Performance Analysis and Optimization of Energy Harvesting Modulation for Multi-User Integrated Data and Energy Transfer

Integrated data and energy transfer(IDET)enables the electromagnetic waves to transmit wireless energy at the same time of data delivery for lowpower devices.In this paper,an energy harvesting modulation(EHM)assisted multi-user IDET system is studied,where all the received signals at the users are exploited for energy harvesting without the degradation of wireless data transfer(WDT)performance.The joint IDET performance is then analysed theoretically by conceiving a practical time-dependent wireless channel.With the aid of the AO based algorithm,the average effective data rate among users are maximized by ensuring the BER and the wireless energy transfer(WET)performance.Simulation results validate and evaluate the IDET performance of the EHM assisted system,which also demonstrates that the optimal number of user clusters and IDET time slots should be allocated,in order to improve the WET and WDT performance.

Multi-Sinusoidal Waveform Shaping for Integrated Data and Energy Transfer in Aging Channels

Integrated data and energy transfer(IDET)is capable of simultaneously delivering on-demand data and energy to low-power Internet of Everything(Io E)devices.We propose a multi-carrier IDET transceiver relying on superposition waveforms consisting of multi-sinusoidal signals for wireless energy transfer(WET)and orthogonal-frequency-divisionmultiplexing(OFDM)signals for wireless data transfer(WDT).The outdated channel state information(CSI)in aging channels is employed by the transmitter to shape IDET waveforms.With the constraints of transmission power and WDT requirement,the amplitudes and phases of the IDET waveform at the transmitter and the power splitter at the receiver are jointly optimised for maximising the average directcurrent(DC)among a limited number of transmission frames with the existence of carrier-frequencyoffset(CFO).For the amplitude optimisation,the original non-convex problem can be transformed into a reversed geometric programming problem,then it can be effectively solved with existing tools.As for the phase optimisation,the artificial bee colony(ABC)algorithm is invoked in order to deal with the nonconvexity.Iteration between the amplitude optimisation and phase optimisation yields our joint design.Numerical results demonstrate the advantage of our joint design for the IDET waveform shaping with the existence of the CFO and the outdated CSI.

NOMA Empowered Energy Efficient Data Collection and Wireless Power Transfer in Space-Air-Ground Integrated Networks

As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.

Optical Based Gradient-Weighted Class Activation Mapping and Transfer Learning Integrated Pneumonia Prediction Model

Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems.However,hospital medical resources are limited,and sometimes the workload of physicians is too high,which can affect their judgment.Therefore,a good medical assistance system is of great significance for improving the quality of medical care.This study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping(Grad-CAM).Pneumonia is a common lung disease that is generally diagnosed using X-rays.However,in areaswith limited medical resources,a shortage of medical personnel may result in delayed diagnosis and treatment during the critical period.Additionally,overworked physicians may make diagnostic errors.Therefore,having an X-ray pneumonia diagnosis assistance system is a significant tool for improving the quality of medical care.The result indicates that the best results were obtained by a ResNet50 pretrained model combined with a fully connected classification layer.A retraining procedure was designed to improve accuracy by using gradient-weighted class activation mapping(Grad-CAM),which detects the misclassified images and adds weights to them.In the evaluation tests,the final combined model is named Grad-CAM Based Pneumonia Network(GCPNet)out performed its counterparts in terms of accuracy,precision,and F1 score and reached 97.2%accuracy.An integrated system is proposed to increase model performance where Grad-CAM and transfer learning are combined.Grad-CAM is used to generate the heatmap,which shows the region that the model is focusing on.The outcomes of this research can aid in diagnosing pneumonia symptoms,as themodel can accurately classify chest X-ray images,and the heatmap can assist doctors in observing the crucial areas.

UAV-Aided Data and Energy Integrated Network: System Design and Prototype Development

Terminal devices deployed in outdoor environments are facing a thorny problem of power supply.Data and energy integrated network(DEIN)is a promising technology to solve the problem,which simultaneously transfers data and energy through radio frequency signals.State-of-the-art researches mostly focus on theoretical aspects.By contrast,we provide a complete design and implementation of a fully functioning DEIN system with the support of an unmanned aerial vehicle(UAV).The UAV can be dispatched to areas of interest to remotely recharge batteryless terminals,while collecting essential information from them.Then,the UAV uploads the information to remote base stations.Our system verifies the feasibility of the DEIN in practical applications.

Probability estimation method for fatigue crack growth rate based on a∼t data from service

Purpose–The study aims to provide a basis for the effective use of safety-related information data and a quantitative assessment way for the occurrence probability of the safety risk such as the fatigue fracture of the key components.Design/methodology/approach–The fatigue crack growth rate is of dispersion,which is often used to accurately describe with probability density.In view of the external dispersion caused by the load,a simple and applicable probability expression of fatigue crack growth rate is adopted based on the fatigue growth theory.Considering the isolation among the pairs of crack length a and crack formation time t(a∼t data)obtained from same kind of structural parts,a statistical analysis approach of t distribution is proposed,which divides the crack length in several segments.Furthermore,according to the compatibility criterion of crack growth,that is,there is statistical development correspondence among a∼t data,the probability model of crack growth rate is established.Findings–The results show that the crack growth rate in the stable growth stage can be approximately expressed by the crack growth control curve da/dt=5 Q•a,and the probability density of the crack growth parameter Q represents the external dispersion;t follows two-parameter Weibull distribution in certain a values.Originality/value–The probability density f(Q)can be estimated by using the probability model of crack growth rate,and a calculation example shows that the estimation method is effective and practical.

Influence of O-O formation pathways and charge transfer mediator on lipid bilayer membrane-like photoanodes for water oxidation

Inspired by the function of crucial components in photosystemⅡ(PSⅡ),electrochemical and dyesensitized photoelectrochemical(DSPEC)water oxidation devices were constructed by the selfassembly of well-designed amphipathic Ru(bda)-based catalysts(bda=2,2'-bipyrdine-6,6'-dicarbonoxyl acid)and aliphatic chain decorated electrode surfaces,forming lipid bilayer membrane(LBM)-like structures.The Ru(bda)catalysts on electrode-supported LBM films demonstrated remarkable water oxidation performance with different O-O formation mechanisms.However,compared to the slow charge transfer process,the O-O formation pathways did not determine the PEC water oxidation efficiency of the dyesensitized photoanodes,and the different reaction rates for similar catalysts with different catalytic paths did not determine the PEC performance of the DSPECs.Instead,charge transfer plays a decisive role in the PEC water oxidation rate.When an indolo[3,2-b]carbazole derivative was introduced between the Ru(bda)catalysts and aliphatic chain-modified photosensitizer in LBM films,serving as a charge transfer mediator for the tyrosine-histidine pair in PSⅡ,the PEC water oxidation performance of the corresponding photoanodes was dramatically enhanced.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

A comprehensive review of heart rate variability as an indicator in the regulation of the autonomic nervous system by acupuncture:a bibliometric analysis

This study sought to conduct a bibliometric analysis of acupuncture studies focusing on heart rate variability(HRV)and to investigate the correlation between various acupoints and their effects on HRV by utilizing association rule mining and network analysis.A total of 536 publications on the topic of acupuncture studies based on HRV.The disease keyword analysis revealed that HRV-related acupuncture studies were mainly related to pain,inflammation,emotional disorders,gastrointestinal function,and hypertension.A separate analysis was conducted on acupuncture prescriptions,and Neiguan(PC6)and Zusanli(ST36)were the most frequently used acupoints.The core acupoints for HRV regulation were identified as PC6,ST36,Shenmen(HT7),Hegu(LI4),Sanyinjiao(SP6),Jianshi(PC5),Taichong(LR3),Quchi(LI11),Guanyuan(CV4),Baihui(GV20),and Taixi(KI3).Additionally,the research encompassed 46 reports on acupuncture animal experiments conducted on HRV,with ST36 being the most frequently utilized acupoint.The research presented in this study offers valuable insights into the global research trend and hotspots in acupuncture-based HRV studies,as well as identifying frequently used combinations of acupoints.The findings may be helpful for further research in this field and provide valuable information about the potential use of acupuncture for improving HRV in both humans and animals.

Power Splitting Based SWIPT in Network-Coded Two-Way Networks with Data Rate Fairness:An Information-Theoretic Perspective

This paper investigates the simultaneous wireless information and powertransfer(SWIPT) for network-coded two-way relay network from an information-theoretic perspective, where two sources exchange information via an SWIPT-aware energy harvesting(EH) relay. We present a power splitting(PS)-based two-way relaying(PS-TWR) protocol by employing the PS receiver architecture. To explore the system sum rate limit with data rate fairness, an optimization problem under total power constraint is formulated. Then, some explicit solutions are derived for the problem. Numerical results show that due to the path loss effect on energy transfer, with the same total available power, PS-TWR losses some system performance compared with traditional non-EH two-way relaying, where at relatively low and relatively high signalto-noise ratio(SNR), the performance loss is relatively small. Another observation is that, in relatively high SNR regime, PS-TWR outperforms time switching-based two-way relaying(TS-TWR) while in relatively low SNR regime TS-TWR outperforms PS-TWR. It is also shown that with individual available power at the two sources, PS-TWR outperforms TS-TWR in both relatively low and high SNR regimes.

An improved transfer learning strategy for short-term cross-building energy prediction usingdata incremental

The available modelling data shortage issue makes it difficult to guarantee the performance of data-driven building energy prediction(BEP)models for both the newly built buildings and existing information-poor buildings.Both knowledge transfer learning(KTL)and data incremental learning(DIL)can address the data shortage issue of such buildings.For new building scenarios with continuous data accumulation,the performance of BEP models has not been fully investigated considering the data accumulation dynamics.DIL,which can learn dynamic features from accumulated data adapting to the developing trend of new building time-series data and extend BEP model's knowledge,has been rarely studied.Previous studies have shown that the performance of KTL models trained with fixed data can be further improved in scenarios with dynamically changing data.Hence,this study proposes an improved transfer learning cross-BEP strategy continuously updated using the coarse data incremental(CDI)manner.The hybrid KTL-DIL strategy(LSTM-DANN-CDI)uses domain adversarial neural network(DANN)for KLT and long short-term memory(LSTM)as the Baseline BEP model.Performance evaluation is conducted to systematically qualify the effectiveness and applicability of KTL and improved KTL-DIL.Real-world data from six-type 36 buildings of six types are adopted to evaluate the performance of KTL and KTL-DIL in data-driven BEP tasks considering factors like the model increment time interval,the available target and source building data volumes.Compared with LSTM,results indicate that KTL(LSTM-DANN)and the proposed KTL-DIL(LSTM-DANN-CDI)can significantly improve the BEP performance for new buildings with limited data.Compared with the pure KTL strategy LSTM-DANN,the improved KTL-DIL strategy LSTM-DANN-CDI has better prediction performance with an average performance improvement ratio of 60%.

Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.

Data Transfer Over the Internet for Real Time Applications

Efficient real time data exchange over the Internet plays a crucial role in the successful application of web-based systems. In this paper, a data transfer mechanism over the Internet is proposed for real time web based applications. The mechanism incorporates the eXtensible Markup Language （XML） and Hierarchical Data Format （HDF） to provide a flexible and efficient data format. Heterogeneous transfer data is classified into light and heavy data, which are stored using XML and HDF respectively; the HDF data format is then mapped to Java Document Object Model （JDOM） objects in XML in the Java environment. These JDOM data objects are sent across computer networks with the support of the Java Remote Method Invocation （RMI） data transfer infrastructure. Client＇s defined data priority levels are implemented in RMI, which guides a server to transfer data objects at different priorities. A remote monitoring system for an industrial reactor process simulator is used as a case study to illustrate the proposed data transfer mechanism.

Does Checklist Implementation Improve Quantity of Data Transfer: An Observation in Postanesthesia Care Unit (PACU)

Background: Omission of patient information in perioperative communication is closely linked to adverse events. Use of checklists to standardize the handoff in the post anesthesia care unit (PACU) has been shown to effectively reduce medical errors. Objective: Our study investigates the use of a checklist to improve quantity of data transfer during handoffs in the PACU. Design: A cross-sectional observational study. Setting: PACU at Memorial Sloan Kettering Cancer Center (MSKCC);June 13, 2016 through July 15, 2016. Patients, other participants: We observed the handoff reports between the nurses, PACU midlevel providers, anesthesia staff, and surgical staff. Intervention: A physical checklist was provided to all anesthesia staff and recommended to adhere to the list at all observed PACU handoffs. Main outcome measure: Quantity of reported handoff items during 60 pre- and 60 post-implementation of a checklist. Results: Composite value from both surgical and anesthesia reports showed an increase in the mean report of 8.7 items from pre-implementation period to 10.9 post-implementation. Given that surgical staff reported the mean of 5.9 items pre-implementation and 5.5 items post-implementation without intervention, improvements in anesthesia staff report with intervention improved the overall handoff data transfer. Conclusions: Using a physical 12-item checklist for PACU handoff increased overall data transfer.

Data Augmentation Technology Driven By Image Style Transfer in Self-Driving Car Based on End-to-End Learning

With the advent of deep learning,self-driving schemes based on deep learning are becoming more and more popular.Robust perception-action models should learn from data with different scenarios and real behaviors,while current end-to-end model learning is generally limited to training of massive data,innovation of deep network architecture,and learning in-situ model in a simulation environment.Therefore,we introduce a new image style transfer method into data augmentation,and improve the diversity of limited data by changing the texture,contrast ratio and color of the image,and then it is extended to the scenarios that the model has been unobserved before.Inspired by rapid style transfer and artistic style neural algorithms,we propose an arbitrary style generation network architecture,including style transfer network,style learning network,style loss network and multivariate Gaussian distribution function.The style embedding vector is randomly sampled from the multivariate Gaussian distribution and linearly interpolated with the embedded vector predicted by the input image on the style learning network,which provides a set of normalization constants for the style transfer network,and finally realizes the diversity of the image style.In order to verify the effectiveness of the method,image classification and simulation experiments were performed separately.Finally,we built a small-sized smart car experiment platform,and apply the data augmentation technology based on image style transfer drive to the experiment of automatic driving for the first time.The experimental results show that:(1)The proposed scheme can improve the prediction accuracy of the end-to-end model and reduce the model’s error accumulation;(2)the method based on image style transfer provides a new scheme for data augmentation technology,and also provides a solution for the high cost that many deep models rely heavily on a large number of label data.

Time Optimization of Multiple Knowledge Transfers in the Big Data Environment

In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfer is one of the mostimportant aspects to improve knowledge transfer efficiency. Based on the analysis of thecomplex characteristics of knowledge transfer in the big data environment, multipleknowledge transfers can be divided into two categories. One is the simultaneous transferof various types of knowledge, and the other one is multiple knowledge transfers atdifferent time points. Taking into consideration the influential factors, such as theknowledge type, knowledge structure, knowledge absorptive capacity, knowledge updaterate, discount rate, market share, profit contributions of each type of knowledge, transfercosts, product life cycle and so on, time optimization models of multiple knowledgetransfers in the big data environment are presented by maximizing the total discountedexpected profits (DEPs) of an enterprise. Some simulation experiments have beenperformed to verify the validity of the models, and the models can help enterprisesdetermine the optimal time of multiple knowledge transfer in the big data environment.

Enhanced Data Transmission Rate of XCTD Profiler Based on OFDM

An expendable conductivity-temperature-depth profiler(XCTD)is one of the most important instruments used to obtain hydrological data,such as temperature and conductivity,and detect ocean depth in a large area.However,the XCTD channel provides poor time-varying performance,narrowband,and low signal-to-noise ratio(SNR),which severely restricts the data transmission rate.In contrast to conventional single-carrier modulation techniques,such as amplitude-shift keying and differential phase-shift keying,this article provides a new method,based on orthogonal frequency division multiplexing(OFDM)to enhance the data transmission rate of deep-sea abandoned profilers.We apply the OFDM to enhance the SNR of the XCTD,which is achieved by reducing the data transmission rate of each sub-channel.Moreover,the bandwidth utilization may be improved by increasing the number of subcarriers in a given bandwidth,which enhances the data transmission rate.Based on analysis of the XCTD channel model,OFDM with different parameters such as constellation mapping,number of subcarriers,subcarrier spacing,signal period and cyclic prefix are achieved.To verify the effectiveness of the OFDM,this study investigates the influence of different parameters on the data transmission rate at different noise levels,i.e.,-20 dB and-40 d B.

Spectrum Prediction Based on GAN and Deep Transfer Learning:A Cross-Band Data Augmentation Framework

This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes.The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency.Moreover,the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very limited.To address the above issues,we develop a crossband data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network(GAN)and deep transfer learning.Firstly,through the similarity measurement,we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency band.Then,through the data augmentation by feeding the small amount of the target data into the pre-trained GAN,temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from GAN.Finally,experiment results demonstrate the effectiveness of the proposed framework.

Maximum Data Generation Rate Routing Protocol Based on Data Flow Controlling Technology for Rechargeable Wireless Sensor Networks

For rechargeable wireless sensor networks,limited energy storage capacity,dynamic energy supply,low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario.Therefore,before data delivery,a sensor has to update its waking schedule continuously and share them to its neighbors,which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets.In this work,we propose the maximum data generation rate routing protocol based on data flow controlling technology.For a sensor,it does not share its waking schedule to its neighbors and cache any waking schedules of other sensors.Hence,the energy consumption for time synchronization,location information and waking schedule shared will be reduced significantly.The saving energy can be used for improving data collection rate.Simulation shows our scheme is efficient to improve packets generation rate in rechargeable wireless sensor networks.

Create Your Own Data and Energy Integrated Communication Network:A Brief Tutorial and a Prototype System

In order to satisfy the ever-increasing energy appetite of the massive battery-powered and batteryless communication devices,radio frequency(RF)signals have been relied upon for transferring wireless power to them.The joint coordination of wireless power transfer(WPT)and wireless information transfer(WIT)yields simultaneous wireless information and power transfer(SWIPT)as well as data and energy integrated communication network(DEIN).However,as a promising technique,few efforts are invested in the hardware implementation of DEIN.In order to make DEIN a reality,this paper focuses on hardware implementation of a DEIN.It firstly provides a brief tutorial on SWIPT,while summarising the latest hardware design of WPT transceiver and the existing commercial solutions.Then,a prototype design in DEIN with full protocol stack is elaborated,followed by its performance evaluation.