A continuous oscillation in the steady state causes a reduction in the PV module output power. In addition it cannot operate the module at its maximum output power in rapidly changing of weather conditions.So,thereisaneed ofMPPT system tosampletheoutputofthecellsandapplythe properresistance (load)toobtainmaximum powerfor anygiven environmentalconditions.Anewmethod to tracktheglobalMPPispresented,whichisbasedon Ant Colony Optimization (ACO) combined with ParticleSwarm Optimization (PSO)thatcontrollinga DC-DC converterconnectedattheoutputof PV array, such thatitmaintainsaconstantinput-powerload.This modelindicatestheDC-DC converteris an interleaved boostconvertertopologywhich willincreasethe efficiencyand reducetheripplefactorwhich iseasily controland greaterstabilitycan beachieved. By using thismodel wegetverylowconduction andswitching lossesthenswitchingfrequencyisimprovedand sizeof thesystem alsoreduced.Theproposedmethodhasthe advantagethatitcan beapplied ineitherstandaloneor grid-connectedPV systemscomprisingPVarrays with unknown electricalcharacteristicsanddoesnotrequire knowledge aboutthe PVmodules configuration.

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In recent years, as one of the emerging biometrics technologies, iris recognition has drawn wide attentions. It has many advantages such as uniqueness, low false recognition rate and so it has broad applications. It mainly uses pattern recognition and image processing methods to describe and match the iris feature of the eyes, and then realizes personal authentication. In image processing field, local image descriptor plays an important role for object detection, image recognition, etc. Till now, a lot of local image descriptors have been proposed. Among all kinds of local image descriptors, it is well-known that LBP is a popular and powerful one, which has been successfully adopted for many different applications such as face recognition, texture classification, object recognition, etc. Currently, a new trend of the research on LBP is to encode the directional information instead of intensity information. LLDP is an LBP-like descriptor that operates in the local line-geometry space. We used modified finite radon transform (MFRAT) to implement the LLDP descriptor for iris recognition and obtained 80.03% accuracy.