Energy Efficiency and Superlative TTT for Equitable RLF and Ping Pong in LTE Networks

Mobile Networks and Applications - Data hungry users engage radio resources over long periods of time thus resulting into higher energy consumption by Base Stations (BSs). Mobile operators’...     

Template‐assisted hydrophobic porous silica membrane: A purifier sieve for organic solvents

Self‐supporting, interconnected, porous inorganic (silicon/carbon) membranes were obtained by the calcination of composite membranes at 900 °C with a porous polymer template obtained by binary‐phase solid‐state photopolymerization at ?60 °C with acrylate monomers followed by chemical vapor deposition. The physicochemical properties of all of the membranes were characterized by scanning electron microscopy, energy‐dispersive spectroscopy (EDS), Fourier transform infrared (FTIR) spectroscopy, X‐ray powder diffraction (XRD), thermogravimetric analysis (TGA), and the measurement of water contact angles, water uptake, and percentage of porosity. The membranes showed contact angles ranging from 127 to 130 ± 0.5°, which were close to those of superhydrophobicity. The EDS, FTIR spectroscopy, XRD, and TGA results confirmed the formation of the Si–C composite structure after calcination and an increase in the thermal stability. The polymer and composite membranes were found to be hydrophilic, whereas Si–C was hydrophobic. The poly(ethylene glycol) diacrylate derived Si–C membrane showed a good absorption efficiency for tinted toluene from water compared to the others. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 45822.     

QoS aware cross layer paradigm for urban development applications in IoT

Wireless Networks - Large number of services and heterogeneity of the objects have made Internet of Things (IoT) a complex paradigm. It is necessary to placate the requirements of quality of...     

Modelling and optimization of syngas production by methane dry reforming over samarium oxide supported cobalt catalyst: response surface methodology and artificial neural networks approach

The reforming of methane by carbon dioxide for the production of syngas is a potential technological route for the mitigation of greenhouse gases. However, the process is highly endothermic and often accompanied by catalyst deactivation from sintering and carbon deposition. Besides, the applications of dissimilar catalytic systems in methane dry reforming have made it difficult to obtain generalized optimum conditions for the desired products. Hence, optimization studies of any catalytic system often resulted in a unique optimum condition. The present study aimed to investigate optimum conditions of variables such as methane (CH₄) partial pressure, carbon dioxide (CO₂) partial pressure and reaction temperature that will maximize syngas yields from methane dry reforming over samarium oxide supported cobalt (Co/Sm₂O₃) catalyst. The Co/Sm₂O₃ catalyst was synthesized using wet-impregnation method and characterized by thermogravimetric analysis), field emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray powder diffraction and nitrogen (N₂) physisorption. Syngas production by methane dry reforming over the synthesized Co/Sm₂O₃ catalyst was investigated in a stainless steel fixed-bed reactor. The process variables (CH₄ partial pressure, CO₂ partial pressure and reaction temperature) for the syngas production were optimized using response surface methodology (RSM). The RSM and artificial neural networks (ANNs) were used to predict the syngas production from the experimental data. The comparative analysis between the two models showed that the ANN model has better prediction of the syngas yields compared to the RSM model as evident from the good agreement between the observed and the predicted values. At maximum desirability value of 0.97, optimum CH₄ and CO₂ partial pressures of 47.9 and 48.9 kPa were obtained at reaction temperature of 735 °C resulting in syngas yield of ~79.4 and 79.0% for hydrogen (H₂) and carbon monoxide (CO), respectively.     

Cyber Secure Framework for Smart Containers Based on Novel Hybrid DTLS Protocol

The Internet of Things (IoTs) is apace growing, billions of IoT devices are connected to the Internet which communicate and exchange data among each other. Applications of IoT can be found in many fields of engineering and sciences such as healthcare, traffic, agriculture, oil and gas industries, and logistics. In logistics, the products which are to be transported may be sensitive and perishable, and require controlled environment. Most of the commercially available logistic containers are not integrated with IoT devices to provide controlled environment parameters inside the container and to transmit data to a remote server. This necessitates the need for designing and fabricating IoT based smart containers. Due to constrained nature of IoT devices, these are prone to different cyber security attacks such as Denial of Service (DoS), Man in Middle (MITM) and Replay. Therefore, designing efficient cyber security framework are required for smart container. The Datagram Transport Layer Security (DTLS) Protocol has emerged as the de facto standard for securing communication in IoT devices. However, it is unable to minimize cyber security attacks such as Denial of Service and Distributed Denial of Service (DDoS) during the handshake process. The main contribution of this paper is to design a cyber secure framework by implementing novel hybrid DTLS protocol in smart container which can efficiently minimize the effects of cyber attacks during handshake process. The performance of our proposed framework is evaluated in terms of energy efficiency, handshake time, throughput and packet delivery ratio. Moreover, the proposed framework is tested in IoT based smart containers. The proposed framework decreases handshake time more than 9% and saves 11% of energy efficiency for transmission in compare of the standard DTLS, while increases packet delivery ratio and throughput by 83% and 87% respectively.     

Design of a compact multiband circularly polarized antenna for global navigation satellite systems and 5G/B5G applications

Design of a multiband circularly polarized antenna is proposed in this article. The antenna has a simple and compact form factor by employing single‐feed stacked patch structure. It exhibits good performance at the global navigation satellite system (GNSS) frequency bands of L1, L2, and L5 and cellular communications frequency band of 2.3 GHz. The antenna has a 3‐dB axial ratio bandwidth of 1.1%, 1.0%, 4.1%, and 1.5% at the four operating bands of L1 (1.575 GHz), L2 (1.227 GHz), L5 (1.176 GHz), and 2.3 GHz. The antenna also achieves a gain of more than 2.2 dBiC and efficiency of more than 70% at the four frequencies. A detailed parametric study is carried out to investigate the importance of different structural elements on the antenna performance. Results are verified through close agreement of simulations and experimental measurements of the fabricated prototype. Good impedance matching, axial ratio bandwidth, and radiation characteristics at the four operating bands along with small profile and mechanically stable structure make this antenna a good candidate for current and future GNSS devices, mobile terminals, and small satellites for 5G/Beyond 5G (5G/B5G) applications.     

The Influence of the Coarse Wool Treatment on Fiber Structure and Physico-Mechanical Parameters

In this research, the influence of slendering on coarse wool fiber which then improved by m-TGase treatment was studied. The coarse wool fiber was first treated by reducing agent then it was stretched at different conditions over various periods of times and temperatures and finally steam set. The wool fibers were then tested for some mechanical and physical properties. To overcome tenacity loss of the fibers as a result of slendering treatment, after-treatment with microbial trans-glutaminases was examined. The results show that the fineness of the fiber was improved about 17% but the tenacity decreased about 15%. XRD analysis show that the degree of crystallinity related to stretching ratio and this increasing of stretching alters the degree of crystallinity. SEM shows that the cuticle of the treated samples causes excessive damage and the reducing agent can affect on wool surface. Furthermore, alkaline solubility was increased by reduction treatment, but m-TGase can compensate a little. The fiber yellowness was significantly decreased after slendering.     

CO2 capture using membrane contactors: a systematic literature review

With fossil fuel being the major source of energy, CO₂ emission levels need to be reduced to a minimal amount namely from anthropogenic sources. Energy consumption is expected to rise by 48% in the next 30 years, and global warming is becoming an alarming issue which needs to be addressed on a thorough technical basis. Nonetheless, exploring CO₂ capture using membrane contactor technology has shown great potential to be applied and utilised by industry to deal with post- and pre-combustion of CO₂. A systematic review of the literature has been conducted to analyse and assess CO₂ removal using membrane contactors for capturing techniques in industrial processes. The review began with a total of 2650 papers, which were obtained from three major databases, and then were excluded down to a final number of 525 papers following a defined set of criteria. The results showed that the use of hollow fibre membranes have demonstrated popularity, as well as the use of amine solvents for CO₂ removal. This current systematic review in CO₂ removal and capture is an important milestone in the synthesis of up to date research with the potential to serve as a benchmark databank for further research in similar areas of work. This study provides the first systematic enquiry in the evidence to research further sustainable methods to capture and separate CO₂.     

A Novel Action Transformer Network for Hybrid Multimodal Sign Language Recognition

Sign language fills the communication gap for people with hearing and speaking ailments. It includes both visual modalities, manual gestures consisting of movements of hands, and non-manual gestures incorporating body movements including head, facial expressions, eyes, shoulder shrugging, etc. Previously both gestures have been detected; identifying separately may have better accuracy, but much communicational information is lost. A proper sign language mechanism is needed to detect manual and non-manual gestures to convey the appropriate detailed message to others. Our novel proposed system contributes as Sign Language Action Transformer Network (SLATN), localizing hand, body, and facial gestures in video sequences. Here we are expending a Transformer-style structural design as a “base network” to extract features from a spatiotemporal domain. The model impulsively learns to track individual persons and their action context in multiple frames. Furthermore, a “head network” emphasizes hand movement and facial expression simultaneously, which is often crucial to understanding sign language, using its attention mechanism for creating tight bounding boxes around classified gestures. The model’s work is later compared with the traditional identification methods of activity recognition. It not only works faster but achieves better accuracy as well. The model achieves overall 82.66% testing accuracy with a very considerable performance of computation with 94.13 Giga-Floating Point Operations per Second (G-FLOPS). Another contribution is a newly created dataset of Pakistan Sign Language for Manual and Non-Manual (PkSLMNM) gestures.