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Vinodhini, V.
- Performance Evaluation of Common Effluent Treatment Plant for Tanneries at Vaniyambadi, Vellore, Tamil Nadu
Abstract Views :161 |
PDF Views:2
Authors
Affiliations
1 School of Biotechnology, Chemical and Biomedical Engineering, Environmental Biotechnology Division, VIT University, Vellore-632 014, Tamil Nadu, IN
1 School of Biotechnology, Chemical and Biomedical Engineering, Environmental Biotechnology Division, VIT University, Vellore-632 014, Tamil Nadu, IN
Source
Nature Environment and Pollution Technology, Vol 7, No 3 (2008), Pagination: 385-390Abstract
The present study has been undertaken to evaluate performance efficiency of the Common Effluent Treatment Plant (CETP) at Vaniyambadi, Vellore. Waste water samples were collected at four different stages of the treatment units and analysed for various physico-chemical parameters such as pH, temperature, turbidity, EC, TSS, TDS, BOD, COD and heavy metals. The values obtained for different parameters were compared with standard values given by National Environmental Quality Standards (NEQS) for waste water quality. The CETP has been working with the norms of NEQS and Tamil Nadu Pollution Control Board (TNPCB) and meeting the standard discharge limits.Keywords
Common Effluent Treatment Plant, Tannery Waste, Biological Treatment, BOD, COD.Full Text
- A Behavioral Approach to Detect Somnolence of CAB Drivers Using Convolutional Neural Network
Abstract Views :247 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Sona College of Technology, IN
1 Department of Computer Science and Engineering, Sona College of Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 3 (2021), Pagination: 2359-2363Abstract
The Road Traffic Accident Statistics concluded that fatal road accident 60% is caused by vehicle collision of taxi drivers. World Health Organization (WHO) is constantly initiating global road safety measures to minimize road accidents but, the cause for fatal injuries is primarily due to driving fatigue. Most people rely on cabs as the main transport. To provide obligate care of passengers, a computer vision-based technique is needed to detect the somnolence of drivers. Our proposed model CabSafety is based non-intrusive computer vision technique using Convolutional Neural Network (CNN). A tiny camera is fixed focusing the driver’s face to monitor the behavioral changes like an eye blink, yawing, watery eye, mouth movement, and head position. The measures of the driver’s eye are concentrated to identify sleepiness under stimulator or test conditions. The efficiency of the proposed model provides better results compared to the existing technique. The image from the camera is processed by OpenCV and Keras/Tensor flow. CNN classifier is used to detect eye status. The prediction from the CNN classifier produces an alarm to alert the driver.Keywords
Road Accident, Drowsy Driver, Eye Tracking, Convolutional Neural Network.
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