The looming energy crisis, heightened by the continuing depletion of fossil fuels, accentuates the need for deployment of renewable energy resources in Bangladesh, now more than ever before. Though hydrocarbon resources in the country are limited, the substantial availability of renewable energy sources in the form of solar, biomass, hydropower and wind energy offers opportunities of sustainable energy based development. Motivated by this auspice, the government of Bangladesh and different non-government organizations have been working towards the dissemination of renewable energy based technologies throughout the country. Though this diffusion of renewable energy sources is yet to assume extensive commercial dimensions and widespread implementations, the advancement has been significant in recent years. With the objective of reviewing this progress, this paper presents a comprehensive study of the contemporary renewable energy scenario in Bangladesh in terms of distribution, research and infrastructural development in the country. In addition to this, installed capacity has been calculated to assess the relative contributions of the five renewable energy sectors of Bangladesh.
This paper presents a simple yet efficient feature extraction algorithm for face recognition based on the principle of spectral domain cross-correlation. Instead of considering the spatial data of a face image as a whole, spectral feature is extracted from the each row of the spatial data individually. As each of these rows bears distinct characteristic of the face image, considering row-wise Fourier domain representation of all of them ensures the extraction of detail variation in face geometry. It is shown that the cross-correlations obtained considering pairs of spectral representations of consecutive rows provide a signature of the particular face image reflecting the variation in the face geometry along the vertical direction. In a similar fashion, a horizontal signature can be obtained considering spectral crosscorrelations along consecutive columns. In the proposed method, both of these horizontal and vertical signatures are utilized in order to obtain a distinguishable feature space for a particular person. It is found that the proposed feature extraction algorithm offers advantages of simple practical implementation with a high degree of face recognition accuracy.
This paper presents a simple yet efficient face recognition technique, where a feature extraction algorithm is proposed based on the principle of spectral domain crosscorrelation. Instead of considering the spatial variation of a face image as a whole, first we concentrate on spectral variation of each row of the image individually, which is obtained using discrete cosine transform (DCT). As each of these rows could carry distinct characteristic of the face image, considering all of them would ensure the extraction of the variation in face geometry without discarding any information even in a minute scale. It is shown that the cross-correlations in the DCT-domain considering pairs of consecutive rows provide a signature of the particular face image reflecting the variation in the face geometry along the vertical direction. In a similar fashion, a horizontal signature can be obtained considering DCT-domain cross-correlations along consecutive columns. However, it is observed that in comparison to horizontal features, vertical features offer better within-class compactness and between-class separation. In the proposed method, these two signatures are utilized combinedly in order to obtain a distinguishable feature space. From extensive simulations on standard databases, it is found that the proposed feature extraction algorithm offers advantages of simple practical implementation with a high degree of face recognition accuracy.
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