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Journal articles on the topic 'Adaptive sensing'

To see the other types of publications on this topic, follow the link: Adaptive sensing.

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Davenport, Mark A., Andrew K. Massimino, Deanna Needell, and Tina Woolf. "Constrained Adaptive Sensing." IEEE Transactions on Signal Processing 64, no. 20 (October 15, 2016): 5437–49. http://dx.doi.org/10.1109/tsp.2016.2597130.

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2

Feng, Yan, and Xiaodong Wang. "Adaptive Multiband Spectrum Sensing." IEEE Wireless Communications Letters 1, no. 2 (April 2012): 121–24. http://dx.doi.org/10.1109/wcl.2012.022012.110230.

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3

Sun, Fang, Dongyue Xiao, Wei He, and Ran Li. "Adaptive Image Compressive Sensing Using Texture Contrast." International Journal of Digital Multimedia Broadcasting 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/3902543.

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The traditional image Compressive Sensing (CS) conducts block-wise sampling with the same sampling rate. However, some blocking artifacts often occur due to the varying block sparsity, leading to a low rate-distortion performance. To suppress these blocking artifacts, we propose to adaptively sample each block according to texture features in this paper. With the maximum gradient in 8-connected region of each pixel, we measure the texture variation of each pixel and then compute the texture contrast of each block. According to the distribution of texture contrast, we adaptively set the sampling rate of each block and finally build an image reconstruction model using these block texture contrasts. Experimental results show that our adaptive sampling scheme improves the rate-distortion performance of image CS compared with the existing adaptive schemes and the reconstructed images by our method achieve better visual quality.

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4

Zhang, Xiaohua, Jiawei Chen, Hongyun Meng, and Xiaolin Tian. "Self-adaptive structured image sensing." Optical Engineering 51, no. 12 (December 4, 2012): 127001. http://dx.doi.org/10.1117/1.oe.51.12.127001.

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5

Malloy, Matthew L., and Robert D. Nowak. "Near-Optimal Adaptive Compressed Sensing." IEEE Transactions on Information Theory 60, no. 7 (July 2014): 4001–12. http://dx.doi.org/10.1109/tit.2014.2321552.

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6

Chen, Zichong, Juri Ranieri, Runwei Zhang, and Martin Vetterli. "DASS: Distributed Adaptive Sparse Sensing." IEEE Transactions on Wireless Communications 14, no. 5 (May 2015): 2571–83. http://dx.doi.org/10.1109/twc.2014.2388232.

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7

He, Lihan, Shihao Ji, Waymond R. Scott, and Lawrence Carin. "Adaptive Multimodality Sensing of Landmines." IEEE Transactions on Geoscience and Remote Sensing 45, no. 6 (June 2007): 1756–74. http://dx.doi.org/10.1109/tgrs.2007.894933.

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8

Li, Ran, Xiaomeng Duan, and Yongfeng Lv. "Adaptive compressive sensing of images using error between blocks." International Journal of Distributed Sensor Networks 14, no. 6 (June 2018): 155014771878175. http://dx.doi.org/10.1177/1550147718781751.

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Block compressive sensing of image results in blocking artifacts and blurs when reconstructing images. To solve this problem, we propose an adaptive block compressive sensing framework using error between blocks. First, we divide image into several non-overlapped blocks and compute the errors between each block and its adjacent blocks. Then, the error between blocks is used to measure the structure complexity of each block, and the measurement rate of each block is adaptively determined based on the distribution of these errors. Finally, we reconstruct each block using a linear model. Experimental results show that the proposed adaptive block compressive sensing system improves the qualities of reconstructed images from both subjective and objective points of view when compared with image block compressive sensing system.

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9

JIANG, Chunxiao, Hongyang CHEN, Peisen ZHAO, Nengqiang HE, Canfeng CHEN, and Yong REN. "Adaptive Channel Sensing for Asynchronous Cooperative Spectrum Sensing Scheme." IEICE Transactions on Communications E96.B, no. 3 (2013): 918–22. http://dx.doi.org/10.1587/transcom.e96.b.918.

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10

Balasuriya, Arjuna, Henrik Schmidt, and Michael B. Benjamin. "Nested Autonomy ‐ Adaptive and collaborative sensing with hybrid sensing networks." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3905. http://dx.doi.org/10.1121/1.2935891.

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11

Luo, Meng-ru, and Si-wang Zhou. "Adaptive Wavelet Packet Image Compressed Sensing." Journal of Electronics & Information Technology 35, no. 10 (February 27, 2014): 2371–77. http://dx.doi.org/10.3724/sp.j.1146.2013.00022.

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12

Skubic, Marjan, and Leon Devjak. "Robotized Assembly using Adaptive Visual Sensing." IFAC Proceedings Volumes 31, no. 7 (May 1998): 159–64. http://dx.doi.org/10.1016/s1474-6670(17)40274-6.

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13

Connolly, Martin, Ivana Dusparic, Georgios Iosifidis, and Melanie Bouroche. "Adaptive Reward Allocation for Participatory Sensing." Wireless Communications and Mobile Computing 2018 (August 7, 2018): 1–15. http://dx.doi.org/10.1155/2018/6353425.

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Participatory sensing is a paradigm through which mobile device users (or participants) collect and share data about their environments. The data captured by participants is typically submitted to an intermediary (the service provider) who will build a service based upon this data. For a participatory sensing system to attract the data submissions it requires, its users often need to be incentivized. However, as an environment is constantly changing (for example, an accident causing a buildup of traffic and elevated pollution levels), the value of a given data item to the service provider is likely to change significantly over time, and therefore an incentivization scheme must be able to adapt the rewards it offers in real-time to match the environmental conditions and current participation rates, thereby optimizing the consumption of the service provider’s budget. This paper presents adaptive reward allocation (ARA), which uses the Lyapunov Optimization method to provide adaptive reward allocation that optimizes the consumption of the service provider’s budget. ARA is evaluated using a simulated participatory sensing environment with experimental results showing that the rewards offered to participants are adjusted so as to ensure that the data captured matches the dynamic changes occurring in the sensing environment and takes the response rate into account while also seeking to optimize budget consumption.

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14

Braun, Gabor, Sebastian Pokutta, and Yao Xie. "Info-Greedy Sequential Adaptive Compressed Sensing." IEEE Journal of Selected Topics in Signal Processing 9, no. 4 (June 2015): 601–11. http://dx.doi.org/10.1109/jstsp.2015.2400428.

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15

Brotzge, Jerald A., Kelvin Droegemeier, and David J. McLaughlin. "Collaborative Adaptive Sensing of the Atmosphere." Transportation Research Record: Journal of the Transportation Research Board 1948, no. 1 (January 2006): 144–51. http://dx.doi.org/10.1177/0361198106194800116.

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16

Castro, Rui M., Gabor Lugosi, and Pierre-Andre Savalle. "Detection of Correlations With Adaptive Sensing." IEEE Transactions on Information Theory 60, no. 12 (December 2014): 7913–27. http://dx.doi.org/10.1109/tit.2014.2364713.

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17

Tajer, Ali, Rui M. Castro, and Xiaodong Wang. "Adaptive Sensing of Congested Spectrum Bands." IEEE Transactions on Information Theory 58, no. 9 (September 2012): 6110–25. http://dx.doi.org/10.1109/tit.2012.2201369.

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18

Schwartz, C., E. Ribak, and S. G. Lipson. "Bimorph adaptive mirrors and curvature sensing." Journal of the Optical Society of America A 11, no. 2 (February 1, 1994): 895. http://dx.doi.org/10.1364/josaa.11.000895.

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19

Averbuch, Amir, Shai Dekel, and Shay Deutsch. "Adaptive Compressed Image Sensing Using Dictionaries." SIAM Journal on Imaging Sciences 5, no. 1 (January 2012): 57–89. http://dx.doi.org/10.1137/110820579.

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20

Gao, Fei, Xiaohua Feng, Ruochong Zhang, Siyu Liu, and Yuanjin Zheng. "Adaptive Photoacoustic Sensing Using Matched Filter." IEEE Sensors Letters 1, no. 5 (October 2017): 1–3. http://dx.doi.org/10.1109/lsens.2017.2738012.

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21

Fontanella, J. C. "Wavefront sensing deconvolution and adaptive optics." Journal of Optics 16, no. 6 (November 1985): 257–68. http://dx.doi.org/10.1088/0150-536x/16/6/002.

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22

He, Shibo, Elhadi M. Shakshuki, Guoqiang Mao, and Jianping He. "Adaptive Sensing in Emerging Sensor Networks." International Journal of Distributed Sensor Networks 11, no. 12 (January 2015): 794058. http://dx.doi.org/10.1155/2015/794058.

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23

Bull, Adam D. "Spatially-adaptive sensing in nonparametric regression." Annals of Statistics 41, no. 1 (February 2013): 41–62. http://dx.doi.org/10.1214/12-aos1064.

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24

Li, Linhao, Zhiqiang Zhou, Bo Wang, Lingjuan Miao, Zhe An, and Xiaowu Xiao. "Domain Adaptive Ship Detection in Optical Remote Sensing Images." Remote Sensing 13, no. 16 (August 10, 2021): 3168. http://dx.doi.org/10.3390/rs13163168.

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With the successful application of the convolutional neural network (CNN), significant progress has been made by CNN-based ship detection methods. However, they often face considerable difficulties when applied to a new domain where the imaging condition changes significantly. Although training with the two domains together can solve this problem to some extent, the large domain shift will lead to sub-optimal feature representations, and thus weaken the generalization ability on both domains. In this paper, a domain adaptive ship detection method is proposed to better detect ships between different domains. Specifically, the proposed method minimizes the domain discrepancies via both image-level adaption and instance-level adaption. In image-level adaption, we use multiple receptive field integration and channel domain attention to enhance the feature’s resistance to scale and environmental changes, respectively. Moreover, a novel boundary regression module is proposed in instance-level adaption to correct the localization deviation of the ship proposals caused by the domain shift. Compared with conventional regression approaches, the proposed boundary regression module is able to make more accurate predictions via the effective extreme point features. The two adaption components are implemented by learning the corresponding domain classifiers respectively in an adversarial training way, thereby obtaining a robust model suitable for both of the two domains. Experiments on both supervised and unsupervised domain adaption scenarios are conducted to verify the effectiveness of the proposed method.

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25

S. Sureshkrishna, S. Varalakshmi, K. Senthil Kumar, A. K. Gnanasekar,. "An Effective Adaptive Threshold Based Compressive Spectrum Sensing in Cognitive Radio Networks." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 18, 2021): 1220–24. http://dx.doi.org/10.17762/itii.v9i1.260.

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Spectrum sensing is playing a vital role in Cognitive Radio networks. Wideband spectrum sensing increases the speed of sensing but which in turn requires higher sampling rate and also increases the complexity of hardware and also power consumption. Compression based sensing reduces the sampling rate by using Sub-Nyquist sampling but the compression and the reconstruction problem exists. In compression based spectrum sensing, noise uncertainty is one of the major performance degradation factor. To reduce this degradation, compressive measurements based sensing with adaptive threshold is proposed. In this technique compressed signal is sensed without any reconstruction of the signal. When the nodes are mobile in the low SNR region, the noise uncertainty degrades the performance of spectrum sensing. To conquer this problem, noise variance is estimated using parametric estimation technique and the threshold is varied adaptively. In the low SNR region, this proposed technique reduces the effect of noise and improves the spectrum sensing performance.

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26

Li, Lingling, Pujiang Liang, Jingjing Ma, Licheng Jiao, Xiaohui Guo, Fang Liu, and Chen Sun. "A Multiscale Self-Adaptive Attention Network for Remote Sensing Scene Classification." Remote Sensing 12, no. 14 (July 10, 2020): 2209. http://dx.doi.org/10.3390/rs12142209.

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High-resolution optical remote sensing image classification is an important research direction in the field of computer vision. It is difficult to extract the rich semantic information from remote sensing images with many objects. In this paper, a multiscale self-adaptive attention network (MSAA-Net) is proposed for the optical remote sensing image classification, which includes multiscale feature extraction, adaptive information fusion, and classification. In the first part, two parallel convolution blocks with different receptive fields are adopted to capture multiscale features. Then, the squeeze process is used to obtain global information and the excitation process is used to learn the weights in different channels, which can adaptively select useful information from multiscale features. Furthermore, the high-level features are classified by many residual blocks with an attention mechanism and a fully connected layer. Experiments were conducted using the UC Merced, NWPU, and the Google SIRI-WHU datasets. Compared to the state-of-the-art methods, the MSAA-Net has great effect and robustness, with average accuracies of 94.52%, 95.01%, and 95.21% on the three widely used remote sensing datasets.

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27

Wang, Xiao-Dong, Yun-Hui Li, Zhi Wang, Wen-Guang Liu, Dan Liu, and Jia-Ning Wang. "Self-adaptive block-based compressed sensing imaging for remote sensing applications." Journal of Applied Remote Sensing 14, no. 01 (March 3, 2020): 1. http://dx.doi.org/10.1117/1.jrs.14.016513.

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28

S. Arulananth, T., R. Satheesh, and P. Bhaskara Reddy. "Performance Evaluation of Frame based Adaptive Compressed Sensing and Non-Adaptive Compressed Sensing based on Average Frame Signal to Noise Ratio." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 202. http://dx.doi.org/10.14419/ijet.v7i4.10.20836.

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The primary inspiration of our work is to discovering upgrades in the current Compressed Sensing procedure that utilizations Non Adaptive Projection Matrix rule. Normal Frame Signal-to-Noise Ratio (AFSNR) is intended to evaluate the show of the Frame-Based Adaptive Compressed Sensing with the Non-Adaptive Compressed Sensing (CS). It is a developing sign securing strategy and straight gathers the signs in a compacted shape on the off chance that they are meager on some specific premise. Proposed approach utilizes Adaptive Projection Matrix in light of edge examination which gives fundamentally enhanced discourse recreation quality and decreases the noise levels.

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29

Xu Feng, Wang Xia, Zheng XiaoDong, and Wang Hao. "An Adaptive Compressed Sensing Method in Speech." International Journal of Advancements in Computing Technology 4, no. 8 (May 31, 2012): 36–43. http://dx.doi.org/10.4156/ijact.vol4.issue8.5.

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30

Anderson, S. J. "Adaptive remote sensing with HF skywave radar." IEE Proceedings F Radar and Signal Processing 139, no. 2 (1992): 182. http://dx.doi.org/10.1049/ip-f-2.1992.0022.

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31

Lu, Xiao-xia, and Si-kun Li. "Adaptive Remote Sensing Texture Compression on GPU." International Journal of Image, Graphics and Signal Processing 2, no. 1 (November 3, 2010): 46–52. http://dx.doi.org/10.5815/ijigsp.2010.01.06.

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32

Mohammed, Gamal Abdel Fadeel. "ADAPTIVE SPECTRUM SENSING IN COGNITIVE RADIO NETWORKS." JES. Journal of Engineering Sciences 40, no. 3 (May 1, 2012): 867–75. http://dx.doi.org/10.21608/jesaun.2012.114416.

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33

Sreenivasan, Rajagopal, Sasirekha GVK, and Jyotsna Bapat. "Adaptive Cooperative Spectrum Sensing Using Group Intelligence." International journal of Computer Networks & Communications 3, no. 3 (May 31, 2011): 31–46. http://dx.doi.org/10.5121/ijcnc.2011.3303.

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34

Abdul, Rishbiya, Riya Kuriakose, Sibila M., Lakshmi C., and Reshmi S. "Adaptive Spectrum Sensing in Cognitive Radio Networks." International Journal of Computer Applications 179, no. 45 (May 18, 2018): 10–16. http://dx.doi.org/10.5120/ijca2018917117.

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35

Fang, Jun, Yanning Shen, Linxiao Yang, and Hongbin Li. "Adaptive one-bit quantization for compressed sensing." Signal Processing 125 (August 2016): 145–55. http://dx.doi.org/10.1016/j.sigpro.2016.01.020.

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36

Warnell, Garrett, Sourabh Bhattacharya, Rama Chellappa, and Tamer Basar. "Adaptive-Rate Compressive Sensing Using Side Information." IEEE Transactions on Image Processing 24, no. 11 (November 2015): 3846–57. http://dx.doi.org/10.1109/tip.2015.2456425.

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37

Hou, Jing. "Time-sharing wave-front-sensing adaptive optics." Journal of the Optical Society of America A 21, no. 2 (February 1, 2004): 223. http://dx.doi.org/10.1364/josaa.21.000223.

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38

Hursky, Paul. "Comparison of adaptive and compressed sensing beamformers." Journal of the Acoustical Society of America 144, no. 3 (September 2018): 1943. http://dx.doi.org/10.1121/1.5068504.

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39

Mangia, Mauro, Fabio Pareschi, Riccardo Rovatti, and Gianluca Setti. "Adaptive Matrix Design for Boosting Compressed Sensing." IEEE Transactions on Circuits and Systems I: Regular Papers 65, no. 3 (March 2018): 1016–27. http://dx.doi.org/10.1109/tcsi.2017.2766247.

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40

Wang, Jun, Xin Ren, Shaowen Zhang, Daiming Zhang, Husheng Li, and Shaoqian Li. "Adaptive Bistable Stochastic Resonance Aided Spectrum Sensing." IEEE Transactions on Wireless Communications 13, no. 7 (July 2014): 4014–24. http://dx.doi.org/10.1109/twc.2014.2317779.

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41

Arias-Castro, Ery, Emmanuel J. Candes, and Mark A. Davenport. "On the Fundamental Limits of Adaptive Sensing." IEEE Transactions on Information Theory 59, no. 1 (January 2013): 472–81. http://dx.doi.org/10.1109/tit.2012.2215837.

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42

Sobron, Iker, Paulo S. R. Diniz, Wallace A. Martins, and Manuel Velez. "Energy Detection Technique for Adaptive Spectrum Sensing." IEEE Transactions on Communications 63, no. 3 (March 2015): 617–27. http://dx.doi.org/10.1109/tcomm.2015.2394436.

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43

Newlin. "EDGE SENSING DEMOSAICING USING ADAPTIVE WEIGHTED INTERPOLATION." American Journal of Applied Sciences 10, no. 4 (April 1, 2013): 418–25. http://dx.doi.org/10.3844/ajassp.2013.418.425.

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44

Zatorre, Guillermo, Nicolás Medrano, María Teresa Sanz, Belén Calvo, Pedro A. Martinez, and Santiago Celma. "Designing Adaptive Conditioning Electronics for Smart Sensing." IEEE Sensors Journal 10, no. 4 (April 2010): 831–38. http://dx.doi.org/10.1109/jsen.2009.2033463.

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45

Hadizadeh, Hadi, and Ivan V. Bajic. "Soft Video Multicasting Using Adaptive Compressed Sensing." IEEE Transactions on Multimedia 23 (2021): 12–25. http://dx.doi.org/10.1109/tmm.2020.2975420.

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46

Safavi, Seyed Hamid, and Farah Torkamani‐Azar. "Sparsity‐aware adaptive block‐based compressive sensing." IET Signal Processing 11, no. 1 (February 2017): 36–42. http://dx.doi.org/10.1049/iet-spr.2016.0176.

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47

Jeon, Wha Sook, and Dong Geun Jeong. "Adaptive sensing scheduling for cognitive radio systems." Computer Networks 56, no. 14 (September 2012): 3318–32. http://dx.doi.org/10.1016/j.comnet.2012.06.002.

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48

Xu, Ping, Bingqiang Chen, Lingyun Xue, Jingcheng Zhang, and Lei Zhu. "A Prediction-Based Spatial-Spectral Adaptive Hyperspectral Compressive Sensing Algorithm." Sensors 18, no. 10 (September 30, 2018): 3289. http://dx.doi.org/10.3390/s18103289.

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In order to improve the performance of storage and transmission of massive hyperspectral data, a prediction-based spatial-spectral adaptive hyperspectral compressive sensing (PSSAHCS) algorithm is proposed. Firstly, the spatial block size of hyperspectral images is adaptively obtained according to the spatial self-correlation coefficient. Secondly, a k-means clustering algorithm is used to group the hyperspectral images. Thirdly, we use a local means and local standard deviations (LMLSD) algorithm to find the optimal image in the group as the key band, and the non-key bands in the group can be smoothed by linear prediction. Fourthly, the random Gaussian measurement matrix is used as the sampling matrix, and the discrete cosine transform (DCT) matrix serves as the sparse basis. Finally, the stagewise orthogonal matching pursuit (StOMP) is used to reconstruct the hyperspectral images. The experimental results show that the proposed PSSAHCS algorithm can achieve better evaluation results—the subjective evaluation, the peak signal-to-noise ratio, and the spatial autocorrelation coefficient in the spatial domain, and spectral curve comparison and correlation between spectra-reconstructed performance in the spectral domain—than those of single spectral compression sensing (SSCS), block hyperspectral compressive sensing (BHCS), and adaptive grouping distributed compressive sensing (AGDCS). PSSAHCS can not only compress and reconstruct hyperspectral images effectively, but also has strong denoise performance.

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49

Xuan Wang, Wenxuan Shi, and Dexiang Deng. "Block Compressed Sensing of Multispectral Remote Sensing Image by Adaptive Filtering Prediction." International Journal of Advancements in Computing Technology 5, no. 6 (March 31, 2013): 22–30. http://dx.doi.org/10.4156/ijact.vol5.issue6.4.

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50

Sugitani, Yuji. "Sensing technololgy for in process control. (2). Sensing technology in adaptive control." Journal of the Japan Welding Society 60, no. 2 (1991): 133–38. http://dx.doi.org/10.2207/qjjws1943.60.133.

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