Sharif Digital Repository

In this paper, the performance of some popular direct interconnection networks, namely the mesh, torus and hypercube, are studied with adaptive wormhole routing for different traffic patterns. We investigate the effect of the number of virtual channels and depth of their buffers on the performance of such strictly orthogonal topologies under uniform, hot-spot and matrix-transpose traffic patterns for generated messages, while the total buffer size associated to each physical channel is kept constant. In addition we analyze the effect of escape channel buffer length on the performance of a fully adaptive routing algorithm. It is shown that the optimal number of virtual channels and buffer... 

Self-adaptation is a well-known technique to handle growing complexities of software systems, where a system autonomously adapts itself in response to changes in a dynamic and unpredictable environment. With the increasing need for developing self-adaptive systems, providing a model and an implementation platform to facilitate integration of adaptation mechanisms into the systems and assuring their safety and quality is crucial. In this paper, we target Track-based Traffic Control Systems (TTCSs) in which the traffic flows through pre-specified sub-tracks and is coordinated by a traffic controller. We introduce a coordinated actor model to design self-adaptive TTCSs and provide a general... 

In this paper, an enhanced local mean-field model that is suitable for simulating the electroencephalogram (EEG) in different depths of anesthesia is presented. The main building elements of the model (e.g., excitatory and inhibitory populations) are taken from Steyn-Ross and Bojak and Liley mean-field models and a new slow ionic mechanism is included in the main model. Generally, in mean-field models, some sigmoid-shape functions determine firing rates of neural populations according to their mean membrane potentials. In the enhanced model, the sigmoid function corresponding to excitatory population is redefined to be also a function of the slow ionic mechanism. This modification adapts the... 

In this paper, an approach is proposed for controlling the uncertain Lorenz system. Based on an identification technique, a controller is designed that guarantees the regulation of all states in the presence of system uncertainty. Since in some applications the challenging problem of output tracking is desired, we have proposed several effective set-point tracking control techniques. The control schemes that are based on the feedback linearization method, can stabilize the internal dynamics of the system. Simulation results have illustrated the effectiveness of the proposed schemes. © 2005 Elsevier Ltd. All rights reserved  

This paper introduces adaptive consensus, a spatio-temporal adaptive method to improve convergence behavior of the current consensus fusion schemes. This is achieved by introducing a time adaptive weighting method for updating each sensor data in each iteration. Adaptive consensus method will improve node convergence rate, average convergence rate and the variance of error over the network. A mathematical formulation of the method according to the adaptive filter theory as well as derivation of the time adaptive weights and convergence conditions are presented. The analytical results are verified by simulation as well. ©2006 IEEE  

The star graph was introduced as an attractive alternative to the well-known hypercube and its properties have been well studied in the past. Most of these studies have focused on topological properties and algorithmic aspects of this network. Although several analytical models have been proposed in the literature for different interconnection networks, none of them have dealt with star graphs. This paper proposes the first analytical model to predict message latency in wormhole-switched star interconnection networks with fully adaptive routing. The analysis focuses on a fully adaptive routing algorithm which has shown to be the most effective for star graphs. The results obtained from... 

This paper presents a new adaptive lifting scheme transform for detecting user-selected objects in a sequence of images. In our algorithm, we first select a set of object features in the wavelet transform domain and then build an adaptive transform by using the selected features. The adaptive transform is constructed based on adaptive prediction in a lifting scheme procedure. Adaptive prediction is performed such that, the large coefficients in the high-pass component of the non-adaptive transform vanishes in the high-pass component of the adaptive transform. Finally, both the non-adaptive and adaptive transforms are applied to a given test image and the transform domain coefficients are... 

The adaptive capability of filters is known to be increased by incorporating a neural network into the filtering procedure. In this paper, an adaptive algorithm for tracking maneuvering targets based on neural networks is proposed. This algorithm is implemented with two filters based on the current statistical model and a multilayer feedforward neural network. The two filters track the same maneuvering target in parallel and the neural network automatically considers all the state information of the two filters and adaptively adjusts the process variance of one of them to achieve better performance in different target maneuver tracking. Simulations results show that the proposed adaptive... 

Segmentation is a very important stage of Farsi/Arabie character recognition systems. A new segmentation algorithm -for multi font Farsi/Arabic texts- based on the conditional labeling of the up contour and down contour is presented. A pre-processing technique is used to adjust the local base line for each subword. This algorithm uses adaptive base line for each subword to improve the segmentation results. This segmentation algorithm, in addition to up and down contours, takes advantage of their curvatures also. The algorithm was tested on a data set of printed Farsi texts, containing 22236 characters, in 18 different fonts. 97% of characters were correctly segmented. © 2005 IEEE  

An application of the multiple models adaptive control based on switching and tuning to a flexible transmission system will be presented. This approach has been considered in order to assure high control performance in the presence of large load variation on the system. The advantages of the multiple models adaptive control system with respect to the classical adaptive control will be illustrated via the experimental results. It will also be shown that the robustness of the adaptive control system can be improved with the appropriate shaping of a sensitivity function. The use of a recently developed parameter estimation algorithm based on the minimization of the closed-loop output error in... 

Trip production has long been considered as a major element in trip demand estimation. Many models have been presented for this purpose. Models use socio-economic variables in order to predict trip production. This paper develops an Adaptive-Network-based Fuzzy Inference System (ANFIS) models to predict school trip production. ANFIS can construct an input-output mapping based on both human knowledge and stipulated input-output data pairs. In order to improve models' generalization capability, a heuristic algorithm is used to generate reasonable initial values for data loss in training data set. Models with different Membership Functions (MFs) were trained, validated and tested with real data... 

In this paper, we propose an adaptive object tracking algorithm in crowded scenes. The amplitudes of of Undecimated Wavelet Packet Tree coefficients for some selected pixels at the object border are used to create a Feature Vector (FV) corresponding to that pixel. The algorithm uses these FVs to track the pixels of small square blocks located at the vicinity of the object boundary. The search window is adapted through the use of texture information of the scene by finding the direction and speed of the object motion. Experimental results show a good object tracking performance in crowds that include object translation, rotation, scaling and partial occlusion. Copyright - World Automation... 

Adaptive time-frequency representations have many advantages compared with conventional methods. In this paper, a new method is proposed to adapt Smoothed Pseudo WignerVille distribution to match signal's time-frequency content. It is based on maximizing a local time-frequency concentration measure for different time and frequency smoothing window lengths. Subsequently, the optimized values are used for constructing an adaptive kernel over time. The proposed transform is then applied to vibration signals of healthy and cracked shafts which are acquired through run-up, and the crack signature is obtained. Results show that enhanced improvement in resolution is obtained while the computational... 

In this paper, an adaptive multi-model CMAC-based controller (AMCBC) in conjunction with a supervisory controller is developed for uncertain nonlinear MIMO systems. AMCBC is a kind of adaptive feedback linearizing controller where nonlinearity terms are approximated with multiple CMAC neural networks With the help of a supervisory controller, the resulting close-loop system is globally stable. The proposed control system is applied to control a robotic manipulators, where some varying tasks are repeated but information on the load is not defined; it is unknown and varying. It is shown how the proposed controller is effective because of its capability to memorize the control skill for each... 

Image-Adaptive watermarking systems exploit visual models to adapt the watermark to local properties of the host image. This leads to a watermark power enhancement, hence an improved resilience against different attacks, while keeping the mark imperceptible. Visual models consider different properties of the human visual system, such as frequency sensitivity, luminance sensitivity and contrast masking. Entropy masking is another human visual system's characteristic, which rarely has been addressed in visual models. In this paper we have utilized this masking effect to improve the robustness of Image-Adaptive watermarks while keeping their transparency. Experimental results show a significant... 

In this paper, we focus on adaptive and wavelet based systems in radar signal processing, and a new algorithm to Doppler compensation is developed. The new wavelet-based method for ambiguity surface smoothing that applies the three dimensions dual tree wavelet transform and adapt constant false alarm rate, is proposed. The model captures the dependence between a wavelet coefficient and its parent. Simulation results show that new approach is better than older algorithms. We have simulated this new method for bistatic FM-based passive coherent receiver  

Self-adaptive systems are systems that automatically adapt in response to environmental and internal changes, such as possible failures and variations in resource availability. Such systems are often realized by a MAPE-K feedback loop, where Monitor, Analyze, Plan and Execute components have access to a runtime model of the system and environment which is kept in the Knowledge component. In order to provide guarantees on the correctness of a self-adaptive system at runtime, the MAPE-K feedback loop needs to be extended with assurance techniques. To address this issue, we propose a coordinated actor-based approach to build a reusable and scalable model@runtime for self-adaptive systems in the...