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With the improvement and development in the automotive, the safety related aspects are also becoming more important. Hence there is a stringent demand for the Functional Safety and reliability. In these years, most of the vehicles are made with electrical and electronic components and systems which include lots of Electronic Controller Units (ECUs), electronic sensors, bus systems with coding. Due to the complexity in application of these electrical, electronics and programmable electronics, it is necessary to analyze the potential risk of malfunction for automotive systems. Thus, ISO 26262 has been introduced for automotive electrical/electronic (E/E) systems which ensure the complete safety installation of all ECUs, E/E systems its technical as well as management issues. In this paper, functional safety in accordance with ISO 26262 Part 3 of an electric traction inverter is done, the Functional safety report is generated in MEDINI TOOL and the short circuit fault of traction inverter is considered for Functional safety using MATLAB/SIMULINK.

Motivation: Rapidly advancing genome technology has allowed access to a large number of diverse genomes and annotation data. We have defined a systems model that integrates assembly data, comparative genomics, gene predictions, mRNA and EST alignments and physiological tissue expression. Using these as predictive parameters, we engineered a machine learning approach to decipher putative active regions in the genome., Results: Analysis of genomic sequences showed nucleosome-free region (NFR) modules containing a higher percentage of conserved regions, RNA-encoding sequences, CpG islands, splice sites and GC-rich areas. In contrast, random in silico fragments revealed higher percentages of DNA repeats and a lower conservation. The larger conserved sequences from the Vista enhancer browser (VEB) showed a greater percentage of short DNA sequence matches and RNA coding regions in multiple species. Our model can predict small regulatory regions in the genome with >95% prediction accuracy using NFR modules and >85% prediction accuracy with VEB elements. Ultimately, this systems model can be applied to any organism to identify candidate transcriptional modules on a genome scale.

Modeling gene regulatory networks requires recognition of active transcriptional sites in the genome. For this reason, we present a novel approach for inferring active transcriptional regulatory modules in a genome using an established systems model of bit encoded DNA sequences. Our analysis showed variations in several properties between random and functional sequences. Cross correlation within random and functional groups uncovered a wave pattern associated with functional sequences. Using the exclusive-OR (XOR) logic gate, we formulated a scheme to threshold signals that may correlate to putative active transcriptional modules from a population of random genomic fragments. It is our intent to use this as a basis for identifying novel regulatory sites in the genome.

The identification of transcriptional regulatory modules within mammalian genomes is a prerequisite to understanding the mechanisms controlling regulated gene expression. While high-throughput microarray- and sequencing-based approaches have been used to map the genomic locations of sites of nuclease hypersensitivity or target DNA sequences bound by specific protein factors, the identification of regulatory elements using functional assays, which would provide important complementary data, has been relatively rare. Here we present a method that permits the functional identification of active transcriptional regulatory modules using a simple procedure for the isolation and analysis of DNA derived from nucleosome-free regions (NFRs), the 2% of the cellular genome that contains these elements. The more than 100 new active regulatory DNAs identified in this manner from F9 cells correspond to both promoter-proximal and distal elements, and display several features predicted for endogenous transcriptional regulators, including localization within DNase-accessible chromatin and CpG islands, and proximity to expressed genes. Furthermore, comparison with published ChIP-seq data of ES-cell chromatin shows that the functional elements we identified correspond with genomic regions enriched for H3K4me3, a histone modification associated with active transcriptional regulatory elements, and that the correspondence of H3K4me3 with our promoter-distal elements is largely ES-cell specific. The majority of the distal elements exhibit enhancer activity. Importantly, these functional DNA fragments are an average 149 bp in length, greatly facilitating future applications to identify transcription factor binding sites mediating their activity. Thus, this approach provides a tool for the high-resolution identification of the functional components of active promoters and enhancers.