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Journal articles on the topic 'Compressed sensing, compressive camera identification'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Li, Zhong, Liyang Chen, Kai Liu, Xun Ma, and Yong Wang. "Analysis and identification of transformer acoustic signal based on compressed sensing." Journal of Physics: Conference Series 2656, no. 1 (2023): 012016. http://dx.doi.org/10.1088/1742-6596/2656/1/012016.

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Abstract The operation state of the transformer affects the operation of the whole power system. Aiming at the application of online monitoring of transformer operation, based on compressive sensing theory and wavelet packet analysis technology, a transformer acoustic fault diagnosis method based on compressive sensing is proposed. Firstly, the partial Hadamard matrix is constructed as the observation matrix to compress the acoustic signal of the transformer. The energy decomposition of the compressed signal is completed based on the wavelet packet. Finally, the feature selection is completed

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Chou, Ching-Yao, Yo-Woei Pua, Ting-Wei Sun, and An-Yeu (Andy) Wu. "Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis." Sensors 20, no. 11 (2020): 3279. http://dx.doi.org/10.3390/s20113279.

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Nowadays, user identification plays a more and more important role for authorized machine access and remote personal data usage. For reasons of privacy and convenience, biometrics-based user identification, such as iris, fingerprint, and face ID, has become mainstream methods in our daily lives. However, most of the biometric methods can be easily imitated or artificially cracked. New types of biometrics, such as electrocardiography (ECG), are based on physiological signals rather than traditional biological traits. Recently, compressive sensing (CS) technology that combines both sampling and

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Wang, Zhen, Shijie Gao, and Lei Sheng. "Feasibility of Laser Communication Beacon Light Compressed Sensing." Sensors 20, no. 24 (2020): 7257. http://dx.doi.org/10.3390/s20247257.

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The Compressed Sensing (CS) camera can compress images in real time without consuming computing resources. Applying CS theory in the Laser Communication (LC) system can minimize the assumed transmission bandwidth (normally from a satellite to a ground station) and minimize the storage costs of beacon light-spot images; this can save more than ten times the typical bandwidth or storage space. However, the CS compressive process affects the light-spot tracking and key parameters in the images. In this study, we quantitatively explored the feasibility of the CS technique to capture light-spots in

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4

Matin, Amir, and Xu Wang. "Compressive Coded Rotating Mirror Camera for High-Speed Imaging." Photonics 8, no. 2 (2021): 34. http://dx.doi.org/10.3390/photonics8020034.

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We develop a novel compressive coded rotating mirror (CCRM) camera to capture events at high frame rates in passive mode with a compact instrument design at a fraction of the cost compared to other high-speed imaging cameras. Operation of the CCRM camera is based on amplitude optical encoding (grey scale) and a continuous frame sweep across a low-cost detector using a motorized rotating mirror system which can achieve single pixel shift between adjacent frames. Amplitude encoding and continuous frame overlapping enable the CCRM camera to achieve a high number of captured frames and high tempor

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Ali B H, Baba Fakruddin, and Prakash Ramachandran. "Compressive Domain Deep CNN for Image Classification and Performance Improvement Using Genetic Algorithm-Based Sensing Mask Learning." Applied Sciences 12, no. 14 (2022): 6881. http://dx.doi.org/10.3390/app12146881.

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The majority of digital images are stored in compressed form. Generally, image classification using convolution neural network (CNN) is done in uncompressed form rather than compressed one. Training the CNN in the compressed domain eliminates the requirement for decompression process and results in improved efficiency, minimal storage, and lesser cost. Compressive sensing (CS) is one of the effective and efficient method for signal acquisition and recovery and CNN training on CS measurements makes the entire process compact. The most popular sensing phenomenon used in CS is based on image acqu

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Wei, Ziran, Jianlin Zhang, Zhiyong Xu, and Yong Liu. "Optimization Methods of Compressively Sensed Image Reconstruction Based on Single-Pixel Imaging." Applied Sciences 10, no. 9 (2020): 3288. http://dx.doi.org/10.3390/app10093288.

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According to the theory of compressive sensing, a single-pixel imaging system was built in our laboratory, and imaging scenes are successfully reconstructed by single-pixel imaging, but the quality of reconstructed images in traditional methods cannot meet the demands of further engineering applications. In order to improve the imaging accuracy of our single-pixel camera, some optimization methods of key technologies in compressive sensing are proposed in this paper. First, in terms of sparse signal decomposition, based on traditional discrete wavelet transform and the characteristics of coeff

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Szajewska, Anna. "Simulation of the Operation of a Single Pixel Camera with Compressive Sensing in the Long-Wave Infrared." Pomiary Automatyka Robotyka 25, no. 2 (2021): 53–60. http://dx.doi.org/10.14313/par_240/53.

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Imaging with the use of a single pixel camera and based on compressed sensing (CS) is a new and promising technology. The use of CS allows reconstruction of images in various spectrum ranges depending on the spectrum sensibility of the used detector. During the study image reconstruction was performed in the LWIR range based on a thermogram from a simulated single pixel camera. For needs of reconstruction CS was used. A case analysis showed that the CS method may be used for construction of infrared-based observation single pixel cameras. This solution may also be applied in measuring cameras.

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Yu, Wen-Kai. "Super Sub-Nyquist Single-Pixel Imaging by Means of Cake-Cutting Hadamard Basis Sort." Sensors 19, no. 19 (2019): 4122. http://dx.doi.org/10.3390/s19194122.

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Single-pixel imaging via compressed sensing can reconstruct high-quality images from a few linear random measurements of an object known a priori to be sparse or compressive, by using a point/bucket detector without spatial resolution. Nevertheless, random measurements still have blindness, limiting the sampling ratios and leading to a harsh trade-off between the acquisition time and the spatial resolution. Here, we present a new compressive imaging approach by using a strategy we call cake-cutting, which can optimally reorder the deterministic Hadamard basis. The proposed method is capable of

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9

Zhou, Shihua, Xinhai Yu, Xuan Li, Yue Wang, Kaibo Ji, and Zhaohui Ren. "Gearbox Fault Diagnosis Based on Compressed Sensing and Multi-Scale Residual Network with Lightweight Attention Mechanism." Mathematics 13, no. 9 (2025): 1393. https://doi.org/10.3390/math13091393.

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As a core component of mechanical transmission systems, gear damage status significantly impacts the safety and efficiency of an overall mechanical system. However, existing fault diagnosis methods often struggle to extract features effectively in complex application scenarios characterized by conditions such as high temperature, high humidity, and high-level vibrations. Consequently, they exhibit poor adaptability and limited anti-noise capabilities. To address these limitations and enhance the adaptability and precision of gear fault diagnosis (GFD), a novel compressive sensing lightweight a

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10

Sun, Ting-Wei, Danish Ali, and Ayeu (Andy) Wu. "Compressed-Domain ECG-based Biometric User Identification Using Task-Driven Dictionary Learning." ACM Transactions on Computing for Healthcare 3, no. 3 (2022): 1–15. http://dx.doi.org/10.1145/3461701.

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In recent years, user identification has become crucial for authorized machine access. Electrocardiography (ECG) is a new and rising biometrics signature. Rather than traditional biological traits, ECG cannot be easily imitated. In the long-term monitoring system, the wireless wearable ECG biomedical sensor nodes are resource-limited. Recently, compressive sensing (CS) technology is extensively applied to reduce the power of data transmission and acquisition. The prior CS-based reconstruction process aims at improving energy efficiency with different schemes, and they focus on the performance

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11

Shujia Wan, Qiong Gong, Hongjuan Wang, Shibang Ma, and Yi Qin. "Compressed optical image encryption in the diffractive-imaging-based scheme by input plane and output plane random sampling." Optica Applicata 52, no. 1 (2022). http://dx.doi.org/10.37190/oa220104.

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The successful recovery of the plaintext in the simplified diffractive-imaging-based encryption (S-DIBE) scheme needs to record one intact axial intensity map as the ciphertext. By aid of compressive sensing, we propose here a new image encryption approach, referred to as compressed DIBE (C-DIBE), which allows further compression of the intensity map. The plaintext is sampled before being sent to DIBE. Afterwards, the intensity map recorded by the CCD camera is also processed by such sampling operation to generate the ciphertext. For decryption, we first obtain the sparse plaintext using the p

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Kong, Yeseul, Seung Hwan Lee, and Gyuhae Park. "Phase-based Full-field Displacement Measurement with Phase Nonlinearity Weighting Process." e-Journal of Nondestructive Testing 29, no. 7 (2024). http://dx.doi.org/10.58286/29739.

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The camera, as a non-contact sensor, has evolved into a robust tool for measuring the full-field vibration of complex structures. It offers distinct advantages over traditional contact sensors, including flexible positioning, simultaneous multi-point tracking, and high spatial resolution. Vibration imaging techniques have been developed to identify structural operational conditions through feature extraction methods. Phase-based vibration imaging techniques can visualize full-field operational shapes or vibrational features in a less-supervised manner. While phase-based optical flow with Gabor

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13

"Wideband Cognitive Radio based on Scheduled Sequential Compressed Spectrum Sensing." International Journal of Recent Technology and Engineering 8, no. 2 (2019): 4691–95. http://dx.doi.org/10.35940/ijrte.b3515.078219.

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The cooperation for big data applications through the cognitive radio innovation requires wideband spectrum sensing. Conversely, it is expensive to employ long haul wideband detecting and is particularly troublesome within the sight of vulnerability. For example, more noise, obstruction, anomalies, as well as channel blurring. In this article, we project the planning of successive compacted range detecting which together endeavors compressive sensing (CS) and consecutive occasional identification procedures to accomplish increasingly exact and convenient wideband detecting. Rather than summoni

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14

Xia, Xin, Chuanliang He, Yingjie Lv, et al. "Power Quality Data Compression and Disturbances Recognition Based on Deep CS-BiLSTM Algorithm With Cloud-Edge Collaboration." Frontiers in Energy Research 10 (April 26, 2022). http://dx.doi.org/10.3389/fenrg.2022.874351.

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The current disturbance classification of power quality data often has the problem of low disturbance recognition accuracy due to its large volume and difficult feature extraction. This paper proposes a hybrid model based on distributed compressive sensing and a bi-directional long-short memory network to classify power quality disturbances. A cloud-edge collaborative framework is first established with distributed compressed sensing as an edge-computing algorithm. With the uploading of dictionary atoms of compressed sensing, the data transmission and feature extraction of power quality is ach

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Xia, Xin, Chuanliang He, Yingjie Lv, et al. "Power Quality Data Compression and Disturbances Recognition Based on Deep CS-BiLSTM Algorithm With Cloud-Edge Collaboration." Frontiers in Energy Research 10 (April 26, 2022). http://dx.doi.org/10.3389/fenrg.2022.874351.

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The current disturbance classification of power quality data often has the problem of low disturbance recognition accuracy due to its large volume and difficult feature extraction. This paper proposes a hybrid model based on distributed compressive sensing and a bi-directional long-short memory network to classify power quality disturbances. A cloud-edge collaborative framework is first established with distributed compressed sensing as an edge-computing algorithm. With the uploading of dictionary atoms of compressed sensing, the data transmission and feature extraction of power quality is ach

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16

Jadhav, Sharad T., Kirti Mahajan, Faizur Rashid, and Gavendra Singh. "Fractional Fire Hawk Optimization-Based Compressive Sensing for Compression and Recovery of Medical Images." International Journal of Image and Graphics, February 17, 2025. https://doi.org/10.1142/s0219467826500427.

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Due to the dramatic rise in the number of medical images utilized for identification and treatment, the storage of medical images is an important issue. Finding the domain in which an image is represented and accurately recovering it in order to produce a high-quality result is one of the most significant challenges in compressive sensing. Hence, an optimal technique is introduced using the proposed Fractional Fire Hawk Optimization (FFHO)-based collaborative recovery for compression and recovery of medical images. This technique involves two steps, such as the compression and recovery steps.

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17

Hoffmann, Alex Paul, Mark B. Moldwin, Brady P. Strabel, and Lauro V. Ojeda. "Enabling Boomless CubeSat Magnetic Field Measurements with the Quad‐Mag Magnetometer and an Improved Underdetermined Blind Source Separation Algorithm." Journal of Geophysical Research: Space Physics, August 25, 2023. http://dx.doi.org/10.1029/2023ja031662.

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AbstractIn situ magnetic field measurements are often difficult to obtain due to the presence of stray magnetic fields generated by spacecraft electrical subsystems. The conventional solution is to implement strict magnetic cleanliness requirements and place magnetometers on a deployable boom. However, this method is not always feasible on low‐cost platforms due to factors such as increased design complexity, increased cost, and volume limitations. To overcome these problems, we propose using the Quad‐Mag CubeSat magnetometer with an improved Underdetermined Blind Source Separation (UBSS) nois

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