National Institute of Technology Calicut

About: National Institute of Technology Calicut is a education organization based out in Kozhikode, Kerala, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 3627 authors who have published 4638 publications receiving 50830 citations. The organization is also known as: Calicut Regional Engineering College & NIT Calicut.

HFO refrigerants: A review of present status and future prospects.

Abstract: HFCs (Hydrofluorocarbons) are one of the most widely used refrigerants worldwide. But, HFCs are heading towards complete phase out in the near future due to environmental concerns. A high global warming potential (GWP) of HFCs is the main reason for its phase-out. As a result, researchers, engineers and technicians associated with the refrigeration sector are looking for a suitable environment-friendly alternative. Recent past has witnessed the emergence of a new group of environment-friendly refrigerants known as the HFOs (Hydrofluoro-olefins). The present review is focussed on addressing various aspects of HFO refrigerants such as the thermodynamic and transport properties, flammability and oil compatibility, boiling and condensation heat transfer performance and their performance in actual vapour compression refrigeration systems. Few suggestions for future research work related to HFO refrigerants are also discussed.

EEG-Based Emotion Recognition Using Statistical Measures and Auto-Regressive Modeling

Abstract: In this paper, a novel approach towards classification of various human emotions based on statistically weighed autoregressive modeling of Electroencephalogram (EEG) signals is discussed. The proposed algorithm was proven to be superior to many related works, in distinguishing different emotions such as happiness, fear, sadness etc. The findings discussed are based on the results obtained using benchmark emotion based EEG database called DEAP. In this work, epochs were extracted from data using statistical measures such as Shannon Entropy and higher order auto-regressive model was fit to the extracted features. The model was used for classifying human emotions by feeding it into a multi-class Support Vector Machine (MCSVM). The proposed algorithm is proven to be more efficient than existing algorithms as a classification accuracy of 94.097% was obtained.

Shadow Detection and Removal from a Single Image Using LAB Color Space

Abstract: A shadow appears on an area when the light from a source cannot reach the area due to obstruction by an object. The shadows are sometimes helpful for providing useful information about objects. However, they cause problems in computer vision applications, such as segmentation, object detection and object counting. Thus shadow detection and removal is a pre-processing task in many computer vision applications. This paper proposes a simple method to detect and remove shadows from a single RGB image. A shadow detection method is selected on the basis of the mean value of RGB image in A and B planes of LAB equivalent of the image. The shadow removal is done by multiplying the shadow region by a constant. Shadow edge correction is done to reduce the errors due to diffusion in the shadow boundary.

Influence of scale on SWAT model calibration for streamflow in a river basin in the humid tropics.

Abstract: The Soil Water Assessment Tool (SWAT) was applied to the 2,530 km2 Chaliyar river basin in Kerala, India to investigate the influence of scale on the model parameters. The study was carried out in this river basin at two scales. Parameters such as land use, soil type, topography and management practices are similar at these scales. The model was initially calibrated for streamflow and then validated. Critical parameters were the curve number (CN2), soil evaporation compensation factor (ESCO), available water holding capacity (SOL_AWC), average slope length (SLSUBBSN), and base flow alpha factor (ALPHA_BF). Using the optimized value of various parameters, stream flow was estimated from parts of the basin at two different scales—an area of 2,361.58 km2 and an area of 1,013.15 km2. The streamflow estimates at both these scales were statistically analysed by computing the coefficient of determination (R 2) and the Nash–Sutcliffe efficiency (ENS). Results indicate that the SWAT model could simulate streamflow at both scales reasonably well with very little difference between the observed and computed values. However, the results also indicate that there may be greater uncertainty in SWAT streamflow estimates as the size of the watershed increases.