فهرست مطالب مهدی چهل امیرانی

Using of compressive sensing in radar systems caused to eliminate the matched filter from receiver, and to reduce the required receiver analog-to-digital conversion bandwidth in radar systems. One of compressive sensing parameters is measurement matrix. Measurement matrix for radar systems is usually random matrix. Although exact recovery of signal using random matrices is possible with high probability and this matrix is incoherent with every basis matrix but implementation of that is impossible in practice. So it is useful to use deterministic matrices as measurement matrix. One of these matrices is Alltop matrix that obtained from Alltop sequence. There are limitations in use of this matrix for compressive sensing. We not only will resolve These limitations in this article but also will present a suitable alternative for matched filter block based on compressive sensing that has better performance in comparison to matched filter and can reconstruct radar targets with lower error than matched filter.

Mammography is the most common and effective screening method for breast cancer detection. In this paper a computer aided system for classification of benign and malignant tumors in digital mammogram is presented. First, a median filter is used for noise reduction, and then artifacts and pectoral muscle are removed to make the mammogram ready for segmentation. For segmentation of mammogram, a new contrast enhancement method is presented which employs the difference of two complement enhanced images and then a histogram based fuzzy C-means (HFCM) clustering are used for region-of-interest (ROI) extraction. Then, some geometrical and textural features are extracted, and finally linear support vector machine and decision tree classifier are used to classify the region of interest into benign and malignant classes. The proposed algorithm is validated on the MIAS and DDSM databases. The experimental results showed that the performance of the proposed method is promising compared to the other methods evaluated.

Cardiac arrhythmia, especially in the early hours, can stop heart activity and cause death. Since mistake in decision making is one of the most important causes of death in patients in cardiac intensive care units, the identification and classification of cardiac arrhythmias using the ECG signal is a valuable information source for diagnosing patients with heart abnormalities in times of crisis and warfare.

At first, the noises of ECG signal are removed using digital filters and discrete wavelet transform (DWT). Then, Kurtogram of each QRS complex is obtained using spectral kurtosis analysis. Informative features are obtained from segments of Kurtogram function. Finally, K-nearest neighbor classifier is used to determine the normality of person or its arrhythmia type is detected. 

In this paper, ECG signals from MIT-BIH signal are used. ECG signals of normal persons and four arrhythmias including APB, PVC, LBBB, and RBBB are chosen for classification. Obtained results show that proposed method achieves the accuracy of 98.51% for classification of ECG signals. Since accuracy of cardiac arrhythmia detection is an important and vital issue in medicine, the proposed method can be used by cardiologists to make a robust decision.

Considering the low computational complexity of the proposed method and obtained results, it can be used for fast and accurate cardiac arrhythmia detection, which is a special care resource and important task in physician within war.

The deliberate and unintentional attacks of the enemy in order to destroy and damag the country’s communications infrastructure as an undeniable fact, underscores the importance of the structuralist defense approach. In this regard, the balancing of the defense and offense budgets to prevent any increase in the index of service expectations was studied.To do so ,Increasing the security of the country’s most important infrastructures from the passive defense approach was investigated , and a comprehensive defense-based algorithm to increase the power and reduce vulnerability against attackers’s deliberate attacking was provided. Furthermore, Extensive pressure control system has been selected as a case study, and considering the risk of attacks, a comprehensive defense algorithm has been undertaken.In order to increase the security of the extensive pressure control system, two methods of hardware and software were proposed, which can reduce the risk of vulnerability to a great extent and ultimately provide the system security.The use of shielding using different materials as a hardware method for coping with field attacks has been investigated and its desirable results have been provided by FEKO software. Morover, in this study, the information coding as a proposed software method, guarantees the security of information transmition. In this paper, while presenting a constructed algorithm to meet the requirements of passive defense in the vital infrastructures of the country, we have reviewed practical solutions to increase the security of information transmission that can be used to deal with cyber-field attacks.

Cognitive control deficits are seen in many psychological and brain disorders. The dual mechanism of control (DMC) theory assumes two proactive (PC) and reactive control (RC) modes for cognitive control and uses the AX version of continuous performance test (AX-CPT) as the main research paradigm. This test determines deficits in cognitive control modes in various disorders. Midbrain dopamine signal plays an important role in the pathophysiology of various disorders with cognitive control deficits and plays a key role in DMC theory.
The present study was a computer simulation to investigate the effect of midbrain dopamine signal manipulation on cognitive control deficits. Simulation is based on LEABRA cognitive architecture and using the PBWM model as a model of working memory and PVLV model as a model of midbrain dopaminergic system. This investigation has been implemented in the emergent computer simulation software where the AX-CPT is presented to the model and the model performance was measured. Simulation results are calculated in three proactive control behavioral index (PCB), PC, and RC indices.
With increasing gain of phasic dopamine signal from 0 to 100 percent, a 15 to 45 percent changes occurred in the trend of curves. Increasing up to 15 percent, the error indices sharply decreased, PC was reduced, RC is increased, and PCB tends from PC to RC. Increasing from 15 to 45 percent, PC was increased, RC was reduced, and PCB tends from RC to PC. These trends were damped between 45 to 100 percent enhancement. Indices related to the average reaction time showed a reversed pattern of error indices.
The results of the error indices by decreasing phasic dopamine level indicate an increase in PC deficit and RC improvement as well as a greater tendency toward RC. These results are consistent with the hypodopaminergic pattern and DMC mechanism deficits in attention deficit hyperactivity disorder, depression, negative symptoms of schizophrenia as well as chronic addiction of cocaine and alcohol and Parkinson's disease.