Age of Information Based User Scheduling and Data Assignment in Multi-User Mobile Edge Computing Networks:An Online Algorithm

This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.

Seismic anisotropy and upper mantle dynamics in Alaska:A review of shear wave splitting analyses

Shear wave splitting(SWS)is regarded as the most effective geophysical method to delineate mantle flow fields by detecting seismic azimuthal anisotropy in the earth's upper mantle,especially in tectonically active regions such as subduction zones.The Aleutian-Alaska subduction zone has a convergence rate of approximately 50 mm/yr,with a trench length reaching nearly 2800 km.Such a long subduction zone has led to intensive continental deformation and numerous strong earthquakes in southern and central Alaska,while northern Alaska is relatively inactive.The sharp contrast makes Alaska a favorable locale to investigate the impact of subduction on mantle dynamics.Moreover,the uniqueness of this subduction zone,including the unusual subducting type,varying slab geometry,and atypical magmatic activity and composition,has intrigued the curiosity of many geoscientists.To identify different sources of seismic anisotropy beneath the Alaska region and probe the influence of a geometrically varying subducting slab on mantle dynamics,extensive SWS analyses have been conducted in the past decades.However,the insufficient station and azimuthal coverage,especially in early studies,not only led to some conflicting results but also strongly limited the in-depth investigation of layered anisotropy and the estimation of anisotropy depth.With the completion of the Transportable Array project in Alaska,recent studies have revealed more detailed mantle structures and characteristics based on the dense station coverage and newly collected massive seismic data.In this study,we review significant regional-and continental-scale SWS studies in the Alaska region and conclude the mantle flow fields therein,to understand how a geometrically varying subducting slab alters the regional mantle dynamics.The summarized mantle flow mechanisms are believed to be conducive to the understanding of seismic anisotropy patterns in other subduction zones with a complicated tectonic setting.

Link Budget and Enhanced Communication Distance for Ambient Internet of Things

Backscatter communications will play an important role in connecting everything for beyond 5G(B5G)and 6G systems.One open challenge for backscatter communications is that the signals suffer a round-trip path loss so that the communication distance is short.In this paper,we first calculate the communication distance upper bounds for both uplink and downlink by measuring the tag sensitivity and reflection coefficient.It is found that the activation voltage of the envelope detection diode of the downlink tag is the main factor limiting the back-scatter communication distance.Based on this analysis,we then propose to implement a low-noise amplifier(LNA)module before the envelope detection at the tag to enhance the incident signal strength.Our experimental results on the hardware platform show that our method can increase the downlink communication range by nearly 20 m.

Fiber-Optic Sensors and Their Practical Research in the Internet of Things

With the rapid development of the Internet of Things(IoT)technology,fiber-optic sensors,as a kind of high-precision and high-sensitivity measurement tool,are increasingly widely used in the field of IoT.This paper outlines the advantages of fiber-optic sensors over traditional sensors,such as high precision,strong resistance to electromagnetic interference,and long transmission distance.On this basis,the paper discusses the application scenarios of fiber-optic sensors in the Internet of Things,including environmental monitoring,intelligent industry,medical and health care,intelligent transportation,and other fields.It is hoped that this study can provide theoretical support and practical guidance for the further development of fiber-optic sensors in the field of the Internet of Things,as well as promote the innovation and application of IoT.

Automatic detection of small bowel lesions with different bleeding risks based on deep learning models

BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some unresolved challenges.AIM To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks,and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.METHODS The proposed model represents a two-stage method that combined image classification with object detection.First,we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images,normal SB mucosa images,and invalid images.Then,the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding,and the location of the lesion was marked.We constructed training and testing sets and compared model-assisted reading with physician reading.RESULTS The accuracy of the model constructed in this study reached 98.96%,which was higher than the accuracy of other systems using only a single module.The sensitivity,specificity,and accuracy of the model-assisted reading detection of all images were 99.17%,99.92%,and 99.86%,which were significantly higher than those of the endoscopists’diagnoses.The image processing time of the model was 48 ms/image,and the image processing time of the physicians was 0.40±0.24 s/image(P<0.001).CONCLUSION The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images,which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.

Virtual sample generation for model-based prognostics and health management of on-board high-speed train control system

In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment.A virtual sample generation solution based on Generative Adversarial Network(GAN)is proposed to overcome this shortcoming.Aiming at augmenting the sample classes with the imbalanced data problem,the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models.Under the PHM framework of the on-board train control system,the virtual sample generation principle and the detailed procedures are presented.With the enhanced class-balancing mechanism and the designed sample augmentation logic,the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status.Practical data from a specific type of on-board train control system is employed for the validation of the presented solution.The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance(CBM)operations.

Virtual Keyboard:A Real-Time Hand Gesture Recognition-Based Character Input System Using LSTM and Mediapipe Holistic

In the digital age,non-touch communication technologies are reshaping human-device interactions and raising security concerns.A major challenge in current technology is the misinterpretation of gestures by sensors and cameras,often caused by environmental factors.This issue has spurred the need for advanced data processing methods to achieve more accurate gesture recognition and predictions.Our study presents a novel virtual keyboard allowing character input via distinct hand gestures,focusing on two key aspects:hand gesture recognition and character input mechanisms.We developed a novel model with LSTM and fully connected layers for enhanced sequential data processing and hand gesture recognition.We also integrated CNN,max-pooling,and dropout layers for improved spatial feature extraction.This model architecture processes both temporal and spatial aspects of hand gestures,using LSTM to extract complex patterns from frame sequences for a comprehensive understanding of input data.Our unique dataset,essential for training the model,includes 1,662 landmarks from dynamic hand gestures,33 postures,and 468 face landmarks,all captured in real-time using advanced pose estimation.The model demonstrated high accuracy,achieving 98.52%in hand gesture recognition and over 97%in character input across different scenarios.Its excellent performance in real-time testing underlines its practicality and effectiveness,marking a significant advancement in enhancing human-device interactions in the digital age.

Friend or foe? Using eye-tracking technology to investigate the visual discrimination ability of giant pandas

The role that visual discriminative ability plays among giant pandas in social communication and individual discrimination has received less attention than olfactory and auditory modalities.Here,we used an eye-tracker technology to investigate pupil fixation patterns for 8 captive male giant pandas Ailuropoda melanoleuca.We paired images(N=26)of conspecifics against:1)sympatric predators(gray wolves and tigers),and non-threatening sympatric species(golden pheasant,golden snub-nosed monkey,takin,and red panda),2)conspecifics with atypical fur colora-tion(albino and brown),and 3)zookeepers/non-zookeepers wearing either work uniform or plain clothing.For each session,we tracked the pan-da's pupil movements and measured pupil first fixation point(FFP),fixation latency,total fixation count(TFC),and duration(TFD)of attention to each image.Overall,pandas exhibited similar attention(FFPs and TFCs)to images of predators and non-threatening sympatric species.Images of golden pheasant,snub-nosed monkey,and tiger received less attention(TFD)than images of conspecifics,whereas images of takin and red panda received more attention,suggesting a greater alertness to habitat or food competitors than to potential predators.Pandas'TFCs were greater for images of black-white conspecifics than for albino or brown phenotypes,implying that familiar color elicited more interest.Pandas reacted differently to images of men versus women.For images of women only,pandas gave more attention(TFC)to familiar combinations(uniformed zookeepers and plain-clothed non-zookeepers),consistent with the familiarity hypothesis.That pandas can use visual perception to discriminate intra-specifically and inter-specifically,including details of human appearance,has applications for panda conservation and captive husbandry.

Potential Transmission Choice for Internet of Things(IoT):Wireless and Batteryless Communications and Open Problems

The 5th generation mobile communications aims at connecting everything and future Internet of Things(IoT)will get everything smartly connected.To realize it,there exist many challenges.One key challenge is the battery problem for small devices,such as sensors or tags.Batteryless backscatter,also referred to as or battery-free backscatter,is a new potential technology to address this problem.One early and typical type of batteryless backscatter is ambient backscatter.Generally,batteryless backscatter utilizes environmental wireless signals to enable battery-free devices to communicate with each other.These devices first harvest energy from ambient wireless signals and then backscatter these signals so as to transmit their own information.This paper reviews the current studies about batteryless backscatter,including various backscatter schemes and theoretical works,and then introduces open problems for future research.

Membership Proof and Verification in Authenticated Skip Lists Based on Heap

How to keep cloud data intact and available to users is a problem to be solved. Authenticated skip list is an important data structure used in cloud data integrity verification. How to get the membership proof of the element in authenticated skip list efficiently is an important part of authentication. Kaouthar Blibech and Alban Gabillon proposed a head proof and a tail proof algorithms for the membership proof of elements in the authenticated skip list. However, the proposed algorithms are uncorrelated each other and need plateau function. We propose a new algorithm for computing the membership proof for elements in the authenticated skip list by using two stacks, one is for storing traversal chain of leaf node, the other is for storing authentication path for the leaf. The proposed algorithm is simple and effective without needing plateau function. It can also be applicable for other similar binary hash trees.

Reform and Practice on International Software Talent Training Mode

In view of the increasingly rapid development of global economic integration and combined with the existing modes of training international software engineering talents in China,this paper deeply analyzes and obtains the existing problems in the current teaching process,and proposes various teaching reform measures under the guidance of CDIO higher engineering education thought.Through many years of teaching practice experience,we can find that our reform has achieved remarkable results.

An Update Method of Decision Implication Canonical Basis on Attribute Granulating

Decision implication is a form of decision knowledge represen-tation,which is able to avoid generating attribute implications that occur between condition attributes and between decision attributes.Compared with other forms of decision knowledge representation,decision implication has a stronger knowledge representation capability.Attribute granularization may facilitate the knowledge extraction of different attribute granularity layers and thus is of application significance.Decision implication canonical basis(DICB)is the most compact set of decision implications,which can efficiently represent all knowledge in the decision context.In order to mine all deci-sion information on decision context under attribute granulating,this paper proposes an updated method of DICB.To this end,the paper reduces the update of DICB to the updates of decision premises after deleting an attribute and after adding granulation attributes of some attributes.Based on this,the paper analyzes the changes of decision premises,examines the properties of decision premises,designs an algorithm for incrementally generating DICB,and verifies its effectiveness through experiments.In real life,by using the updated algorithm of DICB,users may obtain all decision knowledge on decision context after attribute granularization.

Reform of Human-Computer Interaction Course based on Mobile Application Interaction Design

With the popularity of new intelligent mobile devices in people’s lives,the development of mobile applications has paid increasing attention to the interactive experience of users.As the content of traditional Human-Computer Interaction(HCI)course and teaching material is out of date,it cannot meet the needs of mobile application interaction design and enterprises for students.Therefore,we need a new generation HCI course based on intelligent mobile devices to study the relationship between users and systems.The HCI course not only teaches students HCI theory and model,but also needs to cultivate students’interaction-oriented design practical ability.This paper proposes a set of HCI teaching material design and teaching methods for improving HCI class quality on mobile application interaction design,so as to make students more suitable for the employment requirements of enterprises.

Discrimination between leucine-rich glioma-inactivated 1 antibody encephalitis and gamma-aminobutyric acid B receptor antibody encephalitis based on ResNet18

This study aims to discriminate between leucine-rich glioma-inactivated 1(LGI1)antibody encephalitis and gammaaminobutyric acid B(GABAB)receptor antibody encephalitis using a convolutional neural network(CNN)model.A total of 81 patients were recruited for this study.ResNet18,VGG16,and ResNet50 were trained and tested separately using 3828 positron emission tomography image slices that contained the medial temporal lobe(MTL)or basal ganglia(BG).Leave-one-out cross-validation at the patient level was used to evaluate the CNN models.The receiver operating characteristic(ROC)curve and the area under the ROC curve(AUC)were generated to evaluate the CNN models.Based on the prediction results at slice level,a decision strategy was employed to evaluate the CNN models’performance at patient level.The ResNet18 model achieved the best performance at the slice(AUC=0.86,accuracy=80.28%)and patient levels(AUC=0.98,accuracy=96.30%).Specifically,at the slice level,73.28%(1445/1972)of image slices with GABAB receptor antibody encephalitis and 87.72%(1628/1856)of image slices with LGI1 antibody encephalitis were accurately detected.At the patient level,94.12%(16/17)of patients with GABAB receptor antibody encephalitis and 96.88%(62/64)of patients with LGI1 antibody encephalitis were accurately detected.Heatmaps of the image slices extracted using gradient-weighted class activation mapping indicated that the model focused on the MTL and BG for classification.In general,the ResNet18 model is a potential approach for discriminating between LGI1 and GABAB receptor antibody encephalitis.Metabolism in the MTL and BG is important for discriminating between these two encephalitis subtypes.

Study on key technologies of GNSS-based train state perception for traincentric railway signaling

The application of Global Navigation Satellite Systems(GNSSs)in the intelligent railway systems is rapidly developing all over the world.With the GNSs-based train positioning and moving state perception,the autonomy and flexibility of a novel train control system can be greatly enhanced over the existing solutions relying on the track-side facilities.Considering the safety critical features of the railway signaling applications,the GNSS stand-alone mode may not be sufficient to satisfy the practical requirements.In this paper,the key technologies for applying GNSS in novel train-centric railway signaling systems are investigated,including the multi-sensor data fusion,Virtual Balise(VB)capturing and messaging,train integrity monitoring and system performance evaluation.According to the practical characteristics of the novel train control system under the moving block mode,the details of the key technologies are introduced.Field demonstration results of a novel train control system using the presented technologies under the practical railway operation conditions are presented to illustrate the achievable performance feature of autonomous train state perception using BeiDou Navigation Satellite System(BDS)and related solutions.It reveals the great potentials of these key technologies in the next generation train control system and other GNSS-based railway implementations.

A review of high performance computing applications in high-speed rail systems

Further improving the railway innovation capacity and technological strength is the important goal of the 14th Five-Year Plan for railway scientific and technological innovation.It includes promoting the deep integration of cutting-edge technologies with the railway systems,strengthening the research and application of intelligent railway technologies,applying green computing technologies and advancing the collaborative sharing of transportation big data.The high-speed rail system tasks need to process huge amounts of data and heavy workload with the requirement of ultra-fast response.Therefore,it is of great necessity to promote computation efficiency by applying High Performance Computing(HPC)to high-speed rail systems.The HPC technique is a great solution for improving the performance,efficiency,and safety of high-speed rail systems.In this review,we introduce and analyze the application research of high performance computing technology in the field of highspeed railways.These HPC applications are cataloged into four broad categories,namely:fault diagnosis,network and communication,management system,and simulations.Moreover,challenges and issues to be addressed are discussed and further directions are suggested.

An analysis of ridesharing trip time using advanced text mining techniques

The time cost of ridesharing rental represents a crucial factor influencing users'decisions to rent a car.Researchers have explored this aspect through text analysis and questionnaires.However,the current research faces limitations in terms of data quantity and analysis methods,preventing the extraction of key information.Therefore,there is a need to further optimize the level of public opinion analysis.This study aimed to investigate user perspectives concerning travel time in ridesharing,both pre and post-pandemic,within the Twitter application.Our analysis focused on a dataset from users residing in the USA and India,with considerations for demographic variables such as age and gender.To accomplish our research objectives,we employed Latent Dirichlet Allocation for topic modeling and BERT for sentiment analysis.Our findings revealed significant influences of the pandemic and the user's country of origin on sentiment.Notably,there was a discernible increase in positive sentiment among users from both countries following the pandemic,particularly among older individuals.These findings bear relevance to the ridesharing industry,offering insights that can aid in establishing benchmarks for improving travel time.Such improvements are instrumental in enabling ridesharing companies to effectively compete with other public transportation alternatives.

A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.

Efficacy of Chinese Medicine Treatment Based on Syndrome Differentiation for Primary Insomnia:A Randomized Placebo Controlled Triple-Blinded Trial

Objective:To assess efficacy of Chinese medicine(CM)on insomnia considering characteristics of treatment based on syndrome differentiation.Methods:A total of 116 participants aged 18 to 65 years with moderate and severe primary insomnia were randomized to the placebo(n=20)or the CM group(n=96)for a 4-week treatment and a 4-week follow-up.Three CM clinicians independently prescribed treatments for each patient based on syndromes differentiation.The primary outcome was change in total sleep time(TST)from baseline.Secondary endpoints included sleep onset latency(SOL),wake time after sleep onset(WASO),sleep efficiency,Pittsburgh Sleep Quality Index(PSQI)and CM symptoms.Results:The CM group had an average 0.6 h more(95%confidence interval(CI):0.3–0.9,P<0.001)TST and 34.1%(10.3%–58.0%,P=0.005)more patients beyond 0.5 h TST increment than that of the placebo group.PSQI was changed–3.3(–3.8 to–2.7)in the CM group,a–2.0(–3.2 to–0.8,P<0.001)difference from the placebo group.The CM symptom score in the CM group decreased–2.0(–3.3 to–0.7,P=0.003)more than the placebo group.SOL and WASO changes were not significantly different between groups.The analysis of prescriptions by these clinicians revealed blood deficiency and Liver stagnation as the most common syndromes.Prescriptions for these clinicians displayed relative stability,while the herbs varied.All adverse events were mild and were not related to study treatment.Conclusions:CM treatment based on syndrome differentiation can increase TST and improve sleep quality of primary insomnia.It is effective and safe for primary insomnia.In future studies,the long-term efficacy validation and the exploratory of eutherapeutic clinicians'fixed herb formulas should be addressed.(Registration No.NCT01613183).

Selection of optimal window length using STFT for quantitative SNR analysis of LFM signal

An adaptive approach to select analysis window param- eters for linear frequency modulated （LFM） signals is proposed to obtain the optimal 3 dB signal-to-noise ratio （SNR） in the short- time Fourier transform （STFT） domain. After analyzing the instan- taneous frequency and instantaneous bandwidth to deduce the relation between the window length and deviation of the Gaus- sian window, high-order statistics is used to select the appropriate window length for STFT and get the optimal SNR with the right time-frequency resolution according to the signal characteristic under a fixed sampling rate. Computer simulations have verified the effectiveness of the new method.