Debdatta Kandar

An Approach to Convergence Between LTE and DSRC

With the rapid advancements of technologies and parallel development of intelligent machines in the field of wireless communication, customer needs also arises and ubiquitous network services are expected. In order to support customer’s demand there is huge need of hybrid heterogeneous network. In this paper, we propose the collaboration of cellular technology like Long Term Evolution (LTE) with traditional vehicular technology i.e., the Dedicated Short Range Communication (DSRC). In this paper we proposed a LTE/DSRC hybrid architecture which can establish a bridge between LTE/DSRC communication. We proposed no changes to the already available standards but we modify some of the configurable parameters in the PHY layers. We concentrate on the OFDM modulation which is the common feature for both LTE and DSRC.

Despeckling of SAR Image Based on Fuzzy Inference System

Synthetic Aperture Radar (SAR) is a type of imaging radar system that is widely used for remote sensing of Earth. It is observed that the images obtained from the SAR systems are often corrupted with speckle noise which reduces the visibility of the image. Preprocessing such images is often essential to enhance the clarity of the image for acquiring the required information present in it. A nonlinear filtering technique is proposed in this work, based on fuzzy inference rule-based systems, which uses fuzzy sets and fuzzy rules that operates on the luminance difference between the central pixel and its neighbors in a 3 × 3 window to reduce the presence of speckle noise in the SAR images. A comparative evaluation is performed on the proposed filter with three other existing filtering methods namely Mean, Median and FIRE filter for mixed noise to evaluate its performance.

A Novel Weighted Vehicular Network Clustering Scheme

In today’s world of emerging success in trending technologies, one of the most open and vast area of wireless communication research is the Vehicular Ad-hoc Network (VANET). Incorporating the trending technologies with the growing internet services, and deploying these into Intelligent Transportation System (ITS) is one of the open challenges. Therefore, for efficient vehicular network in ITS, in the urban areas as the nodes are mobile therefore clustering of these nodes comes into the picture. Many clustering algorithms are proposed based on different metrics and different results are obtained. This paper attempts to proposed a new clustering procedure where direction, distance, speed and transmission range are considered so as to obtain stable clusters and a weighted value is computed to select cluster head. The simulation results were simulated in NS-2.

Uplink Communication Between WiMAX and IEEE 802.11P Using Sub-channelization

In this chapter, an integrated architecture of Vehicular standards and Mobile standards for Efficient Intelligent Transportation system is proposed. By combining these two technologies, many research challenges have been adopted to establish a communication between different standards. In this chapter, we considered the hybrid scenario of Uplink and Downlink between IEEE 802.11p and IEEE 802.16d systems. We addressed the problem of sub-channelization by establishing communication between different subcarriers. We provide an analytical solution based on the Modulation schemes as well as AMC (Adaptive and Modulation Coding). The simulation results are provided to compare the performance of efficiency of payload, coverage range and data capacity with different modulation schemes and sub-channelization.

Impulse Noise Reduction in Digital Images Using Fuzzy Logic and Artificial Neural Network

Impulse noise is the most common types of noise; it degrades the quality of images and must be removed before performing any high level image processing. In this work, we have proposed a hybrid impulse noise filter, it is implemented in two phases, in the first phase, fuzzy rules are used to detect the pixels affected by impulse noise and in the second phase, artificial neural network is used to remove noise from the affected pixel. The proposed filter is comparatively evaluated with some of the popular impulse noise filter based on peak signal-to-noise ratio and edge preservative factor, it was found that the proposed filter reduces impulse noise and simultaneously preserves image details. For highly corrupted images, the proposed filter can be used, recursively.

OFDM Simulation in Combined WiMAX/DSRC Mechanism for Intelligent Transportation System

Wireless and mobile communications are the current trends of technologies, which are rapidly expanding. Considering the robustness as well as minimizing the cost of the WiMAX base station, incorporating WiMAX with other existing technologies such as DSRC, which provides efficiency when it comes to vehicular communication as these are mobile, fading as well as data loss should not be compromised as well as competing in coverage and data rate in a vehicular network. Multistandard convergence and efficient bandwidth usage are the major business trends in the era of wireless communication. WiMAX–DSRC convergence is an ideal technology that provides the best of both worlds: the low cost features of DSRC and the new features in WiMAX. In order to create an environment of heterogeneous networks, the authors investigated the differences between the two technologies (WiMAX and DSRC). Both technologies have orthogonal frequency division multiplexing (OFDM) transmission in the physical layer with different standards. The authors thoroughly investigated and successfully established OFDM transmission between DSRC and WiMAX systems. Therefore by modifying the configurable parameters, in this paper, considering DSRC-OFDM and WiMAX-OFDM, data transmission is simulated in MATLAB®.

Comparative Evaluation of Radial Basis Function Network Transfer Function for Filtering Speckle Noise in Synthetic Aperture Radar Images

Synthetic aperture radar (SAR) is imaging radar. It uses the movement of the antenna to synthesize a very large antenna and produces good resolution images of the mapped area. SAR images are polluted with speckle noise. Speckle noise inherently exists in all types of coherent imagery including SAR. It reduces the quality of the image and causes serious difficulty in automatic interpretation of the images. The radial basis function network (RBFN) is gaining importance in speckle noise filtering. RBFN is an artificial neural network (ANN) that has only one hidden layer known as the radial center. In this paper we have used Gaussian function, thin plate spline function, quadratic function, and inverse quadratic function as radial centers to design and implement different RBFNs. The performance of these RBFNs for filtering speckle noise is evaluated based on mean square error(MSE) achieved during training and peak signal-to-noise ratio (PSNR).

A Hybrid Face Recognition Scheme Using Contour and Gabor Wavelet

A face recognition system based on the hybrid approach is proposed in this paper. We have combined both the global and local features of a face using Gabor wavelet transform and a face contour. Gabor transform helps in extracting the features of a face, that is, the position of eyes, tip of the nose, and the corner of the mouth, whereas a face contour helps to extract the structure of a face. A combination of these techniques helps to extract the best features.