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107 articles for “mean square error”
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An Improved Power Line Interference Reduction Approach Based on Combination of Mirror Extension and IIR filtering Through a Data Driven Mechanism
Abstract: Electrocardiogram (ECG) is a clinical sign monitoring measurement of the cardiac abnormalities. Like other biomedical signals, the ECG signal is also contaminated by various kinds of noise and artifacts such as power line interference, base line wandering, muscle artifacts and electrode artifacts. The present paper proposes a scheme for power line interference (PLI) reduction from ECG signal. It makes use of the concept of mirror method, empirical mode decomposition (EMD) …
Published in Current Trends in Signal Processing · Vol. 7, Issue 2, 2017 · pp. 13–21 Read article
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A Low Delay Two-Channel Quadrature Mirror Filter Bank using IIR Filter
Abstract: IIR filters have the advantage of low delay, high selectivity and stopband attenuation as compared to their counterpart FIR filters. In the present work, a low delay two-channel Quadrature Mirror Filter (QMF) bank using IIR filter has been proposed. The IIR prototype filter has been designed by approximating the FIR lowpass filter using the least square approximation. The length of the IIR filter has been selected using the bi-section method. …
Published in Current Trends in Signal Processing · Vol. 7, Issue 2, 2017 · pp. 1–7 Read article
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Estimation of Population Mean Using Ratio cum Product Imputation Techniques in Sample Survey
Abstract: In this paper, three generalized classes of ratio cum product compromised imputation techniques in single phase sampling scheme and its properties have been studied. Their mean square error (MSE) expressions and percentage relative efficiency (PRE) are compared with the sample mean, ratio, compromised and Ahmed method of imputation in case of missing data, and necessary conditions are derived. Further, theoretical results have been verified through empirical studies with the help …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 38–49 Read article
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Analysis of Air Pollutants in Pre-pandemic and Pandemic Era
Abstract: The paper deals with the impact of the Coronavirus pandemic induced lockdown on the air quality levels of cities of India like Delhi, Mumbai, Kolkata and Bengaluru. PM2.5 and PM10 are the main pollutants on which this study is based. This project compares the actual data of PM2.5 and PM10 of each city with the hypothetical data if the pandemic caused lockdown had not occurred. The hypothetical data was obtained …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 11, Issue 2, 2021 · pp. 19–25 Read article
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Machine Learning-Based Channel Estimation in 5G, Beyond-5G, and 6G Networks: Recent Advances and Future Directions
Abstract: Accurate channel estimation is one of the most fundamental challenges in modern wireless communication systems. In fifth- generation (5G) New Radio (NR) and emerging sixth-generation (6G) networks, precise knowledge of the wireless channel is essential for achieving reliable data transmission, high spectral efficiency, and low Bit Error Rate (BER). Conventional estimation techniques such as Least Squares (LS) and Minimum Mean Square Error (MMSE) rely on mathematical channel models and predefined …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 2, 2026 Read article
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Estimation of Population Mean Under SRSWOR Using Imputation Technique
Abstract: This research work suggests four log-type imputation methods using auxiliary information for missing observations in a data set. These methods consist of corresponding point estimator for estimating population mean under simple random sampling without replacement. Bias and mean square error expressions are derived up-to first order of approximation. Percent relative efficiency of suggested estimators is compared with the existing estimators using four population real data sets. And it is empirically …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 2, 2018 · pp. 12–20 Read article
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A Factorial Investigation of Hyperparameter Tuning Strategies for Lasso- Based Genomic Prediction
Abstract: In an earlier comparative study of machine-learning methods for genomic prediction of wheat grain yield, we reported a counter-intuitive result: automated nested-cross-validation tuning of the Lasso regularization penalty reduced mean predictive ability relative to a fixed, arbitrarily chosen penalty (mean Pearson r falling from 0.408 to 0.349 across four environments), the opposite of the expected effect of hyperparameter tuning. We hypothesized two possible explanations at the time — high-variance penalty …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 · pp. 42–51 Read article
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Invisible Watermarking on RGB Image with the Combination of 7-DWT and SVD
Abstract: In this study, we present a powerful watermarking system based on seven level discrete wavelet transform and singular value decomposition for RGB picture where R signifies red color, G indicates green color and B means blue color. It provides improved security and performance in terms of mean square error, peak signal noise ratio, structural similarity matrix, normalized cross correlation and execution time. During the experiment, we increase peak signal noise …
Published in Journal of Open Source Developments · Vol. 2, Issue 3, 2015 · pp. 12–19 Read article
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A Suboptimal Nonlinear Duty-cycle Modulation Scheme
Abstract: The DCM (duty-cycle modulation) technique is increasingly used in industrial electronics applications, including instrumentation systems, interfacing drivers and signal transmission chains. However, in existing research works related to new applications of DCM technique, the linear approximation policy is used for the sake of structural simplicity and low implementation cost, at the expends of rigorous analysis and low approximation errors. In this paper, a suboptimal nonlinear DCM scheme is developed. It …
Published in Journal of Electronic Design Technology · Vol. 7, Issue 1, 2016 · pp. 22–31 Read article
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Estimation of Semi-Blind Channel for Two-Way Relay by Using CRB: Pilot-based and Random Pilot-based
Abstract: The main purpose of semi-blind channel estimation scheme is for frequency-selective channel estimation, for multi-input and multi-output (MIMO) on relay networks, for using high bandwidth efficient use of spectrum. This scheme motivate to the low complexity of system to allow exchange information users to users by using an intermediate relay node. Because of superposition of signals at the relay node, section receiving signal at the user terminals is affected by …
Published in Current Trends in Signal Processing · Vol. 7, Issue 2, 2017 · pp. 22–31 Read article
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Nonlinear Channel Equaliser using Discrete Gabor Transform
Abstract: ABSTRACTThe adaptive equaliser makes use of adaptive digital filters whose filter coefficients are modified depending on the channel characteristics at the front end of the receiver. The noise introduced in the channel gets nullified and hence the signal-to-noise ratio of the receiver improves. Discrete Gabor Transform (DGT) helps decorrelate input data because of which the convergence speed of the Mean Square Error (MSE) improves considerably. It is found that the …
Published in Journal of Computer Technology & Applications · Vol. 1, Issue 1-3, 2011 · pp. 63–74 Read article
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Reduced Partial Product Booth Multiplier with An Effective Error Compensation Circuit
Abstract: Reduced partial product Booth multiplier is mainly used in multimedia and digital signal processing (DSP) systems. In this paper, design of reduced partial product (constant-width) Booth multiplier is introduced. Here the least significant bit (LSB) of partial product matrix of Booth multiplier is removed and an error compensation function is derived for reducing truncation error. A simple error compensation circuit (SC generator) is also introduced to reduce the computations that …
Published in Trends in Electrical Engineering · Vol. 5, Issue 2, 2015 · pp. 42–49 Read article
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Seasonal ARIMA for Forecasting Sea Surface Temperature of the North Zone of the Bay of Bengal
Abstract: The behavior of the sea surface temperature (SST) of the north zone of the Bay of Bengal plays an important role for understanding climate changes over Bangladesh. The monthly average of SST of this zone is used in this study which is obtained from January 1900 to December 2009. Box and Jenkins method is used to fit a seasonal auto regressive integrated moving average (ARIMA) model used in forecasting SST …
Published in Research & Reviews : Journal of Statistics · Vol. 2, Issue 2, 2013 · pp. 23–31 Read article
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Time Series Forecasting of Electricity Consumption: A Comparative Analysis of ARIMA and SARIMA Models
Abstract: Accurate electricity demand forecasting plays a vital role in energy planning, efficient power system operation, and sustainable resource management. This study conducts a comparative evaluation of the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models using ten years of monthly electricity consumption data collected from a national electricity regulatory authority. The performance of both models is assessed using forecasting accuracy metrics, including Mean Absolute Error …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 43–53 Read article
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Integrative Machine Learning Approaches for Predicting the Rheological Behaviour of Soft Magnetorheological Elastomers
Abstract: Magnetorheological Elastomers (MREs) are advanced composite materials known for their ability to alter mechanical properties under external magnetic fields, making them highly valuable in adaptive damping systems, vibration control, and smart devices. The accurate prediction of rheological behavior in soft MREs remains a significant challenge due to the complex interplay between material composition and magnetic fields. To address this challenge, this study employs a multi-pronged approach that integrates traditional material …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 1083–1096 Read article
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Digital Twin Assisted Intelligent Prediction of Polymer Composite Degradation Under Environmental Exposure
Abstract: Polymer matrix composites (PMCs) deployed in aerospace, marine, automotive, and renewable-energy structures are continuously subjected to coupled environmental stressors — ultraviolet (UV) radiation, moisture ingress, thermal cycling, and mechanical loading — that progressively degrade their mechanical performance. Conventional accelerated ageing tests and empirical lifetime models are time-consuming, destructive, and poorly suited to in-service, asset-specific degradation forecasting. This paper proposes a Digital Twin (DT) assisted intelligent prediction framework that fuses a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Optimizing Performance Characteristics, Thermal Stability, and Manufacturing Performance of Polymer Nanocomposites Using Artificial Intelligence
Abstract: Artificial intelligence (AI) has proven an efficient method to optimize the design and manufacture of polymer nanocomposites, allowing the proper prediction of the behavior of the materials and the results of the processing. This work proposes an AI-based framework to enhance the performance characteristics, thermal stability and manufacturing performance of advanced polymer nanocomposites. The input variables of the proposed framework are the material composition, the nanoparticle concentration, the particle size, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Hybrid Machine Learning and Finite Element Framework for Predicting Damage Behavior in Fiber-Reinforced Polymer Composites
Abstract: Fiber Reinforced Polymer (FRP) composites have broad spread use in aerospace, automotive, marine and structural applications due to its high specific strength, stiffness and corrosion resistance. The various damage mechanisms such as matrix cracking, fiber breakage, delamination and interfacial failure, however, make the forecasting of damage particularly complex. In this work, a hybrid machine learning (ML) and finite element (FE) system is proposed for predicting the damage behavior of FRP …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Harnessing Machine Learning for Stock Movement Prediction: A Review of Current Approaches
Abstract: Stock price prediction is a crucial task in financial analysis, aiding investors and traders in making informed decisions. This study investigates the use of deep learning methods, particularly Long Short-Term Memory (LSTM) networks, for predicting stock prices based on historical market data. The dataset, sourced from Yahoo Finance, consists of time-series stock price data, which is preprocessed, feature-engineered, and visualized to improve prediction accuracy. The model's performance is assessed using …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 29–40 Read article
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Optimization of Liquid Metal Nanocomposites and Biogas Addition Rate Using ANN-GA
Abstract: In this study, the liquid metal nanocomposites were investigated using artificial neural network (ANN) prediction capabilities for Compression Ignition (CI) engine performance. The independent input variables selected were load (20-100%), Liquid-metal nanocomposites Doped Rate (NDR, 0-50 ppm), and Biogas Flow Rate (BFR, 0.5-1.0 kg/h). The Central Composite Face-Centered Design (CCFCD) was used in conjunction with the selected input variables and output parameters to assist in the preparation of the Design …
Published in Journal of Polymer & Composites · Vol. 11, Issue 11, 2023 · pp. 12–27 Read article