Journal articles: 'Optical and molecular fingerprints'

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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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Yao, Haizi, Weiwei Zhang, Wenfu Liu, and Hongying Mei. "Resolved terahertz spectroscopy of tiny molecules employing tunable spoof plasmons in an otto prism configuration." Journal of Optics 24, no. 4 (March 7, 2022): 045301. http://dx.doi.org/10.1088/2040-8986/ac5537.

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Abstract Sensitive detection of terahertz fingerprint absorption spectrum for tiny molecules is essential for bioanalysis. However, it is extremely challenging for traditional terahertz spectroscopy measurement because of the weak spectral response caused by the large mismatch between terahertz wavelengths and biomolecular dimensions. Here, we proposed a wideband-tunable metal plasmonic terahertz biosensor to detect tiny biomolecules, employing attenuated total reflection in an Otto prism configuration and tightly confined spoof surface plasmons on the grooved metal surface. Benefitting from the plasmonic electric field enhancement, such a biosensor is able to identify the molecular terahertz fingerprints. As a proof of concept, a hypothetical molecule modeled by the Lorentz model with two vibrational modes is used as the sensing analytes. Simulation results show that the absorption of two vibrational modes of analytes can be selectively enhanced up to ten times by plasmonic resonance, and their fingerprints can be resolved by sweeping incident angle in a wide waveband. Our work provides an effective approach for the highly sensitive identification of molecular fingerprints in fields of biochemical sensing for tiny analytes.

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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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Mohammed, Fatima, and Samira A. Mahdi. "Detection Measurements of Some Drugs Materials in Fingerprints." NeuroQuantology 20, no. 5 (May 18, 2022): 483–87. http://dx.doi.org/10.14704/nq.2022.20.5.nq22198.

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The major goal of this project is to use the Gas Chromatography–Mass Spectrometry (GC-MS) instrument to evaluate the fingerprints of illicit amphetamine. Samples of amphetamine were obtained from the Iraqi Narcotics Control Bureau. The fingerprints were examined before and after contamination with anesthetic powder. The results showed a clear difference between the spectrum of the pure fingerprint and the spectrum of the contaminated fingerprint with amphetamine. The highest peak of the drug sample was recorded at the time (6.80) minutes and Abundance 120000, and in contrast the appearance of the same peak in the fingerprint sample at the time (6.80) and Abundance 120000, and thus we find that the (GC-MS) device is sensitive for a small amount of drugs and can be used to detect it in the fingerprints.

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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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Ulrich, Georg, Emanuel Pfitzner, Arne Hoehl, Jung-Wei Liao, Olga Zadvorna, Guillaume Schweicher, Henning Sirringhaus, et al. "Thermoelectric nanospectroscopy for the imaging of molecular fingerprints." Nanophotonics 9, no. 14 (August 21, 2020): 4347–54. http://dx.doi.org/10.1515/nanoph-2020-0316.

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AbstractWe present a nanospectroscopic device platform allowing simple and spatially resolved thermoelectric detection of molecular fingerprints of soft materials. Our technique makes use of a locally generated thermal gradient converted into a thermoelectric photocurrent that is read out in the underlying device. The thermal gradient is generated by an illuminated atomic force microscope tip that localizes power absorption onto the sample surface. The detection principle is illustrated using a concept device that contains a nanostructured strip of polymethyl methacrylate (PMMA) defined by electron beam lithography. The platform’s capabilities are demonstrated through a comparison between the spectrum obtained by on-chip thermoelectric nanospectroscopy with a nano-FTIR spectrum recorded by scattering-type scanning near-field optical microscopy at the same position. The subwavelength spatial resolution is demonstrated by a spectral line scan across the edge of the PMMA layer.

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Alali, Haifa, Yukai Ai, Yong-Le Pan, Gorden Videen, and Chuji Wang. "A Collection of Molecular Fingerprints of Single Aerosol Particles in Air for Potential Identification and Detection Using Optical Trapping-Raman Spectroscopy." Molecules 27, no. 18 (September 14, 2022): 5966. http://dx.doi.org/10.3390/molecules27185966.

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Characterization, identification, and detection of aerosol particles in their native atmospheric states remain a challenge. Recently, optical trapping-Raman spectroscopy (OT-RS) has been developed and demonstrated for characterization of single, airborne particles. Such particles in different chemical groups have been characterized by OT-RS in recent years and many more are being studied. In this work, we collected single-particle Raman spectra measured using the OT-RS technique and began construction of a library of OT-RS fingerprints that may be used as a reference for potential detection and identification of aerosol particles in the atmosphere. We collected OT-RS fingerprints of aerosol particles from eight different categories including carbons, bioaerosols (pollens, fungi, vitamins, spores), dusts, biological warfare agent surrogates, etc. Among the eight categories, spectral fingerprints of six groups of aerosol particles have been published previously and two other groups are new. We also discussed challenges, limitations, and advantages of using single-particle optical trapping-Raman spectroscopy for aerosol-particle characterization, identification, and detection.

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Palma, J., C. Liessner, and S. Mil'Shtein. "Contactless optical scanning of fingerprints with 180° view." Scanning 28, no. 6 (March 14, 2007): 301–4. http://dx.doi.org/10.1002/sca.4950280601.

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Asing, Md Eaqub Ali, and Sharifah Bee Abd Hamid. "SERS-Modeling in Molecular Sensing." Advanced Materials Research 1109 (June 2015): 223–26. http://dx.doi.org/10.4028/www.scientific.net/amr.1109.223.

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Surface enhanced Raman spectroscopy (SERS) is an ultrasensitive vibrational spectroscopic technique that useful tools in detecting biomolecules at near or on the surface of plasmonic nanostructures. Unique physicochemical and optical properties of noble metal nanostructures allow the assimilation of biomolecular probes and exhibit distinctive spectra, prompting the development of a plethora of biosensing platforms in molecular diagnostics. In SERS biosensor, signal to noise ration such as recognition and transducer elements that provide fingerprint spectrum at the lower limit of detection with specific binding or hybridized event, increasing reliability and sensitivity. Since the localized surface plasmon resonance (LSPR) of nanoparticle lies at the heart of SERS. It is essential to control all of the LSPR influencing factors in highly sensitivity signal strength that ensures reproducibility of SERS signals. SERS active substrates, transducer elements, metal surfaces modification, interparticle spacing, dielectric environment and selection of biorecognition molecules contribute in SERS signal strength. Modified metal structure with bioprobe and Raman reporter molecules provides a strong signature fingerprints that surely contribute to noble biosensor structural designing. We reviewed here ideal fabrication of nanostructure for SERS application in molecular sensing research fields.

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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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Zhang, Kang, Shu Huang, Eryun Liu, and Heng Zhao. "LFLDNet: Lightweight Fingerprint Liveness Detection Based on ResNet and Transformer." Sensors 23, no. 15 (August 1, 2023): 6854. http://dx.doi.org/10.3390/s23156854.

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With the rapid development of fingerprint recognition systems, fingerprint liveness detection is gradually becoming regarded as the main countermeasure to protect the fingerprint identification system from spoofing attacks. Convolutional neural networks have shown great potential in fingerprint liveness detection. However, the generalization ability of the deep network model for unknown materials, and the computational complexity of the network, need to be further improved. A new lightweight fingerprint liveness detection network is here proposed to distinguish fake fingerprints from real ones. The method includes mainly foreground extraction, fingerprint image blocking, style transfer based on CycleGan and an improved ResNet with multi-head self-attention mechanism. The proposed method can effectively extract ROI and obtain the end-to-end data structure, which increases the amount of data. For false fingerprints generated from unknown materials, the use of CycleGan network improves the model generalization ability. The introduction of Transformer with MHSA in the improved ResNet improves detection performance and reduces computing overhead. Experiments on the LivDet2011, LivDet2013 and LivDet2015 datasets showed that the proposed method achieves good results. For example, on the LivDet2015 dataset, our methods achieved an average classification error of 1.72 across all sensors, while significantly reducing network parameters, and the overall parameter number was only 0.83 M. At the same time, the experiment on small-area fingerprints yielded an accuracy of 95.27%.

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Ho, Louise, Michael Pepper, and Philip Taday. "Signatures and fingerprints." Nature Photonics 2, no. 9 (September 2008): 541–43. http://dx.doi.org/10.1038/nphoton.2008.174.

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Bobrovnikov, Sergei, Evgeny Gorlov, Viktor Zharkov, Sergei Murashko, Alexander Vorozhtsov, and Alexander Stykon. "Investigation of the Process of Evolution of Traces of Explosives Carried by Fingerprints Using Polarimetric Macrophotography and Remote LF/LIF Method." Photonics 10, no. 7 (June 28, 2023): 740. http://dx.doi.org/10.3390/photonics10070740.

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The results of a study of the degradation of the cyclotrimethylenetrinitramine (RDX) traces carried in fingerprints depending on the fingerprint number are presented. The surface concentration of the trace was assessed using macrophotography in polarized light and by the method of laser fragmentation/laser-induced fluorescence. A technique for estimating the surface concentration of RDX traces in sweat-fat fingerprints based on pixel-by-pixel scanning of macrophotographs is described. The data of parallel experiments on remote laser detection of RDX particles in fingerprints are presented. A comparison shows that the results of the direct measurements of the total trace area are in good agreement with the LF/LIF response data.

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Roberge, Danny, and Colin Soutar. "Operating-curve selection for optical and digital correlation of fingerprints." Applied Optics 37, no. 32 (November 10, 1998): 7545. http://dx.doi.org/10.1364/ao.37.007545.

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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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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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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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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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Voronin, Kirill V., Unai Aseguinolaza Aguirreche, Rainer Hillenbrand, Valentyn S. Volkov, Pablo Alonso-González, and Alexey Y. Nikitin. "Nanofocusing of acoustic graphene plasmon polaritons for enhancing mid-infrared molecular fingerprints." Nanophotonics 9, no. 7 (May 18, 2020): 2089–95. http://dx.doi.org/10.1515/nanoph-2020-0164.

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AbstractMid-infrared (mid-IR) optical spectroscopy of molecules is of large interest in physics, chemistry, and biology. However, probing nanometric volumes of molecules is challenging because of the strong mismatch of their mid-infrared absorption and scattering cross-sections with the free-space wavelength. We suggest overcoming this difficulty by nanofocusing acoustic graphene plasmon polaritons (AGPs) – oscillations of Dirac charge carriers coupled to electromagnetic fields with extremely small wavelengths – using a taper formed by a graphene sheet above a metallic surface. We demonstrate that due to the appreciable field enhancement and mode volume reduction, the nanofocused AGPs can efficiently sense molecular fingerprints in nanometric volumes. We illustrate a possible realistic sensing sсenario based on AGP interferometry performed with a near-field microscope. Our results can open new avenues for designing tiny sensors based on graphene and other 2D polaritonic materials.

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Filipek, Patrycja, Hubert Hellwig, Agata Szlapa-Kula, and Michał Filapek. "Simple Donor–π–Acceptor Compounds Exhibiting Aggregation-Induced Emission as Hidden Fingerprints Detecting Agents." Molecules 28, no. 22 (November 14, 2023): 7597. http://dx.doi.org/10.3390/molecules28227597.

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Latent fingerprints are a significant carrier of information for a court expert. To detect this type of forensic trace, what is necessary is a method that is easy to use, compact, and versatile. The research aimed to investigate the physicochemical properties of luminescent substances of donor–π–acceptor systems in terms of their potential use in detecting hidden fingerprints. During the research, a group of fluorene compounds consisting of the (-CH=C(CN)(COOR)) moiety was designed and successfully synthesized. The optical, electrochemical, and aggregation-induced emission properties were studied. The aggregation-induced emission of compounds has been studied in the mixture of THF (as a good solvent) and water (as a poor solvent) with different water fractions ranging from 0% to 99%. Due to the molecular structure, substances showed different affinities to organic traces. As a result, it was noticed that all compounds showed the AIE phenomenon, while during tests on latent fingerprints, it was observed that two substances had particularly forward-looking features in this field.

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Wang, Qu, Haiyong Luo, Aidong Men, Fang Zhao, and Yan Huang. "An Infrastructure-Free Indoor Localization Algorithm for Smartphones." Sensors 18, no. 10 (October 3, 2018): 3317. http://dx.doi.org/10.3390/s18103317.

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Accurate indoor positioning technology provides location-based service for a variety of applications. However, most existing indoor localization approaches (e.g., Wi-Fi and Bluetooth-based methods) rely heavily on positioning infrastructure, which prevents their large-scale deployment and limits the range at which they are applicable. Here, we proposed an infrastructure-free indoor positioning and tracking approach, termed LiMag, which used ubiquitous magnetic field and ambient lights (e.g., fluorescent, incandescent, and light-emitting diodes (LEDs)) without containing modulated information. We conducted an in-depth study on both the advantages and the challenges in leveraging magnetic field and ambient light intensity for indoor localization. Based on the insights from this study, we established a hybrid observation model that took full advantage of both the magnetic field and ambient light signals. To address the low discernibility of the hybrid observation model, LiMag first generated a single-step fingerprint model by vectorizing consecutive hybrid observations within each step. In order to accurately track users, a lightweight single-step tracking algorithm based on the single-step fingerprints and the particle filter framework was designed. LiMag leveraged the walking information of users and several single-step fingerprints to generate long trajectory fingerprints that exhibited much higher location differentiation ability than the single-step fingerprint. To accelerate particle convergence and eliminate the accumulative error of single-step tracking algorithm, a long trajectory calibration scheme based on long trajectory fingerprints was also introduced. An undirected weighted graph model was constructed to decrease the computational overhead resulting from this long trajectory matching. In addition to typical indoor scenarios including offices, shopping malls and parking lots, we also conducted experiments in more challenging scenarios, including large open-plan areas as well as environments characterized by strong sunlight. Our proposed algorithm achieved a 75th percentile localization accuracy of 1.8 m and 2.2 m, respectively, in the office and shopping mall tested. In conclusion, our LiMag algorithm provided location-based service of infrastructure-free with significantly improved localization accuracy and coverage, as well as satisfactory robustness inside complex indoor environments.

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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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Liu, Yuting, Mengzhou Bi, Xuewen Zhang, Na Zhang, Guohui Sun, Yue Zhou, Lijiao Zhao, and Rugang Zhong. "Machine Learning Models for the Classification of CK2 Natural Products Inhibitors with Molecular Fingerprint Descriptors." Processes 9, no. 11 (November 19, 2021): 2074. http://dx.doi.org/10.3390/pr9112074.

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Casein kinase 2 (CK2) is considered an important target for anti-cancer drugs. Given the structural diversity and broad spectrum of pharmaceutical activities of natural products, numerous studies have been performed to prove them as valuable sources of drugs. However, there has been little study relevant to identifying structural factors responsible for their inhibitory activity against CK2 with machine learning methods. In this study, classification studies were conducted on 115 natural products as CK2 inhibitors. Seven machine learning methods along with six molecular fingerprints were employed to develop qualitative classification models. The performances of all models were evaluated by cross-validation and test set. By taking predictive accuracy(CA), the area under receiver operating characteristic (AUC), and (MCC)as three performance indicators, the optimal models with high reliability and predictive ability were obtained, including the Extended Fingerprint-Logistic Regression model (CA = 0.859, AUC = 0.826, MCC = 0.520) for training test andPubChem fingerprint along with the artificial neural model (CA = 0.826, AUC = 0.933, MCC = 0.628) for test set. Meanwhile, the privileged substructures responsible for their inhibitory activity against CK2 were also identified through a combination of frequency analysis and information gain. The results are expected to provide useful information for the further utilization of natural products and the discovery of novel CK2 inhibitors.

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Yuille, A. L., and T. Poggio. "Fingerprints theorems for zero crossings." Journal of the Optical Society of America A 2, no. 5 (May 1, 1985): 683. http://dx.doi.org/10.1364/josaa.2.000683.

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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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Rao, S. Madhusudana. "Method for producing correct fingerprints." Applied Optics 47, no. 1 (December 20, 2007): 25. http://dx.doi.org/10.1364/ao.47.000025.

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Tran, Lisa, Hye-Na Kim, Ningwei Li, Shu Yang, Kathleen J. Stebe, Randall D. Kamien, and Martin F. Haase. "Shaping nanoparticle fingerprints at the interface of cholesteric droplets." Science Advances 4, no. 10 (October 2018): eaat8597. http://dx.doi.org/10.1126/sciadv.aat8597.

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The ordering of nanoparticles into predetermined configurations is of importance to the design of advanced technologies. Here, we balance the interfacial energy of nanoparticles against the elastic energy of cholesteric liquid crystals to dynamically shape nanoparticle assemblies at a fluid interface. By adjusting the concentration of surfactant that plays the dual role of tuning the degree of nanoparticle hydrophobicity and altering the molecular anchoring of liquid crystals, we pattern nanoparticles at the interface of cholesteric liquid crystal emulsions. In this system, interfacial assembly is tempered by elastic patterns that arise from the geometric frustration of confined cholesterics. Patterns are tunable by varying both surfactant and chiral dopant concentrations. Adjusting the particle hydrophobicity more finely by regulating the surfactant concentration and solution pH further modifies the rigidity of assemblies, giving rise to surprising assembly dynamics dictated by the underlying elasticity of the cholesteric. Because particle assembly occurs at the interface with the desired structures exposed to the surrounding water solution, we demonstrate that particles can be readily cross-linked and manipulated, forming structures that retain their shape under external perturbations. This study serves as a foundation for better understanding inter-nanoparticle interactions at interfaces by tempering their assembly with elasticity and for creating materials with chemical heterogeneity and linear, periodic structures, essential for optical and energy applications.

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Flanagan, Padraic J., and Jacqueline M. Cole. "Clustering a database of optically absorbing organic molecules via a hierarchical fingerprint scheme that categorizes similar functional molecular fragments." Journal of Chemical Physics 156, no. 15 (April 21, 2022): 154110. http://dx.doi.org/10.1063/5.0087603.

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A measure of chemical similarity is only useful if it implies similarity in some relevant property space. Typically, similarity calculations operate by assigning each molecule a chemical fingerprint: a fixed-length vector of bits where the on-bits signify the presence of a certain feature. Common fingerprinting schemes, such as extended-connectivity fingerprints, are by definition general and fail to capture much of the domain-specific theory that underpins similarity in a specific domain. In this work, a hierarchical fingerprinting scheme is developed that is bespoke to a database of ∼4500 organic molecules and their cognate optical absorption spectral properties. Our fingerprinting scheme incorporates molecular fragmentation and domain-specific chemical intuition into an algorithm that categorizes each fragment as being one of a core chemical group, a substituent, or a bridge. The algorithm is applied to every molecule in the database to generate a pool of chemically relevant fragments that are labeled according to their structural category. The fingerprint of each molecule is then composed of a nested Python dictionary specifying the unique identifiers of its constituent fragment entities and the structural links between them to give a hierarchical molecular encoding scheme. Four case studies show the application of our fingerprinting scheme to the subject database. In each case, the clustered molecules display a host of interesting chemical trends. The application that was used to develop and implement this bespoke fingerprinting scheme, referred to as ChemCluster, also exposes a host of other cheminformatics tools pertaining to this database, a selection of which is demonstrated in this work. The enhanced similarity comparisons afforded by our fingerprinting scheme, as well as the large repository of categorized fragments generated during its development, constitute the first step toward using this database in a data-driven materials discovery workflow.

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Yang, Chan-Shan, Yi-Sheng Cheng, Young-Chou Hsu, Yi-Cheng Chung, Jing-Ting Hung, Chien-Hao Liu, Jin-Chen Hsu, et al. "Hybrid Graphene-Based Photonic-Plasmonic Biochemical Sensor with a Photonic and Acoustic Cavity Structure." Crystals 11, no. 10 (September 28, 2021): 1175. http://dx.doi.org/10.3390/cryst11101175.

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In this study, we propose a biochemical sensor that features a photonic cavity integrated with graphene. The tunable hybrid plasmonic-photonic sensor can detect the molecular fingerprints of biochemicals with a small sample volume. The stacking sequence of the device is “ITO grating/graphene/TiO2/Au/Si substrate”, which composes a photonic band gap structure. A defect is created within the ITO gratings to form a resonant cavity. The plasmonic-photonic energy can be confined in the cavity to enhance the interaction between light and the analyte deposited in the cavity. The finite element simulation results indicated that the current sensor exhibits very high values in resonance shift and sensitivity. Moreover, the resonance spectrum with a broad resonance linewidth can identify the molecular vibration bands, which was exemplified by the fingerprint detections of protein and the chemical compound CBP. The sensor possesses an electrical tunability by including a graphene layer, which allowed us to tune the effective refractive index of the cavity to increase the sensor’s sensing performance. In addition, our device admits a phononic bandgap as well, which was exploited to sense the mechanical properties of two particular dried proteins based on the simplified elastic material model instead of using the more realistic viscoelastic model. The dual examinations of the optical and mechanical properties of analytes from a phoxonic sensor can improve the selectivity in analyte detections.

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Pankin, Dmitrii, Mikhail Smirnov, Anastasia Povolotckaia, Alexey Povolotskiy, Evgenii Borisov, Maksim Moskovskiy, Anatoly Gulyaev, et al. "DFT Modelling of Molecular Structure, Vibrational and UV-Vis Absorption Spectra of T-2 Toxin and 3-Deacetylcalonectrin." Materials 15, no. 2 (January 15, 2022): 649. http://dx.doi.org/10.3390/ma15020649.

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This paper discusses the applicability of optical and vibrational spectroscopies for the identification and characterization of the T-2 mycotoxin. Vibrational states and electronic structure of the T-2 toxin molecules are simulated using a density-functional quantum-mechanical approach. A numerical experiment aimed at comparing the predicted structural, vibrational and electronic properties of the T-2 toxin with analogous characteristics of the structurally similar 3-deacetylcalonectrin is performed, and the characteristic spectral features that can be used as fingerprints of the T-2 toxin are determined. It is shown that theoretical studies of the structure and spectroscopic features of trichothecene molecules facilitate the development of methods for the detection and characterization of the metabolites.

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Zhao Cheng, 赵成, 黄立华 Huang Lihua, 凌丽青 Ling Liqing, 郭凯 Guo Kai, 屈建峰 Qu Jianfeng, 张善华 Zhang Shanhua, 李红霞 Li Hongxia, 庄锡峰 Zhuang Xifeng, and 黄惠杰 Huang Huijie. "Detection of Latent Fingerprints on Porous Paper." Chinese Journal of Lasers 45, no. 7 (2018): 0704003. http://dx.doi.org/10.3788/cjl201845.0704003.

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Karsch, Nicholas, Hendrik Schulte, Thomas Musch, and Christoph Baer. "A Novel Localization System in SAR-Demining Applications Using Invariant Radar Channel Fingerprints." Sensors 22, no. 22 (November 10, 2022): 8688. http://dx.doi.org/10.3390/s22228688.

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In this paper, we present a novel two dimensional (2D) frequency-modulated continuous-wave (FMCW) localization method for handheld systems based on the extraction of distinguishable subchannel fingerprints. Compared with other concepts, only one subdivided radar source channel is needed in order to instantly map a one-dimensional measurement to higher-dimensional space coordinates. The additional information of the detected target is implemented with low-cost hardware component features, which exhibit distinguishable space-dependent fingerprint codes. Using the given a priori information of the hardware thus leads to a universally applicable extension for low-cost synthetic aperture radar (SAR)-demining purposes. In addition to the description of the system concept and its requirements, the signal processing steps and the hardware components are presented. Furthermore, the 2D localization accuracy of the system and the classification accuracy of the frequency-coded fingerprints are described in a defined test environment to proof the operational reliability of the realized setup, reaching a classification accuracy of 94.7% and an averaged localization error of 4.9 mm.

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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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Locharoenrat, Kitsakorn, and Pattareeya Damrongsak. "Optical imaging of artificial latent fingerprints using rhodamine 6G and Au-core/Pd-shell nanorods." Ukrainian Journal of Physical Optics 20, no. 3 (2019): 106–12. http://dx.doi.org/10.3116/16091833/20/3/106/2019.

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Dubey, Satish Kumar, Dalip Singh Mehta, Arun Anand, and Chandra Shakher. "Simultaneous topography and tomography of latent fingerprints using full-field swept-source optical coherence tomography." Journal of Optics A: Pure and Applied Optics 10, no. 1 (January 1, 2008): 015307. http://dx.doi.org/10.1088/1464-4258/10/01/015307.

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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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Gao, Zhang, Xiao, and Li. "Kullback–Leibler Divergence Based Probabilistic Approach for Device-Free Localization Using Channel State Information." Sensors 19, no. 21 (November 3, 2019): 4783. http://dx.doi.org/10.3390/s19214783.

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Recently, people have become more and more interested in wireless sensing applications, among which indoor localization is one of the most attractive. Generally, indoor localization can be classified as device-based and device-free localization (DFL). The former requires a target to carry certain devices or sensors to assist the localization process, whereas the latter has no such requirement, which merely requires the wireless network to be deployed around the environment to sense the target, rendering it much more challenging. Channel State Information (CSI)—a kind of information collected in the physical layer—is composed of multiple subcarriers, boasting highly fined granularity, which has gradually become a focus of indoor localization applications. In this paper, we propose an approach to performing DFL tasks by exploiting the uncertainty of CSI. We respectively utilize the CSI amplitudes and phases of multiple communication links to construct fingerprints, each of which is a set of multivariate Gaussian distributions that reflect the uncertainty information of CSI. Additionally, we propose a kind of combined fingerprints to simultaneously utilize the CSI amplitudes and phases, hoping to improve localization accuracy. Then, we adopt a Kullback–Leibler divergence (KL-divergence) based kernel function to calculate the probabilities that a testing fingerprint belongs to all the reference locations. Next, to localize the target, we utilize the computed probabilities as weights to average the reference locations. Experimental results show that the proposed approach, whatever type of fingerprints is used, outperforms the existing Pilot and Nuzzer systems in two typical indoor environments. We conduct extensive experiments to explore the effects of different parameters on localization performance, and the results demonstrate the efficiency of the proposed approach.

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Wei, Yang, Aimin Yan, Jiabin Dong, Zhijuan Hu, and Jingtao Zhang. "Optical image encryption using QR code and multilevel fingerprints in gyrator transform domains." Optics Communications 403 (November 2017): 62–67. http://dx.doi.org/10.1016/j.optcom.2017.06.087.

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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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Pirri, Angela, Roman N. Maksimov, Jiang Li, Matteo Vannini, and Guido Toci. "Achievements and Future Perspectives of the Trivalent Thulium-Ion-Doped Mixed-Sesquioxide Ceramics for Laser Applications." Materials 15, no. 6 (March 11, 2022): 2084. http://dx.doi.org/10.3390/ma15062084.

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This paper is devoted to reviewing the latest results achieved in solid-state lasers based on thulium-doped mixed-sesquioxide ceramics, i.e., (Lu,Sc,Y)2O3. The near- and mid-infrared regions are of interest for many applications, from medicine to remote sensing, as they match molecular fingerprints and cover several atmospheric transparency windows. These matrices are characterized by a strong electron–phonon interaction—which results in a large splitting of the ground state—and by a spectral broadening of the optical transition suitable for developing tunable and short-pulse lasers. In particular, the manuscript reports on the trivalent thulium laser transitions at 1.5, 1.9, and 2.3 µm, along with the thermal and optical characteristics of the (Lu,Sc,Y)2O3 ceramics, including the fabrication techniques, spectroscopic and optical properties, and laser performances achieved in different pumping regimes, such as continuous-wave (CW), quasi-CW, and pulsed modes. A comparison of the performance obtained with these mixed-sesquioxide ceramics and with the corresponding crystals is reported.

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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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Day, Joanna S., Howell G. M. Edwards, Steven A. Dobrowski, and Alison M. Voice. "The detection of drugs of abuse in fingerprints using Raman spectroscopy I: latent fingerprints." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 60, no. 3 (February 2004): 563–68. http://dx.doi.org/10.1016/s1386-1425(03)00263-4.

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Bottari, Giovanni, Roberto Caciuffo, Marianna Fanti, David A. Leigh, Stewart F. Parker, and Francesco Zerbetto. "Solid-State Fingerprints of Molecular Threading Detected by Inelastic Neutron Scattering." ChemPhysChem 3, no. 12 (December 16, 2002): 1038–41. http://dx.doi.org/10.1002/cphc.200290007.

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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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Yang, Ting, Huizhi Yang, Guixia Ling, and Guoxiang Sun. "Evaluating the quality consistency of Keteling capsules by three-dimensional quantum fingerprints and HPLC fingerprint." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 270 (April 2022): 120820. http://dx.doi.org/10.1016/j.saa.2021.120820.

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Soriano, Miguel C., Luciano Zunino, Laurent Larger, Ingo Fischer, and Claudio R. Mirasso. "Distinguishing fingerprints of hyperchaotic and stochastic dynamics in optical chaos from a delayed opto-electronic oscillator." Optics Letters 36, no. 12 (June 7, 2011): 2212. http://dx.doi.org/10.1364/ol.36.002212.

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Godwal, Y., M. T. Taschuk, S. L. Lui, Y. Y. Tsui, and R. Fedosejevs. "Development of laser-induced breakdown spectroscopy for microanalysis applications." Laser and Particle Beams 26, no. 1 (March 2008): 95–104. http://dx.doi.org/10.1017/s0263034608000128.

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AbstractLaser induced breakdown spectroscopy is a fast non-contact technique for the analysis of the elemental composition of any sample. Our focus is to advance this technique into a regime where we use pulse energies below 100 µJ. This regime is referred to as micro-laser-induced breakdown spectroscopy or µLIBS. At present we have concentrated on two application areas : (1) The imaging of latent fingerprints and (2) the extension to laser ablation followed by laser-induced fluorescence (LA-LIF) for very high sensitivity analysis of contaminants in water. Preliminary pulse emission scaling of Na in latent fingerprints has been investigated for ~130 fs, 266 nm pulses with energies below 15 µJ. The lowest energy for reliable single shot detection of Na is approximately 3.5 µJ. A 2D map of a fingerprint on a Si wafer has been successfully demonstrated using 5 µJ pulses. In LA-LIF the detection sensitivity of micro-laser-induced breakdown spectroscopy (µLIBS) is improved by coupling it with a second resonant probe pulse. This technique was investigated for the detection of Pb at low concentrations when ablated by 266 nm, 170 µJ pulses. After a short delay the resulting plume was re-excited with a nanosecond laser pulse tuned to a specific transition of Pb. In the case of the resonant dual-pulse LIBS the limit of detection was found to be approximately 60 ppb for Pb in water for 1000 shots. It is expected that this result could be implemented with fiber or microchip lasers with multi-kHz repetition rates and fiber Bragg grating tuning elements. The results are promising for the development of portable µLIBS water monitoring systems and portable fingerprint scanners.

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Abanov, Ar, Andrey V. Chubukov, and Jörg Schmalian. "Fingerprints of spin mediated pairing in cuprates." Journal of Electron Spectroscopy and Related Phenomena 117-118 (June 2001): 129–51. http://dx.doi.org/10.1016/s0368-2048(01)00251-1.

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Day, Joanna S., Howell G. M. Edwards, Steven A. Dobrowski, and Alison M. Voice. "The detection of drugs of abuse in fingerprints using Raman spectroscopy II: cyanoacrylate-fumed fingerprints." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 60, no. 8-9 (July 2004): 1725–30. http://dx.doi.org/10.1016/j.saa.2003.09.013.

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Tufillaro, N. B., Hernán G. Solari, and R. Gilmore. "Relative rotation rates: Fingerprints for strange attractors." Physical Review A 41, no. 10 (May 1, 1990): 5717–20. http://dx.doi.org/10.1103/physreva.41.5717.

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Journal articles on the topic 'Optical and molecular fingerprints'

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