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Photoionisation detection of a single Er3+ ion with sub-100-ns time resolution

National Science Review 2024年第4期11卷 38-45页

作者： Yangbo Zhang Wenda Fan Jiliang Yang Hao Guan Qi Zhang Xi Qin Changkui Duan Gabriele G.de Boo Brett C.Johnson Jeffrey C.Mc Callum Matthew J.Sellars Sven Rogge Chunming Yin Jiangfeng Du CAS Key Laboratory of Microscale Magnetic Resonance and School of Physical Sciences University of Science and Technology of China CAS Center for Excellence in Quantum information and Quantum Physics University of Science and Technology of China Hefei National Laboratory University of Science and Technology of China Centre of Excellence for Quantum Computation and communication Technology School of Physics University of New South Wales Centre of Excellence for Quantum Computation and communication Technology School of Engineering RMIT University Centre of Excellence for Quantum Computation and communication Technology School of Physics University of Melbourne Centre of Excellence for Quantum Computation and communication Technology Research School of Physics and Engineering Australian National University

Efficient detection of single optical centres in solids is essential for quantum information processing, sensing and single-photon generation *** this work,we use radio-frequency（RF） reflectometry to electrically detect the photoionisation induced by a single Er3+ion in *** high bandwidth and sensitivity of the RF reflectometry provide sub-100-ns time resolution for the photoionisation *** this technique,the optically excited state lifetime of a single Er3+ion in a Si nano-transistor is measured for the first time to be 0.49±0.04μ*** results demonstrate an efficient approach for detecting a charge state change induced by Er excitation and *** approach could be used for fast readout of other single optical centres in solids and is attractive for large-scale integrated optical quantum systems thanks to the multi-channel RF reflectometry demonstrated with frequency multiplexing techniques.

关键词： single optical centres erbium ions RF reflectometry photoionisation detection

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek department of information Technology Lebanese French UniversityErbilIraq department of Computer Science University of ZakhoZakhoKurdistan RegionIraq department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic department of Cybernetics and Biomedical engineering VSB‐Technical University of OstravaOstravaCzech Republic department of Computer Network and information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq information and communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Direction finding of bistatic MIMO radar in strong impulse noise

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Journal of Systems engineering and electronics 2024年第4期35卷 888-898页

作者： CHEN Menghan GAO Hongyuan DU Yanan CHeng Jianhua ZHANG Yuze College of information and communication engineering Harbin Engineering UniversityHarbin 150001China College of Intelligent Systems Science and engineering Harbin Engineering UniversityHarbin 150001China

For bistatic multiple-input multiple-output(MIMO)radar,this paper presents a robust and direction finding method in strong impulse noise *** means of a new lower order covariance,the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood(ML)estimation method.A quantum equilibrium optimizer algorithm(QEOA)is devised to resolve the corresponding objective function for efficient and accurate direc-tion *** results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations,e.g.,locating coherent signal sources with very few snapshots in strong impulse *** than that,the Cramér-Rao bound(CRB)under impulse noise environment has been drawn to test the capability of the presented method.

关键词： bistatic multiple-input multiple-output(MIMO)radar impulse noise direction finding lower order covariance quan-tum equilibrium optimizer algorithm(QEOA) maximum likeli-hood estimation method Cramér-Rao bound(CRB)

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Orbit Weighting Scheme in the Context of Vector Space information Retrieval

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Computers, Materials & Continua 2024年第7期80卷 1347-1379页

作者： Ahmad Ababneh Yousef Sanjalawe Salam Fraihat Salam Al-E’mari Hamzah Alqudah department of Computer Science Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan department of Cybersecurity Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346Ajman13306United Arab Emirates information Security department Faculty of Information TechnologyUniversity of PetraAmman11196Jordan department of Data Science and Artificial Intelligence Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and *** conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size *** proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing *** research focuses on evaluating OWS’s impact on model accuracy using information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model *** compare OWS-based retrieval models against others using different schemes,including tf-idf variations and *** reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index *** highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

关键词： information retrieval orbit weighting scheme semantic text analysis Tf-Idf weighting scheme vector space model

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Controlled release of dexamethasone from fibrin sealant for intratympanic administration in inner ear therapy

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Journal of Otology 2024年第1期19卷 55-58页

作者： Jing Zou department of Otolaryngology-Head and Neck Surgery Changhai HospitalSecond Military Medical UniversityShanghai200433China Faculty of information Technology and communication Sciences Tampere UniversityTampereFinland

The aim of the present work was to show the sustainability of fibrin sealant in releasing dexamethasone and adjust the protocol for clinical application of the novel method in the treatment of Meniere’s disease (MD) and sudden sensorineural hearing loss (SSHL).Gelation occurred shortly after mixing dexamethasone-containing fibrinogen with *** was constantly released for at least 16 d at a stable level after 7d in protocol 1 (low-dose),while it was robustly released within 4 d and slowed afterward until 10 d in protocol 2(high-dose).There were significant differences among the time points in Protocol 2 (p1,ANOVA),and the exponential model with the formula y=15.299*e~(-0.483*t) fits the *** estimated concentration of dexamethasone released on 7 d in protocol 2 was slightly lower than that observed in protocol *** fibrin sealant is capable of constantly releasing dexamethasone with adjustable *** and minimally invasive administration of the material can be achieved in the clinic by sequential injections of the fluids using a soft-tipped catheter.

关键词： Intratympanic drug delivery Controlled release Corticosteroids Meniere's disease Sudden sensorineural hearing loss

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Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

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Computers, Materials & Continua 2024年第2期78卷 2047-2066页

作者： Muhammad Rizwan Muhammad Faheem Mushtaq Maryam Rafiq Arif Mehmood Isabel de la Torre Diez Monica Gracia Villar Helena Garay Imran Ashraf department of Artificial Intelligence The Islamia University of BahawalpurBahawalnagar63100Pakistan department of information Technology Khwaja Fareed University of Engineering and ITRYKhan64200Pakistan department of Computer Science&information Technology The Islamia University of BahawalpurBahawalnagar63100Pakistan department of Signal Theory and communications and Telematic engineering Unviersity of ValladolidPaseo de Belén 15Valladolid47011Spain Faculty of Social Science and Humanities Universidad Europea del Atlántico Isabel Torres 21Santander39011Spain department of Project Management Universidad Internacional IberoamericanaCampeche24560México Fundación Universitaria Internacional de Colombia Bogotá111611Colombia Universidad Internacional Iberoamericana AreciboPuerto Rico00613USA Universidade Internacional do Cuanza Cuito46703Angola department of information and communication engineering Yeungnam UniversityGyeongsan38541South Korea

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media *** study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the ***,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic *** baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the *** FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to ***,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual *** proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText *** proposed model better captures the contextual features of the relatively small sample class and aids in the detection of

关键词： Depression classification deep learning FastText machine learning

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Network Intrusion Traffic Detection Based on Feature Extraction

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Computers, Materials & Continua 2024年第1期78卷 473-492页

作者： Xuecheng Yu Yan Huang Yu Zhang Mingyang Song Zhenhong Jia College of information Science and engineering Xinjiang UniversityUrumqi830046China Network department China Mobile Communications Group Xinjiang CoLtd.UrumqiUrumqi830011China Xinjiang Signal Detection and Processing Key Laboratory Xinjiang UniversityUrumqi830000China

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal ***,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion *** method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after *** network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between *** datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the ***,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic *** empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing *** results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

关键词： Network intrusion traffic detection PCA Hotelling’s T^(2) BiLSTM

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Cluster-Based Massive Access for Massive MIMO Systems

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China communications 2024年第1期21卷 24-33页

作者： Shiyu Liang Wei Chen Zhongwen Sun Ao Chen Bo Ai State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100091China School of electronic and information engineering Beijing Jiaotong UniversityBeijing 100091China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong UniversityBeijing 100091China Key Laboratory of Railway Industry of Broadband Mobile information communications Beijing Jiaotong UniversityBeijing 100091China Beijing engineering Research Center of High-speed Railway Broadband Mobile communications Beijing Jiaotong UniversityBeijing 100091China School of information engineering Zhengzhou UniversityZhengzhou 450001China

Massive machine type communication aims to support the connection of massive devices,which is still an important scenario in *** this paper,a novel cluster-based massive access method is proposed for massive multiple input multiple output *** exploiting the angular domain characteristics,devices are separated into multiple clusters with a learned cluster-specific dictionary,which enhances the identification of active *** detected active devices whose data recovery fails,power domain nonorthogonal multiple access with successive interference cancellation is employed to recover their data via *** results show that the proposed scheme and algorithm achieve improved performance on active user detection and data recovery.

关键词： compressive sensing dictionary learning multiuser detection random access

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Economic Feasibility Assessment of Motorized PV Louver System Considering the Near Shading Parameter, Array Incidence Losses and Other Environmental Factors

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World Journal of engineering and Technology 2024年第3期12卷 540-551页

作者： Hussein Safwat Hasan Humor Hwang department of information and communications engineering Myongji University Yongin Republic of Korea

Seoul has good weather settings for incorporating renewable energies, hence, given its small land area living mode was mostly set in an apartment condition it is an ideal place for building applied photovoltaic (BAPV) for solar energy harvesting. On the other hand, the BAPV energy self-consumption hasn’t been thoroughly examined considering the overall energy consumption requirement. Therefore, presented in this communication are the viability of PVL to produce electricity from solar energy and insights on modulating and improving energy harvesting efficiency. To accomplish this objective, three major factors were considered: 1) the photovoltaic (PV) positioning;2) the solar tracking scenario;and 3) the mechanistic system energy consumption. The overall louver energy generation was thoroughly scrutinized from the net energy conception of the BAPV up to the mechanistic module energy expenditure. This work intends to provide insights into the economic feasibility of BAPV assessing its technological profitability in the specified location and building size.

关键词： Photovoltaic Louvers Energy Consumption Tracking System Building Integrated Photovoltaic PVsyst

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Iterative Signal Detection Based Soft Interference Cancellation for Satellite-Terrestrial Downlink NOMA Systems

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China communications 2024年第8期21卷 249-263页

作者： Sun Meng Zhang Qi Yao Haipeng Gao Ran Xin Xiangjun Tian Feng Feng Weiying Chen Dong School of electronic engineering Beijing University of Posts and Telecommunications(BUPT)Beijing 100876China Beijing Key Laboratory of Space-Ground Interconnection and Convergence State Key Laboratory of Information Photonics and Optical CommunicationsSchool of Electronic EngineeringBeijing University of Posts and Telecommunications(BUPT)Beijing 100876China School of information and communication engineering Beijing University of Posts and Telecommunications(BUPT)Beijing 100876China Beijing Institute of Technology Beijing 100081China Beihang university Beijing 100191China China Academy of Space Technology(CAST) Beijing 100094China

To improve the bit error rate(BER)performance of multi-user signal detection in satelliteterrestrial downlink non-orthogonal multiple access(NOMA)systems,an iterative signal detection algorithm based on soft interference cancellation with optimal power allocation is *** that power allocation has a significant impact on BER performance,the optimal power allocation is obtained by minimizing the average BER of NOMA *** to the allocated powers,successive interference cancellation(SIC)between NOMA users is performed in descending power *** each user,an iterative soft interference cancellation is performed,and soft symbol probabilities are calculated for soft *** improve detection accuracy and without increasing the complexity,the aforementioned algorithm is optimized by adding minimum mean square error(MMSE)signal estimation before detection,and in each iteration soft symbol probabilities are utilized for soft-decision of the current user and also for the update of soft interference of the previous *** results illustrate that the optimized algorithm ***-IDBSIC significantly outperforms joint multi-user detection and SIC detection by 7.57dB and 8.03dB in terms of BER performance.

关键词： non-orthogonal multiple access(NOMA) resource allocation satellite-terrestrial communication signal detection soft interference cancellation

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