Effect of Temperature &Supply Voltage Variation on Stability of 9T SRAM Cell at 45 nm Technology for Various Process Corners

Due to the continuous rising demand of handheld devices like iPods, mobile, tablets;specific applications like biomedical applications like pacemakers, hearing aid machines and space applications which require stable digital systems with low power consumptions are required. As a main part in digital system the SRAM (Static Random Access Memory) should have low power consumption and stability. As we are continuously moving towards scaling for the last two decades the effect of this is process variations which have severe effect on stability, performance. Reducing the supply voltage to sub-threshold region, which helps in reducing the power consumption to an extent but side by side it raises the issue of the stability of the memory. Static Noise Margin of SRAM cell enforces great challenges to the sub threshold SRAM design. In this paper we have analyzed the cell stability of 9T SRAM Cell at various processes. The cell stability is checked at deep submicron (DSM) technology. In this paper we have analyzed the effect of temperature and supply voltage (Vdd) on the stability parameters of SRAM which is Static Noise Margin (SNM), Write Margin (WM) and Read Current. The effect has been observed at various process corners at 45 nm technology. The temperature has a significant effect on stability along with the Vdd. The Cell has been working efficiently at all process corners and has 50% more SNM from conventional 6T SRAM and 30% more WM from conventional 6T SRAM cell.

Feedback Reliability Ratio of an Intrusion Detection System

The behavior and nature of attacks and threats to computer network systems have been evolving rapidly with the advances in computer security technology. At the same time however, computer criminals and other malicious elements find ways and methods to thwart such protective measures and find techniques of penetrating such secure systems. Therefore adaptability, or the ability to learn and react to a consistently changing threat environment, is a key requirement for modern intrusion detection systems. In this paper we try to develop a novel metric to assess the performance of such intrusion detection systems under the influence of attacks. We propose a new metric called feedback reliability ratio for an intrusion detection system. We further try to modify and use the already available statistical Canberra distance metric and apply it to intrusion detection to quantify the dissimilarity between malicious elements and normal nodes in a network.

Reversible Digital Image Watermarking Scheme Using Bit Replacement and Majority Algorithm Technique

The current paper presents a new digital watermarking method through bit replacement technology, which stores mul-tiple copies of the same data that is to be hidden in a scrambled form in the cover image. In this paper an indigenous approach is described for recovering the data from the damaged copies of the data under attack by applying a majority algorithm to find the closest twin of the embedded information. A new type of non-oblivious detection method is also proposed. The improvement in performance is supported through experimental results which show much enhancement in the visual and statistical invisibility of hidden data.

Soil Quality Mapping Studies Using Nematodes as Bioindicators

Soil quality is one of the most important factors in sustaining the global biosphere and developing sustainable agricultural practices. Land use and management practices greatly impact the direction and degree of soil quality changes in time and space. Understanding the effects of land use and management practices on soil quality and its indicators has been identified as one of the most important goals for modern soil science. Soil quality mapping study represents a method for assessing and mapping soil quality changes in time and space in small units. For the present study, changes in the physical, chemical parameters and nematode density of the soils in the rural and urban areas of Thiruvananthapuram district, Kerala, were determined. The soil samples were collected from seven different categories of contaminated soils namely coastal area, sewage disposal area, industrial area, road-side area, agricultural area, market area and gasoline station area, and also from two control stations in rural and urban areas. The soil physico-chemical parameters and nematode density were determined. Geostatistics combined with GIS was applied to analyze the spatial variability of soil physico-chemical characteristics and nematode density. This soil quality mapping study provides a basis for identifying tension zones and serves as a triggering mechanism for implementation of soil contamination mitigating strategies.

An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference

The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.