BITS Faculty Publications
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Item Efficient ASIC Implementation of Artificial Neural Network with Posit Representation of Floating-Point Numbers(Springer, 2023-07) Gupta, Anu; Gupta, RajivThis paper presents a low-power ASIC architecture of a feedforward Artificial Neural Network using Posit representation. The ASIC Posit shows 50% improvement over ASIC using IEEE 754 format in terms of Power and Silicon Area and is also 13% faster while achieving the same accuracy. The same design using the FPGA platform consumes more power than the ASIC design. The designs are done using Cadence RTL Encounter with TSMC 180 nm technology node.Item Low-cost Artificial Intelligence Enhanced Hardware Design for Data Augmentation(IEEE, 2023) Gupta, Rajiv; Gupta, AnuThis paper presents a novel low-cost hardware implementation of data augmentation using artificial neural networks for a low-power, low-cost Water Quality Indexing application. Multilayer Perceptron (MLP) feedforward network with backpropagation learning has been designed to predict the data of DO and EC using pH and ORP as the input vector. This reduces the requirement for costly sensor electrodes, decreasing the design's cost. The design has been implemented on both ASIC and Embedded platforms. The Augmentation ANN predicts DO and EC with a 98% accuracy rate and achieves a 92% reduction in cost. The results have been presented and compared with standard WQI device.Item Adaptive Shrinkage Function Optimization by Differential Evolution(IEEE, 2008) Gupta, Karunesh Kumar; Gupta, RajivIn this paper, a new wavelet shrinkage denoising algorithm is presented. The algorithm uses wavelet transform (WT) to extract information about sharp variation in multiresolution images and applies shrinkage function adapting the image features. The features are detected by energy of neighboring pixels, whereas in standard wavelet methods, the empirical wavelet coefficients shrink pixel by pixel, on the basis of their individual magnitude. The shrinkage function is optimized by differential Evolution (DE)Item Feature Adaptive Wavelet Shrinkage for Image Denoising(IEEE, 2007) Gupta, Karunesh Kumar; Gupta, RajivIn this paper, a new wavelet shrinkage denoising algorithm is presented. The algorithm uses wavelet transform (WT) to extract information about sharp variation in multiresolution images and applies shrinkage function adapting the image features. The shrinkage function depends on energy of neighboring pixels, whereas in standard wavelet methods, the empirical wavelet coefficients shrink pixel by pixel, on the basis of their individual magnitude. Experiments show that wavelet shrinkage algorithm which uses neighboring pixels energy improves the denoising performance and achieves better peak signal to noise ratio compared to other thresholding algorithms. Due to its low complexity, the proposed algorithm is very suitable for hardware implementationItem A review of emerging trends on water quality measurement sensors(IEEE, 2015) Gupta, Karunesh Kumar; Gupta, RajivNew concepts and techniques are replacing traditional methods of water quality parameters measurement systems. In modern sensor era, Optical Sensors (OS), Microelectronic Mechanical Systems (MEMS) and Bio-Sensors are important sensing techniques for different water quality parameter detection. Furthermore, these sensors are highly selective, sensitive, economical and user-friendly with quick response. This paper comprehensively reviews and discuss role of emerging techniques in detection of important water quality parameters i.e Dissolved Oxygen, Turbidity, pH, E-Coli, Effective chlorination, Biochemical Oxygen Demand (B.O.D) and fluoride. In addition, also explains why modern water quality parameters sensing techniques are preferable option for detection of above mentioned parameters. A dedicated part of this paper also discusses the significant advantages and limitations of new available techniques.Item Soft computing framework for assessment of water quality in distribution network(IEEE, 2017) Gupta, Karunesh Kumar; Gupta, RajivModern techniques are replacing traditional methods of water quality parameter measurement systems. Continuous effective online monitoring with user friendly decision support system is one of the essential challenges of water quality monitoring system. To achieve the goal of development of user friendly decision support system for monitoring of potable water in distribution network, this paper introduces soft computing framework, mainly consist of Python programming framework and fuzzy sets. In so far, we have exploited the properties of NumPy and Matplotlib libraries of Python for user interface and fuzzy sets for decision support system. The proposed decision support system collects and utilizes the data points generated from integrated Multi Sensor Array and process the obtained data set through rule based fuzzy sets. Effective user interface and decision making are essential prerequisite of any decision support system. Therefore, we developed Rule Based Decision Support System (RBDSS) strategy to measure the extent of potability of water in distribution network. Based on extensive research, five water quality parameters has been considered to implement decision support system i.e pH, Dissolved Oxygen (D.O.), Electrical Conductivity (E.C.), Oxygen Reduction Potential (O.R.P) and Temperature. The conducted study to test the feasibility of proposed decision support system testify the plausibility of framework in water quality distribution network.Item Wavelet Based Speckle Filtering of the SAR Images(IREIT, 2015) Gupta, Rajiv; Gupta, Karunesh KumarSynthetic Aperture Radar (SAR) images are corrupted by speckle noise due to random interference of electromagnetic waves. The speckle degrades the quality of the image and makes difficult to interpret, analysis and classification of the SAR images. This paper proposes a method that reduces the speckle and preserves the features. The features are preserved by using scale-space correlation between the scales. The results show that the proposed method is better than widely used filters based on the spatial domain, such as Lee, Kuan, Frost, Ehfrost, Median, Gamma filters in terms of feature preservation. Moreover the proposed method achieves a wide range of balances between speckle reduction and feature preservation, and thus is applicable in different applications such as road detection, bridge, and ribbon like structure. Furthermore, the proposed method does not require prior modeling of either the image or noise statistics. It uses the variance of the detail wavelets coefficients to estimate noise varianceItem Despeckle and Geographical Feature Extraction in SAR Images by Wavelet Transform(Elsevier, 2007-12) Gupta, Karunesh Kumar; Gupta, RajivThis paper presents a method to despeckle Synthetic Aperture Radar (SAR) image, and then extract geographical features in it. In this research work, speckle is reduced by multiscale analysis in wavelet domain. In terms of geographical feature preservation the result shows that the method is better compared to spatial domain filters, such as Lee, Kuan, Frost, Ehfrost, Median, Gamma filters. The geographical features such as roads, airport runways, rivers and other ribbon-like shape structures are detected by the new wavelet-based method as proposed by Yuan Yan Tang. Experimental results show that the proposed method extracts geographical features of different width as well as different gray levels.Item An algorithm for road enhancement in SAR images using wavelet transform(Springer, 2007-12) Gupta, Karunesh Kumar; Gupta, RajivImage denoising and enhancement plays an important role in the field of Synthetic Aperture Radar (SAR) imagery. The geographical features detection applications such as road detection are very demanding. The objective of image enhancement is to improve the visibility of lowcontrast features while suppressing the speckle. It improves the visible quality of the image. Image has locally varying statistics, has different edges and smoothness in it. Speckle reduction can be done on an image by wavelet analysis. Wavelet gives a superior performance in speckle reduction due to properties such as sparsity and multi resolution.Item Feature preserving speckle filtering of the SAR images by wavelet transform(Springer, 2008-07) Gupta, Karunesh Kumar; Gupta, RajivSynthetic Aperture Radar (SAR) images are corrupted by speckle noise due to random interference of electromagnetic waves. The speckle degrades the quality of the image and makes it difficult to interpret, analyse and classify. This paper proposes a method that reduces the speckle and preserves the features by using scale-space correlation between the scales. The results show that the proposed method is better than the widely used filters based on the spatial domain, such as Lee, Kuan, Frost, Ehfrost, Median, Gamma filters in terms of feature preservation. Moreover the proposed method achieves a wide range of balances between speckle reduction and feature preservation, and thus is applicable in different applications such as road detection, detection/ identification of bridge, and ribbon like structures. Furthermore, the proposed method does not require prior modeling of either the image or noise statistics. It uses the variance of the detail wavelets coefficients to estimate noise variance.