BL_Wiener Denoising Method for Removal of Speckle Noise in Ultrasound Image

Medical imaging techniques are extremely important tools in medical diagnosis. One of these important imaging techniques is ultrasound imaging. However, during ultrasound image acquisition process, the quality of image can be degraded due to corruption by speckle noise. The enhancement of ultrasound images quality from the 2D ultrasound imaging machines is expected to provide medical practitioners more reliable medical images in their patients’ diagnosis. However, developing a denoising technique which could remove noise effectively without eliminating the image’s edges and details is still an ongoing issue. The objective of this paper is to develop a new method that is capable to remove speckle noise from the ultrasound image effectively. Therefore, in this paper we proposed the utilization of Bilateral Filter and Adaptive Wiener Filter (BL_Wiener denoising method) for images corrupted by speckle noise. Bilateral Filter is a non-linear filter effective in removing noise, while Adaptive Wiener Filter balances the tradeoff between inverse filtering and noise smoothing by removing additive noise while inverting blurring. From our simulation results, it is found that the BL_Wiener method has improved between 0.89 [dB] to 3.35 [dB] in terms of PSNR for test images in different noise levels in comparison to conventional methods

Design of U-PPC-Type II for nonlinear systems

In this study, a new U-PPC-Type II (U-model Pole Placement Control Type II) control system design procedure is proposed based on the U-model principle. The objective of a U-PPC-Type II design is to determine a linear controller Gc from a specified closed loop linear transfer function Gcls . The study also compares the new design procedure with a U-PPC-Type I based design procedure. For demonstration of the effectiveness of the proposed new procedure, U-PPC-Type II is designed for both a linear dynamic model and a Hammerstein (nonlinear dynamic) model. The simulation results are presented with discussions and graphical illustrations

Comparative study on pole placement controller design with U-Model approach & classical approach

© 2016 TCCT. As a universal framework, U-model has established an enabling design prototype for the control of non-linear dynamic plants with concise and applicable linear approaches. This study is devoted to a remaining fundamental research question, that is, while U-model methodology is applied to a linear dynamic plant control system design, how different it is from classical linear approaches. Taking up an initial research, this comparative study uses pole placement controller design as an example to implement with two approaches in terms of U-Model based design and classical control design. Design efficiency and effectiveness are compared analytically and computationally via numerical experiment, which justifies the superiority of U-model in designing linear control systems (or at least in designing pole placement control). In addition, the study provides benchmark examples for users with their ad hoc applications

Breast cancer diagnosis system using hybrid support vector machine-artificial neural network

Breast cancer is the second most common cancer occurring in women. Early detection through mammogram screening can save more women’s lives. However, even senior radiologists may over-diagnose the clinical condition. Machine learning (ML) is the most used technique in the diagnosis of cancer to help reduce human errors. This study is aimed to develop a computer-aided detection (CAD) system using ML for classification purposes. In this work, 80 digital mammograms of normal breasts, 40 of benign and 40 of malignant cases were chosen from the mini MIAS dataset. These images were denoised using median filter after they were segmented to obtain a region of interest (ROI) and enhanced using histogram equalization. This work compared the performance of artificial neural network (ANN), support vector machine (SVM), reduced features of SVM and the hybrid SVM-ANN for classification process using the statistical and gray level co-occurrence matrix (GLCM) features extracted from the enhanced images. It is found that the hybrid SVM-ANN gives the best accuracy of 99.4% and 100% in differentiating normal from abnormal, and benign from malignant cases, respectively. This hybrid SVM-ANN model was deployed in developing the CAD system which showed relatively good accuracy of 98%

Ensemble-Empirical-Mode-Decomposition based micro-Doppler signal separation and classification

The target echo signals obtained by Synthetic Aperture Radar (SAR) and Ground Moving Target Indicator (GMTI platforms are mainly composed of two parts, the micro-Doppler signal and the target body part signal. The wheeled vehicle and the track vehicle are classified according to the different character of their micro-Doppler signal. In order to overcome the mode mixing problem in Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) is employed to decompose the original signal into a number of Intrinsic Mode Functions (IMF). The correlation analysis is then carried out to select IMFs which have a relatively high correlation with the micro-Doppler signal. Thereafter, four discriminative features are extracted and Support Vector Machine (SVM) classifier is applied for classification. The experimental results show that the features extracted after EEMD decomposition are effective, with up 90% success rate for classification using one feature. In addition, these four features are complementary in different target velocity and azimuth angles

Ensemble-Empirical-Mode-Decomposition based micro-Doppler signal separation and classification

The target echo signals obtained by Synthetic Aperture Radar (SAR) and Ground Moving Target Indicator (GMTI platforms are mainly composed of two parts, the micro-Doppler signal and the target body part signal. The wheeled vehicle and the track vehicle are classified according to the different character of their micro-Doppler signal. In order to overcome the mode mixing problem in Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) is employed to decompose the original signal into a number of Intrinsic Mode Functions (IMF). The correlation analysis is then carried out to select IMFs which have a relatively high correlation with the micro-Doppler signal. Thereafter, four discriminative features are extracted and Support Vector Machine (SVM) classifier is applied for classification. The experimental results show that the features extracted after EEMD decomposition are effective, with up 90% success rate for classification using one feature. In addition, these four features are complementary in different target velocity and azimuth angles

Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review

This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated

Model-free robust decentralised control of multi-input-multi-output nonlinear interconnected dynamic systems

This study presents a generalised framework for model-free robust decentralised control (MFRDC) of interconnected MIMO dynamic systems, with the goal of significantly reducing complexity in model-based design. A model-free robust sliding mode control, by Lyapunov differential inequality, is presented to achieve simultaneous nonlinear, dynamic, interaction/coupling inversion/cancellation (NDII) for such MIMO systems, which treats the plant/process as a total uncertainty from input to output. The U-control platform is presented, to integrate separately independently designed NDII and an invariant controller (IC) into a complete double loop control system. The associated robust stability and other properties are analysed to provide reference for applications. Two simulated tracking control tests are presented for functionally numerical demonstration, validation of the analytical results and illustration of the transparent procedure for general expansion/applications. These are: a coupled inverted pendulum and a two-input and two-output (TITO) non-affine nonlinear dynamic plant

Aerodynamic and aeroacoustic analysis of a harmonically morphing airfoil using dynamic meshing

This work explores the aerodynamic and aeroacoustic responses of an airfoil fitted with a harmonically morphing Trailing Edge Flap (TEF). An unsteady parametrization method adapted for harmonic morphing is introduced, and then coupled with dynamic meshing to drive the morphing process. The turbulence characteristics are calculated using the hybrid Stress Blended Eddy Simulation (SBES) RANS-LES model. The far-field tonal noise is predicted using the Ffowcs-Williams and Hawkings (FW-H) acoustic analogy method with corrections to account for spanwise effects using a correlation length of half the airfoil chord. At various morphing frequencies and amplitudes, the 2D aeroacoustic tonal noise spectra are obtained for a NACA 0012 airfoil at a low angle of attack (AoA = 4°), a Reynolds number of 0.62 × 106, and a Mach number of 0.115, respectively, and the dominant tonal frequencies are predicted correctly. The aerodynamic coefficients of the un-morphed configuration show good agreement with published experimental and 3D LES data. For the harmonically morphing TEF case, results show that it is possible to achieve up to a 3% increase in aerodynamic efficiency (L/D). Furthermore, the morphing slightly shifts the predominant tonal peak to higher frequencies, possibly due to the morphing TEF causing a breakup of large-scale shed vortices into smaller, higher frequency turbulent eddies. It appears that larger morphing amplitudes induce higher sound pressure levels (SPLs), and that all the morphing cases induce the shift in the main tonal peak to a higher frequency, with a maximum 1.5 dB reduction in predicted SPL. The proposed dynamic meshing approach incorporating an SBES model provides a reasonable estimation of the NACA 0012 far-field tonal noise at an affordable computational cost. Thus, it can be used as an efficient numerical tool to predict the emitted far-field tonal noise from a morphing wing at the design stage

Integrated supply–demand energy management for optimal design of off-grid hybrid renewable energy systems for residential electrification in arid climates

The growing research interest in hybrid renewable energy systems (HRESs) has been regarded as a natural and yet critical response to address the challenge of rural electrification. Based on a Bibliometric analysis performed by authors, it was concluded that most studies simply adopted supply-side management techniques to perform the design optimization of such a renewable energy system. To further advance those studies, this paper presents a novel approach by integrating demand-supply management (DSM) with particle swarm optimization and applying it to optimally design an off-grid hybrid PV-solar-diesel-battery system for the electrification of residential buildings in arid environments, using a typical dwelling in Adrar, Algeria, as a case study. The proposed HRES is first modelled by an in-house MATLAB code based on a multi-agent system concept and then optimized by minimizing the total net present cost (TNPC), subject to reliability level and renewable energy penetration. After validation against the HOMER software, further techno-economic analyses including sensitivity study are undertaken, considering different battery technologies. By integrating the proposed DSM, the results have shown the following improvements: with RF = 100%, the energy demand and TNPC are reduced by 7% and 18%, respectively, compared to the case of using solely supply-side management. It is found that PV-Li-ion represents the best configuration, with TNPC of $23,427 and cost of energy (COE) of 0.23$/kWh. However, with lower RF values, the following reductions are achieved: energy consumption (19%) and fuel consumption or CO 2 emission (57%), respectively. In contrast, the RF is raised from 15% (without DSM) to 63% (with DSM). It is clear that the optimal configuration consists of wind-diesel, with COE of 0.21 $/kWh, smaller than that obtained with a stand-alone diesel generator system. The outcomes of this work can provide valuable insights into the successful design and deployment of HRES in Algeria and surrounding regions