Robust Stability Analysis of Smith Predictor Based Interval Fractional-Order Control Systems:A Case Study in Level Control Process

The robust stability study of the classic Smith predictor-based control system for uncertain fractional-order plants with interval time delays and interval coefficients is the emphasis of this work.Interval uncertainties are a type of parametric uncertainties that cannot be avoided when modeling real-world plants.Also,in the considered Smith predictor control structure it is supposed that the controller is a fractional-order proportional integral derivative(FOPID)controller.To the best of the authors'knowledge,no method has been developed until now to analyze the robust stability of a Smith predictor based fractional-order control system in the presence of the simultaneous uncertainties in gain,time-constants,and time delay.The three primary contributions of this study are as follows:ⅰ)a set of necessary and sufficient conditions is constructed using a graphical method to examine the robust stability of a Smith predictor-based fractionalorder control system—the proposed method explicitly determines whether or not the FOPID controller can robustly stabilize the Smith predictor-based fractional-order control system;ⅱ)an auxiliary function as a robust stability testing function is presented to reduce the computational complexity of the robust stability analysis;andⅲ)two auxiliary functions are proposed to achieve the control requirements on the disturbance rejection and the noise reduction.Finally,four numerical examples and an experimental verification are presented in this study to demonstrate the efficacy and significance of the suggested technique.

A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments

Future 6G communications are envisioned to enable a large catalogue of pioneering applications.These will range from networked Cyber-Physical Systems to edge computing devices,establishing real-time feedback control loops critical for managing Industry 5.0 deployments,digital agriculture systems,and essential infrastructures.The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised.While full automation will enhance industrial efficiency significantly,it concurrently introduces new cyber risks and vulnerabilities.In particular,unattended systems are highly susceptible to trust issues:malicious nodes and false information can be easily introduced into control loops.Additionally,Denialof-Service attacks can be executed by inundating the network with valueless noise.Current anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical models.Solutions based on an exhaustive data collection to detect anomalies are precise but extremely slow.Standard models,with their limited understanding of mobile networks,can achieve precision rates no higher than 75%.Therefore,more general and transversal protection mechanisms are needed to detect malicious behaviors transparently.This paper introduces a probabilistic trust model and control algorithm designed to address this gap.The model determines the probability of any node to be trustworthy.Communication channels are pruned for those nodes whose probability is below a given threshold.The trust control algorithmcomprises three primary phases,which feed themodel with three different probabilities,which are weighted and combined.Initially,anomalous nodes are identified using Gaussian mixture models and clustering technologies.Next,traffic patterns are studied using digital Bessel functions and the functional scalar product.Finally,the information coherence and content are analyzed.The noise content and abnormal information sequences are detected using a Volterra filter and a bank of Finite Impulse Response filters.An experimental validation based on simulation tools and environments was carried out.Results show the proposed solution can successfully detect up to 92%of malicious data injection attacks.

Investigating the Use of Email Application in Illiterate and Semi-Illiterate Population

The use of electronic communication has been significantly increased over the last few decades.Email is one of the most well-known means of electronic communication.Traditional email applications are widely used by a large population;however,illiterate and semi-illiterate people face challenges in using them.A major population of Pakistan is illiterate that has little or no practice of computer usage.In this paper,we investigate the challenges of using email applications by illiterate and semi-illiterate people.In addition,we also propose a solution by developing an application tailored to the needs of illiterate/semi-illiterate people.Research shows that illiterate people are good at learning the designs that convey information with pictures instead of text-only,and focus more on one object/action at a time.Our proposed solution is based on designing user interfaces that consist of icons and vocal/audio instructions instead of text.Further,we use background voice/audio which is more helpful than flooding a picture with a lot of information.We tested our application using a large number of users with various skill levels(from no computer knowledge to experts).Our results of the usability tests indicate that the application can be used by illiterate people without any training or third-party’s help.

Contribution of the Earth's gravitational potential to variations in orbital motion of short-period comets

We present simulation results on evolution development of orbital motion of short-period comets with the revolution period not exceeding 6-7 years, namely comets 21P/Giacobini-Zinner, 26P/Grigg- Skjellerup and 7P/Pons-Winnecke. The calculations cover the range from the date of the object＇s discovery to 2100. Variations in the objects＇ orbital elements under the action of gravity disturbances, taking Earth＇s gravitational potential into account when the small body approaches, are analyzed. Corrected dates of peri- helion passages can be used for scheduling observations.

Analysis of Factors that Affect Government Digitization:A Pilot Case Study of Pakistan

One of the greatest factors that affects the economic condition of a country is its institutions.In the model of good governance,the primary elements for stronger institution include efficiency,transparency,and accountability;and technology plays a major role in improving these elements.However,there are myriad of challenges when it comes to practical integration of technology in these institutions for efficiency.It is more challenging when a country is developing and one that is already weak economically.It is also important to mention that the challenges of digitization in public sector is not limited to developing countries only.It is equally challenging,even today,in already developed countries to digitally transform their public institutions for improved policymaking and for responsive service delivery.Many factors contribute to the failure of such digitization initiatives,more so within developing countries.And the purpose of this paper is to identify those factors,to measure the significance of each of those factors,and to realize and overcome them.This research considered the case study of Pakistan;however,the results are very likely to match the conditions of other developing regions around the world.Through questionnaires and interviews,valuable feedback was gathered from up to 25 senior government officers that are closely associated with digitization initiatives in public sector.The feedback to the questions were overall unanimous.The results indicate the most significant of factors that affect government digitization in this developing region,including some factors that were not expected.

Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks.Video surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this domain.This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis.Managing theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic.The Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in Kaaba.The crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major flow.We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow.Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.

Evaluation of Hypervisor Stability towards Insider Attacks

Virtualization technology plays a key role in cloud computing.Thus,the security issues of virtualization tools（hypervisors,emulators,etc.） should be under precise consideration.However,threats of insider attacks are underestimated.The virtualization tools and hypervisors have been poorly protected from this type of attacks.Furthermore,hypervisor is one of the most critical elements in cloud computing infrastructure.Firstly,hypervisor vulnerabilities analysis is provided.Secondly,a formal model of insider attack on hypervisor is developed.Consequently,on the basis of the formal attack model,we propose a new methodology of hypervisor stability evaluation.In this paper,certain security countermeasures are considered that should be integrated in hypervisor software architecture.

IRMIRS:Inception-ResNet-Based Network for MRI Image Super-Resolution

Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues.These challenges are increasing the interest in the quality of medical images.Recent research has proven that the rapid progress in convolutional neural networks(CNNs)has achieved superior performance in the area of medical image super-resolution.However,the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance(MR)images,adding extra noise in the models and more memory consumption.Furthermore,conventional deep CNN approaches used layers in series-wise connection to create the deeper mode,because this later end layer cannot receive complete information and work as a dead layer.In this paper,we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as IRMRIS.In our proposed approach,a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling filters.Furthermore,a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input image.Quantitative and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.

SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia communication.Versatile Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)videos.This article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video communication.The advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon limit.Turbo codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel fashion.Constituent RSC encoders may be operating on the same or different architectures and code rates.The proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video communication.Moreover,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−4.The Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

MDEV Model:A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful;thus,catching it early is crucial.Medical physicians’time is limited in outdoor situations due to many patients;therefore,automated systems can be a rescue.The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’experience.Therefore,radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays.In medical classifications,deep convolution neural networks are commonly used.This research aims to use deep pretrained transfer learning models to accurately categorize CXR images into binary classes,i.e.,Normal and Pneumonia.The MDEV is a proposed novel ensemble approach that concatenates four heterogeneous transfer learning models:Mobile-Net,DenseNet-201,EfficientNet-B0,and VGG-16,which have been finetuned and trained on 5,856 CXR images.The evaluation matrices used in this research to contrast different deep transfer learning architectures include precision,accuracy,recall,AUC-roc,and f1-score.The model effectively decreases training loss while increasing accuracy.The findings conclude that the proposed MDEV model outperformed cutting-edge deep transfer learning models and obtains an overall precision of 92.26%,an accuracy of 92.15%,a recall of 90.90%,an auc-roc score of 90.9%,and f-score of 91.49%with minimal data pre-processing,data augmentation,finetuning and hyperparameter adjustment in classifying Normal and Pneumonia chests.

Data Augmentation and Random Multi-Model Deep Learning for Data Classification

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML models.The increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen examples.Deep learning(DL)models have a lot of parameters,and they frequently overfit.Effectively,to avoid overfitting,data plays a major role to augment the latest improvements in DL.Nevertheless,reliable data collection is a major limiting factor.Frequently,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in batches.In this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for classification.We present a methodology for using Generative Adversarial Networks(GANs)to generate images for data augmenting.Through experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model efficiency.Experimenting across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

Text Augmentation-Based Model for Emotion Recognition Using Transformers

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally intelligentmachines.Graph-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC tasks.However,their limited ability to collect and acquire contextual information hinders their effectiveness.We propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address this.The proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human emotions.Themodel used text augmentation techniques to producemore training data,improving the proposed model’s accuracy.Transformer encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual information.This integration improves the accuracy and robustness of the proposed model.Furthermore,we present a method for balancing the training dataset by creating enhanced samples from the original dataset.By balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed model.Experimental results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ERC.TA-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based encoding.The balanced dataset and the additional training samples also enhance its resilience.These findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

一种新的视频图像分块匹配压缩算法

视频图像压缩的分形压缩方法是当今图像压缩编码中的热门话题,通过研究双变量分形函数空间,得出了一些关于双变量分形函数的有用的结果;提出了一种基于双变量分形函数空间理论的帧内分块匹配视频图像压缩算法;给出了算法框图和帧间编码的有效花费函数,形成了一种新的视频图像分块匹配压缩算法,在计算机上实现效果良好。

Aerodynamic Effects Compensation on Multi-Rotor UAVs Based on a Neural Network Control Allocation Approach

This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks.Thus,the system performance can be improved by replacing the classic allocation matrix,without using the aerodynamic inflow equations directly.The network training is performed offline,which requires low computational power.The target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation algorithm.Such a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work,because of its small size.We compared the mechanical torques commanded by the flight controller,i.e.,the control input,to those actually generated by the actuators and established at the aircraft.It was observed that the proposed neural network was able to closely match them,while the classic allocation matrix could not achieve that.The allocation error was also determined in both cases.Furthermore,the closed-loop performance also improved with the use of the proposed neural network control allocation,as well as the quality of the thrust and torque signals,in which we perceived a much less noisy behavior.