Thursday, February 3, 2022

Low Power Low Voltage Bulk Driven Balanced OTA


Low Power Low Voltage Bulk Driven Balanced OTA

The last few decades, a great deal of attention has been paid to low-voltage (LV) low-power (LP) integrated circuits design since the power consumption has become a critical issue. Among many techniques used for the design of LV LP analog circuits, the Bulk-driven principle offers a promising route towards this design for many aspects mainly the simplicity and using the conventional MOS technology to implement these designs. This paper is devoted to the Bulk-driven (BD) principle and utilizing this principle to design LV LP building block of Operational Transconductance Amplifier (OTA) in standard CMOS processes and supply voltage 0.9V. The simulation results have been carried out by the Spice simulator using the 130nm CMOS technology from TSMC.

Handling Trust in A Cloud Based Multi Agent System

 Cloud computing is an opened and distributed network that guarantees access to a large amount of data and IT infrastructure at several levels (software, hardware...). With the increase demand, handling clients' needs is getting increasingly challenging. Responding to all requesting clients could lead to security breaches, and since it is the provider's responsibility to secure not only the offered cloud services but also the data, it is important to ensure clients reliability. Although filtering clients in the cloud is not so common, it is required to assure cloud safety. In this paper, by implementing multi agent systems in the cloud to handle interactions for the providers, trust is introduced at agent level to filtrate the clients asking for services by using Particle Swarm Optimization and acquaintance knowledge to determine malicious and untrustworthy clients. The selection depends on previous knowledge and overall rating of trusted peers. The conducted experiments show that the model outputs relevant results, and even with a small number of peers, the framework is able to converge to the best solution. The model presented in this paper is a part of ongoing work to adapt interactions in the cloud.

For more details:

Wednesday, February 2, 2022



Author :  Mahesh S. Khadka*, K. M. George, N. Park and J. B. Kima

Affiliation :  aDepartment of Economics and Legal Studies in Business, Oklahoma State University, Stillwater, OK 74078, USA

Country :  USA

Category :  Information Technology Management

Volume, Issue, Month, Year :  4, 3, August, 2012

This paper presents performance analysis of hybrid model comprise of concordance and Genetic Programming (GP) to forecast financial market with some existing models. This scheme can be used for in depth analysis of stock market. Different measures of concordances such as Kendall’s Tau, Gini’s Mean Difference, Spearman’s Rho, and weak interpretation of concordance are used to search for the pattern in past that look similar to present. Genetic Programming is then used to match the past trend to present trend as close as possible. Then Genetic Program estimates what will happen next based on what had happened next. The concept is validated using financial time series data (S&P 500 and NASDAQ indices) as sample data sets. The forecasted result is then compared with standard ARIMA model and other model to analyse its performance.

For more details: