2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA

We present a new approach for face recognition system. The method is based on 2D face image features using subset of non-correlated and Orthogonal Gabor Filters instead of using the whole Gabor Filter Bank, then compressing the output feature vector using Linear Discriminant Analysis (LDA). The face image has been enhanced using multi stage image processing technique to normalize it and compensate for illumination variation. Experimental results show that the proposed system is effective for both dimension reduction and good recognition performance when compared to the complete Gabor filter bank. The system has been tested using CASIA, ORL and Cropped YaleB 2D face images Databases and achieved average recognition rate of 98.9 %

A study of the gravitational wave form from pulsars II

We present analytical and numerical studies of the Fourier transform (FT) of the gravitational wave (GW) signal from a pulsar, taking into account the rotation and orbital motion of the Earth. We also briefly discuss the Zak-Gelfand Integral Transform. The Zak-Gelfand Integral Transform that arises in our analytic approach has also been useful for Schrodinger operators in periodic potentials in condensed matter physics (Bloch wave functions).Comment: 6 pages, Sparkler talk given at the Amaldi Conference on Gravitational waves, July 10th, 2001. Submitted to Classical and Quantum Gravit

The Zero-Removing Property and Lagrange-Type Interpolation Series

The classical Kramer sampling theorem, which provides a method for obtaining orthogonal sampling formulas, can be formulated in a more general nonorthogonal setting. In this setting, a challenging problem is to characterize the situations when the obtained nonorthogonal sampling formulas can be expressed as Lagrange-type interpolation series. In this article a necessary and sufficient condition is given in terms of the zero removing property. Roughly speaking, this property concerns the stability of the sampled functions on removing a finite number of their zeros

A critical institutionalist analysis of youth participation in Jordan's spatial planning the case of Amman 2025

By 2050, it is estimated that 84% of the population in the Global South will be living in urban areas. As a country of the Global South, Jordan has experienced dramatic growth of urban areas over the past decades. Cities in Jordan contain 83% of the population, of which, it is estimated, 24% are in the age group 15–24. Youth input, effort and experience in the planning process are recognised by academic research and international aid donors as a significant element in catalysing positive social and economic change and ensuring sustainable development across the Global South. Consequently, this research aims to investigate whether young groups’ vision and aspirations for, and perspectives on the city of Amman were translated into strategies or projects in urban policy. In doing so, it aims to explore the wide range of institutional challenges and opportunities that either hinder or encourage youth participation in policymaking. To achieve this aim, this study followed the inductive–deductive cycle of knowledge. This research starts with a critical literature review of theories in planning, governance, youth participation and spatial planning in Jordan. Healey’s systemic institutional design for collaborative planning was employed to critically analyse the planning system (hard infrastructure) and planning practice (soft infrastructure) within the chosen case study of Amman 2025. Amman 2025 is a significant and unique strategic spatial planning project in Jordan designed to encourage public participation in the policymaking process regarding urban development in Amman. New primary data were collected through extensive qualitative research in the form of semi-structured interviews and focus groups to cover the period from the start of Amman 2025, in 2006, to the conducting of data collection in 2015. With the research objectives in mind, a thematic analysis was conducted to identify salient themes in order to address the research aims. The findings of this study show that youth participation in Jordan is neither inherent in the legal, political and administrative framework of the planning system nor in the embedded institutional settings within the planning practice of Jordan. Most importantly, cultural imperialism in Jordan weakens young people’s chances of being considered for any decision-making roles relevant to planning activities. Enhancing youth participation in spatial planning in Jordan requires the institutional capacity of urban governance to be built up to enable a more collaborative planning practice, in addition to applying principles of good governance in the planning system

Nanoparticles Decorated on Resin Particles and Their Flame Retardancy Behavior for Polymer Composites

New nanocomposites have been developed by doping of amberlite IR120 resin with spherical TiO2 nanoparticles in the presence of maleate diphosphate. Polystyrene composites of resin, maleate diphosphate, and resin-maleate diphosphate were prepared individually. This is in addition to preparation of polymer nanocomposites of polystyrene-resin doped TiO2 nanoparticles-maleate diphosphate. The flame retardancy and thermal stability properties of these developed polymer composites were evaluated. The inclusion of resin and resin doped nanoparticles improved the fire retardant behavior of polystyrene composites and enhanced their thermal stability. Synergistic behavior between flame retardant, resin, and nanoparticles was detected. The rate of burning of the polymer nanocomposites was recorded as 10.7 mm/min achieving 77% reduction compared to pure polystyrene (46.5 mm/min). The peak heat release rate (PHRR) of the new polymer composites has reduced achieving 46% reduction compared to blank polymer. The morphology and dispersion of nanoparticles on resin and in polymer nanocomposites were characterized using transmission and scanning electron microscopy, respectively. The flame retardancy and thermal properties were evaluated using UL94 flame chamber, cone tests, and thermogravimetric analysis, respectively

Integrating transposable elements in the 3D genome

Chromosome organisation is increasingly recognised as an essential component of genome regulation, cell fate and cell health. Within the realm of transposable elements (TEs) however, the spatial information of how genomes are folded is still only rarely integrated in experimental studies or accounted for in modelling. Whilst polymer physics is recognised as an important tool to understand the mechanisms of genome folding, in this commentary we discuss its potential applicability to aspects of TE biology. Based on recent works on the relationship between genome organisation and TE integration, we argue that existing polymer models may be extended to create a predictive framework for the study of TE integration patterns. We suggest that these models may offer orthogonal and generic insights into the integration profiles (or "topography") of TEs across organisms. In addition, we provide simple polymer physics arguments and preliminary molecular dynamics simulations of TEs inserting into heterogeneously flexible polymers. By considering this simple model, we show how polymer folding and local flexibility may generically affect TE integration patterns. The preliminary discussion reported in this commentary is aimed to lay the foundations for a large-scale analysis of TE integration dynamics and topography as a function of the three-dimensional host genome

Source printer identification using convolutional neural network and transfer learning approach

In recent years, Source printer identification has become increasingly important for detecting forged documents. A printer's distinguishing feature is its fingerprints. Each printer has a unique collection of fingerprints on every printed page. A model for identifying the source printer and classifying the questioned document into one of the printer classes is provided by source printer identification. A paper proposes a new approach that trains three different approaches on the dataset to choose the more accurate model for determining the printer's source. In the first, some pre-trained models are used as feature extractors, and support vector machine (SVM) is used to classify the generated features. In the second, we construct a two-dimensional convolutional neural network (2D-CNN) to address the source printer identification (SPI) problem. Instead of SoftMax, 2D-CNN is employed for feature extractors and SVM as a classifier. This approach obtains 93.75% 98.5% accuracy for 2D-CNN-SVM in the experiments. The SVM classifier enhanced the 2D-CNN accuracy by roughly 5% over the initial configuration. Finally, we adjusted 13 already-pre-trained CNN architectures using the dataset. Among the 13 pre-trained CNN models, DarkNet-19 has the greatest accuracy of 99.2 %. On the same dataset, the suggested approaches achieve well in terms of classification accuracy than the other recently released algorithms.