Sharif Digital Repository

Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural... 

The main focus of this paper is on the estimation of target motion parameters using bearing measurements. Here, based on a linearization of measurements, a recursive least squares (RLS) solution is developed. The performance of the proposed method is compared to PLE which uses the same approach in measurement linearization. Simulation results show the excellence of the proposed method over the PLE, and RLS results are in vicinity of the CRLB  

In this paper, a new method is proposed for solving bearing-only tracking when the target moves with constant velocity. The proposed method is compared to Pseudo Linear Estimator. Simulation results show that the proposed method outperforms PLE and estimation error is in vicinity of the CRLB  

Multi-sensor tracking is widely used for augmentation of tracking accuracy using data fusion. A basic requirement for such applications is the real time temporal synchronization and spatial registration of two sensory data. In this study a new method for time and space coordination of two tracking sensor measurements has been presented. For spatial registration we used a body coordinate system and then applied the effect of the level arm. The time synchronization was done based on least mean square (LMS) error method. This method was implemented to synchronize the position and orientation of an object using Inertial (1IMU) and Optical (Optotrak) tracking systems. The results of synchronized... 

Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR... 

Most of the publications regarding the maxim covering location problem (MCLP) address the case where the decision is to be made for one period. In this paper, we deal with a rather untouched version of MCLP which is called dynamic MCLP (DMCLP). In order to solve this problem, a simulated annealing (SA) has been presented. The proposed solution algorithm is capable of solving problems with up to 2500 demand nodes and 200 potential facilities with a fair amount of exactness. Our experiments showed that the proposed approach finds solutions with errors less than one percent  

In this paper, we present a new method to solve tracking problem. The proposed method combines sparse representation and motion estimation to track an object. Recently. sparse representation has gained much attention in signal processing and computer vision. Sparse representation can be used as a classifier but has high time complexity. Here, we utilize motion information in order to reduce this computation time by not calculating sparse codes for all the frames. Experimental results demonstrates that the achieved result are accurate enough and have much less computation time than using just a sparse classifier  

In this work we present a sparse dictionary learning method, specifically tuned to solve the tracking problem. Recently, sparse representation has drawn much attention because of its genuineness and strong mathematical background. In this paper we present an online method for dictionary learning which is desirable for problems such as tracking. Online learning methods are preferable because the whole data are not available at the current time. The presented method tries to use the advantages of the generative and discriminative models to achieve better performance. The experimental results show our method can overcome many tracking challenges  

Since tracking algorithms should be robust with respect to appearance changes, online algorithms has been investigated recently instead of offline ones which has shown an acceptable performance in controlled environments. The most challenging issue in online algorithms is updating of the model causing tracking failure because of introducing small errors in each update and disturbing the appearance model (drift). in this paper, we propose an online generative tracking algorithm in order to overcome the challenges such as occlusion, object shape changes, and illumination variations. In each frame, color distribution of target candidates is obtained and the candidate having the lowest distance... 

Human tracking is an essential step in many computer vision-based applications. As single view tracking may not be sufficiently robust and accurate, tracking based on multiple cameras has been widely considered in recent years. This paper presents a distributed human tracking method in a smart camera network and introduces a particle filter design based on Histogram of Oriented Gradients (HOG) and color histogram. The proposed adaptive motion model also estimates the target speed from the history of its latest displacement and improves the robustness of the tracker by decreasing the probability of missing targets. In addition, a distributed data fusion method is proposed which fuses the... 

The fusion of the optical and inertial tracking systems seems an attractive solution to solve the shadowing problem of the optical tracking systems, and remove the time integration troubles of the inertial sensors. We developed a fusion algorithm for this purpose, based on the Kalman filter, and examined its efficacy to improve the position and orientation data, obtained by each individual system. Experimental results indicated that the proposed fusion algorithm could effectively estimate the 2 seconds missing data of the optical tracker  

Non Line of Sight (NLOS) propagation is a challenging issue which the performance of network based wireless localization is limited by errors primarily caused by NLOS corruption. NLOS is inherently a dominant source of errors in metropolitan area wireless networks, like cellular one, so in this paper we discuss our proposed algorithm within cellular network terminology. This paper contributes two novel algorithms. The first one is to extract the NLOS state of base station (BS) and the second one is a correction algorithm to enhance the measurements accuracy. Our proposed algorithm discuses a novel localization technique to estimate true mobile terminal (MT) location from a set of possibly... 

Object tracking is a difficult and primary task in many video processing applications. Because of the diversity of various video processing tasks, there exists no optimum method that can perform properly for all applications. Histogram-based particle filtering is one of the most successfu1 object tracking methods. However, for dealing with visual tracking in real world conditions (such as changes in illumination and pose) is still a challenging task. In this paper, we have proposed a color-based adaptive histogram particle filtering method that can update the target model. We have used the Bhattacharyya coefficients to measure the likelihood between two color histograms. Our experimental... 

This paper examines the problem of determining optimal sensors trajectories for localization of a moving radio source based on Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) measurements in situations in which sensors are constrained both in their movements and regions of operation. By considering the movement of the source and constrained movement of the sensors, a constraint problem is formed which is solved to determine optimal trajectories of the sensors for source tracking. The validity of the proposed algorithm is assessed by two different simulation scenarios and the results verify its proper operation with estimation error decreasing in consecutive steps... 

In this research, a web-based simulator is developed, which can be used for generating image sequences of moving spermatozoa cells. It can be used for assessment of multiple object tracking algorithms, especially Computer Aided Sperm Analysis (CASA) systems. The developed software has many useful parameters such as blurring images or adding noise and it also gives full control of sperm counts and types. To illustrate performance of the developed simulator, three parameters (spermatozoa population, standard deviation of Gaussian blur filter and noise intensity) have been swept and the results of three different multiple object tracking algorithms were compared as an application of this... 

This paper, describes a solution for tracking multiple soccer players, simultaneously, in soccer ground. It adapts Kalman filter for tracking multiple players. Adapting Kalman filter is divided to four main tasks. The first task is defining the state vector for multiple object tracking. The second task is determining a motion model for estimating the position of soccer players in the next frame. The third task is defining an observation method for detecting soccer players in each frame. Finally, the fourth task is tuning the measurement noise covariance and estimating noise covariance. In the third task, a novel observation method for detecting soccer players is proposed. This method divides... 

Design of a vision-based target tracking control strategy for a dual-rotor UAV with tilting rotors is presented. In this research, the under-study UAV is equipped with a sliding mass that can slide over a motorized linear track attached to the UAV's fuselage enabling it to perform sophisticated maneuvers by monitoring its five controllable degrees of freedom. The dynamic model of the system is first derived and then a model-based controller using vision based pose estimation, obtained via an onboard down-looking camera, is designed enabling the UAV to: (1) pitch hover in position, (2) servo towards a target position, and (3) track a moving target. Simulations show the premise of the proposed... 

In this paper, we propose what we believe is a novel sensitive micro-optoelectromechanical systems (MOEMS) accelerometer based on intensity modulation by using a one-dimensional photonic crystal. The optical sensing system of the proposed structure includes an air-dielectric multilayer photonic bandgap material, a laser diode (LD) light source, a typical photodiode (1550 nm) and a set of integrated optical waveguides. The proposed sensor provides several advantages, such as a relatively wide measurement range, good linearity in the whole measurement range, integration capability, negligible cross-axis sensitivity, high reliability, and low air-damping coefficient, which results in a wider... 

This paper presents a new approach to sensor placement strategy in emitter localization problem which is based on Time Difference of Arrival (TDOA) measurements. The advantage of this method is its flexibility and capability of positioning sensors in constrained or non-stationary situations in which the positions of the sensors are restricted to certain portions of the space and/or needed to be changed repeatedly. The validity of the proposed algorithm is assessed by three different simulation scenarios and the results verify its proper operation  

In this paper, a sparsity-aware target localization method in multiple-input-multiple-output (MIMO) radars by utilizing time difference of arrival (TDOA) measurements is proposed. This method provides a maximum likelihood (ML) estimator for target position by employing compressive sensing techniques. Also, for fast convergence and mitigating the mismatch problem due to grid discretization, we address a block-based search coupled with an adaptive dictionary learning technique. The Cramer-Rao lower bound for this model is derived as a benchmark. Simulations results are included to verify the localization performance