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RISS 인기검색어
- 검색키워드
- 저자 : Murukesh C. (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Secured Authentication through Integration of Gait and Footprint for Human Identification
C. Murukesh,K. Thanushkodi,Preethi Padmanabhan,Feroze Naina Mohamed D 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.6
Gait Recognition is a new technique to identify the people by the way they walk. Human gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. The proposed method makes a simple but efficient attempt to gait recognition. For each video file, spatial silhouettes of a walker are extracted by an improved background subtraction procedure using Gaussian Mixture Model (GMM). Here GMM is used as a parametric probability density function represented as a weighted sum of Gaussian component densities. Then, the relevant features are extracted from the silhouette tracked from the given video file using the Principal Component Analysis (PCA) method. The Fisher Linear Discriminant Analysis (FLDA) classifier is used in the classification of dimensional reduced image derived by the PCA method for gait recognition. Although gait images can be easily acquired, the gait recognition is affected by clothes, shoes, carrying status and specific physical condition of an individual. To overcome this problem, it is combined with footprint as a multimodal biometric system. The minutiae is extracted from the footprint and then fused with silhouette image using the Discrete Stationary Wavelet Transform (DSWT). The experimental result shows that the efficiency of proposed fusion algorithm works well and attains better result while comparing with other fusion schemes.
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Lavanya R.,Murukesh C.,Shanker N. R. 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.3
In this paper, a microclimatic data based occupancy regression controller (ORC) is proposed for heating, ventilation, and air conditioning (HVAC) systems and is termed microclimatic HVAC (M-HVAC). Microclimatic data consist of various measurements such as PIR, CO2, humidity, wall, floor and roof temperatures, as well as human skin temperature for the prediction of the optimal thermal setpoint temperature in an M-HVAC system. Microclimatic conditions have a major role in building energy consumption and indoor thermal comfort. Human skin temperature, wall, floor and roof temperatures in the room are obtained through a thermal camera. ORC controller performance is evaluated on the SiO2 nanocoated room walls for high energy savings. Up until now, researchers have focused on the optimization of thermal setpoint temperature (SPT) using indoor air temperature and room occupant count data, but have never addressed the microclimatic conditions. ORC predicts the optimal SPT after including the microclimate data. M-HVAC systems implement the ORC using a Raspberry Pi board connected with sensors and a thermal camera. ORC leads to thermal comfort in a room and reduces energy consumption. ORC improves prediction accuracy through regression analysis and reduces the energy cost of about 23.9% when compared to the traditional method. ORC provides high thermal comfort of about 97% with higher energy savings than the traditional method of temperature setpoint.
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