Development of an Adaptive Fuzzy Extended Kalman Filter (AFEKF) and an Adaptive Fuzzy Unscented Kalman Filter (AFUKF) for the state estimation of unmanned aerial vehicles (UAVs) were presented in this paper based on real flight data of a fixed wing airplane. The Adaptive Neuro Fuzzy extension helps to estimate the values of the EKF's and UKF's Rk covariance matrix at each sampling instant when measurement update step is carried out. The ANFIS monitors the EKF's and UKF's performances attempt to eliminate the gap between theoretical and real innovation sequences' covariance. The investigations show that AFUKF can provide better performance in accuracy and less error than the AFEKF in case of real flight data for maneuvering fixed wing UAVs.

Lung cancer is a form of malignant tumor distinguished by aggressive multiplication of abnormal cells in lung tissues. If we can assure the detection of lung cancer in the early stage, then we have a chance to increase the survival rate by five years as effective treatment is still available at this stage. Many researchers in the field of image processing sector have built various systems to detect cancer by using image processing techniques. Internationally TNM (Tumor, Nodule, Metastases respectively) method is followed by a physician and radiologist to describe the stage of lung cancer. Our proposed system uses image processing techniques to detect and classify the tumor according to the TNM staging method. First, a series of image processing techniques are performed in a Computed tomography (CT) image. Then, features are extracted to identify the region of interest (ROI). In our proposed system, the classification approach is different from the reviewed existing systems, and the detection rate is comparatively high.

In pharmaceutical industry, dissolution testing is part of the target product quality that essentials are in the approval of new products. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. RAMAN and Near Infrared (NIR) spectroscopy are two complementary methods, that provide information on the physical and chemical properties of the tablets and can help in predicting their dissolution profiles. This work aims to use the information collected by these methods to support the decision of how much of the dissolution profile should be measured and which methods to use, so that by estimating the remaining part, the accuracy requirement of the industry is met. Artificial neural network models were created, in which parts of the measured dissolution profiles, along with the spectroscopy data and the measured compression curves were used as an input to estimate the remaining part of the dissolution profiles. It was found that by measuring the dissolution profiles for 30 minutes, the remaining part was estimated within the acceptance limits of the f2 similarity factor. Adding further spectroscopy methods along with the measured parts of the dissolution profile significantly increased the prediction accuracy.

Switched Reluctance Motor (SRM) drive is being gradually used in industrial applications, including electric vehicles (EVs), due to several advantages over conventional motors. However, the nonlinear magnetic characteristics of the motor make its controller very complicated. This paper presents a simplified procedure to obtain the optimal switching angles under hysteresis current control SRM drive over a wide range of speeds. A multi-objective optimization technique is applied to determine the optimal switch on and switch off angles that achieve the optimum combination of maximum average torque with minimum torque ripple and copper loss. A searching algorithm is developed for each operating point to define the maximum average torque and the minimum torque ripple and copper losses, as they vary for different currents and motor speeds. Then the optimal values of switching angles are stored in the lookup tables to build a MATLAB model of the SRM drive system. Finally, simulation and experimental results are presented to show the validity and effectiveness of the proposed controller.

Power converters play an essential role in Photovoltaic (PV) system to maximize the power transfer to the electrical grid. However, the generated harmonics in the grids due to these power converters and nonlinear loads are considered one of the encountered problems to overcome. This paper presents a decoupled control of PV field real power and reactive power injected to the high voltage network via a PWM inverter by using fuzzy logic controllers. Elsewhere, a procedure based on a coordinated active and LCL filtering is proposed to mitigate the harmonic current introduced by a nonlinear load and the inverter itself in such a way to enhance the power quality injected into the grid. The results obtained in the present study show the good performance of the suggested hybrid filtering approach and demonstrate that almost all harmonics orders of the grid current are well mitigated; the current Total Harmonic Distortion (THD) meets its standard and consequently the power quality is considerably enhanced.

A design of MIMO antenna with four elements each one consists of two polarized ports is proposed in this research. The design of each elements based on ZORA which is applied to get the advantage of band frequency flexibility. The proposed MIMO antenna is operated as a single and dual band by adjusting a ground structure. The resonance frequency is 3.9 GHz for single band with bandwidth 1 GHz (3.4–4.4) GHz at -10 dB with −27 dB isolation. The resonance frequencies for dual band are 4.95 and 7 GHz with bandwidth 1.23 GHz (4.03–5.26) GHz and 410 MHz (6.88–7.29) GHz at −10 dB and less than −12 dB isolation for both bands. The obtained size of FR-4 PCB for single band is 77 × 150 mm2 while 82 × 150 mm2 for dual band which are suitable for future smartphone.

In the present study, we attempt to estimate the severity of depression using a Convolutional Neural Network (CNN). The method is special because an auto- and cross-correlation structure has been crafted rather than using an actual image for the input of the network. The importance to investigate the possibility of this research is that depression has become one of the leading mental disorders in the world. With its appearance, it can significantly reduce an individual's quality of life even at an early stage, and in severe cases, it may threaten with suicide. It is therefore important that the disorder be recognized as early as possible. Furthermore, it is also important to determine the disorder severity of the individual, so that a treatment order can be established. During the examination, speech acoustic features were obtained from recordings. Among the features, MFCC coefficients and formant frequencies were used based on preliminary studies. From its subsets, correlation structure was created. We applied this quadratic structure to the input of a convolutional network. Two models were crafted: single and double input versions. Altogether, the lowest RMSE value (10.797) was achieved using the two features, which has a moderate strength correlation of 0.61 (between estimated and original).

This paper presents a new converter topology for grid connected Hybrid Renewable Energy System (HRES). The proposed topology named Dual Input Z-source Indirect Matrix Converter (DIZIMC) consists of an Ultra Sparse Z-source Matrix Converter (USZMC) interfacing PV and Wind Turbine (WT) to the grid. The DC link of the proposed converter is replaced by Interconnection Sources System (ISS). The ISS operates according to whether the sources produce energy or not. It allows connecting the sources individually or simultaneously and even isolates them if necessary. In the other hand, the DIZIMC provides several advantages such as reduced number of IGBTs and compact size inherited from USZMC, the use of Z-sources instead of conventional DC/DC converters keep the matrix configuration of the global structure. The global model of the proposed system was tested by simulation under Matlab/Simpowersys environment. The obtained results show clearly freedom in connecting the operational sources, also a better quality of energy injected to the grid.

The main aim of this study is to investigate the ageing process of Low Voltage cables in smart grid. In addition, the behavior of ageing phenomenon has also been investigated. The Low Voltage cable networks were developed decades ago and it is of utmost importance to make comprehensive study over the health of existing Low Voltage cable networks so as to avoid any unprecedented damage. Incessantly increasing energy demand and distributed generation makes it among top priorities to investigate the ageing process of Low Voltage cables.The effect of thermal stress over dielectrics was investigated as a part of research. To be able to determine these effects the measurements were done on PVC insulated (both cores and jacket) cable by measuring tanδ (dissipation factor), capacitance, return voltage of cable and hardness of insulations. In order to determine thermal effects on dielectric, the measurements were done in different ageing temperature ranges i.e. at 110 °C, 125 °C and 140 °C.The results of this study support the expectations. The mechanical and the electrical parameters of cable insulation are affected by thermal stress. The dissipation factor and the hardness are increased while the decay voltage slope ( Sd ) is decreased by ageing.

The paper presents a comparative analysis of different commercial and academic software. The comparison aims to examine how the integrated adaptive grid refinement methodologies can deal with challenging, electromagnetic-field related problems. For this comparison, two bench-mark problems were examined in the paper. The first example is a solution of an L-shape domain like test problem, which has a singularity at a certain point in the geometry. The second problem is an induction heated aluminum rod, which accurate solution needs to solve non-linear, coupled physical fields. The accurate solution of this problem requires applying adaptive mesh generation strategies or applying a very fine mesh in the electromagnetic domain, which can significantly increase the computational complexity. The results show that the fully-hp adaptive meshing strategies, which are integrated into Agros Suite, can significantly reduce the task's computational complexity compared to the automatic h-adaptivity, which is part of the examined, popular commercial solvers.