RTL2RTL Formal Equivalence: Boosting the Design Confidence

Increasing design complexity driven by feature and performance requirements and the Time to Market (TTM) constraints force a faster design and validation closure. This in turn enforces novel ways of identifying and debugging behavioral inconsistencies early in the design cycle. Addition of incremental features and timing fixes may alter the legacy design behavior and would inadvertently result in undesirable bugs. The most common method of verifying the correctness of the changed design is to run a dynamic regression test suite before and after the intended changes and compare the results, a method which is not exhaustive. Modern Formal Verification (FV) techniques involving new methods of proving Sequential Hardware Equivalence enabled a new set of solutions for the given problem, with complete coverage guarantee. Formal Equivalence can be applied for proving functional integrity after design changes resulting from a wide variety of reasons, ranging from simple pipeline optimizations to complex logic redistributions. We present here our experience of successfully applying the RTL to RTL (RTL2RTL) Formal Verification across a wide spectrum of problems on a Graphics design. The RTL2RTL FV enabled checking the design sanity in a very short time, thus enabling faster and safer design churn. The techniques presented in this paper are applicable to any complex hardware design.Comment: In Proceedings FSFMA 2014, arXiv:1407.195

Structural, optical and nanomechanical properties of (1 1 1) oriented nanocrystalline ZnTe thin films

Structural, optical and nanomechanical properties of nanocrystalline Zinc Telluride (ZnTe) films of thickness upto 10 microns deposited at room temperature on borosilicate glass substrates are reported. X-ray diffraction patterns reveal that the films were preferentially oriented along the (1 1 1) direction. The maximum refractive index of the films was 2.74 at a wavelength of 2000 nm. The optical band gap showed strong thickness dependence. The average film hardness and Young’s modulus obtained from loaddisplacement curves and analyzed by Oliver-Pharr method were 4 and 70 GPa respectively. Hardness of (1 1 1) oriented ZnTe thin films exhibited almost 5 times higher value than bulk. The studies show clearly that the hardness increases with decreasing indentation size, for indents between 30 and 300 nm in depth indicating the existence of indentation size effect. The coefficient of friction for these films as obtained from the nanoscratch test was ∼0.4.Financial support in the form of fellowships to MSRNK and SK from the ACRHEM project of DRDO is acknowledged

GONG p-mode frequency changes with solar activity

We present a correlation analysis of GONG p-mode frequencies with nine solar activity indices for the period 1995 August to 1997 August. This study includes spherical harmonic degree in the range 2 to 150 and the frequency range of 1500-3500 \mu Hz. Using three statistical tests, the measured mean frequency shifts show strong to good correlation with activity indices. A decrease of 0.06 \mu Hz in frequency, during the descending phase of solar cycle 22 and an increase of 0.04 \mu Hz in the ascending phase of solar cycle 23 is observed. These results provide the first evidence for change in p-mode frequencies around the declining phase of solar cycle 22 and beginning of new cycle 23. This analysis further confirms that the temporal behaviour of the solar frequency shifts closely follow the phase of the solar activity cycle.Comment: 14 pages, 3 figures. To appear in Ap.

Solar cycle induced variations in GONG p-mode frequencies and splittings

We have analysed the recently available GONG p-mode frequencies and splitting coefficients for a period of three and half years, including the rapidly rising phase of solar cycle 23. The analysis of mean frequency shift with different activity indices shows that the shift is equally correlated with both magnetic and radiative indices. During the onset of the new cycle 23, we notice that the change in $b_4$ splitting coefficient is more prominent than the change in $b_2$. We have estimated the solar rotation rate with varying depth and latitude. In the equatorial region, the rotation first increases with depth and then decreases, while an opposite behaviour is seen in the polar region. We also find a small but significant temporal variation in the rotation rate at high latitudes.Comment: Uses aastex, To appear in Astrophysical Journal, October 10, 2000 issu

A Survey on Graph Database Management Techniques for Huge Unstructured Data

Data analysis, data management, and big data play a major role in both social and business perspective, in the last decade. Nowadays, the graph database is the hottest and trending research topic. A graph database is preferred to deal with the dynamic and complex relationships in connected data and offer better results. Every data element is represented as a node. For example, in social media site, a person is represented as a node, and its properties name, age, likes, and dislikes, etc and the nodes are connected with the relationships via edges. Use of graph database is expected to be beneficial in business, and social networking sites that generate huge unstructured data as that Big Data requires proper and efficient computational techniques to handle with. This paper reviews the existing graph data computational techniques and the research work, to offer the future research line up in graph database management

First passage time of N excluded volume particles on a line

Motivated by recent single molecule studies of proteins sliding on a DNA molecule, we explore the targeting dynamics of N particles ("proteins") sliding diffusively along a line ("DNA") in search of their target site (specific target sequence). At lower particle densities, one observes an expected reduction of the mean first passage time proportional to 1/N**2, with corrections at higher concentrations. We explicitly take adsorption and desorption effects, to and from the DNA, into account. For this general case, we also consider finite size effects, when the continuum approximation based on the number density of particles, breaks down. Moreover, we address the first passage time problem of a tagged particle diffusing among other particles.Comment: 9 pages, REVTeX, 6 eps figure

Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms

India is often described as a country of villages, where a majority of the population depends on agriculture for their livelihood. The landscape of Indian agriculture is approximately 159.7 million hectares. Agriculture plays a pivotal role in India's Gross Domestic Product (GDP), accounting for about 18% of the nation's economic output. Diseases and pests can have detrimental effects on crops, leading to reduced yields. These challenges can include the spread of plant diseases, infestations by insects or other pests, and the overall degradation of crop health. Early detection of diseases in crops is crucial for several reasons. Detecting diseases at an early stage allows for prompt intervention, such as applying appropriate pesticides or taking preventive measures. The main aim of this study is to develop a highly effective method for plant leaf disease detection using computer vision techniques. Here, leaf disease detection comprises histogram equalization, denoising, image color threshold masking, feature descriptors such as Haralick textures, Hu moments, and color histograms to extract the salient features of leaf images. These features are then used to classify the images by training Logistic Regression, Linear Discriminant Analysis, K-nearest neighbor, decision tree, Random Forest, and Support Vector Machine algorithms using K-fold validation. K-fold validation is used to separate the validation samples from the training samples, and the K indicates the number of times this is repeated for the generalization. The training and validation processes are performed in two approaches. The first approach uses default hyperparameters with segmented and non-segmented images. In the second approach, all hyperparameters of the models are optimized to train segmented datasets. The classification accuracy improved by 2.19% by utilizing segmentation and hyperparameter tuning further improved by 0.48%. The highest average classification accuracy of 97.92% is achieved using the Random Forest classifier to classify 40 classes of 10 different plant species. Accurate detection of plant disease leads to the sustained growth of plants throughout the growing span of the plants