Texture Units Research Articles

Optimum texture shape under different lubrication conditions applied to the start-up phase of journal bearings

This paper presents a parameterized model to optimize the texture units using an improved particle swarm optimization. The texture units are optimized under three lubrication states: boundary lubrication, mixed lubrication, and hydrodynamic lubrication, by utilizing the Stribeck curve. The optimal texture units can be applied to journal bearings and thrust bearings in different operating conditions to achieve maximum load-carrying capacity. The paper applies the optimal textures under the three lubrication states to the start-up stage of journal bearings to examine the lubrication performance and dynamic characteristics of the textured journal bearing system. The results demonstrate that compared to the untextured journal-bearing system, the introduction of optimal textures improves the tribological performance of the journal-bearing system, and the optimal textured units under hydrodynamic lubrication have the best friction reduction effect. Furthermore, it is found that the introduction of textures has no significant effect on the dynamic characteristics of the journal bearings.

Friction-Wear and Noise Characteristics of Friction Disks with Circular Texture.

The reduction of friction-induced noise is a crucial research area for enhancing vehicle comfort, and this paper proposes a method based on circular pit texture to achieve this goal. We conducted a long-term sliding friction test using a pin-on-disc friction and a wear test bench to verify the validity of this method. To compare the friction noise of different surfaces, texture units with varying line densities were machined on the surface of friction disk samples. The resulting friction-wear and noise characteristics of the samples were analyzed in conjunction with the microscopic morphology of the worn surfaces. The results indicate that surfaces with textures can delay the onset of squeal noise, and the pattern of its development differs from that of smooth surfaces. The noise reduction effect is most evident due to the proper distribution of textures that can form furrow-like wear marks at the wear interface. The finite element results demonstrate that this morphology can improve pressure distribution at the leading point and reduce the tendency of system instability.

An efficient unsupervised approach for textile defect detection through unifying texture reconstruction and semantic localization

Deep learning has shown promise in textile defect detection, but its reliance on large high-quality labeled datasets poses challenges in real-world industrial applications. This study presents a novel unsupervised defect detection framework that effectively detects various types of texture defects with limited defect-free texture samples. The framework integrates texture and semantic information using a bilateral-branch network architecture (TSUBB-Net). Specifically, TSUBB-Net employs a weighted centering loss to cluster complex texture units, emphasizing semantic information within defect regions through a channel attention mechanism. It further fuses contextual semantic information to achieve precise defect localization. Thus, the efficient fusion method combines texture and semantic information, enabling the representation of complex texture structures and mitigating the impact of image acquisition quality on defect recognition. To evaluate the effectiveness of our proposed method, we build a unique dedicated database of textile defect image segmentation, which serves as the benchmark for textile defect detection. Experimental results demonstrate that TSUBB-Net surpasses state-of-the-art methods, exhibiting excellent performance in textile defect detection. The proposed framework holds significant potential for practical applications in the textile industry, improving defect detection capabilities.

Combined effects of tool surface texturing, cutting parameters and minimum quantity lubrication (MQL) pressure on micro-grinding of BK7 glass

Capability of sintered micro-grinding tool is limited by low chip space, ineffective lubrication access and premature loading which results in high cutting forces and brittle mode fractures on the work surfaces. Usage of textured tool is one of the methods to avoid aforesaid phenomena and to achieve high cutting performance. On machine fabrication of textured micro-tools and correlating their cutting mechanism with texture dimension are some of the primary challenges. A new electric discharge texturing method for on-machine fabrication of textured tools is devised and the tools with optimum intermittent ratio but varying texture dimensions are fabricated. Micro-grinding experiments are carried out on BK7 glass to establish the correlation between cutting performance with texture dimension, cutting parameters and MQL (minimum quantity lubrication) pressure. It is concluded that tool with small size and large number of texture units reduced the cutting forces, surface roughness and edge chipping by 25–35 % of those obtained by a tool without texture at depth of cut 10 μm and feed rate 5 mm/min. The amount of reductions of these two process parameters even reached to 50–60 % at high depth of cut 40 μm and feed rate 11 mm/min. The reduction in output parameters is supported by the observation of effective migration of chips in the passive areas. It is also established in this study that MQL spray droplet size should be very close to the texture dimension to ensure maximum cutting fluid access in the machining zone. Analytical derivation of penetration depth for textured tools indicated delayed chances of ductile to brittle transition when the tools with small size texture units are used in place of the tools with large size texture units.

Romance «Blacked Night» by O. Tadeush based on Sappho’s poem: dialogue in “large time”

Romance «Blacked Night» by O. Tadeush based on Sappho’s poem: dialogue in “large time”

Content-Based Light Field Image Compression Method With Gaussian Process Regression

Light field (LF) imaging enables new possibilities for digital imaging, such as digital refocusing, changing of focus plane, changing of viewpoint, scene-depth estimation, and 3D scene reconstruction, by capturing both spatial and angular information of light rays. However, one main problem in dealing with LF data is its sheer volume. In this context, efficient compression methods are needed for such a particular type of content. In this paper, we propose a content-based LF image-compression method with Gaussian process regression to improve the compression efficiency and accelerate the prediction procedure. First, the LF image is fed to the intra-frame codec of HEVC. In the prediction procedure, the prediction units (PUs) are classified as non-homogenous texture units, homogenous texture units, and visually flat units, based on the content property of the LF image. For each category, we design a corresponding Gaussian process regression (GPR)-based prediction method. Moreover, we propose a classification mechanism to exactly decide to which category the current PU belongs, so as to adjust the trade-off between the computational burden and the LF image coding efficiency. Experimental results demonstrate that the proposed LF image compression method is superior to several other state-of-the-art compression methods in terms of different quality metrics. Furthermore, the proposed method can also achieve a good visual quality of views rendered from decoded LF contents.

Classification of terrain based on geo-environmental parameters and their relationship with land use/land cover in Bansloi River basin, Eastern India: RS-GIS approach

Terrain classification is a process of classifying the Earth’s surface into units or sub-units based on the similarities in terrain properties, geomorphological characteristics, geological characteristics, morphometric properties, soil characteristics, etc. Thus, it possibly represents a qualitative and quantitative description of landforms. The present study intends to carry out terrain classification and to figure out the relationship between terrain classes and land use/land cover in plateau fringe watershed of Bansloi River, Eastern India. Terrain has been classified based on the hierarchical method using geomorphological, lithological, morphometric diversity and soil texture units in GIS environment. Nine morphometric variables (relative relief, dissection index, drainage density, slope, etc.) have been used for deriving morphometric diversity of this basin based on simple compositing method. Land use/land cover categories have been performed by means of supervised classification on Landsat image. In Bansloi River basin, 112 terrain classes have been identified and correlated with land use/land cover. Presently, within 65.87% areas of the total basin agriculture, activities are practiced. Structural origin terrain classes are mainly covered by vegetation, and fluvial origin terrain classes are in use of agricultural activities and built-up land. These micro-level terrain classes and land use/land cover map can be used for agricultural planning and watershed management.

Landscape texture in anthropogenically transformed regions: The example of Upper Silesia and the Dąbrowa Coal Basin (southern Poland)

In recent years, we see growing importance of research on landscape texture, which enables scientists to assess landscape as to its esthetic (visual), planning, as well as ecological aspects. Analyses of landscape texture result in identification of landscape zones, classified according to their habitability, recreational potential and suitability for industry, which plays a crucial role for work on planning and strategic documents. The study area covers 12 selected municipalities of Upper Silesia and the Dąbrowa Coal Basin, which are highly industrialized regions. Combining an analysis of the degree of landscape enclosure/openness with an analysis of morphological diversity in the study area, the author identifies landscape texture units in accordance with the new, more detailed typology. This results in the emergence of 36 landscape texture types that take into account the land relief forms in the study area. For the needs of further analyses, these types are classified into three groups: open, mosaic and enclosed landscapes.

Soil texture derived from topography in North-eastern Amazonia

ABSTRACTWe present a 1:100,000 scale soil texture map of Paragominas county (Pará, Brazil), covering 19,330 km2. The method allows rapid production of a soil texture map of a large area where the strength of a duricrust controls the relief. It is based on an easily accessible explanatory variable, topography, which is represented using a Digital Elevation Model. The method makes it possible to map the spatial distribution of the texture of the topsoil layer. Modelling was complemented by field observations to identify the laws governing the spatial organisation of soil textures. The spatial variability of the elevation above sea-level of the duricrust was obtained by Kriging. The error rate of the resulting map is 26%, and the observations of the four soil texture units were respectively 78%, 90%, 41% and 60% accurately located.

Multi-scale density detection for yarn-dyed fabrics with deformed repeat patterns

A new method combining the characteristics of macro-scale texture repeat patterns and micro-scale interwoven yarns of fabric images was proposed for yarn-dyed fabric density detection. The method was formulated in a research framework of multi-scale image processing and analysis. Firstly, a structure–texture decomposition approach was used to extract texture information and woven pattern details from the macro-scale fabric image. Secondly, a texture unit detection model was proposed to extract the texture units and to detect the yarn skewness in these texture units. Thirdly, a simple yet effective image registration method and a lightness gradient projection method were adopted to analyze the micro-scale fabric image and obtain the yarn locations in a texture unit. Finally, the average fabric density was calculated by coupling the near-regular features of texture units and yarn locations. The experiments showed that the proposed method was effective in detecting hundreds of yarns in the fabric samples and the computation time was very reasonable.

Optimal Piecewise Linear Function Approximation for GPU-Based Applications

Many computer vision and human-computer interaction applications developed in recent years need evaluating complex and continuous mathematical functions as an essential step toward proper operation. However, rigorous evaluation of these kind of functions often implies a very high computational cost, unacceptable in real-time applications. To alleviate this problem, functions are commonly approximated by simpler piecewise-polynomial representations. Following this idea, we propose a novel, efficient, and practical technique to evaluate complex and continuous functions using a nearly optimal design of two types of piecewise linear approximations in the case of a large budget of evaluation subintervals. To this end, we develop a thorough error analysis that yields asymptotically tight bounds to accurately quantify the approximation performance of both representations. It provides an improvement upon previous error estimates and allows the user to control the tradeoff between the approximation error and the number of evaluation subintervals. To guarantee real-time operation, the method is suitable for, but not limited to, an efficient implementation in modern graphics processing units, where it outperforms previous alternative approaches by exploiting the fixed-function interpolation routines present in their texture units. The proposed technique is a perfect match for any application requiring the evaluation of continuous functions; we have measured in detail its quality and efficiency on several functions, and, in particular, the Gaussian function because it is extensively used in many areas of computer vision and cybernetics, and it is expensive to evaluate.

Experimental Study on the Friction Characteristics of Textured Steel Surface with Ring-Shaped Pits under Lubricated Sliding Conditions

Surface texturing has been recognized to be an effective approach to modify the tribological performances of sliding surfaces. A ring-shaped texture has been fabricated on the surface of AISI1045 steel using a pulse laser, and reciprocating sliding tests were performed on a variable load tribometer (VLT). Frictional force of the textured surfaces was investigated under various geometrical parameters (inner radius, external radius, pitch, and offset ratio of texture units) and operating conditions (velocity and load). The results show that the width of the ring-shaped texture has a significant influence on antifriction, an optimum width for minimum friction is about 200 µm, and the staggered array of texture units is an effective way of reducing frictional force and wear.

IConn

Recently, the graphics processing unit (GPU) has made significant progress as a general-purpose parallel processor. The CPU and GPU cooperate together to solve data-parallel and control-intensive real-world applications in an optimized fashion. For example, emerging heterogeneous computing architectures such as Intel Sandy Bridge and AMD Fusion integrate the functionality of the CPU and GPU in a single die. However, the single-die CPU-GPU heterogeneous computing architecture faces the challenge of tight budget of die area. The conventional homogenous interconnect fails to provide satisfactory performance by fully exploiting the given area budget in the heterogeneous processing era. In this article, we aim to implement an interconnect network within an area budget for a CPU-GPU heterogeneous computing architecture. We propose iConn, a 2D mesh-style on-chip heterogeneous communication infrastructure. In iConn, a set of GPU logical units such as the stream processors, the texture units, and the rendering output units form a computing unit (CU). Differing from conventional homogenous router design, iConn adopts nonuniform on-chip routers in order to meet the unique communication demands from each single CPU and CU. The routers can also dynamically allocate their buffers across all virtual channels (VCs) to meet the latency requirements of CPUs and CUs. Moreover, the memory controller scheduling algorithm is modified from traditional load-over-store scheduling in order to prioritize the traffic. Our simulation results show that iConn improves the performance of CPUs by 23.0% and CUs by 9.4%.

Forest vegetation texture measurement of remote sensing images based on the blue noise theory

Forest vegetation remote sensing image segmentation is an important kind of target,and effective determination of the scale of forest vegetation texture segmentation is an important research topic. This paper presents a method in which the blue noise theory is used to describe the characteristics of remote sensing images for forest vegetation texture. This is a new method for vegetation texture characterization and texture scale calculation.The correspondence between the research scale morphology and vegetation textures can be used in the selected detection area to iteratively search for blue noise characteristics. Iteration consists of the reduction of the region size through the geometric transformation and the obtaining of a spectral response region by fast Fourier transform so as to extract the blue noise characteristics from the spectral response. For regions with blue noise characteristics,the intensity distribution of forest vegetation texture is computed,and the texture size is calculated based on the current size of the area. Experimental results show that the gray scale and the distribution of forest vegetation texture units can be accurately measured,which lays reliable foundation for further texture segmentation.

Pattern based Dimensionality Reduction Model for Age Classification

The two most popular statistical methods used to measure the textural information of images are the Grey Level Cooccurrence Matrix (GLCM) and Texture Units (TU) approaches. The novelty of the present paper is, it combines TU and GLCM features by deriving a new model called “Pattern based Second order Compressed Binary (PSCB) image” to classify human age in to four groups. The proposed PSCB model reduces the given 5 x 5 grey level image into a 2 x 2 binary image, while preserving the significant features of the texture. The proposed method intelligently compressed a 5x5 window into a 2x2 window and derived TU on them. Thus the derived TU also represents a TU of a 5x5 window. The TU of the proposed PSCB model ranges from 0 to 15, thus it overcomes the previous disadvantages in evaluating TU’s.

Random Texture Defect Detection Using 1-D Hidden Markov Models Based on Local Binary Patterns

In this paper a novel method for the purpose of random texture defect detection using a collection of 1-D HMMs is presented. The sound textural content of a sample of training texture images is first encoded by a compressed LBP histogram and then the local patterns of the input training textures are learned, in a multiscale framework, through a series of HMMs according to the LBP codes which belong to each bin of this compressed LBP histogram. The hidden states of these HMMs at different scales are used as a texture descriptor that can model the normal behavior of the local texture units inside the training images. The optimal number of these HMMs (models) is determined in an unsupervised manner as a model selection problem. Finally, at the testing stage, the local patterns of the input test image are first predicted by the trained HMMs and a prediction error is calculated for each pixel position in order to obtain a defect map at each scale. The detection results are then merged by an inter-scale post fusion method for novelty detection. The proposed method is tested with a database of grayscale ceramic tile images.

SOIL CARBON RELATIONSHIPS WITH TERRAIN ATTRIBUTES, ELECTRICAL CONDUCTIVITY, AND A SOIL SURVEY IN A COASTAL PLAIN LANDSCAPE

Soil organic carbon (SOC) estimation at the landscape level is critical for assessing impacts of management practices on C sequestration and soil quality. We determined relationships between SOC, terrain attributes, field scale soil electrical conductivity (EC), soil texture and soil survey map units in a 9 ha coastal plain field (Aquic and Typic Paleudults) historically managed by conventional means. The site was composite sampled for SOC (0-30 cm) within 18.3 × 8.5-m grids (n = 496), and two data sets were created from the original data. Ordinary kriging, co-kriging, regression kriging and multiple regression were used to develop SOC surfaces that were validated with an independent data set (n = 24) using the mean square error (MSE). The SOC was relatively low (26.13 Mg ha−1) and only moderately variable (CV = 21%), and showed high spatial dependence. Interpolation techniques produced similar SOC maps but the best predictor was ordinary kriging (MSE = 9.11 Mg2 ha−2) while regression was the worst (MSE = 20.65 Mg2 ha−2). Factor analysis indicated that the first three factors explained 57% of field variability; compound topographic index (CTI), slope, EC and soil textural fractions dominated these components. Elevation, slope, CTI, silt content and EC explained up to 50% of the SOC variability (P ≤ 0.01) suggesting that topography and historical erosion played a significant role in SOC distribution. Field subdivision into soil map units or k-mean clusters similarly decreased SOC variance (about 30%). The study suggests that terrain attributes and EC surveys can be used to differentiate zones of variable SOC content, which may be used as bench marks to evaluate field-level impact of management practices on C sequestration.

A low-power 3D rendering engine with two texture units and 29-Mb embedded DRAM for 3G multimedia terminals

A low-power three-dimensional (3-D) rendering engine with two texture units and 29-Mb embedded DRAM is designed and integrated into an LSI for mobile third-generation (3G) multimedia terminals. Bilinear MIPMAP texture-mapped 3-D graphics can be realized with the help of low-power pipeline structure, optimization of datapath, extensive clock gating, texture address alignment, and the distributed activation of embedded DRAM. The scalable performance reaches up to 100 Mpixels/s and 400 Mtexels/s at 50 MHz. The chip is implemented with 0.16-/spl mu/m pure DRAM process to reduce the fabrication cost of the embedded-DRAM chip. The logic with DRAM takes 46 mm/sup 2/ and consumes 140 mW at 33-MHz operation, respectively. The 3-D graphics images are successfully demonstrated by using the fabricated chip on the prototype PDA board.

Comparison of Gray-Level Reduction and Different Texture Spectrum Encoding Methods for Land-Use Classification Using a Panchromatic Ikonos Image

In this paper, we evaluate the potential of a frequencybased contextual classifier (FBC) for land-use classification with a panchromatic Ikonos image. To capture the spatial arrangement of image gray-level values and use such information in image classification, we applied texture spectrum (TS) directly in the FBC. The effects of several data preprocessing and reduction methods on the performance of the FBC are also evaluated. The methods include four gray-level reduction (GLR) techniques and several modifications to the TS technique. The purpose of data reduction is to improve the classification efficiency of the FBC. The GLR schemes were min-max linear compression (LC), gray level binning (BN), histogram equalization (HE), and piece-wise nonlinear compression (PC). Instead of using the texture measures derived from the texture spectrum, we directly applied texture spectra of various sizes in the classification. We modified the encoding algorithm in the TS and were able to reduce the number of texture units from its original 6561 to 256, 81, and 16, leading to as much as a 410 times computation efficiency. The original image and GLR images were subsequently classified with the FBC. We compared the classification accuracies and found that the GLR methods resulted in accuracies similar to that of the original image (within 0.03 kappa value). There was little difference in classification accuracy (within 0.03 kappa value) among the three modified TS methods, which were all outperformed by the original TS method. All TS methods performed considerably better than the use of the original image and the GLR methods.

Distributed rendering of interactive soft shadows

Recently several distributed rendering systems have been developed that exploit a cluster of commodity computers by connecting host graphics cards over a fast network to form a compositing pipeline. This paper introduces an algorithm for one such system to parallelize contributions from arbitrary numbers of OpenGL lights, for example to compute approximate soft shadows by sampling area light sources. The algorithm renders multiple shadow maps across nodes of the cluster by factoring the OpenGL lighting equation into illumination and material properties. Additional parallelism is achieved by utilizing multiple texture units within each graphics card. Illumination by L point sources can be rendered by ( L/ K)+1 nodes where K is the number of texture units on each graphics card. For walkthrough applications each node requires a single rendering pass, while for scenes with dynamic lights or geometry K+1 passes are needed per node. We present results using fragment shaders on nVidia GeForce4 Ti4600 and ATI Radeon 8500 graphics cards, and a cycle-accurate simulation of the compositing operators. These results show interactive frame rates for walkthroughs and dynamic models rendering shadows with 32 light sources on 9 nodes of a Sepia-2a distributed rendering cluster.