Small Size Blocks Research Articles

Разработка технологии проведения и крепления горной выработки в зоне тектонически-ослабленных пород

Presence of tectonically weakened and disturbed zones leads to decreased underground excavation rates and reduced safety of the mining operations. As a rule, such zones are bounded by vertical and subvertical disturbances that contribute to weakening and loss of stability of the rock and ore mass. Disturbance of the natural state, in particular driving of a mine workings, provokes irreversible deformations due to redistribution and concentration of stresses within the boundary rock mass, which is more rigid. At the same time no significant stresses are formed within the rock mass of the tectonic zone due to its caving. Therefore, when developing solutions for driving and supporting mining excavations within the tectonic zone, the rock mass should be considered as weakened and highly fragmented with unbonded structural blocks. The particle-size distribution within the rock mass of the tectonic zone varies in a wide range of sized from several to tens of centimetres. In this context, the mining and rock reinforcement technology presented in this paper accounts for the possible free caving of the non-bonded host rock mass made up of small-sized blocks. The paper discusses physical and mechanical characteristics and the stress-and-strain state of the rock mass within the tectonically weakened zone and the boundary host rocks, and a technology of driving and supporting underground mine workings is proposed on this basis.

The concept of reconstructing the spatial-planning structure of historical quarters using small-sized block sections

The method of reconstruction of historical quarters of the city of Samara is considered. The proposed approach is based on the method of permanent reconstruction of block buildings using small-sized block sections. To substantiate the proposed approach, a number of objects from world practice are analyzed. The features of the architectural and planning structure of Samara's historical quarters and the sociocultural conditions for the formation of the modern architectural appearance of the Samara courtyard are revealed. An experimental model for the reconstruction of the Samara quarter is proposed.

Ramps as modifiers of deformation of mass-transport deposits flow cells: A case study in the Santos Basin, SE Brazil

Mass-transport deposits (MTDs) are one of the several deepwater deposits and correspond to an important part of the gravitational sedimentation in the basins. MTDs register several pieces of evidence about their formation, which can indicate flow direction, level of internal deformation, and position in the deposit, for example. During the transport process, the flow can interact with the substrate and change its characteristics. Consequently, different structures are formed internally and in the basal surface according to the flow modification. In this study, 3D seismic data located in the northern Santos Basin was used to investigate the relationships between different MTD kinematic structures and styles of basal interaction. After conducting a comprehensive interpretation of the deposit, which involved morphological analysis, examination of basal and upper surfaces, and the assessment of internal facies distribution, we proceeded to correlate various structural elements, with the purpose of providing a clearer understanding of the sedimentation processes that took place within the deposit. On the basal surface, three distinct features (grooves, striations, and deformational elements) along with ramps, were interpreted. Apart from this, the distribution of large and small-size blocks, and normal and reverse faults within this MTD indicates that there were major changes in the flow behavior during the transport. Thus, we propose a model for the MTD transformation which considers ramp generation as a modifier of the MTD deformation and, consequently, as responsible for creating second-order flow cells, facilitating the substrate deformation, and providing erosive blocks near the flow base. In this model, the studied MTD was divided in five zones according to the nature of basal interaction. Typically, these zones are border by ramps. The variation in the internal structures suggest fluctuations in the flow velocity. Altogether, our findings indicate that the flow exhibited a compartmentalized behavior, giving rise to second-order flow cells.

PP-JPEG: A Privacy-Preserving JPEG Image-Tampering Localization.

The widespread availability of digital image-processing software has given rise to various forms of image manipulation and forgery, which can pose a significant challenge in different fields, such as law enforcement, journalism, etc. It can also lead to privacy concerns. We are proposing that a privacy-preserving framework to encrypt images before processing them is vital to maintain the privacy and confidentiality of sensitive images, especially those used for the purpose of investigation. To address these challenges, we propose a novel solution that detects image forgeries while preserving the privacy of the images. Our method proposes a privacy-preserving framework that encrypts the images before processing them, making it difficult for unauthorized individuals to access them. The proposed method utilizes a compression quality analysis in the encrypted domain to detect the presence of forgeries in images by determining if the forged portion (dummy image) has a compression quality different from that of the original image (featured image) in the encrypted domain. This approach effectively localizes the tampered portions of the image, even for small pixel blocks of size 10×10 in the encrypted domain. Furthermore, the method identifies the featured image's JPEG quality using the first minima in the energy graph.

Analysis of seismically‐isolated two‐block systems using a multi–rocking‐body dynamic model

AbstractA novel multibody rocking model is developed to investigate the dynamic response of two stacked rigid blocks placed on a linear base isolation device. The model is used to investigate the dynamic response of a realistic statue‐pedestal system subject to pulse‐like ground motions. The analysis shows that, in general, base isolation increases the safety level of the rocking system. However, for large period pulses or small size blocks, the isolator can amplify the ground motion, resulting in a lower minimum overturning acceleration than for the nonisolated system. Further, the amplification or shock spectrum of a linear mass‐dashpot‐spring oscillator, was found to be the reciprocal of the minimum nondimensional overturning acceleration of the investigated rocking system. Novel rocking spectra are obtained by normalizing the frequency of the pulse by the frequency of the isolator. The analysis also demonstrates how the dynamic response of the two stacked blocks is equivalent to that of a single‐block configuration coincident with the whole system assumed monolithic or the upper block alone, whichever is more slender.

Covariance-Free Variational Bayesian Learning for Correlated Block Sparse Signals

We consider the problem of estimating channel in massive machine type communication (mMTC) systems. The sparse device activity in a mMTC system makes the channel block-sparse, with intra-block correlation. Block-sparse Bayesian learning (B-SBL) is a powerful framework for estimating such signals. The existing B-SBL algorithms become computationally expensive for high-dimensional problems, which is common in mMTC systems. This is because of large number of devices in a mMTC system, they invert a large-dimensional matrix to calculate the covariance matrix. To address this problem, we exploit variational Bayesian inference, and design a novel covariance-free variational B-SBL algorithm which inverts multiple small-sized block matrices, instead of inverting a complete big-sized matrix. The complexity is further reduced by avoiding explicit computation of the covariance matrix. The proposed algorithm, instead of performing costly matrix inversions, solves multiple linear systems to calculate an unbiased estimate of the posterior statistics. The proposed algorithm is numerically shown to estimate the mMTC channel with a much lesser complexity, and that too without compromising the reconstruction performance.

A multiple transform based approach for robust and blind image copyright protection

SummaryDigital image sharing and utilization have increasing in a speedy manner at present time. Therefore, copyright/ownership protection for digital images has been an essential requirement in the modern world of digital advancements. This work offers an image watermarking scheme to provide ownership/copyright verification in an effective manner with no conciliation with imperceptibility. The image is first partitioned into small size blocks. During embedding, a multilevel transform domain‐based framework is employed to embed the watermark information into blocks. Additionally, the block selection process is made randomize (key‐based) to offer high security against illegal manipulations/access. Before embedding, the watermark is encrypted using the Arnold transform‐based approach for additional security. The scheme has blind nature, high imperceptibility and it is robust enough to endure a different variety of processing attacks. Experimental results on different images illustrate that the proposed watermarking approach has high imperceptibility, high robustness, decent embedding capacity, and significant security features. The relative comparison with the existing robust watermarking schemes (having the same payload) shows the superiority of the proposed work over existing methods presented in the recent past.

State Space Partitioning Based on Constrained Spectral Clustering for Block Particle Filtering

The particle filter (PF) is a powerful inference tool widely used to estimate the filtering distribution in non-linear and/or non-Gaussian problems. To overcome the curse of dimensionality of PF, the block PF (BPF) inserts a blocking step to partition the state space into several subspaces or blocks of smaller dimension so that the correction and resampling steps can be performed independently on each subspace. Using blocks of small size reduces the variance of the filtering distribution estimate, but in turn the correlation between blocks is broken and a bias is introduced. When the dependence relationships between state variables are unknown, it is not obvious to decide how to split the state space into blocks and a significant error overhead may arise from a poor choice of partitioning. In this paper, we formulate the partitioning problem in the BPF as a clustering problem and we propose a state space partitioning method based on spectral clustering (SC). We design a generalized BPF algorithm that contains two new steps: (i) estimation of the state vector correlation matrix from predicted particles, (ii) SC using this estimate as the similarity matrix to determine an appropriate partition. In addition, a constraint is imposed on the maximal cluster size to prevent SC from providing too large blocks. We show that the proposed method can bring together in the same blocks the most correlated state variables while successfully escaping the curse of dimensionality.

Ultrasound-induced release of nimodipine from drug-loaded block copolymers: In vitro analysis

RationaleAdministration of nimodipine has been demonstrated to prevent secondary ischemic complications following subarachnoid hemorrhage and to improve functional outcome. Nanocarriers have been increasingly used for intrathecal nimodipine delivery with sustained drug release. However, no technique for an on-demand drug release at the time of vasospasm development has been established yet. The aim of this in vitro study was to produce nimodipine-loaded copolymers and to evaluate the possibility of on-demand drug release by means of ultrasound. MethodsThe copolymers were produced using Pluronic® F-127, and then loaded with nimodipine by applying the direct dissolution method. The drug release profile of bound nimodipine to copolymers was assessed using the dialysis bag method. The spontaneous as well as the ultrasound-guided (1 MHz with 1.7 W/cm2) drug-release was evaluated. ResultsA significant increase in nimodipine-release could be induced on-demand by ultrasound application for 30 or 60 s. While the maximum induced drug-release rate 24 h post treatment compared to the spontaneous drug-release rate was higher in the 5% and 15%-solution (p = 0.004), this was not the case in the 10%-solution. Comparing the two treatment durations, a higher drug-release was found in the 5% and 10%-solution after 5 min (p = 0.0002), 15 min (p = 0.0005) and 30 min (p = 0.0003) in the group receiving 60 s sonication. ConclusionsBy using the direct dissolution method, small-sized block copolymers comprising Pluronic® F-127 and nimodipine were successfully produced. In vitro analyses revealed that a significant increase in nimodipine-release rate could be efficiently induced on-demand after application of ultrasound for 60 s.

A novel deep learning framework for double JPEG compression detection of small size blocks

Double JPEG compression detection plays a vital role in multimedia forensics, to find out whether a JPEG image is authentic or manipulated. However, it still remains to be a challenging task in the case when the quality factor of the first compression is much higher than that of the second compression, as well as in the case when the targeted image blocks are quite small. In this work, we present a novel end-to-end deep learning framework taking raw DCT coefficients as input to distinguish between single and double compressed images, which performs superior in the above two cases. Our proposed framework can be divided into two stages. In the first stage, we adopt an auxiliary DCT layer with sixty-four 8 × 8 DCT kernels. Using a specific layer to extract DCT coefficients instead of extracting them directly from JPEG bitstream allows our proposed framework to work even if the double compressed images are stored in spatial domain, e.g. in PGM, TIFF or other bitmap formats. The second stage is a deep neural network with multiple convolutional blocks to extract more effective features. We have conducted extensive experiments on three different image datasets. The experimental results demonstrate the superiority of our framework when compared with other state-of-the-art double JPEG compression detection methods either hand-crafted or learned using deep networks in the literature, especially in the two cases mentioned above. Furthermore, our proposed framework can detect triple and even multiple JPEG compressed images, which is scarce in the literature as far as we know.

BLUE SCREEN VIDEO FORGERY DETECTION AND LOCALIZATION USING AN ENHANCED 3-STAGE FOREGROUND ALGORITHM

The availability of easy to use video editing software has made it easy for cyber criminals to combine different videos from different sources using blue screen composition technology. This, makes the authenticity of such digital videos questionable and needs to be verified especially in the court of law. Blue Screen Composition is one of the ways to carry out video forgery using simple to use and affordable video editing software. Detecting this type of video forgery aims at revealing and observing the facts about a video so as to conclude whether the contents of the video have undergone any unethical manipulation. In this work, we propose an enhanced 3-stage foreground algorithm to detect Blue Screen manipulation in digital video. The proposed enhanced detection technique contains three (3) phases, extraction, detection and tracking. In the extraction phase, a Gaussian Mixture Model (GMM) is used to extract foreground element from a target video. Entropy function as a descriptive feature of image is extracted and calculated from the target video in the detection phase. The tracking phase seeks to use Minimum Output Sum of Squared Error (MOSSE) object tracking algorithm to fast track forged blocks of small sizes in a digital video. The result of the experiments demonstrates that the proposed detection technique can adequately detect Blue Screen video forgery when the forged region is small with a true positive detection rate of 98.02% and a false positive detection rate of 1.99%. The result of this our research can be used to

Influence of Austenitizing Parameters on the Microstructures and Mechanical Properties of 22MnB5 Hot Stamping Steel Produced by TSCR Process

Thin gauge hot strip steel can be produced by thin slab continuous casting and rolling (TSCR) process without the aid of cold rolling. However, few attempts have been made to produce hot stamping steel by using TSCR technology. As given steel, the hot stamping steel produced by the TSCR process was used to study the influence of austenitizing parameters on the final microstructures and mechanical properties. A Gleeble3500 thermal simulator was employed to heat the samples to obtain different austenite microstructures. After quenching, the Vickers hardness examination along the thickness of the sample and tensile testing were conducted. The microstructure was observed using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The results showed that on the condition of relatively lower temperature and shorter holding time, the sample exhibited better mechanical properties at the cost of microstructure uniformity along the thickness, although higher austenitizing temperature enhanced the carbide dissolution which would increase the effective carbon content in the martensite matrix. Lower heating temperature led to small prior austenite grain size and small blocks which contributed greatly to the mechanical properties.

Multi-stage all-zero block detection for HEVC coding using machine learning

Compared with deadzone hard-decision quantization (HDQ), rate-distortion optimized quantization (RDOQ) in HEVC brings non-negligible coding gain, however consumes considerable computations caused by exhaustive search over multiple candidates to determine optimal output level. Benefiting from efficient prediction in HEVC, transform blocks are frequently quantized to all zero, especially in small-size blocks. It is worthwhile to detect all zero block (AZB) for transform blocks to bypass subsequent computation-intensive RDOQ. Traditional thresholding based AZB detection algorithms are well-suited for deadzone quantized blocks, however miss partial optimal results in RDOQ and suffer from more or less accuracy degradation in RDOQ. This paper proposes a novel multi-stage AZB detection algorithm for RDOQ blocks with good tradeoff between complexity and accuracy. At the first stage, genuine all zero blocks (G_AZB) which are quantized to all zero both in HDQ and RDOQ are prejudged by comparison with conservative threshold determined by mathematical derivation for deadzone HDQ. At the second stage, an adaptive threshold model is built using adaptive deadzone offset by simulating the behavior patterns existing in RDOQ, aiming to further detect the pseudo AZB (P_AZB) which are quantized to all zero in RDOQ however not all zero in HDQ. At the final stage, machine learning based detection is proposed to classify the remaining “cunning” all zero blocks using eight distinguished RDO-related features, by which subtle working mechanism in RDOQ is leveraged. The experimental results demonstrate that the proposed algorithm achieves up to 7.471% total coding computation saving with 0.064% BD-RATE increment compared with RDOQ on average. Moreover, the average FNR and FPR detection accuracies are 6.3% and 6.5% respectively.

New Order-Revealing Encryption with Shorter Ciphertexts

As data outsourcing services have been becoming common recently, developing skills to search over encrypted data has received a lot of attention. Order-revealing encryption (OREnc) enables performing a range of queries on encrypted data through a publicly computable function that outputs the ordering information of the underlying plaintexts. In 2016, Lewi et al. proposed an OREnc scheme that is more secure than the existing practical (stateless and non-interactive) schemes by constructing an ideally-secure OREnc scheme for small domains and a “domain-extension” scheme for obtaining the final OREnc scheme for large domains. They encoded a large message into small message blocks of equal size to apply them to their small-domain scheme, thus their resulting OREnc scheme reveals the index of the first differing message block. In this work, we introduce a new ideally-secure OREnc scheme for small domains with shorter ciphertexts. We also present an alternative message-block encoding technique. Combining the proposed constructions with the domain-extension scheme of Lewi et al., we can obtain a new large-domain OREnc scheme with shorter ciphertexts or with different leakage information, but longer ciphertexts.

Reordering sparse matrices into block-diagonal column-overlapped form

Many scientific and engineering applications necessitate computing the minimum norm solution of a sparse underdetermined linear system of equations. The minimum 2-norm solution of such systems can be obtained by a recent parallel algorithm, whose numerical effectiveness and parallel scalability are validated in both shared- and distributed-memory architectures. This parallel algorithm assumes the coefficient matrix in a block-diagonal column-overlapped (BDCO) form, which is a variant of the block-diagonal form where the successive diagonal blocks may overlap along their columns. The total overlap size of the BDCO form is an important metric in the performance of the subject parallel algorithm since it determines the size of the reduced system, solution of which is a bottleneck operation in the parallel algorithm. In this work, we propose a hypergraph partitioning model for reordering sparse matrices into BDCO form with the objective of minimizing the total overlap size and the constraint of maintaining balance on the number of nonzeros of the diagonal blocks. Our model makes use of existing partitioning tools that support fixed vertices in the recursive bipartitioning paradigm. Experimental results validate the use of our model as it achieves small overlap size and balanced diagonal blocks.

Nearly Resolution V Plans on Blocks of Small Size

In the present paper we aim at constructing plans (designs) for symmetrical experiments on small blocks such that factorial effects (main effects or interactions) are ‘orthogonal through the block factor’. Specifically, given an initial plan, we derive sufficient conditions on the set of ‘generators’ for a desired change in the relation between a pair of factorial effects in the generated plan—non-orthogonality turning to orthogonality and aliased effects turn into non-aliased. We illustrate the theory with plans for two- and three-level factors on blocks of size four or less, estimating main effects as well as two-factor interactions. The plans constructed have the following features. They are inter-class orthogonal, in the sense that each effect of interest is orthogonal to all effects except the ones in its own class. The sizes of the classes are small, so that amount of non-orthogonality is limited. In particular, in the plans for experiments with three-level factors, most of the main effects are orthogonal to all others.

A hybrid symmetry–PSO approach to finding the self-equilibrium configurations of prestressable pin-jointed assemblies

For pin-jointed assemblies with many members or self-stress states, the form-finding problem using conventional methods generally involves considerable computational complexities due to the large size of the solution spaces. Here, we propose an improved form-finding method for prestressable pin-jointed structures by combining symmetry-based qualitative analysis with particle swarm optimization. Expressed in the symmetry-adapted coordinate system, the nodal coordinate vectors of a structure with specific symmetry and topology are independently extracted from the key blocks of the small-sized force density matrices associated with rigid-body translations. Then, the first block of the equilibrium matrix is computed, in which the null space reveals integral self-stress states. Particle swarm optimization is introduced and adapted to find feasible prestress modes, where the uniformity and unilaterality conditions of the members are considered. Besides, the QR decomposition with column pivoting is adopted for efficient computations on the null space of these blocks. The QR decompositions of the small-sized blocks of the force density matrix and the equilibrium matrix are performed iteratively, to simultaneously find a stable self-equilibrium configuration and a feasible prestress mode. Representative examples show the presented method is computationally efficient and accurate for the form-finding of symmetric tensegrities and prestressed cable–strut structures.

Near-Linear Time Algorithm for $n$-Fold ILPs via Color Coding

We study an important case of integer linear programs (ILPs) of the form $\max\{c^Tx \ \vert\ \mathcal Ax = b, l \leq x \leq u,\, x \in \mathbb{Z}^{n t} \} $ with $n t$ variables and lower and upper bounds $\ell, u\in\mathbb Z^{nt}$. In $n$-fold ILPs nonzero entries only appear in the first $r$ rows of the matrix $\mathcal A$ and in small blocks of size $s\times t$ along the diagonal underneath. Despite this restriction, many optimization problems can be expressed in this form. It is known that $n$-fold ILPs are fixed-parameter tractable (FPT) regarding the parameters $s, r,$ and $\Delta$, where $\Delta$ is the greatest absolute value of any entry in $\mathcal A$. The state-of-the-art technique is a local search algorithm that subsequently moves in an improving direction where the number of iterations and the search for such an improving direction each take time $\Omega(n)$. This leads to a running time quadratic in $n$. We introduce a technique based on color coding which allows us to compute these improving directions in logarithmic time after a single initialization step. This yields an algorithm for $n$-fold ILPs with a running time that is near-linear in $nt$, the number of variables. More precisely, our algorithm runs in time $(rs\Delta)^{\mathcal{O}(r^2s + s^2)} L^2 nt \log^{\mathcal{O}(1)}(nt)$, where $L$ is the encoding length of the largest integer in the input. Further, in contrast to the algorithms in recent literature, we do not need to solve the LP relaxation in order to handle unbounded variables. Instead we give a structural lemma to introduce appropriate bounds. On the other hand, if we are given such an LP solution, the running time can be decreased by a factor of $L$.

Generalized Feedback Vertex Set Problems on Bounded-Treewidth Graphs: Chordality is the Key to Single-Exponential Parameterized Algorithms

It has long been known that Feedback Vertex Set can be solved in time $2^{\mathcal{O}(w\log w)}n^{\mathcal{O}(1)}$ on $n$-vertex graphs of treewidth $w$, but it was only recently that this running time was improved to $2^{\mathcal{O}(w)}n^{\mathcal{O}(1)}$, that is, to single-exponential parameterized by treewidth. We investigate which generalizations of Feedback Vertex Set can be solved in a similar running time. Formally, for a class $\mathcal{P}$ of graphs, the Bounded $\mathcal{P}$-Block Vertex Deletion problem asks, given a graph~$G$ on $n$ vertices and positive integers~$k$ and~$d$, whether $G$ contains a set~$S$ of at most $k$ vertices such that each block of $G-S$ has at most $d$ vertices and is in $\mathcal{P}$. Assuming that $\mathcal{P}$ is recognizable in polynomial time and satisfies a certain natural hereditary condition, we give a sharp characterization of when single-exponential parameterized algorithms are possible for fixed values of $d$: if $\mathcal{P}$ consists only of chordal graphs, then the problem can be solved in time $2^{\mathcal{O}(wd^2)} n^{\mathcal{O}(1)}$, and if $\mathcal{P}$ contains a graph with an induced cycle of length $\ell\ge 4$, then the problem is not solvable in time $2^{o(w\log w)} n^{\mathcal{O}(1)}$ even for fixed $d=\ell$, unless the ETH fails. We also study a similar problem, called Bounded $\mathcal{P}$-Component Vertex Deletion, where the target graphs have connected components of small size rather than blocks of small size, and we present analogous results. For this problem, we also show that if $d$ is part of the input and $\mathcal{P}$ contains all chordal graphs, then it cannot be solved in time $f(w)n^{o(w)}$ for some function $f$, unless the ETH fails.

Efficient Solution of Electromagnetic Scattering From Dielectric Objects via Characteristic Basis Function Method Based on Large-Size Blocks With Multilevel Subdivision

The characteristic basis function method (CBFM) based on large-size blocks with multilevel subdivision is proposed to improve the analysis of electromagnetic scattering from dielectric objects. The small-size blocks' strategy is widely used to alleviate the computational burden on generating the characteristic basis functions (CBFs) in the perfectly electrical conducting (PEC) object problems. However, when transplanted into the dielectric objects, this strategy performs poorly according to our empirical results. For adapting the CBFM to dielectric objects, the large-size blocks' strategy is proposed for the first time. Primarily, it remarkably reduces the number of CBFs required for the reduced matrix. Furthermore, the multilevel subdivision on the blocks is adopted to convert the full-rank impedance matrices of the blocks into hierarchical matrices, leading to great advantage in two aspects. In the CBFs' generation, the self-impedance matrices with hierarchical structure can be easily inversed by a recursive inversion algorithm based on the Sherman-Morrison-Woodbury (SMW) formula, which significantly cuts down the consumption in terms of CPU time and memory requirement. In addition, the hierarchical structure is taken advantage of by the multiscale adaptive cross approximation (MS-ACA) to accelerate the filling of the mutual-impedance matrices of the adjacent blocks. The numerical results of several dielectric objects are presented to demonstrate the accuracy and efficiency of the proposed method.