This paper deals with a new control technique of system parameter evaluation for a parallel three phase active filters (AF) to eliminate harmonics and to compensate reactive power of non-linear loads. The work is motivated by the need to minimize the burden on the dc-link capacitor voltage under dynamic load conditions. Genetic Algorithm is used to obtain an optimized control of dc-link voltage. A three phase diode rectifier with R-L loading is used as a non-linear load. The system dynamic performance is analyzed based on its transient response to load change and load unbalancing. Moreover, the robustness of the system to filter parameter variation is also studied. The AF is found effective to meet IEEE-519 standard recommendations on harmonics level.
The Low-Dropout voltage regulators are significant part of VLSI design integrated chips and are used to provide steady supply voltage to noise sensitive analog/RF circuit blocks. It belongs to class of linear regulators designed to minimize the saturation of output pass transistor and its drive requirements. A capacitor-less low-dropout regulator with improved performance push-pull power transistor is described in the paper. The proposed LDO is stable over a wide range of load current and implemented in 65-nm CMOS process technology. The simulation illustrates that the regulator is able to convert V IN of 0.65V-1.5V to output voltage of 0.5V. This LDO achieves power supply rejection of >40dB at 1 kHz operating frequency. It consumes a quiescent current less than 10 uA. It is capable of delivering a maximum load current of 70 mA with a dropout voltage of less than 220 mV.
Traffic Sign Recognition (TSR) framework is a significant part of Intelligent Transport System (ITS) as traffic signs help the drivers to drive all the more securely and proficiently. This paper speaks to another approach for TSR framework where location of traffic sign is done utilizing fuzzy rules based shading division strategy and recognition is refined utilizing Speeded Up Robust Features (SURF) descriptor, prepared by artificial neural network (ANN) classifier. In the identification step, the locale of intrigue (sign region) is divided utilizing an arrangement of fuzzy rules relying upon the tint and immersion estimations of every pixel in the HSV shading space, present prepared on channel undesirable area. At long last the recognition of the traffic sign is executed utilizing ANN classifier upon the preparation of SURF features descriptor. The proposed framework mimicked on disconnected street scene pictures caught under various brightening conditions. The discovery calculation demonstrates a high robustness and the recognition rate is very palatable. The execution of the ANN display is delineated as far as cross entropy, confusion matrix and receiver operating characteristic (ROC) curves. Likewise, exhibitions of some classifier, for example, Support Vector Machine (SVM), Decision Trees, Ensembles Learners (Adaboost) and K Nearest Neighbor (KNN) classifier are surveyed with ANN approach. The recreation comes about represent that recognition utilizing ANN demonstrate is higher than classifiers expressed previously.
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