A hybrid model for spelling error detection and correction for Urdu language

Neural Computing and Applications - Detecting and correcting misspelled words in a written text are of great importance in many natural language processing applications. Errors can be broadly...     

BSLPSDN: Base station location privacy via software-defined networking (SDN) in wireless sensor networks

Base station's location privacy in a wireless sensor network (WSN) is critical for information security and operational availability of the network. A key part of securing the base station from potential compromise is to secure the information about its physical location. This paper proposes a technique called base station location privacy via software-defined networking (SDN) in wireless sensor networks (BSLPSDN). The inspiration comes from the architecture of SDN, where the control plane is separated from the data plane, and where control plane decides the policy for the data plane. BSLPSDN uses three categories of nodes, namely, a main controller to instruct the overall operations, a dedicated node to buffer and forward data, and lastly, a common node to sense and forward the packet. We employ three kinds of nodes to collaborate and achieve stealth for the base station and thus protecting it against the traffic-analysis attacks. Different traits of the WSN including energy status and traffic density can actively be monitored by BSLPSDN, which positively affects the energy goals, expected life of the network, load on common nodes, and the possibility of creating diversion in the wake of an attack on the base station. We incorporated multiple experiments to analyze and evaluate the performance of our proposed algorithm. We use single controller with multiple sensor nodes and multiple controllers with multiple sensor nodes to show the level of anonymity of BS. Experiments show that providing BS anonymity via multiple controllers is the best method both in terms of energy and privacy.     

Power Control for Cognitive Radio Networks: A Game Theoretic Approach

In communication industry one of the most rapidly growing area is wireless technology and its applications. The efficient access to radio spectrum is a requirement to make this communication feasible for the users that are running multimedia applications and establishing real-time connections on an already overcrowded spectrum. In recent times cognitive radios (CR) are becoming the prime candidates for improved utilization of available spectrum. The unlicensed secondary users share the spectrum with primary licensed user in such manners that the interference at the primary user does not increase from a predefined threshold. In this paper, we propose an algorithm to address the power control problem for CR networks. The proposed solution models the wireless system with a non-cooperative game, in which each player maximize its utility in a competitive environment. The simulation results shows that the proposed algorithm improves the performance of the network in terms of high SINR and low power consumption.

     

Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect IoT networks have been developed. A distributed architecture based on an intrusion detection system (IDS) ensures that a dynamic, scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is available. In this study, we examined the time-related aspects of network traffic data. We presented an intrusion detection model based on a two-layered bidirectional long short-term memory (Bi-LSTM) with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark dataset. We showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy, precision, recall and F1 score.     

Nanograin Mn-Zn ferrite smart cores to miniaturize electronic devices

Conventional ceramic and sol-gel auto combustion routes were adopted to develop Mn-Zn ferrite cores. To control high frequency (>500 kHz) losses, zirconia (0.2 wt%) and calcia (0.04 wt%) were added in Mn_0.57Zn_0.35Fe_2.08O₄. The results revealed that Mn-Zn ferrite smart cores synthesized by auto combustion process have superior properties than conventionally prepared cores. It is believed that the presence of unique properties such as nanograin microstructure, light weight and short height (thickness) dimensions have played their role to enhance the magnetic impedance of smart core to manifold. Fabricated smart core excellently performed in a test frequency band of 3-15 MHz.     

Influence of metal‐oxide‐supported bentonites on the pyrolysis behavior of polypropylene and high‐density polyethylene

In this article, we report on the pyrolysis of polypropylene (PP) and high‐density polyethylene (HDPE) in the absence and presence of plain and metal‐oxide‐impregnated bentonite clays [BCs; acid‐washed bentonite clay (AWBC), Zn/AWBC, Ni/AWBC, Co/AWBC, Fe/AWBC, and Mn/AWBC] into useful products. Thermal and catalytic runs were performed at 300°C in the case of PP and at 350°C in the case of HDPE for a contact time of 30 min. The effects of different catalysts and their concentrations on the overall yields and the yields of liquid, gas, and residue were studied. The efficacy of each catalyst is reported on the basis of the highest liquid yields (in weight percentage). The derived liquid products were analyzed by Fourier transform infrared spectroscopy and gas chromatography–mass spectroscopy; this confirmed the presence of paraffins, olefins, and naphthenes. The results indicate the catalytic role of impregnated BCs compared to plain BC with the optimum efficiency shown by Co/AWBC in the case of PP and Zn/AWBC in the case of HDPE toward the formation of liquid products in a desirable C range with the enrichment of olefins and naphthenes in the case of PP and paraffins and olefins in the case of HDPE compared to the thermal run. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41221.     

Improving properties of silicone rubber composites using macromolecular silane coupling agent (MMSCA)

Two types of vinyl silicone oil (VSO), allyl‐capped hyperbranched polycarbosilane (HBP), and triethoxysilane (TES) were employed to synthesize macromolecular silane coupling agent (MMSCA) by hydrosilylation. VSOs, HBP, and the hydrosilylated products were used as crosslinker, respectively, to improve weak mechanical properties of silicone rubber (SR). Structures of the crosslinkers were studied by gel permeation chromatography (GPC), Fourier transform infrared spectroscopy (FTIR), and nuclear magnetic resonance (NMR). Crosslinking density test and scanning electron microscope (SEM) observation showed an increased interaction between silicone rubber and fumed silica by the use of MMSCA. Mechanical properties of the resulted composites using MMSCAs were increased to varying degrees compared with those possessing crosslinkers without ethoxy group. MMSCAs were effective for further property enhancements of composites. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43415.     

Considering Industry 4.0 aspects in the supply chain for an SME

Industry 4.0 is a buzzword in current research and in the manufacturing environment. However, despite this limited research has focused on how a company can determine the likely benefits of Industry 4.0 technologies and its influence on the supply chain. Additionally, the aspect of customer experience is gaining significant importance in the new Industry 4.0 supply chain. There is a research gap in implementation strategies of Industry 4.0 concepts for small and medium sized enterprises (SMEs). This paper addresses this gap through an in-depth case study of an SME and highlights how the company can develop competitive advantage. First, a framework was developed to determine the realization of Industry 4.0 predicted benefits for SMEs, which was tested in a case company. Based on input from the case the five-step framework for Industry 4.0 realization is adjusted. It enables practitioners to determine the actual status for their company towards Industry 4.0 realization and thereby functions as a guide for further implementation strategies.     

Modification of the structural and electrical properties of graphene layers by Pt adsorbates

The properties of graphene are strongly affected by metal adsorbates and clusters on graphene. Here, we study the effect of a thin layer of platinum (Pt) metal on exfoliated single, bi- and trilayer graphene and on chemical vapor deposition-grown single-layer graphene by using Raman spectroscopy and transport measurements. The Raman spectra and transport measurements show that Pt affects the structure as well as the electronic properties of graphene. The shift of peak frequencies, intensities and widths of the Raman bands were analyzed after the deposition of Pt with different thicknesses (1, 3, 5 nm) on the graphene. The shifts in the G and 2D peak positions of the Raman spectra indicate the n-type doping effect by the Pt metal. The doping effect was also confirmed by gate-voltage dependent resistivity measurements. The doping effect by the Pt metal is stable under ambient conditions, and the doping intensity increases with the increasing Pt deposition without inducing a severe degradation of the charge carrier mobility.