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Medical Imaging 2025: Image Processing

作者： Yao, Tianyuan Li, Zhiyuan Kanakaraj, Praitayini Archer, Derek B. Schilling, Kurt Beason-Held, Lori Resnick, Susan Landman, Bennett A. Huo, Yuankai Department of Computer Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Neurology Vanderbilt University Medical Center NashvilleTN United States Department of Biomedical Engineering Vanderbilt University NashvilleTN United States Laboratory of Behavioral Neuroscience National Institute on Aging BaltimoreMD United States

ISBN: (纸本)9781510685901

Diffusion-weighted Magnetic Resonance Imaging (dMRI) is an essential tool in neuroimaging. It is arguably the sole noninvasive technique for examining the microstructural properties and structural connectivity of the brain. Recent years have seen the emergence of machine learning and data-driven approaches that enhance the speed, accuracy, and consistency of dMRI data analysis. However, traditional deep learning models often fell short, as they typically utilize pixel-level or volumetric patch-level embeddings similar to those used in structural MRI, and do not account for the unique distribution of various gradient encodings. In this paper, we propose a novel method called Polyhedra Encoding Transformer (PE-Transformer) for dMRI, designed specifically to handle spherical signals. Our approach involves projecting an icosahedral polygon onto a unit sphere to resample signals from predetermined directions. These resampled signals are then transformed into embeddings, which are processed by a transformer encoder that incorporates orientational information reflective of the icosahedral structure. Through experimental validation with various gradient encoding protocols, our method demonstrates superior accuracy in estimating multi-compartment models and Fiber Orientation Distributions (FOD), outperforming both conventional CNN architectures and standard transformers. © 2025 SPIE.

关键词： Network coding

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Joint Optimization for Cooperative Service-Caching, Computation-Offloading, and Resource-Allocations Over EH/MEC 6G Ultra-Dense Mobile Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Chen, Zhian Wang, Fei Zhang, Xi Southwest University College of Electronic and Information Engineering Chongqing400715 China Texas A&M University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States

Service-caching, computation-offloading, and mobile edge-computing (MEC) have been widely recognized as three key 6G wireless technologies which can efficiently support implementing the ultra-dense networks (UDNs) with massive small-cell base stations (SBSs). But, these impose the new challenges for the UDNs to solely rely on grid power for energy supplying and to jointly optimize service-caching, computation-offloading, and resource-allocations. To overcome the above described difficulties, integrating energy-harvesting (EH) techniques with MEC-enabled 6G UDNs, we propose to develop the joint optimization schemes for cooperative service-caching, computation-offloading, and resource-allocations. In our considered UDNs, there exist a large number of EH-based stationary users (SUs) or mobile users (MUs), and a mixture of on-grid SBSs powered by electric grid and off-grid SBSs power-supplied by solar, radio frequency (RF) energy, etc. Specifically, first we formulate an energy minimization problem under a non-linear RF-energy EH model to minimize the sum of weighted energy consumption of users and off-grid SBSs. Second, for scenarios with SUs, we develop a two-timescale based joint cooperative service-caching, computation-offloading, and resource-allocations scheme using the hierarchical multi-agent deep reinforcement learning. We derive cooperative service-caching in each time frame, and then derive computation-offloading and resource-allocations in each time slot. Third, we extend our work to scenarios with MUs, where MUs can move with certain trajectories at low speeds. Finally, we validate and evaluate the performances of our proposed schemes through the extensive simulations. © 2002-2012 IEEE.

关键词： Energy utilization

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Distill & Contrast: A New Graph Self-Supervised Method With Approximating Nature Data Relationships

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IEEE Transactions on Knowledge and Data engineering 2025年第6期37卷 3284-3297页

作者： He, Dongxiao Zhao, Jitao Guo, Rui Feng, Zhiyong Huo, Cuiying Jin, Di Pedrycz, Witold Zhang, Weixiong Tianjin University College of Intelligence and Computing Tianjin300350 China Department of Measurement and Control Systems Gliwice44-100 Poland University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Hong Kong Polytechnic University Department of Data Science and Ai Hong Kong

Contrastive Learning (CL) has emerged as a popular self-supervised representation learning paradigm that has been shown in many applications to perform similarly to traditional supervised learning methods. A key component of CL is mining the latent discriminative relationships between positive and negative samples and using them as self-supervised labels. We argue that this discriminative contrastive task is, in essence, similar to a classification task, and the "either positive or negative"hard label sampling strategies are arbitrary. To solve this problem, we explore ideas from data distillation, which considers probabilistic logit vectors as soft labels to transfer model knowledge. We attempt to abandon the classical hard sampling labels in CL and instead explore self-supervised soft labels. We adopt soft sampling labels that are extracted, without supervision, from the inherent relationships in data pairs to retain more information. We propose a new self-supervised graph learning method, Distill and Contrast (D&C), for learning representations that closely approximate natural data relationships. D&C extracts node similarities from the features and structures to derive soft sampling labels, which also eliminate noise in the data to increase robustness. Extensive experimental results on real-world datasets demonstrate the effectiveness of the proposed method. © 2025 IEEE.

关键词： Self-supervised learning

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Space division and theory of graph-coloration-based protocol for multi-channel multi-transceiver operation in Internet of Vehicles

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EURASIP Journal on Wireless Communications and Networking 2025年第1期2025卷 1-26页

作者： Khamer, Lazhar Zaidi, Sofiane Calafate, Carlos T. Department of Mathematics and Computer Science Research Laboratory on Computer Science’s Complex Systems (RELA(CS)2 University of Oum El Bouaghi Oum El Bouaghi Algeria Department of Computer Science University of Souk Ahras Souk Ahras Algeria Department of Computer Engineering (DISCA) Universitat Politècnica de València Valencia Spain

Data exchange in the Internet of Vehicles (IoV) is challenging due to high interference, hidden terminal problems, and frequent transmission collisions. To overcome these issues and enhance overall network performance, we propose a Space Division and theory of Graph Coloration-based protocol for Multi-Channel Multi-Transceiver operation of the MAC layer (SDGC-MCMT) in the IoV. The protocol dynamically assigns service channels to vehicles based on their positions and schedules the transmission of packets across multiple transceivers to enhance efficiency and enable synchronous communication. Furthermore, it uses an adaptive channel busyness-based scheme when the number of available transceivers is lower than the number of service channels to improve data delivery. Simulation experiments, targeting packet delivery ratio, multi-hop channel access latency, and collision probability reveal the effectiveness of SDGC-MCMT in improving network performance compared to the IEEE 1609.4 protocol.

关键词： Internet of Vehicles Multi-channel MAC operation Multi-transceiver Graph coloration theory

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Design of Distributed Nonlinear Observers Without Using Global Inputs

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IEEE Transactions on Automatic Control 2025年

作者： Cao, Ganghui Wang, Jinzhi Polycarpou, Marios M. Peking University State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Beijing100871 China University of Cyprus KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering Nicosia1678 Cyprus

This paper investigates the design of distributed observers for a class of nonlinear systems. The designed distributed observers reside in a network of sensor nodes. The communication links in the network enable each sensor node to estimate all states of the nonlinear system, even though each one can only collect a small portion of the system's outputs. However, all inputs of the system are still needed at each node to carry out a correct state estimation. For this problem, the paper further proposes a distributed nonlinear observer without using global inputs, in which each sensor node only needs partial input information of the nonlinear system. © 1963-2012 IEEE.

关键词： Nonlinear simulations

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Design of awideband symmetric large back-off range Doherty power amplifier based on impedance and phase hybrid optimization

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Frontiers of Information Technology & Electronic engineering 2025年第1期26卷 146-156页

作者： Zhongpeng NI Jing XIA Xinyu ZHOU Wa KONG Wence ZHANG Xiaowei ZHU School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

The present paper proposes an optimization design method for the Doherty output matching network(OMN)using impedance–phase hybrid objective function constraints,which possesses the capability of enhancing the efficiency consistency of the Doherty power amplifier(DPA)using integrated enhancing reactance(IER)during the back-off power(BOP)*** calculating the desired reactance for an extended BOP range and combining it with the two-impedance matching method,the S-parameters of the OMN are ***,the impedance and phase constraints of the OMN are proposed to narrow the distribution range of the ***,a fragmenttype structure is employed in the OMN optimization so as to enhance the flexibility of the circuit optimization *** validate the proposed method,a 1.7–2.5 GHz symmetric DPA with a large BOP range was designed and *** results demonstrate that across the entire operating frequency band,the saturated output power is>44 dBm,and the efficiency ranges from 45%to 55%at a 9-dB BOP.

关键词： Back-off power range Doherty power amplifier Fragment-type structure Impedance-phase hybrid function

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On-chip GaAs carrier lifetime investigation

arXiv

引用

arXiv 2025年

作者： Prayoonyong, Chawaphon Szydzik, Crispin Ren, Guanghui Corcoran, Bill Dubey, Aditya Murray, Caitlin E. Mitchell, Toby Mitchell, Arnan Photonic Communications Laboratory Dept. Electrical and Computer Systems Engineering Monash University ClaytonVIC Australia MelbourneVIC Australia

We propose an alternative way to determine GaAs carrier lifetime using pump-probe measurement based on fibre optics and integrated waveguides. We find that our GaAs samples have the lifetime ranging from 30-80 ps, supporting the bandwidth ≥ 12.5 GHz. The platform utilised in this work could offer a cost-effective way to investigate photocarrier lifetime. Moreover, it may have the potential for high-speed switches and detectors, or could be exploited for possible all-optical modulation. © 2025, CC BY-NC-ND.

关键词： Gallium arsenide

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A New Heterogeneous Hybrid Massive MIMO Receiver With an Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 17-29页

作者： Shu, Feng Shi, Baihua Chen, Yiwen Bai, Jiatong Li, Yifan Liu, Tingting Han, Zhu You, Xiaohu Hainan University School of Information and Communication Engineering Haikou570228 China Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Hainan University Collaborative Innovation Center of Information Technology Haikou570228 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210003 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China

Massive multiple input multiple output (MIMO) antenna arrays eventuate a huge amount of circuit costs and computational complexity. To satisfy the needs of high precision and low cost in future green wireless communication, the conventional hybrid analog and digital MIMO receive structure emerges a natural choice. But it exists an issue of the phase ambiguity in direction of arrival (DOA) estimation and requires at least two time-slots to complete one-time DOA measurement with the first time-slot generating the set of candidate solutions and the second one to find a true direction by received beamforming over this set, which will lead to a low time-efficiency. To address this problem,a new heterogeneous sub-connected hybrid analog and digital ( H2 AD) MIMO structure is proposed with an intrinsic ability of removing phase ambiguity, and then a corresponding new framework is developed to implement a rapid high-precision DOA estimation using only single time-slot. The proposed framework consists of two steps: 1) form a set of candidate solutions using existing methods like MUSIC;2) find the class of the true solutions and compute the class mean. To infer the set of true solutions, we propose two new clustering methods: weight global minimum distance (WGMD) and weight local minimum distance (WLMD). Next, we also enhance two classic clustering methods: accelerating local weighted k-means (ALW-K-means) and improved density. Additionally, the corresponding closed-form expression of Cramer-Rao lower bound (CRLB) is derived. Simulation results show that the proposed frameworks using the above four clustering can approach the CRLB in almost all signal to noise ratio (SNR) regions except for extremely low SNR (SNR © 2024 The Authors.

关键词： Signal to noise ratio

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Communication and Control Co-design in Non-cooperative Games

arXiv

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arXiv 2025年

作者： Aggarwal, Shubham Başar, Tamer Maity, Dipankar UrbanaIL61801 United States Department of Electrical & Computer Engineering Coordinated Science Laboratory UIUC United States Department of Electrical and Computer Engineering University of North Carolina at Charlotte CharlotteNC28223 United States

In this article, we revisit a communication-control co-design problem for a class of two-player stochastic differential games on an infinite horizon. Each ‘player’ represents two active decision makers, namely a scheduler and a remote controller, which cooperate to optimize over a global objective while competing with the other player. Each player’s scheduler can only intermittently relay state information to its respective controller due to associated cost/constraint to communication. The scheduler’s policy determines the information structure at the controller, thereby affecting the quality of the control inputs. Consequently, it leads to the classical communication-control trade-off problem. A high communication frequency improves the control performance of the player on account of a higher communication cost, and vice versa. Under suitable information structures of the players, we first compute the Nash controller policies for both players in terms of the conditional estimate of the state. Consequently, we reformulate the problem of computing Nash scheduler policies (within a class of parametrized randomized policies) into solving for the steady-state solution of a generalized Sylvester equation. Since the above-mentioned reformulation involves infinite sum of powers of the policy parameters, we provide a projected gradient descent-based algorithm to numerically compute a Nash equilibrium using a truncated polynomial approximation. Finally, we demonstrate the performance of the Nash control and scheduler policies using extensive numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Accurate, provable, and fast nonlinear tomographic reconstruction: A variational inequality approach

arXiv

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arXiv 2025年

作者： Lou, Mengqi Verchand, Kabir Aladin Fridovich-Keil, Sara Pananjady, Ashwin Georgia Institute of Technology School of Industrial and Systems Engineering United States School of Electrical and Computer Engineering United States University of Cambridge Statistical Laboratory United Kingdom Stanford University Department of Electrical Engineering United States

We consider the problem of signal reconstruction for computed tomography (CT) under a nonlinear forward model that accounts for exponential signal attenuation, a polychromatic X-ray source, general measurement noise (e.g. Poisson shot noise), and observations acquired over multiple wavelength windows. We develop a simple iterative algorithm for single-material reconstruction, which we call EXACT (EXtragradient Algorithm for Computed Tomography), based on formulating our estimate as the fixed point of a monotone variational inequality. We prove guarantees on the statistical and computational performance of EXACT under practical assumptions on the measurement process. We also consider a recently introduced variant of this model with Gaussian measurements, and present sample and iteration complexity bounds for EXACT that improve upon those of existing algorithms. We apply our EXACT algorithm to a CT phantom image recovery task and show that it often requires fewer X-ray projection exposures, lower source intensity, and less computation time to achieve similar reconstruction quality to existing methods. © 2025, CC BY.

关键词： computerized tomography

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