Effective layer-based segmentation of compound images using morphology

As smart devices have become commonly used to access internet banking applications, these devices constitute appealing targets for fraudsters. Impersonation attacks are an essential concern for internet banking providers. Therefore, user authentication countermeasures based on biometrics, whether physiological or behavioral, have been developed, including those based on touch dynamics biometrics. These measures take into account the unique behavior of a person when interacting with touchscreen devices, thus hindering identitification fraud because it is hard to impersonate natural user behaviors. Behavioral biometric measures also balance security and usability because they are important for human interfaces, thus requiring a measurement process that may be transparent to the user. This paper proposes an improvement to Biotouch, a supervised Machine Learning-based framework for continuous user authentication. The contributions of the proposal comprise the utilization of multiple scopes to create more resilient reasoning models and their respective datasets for the improved Biotouch framework. Another contribution highlighted is the testing of these models to evaluate the imposter False Acceptance Error (FAR). This proposal also improves the flow of data and computation within the improved framework. An evaluation of the multiple scope model proposed provides results between 90.68% and 97.05% for the harmonic mean between recall and precision (F1 Score). The percentages of unduly authenticated imposters and errors of legitimate user rejection (Equal Error Rate (EER)) are between 9.85% and 1.88% for static verification, login, user dynamics, and post-login. These results indicate the feasibility of the continuous multiple-scope authentication framework proposed as an effective layer of security for banking applications, eventually operating jointly with conventional measures such as password-based authentication.

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Compress compound images in H.264/MPEG-4 AVC by fully exploiting spatial correlation

2009 IEEE International Symposium on Circuits and Systems ◽

10.1109/iscas.2009.5118388 ◽

2009 ◽

Cited By ~ 2

Author(s):

Cuiling Lan ◽

Feng Wu ◽

Guangming Shi

Keyword(s):

Spatial Correlation ◽

Compound Images

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An Environmental Study on Tornado Path-Length, Longevity, and Width

Weather and Forecasting ◽

10.1175/waf-d-20-0230.1 ◽

2021 ◽

Author(s):

Jonathan M. Garner ◽

William C. Iwasko ◽

Tyler D. Jewel ◽

Richard L. Thompson ◽

Bryan T. Smith

Keyword(s):

Path Length ◽

Rotational Velocity ◽

Convective Available Potential Energy ◽

Environmental Study ◽

Synoptic Scale ◽

Near Surface ◽

Low Level ◽

Wind Profiles ◽

Radar Information ◽

Effective Layer

AbstractA dataset maintained by the Storm Prediction Center (SPC) of 6300 tornado events from 2009–2015, consisting of radar-identified convective modes and near-storm environmental information obtained from Rapid Update Cycle and Rapid Refresh model analysis grids, has been augmented with additional radar information related to the low-level mesocyclones associated with tornado longevity, path-length, and width. All EF2–EF5 tornadoes, in addition to randomly selected EF0–EF1 tornadoes, were extracted from the SPC dataset, which yielded 1268 events for inclusion in the current study. Analysis of that data revealed similar values of the effective-layer significant tornado parameter for the longest-lived (60+ min) tornadic circulations, longest-tracked (≥ 68 km) tornadoes, and widest tornadoes (≥ 1.2 km). However, the widest tornadoes occurring west of –94° longitude were associated with larger mean-layer convective available potential energy, storm-top divergence, and low-level rotational velocity. Furthermore, wide tornadoes occurred when low-level winds were out of the southeast resulting in large low-level hodograph curvature and near-surface horizontal vorticity that was more purely streamwise compared to long-lived and long-tracked events. On the other hand, tornado path-length and longevity were maximized with eastward migrating synoptic-scale cyclones associated with strong southwesterly wind profiles through much of the troposphere, fast storm motions, large values of bulk wind difference and storm-relative helicity, and lower buoyancy.

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North American Supercell Environments in Atmospheric Reanalyses and RUC-2

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-18-0015.1 ◽

2019 ◽

Vol 58 (1) ◽

pp. 71-92 ◽

Cited By ~ 10

Author(s):

Austin T. King ◽

Aaron D. Kennedy

Keyword(s):

North American ◽

Grid Point ◽

Convective Available Potential Energy ◽

Kinematic Parameters ◽

The North ◽

Composite Parameter ◽

Analysis Package ◽

North American Regional Reanalysis ◽

Regional Reanalysis ◽

Effective Layer

AbstractA suite of modern atmospheric reanalyses is analyzed to determine how they represent North American supercell environments. This analysis is performed by comparing a database of Rapid Update Cycle (RUC-2) proximity soundings with profiles derived from the nearest grid point in each reanalysis. Parameters are calculated using the Sounding and Hodograph Analysis and Research Program in Python (SHARPpy), an open-source Python sounding-analysis package. Representation of supercell environments varies across the reanalyses, and the results have ramifications for climatological studies that use these datasets. In particular, thermodynamic parameters such as the convective available potential energy (CAPE) show the widest range in biases, with reanalyses falling into two camps. The North American Regional Reanalysis (NARR) and the Japanese 55-year Reanalysis (JRA-55) are similar to RUC-2, but other reanalyses have a substantial negative bias. The reasons for these biases vary and range from thermodynamic biases at the surface to evidence of convective contamination. Overall, it is found that thermodynamic biases feed back to other convective parameters that incorporate CAPE directly or indirectly via the effective layer. As a result, significant negative biases are found for indices such as the supercell composite parameter. These biases are smallest for NARR and JRA-55. Kinematic parameters are more consistent across the reanalyses. Given the issues with thermodynamic properties, better segregation of soundings by storm type is found for fixed-layer parameters than for effective-layer shear parameters. Although no reanalysis can exactly reproduce the results of earlier RUC-2 studies, many of the reanalyses can broadly distinguish between environments that are significantly tornadic versus nontornadic.

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Enhanced Hybrid Compression Models for Compound Images

Communications in Computer and Information Science - Trends in Computer Science, Engineering and Information Technology ◽

10.1007/978-3-642-24043-0_27 ◽

2011 ◽

pp. 265-274

Author(s):

D. Maheswari ◽

V. Radha

Keyword(s):

Compound Images

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Sparse Decomposition Technique for Segmentation and Compression of Compound Images

Journal of Intelligent Systems ◽

10.1515/jisys-2017-0360 ◽

2018 ◽

Vol 29 (1) ◽

pp. 515-528

Author(s):

V.N. Manju ◽

A. Lenin Fred

Keyword(s):

Huffman Coding ◽

Gray Wolf ◽

Decomposition Technique ◽

Sparse Decomposition ◽

Compound Images ◽

Block Based ◽

Compressed Data ◽

Fuzzy C Means Algorithm ◽

Original Information ◽

Compound Image

Abstract Compression of compound records and images can be more cumbersome than the original information since they can be a mix of text, picture and graphics. The principle requirement of the compound record or images is the nature of the compressed data. In this paper, diverse procedures are used under block-based classification to distinguish the compound image segments. The segmentation process starts with separation of the entire image into blocks by spare decomposition technique in smooth blocks and non smooth blocks. Gray wolf-optimization based FCM (fuzzy C-means) algorithm is employed to segment background, text, graphics, images and overlap, which are then individually compressed using adaptive Huffman coding, embedded zero wavelet and H.264 coding techniques. Exploratory outcomes demonstrate that the proposed conspire expands compression ratio, enhances image quality and additionally limits computational complexity. The proposed method is implemented on the working platform of MATLAB.

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Examination of the Predictability of Nocturnal Tornado Events in the Southeastern United States

Weather and Forecasting ◽

10.1175/waf-d-18-0162.1 ◽

2019 ◽

Vol 34 (2) ◽

pp. 467-479 ◽

Cited By ~ 1

Author(s):

Ryan C. Bunker ◽

Ariel E. Cohen ◽

John A. Hart ◽

Alan E. Gerard ◽

Kim E. Klockow-McClain ◽

...

Keyword(s):

United States ◽

Predictive Power ◽

Meteorological Parameters ◽

Southeastern United States ◽

Time Frame ◽

Assessment Model ◽

The Other ◽

Risk Assessment Model ◽

Multiple Parameters ◽

Effective Layer

Abstract Tornadoes that occur at night pose particularly dangerous societal risks, and these risks are amplified across the southeastern United States. The purpose of this study is to highlight some of the characteristics distinguishing the convective environment accompanying these events. This is accomplished by building upon previous research that assesses the predictive power of meteorological parameters. In particular, this study uses the Statistical Severe Convective Risk Assessment Model (SSCRAM) to determine how well convective parameters explain tornado potential across the Southeast during the months of November–May and during the 0300–1200 UTC (nocturnal) time frame. This study compares conditional tornado probabilities across the Southeast during November–May nocturnal hours to those probabilities for all other November–May environments across the contiguous United States. This study shows that effective bulk shear, effective storm-relative helicity, and effective-layer significant tornado parameter yield the strongest predictability for the November–May nocturnal Southeast regime among investigated parameters. This study demonstrates that November–May southeastern U.S. nocturnal predictability is generally similar to that within other regimes across the contiguous United States. However, selected ranges of multiple parameters are associated with slightly better predictability for the nocturnal Southeast regime. Additionally, this study assesses conditional November–May nocturnal tornado probabilities across a coastal domain embedded within the Southeast. Nocturnal coastal tornado predictability is shown to generally be lower than the other regimes. All of the differences highlight several forecast challenges, which this study analyzes in detail.

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