Efficient Built In Self Repair Strategy for Embedded SRAM with selecteble redundancy

Built-in self -test (BIST) refers to those testing techniques where additional hardware is added to a design so that testing is accomplished without the aid of external hardware. Usually, a pseudo-random generator is used to apply test vectors to the circuit under test and a data compactor is used to produce a signature. To increase the reliability and yield of embedded memories, many redundancy mechanisms have been proposed. All the redundancy mechanisms bring penalty of area and complexity to embedded memories design. Considered that compiler is used to configure SRAM for different needs, the BISR had better bring no change to other modules in SRAM. To solve the problem, a new redundancy scheme is proposed in this paper. Some normal words in embedded memories can be selected as redundancy instead of adding spare words, spare rows, spare columns or spare blocks. Built-In Self-Repair (BISR) with Redundancy is an effective yield-enhancement strategy for embedded memories. This paper proposes an efficient BISR strategy which consists of a Built-In Self-Test (BIST) module, a Built-In Address-Analysis (BIAA) module and a Multiplexer (MUX) module. The BISR is designed flexible that it can provide four operation modes to SRAM users. Each fault address can be saved only once is the feature of the proposed BISR strategy. In BIAA module, fault addresses and redundant ones form a one- to- one mapping to achieve a high repair speed. Besides, instead of adding spare words, rows, columns or blocks in the SRAMs, users can select normal words as redundancy

Redundancy Elimination with Coverage Preserving algorithm in Wireless Sensor Network

In Wireless Sensor Network, the sensor nodes are deployed using random or deterministic deployment methods. Many applications prefer random deployment for deploying the sensor nodes. Random deployment is the main cause of redundancy. Detection and elimination of redundant sensor nodes while preserving coverage is very important issue after the sensor nodes are deployed randomly in the region of interest. The redundancy elimination with coverage preserving algorithm is proposed in this paper and the results are presented. The proposed algorithm determines redundant sensor nodes and also the sensor nodes which provide the least coverage of region of interest. If two sensor nodes cover same area or if the Euclidian distance between two nodes is less than 25% of sensing range of a sensor node, the sensor which is not located at optimal position will be deactivated, so that, it reduces the number of optimal nodes required to cover complete region of interest. This in turn increases the lifetime of the network. The simulation results illustrate that the proposed algorithm preserves 100% coverage or region of interest by removing redundant nodes and also the nodes which provide the least coverage of region of interest. It also reduces the number of optimal nodes required to provide 100% coverage of region of interest

Molecular mapping of flowering time genes in chickpea (Cicer arietinum L.)

Flowering time is an important component of adaptation and productivity of chickpea (Cicer arietinum L.) in semi-arid environments characterized by terminal drought stress. The present study was aimed at identifying molecular markers linked to flowering time genes in four F2 populations of chickpea. Genetic studies revealed that flowering time was determined by a single major gene in the crosses ICCV 96029 × CDC Frontier, BGD 132 × CDC Frontier and ICC 16641 × CDC Frontier. Whereas in the cross ICC 5810 × CDC Frontier, it was under digenic control with complementary gene action. The intra-specific genetic map developed consisted of 77 markers, spanning 262.25 cM in the cross ICCV 96029 × CDC Frontier and 76 markers with 335.74 cM map distance in the cross ICC 5810 × CDC Frontier. The genetic map of BGD 132 × CDC Frontier consisted of 68 markers covering 311.10 cM map distance and that of ICC 16641 × CDC Frontier had 67 SSRs with 385.13 cM genome coverage. Consensus map developed from four populations consisted 111 SSRs and covered the map distance of 364.44 cM. QTL analysis detected altogether seven major (Qefl1-2, Qefl2-1, Qefl2-2, Qefl2-3, Qefl2-4, Qefl3-3, Qefl4-1) and three minor QTLs (Qefl1-1, Qefl3-1, Qefl3-2) for flowering time that are distributed on linkage groups CaLG01, CaLG03, CaLG04, CaLG06 and CaLG08 of chickpea genetic map. Analysis of QTL regions provided important candidate genes like SUVR5, SET6, HOS1, TEM1, EFL6, JMJ11 and homeotic genes like AP2, ANT, SPT, AHL27 and PTL, that are known to be involved in various functions like regulation of flowering time and flower development. Flowering time was positively correlated with key phenological traits and showed no correlation with grain yield in all the crosses. Flowering time showed positive correlation with 100 seed weight in all the crosses except in the cross ICC 16641 × CDC Frontier, where the correlation was non-significant. Harvest index was negatively associated with flowering time. The identified genomic regions with linked markers can be deployed for introgressing early flowering trait into elite chickpea cultivars through marker-assisted selection (MAS) to develop early maturing cultivars better adapted to terminal stress conditions

Induction of Androgenesis in Pearl Millet

Breeding efforts in Pearl millet (Pennisetum glaucum (L.) R.Br), one of the most widely cultivated drought- and high-temperature tolerant C4 cereals, are aimed at maximum exploitation of hybrid vigor for both grain and forage yields. Until now, very limited work has been carried out on in vitro production of haploids in pearl millet; while it is being employed as the pollinator which will be further eliminated, resulting in haploids of the recipient species, e.g. wheat, oat. Anther culture experiments were carried out with seven genotypes 841-P3, 843-22B, ICMB 93333, ICMB 89111, XL-51, 4201 and 86-M34 tested on 12 different culture media. Androgenic embryos were induced in the frequency of 13.7, 9.51 and 7.58 % from 841-P3, ICMB 93333 and XL-51 cultivars. Inclusion of 4% maltose as additional carbon source resulted in higher number of multicellular microspores among the responsive genotypes. These experiments form a promising basis to further develop double haploid protocol for pearl mille breeding in the arid and semi-arid regions

Preparation and Characterization of NiO Thin Films by DC Reactive Magnetron Sputtering

Nickel oxide (NiO) thin films were successfully deposited on Corning 7059 glass substrates at different oxygen partial pressures in the range of 1 × 10 – 4 to 9 × 10 – 4 mbar using dc reactive magnetron sputtering technique. Structural properties of NiO films showed polycrystalline nature with cubic structure along (220) orientation. The optical transmittance and band gap values of the films increased with increasing the oxygen partial pressure from 1 × 10 – 4 to 5 × 10 – 4 mbar and decreased on further increasing the oxygen partial pressure. Using Scanning Electron Microscopy (SEM), fine grains were observed at oxygen partial pressure of 5 × 10 – 4 mbar. The film resistivity decreases from 90.48 to 13.24 Ω cm with increase in oxygen partial pressure to 5 × 10 – 4 mbar and then increased on further increasing the oxygen partial pressure. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3025

Effect of SEN immersion depth on mold flow profile and slag entrapment during continuous casting of steel

Mold flux entrapment during continuous casting of steel contributes to both surface and sub-surface defects in the final product. Continuous casting operating parameters such as casting speed, SEN immersion depth, SEN port geometry, argon flow, and mold EMS significantly affect the mold flow conditions and flow profile. During continuous casting operation, SEN immersion depth is continuously varied to avoid localized erosion of SEN, and it impacts the flow dynamics in the mold. In the present work, water modeling studies were carried out for a wide range of mold widths (1200-1800 mm) and casting speeds (0.8-1.4 m/min) on a 0.5 scaled down water model to optimize casting speed for different combinations of SEN immersion depth and mold width. Results from water modeling were further validated using nail board studies in the actual plant. A safe operating matrix was identified from these experiments to avoid mold slag entrapment during continuous casting

Deep Convolutional Neural Network Architecture for Plant Seedling Classification

Weed control is essential in agriculture since weeds reduce yields, increase production cost, impede harvesting, and degrade product quality. As a result, it is indeed critical to recognize weeds early in their vegetation cycle to evade negative impacts to crop growth. Earlier traditional methods used machine learning to determine crops along with weed species, but they had issues with weed detection efficiency at early growth stages. The current work proposes the implementation of a deep learning method that provides accurate results for precise weed recognition. Two different deep convolution neural networks have been used for our classification framework, namely Efficient Net B2 and Efficient Net B4. The plant seedlings dataset is utilized to investigate the proposed work. The evaluation metrics average accuracy, precision, recall, and F1-score were used. The findings demonstrate that the proposed approach is capable of differentiating between 12 species of a plant seedling dataset which contains 3 crops and 9 weeds. The average classification accuracy and F1 score are 99.00% for our Efficient Net B4 model and 97.00% for the Efficient Net B2. In addition, the proposed Efficient Net-B4 model performance is compared to the one of existing models on the plant seedlings dataset and the results showed that the proposed model Efficient Net B4 has superior performance. We intend to detect diseases in the identified plant species in our future research