Flexible Deep Learning in Edge Computing for Internet of Things

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Traditional edge computing models have rigid characteristics. Flexible edge computing architecture solves rigidity in IoT edge computing. Proposed model combines deep learning into edge computing and flexible edge computing architecture using multiple agents. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. FEC architecture is a flexible and advanced IoT system model characterized by environment adaptation ability and user orientation ability. In the performance evaluation, we test the performance of executing deep learning tasks in FEC architecture for edge computing environment. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT

Impact of Rural-urban Labour Migration on Education of Children: A Case Study of Left behind and Accompanied Migrant Children in India

In developing countries, seasonal labour migration from rural to urban or from backward to developed region is a household livelihood strategy to cope with poverty. In this process, the children of those migrants are the worst affected whether they accompany their parents or are left behind in the villages. The present paper explores the impact of temporary labour migration of parent(s) on school attendance of the children between 6–14 years and their dropping out from the school through an analysis of the cases from both the ends of migration stream in India. Data was collected from thirteen construction sites of Varanasi Uttar Pradesh and nine villages of Bihar by applying both qualitative and quantitative techniques. It is evident from the study that the migrants through remittances improve school accessibility for the left behind children and bridge gender gap in primary school education. However, among the accompanying migrant children of construction workers, many remain out of school and many are forced to drop out and some of them become vulnerable to work as child labour due to seasonal mobility of their parents. Thus, mainstreaming these children in development process is a big challenge in attaining the goal of universal primary education and inclusive growth in the country like India

Flexible Deep Learning in Edge Computing for Internet of Things

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Traditional edge computing models have rigid characteristics. Flexible edge computing architecture solves rigidity in IoT edge computing. Proposed model combines deep learning into edge computing and flexible edge computing architecture using multiple agents. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. FEC architecture is a flexible and advanced IoT system model characterized by environment adaptation ability and user orientation ability. In the performance evaluation, we test the performance of executing deep learning tasks in FEC architecture for edge computing environment. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT

Efficient Circuit Configuration to Reduce Comparator Requirement of 8-Bit Flash Analog to Digital Convertor

Need constantly exists for converters with higher resolution, faster conversion speed and lower power dissipation. High-speed analog to digital converters (ADC’s) have been based on flash architecture, because all comparators sample the analog input voltage simultaneously, this ADC is thus inherently fast. Unfortunately, flash ADC requires 2N - 1 comparators to convert N bit digital code from an analog sample. This makes flash ADC’s unsuitable for high-resolution applications. This paper demonstrates a simple technique to reduce comparator requirement of 8-bit flash ADC that requires as few as 65 comparators for 8-bit conversion. In this approach, the analog input range is partitioned into 64 quantization cells, separated by 63 boundary points. A 6-bit binary code 000000 to 111111 is assigned to each cell. A 8-bit flash converter requires 256 comparators, while proposed technique reduces number of comparator requirements to 65 for 8-bit conversion. DOI: 10.17762/ijritcc2321-8169.150711

Time to death in the presence of E. coli: a mass-scale method for assaying pathogen resistance in Drosophila

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Hierarchical Approach for Total Variation Digital Image Inpainting

Abstract The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consuming process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting throug

Investigations of Gold-Graphene Nanocomposite for ORR in Aqueous Electrolytes

Oxygen reduction reaction (ORR) is an essential reaction step in fuel cell and metal-air batteries. The kinetics of ORR is very sluggish; it requires very high potential to occur. Many interesting articles have been published to enhance the kinetics. In this direction, we are working on metal nanoparticles modified graphene sheet. Gold nanoparticles are attached on two dimensional graphene sheets by in-situ reduction of metal ion in an aqueous reaction mixture. The synthesized nanocomposite is characterized by powder XRD, XPS and Raman spectroscopy. Microscopy image shows gold nanoparticles are attached to graphene sheets. ORR is studied in 0.1 M KOH and 0.1 M K2SO4 electrolytes. O2 reduction in aqueous electrolytes produces water molecules on gold-graphene nanocomposite

An Experimental Study on Utilization of Iron Ore Tailings (IOT) and Waste Glass Powder in Concrete

Cement manufacturing industry is one of the carbon dioxide emitting sources besides deforestation burning of fossil fuels. The global warming is caused by the emission of green house gases, such as CO2, to the atmosphere. Among the greenhouse gases, CO2 contributes about 65% of global warming. The global cement industry contributes about 7% of green house gas emission to the earth’s atmosphere. In order to address environmental effects associated with cement manufacturing, there is a need to develop alternative binders to make concrete. Consequently extensive research is on going into the use of cement replacements, using many waste materials industrial by products. Efforts have been made in the concrete industry to use waste glass as partial replacement of cement and also in recent years almost every mineral producing country is facing the problem of better utilization of mine waste because of its accumulation lack of suitable storage space. In this study, finely powdered waste glass from industries and Iron Ore Tailings (IOT) produced from mining areas are used as a partial replacement  of cement and fine aggregates in concrete respectively.  This work examines the possibility of using Glass powder and iron ore tailing as a partial replacement of cement and fine aggregate in concrete. In the present study  Glass powder and Iron Ore Tailing  ( IOT )  are partially replaced by 10%, 20%, 30% and  40%  tested for its compressive, flexural strength for 7, 28 and 56 days of curing and were compared with those of conventional concrete. Keywords: Glass Powder – GP, Iron Ore Tailings – IOT, Conventional Concrete - C

Metabolite and enzyme profiles of glycogen metabolism in Methanococcoides methylutens

When a buffered anaerobic cell suspension of Methanococcoides methylutens was maintained under methanol-limited conditions, intracellular glycogen and hexose phosphates were consumed rapidly and a very small amount of methane formed at 4 h of a starvation period. When methanol was supplemented after a total of 20 h of starvation, a reverse pattern was observed: the glycogen level and the hexose phosphate pool increased, and formation of methane took place after a lag period of 90 min. A considerable amount of methane was formed in 120 min after its detection with a rate of 0.18 µmol mg-1 protein min-1. When methane formation decreased after 270 min of incubation and finally came to a halt, probably due to complete assimilation of supplemented methanol, the levels of glycogen and hexose monophosphates decreased once again. However fructose 1,6-diphosphate levels showed a continuous increase even after exhaustion of methane formation. In contrast to the hexose phosphate pool, levels of other metabolites showed a small increase after addition of methanol. The enzyme profile of glycogen metabolism showed relatively high levels of triose phosphate isomerase. Glyceraldehyde 3-phosphate dehydrogenase reacted with NADPH with a three-fold higher activity as compared to that with NADH