Yudong Yao

ORCID: 0000-0003-3868-0593
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About
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Research Areas
  • Wireless Communication Networks Research
  • Advanced MIMO Systems Optimization
  • Advanced Wireless Communication Techniques
  • Radiomics and Machine Learning in Medical Imaging
  • Cognitive Radio Networks and Spectrum Sensing
  • Cooperative Communication and Network Coding
  • Wireless Signal Modulation Classification
  • AI in cancer detection
  • Wireless Networks and Protocols
  • COVID-19 diagnosis using AI
  • Advanced Wireless Network Optimization
  • Lung Cancer Diagnosis and Treatment
  • Medical Imaging Techniques and Applications
  • Cell Image Analysis Techniques
  • Full-Duplex Wireless Communications
  • Advanced Neural Network Applications
  • Digital Imaging for Blood Diseases
  • Cardiovascular Health and Disease Prevention
  • Power Line Communications and Noise
  • Face and Expression Recognition
  • Advanced Wireless Communication Technologies
  • Blind Source Separation Techniques
  • Image Processing Techniques and Applications
  • Wireless Communication Security Techniques
  • Image Retrieval and Classification Techniques

Stevens Institute of Technology
2016-2025

Northwestern Polytechnical University
2023-2025

Ningbo University
2021-2025

Northeastern University
2014-2024

HBIS (China)
2024

College of Staten Island
2023

Northeastern University
2023

Optica
2023

Qualcomm (United Kingdom)
1995-2023

Hong Kong Polytechnic University
2023

Deep learning (DL) is a new machine (ML) methodology that has found successful implementations in many application domains. However, its usage communications systems not been well explored. This paper investigates the use of DL modulation classification, which major task systems. The relies on massive amount data and, for research and applications, this can be easily available Furthermore, unlike ML, advantage requiring manual feature selections, significantly reduces complexity...

10.1109/tnnls.2018.2850703 article EN IEEE Transactions on Neural Networks and Learning Systems 2018-07-24

Drones, also known as mini-unmanned aerial vehicles, have attracted increasing attention due to their boundless applications in communications, photography, agriculture, surveillance, and numerous public services. However, the deployment of amateur drones poses various safety, security, privacy threats. To cope with these challenges, drone surveillance has become a very important but largely unexplored topic. In this article, we first present brief survey show state-of-the-art studies on...

10.1109/mcom.2017.1700452 article EN IEEE Communications Magazine 2018-01-01

In this correspondence, an adaptive cooperation diversity scheme with best-relay selection is proposed for multiple-relay cognitive radio networks to improve the performance of secondary transmissions while ensuring quality service (QoS) primary transmissions. Exact closed-form expressions outage probability transmissions, referred as probability, are derived under constraint satisfying a required (primary probability) both traditional non-cooperation and schemes over Rayleigh fading...

10.1109/tsp.2010.2053708 article EN IEEE Transactions on Signal Processing 2010-06-25

We investigate the problem of achieving global optimization for distributed channel selections in cognitive radio networks (CRNs), using game theoretic solutions. To cope with lack centralized control and local influences, we propose two special cases interaction to study this problem. The first is altruistic game, which each user considers payoffs itself as well its neighbors rather than considering only. second congestion minimizes number competing neighbors. It shown that proposed games,...

10.1109/jstsp.2011.2176916 article EN IEEE Journal of Selected Topics in Signal Processing 2011-11-22

A computer-aided diagnosis (CAD) system based on mammograms enables early breast cancer detection, diagnosis, and treatment. However, the accuracy of existing CAD systems remains unsatisfactory. This paper explores a method feature fusion with convolutional neural network (CNN) deep features. First, we propose mass detection CNN features unsupervised extreme learning machine (ELM) clustering. Second, build set fusing features, morphological texture density Third, an ELM classifier is...

10.1109/access.2019.2892795 article EN cc-by-nc-nd IEEE Access 2019-01-01

We investigate the problem of distributed channel selection using a game-theoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where availability statistics and number secondary users are apriori unknown. formulate as game which is proved to be exact potential game. However, due lack information about other restriction that time-varying with unknown statistics, task achieving Nash equilibrium (NE) points challenging. Firstly, we propose genie-aided algorithm...

10.1109/twc.2012.020812.110025 article EN IEEE Transactions on Wireless Communications 2012-02-13

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social healthcare system. Rapid detection COVID-19 cases significant step to fight against this virus as well release off the OBJECTIVE: One critical factors behind rapid spread pandemic lengthy clinical testing time. imaging tool, such Chest X-ray (CXR), can speed up...

10.3233/xst-200715 article EN other-oa Journal of X-Ray Science and Technology 2020-08-04

This paper investigates the issue of spatial-temporal opportunity detection for spectrum-heterogeneous cognitive radio networks, where at a given time secondary users (SUs) different locations may experience spectrum access opportunities. Most prior studies address either spatial or temporal sensing in isolation and explicitly implicitly assume that all SUs share same opportunity. However, this assumption is not realistic traditional non-cooperative (NCS) cooperative (CS) schemes are very...

10.1109/twc.2012.122212.111638 article EN IEEE Transactions on Wireless Communications 2013-02-01

Modulation classification is one of the key tasks for communications systems monitoring, management, and control addressing technical issues, including spectrum awareness, adaptive transmissions, interference avoidance. Recently, deep learning (DL)-based modulation has attracted significant attention due to its superiority in feature extraction accuracy. In DL-based classification, major challenge preprocess a received signal represent it proper format before feeding into neural networks....

10.1109/tnnls.2021.3085433 article EN IEEE Transactions on Neural Networks and Learning Systems 2021-06-14

With the widespread popularity of Internet Things and various intelligent medical devices, amount data is rising sharply, thus processing has become increasingly challenging. Mobile edge computing technology allows power to be allocated at closer users, which enables efficient offloading for healthcare systems. However, existing studies on seldom guarantee effective privacy security. Moreover, research equipping architectures with Blockchain neglect delay energy consumption costs incurred in...

10.1109/tnet.2023.3274631 article EN IEEE/ACM Transactions on Networking 2023-06-05

Early, express, and reliable detection of cancer can provide a favorable prognosis decrease mortality. Tumor biomarkers have been proven to be closely related tumor occurrence development. Conventional biomarker based on genomic, proteomic, metabolomic methods is time equipment-consuming always needs specific target marker. Surface-enhanced Raman scattering (SERS), as non-invasive ultrasensitive label-free vibrational spectroscopy technique, detect cancer-related biomedical changes in...

10.1007/s00216-023-04730-7 article EN cc-by Analytical and Bioanalytical Chemistry 2023-05-17

Relying on a kurtosis-type criterion, we develop low-complexity blind carrier frequency offset (CFO) estimator for orthogonal frequency-division multiplexing (OFDM) systems. We demonstrate analytically how identifiability and performance of this CFO depend the channel's selectivity input distribution. show that approach can be applied to estimation in multi-input multi-output multiuser OFDM The issues channel nulls, interference, effects multiple antennas are addressed analytically, tested...

10.1109/tcomm.2004.840623 article EN IEEE Transactions on Communications 2005-01-01

In orthogonal frequency-division multiple access (OFDMA), closely spaced subcarriers are assigned to different users for parallel signal transmission. An interleaved subcarrier-assignment scheme is preferred because it provides maximum frequency diversity and increases the capacity in frequency-selective fading channels. The overlapping, but each other such that there no intercarrier interference (ICI). Carrier-frequency offsets (CFOs) between transmitter receiver destroy orthogonality...

10.1109/tcomm.2004.833183 article EN IEEE Transactions on Communications 2004-09-01

A microcell interference model termed the Nakagami m/sub x//m/sub y/ is introduced. The desired signal and cochannel interferers are assumed to have statistics but with different amounts of fading. special case this obtained when has while subject Rayleigh probability density function signal-to-interference ratio in derived. This also compared a Rician/Rayleigh microcellular model. Expressions for outage probabilities systems Numerical results show that, medium/large cell systems, lower...

10.1109/25.142770 article EN IEEE Transactions on Vehicular Technology 1992-05-01
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