Low Area, Low Power and High Bandwidth Operational Amplifier by 130nm CMOS Technology

A low power and high bandwidth CMOS Operational Amplifier has been designed in 130 nm CMOS Technology by Miller compensation technique and obtained a gain of 107 dB. A loop feedback is used to increase the bandwidth and results the final 3dB bandwidth 65 KHz and Unity gain bandwidth 2.3GHz. The proposed opamp providing 318 dB CMRR, 137 dB PSRR, 4.25 V/us Slew rate and 0.7 mW power dissipation. The overall design is simulated in 130nm digital CMOS technology in PSpice

A High Data Rate Wireless System using STBC MIMO Technique to Control Microgrid in Smart Grid System for Remote Areas

The rising requirement on real time application to attain high throughput, reliable wireless system and network capacity for third generation Universal Mobile Telecommunication System, a MIMO (Multiple Input Multiple Output) technique is mainly smart technique in wireless communication system and it is very trendy for high data rate capacity and beside multipath fading.This paper presents MIMO technique with Space Time Block Code (STBC) multiplexing. The result of using these MIMO techniques is higher data rate or longer transmits range with not requiring any extra bandwidth or transmits power. These Space Time Block Code techniques are examined for performance according to their bit-error rates using16-QAM modulation scheme for getting high signal to noise ratio (SNR). There are numerous standardized wired and wireless communication technologies existing for different smart grid applications. To get high transmission ,there are several methods from which 16 QAM has been used to reduce time delay

Design of High Performance Phase Locked Loop for UHF Band in 180 nm CMOS Technology

Abstract: The aim of this study was to design low phase noise 2.4 GHz ring oscillator with low power dissipation and small die area. This study presents the design of high performance PLL for UHF band. This PLL has been realized in 180 nm by Virtuoso Analog Design Environment of Cadence tool. After simulating various stages of the ring oscillators, a three-stage ring oscillator has been selected for the implementation of the PLL. A zero dead zone Phase Frequency Detector (PFD) and Charge Pump (CP) with loop filter have been designed and used in the PLL. The PLL has designed with lowest phase noise of-122.2 dBc/Hz @ 10 MHz offset frequency and figure of merit-134 dBc/Hz. The layout of complete PLL has been designed by Virtuoso LayoutXL tool of Cadence. The total area required to implement the PLL without package is (0.093 × 0.09783 mm) 0.0091 mm 2

Automatic detection of sleep breathing disorder using Bayesian optimization algorithm from single-lead electrocardiogram

Background/objective: Deep learning paradigm is very popular for image classification problems and has proven its significance in all domains. The tuning of hyperparameter for deep neural network algorithm is a very tedious task and is performed mostly in the trial-and-error method. We propose a Bayesian optimization algorithm (BOA) to tune hyperparameter in pre-trained GooLeNet architecture to detect sleep breathing disorders using single-lead ECG. We aim to perform automatic detection of sleep apnea using single-lead ECG rather than polysomnography as it is easy to record and implement. Method: The physionet sleep apnea data is used for training and testing of the model proposed. Three different solvers adam, rmsprop, and sgdm are used in pre-trained GoogLeNet architecture for the classification of sleep breathing disorder using single-lead ECG while rest all other hyperparameters are altered too. Result: To detect automatic sleep breathing disorder (SBD) in BOA using pre-trained GoogLeNet and solvers adam, rmsprop, and sgdm the sgdm optimizer is showing the best result as the loss is least in this case but processing times for each are different. Discussion/conclusion: We conclude that the BOA was used to identify the most suitable classifier for the automatic detection of SBD

Analysis and Risk Estimation System for Heart Attack Using EDENN Algorithm

Heart related diseases are very common in the present scenario. In the past two decades the number of heart patients have increased to a large extent. Due to this abrupt rise in the number of patients, the death count has also increased. Thus, an efficient and accurate system must be developed for the diagnosis of heart related diseases, as the present methods available are not accurate enough and are insufficient for the Heart Attack (HA) and its Risk Analysis (RA). This paper propounds a system for HA risk estimation by the use of an Enhanced Deep Elman Neural Network (EDENN). In this system a Photoplethysmography (PPG) signal is inputted and pre-processed for noise removal. Further, Signal Decomposition (SD) is done, and the vital signs are estimated like Blood Pressure (BP), Respiratory Rate (RR) and Cardiac Autonomic Nervous System (CANS). For the BP estimation, Modified Maximum Amplitude Algorithm (MMAA) method is used and for the decomposed signal processing the Improved Incremental Merge Segmentation (IIMS) is used. As for features, Variation of amplitude, frequency and intensity are calculated and merged

Analysis and risk estimation system for heart attack using EDENN algorithm

Heart related diseases are very common in the present scenario. In the past two decades the number of heart patients have increased to a large extent. Due to this abrupt rise in the number of patients, the death count has also increased. Thus, an efficient and accurate system must be developed for the diagnosis of heart related diseases, as the present methods available are not accurate enough and are insufficient for the Heart Attack (HA) and its Risk Analysis (RA). This paper propounds a system for HA risk estimation by the use of an Enhanced Deep Elman Neural Network (EDENN). In this system a Photoplethysmography (PPG) signal is inputted and pre-processed for noise removal. Further, Signal Decomposition (SD) is done, and the vital signs are estimated like Blood Pressure (BP), Respiratory Rate (RR) and Cardiac Autonomic Nervous System (CANS). For the BP estimation, Modified Maximum Amplitude Algorithm (MMAA) method is used and for the decomposed signal processing the Improved Incremental Merge Segmentation (IIMS) is used. As for features, Variation of amplitude, frequency and intensity are calculated and merged