Indian Institute of Technology Kharagpur

About: Indian Institute of Technology Kharagpur is a education organization based out in Kharagpur, India. It is known for research contribution in the topics: Computer science & Dielectric. The organization has 16887 authors who have published 38658 publications receiving 714526 citations.

Nonlinear channel equalization for QAM signal constellation using artificial neural networks

Abstract: Application of artificial neural networks (ANN's) to adaptive channel equalization in a digital communication system with 4-QAM signal constellation is reported in this paper. A novel computationally efficient single layer functional link ANN (FLANN) is proposed for this purpose. This network has a simple structure in which the nonlinearity is introduced by functional expansion of the input pattern by trigonometric polynomials. Because of input pattern enhancement, the FLANN is capable of forming arbitrarily nonlinear decision boundaries and can perform complex pattern classification tasks. Considering channel equalization as a nonlinear classification problem, the FLANN has been utilized for nonlinear channel equalization. The performance of the FLANN is compared with two other ANN structures [a multilayer perceptron (MLP) and a polynomial perceptron network (PPN)] along with a conventional linear LMS-based equalizer for different linear and nonlinear channel models. The effect of eigenvalue ratio (EVR) of input correlation matrix on the equalizer performance has been studied. The comparison of computational complexity involved for the three ANN structures is also provided.

Comparison of similarity metrics for texture image retrieval

Abstract: Similarity metrics plays an important role in content-based image retrieval. The paper compares nine image similarity measures - Manhattan (L1), weighted-mean-variance (WMV), Euclidean (L2), Chebychev (L/spl infin/), Mahalanobis, Canberra, Bray-Curtis, squared chord and squared chi-squared distances - for texture image retrieval. A large texture database of 1856 images, derived from the Brodatz album, is used to check the retrieval performance. Features of all the database images were extracted using the Gabor wavelet. Experimental results on the Brodatz texture database indicate that the retrieval performance can be improved significantly by using the Canberra and Bray-Curtis distance metrics as compare to traditional Euclidean and Mahalanobis distance based approaches.

State of Health Estimation of Lithium-Ion Batteries Using Capacity Fade and Internal Resistance Growth Models

Abstract: In this paper, a method for the estimation of remaining useful lifetime (RUL) of lithium-ion batteries has been presented based on a combination of its capacity degradation and internal resistance growth models. The capacity degradation model is developed recently based on battery capacity test data. An empirical model for internal resistance growth is also developed based on electrochemical-impedance spectroscopy (EIS) test data. The obtained models are used in a particle filtering (PF) framework for making end-of-lifetime (EOL) predictions at various phases of its lifecycle. Further, the above two models were fused together to obtain a new degradation model for RUL estimation. It has been observed that the fused degradation model has improved the standard deviation of prediction as compared to the individual degradation models by maintaining satisfactory prediction accuracy. The effect of parameter variations on the performance of the PF algorithm has also been studied. Finally, the predictions are validated with experimental data. From the results it can be observed that with the availability of longer volume of data, the prediction accuracy gradually improves. The prognostics framework proposed in this paper provides a structured way for monitoring the state of health (SoH) of a battery.

Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges

Abstract: The scarcity of groundwater storage change data at the global scale hinders our ability to monitor groundwater resources effectively. In this study, we assimilate a state-of-the-art terrestrial water storage product derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into NASA's Catchment land surface model (CLSM) at the global scale, with the goal of generating groundwater storage time series that are useful for drought monitoring and other applications. Evaluation using in situ data from nearly 4,000 wells shows that GRACE data assimilation improves the simulation of groundwater, with estimation errors reduced by 36% and 10% and correlation improved by 16% and 22% at the regional and point scales, respectively. The biggest improvements are observed in regions with large interannual variability in precipitation, where simulated groundwater responds too strongly to changes in atmospheric forcing. The positive impacts of GRACE data assimilation are further demonstrated using observed low-flow data. CLSM and GRACE data assimilation performance is also examined across different permeability categories. The evaluation reveals that GRACE data assimilation fails to compensate for the lack of a groundwater withdrawal scheme in CLSM when it comes to simulating realistic groundwater variations in regions with intensive groundwater abstraction. CLSM-simulated groundwater correlates strongly with 12-month precipitation anomalies in low-latitude and midlatitude areas. A groundwater drought indicator based on GRACE data assimilation generally agrees with other regional-scale drought indicators, with discrepancies mainly in their estimated drought severity.