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Journal articles on the topic 'Deep Fingerprinting'

Author: Grafiati

Published: 29 June 2021

Last updated: 18 February 2022

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1

Guo, Maohua, Jinlong Fei, and Yitong Meng. "Deep Nearest Neighbor Website Fingerprinting Attack Technology." Security and Communication Networks 2021 (July 14, 2021): 1–14. http://dx.doi.org/10.1155/2021/5399816.

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By website fingerprinting (WF) technologies, local listeners are enabled to track the specific website visited by users through an investigation of the encrypted traffic between the users and the Tor network entry node. The current triplet fingerprinting (TF) technique proved the possibility of small sample WF attacks. Previous research methods only concentrate on extracting the overall features of website traffic while ignoring the importance of website local fingerprinting characteristics for small sample WF attacks. Thus, in the present paper, a deep nearest neighbor website fingerprinting (DNNF) attack technology is proposed. The deep local fingerprinting features of websites are extracted via the convolutional neural network (CNN), and then the k-nearest neighbor (k-NN) classifier is utilized to classify the prediction. When the website provides only 20 samples, the accuracy can reach 96.2%. We also found that the DNNF method acts well compared to the traditional methods in coping with transfer learning and concept drift problems. In comparison to the TF method, the classification accuracy of the proposed method is improved by 2%–5% and it is only dropped by 3% when classifying the data collected from the same website after two months. These experiments revealed that the DNNF is a more flexible, efficient, and robust website fingerprinting attack technology, and the local fingerprinting features of websites are particularly important for small sample WF attacks.

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Song, Pingfan, Yonina C. Eldar, Gal Mazor, and Miguel R. D. Rodrigues. "HYDRA: Hybrid deep magnetic resonance fingerprinting." Medical Physics 46, no. 11 (September 10, 2019): 4951–69. http://dx.doi.org/10.1002/mp.13727.

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3

Cohen, Ouri, Bo Zhu, and Matthew S. Rosen. "MR fingerprinting Deep RecOnstruction NEtwork (DRONE)." Magnetic Resonance in Medicine 80, no. 3 (April 6, 2018): 885–94. http://dx.doi.org/10.1002/mrm.27198.

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4

Oh, Se Eun, Saikrishna Sunkam, and Nicholas Hopper. "p1-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning." Proceedings on Privacy Enhancing Technologies 2019, no. 3 (July 1, 2019): 191–209. http://dx.doi.org/10.2478/popets-2019-0043.

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Abstract Recent advances in Deep Neural Network (DNN) architectures have received a great deal of attention due to their ability to outperform state-of-the-art machine learning techniques across a wide range of application, as well as automating the feature engineering process. In this paper, we broadly study the applicability of deep learning to website fingerprinting. First, we show that unsupervised DNNs can generate lowdimensional informative features that improve the performance of state-of-the-art website fingerprinting attacks. Second, when used as classifiers, we show that they can exceed performance of existing attacks across a range of application scenarios, including fingerprinting Tor website traces, fingerprinting search engine queries over Tor, defeating fingerprinting defenses, and fingerprinting TLS-encrypted websites. Finally, we investigate which site-level features of a website influence its fingerprintability by DNNs.

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Oh, Se Eun, Nate Mathews, Mohammad Saidur Rahman, Matthew Wright, and Nicholas Hopper. "GANDaLF: GAN for Data-Limited Fingerprinting." Proceedings on Privacy Enhancing Technologies 2021, no. 2 (January 29, 2021): 305–22. http://dx.doi.org/10.2478/popets-2021-0029.

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Abstract We introduce Generative Adversarial Networks for Data-Limited Fingerprinting (GANDaLF), a new deep-learning-based technique to perform Website Fingerprinting (WF) on Tor traffic. In contrast to most earlier work on deep-learning for WF, GANDaLF is intended to work with few training samples, and achieves this goal through the use of a Generative Adversarial Network to generate a large set of “fake” data that helps to train a deep neural network in distinguishing between classes of actual training data. We evaluate GANDaLF in low-data scenarios including as few as 10 training instances per site, and in multiple settings, including fingerprinting of website index pages and fingerprinting of non-index pages within a site. GANDaLF achieves closed-world accuracy of 87% with just 20 instances per site (and 100 sites) in standard WF settings. In particular, GANDaLF can outperform Var-CNN and Triplet Fingerprinting (TF) across all settings in subpage fingerprinting. For example, GANDaLF outperforms TF by a 29% margin and Var-CNN by 38% for training sets using 20 instances per site.

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Zhang, Qiang, Pan Su, Zhensen Chen, Ying Liao, Shuo Chen, Rui Guo, Haikun Qi, et al. "Deep learning–based MR fingerprinting ASL ReconStruction (DeepMARS)." Magnetic Resonance in Medicine 84, no. 2 (February 4, 2020): 1024–34. http://dx.doi.org/10.1002/mrm.28166.

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7

Zhao, Jingjing, Qingyue Hu, Gaoyang Liu, Xiaoqiang Ma, Fei Chen, and Mohammad Mehedi Hassan. "AFA: Adversarial fingerprinting authentication for deep neural networks." Computer Communications 150 (January 2020): 488–97. http://dx.doi.org/10.1016/j.comcom.2019.12.016.

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8

Lee, Woongsup, Seon Yeob Baek, and Seong Hwan Kim. "Deep-Learning-Aided RF Fingerprinting for NFC Security." IEEE Communications Magazine 59, no. 5 (May 2021): 96–101. http://dx.doi.org/10.1109/mcom.001.2000912.

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9

Li, Tao, Hai Wang, Yuan Shao, and Qiang Niu. "Channel state information–based multi-level fingerprinting for indoor localization with deep learning." International Journal of Distributed Sensor Networks 14, no. 10 (October 2018): 155014771880671. http://dx.doi.org/10.1177/1550147718806719.

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With the rapid growth of indoor positioning requirements without equipment and the convenience of channel state information acquisition, the research on indoor fingerprint positioning based on channel state information is increasingly valued. In this article, a multi-level fingerprinting approach is proposed, which is composed of two-level methods: the first layer is achieved by deep learning and the second layer is implemented by the optimal subcarriers filtering method. This method using channel state information is termed multi-level fingerprinting with deep learning. Deep neural networks are applied in the deep learning of the first layer of multi-level fingerprinting with deep learning, which includes two phases: an offline training phase and an online localization phase. In the offline training phase, deep neural networks are used to train the optimal weights. In the online localization phase, the top five closest positions to the location position are obtained through forward propagation. The second layer optimizes the results of the first layer through the optimal subcarriers filtering method. Under the accuracy of 0.6 m, the positioning accuracy of two common environments has reached, respectively, 96% and 93.9%. The evaluation results show that the positioning accuracy of this method is better than the method based on received signal strength, and it is better than the support vector machine method, which is also slightly improved compared with the deep learning method.

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Fang, Zhenghan, Yong Chen, Sheng‐Che Hung, Xiaoxia Zhang, Weili Lin, and Dinggang Shen. "Submillimeter MR fingerprinting using deep learning–based tissue quantification." Magnetic Resonance in Medicine 84, no. 2 (December 19, 2019): 579–91. http://dx.doi.org/10.1002/mrm.28136.

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11

Jian, Tong, Bruno Costa Rendon, Emmanuel Ojuba, Nasim Soltani, Zifeng Wang, Kunal Sankhe, Andrey Gritsenko, Jennifer Dy, Kaushik Chowdhury, and Stratis Ioannidis. "Deep Learning for RF Fingerprinting: A Massive Experimental Study." IEEE Internet of Things Magazine 3, no. 1 (March 2020): 50–57. http://dx.doi.org/10.1109/iotm.0001.1900065.

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12

Bhat, Sanjit, David Lu, Albert Kwon, and Srinivas Devadas. "Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning." Proceedings on Privacy Enhancing Technologies 2019, no. 4 (October 1, 2019): 292–310. http://dx.doi.org/10.2478/popets-2019-0070.

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Abstract In recent years, there have been several works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user visits. While the current state-of-the-art attack, which uses deep learning, outperforms prior art with medium to large amounts of data, it attains marginal to no accuracy improvements when both use small amounts of training data. In this work, we propose Var-CNN, a website fingerprinting attack that leverages deep learning techniques along with novel insights specific to packet sequence classification. In open-world settings with large amounts of data, Var-CNN attains over 1% higher true positive rate (TPR) than state-of-the-art attacks while achieving 4× lower false positive rate (FPR). Var-CNN’s improvements are especially notable in low-data scenarios, where it reduces the FPR of prior art by 3.12% while increasing the TPR by 13%. Overall, insights used to develop Var-CNN can be applied to future deep learning based attacks, and substantially reduce the amount of training data needed to perform a successful website fingerprinting attack. This shortens the time needed for data collection and lowers the likelihood of having data staleness issues.

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13

Chen, Mantun, Yongjun Wang, Zhiquan Qin, and Xiatian Zhu. "Few-Shot Website Fingerprinting Attack with Data Augmentation." Security and Communication Networks 2021 (September 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/2840289.

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This work introduces a novel data augmentation method for few-shot website fingerprinting (WF) attack where only a handful of training samples per website are available for deep learning model optimization. Moving beyond earlier WF methods relying on manually-engineered feature representations, more advanced deep learning alternatives demonstrate that learning feature representations automatically from training data is superior. Nonetheless, this advantage is subject to an unrealistic assumption that there exist many training samples per website, which otherwise will disappear. To address this, we introduce a model-agnostic, efficient, and harmonious data augmentation (HDA) method that can improve deep WF attacking methods significantly. HDA involves both intrasample and intersample data transformations that can be used in a harmonious manner to expand a tiny training dataset to an arbitrarily large collection, therefore effectively and explicitly addressing the intrinsic data scarcity problem. We conducted expensive experiments to validate our HDA for boosting state-of-the-art deep learning WF attack models in both closed-world and open-world attacking scenarios, at absence and presence of strong defense. For instance, in the more challenging and realistic evaluation scenario with WTF-PAD-based defense, our HDA method surpasses the previous state-of-the-art results by nearly 3% in classification accuracy in the 20-shot learning case. An earlier version of this work Chen et al. (2021) has been presented as preprint in ArXiv (https://arxiv.org/abs/2101.10063).

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14

Luo, Junhai, and Huanbin Gao. "Deep Belief Networks for Fingerprinting Indoor Localization Using Ultrawideband Technology." International Journal of Distributed Sensor Networks 12, no. 1 (January 2016): 5840916. http://dx.doi.org/10.1155/2016/5840916.

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15

Merchant, Kevin, Shauna Revay, George Stantchev, and Bryan Nousain. "Deep Learning for RF Device Fingerprinting in Cognitive Communication Networks." IEEE Journal of Selected Topics in Signal Processing 12, no. 1 (February 2018): 160–67. http://dx.doi.org/10.1109/jstsp.2018.2796446.

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16

Chen, Zhenghua, Han Zou, JianFei Yang, Hao Jiang, and Lihua Xie. "WiFi Fingerprinting Indoor Localization Using Local Feature-Based Deep LSTM." IEEE Systems Journal 14, no. 2 (June 2020): 3001–10. http://dx.doi.org/10.1109/jsyst.2019.2918678.

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17

Zeng, Xiangsheng, Limin Xiao, Ming Zhao, Xibin Xu, and Yunzhou Li. "Transformable Fingerprinting with Deep Metric Learning Approach for Indoor Localization." Journal of Physics: Conference Series 1575 (June 2020): 012001. http://dx.doi.org/10.1088/1742-6596/1575/1/012001.

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18

Sun, Haotai, Xiaodong Zhu, Yuanning Liu, and Wentao Liu. "WiFi Based Fingerprinting Positioning Based on Seq2seq Model." Sensors 20, no. 13 (July 5, 2020): 3767. http://dx.doi.org/10.3390/s20133767.

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Indoor positioning technologies are of great use in GPS-denied areas. They can be partitioned into two types of systems—infrastructure-free based and infrastructure-dependent based. WiFi based indoor positioning system is somewhere between the infrastructure-free and infrastructure-dependent systems. The reason is that in WiFi based systems, Access Points (APs) as pre-installed infrastructures are necessary. However, the APs do not need to be specially installed, because WiFi APs are already widely deployed in many indoor areas, for example, offices, malls and airports. This feature makes WiFi based indoor positioning suitable for many practical applications. In this paper, a seq2seq model based, deep learning method is proposed for WiFi based fingerprinting. The model can learn from different length of training sequences, and thus can exploit the context information for positioning. The context information denotes the information contained in the sequence, which can help finding the correspondences between RSS fingerprints and the coordinate positions. A simple example piece of context information is human walking routine (such as no sharp turns). The proposed method shows an improvement with an open source dataset, when compared against deep learning based counterpart methods.

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19

Chen, Yong, Zhenghan Fang, Sheng-Che Hung, Wei-Tang Chang, Dinggang Shen, and Weili Lin. "High-resolution 3D MR Fingerprinting using parallel imaging and deep learning." NeuroImage 206 (February 2020): 116329. http://dx.doi.org/10.1016/j.neuroimage.2019.116329.

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20

Wu, Qingyang, Carlos Feres, Daniel Kuzmenko, Ding Zhi, Zhou Yu, Xin Liu, and Xiaoguang ‘Leo’ Liu. "Deep learning based RF fingerprinting for device identification and wireless security." Electronics Letters 54, no. 24 (November 2018): 1405–7. http://dx.doi.org/10.1049/el.2018.6404.

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21

Wang, Xuyu, Lingjun Gao, and Shiwen Mao. "CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach." IEEE Internet of Things Journal 3, no. 6 (December 2016): 1113–23. http://dx.doi.org/10.1109/jiot.2016.2558659.

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22

Soro, Bedionita, and Chaewoo Lee. "A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization." Sensors 19, no. 8 (April 14, 2019): 1790. http://dx.doi.org/10.3390/s19081790.

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The performance of an Artificial Neural Network (ANN)-based algorithm is subject to the way the feature data is extracted. This is a common issue when applying the ANN to indoor fingerprinting-based localization where the signal is unstable. To date, there is not adequate feature extraction method that can significantly mitigate the influence of the receiver signal strength indicator (RSSI) variation that degrades the performance of the ANN-based indoor fingerprinting algorithm. In this work, a wavelet scattering transform is used to extract reliable features that are stable to small deformation and rotation invariant. The extracted features are used by a deep neural network (DNN) model to predict the location. The zeroth and the first layer of decomposition coefficients were used as features data by concatenating different scattering path coefficients. The proposed algorithm has been validated on real measurements and has achieved good performance. The experimentation results demonstrate that the proposed feature extraction method is stable to the RSSI variation.

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23

Lori, Nicolás F., Ivo Ramalhosa, Paulo Marques, and Victor Alves. "Deep Learning Based Pipeline for Fingerprinting Using Brain Functional MRI Connectivity Data." Procedia Computer Science 141 (2018): 539–44. http://dx.doi.org/10.1016/j.procs.2018.10.129.

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24

Cao, Peng, Di Cui, Vince Vardhanabhuti, and Edward S. Hui. "Development of fast deep learning quantification for magnetic resonance fingerprinting in vivo." Magnetic Resonance Imaging 70 (July 2020): 81–90. http://dx.doi.org/10.1016/j.mri.2020.03.009.

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25

Rahman, Mohammad Saidur, Mohsen Imani, Nate Mathews, and Matthew Wright. "Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks With Adversarial Traces." IEEE Transactions on Information Forensics and Security 16 (2021): 1594–609. http://dx.doi.org/10.1109/tifs.2020.3039691.

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26

Koike-Akino, Toshiaki, Pu Wang, Milutin Pajovic, Haijian Sun, and Philip V. Orlik. "Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach." IEEE Access 8 (2020): 84879–92. http://dx.doi.org/10.1109/access.2020.2991129.

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27

Li, Bo, and Ediz Cetin. "Design and Evaluation of a Graphical Deep Learning Approach for RF Fingerprinting." IEEE Sensors Journal 21, no. 17 (September 1, 2021): 19462–68. http://dx.doi.org/10.1109/jsen.2021.3088137.

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28

Pulls, Tobias, and Rasmus Dahlberg. "Website Fingerprinting with Website Oracles." Proceedings on Privacy Enhancing Technologies 2020, no. 1 (January 1, 2020): 235–55. http://dx.doi.org/10.2478/popets-2020-0013.

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AbstractWebsite Fingerprinting (WF) attacks are a subset of traffic analysis attacks where a local passive attacker attempts to infer which websites a target victim is visiting over an encrypted tunnel, such as the anonymity network Tor. We introduce the security notion of a Website Oracle (WO) that gives a WF attacker the capability to determine whether a particular monitored website was among the websites visited by Tor clients at the time of a victim’s trace. Our simulations show that combining a WO with a WF attack—which we refer to as a WF+WO attack—significantly reduces false positives for about half of all website visits and for the vast majority of websites visited over Tor. The measured false positive rate is on the order one false positive per million classified website trace for websites around Alexa rank 10,000. Less popular monitored websites show orders of magnitude lower false positive rates.We argue that WOs are inherent to the setting of anonymity networks and should be an assumed capability of attackers when assessing WF attacks and defenses. Sources of WOs are abundant and available to a wide range of realistic attackers, e.g., due to the use of DNS, OCSP, and real-time bidding for online advertisement on the Internet, as well as the abundance of middleboxes and access logs. Access to a WO indicates that the evaluation of WF defenses in the open world should focus on the highest possible recall an attacker can achieve. Our simulations show that augmenting the Deep Fingerprinting WF attack by Sirinam et al. [60] with access to a WO significantly improves the attack against five state-of-the-art WF defenses, rendering some of them largely ineffective in this new WF+WO setting.

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Dai, Peng, Yuan Yang, Manyi Wang, and Ruqiang Yan. "Combination of DNN and Improved KNN for Indoor Location Fingerprinting." Wireless Communications and Mobile Computing 2019 (March 6, 2019): 1–9. http://dx.doi.org/10.1155/2019/4283857.

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Fingerprinting based on Wi-Fi Received Signal Strength Indicator (RSSI) has been widely studied in recent years for indoor localization. While current algorithms related to RSSI Fingerprinting show a much lower accuracy than multilateration based on time of arrival or the angle of arrival techniques, they highly depend on the number of access points (APs) and fingerprinting training phase. In this paper, we present an integrated method by combining the deep neural network (DNN) with improved K-Nearest Neighbor (KNN) algorithm for indoor location fingerprinting. The improved KNN is realized by boosting the weights on K-nearest neighbors according to the number of matching access points. This will overcome the limitation of the original KNN algorithm on ignoring the influence of the neighboring points, which directly affect localization accuracy. The DNN algorithm is first used to classify the Wi-Fi RSSI Fingerprinting dataset. Then these possible locations in a certain class are also classified by the improved KNN algorithm to determine the final position. The proposed method is validated inside a room within about 13⁎9 m2. To examine its performance, the presented method has been compared with some classical algorithms, i.e., the random forest (RF) based algorithm, the KNN based algorithm, the support vector machine (SVM) based algorithm, the decision tree (DT) based algorithm, etc. Our real-world experiment results indicate that the proposed method is less dependent on the dense of access points and indoor radio propagation interference. Furthermore, our method can provide some preliminary guidelines for the design of indoor Wi-Fi test bed.

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Fernandez-Cortes, A., R. Perez-Lopez, S. Cuezva, J. M. Calaforra, J. C. Cañaveras, and S. Sanchez-Moral. "Geochemical Fingerprinting of Rising Deep Endogenous Gases in an Active Hypogenic Karst System." Geofluids 2018 (December 6, 2018): 1–19. http://dx.doi.org/10.1155/2018/4934520.

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The hydrothermal caves linked to active faulting can potentially harbour subterranean atmospheres with a distinctive gaseous composition with deep endogenous gases, such as carbon dioxide (CO2) and methane (CH4). In this study, we provide insight into the sourcing, mixing, and biogeochemical processes involved in the dynamic of deep endogenous gas formation in an exceptionally dynamic hypogenic karst system (Vapour Cave, southern Spain) associated with active faulting. The cave environment is characterized by a prevailing combination of rising warm air with large CO2 outgassing (>1%) and highly diluted CH4 with an endogenous origin. The δ13CCO2 data, which ranges from −4.5 to −7.5‰, point to a mantle-rooted CO2 that is likely generated by the thermal decarbonation of underlying marine carbonates, combined with degassing from CO2-rich groundwater. A pooled analysis of δ13CCO2 data from exterior, cave, and soil indicates that the upwelling of geogenic CO2 has a clear influence on soil air, which further suggests a potential for the release of CO2 along fractured carbonates. CH4 molar fractions and their δD and δ13C values (ranging from −77 to −48‰ and from −52 to −30‰, respectively) suggest that the methane reaching Vapour Cave is the remnant of a larger source of CH4, which was likely generated by microbial reduction of carbonates. This CH4 has been affected by a postgenetic microbial oxidation, such that the gas samples have changed in both molecular and isotopic composition after formation and during migration through the cave environment. Yet, in the deepest cave locations (i.e., 30 m below the surface), measured concentration values of deep endogenous CH4 are higher than in atmospheric with lighter δ13C values with respect to those found in the local atmosphere, which indicates that Vapour Cave may occasionally act as a net source of CH4 to the open atmosphere.

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Kaiser, Patricia, Maya Bode, Astrid Cornils, Wilhelm Hagen, Pedro Martínez Arbizu, Holger Auel, and Silke Laakmann. "High-resolution community analysis of deep-sea copepods using MALDI-TOF protein fingerprinting." Deep Sea Research Part I: Oceanographic Research Papers 138 (August 2018): 122–30. http://dx.doi.org/10.1016/j.dsr.2018.06.005.

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32

Buin, Andrei, Hung Yi Chiang, S. Andrew Gadsden, and Faraz A. Alderson. "Permutationally Invariant Deep Learning Approach to Molecular Fingerprinting with Application to Compound Mixtures." Journal of Chemical Information and Modeling 61, no. 2 (February 4, 2021): 631–40. http://dx.doi.org/10.1021/acs.jcim.0c01097.

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Ali, Irsan Taufik, Abdul Muis, and Riri Fitri Sari. "A DEEP LEARNING MODEL IMPLEMENTATION BASED ON RSSI FINGERPRINTING FOR LORA-BASED INDOOR LOCALIZATION." EUREKA: Physics and Engineering, no. 1 (January 30, 2021): 40–59. http://dx.doi.org/10.21303/2461-4262.2021.001620.

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LoRa technology has received a lot of attention in the last few years. Numerous success stories about using LoRa technology for the Internet of Things in various implementations. Several studies have found that the use of LoRa technology has the opportunity to be implemented in indoor-based applications. LoRa technology is found more stable and is more resilient to environmental changes. Environmental change of the indoor is a major problem to maintain accuracy in position prediction, especially in the use of Received Signal Strength (RSS) fingerprints as a reference database. The variety of approaches to solving accuracy problems continues to improve as the need for indoor localization applications increases. Deep learning approaches as a solution for the use of fingerprints in indoor localization have been carried out in several studies with various novelties offered. Let’s introduce a combination of the use of LoRa technology's excellence with a deep learning method that uses all variations of measurement results of RSS values at each position as a natural feature of the indoor condition as a fingerprint. All of these features are used for training in-deep learning methods. It is DeepFi-LoRaIn which illustrates a new technique for using the fingerprint data of the LoRa device's RSS device on indoor localization using deep learning methods. This method is used to find out how accurate the model produced by the training process is to predict the position in a dynamic environment. The scenario used to evaluate the model is by giving interference to the RSS value received at each anchor node. The model produced through training was found to have good accuracy in predicting the position even in conditions of interference with several anchor nodes. Based on the test results, DeepFi-LoRaIn Technique can be a solution to cope with changing environmental conditions in indoor localization

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Kim, Beom-Hun, and Jae-Young Pyun. "ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks." Sensors 20, no. 11 (May 29, 2020): 3069. http://dx.doi.org/10.3390/s20113069.

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Securing personal authentication is an important study in the field of security. Particularly, fingerprinting and face recognition have been used for personal authentication. However, these systems suffer from certain issues, such as fingerprinting forgery, or environmental obstacles. To address forgery or spoofing identification problems, various approaches have been considered, including electrocardiogram (ECG). For ECG identification, linear discriminant analysis (LDA), support vector machine (SVM), principal component analysis (PCA), deep recurrent neural network (DRNN), and recurrent neural network (RNN) have been conventionally used. Certain studies have shown that the RNN model yields the best performance in ECG identification as compared with the other models. However, these methods require a lengthy input signal for high accuracy. Thus, these methods may not be applied to a real-time system. In this study, we propose using bidirectional long short-term memory (LSTM)-based deep recurrent neural networks (DRNN) through late-fusion to develop a real-time system for ECG-based biometrics identification and classification. We suggest a preprocessing procedure for the quick identification and noise reduction, such as a derivative filter, moving average filter, and normalization. We experimentally evaluated the proposed method using two public datasets: MIT-BIH Normal Sinus Rhythm (NSRDB) and MIT-BIH Arrhythmia (MITDB). The proposed LSTM-based DRNN model shows that in NSRDB, the overall precision was 100%, recall was 100%, accuracy was 100%, and F1-score was 1. For MITDB, the overall precision was 99.8%, recall was 99.8%, accuracy was 99.8%, and F1-score was 0.99. Our experiments demonstrate that the proposed model achieves an overall higher classification accuracy and efficiency compared with the conventional LSTM approach.

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Laska, Marius, and Jörg Blankenbach. "DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization." Sensors 21, no. 6 (March 12, 2021): 2000. http://dx.doi.org/10.3390/s21062000.

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Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

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Hamilton, Jesse I., Danielle Currey, Sanjay Rajagopalan, and Nicole Seiberlich. "Deep learning reconstruction for cardiac magnetic resonance fingerprinting T 1 and T 2 mapping." Magnetic Resonance in Medicine 85, no. 4 (October 26, 2020): 2127–35. http://dx.doi.org/10.1002/mrm.28568.

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Trinh, Hoang Duy, Engin Zeydan, Lorenza Giupponi, and Paolo Dini. "Detecting Mobile Traffic Anomalies Through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach." IEEE Access 7 (2019): 152187–201. http://dx.doi.org/10.1109/access.2019.2947742.

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Sankhe, Kunal, Mauro Belgiovine, Fan Zhou, Luca Angioloni, Frank Restuccia, Salvatore D'Oro, Tommaso Melodia, Stratis Ioannidis, and Kaushik Chowdhury. "No Radio Left Behind: Radio Fingerprinting Through Deep Learning of Physical-Layer Hardware Impairments." IEEE Transactions on Cognitive Communications and Networking 6, no. 1 (March 2020): 165–78. http://dx.doi.org/10.1109/tccn.2019.2949308.

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Li, Qiang, Yue Zhao, Xu Zhang, Yuquan Wei, Linlin Qiu, Zimin Wei, and Fuheng Li. "Spatial heterogeneity in a deep artificial lake plankton community revealed by PCR-DGGE fingerprinting." Chinese Journal of Oceanology and Limnology 33, no. 3 (March 4, 2015): 624–35. http://dx.doi.org/10.1007/s00343-015-4184-9.

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Khajehim, Mahdi, Thomas Christen, Fred Tam, and Simon J. Graham. "Streamlined magnetic resonance fingerprinting: Fast whole-brain coverage with deep-learning based parameter estimation." NeuroImage 238 (September 2021): 118237. http://dx.doi.org/10.1016/j.neuroimage.2021.118237.

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Yang, Y., C. Toth, and D. Brzezinska. "A 3D MAP AIDED DEEP LEARNING BASED INDOOR LOCALIZATION SYSTEM FOR SMART DEVICES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 391–97. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-391-2020.

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Abstract. Indoor positioning technologies represent a fast developing field of research due to the rapidly increasing need for indoor location-based services (ILBS); in particular, for applications using personal smart devices. Recently, progress in indoor mapping, including 3D modeling and semantic labeling started to offer benefits to indoor positioning algorithms; mainly, in terms of accuracy. This work presents a method for efficient and robust indoor localization, allowing to support applications in large-scale environments. To achieve high performance, the proposed concept integrates two main indoor localization techniques: Wi-Fi fingerprinting and deep learning-based visual localization using 3D map. The robustness and efficiency of technique is demonstrated with real-world experiences.

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Tiku, Saideep, Prathmesh Kale, and Sudeep Pasricha. "QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices." ACM Transactions on Cyber-Physical Systems 5, no. 4 (October 31, 2021): 1–30. http://dx.doi.org/10.1145/3461342.

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Indoor localization services are a crucial aspect for the realization of smart cyber-physical systems within cities of the future. Such services are poised to reinvent the process of navigation and tracking of people and assets in a variety of indoor and subterranean environments. The growing ownership of computationally capable smartphones has laid the foundations of portable fingerprinting-based indoor localization through deep learning. However, as the demand for accurate localization increases, the computational complexity of the associated deep learning models increases as well. We present an approach for reducing the computational requirements of a deep learning-based indoor localization framework while maintaining localization accuracy targets. Our proposed methodology is deployed and validated across multiple smartphones and is shown to deliver up to 42% reduction in prediction latency and 45% reduction in prediction energy as compared to the best-known baseline deep learning-based indoor localization model.

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Ma, Haishu, Zongzheng Ma, Lixia Li, and Ya Gao. "Deep learning approach for UHF RFID-based indoor localization." International Journal of RF Technologies 12, no. 1 (August 24, 2021): 1–13. http://dx.doi.org/10.3233/rft-200256.

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Due to the proliferation of the IoT devices, indoor location-based service is bringing huge business values and potentials. The positioning accuracy is restricted by the variability and complexity of the indoor environment. Radio Frequency Identification (RFID), as a key technology of the Internet of Things, has became the main research direction in the field of indoor positioning because of its non-contact, non-line-of-sight and strong anti-interference abilities. This paper proposes the deep leaning approach for RFID based indoor localization. Since the measured Received Signal Strength Indicator (RSSI) can be influenced by many indoor environment factors, Kalman filter is applied to erase the fluctuation. Furthermore, linear interpolation is adopted to increase the density of the reference tags. In order to improve the processing ability of the fingerprint database, deep neural network is adopted together with the fingerprinting method to optimize the non-linear mapping between fingerprints and indoor coordinates. The experimental results show that the proposed method achieves high accuracy with a mean estimation error of 0.347 m.

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Zheng, Lili, Bin-Jie Hu, Jinguang Qiu, and Manman Cui. "A Deep-Learning-Based Self-Calibration Time-Reversal Fingerprinting Localization Approach on Wi-Fi Platform." IEEE Internet of Things Journal 7, no. 8 (August 2020): 7072–83. http://dx.doi.org/10.1109/jiot.2020.2981723.

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Liu, Ruolin, Bai Guo, Maoyu Wang, Weiqiang Li, Tao Yang, Hongfei Ling, and Tianyu Chen. "Isotopic fingerprinting of dissolved iron sources in the deep western Pacific since the late Miocene." Science China Earth Sciences 63, no. 11 (August 5, 2020): 1767–79. http://dx.doi.org/10.1007/s11430-020-9648-6.

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Yin, Yuqing, Changze Song, Ming Li, and Qiang Niu. "A CSI-Based Indoor Fingerprinting Localization with Model Integration Approach." Sensors 19, no. 13 (July 7, 2019): 2998. http://dx.doi.org/10.3390/s19132998.

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Convenient indoor positioning has become an urgent need due to the improvement it offers to quality of life, which inspires researchers to focus on device-free indoor location. In areas covered with Wi-Fi, people in different locations will to varying degrees have an impact on the transmission of channel state information (CSI) of Wi-Fi signals. Because space is divided into several small regions, the idea of classification is used to locate. Therefore, a novel localization algorithm is put forward in this paper based on Deep Neural Networks (DNN) and a multi-model integration strategy. The approach consists of three stages. First, the local outlier factor (LOF), the anomaly detection algorithm, is used to correct the abnormal data. Second, in the training phase, 3 DNN models are trained to classify the region fingerprints by taking advantage of the processed CSI data from 3 antennas. Third, in the testing phase, a model fusion method named group method of data handling (GMDH) is adopted to integrate 3 predicted results of multiple models and give the final position result. The test-bed experiment was conducted in an empty corridor, and final positioning accuracy reached at least 97%.

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Kim, Byungjai, Michael Schär, HyunWook Park, and Hye-Young Heo. "A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging." NeuroImage 221 (November 2020): 117165. http://dx.doi.org/10.1016/j.neuroimage.2020.117165.

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Schmickler, B., D. E. Jacob, and S. F. Foley. "Eclogite xenoliths from the Kuruman kimberlites, South Africa: geochemical fingerprinting of deep subduction and cumulate processes☆." Lithos 75, no. 1-2 (July 2004): 173–207. http://dx.doi.org/10.1016/j.lithos.2003.12.012.

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Boehm, P. D., and P. D. Carragher. "Location of natural oil seep and chemical fingerprinting suggest alternative explanation for deep sea coral observations." Proceedings of the National Academy of Sciences 109, no. 40 (August 8, 2012): E2647. http://dx.doi.org/10.1073/pnas.1209658109.

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Cao, Licheng, Tao Jiang, and Jingke He. "Fingerprinting sand from Asian rivers to the deep central South China Sea since the Late Miocene." GSA Bulletin 133, no. 9-10 (January 21, 2021): 1964–78. http://dx.doi.org/10.1130/b35845.1.

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Abstract The complex sedimentary processes from source to sink lead to a substantial fractionation of sediment size and composition. Relatively coarse-grained, continent-derived detritus is rarely transported and deposited in the deep ocean, and the terminus of this sediment routing system is poorly understood. Sandy turbidite deposits within the Upper Miocene–Pleistocene strata drilled in the deep central South China Sea during the International Ocean Discovery Program (IODP) Expedition 349 provide valuable samples for evaluating the evolution of sediment contributions from different Asian landmasses. This study reconstructs this ancient source-to-sink system based on an integration of heavy mineral and detrital zircon analyses (including U-Pb age, trace element, grain size and shape), obtained from IODP sites U1431 and U1432, as well as a zircon age-based mixture modeling of well-defined provenance end-members. The results show several provenance shifts that correspond to more complex and dynamic source-to-sink scenarios than previously envisaged. Certain source areas, like East Vietnam, present a different provenance signature than that of today. Multiple provenances have been mixed and diluted during sediment transport, exhibiting a large regional variability. We interpret that siliciclastic turbidite deposits in the central South China Sea were mainly derived from East Vietnam during the early Late Miocene and Pliocene, and the Pearl River Basin during the late Late Miocene and Pleistocene. Additional, but less significant, contributions from the Red and Mekong river basins and coastal Southeast China are also observed.

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