Sharif Digital Repository

In this paper, we propose a mapping from low level feature space to the semantic space drawn by the users through relevance feedback to enhance the performance of current content based image retrieval (CBIR) systems. The proposed approach makes a rule base for its inference and configures it using the feedbacks gathered from users during the life cycle of the system. Each rule makes a hypercube (HC) in the feature space corresponding to a semantic concept in the semantic space. Both short and long term strategies are taken to improve the accuracy of the system in response to each feedback of the user and gradually bridge the semantic gap. A scoring paradigm is designed to determine the... 

Feature relevance estimation is one of the most successful techniques used for improving the retrieval results of a content-based image retrieval (CBIR) system based on users' feedbacks. In this class of approaches, the weights of the feature elements (FEs) are adjusted based on the relevance feedbacks (RFs) given by the users to reduce the so-called semantic gap in the underlying CBIR system. An analytical approach is proposed in this paper to convert the users' feedbacks to the appropriate FE weights by solving a constrained optimization problem. Experiments on a set of 11,000 images from the Corel database show that the proposed approach outperforms other existing short-term RF approaches... 

To reduce the semantic gap in the content based image retrieval (CBIR) systems we propose a fuzzy rule base approach. By submitting a query to the proposed system, it first extracts its low-level features and then checks its rule base for determining the proper weight vector for its distance measure. It then uses this weight vector to determine what images are more similar to the query image. For the training purpose, an algorithm is provided by which the system adjusts its fuzzy rules' parameters by gathering the trainers' opinions on which and how much the image pairs are relevant. For further improving the performance of the system, a feature space dimensionality reduction method is also... 

To evaluate the performance of the distributed medium access control layer of the emerging ultrawideband and 60-GHz millimeter wave (mmWave) wireless personal area networks based on ECMA-368 and ECMA-387 standards, the first step is to determine the beacon period length (BPL) of the superframe in a given network. In this paper, we provide an analytical model for the probability mass function (PMF) of the BPL as a function of the network dimensions, number of beaconing devices, antenna beamwidth, and the transmission range of the devices. To enable devices with steerable directional antennas in the ECMA-387 standard to have simultaneous communications with neighbors in their different antenna... 

A probabilistic method is developed here to model the beacon period length (BPL) defined in the ECMA-368 and ECMA-387 standards for the distributed reservation protocol. The model computes the probability mass function (PMF) of the BPL in a superframe which will be used in modeling of throughput, delay, fairness, power and other major distributed medium access control layer parameters of the emerging ultra wideband and 60- GHz millimeter wave wireless personal area networks. The model relates the PMF of the BPL to the network density, antenna beamwidth, and the transmission range of the devices assuming that the devices are distributed in an outdoor environment based on Two-dimensional... 

To evaluate the performance of distributed mediumaccess control (MAC) layer of emerging ultra wideband (UWB) and 60-GHz millimeter wave (mmWave) wireless personal area networks (WPANs) based on distributed reservation protocol (DRP) of ECMA-368 and ECMA-387 standards, we provide an analytical model for the probability distribution of steady state throughput as a function of the network dimensions size, number of devices, antenna beamwidth, transmission range, bit error rate (BER), antenna disturbance rate (ADR), aggregation length, payload size, and incoming frame rate. Using this model, the optimal payload size is computed as an example of optimization problem.We also propose a formula for... 

Compressed images such as JPEG when transmitted over communication channels are susceptible to channel noise and/or erasure, which causes losses of blocks of pixels or macroblocks. Recovery of a large block in an image is a difficult task. In this paper we propose a novel method that addresses this problem and can exactly recover a lost block. Moreover, the proposed algorithm is fast enough to be implemented in real time. Simulation results show that our method is faster than previous methods proposed in the literature with less distortion  

In the internet telephony, loss of IP packets causes instantaneous discontinuities in the received speech. In this paper, we have focused on finding an error resilient method for this problem. Our proposed method creates artificial correlation between speech samples that pre-distorts the speech signal. The receiver uses this correlation to reconstruct the lost speech packets. An appropriate speech enhancement technique is designed for the reduction of the processing error in the recovered speech caused by the speech codecs. The SegSNR results show the superiority of our proposed speech enhancement method over a recently proposed one. © 2007 IEEE  

The spatial sparsity of targets in the radar scene is widely used in multiple-input multiple-output (MIMO) radar signal processing, either to improve the detection/estimation performance of the radar or to reduce the cost of the conventional MIMO radars (e.g., by reducing the number of antennas). While sparse target estimation is the main challenge in such an approach, here, we address the design of a compressive-sensing-based MIMO radar, which facilitates such estimations. In particular, we propose an efficient solution for the problem of joint power allocation and antenna placement based on minimizing the number of transmit antennas while constraining the coherence of the sensing matrix....