Error resilient video over multimedia broadcast multicast services (MBMS)

With available data rates for mobile devices constantly increasing, services such as video broadcast and multicast are becoming feasible. A new standard called Multimedia Broadcast Multicast Services (MBMS) is being developed by 3GPP to enable new class of spectrum-efficient multimedia services. Multicast services are expected to serve a diverse user base with varying connectivity and capabilities. We analyze the problem of video error resilience in MBMS services that is critical to maintain consistent quality for end users. The existing error resilience techniques for IP multicasting are not applicable in the MBMS environment. In this paper, we present error resilience techniques that are applicable within the context of the MBMS standard. We propose an Intra Block Refresh method for MBMS services and the results show improved performance. We develop a methodology that can be applied to adapting traditional error resilience tools for the MBMS environment.     

Uncover the cover to recover the hidden secret - A separable reversible data hiding framework

The volatile development in the multimedia cognitive content is changing the global set-up towards a cloud-based architecture which is helped us with a massive amount of computer storage and the highest computational platform. Cost-saving and elasticity of services will be provided by progressive cloud computing technology for users. With the advancement in multimedia technology, the data owners outsource their private multimedia data on the hybrid cloud. Meantime the cloud servers also carry out some highly computationally expensive tasks. Nevertheless, there is an opportunity for security infracts possible in the public cloud environment. It makes an alarm for a cloud environment in security aspects. Before outsourcing multimedia data, an encryption technique is needed for safeguarding against several attacks. But performing the same is a significant challenge. A new research area was recently awakened on privacy-preserving Reversible Data Hiding (RDH) especially for multimedia data over the outsourced environment. A novel RDH for an encrypted image was proposed in this paper by using the (Most Significant Bit) MSB difference of the pixel value. By using this method, any third-party people can embed the ciphertext in the cipher image without the knowledge of the cover and secret. A person with decryption keys can get back the secret and the cover without any loss. The proposed work achieves the embedding capacity up to 1 bpp (bits per pixel) with the encryption quality of near-zero correlation and uniform histogram. The decrypted image is also retrieved with infinite Peak Signal to Noise Ratio (PSNR), unit Structural Similarity Index Metric (SSIM) and zero Bit Error Rate (BER).

     

Study on microstructure, piezoelectric and dielectric properties of 3-3 porous PZT composites

Porous lead zirconate titanate (PZT) composites are used for low frequency hydrophones due to their high hydrostatic figure of merit, and low sound velocity. Porous PZT composite materials manufactured using varied percentage of polymethyl methacrylate (PMMA) as a pore-forming agent was studied, to understand the effect of PMMA on microstructure and dielectric properties. Properties such as the density, porosity, transverse piezoelectric coefficient, dielectric constant, and dielectric loss factor were studied as a function of PMMA. Porous PZT composites (with 30% PMMA) show lower piezoelectric transverse coefficient (−78 pC/N) and dielectric constant (116) as compared to homogeneous PZT. At a frequency of 1 MHz, the dielectric constant decreases and dielectric loss increases with increase in PMMA. This paper describes the effect of polymethyl methacrylate (PMMA) as a pore-forming agent on dielectric properties and microstructure of porous PZT composites.     

Fusion of confusion and diffusion: a novel image encryption approach

Recently, the development of confidentiality and authenticity ensured by image encryption has been one of the key advancements in the field of secured wireless communication. The proposed work focuses on providing confusion, diffusion and permutation inherently in the system. The input grayscale image is shuffled by employing the Henon algorithm and subsequently separated into 8 bit planes. With the aid of a secret key of 256 bits, chaotic sequences are generated for each bit plane. Later, the logistic map is adapted on the chaotic sequences to obtain the scrambled image. Furthermore, this image is shuffled with Zaslavskii and Hilbert Space Filling Curve algorithms, which establishes the confusion stage. To incorporate the diffusion stage, a self-invertible matrix is generated by a latin square image cipher and a secret key. This matrix and the scrambled image underwent the Hill cipher to build the encrypted image followed by a row-column transformation to ensure multifold security. The proposed compound activities of encryption are successfully implemented on the laboratory virtual instrumentation engineering workbench 2013 platform. Noticeably, the established processes of image encryption are tested in the universal software radio peripheral environment and transceived via an additive white Gaussian noise (AWGN) channel. More specifically, the influence of natural and unnatural (cropping attack) noise on the characteristics of the encrypted image while sharing through AWGN channel has been investigated. Security analysis is performed by computing the unified average changing intensity, number of pixels change rate, correlation value, large key space to defy brute force attack, strong key sensitivity and uniform gray value distribution on encryption.     

Real Time Authenticated Spectrum Access and Encrypted Image Transmission via Cloud Enabled Fusion centre

Wireless Personal Communications - Spectrum scarcity and the number of mobile users accessing the spectrum are increasing radically, as a complement to this, under utilization of the licensed...     

Transreceiving of encrypted medical image – a cognitive approach

Multimedia Tools and Applications - Recently, there is an increasing demand for efficient and secure transreception of medical images in telemedicine applications. Though a fixed spectrum is...     

Human Stress Recognition by Correlating Vision and EEG Data

Because stress has such a powerful impact on human health, we must be able to identify it automatically in our everyday lives. The human activity recognition (HAR) system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions. Using the multimodal dataset DEAP (Database for Emotion Analysis using Physiological Signals), this paper presents deep learning (DL) technique for effectively detecting human stress. The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when fatal. Based on visual and EEG (Electroencephalogram) data, this research aims to enhance the performance and extract the dominating characteristics of stress detection. For the stress identification test, we utilized the DEAP dataset, which included video and EEG data. We also demonstrate that combining video and EEG characteristics may increase overall performance, with the suggested stochastic features providing the most accurate results. In the first step, CNN (Convolutional Neural Network) extracts feature vectors from video frames and EEG data. Feature Level (FL) fusion that combines the features extracted from video and EEG data. We use XGBoost as our classifier model to predict stress, and we put it into action. The stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree (DT), Random Forest (RF), AdaBoost, Linear Discriminant Analysis (LDA), and K-Nearest Neighborhood (KNN). When we compared our technique to existing state-of-the-art approaches, we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.     

Greedy Weight Matrix Topological Adaptive Ad Hoc On-demand Multipath Distance Vector Protocol for QOS Improvement in MANET

Wireless Personal Communications - The Mobile Ad Hoc Network (MANET) dominates the wireless network environment and the output of MANET is significantly affected by the...     

Dimensional stability and hardness improvement of southern yellow pine wood using divinylbenzene dioxide

Wood is commonly used as a construction material for floors, outdoor decks, house framing, bridges etc. However, the dimensional instability of wood has limited its use in these and other applications. This is due in part to the constant adsorption and release of water which results in swelling and shrinking leading to the physical degradation of the wood (Kumar 1994). A variety of treatment methods has been developed in an attempt to improve the hardness, stability, stiffness, and dimensional stability of wood. However, many of these methods are not economic or commercially scalable. There still exists a need for a commercially viable wood treating technology which will provide increased dimensional stability and hardness. This article details a wood treating method using a difunctional epoxide cured with a catalyst at low temperature inside of the wood to prepare a wood polymer composite (WPOC). Analysis by scanning electron microscopy (SEM) and micro infrared spectroscopy (micro IR) showed the epoxy resin was distributed uniformly throughout the wood including the cell wall. The epoxide treated wood compared to untreated wood showed improvements in hydrophobicity (water repellency), anti-swelling efficiency (ASE) as a measure of dimensional stability, hardness, and three-point bending dynamic mechanical analysis (DMA) properties.     

Edge detection using Chebyshev's orthogonal polynomial and brain extraction from magnetic resonance images of human head

In this article, we propose a new edge detecting method based on the transform coefficients obtained by a point spread function constructed out of Chebyshev's orthogonal polynomials. This edge detector finds edges similar to that of Prewitt and Roberts but is robust against additive and multiplicative noises. We also propose a new scheme to extract brain portion from the magnetic resonance images (MRI) of human head scan by making use of the of the new edge detector. The proposed scheme involves edge detection, morphological operations, and largest connected component analysis. Experiments conducted by applying the proposed scheme on 19 volumes of MRI collected from Internet Brain Segmentation Repository (IBSR) show that the proposed brain extraction scheme performed better than the popular Brain Extraction Tool (BET). The performance of the proposed scheme is measured by computing the Dice coefficient (D) and Jaccard similarity index (J). The proposed method produced a value of 0.9068 for D and 0.8321 for J.