Artificial neural networks as building blocks of mixed signal FPGA

Ever since the deployment of FPAAs, efforts are on the way to minimize the silicon area to realize an arbitrary system. A relatively new concept which has been tested and tried in this direction is the use of Artificial neural networks (ANNs) as Configurable Analog Blocks (CABs). Conventional ANNs however suffer with lengthy training period. In this paper ANNs with differential feedback technique are explored. It has been found out that they perform better than the conventional ANNs

Information geometry of differentially fed artificial neural networks

A new class of neural networks with differential feedback are presented. The different orders of differential feedback form a manifold of hyperplanes, one among them called eigen-plane corresponding to /spl infin/ order feedback. In this paper information geometry is used to explore the interesting properties of this plane

Indoor Study on Airborne Fungi in Swine House of Bangalore, India

In rural areas of India and other tropical as well as temperate countries a large number of people are occupationally involved with different types of animal sheds. In these sheds, a wide range of fungal growth substrates like moldy livestock foods, moldy hay, bedding of animals and their excreta are present, which could provide a huge airborne fungal spore load making these places unhygienic for the animal workers. The nature and seasonal variations of fungi have been investigated in the environments within partially and completely enclosed swine house during one-year period by fortnightly sampling from January 2011 to December 2011, using an Andersen two stage viable air sampler. The air samples were collected from indoor swine houses in Hessaraghatta village, Bangalore. A total of 69.11 CFU/m3 airborne spore and 25 species representing 14 genera were recorded which included Acremonium, Alternaria sp, A. alternata, Aspergillus sp, A. flavus, A. fumigatus, A. niger, Botrytis sp, Cladosporium sp, C. cladosporioides, C. herbarum, C.lunata, Curvularia sp, Fusarium sp, F.moniliforme, F.oxysporum, Mucor sp, Nigrospora sp, Pencillium sp, P. nigricans, Phoma, Rhizopus sp, Rhizopus oryzae, Scopulariopsis sp, Trichoderma sp, and 1 unidentified genera. The aim of the present study was performed to evaluate the quality and magnitude of exposure to airborne fungi in indoor air and to compare the seasonal variation of fungal genera with regard to these environments

PLANNING SCHEDULING OF GRADE SEPARATOR

Planning, scheduling plays a vital role in improving the prospects of successful implementation of infrastructure projects. Project planning involves defining and coordinating activities and work tasks, preparing work schedules, assigning and allocating resources to competing activities and developing an acceptable budget. The scheduling is just one of the tools will be used to manage activities and duration.. The scope of this work is to Consistent view of project status and issues, is to ensure that the project is completed within the allocated and approved budget; Work Break Down structure will be prepared, duration and predecessors for each activity will be assigned, Critical Path will be determined, Resource analyzing and leveling will be done for entire,. Estimating is to assign resources to each activity in the activity list. Total duration and cost will come to know by using Microsoft Project. Based on prediction of future traffic at junction on chord road the grade separator is been projected. Accordingly the planning scheduling of grade separator at junction on chord road is been prepared. An effective planning, scheduling will help in emerging problems and taking time correctively action

A prospective comparative study in the clinical outcome of trochanteric and subtrochanteric fracture femur with proximal femoral nail versus dynamic hip screw

Background: Trochanteric fractures are the most common fractures encountered accounting for 50% of all hip fractures. Subtrochanteric femur fractures have high rate of complications associated with their management. 10%–34% of all hip fractures occur in the subtrochanteric region.The study was to compare the clinical outcome of trochanteric and subtrochanteric fracture femur with proximal femoral nail (PFN) versus dynamic hip screw (DHS).Methods: A prospective study of 50 patients with intertrochanteric and subtrochanteric fracture among which 30 were treated with Proximal Femoral Nail and 20 with Dynamic Hip Screw at SSIMS-SPARSH Davangere, Karnataka, India between June 2015 to November 2016. At final follow up results were assessed with Modified Harris Hip score.Results: Among the PFN Intertrochanteric fracture group, 9 patients showed excellent outcome, 6 patients showed good outcome and 2 patients showed fair outcome and 1 patient showed poor outcome. Among the PFN subtrochanteric fracture group, 7 patients showed excellent outcome, 3 patients showed good outcome and 1 patients showed fair outcome and 1 patient showed poor outcome. Among the DHS intertrochanteric fracture group, 3 patients showed excellent outcome, 3 patients showed good outcome and 2 patients showed fair outcome and 2 patient showed poor outcome. Among the DHS subtrochanteric fracture group, 1 patients showed excellent outcome, 2 patients showed good outcome and 3 patients showed fair outcome and 4 patient showed poor outcome. Conclusions: Fractures of the trochanteric region of the femur need a proper selection of implant based on fracture pattern. DHS has excellent results when used on stable fractures. For unstable fractures, PFN is the implant of choice. In case of subtrochanteric fractures PFN has better results in both stable and unstable fractures compared to DHS with less failure rates and restoring better hip biomechanics

Compact optimized deep learning model for edge: a review

Most real-time computer vision applications, such as pedestrian detection, augmented reality, and virtual reality, heavily rely on convolutional neural networks (CNN) for real-time decision support. In addition, edge intelligence is becoming necessary for low-latency real-time applications to process the data at the source device. Therefore, processing massive amounts of data impact memory footprint, prediction time, and energy consumption, essential performance metrics in machine learning based internet of things (IoT) edge clusters. However, deploying deeper, dense, and hefty weighted CNN models on resource-constraint embedded systems and limited edge computing resources, such as memory, and battery constraints, poses significant challenges in developing the compact optimized model. Reducing the energy consumption in edge IoT networks is possible by reducing the computation and data transmission between IoT devices and gateway devices. Hence there is a high demand for making energy-efficient deep learning models for deploying on edge devices. Furthermore, recent studies show that smaller compressed models achieve significant performance compared to larger deep-learning models. This review article focuses on state-of-the-art techniques of edge intelligence, and we propose a new research framework for designing a compact optimized deep learning (DL) model deployment on edge devices

Studies on Seasonal Variation of Indoor Airborne Fungal Spores in Rabbit House

The indoor airborne fungal spore survey has been conducted for one year to assess the seasonal variation of the fungal flora in a rabbit house situated at Hessaraghatta village, near Bangalore city. The investigation was carried out by using an Andersen two stage viable sampler, at monthly intervals over a period of 12 months from January 2011 to December 2011. A total of 1.16 x 104 CFU/m3 belonging to fifteen different genera, excluding some unidentified ones were recorded. The differences in distribution among these fungi for seasonal and meteorological factors were correlated and the mean significant difference was expressed statistically at 0.05% and 0.01% level of significanc

Qualitative Analysis of Indoor and Outdoor Airborne Fungi in Cowshed

Air pollution is one of the most serious problems to human health. Fungi are the causal agents for different diseases in animals, plants, and human beings. Otomycosis, chronic bronchitis, emphysema, asthma, allergy, and systemic mycosis are among the fungal diseases caused. The present study was conducted to analyze the monthly incidence of airborne fungi, seasonal variation, and influence of meteorological parameters in indoor and outdoor fungi of cowshed at Hesaraghatta village, Bangalore. An aeromycological survey of indoor and outdoor area of cowshed at Hesaraghatta village in Bangalore city was carried out using the Andersen two-stage sampler onto a petri dish containing malt extract agar from January 2011 to December 2011. Altogether, 29 species belonging to 13 genera from indoor and 26 species belonging to 12 genera were recorded from outdoor environment of the cowshed; the dominant fungal species identified were Cladosporium sp., Aspergillus sp., and Alternaria alternata. Seasonal occurrence of fungal spores in both indoor and outdoor of the cowshed revealed that maximum spores were recorded in summer season followed by winter and rainy season

Deep learning-based switchable network for in-loop filtering in high efficiency video coding

The video codecs are focusing on a smart transition in this era. A future area of research that has not yet been fully investigated is the effect of deep learning on video compression. The paper’s goal is to reduce the ringing and artifacts that loop filtering causes when high-efficiency video compression is used. Even though there is a lot of research being done to lessen this effect, there are still many improvements that can be made. In This paper we have focused on an intelligent solution for improvising in-loop filtering in high efficiency video coding (HEVC) using a deep convolutional neural network (CNN). The paper proposes the design and implementation of deep CNN-based loop filtering using a series of 15 CNN networks followed by a combine and squeeze network that improves feature extraction. The resultant output is free from double enhancement and the peak signal-to-noise ratio is improved by 0.5 dB compared to existing techniques. The experiments then demonstrate that improving the coding efficiency by pipelining this network to the current network and using it for higher quantization parameters (QP) is more effective than using it separately. Coding efficiency is improved by an average of 8.3% with the switching based deep CNN in-loop filtering