Earticle

VANET is one of the most escalating technologies that have been adopted due to its innovative applications and services. It has the potential to permit the vehicles on the roads to communicate intelligently in the absence of any fixed infrastructure. There are several challenges related to MAC and Routing Layer that needs attention for the intended implementation of VANET. Considering the challenge of optimisation of MAC Layer in VANET, work has been done here. The numerous Routing Protocols such as AODV, ADV, DSDV and GOD have been considered for the Cross layer Optimisation of 802.11b MAC Interface. The queue size has been optimised in terms of QoS parameters namely Throughput Rate, Packet Collision Rate, Packet Drop Rate and Broadcast Rate. The simulative investigations have been done for the aforementioned standard routing protocols by varying queue size and Optimised Routing Protocol for 802.11b MAC has also been identified.

Micro-EDM is a non-contact process based on the thermoelectric energy between a tool electrode and a workpiece. In μEDM process, the mechanism of material removal is melting and evaporation. The thermal energy in the discharge plasma helps remove material from the workpiece, at the same time deteriorates the quality and integrity of the workpiece surface. The material removal phenomenon in μEDM of partially conductive and non-conductive materials is very complex. This paper presents a novel approach to model the effects of electrical and non-electrical parameters on the material removal phenomenon and surface integrity for a non-conductive ceramic material. The fuzzy logic modeling system is employed for predicting the μEDM process responses. The trends in the material removal rate and hardness values with the chosen electrical and non-electrical parameter for the model and obtained using AOM approach are compared. The average deviation between the model predictions and the results obtained using AOM plots is less than 10%. The material removal rate (MRR) decreases linearly with voltage, indicating a difference in material removal mechanism in the μEDM of non-conductive materials.

In indoor Light Fidelity (Li-Fi) system the position of transmitter and receiver bring different impact on coverage area. Due to Line of sight (LOS) characteristics of Li-Fi, coverage area of a light emitting diode (LED) transmitter plays a vital role for efficient reception of data. In this paper we considered the case when 1 optical receiver can simultaneously receive signal of 2 LED transmitters and present an optimal solution of controlling coverage area of LED transmitter using dimming factor. This paper focuses on efficient reception of data by controlling coverage area of multiple LED transmitters. The performance of proposed dimming factor approach is evaluated by using different dimming based modulation schemes.

This paper presents a computationally efficient encryption scheme for RGB images. Firstly, the original plain-image is decomposed to three component images of R, G and B. Secondly, an improved gravitation model is used for value transformation by setting different encryption keys on each component. Thirdly, the improved Arnold transform is applied for position permutation. Finally, we obtain the encrypted image by composing the three component cipher-images. Experimental results show that encrypted image achieved by our algorithm can effectively resist security attacks.

Low energy hydrogen ion shower discharge was produced using a gas discharge ion source to alter the wettability and dyeability properties of cotton fabric. Ion showers of 400 and 600 eV were used and samples were immersed in the discharge for 15, 30, 45 and 60 minutes. Wettability was measured via the capillary rise method by calculating the velocity of the rising liquid front. Results were then crosschecked using the Lucas-Washburn equation by calculating the advancing contact angles. Color properties such as reflectance, tinctorial strength (K/S) and CIEL*A*B* values were measured. Treatment led to various physicochemical alterations depending on beam energy and processing times. High energy shower led to fiber abrasion which gave rise to decreased water-sorption property. Low energy shower and longer treatment duration enhanced hydroxyl groups (O-H) promoting surface activation. Both treatment levels strengthened carbon triple bonds (C≡C) resulting in better dyeability.

Many sorting algorithms have been proposed and implemented in previous years. These algorithms are usually judged by their performance in term of algorithm growth rate according to the input size. Efficient sorting algorithm implementation is important for optimizing the use of other algorithms such as searching algorithms, load balancing algorithms, etc. In this paper, parallel Quicksort, parallel Merge sort, and parallel Merge-Quicksort algorithms are evaluated and compared in terms of the running time, speedup, and parallel efficiency. These sorting algorithms are implemented using Message Passing Interface (MPI) library, and results have been conducted using IMAN1 supercomputer. Results show that the run time of parallel Quicksort algorithm outperforms both Merge sort and Merge-Quicksort algorithms. Moreover, on large number of processors, parallel Quicksort achieves the best parallel efficiency of up to 88%, while Merge sort and Merge-Quicksort algorithms achieve up to 49% and 52% parallel efficiency, respectively.

This paper describes design and implementation of a three-dimensional game based on a brain-computer interface (BCI). The interaction between player and game application is controlled by translating brain signals into user-key input through electroencephalogram software. To evaluate the game performance, we compare the scores of the BCI interface with those of an existing non-BCI interface. The experiments results show that the players can control the game with BCI although the game speed is low.

Modern technology is developing by leaps and bounds, and more and more people begin using wearable technology devices. Recently, users have been using this kind of devices such as Fitbit, Apple Watch and Samsung wrist trackers so as to keep track of their health data such as consumed calories, running miles and steps, and even sleeping time. Many users wear their devices nearly 24/7, providing a thorough weekly health analysis in the devices’ applications installed in their mobile phones. However, few people really use wearable devices to diagnose or identify common diseases which can be captured by the fluctuations or major changes in data captured by the devices. Hence, integrating with machine learning technology, we attempt to figure out a solution to detect and diagnose some diseases based on the daily health data collected by wearable devices. Aiming at this, we collected data and experimented using a classification-based machine learning method, namely Support Vector Machine, to simulate a verisimilar ambient to monitor certain users’ health conditions.