Sharif Digital Repository

Hip dislocation is one of the most frequent complications after total hip arthroplasty. Impingement and dislocation might be caused due to misalignment of the acetabular cup during surgery, or performing dislocation-prone activities afterwards. A finite element model was developed to predict the impingement and dislocation behavior of the prosthetic joint, for different combinations of cup orientation and patient maneuver. Four dislocation-prone activities of daily life and 25 cup orientations were analyzed to determine how close they are to the impingement and subsequent dcislocation events. The angular margin results obtained indicated that the sit-to-stand and standing while bending at... 

Laminated composite beams with non-uniform thickness are being used as primary structural elements in a wide range of advanced engineering applications. Tapered composite structures, formed by terminating some of the plies, create geometry and material discontinuities that act as sources for delamination initiation and propagation. Any small damage or delamination in these structures can progress rapidly without any visible external signs. Due to this reason early detection of damage in these systems during their service life is receiving increasing attention. The presence of a crack in a component or structure leads to changes in its global dynamic characteristics results in decreases in... 

This paper deals with the problem of vibration based health monitoring (VHM) in multi-stable asymmetrical laminated composite structures. Multi-stable asymmetrical laminates can be snapped between two or more geometries. The ability of snapping between two or more geometries in multistable composite structures makes them vulnerable to delaminate. Because of these reason early detection of damage in these systems during their service life is receiving increasing attention. In the present work, an efficient and accurate finite element model (FEM) based on layer-wise theory by considering the electro-mechanical coupling effect has been developed to investigate the actuation and /or sensing... 

Recognition and correction of inhomogeneous displacement caused by patient's movement has been recently discussed as an interesting topic in medical image processing. Considering consistency in general structure of the image during distortion, histogram could be employed as a fast implementation method in feature domain. Accordingly, attribute vectors could be defined for each pixel based on spatial features to find corresponding points in two images. Consequently a point-based and non-rigid transformation approach will be designed. A B-spline image registration has been applied to match those pairs with a defined smoothness factor. This algorithm is a step-by-step registration process... 

[No abstract available]  

In this paper we introduce and propose novel signaling methods and receiver structures based on advanced binary optical logic gates for fiber-optic code division multiple access (FO-CDMA) systems using all-optical signal processing. In the proposed system the users of the network are categorized into multiple classes. Users of each class transmit at the same power level but different from the levels of the other classes' users. Using a combination of optical OR, AND and XNOR logic gates for the receiver structure we show that such a network not only takes the full advantages of all-optical signal processing but also demonstrates a considerable throughput efficiency when compared to ordinary... 

Similarity measure is an important key in image registration. Most traditional intensity-based similarity measures (e.g. sum-of-squared-differences, correlation coefficient, mutual information and correlation ratio) assume a stationary image and pixel-by-pixel independence. These similarity measures ignore the correlation among pixel intensities; hence, a perfect image registration cannot be achieved especially in the presence of spatially varying intensity distortions and outlier objects that appear in one image but not in the other. It is supposed here that non-stationary intensity distortion (such as bias field) has a sparse representation in the transformation domain. Based on this... 

The registration of images is a fundamental task in numerous applications in medical image processing. Similarity measure is an important key in intensity based image registration. Here, we propose correntropy measure as similarity measure in mono modal setting. Correntropy is a important measure between two random variables based on information theoretic learning and kernel methods. This measure is useful in non-Gaussian signal processing. In this paper, this measure is used in image registration. Here, we analytically illustrate that this measure is robust in presence of spiky noise (impulsive noise). The experimental results show that the proposed similarity has better performance than... 

Similarity measure is an important key in image registration. Most traditional intensity-based similarity measures (e.g., SSD, CC, MI, and CR) assume stationary image and pixel by pixel independence. Hence, perfect image registration cannot be achieved especially in presence of spatially-varying intensity distortions and outlier objects that appear in one image but not in the other. Here, we suppose that non stationary intensity distortion (such as Bias field or Outlier) has sparse representation in transformation domain. Based on this as-sumption, the zero norm (ℓ0)of the residual image between two registered images in transform domain is introduced as a new similarity measure in presence... 

Similarity measure is an important block in image registration. Most traditional intensity-based similarity measures (e.g., sum-of-squared-difference, correlation coefficient, and mutual information) assume a stationary image and pixel-by-pixel independence. These similarity measures ignore the correlation between pixel intensities; hence, perfect image registration cannot be achieved, especially in the presence of spatially varying intensity distortions. Here, we assume that spatially varying intensity distortion (such as bias field) is a low-rank matrix. Based on this assumption, we formulate the image registration problem as a nonlinear and low-rank matrix decomposition (NLLRMD).... 

Similarity measure is an important part of image registration. The main challenge of similarity measure is lack of robustness to different distortions. A well-known distortion is spatially-varying intensity distortion. Its main characteristic is correlation among pixels. Most traditional intensity based similarity measures (e.g., SSD, MI) assume stationary image and pixel to pixel independence. Hence, these similarity measures are not robust against spatially-varying intensity distortion. Here, we suppose that non-stationary intensity distortion has a sparse representation in transform domain, i.e. its distribution has high peak at origin and a long tail. We use two viewpoints of Maximum... 

In this paper, we propose the Object Pursuing based Efficient Routing Algorithm (OPERA) suitable for vehicular ad hoc networks (VANETs), esp. in sparse situations. The proposed algorithm is applicable for both moving and fixed destinations. It is based on considering static nodes at each intersection. In this algorithm, we optimize the decision making at intersections, with respect to the connectivity and feasibilty of the roads. To this end, we consider the average delay of each road as the connectivity metric, and the vehicle availability in the transmission range of the intersection as the feasibility metric. By exploiting the related metrics, we select the next road to forward the packet... 

Similarity measure is a main core of image registration algorithms. Spatially varying intensity distortion is an important challenge, which affects the performance of similarity measures. Correlation among the pixels is the main characteristic of this distortion. Similarity measures such as sum-of-squared-differences (SSD) and mutual information ignore this correlation; hence, perfect registration cannot be achieved in the presence of this distortion. In this paper, we model this correlation with the aid of the low rank matrix theory. Based on this model, we compensate this distortion analytically and introduce rank-regularized SSD (RRSSD). This new similarity measure is a modified SSD based... 

We show that multiple Abelian sectors with Stückelberg mass-mixing simply break supersymmetry via Fayet-Iliopoulos D-terms and straightforwardly mediate it to the other sectors. This mechanism naturally realizes a split supersymmetry spectrum for soft parameters. Scalar squared-masses (holomorphic and non-holomorphic) are induced through sizable portals and are not suppressed. Gaugino masses, a-terms and a μ-like term are generated by higher-dimensional operators and are suppressed. The hypercharge is mixed with extra U(1)'s, it's D-term in non-vanishing and supersymmetry is broken in the visible sector too. Scalar tachyonic directions are removed by unsuppressed interactions and hypercharge... 

We present a constant-time randomized distributed algorithms in the congested clique model that computes an O(α)-vertex-coloring, with high probability. Here, α denotes the arboricity of the graph, which is, roughly speaking, the edge-density of the densest subgraph. Congested clique is a well-studied model of synchronous message passing for distributed computing with all-to-all communication: per round each node can send one O(log n)-bit message algorithm to each other node. Our O(1)-round algorithm settles the randomized round complexity of the O(α)-coloring problem. We also explain that a similar method can provide a constant-time randomized algorithm for decomposing the graph into O(α)...