The Modares Journal of Electrical Engineering
Year:2015 | Volume:15 | Issue:4 |Papers Count:6

The art of steganography is used to hide the relationship and secret messages between sender and receiver for the sake of information security in communication networks. Capacity, imperceptibility and robustness are three important pillars of steganography requirements. Increasing each of these factors in steganography may result in decreasing the other factors. Optimization methods with respect to an acceptable value for one factor can be used to increase the other factors. In this paper by specifying the scope of PSNR as a measure of imperceptibility and in order to increase capacity, steganography is conducted using PSO algorithm. In the proposed method considering the order of each bit-plane of cover image, steganography is run with a matrix encoding method. In the present study the capacity of stego images for famous cover images is examined. The results show that the proposed method in comparison with some recent ones provides better PSNR in addition to increasing capacity.

This paper presents a novel approach for driving stress assessment by fuzzy clustering. In previous researches, stress during real- world driving tasks has been detected in discrete levels, but in this study, we demonstrated that considering fixed-levels for stress in long periods is not authentic. Without employing discrete levels of stress, data remains unlabeled, so a clustering method has been proposed to compensate for the lack of feasibility of classification. Due to uncertainties, the clusters can be defined in terms of fuzzy sets. Furthermore, using fuzzy clustering methods, data overlap is considered. In the proposed algorithm, using membership values generated by fuzzy c-means, and weights assigned by fuzzy inference system (FIS), we present an automatic continuous criteria for stress in short time intervals. The continuous scale is defined between 0 and 100, where higher values represent higher stress levels. Our findings not only confirm rough results of previous studies, but also indicate improvements in precision and accuracy of stress assessment.

A new image encryption algorithm is proposed, in which a novel chaotic map is introduced to generate the random sequence. The mentioned sequence is employed to produce gray level values. The original image is encrypted by applying the XOR operator to every pixel using the mentioned values. Our chaotic map provides a large degree of randomization compared to existing approaches and therefore, the correlation between adjacent pixels in the encrypted image is reduced substantially. The security analysis demonstrates that the new algorithm is highly secure. As it is shown in the experimental results, our algorithm improves entropy, key sensitivity, and correlation. Specifically, the amounts of entropy and correlation measures are very close to the optimal values. It is also very robust against the noise. The PSNR of decrypted images are degraded slightly with the increasing noise strength. Additionally, the suggested approach leads to smoother histograms in comparison to the previous algorithms.

In this work, we introduce MRE2C method for classifying multi relational data. Multi-relational data are stored on relational databases where they consist of multiple relations that are linked together by entity-relationship links. MRE2C creates multiple different feature subsets of relational database and then applies traditional classifiers as base classifiers. Final by using a proposed two-step combining classifier method, the results of base classifiers are combined. In first step, the proposed method uses local voting to create meta-features and then it learns meta learner to combine predication of base classifiers. Testing has been performed on two databases and six benchmark tasks. We compare our proposed method with other state-of-the-art multi relational classification methods which use different approaches to deal with multi relational setting. We showed that the proposed method achieves promising results in experiments.

This paper uses data fusion based on fuzzy integral theory for stator winding inter-turn short circuit fault diagnosis in induction motors. Time-domain features are extracted from current signals, and a technique is proposed to choose appropriate features. The fuzzy c-mean analysis method is employed to classify different modes. It is used to choose the membership values of each feature for each fault mode. Finally, different features are fused at feature-level and decision-level using fuzzy integral data fusion to produce diagnostic results. Results show that fuzzy data fusion method performs very well for fault diagnosis in a 4hp laboratory induction motor.

In this paper, a novel methodology is proposed to improve performance of the Networked Control System (NCS) in the face of random time-delays, using Model Predictive Controller (MPC) approach. For this purpose, a new state-feedback MPC structure is developed to cope with random network time-delays when the system is subjected to uncertainties with state and control constraints. The main idea is to reduce the disturbing effect of random network time-delays on regulatory performance of the NCS using a new robust formulation in MPC design. A terminal penalty constraint has been added to the finite horizon objective function to guarantee the stability of the system stability. Finally, applicability of the presented method is evaluated in a real pilot plant within a NCS configuration, being realized by an industrial Ethernet and Foundation Fieldbus technology. It is demonstrated that the proposed online methodology is effective to provide a better performance, having faster response, smaller overshoot and stronger robustness compared to the conventional MPC method with less aggressive control actions.