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Design Of Analog Cmos Integrated Circuits

Thank you for downloading design of analog cmos integrated circuits. Maybe you have knowledge that, people have look hundreds times for their chosen books like this design of analog cmos integrated circuits, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some harmful virus inside their computer. design of analog cmos integrated circuits is available in our book collection an online access to it is set as public so you can download it instantly. Our digital library spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the design of analog cmos integrated circuits is universally compatible with any devices to read.

/pdf/advanced-nanomaterials-based-electrochemical-biosensors-for-bv2nfk3x.pdf

Advanced Nanomaterials-Based Electrochemical Biosensors for Catecholamines Detection: Challenges and Trends

2015 IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS)

Determining the bearing capacity of a strip footing under inclined loading is crucial in designing foundations. Due to the complex correlations, the subject area remains predominantly unexplored, or it has been simulated using only limited datasets. This paper presents the development of a prediction model based on machine learning (ML), leveraging advanced hybrid artificial neural network (ANN) models for estimating the bearing capacity of strip footings under inclined loading. The ANN models are hybridized with four different optimization algorithms, ant colony optimization (ACO), artificial lion optimization (ALO), the imperialist competitive algorithm (ICA), and shuffled complex evolution (SCE), which enhance the accuracy and efficiency of the predictive capabilities of ANN. The models are trained on a dataset of 920 records, and their performance is evaluated using a range of significant performance metrics. The ANN-ICA model achieved the highest rank in the score analysis (R2 =0.912, RMSE=0.165 in testing), followed by ANN-ALO and ANN-ACO. To reinforce the trustworthiness of the predictions, external validation is employed, and visual analysis is conducted using the Taylor diagram. The findings suggest that the proposed models are robust, and the incorporation of optimization techniques has improved the performance of traditional ANNs. The research findings have significant implications for the field of geotechnical engineering, providing engineers and researchers with valuable insights into the applicability of hybrid artificial neural network (ANN) models and alternative machine learning (ML)-based prediction models in assessing the bearing capacity of strip footings. 

Optimized neural network-based state-of-the-art soft computing models for the bearing capacity of strip footings subjected to inclined loading

The complexity of concrete's composition makes it difficult to predict its compressive strength, which is a highly valuable and desired characteristic. Traditional methods for prediction are expensive and time-consuming, resulting in limited data availability. However, modern soft-computing models have emerged as a reliable solution for accurately forecasting concrete's compressive strength. The research proposes a novel Deep Neural Network (DNN), Multivariate Adaptive Regression Splines (MARS) and Extreme Learning Machine (ELM) based machine learning (ML) models for forecasting the compressive strength of concrete added with various proportions of fly ash and silica fume. For this purpose, a dataset of 144 trials, having 8 input parameters is taken from the literature. The performance of the models is confirmed using various statistical parameters. Rank Analysis reveals that DNN is the best-performing model (Rank =52, RTR2=0.983 and RTs2=0.954), closely followed by MARS (Rank =38, RTR2=0.974 and RTs2=0.956); while ELM lags behind the other two counterparts. The results are further confirmed using an error matrix, external validation and AIC criteria. The visual interpretation is provided using the Taylor diagram. MARS has the edge over the other two models in terms of providing a user-friendly solution. 

Soft computing-based prediction models for compressive strength of concrete

In the recent times, the automobile vehicle equipped with the advance technology and luxuries is nevertheless the driver who violates the traffic rules intentionally or unintentionally ignoring the traffic lights. In this scenario, there is a need of up gradation in the conventional traffic management system to make it vehicle assisted by using the Internet-of-things. Therefore, this paper proposes a novel intelligent traffic light management system using Internet-of-things. In this proposed system, the vehicle and traffic light can communicate with each other with the help of cloud system.

IOT-Based Smart Traffic Light System for Smart Cities

Electromyogram (EMG) signals are widely used in rehabilitation, medical, engineering, robotic, and industrial fields. For amputee’s residual muscles control, EMG signals have been utilized. Multiple-level approximation and detail coefficients of EMG signals were studied here. In this study, EMG signals were extracted from 20 healthy human subjects by multilevel coefficients of wavelet. After the EMG data acquisition, preprocessing was done followed by discrete wavelet transform (DWT) de-noising and feature extraction by db4 wavelet. Three-level decomposition was achieved with DWT technique, and higher classification accuracy was observed by support vector machine (SVM) classifier with principal component analysis. This paper proposed a combination of a highly accurate algorithm for the pattern recognition of EMG signal with different movements. The finding of this paper could be utilized with real-time applications.

DWT-Based Hand Movement Identification of EMG Signals Using SVM

A fully dynamic latched comparator has been designed to meet the requirement of high speed and low power consumption. Such comparators are used in high speed data converters. In this work, dynamic comparators are designed in two different technologies and compared on the basis of delay, offset voltage and power consumption. These comparators work on the concept of charge sharing. Main focus is given towards reduction of both propagation delay and the power dissipation, which will be beneficial in improving performance of the comparator. 90nm CMOS technology is used to simulate the design with 1 V supply voltage. Hspice is used for designing and functional verification.

A high speed power efficient dynamic comparator designed in 90nm CMOS technology

In this paper a high gain low noise Op-Amp has been designed. In designing of a high gain Op-Amp, for large values of coupling capacitor, gain will decrease. Since our requirement was to increase the gain, so we have designed a three stage Op-Amp. Our designed circuit provides gain of 78.4 dB, which is very much larger than two stage Op-Amp. There is a trade-off between various parameters like Phase margin, gain, slew rate etc. For example, to achieve larger values of GBW, PM will decrease. We have compared the results for two values of input common mode range. Improvement in the designed circuit is done to achieve the desired GBW by recalculating the transistor's W/L ratios and then simulating the results. Gain Bandwidth product of 176.9 MHz and Phase Margin greater than 60 degrees is achieved but at the cost of power dissipation and area. The Op-Amp is designed in gpdk 180 nm CMOS technology.

Design of 3 stage low noise operational amplifier

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