A Reconstruction Method for Cross-Cut Shredded Documents Based on the Extreme Learning Machine Algorithm

Reconstruction of cross-cut shredded text documents (RCCSTD) has important applications for information security and judicial evidence collection. The traditional method of manual construction is a very time-consuming task, so the use of computer-assisted efficient reconstruction is a crucial research topic. Fragment consensus information extraction and fragment pair compatibility measurement are two fundamental processes in RCCSTD. Due to the limitations of the existing classical methods of these two steps, only documents with specific structures or characteristics can be spliced, and pairing error is larger when the cutting is more fine-grained. In order to reconstruct the fragments more effectively, this paper improves the extraction method for consensus information and constructs a new global pairwise compatibility measurement model based on the extreme learning machine algorithm. The purpose of the algorithm's design is to exploit all available information and computationally suggest matches to increase the algorithm's ability to discriminate between data in various complex situations, then find the best neighbor of each fragment for splicing according to pairwise compatibility. The overall performance of our approach in several practical experiments is illustrated. The results indicate that the matching accuracy of the proposed algorithm is better than that of the previously published classical algorithms and still ensures a higher matching accuracy in the noisy datasets, which can provide a feasible method for RCCSTD intelligent systems in real scenarios.

Comparison of Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction, With and Without Framing Feature Selection, to Test the Shahada Recitation

Voice research for feature extraction using MFCC. Introduction with feature extraction as the first step to get features. Features need to be done further through feature selection. The feature selection in this research used the Dominant Weight feature for the Shahada voice, which produced frames and cepstral coefficients as the feature extraction. The cepstral coefficient was used from 0 to 23 or 24 cepstral coefficients. At the same time, the taken frame consisted of 0 to 10 frames or eleven frames. Voting as many as 300 samples of recorded voices were tested on 200 voices of both male and female voice recordings. The frequency used was 44.100 kHz 16-bit stereo. This research aimed to gain accuracy by selecting the right features on the frame using MFCC feature extraction and matching accuracy with frame feature selection using the Dominant Weight Normalization (NBD). The accuracy results obtained that the MFCC method with the selection of the 9th frame had a higher accuracy rate of 86% compared to other frames. The MFCC without feature selection had an average of 60%. The conclusion was that selecting the right features in the 9th frame impacted the accuracy of the voice of shahada recitation.

How Well Do We Know Europa’s Topography? An Evaluation of the Variability in Digital Terrain Models of Europa

Jupiter’s moon Europa harbors one of the most likely environments for extant extraterrestrial life. Determining whether Europa is truly habitable requires understanding the structure and thickness of its ice shell, including the existence of perched water or brines. Stereo-derived topography from images acquired by NASA Galileo’s Solid State Imager (SSI) of Europa are often used as a constraint on ice shell structure and heat flow, but the uncertainty in such topography has, to date, not been rigorously assessed. To evaluate the current uncertainty in Europa’s topography we generated and compared digital terrain models (DTMs) of Europa from SSI images using both the open-source Ames Stereo Pipeline (ASP) software and the commercial SOCET SET® software. After first describing the criteria for assessing stereo quality in detail, we qualitatively and quantitatively describe both the horizontal resolution and vertical precision of the DTMs. We find that the horizontal resolution of the SOCET SET® DTMs is typically 8–11× the root mean square (RMS) pixel scale of the images, whereas the resolution of the ASP DTMs is 9–13× the maximum pixel scale of the images. We calculate the RMS difference between the ASP and SOCET SET® DTMs as a proxy for the expected vertical precision (EP), which is a function of the matching accuracy and stereo geometry. We consistently find that the matching accuracy is ~0.5 pixels, which is larger than well-established “rules of thumb” that state that the matching accuracy is 0.2–0.3 pixels. The true EP is therefore ~1.7× larger than might otherwise be assumed. In most cases, DTM errors are approximately normally distributed, and errors that are several times the derived EP occur as expected. However, in two DTMs, larger errors (differences) occur and correlate with real topography. These differences primarily result from manual editing of the SOCET SET® DTMs. The product of the DTM error and the resolution is typically 4–8 pixel2 if calculated using the RMS image scale for SOCET SET® DTMs and the maximum images scale for the ASP DTMs, which is consistent with recent work using martian data sets and suggests that the relationship applies more broadly. We evaluate how ASP parameters affect DTM quality and find that using a smaller subpixel refinement kernel results in DTMs with smaller (better) resolution but, in some cases, larger gaps, which are sometimes reduced by increasing the size of the correlation kernel. We conclude that users of ASP should always systematically evaluate the choice of parameters for a given dataset.

The impact of ear growth on identification rates using an ear biometric system in young infants

Background Accurate patient identification is essential for delivering longitudinal care. Our team developed an ear biometric system (SEARCH) to improve patient identification. To address how ear growth affects matching rates longitudinally, we constructed an infant cohort, obtaining ear image sets monthly to map a 9-month span of observations. This analysis had three main objectives: 1) map trajectory of ear growth during the first 9 months of life; 2) determine the impact of ear growth on matching accuracy; and 3) explore computer vision techniques to counter a loss of accuracy. Methodology Infants were enrolled from an urban clinic in Lusaka, Zambia. Roughly half were enrolled at their first vaccination visit and ~half at their last vaccination. Follow-up visits for each patient occurred monthly for 6 months. At each visit, we collected four images of the infant’s ears, and the child’s weight. We analyze ear area versus age and change in ear area versus age. We conduct pair-wise comparisons for all age intervals. Results From 227 enrolled infants we acquired age-specific datasets for 6 days through 9 months. Maximal ear growth occurred between 6 days and 14 weeks. Growth was significant until 6 months of age, after which further growth appeared minimal. Examining look-back performance to the 6-month visit, baseline pair-wise comparisons yielded identification rates that ranged 46.9–75%. Concatenating left and right ears per participant improved identification rates to 61.5–100%. Concatenating images captured on adjacent visits further improved identification rates to 90.3–100%. Lastly, combining these two approaches improved identification to 100%. All matching strategies showed the weakest matching rates during periods of maximal growth (i.e., <6 months). Conclusion By quantifying the effect that ear growth has on performance of the SEARCH platform, we show that ear identification is a feasible solution for patient identification in an infant population 6 months and above.

Map-Matching Using Hidden Markov Model and Path Choice Preferences under Sparse Trajectory

In the field of map matching, algorithms using topological relationships of road networks along with other data are normally suitable for high frequency trajectory data. However, for low frequency trajectory data, the above methods may cause problems of low matching accuracy. In addition, most past studies only use information from the road network and trajectory, without considering the traveler’s path choice preferences. In order to address the above-mentioned issue, we propose a new map matching method that combines the widely used Hidden Markov Model (HMM) with the path choice preference of decision makers. When calculating transition probability in the HMM, in addition to shortest paths and road network topology relationships, the choice preferences of travelers are also taken into account. The proposed algorithm is tested using sparse and noisy trajectory data with four different sampling intervals, while compared the results with the two underlying algorithms. The results show that our algorithm can improve the matching accuracy, especially for higher frequency locating trajectory. Importantly, the method takes into account the route choice preferences while correcting deviating trajectory points to the corresponding road segments, making the assumptions more reasonable. The case-study is in the city of Beijing, China.

Improving Matching Accuracy of Underwater Gravity Matching Navigation Based on Iterative Optimal Annulus Point Method with a Novel Grid Topology

In this study, we improve the matching accuracy of underwater gravity matching navigation. Firstly, the Iterative Optimal Annulus Point (IOAP) method with a novel grid topology is proposed for breaking through the inherent grid structure limit of the canonical gravity matching algorithm and enhancing its underwater gravity matching accuracy. The theory of IOAP is as follows: (1) small-annulus matching and positioning mechanism on the tracking starting point is developed by employing the starting point and drift error of the INS (Inertial Navigation System), the fixed rotation angle, etc. The optimal matching location of the starting point is obtained by matching and comparing the matched points in this small-annulus grid, which contributes to heightening the initial-position error insensitivity of the algorithms. (2) Variable-angle three-layer annulus matching and positioning mechanisms on the tracking ending point were constructed by using the optimal matching location of the starting point and combining the tracking direction-and-distance information of the INS and the cumulative drift error, etc. It is used to generate the annulus matching points with the ring-type grid topology. (3) The optimal matching position of the ending point in this annulus is obtained by iteratively calculating the evaluation index value of the matching points and following the evaluation index optimal rule. Secondly, we comprehensively consider the main performance evaluation indexes of the underwater gravity matching algorithms, such as the statistical indicators of the matching accuracy, the average matching time and the matching success rate, and take them as a basis of the pros and cons of the matching analysis. Furthermore, under conditions that include different scale searching regions or different reference-angle ring radii, the statistical results verify that the IOAP had a different matching ability and better robustness. Finally, several trajectories with the starting points from different areas and the ending points in different gravity ranges are tested and compared to carry out the numerical simulations. These results indicate that the IOAP has many advantages, such as a high matching accuracy and strong positioning applicability in different gravity regions. Compared with the TERCOM (terrain contour matching algorithm), its average matching accuracy was the highest, increased by 40.39%.

A trajectory map matching algorithm via exploring the average directional features of continuous windows

Many location-based services require a pre-processing step of map matching. Due to the error of the original position data and the complexity of the road network, the matching algorithm will have matching errors when the complex road network is implemented, which is therefore challenging. Aiming at the problems of low matching accuracy and low efficiency of existing algorithms at Y-shaped intersections and roundabouts, this paper proposes a space-time-based continuous window average direction feature trajectory map matching algorithm (STDA-matching). Specifically, the algorithm not only adaptively generates road network topology data, but also obtains more accurate road network relationships. Based on this, the transition probability is calculated by using the average direction feature of the continuous window of the track points to improve the matching accuracy of the algorithm. Secondly, the algorithm simplifies the trajectory by clustering the GPS trajectory data aggregation points to improve the matching efficiency of the algorithm. Finally, we use a real and effective data set to compare the algorithm with the two existing algorithms. Experimental results show that our algorithm is effective.

An End-to-End Point of Interest (POI) Conflation Framework

Point of interest (POI) data serves as a valuable source of semantic information for places of interest and has many geospatial applications in real estate, transportation, and urban planning. With the availability of different data sources, POI conflation serves as a valuable technique for enriching data quality and coverage by merging the POI data from multiple sources. This study proposes a novel end-to-end POI conflation framework consisting of six steps, starting with data procurement, schema standardisation, taxonomy mapping, POI matching, POI unification, and data verification. The feasibility of the proposed framework was demonstrated in a case study conducted in the eastern region of Singapore, where the POI data from five data sources was conflated to form a unified POI dataset. Based on the evaluation conducted, the resulting unified dataset was found to be more comprehensive and complete than any of the five POI data sources alone. Furthermore, the proposed approach for identifying POI matches between different data sources outperformed all baseline approaches with a matching accuracy of 97.6% with an average run time below 3 min when matching over 12,000 POIs to result in 8699 unique POIs, thereby demonstrating the framework’s scalability for large scale implementation in dense urban contexts.