Miracle-dz/UniFAR-FTUs huggingface dataset:
Kernel Principal Component Analysis Feature Detection for Underwater Images

Sonar systems are used in a variety of military and civil applications. The image feature extraction and detection process is a very difficult one due to the presence of distortion of images like blurring, illumination change and image rotation at various angle. Lowe (2004) presented SIFT for extracting distinctive invariant features from images that can be invariant to image scale and rotation. Then it was widely used in image mosaic, recognition, retrieval and etc. After Lowe, Ke and Sukthankar used PCA to normalize gradient patch instead of histograms [1]. They showed that PCA-based local descriptors were also distinctive and robust to image deformations. But the methods of extracting robust features were still very slow. Bay and Tuytelaars (2006) speeded up robust features and used integral images for image convolutions and Fast-Hessian detector [2]. Their experiments turned out that it was faster and it works well. There are two considerations to using local descriptors in these applications. First, we must localize the interest point in position and scale. Typically, interest points are placed at local peaks in a scale-space search, and filtered to preserve only those that are likely to remain stable over transformations. Second, we must build a description of the interest point; ideally, this description should be distinctive (reliably differentiating one interest point from others), concise, and invariant over transformations caused by changes in camera pose and lighting. While the localization and description aspects of interest point algorithms are often designed together, the solutions to these two problems are independent [3]. This paper focuses on approaches to the Kernel PCA using SVM kernels. PCA is used for feature extraction and dimension reduction. By combining PCA and Scale Invariant Feature transform (SIFT), the method gives both distinctive and more compact. Its matching accuracy and matching speed also better for underwater images. Then when compare to PCA SIFT the KPCA method is well suited in accuracy and speed. The paper is organized as follows: Section II discusses the Review of SIFT algorithm. Section III presents the details of PCA SIFT algorithm. Section IV represents the proposed method KPCA feature detection for underwater images. The Experimental setup and performance evaluation are presented in section V. Finally, conclusions are given in section VI
Hardware versus hybrid data prefetching in multimedia processors: a case study

Data prefetching is a promising technique for hiding the penalties due to compulsory cache misses. In this paper we present a case study on two types of data prefetching in the context of multimedia processing: a purely hardware-based technique and a more low-cost hybrid hardware/software technique. Moreover, we also propose a technique for increasing the so-called prefetch distance in hardware prefetching and a scheme to reduce trashing in the data cache. Our results demonstrate that the low-cost hybrid prefetching scheme slightly outperforms hardware-based prefetching for the code segments for which both solutions have been applied, while hardware prefetching potentially allows more code to benefit from the prefetching.
Investigation of particle size and reinforcement content on mechanical properties and fracture behavior of A356-Al2O3 composite fabricated by vortex method

The aim of the present study is to investigate the effect of alumina particle size and its amount on relative density, mechanical properties, and fracture behavior of Al - Al 2 O 3 composite. To manufacture micro and nano-composites, alumina particles with various sizes of 50 µm, 10 µm, and 20 nm were separately milled with aluminum powders, and subsequently different volume fractions of milled powders were injected by argon gas into molten alloy and incorporated into A356 matrix by a mechanical stirrer (vortex method). Composites were fabricated at various casting temperatures, viz. 750, 800, and 900℃. Microstructural characterizations revealed that the dendrite size of the Aluminum matrix nano composite (AMNCs) is smaller than that of the non-reinforced alloy. The Scanning electron microscope (SEM) micrographs revealed that the Al 2 O 3 particles were surrounded by silicon eutectic and inclined to move toward inter-dendritic regions. Nano-particles were dispersed uniformly in the matrix when the volume fraction of nano-particles in the composite was less than 3.5 vol.%. The porosity content of the composites increased with increasing volume fraction and decreasing particle size. Also, hardness and tensile strength of the composites improved with decreasing particle size and increasing reinforcement content. The significant improvements in hardness and tensile strength were respectively attained in the nano-composites, reinforced with 1.5 and 2.5 vol.% Al 2 O 3 nano-particles. Alumina particle cracking was observed in the fracture surface of the micro-composites. Agglomerated nano-particles were observed on dendrites in the fracture surface of nano-composites.
BossaNova at ImageCLEF 2012 Flickr Photo Annotation Task

We present the BossaNova scheme for the ImageCLEF 2012 Flickr Photo Annotation Task. BossaNova is a mid-level image represen- tation, recently developed by our team, that enriches the Bag-of-Words representation, by keeping a histogram of distances between the descrip- tors found in the image and those in the codebook. Our scheme has the advantage of being conceptually simple, non-parametric, and eas- ily adaptable. Compared to other schemes existing in the literature to add information to the Bag-of-Words model, it leads to much more com- pact representations. Furthermore, it complements well the cutting-edge Fisher Vector representations, showing even better results when em- ployed in combination with them. In our participation, we submitted four purely visual runs. Our best result (MiAP = 34.37%) achieved the second rank by MiAP measure among the 28 purely visual submissions and the 18 teams.
Effect of Clay on Thermal, Mechanical and Gas Barrier Properties of Biodegradable Poly(lactic acid)/Poly(butylene succinate) (PLA/PBS) Nanocomposites

Abstract A series of poly(lactic acid) (PLA)/poly(butylene succinate) (PBS)/layered silicate nancomposites were prepared by simple melt extrusion of PLA/PBS/Cloisite 30BX (organically modified MMT) clay. Extruded and compression moulded samples containing 1, 3, 5, 7 and 10 wt. % of clay having 80 and 20 wt. % of PLA and PBS respectively, were prepared to investigate morphological, thermal, mechanical and gas barrier properties of these biodegradable nanocomposites. Wide angle X-ray diffraction (WAXD) was used to assess the periodic distance of the clay layers. WAXD indicated an exfoliated structure for nanocomposites containing 1 and 3 wt. % of C30BX, while, dominantly intercalated structures were noticed for nanocomposites having 5, 7 and 10 wt. % of C30BX. Transmission Electron Microscopy (TEM) images confirmed mixed morphology of intercalated and exfoliated structure even for nanocomposites having 1 and 3 wt. % of C30BX, while some clusters or agglomerated tactoids were detected for higher clay containing (>3 wt.%) nanocomposites. Thermo gravimetric analysis (TGA) revealed the thermal stabilities of polymer blend and their nancomposites. Nanocomposite with 3 wt. % C30BX clay showed enhanced thermal stability compared to other nanocomposites. The effect on the crystallinity, investigated by Modulated Differential Scanning Calorimetry (MDSC) showed slight improvement for nanocomposites having 1, 3, 7 and 10 wt. % of C30BX studied. However, nanocomposites having 5 wt. % of clay content showed higher crystallinity compared to the rest of the nanocomposites. Mechanical properties (tensile strength, % elongation at break and Young's modulus) were measured by Instron Universal Testing Machine. Tensile strength and Young's modulus initially increased for nanocomposites of up to 3 wt. % of clay but then decreased with the introduction of more clay. Oxygen permeabilities of the nanocomposites were also measured. Barrier properties were improved by approximately 26% with the increase of clay content in these nanocomposites.
Decompositions of All Different, Global Cardinality and Related Constraints

We show that some common and important global constraints like ALL-DIFFERENT and GCC can be decomposed into simple arithmetic constraints on which we achieve bound or range consistency, and in some cases even greater pruning. These decompositions can be easily added to new solvers. They also provide other constraints with access to the state of the propagator by sharing of variables. Such sharing can be used to improve propagation between constraints. We report experiments with our decomposition in a pseudo-Boolean solver.
Learning and memory in amnestic mild cognitive impairment: Contribution of working memory

Abstract In addition to deficits in delayed recall, recent research suggests that participants with amnestic mild cognitive impairment (aMCI) demonstrate diminished use of strategic encoding strategies during learning. Few studies have explored the cognitive mechanisms underlying this deficit. The aim of this study was to investigate in aMCI whether components of working memory (executive attention – attention set-shifting, dividing and focusing attention; and episodic buffer functions – strategic retrieval and manipulation of information) predict strategic encoding strategies during learning (semantic clustering). Thirty-three participants with aMCI and 33 healthy older adults (HOA) were administered neuropsychological tests assessing word-list learning and working memory. The aMCI group demonstrated significant impairment in acquisition, retrieval of information, and decreased use of semantic clustering strategies. Use of semantic clustering in the aMCI group was not predicted by measures of executive attention or phonemic verbal fluency, but was predicted by semantic verbal fluency performance. In the HOA group, semantic clustering was strongly related to semantic verbal fluency. These findings suggest that in aMCI, diminished strategic encoding strategies during learning (semantic clustering) is selectively related to the strategic function of the episodic buffer, but only when in interaction with the manipulation and retrieval of semantic associations. (JINS, 2010, 16, 342–351.)
Practical and Incremental Maintenance of Software Resources in Consumer Electronics Products

We introduce a method, so called FC method, for maintaining software resources, such as source codes and design documents, in consumer electronics products. Because a consumer electronics product is frequently and rapidly revised, software components in such product are also revised in the same way. However, it is not so easy for software engineers to follow the revision of the product because requirements changes for the product, including the changes of its functionalities and its hardware components, are largely independent of the structure of current software resources. FC method lets software engineers to restructure software resources, especially design documents, stepwise so as to follow the requirements changes for the product easily. We report an application of this method in our company to validate it. From the application, we can confirm that the quality of software was improved about in twice, and that efficiency of development process was also improved over four times.
Reduced parahippocampal connectivity produces schizophrenia-like memory deficits in simulated neural circuits with reduced parahippocampal connectivity.

CONTEXT Episodic memory impairments are well characterized in schizophrenia, but their neural origin is unclear. OBJECTIVE To determine whether the episodic memory impairments in schizophrenia may originate from reduced parahippocampal connectivity. DESIGN Experimental in silico model. SETTING Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands. INTERVENTIONS A new, in silico medial temporal lobe model that simulates normal performance on a variety of episodic memory tasks was devised. The effects of reducing parahippocampal connectivity in the model (from perirhinal and parahippocampal cortex to entorhinal cortex and from entorhinal cortex to hippocampus) were evaluated and compared with findings in schizophrenic patients. Alternative in silico neuropathologies, increased noise and loss of hippocampal neurons, were also evaluated. RESULTS In the model, parahippocampal processing subserves integration of different cortical inputs to the hippocampus and feature extraction during recall. Reduced connectivity in this area resulted in a pattern of deficits that closely mimicked the impairments in schizophrenia, including a mild recognition impairment and a more severe impairment in free recall. Furthermore, the schizophrenic model was not differentially sensitive to interference, also consistent with behavioral data. Notably, neither increased noise levels nor a reduction of hippocampal nodes in the model reproduced this characteristic memory profile. CONCLUSIONS Taken together, these findings highlight the importance of parahippocampal neuropathology in schizophrenia, demonstrating that reduced connectivity in this region may underlie episodic memory problems associated with the disorder.
Chromosome Segregation in Vibrio cholerae

The study of chromosome segregation is currently one of the most exciting research frontiers in cell biology. In this review, we discuss our current knowledge of the chromosome segregation process in Vibrio cholerae, based primarily on findings from fluorescence microscopy experiments. This bacterium is of special interest because of its eukaryotic feature of having a divided genome, a feature shared with 10% of known bacteria. We also discuss how the segregation mechanisms of V. cholerae compare with those in other bacteria, and highlight some of the remaining questions regarding the process of bacterial chromosome segregation.
Relationship between body mass index and dental caries among adolescent children in South India.

OBJECTIVE To evaluate the relationship between body mass index (BMI) and dental caries and to study the role of sweet consumption in predicting this relationship among adolescent children in Udupi district, India. METHODS The study population consisted of 463 school children in the 13-15-year age group. Anthropometric (height in metres and weight in kilograms) and caries measurements and decayed missing filled teeth (DMFT) index, were carried out by a trained recorder according to standard criteria. RESULTS The majority of the children were having low normal weight (BMI < 25) with 18.6% classified as overweight (BMI 25-29.9) and 3.5% as obese (BMI > 30). The frequency of sweet consumption significantly increased from low normal weight children to overweight and obese children. Analysis showed that the obese group of children had more caries than the overweight and low normal weight children. Correlation analysis showed significant positive relation with BMI, decayed teeth (DT) [r = 0.254, p < 0.001] and DMFT (r = 0.242, p < 0.001). Binomial logistic regression showed that males (OR = 2.09, CI = 1.01, 4.33), obese/overweight children (OR = 3.68, CI = 1.79, 7.56) and those who consumed sweets more than once a day (OR = 3.13, CI = 1.25, 7.85) were more likely to have high caries experience. CONCLUSION There was a significant association between overweight/obesity and caries experience among school children of the Udupi district. Obesity and dental caries have common risk determinants and require a comprehensive multidisciplinary approach by both medical and dental healthcare professionals.
Spatially adaptive image denoising under overcomplete expansion

This paper presents a novel wavelet-based image denoising algorithm under overcomplete expansion. In order to optimize the denoising performance, we make a systematic study of both signal and noise characteristics under overcomplete expansion. High-band coefficients are viewed as the mixture of non-edge class and edge class observing different probability models. Based on improved statistical modeling of wavelet coefficients, we derive optimal MMSE estimation strategies to suppress noise for both non-edge and edge coefficients. We have achieved fairly better objective performance than most recently-published wavelet denoising schemes.
Gaussian Accelerated Molecular Dynamics in NAMD

Gaussian accelerated molecular dynamics (GaMD) is a recently developed enhanced sampling technique that provides efficient free energy calculations of biomolecules. Like the previous accelerated molecular dynamics (aMD), GaMD allows for “unconstrained” enhanced sampling without the need to set predefined collective variables and so is useful for studying complex biomolecular conformational changes such as protein folding and ligand binding. Furthermore, because the boost potential is constructed using a harmonic function that follows Gaussian distribution in GaMD, cumulant expansion to the second order can be applied to recover the original free energy profiles of proteins and other large biomolecules, which solves a long-standing energetic reweighting problem of the previous aMD method. Taken together, GaMD offers major advantages for both unconstrained enhanced sampling and free energy calculations of large biomolecules. Here, we have implemented GaMD in the NAMD package on top of the existing aMD feature and validated it on three model systems: alanine dipeptide, the chignolin fast-folding protein, and the M3 muscarinic G protein-coupled receptor (GPCR). For alanine dipeptide, while conventional molecular dynamics (cMD) simulations performed for 30 ns are poorly converged, GaMD simulations of the same length yield free energy profiles that agree quantitatively with those of 1000 ns cMD simulation. Further GaMD simulations have captured folding of the chignolin and binding of the acetylcholine (ACh) endogenous agonist to the M3 muscarinic receptor. The reweighted free energy profiles are used to characterize the protein folding and ligand binding pathways quantitatively. GaMD implemented in the scalable NAMD is widely applicable to enhanced sampling and free energy calculations of large biomolecules.
Towards optimization of patterned superhydrophobic surfaces

Experimental and theoretical study of wetting properties of patterned Si surfaces with cylindrical flat-top pillars of various sizes and pitch distances is presented. The values of the contact angle (CA), contact angle hysteresis (CAH) and tilt angle (TA) are measured and compared with the theoretical values. Transition from the composite solid–liquid–air to the homogeneous solid–liquid interface is investigated. It is found that the wetting behaviour of a patterned hydrophobic surface depends upon a simple non-dimensional parameter, the spacing factor, equal to the pillar diameter divided by the pitch. The spacing factor controls the CA, CAH and TA in the composite interface regime, as well as destabilization and transition to the homogeneous interface. We show that the assumption that the CAH is a consequence of the adhesion hysteresis and surface roughness leads to the theoretical values of the CAH that are in a reasonably good agreement with the experimental values. By decreasing the spacing factor, the values of CA=170°, CAH=5° and TA=3° are achieved. However, with further decreasing of the spacing factor, the composite interface destabilizes.
Distributed and Multiplexed Fibre Grating Sensors, Including Discussion of Problem Areas (Special Issue on Optical Fiber Sensors)

A short review of distributed and multiplexed sensor technology, based on fibre gratings, is given. This is followed by details of more specific work in this area at the University of Southampton, particularly grating fabrication, distributed and multiplexed addressing and important practical aspects such as temperature and strain discrimination. The paper concludes with a short discussion of the problems that must be avoided in order to construct viable systems for engineering requirements.
Structure and possible origins of stacking faults in gamma-yttrium disilicate

Parallel stacking faults on (010) planes are frequently observed in hot-pressed Y2Si2O7. A combination of conventional dark-field imaging and high-resolution transmission electron microscopy was used to investigate the structure of these faults. It was found that they consisted of the repeat of one layer of the two-layer γ-Y2Si2O7 structure with an associated in-plane rigid body displacement. The resulting structure was confirmed by image simulation of high-resolution images from two perpendicular projections. A model for the formation of the stacking faults is proposed as a consequence of a transformation from β-Y2Si2O7 to γ-Y2Si2O7 in the hot pressing.
A Systematic Approach to Network Coding Problems using Conflict Graphs

We present a new approach to network coding problems that could lead to a systematic method for deciding solvability of a given network. The approach is based on a graph theoretic formulation of the problem. The constraints at each node in the network are represented using hyperedges in a ‘conflict’ hypergraph. This representation reduces the solvability question to that of finding a stable set with certain properties in a hypergraph. The approach is sufficiently general to allow even non-linear codes by suitably modifying the conflict graph. We also demonstrate the use of the conflict graph idea in the context of a multicast crossbar switch. Using examples, we show that the rate region of a multicast switch strictly improves with network coding at the inputs, and derive an outer bound on the rate region when intra-session network coding is allowed.
Class specific dynamic feature selection technique — Towards human movement based biometrics application

Classification of the biometrics data for identity validation can be considered as a single-class problem. Each class can be represented by a unique set of features. However, current feature selection techniques consider the entire database and identify the feature-set that is suitable for representing all available classes. This may not be the best representation of the biometrics data of each individual because different people may have difference in the most suitable features to represent their biometric data. In this study, a class-specific dynamic feature selection method has been proposed and experimentally validated using dynamic signatures. This method is based on the variance within the feature set, where the features with smaller variance are selected and the ones with larger variance are rejected. A comparison was made with other feature selection methods, and the results show that there were differences in the features representing different classes. A significant improvement in the classification accuracy and specificity and sensitivity was also observed when using this feature selection technique.