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Due to great advances in the field of information technology, the need for a more reliable authentication system has been growing rapidly for protecting the individual or organizational assets from online frauds. In the past, many authentication techniques have been proposed like password and tokens but these techniques suffer from many shortcomings such as offline attacks (guessing) and theft. To overcome these shortcomings, in this paper brain signal based authentication system is proposed. A Brain–Computer Interface (BCI) is a tool that provides direct human–computer interaction by analyzing brain signals. In this study, a person authentication approach that can effectively recognize users by generating unique brain signal features in response to pictures of different objects is presented. This study focuses on a P300 BCI for authentication system design. Also, three classifiers were tested: Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor, and Quadratic Support Vector Machine. The results showed that the proposed visual stimuli with pictures as selection attributes obtained significantly higher classification accuracies (97%) and information transfer rates (37.14 bits/min) as compared to the conventional paradigm. The best performance was observed with the QDA as compare to other classifiers. This method is advantageous for developing brain signal based authentication application as it cannot be forged (like Shoulder surfing) and can still be used for disabled users with a brain in good running condition. The results show that reduced matrix size and modified visual stimulus typically affects the accuracy and communication speed of P300 BCI performance.

A novel improved meta heuristic method, Hybrid salp swarm using sine cosine algorithm (Hybrid SS-SC) for maximum power point tracking (MPPT) under partial shading condition is proposed in this paper. The proposed algorithm envisages finding the best GMPP. The simulation studies clearly demonstrate that the method compares favourably with the conventional P&O method as well as with some other nature inspired algorithms in tracking GMPP in less time.

This paper explores the application of artificial neural networksfor online identification of a multimachine power system. A recurrent neural networkhas been proposedas the identifier of the two area, four machine system which is a benchmark system for studying electromechanical oscillations in multimachine power systems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of the paper is on investigating the performance of the variants of the Backpropagation algorithm in training the neural identifier. The paper also compares the performances of the neural identifiers trained using variants of the Backpropagation algorithm over a wide range of operating conditions . The simulation results establish a satisfactory performance of the trained neural identifiers in identification of the test power system.

This paper presents a neural network predictive controller for the UPFC to improve the transient stability performance of the power system. A neural network model for the power system is trained using the backpropagation learning method employing the Levenberg-Marquardt algorithm for faster convergence. This neural identifier is then utilized during predictive control of the UPFC. The damped Gauss-Newton method employing 'backtracking' as the line search method for step selection is used by the predictive controller to predict the future control inputs. The 4- machine 2-area power system which is a benchmark power system is used to demonstrate the performance of the proposed controller. The system under consideration is simulated for different transients over a range of operating conditions using Matlab/Simulink. The proposed neural network predictive controller exhibits superior damping performance in comparison to the conventional PI controller. The simulation results also establish convergence of the minimization algorithm to an acceptable solution within single iteration.

The purpose of this study was to investigate bacterial load in ready-to-sale sheep meat with special reference to Salmonella. Samples were collected from 100 sheep carcasses from retail meat shops in domestic markets. On carcasses, where bacterial counts were obtained, the mean of the log 10 aerobic plate count was 7.26 cfu g -1 , and that of total coliform count and total Escherichia coli count was 4.11 log 10 cfu g -1 and 3.03 log 10 cfu g -1 , respectively. All the samples (100) were found positive for coliforms, 49.0% were positive for E. coli and 3.0% were positive for Salmonella. The isolates were serotyped as Salmonella infantis having antigenic structure 6, 7: r: 1, 5. Antibiogram revealed highest (100.0%) sensitivity towards amikacin, ceftriaxone, ciprofloxacin, chloramphenicol, colistin sulphate, gentamicin and nalidixic acid followed by cefuroxime and tetracycline (66.67% each) and cotrimoxazole (33.33%). All the strains were resistant to ampicillin.

The paper presents an investigation in to the effect of neural network predictive control of UPFC on the transient stability performance of a multimachine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers, and an improved damping of the power oscillations as compared to the conventional PI controller., {"references":["P. Kundur, J. Paserba, V. Ajjarapu, G. Andersson, A. Bose, C. Canizares, N. Hatziargyriou, D. Hill, A. Stankovic, C. Taylor, T. V. Cutsem, and V. Vittal, \"Definition and classification of power system stability,\" IEEE Trans. Power Systems, vol. 19, no. 2, pp. 1387-1401, 2004.","N. G. Hingorani, and L. Gyuvgi, Understanding FACTS concepts & technology of flexible AC transmission systems. Delhi: IEEE Press, 2001, ch. 1.","M. Vilathgamuwa, X. Zhu, and S. S. Choi, \"A robust control method to improve the performance of a unified power flow controller,\" Electrical Power System Research, vol. 55, pp. 103-111, 2000.","B. C. Pal, \"Robust damping of interarea oscillations with unified power flow controller,\" IEE Proceedings on Generation, Transmission, Distribution, vol. 149, no. 6, pp. 733-738, 2002.","A. H. M. A. Rahim, and S. A. Al-Baiyat, \"A robust damping controller design for a unified power flow controller,\" in Proc. 39th IEEE Universities Power Engineering Conference, (UPEC), IEEE, Bristol, 2004, pp. 265-269.","A.H M.A. Rahim, J. M. Bakhashwain, and S. A. Al-Baiyat, \"Robust damping controls for a unified power flow controller,\" International Journal of Emerging Electric Power Systems, vol. 6, no. 2, pp. 1-21, 2006.","S. A. Taher, S. Akbari, A. Abdolalipour, and R. Hematti, \"Robust decentralized controller design for UPFC using µ-synthesis,\" Communications in Nonlinear Science and Numerical Simulation, vol. 15, pp. 2149-2161, 2010.","A.T. Al-Awami, Y. L. Abdel-Magid, and M. A. Abido, \"Simultaneous stabilization of power system using UPFC-based controllers,\" Electric Power Components and Systems, vol. 34, no. 9, pp. 941-959, 2006.","K. Wang, B. Yan, M. L. Crow, and D. 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This paper demonstrates an effective application of artificial neural networks for online identification of a multimachine power system. The paper presents a recurrent neural network as the identifier of the benchmark two area, four machine system. This neural identifier is trained using the static Backpropagation algorithm. The trained neural identifier is then tested using datasets generated by simulating the system under consideration at different operating points and a different loading condition. The test results clearly establish a satisfactory performance of the trained neural identifier in identification of the power system considered.