Implementation of Extracted Timing Methodology on Process Monitor for Silicon Characterization

Process variations are playing a key role in defining the behaviour of an IP. These process variations can accurately measure using process monitor. In order to verify process variations, the process monitor should meet all timing requirements. Static Timing Analysis (STA) uses best case/ and worst analysis overly pessimistic, and could be optimistic also in some cases. Static Timing Analysis (STA) is a method for estimating yield of a circuit in terms of timing activities. Model extraction is a technique that accurately captures the characteristics of interface logic of a design in the form of a timing library model and provides a capacity improvement in timing verification by more than two orders of magnitude. Extracted timing model is an efficient timing library model to get accurate timing arcs of the circuit. This paper describes Methodology for creating timing models and also the flow to develop IP (process monitor) ETMs (.lib) using Synopsys Primetime tool, which can be used in any SOC and ETMs (Extracted timing models)with necessary time-budgeting instead of IP Netlists. Generated ETM with and without annotation delays and compared the library file. And the process monitor2019;s ring oscillator is designed through Verilog code using cadence tool

Study of prevalence of thyroid peroxidase antibodies in preterm deliveries and recurrent pregnancy loss

Background: Evaluation of thyroid disorder in pregnancy is essential for maternal health, obstetrical outcome and neurodevelopment of the child. Euthyroid pregnant women having positive thyroid peroxidase antibodies have an elevated risk of miscarriage, premature birth, gestational hypertension, and intrauterine fetal demise. Thyroid autoimmunity (TAI) and subclinical hypothyroidism (SCH) have been connected with adverse outcomes in pregnancy and foetus. The present aim of the study was to estimate the prevalence of TPO antibodies in recurrent pregnancy losses, first trimester abortions and preterm deliveries.Methods: This was a hospital-based cross-sectional prevalence study conducted for 18 months. The study consists of 100 women who had preterm deliveries and miscarriages attending to department of obstetrics and gynaecology, Narayana medical college and hospital, Nellore, Andhra Pradesh.Results: In our study out of 100 cases, 11 had high thyroid peroxidase antibody (TPOAb) levels, of which 9 had preterm deliveries and 2 had miscarriages. Out of 100 cases 5 cases had elevated T3 levels, 6 cases had elevated T4 levels and 24 cases had elevated TSH levels.Conclusions: There was a statistically significant association of thyroid peroxidase antibodies (TPOAb) with T3, T4, and, TSH (P<0.05) and it leads to developing hypothyroidism during pregnancy. The presence of TPOAb in pregnant women significantly increases the risk of preterm delivery. The screening of TSH and thyroid peroxidase antibodies is essential during pregnancy to avoid complications. So, screening T3, T4, TSH and thyroid peroxidase antibodies are essential during pregnancy to avoid complications.

IMPLEMENTATION OF LOW POWER AND DELAY SCALABLE CHANNEL PARALLEL NAND FLASH MEMORY CONTROLLER ARCHITECTURE USING ALU

RISC refers to Reduced Instruction Set Computer. Which means the computer that consists of RISC processor contains reduced (simple) instructions for performing necessary and required operations. Any chip if considered as processor, it should have the capability of performing certain operations like arithmetic, logical, control and data transfer. For performing these operations, a processor should contain some major blocks as Control unit (CU), Flexible computational unit (FCU), Program counter (PC), Accumulator, Instruction register, Memory and additional logic. RISC actually enhances the performance of processor by considering the factors like simple architecture construction and instruction set, easy instruction set for decoding and simplified control architecture. This paper proposes a simple 32 bit RISC processor by using Peres reversible logic gates, which is expected to reduce the size then the conventional architecture that is based on carry save logic adder approach. The synthesis and simulation is carried out using XILINX ISE 12.3i and HDL is developed using VERILOG language

ESTIMATION OF PHYTOCHEMICALS SCREENING AND SUN PROTECTION FACTOR (SPF) NUMBER IN COMMONLY USED ETHANOLIC FRUIT EXTRACTS

The aim of the present study is phytochemical screening and the ultraviolet absorption properties of ethanolic herbal extracts of some commonly used vegetable sources by determining the sun protection factor (spf) number. The invitro SPF number is determined according to the spectrophotomertic method described by Mansur et.al.,. Ethanolic herbal extracts were prepared and after dilution with alcoholic solutions the absorbance were recorded between 290-320 using uv-vis spectrophotometry. It was observed that all of the ethanolic herbal extract showed some UV protection capability. keywords :sun protection factor, spectrophotomertic , ethanolic extrac

A membrane network of receptors and enzymes for adenine nucleotides and nucleosides

AbstractMost cells express more than one receptor plus degrading enzymes for adenine nucleotides or nucleosides, and cellular responses to purines are rarely compatible with the actions of single receptors. Therefore, these receptors are viewed as components of a combinatorial receptor web rather than self-dependent entities, but it remained unclear to what extent they can associate with each other to form signalling units. P2Y1, P2Y2, P2Y12, P2Y13, P2X2, A1, A2A receptors and NTPDase1 and -2 were expressed as fluorescent fusion proteins which were targeted to membranes and signalled like the unlabelled counterparts. When tested by FRET microscopy, all the G protein-coupled receptors proved able to form heterooligomers with each other, and P2Y1, P2Y12, P2Y13, A1, A2A, and P2X2 receptors also formed homooligomers. P2Y receptors did not associate with P2X, but G protein-coupled receptors formed heterooligomers with NTPDase1, but not NTPDase2. The specificity of prototypic interactions (P2Y1/P2Y1, A2A/P2Y1, A2A/P2Y12) was corroborated by FRET competition or co-immunoprecipitation. These results demonstrate that G protein-coupled purine receptors associate with each other and with NTPDase1 in a highly promiscuous manner. Thus, purinergic signalling is not only determined by the expression of receptors and enzymes but also by their direct interaction within a previously unrecognized multifarious membrane network

A machine learning system for automated whole-brain seizure detection

Epilepsy is a chronic neurological condition that affects approximately 70 million people worldwide. Characterised by sudden bursts of excess electricity in the brain, manifesting as seizures, epilepsy is still not well understood when compared with other neurological disorders. Seizures often happen unexpectedly and attempting to predict them has been a research topic for the last 30 years. Electroencephalograms have been integral to these studies, as the recordings that they produce can capture the brain’s electrical signals. The diagnosis of epilepsy is usually made by a neurologist, but can be difficult to make in the early stages. Supporting para-clinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and instigate treatment earlier. However, electroencephalogram capture and interpretation is time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity generalised across different regions of the brain and across multiple subjects may be a solution. This paper explores this idea further and presents a supervised machine learning approach that classifies seizure and non-seizure records using an open dataset containing 342 records (171 seizures and 171 non-seizures). Our approach posits a new method for generalising seizure detection across different subjects without prior knowledge about the focal point of seizures. Our results show an improvement on existing studies with 88% for sensitivity, 88% for specificity and 93% for the area under the curve, with a 12% global error, using the k-NN classifier

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisakis model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: Linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers error rates and then to evaluate the information expressed by the classifiers confusion matrices. © Springer Science+Business Media, LLC 2011