Sybil Attack with RSU Detection and Location Privacy in Urban VANETs: An Efficient EPORP Technique

In recent years, Vehicular ad hoc networks (VANETs) could facilitate the decision-making progress of the drivers for example trip planning with the consideration of traffic. In the VANET, the Sybil attack is a very serious attack that collapses the security. In literature, some of the methods are reviewed to detect Sybil attacks in VANETs, but it fails to achieve Sybil attack detection. Hence, in this paper, Emperor Penguin Optimization-based Routing protocol (EPORP) is developed for detecting the Sybil attack which enhances the VANETs security. The main motive of the research is detecting the Sybil attack in VANETs for enhancing the secure operation. In the proposed approach, the Sybil attack will be detected with the help of the Rumour riding technique. To enhance the security of the VANETs, the Split XOR (SXOR) operation is utilized. In the SXOR operation, the optimal key is selected with the help of Emperor Penguin Optimization (EPO). The proposed method is implemented in the NS2 platform and performances are evaluated by metrics such as delay, throughput, delay, encryption time, and decryption time. The proposed method is compared with existing methods such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA) respectively. While analyzing the delivery ratio, the proposed method has 0.96 s, and the WOA, PSO, and FA are 0.94, 0.92, and 0.90 respectively. From the analysis, the proposed method has a high delivery ratio value compared with the WOA, PSO, and FA methods. Similarly, the other parameters are analyzed and compared with the existing methods.

     

Epitomic Location Recognition

This paper presents a novel method for location recognition, which exploits an epitomic representation to achieve both high efficiency and good generalization. A generative model based on epitomic image analysis captures the appearance and geometric structure of an environment while allowing for variations due to motion, occlusions, and non-Lambertian effects. The ability to model translation and scale invariance together with the fusion of diverse visual features yields enhanced generalization with economical training. Experiments on both existing and new labeled image databases result in recognition accuracy superior to state of the art with real-time computational performance.     

Influence ofβ-cyclodextrin as an encapsule and as an inclusion complex dopant on conducting polyaniline

An investigation on the effect ofβ-cyclodextrin (CD) in both free and inclusion-complexed forms with a guest anionic metal complex, dioxalatodiaquochromate(III) (DDC), on the characteristics of conducting polyaniline (PANI) is carried out. Four materials, PANI (i.e. PANI-SO ₄ ²⁻ ), PANI-DDC, PANI-CD and PANI-CD + DDC were prepared byin situ chemical oxidative polymerization in aqueous H ₂SO₄ at pH 1 and subjected to electrical conductivity and spectral (IR and UV-vis bd measurements. DDC and CD when separately incorporated, reduce the conductivity of PANI by about half whilst their inclusion complex CD + DDC enhances it. Spectral characterization reveals that DDC as a dopant and CD as an encapsule exhibit their effects through adverse interaction with imine-amine N centres and benzenoid moiety of PANI. The inclusion complex CD + DDC, on the contrary, functions as a dopant by lying in between the chains and seems to promote the extended conformation of PANI chain and hence theπ -electron delocalization. Exposure of the material to methanol vapour causes a decrease in conductivity in PANI and PANI-CD while an increase in PANI-CD + DDC. This study makes explicit the distinct role of CD as an encapsule and CD + DDC inclusion complex as a dopant in altering the electrical property of PANI.     

Magnetic Properties of Mn‐doped SnO2 Thin Films Prepared by the Sol–Gel Dip Coating Method for Dilute Magnetic Semiconductors

Manganese‐doped tin oxide (SnO₂:Mn) thin films were deposited on glass substrates by the sol–gel dip coating technique. The effect on structural, morphological, magnetic, electrical, and optical properties in the films with different Mn concentrations (0–5 mol%) were investigated. X‐ray diffraction patterns (XRD) showed the deterioration of crystallinity with increase in Mn‐doping concentration. Scanning electron microscopy (SEM) studies showed an inhibition of grain growth with an increase in Mn concentration. X ray photoelectron spectroscopy (XPS) revealed the presence of Sn⁴⁺ and Mn³⁺ in SnO₂: Mn films. SnO₂: Mn films show ferromagnetic and paramagnetic behavior. These SnO₂:Mn films acquire n‐type conductivity for 0–3 mol% (SnO₂ ‐ Sn_0.97Mn_0.03O_{2) ‐}doping concentration and p type for 5 mol% Mn‐doping concentration(Sn_0.95Mn_0.05O₂) in SnO₂ films. An average transmittance of > 75% (in UV‐Vis region) was observed for all the SnO₂:Mn films. Optical band gap energy of SnO₂: Mn films were found to vary in the range 3.55 to 3.71 eV with the increase in Mn‐doping concentration. Photoluminescence (PL) spectra of the films exhibited an increase in the emission intensity with increase in Mn‐doping concentration which may be due to structural defects or luminescent centers, such as nanocrystals and defects in the SnO₂. Such SnO₂:Mn films with structural, magnetic and optical properties can be used as dilute magnetic semiconductors.     

Distance metric-based time-efficient fuzzy algorithm for abnormal magnetic resonance brain image segmentation

Image segmentation is one of the significant computational applications of the biomedical field. Automated computational methodologies are highly preferred for medical image segmentation since these techniques are immune to human perception error. Artificial intelligence (AI)-based techniques are often used for this process since they are superior to other automated techniques in terms of accuracy and convergence time period. Fuzzy systems hold a significant position among the AI techniques because of their high accuracy. Even though these systems are exceptionally accurate, the time period required for convergence is exceedingly high. In this work, a novel distance metric-based fuzzy C-means (FCM) algorithm is proposed to tackle the low-convergence-rate problem of the conventional fuzzy systems. This modified approach involves the concept of distance-based dimensionality reduction of the input vector space that substantially reduces the iterative time period of the conventional FCM algorithm. The effectiveness of the modified FCM algorithm is explored in the context of magnetic resonance brain tumor image segmentation. Experimental results show promising results for the proposed approach in terms of convergence time period and segmentation efficiency. Thus, this algorithm proves to be highly feasible for time-oriented real-time applications.     

A pattern based PSO approach for block matching in motion estimation

Block matching motion estimation is a popular method in developing video coding applications. A new algorithm has been proposed for reducing the number of search points using a pattern based particle swarm optimization (PSO) for motion estimation. The conventional particle swarm optimization has been modified to provide accurate solutions in motion estimation problems. This leads to very low computational cost and good estimation accuracy. Due to the center biased nature of the videos, the proposed approach uses an initial pattern to speed up the convergence of the algorithm. Simulation results show that improvements over other fast block matching motion estimation algorithms could be achieved with 31%～63% of search point reduction, without degradation of image quality.     

Remote sensing of phytoplankton functional types

The principal goal in early missions of satellite-borne visible spectral radiometry (ocean colour) was to create synoptic fields of phytoplankton biomass indexed as concentration of chlorophyll-a. In the context of climate change, a major application of the results has been in the modelling of primary production and the ocean carbon cycle. It is now recognised that a partition of the marine autotrophic pool into a suite of phytoplankton functional types, each type having a characteristic role in the biogeochemical cycle of the ocean, would increase our understanding of the role of phytoplankton in the global carbon cycle. At the same time, new methods have been emerging that use visible spectral radiometry to map some of the phytoplankton functional types. Here, we assess the state of the art, and suggest paths for future work.     

Fast and accurate fuzzy C‐means algorithm for MR brain image segmentation

Fuzzy theory based intelligent techniques are widely preferred for medical applications because of high accuracy. Among the fuzzy based techniques, Fuzzy C‐Means (FCM) algorithm is popular than the other approaches due to the availability of expert knowledge. But, one of the hidden facts is that the computational complexity of the FCM algorithm is significantly high. Since medical applications need to be time effective, suitable modifications must be made in this algorithm for practical feasibility. In this study, necessary changes are included in the FCM approach to make the approach time effective without compromising the segmentation efficiency. An additional data reduction approach is performed in the conventional FCM to minimize the computational complexity and the convergence rate. A comparative analysis with the conventional FCM algorithm and the proposed Fast and Accurate FCM (FAFCM) is also given to show the superior nature of the proposed approach. These techniques are analyzed in terms of segmentation efficiency and convergence rate. Experimental results show promising results for the proposed approach. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 188–195, 2016