Smaller Sub-blocks Research Articles

Effect of microstructure on sag resistance of ultra-high strength spring steel evaluated by hysteresis loop

Effect of microstructure on sag resistance of ultra-high strength spring steel evaluated by hysteresis loop

POCache: Toward robust and configurable straggler tolerance with parity-only caching

Stragglers (i.e., nodes with slow performance) are prevalent and incur performance instability in large-scale storage systems, yet it is challenging to detect stragglers in practice. We make a case by showing how erasure-coded caching provides robust straggler tolerance without relying on timely and accurate straggler detection, while incurring limited redundancy overhead in caching. We first analytically motivate that caching only parity blocks can achieve effective straggler tolerance. To this end, we present POCache , a parity-only caching design that provides robust straggler tolerance. To limit the erasure coding overhead, POCache slices blocks into smaller subblocks and parallelizes the coding operations at the subblock level. It further adopts a configurable straggler-aware cache algorithm (CSAC) that takes into account both file access popularity and straggler estimation to decide which parity blocks should be cached. CSAC enables POCache to configure various cache admission and eviction algorithms with straggler awareness and supports cache prefetching. We implement a POCache prototype atop Hadoop 3.1 HDFS, while preserving the performance and functionalities of normal HDFS operations. Extensive experiments on both local and Amazon EC2 clusters show that in the presence of stragglers, POCache can reduce the read latency by up to 87.9% compared to vanilla HDFS. • POCache applies parity-only caching to achieve robust straggler tolerance. • Mathematical analysis shows the effectiveness of caching only parity blocks. • We design a configurable straggler-aware cache algorithm for POCache. • We implement a POCache prototype atop Hadoop 3.1 HDFS. • We evaluate POCache on both local and Amazon EC2 clusters.

LIDAR AND PHOTOGRAMMETRIC DATASETS INTEGRATION USING SUB-BLOCK OF IMAGES

Abstract. Imagery and Lidar datasets have been used frequently to extract geoinformation. Datasets in the same mapping or geodetic frame is a fundamental condition for this application. Nowadays, Direct Sensor Orientation (DSO) can be considered as a mandatory technology to be used in the aerial photogrammetric survey. Although the DSO provides a high degree of automation process due to the GNSS/INS technologies, the accuracies of the obtained results from the imagery and Lidar surveys are dependent on the quality of a group of parameters that models accurately the user conditions of the system at the moment the job is performed. This paper shows the study that was performed to improve the tridimensional accuracies of the aerial imagery and Lidar datasets integration using the 3D photogrammetric intersection of single models (pairs of images) with Exterior Orientation Parameters (EOP) estimated from DSO. A Bundle Adjustment with additional parameters (BBA) of a small sub-block of images is used to refine the Interior Orientation Parameters (IOP) and EOP in the job condition. In the 3D photogrammetric intersection experiments using the proposed approach, the horizontal and vertical accuracies, estimated by the Root Mean Square Error (RMSE) of the 3D discrepancies from the Lidar checkpoints, increased around of 25% and 75% respectively.

Content Based Image Retrieval for Community Retrieval from Given Nationality using an Efficient Combination Algorithm

There is tremendous requirement of such technique which can fulfill the entire requirement for retrieval of an image from available dataset which comes under computer vision. In this paper we discussed about the one of the application of CBIR using an efficient combination of two techniques. The application is retrieval of people images from database that comes under minority. In this paper we used an efficient combination of color image histogram technique and edge orientation histogram technique by dividing original image into small subblocks. The feature vector is formed by combination of two features obtained by above methodologies. The final features obtained by query image will be compared with the feature vector of database images using a new Canberra Distance classifier. Proposed method is designed for multiple self-prepared and some collected from internet databases. Our method includes the efficient integration of features such as color, texture, shape and orientation. The proposed method is compared with state of art techniques to prove the stable and highest accuracy of proposed work.

Geoblocks Recognition and Delineation of the Earthquake Prone Areas in the Tuan Giao Area (Northwest Vietnam)

The goal of the paper is to reveal the hierarchical block structure in the Tuan Giao area (Northwest Vietnam) and, on that base, to identify areas prone to earthquakes with M ≥ 5. Four large geoblocks of the second rank have been delineated on the basis of the joint analysis of geological, geophysical, geomorphic, and remote sensing data. The second rank geoblocks have been divided into smaller sub-blocks of the third rank. The recent geodynamics of the geoblocks have been characterized using geomorphic, seismological, gravity, and GPS data.The system of the delineated geoblocks is viewed as the Geodynamic Blocks model. The areas prone to earthquakes with M ≥ 5 have been identified using the pattern recognition algorithm CORA-3. The objects of the recognition were defined as circular areas, where boundaries of the geoblocks intersect each other. The recognition results confirm high seismic potential of the study region and provide information on potential earthquake sources for seismic hazard assessment: a number of boundary intersections have been identified as areas prone to earthquakes with M ≥ 5, where events of such magnitude have not been recorded up to date. Mordern geothermal activity and anomalies of dissolved methane (up to 10000 nL/L), helium and hydrogen in the hot mineral water springs prove high permeability of the study area and its active seismo-tectonic state.

Cross spectral iris recognition for surveillance based applications

Human iris has been explored as one of the most promising biometric traits since last many years. This paper presents a new ingenious feature extraction approach which is based on the texture variations of the iris template. A 2D Gabor filter bank is first employed to reveal the iris texture at different scales and orientations. Each filtered iris template is then partitioned into smaller sub-blocks. Contemplating the iris texture variations at micro-levels, two-level template partitioning is employed here. Difference of Variance (DoV) of corresponding first and second level sub-blocks, from each filtered image, then forms the feature set of the iris. Performance of the proposed iris recognition scheme is first tested with the benchmark IITD iris database to find the optimal window size of the filter bank. Thereafter, to prove the efficacy of the proposed approach in surveillance based applications, cross-spectral iris matching experiments (i.e. visible wavelength (VW) to near infrared (NIR) matching) are performed using PolyU cross-spectral database. Experiments show that the proposed approach achieves outperforming results for both IITD and PolyU databases.

Fast iterative solution of the Bethe–Salpeter eigenvalue problem using low-rank and QTT tensor approximation

In this paper, we study and implement the structural iterative eigensolvers for the large-scale eigenvalue problem in the Bethe-Salpeter equation (BSE) based on the reduced basis approach via low-rank factorizations in generating matrices, introduced in the previous paper. The approach reduces numerical costs down to $\mathcal{O}(N_b^2)$ in the size of atomic orbitals basis set, $N_b$, instead of practically intractable $\mathcal{O}(N_b^6)$ complexity scaling for the direct diagonalization of the BSE matrix. As an alternative to rank approximation of the static screen interaction part of the BSE matrix, we propose to restrict it to a small active sub-block, with a size balancing the storage for rank-structured representations of other matrix blocks. We demonstrate that the enhanced reduced-block approximation exhibits higher precision within the controlled numerical cost, providing as well a distinct two-sided error estimate for the BSE eigenvalues. It is shown that further reduction of the asymptotic computational cost is possible due to ALS-type iteration in block tensor train (TT) format applied to the quantized-TT (QTT) tensor representation of both long eigenvectors and rank-structured matrix blocks. The QTT-rank of these entities possesses almost the same magnitude as the number of occupied orbitals in the molecular systems, $N_o$, hence the overall asymptotic complexity for solving the BSE problem can be estimated by $\mathcal{O}(\log(N_o) N_o^{2})$. We confirm numerically a considerable decrease in computational time for the presented iterative approach applied to various compact and chain-type molecules, while supporting sufficient accuracy.

Reversible data hiding based on flexible block-partition and adaptive block-modification strategy

Abstract A novel reversible data hiding (RDH) method based on flexible block-partition and adaptive pixel-modification strategy is proposed in this paper, which is implemented from the following two aspects. One aims at partitioning flexibly a smooth block into non-overlapped sub-blocks of arbitrary size according to a local complexity measurement. After partition, since each resulting sub-block can be treated as an independent embedding unit, this block can be embedded with more data bits. The other is that the different pixel modification method is utilized for blocks (or sub-blocks) of different levels. Specifically, for a block, if it is not suitable for further division, only the maximum and minimum are modified at the same time so as to keep the distortion as low as possible. If it is divided into smaller sub-blocks, two pixel-modification schemes are designed for sub-blocks of size 1 × 3 and other sizes, respectively. One is that a 1 × 3 block can be embedded with 2 data bits by only modifying the maximum of this block. In this way, the embedding distortion is further decreased. The other is that in order to exploit better redundancy, two largest and two smallest pixels are modified simultaneously so that at most 4 data bits are embedded into a sub-block. Extensive experiments verify that the proposed method outperforms Peng et al.’s, Wang et al.’s, Li et al.’s, Sachnev et al.’s and Hong et al.’s works.

Efficient volume rendering methods for out-of-Core datasets by semi-adaptive partitioning

Volume rendering methods are widely used for the high-quality visualization of various 3D datasets, especially scalar field datasets (e.g., 3D images). However, when rendering datasets with ultra-high spatial resolutions, which occupy massive (out-of-core) storage space, some traditional in-core volume rendering algorithms cannot function because the large input data can hardly be handled in the main memory. Simple modifications based on disk cache I/O do not perform well because of the overheads associated with external memory access. To solve this problem, this paper describes a semi-adaptive partitioning strategy and an efficient out-of-core visualization framework with improved volume rendering algorithms. Under this new partitioning strategy, an out-of-core dataset is spatially divided into small sub-blocks of different sizes, which are organized by a binary space partitioning (BSP) tree. Each sub-block can be loaded into the fast texture memory of the graphics hardware to be rendered by our improved volume rendering algorithms. The final result is obtained by composing the projection images of all sub-blocks after traveling the BSP tree according to the viewpoint. Experimental results indicate that the new methods are effective and efficient for visualizing out-of-core 3D scalar field datasets.

Implementation of computation-reduced DCT using a novel method

The discrete cosine transform (DCT) performs a very important role in the application of lossy compression for representing the pixel values of an image using lesser number of coefficients. Recently, many algorithms have been devised to compute DCT. In the initial stage of image compression, the image is generally subdivided into smaller subblocks, and these subblocks are converted into DCT coefficients. In this paper, we present a novel DCT architecture that reduces the power consumption by decreasing the computational complexity based on the correlation between two successive rows. The unwanted forward DCT computations in each 8 × 8 sub-image are eliminated, thereby making a significant reduction of forward DCT computation for the whole image. This algorithm is verified with various high- and less-correlated images, and the result shows that image quality is not much affected when only the most significant 4 bits per pixel are considered for row comparison. The proposed architecture is synthesized using Cadence SoC Encounter® with TSMC 180 nm standard cell library. This architecture consumes 1.257 mW power instead of 8.027 mW when the pixels of two rows have very less difference. The experimental result shows that the proposed DCT architecture reduces the average power consumption by 50.02 % and the total processing time by 61.4 % for high-correlated images. For less-correlated images, the reduction in power consumption and the total processing time is 23.63 and 35 %, respectively.

Local gradient‐based illumination invariant face recognition using local phase quantisation and multi‐resolution local binary pattern fusion

A local-based illumination insensitive face recognition algorithm is proposed which is the combination of image normalisation and illumination invariant descriptors. Illumination insensitive representation of image is obtained based on the ratio of gradient amplitude to the original image intensity and partitioned into smaller sub-blocks. Local phase quantisation and multi-scale local binary pattern, extract the sub-regions characteristics. Distance measurements of local nearest neighbour classifiers are fused at the score level to find the best match and decision-level fusion combines the results of two matching techniques. Entropy, class posterior probability and mutual information are utilised as the weights of fusion components. Simulation results on the YaleB, Extended YaleB, AR, Multi-PIE and FRGC databases show the improved performance of the proposed algorithm under severe illumination with low computational complexity and no reconstruction or training requirement.

An adaptive vector quantization approach for image segmentation based on SOM network

Image segmentation can be viewed as an unsupervised clustering process of pixels in the image being processed. Existing segmentation algorithms in common use often take single pixel as processing unit and segment an image mainly based on the gray value information of the image pixels. However, the spatially structural information between pixels provides even more important information on the image contents. In order to effectively exploit both the gray value and the spatial information of pixels, this paper presents an adaptive image segmentation approach based on Vector Quantization (VQ) technique. In the method, the image to be segmented is divided into small sub-blocks with each sub-block constituting a vector and the vectors are clustered by using the VQ method to implement the segmentation. The self-organizing map (SOM) neural network is adopted for realizing the VQ algorithm adaptively. In order to resolve the problem of determining the codebook size (i.e., the segment number) for the SOM-based VQ segmentation approach, an adaptive search algorithm for estimating the optimum codebook size is developed by minimizing the ratio of within-class scatter to between-class scatter in the segmentation process. Experiments have been conducted in the work by using real brain MRI images from the Internet Brain Segmentation Repository (IBSR) and some other databases and comparison studies with other algorithms in the state-of-the-art are performed. The experimental results are evaluated both via subjective comparison with human vision and also via quantitative evaluation in terms of the average overlap metric, which shows that the proposed method outperforms the other existing algorithms.

Data hiding method in binary images based on block masking for key authentication

Abstract This paper proposes a new data hiding method for binary images that relies on block masking to distribute keys to two parts and then authenticates the right authorized part. The proposed method divides a cover image into small sub-blocks and designs key pairs that determine both where the bit is to be embedded and whether it is possible to embed it there. Furthermore, the key pairs are required to extract the secret data from the stego-image. Experimental results demonstrate a higher capacity and less distortion compared with previous methods since almost all data are hidden in the edge areas.

Flexible Multi-Block OFDM Transmission for High-Speed Fiber-Wireless Networks

Orthogonal frequency-division multiplexing (OFDM) has been widely used in fiber-wireless (FiWi) networks, but it suffers from reduced spectral efficiency, high peak-to-average power ratio (PAPR), and severe sensitivity to carrier frequency offset (CFO). In this paper, we propose a flexible multi-block OFDM (MB-OFDM) transmission scheme to simultaneously solve those problems. First, one guard interval is shared by multiple blocks by exploiting the slow time-varying property of the fiber-wireless channel, so the spectral efficiency could be typically improved by about 10%. Second, the proposed scheme further divides every data block into multiple small sub-blocks, whereby each sub-block is generated by an inverse fast Fourier transform (IFFT) of smaller size accordingly. It thus provides a flexible compromise between the multi-carrier and single-carrier transmissions, and reduces the PAPR and the sensitivity to CFO. In addition, a hybrid-domain channel equalization method is proposed to detect the MB-OFDM signal by utilizing the single-carrier and multi-carrier transmission formats successively. Simulation results are provided to demonstrate the enhanced performance of the proposed scheme.

An image segmentation method based on the fusion of vector quantization and edge detection with applications to medical image processing

In the existing segmentation algorithms, most of them take single pixel as processing unit and segment an image mainly based on the gray value information of the image pixels. However, the spatially structural information between pixels of an image provides even more important information of the image. In order to effectively exploit both the gray value and the spatial information of image pixels, this paper proposes a fusion method for image segmentation by jointly utilizing vector quantization and edge detection methods. In the method, the image to be segmented is divided into small sub-blocks with each sub-block constituting a vector and the vectors are classified into two patterns, called the edge pattern and non-edge pattern, by using an edge detection algorithm. The image is then processed further, in which a Boundary Detection (BD) algorithm is developed for extracting the refined boundary curves in the edge pattern vectors and a Vector Quantization (VQ) approach is presented for segmenting the non-edge pattern vectors. In addition, an SOM neural network is proposed for realizing the VQ algorithm adaptively. Finally, a fusion scheme is designed to synthesize the results of VQ and BD to accomplish the segmentation. Simulation experiments and comparison studies have been conducted with applications to medical image processing in the paper, and the results validate the effectiveness of the proposed method.

Paleoproterozoic structural evolution of the southern segment of the Jiao-Liao-Ji Belt, North China Craton

Abstract The Paleoproterozoic Jiao-Liao-Ji Belt separates the Eastern Block of the North China Craton into two small sub-blocks: the northern Longgang and the southern Rangrim blocks. However, it still remains unknown or controversial about the subduction polarity, collisional deformation and kinematics between two sub-blocks. The southern segment of the belt consists of the Paleoproterozoic Fenzishan and Jingshan groups, and Paleoproterozoic high pressure mafic granulites and serpentinites blocks which are located in the Jiaodong Complex. All of which are separated from the Jiaodong Complex of Neoarchean TTG gneisses by STZ1 ductile shear zones. Structural analysis in this study indicates that most of the rocks in all the units of the southern segment of the Jiao-Liao-Ji Belt underwent three distinct episodes of folding (D1 to D3) and two stage of ductile thrust shearing (STZ1 coeval to D1 and D2, STZ2 between D2 and D3). The D1 deformation formed penetrative axial planar foliations (S1), bedding-parallel ductile shear zone, mineral stretching lineations (L1), and rarely preserved small isoclinal D1 folds in the Jingshan and Fenzishan groups. In the Jingshan Group, however, penetrative deformational transposition resulted in stacking of sedimentary compositional layers which are separated by bedding-parallel ductile shear zones (STZ1) at a period of about 1956 Ma to 1914 Ma. The kinematic indicators of STZ1 in the Jingshan Group with resultant prograde peak metamorphism up to granulite facies grade and the Fenzishan Group with peak metamorphism up to amphibolite facies grade indicate NW-directed compression. D2 resulted in crustal thickening with retrograded medium pressure granulite facies grade at about 1914–1893 Ma. The D2 deformation produced NW-verging asymmetric and recumbent folds, interpreted to have resulted from basement-involved thicken-skin structures. The Jiaodong Complex was also involved into the development of WNW-verging asymmetric tight folds associated with D2 in the Jingshan and the Fenzishan groups. Ongoing collision led to the development of transpressional ductile shearing (STZ2), forming the transpressional Taipingzhuang dextral ductile shear zone between the Jingshan Group and the southern Archean Complex and the transpressional Tading-Xiadian sinistral ductile shear zone between the Jingshan Group and the northern Archean Complex. All three lithotectonic units were superposed during the late D3 deformation with amphibolite facies metamorphism. The D3 deformation developed WNW-trending open to tight upright folds at about 1893–1875 Ma. The structural pattern resulting from superimposition of D2 and D3 is a composite synform in the Fenzishan and Jingshan groups. The structural events of D1 and STZ1, and D2 and STZ2 deformation were possibly responsible for fast syn-collisional exhumation of the high pressure mafic granulites. The structural patterns and deformational history of the Fenzishan and Jingshan groups suggest a southeastward-directed oblique subduction beneath the northwestern margin of the Rangrim Block, and that the final scissor-shaped closure of the rift led to collision between the two blocks to form the coherent basement of the Eastern Block of the North China Craton.

Block-based Kernel Semi-supervised Discriminant Projection

Abstract The paper developed a new feature extraction method, termed Block-based Kernel Semi-supervised Discriminant Projection (BKSDP). Its main idea is that each face image is partitioned into smaller sub-blocks which are reshaped into a large column vector. Then all these column vectors are mapped into a higher dimensional feature space through a predefined kernel function. Finally, the semi-supervised discriminant analysis is performed over the feature space by integrating the intrinsic manifold structure of the labeled and unlabeled data. Experiment results conducted on ORL and YALA face databases demonstrate the effectiveness of the proposed method.

Bandwidth-efficient frequency-domain equalization for single carrier multiple-input multiple-output underwater acoustic communications

This paper proposes a single carrier (SC) receiver scheme with bandwidth-efficient frequency-domain equalization (FDE) for underwater acoustic (UWA) communications employing multiple transducers and multiple hydrophones. Different from the FDE methods that perform FDE on a whole data block, the proposed algorithm implements an overlapped-window FDE by partitioning a large block into small subblocks. A decision-directed channel estimation scheme is incorporated with the overlapped-window FDE to track channel variations and improve the error performance. The proposed algorithm significantly increases the length of each block and keeps the same number of training symbols per block, hence achieving better data efficiency without performance degradation. The proposed scheme is tested by the undersea data collected in the Rescheduled Acoustic Communications Experiment (RACE) in March 2008. Without coding, the 2-by-12 MIMO overlapped-window FDE reduces the average bit error rate (BER) over traditional SC-FDE schemes by 74.4% and 84.6% for the 400 m and 1000 m range systems, respectively, at the same data efficiency. If the same BER performance is required, the proposed algorithm has only 8.4% transmission overhead, comparing to over 20% overhead in other existing UWA OFDM and SC-FDE systems. The improved data efficiency and/or error performance of the proposed FDE scheme is achieved by slightly increased computational complexity over traditional SC-FDE schemes.

Selective image similarity measure for bronchoscope tracking based on image registration

We propose a selective method of measurement for computing image similarities based on characteristic structure extraction and demonstrate its application to flexible endoscope navigation, in particular to a bronchoscope navigation system. Camera motion tracking is a fundamental function required for image-guided treatment or therapy systems. In recent years, an ultra-tiny electromagnetic sensor commercially became available, and many image-guided treatment or therapy systems use this sensor for tracking the camera position and orientation. However, due to space limitations, it is difficult to equip the tip of a bronchoscope with such a position sensor, especially in the case of ultra-thin bronchoscopes. Therefore, continuous image registration between real and virtual bronchoscopic images becomes an efficient tool for tracking the bronchoscope. Usually, image registration is done by calculating the image similarity between real and virtual bronchoscopic images. Since global schemes to measure image similarity, such as mutual information, squared gray-level difference, or cross correlation, average differences in intensity values over an entire region, they fail at tracking of scenes where less characteristic structures can be observed. The proposed method divides an entire image into a set of small subblocks and only selects those in which characteristic shapes are observed. Then image similarity is calculated within the selected subblocks. Selection is done by calculating feature values within each subblock. We applied our proposed method to eight pairs of chest X-ray CT images and bronchoscopic video images. The experimental results revealed that bronchoscope tracking using the proposed method could track up to 1600 consecutive bronchoscopic images (about 50s) without external position sensors. Tracking performance was greatly improved in comparison with a standard method utilizing squared gray-level differences of the entire images.

A successive termination and elimination method for fast H.264/AVC SATD-based inter mode decision

A successive termination and elimination (STE) method to achieve fast inter mode decision is proposed. The termination detection starts from residual homogeneous detection and then spatial homogeneous detection is performed for each 16×16 macroblock. For either the residual or spatial homogeneous case, the authors can directly terminate the inter prediction and choose the 16×16 mode as the best inter mode. For non-homogeneous cases, the authors then carry out the 8×8 subblock motion estimation. Based on the cost analyses of the 8×8 and 16×16 modes, the elimination detection method, which could help to remove unlikely 8×16 and 16×8 modes, is also suggested. Similarly, the STE method for each 8×8 block can also be applied to decide if the inter prediction needs to be further performed for smaller subblocks. Once the algorithm reaches the termination stage, the best inter mode will be decided by selecting the least cost among all searched modes. Experimental results reveal that the proposed STE method can save about 56% of coding time in inter prediction with a slight performance degradation compared with the original method proposed by JM10.1 and outperforms some existing methods in both coding performance and coding time.