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Turkey established the TUSAGA-AKTIF CORS Network in May 2009. Network software and central server were updated in 2016. With this update, GLONASS message type was determined for Flachen Korrektur Parameter (FKP), Master Auxiliary ...
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Turkey established the TUSAGA-AKTIF CORS Network in May 2009. Network software and central server were updated in 2016. With this update, GLONASS message type was determined for Flachen Korrektur Parameter (FKP), Master Auxiliary Concept (MAC) and Virtual Reference Station (VRS) network-based real time kinematic (NRTK) techniques. A 64 bit central server and the Trimble Pivot Platform network software were also acquired with this update. To the best of our knowledge, there has not been a comprehensive accuracy and precision test of the new system yet. In this paper, we aimed to create empirical accuracy and precision model of FKP, MAC and VRS NRTK techniques of the updated system as a function of baseline distance and occupation time. It is intended that surveyors can perform mission planning according to the requirements of accuracy and precision using these models. Seven test points and two check points were chosen to conduct the experiment. The baseline lengths with respect to the closest continuously operating reference stations (CORS) station were determined as 5-20-40-50 km approximately. Three thousand epochs with 2-s sampling interval were obtained for northing, easting and ellipsoidal height coordinate components of NRTK techniques at each point. Assumed true coordinates of each test point were determined by static survey using the GAMIT/GLOBK scientific software. In terms of accuracy and precision, our results show that empirical accuracy model depends only on the occupation time while empirical precision model depends on both the baseline length with respect to the closest CORS station and the occupation time for each NRTK technique. The results indicate that estimated accuracy and precision models can be safely used for mission planning purposes.
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As a relatively new biometric trait, Finger-Knuckle-Print (FKP) plays a vital role in establishing a personal authentication system in modern society due to its rich discriminative features, low time cost in image capture and user...
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As a relatively new biometric trait, Finger-Knuckle-Print (FKP) plays a vital role in establishing a personal authentication system in modern society due to its rich discriminative features, low time cost in image capture and user-friendliness. However, most existing KFP descriptors are hand-crafted and fail to work well with limited training samples. In this paper, we propose a feature learning method for few-shot FKP recognition by jointly learning compact multi-view hash codes (JLCMHC) of a FKP image. We first form the multi-view data vectors (MVDV) to exploit the multiple feature-specific information from a FKP image. Then, we learn a feature projection to encode the MVDV into compact binary codes in an unsupervised manner, where 1) the variance of the learned feature codes on each view is maximized and 2) the difference of the inter-view binary codes is enlarged, so that the redundant information in MVDV is reduced and more informative features can be obtained. Lastly, we pool the binary codes into block-wise statistics features as the final descriptor for FKP representation and recognition. Experimental results on the existing benchmark FKP databases clearly show that the JLCMHC method outperforms the state-of-the-art FKP descriptors. ? 2021 Elsevier Ltd. All rights reserved.
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Abstract In this paper, a contactless person verification system based on score level fusion of 2D and 3D finger knuckle patterns. In particular, four types of scores extracted from 3D forefinger FKP (Finger Knuckle Print), 3D mid...
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Abstract In this paper, a contactless person verification system based on score level fusion of 2D and 3D finger knuckle patterns. In particular, four types of scores extracted from 3D forefinger FKP (Finger Knuckle Print), 3D middle FKP, 2D forefinger FKP and 2D middle FKP are merged to attain higher accuracy for personal recognition systems. The Tan and Triggs normalization technique (TT) is applied on the depth of 3D FKP image (fore and middle finger) to acquire TT 3D FKP image. Then, a novel and efficient scheme to extract features from TT 3D FKP image, namely Monogenic Local Phase Quantization (MLPQ) is utilized. Also, the MLPQ descriptor is applied on 2D FKP image (fore and middle finger) to extract features. The main idea of MLPQ descriptor is, first, the monogenic filters are applied to decompose TT 3D FKP image or 2D FKP image into three complementary parts: Bandpass, vertical and horizontal Bandpass components. Later, Local Phase Quantization (LPQ) is utilized to encode these complementary components. The encoded components are divided into M × M non-overlapped rectangular sub-regions to calculate their histograms. These histograms sequences are concatenated to build a large feature vector. The kernel fisher analysis (KFA) is used as a dimensionality reduction technique to build the monogenic Local Phase Quantization (MLPQ) feature vector for 3D or 2D FKP recognition. Finally, the cosine distance is used to ascertain the identity of the person. Experimental results using publicly available PolyU FKP dataset show that the presented framework notably attained lower error rates and outperformed the state-of-the-art technique.
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Biometric authentication is an effective method for automatically recognizing a person's identity. In our previous paper, we have considered palm print for human authentication. Recently, it has been found that the Finger Knuckle ...
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Biometric authentication is an effective method for automatically recognizing a person's identity. In our previous paper, we have considered palm print for human authentication. Recently, it has been found that the Finger Knuckle Print (FKP), which refers to the inherent skin patterns of the outer surface around the phalangeal joint of one's finger, has high capability to discriminate different individuals, making it an emerging biometric identifier. In this paper, the local convex direction map of the FKP image is extracted. Then, the local features of the enhanced FKP are extracted using the Scale Invariant Feature Transform (SIR), the Speeded Up Robust Features (SURF) and frequency feature. SIFT are formed by means of local patterns around key-points from scale space decomposed image. Feature vectors through SURF are formed by means of local patterns around key-points which are detected using scaled up filter. The frequency range of pixel levels in each image is employed by using Empirical Mode Decomposition (EMD). For the authentication of FKP image, we used shortest distance between the query image and the database, to evaluate their similarity. Here, we use PolyU FKP database images to examine the performance of the proposed system. The proposed biometric system is implemented in MATLAB and compared with the previous palm print human authentication system. For the same person, the matching score of the two methods are fused for the multimodal biometric recognition. The experimental results demonstrated the efficiency and effectiveness of this new biometric characteristic.
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GLONASS system; It has become the second system operating on a global scale after the GPS system in the world, after completing the satellite constellation and using it at full capacity as of 8 December 211. Due to the increasing ...
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GLONASS system; It has become the second system operating on a global scale after the GPS system in the world, after completing the satellite constellation and using it at full capacity as of 8 December 211. Due to the increasing need for high accuracy and precision real-time location information, CORS networks have become widespread in the world. In Turkey, it was established as CORS-TR and opened for use in December 28. Comprehensive studies investigating the effects of Network-Based RTK techniques (VRS, FKP, and MAC) in the CORS- TR network are very limited due to the fact that the GLONASS system has been used at full capacity recently. In this paper, it is aimed to determine the effect of measurements derived from the Network-Based RTK techniques in the CORS-TR network of the GLONASS system on the location accuracy, and thus to make a business plan according to the accuracy and precision requirements of all civil and military users. For this purpose, simultaneous measurements were made with 6 GNSS receiver devices of the same brand and model. A total of 38,98 epoch data (northing value, easting value, and ellipsoidal height: projection coordinates (ITRF96 Datum, 25. Reference Epoch)) were collected at one-second intervals in each technique and for seven days of measurements. As a result of the evaluation and analysis of the data sets obtained with the measurements; It has been observed that the GLONASS system has a positive effect on position accuracy, but in some cases, it also has disruptive effects. It has been observed that the most important contribution is to increase the number of visible satellites and to enable measurements with GLONASS satellites in cases where GPS satellites alone are not sufficient, especially in areas where the satellite elevation angle is narrowed, such as city centers, and forest areas.
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Temporal correlation in network based Real-Time Kinematic (NRTK) data exists due to the unmodeled multipath, tropospheric (especially for wet delay) and ionospheric variations, and slowly changing spatial geometry of satellites. I...
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Temporal correlation in network based Real-Time Kinematic (NRTK) data exists due to the unmodeled multipath, tropospheric (especially for wet delay) and ionospheric variations, and slowly changing spatial geometry of satellites. Ignoring the temporal correlation can result in too much confidence being placed in the estimated uncertainty of the coordinates. Temporally correlated errors occur when the magnitude of an error is constant or similar over time. In this study, temporal correlation lengths of commonly used Virtual Reference Station (VRS), Flachen Korrektur Parameter (FKP) and Master Auxiliary Concept (MAC) NRTK techniques were computed for the different length of baselines between the rover and the master station (closest CORS station) in the Turkish GNSS active network (TUSAGA-Aktif). Thus, appropriate time separation between the measurements for revisits of the points was determined in order to avoid overly optimistic coordinate uncertainty and unreliable coordinate checking. The results show that there is no clear relationship between the correlation length and the baseline distance. Correlation lengths of FKP and VRS are similar, whereas MAC produced a much longer correlation time for the horizontal component. When the correlation length of each technique is averaged, the correlation lengths are estimated as 17-13-16 min for northing, easting projection coordinates and ellipsoidal height, respectively. According to the measurements using different geodetic-grade receivers, the results show that temporal correlation length varies between the receivers. (C) 2018 Elsevier Ltd. All rights reserved.
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The geostationary satellite system will be able to disseminate data to a wider area and a greater number of users simultaneously. The authors tested the network-based RTK-GPS positioning using the area correction parameter (FKP) v...
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The geostationary satellite system will be able to disseminate data to a wider area and a greater number of users simultaneously. The authors tested the network-based RTK-GPS positioning using the area correction parameter (FKP) via the geostationary satellite-based communication line. They investigated network-based RTK-GPS positioning at several fixed points located inside the GPS reference station network area.
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Today An Extensive Range Of Systems Require Sophisticated Identification To Verify Or Determine The Identity Of A Person. The Principle Of These Systems Is To Make Sure That The Services Provided Are Accessible By A Genuine User O...
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Today An Extensive Range Of Systems Require Sophisticated Identification To Verify Or Determine The Identity Of A Person. The Principle Of These Systems Is To Make Sure That The Services Provided Are Accessible By A Genuine User Only. In This Paper, An Efficient And Robust Multi-Modal Biometric System Is Proposed That Used Finger Knuckle Print (FKP) And Ins As Input Biometric Modalities For Verification. To Extract Feature Set From FKP, Scale Invariant Feature Transform (SIFT) And Speeded Up Robust Features (SURF) Methods Are Used. Log-Gabor Wavelet Is Used To Extract Iris Feature Set. In Proposed System, Matching Scores Of Two Biometric Modalities FKP And iris Are Fused For Verification. The Performance Of Proposed System Has Been Examined By Public Database Polyu For FKP And CASIA Iris Database In MATLAB R2009a.
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