In a connected graph $ G $, two adjacent vertices are said to be neighbors of each other. A verte... more In a connected graph $ G $, two adjacent vertices are said to be neighbors of each other. A vertex $ v $ adjacently distinguishes a pair $ (x, y) $ of two neighbors in $ G $ if the number of edges in $ v $-$ x $ geodesic and the number of edges in $ v $-$ y $ geodesic differ by one. A set $ S $ of vertices of $ G $ is a neighbor-distinguishing set for $ G $ if every two neighbors in $ G $ are adjacently distinguished by some element of $ S $. In this paper, we consider two families of generalized Petersen graphs and distinguish every two neighbors in these graphs by investigating their minimum neighbor-distinguishing sets, which are of coordinately two.
Mehran University Research Journal of Engineering and Technology
A novel hybrid design based electronic voting system is proposed, implemented and analyzed. The p... more 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.
International Journal of Advanced Computer Science and Applications
Wireless services appearing in the next generation wireless standard i.e. 6G include Internet of ... more Wireless services appearing in the next generation wireless standard i.e. 6G include Internet of Everything (IoE), Holographic communications, smart transportation and smart cities require exponential rise in the bandwidth in addition to other requirements. The current static spectrum allocation policy does not allow any new entrant to exploit already grid-locked Radio Frequency (RF) spectrum. Hence, quest for larger bandwidth can be fulfilled through other technologies. These include exploiting sub-Terahertz band, Visible Light Communication and Cognitive Radio scheme or exploiting of RF bands in opportunistic fashion. Cognitive Radio is one of those engines to exploit the RF spectrum in secondary style. Cognitive Radio can use artificial intelligence driven algorithms to complete the task. Several intelligent algorithms can be used for better forecasting of spectral holes. Convolutional Neural Network (CNN) is a Deep Learning algorithm that can be used to predict the presence of a spectral hole that can be opportunistically exploited for efficient utilization of RF spectrum in secondary fashion. This paper investigates the performance of CNN for metropolitan Karachi city of Pakistan so that the users can be provided with uninterrupted access to the network even under busy hours. Dataset for the proposed setup is collected for 1805 MHz frequency band through NI 2901 Universal Software Radio Peripheral (USRP) devices. The root mean square error (RMSE) for the predicted results using CNN appears to be 81.02 at epoch of 200 and mini-batch loss of 3281.8. Based on the predicted results, it was concluded that CNN can be useful for investigating the possible opportunistic usage of RF spectrum; however, further investigation is required with different datasets.
International Journal of Advanced Computer Science and Applications
Marriage of Internet of Everything (IoE) and Cognitive Radio driven technologies seems near under... more Marriage of Internet of Everything (IoE) and Cognitive Radio driven technologies seems near under the umbrella of 6G and 6G+ communication standard. The expected new services that will be introduced in 6G communication will require high data rates for transmission. The learning based algorithms will play a key role towards successful implementation of these novel technologies and evolving next generation wireless standards for providing ubiquitous connectivity. This paper investigates performance of two artificial neural network (ANN) based algorithms for Karachi. These include Nonlinear autoregressive exogenous Algorithm (NARX) and cascade feed forward back propagation neural network (CFFBNN) scheme. A dataset for Karachi is also developed for 1805 MHZ. The results of the two algorithms are compared that show Mean Square Error (MSE) for CFFBNN is 6.8877e-5 at epoch 16 and MSE for NARX is 3.1506e-11 at epoch 26. Hence, exploiting computational performance, NARX performs much superior than the classis CFFBNN algorithm.
In a connected graph $ G $, two adjacent vertices are said to be neighbors of each other. A verte... more In a connected graph $ G $, two adjacent vertices are said to be neighbors of each other. A vertex $ v $ adjacently distinguishes a pair $ (x, y) $ of two neighbors in $ G $ if the number of edges in $ v $-$ x $ geodesic and the number of edges in $ v $-$ y $ geodesic differ by one. A set $ S $ of vertices of $ G $ is a neighbor-distinguishing set for $ G $ if every two neighbors in $ G $ are adjacently distinguished by some element of $ S $. In this paper, we consider two families of generalized Petersen graphs and distinguish every two neighbors in these graphs by investigating their minimum neighbor-distinguishing sets, which are of coordinately two.
Mehran University Research Journal of Engineering and Technology
A novel hybrid design based electronic voting system is proposed, implemented and analyzed. The p... more 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.
International Journal of Advanced Computer Science and Applications
Wireless services appearing in the next generation wireless standard i.e. 6G include Internet of ... more Wireless services appearing in the next generation wireless standard i.e. 6G include Internet of Everything (IoE), Holographic communications, smart transportation and smart cities require exponential rise in the bandwidth in addition to other requirements. The current static spectrum allocation policy does not allow any new entrant to exploit already grid-locked Radio Frequency (RF) spectrum. Hence, quest for larger bandwidth can be fulfilled through other technologies. These include exploiting sub-Terahertz band, Visible Light Communication and Cognitive Radio scheme or exploiting of RF bands in opportunistic fashion. Cognitive Radio is one of those engines to exploit the RF spectrum in secondary style. Cognitive Radio can use artificial intelligence driven algorithms to complete the task. Several intelligent algorithms can be used for better forecasting of spectral holes. Convolutional Neural Network (CNN) is a Deep Learning algorithm that can be used to predict the presence of a spectral hole that can be opportunistically exploited for efficient utilization of RF spectrum in secondary fashion. This paper investigates the performance of CNN for metropolitan Karachi city of Pakistan so that the users can be provided with uninterrupted access to the network even under busy hours. Dataset for the proposed setup is collected for 1805 MHz frequency band through NI 2901 Universal Software Radio Peripheral (USRP) devices. The root mean square error (RMSE) for the predicted results using CNN appears to be 81.02 at epoch of 200 and mini-batch loss of 3281.8. Based on the predicted results, it was concluded that CNN can be useful for investigating the possible opportunistic usage of RF spectrum; however, further investigation is required with different datasets.
International Journal of Advanced Computer Science and Applications
Marriage of Internet of Everything (IoE) and Cognitive Radio driven technologies seems near under... more Marriage of Internet of Everything (IoE) and Cognitive Radio driven technologies seems near under the umbrella of 6G and 6G+ communication standard. The expected new services that will be introduced in 6G communication will require high data rates for transmission. The learning based algorithms will play a key role towards successful implementation of these novel technologies and evolving next generation wireless standards for providing ubiquitous connectivity. This paper investigates performance of two artificial neural network (ANN) based algorithms for Karachi. These include Nonlinear autoregressive exogenous Algorithm (NARX) and cascade feed forward back propagation neural network (CFFBNN) scheme. A dataset for Karachi is also developed for 1805 MHZ. The results of the two algorithms are compared that show Mean Square Error (MSE) for CFFBNN is 6.8877e-5 at epoch 16 and MSE for NARX is 3.1506e-11 at epoch 26. Hence, exploiting computational performance, NARX performs much superior than the classis CFFBNN algorithm.
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Papers by Shabbar Naqvi