Bindu Priyadarshini

Bio: Bindu Priyadarshini is an academic researcher from Indian Institutes of Technology. The author has contributed to research in topics: Current conveyor & Operational transconductance amplifier. The author has an hindex of 3, co-authored 11 publications receiving 22 citations.

PSO Based Design of Current CCII-PID Controller for the Speed Control of BLDC Motor

Abstract: A conventional DC motor has remained the area of interest for a wide range of applications. With the invention of BLDC motor which has many advantages over conventional DC motor, it has found it application in almost all the application by replacing conventional DC motor. Here, in this paper, we analyze the performance of BLDC motor using PID controller. To design the PID controller we have used Particle Swarm Optimization (PSO) based algorithm. The seminal work of this paper is to realize the controller circuit using only one second generation Current Conveyer (CCII) circuit. The performance has been validated using simulation with handy CCII IC AD844 in Multisim. Since, the transient behavior of a motor is of main concern, so this will be worthy to mention that the paper gives results in time domain. Moreover, the paper is not restricted up to only transient response, however, it also gives various steady state parameter like error in the output when transient dies out which is usually considered after 5τ sec in the literature.

A series expansion method aided design of CCII controller for a TITO system

Abstract: Current mode circuits have attracted the attention of the design engineers due to their unique characteristics that are not found in their voltage mode counterparts. This article presents a series expansion method based design of current mode circuits for the control of a two input two output (TITO) system. The unique contribution of this work is the realization of the TITO controller using the second generation current conveyor circuits. The controller was practically implemented using AD844 integrated circuits. Simulation and the experimental results are presented for a pragmatic case study to demonstrate the efficacy of the proposed design methodology.

A Compact Floating and Grounded Memristor model using Single Active Element

Abstract: Memristor with defined operational specifications is a challenging task that promotes innovation in emulation circuit modelling. This article presents a single active block based compact memristor emulator architecture which is suitable for both floating and grounded circuit topologies. It effectively operates in incremental/decremental modes in both the configurations. The proposed model utilizes only one Differential Voltage Current Conveyor Transconductance Amplifier (DVCCTA) as an active block along with a few passive components. It discourages the use of extra sub-circuit components like multiple active blocks, summers and multipliers. The circuit is intended to work at ±1 V of dc power supply and has a power consumption of 8.74 mW. The proposed emulator model exhibits all the characteristics of an ideal memristor with an operating frequency of 12.8 MHz. It has wide working voltage range of 50–500 mV. The memristor model is built and simulated using the TSMC 0.18 μm CMOS process parameter. The reliability and effectiveness of the proposed model is verified through non-ideal, Monte-Carlo, process corner, non-volatility and temperature variation analysis. Furthermore, the memristor prototype is constructed on the breadboard using discrete ICs AD844AN and CA3080. The experimental outcome is found in accordance with the simulation. The applicability of the proposed memristor model is verified by implementing a memristor based Schmitt trigger circuit.

Electron transport within the wurtzite and zinc-blende phases of gallium nitride and indium nitride

Abstract: Wide energy gap semiconductors are broadly recognized as promising materials for novel electronic and opto-electronic device applications. As informed device design requires a firm grasp on the material properties of the underlying electronic materials, the electron transport that occurs within the wide energy gap semiconductors has been the focus of considerable study over the years. In an effort to provide some perspective on this rapidly evolving and burgeoning field of research, we review analyzes of the electron transport within some wide energy gap semiconductors of current interest in this paper. In order to narrow the scope of this review, we will primarily focus on the electron transport that occurs within the wurtzite and zinc-blende phases of gallium nitride and indium nitride in this review, these materials being of great current interest to the wide energy gap semiconductor community; indium nitride, while not a wide energy gap semiconductor in of itself, is included as it is often alloyed with other wide energy gap semiconductors, the resultant alloy often being a wide energy gap semiconductor itself. The electron transport that occurs within zinc-blende gallium arsenide will also be considered, albeit primarily for bench-marking purposes. Most of our discussion will focus on results obtained from our ensemble semi-classical three-valley Monte Carlo simulations of the electron transport within these materials, our results conforming with state-of-the-art wide energy gap semiconductor orthodoxy. A brief tutorial on the Monte Carlo electron transport simulation approach, this approach being used to generate the results presented herein, will also be provided. Steady-state and transient electron transport results are presented. The evolution of the field, a survey of the current literature, and some applications for the results presented herein, will also be featured. We conclude our review by presenting some recent developments on the electron transport within these materials. This review is the latest in a series of reviews that have been published on the electron transport processes that occur within the class of wide energy semiconductor materials. The results and references have been updated to include the latest developments in this rapidly evolving field of study.

Automatic Classification of ECG Signalswith Features Extracted Using WaveletTransform and Support Vector Machines

Abstract: Electrocardiogram (ECG) is one of the most widely used techniques for diagnosing cardio vascular diseases. Automatic beat segmentation and classification of ECG signal is paramount since scrutinizing each and every beat is a tedious job for even the most experienced cardiologist. In this paper, we have accurately classified and differentiated Normal and abnormal heartbeats such as left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contractions (APC) and premature ventricular contractions (PVC), atrial premature beat (APB), Paced beats and Fusion beats with adequate levels of accuracy. At first the multi resolution analysis of ecg signal is done to denoised and extract 25 features. The mother wavelet used for decomposition was db4. The classification is implemented by using OAO (One Against One) SVM (Support Vector Machine). 7 SVM’s were trained and final grouping is done by maximum voting. ECG signals are obtained from the open source MIT-BIH cardiac arrythmia database. Experiments reveal that the overall classification accuracy is well above 97 % for all the classes.

Automatic Classification of ECG Signals with Features Extracted Using Wavelet Transform and Support Vector Machines

Abstract: Electrocardiogram (ECG) is one of the most widely used techniques for diagnosing cardio vascular diseases. Automatic beat segmentation and classification of ECG signal is paramount since scrutinizing each and every beat is a tedious job for even the most experienced cardiologist. In this paper, we have accurately classified and differentiated Normal and abnormal heartbeats such as left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contractions (APC) and premature ventricular contractions (PVC), atrial premature beat (APB), Paced beats and Fusion beats with adequate levels of accuracy. At first the multi resolution analysis of ecg signal is done to denoised and extract 25 features. The mother wavelet used for decomposition was db4. The classification is implemented by using OAO (One Against One) SVM (Support Vector Machine). were trained and final grouping is done by maximum voting. ECG signals are obtained from the open source MIT-BIH cardiac arrythmia database. Experiments reveal that the overall classification accuracy is well above 97 % for all the classes.

Design and Implementation of Particle Swarm Optimization (PSO) Tuned PID Controller for Speed Control of Permanent Magnet Brush Less DC (PMBLDC) Motor

Abstract: This paper presents the design and implementation of Particle Swarm Optimization (PSO) tuned PID controller for speed control of Permanent Magnet Brush Less DC (PMBLDC) motor using LabVIEW. PMBLDC motor is widely used in industrial applications like aeronautics, robotics, medical, food, chemical and automated industrial equipments. It is a synchronous motor with high efficiency, high torque-to-weight ratio and faster dynamic response. For effective speed control of PMBLDC motor, PID controller is tuned using Particle Swarm Optimization (PSO) algorithm due to its simplicity and fast convergence to optimum values. When compared with traditional Zeigler-Nichols method (Z-N) and evolutionary Genetic Algorithm (GA), PID controller tuned with PSO algorithm provides an improved time response performance with faster settling time, negligible peak overshoot and effective speed control. This is emphasized by considering numerical examples of various systems like non-oscillatory, oscillatory, integrating and non-minimum phase. The results obtained indicate that PSO-PID performs better than the other two methods. Hence, it is concluded that PSO-PID algorithm is ideal to control the speed of PMBLDC motor effectively with increased efficiency.

Electrocardiogram device and methods

Abstract: Devices and methods are described that provide improved diagnosis from the processing of physiological data. The methods include use of multiple algorithms and intelligently combing the results of multiple algorithms to provide a single optimized diagnostic result. The algorithms are adaptive and may be customized for particular data sets or for particular patients. Examples are shown with applications to electrocardiogram data, but the methods taught are applicable to many types of physiological data.