Target threat assessment using fuzzy sets theory

The threat evaluation is significant component in target classification process and is significant in military and non military applications. Small errors or mistakes in threat evaluation and target classification especial in military applications can result in huge damage of life and property. Threat evaluation helps in case of weapon assignment, and intelligence sensor support system. It is very important factor to analyze the behavior of enemy tactics as well as our surveillance. This paper represented a precise description of the threat evaluation process using fuzzy sets theory. A review has been carried out regarding which parameters that have been suggested for threat value calculation. For the first time in this paper, eleven parameters are introduced for threat evaluation so that this parameters increase the accuracy in designed system. The implemented threat evaluation system has been applied to a synthetic air defense scenario and four real time dynamic air defense scenarios. The simulation results show the correctness, accuracy, reliability and minimum errors in designing of threat evaluation syste

A new model for threat assessment in data fusion based on fuzzy evidence theory

In this paper a new method for threat assessment is proposed based on Fuzzy Evidence Theory. The most widely classical and intelligent methods used for threat assessment systems will be Evidence or Dempster Shafer and Fuzzy Sets Theories. The disadvantage of both methods is failing to calculate of uncertainty in the data from the sensors and the poor reliability of system. To fix this flaw in the system of dynamic targets threat assessment is proposed fuzzy evidence theory as a combination of both Dempster- Shafer and Fuzzy Sets Theories. In this model, the uncertainty in input data from the sensors and the whole system is measured using the best measure of the uncertainty. Also, a comprehensive comparison is done between the uncertainty of fuzzy model and fuzzy- evidence model (proposed method). This method applied to a real time scenario for air threat assessment. The simulation results show that this method is reasonable, effective, accuracy and reliability

Ocular Artifact Detection and Removing from EEG by wavelet families: A Comparative Study

The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. Biological artifacts like ocular artifact (OA) are one of the main interferences in EEG recordings. Eye blinks and movements of the eyeballs produce a signal known as electrooculogram (EOG) that these are 10 to 100 times stronger than the EEG signal. Due to the frequency range of EEG signal and OA which has overlapping with each other, identify and removing of the EOG artifacts are one of the main challenges for researchers, because an incorrect denoising may lose some of the important information of EEG signals. In this context, our aim is to propose a technique based on wavelet transform for accurate identification of the blink artifact zone and removal of EEG signals. We propose using absolute value of signal reconstructed details for blink zone detection and the efficiency of wavelet families to remove the blink artifact which is evaluated by calculating the mean squared error (MSE) between denoised and clean EEG signals and comparing with the results before and after artifact removing show that db7, sym7, coif5, rbio1.5 and dmey at 4th level are preferable and effective in blink artifact zone detection and db7, coif5, dmey, db5 and db9 respectively provide the best result for blink artifact removing with minimum loss important information. Keywords: EEG, EOG, OA, Wavelet transform, MS

A New Method to Classify Breast Cancer Tumors and Their Fractionation

http://dx.doi.org/10.5902/2179460X19428In this paper, suspicious breast tumors were classified by using the neural network and the growth area method has been used for a fractionation of the benign or malignant areas of the normal tissue. Features extracted from input tissues are including statistical features and characteristics of spatial dependence. The advantage of this method is using of phase adaptive threshold based on entropy which leads to more accurate extraction of tumors and also corresponded with the nature of mammogram images. As a result, this method mimics of the human eye operation to detect abnormal masses. Database used in this paper is the MIAS mammogram database including 238 normal, benign and malignant mammograms. The accuracy obtained with 38 features is equal to 86.66% for detecting abnormal masses and 38.05 % for normal masses.In this paper, suspicious breast tumors were classified by using the neural network and the growth area method has been used for a fractionation of the benign or malignant areas of the normal tissue. Features extracted from input tissues are including statistical features and characteristics of spatial dependence. The advantage of this method is using of phase adaptive threshold based on entropy which leads to more accurate extraction of tumors and also corresponded with the nature of mammogram images. As a result, this method mimics of the human eye operation to detect abnormal masses. Database used in this paper is the MIAS mammogram database including 238 normal, benign and malignant mammograms. The accuracy obtained with 38 features is equal to 86.66% for detecting abnormal masses and 38.05 % for normal masses

TransDoubleU-Net: Dual Scale Swin Transformer With Dual Level Decoder for 3D Multimodal Brain Tumor Segmentation

Segmenting brain tumors in MR modalities is an important step in treatment planning. Recently, the majority of methods rely on Fully Convolutional Neural Networks (FCNNs) that have acceptable results for this task. Among various networks, the U-shaped architecture known as U-Net, has gained enormous success in medical image segmentation. However, absence of long-range association and the locality of convolutional layers in FCNNs can create issues in tumor segmentation with different tumor sizes. Due to the success of Transformers in natural language processing (NLP) as a result of using self-attention mechanism to model global information, some studies designed different variations of vision based U-Shaped Transformers. So, to get the effectiveness of U-Net we proposed TransDoubleU-Net which consists of double U-shaped nets for 3D MR Modality segmentation of brain images based on dual scale Swin Transformer for the encoder part and dual level decoder based on CNN and Transformers for better localization of features. The model’s core uses the shifted windows multi-head self-attention of Swin Transformer and skip connections to CNN based decoder. The outputs are evaluated on BraTS2019 and BraTS2020 datasets and showed promising results in segmentation

An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System

<p/> <p>This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF) neural network with a hybrid learning algorithm (HLA) has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT) is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI) with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR) of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL) indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%.</p

Ocular Artifact Detection and Removing from EEG using wavelet families: A Comparative Study

The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. Biological artifacts like ocular artifact (OA) are one of the main interferences in EEG recordings. Eye blinks and movements of the eyeballs produce a signal known as electrooculogram (EOG) that these are 10 to 100 times stronger than the EEG signal. Due to the frequency range of EEG signal and OA which has overlapping with each other, identify and removing of the EOG artifacts are one of the main challenges for researchers, because an incorrect denoising may lose some of the important information of EEG signals. In this context, our aim is to propose a technique based on wavelet transform for accurate identification of the blink artifact zone and removal of EEG signals. We propose using absolute value of signal reconstructed details for blink zone detection and the efficiency of wavelet families to remove the blink artifact which is evaluated by calculating the mean squared error (MSE) between denoised and clean EEG signals and comparing with the results before and after artifact removing show that db7, sym7, coif5, rbio1.5 and dmey at 4th level are preferable and effective in blink artifact zone detection and db7, coif5, dmey, db5 and db9 respectively provide the best result for blink artifact removing with minimum loss of important information

Predicting and Monitoring of the Elderly Falls Based on Modeling of the Motion Patterns Obtained From Video Sequences

Objectives: Many countries are faced with the growing population of the elderly each year and so designing an appropriate system for monitoring of various elderly states is necessity. Every year thousands of the elderly suffer serious damages such as articular fractures, broken bones and even death due to their fall Methods & Materials: In this paper, based on the analysis of images taken from the elderly&rsquo;s movement, an efficient system has been proposed that, in the first phase, simulates the movement of the elderly by detecting their abnormal walking. The, by combining several important features, including an estimate of body angle, representation of the motion and estimate of the magnitude and direction of movement, the speed of the falling is calculated. This system has been implemented on a set of 57425 video frames received from the elderly residing in Farzanegan Health Care Center in Mashhad and the video sequences containing the actual occurrence the of falling. All the sequences were randomly converted into four Movie categories with these details: AVI format, 120&times;160 pixels resolution and 15 fps. Results: Simulation of algorithm distinguishes the proposed system from similar ones, particularly due to its intelligent monitoring and its real time detection of the elderly&rsquo;s fall. The average accuracy (AAC), detection rate (DR) and insignificant false alarm rate (FAR) are 94%, 92.91% and 5.52% respectively in acceptable level. The 92% sensitivity and 94.47% specificity indicate the ability of the system in identifying the incidents similar to the fall. Conclusion: Many advantages such as high speed in data processing, unique accuracy and sensitivity and time parsimony make a system has particular performance and implementation of it due to intelligent monitoring and Real-Time tracking of seniors in Health Care Center and houses