Browsing by Author "Narasimhadhan, A.V."

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1-D CNN for Mineral Classification using Hyperspectral Data
(Institute of Electrical and Electronics Engineers Inc., 2023) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.
Hyperspectral Image (HSI) is a potent remote sensing (RS) technique, capturing images over numerous narrow, contiguous spectral bands. Unlike traditional RS methods, HSI offers detailed spectral insights for each pixel, enhancing comprehension of the Earth's surface and its contents. Initially intended for mining and geology, its application has expanded across various domains. Yet, mineral identification poses challenges due to spectral signature variations and limited ground truth. Despite various advanced algorithms, including machine learning, no dedicated Deep Learning (DL) expert system exists for mineral classification in HSI. DL models require abundant training data and ground-truth, which are scarce in hyperspectral mineral data. Introducing the 1-D CNN model as a proposed method, we focus on enhancing mineral classification by increasing the available training data. The utilization of augmented training samples through the 1-D CNN model tackles the challenge of limited ground truth data, enabling accurate classification of mineral classes. Â© 2023 IEEE.
A Compact Dual-band Hat-Shaped Antenna with Band-Specific Behavior Using Harmonic Mixer for Passive Neural Monitoring
(Institute for Ionics, 2024) Gopavajhula, D.S.; Kumar, S.; Narasimhadhan, A.V.; Song, H.
This work proposes a hat-shaped dual-band antenna with band-specific behavior using a harmonic mixer for the passive wireless neural monitoring system. The antenna is designed to work in coherence with a harmonic mixer of 2nd order. The antenna covers a volume of 16 × 16 × 1.6 mm3. The performance of the antenna is found to be satisfactory by conducting experiments using both homogeneous and heterogeneous media mimicking human tissue after covering it with a biocompatible PDMS layer. The lower and higher resonant bands extend from 3.75 to 3.9 GHz and 7.05 to 8.2 GHz, respectively, supporting communication at high data rates up to 20 Mbps. A directive gain of 1.29 dB in the lower band and 1.39 dB in the higher band makes it a good choice for implantable medical devices. A six-layer head model was considered for SAR evaluation with a penetration depth of 10 mm for safe operation as per IEEE C95.1-1999 standard. Based on this simulation, the maximum input power that can be fed to the antenna for safe operation is found to be 8.46 mW. The link budget analysis reveals that a satisfactory communication link may potentially be established up to a distance of 7 and 1.5 m between implantable and interrogator antennae with corresponding data rates of 1 Mbps and 20 Mbps, respectively. © The Author(s), under exclusive licence to Shiraz University 2023.
A Comparative Study of Illumination Invariant Techniques in Video Tracking Perspective
(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2020) Asha, C.S.; Narasimhadhan, A.V.
Object tracking is being utilized in the field of computer vision over decades for video surveillance, human–computer interaction and robotic applications. Even though the state-of-the-art tracking technology is rapidly growing, few issues are still challenging such as illumination variation, pose variation, scale changes, occlusion, etc. Among these challenges, sudden illumination variation is more complicated which is not solved completely. Most of the current trackers, indeed work under controlled illumination conditions in outdoor and indoor environments. In this work, we study the effect of adding the photometric normalization techniques prior to tracking in order to minimize the drift during abrupt light changes of the median flow tracker (MFT). The tracker under investigation is based on the optical flow method and achieved remarkable results in the tracking literature. However, it drifts off during sudden illumination variation. To resolve this problem, pre-processing technique is incorporated just before tracking. Hence, we present an experimental study of various pre-processing techniques to improve the accuracy of the MFT. A total of eight state-of-the-art normalization techniques are summarized and tested in video tracking perspective. The experiments are carried out with the video sequences obtained from the object tracking benchmark dataset posing sudden illumination change as a challenge to analyze the modified tracker. A comparative analysis indicates that the modified tracker outperforms the baseline tracker in terms of precision score and overlap score. © 2019 IETE.
A High Gain Zero Index Metamaterial for Radome Applications
(Institute of Electrical and Electronics Engineers Inc., 2020) Babu, M.A.; Krishnak, K.; Narasimhadhan, A.V.
Antennas in radar systems are protected on the outside by dielectric materials called radomes. Radomes are prone to boresight error and loss of electromagnetic energy. Metamaterials can be used in enhancing the performance of radome by eliminating boresight error and alleviating loss of energy. This paper proposes a compact single layer radome made up of an S-shaped zero-index metamaterial that operates at X-band frequency i.e., 10 GHz. A radome is designed by the periodic repetition of 10 x 7 S-ring unit cells. Radome is realized on Rogers RT/Duriod 5870 substrate (having permittivity Ïµr = 2.33) with dimensions of 35 mm x 35mm x 0.5mm. The proposed design provides an overall gain of 9.8dB (18% more than antenna), eliminates the boresight error and has a wider band of operation from 9 to 11 GHz. Â© 2020 IEEE.
A Meaningful Reformulation of Relative Spectral Discrimination Power to Analyze Hyperspectral Data
(Institute of Electrical and Electronics Engineers Inc., 2023) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.
Spectral matching algorithms (SMAs) discriminate and distinguish spectral signatures of earth surface features by comparing with their ground-truth spectra. Though different SMAs developed based on different theoretical strategies, choosing an effective SMA is still a challenging task. To study the performance of SMAs in distinguishing spectral signatures, few performance measure are developed and relative spectral discrimination power (RSDPW) is one such a measure. RSDPW discriminates how one spectral signature is distinct from another relative to a reference spectral signature. Classical way of measuring RSDPW do not takes into account of spectral matching between the two spectral signatures to be discriminated. Therefore, in this paper, a reformulation for RSDPW is presented to get a good idea about the spectral diversity of spectral signatures to measure RSDPW in a more meaningful manner and also to make it perspicacious. The experimental results show that the proposed reformulated RSDPW not only a meaningful way to measure it but also robust/standard enough to compare various SMAs by measuring it. Additionally, the range of RSDPW values for different levels of discrimination is demonstrated for the present study. Â© 2023 IEEE.
A Method for QRS Delineation Based on STFT Using Adaptive Threshold
(Elsevier, 2015) Shaik, B.S.; Naganjaneyulu, G.V.S.S.K.R.; Chandrasheker, T.; Narasimhadhan, A.V.
Electrocardiogram (ECG) is the electrical manifestation of the contractile activity of the heart. In this work, it is proposed to utilize an adaptive threshold technique on spectrogram computed using Short Time Fourier Transform (STFT) for QRS complex detection in electrocardiogram (ECG) signal. The algorithm consists of preprocessing the raw ECG signal to remove the power-line interference, computing the STFT, applying adaptive thresholding technique and followed by identifying QRS peaks. Sensitivity, Specificity and Detection error rate are calculated on MIT-BIH database using the proposed method, which yields a competitive results when compared with the state of art in QRS detection. Â© 2015 The Authors.
A multi clue heuristic based algorithm for table detection
(Institute of Electrical and Electronics Engineers Inc., 2017) Naganjaneyulu, G.V.S.S.K.R.; Sathwik, N.V.; Narasimhadhan, A.V.
Research in the field of document analysis and document recognition experienced reverent growth in the past decade as automation of the office document became essential for daily life. Text in documents can take different forms like hand written text, printed text, headings signatures, tables and graphics. Extraction of tables plays a crucial role in layout analysis, and retaining the important information present in tables. In this work, a multi clue heuristic based table detection algorithm using hough lines and corner harris corner is proposed. Hough lines and harris corner points are extracted from the document in two parallel process. The clues extracted from both the process are matched using nearest neighbor framework to yield tables from the documents. The proposed algorithm is a simple paradigm for extraction of tables that are formed by lines. The performance of the proposed algorithm is tested on different types of documents that contain tables to observe an accuracy of 89.7 %. Â© 2016 IEEE.
A novel approach for QRS delineation in ECG signal based on chirplet transform
(Institute of Electrical and Electronics Engineers Inc., 2016) Shaik, B.S.; Naganjaneyulu, G.V.S.S.K.R.; Narasimhadhan, A.V.
ECG analysis is used significantly in diagnosis, and biometrics. QRS complex detection is an important step in any application involving ECG signal. In this work, a novel approach for QRS complex detection based on chirplet transform is proposed. The QRS detection algorithm proposed in this work mainly consists of four steps. A preprocessing step to remove power line interference, computation of chirplet transform, an adaptive threshold technique for detecting possible QRS complex peaks, and followed by a decision making step. The performance of proposed algorithm for QRS complex detection is evaluated on MIT-BIH database and compared with the results of different algorithms in the state of art. The performance of the algorithm is comparable with the state of art of QRS complex detection. Â© 2015 IEEE.
A novel method for logo detection based on curvelet transform using GLCM features
(Springer Verlag service@springer.de, 2018) Naganjaneyulu, G.V.S.S.K.R.; Sai Krishna, C.; Narasimhadhan, A.V.
Automatic logo detection is a key tool for document retrieval, document recognition, document classification, and authentication. It helps in office automation as it enables the effective identification of source of a document. In this paper, a novel approach for logo detection using curvelet transform is proposed. The curvelet transform is employed for logo detection because of its ability to represent curved singularities efficiently when compared to wavelet and ridgelet transforms. The proposed algorithm consists of five steps, namely segmentation, noncandidate elimination, computation of curvelet coefficients, gray level co-occurrence matrix (GLCM) features extraction, followed by classification using a pretrained support vector machine classifier. The proposed algorithm is tested on a standard dataset, and the performance is compared with the state-of-the-art methods. The results show good improvement in the accuracy when compared with the competitors. Â© Springer Nature Singapore Pte Ltd. 2018.
A novel method for pitch detection via instantaneous frequency estimation using polynomial chirplet transform
(Institute of Electrical and Electronics Engineers Inc., 2017) Naganjaneyulu, G.V.S.S.K.R.; Ramana, M.V.; Narasimhadhan, A.V.
Speech processing and synthesis have been the interests of many researchers for the past few decades. One of the primary task in speech processing is the estimation of fundamental frequency of speech (also known as pitch). Speech is a non stationary signal whose frequency varies arbitrarily with time. Linear time frequency analysis tools such as short time fourier transform may not be convenient for estimation of pitch of speech. Polynomial chirplet transform models the frequency of speech signal by a higher order polynomial of time, which makes it suitable for analysis of speech to extract pitch. In this work, a novel algorithm is proposed for pitch detection in speech by estimating instantaneous frequency (IF) using polynomial chirplet transform. The proposed algorithm is applied on a part of TIMIT speech database to find the pitch of speech of different male and female persons. Â© 2016 IEEE.
A robust framework for quality enhancement of aerial remote sensing images
(Elsevier B.V., 2018) Karuna Kumari, E.; Das, D.; Suresh, S.; Lal, S.; Narasimhadhan, A.V.
This paper proposes a robust framework for quality restoration of remotely sensed aerial images. Proposed framework works in three steps: (1) Efficient color balancing and saturation adjustment, (2) Efficient color restoration, (3) Modified contrast enhancement using particle swarm optimization (PSO). In order to show the robustness, step-wise results of proposed framework is illustrated. Several aerial images from two publically available datasets are tested to support the robustness of the proposed framework over existing image quality restoration methods. The experimental results of proposed framework and other existing quality restoration methods are compared in terms of NIQMC, BIQME, MICHELSON, DE, EME and PIXDIST along with visual experimental results. Based on experimental results conducted on several aerial images suggest that the proposed framework is outperform over existing quality restoration methods. © 2018 Elsevier B.V.
Adaptive Learning Rate for Visual Tracking Using Correlation Filters
(2016) Asha, C.S.; Narasimhadhan, A.V.
Visual tracking is a difficult problem in computer vision due to illumination, pose, scale, appearance variations of object. Most of the trackers use either gray scale/color information or gradient information for image description. However the use of multiple channel features provide more information than single feature alone. Recently correlation filter based video tracking gained popularity due to its efficiency and high frame rate. Existing correlation filters use fixed learning rate to update filter template in every frame. In this paper, a method for adapting learning rate in correlation filter (CF) is presented which depends on the position of target in the present and previous frames (target velocity). This method uses integral channel features in correlation filter framework with adaptive learning rate to efficiently track the object. We experiment this technique on 12 challenging video sequences from visual object tracking (VOT challenges) datasets. Proposed technique can track any object irrespective of illumination variance, occlusion, scale change and outperforms the state-of-the-art trackers. � 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
Adaptive Learning Rate for Visual Tracking Using Correlation Filters
(Elsevier B.V., 2016) Asha, C.S.; Narasimhadhan, A.V.
Visual tracking is a difficult problem in computer vision due to illumination, pose, scale, appearance variations of object. Most of the trackers use either gray scale/color information or gradient information for image description. However the use of multiple channel features provide more information than single feature alone. Recently correlation filter based video tracking gained popularity due to its efficiency and high frame rate. Existing correlation filters use fixed learning rate to update filter template in every frame. In this paper, a method for adapting learning rate in correlation filter (CF) is presented which depends on the position of target in the present and previous frames (target velocity). This method uses integral channel features in correlation filter framework with adaptive learning rate to efficiently track the object. We experiment this technique on 12 challenging video sequences from visual object tracking (VOT challenges) datasets. Proposed technique can track any object irrespective of illumination variance, occlusion, scale change and outperforms the state-of-the-art trackers. Â© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
An Improved Method for Speech Enhancement Using Convolutional Neural Network Approach
(Institute of Electrical and Electronics Engineers Inc., 2022) Mahesh Kumar, T.N.; Hegde, P.; Deepak, K.T.; Narasimhadhan, A.V.
In the speech processing domain Speech enhancement is one of the most widely used techniques. With the development of deep neural networks and the availability of powerful hardware, multiple deep learning-based speech enhancement models have come up in recent years. In this work, the speech enhancement technique using a Convolutional Neural Network(CNN) as Denoising Autoencoders (DAEs) is investigated and compared with the conventional feed-forward topology. Further, The proposed model is analyzed at various SNR levels to process the corrupted english speech and also tested on unseen speech data which includes additional SNR levels. It is observed from simulation results that the proposed model outperforms the existing model in terms of Perceptual Evaluation of Speech Quality (PESQ) and Log Spectral Distance (LSD). The network achieved 3% higher scores than feed-forward neural networks, and it is found that the convolutional DAEs perform better than feed-forward counterparts. Â© 2022 IEEE.
Approximate Finite Rate of Innovation Based Seismic Reflectivity Estimation
(Birkhauser, 2024) Sudhakar Reddy, P.S.; Raghavendra, B.S.; Narasimhadhan, A.V.
Reflectivity inversion is an important deconvolution problem in reflection seismology that helps to describe the subsurface structure. Generally, deconvolution techniques iteratively work on the seismic data for estimating reflectivity. Therefore, these techniques are computationally expensive and may be slow to converge. In this paper, a novel method for estimating reflectivity signals in seismic data using an approximate finite rate of innovation (FRI) framework, is proposed. The seismic data is modeled as a convolution between the Ricker wavelet and the FRI signal, a Dirac impulse train. Relaxing the accurate exponential reproduction limitation given by generalised Strang-Fix (GSF) conditions, we develop a suitable sampling kernel utilizing Ricker wavelet which allows us to estimate the reflectivity signal. The experimental results demonstrate that the proposed approximate FRI framework provides a better reflectivity estimation than the deconvolution technique for medium-to-high signal-to-noise ratio (SNR) regimes with nearly 18% of seismic data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Assessment of speckle denoising in ultrasound carotid images using least square Bayesian estimation approach
(2017) Nagaraj, Y.; Asha, C.S.; Narasimhadhan, A.V.
The ultrasound carotid images affected by speckle noise, which highly reduces the image quality and effects the human interpretation. Speckle removal is substantial and critical step for preprocessing of ultrasound carotid images. For robust diagnosis, the carotid images must be free of noise and clear in clinical practices. The carotid ultrasound images have multiplicative noise and is very difficult to remove as compared to additive noise. To address this issue we propose to use Bayesian least square estimation in the logarithmic space. The proposed algorithm is tested on 50 ultrasound B mode carotid images and the performance of the algorithm is compared with the existing algorithms like Median filter, Speckle Reducing Anisotropic Diffusion(SRAD), Non Local Mean (NLM) filter, Total Variation (TV), Detail Preserving Anisotropic Diffusion(DPAD) filter, Lee filter, Frost filter and Wavelet filter. Experimental result shows that proposed algorithm capable of achieving better results as compared to the other methods in terms of signal to noise ratio (SNR), peak signal to noise ratio (PSNR), Correlation of Coefficient (CoC), Structural Similarity Index Map (SSIM) and Image Quality Index(IQI) measures. As per visual inspection concerned the proposed approach is more effective in terms of suppression of noise and image enhancement. � 2016 IEEE.
Assessment of speckle denoising in ultrasound carotid images using least square Bayesian estimation approach
(Institute of Electrical and Electronics Engineers Inc., 2017) Yamanakkanavar, Y.; Asha, C.S.; Narasimhadhan, A.V.
The ultrasound carotid images affected by speckle noise, which highly reduces the image quality and effects the human interpretation. Speckle removal is substantial and critical step for preprocessing of ultrasound carotid images. For robust diagnosis, the carotid images must be free of noise and clear in clinical practices. The carotid ultrasound images have multiplicative noise and is very difficult to remove as compared to additive noise. To address this issue we propose to use Bayesian least square estimation in the logarithmic space. The proposed algorithm is tested on 50 ultrasound B mode carotid images and the performance of the algorithm is compared with the existing algorithms like Median filter, Speckle Reducing Anisotropic Diffusion(SRAD), Non Local Mean (NLM) filter, Total Variation (TV), Detail Preserving Anisotropic Diffusion(DPAD) filter, Lee filter, Frost filter and Wavelet filter. Experimental result shows that proposed algorithm capable of achieving better results as compared to the other methods in terms of signal to noise ratio (SNR), peak signal to noise ratio (PSNR), Correlation of Coefficient (CoC), Structural Similarity Index Map (SSIM) and Image Quality Index(IQI) measures. As per visual inspection concerned the proposed approach is more effective in terms of suppression of noise and image enhancement. Â© 2016 IEEE.
ATGP based Change Detection in Hyperspectral Images
(IEEE Computer Society, 2022) Yadav, P.P.; Bobate, N.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.
Hyperspectral images (HSIs) due to advancements in spatial-spectral resolutions and availability of multi-temporal information is in demand for many remote sensing (RS) applications including change detection (CD). The high dimensionality of HSIs and limited availability of HSI-CD data sets with ground-truth change maps make CD not so easy but a difficult task. Though there are many classical and deep learning (DL) based algorithms to detect changes, the performance of classical algorithms is not up to the satisfactory level and the final performance of DL models depend on efficiency of pre-detection techniques which provide prior knowledge on changed and unchanged areas that are required to get appropriate training samples to learn to detect changes. Classical and DL approaches consider change information at pixel level i.e. pixel to pixel change either by comparing the corresponding pixels alone or with their local neighborhood pixels. Therefore, identification of features for every pixel that relate the most significant information of the whole HSI-CD data in a simple and an efficient way to detect changes effectively is the need of the hour. In addition, there is not much comprehensive study on developing CD algorithms that not only simple to use but also as efficient as that of DL models is available. Therefore, in this paper, an endmember based feature extraction is proposed to detect changes in HSI. An automatic target generation process (ATGP) algorithm is adapted to extract endmembers present in the HSI-CD data set. Then, various spectral matching algorithms are used to measure endmember relations for all the pixels so that dimensionality of the data is reduced as well as the effective features to detect changes can be extracted. The experimental results on three benchmark HSI-CD data sets show that proposed ATGP based change vector analysis (CVA) algorithm yields remarkable results on comparing both with the classical as well as DL based CD approaches. Â© 2022 IEEE.
Automatic method for contrast enhancement of natural color images
(Korean Institute of Electrical Engineers, 2015) Shyam, L.; Narasimhadhan, A.V.
The contrast enhancement is great challenge in the image processing when images are suffering from poor contrast problem. Therefore, in order to overcome this problem an automatic method is proposed for contrast enhancement of natural color images. The proposed method consist of two stages: in first stage lightness component in YIQ color space is normalized by sigmoid function after the adaptive histogram equalization is applied on Y component and in second stage automatic color contrast enhancement algorithm is applied on output of the first stage. The proposed algorithm is tested on different NASA color images, hyperspectral color images and other types of natural color images. The performance of proposed algorithm is evaluated and compared with the other existing contrast enhancement algorithms in terms of colorfulness metric and color enhancement factor. The higher values of colorfulness metric and color enhancement factor imply that the visual quality of the enhanced image is good. Simulation results demonstrate that proposed algorithm provides higher values of colorfulness metric and color enhancement factor as compared to other existing contrast enhancement algorithms. The proposed algorithm also provides better visual enhancement results as compared with the other existing contrast enhancement algorithms. © The Korean Institute of Electrical Engineers.
Automatic Segmentation of Intima Media Complex in Carotid Ultrasound Images Using Support Vector Machine
(Springer Verlag, 2019) Yamanakkanavar, Y.; Hema Sai Teja, A.; Narasimhadhan, A.V.
The intima media thickness (IMT) of common carotid artery is a reliable measure of cardiovascular diseases. The quantification of IMT is the biomarker for clinical diagnosis of the risk of stroke. For robust measurement of IMT, the ultrasound carotid images must be free of speckle noise. To reduce the effect of speckle noise in the carotid ultrasound image, we propose to use Bayesian least square estimation filter. In addition, the enhancement step based on total variation-L1(TV-L1) norm is performed to improve the robustness. Further more, we present a fully automated region of interest and segmentation of intima media complex based on support vector machine. The quantitative evaluation is carried out on 49 carotid ultrasound images. The proposed algorithm is compared with gradient-based methods like model based, dynamic programming, snake algorithm, and classifier-based segmentation using a neural network algorithm. The performance of the experimental result shows that the proposed method is robust in quantifying the IMT in carotid ultrasound images. © 2018, King Fahd University of Petroleum & Minerals.