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Ebrahim Karami

Publications and source records attributed to Ebrahim Karami.

18 recordsLinked to original sources

Estimation and Tracking of AP-diameter of the Inferior Vena Cava in Ultrasound Images Using a Novel Active Circle Algorithm

Medical research suggests that the anterior-posterior (AP)-diameter of the inferior vena cava (IVC) and its associated temporal variation as imaged by bedside ultrasound is useful in guiding fluid resuscitation of the critically-ill patient. Unfortunately, indistinct edges and gaps in vessel walls are frequently present which impede accurate estimation of the IVC AP-diameter for both human operators and segmentation algorithms. The majority of research involving use of the IVC to guide fluid resuscitation involves manual measurement of the maximum and minimum AP-diameter as it varies over time. This effort proposes using a time-varying circle fitted inside the typically ellipsoid IVC as an efficient, consistent and novel approach to tracking and approximating the AP-diameter even in the context of poor image quality. In this active-circle algorithm, a novel evolution functional is proposed and shown to be a useful tool for ultrasound image processing. The proposed algorithm is compared with an expert manual measurement, and state-of-the-art relevant algorithms. It is shown that the algorithm outperforms other techniques and performs very close to manual measurement.

cs.CV

Adaptive Polar Active Contour for Segmentation and Tracking in Ultrasound Videos

Detection of relative changes in circulating blood volume is important to guide resuscitation and manage a variety of medical conditions including sepsis, trauma, dialysis and congestive heart failure. Recent studies have shown that estimates of circulating blood volume can be obtained from the cross-sectional area (CSA) of the internal jugular vein (IJV) from ultrasound images. However, accurate segmentation and tracking of the IJV in ultrasound imaging is a challenging task and is significantly influenced by a number of parameters such as the image quality, shape, and temporal variation. In this paper, we propose a novel adaptive polar active contour (Ad-PAC) algorithm for the segmentation and tracking of the IJV in ultrasound videos. In the proposed algorithm, the parameters of the Ad-PAC algorithm are adapted based on the results of segmentation in previous frames. The Ad-PAC algorithm is applied to 65 ultrasound videos captured from 13 healthy subjects, with each video containing 450 frames. The results show that spatial and temporal adaptation of the energy function significantly improves segmentation performance when compared to current state-of-the-art active contour algorithms.

eess.IV

Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformations

Image identification is one of the most challenging tasks in different areas of computer vision. Scale-invariant feature transform is an algorithm to detect and describe local features in images to further use them as an image matching criteria. In this paper, the performance of the SIFT matching algorithm against various image distortions such as rotation, scaling, fisheye and motion distortion are evaluated and false and true positive rates for a large number of image pairs are calculated and presented. We also evaluate the distribution of the matched keypoint orientation difference for each image deformation.

cs.CV

A Semi-Automated Technique for Internal Jugular Vein Segmentation in Ultrasound Images Using Active Contours

The assessment of the blood volume is crucial for the management of many acute and chronic diseases. Recent studies have shown that circulating blood volume correlates with the cross-sectional area (CSA) of the internal jugular vein (IJV) estimated from ultrasound imagery. In this paper, a semi-automatic segmentation algorithm is proposed using a combination of region growing and active contour techniques to provide a fast and accurate segmentation of IJV ultrasound videos. The algorithm is applied to track and segment the IJV across a range of image qualities, shapes, and temporal variation. The experimental results show that the algorithm performs well compared to expert manual segmentation and outperforms several published algorithms incorporating speckle tracking.

eess.IV

Performance Analysis of Decision Directed Maximum Likelihood MIMO Channel Tracking Algorithm

In this paper, the performance of decision directed (DD) maximum likelihood (ML) channel tracking algorithm is analyzed. The ML channel tracking algorithm presents efficient performance especially in the decision directed mode of the operation. In this paper, after introducing the method for analysis of DD algorithms, the performance of ML Multiple-Input Multiple-Output (MIMO) channel tracking algorithm in the DD mode of operation is analyzed. In this method channel tracking error is evaluated for given decision error rate. Then, the decision error rate is approximated for given channel tracking error. By solving these two derived equations jointly, both the decision error rate and the channel tracking error are computed. The presented analysis is compared with simulation results for different channel ranks, Doppler frequency shifts, and SNRs, and it is shown that the analysis is a good match for simulation results especially in high rank MIMO channels and high Doppler shifts.

eess.SP

A Novel Detection Algorithm Efficient for Turbo coded CDMA Signals in Detect and Forward Cooperative Channels

In this paper, a new detection algorithm is proposed for turbo coded Code Division Multiple Access (CDMA) signals in detect and forward cooperative channels. Use of user cooperation makes much improvement in the performance of CDMA systems. Due to the special structure of CDMA systems, cooperative schemes increase the sum and cutoff capacities of CDMA based wireless systems and improve the quality of user-partner link which enhances the overall performance of the system. In this paper, a new combining scheme is proposed that makes the receiver more robust against the decision errors in the partner link. This structure is simulated for punctured 1/2 rate 4 states turbo code in a channel with first-order Markov time variation and different Rice factor variances. Through various simulations, it is shown when the channel estimates are available in the partner and receiver, the cooperation between users provides much diversity gain especially while using the new proposed combining algorithm.

eess.SP

Multisensor Data Fusion for Water Quality Monitoring using Wireless Sensor Networks

In this paper, the application of hierarchical wireless sensor networks in water quality monitoring is investigated. Adopting a hierarchical structure, the set of sensors is divided into multiple clusters where the value of the sensing parameter is almost constant in each cluster. The members of each cluster transmit their sensing information to the local fusion center (LFC) of their corresponding cluster, where using some fusion rule, the received information is combined, and then possibly sent to a higher-level central fusion center (CFC). A two-phase processing scheme is also envisioned, in which the first phase is dedicated to detection in the LFC, and the second phase is dedicated to estimation in both the LFC and the CFC. The focus of the present paper is on the problem of decision fusion at the LFC: we propose hard- and soft-decision maximum a posteriori (MAP) algorithms, which exhibit flexibility in minimizing the total cost imposed by incorrect detections in the first phase. The proposed algorithms are simulated and compared with conventional fusion techniques. It is shown that the proposed techniques result in lower cost. Furthermore, when the number of sensors or the amount of contamination increases, the performance gap between the proposed algorithms and the existing methods also widens.

eess.SP

Identification of GSM and LTE Signals Using Their Second-order Cyclostationarity

Automatic signal identification (ASI) has various millitary and commercial applications, such as spectrum surveillance and cognitive radio. In this paper, a novel ASI algorithm is proposed for the identification of GSM and LTE signals, which is based on the pilot-induced second-order cyclostationarity. The proposed algorithm provides a very good performance at low signal-to-noise ratios and short observation times, with no need for channel estimation, and timing and frequency synchronization. Simulations and off-the-air signals acquired with the ThinkRF WSA4000 receiver are used to confirm the findings.

eess.SP

Tracking of the Internal Jugular Vein in Ultrasound Images Using Optical Flow

Detection of relative changes in circulating blood volume is important to guide resuscitation and manage variety of medical conditions including sepsis, trauma, dialysis and congestive heart failure. Recent studies have shown that estimates of circulating blood volume can be obtained from ultrasound imagery of the of the internal jugular vein (IJV). However, segmentation and tracking of the IJV is significantly influenced by speckle noise and shadowing which introduce uncertainty in the boundaries of the vessel. In this paper, we investigate the use of optical flow algorithms for segmentation and tracking of the IJV and show that the classical Lucas-Kanade (LK) algorithm provides the best performance among well-known flow tracking algorithms.

eess.IV

Identification of SM-OFDM and AL-OFDM Signals Based on Their Second-Order Cyclostationarity

Automatic signal identification (ASI) has important applications to both commercial and military communications, such as software defined radio, cognitive radio, spectrum surveillance and monitoring, and electronic warfare. While ASI has been intensively studied for single-input single-output systems, only a few investigations have been recently presented for multiple-input multiple-output systems. This paper introduces a novel algorithm for the identification of spatial multiplexing (SM) and Alamouti coded (AL) orthogonal frequency division multiplexing (OFDM) signals, which relies on the second-order signal cyclostationarity. Analytical expressions for the second-order cyclic statistics of SM-OFDM and AL-OFDM signals are derived and further exploited for the algorithm development. The proposed algorithm provides a good identification performance with low sensitivity to impairments in the received signal, such as phase noise, timing offset, and channel conditions.

eess.SP

Cluster Size Optimization in Cooperative Spectrum Sensing

In this paper, we study and optimize the cooperation cluster size in cooperative spectrum sensing to maximize the throughput of secondary users (SUs). To calculate the effective throughput, we assume each SU spends just 1 symbol to negotiate with the other SUs in its transmission range. This is the minimum overhead required for each SU to broadcast its sensing decision to the other members of the cluster. When the number of SUs is large, the throughput spent for the negotiation is noticeable and therefore increasing the cooperation cluster size does not improve the effective throughput anymore. In this paper, we calculate the effective throughput as a function of the cooperation cluster size, and then we maximize the throughput by finding the optimal cluster size. Various numerical results show that when decisions are combined by the OR-rule, the optimum cooperation cluster size is less than when the AND-rule is used. On the other hand, the optimum cluster size monotonically decreases with the increase in the average SNR of the SUs. Another interesting result is that when the cluster size is optimized the OR-rule always outperforms the AND-rule.

eess.SP

Stochastic Models of Coalition Games for Spectrum Sharing in Large Scale Interference Channels

In this paper, we present a framework for the analysis of self-organized distributed coalition formation process for spectrum sharing in interference channel for large-scale ad hoc networks. In this approach, we use the concept of coalition clusters within the network where mutual interdependency between different clusters is characterized by the concept of spatial network correlation. Then by using stochastic models of the process we give up some details characteristic for coalition game theory in order to be able to include some additional parameters for network scaling. Applications of this model are a) Estimation of average time to reach grand coalition and its variance through closed-form equations. These parameters are important in designing the process in a dynamic environment. b) Dimensioning the coalition cluster within the network c) Modelling the network spatial correlation characterizing mutual visibility of the interfering links. d) Modeling of the effect of the new link activation/inactivation on the coalition forming process. e) Modeling the effect of link mobility on the coalition-forming process.

eess.SP

Optimization of Scheduling in Wireless Ad-Hoc Networks Using Matrix Games

In this paper, we present a novel application of matrix game theory for optimization of link scheduling in wireless ad-hoc networks. Optimum scheduling is achieved by soft coloring of network graphs. Conventional coloring schemes are based on assignment of one color to each region or equivalently each link is member of just one partial topology. These algorithms based on coloring are not optimal when links are not activated with the same rate. Soft coloring, introduced in this paper, solves this problem and provide optimal solution for any requested link usage rate. To define the game model for optimum scheduling, first all possible components of the graph are identified. Components are defined as sets of the wireless links can be activated simultaneously without suffering from mutual interference. Then by switching between components with appropriate frequencies (usage rate) optimum scheduling is achieved. We call this kind of scheduling as soft coloring because any links can be member of more than one partial topology, in different time segments. To simplify this problem, we model relationship between link rates and components selection frequencies by a matrix game which provides a simple and helpful tool to simplify and solve the problem. This proposed game theoretic model is solved by fictitious playing method. Simulation results prove the efficiency of the proposed technique compared to conventional scheduling based on coloring

cs.NI

Joint Optimization of Scheduling and Routing in Multicast Wireless Ad-Hoc Network Using Soft Graph Coloring and Non-linear Cubic Games

In this paper we present matrix game-theoretic models for joint routing, network coding, and scheduling problem. First routing and network coding are modeled by using a new approach based on compressed topology matrix that takes into account the inherent multicast gain of the network. The scheduling is optimized by a new approach called network graph soft coloring. Soft graph coloring is designed by switching between different components of a wireless network graph, which we refer to as graph fractals, with appropriate usage rates. The network components, represented by graph fractals, are a new paradigm in network graph partitioning that enables modeling of the network optimization problem by using the matrix game framework. In the proposed game which is a nonlinear cubic game, the strategy sets of the players are links, path, and network components. The outputs of this game model are mixed strategy vectors of the second and the third players at equilibrium. Strategy vector of the second player specifies optimum multi-path routing and network coding solution while mixed strategy vector of the third players indicates optimum switching rate among different network components or membership probabilities for optimal soft scheduling approach. Optimum throughput is the value of the proposed nonlinear cubic game at equilibrium. The proposed nonlinear cubic game is solved by extending fictitious playing method. Numerical and simulation results prove the superior performance of the proposed techniques compared to the conventional schemes using hard graph coloring.

eess.SP

Doppler Spread Estimation in MIMO Frequency-selective Fading Channels

One of the main challenges in high-speed mobile communications is the presence of large Doppler spreads. Thus, accurate estimation of maximum Doppler spread (MDS) plays an important role in improving the performance of the communication link. In this paper, we derive the data-aided (DA) and non-data-aided (NDA) Cramer-Rao lower bounds (CRLBs) and maximum likelihood estimators (MLEs) for the MDS in multiple-input multiple-output (MIMO) frequency-selective fading channel. Moreover, a lowcomplexity NDA-moment-based estimator (MBE) is proposed. The proposed NDA-MBE relies on the second- and fourth-order moments of the received signal, which are employed to estimate the normalized squared autocorrelation function of the fading channel. Then, the problem of MDS estimation is formulated as a non-linear regression problem, and the least-squares curvefitting optimization technique is applied to determine the estimate of the MDS. This is the first time in the literature when DAand NDA-MDS estimation is investigated for MIMO frequency-selective fading channel. Simulation results show that there is no significant performance gap between the derived NDA-MLE and NDA-CRLB even when the observation window is relatively small. Furthermore, the significant reduced-complexity in the NDA-MBE leads to low root-mean-square error (NRMSE) over a wide range of MDSs when the observation window is selected large enough.

eess.SP

Low Complexity Time Domain Semi-Blind MIMO-OFDM Channel Estimation Using Adaptive Bussgang Algorithm

In this paper, a low complexity time domain semi-blind algorithm is proposed to estimate and track the time varying MIMO OFDM channels. First, the proposed least mean squares (LMS) based algorithm is developed for the training mode and then is extended for the blind mode of the operation by combining with the decision direction (DD) or adaptive Bussgang algorithm (ABA) techniques. In the blind mode, because of decision errors, a smaller step size is considered for the LMS algorithm and the channel estimation is run a few times to improve its precision. In each round of the estimation in the blind mode, the step size is decreased to form some kind of annealing. Both DD LMS and ABA LMS techniques are simulated and compared to the full training case and MSE of channel estimation error is considered as comparison criterion. It is shown for 2x4 DD LMS and for 4x4 ABA LMS algorithms present near full training case estimation error. Of course in some scenarios the former proposed technique performs better and in other scenarios the latter is better and therefore combine of it can be very interesting in all channel conditions.

eess.SP

Windowed Overlapped frequency-domain Block Filtering Approach for Direct Sequence Signal Acquisition

This paper applies a windowed frequency-domain overlapped block filtering approach for the acquisition of direct sequence signals. The windows, as a novel viewpoint, not only allow pulse shaping without a front end pulse shaping filter, but also improve the performance of the spectrum sensing unit which can efficiently be implemented into this frequency-domain receiver and may further be used for spectrum sensing in cognitive radios or narrowband interference cancellation in military radios. The proposed receiver is applicable for initial time synchronization of different signals containing a preamble. These signals include single carrier, constant-envelope single-carrier, multi-carrier and even generalized-multi-carrier signals, which makes the proposed receiver structure a universal unit. Furthermore, the receiver can be used to perform filtering with long codes and compute the sliding correlation of an unknown periodic preamble. It can further be modified to handle large Doppler shifts. We will also demonstrate the computational complexity and analysis of the acquisition performance in Rayleigh and Rician fading channels.

eess.SP

Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images

Fast and robust image matching is a very important task with various applications in computer vision and robotics. In this paper, we compare the performance of three different image matching techniques, i.e., SIFT, SURF, and ORB, against different kinds of transformations and deformations such as scaling, rotation, noise, fish eye distortion, and shearing. For this purpose, we manually apply different types of transformations on original images and compute the matching evaluation parameters such as the number of key points in images, the matching rate, and the execution time required for each algorithm and we will show that which algorithm is the best more robust against each kind of distortion. Index Terms-Image matching, scale invariant feature transform (SIFT), speed up robust feature (SURF), robust independent elementary features (BRIEF), oriented FAST, rotated BRIEF (ORB).

cs.CV