Sensors

28 pages, 85416 KiB

Open AccessArticle

ENGDM: Enhanced Non-Isotropic Gaussian Diffusion Model for Progressive Image Editing

by Xi Yu, Xiang Gu, Xin Hu and Jian Sun

Sensors 2025, 25(10), 2970; https://doi.org/10.3390/s25102970 (registering DOI) - 8 May 2025

Diffusion models have made remarkable progress in image generation, leading to advancements in the field of image editing. However, balancing editability with faithfulness remains a significant challenge. Motivated by the fact that more novel content will be generated when larger variance noise is [...] Read more.

Diffusion models have made remarkable progress in image generation, leading to advancements in the field of image editing. However, balancing editability with faithfulness remains a significant challenge. Motivated by the fact that more novel content will be generated when larger variance noise is applied to the image, in this paper, we propose an Enhanced Non-isotropic Gaussian Diffusion Model (ENGDM) for progressive image editing, which introduces independent Gaussian noise with varying variances to each pixel based on its editing needs. To enable efficient inference without retraining, ENGDM is rectified into an isotropic Gaussian diffusion model (IGDM) by assigning different total diffusion times to different pixels. Furthermore, we introduce reinforced text embeddings, using a novel editing reinforcement loss in the latent space to optimize text embeddings for enhanced editability. And we introduce optimized noise variances by employing a structural consistency loss to dynamically adjust the denoising time steps for each pixel for better faithfulness. Experimental results on multiple datasets demonstrate that ENGDM achieves state-of-the-art performance in image-editing tasks, effectively balancing faithfulness to the source image and alignment with the desired editing target. Full article

(This article belongs to the Section Sensing and Imaging)

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18 pages, 3491 KiB

Open AccessArticle

RT-4M: Real-Time Mosaicing Manager for Manual Microscopy System

by Nobuhito Mori, Yoshihiro Miyazaki, Tatsuya Oda and Yasuyuki S. Kida

Sensors 2025, 25(10), 2968; https://doi.org/10.3390/s25102968 - 8 May 2025

Abstract

The creation of virtual slides, i.e., high-resolution digital images of biological samples, is expensive, and existing manual methods often suffer from stitching errors and additional reimaging costs. To address these issues, we propose a real-time mosaicing manager for manual microscopy (RT-4M) that performs [...] Read more.

The creation of virtual slides, i.e., high-resolution digital images of biological samples, is expensive, and existing manual methods often suffer from stitching errors and additional reimaging costs. To address these issues, we propose a real-time mosaicing manager for manual microscopy (RT-4M) that performs real-time stitching and allows users to preview slides during imaging using existing manual microscopy systems, thereby reducing the need for reimaging. We install it on two different microscopy systems, successfully creating virtual slides of hematoxylin and eosin- and fluorescent-stained tissues obtained from humans and mice. The fluorescent-stained tissues consist of two colors, requiring the manual switching of the filter and an exposure time of 1.6 s per color. Even in the case of the largest dataset in this study (over 900 images), the entire sample is captured without any omissions. Moreover, RT-4M exhibits a processing time of less than one second per registration, indicating that it does not hinder the user’s imaging workflow. Additionally, the composition process reduces the misalignment rate by a factor of 20 compared to existing software. We believe that the proposed software will prove useful in the fields of pathology and bio-research, particularly for facilities with relatively limited budgets. Full article

(This article belongs to the Special Issue Applications of Biomedical Imaging and Sensing Technologies in Disease Diagnosis)

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18 pages, 10917 KiB

Open AccessArticle

A Novel Parallel Multi-Scale Attention Residual Network for the Fault Diagnosis of a Train Transmission System

by Yong Chang, Tengfei Gao, Juanhua Yang, Zongyao Liu and Biao Wang

Sensors 2025, 25(10), 2967; https://doi.org/10.3390/s25102967 - 8 May 2025

Abstract

The data-driven intelligent fault diagnosis method has shown great potential in improving the safety and reliability of train operation. However, the noise interference and multi-scale signal characteristics generated by the train transmission system under non-stationary conditions make it difficult for the network model [...] Read more.

The data-driven intelligent fault diagnosis method has shown great potential in improving the safety and reliability of train operation. However, the noise interference and multi-scale signal characteristics generated by the train transmission system under non-stationary conditions make it difficult for the network model to effectively learn fault features, resulting in a decrease in the accuracy and robustness of the network. This results in the requirements of train fault diagnosis tasks not being met. Therefore, a novel parallel multi-scale attention residual neural network (PMA-ResNet) for a train transmission system is proposed in this paper. Firstly, multi-scale learning modules (MLMods) with different structures and convolutional kernel sizes are designed by combining a residual neural network (ResNet) and an Inception network, which can automatically learn multi-scale fault information from vibration signals. Secondly, a parallel network structure is constructed to improve the generalization ability of the proposed network model for the entire train transmission system. Finally, by using a self-attention mechanism to assign different weight values to the relative importance of different feature information, the learned fault features are further integrated and enhanced. In the experimental section, a train transmission system fault simulation platform is constructed, and experiments are carried out on train transmission systems with different faults under non-stationary conditions to verify the effectiveness of the proposed network. The experimental results and comparisons with five state-of-the-art methods demonstrate that the proposed PMA-ResNet can diagnose 19 different faults with greater accuracy. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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42 pages, 3314 KiB

Open AccessSystematic Review

A Systematic Review of Sensor-Based Methods for Measurement of Eating Behavior

by Delwar Hossain, J. Graham Thomas, Megan A. McCrory, Janine Higgins and Edward Sazonov

Sensors 2025, 25(10), 2966; https://doi.org/10.3390/s25102966 - 8 May 2025

Abstract

The dynamic process of eating—including chewing, biting, swallowing, food items, eating time and rate, mass, environment, and other metrics—may characterize behavioral aspects of eating. This article presents a systematic review of the use of sensor technology to measure and monitor eating behavior. The [...] Read more.

The dynamic process of eating—including chewing, biting, swallowing, food items, eating time and rate, mass, environment, and other metrics—may characterize behavioral aspects of eating. This article presents a systematic review of the use of sensor technology to measure and monitor eating behavior. The PRISMA 2020 guidelines were followed to review the full texts of 161 scientific manuscripts. The contributions of this review article are twofold: (i) A taxonomy of sensors for quantifying various aspects of eating behavior is established, classifying the types of sensors used (such as acoustic, motion, strain, distance, physiological, cameras, and others). (ii) The accuracy of measurement devices and methods is assessed. The review highlights the advantages and limitations of methods that measure and monitor different eating metrics using a combination of sensor modalities and machine learning algorithms. Furthermore, it emphasizes the importance of testing these methods outside of restricted laboratory conditions, and it highlights the necessity of further research to develop privacy-preserving approaches, such as filtering out non-food-related sounds or images, to ensure user confidentiality and comfort. The review concludes with a discussion of challenges and future trends in the use of sensors for monitoring eating behavior. Full article

(This article belongs to the Special Issue Smart Sensing for Dietary Monitoring)

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20 pages, 2857 KiB

Open AccessArticle

NeuroSafeDrive: An Intelligent System Using fNIRS for Driver Distraction Recognition

by Ghazal Bargshady, Hakki Gokalp Ustun, Yasaman Baradaran, Houshyar Asadi, Ravinesh C Deo, Jeroen Van Boxtel and Raul Fernandez Rojas

Sensors 2025, 25(10), 2965; https://doi.org/10.3390/s25102965 - 8 May 2025

Abstract

Driver distraction remains a critical factor in road accidents, necessitating intelligent systems for real-time detection. This study introduces a novel fNIRS-based method to to classify varying levels of driver distraction across diverse simulated scenarios, including cognitive, visual–manual, and auditory sources of inattention. Unlike [...] Read more.

Driver distraction remains a critical factor in road accidents, necessitating intelligent systems for real-time detection. This study introduces a novel fNIRS-based method to to classify varying levels of driver distraction across diverse simulated scenarios, including cognitive, visual–manual, and auditory sources of inattention. Unlike previous work, we evaluated multiple neurophysiological metrics—including oxygenated, deoxygenated, and combined haemoglobin—to identify the most reliable biomarker for distraction detection. Neurophysiological data were collected, and three multi-class classifiers (SVM, KNN, decision tree) were applied across different fNIRS metrics. Our results show that oxygenated haemoglobin outperforms other signals in distinguishing distracted from non-distracted states, while the combined signal performs best in differentiating distraction from baseline. The proposed SVM model achieved ≈ 77.9% accuracy in detecting distracted and relaxed driving states based on brain oxygen levels. Our findings also show that increased distraction correlates with elevated activity in the dorsolateral prefrontal cortex and premotor cortex, whereas driving without distraction exhibits lower neurovascular engagement. This study contributes to affective computing and intelligent transportation systems and could support the development of future driver distraction monitoring systems for safer and more adaptive vehicle control. Full article

(This article belongs to the Special Issue Sensor Technologies and Intelligent Computing for Biometric Signal Analysis and Pattern Recognition)

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10 pages, 373 KiB

Open AccessArticle

Detection of Falls and Frailty in Older Adults with Oldfry: Associated Risk Factors

by Eva Martí-Marco, Enrique J. Vera-Remartínez, Aurora Esteve-Clavero, Irene Carmona-Fortuño, Martín Flores-Saldaña, Jorge Vila-Pascual, Malena Barba-Muñoz and María Pilar Molés-Julio

Sensors 2025, 25(10), 2964; https://doi.org/10.3390/s25102964 - 8 May 2025

Abstract

Objective: To describe the characteristics and outcomes of using the Oldfry technology application in older adults, analyzing changes in frailty and fall risk after its implementation. Design and Methods: Observational, analytical, prospective, cross-sectional, and multicenter study conducted in residential centers in Plana Baja [...] Read more.

Objective: To describe the characteristics and outcomes of using the Oldfry technology application in older adults, analyzing changes in frailty and fall risk after its implementation. Design and Methods: Observational, analytical, prospective, cross-sectional, and multicenter study conducted in residential centers in Plana Baja (Castellón, Spain). A total of 156 older adults over 65 years old participated, selected based on specific criteria and voluntary consent. Sociodemographic, anthropometric, and clinical variables were collected, including fall history, sensory problems, medication use, and standardized cognitive, nutritional, and functional assessment scales. The study was approved by the Ethics Committee of Universitat Jaume I. Results: The sample included 156 individuals (median age: 84 years). Women showed greater functional dependence (Barthel scale) and cognitive impairment (Pfeiffer scale). The Oldfry device detected frailty with statistically significant differences. A direct relationship was found between greater functional dependence and higher fall risk, as well as between higher comorbidity and increased fall risk. An adequate nutritional status was associated with a lower fall risk. Conclusion: The use of Oldfry is crucial for assessing frailty and fall risk in older adults. Factors such as functionality, comorbidities, and nutritional status directly influence fall prevention, highlighting the importance of technological tools in monitoring these risks. Full article

(This article belongs to the Special Issue Fall Detection Based on Wearable Sensors)

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20 pages, 1922 KiB

Open AccessArticle

An Onset Detection Method for Slowly Activated Muscle Based on Marginal Spectrum Entropy

by Xiaolei Huang, Jinzhuang Xiao, Qing Chang and Bin Fang

Sensors 2025, 25(10), 2963; https://doi.org/10.3390/s25102963 - 8 May 2025

Abstract

Muscle activity is composed of fast and slow activations. The detection of the onset time of the electromyogram signal, which is slowly activated, is difficult. This paper proposes a detection method based on marginal spectral entropy (MSE). The surface electromyography (sEMG) signal of [...] Read more.

Muscle activity is composed of fast and slow activations. The detection of the onset time of the electromyogram signal, which is slowly activated, is difficult. This paper proposes a detection method based on marginal spectral entropy (MSE). The surface electromyography (sEMG) signal of the soleus during normal walking was collected by a wireless electromyography acquisition system. The proposed MSE-based detection method is based on the Hilbert–Huang transform (HHT) combined with information entropy. By comparing the changes in MSE before and after muscle activation to plot a trend line, the point of fastest change on the trend line was defined as the onset time of muscle activation. This method was compared with the amplitude threshold method and the Teager–Kaiser energy (TKE) operator method. The results show that the onset time of muscle activation detected by this method is 0.14 s earlier than the amplitude threshold method and 0.16 s earlier than the TKE operator method. The detection results were significantly different (p < 0.05), indicating that this method has higher detection accuracy for the onset time of the sEMG signal, which is slowly activated. Full article

(This article belongs to the Section Biomedical Sensors)

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21 pages, 6664 KiB

Open AccessArticle

The Effect of Filtering on Signal Features of Equine sEMG Collected During Overground Locomotion in Basic Gaits

by Małgorzata Domino, Marta Borowska, Elżbieta Stefanik, Natalia Domańska-Kruppa, Michał Skibniewski and Bernard Turek

Sensors 2025, 25(10), 2962; https://doi.org/10.3390/s25102962 - 8 May 2025

Abstract

In equine surface electromyography (sEMG), challenges related to the reliability and interpretability of data arise, among other factors, from methodological differences, including signal processing and analysis. The aim of this study is to demonstrate the filtering–induced changes in basic signal features in relation [...] Read more.

In equine surface electromyography (sEMG), challenges related to the reliability and interpretability of data arise, among other factors, from methodological differences, including signal processing and analysis. The aim of this study is to demonstrate the filtering–induced changes in basic signal features in relation to the balance between signal loss and noise attenuation. Raw sEMG signals were collected from the quadriceps muscle of six horses during walk, trot, and canter and then filtered using eight filtering methods with varying cut–off frequencies (low–pass at 10 Hz, high–pass at 20 Hz and 40 Hz, and bandpass at 20–450 Hz, 40–450 Hz, 7–200 Hz, 15–500 Hz, and 30–500 Hz). For each signal variation, signal features—such as amplitude, root mean square (RMS), integrated electromyography (iEMG), median frequency (MF), and signal–to–noise ratio (SNR)—along with signal loss metrics and power spectral density (PSD), were calculated. High–pass filtering at 40 Hz and bandpass filtering at 40–450 Hz introduced significant filtering–induced changes in signal features while providing full attenuation of low–frequency noise contamination, with no observed differences in signal loss between these two methods. Other filtering methods led to only partial attenuation of low–frequency noise, resulting in lower signal loss and less consistent changes across gaits in signal features. Therefore, filtering–induced changes should be carefully considered when comparing signal features from studies using different filtering approaches. These findings may support cross-referencing in equine sEMG research related to training, rehabilitation programs, and the diagnosis of musculoskeletal diseases, and emphasize the importance of applying standardized filtering methods, particularly with a high–pass cut–off frequency set at 40 Hz. Full article

(This article belongs to the Special Issue Sensors Technologies for Measurements and Signal Processing)

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15 pages, 6837 KiB

Open AccessArticle

Development of a Printed Sensor and Wireless Measurement System for Urination Monitoring

by Lan Zhang, En Takashi, Jian Lu and Sohei Matsumoto

Sensors 2025, 25(10), 2961; https://doi.org/10.3390/s25102961 - 8 May 2025

Abstract

The development of reliable and efficient sensors is essential for advances in health monitoring technologies. This study focused on the fabrication and evaluation of a multichannel printed sensor electrode designed for long-term stability and effective data acquisition. Using rapid printing technology, we created [...] Read more.

The development of reliable and efficient sensors is essential for advances in health monitoring technologies. This study focused on the fabrication and evaluation of a multichannel printed sensor electrode designed for long-term stability and effective data acquisition. Using rapid printing technology, we created a urine sensor array with extended electrodes for the measurement of urine volume and frequency. The ultrathin design of the sensor electrode, with an average thickness of only 30 microns, ensures both user comfort and measurement accuracy. The sensor electrode dimensions were meticulously designed, measured, and optimized through successful trial manufacturing of the sensor electrode and sensor array. Comprehensive evaluation of the fabricated sensor demonstrated excellent performance, including a high response speed of ≤1 s and long-term stability exceeding 5 weeks. In addition, wireless transmission capabilities and user interfaces were developed for field experiments. Finally, animal experiments were performed to evaluate the field performance of the fabricated sensor. Accordingly, we are confident that the sensor developed herein will contribute to enhancing healthcare in an aging society. Full article

(This article belongs to the Special Issue Ubiquitous Sensors in Environmental Monitor, Precision Agriculture, and Food Safety)

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23 pages, 26231 KiB

Open AccessArticle

Implementation Method of Five-Axis CNC RTOS Kernel Based on gLink-II Bus

by Liangji Chen, Hansong Gao, Huiying Li and Haohao Xu

Sensors 2025, 25(10), 2960; https://doi.org/10.3390/s25102960 - 8 May 2025

Abstract

With the rapid development of Computerized Numerical Control (CNC) systems, traditional industrial communication protocols fail to meet the requirements for high real-time performance and reliability. To address these challenges, an open five-axis CNC system is designed and implemented based on the gLink-II bus [...] Read more.

With the rapid development of Computerized Numerical Control (CNC) systems, traditional industrial communication protocols fail to meet the requirements for high real-time performance and reliability. To address these challenges, an open five-axis CNC system is designed and implemented based on the gLink-II bus protocol. This system features a layered architecture that integrates the Windows operating system with a Real-Time Operating System (RTOS) kernel, along with a multithreaded data interaction structure based on a circular buffer to enhance real-time data transmission performance and improve system responsiveness. In the direct linear interpolation control for five-axis machining, an acceleration and deceleration planning method is introduced, taking into account the kinematic constraints of the rotary axes. This method optimizes velocity and acceleration control. The experimental results show that the system achieves a maximum response error of less than 0.2 milliseconds and an interpolation period of less than 0.5 milliseconds in five-axis coordinated control. The system is capable of efficiently performing data processing and task scheduling, ensuring the stability of the CNC machining process. Full article

(This article belongs to the Section Communications)

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24 pages, 4350 KiB

Open AccessArticle

Domain-Adaptive Direction of Arrival (DOA) Estimation in Complex Indoor Environments Based on Convolutional Autoencoder and Transfer Learning

by Lingyu Shen, Jianfeng Li, Jingjing Pan, Junpeng Shi, Rui Xu, Hao Wang and Weiming Deng

Sensors 2025, 25(10), 2959; https://doi.org/10.3390/s25102959 - 8 May 2025

Abstract

Direction of arrival (DOA) estimation for signal sources in indoor environments has become increasingly important in wireless communications and smart home applications. However, complex indoor conditions, such as multipath effects and noise interference, pose significant challenges to estimation accuracy. This issue is further [...] Read more.

Direction of arrival (DOA) estimation for signal sources in indoor environments has become increasingly important in wireless communications and smart home applications. However, complex indoor conditions, such as multipath effects and noise interference, pose significant challenges to estimation accuracy. This issue is further complicated by domain discrepancies in data collected from different environments. To address these challenges, we propose a deep domain-adaptation-based DOA estimation method. The approach begins with deep feature extraction using a Convolutional Autoencoder (CAE) and employs a Domain-Adversarial Neural Network (DANN) for domain adaptation. By integrating Gradient Reversal Layer (GRL) and Maximum Mean Discrepancy (MMD) loss functions, the model effectively reduces distributional differences between the source and target domains. The CAE-DANN enables transfer learning between data with similar features from different domains. With minimal labeled data from the target domain incorporated into the source domain, the model leverages labeled source data to adapt to unlabeled target data. GRL counters domain shifts, while MMD refines feature alignment. Experimental results show that, in complex indoor environments, the proposed method outperforms other methods in terms of overall DOA prediction performance in both the source and target domains. This highlights a robust and practical solution for high-precision DOA estimation in new environments, requiring minimal labeled data. Full article

(This article belongs to the Special Issue Robust Parameter Estimation with Sensor Arrays in Complex Electromagnetic Environments: 2nd Edition)

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20 pages, 5439 KiB

Open AccessArticle

LarGAN: A Label Auto-Rescaling Generation Adversarial Network for Rare Surface Defects

by Guan Qin, Hanxin Zhang, Ke Xu, Liaoting Pan, Lei Huang, Xuezhong Huang and Yi Wei

Sensors 2025, 25(10), 2958; https://doi.org/10.3390/s25102958 - 8 May 2025

Abstract

Insufficient defect data significantly limits detection accuracy in continuous casting slab production. This limitation arises from the data collection in fast-paced production environments. To address this issue, we propose LarGAN, a data augmentation approach that synthesizes similar and high-quality defect data from a [...] Read more.

Insufficient defect data significantly limits detection accuracy in continuous casting slab production. This limitation arises from the data collection in fast-paced production environments. To address this issue, we propose LarGAN, a data augmentation approach that synthesizes similar and high-quality defect data from a single image. We utilize a progressive GAN framework to ensure a smooth and stable generation process, starting from low-resolution image synthesis and gradually increasing the network depth. We designed a Label Auto-Rescaling strategy to better adapt to defect data with annotation, enhancing both the quality and morphological diversity of the synthesized defects. To validate the generation results, we evaluate not only standard metrics, such as FID, SSIM, and LPIPS, but also performance, through the downstream detection model YOLOv8. Our experimental results demonstrate that the LarGAN model surpasses other single-image generation models in terms of image quality and diversity. Furthermore, the experiments reveal that the data generated by LarGAN effectively enhances the feature space of the original dataset, thereby improving the accuracy and generalization performance of the detection model. Full article

(This article belongs to the Special Issue Object Detection and Recognition Based on Deep Learning)

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13 pages, 2809 KiB

Open AccessArticle

Evaluation of the Temporal Characteristics of Ultrafast Imaging Methods Using Continuous Chirped Pulse Illumination

by Yahui Li, Hang Li, Wanyi Du, Chao Ji, Kai He, Guilong Gao and Jinshou Tian

Sensors 2025, 25(10), 2957; https://doi.org/10.3390/s25102957 - 8 May 2025

Abstract

Ultrafast imaging based on chirped pulse illumination has opened new frontiers, offering a frame rate beyond 1 Tfps for the acquisition of multiple frames in a single–shot. However, the temporal resolving capability is implicitly influenced by parameters in stages of pulse illumination and [...] Read more.

Ultrafast imaging based on chirped pulse illumination has opened new frontiers, offering a frame rate beyond 1 Tfps for the acquisition of multiple frames in a single–shot. However, the temporal resolving capability is implicitly influenced by parameters in stages of pulse illumination and data acquisition. This study delivers a mathematical model to produce a precise investigation, sorting the dominating factors, including the illumination pulse’s bandwidth

λ_{F W H M}

, dispersive propagation length z, and framing module’s spectral resolution

Δ λ

. For a different

λ_{F W H M}

, z has a lower bound to ensure the covered signal is resolved; meanwhile, the time resolution decreases with a larger z. Frame extraction with a narrower

Δ λ

leads to a higher time resolution; however,

Δ λ

must be broad enough for a reasonable signal-to-noise ratio. The theoretical and experimental approaches to evaluate temporal characteristics are discussed, enabling a precise quantitative determination for the community to produce, use, and exploit single-shot ultrafast imaging systems. Full article

(This article belongs to the Section Optical Sensors)

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29 pages, 8418 KiB

Open AccessArticle

Research on the Integration of Sensing and Communication Based on Fractional-Order Fourier Transform

by Mingyan Qi, Yuelong Su, Zhaoyi Wang and Kun Lu

Sensors 2025, 25(10), 2956; https://doi.org/10.3390/s25102956 - 8 May 2025

Abstract

This study investigated the integration of detection and communication techniques. First, the fractional-order Fourier transform (FRFT) is introduced, and the golden section method, parabolic interpolation, and Brent method are applied to search for the optimal fractional-order domain to accurately estimate the parameters of [...] Read more.

This study investigated the integration of detection and communication techniques. First, the fractional-order Fourier transform (FRFT) is introduced, and the golden section method, parabolic interpolation, and Brent method are applied to search for the optimal fractional-order domain to accurately estimate the parameters of the linear frequency modulation (LFM) signal. Second, the three search algorithms and the performance of the integrated sensing and communication waveform are simulated. The Brent method improves the parameter searching efficiency by approximately 30% compared with the golden section method; the bit error ratio (BER) of the integrated LFM signal can reach 10⁻⁴ with a signal-to-noise ratio (SNR) of 3 dB. The results show that the integrated waveform can realize the detection function with guaranteed communication performance. An anti-frequency sweeping interference method based on the fractional domain matching order was also carried out to optimize the detection performance of the integrated waveform. Through the analysis of the difference-frequency signal under frequency sweeping interference, two methods, direct filtering, and pairwise cancellation filtering, are used to suppress the interference signal and detect the target distance. The simulation evaluated the detection performance of the two methods under different signal-to-interference ratios (SIR) and filter widths. The simulation results show that the pairwise cancellation filtering suppresses the frequency sweeping interference by 4–6 dB more than the direct filtering with an SIR ≤ −15 dB. Both filtering methods can correctly extract the target position information under frequency sweeping interference with a low signal-to-interference ratio (SIR). In conclusion, this study provides an effective solution for parameter estimation optimization and frequency-sweeping interference suppression for FRFT-based sensing communication systems. Full article

(This article belongs to the Section Communications)

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