Research Article
Performance Evaluation of Different Threshold Estimation Rules in Denoising EEG Singal with Hamming Window-Based Shrinkage Technique
Issue:
Volume 10, Issue 4, December 2025
Pages:
168-174
Received:
12 August 2025
Accepted:
13 September 2025
Published:
10 October 2025
Abstract: Signal denoising is an integral part of contaminated signal processing to obtain the signal of interest. In this research, a developed system for reliable removal of powerline interference from Electroencephalographic (EEG) signal based on descrete wavelet transform technique is designed which includes soft thresholding –based shrinkage function called hamming window (Ham-WSST). A practical EEG signal was acquired by measurement from Federal Medical Centre, Owerri and contaminated with powerline noise of 50Hz. This was sampled at a frequency of 1000Hz. Due to the new shrinkage function, decomposition level of 7 and daubechies 7 (db7) mother wavelet, the denoising of the powerline noise was extensively performed in combination with Sqtwolog, Rigrsure, Heursure and Minimaxi rule. The outcome results for the four threshold rules of the system were evaluated and compared using power spectral density (PSD), signal to noise (SNR), mean square error (MSE) and maximum absolute error (MAE) estimation functions. The power spectral density result established on the optimum decomposition level of 7 at 0.1 radian normalised frequency was 35.89 dB for Sqtwolog rule, 37.68dB for Rigrsure, 37.68dB for Heursure and for Minimaxi value is 36.52dB. For signal to noise ratio (SNR) the value for Sqwolog is 42.26 dB, Rigrsure is 38.68dB, Heursure is 38.68dB and Minimaxi is 40.55dB. The estimation values for mean square error (MSE) and maximum absolute error (MAE) for Sqtwolog rule, Rigrsure rule, Heursure rule and Minimaxi rule in this order is giving as 0.00147, 0.0046, 0.00492, and 0.00206; 0.1147, 0.1245, 0.1245 and 0.1158. Less PSD value means more noise attenuation at the considered frequency instant, higher value for SNR indicate more signal of interest than noise while lower values of MSE and MAE indicate less error. The research further shows that the window thresholding shrinkage function based on the Hamming window with the Sqtwolog estimation rule is more effective at denoising contaminated EEG signals with powerline noise..
Abstract: Signal denoising is an integral part of contaminated signal processing to obtain the signal of interest. In this research, a developed system for reliable removal of powerline interference from Electroencephalographic (EEG) signal based on descrete wavelet transform technique is designed which includes soft thresholding –based shrinkage function ca...
Show More
Research Article
Minimization of Fuel Consumption in Four Stroke Six Cylinder Engines Using Idle Speed Control
Chinonso Francis Nkeoma Ubbaonu*,
Damian Obioma Dike,
Raymond O. Opara,
Chiedozie Francis Paulinus-Nwamuo,
Ernest Ezugwu
Issue:
Volume 10, Issue 4, December 2025
Pages:
175-184
Received:
10 April 2017
Accepted:
14 June 2017
Published:
14 October 2025
DOI:
10.11648/j.ajset.20251004.12
Downloads:
Views:
Abstract: In a distressed economy with increasing cost of fuel, it is necessary to achieve control of automobile engine over transient behavior and meet performance objectives within the planned life of the vehicle. In this paper the objective is to design a model predictive controller (MPC) that will minimize fuel consumption in a four stroke cylinder engine using idle speed control. Engine speed is most often affected by unexpected disturbances, and the higher the engine speed at idle the higher the consumption of fuel. Non linear dynamic equations of an idle speed engine were obtained and later transformed to equivalent linear equation by linearization method. The designed MPC was integrated with a linearized equation of the plant. The MPC approach was used to forecast and control the four stroke six cylinder engine model in the presence of unexpected disturbance to achieve the desired objective. Simulations were performed in Matlab/Simulink and the results obtained showed that at different instances when disturbances entered the system, with MPC controller in the loop, the instability introduced by the disturbance was eliminated and the referenced idle speed tracked. There by maintaining the engine idle speed at 600 RPM.
Abstract: In a distressed economy with increasing cost of fuel, it is necessary to achieve control of automobile engine over transient behavior and meet performance objectives within the planned life of the vehicle. In this paper the objective is to design a model predictive controller (MPC) that will minimize fuel consumption in a four stroke cylinder engin...
Show More
,
Nnaedozie Chidiebere Moughalu
