Sunil Agrawal

VANET routing protocols: Issues and challenges

In recent years, rapid growth in the number of vehicles on the road has increased demands for communication on the move. A new kind of Ad hoc network with an immense improvement in technological innovations is emerging these days known as VANET (Vehicular ad hoc network). It is an assortment of vehicular nodes that act as mobile hosts establish a transient network without the assistance of any centralized administration or any established infrastructure. Therefore, it is called autonomous & self configured network. In VANET, two kinds of communication can be done to provide a list of applications like emergency vehicle warning, safety etc. These are between various vehicles known as vehicle to vehicle and between vehicles and roadside units known as vehicle to roadside communication. Performance of such kind of communication between vehicles depends on various routing protocols. We have a tendency to survey a number of the recent analysis leads to routing space. In the following sections we present various existing routing protocols with their merits and demerits.

A Comparative Analysis of Thresholding and Edge Detection Segmentation Techniques

and edge detection being one of the important aspects of image segmentation comes prior to feature extraction and image recognition system for analyzing images. It helps in extracting the basic shape of an image, overlooking the minute unnecessary details. In this paper using image segmentation (thresholding and edge detection) techniques different geo satellite images, medical images and architectural images are analyzed. To quantify the consistency of our results error measure is used.

Comparative Analysis of Various Routing Protocols in VANET

To make the drive safer in future Vehicular ad hoc network can ease our life. For its success, it needs efficient routing protocols for communication among vehicles. Such communication can either through road side units (RSUs) or on board units (OBUs) in the vehicles. In this paper we are exploiting various existing routing protocols like AODV, AOMDV, DSR and DSDV by varying the velocity of vehicles and then comparing their performances with respect to throughput, end to end delay, packet delivery ratio and normalized routing load during communication.

Bone vessel image fusion via generalized reisz wavelet transform using averaging fusion rule

Abstract In DSA (Digital Subtraction angiography) image degradation such as prevalence of noise during fusion and blurring of the details is one of the common problems. To improve the performance of the generalized Reisz transform in context of osseous and vascular image fusion it is combined with averaging fusion method. The proposed method is not only able to preserve the spatial consistency of low level features but has also captured the directional structural information. The numerical and the visual assessment clearly depicts that there is negligible amount of noise prevalent in the final fused results which is usually a problem with other state of art fusion methods The performance analysis of the proposed method applied on DSA and mask image fusion demonstrates that it is competitive with state of art fusion methods, especially in combining spatial and structural information. Our proposed method GRWT (Generalized Reisz Wavelet Transform) along with averaging fusion method is able to yield much better results than the GRWT coupled with heuristic fusion model with far less complexity and the computational requirement having a fusion rate of 0.6958.

Color and grey scale fusion of osseous and vascular information

Abstract Presenting an efficient form of gathering, refining and compounding the vital information fusion of osseous and vascular images together has gained increasing momentum in the past. This area has been witnessed with testing of a large variety of fusion based methods. Here in this article an underlying idea of enhancing the fusion quality and increasing the amount of information transfer from source images to fused image has been materialized. The target is achieved by applying a selected sequence of tried and validated techniques for pre- hand processing of the 2D medical data. The series of operations like denoising, enhancement, sharpening and finally the fusion of mask and DSA (Digital Subtraction Angiography) is done before they are finally fused. The results so obtained are able to present a far better visual quality than the raw data acquired from the medical institutes. With this approach of image enhancement prior to fusion we could achieve much better quality of fused images. This improved method of enhancement and fusion is able to achieve QAB/F factor as high as 0.8475 as compared to QAB/F of 0.619 achieved using the dense SIFT fusion algorithm alone by Yu Lui. The high quality of image results obtained offers a revolutionary paradigm in the diagnosis, optimization and planning of surgical or endovascular and cerebrovascular diseases. The entire work is implemented using MATLAB 2012 software

From Multi-Scale Decomposition to Non-Multi-Scale Decomposition Methods: A Comprehensive Survey of Image Fusion Techniques and Its Applications

Image fusion is a well-recognized and a conventional field of image processing. Image fusion provides an efficient way of enhancing and combining pixel-level data resulting in highly informative data for human perception as compared with individual input source data. In this paper, we have demonstrated a comprehensive survey of multi-scale and non-multi-scale decomposition-based image fusion methods in detail. The reference-based and non-reference-based image quality evaluation metrics are summarized together with recent trends in image fusion. Several image fusion applications in various fields have also been reported. It has been stated that though a lot of singular fusion techniques seemed to have given optimum results, the focus of researchers is shifting toward amalgamated or hybrid fusion techniques, which could harness the attributes of both multi-scale and non-multi-scale decomposition methods. Toward the end, the review is concluded with various open challenges for researchers. Thus, the descriptive study in this paper would form basis for stimulating and nurturing advanced research ideas in image fusion.

Osseous and Vascular Information Fusion using Various Spatial Domain Filters

Image fusion techniques aims at generating a composite image by integrating the complementary information from the multiple source images from the same sensor. To achieve a high amout of fidelity in the integrated image has been a major issue of concern with the researchers. It is intriguiging how simple pre-processing techniques can accelerate the fusion rate. In this article the performance evaluation of novel combination schemes of image sharpening and image fusion using spatial domain filters is presented. The performance of the proposed scheme has been verified for bone and vasscular image fusion i.e. multimodal fusion. We have reported weighted bilateral filter fusion technique as the best method in context of objective and subjective evaluation. The entire exhaustive assesment gives a QAB/F factor of 0.769.

Noise Reduction in MR brain image via various transform domain schemes

Despite the phenomenal progress in the field of image denoising it continues to be an active area of research and still holds margin in improving the standard of the denoising techniques. Image denoising has emerged as a significant tool in medical imaging specifically. In this article we have compared and evaluated three transform domain techniques on an MRI test image subjectively and objectively. The performance of Curvelet, Shearlet, and Tetrolet transform with a selective thresholding is evaluated. Shearlet is able to yield the best quality of image denoising. The study aims at analysing the performance of transform domain methods on MRI image at low and high levels of noise.

Node Localization in Wireless Sensor Networks Using the M5P Tree and SMOreg Algorithms

In Wireless Sensor Networks (WSN), Node Localization is of great importance for location aware services. In this paper we propose the use of Time of Arrival (TOA) information with two popular machine learning algorithms M5 tree Model (M5P) and Sequential Minimal Optimization for Regression (SMOreg) for more accurate node localization in WSN. In this paper we also applied the same node localization problem to two previously used artificial neural network models- Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) Network. After that a comparative analysis between all selected algorithms has been made. Simulation results show the superiority of M5P and SMOreg over MLP and RBFN in high noise conditions in terms of root mean square error. At last a comparative analysis between the two new proposed algorithms was made by changing the number of training nodes. Results show that initially the performance of SMOreg is better but there is no improvement in its performance with increasing training samples. On the other hand M5Ps performance can be made better by train it with more number of samples.

Efficient fusion of osseous and vascular details in wavelet domain

The osseous and vascular information fusion is analyzed.Various spatial domain enhancement schemes pre-fusion results in enhanced fusion rate.Occurrence of noise and artifacts in the fused image has been tackled.High fusion rate is achieved which is in agreement with the visual results. Image fusion is important medical image processing technique which aims at fusing complimentary information between two images. A little literature has been found on pre-processing of the source images prior to fusion which directly affects the image fusion quality. In this paper we have proposed an effective image fusion method based on IHS-wavelet transform along with pre-processing of source image in a selected sequence of spatial and transform domain techniques before fusion to create a highly informative fused image. The proposed methodology has outperformed six other state-of-the-art image fusion techniques both in terms of objective evaluation and visual results on osseous and vascular 2-D data