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.

For more details:https://allconferencecfpalerts.com/cfp/view-paper.php?eno=5276

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:https://allconferencecfpalerts.com/cfp/view-paper.php?eno=5261

PERFORMANCE ANALYSIS OF HYBRID FORECASTING MODEL IN STOCK MARKET FORECASTING

 

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:https://allconferencecfpalerts.com/cfp/view-paper.php?eno=5277

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. 

For more details:https://allconferencecfpalerts.com/cfp/view-paper.php?eno=5276

Optimised Malware Detection in Digital Forensics

 

Author :  SaeedAlmarri

Affiliation :  University of Bedfordshire

Country :  United Kingdom

Category :  Networks & Communications

Volume, Issue, Month, Year :  6, 1, January, 2014

Abstract :

On the Internet, malware is one of the most serious threats to system security. Most complex issues and problems on any systems are caused by malware and spam. Networks and systems can be accessed and compromised by malware known as botnets, which compromise other systems through a coordinated attack. Such malware uses anti-forensic techniques to avoid detection and investigation. To prevent systems from the malicious activity of this malware, a new framework is required that aims to develop an optimised technique for malware detection. Hence, this paper demonstrates new approaches to perform malware analysis in forensic investigations and discusses how such a framework may be developed.

Keyword :  Denial of service (DOS), Wireshark, Netstat, TCPView, The Sleuth Kit (TSK), Autopsy, Digital Forensics, Malware analysis, Framework

For More Details:https://allconferencecfpalerts.com/cfp/view-paper.php?eno=5275

Classification of OCT Images for Detecting Diabetic Retinopathy Disease using Machine Learning

Author :  Marwan Aldahami and Umar Alqasemi

Affiliation :  King Abdulaziz University

Country :  Saudi Arabia

Category :  Digital Signal & Image Processing

Volume, Issue, Month, Year :  12, 6, December, 2021

Abstract :

Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease because of their capability to capture micrometer-resolution. An automated technique was introduced to differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160 images were used for classifiers’ training, and 54 images were used for testing. Different features were extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP features has a significant impact on the achieved results. The result has better performance than previously proposed methods in the literature.

Keyword :  Image classification, diabetic retinopathy, support vector machine, optical coherence tomography, retina, machine learning.

 For more details: https://allconferencecfpalerts.com/cfp/view-paper.php?eno=5274

Sensing Method for Two-Target Detection in Time-Constrained Vector Poisson Channel

 

Author :  Muhammad Fahad and Daniel R. Fuhrmann

Affiliation :  Michigan Technological University

Country :  USA

Category :  Digital Signal & Image Processing

Volume, Issue, Month, Year :  12, 6, December, 2021

Abstract :

It is an experimental design problem in which there are two Poisson sources with two possible and known rates, and one counter. Through a switch, the counter can observe the sources individually or the counts can be combined so that the counter observes the sum of the two. The sensor scheduling problem is to determine an optimal proportion of the available time to be allocated toward individual and joint sensing, under a total time constraint. Two different metrics are used for optimization: mutual information between the sources and the observed counts, and probability of detection for the associated source detection problem. Our results, which are primarily computational, indicate similar but not identical results under the two cost functions.

Keyword :  sensor scheduling, vector Poisson channels.

For More Details:https://allconferencecfpalerts.com/cfp/view-paper.php?eno=5266