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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Nako, Julia Tsirimokou, Georgia Psychalinos, Costas Elwakil, Ahmed S. University of Patras Electronics Laboratory Physics Department Rio Patras Greece University of Ioannina Department of Computer Science and Engineering Ioannina Greece University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350378511

A general topology capable of implementing integer and non-integer versions of filters with exponential form of their gain responses in the frequency domain, is introduced in this work. The derivation of the topology is performed by employing a curve-fitting method for approximating the gain response, in each case. The versatility of the presented scheme is confirmed through experimental results obtained using a Field Programmable Analog Array device. A practical design example is also provided, where a combination of the introduced stages is utilized for denoising an electrocardiogram signal. © 2024 IEEE.

关键词： Topology

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Deep CNN Model for Multimodal Medical Image Denoising

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computers, Materials & Continua 2022年第11期73卷 3795-3814页

作者： Walid El-Shafai Amira A.Mahmoud Anas M.Ali El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

In the literature,numerous techniques have been employed to decrease noise in medical image modalities,including X-Ray(XR),Ultrasonic(Us),Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and Positron Emission Tomography(PET).These techniques are organized into two main classes:the Multiple Image(MI)and the Single Image(SI)*** the MI techniques,images usually obtained for the same area scanned from different points of view are used.A single image is used in the entire procedure in the SI *** denoising techniques can be carried out both in a transform or spatial *** paper is concerned with single-image noise reduction techniques because we deal with single medical *** most well-known spatial domain noise reduction techniques,including Gaussian filter,Kuan filter,Frost filter,Lee filter,Gabor filter,Median filter,Homomorphic filter,Speckle reducing anisotropic diffusion(SRAD),Nonlocal-Means(NL-Means),and Total Variation(TV),are ***,the transform domain noise reduction techniques,including wavelet-based and Curvelet-based techniques,and some hybridization techniques are ***,a deep(Convolutional Neural Network)CNN-based denoising model is proposed to eliminate Gaussian and Speckle noises in different medical image *** model utilizes the Batch Normalization(BN)and the ReLU as a basic *** a result,it attained a considerable improvement over the traditional *** previously mentioned techniques are evaluated and compared by calculating qualitative visual inspection and quantitative parameters like Peak Signal-to-Noise Ratio(PSNR),Correlation Coefficient(Cr),and system complexity to determine the optimum denoising algorithm to be applied *** on the quality metrics,it is demonstrated that the proposed deep CNN-based denoising model is efficient and has superior denoising performance over the traditionaldenoising techniques.

关键词： Image enhancement medical imaging speckle noise Gaussian noise denoising filters CNN denoising

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Opinions Are Not Always Positive: Debiasing Opinion Summarization With Model-Specific and Model-Agnostic Methods 30

Opinions Are Not Always Positive: Debiasing Opinion Summariz...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Yanyue Lai, Yilong Wang, Zhenglin Li, Pengfei Zhou, Deyu He, Yulan School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King's College London United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9782493814104

As in the existing opinion summary data set, more than 70% are positive texts, the current opinion summarization approaches are reluctant to generate the negative opinion summary given the input of negative opinions. To address such sentiment bias, two approaches are proposed through two perspectives: model-specific and model-agnostic. For the model-specific approach, a variational autoencoder is proposed to disentangle the input representation into sentiment-relevant and sentiment-irrelevant components through adversarial loss. Therefore, the sentiment information in the input is kept and employed for the following decoding which avoids interference of content information with emotional signals. To further avoid relying on some specific opinion summarization frameworks, a model-agnostic approach based on counterfactual data augmentation is proposed. A dataset with a more balanced emotional polarity distribution is constructed using a large pre-trained language model based on some pairwise and mini-edited principles. Experimental results show that the sentiment consistency of the generated summaries is significantly improved using the proposed approaches, while their semantics quality is unaffected. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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FAS-RIS Communication: Model, Analysis, and Optimization

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9938-9943页

作者： Yao, Junteng Zheng, Jianchao Wu, Tuo Jin, Ming Yuen, Chau Wong, Kai-Kit Adachi, Fumiyuki Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Huizhou University School of Computer Science and Engineering Huizhou516000 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore University College London Department of Electronic and Electrical Engineering LondonWC1E 6BT United Kingdom Yonsei University Yonsei Frontier Laboratory the School of Integrated Technology Seoul03722 Korea Republic of Sendai Japan

This correspondence investigates the novel fluid antenna system (FAS) technology, combining with reconfigurable intelligent surface (RIS) for wireless communications, where a base station (BS) communicates with a FAS-enabled user with the assistance of a RIS. To analyze this technology, we derive the outage probability based on the block-diagonal matrix approximation (BDMA) model. With this, we obtain the upper bound, lower bound, and asymptotic approximation of the outage probability to gain more insights. Moreover, we design the phase shift matrix of the RIS in order to minimize the system outage probability. Simulation results confirm the accuracy of our approximations and that the proposed schemes outperform benchmarks significantly. © 2025 IEEE.

关键词： Benchmarking

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An Improved Method for Approximating the Infinite-Horizon Value Function of the Discrete-Time Switched LQR Problem

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Journal of Systems Science & Complexity 2024年第1期37卷 22-39页

作者： HOU Tan LI Yuanlong LIN Zongli Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904-4743USA

This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR *** particular,the authors propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value *** method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration,and thus,the proposed approach is capable of achieving a better approximation for a given computation time compared with the existing *** numerical examples are presented in this paper to illustrate the effectiveness of the proposed method.

关键词： Optimal control switched linear systems switched LQR problem.

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Grid Services Demonstration Using Service-Oriented DERMS

Grid Services Demonstration Using Service-Oriented DERMS

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Adham, Midrar Keene, Sean J. Slay, Tylor Kolln, Jaime T. Bass, Robert B. Portland State University Department of Electrical & Computer Engineering PortlandOR97201 United States Pacific Northwest National Laboratory RichlandWA99354 United States

ISBN: (纸本)9798350352528

This work demonstrates four grid service use cases using a service-oriented DER Management System within an Energy Grid of Things network. Imposed by a set of rules referred to as the Energy Service Interface, the DER Management System uses IEEE 2030.5 as its primary communication protocol. To simulate the efforts of Grid Operators, Grid Service Providers, and consumers to provide grid services, a co-simulation environment that leverages the GridAPPS-D simulation platform was developed. The results show that a service-oriented DER Management System provides a means of achieving reliable and impactful grid services while protecting customers' privacy and ensuring their agency over their Distributed Energy Resources. © 2025 IEEE.

关键词： DER DERMS Energy Service Interface Grid Services GridAPPS-D

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Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence

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Building Simulation 2023年第7期16卷 1219-1238页

作者： Cary A.Faulkner Dominik S.Jankowski John E.Castellini Jr Wangda Zuo Philipp Epple Michael D.Sohn Ali Taleb Zadeh Kasgari Walid Saad Department of Mechanical Engineering University of Colorado BoulderBoulderCOUSA HySON Institut SonnebergGermany Department of Architectural Engineering Pennsylvania State UniversityUniversity ParkPAUSA Department of Mechanical Engineering Coburg University of Applied SciencesCoburgGermany Energy Analysis and Environmental Impacts Division Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Electrical and Computer Engineering Virginia TechArlingtonVAUSA

Prediction of indoor airflow distribution often relies on high-fidelity,computationally intensive computational fluid dynamics(CFD)*** intelligence(Al)models trained by CFD data can be used for fast and accurate prediction of indoor airflow,but current methods have limitations,such as only predicting limited outputs rather than the entire flow ***,conventional Al models are not always designed to predict different outputs based on a continuous input range,and instead make predictions for one or a few discrete *** work addresses these gaps using a conditional generative adversarial network(CGAN)model approach,which is inspired by current state-of-the-art Al for synthetic image *** create a new Boundary Condition CGAN(BC-CGAN)model by extending the original CGAN model to generate 2D airflow distribution images based on a continuous input parameter,such as a boundary ***,we design a novel feature-driven algorithm to strategically generate training data,with the goal of minimizing the amount of computationally expensive data while ensuring training quality of the Al *** BC-CGAN model is evaluated for two benchmark airflow cases:an isothermal lid-driven cavity flow and a non-isothermal mixed convection flow with a heated *** also investigate the performance of the BC-CGAN models when training is stopped based on different levels of validation error *** results show that the trained BC-CGAN model can predict the 2D distribution of velocity and temperature with less than 5%relative error and up to about 75,ooo times faster when compared to reference CFD *** proposed feature-driven algorithm shows potential for reducing the amount of data and epochs required to train the Al models while maintaining prediction accuracy,particularly when the flow changes non-linearlywith respectto an input.

关键词： artificial intelligence indoor airflow conditional generative adversarial network computational fluid dynamics

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Reinforcement Learning-Based Time-Synchronized Optimized Control for Affine Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5216-5231页

作者： Zhang, Yuxiang Liang, Xiaoling Li, Dongyu Ge, Shuzhi Sam Gao, Bingzhao Chen, Hong Lee, Tong Heng National University of Singapore Department of Electrical and Computer Engineering 117576 Singapore Beihang University School of Cyber Science and Technology Beijing100191 China Tianmushan Laboratory Hangzhou310023 China Tongji University Clean Energy Automotive Engineering Center Shanghai201804 China

The approach of (fixed-) time-synchronized control (FTSC) aims at attaining the outcome where all the system state-variables converge to the origin simultaneously/synchronously. This type of outcome can be the highly essential performance desired in various real-world high-precision control applications. Toward this objective, this article proposes and investigates the development of a time-synchronized reinforcement learning algorithm (TSRL) applicable to a particular class of first- and second-order affine nonlinear systems. The approach developed here appropriately incorporates the norm-normalized sign function into the optimal system control design, leveraging on the special properties of this norm-normalized sign function in attaining time-synchronized stability and control. Concurrently, the actor–critic framework in reinforcement learning (RL) is invoked, and the dual quantities of system control and gradient term of the cost function are decomposed with appropriate time-synchronized control items and unknown actor/critic part and to be learned independently. By additionally employing the adaptive dynamic programming technique, the solution of the Hamilton–Jacobi–Bellman equation is iteratively approximated under this actor–critic framework. As an outcome, the proposed TSRL method optimizes the system control while attaining the notable time-synchronized convergence property. The performance and effectiveness of the proposed method are demonstrated to be effectively applicable via detailed numerical studies and on an autonomous vehicle nonlinear system motion control problem. © 2020 IEEE.

关键词： Synchronization

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Uncovering Spatiotemporal Differences in Cortical Activity Corresponding to two Tasks Using Data-Driven ADASSO Algorithm

Uncovering Spatiotemporal Differences in Cortical Activity C...

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IEEE International Symposium on Biomedical Imaging

作者： Siwei Mai Ali E. Haddad Laleh Najafizadeh Department of Electrical and Computer Engineering Integrated Systems and NeuroImaging Laboratory Rutgers University Department of Computer Engineering University of Basrah

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Identifying spatiotemporal differences in brain functional dynamics corresponding to two tasks is critical for under-standing how specific neural processes contribute to distinct tasks or cognitive functions. Traditional methods rely on imposing assumptions and limits on the location and timing of activities, while machine-learning-based methods generally lack offering interpretable insights. This highlights the need for new data-driven approaches to capture spatial and temporal differences in brain activity between two tasks, while also providing interpretable explanations of the neural processes underlying these differences. In this work, we formulate the problem of finding the spatial and temporal differences in the dynamics of brain function corresponding to two motor imagery (MI) tasks (left hand movement vs right hand movement) as a discriminative discrete basis problem (DDBP). We apply the data-driven asymmetric discriminative associative algorithm (ADASSO) to EEG data collected during these tasks to uncover the key functional components of the brain's functional dynamics that differentiate between them. Results suggest that hand movements are strongly associated with high-confidence activation in the motor cortex, verifying the effectiveness of the ADASSO algorithm in identifying the location and timing of cortical activities that distinguish between the two task classes.

关键词： Hands Brain Heuristic algorithms Motors Electroencephalography Brain-computer interfaces Spatiotemporal phenomena Timing Biomedical imaging

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Stochastic Event-Triggered Estimation With Smart Sensors Over Packet-Dropping Links

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IEEE Transactions on Control of Network Systems 2025年第2期12卷 1756-1768页

作者： Deng, Yahan Yang, Nachuan Li, Yuzhe Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay SAR Hong Kong999077 Hong Kong

The event-triggered scheme (ETS) has been widely used for sensor data scheduling in cyber-physical systems (CPS). Existing literature on the design of ETSs for packet drops deals with the issue of non-Gaussianity of the a posteriori distribution in the system state. On the one hand, the Gaussian assumption only derives an approximate result, while on the other hand, exact results can be obtained by numerical integration but with excessive computational complexity. To this end, in this paper, we propose a stochastic ETS based on acknowledgment (ACK) information for remote state estimation with smart sensors and packet drops. The transmission decision is jointly driven by the holding time at the remote end and the accumulated innovative information. Then, we inductively derive the exact probability density function of the augmented innovative information vector by the Bayesian rule, which is used to obtain the explicit form of the estimation error covariance. These exact theoretical results mean that the design of scheduling parameter sequences no longer relies on experience. Finally, numerical simulations are provided to demonstrate that the empirical results agree with the theoretical results. © 2014 IEEE.

关键词： Packet loss

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