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A novel hybrid design based electronic voting system is proposed, implemented and analyzed. The proposed system uses two voter verification techniques to give better results in comparison to single identification based systems. Finger print and facial recognition based methods are used for voter identification. Cross verification of a voter during an election process provides better accuracy than single parameter identification method. The facial recognition system uses Viola-Jones algorithm along with rectangular Haar feature selection method for detection and extraction of features to develop a biometric template and for feature extraction during the voting process. Cascaded machine learning based classifiers are used for comparing the features for identity verification using GPCA (Generalized Principle Component Analysis) and K-NN (K-Nearest Neighbor). It is accomplished through comparing the Eigen-vectors of the extracted features with the biometric template pre-stored in the election regulatory body database. The results of the proposed system show that the proposed cascaded design based system performs better than the systems using other classifiers or separate schemes i.e. facial or finger print based schemes. The proposed system will be highly useful for real time applications due to the reason that it has 91% accuracy under nominal light in terms of facial recognition.

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Two of the popular refrigeration cycles, VC (Vapor Compression), and VA (Vapor Absorption) are used extensively for refrigeration purposes. In this paper, a system is proposed that works using both cycles powered by an IC (Internal Combustion) engine, where mechanical energy is used to run the VC cycle while exhaust gasses are used to operate the VA cycle. The VC cycle works on R12 refrigerant while LiBr-H 2 O combination is selected for operation of VA cycle. Firstly, the refrigeration system is modeled, followed by a parametric study to investigate the impacts of various operating parameters on the system performance. The results exhibit that for maximum chilling and overall performance, the condenser and evaporator pressures in the VC cycle are obtained as 710 and 340 kPa, respectively, whereas generator and absorber temperatures in VA cycle are 85 and 20 o C, respectively.

We derive probability of detection P d and false alarm P f for spectrum sensing cognitive devices, employing correlated multiple antenna elements using linear test statistic. Detection performance of such sensors is severely degraded due to the correlation among antennas, in addition to that fading channel conditions may further deteriorate the performance. We propose a simple hard decision fusion strategy at the secondary Base Station to improve the performance by exploiting collaborative gain. Region of Convergence (ROC) is also evaluated under OR based fusion strategy. Numerical results certify the proposed proposal.

Abstract-A novel cooperative spectrum sensing algorithm is implemented and analyzed using Raspberry Pi. In the proposed setup, Nokia cell phone is used as a spectrum sensing device while Raspberry Pi functions as a FC device to collect sensing results from local sensing devices. The investigation results of the proposed setup show significant improvement in detection performance as compared to local spectrum sensing techniques. Furthermore, results show a successful communication between sensing nodes and FC.

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