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Computationally Efficient Constraint-Optimized Radar Mismatched Filtering for Waveform-Agile Systems

IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6711-6732页

作者： Jones, Christian C. McCormick, Patrick M. Heintzelman, Matthew B. U.S. Naval Research Laboratory WashingtonDC20375 United States University of Kansas Department of Electrical Engineering and Computer Science LawrenceKS66045 United States University of Kansas Institute for Information Sciences LawrenceKS66045 United States

Mismatched filtering is a well-known approach of range sidelobe suppression in radar pulse compression;however, advances in radar waveform agility and spectrum sharing require the generation of filters to be efficiently computed (for possible real-time operation) with the consideration of possible interference via collisions with other spectral users. Here, a computationally efficient radar mismatched filter (MMF) design framework is proposed, which provides the optimal integrated sidelobe level (ISL) or spectral template match for a constrained signal-to-interference-plus-noise ratio loss via the Lagrange dual problem with known interference. This approach builds off of previous works, which examine ISL MMFs with constrained signal-to-noise ratio loss and provides further extensions, analysis, and bounds for alternative quadratic constraints and design objectives. Furthermore, by leveraging the Toeplitz structure that arises in time-series problems and the bounds of the Lagrange dual function, efficient solvers are developed that leverage infinite-impulse-response-based circulant approximations and/or preconditioned conjugate gradient. The proposed filter design is compared against current linear solvers and is found to have a lower order computational complexity with less required sequential calculations. Filter performance is assessed via hardware-in-the-loop and open-air experimental measurements. © 1965-2011 IEEE.

关键词： Signal to noise ratio

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Additively Manufactured Electric Machine Conductors with Integrated End Turn Heat Exchangers

Additively Manufactured Electric Machine Conductors with Int...

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2022 International Conference on electrical Machines, ICEM 2022

作者： Pecotich, James Klink, David Heins, Greg Bahrani, Behrooz Monash University Power Engineering Advanced Research Laboratory Department of Electrical and Computer System Engineering Melbourne Australia

ISBN: (数字)9781665414326

ISBN: (纸本)9781665414326

Conductors within a loaded electric machine commonly induce the highest loss. Numerous cooling solutions exist for high-power density electric machine conductors with external water jackets being the most common. The inherent disadvantage of water jacket cooling is the thermal resistance between the conductor and the cooling medium, which reduces the maximum cooling capacity, regardless of the jacket design. This restriction commonly leads designers to implement solutions such as end-turn cooling. The advances in additive manufacturing have unlocked new potential for conductor end-turn cooling. This paper investigates an additively-manufactured conductor with an integrated end-turn heat exchanger. The conductor is based around a Formula Student in-hub motor. This concept utilizes air-cooling and demonstrates a steady-state current density of 11 A/mm2 for a AlSi10Mg end-turn heat-exchanger conductor, without the added mass and complexity of liquid cooling solutions. If copper is used instead of AlSi10Mg, this value increases to approximately 22 A/mm2. © 2022 IEEE.

关键词： Electric losses

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Single-shot volumetric fluorescence imaging with neural fields

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advanced Photonics 2025年第2期7卷 92-103页

作者： Oumeng Zhang Haowen Zhou Brandon Y.Feng Elin M.Larsson Reinaldo E.Alcalde Siyuan Yin Catherine Deng Changhuei Yang California Institute of Technology Department of Electrical Engineering Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory California Institute of Technology Division of Biology and Biological Engineering California Institute of Technology Department of Medical Engineering

Single-shot volumetric fluorescence（SVF） imaging offers a significant advantage over traditional imaging methods that require scanning across multiple axial planes, as it can capture biological processes with high temporal resolution. The key challenges in SVF imaging include requiring sparsity constraints, eliminating depth ambiguity in the reconstruction, and maintaining high resolution across a large field of view. We introduce the QuadraPol point spread function（PSF） combined with neural fields, an approach for SVF imaging. This method utilizes a custom polarizer at the back focal plane and a polarization camera to detect fluorescence, effectively encoding the three-dimensional scene within a compact PSF without depth ambiguity. In addition, we propose a reconstruction algorithm based on the neural field technique that provides improved reconstruction quality compared with classical deconvolution methods. QuadraPol PSF, combined with neural fields, significantly reduces the acquisition time of a conventional fluorescence microscope by ～20 times and captures a 100-mm3cubic volume in one shot. We validate the effectiveness of both our hardware and algorithm through all-in-focus imaging of bacterial colonies on sand surfaces and visualization of plant root morphology. Our approach offers a powerful tool for advancing biological research and ecological studies.

关键词： Point spread functions Polarization Fluorescence imaging Reconstruction algorithms Biological imaging Imaging systems Fluorescence Polarizers Cameras Image restoration

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EduPar 2024 Keynote Speaker

EduPar 2024 Keynote Speaker

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Charles E. Leiserson MIT Department of Electrical Engineering and Computer Science MIT Computer Science and Artificial Intelligence Laboratory

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Aside from pure intellectual interest, why do we teach our students parallel computing? Most people would agree that the primary goal is to produce greater application performance. Yet students frequently parallelize code only to discover that it runs disappointingly slower because they don't understand performance. To exploit parallelism effectively, it must operate synergistically with a host of other techniques, including caching, vectorization, algorithms, bit tricks, loop unrolling, using compiler switches, tailoring code to the architecture, exploiting sparsity, changing data representation, metaprogramming, etc. Software performance engineering, which encompasses these techniques, is the science and art of making code run fast or otherwise limiting its consumption of resources, such as energy, memory footprint, network utilization, response time, etc. In this talk, I will argue that the end of Moore's Law makes software performance engineering a critical skill for our students to learn.

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Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

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Journal of Rock Mechanics and Geotechnical engineering 2024年第10期16卷 3982-4003页

作者： Libing Yang Trung Nguyen-Thoi Trung-Tin Tran College of Civil Engineering and Architecture Hunan Institute of Science and TechnologyYueyang414006China Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam Department of Information Technology Greenwich VietnamFPT UniversityDa Nang550000Viet Nam

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal *** technique is expected to improve the accuracy of friction angle prediction *** friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,***-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,***-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering *** improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

关键词： Natural hazards Slope stability Friction angle Clay Soft computing models Geotechnical engineering

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Optimal Control of Connected Automated Vehicles with Event-Triggered Control Barrier Functions: a Test Bed for Safe Optimal Merging

Optimal Control of Connected Automated Vehicles with Event-T...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Sabouni, Ehsan Ahmad, H.M. Sabbir Xiao, Wei Cassandras, Christos G. Li, Wenchao Boston University Division of Systems Engineering Department of Electrical & Computer Engineering BostonMA United States Massachusetts Institute of Technology Computer Science & Artificial Intelligence Laboratory CambridgeMA United States

ISBN: (纸本)9798350335446

We address the problem of safely coordinating a network of Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network. Such problems can be solved through a combination of tractable optimal control problems and Control Barrier Functions (CBFs) that guarantee the satisfaction of all constraints. These solutions can be reduced to a sequence of Quadratic Programs (QPs) which are efficiently solved online over discrete time steps. However, guaranteeing the feasibility of the CBF-based QP method within each discretized time interval requires the careful selection of time steps which need to be sufficiently small. This creates computational requirements and communication rates between agents which may limit the controller's application to real CAVs. We tackle this limitation by adopting an event-triggered control approach for CAVs such that the next QP is triggered by properly defined events with a safety guarantee. We present a laboratory-scale test bed developed to emulate merging roadways using mobile robots as CAVs. We present results to demonstrate how the event-triggered scheme is computationally efficient and can handle measurement uncertainties and noise compared to time-driven control while guaranteeing safety. © 2023 IEEE.

关键词： Merging

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Federated Learning Incorporating Non-Orthogonal Transmission and Unstructured Model Pruning

Federated Learning Incorporating Non-Orthogonal Transmission...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Gao, Siyu Zhao, Ming Zhou, Shengli University of Science and Technology of China Cas Key Laboratory of Wireless-Optical Communications Hefei China University of Connecticut Department of Electrical and Computer Engineering Storrs United States

ISBN: (纸本)9798350351255

In federated learning (FL), the servers are generally seen as omnipotent to distribute the full models to all clients. This is not the case in wireless systems. The broadcast of models inevitably excludes some users with poor channel gain but valuable computation power and diverse local data. In this paper, taking the various computation and communication capabilities of users into account, we propose a federated learning scheme incorporating unstructured model pruning and non-orthogonal transmission. The pruning process divides the model into the core and the extended parts, with the ratio adaptive to the users' achievable working rates. With non-orthogonal transmission, the strong users get both parts of the model while the weak get only the core part. Simulation results show that the proposed scheme involves more users than traditional wireless FL and improves the accuracy of tasks within the given communication rounds. © 2024 IEEE.

关键词： Federated learning

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Hybridization of Wavelet Decomposition and Machine Learning for Brain Waves based Emotion Recognition 1

Hybridization of Wavelet Decomposition and Machine Learning ...

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1st International Conference in advanced Innovation on Smart City, ICAISC 2023

作者： Ali, Mirna Qaisar, Saeed Mian Anurulafchar, Tamanna Effat University Electrical and Computer Engineering Department Jeddah22332 Saudi Arabia LINEACT Laboratory CESI Engineering School Villeurbanne69100 France Effat University Computer Science Department Jeddah22332 Saudi Arabia

ISBN: (纸本)9781665472753

Emotion recognition has sparked the interest of researchers from a variety of disciplines. Studies have demonstrated that brain signals may be utilized to characterize a wide range of emotional states. Electroencephalogram (EEG) measures the cerebral activity. Therefore, by exploiting the EEG signals the emotion states can be determined. In this study the EEG signals undergoes through filtering, segmentation, Wavelet Packet Decomposition (WPD), feature mining, and classification. The machine learning algorithms used for classifications are 'Decision Tree' (DT), 'Support Vector Machine' (SVM), and K-Nearest Neighbor' (K-NN) algorithms are used for categorization. Their performance is compared for automatically identifying the emotion state. It is determined that the best performer is SVM. It has attained 98.2% accuracy, 97.3% precision, 97.3% recall, 98.7% specificity, 97.3% F1, 97.3% kappa, and 99.3% AUC. © 2023 IEEE.

关键词： Emotion Recognition

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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Neural architecture Search for Image Dehazing

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1337-1347页

作者： Mandal, Murari Meedimale, Yashwanth Reddy Satish Kumar Reddy, M. Vipparthi, Santosh Kumar Kalinga Institute of Industrial Technology Deemed to Be University School of Computer Engineering Bhubaneswar751024 India Walmart Global Tech Hyderabad500081 India Texas A&m University Computer Science and Engineering Department College StationTX77843 United States Indian Institute of Technology Ropar Cvpr Laboratory at Department of Electrical Engineering Rupnagar140001 India

Manual design of deep networks require numerous trials and parameter tuning, resulting in inefficient utilization of time, energy, and resources. In this article, we present a neural architecture search (NAS) algorithm - AutoDehaze, to automatically discover effective neural network for single image dehazing. The proposed AutoDehaze algorithm is built on the gradient-based search strategy and hierarchical network-level optimization. We construct a set of search space layouts to reduce memory consumption, avoid the NAS collapse issue, and considerably accelerate the search speed. We propose four search spaces AutoDehaze B, AutoDehaze U1, AutoDehaze U2, and AutoDehaze L, which are inspired by the boat-shaped, U-shaped, and lateral connection-based designs. To the best of authors knowledge, this is a first attempt to present an NAS method for dehazing with a variety of network search strategies. We conduct a comprehensive set of experiments on Reside-Standard (SOTS), Reside-β (SOTS) and Reside-β (HSTS), D-Hazy, and HazeRD datasets. The architectures discovered by the proposed AutoDehaze quantitatively and qualitatively outperform the existing state-of-the-art approaches. The experiments also show that our models have considerably fewer parameters and runs at a faster inference speed in both CPU and GPU devices. © 2020 IEEE.

关键词： Network architecture

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