scholarly journals Invariant behavioural based discrimination for individual representation

2021 ◽  
Vol 11 (1) ◽  
pp. 736
Author(s):  
Wong Yee Leng ◽  
Siti Mariyam Shamsuddin ◽  
Nor Azman Hashim
Keyword(s):  
Main Idea ◽  
Writer Identification ◽  
Biometric Analysis ◽  
Behavioural Adaptation ◽  
Identification Rate ◽  
Novel Approach ◽  
The Moment ◽  
Moment Features ◽  
Authorship Identification ◽  
Cutting Point

Writer identification based on cursive words is one of the extensive behavioural biometric that has involved many researchers to work in. Recently, its main idea is in forensic investigation and biometric analysis as such the handwriting style can be used as individual behavioural adaptation for authenticating an author. In this study, a novel approach of presenting cursive features of authors is presented. The invariants-based discriminability of the features is proposed by discretizing the moment features of each writer using biometric invariant discretization cutting point (BIDCP). BIDCP is introduced for features perseverance to obtain better individual representations and discriminations. Our experiments have revealed that by using the proposed method, the authorship identification based on cursive words is significantly increased with an average identification rate of 99.80%.

scholarly journals Interpreting Locked Photographic Data: The Case of Apollo 17 Photo GPN-2000-00113

Designs ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 8
Author(s):  
Pyrrhon Amathes ◽  
Paul Christodoulides
Keyword(s):  
Experimental Data ◽  
Computer Simulations ◽  
The Moon ◽  
Geometrical Analysis ◽  
Apparent Position ◽  
Apollo Program ◽  
The Earth ◽  
Novel Approach ◽  
The World ◽  
The Moment

Photography can be used for pleasure and art but can also be used in many disciplines of science, because it captures the details of the moment and can serve as a proving tool due to the information it preserves. During the period of the Apollo program (1969 to 1972), the National Aeronautics and Space Administration (NASA) successfully landed humans on the Moon and showed hundreds of photos to the world presenting the travel and landings. This paper uses computer simulations and geometry to examine the authenticity of one such photo, namely Apollo 17 photo GPN-2000-00113. In addition, a novel approach is employed by creating an experimental scene to illustrate details and provide measurements. The crucial factors on which the geometrical analysis relies are locked in the photograph and are: (a) the apparent position of the Earth relative to the illustrated flag and (b) the point to which the shadow of the astronaut taking the photo reaches, in relation to the flagpole. The analysis and experimental data show geometrical and time mismatches, proving that the photo is a composite.

scholarly journals Principles of adaptive element spacing in linear array antennas

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tanzeela Mitha ◽  
Maria Pour
Keyword(s):  
Antenna Arrays ◽  
Communication Systems ◽  
Linear Array ◽  
Main Idea ◽  
Phase Center ◽  
Array Configuration ◽  
Array Antennas ◽  
Center Location ◽  
Novel Approach ◽  
Array Performance

AbstractA novel approach to linear array antennas with adaptive inter-element spacing is presented for the first time. The main idea is based upon electronically displacing the phase center location of the antenna elements, which determine their relative coordinates in the array configuration. This is realized by employing dual-mode microstrip patch antennas as a constitutive element, whose phase center location can be displaced from its physical center by simultaneously exciting two modes. The direction and the amount of displacement is controlled by the amplitude and phase of the modes at the element level. This in turn facilitates reconfiguring the inter-element spacing at the array level. For instance, a uniformly-spaced array could be electronically transformed into a non-uniform one without any mechanical means. The proposed idea is demonstrated in two- and three-element linear antenna arrays. The technique has the potential to control the radiation characteristics such as sidelobe levels, position of the nulls, and the beamwidths in small arrays, which are useful for adaptively controlling the array performance in emerging wireless communication systems and radars.

scholarly journals A Novel Approach for Robust Perceptual Image Hashing

2021 ◽  
Vol 14 (3) ◽  
pp. 38
Author(s):  
Azhar Hadmi ◽  
Awatif Rouijel
Keyword(s):  
Side Information ◽  
Main Idea ◽  
Jpeg Compression ◽  
Image Hashing ◽  
Novel Approach ◽  
Perceptual Hash ◽  
Short Signature ◽  
Robust Image ◽  
Tampered Image ◽  
The Stability

Perceptual image hashing system generates a short signature called perceptual hash attached to an image before transmission and acts as side information for analyzing the trustworthiness of the received image. In this paper, we propose a novel approach to improve robustness for perceptual image hashing scheme for generating a perceptual hash that should be resistant to content-preserving manipulations, such as JPEG compression and Additive white Gaussian noise (AWGN) also should differentiate the maliciously tampered image and its original version. Our algorithm first constructs a robust image, derived from the original input by analyzing the stability of the extracted features and improving their robustness. From the robust image, which does perceptually resemble the original input, we further extract the final robust features. Next, robust features are suitably quantized allowing the generation of the final perceptual hash using the cryptographic hash function SHA1. The main idea of this paper is to transform the original image into a more robust one that allows the extraction of robust features. Generation of the robust image turns out be quite important since it introduces further robustness to the perceptual image hashing system. The paper can be seen as an attempt to propose a general methodology for more robust perceptual image hashing. The experimental results presented in this paper reveal that the proposed scheme offers good robustness against JPEG compression and Additive white Gaussian noise.

Explaining Algorithms

2011 ◽  
pp. 2261-2279
Author(s):  
Tomasz Muldner ◽  
Elhadi Shakshuki
Keyword(s):  
Distance Education ◽  
Main Idea ◽  
Top Down ◽  
Levels Of Abstraction ◽  
Visualization System ◽  
Client Server ◽  
Novel Approach ◽  
Visualization Systems ◽  
Explanation System ◽  
Server Architecture

This article presents a novel approach for explaining algorithms that aims to overcome various pedagogical limitations of the current visualization systems. The main idea is that at any given time, a learner is able to focus on a single problem. This problem can be explained, studied, understood, and tested, before the learner moves on to study another problem. Toward this end, a visualization system that explains algorithms at various levels of abstraction has been designed and implemented. In this system, each abstraction is focused on a single operation from the algorithm using various media, including text and an associated visualization. The explanations are designed to help the user to understand basic properties of the operation represented by this abstraction, for example its invariants. The explanation system allows the user to traverse the hierarchy graph, using either a top-down (from primitive operations to general operations) approach or a bottom-up approach. Since the system is implemented using a client-server architecture, it can be used both in the classroom setting and through distance education.

scholarly journals A Novel Approach for the Detection of Developing Thunderstorm Cells

2019 ◽  
Vol 11 (4) ◽  
pp. 443 ◽  
Author(s):  
Richard Müller ◽  
Stéphane Haussler ◽  
Matthias Jerg ◽  
Dirk Heizenreder
Keyword(s):  
Water Vapor ◽  
Main Idea ◽  
Probability Of Detection ◽  
Base Case ◽  
Convective Clouds ◽  
Critical Success ◽  
Novel Approach ◽  
Forecast Lead Time ◽  
Spatio Temporal ◽  
Atmospheric Motion Vectors

This study presents a novel approach for the early detection of developing thunderstorms. To date, methods for the detection of developing thunderstorms have usually relied on accurate Atmospheric Motion Vectors (AMVs) for the estimation of the cooling rates of convective clouds, which correspond to the updraft strengths of the cloud objects. In this study, we present a method for the estimation of the updraft strength that does not rely on AMVs. The updraft strength is derived directly from the satellite observations in the SEVIRI water vapor channels. For this purpose, the absolute value of the vector product of spatio-temporal gradients of the SEVIRI water vapor channels is calculated for each satellite pixel, referred to as Normalized Updraft Strength (NUS). The main idea of the concept is that vertical updraft leads to NUS values significantly above zero, whereas horizontal cloud movement leads to NUS values close to zero. Thus, NUS is a measure of the strength of the vertical updraft and can be applied to distinguish between advection and convection. The performance of the method has been investigated for two summer periods in 2016 and 2017 by validation with lightning data. Values of the Critical Success Index (CSI) of about 66% for 2016 and 60% for 2017 demonstrate the good performance of the method. The Probability of Detection (POD) values for the base case are 81.8% for 2016 and 89.2% for 2017, respectively. The corresponding False Alarm Ratio (FAR) values are 22.6% (2016) and 36.4% (2017), respectively. In summary, the method has the potential to reduce forecast lead time significantly and can be quite useful in regions without a well-maintained radar network.

scholarly journals Depth Prediction without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

2019 ◽  
Vol 33 ◽  
pp. 8001-8008 ◽  
Author(s):  
Vincent Casser ◽  
Soeren Pirk ◽  
Reza Mahjourian ◽  
Anelia Angelova
Keyword(s):  
Unsupervised Learning ◽  
Moving Objects ◽  
Robot Navigation ◽  
Main Idea ◽  
Object Motion ◽  
Indoor Navigation ◽  
Depth Prediction ◽  
Novel Approach ◽  
Indoor Scenes ◽  
Scene Depth

Learning to predict scene depth from RGB inputs is a challenging task both for indoor and outdoor robot navigation. In this work we address unsupervised learning of scene depth and robot ego-motion where supervision is provided by monocular videos, as cameras are the cheapest, least restrictive and most ubiquitous sensor for robotics. Previous work in unsupervised image-to-depth learning has established strong baselines in the domain. We propose a novel approach which produces higher quality results, is able to model moving objects and is shown to transfer across data domains, e.g. from outdoors to indoor scenes. The main idea is to introduce geometric structure in the learning process, by modeling the scene and the individual objects; camera ego-motion and object motions are learned from monocular videos as input. Furthermore an online refinement method is introduced to adapt learning on the fly to unknown domains. The proposed approach outperforms all state-of-the-art approaches, including those that handle motion e.g. through learned flow. Our results are comparable in quality to the ones which used stereo as supervision and significantly improve depth prediction on scenes and datasets which contain a lot of object motion. The approach is of practical relevance, as it allows transfer across environments, by transferring models trained on data collected for robot navigation in urban scenes to indoor navigation settings. The code associated with this paper can be found at https://sites.google.com/view/struct2depth.

scholarly journals Metabolomic Markers for the Early Selection of Coffea canephora Plants with Desirable Cup Quality Traits

Metabolites ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 214 ◽  
Author(s):  
Gamboa-Becerra ◽  
Hernández-Hernández ◽  
González-Ríos ◽  
Suárez-Quiroz ◽  
Gálvez-Ponce ◽  
...  
Keyword(s):  
Genetically Modified Organisms ◽  
Main Idea ◽  
Coffea Canephora ◽  
Development Stage ◽  
Quality Traits ◽  
Conventional Breeding ◽  
Novel Approach ◽  
Coffee Plants ◽  
Selection Of ◽  
Cup Quality

Genetic improvement of coffee plants represents a great challenge for breeders. Conventional breeding takes a too long time for responding timely to market demands, climatic variations and new biological threads. The correlation of genetic markers with the plant phenotype and final product quality is usually poor. Additionally, the creation and use of genetically modified organisms (GMOs) are often legally restricted and rejected by customers that demand natural products. Therefore, we developed a non-targeted metabolomics approach to accelerate conventional breeding. Our main idea was to identify highly heritable metabolites in Coffea canephora seedlings, which are linked to coffee cup quality. We employed a maternal half-sibs approach to estimate the metabolites heritability in open-pollinated plants in both leaves and fruits at an early plant development stage. We evaluated the cup quality of roasted beans and correlated highly heritable metabolites with sensory quality traits of the coffee beverage. Our results provide new insights about the heritability of metabolites of C. canephora plants. Furthermore, we found strong correlations between highly heritable metabolites and sensory traits of coffee beverage. We revealed metabolites that serve as predictive metabolite markers at an early development stage of coffee plants. Informed decisions can be made on plants of six months old, compared to 3.5 to 5 years using conventional selection methods. The metabolome-wide association study (MWAS) drastically accelerates the selection of C. canephora plants with desirable characteristics and represents a novel approach for the focused breeding of crops.

scholarly journals A Unified Theory for 3D Gear and Thread Metrology

2021 ◽  
Vol 11 (16) ◽  
pp. 7611
Author(s):  
Martin Stein ◽  
Frank Keller ◽  
Anita Przyklenk
Keyword(s):  
Main Idea ◽  
Unified Theory ◽  
The Novel ◽  
Geometry Model ◽  
Novel Approach ◽  
Transverse Profile ◽  
Evaluation Algorithm ◽  
Conical Gears ◽  
Screw Threads ◽  
Actual Geometry

We propose a unified theory for the metrological treatment of helical machine elements such as cylindrical and conical gears, worms, and screw threads. The main idea is to introduce a universal 3D geometry model for threaded components that provides for distinct parameterization using a unique set of geometry parameters and that offers and a functional description of the transverse profile. Using modern 3D coordinate measuring technology, a holistic evaluation algorithm yields the actual geometry as the result of a high dimensional best-fit procedure and form deviations as corresponding residuals. All determinants and evaluation parameters can then be calculated from the set of actual geometry parameters. By applying certain constraints to the model to be fitted, the novel method can be reduced to the established 2D methods and hence meets demands for the comparison of the two procedures. The results of the novel approach have proven to be very stable and they enable the evaluation of areal measurements with no loss of information.

scholarly journals Writer Identification on Multi-Script Handwritten Using Optimum Features

2017 ◽  
Vol 2 (3) ◽  
pp. 178-185 ◽  
Author(s):  
Ahmed Abdullah Ahmed ◽  
Harith Raad Hasan ◽  
Fariaa Abdalmajeed Hameed ◽  
Omar Ismael Al-Sanjary
Keyword(s):  
Document Analysis ◽  
Research Problem ◽  
Writer Identification ◽  
Identification Rate

Recognizing the writer of a text that has been handwritten is a very intriguing research problem in the field of document analysis and recognition. This study tables an automatic way of recognizing the writer from handwritten samples. Even though much has been done in previous researches that have presented other various methods, it is still clear that the field has a room for improvement. This particular method uses Optimum Features based writer characterization. Here, each of the samples written is grouped according to their set of features that are acquired from a computed codebook. This proposed codebook is different from the others which segment the samples into graphemes by fragmenting a certain part of the writing known as ending strokes. The proposed technique is employed to a locate and extract the handwriting fragments from ending zone and then grouped the similar fragments to generate a new cluster known as ending cluster. The cluster that comes in handy in the process of coming up with the ending codebook through picking out the center of the same fragment group. The process is finalized by evaluating the proposed method on four datasets of the various languages. This method being proposed had an impressive 97.12% identification rate which is rates the best result on the ICFHR dataset.

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