Gesture Recognition Based on Computer Vision on a Standalone System

Our project uses computer vision methods gesture recognition in which a camera interfaced to a system captures real time images and after further processing able to recognize the gesture shown to be interpreted. Our project mainly aims at hand gestures and after extracting information we try to produce it as an audio or in some visual form. We have used adaptive background subtraction with Haar classifiers to implement segmentation then we used convex hull and convex defects along with other feature extraction algorithms to interpret the gesture. First, this is implemented on a PC or laptop and then to produce a standalone system, we have to perform all this steps on a system which is dedicated to perform only the given specified task. For this we have chosen Beaglebone Black as a platform to implement our idea. The development comes with ARM Cortex A8 processor supported by NEON processor for video and image processing. It works on a clock frequency of maximum 1 GHz. It is 32 bit processor but it can be used in thumb mode i.e. it can work in 16 bit mode. This board supports Ubuntu, Android with some modification. Our first task is to interface a camera to the board so that it can capture images and store those as matrices followed by our steps to modify the installed Operating System to our purpose and implement all the above processes so that we can come up with a system which can perform gesture recognition

Comparison of CFBP, FFBP, and RBF Networks in the Field of Crack Detection

The issue of crack detection and its diagnosis has gained a wide spread of industrial interest. The crack/damage affects the industrial economic growth. So early crack detection is an important aspect in the point of view of any industrial growth. In this paper a design tool ANSYS is used to monitor various changes in vibrational characteristics of thin transverse cracks on a cantilever beam for detecting the crack position and depth and was compared using artificial intelligence techniques. The usage of neural networks is the key point of development in this paper. The three neural networks used are cascade forward back propagation (CFBP) network, feed forward back propagation (FFBP) network, and radial basis function (RBF) network. In the first phase of this paper theoretical analysis has been made and then the finite element analysis has been carried out using commercial software, ANSYS. In the second phase of this paper the neural networks are trained using the values obtained from a simulated model of the actual cantilever beam using ANSYS. At the last phase a comparative study has been made between the data obtained from neural network technique and finite element analysis

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Not AvailablePresent study is a maiden attempt to assess net ecosystem exchange (NEE) of carbon dioxide ( CO2) flux from jute crop (Corchorus olitorius L.) in the Indo-Gangetic plain by using open-path eddy covariance (EC) technique. Diurnal variations of NEE were strongly influenced by growth stages of jute crop. Daytime peak NEE varied from − 5 μmol m− 2 s− 1 (in germination stage) to − 23 μmol m− 2 s− 1 (in fibre development stage). The ecosystem was net CO2 source during nighttime with an average NEE value of 5–8 μmol m− 2 s− 1. Combining both daytime and nighttime CO2 fluxes, jute ecosystem was found to be a net CO2 sink on a daily basis except the initial 9 days from date of sowing. Seasonal and growth stage-wise NEEs were computed, and the seasonal total NEE over the jute season was found to be − 268.5 gC m− 2 (i.e. 10.3 t CO2 ha- 1). In different jute growth stages, diurnal variations of NEE were strongly correlated (R2 > 0.9) with photosynthetic photon flux density (PPFD). Ecosystem level photosynthetic efficiency parameters were estimated at each growth stage of jute crop using the Michaelis–Menten equation. The maximum values of photosynthetic capacity (Pmax, 63.3 ± 1.15 μmol CO2 m− 2 s− 1) and apparent quantum yield (α, 0.072 ± 0.0045 μmol CO2 μmol photon− 1) were observed during the active vegetative stage, and the fibre development stage, respectively. Results of the present study would significantly contribute to understanding of the carbon flux from the Indian agroecosystems, which otherwise are very sparse.Not Availabl