Relevant bibliographies by topics / Compressed sensing, compressive camera identification

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Compressed sensing, compressive camera identification'

Author: Grafiati
Published: 14 March 2023
Last updated: 31 July 2025

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

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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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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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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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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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Dissertations / Theses on the topic "Compressed sensing, compressive camera identification"

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VALSESIA, DIEGO. "Imaging using random projections: compression, communication, camera identification." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2642257.

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This thesis discusses problems related to imaging applications, focusing on image compression, transmission and source camera identification. Random projections and compressed sensing are shown to be effective techniques to address such problems. We show that random projections can be used to compress multispectral images in a more rate-efficient manner than previously known and with a low-complexity scheme based on compressed sensing that is suitable for usage onboard of spacecrafts. Image measurements obtained through random projections possess peculiar properties that allow to devise novel
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Brorsson, Andreas. "Compressive Sensing: Single Pixel SWIR Imaging of Natural Scenes." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-145363.

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Photos captured in the shortwave infrared (SWIR) spectrum are interesting in military applications because they are independent of what time of day the pic- ture is captured because the sun, moon, stars and night glow illuminate the earth with short-wave infrared radiation constantly. A major problem with today’s SWIR cameras is that they are very expensive to produce and hence not broadly available either within the military or to civilians. Using a relatively new tech- nology called compressive sensing (CS), enables a new type of camera with only a single pixel sensor in the sensor (a SPC).
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Book chapters on the topic "Compressed sensing, compressive camera identification"

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Carmi, Avishy Y. "Compressive System Identification." In Compressed Sensing & Sparse Filtering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38398-4_9.

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Huang, Yongfeng, Qiang Liu, and Cairong Yan. "Research on object re-identification with compressive sensing in multi-camera systems." In Automotive, Mechanical and Electrical Engineering. CRC Press, 2017. http://dx.doi.org/10.1201/9781315210445-82.

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Conference papers on the topic "Compressed sensing, compressive camera identification"

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TAO, FEI, XIN LIU, HAODONG DU, and WENBIN YU. "DISCOVERING FAILURE CRITERIA OF COMPOSITES BY SPARSE IDENTIFICATION AND COMPRESSED SENSING." In Thirty-sixth Technical Conference. Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/asc36/35821.

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A reliable design of a composite structure needs to consider the failure of the composites. Hashin failure criterion is one of the most popular phenomenological models in engineering practice due to its simplicity of application. Although remarkable success has been achieved from the Hashin failure criterion, it does not always fit the experimental results very well. Over the past few years, a few experimental failure data have been collected. It would be of interest to leverage the existing data to improve the prediction of failure criteria. In this paper, we proposed to apply a framework tha
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Farina, A., A. Ghezzi, A. J. M. Lenz, et al. "Multidimensional Compressive Fluorescence Microscopy." In Clinical and Translational Biophotonics. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/translational.2024.jm4a.17.

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A system for multispectral lifetime imaging based on single-pixel camera and compressed-sensing is presented together with two algorithms devoted to the improvement of the spatial resolution and the global reconstruction time.
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