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Microtitre Plate image Augmentation with Generative Adversarial Networks

Microtitre Plate Image Augmentation with Generative Adversar...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Li, Ru Chai, Tingting Kouchaki, Samaneh Clifton, David Yang, Yang Harbin Institute of Technology Weihai China University of Surrey Guildford United Kingdom University of Oxford Oxford United Kingdom Shanghai Jiao Tong University School of Medicine Shanghai China

ISBN: (纸本)9798350368741

Antibiotic Susceptibility Testing (AST) based on microorganism culturing is the gold-standard technique to determine whether a pathogen is susceptible or resistant to available antibiotics. While broth microdilution offers a potential high-throughput method for AST, reading and interpreting microtitre plates can be challenging, even for experienced clinical microbiologists. Machine learning models trained on images of microtitre plates obtained during AST could potentially accelerate and even automate this process. However, these image sets are highly imbalanced since each drug on the plate may exhibit different growth distributions due to varying resistance prevalence and mechanisms, which negatively impacts the performance of trained models. To address this problem, we propose a Generative Adversarial Network (GAN)-based framework, named Culplate-GAN, to augment the dataset with images of plates displaying specific growth levels for particular drugs. The adversarial loss and weight-controlled content loss are introduced to achieve image transformation and content preservation. Moreover, a Multi-Culplate-GAN architecture is designed to generate multilevel outputs with one single input, which are optimized by the proposed domain-based adversarial loss and domain classification loss. We evaluate Culplate-GAN and Multi-Culplate-GAN by training a classifier on an AST Mycobacterium Tuberculosis dataset. Comprehensive results indicate that our method outperforms existing representative augmentation methods and can be generalized to plates containing other bacterial cultures. © 2025 ieee.

关键词： Generative Adversarial Network image Augmentation Microtitre Plate

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Distributed Fault-Tolerant Average-Tracking for Linear Multi-Agent systems 5

Distributed Fault-Tolerant Average-Tracking for Linear Multi...

引用

5th international conference on Control and Robotics (ICCR)

作者： Li, Yu-Ling Liu, Cheng-Lin Jiangnan Univ Sch Internet Things Engn Wuxi Jiangsu Peoples R China

ISBN: (纸本)9798350307627

This paper focuses on implementing average-tracking control for linear multi-agent systems, where each agent is subject to actuator fault and has a unique reference signal. To achieve the average-tracking control, each agent has to acquire the average value of multiple reference signals, while it has only access to its own reference signal. To solve this problem, a fixed-time average estimator is developed for agents to estimate the average value. The actuator faults considered in this study consist of additive and multiplicative faults, which can be observed by a nonlinear fault observer. based on the proposed estimator and the observer, a control protocol is proposed to achieve fault-tolerant average-tracking control. Simulation results demonstrate the effectiveness of the average-tracking control scheme.

关键词： Average-tracking linear multi-agent systems fault tolerant actuator attack

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Conceptualisation of a Parameterizable Low-Pass Filter for Resolving Measurement Data 6

Conceptualisation of a Parameterizable Low-Pass Filter for R...

引用

6th ieee international conference on image Processing, Applications and systems, IPAS 2025

作者： Schmid, Michael Eisenmann, Bastian Aksu, Osman Radosavac, Misel Bierwirth, Florian Herzog, Hans-Georg Concepts Power Supplies BMW AG Department of Functional Safety Munich Germany Low Voltage Distributors BMW AG Department of Active Munich Germany Associate Professorship of Energy Conversion Technology Munich Germany

ISBN: (纸本)9798331506520

The data acquisition process through measurements translates continuous physical quantities, such as temperature or magnetic flux, into discrete digital data sets. Different noise sources, such as quantization or Gaussian noise, distort the sampled points and inhibit subsequent processing during this process. Filtering the measured data is, thereby, a necessary step. In contrast to conventional filter design, this paper describes a novel discrete low-pass filter with a variable shape. It is configured by two independent parameters, allowing it to be sensitive to time-critical peaks at high levels while providing adequate noise suppression at low measured quantities. The theoretical background of this work's filter approach is explained, and guidelines for parameter selection are suggested. In the utilized test data set, the novel approach results in a 19 % better peak preservation while providing the same noise suppression as a conventional low-pass filter. © 2025 ieee.

关键词： discrete digital filter disturbance and response behavior Gaussian filter measurement data parameterizable low-pass signal processing

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Time-series signal Classification based on Adaptive Temporal Swin Transformer Network Model 5

Time-series Signal Classification Based on Adaptive Temporal...

引用

5th international conference on Computer Vision, image and Deep Learning, CVIDL 2024

作者： Zhang, Fangzuo Wuhan University of Technology Wuhan430000 China

ISBN: (纸本)9798350373820

The classification of temporal signals plays a significant role in deep learning tasks. However, it poses unique challenges due to the need for specialized architectures that can effectively capture the temporal dependencies and dynamic characteristics inherent in sequential data. In this paper, we propose an adaptive temporal Swin Transformer classification network model. The model's adaptive window mechanism allows the model to automatically adjust the size and shifting step of the shifting window to adapt to the multi-scale dynamic pattern. In addition, our model improves the data processing format, substantially reduces the computational complexity of processing high-dimensional signal data, and achieves the learning ability for different scale features of brain electrical signals, greatly improving the accuracy of signal classification tasks. Experimental results demonstrate that compared to other classification network models, our model performs better in capturing the complex temporal dependencies. © 2024 ieee.

关键词： Deep learning

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Remote Monitoring System using IoT for Healthcare Applications 10

Remote Monitoring System using IoT for Healthcare Applicatio...

引用

10th international conference on Communication and signal Processing, ICCSP 2024

作者： Vanitha, L. Waseem Fathima, R. Shrija, K. Vinothini, B. Kavitha, S. Snegapriya, V. Ece S.A.Engineering College India

ISBN: (纸本)9798350353068

Healthcare tracking systems have become increasingly important and technology focused. Due to patients not able to receive timely medical attention they undergo many problems from a variety of illness. In order to ensure that patients receive better treatment, the main objective is to establish a dependable internet of Things (IoT) remote healthcare observing method. This would allow medical practitioners to use an IoT centered integrated healthcare structure to observe their patients, whether they are in the hospital or at home. A wireless healthcare detecting scheme that is centered on mobile devices is created. It provides real-time online data about an individual physiological conditions and is primarily composed of sensors, a data acquisition unit, Arduino. The method records, exhibits, and stores the individual's temperature, ECG, heart rate, EEG data. It also transmits these information to the doctor's mobile device via an application. Hence, an internet of Things centered patient observing method can efficiently tracks individuals' health and protect them from critical situation. © 2024 ieee.

关键词： internet of things

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Design of Automatic Emergency Steering Using Model-based System Engineering 11

Design of Automatic Emergency Steering Using Model-Based Sys...

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11th international conference on Emerging Trends in Engineering and technology - signal and Information Processing, ICETET - SIP 2023

作者： Kumar, Raushan Khanapurkar, Milind Akant, Kalyani Surjekar, Neha M. Tech Communication Electronics & Telecommunication Engineering G H Raisoni College of Engineering Nagpur Nagpur India G H Raisoni College of Engineering Nagpur Electronics & Telecommunication Engineering Department Nagpur India Internet of Things & Digital Engineering Tata Consultancy Services Pune India

ISBN: (纸本)9798350348422

One of the hardest automatic driving tasks in the field of intelligent vehicles is collision avoidance. More drivers are often use to braking than to steering in emergency situations, even if the steering is the best maneuver. Manual steering is less effective at avoiding obstacles than automatic emergency steering. Braking, accelerating, steering, or a combination of braking, accelerating & steering can be used to prevent a collision. Because of the complexity involved, the automatic emergency steering system must be designed using a Model-based systems Engineering (MBSE) methodology. Only steering interventions are considered in this paper. © 2023 ieee.

关键词： Collision avoidance

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LDCT image Reconstruction on Edge Attention Graph Convolutional Network with Perceptual Loss and LoDoPaB-CT

LDCT Image Reconstruction on Edge Attention Graph Convolutio...

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2023 international conference in Advances in Power, signal, and Information technology, APSIT 2023

作者： Ramanathan, Shalini Ramasundaram, Mohan National Institute of Technology Tiruchirappalli Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350339369

image reconstruction performs a protruding role in medical image analysis. Low-Dose CT (LDCT) scan images are a common diagnostic procedure to identify diseases in the human body. Recent scanners follow deep learning-based post-processing methods for low-dose imaging. Low-dose CT image reconstruction techniques deteriorate image quality, which has an impact on a physician's diagnosis. Therefore, this paper introduces a novel LDCT image reconstruction method based on the edge attention technique utilized in graph convolutional neural networks. The quality of the outcomes is measured through the perceptual loss function. Experimental assessments are shown on the LoDoPaB-CT benchmark dataset. It is demonstrated that the proposed method produced an improved high-quality image compared to both traditional and deep learning-based reconstruction methods qualitatively and quantitatively. © 2023 ieee.

关键词： image reconstruction

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Make Your Enemy Your Friend: Improving image Rotation Angle Estimation with Harmonics 48

Make Your Enemy Your Friend: Improving Image Rotation Angle ...

引用

48th ieee international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Yu, Kun Hosseini, Morteza Darvish Morshedi Peng, Anjie Zeng, Hui Goljan, Miroslav Binghamton University Suny United States Southwest University of Science and Technology China Guangdong Provincial Key Laboratory of Information Security Technology China

ISBN: (纸本)9781728163277

It is well known that rotation introduces periodic artifacts into the resulting image. By measuring such periodicities, the rotation angle θ can be estimated from the rotated image without the availability of the original unrotated image. However, existing methods suffer from harmonics, especially when θ is small. This paper revisits the harmonics caused by rotation in the cyclic spectrum and points out that such harmonics can help our rotation angle estimation if used effectively. based on this observation, we propose aggregating the magnitudes of the candidate peaks and their harmonics in detecting the rotation-specific peak. Both theoretical analyses and experimental results demonstrate the advantage of the proposed scheme over previous methods. © 2023 ieee.

关键词： cyclic spectrum harmonics image forensics periodic artifacts rotation angle estimation

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MUTUAL INFORMATION ASSISTED GRAPH CONVOLUTION NETWORK FOR COLD-START RECOMMENDATION 49

MUTUAL INFORMATION ASSISTED GRAPH CONVOLUTION NETWORK FOR CO...

引用

49th ieee international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Wang, Wenbo Chen, Ben Liu, Bingquan Wang, Xinxin Yang, Luwei Jiang, Wen Ning, Wei Guan, Jian Harbin Inst Technol Fac Comp Harbin 150001 Peoples R China Alibaba Grp Hangzhou 310000 Peoples R China Harbin Engn Univ Grp Intelligent Signal Proc Harbin 150001 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

To solve the cold-start issue that cold items have no historical interactions to obtain collaborative feature as their representation, existing methods often represent them totally based on content feature obtained from inherent content (i.e., image, video and attributes). However, these methods will lose efficacy in representing the cold item whose inherent content is much different from that of warm items, when the cold item first appears in the test stage. In this paper, we propose a mutual information assisted graph convolution network (MI-GCN), which represents the cold item by simultaneously considering its inherent content and its related users' feature captured by pair-wise mutual information (PMI). In addition, for improving the overall performance, a joint objective function that contains a relation loss and a similarity error loss is employed to achieve a better trade-off of the representation similarity between warm and cold items. Experiments conducted on the Amazon dataset demonstrate the superiority of our method, as compared with the state-of-the-art methods.

关键词： Cold-start recommendation historical interactions inherent content pair-wise mutual information

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MFANet: Multi-Feature Aggregation Network for Multi-focus image Fusion

MFANet: Multi-Feature Aggregation Network for Multi-focus Im...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Zhao, Libo Zhang, Xiaoli Huang, Bo Tian, Mingjie Wang, Zeyu College of Computer Science and Technology Jilin University Changchun130012 China School of Artificial Intelligence Jilin University Changchun130012 China College of Computer Science and Engineering Dalian Minzu University Dalian116600 China

ISBN: (纸本)9798350368741

Existing deep learning-based Multi-focus image Fusion (MFIF) methods often rely on loss functions derived from linear combinations of image quality metrics, leading to complexities in training and only marginal improvements in image quality. Recognizing this, our study identifies input space and scale information as pivotal in enhancing MFIF performance. By augmenting raw spatial images with other feature spaces, i.e., gradient and dense Scale-Invariant Feature Transform (DSIFT), we enhance the model's ability to detect edges, textures, and local structures, facilitating more accurate differentiation between focused and defocused areas. Additionally, smaller scale variations improve focus detection, while multi-scale learning within neural networks effectively suppresses artifacts without affecting focus detection accuracy. To achieve the above enhancements, we introduce the Multi-Feature Aggregation Network (MFANet), which employs a three-branch architecture to perform focused detection process in spatial, gradient, and DSIFT feature spaces. Each branch is equipped with a Pyramid Attention Fusion (PAF) module that utilizes attention mechanisms and a novel Light Spatial Aggregation Pyramid Module (LSAPM) to capture global feature relationships and aggregate multi-scale information. Experimental results demonstrate that MFANet surpasses other state-of-the-art fusion methods in both qualitative and quantitative evaluations. © 2025 ieee.

关键词： feature aggregation image fusion multi-feature input spaces multi-focus pyramid attention

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