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The sensitivity of various solar photovoltaic technologies to dust, temperature and relative humidity is investigated for Qatar's environment. Results obtained show that mono-crystalline PVs have efficiencies as high as 85% compared to 70% for amorphous ones. Also, dust accumulation degrades more critically the efficiency of amorphous and mono-crystalline silicon PVs than the panel's temperature or relative humidity. In addition, the results show that amorphous PVs are more affected by temperature and relative humidity than mono-crystalline PVs. However, amorphous PVs prove to be more robust against dust settlement than mono-crystalline PVs and hence are more suitable for implementation in desert climates like Qatar unless cleaning strategies are devised. It was estimated that 100 days of dust accumulation over mono-crystalline PV panels, caused the efficiency to decrease by around 10%. This limitation makes solar PV an unreliable source of power for unattended or remote devices and thus strongly suggests the challenge of cleaning the panel's surface regularly or injecting technical modifications. Furthermore, the study suggests operating solar PV plants in Qatar from 11:00 am to 02:00 pm to optimize production.Index Terms-PV efficiency, solar transmittance, mono-crystalline and amorphous PV, dust effect, temperature and relative humidity effect

No abstract

Optimal spectral bands selection is a primordial step in multispectral images based systems for face recognition. In this context, we select the best spectral bands using a multilinear sparse decomposition based approach. Multispectral images of 35 subjects presenting 25 different lengths from 480nm to 720nm and three lighting conditions: fluorescent, Halogen and Sun light are groupped in a 3-mode face tensor T of size 35×25×2 . T is then decomposed using 3-mode SVD where three mode matrices for subjects, spectral bands and illuminations are sparsely determined. The 25×25 spectral bands mode matrix defines a sparse vector for each spectral band. Spectral bands having the sparse vectors with the lowest variation with illumination are selected as the best spectral bands. Experiments on two state-of-the-art algorithms, MBLBP and HGPP, showed the effectiveness of our approach for best spectral bands selection.