فهرست مطالب همایون مهدوی نسب

Fire is one of the dangers that can endanger human health in a short time and if it is not controlled in time, it will cause a lot of damage. Therefore, timely and accurate identification of the location of the fire can prevent the consequences of its expansion. In this research, a new method for fire detection is proposed based on the extraction of its temporal-spatial features in video frames. In the proposed algorithm, a multiscale convolutional neural network along with a YOLO (you only look once) network is used to extract spatial features and identify fire candidate regions. Then, fractal analysis based on the temporal blanket method is then used to remove non-moving textures similar to fire and to examine the temporal features of the candidate region. Finally, the fire region is separated from the other parts of the image by fusion the results of the two steps. The evaluation results of the proposed method on three data sets show that the accuracy of fire detection is about 96.1%, while the precision and recall values are 92% and 96.9%, respectively. Experimental results show that the proposed method performs better than existing algorithms and thus confirms the ability of this method for efficient use in the real world.

Implementation of neuromorphic computing systems (NCSs) using digital and analog circuits occup ies a high chip area and consumes high power. With the advancement of nanotechnology, the hybrid Magnetic tunnel junction/Complementary metal–oxide–semiconductor (MTJ/CMOS) circuits have made it possible to implement NCSs with higher density and lower power consumption. However, still there is a gap between the performance of the human brain and NCSs. To mitigate this gap, it is essential to further decrease the energy consumption and the delay of the NCS. The high energy consumption of the MTJ-based NCS is mostly related to the high current needed to switch the MTJ state. Hence, some previous methods tried to perform real-time tracking of the MTJ state by monitoring its voltage and cutting off its current immediately after switching. However, due to the small voltage changes after switching, these methods suffer from a high-power consumption (they need power-hungry amplifiers). In this paper, a new method based on the tracking of MTJ current (instead of voltage) and terminating the MTJ current after switching is proposed. Due to the large changes in the MTJ current after switching (about 40%), there is no need to use an amplifier in the proposed circuit. Therefore, the conventional voltage-mode sensing circuit is replaced with the proposed circuit, to improve the energy efficiency, speed and delay of the NCS. In all state-of-the-art designs, the voltage changes on nodes across the MTJ (PL, FL or both of them) have been used to detect the MTJ switching. However, the proposed circuit detects the MTJ switching by properly sensing the MTJ current and terminates its current immediately. The simulation results in 65-nm CMOS technology confirm that the proposed technique improves the energy consumption and speed of the NCS by 49% and 2.1X compared with the typical NCS.

Addiction is a biological, psychological, and social disease. Several factors are involved in etiology, substance abuse, and addiction which interact with each other and lead to the beginning of drug use and then addiction. Heroin is an addictive drug that, by acting on the central nervous system, reduces the density of neurons in the brain and interferes with decision making. This paper examines the effects of heroin on brain function by studying the relationship between spectral strength of electroencephalogram (EEG) signal and heroin abuse. For this purpose, the resting EEG signal and cognitive activity of 15 healthy individuals and 15 heroin addicts were recorded in 16 channels in one session. The frequency range of EEG signal sub-bands was calculated separately for each individual. Welch method has been used to extract the power of EEG signal frequency sub-bands. The extracted features were examined using Mann-Whitney test and Davies-Bouldin index. The results show that the heroin-dependent group has higher power in delta (in the frontal, central and temporal regions) and theta (in all canals) than in the control group. In the heroin-dependent group, the power of alpha decreased compared to the control group. High alpha sub-bands power in the frontal, temporal and central lobes compared to other frequency sub-bands, as well as in the central, parietal and temporal lobes, the power of the second low alpha sub-band in decreased addicts. According to Davies-Bouldin, the power of the second low alpha sub-band in the T6 channel has a better power to differentiate between healthy and heroin-dependent people.

After the discovery of X-rays with the increasing use of digital imaging systems, medical image processing has become more important. Medical image processing helps specialists in diagnosing diseases. In addition to major digital techniques such as computed tomography (CT) or magnetic resonance imaging (MRI), analog imaging techniques such as endoscopy or radiography are now equipped with digital sensors. By processing images using different methods, the procedure applied to patients can be improved. Algorithms play a key role in noise filtering, segmentation, extraction, and characterization that diagnose diseases. MATLAB software and image processing toolboxes provide a wide range of advanced image processing functions and interactive tools for enhancing and analyzing digital images. In this article, using several algorithms designed in MATLAB, the quality of images is examined and a more appropriate algorithm is selected.

Due to the rapid growth of information technology and exponential increasing of information the need for more and more storage capacity and efficiency has increased. Image compression is an important tool to reduce the redundancy of images data in order to be able to store or transmit them in an efficient manner. When images are limited to a specific and limited family of images like MRI databases of a hospital or facial image database of a university or an organization or fingerprint image databases, this limitation increases the total spatial redundancy. Thus, efficient storage of such images is beneficial, and their compression becomes an appealing application, and this urges algorithms specially tailored for the task of content base image compression to surpass general purpose compression algorithms. The facial images, due to their wide application as the most common images in the organizations and companies are more considerable for image compression. In this paper a new image compression scheme using sparse coding and RLS-DLA redundant dictionary learning is proposed that can be used for compressing of face image databases. In the proposed method, several dictionaries are exploited adaptively based on the required image quality to enhance the overall rate-distortion. The simulation results show that this scheme outperforms the state-of-art algorithms like JPEG2000 by about 0.5 to 1.2 dB for reconstructed images PSNR.

Nowadays, the wireless sensor network (WSN) is used in many different fields and applications. Enemy tracking and wildlife habitats monitoring are the examples of target tracking by using of large number of sensor nodes. The main idea in this area is to find some usable target information such as location, speed and movement direction of the target because they must be available any time. By the way, the sensor nodes in sensor network model have a severe energy limit and cannot be recharged simply. In this paper, an efficient algorithm abbreviated EAASA is presented in order to reduce energy consumption while maintaining the quality of target tracking. The simulation results are compared to the AASA (cluster-based target tracking algorithm) algorithm and show that the proposed algorithm has been able to reduce energy consumption significantly while maintaining tracking quality. This method has increased the life time of the network and reduced the rate of loss of the target.

Edge detection is one of the most important issues in image processing and machine vision. Edge detection in image processing is a low order process, so that the performance of the higher order processes such as object identification, segmentation and coding of images is directly related to the efficiency of this process. The estimation of edge parameters with using gradient vector calculation is usually not accurate. Keeping the structure of edge is one of the most important problems in edge detection, especially in detecting noisy images. For practical applications that accurate edges are needed, subpixel edge detection is done. In this paper a new edge detection method based on edge figure and obtained model from neighboring pixels effect and spatial relation of image pixels is introduced. Then an iterative restoration process based on presented edge detector is suggested. The purpose of this method is to increase the accuracy in recognition of subpixel position, curvature, orientation and change in intensity in noisy images.

One of the most effective strategies for steam generator efficiency enhancement is to improve the control system. For such an improvement، it is essential to have an accurate model for the steam generator of power plant. In this paper، an industrial steam generator is considered as a nonlinear multivariable system for identification. An important step in nonlinear system identification is the development of a nonlinear model. In recent years، artificial neural networks have been successfully used for identification of nonlinear systems in many researches. Wavelet neural networks (WNNs) also are used as a powerful tool for nonlinear system identification. In this paper we present a time delay neural network model and a WNN model in order to identify an industrial steam generator. Simulation results show the effectiveness of the proposed models in the system identification and demonstrate that the WNN model is more precise to estimate the plant outputs.

Active noise control is based on the destructive interference between the primary noise and generated noise from the secondary source. An antinoise of equal amplitude and opposite phase is generated and combined with the primary noise. In this paper، performance of the neural networks is evaluated in active cancellation of sound noise. For this reason، feedforward and recurrent neural networks are designed and trained. After training، performance of the feedforwrad and recurrent networks in noise attenuation are compared. We use Elman network as a recurrent neural network. For simulations، noise signals from a SPIB database are used. In order to compare the networks appropriately، equal number of layers and neurons are considered for the networks. Moreover، training and test samples are similar. Simulation results show that feedforward and recurrent neural networks present good performance in noise cancellation. As it is seen، the ability of recurrent neural network in noise attenuation is better than feedforward network.

Watermarking is used to protect copyright proof. Robustness index is the most important parameter that evaluates watermarking algorithm against different attacks such as noise and compression. In this paper، a novel semi- blind image watermarking algorithm based on joint wavelet transform (WT) and singular value decomposition (SVD) transform is proposed. In this algorithm، a new strategy is used to joint WT and SVD effectively. The most important advantage of this algorithm is robustness against a number of common attacks. Experimental results show that the proposed algorithm improves evaluation parameters more than other methods presented previously in the literature.

Most of the existing image resolution enhancement algorithms assume that the image is clean and noise free، but this assumption is not practically valid. One strategy for interpolation of noisy images is to denoise the image first and then interpolate the denoised image. However، this strategy does not lead to satisfying results because denoising may smooth image details and also other artifacts such as blurring and blocking introduced due to image denoising will be amplified in the following interpolation stage. Thus، in this paper we propose a joint salt and pepper noise removal and resolution enhancement algorithm using dual-tree complex wavelet transform and feedforward neural networks. In this algorithm، the wavelet subbands corresponding to noise free high resolution image are estimated from noisy low resolution image by multi-layer perceptron (MLP). Therefore the noise free high resolution image is obtained by complex wavelet reconstruction of the estimated subbands. Takeing advantages of complex wavelet transform such as nearly shift invariance and directional selectivity the subband estimation by neural networks is done with high accuracy. As it is verified in the experimental results، the proposed algorithm has better performance both subjectively and objectively and is able to maintain the image fine structures well.

The aim of this paper is the introduction of a CMOS OTA basic block that its transconductance gain can be electronically and linearly tuned. This transconductance is proportional to the square root of the bias current. To achieve the maximum output voltage and create a wide range of linear transconductance the CMOS OTA has been used. Then the variation of the transconductance and its effects on the performance of Continuous-time filters has been considered. The novelty of this paper is to show that how the transconductance of a first-Order filter is transformed to high pass and low pass filters and the transfer function of a second-order filter is transformed into high pass، low pass، band pass and band rejection filters. The performance of the proposed circuit is discussed and confirmed through MATLAB and PSPICE-simulation results.

This paper aims at applying H. 264 in medical video compression applications and improving the H. 264 Compression performance with better perceptual quality and low coding complexity. In order to achieve higher compression of medical video، while maintaining high image quality in the region of interest، with low coding complexity، here we propose a new model using H. 264/AVC that uses lossless compression in the region of interest، and very high rate، lossy compression in other regions. This paper proposes a new method to achieve fast intra and inter prediction mode decision that is based on coarse macroblocks for intra and inter prediction mode decision of the background region and finer macroblocks for region of interest. Also the macroblocks of the background region are encoded with the maximum quantization parameter allowed by H. 264/AVC in order to maximize the number of null coefficients. Experimental results show that the proposed algorithm achieves a higher compression rate on medical videos with a higher quality of region of interest with low coding complexity when compared to our previous algorithm and other standard algorithms reported in the literature.

Color and shape are basic characteristics which are used to recognize traffic signs. In this paper, a new speed limit sign detection method in various conditions is proposed. In this method, color image is segmented based on a thresholding technique in HSI color space. Then, corner features are detected using convolution masks and found the location of sign. The first advantage of this method is high accuracy to detect the location of sign. So, the object can be detected with 30% noise level, 30 meters for distances of signs, and for rotated signs. The second advantage of the proposed method is high speed in sign detection. Utimately, the type of sign can be recognized with eliminate redundant information and match between extracted image and database image. If the illumination conditions be ideal, the recognition rate is obtained to 89%.