Number Of Fingerprints Research Articles

Investigation of the Process of Evolution of Traces of Explosives Carried by Fingerprints Using Polarimetric Macrophotography and Remote LF/LIF Method

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.

Time-difference-of-arrival positioning based on visible light communication for harbor-border inspection

In view of the complexity of port ship supervision and the influence of external factors such as electromagnetic interference in harbor-border inspection, an efficient system combining an unmanned aerial vehicle (UAV) and visible light positioning (VLP) is proposed for locating maritime targets. In this system, a rotatable receiver with five photodetectors (PDs) installed obliquely on UAV is designed for expanding the positioning range and allowing a lower flight altitude. On this basis, we propose the Chan-Taylor (CT) method based on time difference of arrival (TDOA) for target positioning. First, the localization problem is reformulated as a weighted least squares (WLS) problem and provides a good initial estimate via the two-step WLS (TWLS) method. Then, based on Taylor expansion of TDOA equations, estimated error is calculated using the initial estimate, which can correct the estimated position of the target iteratively. To offset the error, weighted centroid CT (WCCT) is proposed by endowing different weights based on error difference to estimated results. For further improving accuracy, a restricted-region fingerprinting positioning based on CT (CT-RFP) is proposed. In restricted area determined by CT, a certain number of fingerprints is generated based on received signal strength (RSS) for matching. Simulation results show that CT is significantly improved over the previous methods. Compared with TWLS, the accuracy of CT is improved by 49.71%. For WCCT, the maximum error is reduced from 8.65 to 6.91 cm, which effectively reduces the influence of error. Moreover, CT-RFP can achieve an accuracy within millimeter level via the appropriate number of fingerprints and ensemble runs of CT, even at high noise levels.

Classification models and SAR analysis on thromboxane A2 synthase inhibitors by machine learning methods

ABSTRACT Thromboxane A2 synthase (TXS) is a promising drug target for cardiovascular diseases and cancer. In this work, we conducted a structure-activity relationship (SAR) study on 526 TXS inhibitors for bioactivity prediction. Three types of descriptors (MACCS fingerprints, ECFP4 fingerprints, and MOE descriptors) were utilized to characterize inhibitors, 24 classification models were developed by support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and deep neural networks (DNN). Then we reduced the number of fingerprints according to the contribution of descriptors to the models, and constructed 16 extra models on simplified fingerprints. In general, Model_4D built by DNN algorithm and 67 bits MACCS fingerprints performs best. The prediction accuracy of the model on the test set is 0.969, and Matthews correlation coefficient (MCC) is 0.936. The distance between compound and model (dSTD-PRO) was used to characterize the application domain of the model. In the test set of Model_4D, dSTD-PRO of 91.5% compounds is lower than the corresponding training set threshold (threshold0.90 = 0.1055), and the accuracy of these compounds is 0.983. In addition, the important descriptors were summarized and further analyzed. It showed that aromatic nitrogenous heterocyclic groups were beneficial to improve the bioactivity of TXS inhibitors.

Enrolment and Matching of Fingerprints using Minutiae Tree

Automated fingerprint matching is considered as the most challenging phase of fingerprint recognition since it can be affected by a variety of factors such as noise, skin condition, rotation, distortions and displacements. When there is a large database, the search time to get a matching fingerprint will be relatively high. To reduce the searching time in the database, we have proposed a minutiae tree based indexing method in this paper. The database is represented in the form of a minutiae tree and the fingerprint matching is done by visiting the nodes in the tree where the local configuration of the minutiae is stored. By using this tree structure it is found that the search time or matching time can be considerably reduced and the matching time is independent of the number of fingerprints enrolled in the database. Our framework is scalable and the experiments conducted explores its ability to find correct matches with minimal search time.

Cost-efficient BLE fingerprint database construction approach via multi-quadric RBF interpolation

The demand for indoor localization is becoming urgent, but the traditional location fingerprint approach takes a lot of manpower and time to construct a fine-grained location fingerprint database. To address this problem, we propose to use the approach of combining dynamic collection of fingerprint samples with Radial Basis Function (RBF) interpolation. Specifically, the raw sparse fingerprint database is constructed from a small number of fingerprints collected on a few paths, in which the pedestrian track correction algorithm improves the validity and accuracy of the sparse fingerprint database. Then, the RBF interpolation approach is applied to enrich the sparse fingerprint database, in which the Genetic Algorithm (GA) is used to optimize the free shape parameter and the cut-off radius is determined according to the experimental results. Extensive experiments show that the proposed approach guarantees high interpolation and localization accuracy and also significantly reduces the effort of manual collection of fingerprint samples.

A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building.

Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved.

Scalable and consistent radio map management using participatory sensing

Although Wi-Fi fingerprinting is a promising solution for indoor localization, its widespread use is limited due to the necessity of time-consuming site surveys. Recently, active research has been conducted to reduce site-survey costs with participatory sensing. While previous work focused on the expansion of radio map coverage, in this paper, we deal with the issues on the scalability and consistency of radio map. In participatory sensing, radio map construction should be able to handle massive data collected from many people over a long period with limited storage capacity. The radio map should also guarantee consistency, which means consistent accuracy regardless of the RSS variances caused by environmental dynamics. This paper proposes a scalable and consistent radio map management scheme. Using multiple fingerprints per location, we minimize accuracy degradation caused by the RSS variance problem. To overcome the scalability issue, we control the number of fingerprints by a two-phase fingerprints selection algorithm. For each location, the proposed scheme first clusters the collected fingerprints and removes all fingerprints except for the centroids. Then, an optimal set of fingerprints is found by comparing the fingerprints in neighboring locations. We validate the efficiency of the proposed scheme with real experiments in various environments.

How to Train Your Browser

Cross-Site Scripting (XSS) is one of the most common web application vulnerabilities. It is therefore sometimes referred to as the “buffer overflow of the web.” Drawing a parallel from the current state of practice in preventing unauthorized native code execution (the typical goal in a code injection), we propose a script whitelisting approach to tame JavaScript-driven XSS attacks. Our scheme involves a transparent script interception layer placed in the browser’s JavaScript engine. This layer is designed to detect every script that reaches the browser, from every possible route, and compare it to a list of valid scripts for the site or page being accessed; scripts not on the list are prevented from executing. To avoid the false positives caused by minor syntactic changes (e.g., due to dynamic code generation), our layer uses the concept of contextual fingerprints when comparing scripts. Contextual fingerprints are identifiers that represent specific elements of a script and its execution context. Fingerprints can be easily enriched with new elements, if needed, to enhance the proposed method’s robustness. The list can be populated by the website’s administrators or a trusted third party. To verify our approach, we have developed a prototype and tested it successfully against an extensive array of attacks that were performed on more than 50 real-world vulnerable web applications. We measured the browsing performance overhead of the proposed solution on eight websites that make heavy use of JavaScript. Our mechanism imposed an average overhead of 11.1% on the execution time of the JavaScript engine. When measured as part of a full browsing session, and for all tested websites, the overhead introduced by our layer was less than 0.05%. When script elements are altered or new scripts are added on the server side, a new fingerprint generation phase is required. To examine the temporal aspect of contextual fingerprints, we performed a short-term and a long-term experiment based on the same websites. The former, showed that in a short period of time (10 days), for seven of eight websites, the majority of valid fingerprints stay the same (more than 92% on average). The latter, though, indicated that, in the long run, the number of fingerprints that do not change is reduced. Both experiments can be seen as one of the first attempts to study the feasibility of a whitelisting approach for the web.

Fast fingerprint identification using GPUs

Fingerprints are widely used in a variety of biometric identification systems. The fingerprint matching process is a processing step whose computational requirements limit the size of the fingerprint database that can be dealt with.Fingerprint matching algorithms based on minutiae are one of the most relevant families of biometric identification techniques. The scalability of these models is determined not only by the number of fingerprints but also the number of minutiae per fingerprint. Therefore, processing millions of fingerprints per second requires being able to process hundreds of millions of minutiae per second.In this paper we present a new design of the minutiae based fingerprint matching algorithm presented by Jiang et al. specifically created for GPU based massively parallel architectures. The parallel design allows speed-up ratios of up to 15 with one GPU compared to multi-threaded CPU implementations, and up to 54 using several GPUs in parallel and fingerprint processing rates of between 300,000 and 1,500,000 fingerprints per second.

Review of Sequential Access Method for Fingerprint Identification

Real time fingerprint identification is usually equipped with specific computation machine architecture to optimize speed factor. Focusing on achieving better speed performance of fingerprint identification on common computation machine, a disquisition was conducted on sequential access method for fingerprint identification, with its underlying data structure designed to work with parallel processing. Hypothetically, parallel processing based on multi-cores processor technology, can give faster result without reducing accuracy. If multi core processor was detected, simultaneous processes would run on fingerprint matching-pairs to find its similarity score, respectively. Experiment confirms that speed performance of fingerprint identification using sequential access method with parallel processing outperforms the one without parallel processing. For both strategy, even though using parallel processing confirms faster result, experiment shows that searching time O(n) still linearly depend s on number of fingerprints in database. Avoiding such searching time trend, hypothetically, need strategy of direct access method utilization.

Review of Sequential Access Method for Fingerprint Identification

Real time fingerprint identification is usually equipped with specific computation machine architecture to optimize speed factor. Focusing on achieving better speed performance of fingerprint identification on common computation machine, a disquisition was conducted on sequential access method for fingerprint identification, with its underlying data structure designed to work without and with parallel processing. Hypothetically, parallel processing based on multi cores processor technology, can give faster result without reducing accuracy. Experiment confirms that speed performance of fingerprint identification using sequential access method with parallel processing outperforms the one without parallel processing. For both strategy, even though using parallel processing confirms faster result, experiment shows that searching time O(n) still linearly depends on number of fingerprints in database. Avoiding such searching time trend, hypothetically, need strategy of direct access method utilization.

An algorithm that minimizes audio fingerprints using the difference of Gaussians

Recently, many audio search sites headed by Google have used audio fingerprinting technology to search for the same audio and protect the music copyright using one part of the audio data. However, if there are fingerprints per audio file, then the amount of query data for the audio search increases. In this paper, we propose a novel method that can reduce the number of fingerprints while providing a level of performance similar to that of existing methods. The proposed method uses the difference of Gaussians which is often used in feature extraction during image signal processing. In the experiment, we use the proposed method and dynamic time warping and undertake an experimental search for the same audio with a success rate of 90%. The proposed method, therefore, can be used for an effective audio search.

A simple fingerprinting scheme for large user groups

Digital fingerprinting is an emerging technology to protect multimedia data from piracy, where each distributed copy is labeled with unique identification information. In coded fingerprinting, each codeword of the anti-collusion code is used to represent a user's fingerprint. In real-world applications, the number of potential users can be very large and hence, the used anticollusion code should be easy to construct and have as many codewords as possible. This paper proposes a simple coded fingerprinting scheme, where the anticollusion code is constructed with an identity matrix. The main advantage of our scheme is its simplicity, as a large number of fingerprints can easily be generated to accommodate a large number of users. To improve collusion resistance, some transformations are performed on the original fingerprint in the embedding phase and the corresponding inverse transformations are performed on the extracted sequence in the detection phase.