A nonlinear control scheme is employed in this paper for controlling the speed of DC motors which are driven by DC-DC buck converters. The hybrid robust adaptive backstepping sliding mode control theory is used to design the proposed controller. To handle the sudden load variation effects, the load torque is considered as unknown terms which will estimate using an adaptation law. Exterior turbulences are also considered in the system's dynamical model to illustrate the robustness of the proposed hybrid control scheme. The overall system's stability is established using the Lyapunov theory. At last, simulation results are presented to exhibit the usefulness of the proposed control scheme in the presence of external disturbances and compared with an existing controller. Simulation results reveal that the proposed controller outperforms the existing controller in terms of overshoot and settling time.
This paper introduces a new static output feedback controller design method for continuous-time Takagi-Sugeno fuzzy systems, which can represent a wide class of nonlinear systems. Although many papers discuss state feedback controller design for fuzzy sytems, a fewer number of papers consider an output feedback controller design due to its difficulty. In this paper, our target is to propose an output feedback controller design method. In our control design, the integral structure of the membership functions, which are the same properties as the original membership functions, is utilized. A similar approach for state feedback controller design methods has been in the literature. Our proposed output feedback controller is designed based on a new fuzzy Lyapunov function which includes the double integral of the original membership functions of the system, and it shows a wider stabilizing area than the existing methods. At the end of the paper, a numerical example is provided to illustrate the newly proposed controller design method and to asymptotically stabilize the system.
A directional wideband microstrip line fed rectangular patch antenna has been proposed for the 28 GHz 5G applications. Initially, a conventional rectangular microstrip patch antenna has been designed thereafter to enhance the performances as well as to tune in the desired operating frequency range (24 GHz-30 GHz) a cutting edge with partial ground plane technique is used. At the intermediate stage, a star-shaped parasitic element has been used for the gain enhancement of the antenna. However, the high gain of the antenna is retained by optimizing the dimensions and position of the cutting edge as well as the ground plane without introducing any parasitic element on the patch which is the simplicity of the proposed antenna. The proposed antenna operates at the 28.201 GHz band ranging from 26.984 GHz to 29.551 GHz. It has an average radiation efficiency of 84.5%. As the proposed antenna possesses a smaller size ($\mathbf{20}\times \mathbf{17}\times \mathbf{1.575}\ \mathbf{mm}^{\mathbf{3}}$) and wide bandwidth of 2.567 GHz with a high gain and directivity over the entire operating band, it can be considered as a potential candidate for the 28 GHz millimetre wave (mm-wave) applications.
Intracranial Hemorrhage is a common brain injury that leads to a high mortality rate without prompt recognition. To address these issues, computer-aid diagnosis tools are rapidly being developed along with neural-network-based techniques to provide fast, reliable analysis and achieve accurate diagnosis decisions based on medical images. One of the most interesting applications in computer-aid diagnosis is Image Registration due to its practical features in clinical diagnosis and treatment planning. In this study, we present the non-rigid image registration for the 3D Computed Tomography image dataset of the Intracranial Hemorrhage Brain. By utilizing the affine transformation and a neural network model, we aim to predict the deformation vector field, map the real-world-collected dataset to the reference space and overcome the shifting data problem between the data analysis experiment on standard and real-world medical image analysis. Our test results gave that good registration performance is obtained in a very short time by using a neural network model, and the affine transformation significantly improves the real-world image registration. In addition, according to the distance from the hematoma area change ratio to the brain area change ratio, the characteristics of the major structure are determined to be preserved.
The ever-increasing demand of smart sensors for Internet of things applications, drove the change in outlook towards smart sensor system design. This paper focuses on smart sensing approach using single pristine tin-oxide (SnO2) sensing film based sensor. It is a novel attempt to not only identify but also quantify the bio-marker gases like ammonia (NH3), carbon monoxide (CO) and hydrogen sulphide (H2S) using single sensor with intelligent pattern recognition algorithms and its readout circuit. The presented work uses temperature modulated gas sensor response obtained at different concentrations for mentioned gases with in-house developed sensor for gas discrimination and quantification. This work uses wavelet features with Independent Component Analysis (ICA) based feature extraction technique and proposed Adaptive Gradient Boosting classifier for gas sensor response pattern recognition. The proposed approach provides gas detection accuracy 93.28%. Further, our approach uses maximum likelihood fit to quantify concentration of the predicted gas and reported the average MSE for all three gases is 0.11, which makes the proposed work smart to mitigate the cross-selectivity of MOS based smart sensor design in presence of drift and noise.
