Eight-Chain and Full-Network Models and Their Modified Versions for Rubber Hyperelasticity: A Comparative Study

The eight-chain model, also known as Arruda-Boyce model, is widely used to capture the rate-independent hyperelastic response of rubber-like materials. The parameters of this model are physically based and explained from micromechanics of chain molecules. Despite its excellent performance with only two material parameters to capture bench measurements in uniaxial and pure shear regime, the model is known to be significantly deficient in predicting the equibiaxial data. To ameliorate such drawback, over the years, several modified versions of this successful model have been proposed in the literature. The so-called full-network model is another micromechanically motivated chain model, which has also few modified versions in the literature. For this study, two modified versions of the full-network model have been selected. In this contribution, five modified versions of the Arruda-Boyce model and two modified versions of full-network model are critically compared with the classical eight-chain model for their adequacy in representing equibiaxial data. To do a comparison of all selected models in reproducing the well-known Treloar data, the analytical expressions for the three homogeneous deformation modes, that is, uniaxial tension, equibiaxial tension, and pure shear have been derived and the performances of the selected models are analysed. The comparative study demonstrates that modified Flory-Erman model, Gornet-Desmorat (GD) model, Meissner-Matějka model, and bootstrapped eight-chain model predict well the three deformation modes compare to the classical eight-chain model

A block-based RDWT-SVD image watermarking method using human visual system characteristics

With the rapid growth of internet technology, image watermarking method has become a popular copyright protection method for digital images. In this paper, we propose a watermarking method based on 4×4 image blocks using redundant wavelet transform with singular value decomposition considering human visual system (HVS) characteristics expressed by entropy values. The blocks which have the lower HVS entropies are selected for embedding the watermark. The watermark is embedded by examining U2,1 and U3,1 components of the orthogonal matrix obtained from singular value decomposition of the redundant wavelet transformed image block where an optimal threshold value based on the trade-off between robustness and imperceptibility is used. In order to provide additional security, a binary watermark is scrambled by Arnold transform before the watermark is embedded into the host image. The proposed scheme is tested under various image processing, compression and geometrical attacks. The test results are compared to other watermarking schemes that use SVD techniques. The experimental results demonstrate that our method can achieve higher imperceptibility and robustness under different types of attacks compared to existing schemes. Our method provides high robustness especially under image processing attacks, JPEG2000 and JPEG XR attacks. It has been observed that the proposed method achieves better performance over the recent existing watermarking schemes

Assessment of Wireless Technologies for deployment in Intelligent Transportation System based on Internet of Things

Use of Internet of Things (IoT) with modern wireless network is a trend of the emerging technologies for different systems which can be deployed in various kinds of environment to monitor, communicate with or control the associated elements in the system. The activities e.g., monitoring and communication by IoT can play an important role to design an Intelligent Transportation System (ITS). In this paper, we assess the suitability of IoT enabled wireless technology to be used for ITS. We performed some comparative study to find the best wireless technology that provides reliability, low cost, less power consumption and less data latency for next generation ITS.This technology will reduce energy consumption of the deployed IoT devices as well as ensure safety, efficiency and convenient for transportation systems

On mitigating hop-to-hop congestion problem in IoT enabled Intra-Vehicular communication

Internet of Things enabled Intra-Vehicular Network (IVN) refers to the network where large number of sensors are connected with each other for sharing the vehicle's status information in order to develop a smart vehicular system. The number of sensor nodes in the vehicle has increased significantly due to the increasing vehicular applications. The phenomenon of congestion poses a problem in the IVN where the traffic load and number of sensors are increased. This problem can be resolved by mitigating the limitation of the existing Media Access Control (MAC) protocols. In this paper, we address this issue and proposed a MAC strategy for solving this problem in this network. Furthermore, we discuss the design of IVN scenario and the performance is evaluated in terms of end-to-end delay. The simulation results reveal the effectiveness of our proposal

An Efficient Image Compression Technique using Tchebichef Bit Allocation

The psychovisual technique has brought about significant improvement in pursuing image analysis and image reconstruction. The psychovisual threshold can be utilized to find the optimal bits-budget for image signals. The psychovisual system is developed based on noticeable distortion of the compressed image from an original image in frequency order. This paper proposes an image compression technique using Tchebichef psychovisual threshold for generating an optimal bits-budget of image signals. The bits-budget is designed to replace the main role of quantization tables in image compression. The experimental results show that the proposed bits-budget technique can improve the visual quality of image output by 42 of JPEG compression. The visual image quality of Thcebichef bits allocation produces less artifact effect and distortion of the image pixels. A set of bits-budgets gives excellent improvement in the image quality at a low average bit length of Huffman code than the image coding using quantization tables

An Efficient Solution to Travelling Salesman Problem using Genetic Algorithm with Modified Crossover Operator

The traveling salesman problem (TSP) is a famous NP-hard problem in the area of combinatorial optimization. It is utilized to locate the shortest possible route that visits every city precisely once and comes back to the beginning point from a given set of cities and distance. This paper proposes an efficient and effective solution for solving such a query. A modified crossover method using Minimal Weight Variable, Order Selection Crossover operator, a modified mutation using local optimization and a modified selection method using KMST is proposed. The crossover operator (MWVOSX) chooses a particular order from multiple orders which have the minimum cost and takes the remaining from the other parent in backward and forward order. Then it creates two new offspring. Further, it selects the least weight new offspring from those two offspring. The efficiency of the proposed algorithm is compared to the classical genetic algorithm. Comparisons show that our proposed algorithm provides much efficient results than the existing classical genetic algorithm

IoT Enabled Intra-Vehicular Communication for Designing A Smart Car

Nowadays, the number of sensor nodes in the vehicle has expanded altogether because of the expanding of various vehicular applications. Moreover, the wired connection is not adaptable and flexible due to the internal structure of the vehicle, thus, there is an expanding level of appeal to design a system in which the wired connections with the sensor node are supplanted with wireless connections. Design a wireless sensor system inside the vehicle is quite challenging than other systems, e.g., wireless, sensor and computer network, due to the unpredictable environment inside the vehicle. In this project, we discuss about Internet of Things (IoT) enabled Intra-Vehicular Communication for Designing a Smart Car. IoT enabled Intra-Vehicular Communication (IoT-IVC) refers to the system where huge number of sensors are associated with each other for sharing the car components’ information to build up a smart vehicular system. The marvel of congestion represents an issue in the IoT-IVC while the traffic load and number of sensors are expanded. A new scheduling algorithm is proposed for minimizing the congestion. A Test-Bed is designed in order to validate the proposed algorithm. The experimental results justify the effectiveness of the proposal

IoT enable relay network for demand based light intensity controlled seamless highway lighting system

Highway authority spends huge amount of money per year for Highway Lighting System worldwide (e.g., Malaysia USD 25 Million per year). Internet of Things (IoT) becomes one of the popular concepts that provides prominent solutions in various paradigms. Exploiting IoT, this work develops a prototype for efficient highway lighting system at lower energy consumption. Two key features need to be taken into account for designing such a system are: i) Road users’ point of view (i.e., Comfort and safety) ii) Road service providers’ point of view (i.e., Reduce Energy Consumption and Maintenance Cost). The characteristics of this prototype revile the effectiveness of the proposal

Synthesis of tapioca cellulose-based poly (hydroxamic acid) ligand for heavy metals removal from water

A graft copolymerization was performed using free radical initiating process to prepare the poly(methyl acrylate) grafted copolymer from the tapioca cellulose. The desired material is poly(hydroxamic acid) ligand, which is synthesized from poly(methyl acrylate) grafted cellulose using hydroximation reaction. The tapioca cellulose, grafted cellulose and poly(hydroxamic acid) ligand were characterized by Infrared Spectroscopy and Field Emission Scanning Electron Microscope. The adsorption capacity with copper was found to be good, 210 mg g¡1 with a faster adsorption rate (t1/2 D 10.5 min). The adsorption capacities for other heavy metal ions were also found to be strong such as Fe3C, Cr3C, Co3C and Ni2C were 191, 182, 202 and 173 mg g¡1, respectively at pH 6. To predict the adsorption behavior, the heavy metal ions sorption onto ligand were well-fitted with the Langmuir isotherm model (R2 > 0.99), which suggest that the cellulose-based adsorbent i.e., poly(hydroxamic acid) ligand surface is homogenous and monolayer. The reusability was checked by the sorption/desorption process for six cycles and the sorption and extraction efficiency in each cycle was determined. This new adsorbent can be reused in many cycles without any significant loss in its original removal performances

RERS-CC: Robotic facial recognition system for improving the accuracy of human face identification using HRI

BACKGROUND: Human-Computer Interaction (HCI) is incorporated with a variety of applications for input processing and response actions. Facial recognition systems in workplaces and security systems help to improve the detection and classification of humans based on the vision experienced by the input system. OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements. RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time. CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate