Mechanical characterization and chemical resistances of cured unsaturated polyester resins modified with vinyl ester resins based on recycled poly(ethylene terephthalate)

Unsaturated polyester resin (UP) was prepared from glycolyzed oligomer of poly(ethylene terephthalate) (PET) waste based on diethylene glycol (DEG). New diacrylate and dimethacrylate vinyl ester resins prepared from glycolysis of PET with tetraethylene glycol were blended with UP to study the mechanical characteristics of the cured UP. The vinyl ester resins were used as crosslinking agents for unsaturated polyester resin diluted with styrene, using free‐radical initiator and accelerator. The mechanical properties of the cured UP resins were evaluated. The compressive properties of the cured UP/styrene resins in the presence of different vinyl ester concentrations were evaluated. Increasing the vinyl ester content led to a pronounced improvement in the compression strength. The chemical resistances of the cured resins were evaluated through hot water, solvents, acid, and alkali resistance measurements. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 3175–3182, 2007     

Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4 × 10³⁸ unique IP addresses of devices in the network. IPv6 has introduced new features like Neighbour Discovery Protocol (NDP) and Address Auto-configuration Scheme. IPv6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol (ICMPv6). IPv6 is vulnerable to numerous attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS) which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial implications. Therefore, an Intrusion Detection System (IDS) is a monitoring system of the security of a network that detects suspicious activities and deals with a massive amount of data comprised of repetitive and inappropriate features which affect the detection rate. A feature selection (FS) technique helps to reduce the computation time and complexity by selecting the optimum subset of features. This paper proposes a method for detecting DDoS flooding attacks (FA) based on ICMPv6 messages using a Binary Flower Pollination Algorithm (BFPA-FA). The proposed method (BFPA-FA) employs FS technology with a support vector machine (SVM) to identify the most relevant, influential features. Moreover, The ICMPv6-DDoS dataset was used to demonstrate the effectiveness of the proposed method through different attack scenarios. The results show that the proposed method BFPA-FA achieved the best accuracy rate (97.96%) for the ICMPv6 DDoS detection with a reduced number of features (9) to half the total (19) features. The proven proposed method BFPA-FA is effective in the ICMPv6 DDoS attacks via IDS.     

Hybrid image steganography method using Lempel Ziv Welch and genetic algorithms for hiding confidential data

Multidimensional Systems and Signal Processing - Digital images are commonly used in steganography due to the popularity of digital image transfer and exchange through the Internet. However, the...     

Advanced Authentication Mechanisms for Identity and Access Management in Cloud Computing

Identity management is based on the creation and management of user identities for granting access to the cloud resources based on the user attributes. The cloud identity and access management (IAM) grants the authorization to the end-users to perform different actions on the specified cloud resources. The authorizations in the IAM are grouped into roles instead of granting them directly to the end-users. Due to the multiplicity of cloud locations where data resides and due to the lack of a centralized user authority for granting or denying cloud user requests, there must be several security strategies and models to overcome these issues. Another major concern in IAM services is the excessive or the lack of access level to different users with previously granted authorizations. This paper proposes a comprehensive review of security services and threats. Based on the presented services and threats, advanced frameworks for IAM that provide authentication mechanisms in public and private cloud platforms. A threat model has been applied to validate the proposed authentication frameworks with different security threats. The proposed models proved high efficiency in protecting cloud platforms from insider attacks, single sign-on failure, brute force attacks, denial of service, user privacy threats, and data privacy threats.     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

Adaptive predistortion using threshold decomposition‐based piecewise linear modeling

A new adaptive predistortion approach for nonlinear power amplifiers based on the piecewise linear (PWL) approximation of nonlinear functions is presented. The PWL model is obtained using threshold decomposition (TD) of the input‐output characteristics. The TD based PWL model is used to develop a baseband predistorter using both the direct and the indirect learning architectures. The proposed predistorter is proved to provide a better performance in suppressing nonlinear distortion than conventional polynomial predistorters and a reduced hardware complexity as compared to lookup table (LUT) approach. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

Nanomembrane‐Based,Thermal‐Transport Biosensor for Living Cells

Knowledge of materials' thermal‐transport properties, conductivity and diffusivity, is crucial for several applications within areas of biology, material science and engineering. Specifically, a microsized, flexible, biologically integrated thermal transport sensor is beneficial to a plethora of applications, ranging across plants physiological ecology and thermal imaging and treatment of cancerous cells, to thermal dissipation in flexible semiconductors and thermoelectrics. Living cells pose extra challenges, due to their small volumes and irregular curvilinear shapes. Here a novel approach of simultaneously measuring thermal conductivity and diffusivity of different materials and its applicability to single cells is demonstrated. This technique is based on increasing phonon‐boundary‐scattering rate in nanomembranes, having extremely low flexural rigidities, to induce a considerable spectral dependence of the bandgap‐emission over excitation‐laser intensity. It is demonstrated that once in contact with organic or inorganic materials, the nanomembranes' emission spectrally shift based on the material's thermal diffusivity and conductivity. This NM‐based technique is further applied to differentiate between different types and subtypes of cancer cells, based on their thermal‐transport properties. It is anticipated that this novel technique to enable an efficient single‐cell thermal targeting, allow better modeling of cellular thermal distribution and enable novel diagnostic techniques based on variations of single‐cell thermal‐transport properties.     

Microstructure and mechanical properties of hot extruded Mg–Al–Mn–Ca alloy produced by rapid solidification powder metallurgy

The microstructure and mechanical properties of hot extruded Mg–Al–Mn–Ca alloy was investigated. Both rapid solidified powders and cast billets were extruded at 573, 623 and 673 K to optimize the processing conditions for obtaining better mechanical response. Powder was consolidated to prepare the extrusion billets using both cold compaction and Spark Plasma Sintering at 473 K. The tensile properties of the extruded alloy were then evaluated and correlated to the observed microstructure. The results show that the use of rapid solidified powder could lead to effective grain refinement, which in turn resulted in the improved mechanical response, especially compared to the extruded conventional cast material.