Isolation Enhancement in a Compact Four-Element MIMO Antenna for Ultra-Wideband Applications

Mutual coupling reduction or isolation enhancement in antenna arrays is an important area of research as it severely affects the performance of an antenna.In this paper,a new type of compact and highly isolated Multiple-Input-Multiple-Output(MIMO)antenna for ultra-wideband(UWB)applications is presented.The design consists of four radiators that are orthogonally positioned and confined to a compact 40×40×0.8 mm3 space.The final antenna design uses an inverted L shape partial ground to produce an acceptable reflection coefficient(S11<−10 dB)in an entire UWB band(3.1–10.6)giga hertz(GHz).Moreover,the inter-element isolation has also been enhanced to>20 db for majority of the UWB band.The antenna was fabricated and tested with the vector network analyzer(VNA)and in an anechoic chamber for scattering parameters and radiation patterns.Furthermore,different MIMO diversity performance metrics are also measured to validate the proposed model.The simulation results and the experimental results from the constructed model agree quite well.The proposed antenna is compared with similar designs in recently published literature for various performance metrics.Because of its low envelope correlation coefficient(ECC<0.1),high diversity gain(DG>9.99 dB),peak gain of 4.6 dB,reduced channel capacity loss(CCL<0.4 b/s/Hz),and average radiation efficiency of over 85%,the proposed MIMO antenna is ideally suited for practical UWB applications.

A Quantitative Security Metric Model for Security Controls:Secure Virtual Machine Migration Protocol as Target of Assessment

Quantitative security metrics are desirable for measuring the performance of information security controls. Security metrics help to make functional and business decisions for improving the performance and cost of the security controls. However, defining enterprise-level security metrics has already been listed as one of the hard problems in the Info Sec Research Council's hard problems list. Almost all the efforts in defining absolute security metrics for the enterprise security have not been proved fruitful. At the same time, with the maturity of the security industry, there has been a continuous emphasis from the regulatory bodies on establishing measurable security metrics. This paper addresses this need and proposes a relative security metric model that derives three quantitative security metrics named Attack Resiliency Measure(ARM), Performance Improvement Factor(PIF), and Cost/Benefit Measure(CBM) for measuring the performance of the security controls. For the effectiveness evaluation of the proposed security metrics, we took the secure virtual machine(VM) migration protocol as the target of assessment. The virtual-ization technologies are rapidly changing the landscape of the computing world. Devising security metrics for virtualized environment is even more challenging. As secure virtual machine migration is an evolving area and no standard protocol is available specifically for secure VM migration. This paper took the secure virtual machine migration protocol as the target of assessment and applied the proposed relative security metric model for measuring the Attack Resiliency Measure, Performance Improvement Factor, and Cost/Benefit Measure of the secure VM migration protocol.

Performance Enhancement of Praseodymium Doped Fiber Amplifiers

In this paper,we report a simulation study on the performance enhancement of Praseodymium doped silica fiber amplifiers(PDFAs)in O-band(1270-1350 nm)in terms of small signal gain,power conversion efficiency(PCE),and output optical power by employing bidirectional pumping.The PDFA performance is examined by optimizing the length of Praseodymium doped silica fiber(PDF),its mode-field diameter(MFD)and the concentration of Pr^(3+).A small-signal peak gain of 56.4 dB,power conversion efficiency(PCE)of 47%,and output optical power of around 1.6 W(32 dBm)is observed at optimized parameters for input signal wavelength of 1310 nm.Minimum noise figure(NF)of 4.1 dB is observed at input signal wavelength of 1310 nm.Moreover,the effect of varying the pump wavelength and pump power on output optical power of the amplifier and amplified spontaneous emission(ASE)noise is also investigated,respectively.Finally,the impact of ion-ion interaction(up-conversion effect)on small-signal gain of the amplifier is also studied by considering different values of up-conversion coefficient.

Neural Machine Translation Models with Attention-Based Dropout Layer

In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of alignment.NMT model has obtained state-of-the-art performance for several language pairs.However,there has been little work exploring useful architectures for Urdu-to-English machine translation.We conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and Transformer.Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen data.The trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,respectively.From a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more frequently.The attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear structure.Therefore,we considered refining the attention-based models by defining an additional attention-based dropout layer.Attention dropout fixes alignment errors and minimizes translation errors at the word level.After empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation score.The ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as well.We empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.

Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing

A tremendous amount of vendor invoices is generated in the corporate sector.To automate the manual data entry in payable documents,highly accurate Optical Character Recognition(OCR)is required.This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement.For text localization,the maximally stable extremal region is used,which extracts a word or digit chunk from an invoice.This chunk is later passed to the deep learning model,which performs text recognition.The deep learning model utilizes both convolution neural networks and long short-term memory(LSTM).The convolution layer is used for extracting features,which are fed to the LSTM.The model integrates feature extraction,modeling sequence,and transcription into a unified network.It handles the sequences of unconstrained lengths,independent of the character segmentation or horizontal scale normalization.Furthermore,it applies to both the lexicon-free and lexicon-based text recognition,and finally,it produces a comparatively smaller model,which can be implemented in practical applications.The overall superior performance in the experimental evaluation demonstrates the usefulness of the proposed model.The model is thus generic and can be used for other similar recognition scenarios.

Thermally stratified flow of hybrid nanofluids with radiative heat transport and slip mechanism: multiple solutions

Research on flow and heat transfer of hybrid nanofluids has gained great significance due to their efficient heat transfer capabilities.In fact,hybrid nanofluids are a novel type of fluid designed to enhance heat transfer rate and have a wide range of engineering and industrial applications.Motivated by this evolution,a theoretical analysis is performed to explore the flow and heat transport characteristics of Cu/Al_(2)O_(3) hybrid nanofluids driven by a stretching/shrinking geometry.Further,this work focuses on the physical impacts of thermal stratification as well as thermal radiation during hybrid nanofluid flow in the presence of a velocity slip mechanism.The mathematical modelling incorporates the basic conservation laws and Boussinesq approximations.This formulation gives a system of governing partial differential equations which are later reduced into ordinary differential equations via dimensionless variables.An efficient numerical solver,known as bvp4c in MATLAB,is utilized to acquire multiple(upper and lower)numerical solutions in the case of shrinking flow.The computed results are presented in the form of flow and temperature fields.The most significant findings acquired from the current study suggest that multiple solutions exist only in the case of a shrinking surface until a critical/turning point.Moreover,solutions are unavailable beyond this turning point,indicating flow separation.It is found that the fluid temperature has been impressively enhanced by a higher nanoparticle volume fraction for both solutions.On the other hand,the outcomes disclose that the wall shear stress is reduced with higher magnetic field in the case of the second solution.The simulation outcomes are in excellent agreement with earlier research,with a relative error of less than 1%.

Developing Engagement in the Learning Management System Supported by Learning Analytics

Learning analytics is an emerging technique of analysing student par-ticipation and engagement.The recent COVID-19 pandemic has significantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to 2020.To date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and instructors.Consequently,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notfirst identified.Therefore,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an LMS.The required data was collected from an LMS that is currently utilised by a local Malaysian university.The model was validated by a panel of experts as well as discussions with students.It is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

Dates Fruit Recognition: From Classical Fusion to Deep Learning

There are over 200 different varieties of dates fruit in the world.Interestingly,every single type has some very specific features that differ from the others.In recent years,sorting,separating,and arranging in automated industries,in fruits businesses,and more specifically in dates businesses have inspired many research dimensions.In this regard,this paper focuses on the detection and recognition of dates using computer vision and machine learning.Our experimental setup is based on the classical machine learning approach and the deep learning approach for nine classes of dates fruit.Classical machine learning includes the Bayesian network,Support Vector Machine,Random Forest,and Multi-Layer Perceptron(MLP),while the Convolutional Neural Network is used for the deep learning set.The feature set includes Color Layout features,Fuzzy Color and Texture Histogram,Gabor filtering,and the Pyramid Histogram of the Oriented Gradients.The fusion of various features is also extensively explored in this paper.The MLP achieves the highest detection performance with an F-measure of 0.938.Moreover,deep learning shows better accuracy than the classical machine learning algorithms.In fact,deep learning got 2%more accurate results as compared to the MLP and the Random forest.We also show that classical machine learning could give increased classification performance which could get close to that provided by deep learning through the use of optimized tuning and a good feature set.

Cloud authorization： exploring techniques and approach towards effective access control framework

Despite the various attractive features that Cloud has to offer, the rate of Cloud migration is rather slow, pri- marily due to the serious security and privacy issues that exist in the paradigm. One of the main problems in this regard is that of authorization in the Cloud environment, which is the focus of our research. In this paper, we present a systematic analysis of the existing authorization solutions in Cloud and evaluate their effectiveness against well-established industrial standards that conform to the unique access control require- ments in the domain. Our analysis can benefit organizations by helping them decide the best authorization technique for deployment in Cloud; a case study along with simulation re- sults is also presented to illustrate the procedure of using our qualitative analysis for the selection of an appropriate tech- nique, as per Cloud consumer requirements. From the results of this evaluation, we derive the general shortcomings of the extant access control techniques that are keeping them from providing successful authorization and, therefore, widely adopted by the Cloud community. To that end, we enumer- ate the features an ideal access control mechanisms for the Cloud should have, and combine them to suggest the ultimate solution to this major security challenge - access control as a service （ACaaS） for the software as a service （SaaS） layer. We conclude that a meticulous research is needed to incorpo- rate the identified authorization features into a generic ACaaS framework that should be adequate for providing high level of extensibility and security by integrating multiple accesscontrol models.