Description:
We consider the problem of security in wireless multicasting for a multiple-input multiple-output (MIMO) relay-aided system. The network suffers from a group of adaptive eavesdroppers who can act as both simple eavesdroppers and hostile jammers. This paper formulates the impact of friendly jammers to improve secured communication. We derived the expressions for secrecy multicast capacities considering the absence and presence of friendly jammers. The best relay for transmission is chosen from a group of relays that aids to achieve the maximum secrecy capacity while the best jammer is selected based on competitive interference price. Numerical results show that the achievable secrecy multicast capacity increases significantly in the presence of jammer to nullify the effect of adversaries. Results under different scenarios of varying jamming and relay powers demonstrate the efficacy of friendly jammers in providing physical layer security.
Description:
We consider the problem of security in wireless multicasting for a
Description:
One of the prevalent issues in condition based maintenance (CBM) is to predict the residual life of the equipment. This paper propos-es a novel framework to predict the remnant life of the equipment, called Residual life prediction based on optimally parameterized Wavelet transform and Mute-step Support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications and decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature-signal energy contents. The prediction model is based on multi-step support vector regression (MSVR) and prediction accuracy is improved in comparison with the techniques based on support vector regression (SVR). Performance of RWMS is evaluated in terms of Root Means Square Error (RMSE), studies show that proposed algorithm predicts the residual life of the equipment accurately.
Description:
One of the prevalent issues in condition based maintenance (CBM) is to predict the residual life of the equipment. This paper propos-es a novel framework to predict the remnant life of the equipment, called Residual life prediction based on optimally parameterized Wavelet transform and Mute-step Support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications and decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature-signal energy contents. The prediction model is based on multi-step support vector regression (MSVR) and prediction accuracy is improved in comparison with the techniques based on support vector regression (SVR). Performance of RWMS is evaluated in terms of Root Means Square Error (RMSE), studies show that proposed algorithm predicts the residual life of the equipment accurately.
Description:
Vertical handover mechanism for a WLAN-cellular heterogeneous network could be made efficient with the use of context aware admission control strategy. Existing admission control methods aim to provide satisfactory quality of service, but rely solely on the availability of wireless resources in the target network. We propose that the admission control in WLAN should make use of social connectivity context of users in its coverage area to classify local and global traffic. In this paper, we introduce a novel Social-Connectivity-aware Vertical Handover (SCVH) scheme, which performs admission control using connectivity graph data from the online social networking services. A higher importance of visiting node for users resident in WLAN, advocates a higher priority for granting admission. We employ graph-theoretic concept of centrality to calculate the social importance of potential handing-over nodes. By giving handover precedence to higher-centrality nodes, we achieve an optimal allocation of wireless resources in addition to improved quality of service. The proposed handover strategy offers an additional advantage of reducing global social network traffic.
Description:
It is well known that breast density in mammograms may hinder the accuracy of diagnosis of breast cancer. Although the dense breasts should be processed in a special manner, most of the research has treated dense breast almost the same as fatty. Consequently, the dense tissues in the breast are diagnosed as a developed cancer. In contrast, dense-fatty should be clearly distinguished before the diagnosis of cancerous or not cancerous breast. In this paper, we develop such a system that will automatically analyze mammograms and identify significant features. For feature extraction, we develop a novel system by combining a two-dimensional discrete cosine transform (2D-DCT) and a principal component analysis (PCA) to extract a minimal feature set of mammograms to differentiate breast density. These features are fed to three classifiers: Backpropagation Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN). A majority voting on the outputs of different machine learning tools is also investigated to enhance the classification performance. The results show that features extracted using a combination of DCT-PCA provide a very high classification performance while using a majority voting of classifiers outputs from MLP, SVM, and KNN.
Description:
It is well known that breast density in mammograms may hinder the accuracy of diagnosis of breast cancer. Although the dense breasts should be processed in a special manner, most of the research has treated dense breast almost the same as fatty. Consequently, the dense tissues in the breast are diagnosed as a developed cancer. In contrast, dense-fatty should be clearly distinguished before the diagnosis of cancerous or not cancerous breast. In this paper, we develop such a system that will automatically analyze mammograms and identify significant features. For feature extraction, we develop a novel system by combining a two-dimensional discrete cosine transform (2D-DCT) and a principal component analysis (PCA) to extract a minimal feature set of mammograms to differentiate breast density. These features are fed to three classifiers: Backpropagation Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN). A majority voting on the outputs of different machine learning tools is also investigated to enhance the classification performance. The results show that features extracted using a combination of DCT-PCA provide a very high classification performance while using a majority voting of classifiers outputs from MLP, SVM, and KNN.
Description:
In this paper, we introduce a new hybrid of Hidden Markov Model (HMM), Fuzzy Logic and multiobjective Evolutionary Algorithm (EA) for building a fuzzy model to predict non-linear time series data. In this hybrid approach, the HMM's log-likelihood score for each data pattern is used to rank the data and fuzzy rules are generated using the ranked data. We use multiobjective EA to find a range of trade-off solutions between the number of fuzzy rules and the prediction accuracy. The model is tested on a number of benchmark and more recent financial time series data. The experimental results clearly demonstrate that our model is able to generate a reduced number of fuzzy rules with similar (and in some cases better) performance compared with typical data driven fuzzy models reported in the literature.
Description:
In this paper, we propose a new type of adaptive fuzzy inference system with a view to achieve improved performance for forecasting nonlinear time series data by dynamically adapting the fuzzy rules with arrival of new data. The structure of the fuzzy model utilized in the proposed system is developed based on the log-likelihood value of each data vector generated by a trained Hidden Markov Model. As part of its adaptation process, our system checks and computes the parameter values and generates new fuzzy rules as required, in response to new observations for obtaining better performance. In addition, it can also identify the most appropriate fuzzy rule in the system that covers the new data; and thus requires to adapt the parameters of the corresponding rule only, while keeping the rest of the model unchanged. This intelligent adaptive behavior enables our adaptive fuzzy inference system (FIS) to outperform standard FISs. We evaluate the performance of the proposed approach for forecasting stock price indices. The experimental results demonstrate that our approach can predict a number of stock indices, e.g., Dow Jones Industrial (DJI) index, NASDAQ index, Standard and Poor500 (S&P500) index and few other indices from UK (FTSE100), Germany (DAX) , Australia (AORD) and Japan (NIKKEI) stock markets, accurately compared with other existing computational and statistical methods.
Description:
Spectrum resources are becoming more and more congested as the number of wireless devices are increasing and becoming ubiquitous. Cognitive radios or secondary users (SUs) can provide the solution for better spectrum availability, bandwidth and economic aspects for both the primary service providers and the SUs. We propose a pricing model for spectrum sharing in a single level market where the primary service providers can trade spectrum with the secondary service providers. The proposed pricing model incorporates the reliability of the primary service providers and allowable coverage area, quality of the signal along with the pricing and spectrum bandwidth availability. An iterative distributed algorithm is used to reach the market equilibrium so that both the primary and the secondary service providers are satisfied with the allocated spectrum bandwidth and negotiated price. The performance of the proposed model is demonstrated using extensive numerical results with the stability analysis in reaching the market equilibrium.
Description:
It is well known that the mutations in BRCA1 or BRCA2 gene can cause the hereditary breast cancer. However, it is a tedious and expensive task to identify the mutant genes that impact breast cancer due to the large number of genes and very small number of samples. Furthermore, the expressive energy of the subset of genes in comparison to that of one individual gene at a time is considered to have a profound influence in case of breast cancer. In this paper 7 tumors with BRCA1 mutation and 8 tumors with BRCA2 mutation have been used to identify the subset of discriminative genes. A combination of a non-parametric supervised and an unsupervised statistical method is introduced to analyze the gene expressions and the distinctive genes among the highly expressed genes are identified. The most important genes are filtered using the area under the curve (AUC) measure. These filtered genes are then used to build a hidden Markov model (HMM) to analyse their inter-relationship and identify the best subset among them. In addition, Protein-Protein interaction network is generated to analyse the pathways of the identified genes and their link with BRCA1 or BRCA2. Transcription Factors are identified and Gene Set Enrichment Analysis (GSEA) is calculated for the identified genes subset and the results are compared with the results mentioned in other cancer literature. Experimental results suggest that only 8 genes have been identified out of 3226 genes by the proposed hybrid method. Out of the 8 identified genes, 5 have been linked with breast cancer by other studies. Moreover, 7 genes have been associated with numerous diseases that may result in breast cancer. Furthermore, 8 transcription factors were identified that cover the identified genes and BRCA1 and BRCA2. Lastly, GSEA enrichment score of 0.52 is calculated for the identified genes and it is comparatively better considering the small subset of identified genes.
Description:
We develop a realistic pricing modeling for dynamic spectrum access network considering a number of factors that influence real world trading. Our model incorporates the reputation of sellers, incentive mechanism to attract buyers, timing requirement of the contract, signal quality along with bandwidth size and price of the spectrum resources. Reputation information is accumulated from self experience and/or from the recommendations of other users. Trustworthiness of the recommending SUs is also modeled in this paper. An incentive mechanism is applied to encourage in dissemination of recommendations and to attract the buyers by providing discounts. A utility function for the spectrum trading is modeled such that a buyer can trade with the seller which maximizes its utility based on the above factors and the pricing solution is obtained using the market equilibrium model. The performance of the proposed model is evaluated using simulation results which show that our model benefits both the sellers and the buyers in terms of profit and throughput, respectively.
Description:
Secondary service providers can buy spectrum resources from primary service providers for a short or long period of time and exploit it to solve the problem of spectrum scarcity. This buying decision of spectrum buyers can depend on several factors including pricing of the spectrum, reputation of a seller, and duration of the contract and spectrum quality. However, existing pricing models for dynamic spectrum access consider mainly bandwidth which makes them unsuitable for real-world trading. In this paper, we consider these issues related to the pricing of spectrum sale in terms of microeconomic theories. First, we consider reputation of spectrum sellers and update it dynamically by considering a buyer's own trading experience with the sellers and collecting recommendations on sellers from other buyers. Second, trustworthiness of recommenders as well as incentive to encourage recommendations are modeled. Third, contract duration and spectrum quality are incorporated such that a buyer's utility is formulated as a function of buyer's resource requirement, reputation of seller and trustworthiness of recommenders. Fourth, the model is analyzed using dynamic pricing of the market and the solution is obtained using market equilibrium. Results demonstrate the superiority of our model over the existing microeconomic models for dynamic spectrum trading.
Description:
Spectrum utilization in cognitive radio ad hoc network is a critical task due to the fluctuation of spectrum availability temporally and spatially. Reusing the same channel spatially can effectively improve the spectrum utilization as well as reduce the delay in switching and coordination in different channels. In this paper, a dynamic spectrum sharing method is proposed which allows multiple secondary users to reside in the same channel and use the channel concurrently to maximize the spectrum utilization exploiting variable transmission power and location information, while guaranteeing acceptable level of interference protection to the primary users. Results show that the proposed method successfully outperforms the existing method in maximizing the number of concurrent transmissions as well as the spectrum utilization.
Description:
Artificial immune algorithm has been used widely and successfully in many computational optimization areas, but the theoretical research exploring the convergence rate characteristics of artificial immune algorithm is yet inadequate. In this paper, instead of the traditional eigenvalue estimation of state transition matrix, stochastic processes theory is introduced to study the convergence rate of general artificial immune algorithm. The method begins by analyzing the necessary condition for convergence of artificial immune algorithm and takes it as the sufficient condition for a class of general artificial immune algorithm. Through the definition of Markov chain convergence rate, a probability strong convergence rate estimation method of general artificial immune algorithm is proposed. This method is judged by the final convergence of the best antibody, which overcomes the conservative defect of traditional estimation methods. The simulation results show the correctness of the proposed estimation method, and the estimation method can be used to judge the convergence and convergence rate of a class of artificial immune algorithms. This research has a certain theoretical reference value to optimize the convergence rate in the practical application of artificial immune algorithm.
Description:
This study proposes a modified artificial immune network algorithm for function optimization problems based on idiotypic immune network theory. A hyper-cubic mutation operator was introduced to reduce the heavy computational cost of the traditional opt-AINet algorithm. Moreover, the new symmetrical mutation can effectively improve local search. To maintain population diversity, we also devised an immune selection mechanism based on density and fitness. The global convergence of the algorithm was deduced through the method of pure probability and iterative formula. Simulation results of benchmark function optimization show that the modified algorithm converges more effectively than other immune network algorithms.