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Go to Editorial ManagerTechnically, medical imaging modalities are quantitative, qualitative, and semi-quantitative. Such modalities can generate meaningful and valuable quantitative and qualitative data. Correlating predictive outcomes with quantitative and qualitative data is a difficult process. Thanks to modern computational hardware and advanced machine learning algorithms, it is not a demanding job to perform predictive analysis by cultivating quantitative and qualitative data. Radiomics is a popular topic that studies quantitative data from medical images in order to obtain biologically meaningful information for diagnosis, prognosis, theragnosis, and decision support. Handcrafted radiomics is a process including features based on shape, pixel, and texture-related knowledge from medical scans. In the pursuit of advancing the field of radiomics, we have developed a cutting-edge radiomics training simulator, powered by MATLAB. This tool has been designed for those familiar with MATLAB, making it easy for them to transition into the fascinating world of radiomics. MATLAB's user-friendly interface and strong support in the engineering community provide an ideal platform for this simulator, ensuring aspiring radiomics learners have access to the resources they need for success. Throughout the paper, purpose, design details and methodology of the simulator are described.
This paper proposes robust control for three models of the linear inverted pendulum (one mass linear inverted pendulum model, two masses linear inverted pendulum model and three masses linear inverted pendulum model) which represents the upper, middle and lower body of a bipedal walking robot. The bipedal walking robot is built of light-weight and hard Aluminum sheets with 2 mm thickness. The minimum phase system and non-minimum phase system are studied and investigated for inverted pendulum models. The bipedal walking robot is programmed by Arduino microcontroller UNO. A MATLAB Simulink system is built to embrace the theoretical work. The results showed that one linear inverted pendulum is the worst performance, worst noise rejection and the worst set point tracking to the zero moment point. But two masses linear inverted pendulum models and three masses linear inverted pendulum model have a better performance, a better high-frequency noise rejection characteristic and better set-point tracking to the zero moment point.
Cone-beam computed tomography (CBCT) is an indispensable method that reconstructs three dimensional (3D) images. CBCT employs a mathematical technique of reconstruction, which reveals the anatomy of the patient’s body through the measurements of projections. The mathematical techniques employed in the reconstruction process are classified as; analytical, and iterative. The iterative reconstruction methods have been proven to be superior over the analytical methods, but due to their prolonged reconstruction time those methods are excluded from routine use in clinical applications. The aim of this research is to accelerate the iterative methods by performing the reconstruction process using a graphical processing unit (GPU). This method is tested on two iterative-reconstruction algorithms (IR), the algebraic reconstruction technique (ART), and the multiplicative algebraic reconstruction technique (MART). The results are compared against the traditional ART, and MART. A 3D test head phantom image is used in this research to demonstrate results of the proposed method on the reconstruction algorithms. The simulation results are executed using MATLAB (version R2018b) programming language and computer system with the following specifications: CPU core i7 (2.40 GHz) for the processing, with a NIVDIA GEFORCE GPU. Experimental results indicate, that this method reduces the reconstruction time for the iterative algorithms.
Facial expressions are a form of non-verbal communication, they appear as changes on the surface of the facial skin according to one's inner emotional states, aims, or social communications. Classification of these expressions is a normal process for humans, but it is a challenging task for machines.Lately, interest in facial expression recognition has grown, and many systems have been developed to classify expressions from facial images. Any expression recognition system is comprised of three steps. The first one is face acquisition, then feature extraction, and finally classification. The classification accuracy depends primarily on the feature extraction step. Therefore, in this research we study many texture feature extraction descriptors and compare their results under the same preprocessing circumstances; moreover, we propose two improvements for one of these descriptors, which give better results than the original one. We validate the results on two commonly used databases for expression recognition using Matlab programming language, wishing all of that to be an interesting point for researchers in this field.
Millimeter Wave (mmWave) Massive Multiple Input Multiple Out (MIMO) system is a key technology for future wireless transmission. The system's architecture can differ based on the type of Analog-to-Digital Converters (ADCs) used at the receiver, whether they are all low-resolution or a mix of different resolutions (Mixed-ADCs). Mixed-ADCs is a promising solution to achieve better performance than low-resolution ADC-only architectures by leveraging high-resolution ADCs to capture critical signal components while maintaining energy efficiency through low-resolution ADCs. In this paper, the problem of channel estimation for this system architecture is taken into consideration. A novel compressive-sensing based algorithm, that is called Approximate Conjugate Gradient Pursuit (ACGP), is proposed to estimate the channel coefficients. The performance of the proposed algorithm is investigated under varying system parameters, including different Signal-to-Noise Ratios (SNR), Radio Frequency (RF) chains, ADC resolutions, and numbers of observation frames. Matlab software was used to perform numerical simulations. The results demonstrated that mixed-ADCs architecture outperforms low resolutions only in performance. It was found that ACGP achieves lower Minimum Mean Squared Error (MMSE) compared to Orthogonal Matching Pursuit (OMP) and Least Square (LS), particularly in low SNR conditions showcasing its robustness and efficiency in signal reconstruction, achieving an average enhancement of 30% to 50% at moderate SNR levels. While OMP exhibited faster computation times under various number of observation frames, ACGP maintained stable computational performance, with a slight increase in computation time. For applications where accurate channel estimation is required under noisy environment, the proposed algorithm is an effective choice to meet such requirements.
The main purpose of this paper is to design a robust second order sliding mode controller that can deal with uncertain nonlinear systems. This controller can keep the main advantages of the first order sliding mode controller, such as the ability to make the system asymptotically stable by forcing the error and its derivatives to have a zero value, the simplicity in the operation, and the robustness in the existence of perturbations. In spite of the features that characterize the first order sliding mode controller (1 SMC), it still suffers from the unwanted phenomenon “chattering”, which originates from a discontinuous control part (sign function). In this context, saturation function can be used instead of sign function to reduce this problematic chattering. Different from the saturation function method, the second order sliding mode controller can be used to overcome the chattering; suffered by the first order sliding mode controller and to retain the stability and performance of the system. In this paper, the twisting and the super twisting second-order algorithms of the sliding mode controller were used, and their results were compared with the first order sliding mode controller. So, this subject focused on the chattering problem who suffers from it the 1 SMC and try to reduce it by using the 2 SMC, the uncertain pendulum system was adopted in this work for the purpose of checking the three controllers. The simulations results showed that the second order sliding mode controller has the ability to reduce both the chattering magnitude and the steady state error and achieve an asymptotically stable system. The results were obtained by using MATLAB programming.
The Unified Power Flow Controller (UPFC) is a most complex power electronic device, which can simultaneously control a local bus voltage and optimize power flows in the electrical power transmission system. This paper presents the effect of installing the UPFC on the Iraqi (400 kV) grid transmission system to control the active and reactive power flow by choosing the optimal location and parameters of Unified Power Flow Controllers (UPFCs), which were specified based on the Genetic Algorithm (GA) optimization method. The objectives are improving voltage profile, reducing power losses, treating power flow in overloaded transmission lines, and reducing power generation. The steady state model of UPFC has been adopted on (400 kV) Iraq transmission lines and simulated using the MATLAB programming language. The Newton-Raphson (NR) numerical analysis method has been used for solving the load flow of the system. The practical part has been solved through Power System Simulation for Engineers (PSS\E) software Version 32.0. The Comparative results between the experimental and practical parts obtained from adopting the UPFC were too close and almost the same under different loading conditions, which are (5%, 10%, 15% and 20%) of the total load.
A proposed modern technique for determination the blood group typing by monitoring the agglutination of red blood cells using acousto-optical technique and digital camera. The method based on analysis the digital image of the agglutination process by MATLAB software._x000D_ We present an overview of two acousto-optic sensing approaches; the first demonstrates the cuvette approach while the second is the microscope slide approach. The cuvette approach digital image analyzing depends on the green channel distribution of the original image and count the brighten pixels, while the microscope slide approach passes through series of algorithms started with grayscale filter and end with edge detection it counts the different color pixels._x000D_ The experimental result shown that it is possible to enhance the determination of blood group typing by using acousto-optical technique in both cases of using isohemagglutinating sera as well as the crossmatch test in a short time and high efficiency compared with the traditional methods.
Mobile robots use simultaneous localization and mapping (SLAM) techniques for generating maps of unknown environments through navigating its. In this work, firstly SLAM technique was considered based on extended Kalman filter (EKF) which it was implemented and evaluated at unknown environments with different number of landmarks to estimate mobile robot’s position and build a map for navigated environment at the same time. Then, the detectable landmarks will play an important role in controlling the overall navigation process as well EKF-SLAM technique’s performance. After that, three intelligent optimization algorithms are proposed to enhance the performance of the EKF-SLAM trajectory for the mobile robot, these algorithms are: particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO) and genetic optimization (GA). MATLAB simulation results show that CPSO algorithm outperforms PSO and GA algorithms in terms of minimizing the mean square error (MSE1) with increasing the number of landmarks, where MSE1 is the mean square error of EKF-SLAM according to the actual trajectory. The simulation results show also the performance of EKF-SLAM trajectory is better than the performance of the Odometry trajectory and becomes best with using intelligent optimization algorithms.
In the last two decades, underwater acoustic sensor networks have begun to be used for commercial and non-commercial purposes. In this paper, the focus will be on improving the monitoring performance system of oil pipelines. Linear wireless sensor networks are a model of underwater applications for which many solutions have been developed through several research studies in previous years for data collection research. In underwater environments, there are certain inherent limitations, like large propagation delays, high error rate, limited bandwidth capacity, and communication with short-range. Many deployment algorithms and routing algorithms have been used in this field. In this work a new hierarchical network model proposed with improvement to Smart Redirect or Jump algorithm (SRJ). This improved algorithm is used in an underwater linear wireless sensor network for data transfer to reduce the complexity in routing algorithm for relay nodes which boost delay in communication. This work is implemented using OMNeT++ and MATLAB based on their integration. The results obtained based on throughput, energy consumption, and end to the end delay.
This paper discusses the development of a seven-band coherent wavelength-division multiplexing (WDM) system covering the T to U systems, aiming to enhance the capacity and system efficiency. Seven multiband systems (C+L, S+C+L, S+C+L+U, E+S+C+L, E+S+C+L+U, O+E+S+C+L+U, and T+O+E+S+C+L+U) are designed with 40 GBaud symbol rate, 50 GHz channel spacing, and dual-polarization (DP)-16QAM signaling. The analysis adopted the enhanced Gaussian noise model, considering the amplified spontaneous emission of inline optical amplifiers and nonlinear interference (NLI) from fiber nonlinear optics, including Kerr effect and stimulated Raman scattering (SRS) which it implemented using Matlab (Ver. 2020b) program. The results show that the optimal powers are -4, -5, -5, -4.5, -3.5, -6, and -4.5 dBm for the seven WDM systems, respectively. Further, with a fiber span length of 100 km, the C+L system has the longest transmission reach of 20 span. However, using S+C+L+U system gives the highest bit rate-distance product of 1619 Tbps.km. The O+E+S+C+L+U and T+O+E+S+C+L+U systems are designed with 50 km-span length to reduce the effect of NLI caused by the large numbers of channels (1060 and 1200, respectively).
Computed tomography (CT) imaging is an important diagnostic tool. CT imaging facilitates the internal rendering of a scanned object by measuring the attenuation of beams of X-ray radiation. CT employs a mathematical technique of image reconstruction; those techniques are classified as; analytical and iterative. The iterative reconstruction (IR) methods have been proven to be superior over the analytical methods, but due to their prolonged reconstruction time, those methods are excluded from routine use in clinical applications. In this paper the reconstruction time of an IR algorithm is minimized through the employment of an adaptive region growing segmentation method that focuses the image reconstruction process on a specified region, thus ignoring unwanted pixels that increase the computation time. This method is tested on the iterative algebraic reconstruction technique (ART) algorithm. Some phantom images are used in this paper to demonstrate the effects of the segmentation process. The simulation results are executed using MATLAB (version R2018b) programming language, and a computer system with the following specifications: CPU core i7 (2.40 GHz) for processing. Simulation results indicate that this method will reduce the reconstruction time of the iterative algorithms, and will enhance the quality of the reconstructed image.