78 research outputs found
Performance evaluation of AODV and OLSR under mobility:
No full textWireless mobile ad hoc network is a infrastructureless network where each network node not only acts as a host but also acts as a router. Since the nodes are mobile,the environment is highly dynamic. For these networks to function properly a routing protocol is required that can respond to the rapid changes in the topology. Manyrouting protocols have been developed for accomplishing this task. The objective of this thesis is to study the impact of mobility on the performance of two mobile routing protocols, AODV, which is reactive routing protocol and OLSR, which is proactive routing protocol. Since not many MANETs have been deployed, most of the studies are simulation based. But for this thesis, experiments were conducted on national Open Access Research Testbed (ORBIT) for Next Generation Wireless Networks. We developed a basic framework to analyze the performance of routing protocols. We firstly evaluated the performance in a static environment where nodes are arranged in static linear topology and concluded that OLSR outperformed AODV. To study the mobility, we used Reference Point Group Mobility model that generates real life scenarios. It isclear that there is considerable cost associated with mobility. Both the protocols show decrease in throughput, higher standard deviation, more dead links and higher overhead when compared to their respective performance in static environment. However, the relative performance of AODV and OLSR depends on the mobility scenario. AODV performed better than OLSR for discrete scenario when time snapshots were taken at a lower frequency i.e. every 30 seconds. On the other hand, OLSR performed better in pseudo-continuous scenario when time snapshots were taken at higher frequency i.e. every 5 seconds.M.S.Includes bibliographical references (p. 41-42)by Tanuja Kuma
Color Image Compression Using Vector Quantization and Hybrid Wavelet Transform
No full textAbstractThis paper presents simpler image compression technique using vector quantization and hybrid wavelet transform. Hybrid wavelet transform is generated using Kronecker product of two different transforms. Image is converted to transform domain using hybrid wavelet transform and very few low frequency coefficients are retained to achieve good compression. Vector quantization is applied on these coefficients to increase compression ratio significantly. VQ algorithms are applied on transformed image and codebooks of minimum possible size 16 and 32 are generated. KFCG and KMCG are faster in execution and beats performance of LBG algorithm. KFCG combined with hybrid wavelet transform gives lowest distortion and acceptable image quality at compression ratio 192
Modified CSLBP
No full textImage hashing is an efficient way to handle digital data authentication problem. Image hashing represents quality summarization of image features in compact manner. In this paper, the modified center symmetric local binary pattern (CSLBP) image hashing algorithm is proposed. Unlike CSLBP 16 bin histogram, Modified CSLBP generates 8 bin histogram without compromise on quality to generate compact hash. It has been found that, uniform quantization on a histogram with more bin results in more precision loss. To overcome quantization loss, modified CSLBP generates the two histogram of a four bin. Uniform quantization on a 4 bin histogram results in less precision loss than a 16 bin histogram. The first generated histogram represents the nearest neighbours and second one is for the diagonal neighbours. To enhance quality in terms of discrimination power, different weight factor are used during histogram generation. For the nearest and the diagonal neighbours, two local weight factors are used. One is the Standard Deviation (SD) and other is the Laplacian of Gaussian (LoG). Standard deviation represents a spread of data which captures local variation from mean. LoG is a second order derivative edge detection operator which detects edges well in presence of noise. The proposed algorithm is resilient to the various kinds of attacks. The proposed method is tested on database having malicious and non-malicious images using benchmark like NHD and ROC which confirms theoretical analysis. The experimental results shows good performance of the proposed method for various attacks despite the short hash length
Adaptive CSLBP compressed image hashing
No full textHashing is popular technique of image authentication to identify malicious attacks and it also allows appearance changes in an image in controlled way. Image hashing is quality summarization of images. Quality summarization implies extraction and representation of powerful low level features in compact form. Proposed adaptive CSLBP compressed hashing method uses modified CSLBP (Center Symmetric Local Binary Pattern) as a basic method for texture extraction and color weight factor derived from L*a*b* color space. Image hash is generated from image texture. Color weight factors are used adaptively in average and difference forms to enhance discrimination capability of hash. For smooth region, averaging of colours used while for non-smooth region, color differencing is used. Adaptive CSLBP histogram is a compressed form of CSLBP and its quality is improved by adaptive color weight factor. Experimental results are demonstrated with two benchmarks, normalized hamming distance and ROC characteristics. Proposed method successfully differentiate between content change and content persevering modifications for color images
A framework for cloud cover prediction using machine learning with data imputation
No full textThe climatic conditions of a region are affected by multiple factors. These factors are dew point temperature, humidity, wind speed, and wind direction. These factors are closely related to each other. In this paper, the correlation between these factors is studied and an approach has been proposed for data imputation. The idea is to utilize all these features to obtain the prediction of the total cloud cover of a region instead of removing the missing values. Total cloud cover prediction is significant because it affects the agriculture, aviation, and energy sectors. Based on the imputed data which is obtained as the output of the proposed method, a machine learning-based model is proposed. The foundation of this proposed model is the bi-directional approach of the long short-term memory (LSTM) model. It is trained for 8 stations for two different approaches. In the first approach, 80% of the entire data is considered for training and 20% of the data is considered for testing. In the second approach, 90% of the entire data is accounted for training and 10% of the data is accounted for testing. It is observed that in the first approach, the model gives less error for prediction
- …
