Adaptive

Open Access Article

1 - Low Distance Airplanes Detection and Tracking Visually using Spectral Residual and KLT Composition
Mohammad Anvaripour Sima Soltanpour

10.7508/jist.2014.03.004

This paper presents the method for detection and tracking airplanes which can be observed visually in low distances from sensors. They are used widely for some reasons such as military or unmanned aerial vehicle (UAV) because of their ability to hide from radar signals; More

This paper presents the method for detection and tracking airplanes which can be observed visually in low distances from sensors. They are used widely for some reasons such as military or unmanned aerial vehicle (UAV) because of their ability to hide from radar signals; however they can be detected and viewed by human eyes. Vision based methods are low cost and robust against jamming signals. Therefore, it is mandatory to have some visual approaches to detect airplanes. By this way, we propose spectral density for airplane detection and KLT algorithm for tracking. This approach is a hybrid of two distinct methods which have been presented by researchers and used widely in detection or tracking specific objects. To have accurate detection, image intensity would be adjusted adaptively. Correct detected airplanes would be achievable by eliminating some long optical flow trajectory in image frames. The proposed method would be analyzed and evaluated by comparison with state of the art approaches. The experimental results show the power of our approach in detection of multiple airplanes unless they become too small in presence of other objects and multiple airplanes. We make some test by implementing our approach on an useful database presented by some researchers. Manuscript profile

Open Access Article

2 - Digital Video Stabilization System by Adaptive Fuzzy Kalman Filtering
Mohammad javad Tanakian Mehdi Rezaei Farahnaz Mohanna

10.7508/jist.2013.04.003

Digital video stabilization (DVS) allows acquiring video sequences without disturbing jerkiness, removing unwanted camera movements. A good DVS should remove the unwanted camera movements while maintains the intentional camera movements. In this article, we propose a no More

Digital video stabilization (DVS) allows acquiring video sequences without disturbing jerkiness, removing unwanted camera movements. A good DVS should remove the unwanted camera movements while maintains the intentional camera movements. In this article, we propose a novel DVS algorithm that compensates the camera jitters applying an adaptive fuzzy filter on the global motion of video frames. The adaptive fuzzy filter is a Kalman filter which is tuned by a fuzzy system adaptively to the camera motion characteristics. The fuzzy system is also tuned during operation according to the amount of camera jitters. The fuzzy system uses two inputs which are quantitative representations of the unwanted and the intentional camera movements. Since motion estimation is a computation intensive operation, the global motion of video frames is estimated based on the block motion vectors which resulted by video encoder during motion estimation operation. Furthermore, the proposed method also utilizes an adaptive criterion for filtering and validation of motion vectors. Experimental results indicate a good performance for the proposed algorithm. Manuscript profile

Open Access Article

3 - Video Transmission Using New Adaptive Modulation and Coding Scheme in OFDM based Cognitive Radio
Hassan Farsi Farid Jafarian

10.7508/jist.2013.04.005

As Cognitive Radio (CR) used in video applications, user-comprehended video quality practiced by secondary users is an important metric to judge effectiveness of CR technologies. We propose a new adaptive modulation and coding (AMC) scheme for CR, which is OFDM based sy More

As Cognitive Radio (CR) used in video applications, user-comprehended video quality practiced by secondary users is an important metric to judge effectiveness of CR technologies. We propose a new adaptive modulation and coding (AMC) scheme for CR, which is OFDM based system that is compliant with the IEEE.802.16. The proposed CR alters its modulation and coding rate to provide high quality system. In this scheme, CR using its ability to consciousness of various parameters including knowledge of the white holes in the channel spectrum via channel sensing, SNR, carrier to interference and noise ratio (CINR), and Modulation order Product code Rate (MPR) selects an optimum modulation and coding rate. In this scheme, we model the AMC function using Artificial Neural Network (ANN). Since AMC is naturally a non-liner function, ANN is selected to model this function. In order to achieve more accurate model, Genetic algorithm (GA) and Particle Swarm Optimization (PSO) are selected to optimize the function representing relationship between inputs and outputs of ANN, i.e., AMC model. Inputs of ANN are CR knowledge parameters, and the outputs are modulation type and coding rate. Presenting a perfect AMC model is advantage of this scheme because of considering all impressive parameters including CINR, available bandwidth, SNR and MPR to select optimum modulation and coding rate. Also, we show that in this application, GA rather than PSO is better choice for optimization algorithm. Manuscript profile

Open Access Article

4 - On-road Vehicle detection based on hierarchical clustering using adaptive vehicle localization
Moslem Mohammadi Jenghara Hossein Ebrahimpour Komleh

10.7508/jist.2015.04.004

Vehicle detection is one of the important tasks in automatic driving. It is a hard problem that many researchers focused on it. Most commercial vehicle detection systems are based on radar. But these methods have some problems such as have problem in zigzag motions. Im More

Vehicle detection is one of the important tasks in automatic driving. It is a hard problem that many researchers focused on it. Most commercial vehicle detection systems are based on radar. But these methods have some problems such as have problem in zigzag motions. Image processing techniques can overcome these problems.This paper introduces a method based on hierarchical clustering using low-level image features for on-road vehicle detection. Each vehicle assumed as a cluster. In traditional clustering methods, the threshold distance for each cluster is fixed, but in this paper, the adaptive threshold varies according to the position of each cluster. The threshold measure is computed with bivariate normal distribution. Sampling and teammate selection for each cluster is applied by the members-based weighted average. For this purpose, unlike other methods that use only horizontal or vertical lines, a fully edge detection algorithm was utilized. Corner is an important feature of video images that commonly were used in vehicle detection systems. In this paper, Harris features are applied to detect the corners. LISA data set is used to evaluate the proposed method. Several experiments are applied to investigate the performance of proposed algorithm. Experimental results show good performance compared to other algorithms . Manuscript profile

Open Access Article

5 - Acoustic Noise Cancellation Using an Adaptive Algorithm Based on Correntropy Criterion and Zero Norm Regularization
Mojtaba Hajiabadi

10.7508/jist.2015.03.003

The least mean square (LMS) adaptive algorithm is widely used in acoustic noise cancellation (ANC) scenario. In a noise cancellation scenario, speech signals usually have high amplitude and sudden variations that are modeled by impulsive noises. When the additive noise More

The least mean square (LMS) adaptive algorithm is widely used in acoustic noise cancellation (ANC) scenario. In a noise cancellation scenario, speech signals usually have high amplitude and sudden variations that are modeled by impulsive noises. When the additive noise process is nonGaussian or impulsive, LMS algorithm has a very poor performance. On the other hand, it is well-known that the acoustic channels usually have sparse impulse responses. When the impulse response of system changes from a non-sparse to a highly sparse one, conventional algorithms like the LMS based adaptive filters can not make use of the priori knowledge of system sparsity and thus, fail to improve their performance both in terms of transient and steady state. Impulsive noise and sparsity are two important features in the ANC scenario that have paid special attention, recently. Due to the poor performance of the LMS algorithm in the presence of impulsive noise and sparse systems, this paper presents a novel adaptive algorithm that can overcomes these two features. In order to eliminate impulsive disturbances from speech signal, the information theoretic criterion, that is named correntropy, is used in the proposed cost function and the zero norm is also employed to deal with the sparsity feature of the acoustic channel impulse response. Simulation results indicate the superiority of the proposed algorithm in presence of impulsive noise along with sparse acoustic channel. Manuscript profile

Open Access Article

6 - A Global-Local Noise Removal Approach to Remove High Density Impulse Noise
Ali Mohammad Fotouhi Samane Abdoli Vahid Keshavarzi

10.7508/jist.2017.20.006

Impulse noise removal from images is one of the most important concerns in digital image processing. Noise must be removed in a way that the main and important information of image is kept. Traditionally, the median filter has been the best way to deal with impulse nois More

Impulse noise removal from images is one of the most important concerns in digital image processing. Noise must be removed in a way that the main and important information of image is kept. Traditionally, the median filter has been the best way to deal with impulse noise; however, the image quality obtained in high noise density is not desirable. The aim of this paper is to propose an algorithm in order to improve the performance of adaptive median filter to remove high density impulse noise from digital images. The proposed method consists of two main stages of noise detection and noise removal. In the first stage, noise detection includes two global and local phases and in the second stage, noise removal is also done based on a two-phase algorithm. Global noise detection is done by a pixel classification approach in each block of the image and local noise detection is performed by automatically determining two threshold values in each block. In the noise removal stage only noisy pixels detected from the first stage of the algorithm are processed by estimating noise density and applying adaptive median filter on noise-free pixels in the neighborhood. Comparing experimental results obtained on standard images with other proposed methods proves the success of the proposed algorithm. Manuscript profile

Open Access Article

7 - Balancing Agility and Stability of Wireless Link Quality Estimators
MohammadJavad Tanakian Mehri Mehrjoo

10.7508/jist.2019.03.003

The performance of many wireless protocols is tied to a quick Link Quality Estimation (LQE). However, some wireless applications need the estimation to respond quickly only to the persistent changes and ignore the transient changes of the channel, i.e., be agile and sta More

The performance of many wireless protocols is tied to a quick Link Quality Estimation (LQE). However, some wireless applications need the estimation to respond quickly only to the persistent changes and ignore the transient changes of the channel, i.e., be agile and stable, respectively. In this paper, we propose an adaptive fuzzy filter to balance the stability and agility of LQE by mitigating the transient variation of it. The heart of the fuzzy filter is an Exponentially Weighted Moving Average (EWMA) low-pass filter that its smoothing factor is changed dynamically with fuzzy rules. We apply the adaptive fuzzy filter and a non-adaptive one, i.e., an EWMA with a constant smoothing factor, to several types of channels from short-term to long-term transitive channels. The comparison of the filters outputs shows that the non-adaptive filter is stable for large values of the smoothing factor and is agile for small values of smoothing factor, while the proposed adaptive filter outperforms the other ones in terms of balancing the agility and stability measured by the settling time and coefficient of variation, respectively. Notably, the proposed adaptive fuzzy filter performs in real time and its complexity is low, because of using limited number of fuzzy rules and membership functions. Manuscript profile

Open Access Article

8 - A Two-Stage Multi-Objective Enhancement for Fused Magnetic Resonance Image and Computed Tomography Brain Images
Leena Chandrashekar A Sreedevi Asundi

10.29252/jist.8.30.93

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the imaging techniques for detection of Glioblastoma. However, a single imaging modality is never adequate to validate the presence of the tumor. Moreover, each of the imaging techniques represents a diff More

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the imaging techniques for detection of Glioblastoma. However, a single imaging modality is never adequate to validate the presence of the tumor. Moreover, each of the imaging techniques represents a different characteristic of the brain. Therefore, experts have to analyze each of the images independently. This requires more expertise by doctors and delays the detection and diagnosis time. Multimodal Image Fusion is a process of generating image of high visual quality, by fusing different images. However, it introduces blocking effect, noise and artifacts in the fused image. Most of the enhancement techniques deal with contrast enhancement, however enhancing the image quality in terms of edges, entropy, peak signal to noise ratio is also significant. Contrast Limited Adaptive Histogram Equalization (CLAHE) is a widely used enhancement technique. The major drawback of the technique is that it only enhances the pixel intensities and also requires selection of operational parameters like clip limit, block size and distribution function. Particle Swarm Optimization (PSO) is an optimization technique used to choose the CLAHE parameters, based on a multi objective fitness function representing entropy and edge information of the image. The proposed technique provides improvement in visual quality of the Laplacian Pyramid fused MRI and CT images. Manuscript profile

Open Access Article

9 - Detection of Attacks and Anomalies in the Internet of Things System using Neural Networks Based on Training with PSO Algorithms, Fuzzy PSO, Comparative PSO and Mutative PSO
Mohammad Nazarpour navid nezafati Sajjad Shokouhyar

10.52547/jist.16307.10.40.270

20.1001.1.23221437.2022.10.40.4.0

Integration and diversity of IOT terminals and their applicable programs make them more vulnerable to many intrusive attacks. Thus, designing an intrusion detection model that ensures the security, integrity, and reliability of IOT is vital. Traditional intrusion detect More

Integration and diversity of IOT terminals and their applicable programs make them more vulnerable to many intrusive attacks. Thus, designing an intrusion detection model that ensures the security, integrity, and reliability of IOT is vital. Traditional intrusion detection technology has the disadvantages of low detection rates and weak scalability that cannot adapt to the complicated and changing environment of the Internet of Things. Hence, one of the most widely used traditional methods is the use of neural networks and also the use of evolutionary optimization algorithms to train neural networks can be an efficient and interesting method. Therefore, in this paper, we use the PSO algorithm to train the neural network and detect attacks and abnormalities of the IOT system. Although the PSO algorithm has many benefits, in some cases it may reduce population diversity, resulting in early convergence. Therefore,in order to solve this problem, we use the modified PSO algorithm with a new mutation operator, fuzzy systems and comparative equations. The proposed method was tested with CUP-KDD data set. The simulation results of the proposed model of this article show better performance and 99% detection accuracy in detecting different malicious attacks, such as DOS, R2L, U2R, and PROB. Manuscript profile

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