Sharif Digital Repository

In this paper we propose a content-based text and image retrieval architecture using Fuzzy Adaptive Resonance Theory neural network. This method is equipped with an unsupervised mechanism for dynamic data clustering to deal with incremental information without metadata such as in web environment. Results show noticeable average precision and recall over search results. © 2006 IEEE  

In this study, two methods for solving economic dispatch problems, namely Hopfield neural network and λ iteration method are compared. Three sample of power system with 3, 6 and 20 units have been considered. The time required for CPU, for solving economic dispatch of these two systems has been calculated. It has been shown that for on-line economic dispatch, Hopfield neural network is more efficient and the time required for convergence is considerably smaller compared to classical methods. © 2006 IEEE  

This study presents a powerful application of genetic algorithm (GA) for the minimization of the total harmonic current distortion (THCD) in high-power induction motors fed by voltage source inverters, based on an approximate harmonic model. That is, optimal pulse patterns (switching angles) are determined to have the THCD minimized and the fundamental output voltage regulated concurrently. As the minimization of THCD corresponds to the minimization of harmonic copper losses of the motor windings, GA optimization technique has been utilized, in comparison with conventional optimization methods due to its capabilities of searching the entire solution space with more probability of finding the... 

A novel very light weight approach to isolated word speech recognition is introduced. The approach uses a new simplistic feature set and a neural network recognition system. The algorithm's main processing requirements are FFT computation and a simple neural network comparison, making the method a suitable solution for low price embedded devices. The proposed method is tested on single speaker and multiple speaker test sets and the results are compared with a widely used speech recognition approach, presenting very fast recognition and quite good recognition rate. © 2006 IEEE  

A microwave inverse scattering problem including a method for shape reconstruction of three-dimensional electrically large conducting patches with simple geometries using genetic algorithm is presented. Unknown shape reconstruction algorithm starts from the knowledge of the simulated radar cross-section (RCS) data through back-scattering far-field computation using physical optics approximation. The forward problem involves the computation of the multiple-frequency and multiple-direction RCS of three-dimensional large conducting patches modeled by nonuniform rational B-spline (NURBS) surfaces. The control points of NURBS are the geometrical parameters, which are optimized for the shape... 

This paper presents a comprehensive comparison between job-level dynamic scheduling algorithms on real-time multiprocessor environments using simulation. Earliest Deadline First (EDF) and Least Laxity First (LLF) are two well-known and extensively applied dynamic scheduling algorithms which have been proved to be optimal on uniprocessor systems. However, neither is shown to be optimal on multiprocessors. Many researches have already been done on aforementioned algorithms, but to the best of our knowledge, none of which has compared the efficiency of the two algorithms under similar conditions. Perhaps the main reason is that LLF algorithm is fully dynamic and impractical to implement. In... 

Adaptive Model Predictive Transmission Control Protocol (AMP-TCP) as a new TCP delay-based congestion control algorithm is introduced. Both aspects of design and implementation of the algorithm are described using simulations on the ns-2 network simulator. The design stage is composed of two steps. First, a recursive system identification approach is proposed to capture the network delay dynamics from TCP source view. Second, the proposed modeling is employed to develop an adaptive model predictive TCP congestion control strategy in the absence of any explicit congestion notification. The characteristics and performance of AMP-TCP are investigated using several network simulations. Finally a... 

Interior point methods have shown their powers in solving linear optimization problems and large classes of other optimization problems. However, at present there is still a gap between the practical behavior of these algorithms and their theoretical worst case complexity. The so-called large update interior point methods perform in practice much better than the small update methods which have the best known theoretical complexity. Recently, this gap has been reduced by Peng, Roos and Terlaky by introducing new self regular kernel functions. In this paper, by focusing on linear optimization problem and motivated by the self regular family of kernel functions, we impose some mild condition on... 

The problem of aerodynamic shape optimization of unguided projectiles has been investigated. Two stochastic optimization methods have been applied to solve the problem. These include a Genetic Algorithm (GA) and the recently developed Continuous Ant Colony System (CACS), which is based on the well-known Ant Colony Optimization meta-heuristic. The objective function is defined as the summation of normal force coefficients over a set of given flight conditions. An engineering code (EC) is used to calculate the normal force coefficients over the flight conditions. The obtained results of CACS+EC are compared with those of GA+EC, as well as the results of a previous work (GA +AeroDesign). The... 

We introduce a new approach for sparse decomposition, based on a geometrical interpretation of sparsity. By sparsedecomposition we mean finding sufficiently sparse solutions of underdetermined linear systems of equations. This will be discussed in the context of Blind Source Separation (BSS). Our problem is then underdetermined BSS where there are fewer mixtures than sources. The proposed algorithm is based on minimizing a family of quadratic forms, each measuring the distance of the solution set of the system to one of the coordinate subspaces (i.e. coordinate axes, planes, etc.). The performance of the method is then compared to the minimal 1-norm solution, obtained using the linear... 

Designing efficient acoustic stack and elements for high-frequency (HF) medical ultrasound (US) transducers involves various interrelated parameters. So far, optimizing spatial resolution and acoustic field intensity simultaneously has been a daunting task in the area of HF medical US imaging. Here, we introduce optimized design for a 50-MHz US probe for skin tissue imaging. We have developed an efficient design and simulation approach using Krimholtz, Leedom and Matthaei (KLM) equivalent circuit model and spatial impulse response method by means of Field II software. These KLM model and Field II software are integrated, and a GA algorithm is used to optimize the design of the US transducer... 

In this paper, we present a one-way relaying scheme in which two wireless nodes create an information flow to each other via a single decode-and-forward (DF) relay. We consider an additional secrecy constraint for protection against an honest-but-curious relay. Indeed, while the relay should decode the source message, it should be fully ignorant about the message content. We provide a secure lattice coding strategy based on random difference families (RDF) lattice codes for unidirectional Gaussian relay channels. RDF lattice codes are carved from infinite RDF lattices using a shaping algorithm. By RDF lattice we mean a Construction A lattice with a QC-LDPC code, which is obtained from random... 

Embedded systems have migrated from special-purpose hardware to commodity hardware. These systems have also tended to Mixed-Criticality (MC) implementations, executing applications of different criticalities upon a shared platform. Multi-core processors, which are commonly used to design MC systems, bring out new challenges due to the process variations. Power and frequency asymmetry affects the predictability of embedded systems. In this work, variation-aware techniques are explored to not only improve the reliability of MC systems, but also aid the scheduling and energy saving of them. We leverage the core-to-core (C2C) variations to protect high-criticality tasks and provide full service... 

This study presents a novel method for observation of stage position in a 2D nano-positioning system based on a hybrid Kalman filter. The proposed method obviates the need to measure the stage position directly using complex and costly capacity sensors. Instead, traditional piezo actuators equipped with strain gauge sensors are utilized to measure the deection of the magnification system at the position of actuators. Then, a powerful estimation algorithm called Kalman filter was employed to observe stage displacements. The designed hybrid Kalman filter uses dynamical equations of motion in the prediction step. The piezo actuators deections are measured and exploited to correct the predicted... 

We consider the problem of corrupted radar super-resolution, a generalization of compressed radar super-resolution in which one aims to recover the continuous-valued delay-Doppler pairs of moving objects from a collection of corrupted and noisy measurements. The received signal in this type consists of contributions from objects, outlier and noise. While this problem is ill-posed in general, tractable recovery is possible when both the number of objects and corrupted measurements are limited. In this brief, we propose an atomic norm optimization in order to find the delay-Doppler pairs and the outlier signal. The objective function of our optimization problem encourages both sparsity in the... 

Federated learning (FL) and split learning (SL) are state-of-the-art distributed machine learning techniques to enable machine learning without accessing raw data on clients or end devices. However, their comparative training performance under real-world resource-restricted Internet of Things (IoT) device settings, e.g., Raspberry Pi, remains barely studied, which, to our knowledge, have not yet been evaluated and compared, rendering inconvenient reference for practitioner. This work firstly provides empirical comparisons of FL and SL in real-world IoT settings regarding learning performance and on-device execution overhead. Our analyses demonstrate that the learning performance of SL is... 

The present simulation aims to investigate adding NEPCM nanoparticles to water in the natural convection inside a cavity by using FVM method and SIMPLE algorithm. Nano-encapsulated phase change material (NEPCM) consists of a shell and core with phase change property. The NEPCM particles in base fluid have the ability to transfer heat by absorbing and dissipating heat in the liquid-solid phase change state. In this study, the energy wall phenomenon due to the phase change of NEPCM core has appeared that the whose energy transfer strength is proportional to the latent heat of NEPCM core and the thickness of the energy wall. Moreover, the relationship between the energy wall and the heat... 

This paper addresses the lack of proper Learning from Demonstration (LfD) architectures for Sign Language-based Human–Robot Interactions to make them more extensible. The paper proposes and implements a Learning from Demonstration structure for teaching new Iranian Sign Language signs to a teacher assistant social robot, RASA. This LfD architecture utilizes one-shot learning techniques and Convolutional Neural Network to learn to recognize and imitate a sign after seeing its demonstration (using a data glove) just once. Despite using a small, low diversity data set (~ 500 signs in 16 categories), the recognition module reached a promising 4-way accuracy of 70% on the test data and showed... 

Lift path planning is a significant subtask in constructability analysis, sequencing, and scheduling of congested industrial modular projects, impacting project cost, and safety. Although intuitive lift planning is still prevalent among the practitioners, this manual process might be tedious and error-prone for hundreds of lifts. This research presents an automated lift path planning method for heavy crawler cranes in no-walk scenarios employing a robotics approach. This method treats the lifted object as a three-degree-of-freedom convex mobile robot with discretized rotational and continuous translational motions. The proposed resolution-complete method models the crane capacity chart,... 

We consider the problem of learning a density function from observations of an unknown underlying model in a distributed setting, where the observations are partitioned into different sites. Applying commonly used density estimation methods such as Gaussian Mixture Model (GMM) or Kernel Density Estimation (KDE) to distributed data leads to an extensive amount of communication. A familiar approach to address this issue is to sample a small subset of data and collect them into a central node to run the density estimation algorithms on them. In this paper, we follow an alternative to the sub-sampling approach by proposing the nested Log-Poly model. This model provides an accurate density...