Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)techniques.Especially,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions.From this perspective,an automated AI technique with a digital processing method can be used to improve these signals.This paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG signals.These classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and non-seizure.In addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the brain.Thus,Hadamard coefficients of the EEG signals are obtained via the FWHT.Moreover,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings.Also,a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers.The LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,respectively.The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,respectively.The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals.Eventually,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm-Based Clustering Scheme for Augmenting Network Lifetime in WSNs

In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.

Privacy of Wearable Electronics in the Healthcare and Childcare Sectors: A Survey of Personal Perspectives from Finland and the United Kingdom

The innovative development of Wearable Electronics (WE) is creating exciting opportunities for application across many industries. Two sectors with high potential are healthcare and childcare. However, it is in these two sectors where the challenges of privacy are presumed to be of the highest. In order to ascertain the personal views of people about potential privacy problems in WE application in these two sectors, interviews with questionnaires were conducted in two different countries: Finland and the United Kingdom (UK). The results indicated that the majority of people in both countries are positive about the use of WE in healthcare and childcare environments. However, when more information is added to be read wirelessly, the attitudes become more negative. In general, the application of WE is more favorable in the UK and the reason as to the difference will make for interesting further research. Several interesting viewpoints and concerns were presented in the interviews. It can be concluded that the implementation of WE in these two sectors will require the collaboration of work on several areas and the development of versatile user studies.

MIMO-Terahertz in 6G Nano-Communications:Channel Modeling and Analysis

With the development of wireless mobile communication technology,the demand for wireless communication rate and frequency increases year by year.Existing wireless mobile communication frequency tends to be saturated,which demands for new solutions.Terahertz(THz)communication has great potential for the future mobile communications(Beyond 5G),and is also an important technique for the high data rate transmission in spatial information network.THz communication has great application prospects in military-civilian integration and coordinated development.In China,important breakthroughs have been achieved for the key techniques of THz high data rate communications,which is practically keeping up with the most advanced technological level in the world.Therefore,further intensifying efforts on the development of THz communication have the strategic importance for China in leading the development of future wireless communication techniques and the standardization process of Beyond 5G.This paper analyzes the performance of the MIMO channel in the Terahertz(THz)band and a discrete mathematical method is used to propose a novel channel model.Then,a channel capacity model is proposed by the combination of path loss and molecular absorption in the THz band based on the CSI at the receiver.Simulation results show that the integration of MIMO in the THz band gives better data rate and channel capacity as compared with a single channel.

Deep learning:Applications,architectures,models,tools,and frameworks:A comprehensive survey

Deep Learning(DL)is a subfield of machine learning that significantly impacts extracting new knowledge.By using DL,the extraction of advanced data representations and knowledge can be made possible.Highly effective DL techniques help to find more hidden knowledge.Deep learning has a promising future due to its great performance and accuracy.We need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively.A survey on DL ways,advantages,drawbacks,architectures,and methods to have a straightforward and clear understanding of it from different views is explained in the paper.Moreover,the existing related methods are compared with each other,and the application of DL is described in some applications,such as medical image analysis,handwriting recognition,and so on.

Framework for COVID-19 Segmentation and Classification Based on Deep Learning of Computed Tomography Lung Images

Corona Virus Disease 2019(COVID-19) has affected millions of people worldwide and caused more than6.3 million deaths(World Health Organization, June 2022). Increased attempts have been made to develop deep learning methods to diagnose COVID-19 based on computed tomography(CT) lung images. It is a challenge to reproduce and obtain the CT lung data, because it is not publicly available. This paper introduces a new generalized framework to segment and classify CT images and determine whether a patient is tested positive or negative for COVID-19 based on lung CT images. In this work, many different strategies are explored for the classification task.ResNet50 and VGG16 models are applied to classify CT lung images into COVID-19 positive or negative. Also,VGG16 and ReNet50 combined with U-Net, which is one of the most used architectures in deep learning for image segmentation, are employed to segment CT lung images before the classifying process to increase system performance. Moreover, the image size dependent normalization technique(ISDNT) and Wiener filter are utilized as the preprocessing techniques to enhance images and noise suppression. Additionally, transfer learning and data augmentation techniques are performed to solve the problem of COVID-19 CT lung images deficiency, therefore the over-fitting of deep models can be avoided. The proposed frameworks, which comprised of end-to-end, VGG16,ResNet50, and U-Net with VGG16 or ResNet50, are applied on the dataset that is sourced from COVID-19 lung CT images in Kaggle. The classification results show that using the preprocessed CT lung images as the input for U-Net hybrid with ResNet50 achieves the best performance. The proposed classification model achieves the 98.98%accuracy(ACC), 98.87% area under the ROC curve(AUC), 98.89% sensitivity(Se), 97.99 % precision(Pr), 97.88%F-score, and 1.8974-seconds computational time.

Numerical and Experimental Investigation on the In-Flight Melting Behaviour of Granulated Powders in Induction Thermal Plasmas

An innovative in-flight glass melting technology with thermal plasmas was developed for the purpose of energy conservation and environment protection. In this study, modelling and experiments of argon-oxygen induction thermal plasmas were conducted to investigate the melting behaviour of granulated soda-lime glass powders injected into the plasma. A two-dimensional local thermodynamic equilibrium （LTE） model was performed to simulate the heat and momentum transfer between plasma and particle. Results showed that the particle temperature was strongly affected by the flow rate of carrier gas and the particle size of raw material. A higher flow rate of carrier gas led to lower particle temperature and less energy transferred to particles which resulted in lower vitrification. The incomplete melting of large particles was attributed to the lower central temperature of the particle caused by a larger heat capacity. The numerical analysis explained well the experimental results, which can provide valuable practical guidelines for the process control in the melting process for the glass industry.

Perspectives for Wearable Electronics in Healthcare and Childcare

This paper starts with a literature survey that introduces possibilities of wearable electronics (WE) in different healthcare and childcare applications. Next, 24 personal interviews and an Internet forum survey were conducted in Finland about the use of WE in applications mentioned above. According to the results, most of the people feel positive about clothes used for wireless identification purposes in healthcare and childcare, but when more information about the person is added that can be wirelessly read, the feelings become more negative. Several important points to consider before implementation of WE for healthcare and childcare environments were brought up.

HybridWatermarking and Encryption Techniques for Securing Medical Images

Securing medical data while transmission on the network is required because it is sensitive and life-dependent data.Many methods are used for protection,such as Steganography,Digital Signature,Cryptography,and Watermarking.This paper introduces a novel robust algorithm that combines discrete wavelet transform(DWT),discrete cosine transform(DCT),and singular value decomposition(SVD)digital image-watermarking algorithms.The host image is decomposed using a two-dimensional DWT(2D-DWT)to approximate low-frequency sub-bands in the embedding process.Then the sub-band low-high(LH)is decomposed using 2D-DWT to four new sub-bands.The resulting sub-band low-high(LH1)is decomposed using 2D-DWT to four new sub-bands.Two frequency bands,high-high(HH_(2))and high-low(HL_(2)),are transformed by DCT,and then the SVD is applied to the DCT coefficients.The strongest modified singular values(SVs)vary very little for most attacks,which is an important property of SVD watermarking.The two watermark images are encrypted using two layers of encryption,circular and chaotic encryption techniques,to increase security.The first encrypted watermark is embedded in the S component of the DCT components of the HL_(2)coefficients.The second encrypted watermark is embedded in the S component of the DCT components of the HH2 coefficients.The suggested technique has been tested against various attacks and proven to provide excellent stability and imperceptibility results.

Reinforcement Learning to Improve QoS and Minimizing Delay in IoT

Machine Learning concepts have raised executions in all knowledge domains,including the Internet of Thing(IoT)and several business domains.Quality of Service(QoS)has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors,information and usage.Sen-sor data gathering is an efficient solution to collect information from spatially dis-seminated IoT nodes.Reinforcement Learning Mechanism to improve the QoS(RLMQ)and use a Mobile Sink(MS)to minimize the delay in the wireless IoT s proposed in this paper.Here,we use machine learning concepts like Rein-forcement Learning(RL)to improve the QoS and energy efficiency in the Wire-less Sensor Network(WSN).The MS collects the data from the Cluster Head(CH),and the RL incentive values select CH.The incentives value is computed by the QoS parameters such as minimum energy utilization,minimum bandwidth utilization,minimum hop count,and minimum time delay.The MS is used to col-lect the data from CH,thus minimizing the network delay.The sleep and awake scheduling is used for minimizing the CH dead in the WSN.This work is simu-lated,and the results show that the RLMQ scheme performs better than the base-line protocol.Results prove that RLMQ increased the residual energy,throughput and minimized the network delay in the WSN.

Compact 5G Vivaldi Tapered Slot Filtering Antenna with Enhanced Bandwidth

Compact fifth-generation(5G)low-frequency band filtering antennas(filtennas)with stable directive radiation patterns,improved bandwidth(BW),and gain are designed,fabricated,and tested in this research.The proposed filtennas are achieved by combining the predesigned compact 5G(5.975–7.125 GHz)third-order uniform and non-uniform transmission line hairpin bandpass filters(UTL and NTL HPBFs)with the compact ultrawide band Vivaldi tapered slot antenna(UWB VTSA)in one module.The objective of this integration is to enhance the performance of 5.975–7.125GHz filtennas which will be suitable for modern mobile communication applications by exploiting the benefits of UWB VTSA.Based on NTL HPBF,more space is provided to add the direct current(DC)biassing circuits in cognitive radio networks(CRNs)for frequency reconfigurable applications.To overcome the mismatch between HPBFs and VTSA,detailed parametric studies are presented.Computer simulation technology(CST)software is used for the simulation in this study.Good measured S11 appeared to be<−13 and<−10.54 dB at 5.48–7.73 and 5.9–7.98GHz with peak realized gains of 6.37 and 6.27 dBi,for VTSA with UTL and NTL HPBFs,respectively which outperforms the predesigned filters.Validation is carried out by comparing the measured and simulated results.

Investigating computational models for diagnosis and prognosis of sepsis based on clinical parameters: Opportunities, challenges, and future research directions

This study investigates the use of computational frameworks for sepsis.We consider two dimensions for investi-gation-early diagnosis of sepsis(EDS)and mortality prediction rate for sepsis patients(MPS).We concentrate on the clinical parameters on which sepsis diagnosis and prognosis are currently done,including customized treatment plans based on historical data of the patient.We identify the most notable literature that uses com-putational models to address EDS and MPS based on those clinical parameters.In addition to the review of the computational models built upon the clinical parameters,we also provide details regarding the popular publicly available data sources.We provide brief reviews for each model in terms of prior art and present an analysis of their results,as claimed by the respective authors.With respect to the use of machine learning models,we have provided avenues for model analysis in terms of model selection,model validation,model interpretation,and model comparison.We further present the challenges and limitations of the use of computational models,providing future research directions.This study intends to serve as a benchmark for first-hand impressions on the use of computational models for EDS and MPS of sepsis,along with the details regarding which model has been the most promising to date.We have provided details regarding all the ML models that have been used to date for EDS and MPS of sepsis.

Inner ear barriers to nanomedicine-augmented drug delivery and imaging

There are several challenges to inner ear drug delivery and imaging due to the existence of tight biological barriers to the target structure and the dense bone surrounding it. Advances in imaging and nanomedicine may provide knowledge for overcoming the existing limitations to both the diagnosis and treatment of inner ear diseases. Novel techniques have improved the efficacy of drug delivery and targeting to the inner ear, as well as the quality and accuracy of imaging this structure. In this review, we will describe the pathways and biological barriers of the inner ear regarding drug delivery, the beneficial applications and limitations of the imaging techniques available for inner ear research, the behavior of engineered nanomaterials in inner ear applications, and future perspectives for nanomedicine-based inner ear imaging.

An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

COVID-19 remains to proliferate precipitously in the world.It has significantly influenced public health,the world economy,and the persons’lives.Hence,there is a need to speed up the diagnosis and precautions to deal with COVID-19 patients.With this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical images.This paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)images.The proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT images.The classification stage is firstly applied to detect and classify the input medical CT images.Then,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT images.The classification stage is implemented based on a simple and efficient CNN deep learning model.This model comprises four Rectified Linear Units(ReLUs),four batch normalization layers,and four convolutional(Conv)layers.TheConv layer depends on filters with sizes of 64,32,16,and 8.A2×2windowand a stride of 2 are employed in the utilized four max-pooling layers.A soft-max activation function and a Fully-Connected(FC)layer are utilized in the classification stage to perform the detection process.For the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid approach.The proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,accurately.To summarize the contributions of the paper,we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis system.Once the images are accepted by the system,it is possible to perform further processing through a segmentation process to isolate the regions of interest in the images.The region of interest can be assesses both automatically and through experts.This strategy helps somuch in saving the time and efforts of specialists with the explosion of COVID-19 pandemic in the world.The proposed classification approach is applied for different scenarios of 80%,70%,or 60%of the data for training and 20%,30,or 40%of the data for testing,respectively.In these scenarios,the proposed approach achieves classification accuracies of 100%,99.45%,and 98.55%,respectively.Thus,the obtained results demonstrate and prove the efficacy of the proposed approach for assisting the specialists in automated medical diagnosis services.

Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state classification.With the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various applications.Guided Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of channels.This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be eliminated.This enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset.The(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.

Toward 6G Communication Networks:Terahertz Frequency Challenges and Open Research Issues

Future networks communication scenarios by the 2030s will include notable applications are three-dimensional(3D)calls,haptics communications,unmanned mobility,tele-operated driving,bio-internet of things,and the Nanointernet of things.Unlike the current scenario in which megahertz bandwidth are sufficient to drive the audio and video components of user applications,the future networks of the 2030s will require bandwidths in several gigahertzes(GHz)(from tens of gigahertz to 1 terahertz[THz])to perform optimally.Based on the current radio frequency allocation chart,it is not possible to obtain such a wide contiguous radio spectrum below 90 GHz(0.09 THz).Interestingly,these contiguous blocks of radio spectrum are readily available in the higher electromagnetic spectrum,specifically in the Terahertz(THz)frequency band.The major contribution of this study is discussing the substantial issues and key features of THz waves,which include(i)key features and significance of THz frequency;(ii)recent regulatory;(iii)the most promising applications;and(iv)possible open research issues.These research topics were deeply investigated with the aim of providing a specific,synopsis,and encompassing conclusion.Thus,this article will be as a catalyst towards exploring new frontiers for future networks of the 2030s.

Frequency Reconfigurable Antenna for Multi Standard Wireless and Mobile Communication Systems

In this paper,low profile frequency reconfigurable monopole antenna is designed on FR-4 substrate with a compact size of 30 mm^(3)×20 mm^(3)×1.6 mm^(3).The antenna is tuned to four different modes through three pin diode switches.In Mode 1(SW1 to SW3=OFF),antenna covers a wideband of 3.15–8.51 GHz.For Mode 2(SW1=ON,SW2 to SW3=OFF),the proposed antenna resonates at 3.5 GHz.The antenna shows dual band behavior and covers 2.6 and 6.4 GHz in Mode 3(SW1 and SW2=ON,SW3=OFF).The same antenna covers three different bands of 2.1,5 and 6.4 GHz when operating in Mode 4(SW1 to SW3=ON).The proposed antenna has good radiation efficiency ranges from 70%∼84%,providing adequate average gain of 2.05 dBi in mode 1,1.87 dBi in mode 2,1.4–1.75 dBi in mode 3 and 1.05–1.56 dBi in mode 4.The achieved impedance bandwidths at respective frequencies ranges from 240 to 5000 MHz.The Voltage Standing Waves Ratio(VSWR)of less than 1.5 is achieved for all operating bands.To validate the simulation results,the proposed antenna is fabricated and experimentally tested in antenna measurement laboratory.Due to its reasonably small size and support of multiple bands operation,the proposed antenna can be used in modern communication systems for supporting various applications such as fifth generation(5G)mobile and wireless local area networks(WLAN).

The Effect of Laser Sintering Process Parameters on Cu Nanoparticle Ink in Room Conditions

Copper is an interesting material for printed electronics inks because, for example, of its good conductivity and lower raw material price compared to silver. However, post-processing Cu inks is challenging because of non-conductive copper oxide. In this work, inkjet-printed Cu nanoparticle structures were sintered on a polyimide substrate with a continuous-wave 808-nm diode laser. Laser sintering was tested by varying the sintering parameters (optical power and scanning velocity), and the electrical resistance of the samples was measured. A minimum sheet resistance of approx.90 mΩ/□ was obtained. All tests were run in room conditions. Sintered structures were then analyzed from SEM images. Results showed that laser sintering produces good repeatability, that a scanning velocity increment positively affects the process window, and that multiple sintering cycles do not increase conductivity.

Inflow Outflow Effect and Shock Wave Analysis in a Traffic Flow Simulation

This paper investigates the effect of inflow, outflow and shock waves in a single lane highway traffic flow problem. A constant source term has been introduced to demonstrate the inflow and outflow. The classical Lighthill Whitham and Richards (LWR) model combined with the Greenshields model is used to obtain analytical and numerical solutions. The model is treated as an IBVP and numerical solutions are presented using Lax Friedrichs scheme. Godunov method is also used to present shock wave analysis. The numerical procedures adopted in this investigation yield results which are very much consistent with real life scenario in terms of traffic density and velocity.