Department of Electrical and Electronics Engineering
Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1925
Browse
45 results
Search Results
Item Active Power Filter Control Algorithm using Wavelets(IEEE, 2006) Kumar, Rajneesh; Gupta, Karunesh KumarThis paper presents a wavelet transform (WT) based technique to extract fundamental frequency component from a nonsinusoidal and unbalanced load current in a three phase system. The fundamental frequency component is extracted using multiresolution analysis (MRA). The remaining harmonics can be used by the active filter for compensation. Simulation result obtained for a rectifier load current shows the usefulness of the proposed method.Item Wavelet Based Switching Loss Analysis of MOSFET(IEEE, 2007) Gupta, Karunesh Kumar; Kumar, RajneeshSwitching loss calculation in MOSFET requires device parameters like turn-on and turn-off time, input and output capacitances, parasitic inductances and circuit parameters like voltage, current and operating frequency. Using these parameters switching loss is calculated with given approximate mathematical formulae. This paper presents a wavelet based method for switching loss calculation. It requires only the voltage and current waveforms during switching and calculates the power loss and also provides the frequency content during switching. The information regarding frequency content can be utilized for designing snubber as well as for EMI analysis. Multi Resolution Analysis (MRA) is used to decompose signals in wavelet domain and the signals are transformed in different frequency bands. The power is calculated in each band by multiplication of current and voltage wavelet coefficients. Simulation results are presented for a MOSFET with inductive load to support the method described.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 Hybrid LDPC and STBC Algorithms to Improve BER Reduction in OFDM Systems(Scope, 2013-11) Gupta, Karunesh KumarTHE SEARCH for a good coding algorithm is motivated by the fact that various communication channels require their optimal performance. Orthogonal Frequency Division Multiplexing (OFDM) WiMAX transmission system is used over different fading channels with adaptive modulation and coding (AMC). The computer simulation result shows improved error-correction capability in LDPC codes and STBC codes. The paper proposed hybrid LDPC and STBC method and proved its good bit error performance.Item Wavelet denoising: Comparative analysis and optimization using machine learning(IEEE, 2014) Gupta, Karunesh KumarEven after a phenomenal progress in the quality of image denoising algorithms over the years, there is yet a vast scope of improving the standard of denoised images. This paper presents a new methodology for denoising by integrating the wavelet denoising technique with regression boosted trees. Based on ensemble learning by regression boosted trees, an optimal threshold value is obtained. Its denoising performance is better than Stein's unbiased risk estimator-linear expansion of thresholds (SURE-LET) method which is an up to date denoising algorithm. We have also compared its performance with the other current state of art wavelet based denoising algorithms like ProbShrink, and BiShrink on the basis of their Peak Signal to Noise Ratio (PSNR). Simulations and experimentation results demonstrate that PSNR of our proposed method outperforms the other methods. Extension to Dual Tree-Complex Wavelet Transform (DT-CWT) is also presented.Item Vibro acoustic signal analysis in fault finding of bearing using Empirical Mode Decomposition(IEEE, 2013) Gupta, Karunesh KumarBearing fault is an issue in process and control industries, and has significant impact in the production flow. The behaviour of the machinery can be well understood from the frictional forces of the bearing due to load, and also the wear and tear of the ball bearings. The characteristic of this ball bearing can predict the exact nature of the load and any future malfunction in the operating equipments. The signals generated from these bearings can be of any types i.e., sound or vibration. The acoustic phenomenon is tough to predict in noisy environment, where as the vibration data can be used when the acoustic cannot be the source of information. In general the fault diagnosis in bearing is done by comparing the mathematical interpreted data with vibration signal. This method can only be applicable to those system where the complete information about the ball bearing is known. But, this paper predict the fault in the ball bearing using acoustic and vibration signatures without knowing complete bearing information. Signal processing is used rather than using both signal processing and mathematical formulation all together to predict the fault in the bearing under different states. The signal analysis using FFT fails to analyse the signals of transient and non-stationary in nature. The extraction and analysis of the transient signal can be better done using Empirical Mode Decomposition (EMD) technique.Item Characterization of wireless accelerometer sensor and its industrial applications(IEEE, 2014) Gupta, Karunesh KumarThe basic idea of this paper is to characterize wireless MEMS capacitive accelerometer sensor based on their field of applications. The selection of accelerometers are difficult for certain applications, that demands the sensor to be mount on rotating platform, higher value of g, sensitivity, and wide bandwidth of operation. Whenever higher sensitivity is chosen, the short fall is in the range of g and the bandwidth of operation. This is a serious issue with the sensor as far as industrial applications i.e., ball mill and sag mills are concerned. There is a misconception of using higher value of g (approximately around 500 g) with lower sensitivity in ball mill that is justified in this paper. Generally, the internal frequency of vibration of the ball mill is unknown, and the vibration due to impact during grinding is also random due to non uniformity in the grinding action inside the mill. For such an application, random selection of sensors can mislead the data acquisition and interpretation process. The perplexity of the application demands the characterization of accelerometer, when they are mounted on rotating platform. In this paper the sensor is characterized in mechanical testing lab using lathe machine and later on the same sensor is subjected to measure vibration of the industrial ball mill. Further, the data is transmitted using Zigbee (IEEE 802.15.4), and the RF signal losses during rotation and transmission are also taken care to avoid the high frequency losses due to multiple reflections. Finally, the vibration signatures obtained during experimental phases are analyzed using Fast Fourier Transform (FFT) to characterize the sensor at different operating speeds of the lathe machine.Item Comparative study between VMD and EMD in bearing fault diagnosis(IEEE, 2014) Gupta, Karunesh KumarThis paper proposes a novel Variational mode decomposition (VMD) algorithm for bearing fault diagnosis. The Fast Fourier Transform fails to analyse the transient and non-stationary signals. Discrete Fourier transform and Empirical mode decomposition do not have the ability to attain the accurate Intrinsic mode functions under dynamic system fault conditions because the characteristic of exponentially decaying dc offset is not consistent. EMD is a fully data-driven, not model-based, adaptive filtering procedure for extracting signal components. The EMD technique has high computational complexity and requires a large data series. The proposed technique has high accuracy and convergent speed, and is greatly appropriate for bearing fault diagnosis. This paper illustrates that VMD removes the exponentially decaying dc offset and evaluates its performance compared to EMD.Item Effective sample length selection to avoid dead zone effect in ball mill vibration signal analysis and applications(IEEE, 2015) Gupta, Karunesh KumarThe objective of the paper is to extract and identify the actual spectrum of the ball mill under dynamic conditions using the proposed Synchronized Frequency Estimation method (SFE). Impact statistics in ball mills are nested with complex dynamics, and the identification of the intensity of vibration lack behind the presence of impact dead zone in reference to the data acquisition and its analysis. The selection sample length can change the behavioral pattern for different impact loading cases i.e. the generation of mills natural frequency purely depends on the strength of the impulse excitation strength and the functional characteristics. The unevenness in the impulsive impact dynamics may depict the actual frequency as faux frequency. The improper sample length selection can alter the behavioral pattern for different impact loading. This paper broadly discusses the outcome of the random selection of data sample length and the disciplinary steps in extracting the actual spectrum.