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Physics-Driven Machine Learning for computational Imaging

IEEE SIGNAL PROCESSING MAGAZINE 2023年第1期40卷 28-30页

作者： Wen, Bihan Ravishankar, Saiprasad Zhao, Zhizhen Giryes, Raja Ye, Jong Chul Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Michigan State Univ Dept Computat Math Sci & Engn E Lansing MI 48824 USA Michigan State Univ Dept Sci & Engn E Lansing MI 48824 USA Michigan State Univ Dept Biomed Engn E Lansing MI 48824 USA Univ Illinois Urbana Champaign UIUC Dept Elect & Comp Engn Champaign IL USA Tel Aviv Univ Sch Elect Engn Tel Aviv Israel Korea Adv Inst Sci & Technol Grad Sch Artificial Intelligence Daejeon 34141 South Korea Korea Adv Inst Sci & Technol Dept Math Sci Daejeon 34141 South Korea

Recent years have witnessed a rapidly growing interest in next-generation imaging systems and their combination with machine learning. While model-based imaging schemes that incorporate physics-based forward models, noise models, and image priors laid the foundation in the emerging field of computational sensing and imaging, recent advances in machine learning, from large-scale optimization to building deep neural networks, are increasingly being applied in modern computational imaging. A wide range of machine learning techniques can be applied to enhance the effectiveness and efficiency of computational imaging systems, thus redefining state-of-the-art computational imaging algorithms.

关键词： Special issues and sections Machine learning computational modeling Image processing

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A Simple computational Model of Semantic Priming in 18-Month-Olds

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COGNITIVE SCIENCE 2024年第10期48卷 e13499页

作者： Gliozzi, Valentina Univ Turin Dept Comp Sci Turin Italy Univ Turin Ctr Log Language & Cognit LLC Turin Italy

We propose a simple computational model that describes potential mechanisms underlying the organization and development of the lexical-semantic system in 18-month-old infants. We focus on two independent aspects: (i) on potential mechanisms underlying the development of taxonomic and associative priming, and (ii) on potential mechanisms underlying the effect of Inter Stimulus Interval on these priming effects. Our model explains taxonomic priming between words by semantic feature overlap, whereas associative priming between words is explained by Hebbian links between semantic representations derived from co-occurrence relations between words (or their referents). From a developmental perspective, any delay in the emergence of taxonomic priming compared to associative priming during infancy seems paradoxical since feature overlap per se need not be learned. We address this paradox in the model by showing that feature overlap itself is an emergent process. The model successfully replicates infant data related to Inter Stimulus Interval effects in priming experiments and makes testable predictions.

关键词： computational modeling Infants Semantic priming Conceptual change

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computational modeling of Dynamic Response of a Latent Thermal Energy Storage System With Embedded Heat Pipes

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JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME 2014年第1期136卷 011010-011010页

作者： Nithyanandam, K. Pitchumani, R. Virginia Tech Dept Mech Engn Adv Mat & Technol Lab Blacksburg VA 24061 USA

Concentrating solar power plants (CSPs) are being explored as the leading source of renewable energy for future power generation. Storing sun's energy in the form of latent thermal energy of a phase change material (PCM) is desirable for use on demand including times when solar energy is unavailable. Considering a latent thermal energy storage (LTES) system incorporating heat pipes to enhance heat transfer between the heat transfer fluid (HTF) and the PCM, this paper explores the dynamic response of the LTES system subjected to repeated cycles of charging and discharging. A transient computational analysis of a shell-and-tube LTES embedded with two horizontal heat pipes is performed for repeated charging and discharging of the PCM to analyze the dynamic performance of the LTES, and the augmentation in the cyclic performance of the LTES embedded with heat pipes is investigated. A model low temperature phase change material system is considered in the present study, with the physical results being scalable to high temperature systems used in CSP plants.

关键词： computational modeling embedding Dynamic response Phase change materials LATENT HEAT STORAGE Cache Memory System CHARGE AND DISCHARGE Heat pipes

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Control-Flow modeling with Declare: Behavioral Properties, computational Complexity, and Tools

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第5期32卷 898-911页

作者： Fionda, Valeria Guzzo, Antonella Univ Calabria DeMaCS I-87036 Arcavacata Di Rende Italy

Declarative approaches to control-flow modeling use logic-based languages to formalize a number of constraints that valid traces must satisfy. The most noticeable example is the Declare framework based on linear temporal logic. Despite the interest that Declare has been attracting, the current knowledge about its formal properties was rather limited. The goal of this paper is to fill this gap by: (i) analyzing the behavioral properties of Declare by comparing it with the modeling capabilities of traditional procedural design approaches, in particular, block-structured processes;(ii) analyzing Declare from the computational point of view. As for the former point, we identify both the block-structured processes constructs that can be simulated in Declare and the features of Declare that can be encoded in block-structured processes. As for the latter point, we show that checking whether a given set of Declare patterns admits a satisfying trace is an ${\mathrm {NP}}$ NP -hard problem. In particular, we identify some Declare specifications whose satisfying traces are all of exponential length and some useful Declare fragments where a satisfying trace whose length is polynomially bounded is guaranteed to exist. The paper also discusses the declare2sat prototype system and the results of a thorough experimental validation.

关键词： Lenses computational modeling Process control Tools Analytical models NP-hard problem computational complexity Declarative process modelling linear temporal logic declare process mining

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R-CNN Based Vehicle Object Detection via Segmentation Capabilities in Road Scenes

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IEEE ACCESS 2025年 13卷 3355-3370页

作者： Riaz Chughtai, Bisma Alhasson, Haifa F. Alnusayri, Mohammed Alatiyyah, Mohammed Aljuaid, Hanan Jalal, Ahmad Park, Jeongmin Air Univ Fac Comp & AI Islamabad 44000 Pakistan Qassim Univ Coll Comp Dept Informat Technol Buraydah 52571 Saudi Arabia Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka 72388 Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Al Kharj 16278 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11671 Saudi Arabia Korea Univ Coll Informat Dept Comp Sci & Engn Seoul 02841 South Korea Tech Univ Korea Dept Comp Engn Shihung 15073 Gyeonggi South Korea

In the realm of intelligent transportation systems, vehicle detection and classification stand as pivotal tasks. An effective traffic monitoring system should be capable of detecting, counting, and categorizing moving vehicles. Vehicle classification is a crucial task that can offer insights into road users and help make decisions to reduce congestion, for instance. This paper delves into advanced methodologies for detecting and classifying vehicles on roadways, addressing the limitations of traditional techniques that are often computationally intensive and data acquisition-sensitive. We propose a novel approach that leverages state-of-the-art machine learning and deep learning algorithms to enhance accuracy and efficiency. The proposed model comprises five stages. Initially, all images undergo preprocessing to reduce noise and enhance brightness. In the second stage, the foreground elements are extracted using segmentation techniques. The YOLOv8 algorithm is then used to process these segmented images to identify the vehicles inside them. Next, in the feature extraction phase, the detected vehicles are analyzed using Maximally Stable Estimated Features (MSER), Geometric features and Binary Robust Invariant Scalable Keypoints (BRISK) features. We employ the Recurrent Convolutional Neural Network (R-CNN) classifier for classification. Experimental results from two datasets demonstrate superior performance, with the model achieving an accuracy of 0.94% on the BITVehicle dataset and 0.98% on the Vehicle-OpenImage dataset. Additionally, a comparative analysis was conducted, showcasing the model's performance against the latest techniques in the field.

关键词： Feature extraction Accuracy Vehicle detection YOLO Convolutional neural networks computational modeling Image segmentation Classification algorithms Real-time systems Segmentation BRISK object detection vehicle recognition YOLOv8 recurrent convolutional neural network (RNN) machine learning deep learning deep learning

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Transmission Line Detection Through Auxiliary Feature Registration With Knowledge Distillation

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 9413-9425页

作者： Wang, Yusen Zhou, Wujie Qian, Xiaohong Zhejiang Univ Sci & Technol Sch Informat & Elect Engn Hangzhou 310023 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 308232 Singapore

The periodic inspection of transmission lines ensures a stable power supply across various regions. Deep learning methods, particularly those based on multimodal approaches, have made significant advancements in this field. Current multimodal fusion algorithms do not prioritize the integration of supplementary information from other modal sources. Moreover, these methods frequently depend on substantial parameterization to achieve optimal performance, severely hampering deployment on mobile devices. To enhance the supportive role of the auxiliary modality and minimize model parameters, we proposed the knowledge distillation-based auxiliary feature registration network (AFRNet-S*) for RGB-T transmission line detection (TLD). The method incorporates an auxiliary feature registration module during fusion, utilizing unique complementarity between primary and auxiliary feature modalities for precise feature registration. For knowledge distillation, we devised response-mimicking distillation, semantic supplementary distillation, and cross-view feature polymerization distillation tailored for the student network (AFRNet-S, without knowledge distillation) to achieve model compression. AFRNet-S can learn comprehensive final spatial, precise semantic, and complementary feature integration information from AFRNet-T through these distillation methods. Extensive experiments conducted on an industrial TLD dataset demonstrate that the proposed AFRNet-S* (AFRNet-S with knowledge distillation) outperforms the existing state-of-the-art methods, thereby demonstrating its generality and effectiveness. Our AFRNet-S*, compared to AFRNet-T, has a reduced parameter count from 44.92M to 16.86M and a decrease in FLOPs from 21.13G to 6.41G. This further optimizes the deployment of our network on edge devices, such as unmanned aerial vehicles (drones), in the future. The code and results of our approach are available at https://***/WangYuSenn/AFRNet. Note to Practitioners-This study int

关键词： Performance evaluation computational modeling Power transmission lines Feature extraction Decoding Semantics Mobile handsets Knowledge engineering Training Drones Transmission line detection deep learning knowledge distillation auxiliary feature alignment

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Nonlinear Model Predictive Control for Robotic Pushing of Planar Objects With Generic Shape

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 3006-3013页

作者： Federico, Sara Costanzo, Marco De Simone, Marco Natale, Ciro Univ Campania Luigi Vanvitelli Dipartimento Ingn I-81031 Aversa Italy

Robotic manipulation of objects in cluttered dynamic scenes is challenging for a twofold reason. Object detection and localization are complex due to partial occlusions and high variability in the object classes and manipulation in tight spaces is difficult due to potential collisions. The present letter focuses on the low-level control of the non-prehensile pushing action aimed at moving planar objects of generic shape along a given path with an assigned time law. Based on the continuous and nonlinear dynamics of the system, we propose a nonlinear model predictive controller (NMPC), which avoids the need for linearization and, thus, the hybrid dynamics arising from it. An extensive comparison with a state-of-the-art linear MPC demonstrates that the NMPC can successfully react to more general disturbances, outperforming the linear one. Experimental results confirm the effectiveness of the method in a task where a robot is required to grasp fruits in a container with other obstructing objects (shown in the attached video).

关键词： Robots Shape Splines (mathematics) Vectors computational modeling Analytical models Trajectory Nonlinear dynamical systems Training Robot kinematics Contact modeling dexterous manipulation optimization and optimal control

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Prediction of Received Power in Low-Power and Lossy Networks Deployed in Rough Environments

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Dargie, Waltenegus Tech Univ Dresden Fac Comp Sci D-01062 Dresden Germany

Cost-efficient and low-power sensing nodes enable to monitor various physical environments. Some of these impose extreme operating conditions, subjecting the nodes to excessive heat or rainfall or motion. Rough operating conditions affect the stability of the wireless links the nodes establish and cause a significant amount of packet loss. Adaptive transmission power control (ATPC) enables nodes to adapt to extreme conditions and maintain stable wireless links and often rely on knowledge of the received power as a closed-feedback system to adjust the power of outgoing packets. However, in the presence of a significant packet loss, this knowledge may not reflect the current state of the receiver. In this article, we propose a lightweight n-step predictor that enables transmitters to adapt transmission power in the presence of lost packets. Through extensive practical deployments and testing, we demonstrate that the predictor avoids expensive computation and still achieves an average prediction accuracy exceeding 90% with a low-power radio that supports a transmission rate of 250 kb/s (CC2538) and 85% with a low-power radio that supports 50 kb/s (CC1200).

关键词： Wireless sensor networks Wireless communication Indexes computational modeling Sensors Radio transmitters Receivers Packet loss Topology Monitoring Adaptation Internet of Things link quality prediction low-power networks received power

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A Multispectral Automated Transfer Technique (MATT) for Machine-Driven Image Labeling Utilizing the Segment Anything Model (SAM)

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IEEE ACCESS 2025年 13卷 4499-4516页

作者： Gallagher, James E. Gogia, Aryav Oughton, Edward J. George Mason Univ Dept Geog & Geoinformat Sci Fairfax VA 22030 USA

Segment Anything Model (SAM) is drastically accelerating the speed and accuracy of automatically segmenting and labeling large Red-Green-Blue (RGB) imagery datasets. However, SAM is unable to segment and label images outside of the visible light spectrum, for example, for multispectral or hyperspectral imagery. Therefore, this paper outlines a method we call the Multispectral Automated Transfer Technique (MATT). By transposing SAM segmentation masks from RGB images we can automatically segment and label multispectral imagery with high precision and efficiency. For example, the results demonstrate that segmenting and labeling a 2,400-image dataset utilizing MATT achieves a time reduction of 87.8% in developing a trained model, reducing roughly 20 hours of manual labeling, to only 2.4 hours. This efficiency gain is associated with only a 6.7% decrease in overall mean average precision (mAP) when training multispectral models via MATT, compared to a manually labeled dataset. We consider this an acceptable level of precision loss when considering the time saved during training, especially for rapidly prototyping experimental modeling methods. This research greatly contributes to the study of multispectral object detection by providing a novel and open-source method to rapidly segment, label, and train multispectral object detection models with minimal human interaction. Future research needs to focus on applying these methods to 1) space-based multispectral;and 2) drone-based hyperspectral imagery.

关键词： Image segmentation Sensors Labeling Accuracy Training Manuals computational modeling YOLO Thermal sensors Image resolution Thermal object detection RGB-thermal fusion long-wave infrared (LWIR) multispectral imagery (MSI) computer vision machine learning RGB-LWIR remotely piloted aircraft system (RPAS) unmanned aerial systems (UAS) edge computing segment anything image segmentation

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DTSG-Net: Dynamic Time Series Graph Neural Network and Its Application in Modulation Recognition

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 3742-3754页

作者： Yin, Peng Zhou, Jinchao Ge, Yizheng Chen, Zhuangzhi Univ Chinese Acad Sci Sch Cyber Secur Beijing 101408 Peoples R China Def Ind Secrecy Examinat & Certificat Ctr Beijing 100089 Peoples R China Binjiang Inst Artificial Intelligence Res Ctr Electromagnet Space Secur Hangzhou 310056 Peoples R China Def Ind Secrecy Examinat & Certificat Ctr Beijing 100089 Peoples R China

Modulation recognition of communication signals is of great importance in the context of the Internet of Everything (IoE), as wireless communication technology is a key foundation for implementing the IoE. Recently, graph neural networks (GNNs) have been successfully applied to modulation recognition tasks due to their ability to merge messages transmitted between adjacent nodes in the graph. However, GNN-based models are more computationally intensive when processing long signals, potentially reducing their practicality. In this article, we explore a novel signal representation from a graph perspective and propose a graph-powered modulation recognition framework. We first propose the dynamic time series graph (DTSG) algorithm, which segments the signals and maps each segment into a patch graph, with corresponding patches from different signals sharing connected edges. By integrating DTSG with both GNNs and recurrent neural networks (RNNs), we have designed an end-to-end signal classification framework, DTSG-Net, for modulation recognition. Experimental results on four datasets: 1) RML2016.10a;2) RML2018.01a;3) Sig2019-12;and 4) HKDD_AMC36-demonstrate that our DTSG-Net can achieve high signal modulation classification accuracy (Acc) with minimal computational resources, outperforming existing methods based on signal graph representation in terms of computational resource savings and higher accuracy.

关键词： Modulation Feature extraction Time series analysis Convolutional neural networks Heuristic algorithms Data mining computational modeling Graph neural networks Deep learning Classification algorithms Graph graph neural network (GNN) Internet of Everything (IoE) modulation recognition recurrent neural network (RNN)

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