into the potential of genetic and learning-based algorithms for optimizing qVPNs, and emphasize the significance of path selection and distillation in enabling efficient and reliable quantum communication in multi-organizational settings. Our findings demonstrate that compared to traditional greedy based heuristics, genetic and learning-based algorithms can identify better paths. Furthermore, these algorithms can effectively identify good distillation strategies to mitigate potential noises in gates and quantum channels, while ensuring the necessary quality of service for end users.

quant-ph↗

arXiv2021

Hierarchical Bayesian state-space modeling of age- and sex-structured wildlife population dynamics

Biodiversity is declining at alarming rates worldwide, including for large wild mammals. It is therefore imperative to develop effective population conservation and recovery strategies. Population dynamics models can provide insights into processes driving declines of particular populations of a species and their relative importance. We develop an integrated Bayesian state-space population dynamics model for wildlife populations and illustrate it using a topi population inhabiting the Masai Mara Ecosystem in Kenya. The model is general and integrates ground demographic survey with aerial survey monitoring data. It incorporates population age- and sex-structure and life-history traits and relates birth rates, age-specific survival rates and sex ratio with meteorological covariates, prior population density, environmental seasonality and predation risk. The model runs on a monthly time step, enabling accurate characterization of reproductive seasonality, phenology, synchrony and prolificacy of births and juvenile recruitment. Model performance is evaluated using balanced bootstrap sampling and comparing predictions with aerial population size estimates. The model is implemented using MCMC methods and reproduces several well-known features of the Mara topi population, including striking and persistent population decline, seasonality of births and juvenile recruitment. It can be readily adapted for other wildlife species and extended to incorporate several additional useful features.

stat.AP↗

arXiv2019

Cancer Detection Using Quantum Neural Networks: A Demonstration on a Quantum Computer

Artificial intelligence and machine learning paves the way to achieve greater technical feats. In this endeavor to hone these techniques, quantum machine learning is budding to serve as an important tool. Using the techniques of deep learning and supervised learning in the quantum framework, we are able to propose a quantum neural network and showcase its implementation. We consider the application of cancer detection to demonstrate the working of our quantum neural network. Our focus is to train the network of ten qubits in a way so that it can learn the label of the given data set and optimize the circuit parameters to obtain the minimum error. Thus, through the use of many algorithms, we are able to give an idea of how a quantum neural network can function.

quant-ph↗

arXiv2018

Bayesian MISE convergence rates of Polya urn based density estimators: asymptotic comparisons and choice of prior parameters

Mixture models are well-known for their versatility, and the Bayesian paradigm is a suitable platform for mixture analysis, particularly when the number of components is unknown. Bhattacharya (2008) introduced a mixture model based on the Dirichlet process, where an upper bound on the unknown number of components is to be specified. Here we consider a Bayesian asymptotic framework for objectively specifying the upper bound, which we assume to depend on the sample size. In particular, we define a Bayesian analogue of the mean integrated squared error (Bayesian MISE), and select that form of the upper bound, and also that form of the precision parameter of the underlying Dirichlet process, for which Bayesian MISE of a specific density estimator, which is a suitable modification of the Polya-urn based prior predictive model, converges at a desired rate. As a byproduct of our approach, we investigate asymptotic choice of the precision parameter of the traditional Dirichlet process mixture model; the density estimator we consider here is a modification of the prior predictive distribution of Escobar & West (1995) associated with the Polya urn model. Various asymptotic issues related to the two aforementioned mixtures, including comparative performances, are also investigated.

math.ST↗

arXiv2017

Protein bioelectronics: a review of what we do and do not know

We review the status of protein-based molecular electronics. First we discuss fundamental concepts of electron transfer and transport in and across proteins and proposed mechanisms for these processes. We then describe the immobilization of proteins to solid-state surfaces in both nanoscale and macroscopic approaches, and highlight how different methodologies can alter protein electronic properties. Because immobilizing proteins while retaining biological activity is crucial to the successful development of bioelectronic devices, we discuss this process at length. We briefly discuss computational predictions and their link to experimental results. We then summarize how the biological activity of immobilized proteins is beneficial for bioelectronics devices, and how conductance measurements can shed light on protein properties. Finally, we consider how the research to date could influence the development of future bioelectronics devices.

physics.bio-ph↗

arXiv2016

Wind Speed Data Analysis for Various Seasons during a Decade by Wavelet and S transform

The appropriate weather prediction is a challenging task and it can be feasible with proper wind speed fluctuation analysis. In this current paper daubechies-4 wavelet is used to analyze the winter wind speed fluctuations due to lesser agitated wind data samples of winter. In summer abrupt changes in wind speed occurs which creates difficulty for wavelets to keep proper track of wind speed fluctuations. So, in that case the concept of the S-transform is introduced.

cs.CE↗

arXiv2016

Multiclass Classification of Cervical Cancer Tissues by Hidden Markov Model

In this paper, we report a hidden Markov model based multiclass classification of cervical cancer tissues. This model has been validated directly over time series generated by the medium refractive index fluctuations extracted from differential interference contrast images of healthy and different stages of cancer tissues. The method shows promising results for multiclass classification with higher accuracy.

cs.CV↗

arXiv2016

Temperature-dependent Hysteresis in MAPbI3 Solar Cells

Hysteresis in the current-voltage characteristics of hybrid organic-inorganic perovskite-based solar cells is one of the fundamental aspects of these cells that we do not understand well. One possible cause, suggested for the hysteresis, is polarization of the perovskite layer under applied voltage and illumination bias, due to ion migration within the perovskite. To study this problem systemically current-voltage characteristics of both regular (light incident through the electron conducting contact) and so-called inverted (light incident through the hole conducting contact) perovskite cells were studied at different temperatures and scan rates. We explain our results by assuming that the effects of scan rate and temperature on hysteresis are strongly correlated to ion migration within the device, with the rate-determining step being ion migration at/across the interfaces of the perovskite layer with the contact materials. By correlating between the scan rate with the measurement temperature we show that the inverted and regular cells operate in different hysteresis regimes, with different activation energies of 0.28+-0.04 eV and 0.59+-0.09 eV, respectively. We suggest that the differences, observed between the two architectures are due to different rates of ion migration close to the interfaces, and conclude that the diffusion coefficient of migrating ions in the inverted cells is 3 orders of magnitude higher than in the regular cells, leading to different accumulation rates of ions near the interfaces. Analysis of VOC as a function of temperature shows that the main recombination mechanism is trap-assisted (Shockley-Read Hall, SRH) in the space charge region, similar to what is the case for other thin film inorganic solar cells.

cond-mat.mtrl-sci↗

arXiv2015

Application of S-Transform on Hyper kurtosis based Modified Duo Histogram Equalized DIC images for Pre-cancer Detection

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.

cs.CV↗

arXiv2015

A comparative study between proposed Hyper Kurtosis based Modified Duo-Histogram Equalization (HKMDHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) for Contrast Enhancement Purpose of Low Contrast Human Brain CT scan images

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.

cs.CV↗

arXiv2015

Wavelet based approach for tissue fractal parameter measurement: Pre cancer detection

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.

cs.CV↗

arXiv2015

Diagnosing Heterogeneous Dynamics for CT Scan Images of Human Brain in Wavelet and MFDFA domain

CT scan images of human brain of a particular patient in different cross sections are taken, on which wavelet transform and multi-fractal analysis are applied. The vertical and horizontal unfolding of images are done before analyzing these images. A systematic investigation of de-noised CT scan images of human brain in different cross-sections are carried out through wavelet normalized energy and wavelet semi-log plots, which clearly points out the mismatch between results of vertical and horizontal unfolding. The mismatch of results confirms the heterogeneity in spatial domain. Using the multi-fractal de-trended fluctuation analysis (MFDFA), the mismatch between the values of Hurst exponent and width of singularity spectrum by vertical and horizontal unfolding confirms the same.

cs.CV↗

arXiv2014

A comparative study between seasonal wind speed by Fourier and Wavelet analysis

Wind Energy is a useful resource for Renewable energy purpose. Wind speed plays a vital role for wind energy calculation of certain location. So, it is very much necessary to know the wind speed data characteristics. In this paper fourier and wavelet transform are applied to study the wind speed data. We have compared wind speed of winter with summer by taking their speed into account using various discrete wavelets namely Haar and Daubechies-4 (Db-4). Also the periodicity of wind speed is checked using Continuous Wavelet Transform (MCWT) like Morlet. Thereafter a comparative study is done for detecting the periodicity of both summer and winter. Then wavelet coherence is checked between these two data for extracting the phase coherency information.

cs.CE↗

arXiv2014

Why Lead Methylammonium tri-IODIDE perovskite-based solar cells requires a mesoporous electron transporting scaffold (but not necessarily a hole conductor)

CH3NH3PbI3-based solar cells were characterized with electron beam-induced current (EBIC), and compared to CH3NH3PbI3-xClx ones. A spatial map of charge separation efficiency in working cells shows p-i-n structures for both thin film cells. Effective diffusion lengths, LD, (from EBIC profile) show that holes are extracted significantly more efficiently than electrons in CH3NH3PbI3, explaining why CH3NH3PbI3-based cells require mesoporous electron conductors, while CH3NH3PbI3-xClx ones, where LD values are comparable for both charge types, do not.

physics.chem-ph↗

arXiv2013

An Improved Bayesian Semiparametric Model for Palaeoclimate Reconstruction: Cross-validation Based Model Assessment

Fossil-based palaeoclimate reconstruction is an important area of ecological science that has gained momentum in the backdrop of the global climate change debate. The hierarchical Bayesian paradigm provides an interesting platform for studying such important scientific issue. However, our cross-validation based assessment of the existing Bayesian hierarchical models with respect to two modern proxy data sets based on chironomid and pollen, respectively, revealed that the models are inadequate for the data sets. In this paper, we model the species assemblages (compositional data) by the zero-inflated multinomial distribution, while modelling the species response functions using Dirichlet process based Gaussian mixtures. This modelling strategy yielded significantly improved performances, and a formal Bayesian test of model adequacy, developed recently, showed that our new model is adequate for both the modern data sets. Furthermore, combining together the zero-inflated assumption, Importance Resampling Markov Chain Monte Carlo (IRMCMC) and the recently developed Transformation-based Markov Chain Monte Carlo (TMCMC), we develop a powerful and efficient computational methodology.

stat.AP↗

arXiv2013

Application of Fourier and Wavelet Transform for analysing 300 years Sunspot numbers to Explain the Solar Cycles

In this paper Fourier Transform and Wavelet Transform are applied in case of recent 300 years of sunspot numbers to explain the solar cycles. Here basically parallel study of Fourier and Wavelet analysis are done and we have observed that the better result can be obtained from Wavelet analysis during sunspot number analysis. We are able to show various minima and maxima in the recent ages of solar cycles with this tool. The exact periodicity and other possible periodicities in the cyclic phenomenon of sunspot activity are determined.

cs.CE↗