Veerendra C. Angadi

Veerendra C. Angadi

Technology Transfer Engineer (SME) at WMG, University of Warwick

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Posts

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portfolio

publications

Computational Analysis of Variable Scaling Factor based Invisible Image Watermarking using Hybrid SVD - DCT Compression - Decompression Technique

Published in 2011 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Levengipuram - Kanyakumari, India, 2011

Watermarking is a technique of embedding hidden information in multimedia data imperceptibly, such as image, video, audio and text data. Generally, an original image is transformed and coded watermark image data is embedded in frequency domain watermarking models. The proposed work explores the computational analysis of variable scaling factor (α) based invisible image watermarking process using the combination of Singular Value Decomposition (SVD) and Discrete Cosine Transform (DCT) compression and decompression algorithms. The performance analysis of such an invisible image watermarking system is measured using percentage Compression Ratio (%CR), Peak Signal to Noise Ratio (PSNR), with respect to be embedded and extracted images correspondingly. The algorithms thus developed may find applications in copyright identification, finger printing, transaction tracking, content authentication, device control to name a few.

Recommended citation: M. M. Dixit, P. K. Kulkarni, P. S. Somasagar and V. C. Angadi (2011), "Computational Analysis of Variable Scaling Factor based Invisible Image Watermarking using Hybrid SVD - DCT Compression - Decompression Technique", In 2011 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Levengipuram - Kanyakumari, India.

Variable scaling factor based invisible image watermarking using hybrid DWT - SVD compression - decompression technique

Published in 2012 IEEE Students’ Conference on Electrical Electronics and Computer Science (SCEECS), Bhopal, India, 2012

Variable scaling factor based invisible image watermarking using hybrid DWT - SVD compression - decompression technique.

Recommended citation: M. M. Dixit, P. K. Kulkarni, P. S. Somasagar and V. C. Angadi (2012), "Variable scaling factor based invisible image watermarking using hybrid DWT - SVD compression - decompression technique", In 2012 IEEE Students’ Conference on Electrical, Electronics and Computer Science, Bhopal, India, pp. 1-4.

Quality of Experience based Video Summarization

Published in MSc Dissertation, 2013

The proposed design of video summarization is done by including a sense of feedback in terms of quality of experience from the user. The summarization of the video is done by considering energy associated with the consecutive frame difference. The project exploits the fact that if the energy is very low, it indicates that the motion associated between the frames is less. The maximum threshold for the energy is obtained by the novel numerical mathematical model. The feedback is incorporated in terms of altering the value of the threshold. The project also discusses and analyse the different application associated with proposed video summarization algorithm.

Recommended citation: V. C. Angadi (2013), "Quality of Experience based Video Summarization", MSc Dissertation, University of Sheffield, UK.

Core-Loss Edge Detection and Background Subtraction Techniques for EELS

Published in 2014 Hyperspectral Imaging and Applications Conference, Coventry, UK, 2014

The paper proposes a novel approach for automated background subtraction of core-loss edges. In conventional back-ground subtraction methods, prior knowledge of the pre-edge region is inevitable. We exploit the fact that the core-loss edge is superimposed on an almost exponentially decaying background. Principle component analysis (PCA) is used to detect clustersof positive slope angle between the spectral points. A moving average filter is adopted to minimise the false detection of core-loss edges due to shot noise.

Recommended citation: V. C. Angadi and T. Walther (2014), "Core-Loss Edge Detection and Background Subtraction Techniques for EELS", In 2014 Hyperspectral Imaging and Applications Conferenc, Coventry, UK.

Automated background subtraction for electron energy-loss spectroscopy and application to spectral imaging of semiconductor layers

Published in 2015 19th Microscopy of Semiconducting Materials, Cambridge, UK, 2015

A novel approach for automated background subtraction of core-loss edgeis proposed. In conventional background subtraction methods, prior knowledge of the pre-edge region is inevitable. We exploit the fact that the core-loss edgeis superimposed on an almost exponentially decaying background. Principle component analysis (PCA) is used to detect clusters of positive slope angle between the spectral points. A moving average filter is adopted to minimise thefalse positive detection of core-loss edges due to shot noise.

Recommended citation: V. C. Angadi, T. Walther and C. Abhayaratne (2015), "Automated background subtraction for electron energy-loss spectroscopy and application to spectral imaging of semiconductor layers", In 2015 19th Microscopy of Semiconducting Materials, Cambridge, UK.

Development of automated background subtraction technique for electron energy-loss spectroscopy

Published in 2015 Electron Microscopy and Analysis Group Conference, Manchester, UK, 2015

Development of automated background subtraction technique for electron energy-loss spectroscopy

Recommended citation: V. C. Angadi, C. Abhayaratne and T. Walther (2015), "Development of automated background subtraction technique for electron energy-loss spectroscopy", In 2015 Electron Microscopy and Analysis Group Conference, Manchester, UK.

Automated Background Subtraction Technique for Electron Energy‐loss Spectroscopy and Application to Semiconductor Heterostructures

Published in Journal of Microscopy, 2016

Electron energy‐loss spectroscopy (EELS) has become a standard tool for identification and sometimes also quantification of elements in materials science. This is important for understanding the chemical and/or structural composition of processed materials. In EELS, the background is often modelled using an inverse power‐law function. Core‐loss ionization edges are superimposed on top of the dominating background, making it difficult to quantify their intensities. The inverse power‐law has to be modelled for each pre‐edge region of the ionization edges in the spectrum individually rather than for the entire spectrum. To achieve this, the prerequisite is that one knows all core losses possibly present. The aim of this study is to automatically detect core‐loss edges, model the background and extract quantitative elemental maps and profiles of EELS, based on several EELS spectrum images (EELS SI) without any prior knowledge of the material. The algorithm provides elemental maps and concentration profiles by making smart decisions in selecting pre‐edge regions and integration ranges. The results of the quantification for a semiconductor thin film heterostructure show high chemical sensitivity, reasonable group III/V intensity ratios but also quantification issues when narrow integration windows are used without deconvolution.

Recommended citation: V. C. Angadi, C. Abhayaratne and T. Walther (2016), "Automated background subtraction technique for electron energy‐loss spectroscopy and application to semiconductor heterostructures" Journal of Microscopy, 262(2), pp. 157-166.

Systematic study of background subtraction techniques for EELS

Published in 2016 Electron Microscopy and Analysis Group Conference, Durham, UK, 2016

Systematic study of background subtraction techniques for EELS.

Recommended citation: V. C. Angadi and T. Walther (2016), "Systematic study of background subtraction techniques for EELS", In 2016 Electron Microscopy and Analysis Group Conference, Durham, UK.

Study of phase separation in an InGaN alloy by electron energy loss spectroscopy in an aberration corrected monochromated scanning transmission electron microscope

Published in Journal of Materials Research, 2017

Phase separation of InxGa1−xN into Ga-rich and In-rich regions has been studied by electron energy-loss spectroscopy (EELS) in a monochromated, aberration corrected scanning transmission electron microscope (STEM). We analyze the full spectral information contained in EELS of InGaN, combining for the first time studies of high-energy and low-energy ionization edges, plasmon, and valence losses. Elemental maps of the N K, In M4,5 and Ga L2,3 edges recorded by spectrum imaging at 100 kV reveal sub-nm fluctuations of the local indium content. The low energetic edges of Ga M4,5 and In N4,5 partially overlap with the plasmon peaks. Both have been fitted iteratively to a linear superimposition of reference spectra for GaN, InN, and InGaN, providing a direct measurement of phase separation at the nm-scale. Bandgap measurements are limited in real space by scattering delocalization rather than the electron beam size to ∼10 nm for small bandgaps, and their energetic accuracy by the method of fitting the onset of the joint density of states rather than energy resolution. For an In0.62Ga0.38N thin film we show that phase separation occurs on several length scales.

Recommended citation: T. Walther, X. Wang, V. C. Angadi, P. Ruterana, P. Longo and T. Aoki (2017), "Study of phase separation in an InGaN alloy by electron energy loss spectroscopy in an aberration corrected monochromated scanning transmission electron microscope", Journal of Material Research, 32(5), pp. 983-995.

Determination of bandgap onset by blind deconvolution of electron energy-loss spectra (EELS): 1D EELS vs 2D EEL spectrum images

Published in 2017 20th Microscopy of Semiconducting Materials, Oxford, UK, 2017

Precise determination of the bandgap is important for semiconductor research. It is possible but not straightforward to determine the location of the onset of the Density of States (DOS) from low-loss EELS rather than by optical spectroscopy. A square-root function fit to the low-loss function may work for direct bandgaps but will be affected by the presence of a strong and asymmetric zero-loss peak, phonons, Cerenkov effects and possibly even surface plasmons. Also, the tail of the bulk plasmon will affect the determination of bandgap. Deconvolution methods can be applied to remove these effects. Fourier-log and Richardson-Lucy deconvolution methods are routinely used for one-dimensional (1D) spectra but they tend to enhance noise. An alternative method is 2D deconvolution of a spectrum image. In this method the deconvolution is applied to a spatially or angular resolved EELS, or spectrum image, bringing the extended zero-loss peak to a single point. By this way of deconvolution, even weak information which is hidden by the wide point sperad function of the zero-loss peak can be made visible. In this study, different ways to determine the bandgap of GaAs are compared using blind deconvolution of 1D and 2D EELS. For this, starting from the same 2D spectrum image, the effect of changing the sequence of projection (from 1D to 2D) and deconvolution is thus compared. A Gaussian model of the zero-loss peak is considered as the initial point spread in both cases. A prominent onset of the intensity at low energies is observed and either a square-root function fit or simple smooting and differentiation are used to refine the exact location of the onset. For deconvolution of the 1D vertically projected EELS the value of the band edge apparently decreases with the number of iterations, from ~ 1.8 eV to ~ 0.4 eV (mean value of 0.82 eV, with standard devation of 0.46 eV). For 2D decovolution followed by vertical projection the apparent bandgap stays consistently high, yielding Eg = 1.42 &plusmn 0.03 eV independent of the number of iterations.

Recommended citation: V. C. Angadi, C. Abhayaratne and T. Walther (2017), "Determination of bandgap onset by blind deconvolution of electron energy-loss spectra (EELS): 1D EELS vs 2D EEL spectrum images", In 2017 20th Microscopy of Semiconducting Materials, Oxford, UK.

Evidence of terbium and oxygen co-segregation in annealed AlN:Tb

Published in Applied Physics Letters, 2017

Evidence of terbium and oxygen co-segregation in annealed AlN:Tb.

Recommended citation: V. C. Angadi, F. Benz, I. Tischer, K. Thonke, T. Aoki and T. Walther (2017), "Evidence of terbium and oxygen co-segregation in annealed AlN: Tb" Applied Physics Letters, 110(22), pp. 222102.

Quantitative Electron Energy-loss Spectrum Data Processing for Hyperspectral Imaging in Analytical Transmission Electron Microscopy

Published in PhD Thesis, 2018

In this thesis, a comprehensive automated quantification process of some of the features in electron energy-loss spectroscopy (EELS) is described. For high-loss spectra, two algorithms have been proposed for automated ionization core-loss edge onset detection and quantification. The robustness of the edge detection by estimated exponent method is tested with respect to various parameters for different values of such size of the detection window, specimen thickness and an average white Gaussian noise. For quantification, the pre-edge regions and the integration ranges are automatically chosen based on the edges detected and elemental maps are calculated. A novel way of modelling background in post-edge regions is explored for GaAs high-loss spectra. However, simple post-edge background extrapolation tends to give an overestimation of the net core-loss. Hence, an optimum background is calculated from the error bars of the Poissonian statistics of net core-losses subtended by backgrounds modelled from pre- and post-edge regions. The Richardson-Lucy deconvolution method is explored at high-loss spectra to iteratively reconstruct the single scattering distribution. The ringing artefacts are studied with respect to number of iterations. A baseline correction to conventional linear least-squares method of core-loss quantification is proposed. An example of a high-loss spectrum image (SI) from a Ge based solar cell is used to test the relative quantification of Ga, As and Cu. The improvement from ~ &plusmn 8% to less than &#8818 &plusmn 3% in the quantification of Cu, Ga and As compared to other least-squares fit models are noticed. In some regions due to overlapping of core-losses, the large errors produced by standard leastsquares methods was reduced from ~ &plusmn 15% to &#8818 &plusmn 9%. The thesis also explores the joint fitting of bulk plasmons (InN, GaN and InxGa1-xN) and core-losses of 4d and 3d transitions of In and Ga respectively in the low-loss range < 50 eV. The effective In content in phase separated InxGa1-xN is quantified from two different fit ranges. A correction factor is proposed to correct the effects of a limited fit range arising due to inclusion of a truncated In reference spectrum in joint fitting. In both short and truncation corrected extended fit ranges the values of effective In content has been quantified ~ &#8818 10% with both bulk plasmons and core-losses. Chemical profiling of different regions in an EELS SI is done by mapping the positions of bulk plasmons. The full width at half maximum (FWHM) of bulk plasmon, Wp, is also studied by fitting a Lorentzian function. Regions with (surface) oxide formation or imperfection in the crystal structures due to formation of Tb–O complexes revealed an increase in Wp. The blind measurement of bandgap for a wide-bandgap material, GaN, has also been studied. The bandgap onset detection is compared for different methods such as by fitting a square-root function, derivative method and a novel approach based on the centroids of clusters identified when fit ranges are systematically varied. The square-root fitting and derivative method was applied to density of state (DOS) region between 0 eV to 12 eV to variously modelled backgrounds such as exponential tail extrapolation of ZLP, Richardson-Lucy and Fourier-log deconvolutions etc. The blind measurement of bandgap with square-root fitting to ZLP subtracted GaN spectrum was found to be at 3.28 eV with an R2 of 0.91 and for derivative method it was at 3.31 eV which is in agreement with the literature. The square-root fit applied to different DOS background modelling was able to determine bandgap at 3.52 &plusmn 0.91 eV where as from derivative method it was 3.52 &plusmn 0.41 eV. The bandgap measured using centroid of the highest R2 in the clusters detected using k-means clustering analysis was found to be at 3.52 &plusmn 0.91 eV which is comparable to square-root fits. The blind measurement of bandgap was applied to test for GaAs spectrum. The determined values with derivative and k-means cluster analysis was found to be at 1.40 eV and 1.20 eV, respectively. The precise measurement of bandgaps from EELS for an unknown material is found to be difficult (&#8818 &plusmn 0.41 eV) when compared to optical spectroscopy.

Recommended citation: V. C. Angadi (2018), "Quantitative Electron Energy-loss Spectrum Data Processing for Hyperspectral Imaging in Analytical Transmission Electron Microscopy", Doctoral Thesis, University of Sheffield, UK.

Zero Defect Manufacturing of Microsemiconductors–An Application of Machine Learning and Artificial Intelligence

Published in 2018 5th International Conference on Systems and Informatics (ICSAI), Nanjing, China, 2018

A real-time quality monitoring of the detection and prediction of a defect in fluid dispensing systems is presented. A case study of adhesive placement and dispensing in a semiconductor production system demonstrates the applicability of a combination of PCA to explain the variations in the amount of dispensed fluid syringe needle placement and event-based learning to express the causal relationship between machine and production state with defect types. The resulting definitions of system state and interrelationship of control parameters build the building blocks of Gene Expression Program (GEP) that predicts the formation of droplets and fail or pass product. The results show 99.93 % of accuracy in prediction of defect which is based on the obtained data from glue dispensing model. This integrated solution provides the genetic signature of the glue dispensing process helping to eliminate defects and the adjustment of system state prior to defect formation.

Recommended citation: Z. Huang, V. C. Angadi, M. Danishvar, A. Mousavi and M. Li (2018), "Zero Defect Manufacturing of Microsemiconductors – An Application of Machine Learning and Artificial Intelligence", In 2018 5th International Conference on Systems and Informatics (ICSAI), Nanjing, pp. 449-454.

Predictive Maintenance: When the Digital Shadow Reaches its Limits

Published in ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, 2020

Harmonization of data-driven and physics-based models for predictive maintenance.

Recommended citation: A. Werner, V. C. Angadi, J. Lentes and A. Mousavi (2020), "Predictive Maintenance – When the Digital Shadow reaches limitations", ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, 115(5), pp. 335-339.

A PdM framework Through the Event-based Genomics of Machine Breakdown

Published in The 9th Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modelling, Vancouver, Canada, 2020

A novel event-based predictive maintenance framework based on sensor signal measurements and regressive predictions to minimise machine breakdown and component failure is proposed. Such capabilities will be complemented by Event-Clustering technique to cluster and remove less impact sensor signals and also build breakdown genomics from the root of a failure in order to predict the upcoming machine breakdowns and components failures. The creation of machine breakdown genomics requires the knowledge of systems state observed as well as the state change at specified time intervals (discretization). The proposed framework is applied to a real application case study. An industrial case study of a continuous compression moulding machine that manufactures the plastic bottle closure (caps) in the beverage industry has been considered as an experiment. The machine breakdown genomics theory is tested in this case to build the sequence of events or the genomics of breakdown, where sequences of contiguous events lead to failure or healthy machine status. This is complemented by the Regression Event-Tracker method to estimates the condition monitoring of the components and provide components real-time remaining useful life estimation. The Weibull failure-rate analysis is carried out on the remaining useful life estimates for each element to understand and estimate the mean time to failure for the manufacturing machine.

Recommended citation: M. Danishvar, V. C. Angadi and A. Mousavi (2020), "A PdM framework Through the Event-based Genomics of Machine Breakdown", In The 9th Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modelling, pp. 1-6, Vancouver, Canada.

Causal Modelling for Predicting Machine Tools Degradation in High Speed Production Process

Published in 4th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies, Cambridge, UK, 2020

Causal Modelling for Predicting Machine Tools Degradation in High Speed Production Process.

Recommended citation: V. C. Angadi, A. Mousavi, D. Bartolome, M. Tellarini, M. Fazziani (2020), "Causal Modelling for Predicting Machine Tools Degradation in High Speed Production Process", In 4th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies, Cambridge, UK.

Regressive Event-Tracker: A Causal Prediction Modelling of Degradation in High Speed Manufacturing

Published in 30th International Conference on Flexible Automation and Intelligent Manufacturing, Athens, Greece, 2021

Regressive Event-Tracker: A Causal Prediction Modelling of Degradation in High Speed Manufacturing.

Recommended citation: V. C. Angadi, A. Mousavi, D. Bartolome, M. Tellarini and M. Fazziani (2020), "Regressive Event-Tracker: A Causal Prediction Modelling of Degradation in High Speed Manufacturing", In 30th International Conference on Flexible Automation and Intelligent Manufacturing, Athens, Greece.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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