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1
Kirfel, Alexander, Tobias Scheer, Norbert Jung, and Christoph Busch. "Robust Identification and Segmentation of the Outer Skin Layers in Volumetric Fingerprint Data." Sensors 22, no. 21 (October 27, 2022): 8229. http://dx.doi.org/10.3390/s22218229.
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Despite the long history of fingerprint biometrics and its use to authenticate individuals, there are still some unsolved challenges with fingerprint acquisition and presentation attack detection (PAD). Currently available commercial fingerprint capture devices struggle with non-ideal skin conditions, including soft skin in infants. They are also susceptible to presentation attacks, which limits their applicability in unsupervised scenarios such as border control. Optical coherence tomography (OCT) could be a promising solution to these problems. In this work, we propose a digital signal processing chain for segmenting two complementary fingerprints from the same OCT fingertip scan: One fingerprint is captured as usual from the epidermis (“outer fingerprint”), whereas the other is taken from inside the skin, at the junction between the epidermis and the underlying dermis (“inner fingerprint”). The resulting 3D fingerprints are then converted to a conventional 2D grayscale representation from which minutiae points can be extracted using existing methods. Our approach is device-independent and has been proven to work with two different time domain OCT scanners. Using efficient GPGPU computing, it took less than a second to process an entire gigabyte of OCT data. To validate the results, we captured OCT fingerprints of 130 individual fingers and compared them with conventional 2D fingerprints of the same fingers. We found that both the outer and inner OCT fingerprints were backward compatible with conventional 2D fingerprints, with the inner fingerprint generally being less damaged and, therefore, more reliable.
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Alotaibi, Ashwaq, Muhammad Hussain, Hatim AboAlSamh, Wadood Abdul, and George Bebis. "Cross-Sensor Fingerprint Enhancement Using Adversarial Learning and Edge Loss." Sensors 22, no. 18 (September 15, 2022): 6973. http://dx.doi.org/10.3390/s22186973.
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A fingerprint sensor interoperability problem, or a cross-sensor matching problem, occurs when one type of sensor is used for enrolment and a different type for matching. Fingerprints captured for the same person using various sensor technologies have various types of noises and artifacts. This problem motivated us to develop an algorithm that can enhance fingerprints captured using different types of sensors and touch technologies. Inspired by the success of deep learning in various computer vision tasks, we formulate this problem as an image-to-image transformation designed using a deep encoder–decoder model. It is trained using two learning frameworks, i.e., conventional learning and adversarial learning based on a conditional Generative Adversarial Network (cGAN) framework. Since different types of edges form the ridge patterns in fingerprints, we employed edge loss to train the model for effective fingerprint enhancement. The designed method was evaluated on fingerprints from two benchmark cross-sensor fingerprint datasets, i.e., MOLF and FingerPass. To assess the quality of enhanced fingerprints, we employed two standard metrics commonly used: NBIS Fingerprint Image Quality (NFIQ) and Structural Similarity Index Metric (SSIM). In addition, we proposed a metric named Fingerprint Quality Enhancement Index (FQEI) for comprehensive evaluation of fingerprint enhancement algorithms. Effective fingerprint quality enhancement results were achieved regardless of the sensor type used, where this issue was not investigated in the related literature before. The results indicate that the proposed method outperforms the state-of-the-art methods.
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Sun, Jie, Fang Tian, Ying Zhang, Menghua Wu, Runqian Mao, Zhiyong Le, Dongjin Xu, Hui Cao, and Zhiguo Ma. "Chromatographic Fingerprint and Quantitative Analysis of CommercialPheretima aspergillum(Guang Dilong) and Its Adulterants by UPLC-DAD." International Journal of Analytical Chemistry 2019 (January 9, 2019): 1–10. http://dx.doi.org/10.1155/2019/4531092.
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Guang Dilong is a Traditional Chinese Medicine prepared from the dried body ofPheretima aspergillum(E. Perrier), a species of earthworm. However, preparations of Guang Dilong may be adulterated by other species and a method of quality control is needed. A method was developed to analyze and authenticate commercial Guang Dilong, utilizing ultra-high performance liquid chromatography (UHPLC) coupled with diode array detection (DAD). Equipment included an Acquity UPLC HSS T3 column (100 mm × 2.1 mm, 1.8 μm). The mobile phase consisted of acetonitrile and 0.01% formic acid, pumped at 0.3 mL/min. Wavelength detection was at 260 nm. Twenty-two batches of confirmedP. aspergillumsamples (reference) from different sources and 20 batches of adulterated samples were analyzed to establish a reference fingerprint for commercial Guang Dilong. Five peaks in the fingerprints of the reference batches were identified as characteristic; six characteristic peaks in the fingerprints of the adulterants were identified by comparing their retention time with those of the references. The total 42 batches of samples were compared with the reference fingerprint, and the fingerprints of theP. aspergillumsamples were similar. The UHPLC-DAD method can simultaneously determine the contents of six compounds (hypoxanthine, xanthine, uridine, inosine, guanosine, and adenosine) in the reference and adulterated batches. The six compounds showed good regression (r> 0.9999) within test ranges. The recovery (accuracy) was 98.25 to 101.68%, with relative standard deviation <2.67%. In summary, this UHPLC-DAD method combines chromatographic fingerprint with quantification analysis and could be readily used as an efficient quality control method for Guang Dilong.
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Zhang, Huiqing, and Yueqing Li. "LightGBM Indoor Positioning Method Based on Merged Wi-Fi and Image Fingerprints." Sensors 21, no. 11 (May 25, 2021): 3662. http://dx.doi.org/10.3390/s21113662.
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Smartphones are increasingly becoming an efficient platform for solving indoor positioning problems. Fingerprint-based positioning methods are popular because of the wide deployment of wireless local area networks in indoor environments and the lack of model propagation paths. However, Wi-Fi fingerprint information is singular, and its positioning accuracy is typically 2–10 m; thus, it struggles to meet the requirements of high-precision indoor positioning. Therefore, this paper proposes a positioning algorithm that combines Wi-Fi fingerprints and visual information to generate fingerprints. The algorithm involves two steps: merged-fingerprint generation and fingerprint positioning. In the merged-fingerprint generation stage, the kernel principal component analysis feature of the Wi-Fi fingerprint and the local binary pattern features of the scene image are fused. In the fingerprint positioning stage, a light gradient boosting machine (LightGBM) is trained with mutually exclusive feature bundling and histogram optimization to obtain an accurate positioning model. The method is tested in an actual environment. The experimental results show that the positioning accuracy of the LightGBM method is 90% within a range of 1.53 m. Compared with the single-fingerprint positioning method, the accuracy is improved by more than 20%, and the performance is improved by more than 15% compared with other methods. The average locating error is 0.78 m.
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Gäbler, Hans-Eike, Wilhelm Schink, Simon Goldmann, Andreas Bahr, and Timo Gawronski. "Analytical Fingerprint of Wolframite Ore Concentrates." Journal of Forensic Sciences 62, no. 4 (June 6, 2017): 881–88. http://dx.doi.org/10.1111/1556-4029.13373.
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Qiao, Yan-Ling, Ying-Hao Zhang, Wei Zhang, and Jin-Lan Zhang. "A Rapid Resolution Liquid Chromatographic Method for Fingerprint Analysis of Raw and Processed Caowu (Aconitum kusnezoffii)." Journal of AOAC INTERNATIONAL 92, no. 2 (March 1, 2009): 653–62. http://dx.doi.org/10.1093/jaoac/92.2.653.
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Abstract A sensitive and reliable rapid resolution liquid chromatographic (RRLC) method coupled with diode array detection has been developed for the fingerprint analysis of raw and processed caowu (Aconitum kusnezoffii). The RRLC fingerprints were established with a Zorbax Extend C18 analytical column (4.6 50 mm, 1.8 m) and gradient elution. Analysis run time was <10 min. The method displayed good precision, stability, and repeatability in fingerprint analysis. Characteristic RRLC fingerprints of caowu were generated and used to assess the consistency and differences in the products. Raw and processed caowu from different sources were analyzed under the developed RRLC conditions, yielding contrasting RRLC fingerprints. Comparison of the RRLC fingerprints of processed and raw caowu indicated that the major constituents changed during processing. Meanwhile, a peak area ratio analysis method was applied to assess their chromatographic fingerprints. In characterizing the constituents of caowu, 11 major chromatographic peaks were identified by mass spectrometry and compared with reference standards and reference data. In summary, RRLC fingerprinting is a rapid and useful way to evaluate the quality of raw and processed caowu.
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Kokkinis, Akis, Loizos Kanaris, Antonio Liotta, and Stavros Stavrou. "RSS Indoor Localization Based on a Single Access Point." Sensors 19, no. 17 (August 27, 2019): 3711. http://dx.doi.org/10.3390/s19173711.
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This research work investigates how RSS information fusion from a single, multi-antenna access point (AP) can be used to perform device localization in indoor RSS based localization systems. The proposed approach demonstrates that different RSS values can be obtained by carefully modifying each AP antenna orientation and polarization, allowing the generation of unique, low correlation fingerprints, for the area of interest. Each AP antenna can be used to generate a set of fingerprint radiomaps for different antenna orientations and/or polarization. The RSS fingerprints generated from all antennas of the single AP can be then combined to create a multi-layer fingerprint radiomap. In order to select the optimum fingerprint layers in the multilayer radiomap the proposed methodology evaluates the obtained localization accuracy, for each fingerprint radio map combination, for various well-known deterministic and probabilistic algorithms (Weighted k-Nearest-Neighbor—WKNN and Minimum Mean Square Error—MMSE). The optimum candidate multi-layer radiomap is then examined by calculating the correlation level of each fingerprint pair by using the “Tolerance Based—Normal Probability Distribution (TBNPD)” algorithm. Both steps take place during the offline phase, and it is demonstrated that this approach results in selecting the optimum multi-layer fingerprint radiomap combination. The proposed approach can be used to provide localisation services in areas served only by a single AP.
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Liu, Zhenyu, Bin Dai, Xiang Wan, and Xueyi Li. "Hybrid Wireless Fingerprint Indoor Localization Method Based on a Convolutional Neural Network." Sensors 19, no. 20 (October 22, 2019): 4597. http://dx.doi.org/10.3390/s19204597.
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In the indoor location field, the quality of received-signal-strength-indicator (RSSI) fingerprints plays a key role in the performance of indoor location services. However, changes in an indoor environment may lead to the decline of location accuracy. This paper presents a localization method employing a Hybrid Wireless fingerprint (HW-fingerprint) based on a convolutional neural network (CNN). In the proposed scheme, the Ratio fingerprint was constructed by calculating the ratio of different RSSIs from important contribution access points (APs). The HW-fingerprint combined the Ratio fingerprint and the RSSI to enhance the expression of indoor environment characteristics. Moreover, a CNN architecture was constructed to learn important features from the complex HW-fingerprint for indoor locations. In the experiment, the HW-fingerprint was tested in an actual indoor scene for 15 days. Results showed that the average daily location accuracy of the K-Nearest Neighbor (KNN), Support Vector Machines (SVMs), and CNN was improved by 3.39%, 8.03% and 9.03%, respectively, when using the HW-fingerprint. In addition, the deep-learning method was 4.19% and 16.37% higher than SVM and KNN in average daily location accuracy, respectively.
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Li, Zhuojun, Cui Wu, Bo Xu, Huijun Wang, Pingping Song, Zhenying Liu, and Zhimao Chao. "Gender Discrimination of Flower Buds of Mature Populus tomentosa by HPLC Fingerprint Combined with Chemometrics." International Journal of Analytical Chemistry 2022 (September 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/1281521.
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A high performance liquid chromatography-diode array detector (HPLC-DAD) was used to establish the HPLC fingerprint. Chemometrics methods were used to discriminate against the gender of flower buds of Populus tomentosa based on areas of common peaks calibrated with the HPLC fingerprint. The score plot of principal component analysis (PCA) showed a clear grouping trend (R2X, 0.753; Q2, 0.564) between female and male samples. Two groups were also well discriminated with orthogonal partial least squares-discriminant analysis (OPLS-DA) (R2X, 0.741; R2Y, 0.980; Q2, 0.970). As the hierarchical clustering analysis (HCA) heatmap showed, all samples were separated into two groups. Four compounds were screened out by S-plot and variable importance in projection (VIP > 1.0). Two of them were identified as siebolside B and tremulacin. This study demonstrated that HPLC fingerprints combined with chemometrics can be applied to discriminate against dioecious plants and screen differences, providing a reference for identifying the gender of dioecious plants.
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Grant, Ashleigh, T. J. Wilkinson, Derek R. Holman, and Michael C. Martin. "Identification of Recently Handled Materials by Analysis of Latent Human Fingerprints Using Infrared Spectromicroscopy." Applied Spectroscopy 59, no. 9 (September 2005): 1182–87. http://dx.doi.org/10.1366/0003702055012618.
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Analysis of fingerprints has predominantly focused on matching the pattern of ridges to a specific person as a form of identification. The present work focuses on identifying extrinsic materials that are left within a person's fingerprint after recent handling of such materials. Specifically, we employed infrared spectromicroscopy to locate and positively identify microscopic particles from a mixture of common materials in the latent human fingerprints of volunteer subjects. We were able to find and correctly identify all test substances based on their unique infrared spectral signatures. Spectral imaging is demonstrated as a method for automating recognition of specific substances in a fingerprint. We also demonstrate the use of attenuated total reflectance (ATR) and synchrotron-based infrared spectromicroscopy for obtaining high-quality spectra from particles that were too thick or too small, respectively, for reflection/absorption measurements. We believe the application of this rapid, nondestructive analytical technique to the forensic study of latent human fingerprints has the potential to add a new layer of information available to investigators. Using fingerprints to not only identify who was present at a crime scene, but also to link who was handling key materials, will be a powerful investigative tool.
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Ruan, Ling, Ling Zhang, Tong Zhou, and Yi Long. "An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window." Sensors 20, no. 24 (December 18, 2020): 7269. http://dx.doi.org/10.3390/s20247269.
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The weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal instability, irrelevant fingerprints reduce the positioning accuracy when performing the matching calculation process. Therefore, selecting the appropriate fingerprint data from the database more quickly and accurately is an urgent problem for improving WKNN. This paper proposes an improved Bluetooth indoor positioning method using a dynamic fingerprint window (DFW-WKNN). The dynamic fingerprint window is a space range for local fingerprint data searching instead of universal searching, and it can be dynamically adjusted according to the indoor pedestrian movement and always covers the maximum possible range of the next positioning. This method was tested and evaluated in two typical scenarios, comparing two existing algorithms, the traditional WKNN and the improved WKNN based on local clustering (LC-WKNN). The experimental results show that the proposed DFW-WKNN algorithm enormously improved both the positioning accuracy and positioning efficiency, significantly, when the fingerprint data increased.
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Fan, Zhiyuan, Xue Han, Lingling Xia, Xiaohong Xu, Jing Xie, Quan Zhang, Qing Su, Yanmei Sheng, and Xingliang Xie. "Screening of quality markers of Jie-Geng decoction based on integration of multiple methods." Tropical Journal of Pharmaceutical Research 20, no. 4 (January 24, 2022): 825–32. http://dx.doi.org/10.4314/tjpr.v20i4.24.
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Purpose: To screen quality markers of Jie-Geng decoction (JGD) through multiple analytical methods and integration of network pharmacology and HPLC-ELSD fingerprint.
Methods: Network pharmacology was used to screen the potential bioactive components of JGD. Simultaneously, HPLC-ELSD fingerprint combined with multiple analytical methods was carried out for determination of the chemical compounds in JGD. Subsequently, quality markers for identification of quality variations in JGD were established through the integration of results from network pharmacology and fingerprinting, in combination with similarity analysis, hierarchical clustering analysis (HCA), and orthogonal partial least squares discrimination analysis (OPLS-DA).
Results: A total of 110 compounds responsible for the regulation of 36 target genes in airway inflammation and cough were identified through network pharmacology. Furthermore, 37 characteristic components were obtained through fingerprints. Similarity analysis revealed that the main bioactive compounds in the various batches of JGD were similar. Also, HCA and OPLS-DA analyses were performed to identify the potential quality markers. Glycyrrhizic acid, liquiritin, and platycodin D were selected as quality markers, based on effectiveness, measurability, and distinguishability. Furthermore, quality variations in JGD arose mostly from variations in origin of gancao.
Conclusion: The screened quality markers for JGD are useful in evaluation of factors that affect the quality and variation in JGD. The concept of integration of network pharmacology and fingerprint with multiple analytical methods might be a novel strategy for quality control of Traditional Chinese Medicine (TCM) formulations.
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Jiang, Jehn-Ruey, Hanas Subakti, and Hui-Sung Liang. "Fingerprint Feature Extraction for Indoor Localization." Sensors 21, no. 16 (August 12, 2021): 5434. http://dx.doi.org/10.3390/s21165434.
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This paper proposes a fingerprint-based indoor localization method, named FPFE (fingerprint feature extraction), to locate a target device (TD) whose location is unknown. Bluetooth low energy (BLE) beacon nodes (BNs) are deployed in the localization area to emit beacon packets periodically. The received signal strength indication (RSSI) values of beacon packets sent by various BNs are measured at different reference points (RPs) and saved as RPs’ fingerprints in a database. For the purpose of localization, the TD also obtains its fingerprint by measuring the beacon packet RSSI values for various BNs. FPFE then applies either the autoencoder (AE) or principal component analysis (PCA) to extract fingerprint features. It then measures the similarity between the features of PRs and the TD with the Minkowski distance. Afterwards, k RPs associated with the k smallest Minkowski distances are selected to estimate the TD’s location. Experiments are conducted to evaluate the localization error of FPFE. The experimental results show that FPFE achieves an average error of 0.68 m, which is better than those of other related BLE fingerprint-based indoor localization methods.
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Yuan, Zhengwu, Xupeng Zha, and Xiaojian Zhang. "Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients K-Nearest Neighbor." Sensors 20, no. 18 (September 21, 2020): 5416. http://dx.doi.org/10.3390/s20185416.
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The complex indoor environment makes the use of received fingerprints unreliable as an indoor positioning and localization method based on fingerprint data. This paper proposes an adaptive multi-type fingerprint indoor positioning and localization method based on multi-task learning (MTL) and Weight Coefficients K-Nearest Neighbor (WCKNN), which integrates magnetic field, Wi-Fi and Bluetooth fingerprints for positioning and localization. The MTL fuses the features of different types of fingerprints to search the potential relationship between them. It also exploits the synergy between the tasks, which can boost up positioning and localization performance. Then the WCKNN predicts another position of the fingerprints in a certain class determined by the obtained location. The final position is obtained by fusing the predicted positions using a weighted average method whose weights are the positioning errors provided by positioning error prediction models. Experimental results indicated that the proposed method achieved 98.58% accuracy in classifying locations with a mean positioning error of 1.95 m.
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Costa, Catia, Roger Webb, Vladimir Palitsin, Mahado Ismail, Marcel de Puit, Samuel Atkinson, and Melanie J. Bailey. "Rapid, Secure Drug Testing Using Fingerprint Development and Paper Spray Mass Spectrometry." Clinical Chemistry 63, no. 11 (November 1, 2017): 1745–52. http://dx.doi.org/10.1373/clinchem.2017.275578.
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Abstract BACKGROUND Paper spray mass spectrometry (PS-MS) is a technique that has recently emerged and has shown excellent analytical sensitivity to a number of drugs in blood. As an alternative to blood, fingerprints have been shown to provide a noninvasive and traceable sampling matrix. Our goal was to validate the use of fingerprint samples to detect cocaine use. METHODS Samples were collected on triangular pieces (168 mm2) of washed Whatman Grade I chromatography paper. Following application of internal standard, spray solvent and a voltage were applied to the paper before mass spectrometry detection. A fingerprint visualization step was incorporated into the analysis procedure by addition of silver nitrate solution and exposing the sample to ultraviolet light. RESULTS Limits of detection for cocaine, benzoylecgonine, and methylecgonine were 1, 2, and 31 ng/mL respectively, with relative standard deviations < 33%. No matrix effects were observed. Analysis of 239 fingerprint samples yielded a 99% true-positive rate and a 2.5% false-positive rate, based on the detection of cocaine, benzoylecgonine, or methylecgonine with use of a single fingerprint. CONCLUSIONS The method offers a qualitative and noninvasive screening test for cocaine use. The analysis method developed is rapid (4 min/sample) and requires no sample preparation.
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Liu, Ankang, Lingfei Cheng, and Changdong Yu. "SASMOTE: A Self-Attention Oversampling Method for Imbalanced CSI Fingerprints in Indoor Positioning Systems." Sensors 22, no. 15 (July 29, 2022): 5677. http://dx.doi.org/10.3390/s22155677.
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WiFi localization based on channel state information (CSI) fingerprints has become the mainstream method for indoor positioning due to the widespread deployment of WiFi networks, in which fingerprint database building is critical. However, issues, such as insufficient samples or missing data in the collection fingerprint database, result in unbalanced training data for the localization system during the construction of the CSI fingerprint database. To address the above issue, we propose a deep learning-based oversampling method, called Self-Attention Synthetic Minority Oversampling Technique (SASMOTE), for complementing the fingerprint database to improve localization accuracy. Specifically, a novel self-attention encoder-decoder is firstly designed to compress the original data dimensionality and extract rich features. The synthetic minority oversampling technique (SMOTE) is adopted to oversample minority class data to achieve data balance. In addition, we also construct the corresponding CSI fingerprinting dataset to train the model. Finally, extensive experiments are performed on different data to verify the performance of the proposed method. The results show that our SASMOTE method can effectively solve the data imbalance problem. Meanwhile, the improved location model, 1D-MobileNet, is tested on the balanced fingerprint database to further verify the excellent performance of our proposed methods.
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Yan, Shikai, Wenfeng Xin, Guoan Luo, Yiming Wang, and Yiyu Cheng. "Chemical Fingerprinting of Gardenia jasminoides Fruit Using Direct Sample Introduction and Gas Chromatography with Mass Spectrometry Detection." Journal of AOAC INTERNATIONAL 89, no. 1 (January 1, 2006): 40–45. http://dx.doi.org/10.1093/jaoac/89.1.40.
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Abstract A rapid, rugged, and inexpensive approach is described to develop chemical fingerprints of volatile and semivolatile fractions in herbal medicine. The method is based on the combination of direct sample introduction and gas chromatography (GC) analysis with mass spectrometry detection. In comparison with routine methods, the proposed approach provides the most informative fingerprint and does not demand time-consuming extraction, pretreatment, and cleanup procedures. The approach was applied to establish the GC fingerprint of gardenia fruit (Gardenia jasminoides Ellis), in which 39 components were identified. With the help of principal components analysis, the obtained fingerprint could reveal the variation in and within different herb samples as affected by season and developmental state (wild or cultivated). The results indicated that the proposed approach could serve as a rapid, simple, and effective technique for the quality control of herbal medicines.
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Xing, Xiaoping, and Dan Li. "Identification and Quality Assessment of Chrysanthemum Buds by CE Fingerprinting." Journal of Analytical Methods in Chemistry 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/517402.
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A simple and efficient fingerprinting method for chrysanthemum buds was developed with the aim of establishing a quality control protocol based on biochemical makeup. Chrysanthemum bud samples were successively extracted by water and alcohol. The fingerprints of the chrysanthemum buds samples were obtained using capillary electrophoresis and electrochemical detection (CE-ED) employing copper and carbon working electrodes to capture all of the chemical information. 10 batches of chrysanthemum buds were collected from different regions and various factories to establish the baseline fingerprint. The experimental data of 10 batches electropherogram buds by CE were analyzed by correlation coefficient and the included angle cosine methods. A standard chrysanthemum bud fingerprint including 24 common peaks was established, 12 from each electrode, which was successfully applied to identify and distinguish between chrysanthemum buds from 2 other chrysanthemum species. These results demonstrate that fingerprint analysis can be used as an important criterion for chrysanthemum buds quality control.
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Peng, Yitang, Xiaoji Niu, Jian Tang, Dazhi Mao, and Chuang Qian. "Fast Signals of Opportunity Fingerprint Database Maintenance with Autonomous Unmanned Ground Vehicle for Indoor Positioning." Sensors 18, no. 10 (October 12, 2018): 3419. http://dx.doi.org/10.3390/s18103419.
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Indoor positioning technology based on Received Signal Strength Indicator (RSSI) fingerprints is a potential navigation solution, which has the advantages of simple implementation, low cost and high precision. However, as the radio frequency signals can be easily affected by the environmental change during its transmission, it is quite necessary to build location fingerprint database in advance and update it frequently, thereby guaranteeing the positioning accuracy. At present, the fingerprint database building methods mainly include point collection and line acquisition, both of which are usually labor-intensive and time consuming, especially in a large map area. This paper proposes a fast and efficient location fingerprint database construction and updating method based on a self-developed Unmanned Ground Vehicle (UGV) platform NAVIS, called Automatic Robot Line Collection. A smartphone was installed on NAVIS for collecting indoor Received Signal Strength Indicator (RSSI) fingerprints of Signals of Opportunity (SOP), such as Bluetooth and Wi-Fi. Meanwhile, indoor map was created by 2D LiDAR-based Simultaneous Localization and Mapping (SLAM) technology. The UGV automatically traverse the unknown indoor environment due to a pre-designed full-coverage path planning algorithm. Then, SOP sensors collect location fingerprints and generates grid map during the process of environment-traversing. Finally, location fingerprint database is built or updated by Kriging interpolation. Field tests were carried out to verify the effectiveness and efficiency of our proposed method. The results showed that, compared with the traditional point collection and line collection schemes, the root mean square error of the fingerprinting-based positioning results were reduced by 35.9% and 25.0% in static tests and 30.0% and 21.3% respectively in dynamic tests. Moreover, our UGV can traverse the indoor environment autonomously without human-labor on data acquisition, the efficiency of the automatic robot line collection scheme is 2.65 times and 1.72 times that of the traditional point collection and the traditional line acquisition, respectively.
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Vibert, Benoit, Jean-Marie Le Bars, Christophe Charrier, and Christophe Rosenberger. "Logical Attacks and Countermeasures for Fingerprint On-Card-Comparison Systems." Sensors 20, no. 18 (September 21, 2020): 5410. http://dx.doi.org/10.3390/s20185410.
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Digital fingerprints are being used more and more to secure applications for logical and physical access control. In order to guarantee security and privacy trends, a biometric system is often implemented on a secure element to store the biometric reference template and for the matching with a probe template (on-card-comparison). In order to assess the performance and robustness against attacks of these systems, it is necessary to better understand which information could help an attacker successfully impersonate a legitimate user. The first part of the paper details a new attack based on the use of a priori information (such as the fingerprint classification, sensor type, image resolution or number of minutiae in the biometric reference) that could be exploited by an attacker. In the second part, a new countermeasure against brute force and zero effort attacks based on fingerprint classification given a minutiae template is proposed. These two contributions show how fingerprint classification could have an impact for attacks and countermeasures in embedded biometric systems. Experiments show interesting results on significant fingerprint datasets.
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Pau, Kiu Nai, Vicki Wei Qi Lee, Shih Yin Ooi, and Ying Han Pang. "The Development of a Data Collection and Browser Fingerprinting System." Sensors 23, no. 6 (March 13, 2023): 3087. http://dx.doi.org/10.3390/s23063087.
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The urgent need to protect user privacy and security has emerged as the World Wide Web has become an increasingly necessary part of daily life. Browser fingerprinting is a very interesting topic in the industry of technology security. New technology will always raise new security issues and browser fingerprinting will undoubtedly follow the same process. It has become one of the most popular topics in online privacy because, to date, there is still no exact solution as to how to stop it entirely. The majority of solutions just aim to reduce the likelihood of obtaining a browser fingerprint. Research on browser fingerprinting is unquestionably required since it is essential to educate users, developers, policymakers, and law enforcement about it so that they can make strategic choices based on knowledge. Browser fingerprinting must be recognised in order to defend against privacy problems. A browser fingerprint is described as data gathered by the receiving server to identify a distant device, and it is different from cookies. Websites frequently utilize browser fingerprinting to obtain information about the type and version of the browser, as well as the operating system, and other current settings. It has been known that even when cookies are disabled, fingerprints can be used to fully or partially identify users or devices. In this communication paper, a new insight into the challenge of browser fingerprint is encouraged as a new venture. Thus, the initial way to truly understand the browser fingerprint is the need to collect browser fingerprints. In this work, the process of data collection for browser fingerprinting through scripting, to offer a complete all-in-one fingerprinting test suite, has been thoughtfully divided into appropriate sections and grouped with key information to be carried out. The objective is to gather fingerprint data with no personal identification information and make it an open source of raw datasets in the industry for any future research purposes. To our best knowledge, there are no open datasets made available for browser fingerprints in the research field. The dataset will be widely accessible by anyone interested in obtaining those data. The dataset collected will be very raw and will be in the form of a text file. Thus, the main contribution of this work is to share an open dataset of browser fingerprints along with its collection methodology.
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Brodie, Christopher, Oliver Kracht, and Andreas Hilkert. "Tracing the Geographical Origin of Roasted and Green Coffee Using Isotope Fingerprints." Journal of AOAC INTERNATIONAL 102, no. 2 (March 1, 2019): 653–54. http://dx.doi.org/10.5740/jaoacint.18-0314.
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Abstract Background: Coffee is one of the most popular beverages worldwide, sourced from different geographical regions. To ensure that coffeebeans come from labelled locations, laboratories need an analytical solution that can discriminate geographical origin. Coffee beans have a fingerprint, a unique chemical signature that allows them to be identified: Isotope fingerprints of carbon, nitrogen, sulfur, hydrogen, and oxygen have been reliably used for origin claim verification. Objective: Show that hydrogen and oxygen isotope fingerprints from green and roasted coffee beans can determine the origin of coffee beans. Methods: The coffee beans were initially ground to as fine as possible a powder using a cryo-mill. Following, samples were weighed into tin capsules and introduced to the Thermo Scientific EA IsoLink™ IRMS System via the Thermo Scientific MAS Plus autosampler, where they were pyrolyzed at 1450°C, and converted to H2 and CO for analysis. Results: The hydrogen and oxygen isotope fingerprints of the coffee beans show that they can be clearly differentiated at the continent scale. Conclusions: It is evident that measuring the isotope fingerprint of coffee beans helps support legislation on food integrity and labelling (EC Reg. No. 1169/2011) and product geographical indication/origin (EC Reg. No. 510/2006), therefore protecting consumers and brands. The origin of a coffee bean can be determined using their hydrogen and oxygen isotope fingerprints. Highlights: Hydrogenand oxygen isotope fingerprints can help determine the origin of coffee beans, allowing the label claim to be verified.
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Kauba, Christof, Dominik Söllinger, Simon Kirchgasser, Axel Weissenfeld, Gustavo Fernández Domínguez, Bernhard Strobl, and Andreas Uhl. "Towards Using Police Officers’ Business Smartphones for Contactless Fingerprint Acquisition and Enabling Fingerprint Comparison against Contact-Based Datasets." Sensors 21, no. 7 (March 24, 2021): 2248. http://dx.doi.org/10.3390/s21072248.
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Recent developments enable biometric recognition systems to be available as mobile solutions or to be even integrated into modern smartphone devices. Thus, smartphone devices can be used as mobile fingerprint image acquisition devices, and it has become feasible to process fingerprints on these devices, which helps police authorities carry out identity verification. In this paper, we provide a comprehensive and in-depth engineering study on the different stages of the fingerprint recognition toolchain. The insights gained throughout this study serve as guidance for future work towards developing a contactless mobile fingerprint solution based on the iPhone 11, working without any additional hardware. The targeted solution will be capable of acquiring 4 fingers at once (except the thumb) in a contactless manner, automatically segmenting the fingertips, pre-processing them (including a specific enhancement), and thus enabling fingerprint comparison against contact-based datasets. For fingertip detection and segmentation, various traditional handcrafted feature-based approaches as well as deep-learning-based ones are investigated. Furthermore, a run-time analysis and first results on the biometric recognition performance are included.
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Jiang, Hui, Yuansheng Xiao, Xingya Xue, Hongli Jin, Yang Xiang, Yanfang Liu, and Gaowa Jin. "Computer-Aided Rapid Establishment of Fingerprint of Xiaojin Capsule by HPLC." International Journal of Analytical Chemistry 2021 (January 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/8858501.
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Traditional Chinese medicine (TCM) formulas have a significant clinical efficacy, and the fingerprint technology has been widely accepted to fully reveal the quality of TCM. Whereas, it is a great challenge to establish the fingerprint chromatogram which can fully reflect every single herb material in a short time. In this study, we used Xiaojin capsule (XJC) as a case and developed a rapid fingerprint method based on increasing the column temperature and flow rate simultaneously combined with computer-aided. First, the elution gradient was optimized based on the retention parameters and peak shape parameters of the four linear gradients, and then, the column temperature and flow rate were increased simultaneously to shorten the analysis time. Next, the standard fingerprint chromatogram of XJC, which can reflect every herb material, was generated. Finally, quality markers were screened through unsupervised cluster analysis and supervised orthogonal partial least squares discrimination analysis. Combining computer-aided with increasing column temperature and flow rate simultaneously can develop the rapid method for establishing HPLC fingerprint of XJC, which can fully reflect every single herb material and provide comprehensive quality control. The strategy for establishing HPLC fingerprint of TCM formula could be applied to other traditional Chinese medicine formulas and herbal medicine.
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Lv, Xuzhen, Shuai Feng, Jiacheng Zhang, Sihai Sun, Yannan Geng, Min Yang, Yali Liu, et al. "Application of HPLC Fingerprint Combined with Chemical Pattern Recognition and Multi-Component Determination in Quality Evaluation of Echinacea purpurea (L.) Moench." Molecules 27, no. 19 (September 30, 2022): 6463. http://dx.doi.org/10.3390/molecules27196463.
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Echinacea purpurea (EP) is a common medicinal material for extracting anti-RSV components. However, up to now, there has been no effective and simple method to comprehensively reflect the quality of EP. In our current study, the quality of Echinacea purpurea (L.) Moench samples from six different cultivation locations in China was evaluated by establishing a high-performance liquid chromatography (HPLC) fingerprint, combining chemical pattern recognition and multi-component determination. In this study, the chemical fingerprints of 15 common peaks were obtained using the similarity evaluation system of the chromatographic fingerprints of traditional Chinese medicine (2012A Edition). Among the 15 components, three phenolic acids (caftaric acid, chlorogenic acid and cichoric acid) were identified and determined. The similarity of fingerprints of 16 batches of Echinacea purpurea (L.) Moench samples ranged from 0.905 to 0.998. The similarity between fingerprints of five batches of commercially available Echinacea pupurea (L.) Moench and the standard fingerprint ”R” ranged from 0.980 to 0.997, which proved the successful establishment of the fingerprint. PCA and HCA were performed with the relative peak areas of 15 common peaks (peak 3 as the reference peak) as variables. Anhui and Shaanxi can be successfully distinguished from the other four cultivation areas. In addition, the index components of caftaric acid, chlorogenic acid and cichoric acid were in the range of 1.77–8.60 mg/g, 0.02–0.20 mg/g and 2.27–15.87 mg/g. The results of multi-component index content determination show that the contents of the Shandong cultivation area were higher, followed by Gansu, Henan and Hebei, and the lowest were Anhui and Shaanxi. The results are consistent with PCA and HCA, which proved that the quality of Echinacea purpurea (L.) Moench from different origins was different. HPLC fingerprint combined with chemical pattern recognition and multi-component content determination was a reliable, comprehensive and prospective method for evaluating the quality of Echinacea purpurea (L.) Moench. This method provides a scientific basis for the quality control and evaluation of Echinacea purpurea (L.) Moench.
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Costa, Catia, Mahado Ismail, Derek Stevenson, Brian Gibson, Roger Webb, and Melanie Bailey. "Distinguishing between Contact and Administration of Heroin from a Single Fingerprint using High Resolution Mass Spectrometry." Journal of Analytical Toxicology 44, no. 3 (November 4, 2019): 218–25. http://dx.doi.org/10.1093/jat/bkz088.
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Abstract Fingerprints have been proposed as a promising new matrix for drug testing. In previous work it has been shown that a fingerprint can be used to distinguish between drug users and nonusers. Herein, we look at the possibility of using a fingerprint to distinguish between dermal contact and administration of heroin. Fingerprint samples were collected from (i) 10 patients attending a drug rehabilitation clinic, (ii) 50 nondrug users and (iii) participants who touched 2 mg street heroin, before and after various hand cleaning procedures. Oral fluid was also taken from the patients. All samples were analyzed using a liquid chromatography—high resolution mass spectrometry method validated in previous work for heroin and 6-AM. The HRMS data were analyzed retrospectively for morphine, codeine, 6-acetylcodeine and noscapine. Heroin and 6-AM were detected in all fingerprint samples produced from contact with heroin, even after hand washing. In contrast, morphine, acetylcodeine and noscapine were successfully removed after hand washing. In patient samples, the detection of morphine, noscapine and acetylcodeine (alongside heroin and 6-AM) gave a closer agreement to patient testimony on whether they had recently used heroin than the detection of heroin and 6-AM alone. This research highlights the importance of washing hands prior to donating a fingerprint sample to distinguish recent contact with heroin from heroin use.
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Wang, Wenxu, Damián Marelli, and Minyue Fu. "Fingerprinting-Based Indoor Localization Using Interpolated Preprocessed CSI Phases and Bayesian Tracking." Sensors 20, no. 10 (May 18, 2020): 2854. http://dx.doi.org/10.3390/s20102854.
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Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver’s location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver’s motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios.
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Maimaiti, Zulipiya, Ablajan Turak, Qing Ling Ma, Geyu Liu, and Haji Akbar Aisa. "Quantitative Determination of Marker Compounds and Fingerprint Analysis of the Seeds of Vernonia anthelmintica." International Journal of Analytical Chemistry 2020 (October 30, 2020): 1–12. http://dx.doi.org/10.1155/2020/8859425.
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In traditional Chinese medicine, the seeds of Vernonia anthelmintica (L.) Willd. have been widely used for treatment of cough, skin diseases, diarrhea, fever, schistosomiasis, amoebic dysentery, and gastrointestinal problems, especially in the treatment of vitiligo for thousands of years in China. In this study, an effective, reliable, and accurate high-performance liquid chromatography diode array detector (HPLC-DAD) method was developed for quantitative analysis of 3 marker bioactive compounds and chemical fingerprint of the seeds of V. anthelmintica. Data corresponding to common peak areas and HPLC chromatographic fingerprints were analyzed by exploratory hierarchical cluster analysis (HCA) and principal component analysis (PCA) to extract information of the most significant variables contributing to characterization and classification of the analyzed samples. Based on variety and origin, the high-performance thin layer chromatography (HPTLC) method validated the chemical fingerprint results used to screen the in vitro antioxidant activity of V. anthelmintica. The results show that the developed method has potential application values for the quality consistency evaluation and identification of similar instant V. anthelmintica samples. When considered collectively, this research results provide a scientific basis for the improvement of standardization and specification of V. anthelmintica medicinal materials and provide a pathway for the development and utilization of references for the identification of V. anthelmintica herbs.
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Ganiyev Otabek, Abdullayev Rustam,. "Digital fingerprinting: New opportunities for solving crimes?" Psychology and Education Journal 58, no. 1 (January 1, 2021): 2713–18. http://dx.doi.org/10.17762/pae.v58i1.1154.
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High technologies at the present stage of development of the world community have penetrated into all spheres of human activity. Considering the issue of digital methods of crime investigation, of course, forensic experts are faced with problems of their effectiveness. Today, most Western countries in the investigation of crimes use fingerprint examination, using technology, thereby reducing the time of investigation, ensuring efficiency. And yet, not all forensic specialists use these techniques when taking fingerprints, relying on the reliability and the usual proven practice of the classical method of fingerprinting. As you know, in the countries of the post-Soviet space, this method was used on the basis of scientific approaches developed by such scientists as E. Henry, A. Bertillon, G. Gross, E. Locard, etc. For example, the work of E. Henry formed the basis for fingerprint registration, since the scientific approach to the biological process of damage and restoration of the epidermis during the investigation of a crime, it is necessary to attach special importance, since papillary lines also have the property of recoverability. If the dermis is damaged, then the pattern is not restored in such an area. But the scars that appear at the same time, the scars themselves become the hallmarks of the pattern. For a century, forensic scientists have studied the sweat marks of handprints for identification, since each fingerprint is different. In this analytical article, the authors made an attempt to conduct an analytical review of the implementation practices of digital fingerprinting in different countries and study the extent to which the effectiveness of its application is possible.
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Yu, Tao, Linqing Gui, Tianxin Yu, and Jilong Wang. "Walrasian Equilibrium-Based Incentive Scheme for Mobile Crowdsourcing Fingerprint Localization." Sensors 19, no. 12 (June 14, 2019): 2693. http://dx.doi.org/10.3390/s19122693.
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Mobile crowdsourcing has been exploited to collect enough fingerprints for fingerprinting-based localization. Since the construction of a fingerprint database is time consuming, mobile users should be well motivated to participate in fingerprint collection task. To this end, a Walrasian equilibrium-based incentive mechanism is proposed in this paper to motivate mobile users. The proposed mechanism can eliminate the monopoly of the crowdsourcer, balance the supply and demand of fingerprint data, and maximize the benefit of all participators. In order to reach the Walrasian equilibrium, firstly, the social welfare maximization problem is constructed. To solve the original optimization problem, a dual decomposition method is employed. The maximization of social welfare is decomposed into the triple benefit optimization among the crowdsourcer, mobile users, and the whole system. Accordingly, a distributed iterative algorithm is designed. Through the simulation, the performance of the proposed incentive scheme is verified and analyzed. Simulation results demonstrated that the proposed iterative algorithm satisfies the convergence and optimality. Moreover, the self-reconstruction ability of the proposed incentive scheme was also verified, indicating that the system has strong robustness and scalability.
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Wu, Xiaoxue, Xuemin Gao, Xuan Zhu, Shuyi Zhang, Xinmei Liu, Huayu Yang, Hua Song, and Qing Chen. "Fingerprint Analysis of Cnidium monnieri (L.) Cusson by High-Speed Counter-Current Chromatography." Molecules 24, no. 24 (December 8, 2019): 4496. http://dx.doi.org/10.3390/molecules24244496.
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Cnidium monnieri (L.) Cusson is a popular Traditional Chinese Medicine (TCM) with a variety of bioactivities. However, there are some problems that have affected the development of Cnidium monnieri (L.) Cusson. At present, many methods have been reported for the analysis of coumarins in Cnidium monnieri (L.) Cusson. However, the quality control of coumarins in Cnidium monnieri (L.) Cusson by high-speed counter-current chromatography (HSCCC) has not been reported. In this study, analytical high-speed counter-current chromatography (HSCCC) was successfully used for fingerprint of Cnidium monnieri (L.) Cusson with a two-phase solvent system composed of n-hexane-ethyl acetate-methanol-water at 4:6:6.5:3.5 (v/v). The UV wavelength was set at 254 nm. Six coumarin compounds with high biological activity were selected as indicator compounds for the quality control. The HSCCC fingerprint of the Cnidium monnieri (L.) Cusson was successfully established and there were some differences according to the results of the fingerprint analysis. The present results demonstrate that HSCCC is an established and efficient technique for the fingerprint analysis of Cnidium monnieri (L.) Cusson and can be used to control the quality of Cnidium monnieri (L.) Cusson. In brief, HSCCC is a useful technology for the fingerprint analytical method for TCM.
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Wang, Yuhang, Kun Zhao, Zhengqi Zheng, Wenqing Ji, Shuai Huang, and Difeng Ma. "Indoor Positioning with CNN and Path-Loss Model Based on Multivariable Fingerprints in 5G Mobile Communication System." Sensors 22, no. 9 (April 21, 2022): 3179. http://dx.doi.org/10.3390/s22093179.
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Many application scenarios require indoor positioning in fifth generation (5G) mobile communication systems in recent years. However, non-line of sight and multipath propagation lead to poor accuracy in a traditionally received signal strength-based fingerprints positioning system. In this paper, we propose a positioning method employing multivariable fingerprints (MVF) composed of measurements based on secondary synchronization signals (SSS). In the fingerprint matching, we use MVF to train the convolutional neural network (CNN) location classification model. Moreover, we utilize MVF to train the path-loss model, which indicates the relationship between the distance and the measurement. Then, a hybrid positioning model combining CNN and path-loss model is proposed to optimize the overall positioning accuracy. Experimental results show that all three positioning algorithms based on machine learning with MVF achieve accuracy improvement compared with that of Reference Signal Receiving Power (RSRP)-only fingerprint. CNN achieves best performance among three positioning algorithms in two experimental environments. The average positioning error of hybrid positioning model is 1.47 m, which achieves 9.26% accuracy improvement compared with that of CNN alone.
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Xu, Yang, Fangfei Xu, Yuejie Wang, Xu Wang, and Huiwei Bao. "HPLC Fingerprint Analysis of Rana chensinensis Eggs from Different Habitats and Their Antitussive Effect." International Journal of Analytical Chemistry 2022 (October 22, 2022): 1–12. http://dx.doi.org/10.1155/2022/9229970.
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In this paper, a novel fingerprint method was established for the quality control of Rana chensinensis eggs (RE) by high-performance liquid chromatography (HPLC). Cluster analysis and principal component analysis were performed. Besides, the antitussive effect of RE was explored. The analysis was achieved on a Kromasil 100-5C18 (4.6 mm × 250 mm, 5 μm) column by gradient elution using methanol-0.1% phosphoric acid solution as the mobile phase. The influence of RE on cough latent periods and cough times of mice was investigated via an ammonia cough-inducing experiment. The validated HPLC method was precise, reproducible, and stable. The HPLC fingerprints of 10 batches of RE samples displayed 31 well-resolved common peaks in the chromatogram. Three of these peaks were identified and assigned to 1-methyl hydantoin, estradiol, and 4-cholestene-3-one. The similarities of 10 batches of samples were more than 0.95. RE from different origins could be classified into three groups via SPSS 23.0 software, suggesting RE samples from various provinces (Jilin, Liaoning, and Heilongjiang) can be well distinguished via the established method. High dose and middle dose of the RE group can significantly prolong the cough latent periods of mice ( P < 0.05 or P < 0.01) and inhibit the cough times of mice ( P < 0.01), indicating RE had a good antitussive effect. HPLC fingerprint combined with multicomponent determination can be an efficient and useful method for monitoring the quality of RE. This study also provided a more comprehensive strategy for the quality evaluation of RE.
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Zhang, Lele, Lina Ma, Wuwen Feng, Congen Zhang, Feiya Sheng, Yi Zhang, Chen Xu, et al. "Quality fluctuation detection of an herbal injection based on biological fingerprint combined with chemical fingerprint." Analytical and Bioanalytical Chemistry 406, no. 20 (June 8, 2014): 5009–18. http://dx.doi.org/10.1007/s00216-014-7918-1.
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Yang, Siyuan, Mondher Bouazizi, Yuwen Cao, and Tomoaki Ohtsuki. "Inter-User Distance Estimation Based on a New Type of Fingerprint in Massive MIMO System for COVID-19 Contact Detection." Sensors 22, no. 16 (August 18, 2022): 6211. http://dx.doi.org/10.3390/s22166211.
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In this paper, we address the challenging task of estimating the distance between different users in a Millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (mMIMO) system. The conventional Time of Arrival (ToA) and Angle of Arrival (AoA) based methods need users under the Line-of-Sight (LoS) scenario. Under the Non-LoS (NLoS) scenario, the fingerprint-based method can extract the fingerprint that includes the location information of users from the channel state information (CSI). However, high accuracy CSI estimation involves a huge overhead and high computational complexity. Thus, we design a new type of fingerprint generated by beam sweeping. In other words, we do not have to know the CSI to generate fingerprint. In general, each user can record the Received Signal Strength Indicator (RSSI) of the received beams by performing beam sweeping. Such measured RSSI values, formatted in a matrix, could be seen as beam energy image containing the angle and location information. However, we do not use the beam energy image as the fingerprint directly. Instead, we use the difference between two beam energy images as the fingerprint to train a Deep Neural Network (DNN) that learns the relationship between the fingerprints and the distance between these two users. Because the proposed fingerprint is rich in terms of the users’ location information, the DNN can easily learn the relationship between the difference between two beam energy images and the distance between those two users. We term it as the DNN-based inter-user distance (IUD) estimation method. Nonetheless, we investigate the possibility of using a super-resolution network to reduce the involved beam sweeping overhead. Using super-resolution to increase the resolution of low-resolution beam energy images obtained by the wide beam sweeping for IUD estimation can facilitate considerate improvement in accuracy performance. We evaluate the proposed DNN-based IUD estimation method by using original images of resolution 4 × 4, 8 × 8, and 16 × 16. Simulation results show that our method can achieve an average distance estimation error equal to 0.13 m for a coverage area of 60 × 30 m2. Moreover, our method outperforms the state-of-the-art IUD estimation methods that rely on users’ location information.
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Nejdl, Lukas, Martina Havlikova, Filip Mravec, Tomas Vaculovic, Veronika Faltusova, Kristyna Pavelicova, Mojmir Baron, et al. "UV-Induced fingerprint spectroscopy." Food Chemistry 368 (January 2022): 130499. http://dx.doi.org/10.1016/j.foodchem.2021.130499.
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Qiu, Qin, Lujuan Jiang, Chunying Huang, Lifeng Yu, Dandan Zhen, Meifang Ye, Yuanyuan Liu, et al. "Study on the Spectrum-Effect Correlation of Anti-Inflammatory Active Extract of Sauropus spatulifolius Beille." Journal of Analytical Methods in Chemistry 2022 (May 24, 2022): 1–9. http://dx.doi.org/10.1155/2022/5646546.
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Sauropus spatulifolius Beille (S. spatulifolius) is a commonly used medicine of the Bourau and Yao nationalities. However, the composition of S. spatulifolius is complex, and simple chemical fingerprints cannot accurately evaluate the relationship between its composition and efficacy. In this study, high-performance liquid chromatography (HPLC) method was used to establish the fingerprint of the ethyl acetate extract of S. spatulifolius. Based on the evaluation of the similarity of chromatographic fingerprints of traditional Chinese medicine, combined with cluster analysis and principal component analysis (PCA), the common peaks of fingerprints were evaluated. The anti-inflammatory effect data were extracted through the dimethylbenzene-induced ear-swelling model in mice. The gray relational analysis (GRA) combined with partial least squares regression (PLSR) was used to study the spectrum-effect correlation of S. spatulifolius. As a result, the HPLC fingerprint of the ethyl acetate extract of S. spatulifolius was established, and 18 common peaks were identified. Except for S6, the other similarities are all above 0.915. The reference substance control method was used to identify two absorption peaks, namely, protocatechuic acid and caffeic acid. The cluster analysis results showed that 10 samples from different origins were grouped into four categories, which was consistent with the PCA results. Ethyl acetate extract of 10 batches of S. spatulifolius could significantly inhibit the ear swelling of mice ( P < 0.01 ). Through GRA, the order of the contribution of each chemical component to the anti-inflammatory efficacy was obtained. The results of PLSR showed that the VIP values of peaks 3, 4, and 12 were greater than 1 and were positively correlated with anti-inflammatory activity. In this study, the HPLC fingerprint of the ethyl acetate extract of S. spatulifolius was established. Through the study of the spectrum-effect correlation, the anti-inflammatory active substance of the ethyl acetate extract of S. spatulifolius was obtained. The anti-inflammatory effect of S. spatulifolius was the result of the joint action of multiple ingredients. This research helps to quickly and accurately discover the active ingredient groups of traditional Chinese medicine and provides new ideas and methods for studying the effective substances of traditional Chinese medicine.
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Xu, Jing, Rongrong Zhou, Lin Luo, Ying Dai, Yaru Feng, and Zhihua Dou. "Quality Evaluation of Decoction Pieces of Gardeniae Fructus Based on Qualitative Analysis of the HPLC Fingerprint and Triple-Q-TOF-MS/MS Combined with Quantitative Analysis of 12 Representative Components." Journal of Analytical Methods in Chemistry 2022 (February 26, 2022): 1–13. http://dx.doi.org/10.1155/2022/2219932.
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In this study, quality evaluation (QE) of 40 batches of decoction pieces of Gardeniae Fructus (GF) produced by different manufacturers of herbal pieces was performed by qualitative analysis of the HPLC fingerprint and ultra-fast liquid chromatography (UFLC)-triple-Q-TOF-MS/MS combined with quantitative analysis of multiple components, which we established previously for QE of traditional medicine. First, HPLC fingerprints of 40 samples were determined, and the common peaks in the reference fingerprint were assigned. Second, the components of the common peaks in the HPLC fingerprints were identified by UFLC-triple-Q-TOF-MS/MS. Finally, the contents of the components confirmed by reference substances were measured. The results showed that there were 28 common peaks in the HPLC fingerprints of 40 samples. The components of these 28 common peaks were identified as 13 iridoids, 4 crocins, 7 monocyclic monoterpenoids, 3 organic acids, and 1 flavonoid. Of these, a total of 12 components, including 7 iridoids of geniposide, shanzhiside, geniposidic acid, deacetyl asperulosidic acid methyl ester, gardenoside, scandoside methyl ester, and genipin gentiobioside, 2 crocins such as crocin I and crocin II, 1 monocyclic monoterpenoid of jasminoside B, 1 organic acid of chlorogenic acid, and 1 flavonoid of rutin, were unambiguously identified by comparison with reference substances. There were certain differences in the contents of these 12 components among 40 samples. The geniposide content ranged from 37.917 to 72.216 mg/g, and the total content of the 7 iridoids ranged from 59.931 to 94.314 mg/g.
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Lan, Jing, Gelin Wu, Linlin Wu, Haibin Qu, Ping Gong, Yongjian Xie, Peng Zhou, and Xingchu Gong. "Development of a Quantitative Chromatographic Fingerprint Analysis Method for Sugar Components of Xiaochaihu Capsules Based on Quality by Design Concept." Separations 10, no. 1 (December 26, 2022): 13. http://dx.doi.org/10.3390/separations10010013.
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Background: Xiaochaihu capsule is composed of seven traditional Chinese medicines. The pharmacopoeia only focuses on the quantitative detection of baicalin, which cannot fully reflect the quality of the preparation. Some medium polar components were used to establish the fingerprint of Xiaochaihu capsule, but there was no report on the strong polar components. Methods: A high performance liquid chromatography-corona charged aerosol detection technology was used to establish a fingerprint analysis method for Xiaochaihu capsules following an analytical quality by design approach. Definitive screening designed experiments were used to optimize the method parameters. A stepwise regression method was used to build quantitative models. The method operable design region was calculated using the experimental error simulation method. Plackett–Burman designed experiments were carried out to test robustness. Results: The contents of four components were simultaneously determined. There were seven common peaks in the fingerprint. The common peak area accounted for 91.72%. Both fingerprint and quantitative analysis methods were validated as applicable in the methodology study. The quantitative fingerprint analysis method for sugar components can fill the gap in the detection of strong polar components in the existing methods. It provides a new technology for the comprehensive overall evaluation of Xiaochaihu capsule.
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Habibie, Hanifullah, Rudi Heryanto, Mohamad Rafi, and Latifah Kosim Darusman. "Development of Quality Control Method for Glucofarmaka Antidiabetic Jamu by HPLC Fingerprint Analysis." Indonesian Journal of Chemistry 17, no. 1 (April 1, 2017): 79. http://dx.doi.org/10.22146/ijc.23616.
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Herbal medicines become increasingly popular all over the world for preventive and therapeutic purposes. Quality control of herbal medicines is important to make sure their safety and efficacy. Chromatographic fingerprinting has been accepted by the World Health Organization as one reliable strategy for quality control method in herbal medicines. In this study, high-performance liquid chromatography fingerprint analysis was developed as a quality control method for glucofarmaka antidiabetic jamu. The optimum fingerprint chromatogram were obtained using C18 as the stationary phase and linear gradient elution using 10–95% acetonitrile:water as the mobile phase within 60 minutes of elution and detection at 210 nm. About 20 peaks were detected and could be used as fingerprint of glucofarmaka jamu. To evaluate the analytical performance of the method, we determined the precision, reproducibility, and stability. The result of the analytical performance showed reliable results. The proposed method could be used as a quality control method for glucofarmaka antidiabetic jamu and also for its raw materials.
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Mims, Willie H., Michael A. Temple, and Robert F. Mills. "Active 2D-DNA Fingerprinting of WirelessHART Adapters to Ensure Operational Integrity in Industrial Systems." Sensors 22, no. 13 (June 29, 2022): 4906. http://dx.doi.org/10.3390/s22134906.
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The need for reliable communications in industrial systems becomes more evident as industries strive to increase reliance on automation. This trend has sustained the adoption of WirelessHART communications as a key enabling technology and its operational integrity must be ensured. This paper focuses on demonstrating pre-deployment counterfeit detection using active 2D Distinct Native Attribute (2D-DNA) fingerprinting. Counterfeit detection is demonstrated using experimentally collected signals from eight commercial WirelessHART adapters. Adapter fingerprints are used to train 56 Multiple Discriminant Analysis (MDA) models with each representing five authentic network devices. The three non-modeled devices are introduced as counterfeits and a total of 840 individual authentic (modeled) versus counterfeit (non-modeled) ID verification assessments performed. Counterfeit detection is performed on a fingerprint-by-fingerprint basis with best case per-device Counterfeit Detection Rate (%CDR) estimates including 87.6% < %CDR < 99.9% and yielding an average cross-device %CDR ≈ 92.5%. This full-dimensional feature set performance was echoed by dimensionally reduced feature set performance that included per-device 87.0% < %CDR < 99.7% and average cross-device %CDR ≈ 91.4% using only 18-of-291 features—the demonstrated %CDR > 90% with an approximate 92% reduction in the number of fingerprint features is sufficiently promising for small-scale network applications and warrants further consideration.
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Lin, Chaozhan, Fangle Liu, Runjing Zhang, Meiting Liu, Chenchen Zhu, Jing Zhao, and Shaoping Li. "High-Performance Thin-Layer Chromatographic Fingerprints of Triterpenoids for Distinguishing Between Isodon lophanthoides and Isodon lophanthoides var. gerardianus." Journal of AOAC INTERNATIONAL 102, no. 3 (May 1, 2019): 714–19. http://dx.doi.org/10.5740/jaoacint.18-0305.
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Abstract Background: The aerial parts of Isodon lophanthoides (Buch. Ham. ex D. Don) Hara (IL) has been officially recorded as Isodonis lophanthoidis herba by many provincial quality control standards for Chinese herbal medicines in China. Recently, it has been found that one of its varieties, I. lophanthoides var. gerardianus (Benth.) Hara (ILVG) was pretended to be I. lophanthoidis herba in herbal material markets or cultivated bases. Because of the similarity on appearance, these two close-related species were difficult to be identified by morphological characters, especially when they are dried and sliced. Objective: To establish a rapid and specific method for identification of the two herbal medicines. Method: In this paper, a fingerprint of triterpenoids by HPTLC coupled with a digital profiling was established to identify IL and distinguish it from its substitute, ILVG. The specific HPTLC fingerprints constructed by determining twelve batches of IL samples and thirteen batches of ILVG samples, intuitionally reflected the difference between the two species on HPTLC image and the peak-peak rations of chemical distribution. Results: Authentication results of nine batches of commercial samples by the above established HPTLC fingerprints exhibited coincident conclusion with that by morphological means. Conclusions: The HPTLC fingerprint is proven to be simple, repeatable, specific, and suitable for rapid identification of I. lophanthoidis herba. Highlights: An efficient method for identification and distinguishing Isodon lophanthoides from its substitute, I. lophanthoides var. gerardianus, was established. HPTLC fingerprints of ursane-type triterpenoides were constructed and validated by determining IL and ILVG samples.
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Zhang, Yi, Yue Ding, Tong Zhang, Xiaoyi Jiang, Yaxiong Yi, Lijuan Zhang, Yi Chen, Ting Li, Ping Kang, and Juanjuan Tian. "Quantitative Analysis of Twelve Active Components Combined With Chromatographic Fingerprint for Comprehensive Evaluation of Qinma Prescription by Ultra-Performance Liquid Chromatography Coupled With Diode Array Detection." Journal of Chromatographic Science 57, no. 9 (September 27, 2019): 855–65. http://dx.doi.org/10.1093/chromsci/bmz060.
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Abstract A combination method of ultra-performance liquid chromatography (UPLC) coupled with diode array detection has been developed for quality evaluation of Qinma prescription (QMP), based on chromatographic fingerprint technology with the similarity analysis (SA) and the quantitative analysis of 12 components by hierarchical cluster analysis (HCA). The established method has been validated by linearity, precision, repeatability, stability and recovery tests. The UPLC fingerprints with 17 common peaks of 5 QMP samples prepared by different extraction methods including water decoction extraction, water extraction-ethanol precipitation method, ethanol reflux extraction, ethanol extraction-water precipitation method and methanol ultrasonic extraction were obtained, and the SA results indicated that similarity index was greatly influenced by the large peak. The similarity index ranged from 0.816 to 0.999 basing on 17 peaks, which has been decreased to 0.683–0.999 basing on 16 peaks without the large peak of baicalin (BA). The results of simultaneous quantification of 12 components in these 5 QMP samples proved that BA, gallic acid (GA), wogonoside (WOG) and gentiopicroside (GEN) were the major ingredients in QMP with high contents >1.44 (mg/g), indicating that ethanol reflux was the most effective extraction method. Integrating fingerprint analysis, simultaneous determination and HCA, the established method is rapid, sensitive, accurate and readily applicable. All the results indicated that the combination method can control the quality of QMP and its related traditional Chinese medicinal compounds more comprehensively and scientifically.
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Liu, Yingchun, Guoxiang Sun, Yan Wang, Lanping Yang, and Fangliang Yang. "Monitoring the quality consistency of Weibizhi tablets by micellar electrokinetic chromatography fingerprints combined with multivariate statistical analyses, the simple quantified ratio fingerprint method, and the fingerprint-efficacy relationship." Journal of Separation Science 38, no. 12 (May 15, 2015): 2174–81. http://dx.doi.org/10.1002/jssc.201500145.
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Cao, Hongji, Yunjia Wang, Jingxue Bi, and Hongxia Qi. "An Adaptive Bluetooth/Wi-Fi Fingerprint Positioning Method based on Gaussian Process Regression and Relative Distance." Sensors 19, no. 12 (June 21, 2019): 2784. http://dx.doi.org/10.3390/s19122784.
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Trusted positioning data are very important for the fusion of Bluetooth fingerprint positioning (BFP) and Wi-Fi fingerprint positioning (WFP). This paper proposes an adaptive Bluetooth/Wi-Fi fingerprint positioning method based on Gaussian process regression (GPR) and relative distance (RD), which can choose trusted positioning results for fusion. In the offline stage, measurements of the Bluetooth and Wi-Fi received signal strength (RSS) were collected to construct Bluetooth and Wi-Fi fingerprint databases, respectively. Then, fingerprint positioning error prediction models were built with GPR and data from the fingerprint databases. In the online stage, online Bluetooth and Wi-Fi RSS readings were matched with the fingerprint databases to get a Bluetooth fingerprint positioning result (BFPR) and a Wi-Fi fingerprint positioning result (WFPR). Then, with the help of RD and fingerprint positioning error prediction models, whether the positioning results are trusted was determined. The trusted result is selected as the position estimation result when there is only one trusted positioning result among the BFPR and WFPR. The mean is chosen as the position estimation result when both the BFPR and WFPR results are trusted or untrusted. Experimental results showed that the proposed method was better than BFP and WFP, with a mean positioning error of 2.06 m and a root-mean-square error of 1.449 m.
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Nicoletti, Marcello. "HPTLC fingerprint: a modern approach for the analytical determination of botanicals." Revista Brasileira de Farmacognosia 21, no. 5 (October 2011): 818–23. http://dx.doi.org/10.1590/s0102-695x2011005000131.
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Huang, Lingxia, Liuwei Meng, Liang Yang, Jingyu Wang, Shaojia Li, Yong He, and Di Wu. "A novel method to extract important features from laser induced breakdown spectroscopy data: application to determine heavy metals in mulberries." Journal of Analytical Atomic Spectrometry 34, no. 3 (2019): 460–68. http://dx.doi.org/10.1039/c8ja00442k.
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Pleik, Stefanie, Bernhard Spengler, Dhaka Ram Bhandari, Steven Luhn, Thomas Schäfer, Dieter Urbach, and Dieter Kirsch. "Ambient-air ozonolysis of triglycerides in aged fingerprint residues." Analyst 143, no. 5 (2018): 1197–209. http://dx.doi.org/10.1039/c7an01506b.
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Aging behavior of unsaturated lipids in aged fingerprint residues was analyzed by LC-MS and MALDI-MS. Structure identification helped identify ozonolysis as a major degradation pathway of lipids in fingerprint residues.
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Yang, Yu, Xing-Lin Huang, Zhong-Min Jiang, Xue-Fang Li, Yan Qi, Jie Yu, Xing-Xin Yang, and Mei Zhang. "Quantification of Chemical Groups and Quantitative HPLC Fingerprint of Poria cocos (Schw.) Wolf." Molecules 27, no. 19 (September 27, 2022): 6383. http://dx.doi.org/10.3390/molecules27196383.
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(1)Objective: In this study, a quantitative analysis of chemical groups (the triterpenoids, water-soluble polysaccharides, and acidic polysaccharides) and quantitative high liquid performance chromatography (HPLC) fingerprint of Poria cocos (Schw.) Wolf (PC) for quality control was developed. (2) Methodology: First, three main chemical groups, including triterpenoids, water-soluble polysaccharides, and acidic polysaccharides, in 16 batches of PC were evaluated by ultraviolet spectrophotometry. Afterward, the quantitative fingerprint of PC was established, and the alcohol extract of PC was further evaluated. The method involves establishing 16 batches of PC fingerprints by HPLC, evaluating the similarity of different batches of PC, and identifying eight bioactive components, including poricoic acid B (PAB), dehydrotumulosic acid (DTA), poricoic acid A (PAA), polyporenic acid C (PAC), 3-epidehydrotumulosic acid (EA), dehydropachymic acid (DPA), dehydrotrametenolic acid (DTA-1), and dehydroeburicoic acid (DEA), in PC by comparison with the reference substance. Combined with the quantitative analysis of multi-components by a single marker (QAMS), six bioactive ingredients, including PAB, DTA, PAC, EA, DPA, and DEA, in PC from different places were established. In addition, the multivariate statistical analyses, such as principal component analysis and heatmap hierarchical clustering analysis are more intuitive, and the visual analysis strategy was used to evaluate the content of bioactive components in 16 batches of PC. Finally, the analysis strategy of three main chemical groups in PC was combined with the quantitative fingerprint strategy, which reduced the error caused by the single method. (3) Results: The establishment of a method for the quantification of chemical groups and quantitative HPLC fingerprint of PC was achieved as demonstrated through the quantification of six triterpenes in PC by a single marker. (4) Conclusions: Through qualitative and quantitative chemical characterization, a multi-directional, simple and efficient routine evaluation method of PC quality was established. The results reveal that this strategy can provide an analytical method for the quality evaluation of PC and other Chinese medicinal materials.
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M. Jomaa, Rami, Hassan Mathkour, Yakoub Bazi, and Md Saiful Islam. "End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection." Sensors 20, no. 7 (April 7, 2020): 2085. http://dx.doi.org/10.3390/s20072085.
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Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality.