Wild Goats Optimization Approach for Capacitor Placement Problem

This paper deals with Capacitor Placement (CP) issue. The topic is an optimization problem including two types of variables: capacitor location as an integer variable, capacitor size as a continuous one. To cope with this problem, a new approach entitled Wild Goats Algorithm (WGA) is used. WGA is a new heuristic approach which has been proved recently. In this paper, WGA is successfully implemented to the CP problem with the objective of total loss reduction. Power flow criteria as well as operation constraints are all together accommodated in the process of optimization. Two various scenarios at three load levels are also recognized to cover all possible conditions. The validity of the WGA approach in handling CP problem is assured by testifying it on IEEE 33-bus and 69-bus test systems

Presenting the architecture framework of cyber security governance in the defense organizations of the Islamic Republic of Iran

Objective: The current research is looking for a framework for cyber security governance architecture in defense organizations with a balanced managerial and technical view that considers cyber security not only a set of new techniques and tools from a technical and engineering point of view and does not deal with cyber security only from a managerial point of view.Methodology: The research method is a combination (qualitative and quantitative) whose qualitative phase is done by content analysis and its quantitative phase is based on the findings of the qualitative phase and with the questionnaire tool, and the findings are placed in a Zackman framework.Findings: In today's world and accordingly in organizations; Capitals, assets and resources of organizations are transforming and changing their nature towards cyber capitals, so governments and organizations are well aware of the increasing role and importance of these capitals and are making significant efforts in this regard. On the other hand, cyber threats are widespread, growing and real, and as examples were seen recently in our country, cyber disputes among countries and governments are expanding and are very decisive. This matter is much more important and serious in defense organizations.Originality: Finally, an architectural framework of cyber security governance in defense organizations is presented so that these organizations can achieve the security of the organization in the cyberspace with a comprehensive, structural and proactive view and beyond the managerial, technical and limited perspectives and the existing lack of convergence towards To promote integrity and orderliness

A robust framework epileptic seizures classification based on lightweight structure deep convolutional neural network and wavelet decomposition

Nowadays scientific evidence suggests that epileptic seizures can appear in the brain signals minutes and even hours prior to their occurrence. Advances in predicting epileptic seizures can promise a robust model in which seizures and irreparable injuries at the time of occurrence can be possible. Most of the previous automated solutions are associated with challenges such as the lack of a proper signal descriptor, the existence of a large number of features and, consequently, the time-consuming analysis, which are not considering the uncertainty issue. In this paper, efficient and fastidious classification is performed by analysing the frequency bands of the input EEG signal via discrete wavelet transform, which is relying on the deep convolutional neural network based classification. Using the EEG signals obtained from the CHEG-MIT Scalp EEG database, the implementation in the desired model is performed and the results show that the proposed model has the best response in detecting the disease from the sample signal and with the highest level of certainty to follow. To solve the uncertainty problem, the repeatability algorithm test is arranged and after K-fold cross-validation, the experimental precision of all the three evaluation factors were equal to 99.34%, 99.53%, and 99.76%, respectively

Network coding schemes with efficient LDPC coded MIMO–NOMA in two-way relay networks

The combination of non-orthogonal multiple access (NOMA) and multi-input multi-output (MIMO) approaches has been considered as assuring multiple access for the fifth generation technology. In this study, the performance of a 2 × 2 MIMO- NOMA system with low-density parity check (LDPC) codes is investigated. Redundancy with randomly interleaved differential encoding (R-RIDE) is proposed and applied to LDPC encoded messages by two users. LDPC decoding is done using the sum-product algorithm (SPA), which has two types of decoding methods, hard-decision and soft-decision. For hard-decision, bit-flipping decoder is used and for soft-decision, probability domain, log-domain, and simplified log-domain decoders are used. Bit error rate (BER) versus signal-to-noise ratio (SNR) in (dB) and average mutual information (AMI) in (bps/Hz) versus SNR (dB) are evaluated to compare the performance of the proposed and conventional LDPC schemes in NOMA and MIMO-NOMA systems. Simulation results show that both AMI and BER of the proposed LDPC-R-RIDE in MIMO-NOMA system greatly outperforms conventional LDPC coded schemes in NOMA and MIMO-NOMA systems. Moreover, the proposed R-RIDE-LDPC in MIMO-NOMA system outperforms the proposed scheme in the NOMA system. From the simulation results, LDPC-R-RIDE with simplified log-domain decoder has the best AMI result and BER performance compared with other decoding methods

A Novel Method for Detecting Breast Cancer Location Based on Growing GA-FCM Approach

The main idea of this article is to provide a numerical diagnostic method for breast cancer diagnosis of the MRI images. To achieve this goal, we used the region's growth method to identify the target area. In the area's growth method, based on the similarity or homogeneity of the adjacent pixels, the image is subdivided into distinct areas according to the criteria used for homogeneity analysis to determine their belonging to the corresponding region. In this paper, we used manual methods and use of FCM as the function of genetic algorithm fitness. The presented algorithm is performed for 212 healthy and 110 patients. Results show that GA-FCM method have better performance than hand method to select initial points. The sensitivity of presented method is 0.67. The results of the comparison of the fuzzy fitness function in the genetic algorithm with other technique show that the proposed model is better suited to the Jaccard index with the highest Jaccard values and the lowest Jaccard distance. Among the techniques, the presented works well because of the similarity of techniques and the lowest Jaccard distance. Values close to 0.9 are close to 0.8

A novel steganography algorithm using edge detection and MPC algorithm

With the rapid development of the Internet, preserving the security of confidential data has become a challenging issue. An effective method to this end is to apply steganography techniques. In this paper, we propose an efficient steganography algorithm which applies edge detection and MPC algorithm for data concealment in digital images. The proposed edge detection scheme partitions the given image, namely cover image, into blocks. Next, it identifies the edge blocks based on the variance of their corner pixels. Embedding the confidential data in sharp edges causes less distortion in comparison to the smooth areas. To diminish the imposed distortion by data embedding in edge blocks, we employ LSB and MPC algorithms. In the proposed scheme, the blocks are split into some groups firstly. Next, a full tree is constructed per group using the LSBs of its pixels. This tree is converted into another full tree in some rounds. The resultant tree is used to modify the considered LSBs. After the accomplishment of the data embedding process, the final image, which is called stego image, is derived. According to the experimental results, the proposed algorithm improves PSNR with at least 5.4 compared to the previous schemes

Sleep arousal events detection using PNN-GBMO classifier based on EEG and ECG signals: A hybrid-learning model

Foremost sleep event is the sudden change of sleep stages, mainly from deep sleep to light sleep. The notion is very effective in the detection of sleep disorders. In this paper, the detection of arousal events is performed using an automatic analysis of EEG and ECG signals. Unlike previous methods, which rely solely on the detection of sleep stages, early recognition of change in sleep stages can facilitate the progression of some diseases. Detecting the change in sleep stages is a complex process and requires the expertise of a neurologist. Features can be extracted by three fractal descriptors, Lyapunov exponent and cumulatively discrete wavelet transform. A subset of the features is then applied into the probabilistic neural network optimized by Gases Brownian Motion Optimization (GBMO) algorithm. The set of EEG and ECG signals are samples of the SHHS sleep database that have been incorporated into the learning model with some pre-processing. In addition, solving uncertainty problem of responses, repeatability, and convergence to the minimum error are among the strengths of the proposed model. Compared to the conventional feature extraction and classification methods, outputs were obtained that are more acceptable, and the model for two- and four-class states reached averaged errors of less than 2% and 7% with K-fold cross-validation

Comparison of bit error rate performance for CDMA systems in different fading and AWGN channels

The DS-CDMA (Direct Sequence Code -CDMA (Slow Frequency Div Hopping CDMA), and MC-CDMA (Multi Carrier CDMA) are some of the very common and well known wireless communication techniques that is related to CDMA (Code Division for Multiple Access). Although these wireless techniques are well known, there is still a lack in research related to the performance analysis and comparison of the bit-error-rates (BER) of the wireless techniques with multiple number of users in the presence of fading channels. Thus, in this paper, the BER performance of DS-CDMA, SFH-CDMA and MC-CDMA wireless techniques in Rayleigh and Rician fading channels -users are presented, evaluated, and compared

Sensor based line follower self-driving car (sCar) with Obstacles Avoidance

This paper introduces the sensor based line follower Self-driving Car (sCar) with obstacles avoidance. We develop a Collision Avoidance path-planning Algorithm (CAA) for dual motors controller line follower sCar that has ability for navigate collision avoidance path autonomously through a constraint track from initial position to goal position. A sensor will be mounted in front of the sCar that will detect line and obstacles along the track. A powerful close loop control system is used in the sCar which can calcules collision free path. The sCar senses a line and endeavors a collision free path itself accordingly towards the initial position to desired goal position using a simple feedback mechanism but yet very effective closed loop system. In some situation, there will be multiple destinations and the sCar should able to choose the desired destinations based on CAA applied to the microcontroller Arduino UNO which acts as the center control unit. The CAA will be implemented by C++ programming language. Evaluation results show that CAA calculates collision free path with constant performance which independent on environments

Bone age estimation by deep learning in X-Ray medical images

Patient skeletal age estimation using a skeletal bone age assessment method is a time consuming and very boring process. Today, in order to overcome these deficiencies, computerized techniques are used to replace hand-held techniques in the medical industry, to the extent that this results in better evaluation. The purpose of this research is to minimize the problems of the division of existing systems with deep learning algorithms and the high accuracy of diagnosis. The evaluation of skeletal bone age is the most clinical application for the study of endocrinology, genetic disorders and growth in young people. This assessment is usually performed using the radiologic analysis of the left wrist using the GP (Greulich-Pyle) technique or the TW (Tanner-Whitehouse) technique. Both techniques have many disadvantages, including a lack of human deductions from observations as well as being time-consuming