The objective of this paper is to present the three phase induction motor drive using the cooperation of fuzzy logic controller and proportional plus integral (PI) controller as a hybrid run on field oriented control (FOC) for improving the performance of rotor speed. The system is fed to a three phase induction motor by voltage source inverter that is used space vector modulation (SVM) technique. This system is implemented with the control system on dSPACE programming which is supported by MATLAB/Simulink through a dSPACE -ds1140 interfacing module. In the implementation, the conventional PI controllers are replaced by hybrid fuzzy PI controllers of both an outer speed control loop and two inner currents control loops that are controlled stator flux and rotor torque of the induction motor. The experimental results are compared with conventional PI controllers. As a result, the performance of design model by hybrid fuzzy PI controller is better than the conventional PI controllers. 

With an increase in population of the country day by day and with high growing speed of geographical residential plots, the demand by the public for new set up of electrical power transmission system has become a common mandate. Therefore it is the responsibility of the concerned authority to protect the interests of common man in a smart manner and develop solutions for the growth of country in an intelligent way keeping safety of public as prime importance. This paper proposes an Affine Arithmetic approach of mathematical modelling to estimate the safety parameters of AC transmission line leading to sag, like, temperature, wind loading, ice loading, weight of the conductor, stress, tension, pressure etc., taking into account the uncertainty conditions so that the solutions developed address in real time. The proposed model is executed in MATLAB integrated development environment and gives the complete behavior of sag in transmission lines with respect to each of the safety parameters individually and closely ascertains the safety threshold limits considering 50% factor of safety and 5m ground clearance. The critical safety threshold limit for wind loading is found to be about 3.02 kg/m and the typical value is about 1.51 kg/m. Similarly, the critical safety threshold limit for weight of ice is found to be about 1.45 kg/m, whereas its typical value is about 0.7 kg/m. Extending further, the critical safety threshold limit for conductor weight is found to be about 3.07 kg/m, whereas its typical value is about 1.6 kg/m, which is a close approximation to the typical weight per unit length of industry-grade conductors like Aluminum Conductor Steel Reinforced (ACSR). The critical safety threshold limit for tension in the conductor and the span lengths are found to be 1850 kg and 230m respectively.

Nowadays, fighting fires in rugged mountainous terrains or in high-rise buildings are both difficult and dangerous. High-rise buildings are containing floors at such a height or position that the deployment of external firefighting equipment and rescue operations may not be feasible or practical. Meanwhile, reaching farms located in a rugged mountainous terrain with firefighting vehicles is often impossible. From this perspective, to meet the need for a fast way to extinguish fire in an area that is hard to be approached by the conventional methods, and to offer the highest level of safety for the public and firefighters; fire extinguishing system is proposed in this paper. The system is structured with six units: Quadrotor Unmanned Aerial Vehicle (QUAV), Robot (Release Mechanism), Automatic Electric_Spring Operated Gun, Fire Extinguishing Ball, Collision Avoidance System, and a Camera. Quadrotor will carry a specific payload and be capable of throwing an extinguishing balls in an area that is chosen by the operator. The proposed system has been implemented, constructed, and tested in an actual scenarios. Experimental results demonstrate the feasibility of our drone in extinguishing fire in its initial stages and of being safe to hover over a fire, drop a fire extinguishing ball, fly back to the firefighter, and hover at 2.5 meters in the air so that it can be reloaded with a new ball without losing its stability. 

The aim of our work is to obtain a maximum rate of recognition of abnormal (cancerous) blood cells. We propose the development of a system based on k-means methods, after an RGB channel decomposition by applying the algorithm which can segment our microscopic medical images. It turns out that the proposed system shows better segmentation and classification for the identification and detection of leukemia. The experimental results obtained are very encouraging, which helps hematologists to monitor the evolution of cancerous blood cells and make a good diagnosis.

Energy management system for the household appliances in terms of monitoring and control is described in this paper. Monitoring the energy consumption is the initial step to reduce it. The smart device is designed and developed that will monitor the load device parameters like voltage, current, power factor, power consumption and frequency. The load device is made to turn OFF/ ON based on the status of the device through the mobile application. To get the measured data and to control the load device an Android application has developed, which uses a mobile-enabled Bluetooth to communicate with the Bluetooth low energy module, which is interfaced to the host microcontroller. Bluetooth module is used as it works well for home automation applications. The proposed device has a simple design, low power consumption, cost-effective and easy to interact with the user.

For the past recent decades, image denoising has been analyzed in many fields such as computer vision, statistical signal and image processing. It facilitates an appropriate base for the analysis of natural image models and signal separation algorithms. Moreover, it also turns into an essential part to the digital image acquiring systems to improve qualities of an image. These two directions are vital and will be examined in this work. Noise and Blurring of images are two degrading factors and when an image is corrupted with both blurring and mixed noises, de-noising and de-blurring of the image is very difficult. In this paper, Gauss-Total Variation model (G-TV model) and Gaussian Mixture-Total Variation Model (GM-TV Model) are discussed and results are presented. It is shown that blurring of the image is completely removed using G-TV model; however, image corrupted with blurring and mixed noise can be recovered with GM-TV model.

Epilepsy is a chronic brain disorder which affects normal neuronal activity of the brain. It results in sudden repeated episodes of higher electrical activity due to sensory disturbance. Electroencephalogram (EEG) plays an important role in the diagnosis of epilepsy. Currently, manual observation of EEG is done by experienced neurologist to diagnose epilepsy and related disorders. However, automated system is a promising method for seizure detection and diagnosis. The EEG signals recorded from the patient’s scalp are preprocessed, and classified as seizure and non-seizure based on the extracted signal features. The procedure significantly eliminates the error involved in manual observation. Due to non-linear nature of EEG, joint time-frequency methods are used to analyse the EEG signals. This paper proposes a EEG feature extraction technique using Dual Tree Complex Wavelet Transform (DTℂWT) to overcome the problem of shift variance in DWT. The estimation of improved multi-scale Permutation Entropy (IMPmEn) is done for the level-3 subband of DTℂWT. The performance of the Least Squares Support Vector Machine (LS-SVM) classifier is tested using these features and highest classification accuracy of  99.87 % is obtained on the real time EEG database.