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Item Worldwide, increasingly stringent regulations are coming into force, limiting the exposure of workers to industrial noise. Industrial noise and its consequences are thus growing in importance to employers, local and central government officials, trade unions, occupational hygienists and physicians and insurers. India is not an exception to this. The mining industry in India is facing serious problems over noise due to increasing demand for minerals for which large capacity machines are being deployed producing high noise levels. To know the status and to control the noise, the S&T Department of the Ministry of Coal, Government of India sponsored a research project in the area of noise pollution and its control for opencast projects. To start with, a detailed literature survey was carried out in the area of noise pollution and its control in the mining industry, embracing equipment like from Heavy Earth Moving Machinery (HEMM), compressors, workshops, pneumatic drills, processing plants etc, to know the quantum of work done in and worldwide. The various aspects studied In this project were the daily noise dose and/or noise exposure level of the operators of various types of heavy earth moving machinery and its assessment, noise characteristics at different operating conditions of the machine, analysis of noise coming out from different parts of the machine, analysis of noise at different distances from the machine for different frequency components and the most important one i.e., impact of periodic maintenance on the noise characteristics of machines and to find out with which maintenance schedule there is maximum fluctuation in the noise level and to evolve a technique for attenuating the noise generated from these machines as well as to reduce the operator's exposure to high noise levels. This paper highlights the results of this research project.(Multi-Science Publishing Co. Ltd, Noise analysis of heavy earth moving machinery deployed in opencast mines and development of suitable maintenance guidelines for its attenuation - Part 3) Vardhan, H.; Rao, Y.V.; Karmakar, N.C.2004Item An experimental study was carried out to investigate the principal sources of sound from heavy earth-moving machinery, namely a bulldozer and a front-end loader. Major sound sources were the exhaust and air inlet for the engines and the engine cooling fan on the bulldozer. Sound from the exhaust was an important source at nominal one-third-octave midband frequencies from 25 Hz to 250 Hz; sound from the air inlet was a significant contributor in the range of midband frequencies from 25 Hz to 500 Hz. Cooling fan noise for the bulldozer was important in the frequency range from 315 Hz to 3150 Hz. For the front-end loader, the enclosed cab in which the operator sits provided good noise reduction at frequencies greater than 400 Hz up to 20 kHz. Examination of the spectrum of the sound produced by these and other heavy earth-moving machines can indicate the need for maintenance efforts to restore noise-control capabilities that were originally installed or which should be installed. © 2005 Institute of Noise Control Engineering.(Institute of Noise Control Engineering, Experimental study of sources of noise from heavy earth-moving machinery) Vardhan, H.; Karmakar, N.C.; Rao, Y.V.2005Item Feedback active noise control based on forward-backward LMS predictor(2013) Pavithra, S.; Narasimhan, S.V.In this paper, a new feedback active noise control (FBANC) system based on forward-backward error LMS (FBLMS) predictor is proposed. The misadjustment of the FBLMS predictor is about half that of the forward error LMS (FLMS) predictor. The new ANC system employs FBLMS predictors both for its main path (MP) predictor and for the noise canceler (NC) for the secondary path (SP) identification (SPI). To realize the MP predictor based on the FBLMS concept, a new FXLMS structure is proposed. But for the NC for the SPI, the FBLMS predictor is directly used. The MP predictor based on FBLMS reduces its misadjustment. Further the use of FBLMS predictor for the NC, as it gives a good prediction of primary noise component in the error (residual noise), improves the SNR for SPI. Thus, the improved SP estimate and the reduced misadjustment for the MP predictor achieved result in a significantly better overall noise reduction (of about 8 dB) over the ANC that uses the MP predictor and noise canceler for SPI, both based only on the forward error LMS algorithm. The computational load for the proposed algorithm is about twice that of FBANC that uses only forward error. © 2012 Springer-Verlag London Limited.Item A new non-local maximum likelihood estimation method for Rician noise reduction in magnetic resonance images using the Kolmogorov-Smirnov test(Elsevier, 2014) Rajan, J.; den Dekker, A.J.; Sijbers, J.Denoising algorithms play an important role in the enhancement of magnetic resonance (MR) images. Effective denoising is vital for proper analysis and accurate quantitative measurements from MR images. Maximum Likelihood (ML) estimation methods were proved to be very effective in denoising MR images. Among the ML based methods, the recently proposed non-local maximum likelihood (NLML) approach gained much attention. In the NLML method, the samples for the ML estimation of the true underlying intensity are selected in a non-local way based on the intensity similarity of the pixel neighborhoods. This similarity is generally measured using the Euclidean distance. A drawback of this approach is the usage of a fixed sample size for the ML estimation resulting in over- or under-smoothing. In this work, we propose an NLML estimation method for denoising MR images in which the samples are selected in an adaptive and statistically supported way using the Kolmogorov-Smirnov (KS) similarity test. The method has been tested both on simulated and real data, showing its effectiveness. © 2013 Elsevier B.V.Item Speckle reduction in medical ultrasound images using an unbiased non-local means method(Elsevier Ltd, 2016) Sudeep, P.V.; Ponnusamy, P.; Rajan, J.; Baradaran, H.; Saba, L.; Gupta, A.; Suri, J.S.Enhancement of ultrasound (US) images is required for proper visual inspection and further pre-processing since US images are generally corrupted with speckle. In this paper, a new approach based on non-local means (NLM) method is proposed to remove the speckle noise in the US images. Since the interpolated final Cartesian image produced from uncompressed ultrasound data contaminated with fully developed speckle can be represented by a Gamma distribution, a Gamma model is incorporated in the proposed denoising procedure. In addition, the scale and shape parameters of the Gamma distribution are estimated using the maximum likelihood (ML) method. Bias due to speckle noise is expressed using these parameters and is removed from the NLM filtered output. The experiments on phantom images and real 2D ultrasound datasets show that the proposed method outperforms other related well-accepted methods, both in terms of objective and subjective evaluations. The results demonstrate that the proposed method has a better performance in both speckle reduction and preservation of structural features. © 2016 Elsevier Ltd. All rights reserved.Item Non-local means image denoising using shapiro-wilk similarity measure(Institute of Electrical and Electronics Engineers Inc., 2018) Yamanappa, W.; Sudeep, P.V.; Sabu, M.K.; Rajan, J.Most of the real-time image acquisitions produce noisy measurements of the unknown true images. Image denoising is the post-acquisition technique to improve the signal-to-noise ratio of the acquired images. Denoising is an essential pre-processing step for different image processing applications such as image segmentation, feature extraction, registration, and other quantitative measurements. Among different denoising methods proposed in the literature, the non-local means method is a preferred choice for images corrupted with an additive Gaussian noise. A conventional non-local means filter (CNLM) suppresses noise in a given image with minimum loss of structural information. In this paper, we propose modifications to the CNLM algorithm where the samples are selected statistically using Shapiro-Wilk test. The experiments on standard test images demonstrate the effectiveness of the proposed method. © 2013 IEEE.Item Vibro-acoustic response and sound transmission loss characteristics of truss core sandwich panel filled with foam(Elsevier Masson SAS 62 rue Camille Desmoulins Issy les Moulineaux Cedex 92442, 2018) Arunkumar, M.P.; Jeyaraj, P.; Gangadharan, K.V.; Mailan Chinnapandi, M.C.This paper presents the studies carried out for improving the acoustic behavior of truss core sandwich panel, which is mostly used in aerospace structural applications. The empty space of the truss core is filled with polyurethane foam (PUF) to achieve better vibro-acoustic and sound transmission loss characteristics. Initially equivalent elastic properties of the foam filled truss core sandwich panel are calculated. Then, the vibration response of the panel under a harmonic excitation is obtained based on the equivalent 2D finite element model. Finally, the vibration response is given as an input to the Rayleigh integral code built in-house to obtain the acoustic and sound transmission loss characteristics. The results revealed that PUF filling of the empty space of the truss core, significantly reduces resonant amplitudes of both vibration and acoustic responses. It is also observed that foam filling reduces the overall sound power level significantly. Similarly, sound transmission loss studies revealed that, sudden dips at resonance frequencies are significantly reduced. Also an experiment is conducted on forced vibration response of honeycomb core sandwich panel to show that equivalent 2D model can be used for predicting sound power level and transmission loss behavior. © 2018 Elsevier Masson SASItem Multispectral satellite image denoising via adaptive cuckoo search-based wiener filter(Institute of Electrical and Electronics Engineers Inc., 2018) Suresh, S.; Lal, S.; Chen, C.; Çelik, T.Satellite image denoising is essential for enhancing the visual quality of images and for facilitating further image processing and analysis tasks. Designing of self-tunable 2-D finite-impulse response (FIR) filters attracted researchers to explore its usefulness in various domains. Furthermore, 2-D FIR Wiener filters which estimate the desired signal using its statistical parameters became a standard method employed for signal restoration applications. In this paper, we propose a 2-D FIR Wiener filter driven by the adaptive cuckoo search (ACS) algorithm for denoising multispectral satellite images contaminated with the Gaussian noise of different variance levels. The ACS algorithm is proposed to optimize the Wiener weights for obtaining the best possible estimate of the desired uncorrupted image. Quantitative and qualitative comparisons are conducted with 10 recent denoising algorithms prominently used in the remote-sensing domain to substantiate the performance and computational capability of the proposed ACSWF. The tested data set included satellite images procured from various sources, such as Satpalda Geospatial Services, Satellite Imaging Corporation, and National Aeronautics and Space Administration. The stability analysis and study of convergence characteristics are also performed, which revealed the possibility of extending the ACSWF for real-time applications as well. © 1980-2012 IEEE.Item A 61.2-dB?, 100 Gb/s Ultra-Low Noise Graphene TIA over D-Band Performance for 5G Optical Front-End Receiver(Springer, 2021) Gorre, P.; Vignesh, R.; Song, H.; Kumar, S.This work reports in first time a 100-Gb/s, ultra-low noise, variable gain multi-stagger tuned transimpedance amplifier (VGMST-TIA) over the D-band performance. The whole work is binding into two phases. The first phase involves the modeling and characterization of graphene field-effect transistor (GFET) with an optimized transition frequency of operation. While in the second phase, a TIA design employs a T-shaped symmetrical L-R network at the input, which mitigates the effect of photo diode capacitance and achieves a D-band of operation. The proposed work uses a VGMST to establish TIA, which realizes optimum noise performance. The high gain 3-stage VGMST-TIA effectively minimizes the white noise and illustrates a sharp out-of-band roll-off to achieve considerable noise reduction at high frequencies. The active feedback mechanism controls the transimpedance gain by tuning the control voltage which results better group delay. Besides, an L-C circuit is employed at the output to enhance bandwidth. The full TIA is implemented and fabricated using a commercial nano-manufacturing 9-nm graphene film FET on a silicon wafer using 0.065-?m process. The TIA achieves a flat transimpedance gain of 61.2 dB? with ± 9 ps group delay variation over the entire bandwidth. The proposed TIA measured an impedance bandwidth of 0.2 THz with ultra-low input-referred noise current density of 2.03 pA/?Hz. The TIA supports a 100-Gb/s data transmission due to large bandwidth; therefore, a bit-error-rate (BER) less than 10?12 is achieved. The chip occupies an area of 0.92 * 1.34 mm2 while consuming power of 21 mW under supply of 1.8 V. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.Item A cascaded convolutional neural network architecture for despeckling OCT images(Elsevier Ltd, 2021) Anoop, B.N.; Kalmady, K.S.; Udathu, A.; Siddharth, V.; Girish, G.N.; Kothari, A.R.; Rajan, J.Optical Coherence Tomography (OCT) is an imaging technique widely used for medical imaging. Noise in an OCT image generally degrades its quality, thereby obscuring clinical features and making the automated segmentation task suboptimal. Obtaining higher quality images requires sophisticated equipment and technology, available only in selected research settings, and is expensive to acquire. Developing effective denoising methods to improve the quality of the images acquired on systems currently in use has potential for vastly improving image quality and automated quantitative analysis. Noise characteristics in images acquired from machines of different makes and models may vary. Our experiments show that any single state-of-the-art method for noise reduction fails to perform equally well on images from various sources. Therefore, detailed analysis is required to determine the exact noise type in images acquired using different OCT machines. In this work we studied noise characteristics in the publicly available DUKE and OPTIMA datasets to build a more efficient model for noise reduction. These datasets have OCT images acquired using machines of different manufacturers. We further propose a patch-wise training methodology to build a system to effectively denoise OCT images. We have performed an extensive range of experiments to show that the proposed method performs superior to other state-of-the-art-methods. © 2021 Elsevier Ltd