Block Edge Research Articles

Dual-Polarized Millimeter-Wave Endfire Array Based on Substrate Integrated Mode-Composite Transmission Line

A dual-polarized endfire array is presented in this article for millimeter application. The substrate integrated mode-composite transmission line is introduced to exciting the dual-polarized radiation. Specially, the horizontal polarization is excited by the transverse electromagnetic (TEM) mode, whereas the vertical polarization is based on the TE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> mode. Two metal blocks sandwich the substrates to form a sandwich structure, and an open-ended slot is designed on the block edge to construed complementary sources radiation. Benefiting from the wideband feature of complementary sources, the proposed dual-polarized endfire array achieved a considerable bandwidth performance of 26.9% (24.8–32.5 GHz) based on the standard that both the voltage standing wave ratios of the two ports are less than 2. In terms of the key factor of dual-polarized antenna and isolation, measured results show a high port isolation of over 36 dB and a cross-polarization discrimination rate of over 30 dB. The excellent dual-polarized performance gives the proposed endfire array the potential of application in millimeter-wave mobile communications.

Unplanned Surgery After Transcatheter Closure of Ventricular Septal Defect in Children: Causes and Risk Factors.

Objectives: To evaluate the causes and risk factors of unplanned surgery after transcatheter closure of ventricular septal defect (VSD) in children.Methods: A total of 773 patients with VSD who had the devices transcatheter released between January 2013 and December 2018 in our institution were retrospectively reviewed. Univariate and multivariate analyses were used to identify the risk factors for unplanned surgery.Results: Twenty four patients (3.1%) underwent unplanned surgery after transcatheter closure of VSD. The most common cause for unplanned surgery was new-onset or worsening aortic regurgitation (14/24; 58.3%), followed by occluder migration (4/24; 16.7%), complete atrioventricular block (2/24; 8.3%), severe hemolysis (2/24; 8.3%), residual shunt (1/24; 4.2%), and occluder edge near the tricuspid valve chordae (1/24; 4.2%). Logistic regression analysis revealed that primary aortic valve prolapse (OR: 5.507, 95%CI: 1.673–18.123, P = 0.005); intracristal VSD (OR: 8.731, 95%CI: 2.274–33.527, P = 0.002); eccentric occluder (OR: 4.191, 95%CI: 1.233–14.246, P = 0.022); larger occluder size (OR: 1.645, 95%CI: 1.331–2.033, P < 0.001); and pulmonary artery systolic pressure ≥45 mmHg (OR: 4.003, 95%CI: 1.073–14.941, P = 0.039) were risk factors for unplanned surgery.Conclusions: New-onset or worsening aortic regurgitation was the primary cause for unplanned surgery after transcatheter closure of VSD in children. Primary aortic valve prolapse, intracristal VSD, eccentric occluder, larger occluder size, pulmonary artery systolic pressure ≥45 mmHg could increase the risk of unplanned surgery.

Tunable Knot Segregation in Copolyelectrolyte Rings Carrying a Neutral Segment.

We use Langevin dynamics simulations to study the knotting properties of copolyelectrolyte rings carrying neutral segments. We show that by solely tuning the relative length of the neutral and charged blocks, one can achieve different combinations of knot contour position and size. Strikingly, the latter is shown to vary nonmonotonically with the length of the neutral segment; at the same time, the knot switches from being pinned at the block's edge to becoming trapped inside it. Model calculations relate both effects to the competition between two adversarial mechanisms: the energy gain of localizing one or more of the knot's essential crossings on the neutral segment and the entropic cost of such localization. Tuning the length of the neutral segment sets the balance between the two mechanisms and hence the number of localized essential crossings, which in turn modulates the knot's size. This general principle ought to be useful in more complex systems, such as multiblock copolyelectrolytes, to achieve a more granular control of topological constraints.

Graph based secure cancelable palm vein biometrics

Palm vein is a promising vascular biometric modality that offers contactless and accurate authentication. The complex nature of the vein pattern provides uniqueness to each individual which helps in efficient individual authentication. However, most of the existing palm vein pattern-based works extract point-specific features like bifurcation and endpoints. These methods overlook the unique connections between the vein points. Graphs can help in representing the entire vein structure. However, a direct spatial graph representation of the vein pattern poses template security vulnerabilities. Therefore, a graph representation that uses the structure information, while providing template security is required. Towards this, in this paper, we propose a graph representation for palm veins with vein pattern blocks as nodes and the vein connection between neighbouring blocks as edges. The vein pattern is partitioned into blocks based on random numbers generated using a user-specific key which guarantees template security and cancelability. The method of block generation and edge connection gives uniqueness to this graph. This approach requires no additional processing for providing template security as the feature representation automatically ensures it. Here, even the compromise of key does not affect template security. Extensive experiments with three publicly available datasets prove the efficacy of the method.

Adaptive image inpainting algorithm based on sample block by kriging pretreatment and facet model

We present an improved Criminisi image inpainting algorithm through kriging pretreatment and facet model. We propose three improvement strategies. First, we propose an improved priority. A priority used in Criminisi algorithm consists of confidence term and data term. As iteration progresses, the confidence term is updated and plays a key role in the confidence term of the next edge block. At the same time, the confidence term gradually tends to 0, leading to a weakening of the role of priority, and ultimately affecting the repair effect. We propose an improved priority, which is represented by a piecewise function. Importantly, this improved priority reduces the risk of being weakened during iteration. Second, Criminisi algorithm uses fixed-size sample blocks to repair damaged images, regardless of whether the image content is a textured area or a structure area. We introduce an adaptive method to select sample block size based on the facet model. The size of the sample block is adaptively adjusted for different image contents, thereby improving the quality of the repaired image. Third, we show a weighted sum of squares differences matching principle based on the facet model. The matching formula is determined by the pixel gray value of a sample block and the structure value of four directions, which improves matching accuracy between target block and optimal matching block. Finally, experimental results show that the proposed algorithm is competitive with some state-of-the-art inpainting techniques in terms of both objective metrics and subjective visual inspection.

Extraction of road boundary from MLS data using laser scanner ground trajectory

Abstract Various means of extracting road boundary from mobile laser scanning data based on vehicle trajectories have been investigated. Independent of positioning and navigation data, this study estimated the scanner ground track from the spatial distribution of the point cloud as an indicator of road location. We defined a typical edge block consisting of multiple continuous upward fluctuating points by abrupt changes in elevation, upward slope, and road horizontal slope. Subsequently, such edge blocks were searched for on both sides of the estimated track. A pseudo-mileage spacing map was constructed to reflect the variation in spacing between the track and edge blocks over distance, within which road boundary points were detected using a simple linear tracking model. Experimental results demonstrate that the ground trajectory of the extracted scanner forms a smooth and continuous string just on the road; this can serve as the basis for defining edge block and road boundary tracking algorithms. The defined edge block has been experimentally verified as highly accurate and strongly noise resistant, while the boundary tracking algorithm is simple, fast, and independent of the road boundary model used. The correct detection rate of the road boundary in two experimental data is more than 99.2%.

Edge and identity preserving network for face super-resolution

Face super-resolution (SR) has become an indispensable function in security solutions such as video surveillance and identification system, but the distortion in facial components is a great challenge in it. Most state-of-the-art methods have utilized facial priors with deep neural networks. These methods require extra labels, longer training time, and larger computation memory. In this paper, we propose a novel Edge and Identity Preserving Network for Face SR Network, named as EIPNet, to minimize the distortion by utilizing a lightweight edge block and identity information. We present an edge block to extract perceptual edge information, and concatenate it to the original feature maps in multiple scales. This structure progressively provides edge information in reconstruction to aggregate local and global structural information. Moreover, we define an identity loss function to preserve identification of SR images. The identity loss function compares feature distributions between SR images and their ground truth to recover identities in SR images. In addition, we provide a luminance-chrominance error (LCE) to separately infer brightness and color information in SR images. The LCE method not only reduces the dependency of color information by dividing brightness and color components but also enables our network to reflect differences between SR images and their ground truth in two color spaces of RGB and YUV. The proposed method facilitates the proposed SR network to elaborately restore facial components and generate high quality 8× scaled SR images with a lightweight network structure. Furthermore, our network is able to reconstruct an 128×128 SR image with 215 fps on a GTX 1080Ti GPU. Extensive experiments demonstrate that our network qualitatively and quantitatively outperforms state-of-the-art methods on two challenging datasets: CelebA and VGGFace2.

Blockchain-based mobile edge computing system

With the development of the Internet of Things (IoT), the number of mobile terminal devices is increasing rapidly. Due to high transmission delay and bandwidth limitation, computing power requirements for IoT devices are getting higher and higher. Recently, edge computing is an effective way to reduce system delay, and blockchain solves the security problem of edge computing. In this paper, a three-layer network model, named blockchain-based mobile edge computing system (BMEC), is proposed for clone block identification. Specifically, a neural network based clone block identification (NCBI) method is proposed to prevent clone block attacks. After that, the Prim algorithm is applied to BMEC to generate a weighted undirected graph minimum spanning tree that is composed of edge blocks. This can divide a main chain into several side chains to improve the transaction speed of blockchain. Finally, the blockchain is constructed based on the time slicing round-robin scheduling algorithm to control resources from edge servers and regulate edge devices’ activities based on the predefined rules of priority, application type, and past behavior. Experimental results show that our clone block identification method can achieve block validation effectively in BMEC, and our construction of blockchain delay is lower than conventional edge computing methods.

Region of Interest-Based Coding Technique of Medical Images Using Varying Grading of Compression

A region of interest (ROI)-based compression method for medical image datasets is a requirement to maintain the quality of the diagnostically important region of the image. It is always a better option to compress the diagnostic important region in a lossless manner and the remaining portion of the image with a near-lossless compression method to achieve high compression efficiency without any compromise of quality. The predictive ROI-based compression on volumetric CT medical image is proposed in this paper; resolution-independent gradient edge detection (RIGED) and block adaptive arithmetic encoding (BAAE) are employed to ROI part for prediction and encoding that reduce the interpixel and coding redundancy. For the non-ROI portion, RIGED with an optimal threshold value, quantizer with optimal [Formula: see text]-level and BAAE with optimal block size are utilized for compression. The volumetric 8-bit and 16-bit standard CT image dataset is utilized for the evaluation of the proposed technique, and results are validated on real-time CT images collected from the hospital. Performance of the proposed technique in terms of BPP outperforms existing techniques such as JPEG 2000, M-CALIC, JPEG-LS, CALIC and JP3D by 20.31%, 19.87%, 17.77%, 15.58% and 13.66%, respectively.

Agricultural Productive Service System Based on the Block Chain and Edge Computing

With the rapid development of the service sector in economic growth, productive service industry has become a growing field of people’s attention. Agricultural productive services are a supporting point to promote the development of modern agriculture, and at the same time, they also point out the direction for promoting the transformation of the agricultural development mode, which has very important strategic significance. In order to analyze and improve the rural productive service system and solve the “three rural” issues, this article designs an agricultural productive service analysis framework from the perspective of farmers, statistics of the investment in agricultural productive services in a certain region from 2015 to 2019, the expenditure of agricultural productive services in agriculture, forestry, fishery, and animal husbandry, and per capita wage income and operating income of farmers in the area under agricultural productive services, and in the process of agricultural production, farmers need agricultural productive services from the perspective of the whole process, land preparation, seeding, pest control, fertilization, and harvesting risks. The data in this experiment are stored in a distributed manner through the block chain, and the data are stored in the corresponding part in the chronological order; the data are transmitted to the edge server of the experimental network in the way of point-to-point transmission, and then edge computing technology is used to calculate and analyze the data collected in the experiment. The final result shows that, in this region, the input of the main agricultural producer service industry is growing very fast, the scale of productive services is expanding, and more and more attention is paid to the producer services in the process of agricultural production, but the internal composition of agricultural producer services is unbalanced. With the continuous improvement of the scale of agricultural productive services, the per capita income of farmers and the per capita income of management are also increasing year by year. When the scale of agricultural productive services reaches 0.09 in 2019, the per capita wage income of farmers has reached 3900 yuan, and the per capita income of farmers has reached 3750 yuan. And in the perspective of various risks in agricultural production, the higher the risk coefficient, the stronger the risk preference of farmers and the higher the risk investment in agricultural production services.

Block Adjustment-Based Radiometric Normalization by Considering Global and Local Differences

For radiometric normalization (RN) of multiple remote sensing (MRS) images within large-scale coverage, the traditional methods ignore the error accumulation and adaptive allocation of cumulative errors caused by the transfer paths in the classical one-after-another pipeline. To this end, a block adjustment-based RN method of MRS images is proposed by considering the global and local radiometric differences (RDs) in this letter. First, the block adjustment-based global RN is conducted to eliminate the global differences of MRS images. This step is independent of transfer paths so that it breaks through the corresponding error accumulation and uneven distribution in the one-after-another pipeline. Second, two local strategies based on block adjustment and edge optimization are further adopted to remove the local residual RDs. In the experiments, it demonstrates that the proposed method can obtain MRS images with a balanced and appealing visual effect, which outperforms the moment matching (MM) method and the popular ENVI software.

An Enhanced Adaptive Block Truncation Coding with Edge Quantization Scheme

Recently, image compression using adaptive block truncation coding based on edge quantization (ABTC-EQ) was proposed by Mathews and Nair. Their approach deals with an image for two types of blocks, edge blocks and non-edge blocks. Different from using the bi-clustering approach on all blocks in previous block truncation coding (BTC)-like schemes, ABTC-EQ adopts tri-clustering to tackle edge blocks. The compression ratio of ABTC-EQ is reduced, but the visual quality of the reconstructed image is significantly improved. However, it is observed that ABTC-EQ uses 2 bits to represent the index of three clusters in a block. We can only use an average of 5/3 bits by variable-length code to represent the index of each cluster. On the other hand, there are two observations on the quantization levels in a block. The first observation is that the difference between the two quantization values is often smaller than the quantization values themselves. The second observation is that more clusters may enhance the visual quality of the reconstructed image. Based on variable-length coding and the above observations, we design variants of ABTC-EQ to enhance the visual quality of the reconstructed image and compression ratio.

Perturbative many-body transfer

The transfer of excitations between different locations of a quantum many-body system is of primary importance in many research areas, from transport properties in spintronics and atomtronics to quantum state transfer in quantum information processing. We address the transfer of n > 1 bosonic and fermionic excitations between the edges of a one-dimensional chain modelled by a quadratic hopping Hamiltonian, where the block edges, embodying the sender and the receiver sites, are weakly coupled to the quantum wire. We find that perturbative high-quality transfer is attainable in the weak-coupling limit, for both bosons and fermions, only for certain modular arithmetic equivalence classes of the wire’s length. Finally we apply our findings to the transport of spins and the charging of a many-body quantum battery.

Efficient FPGA-based graph processing with hybrid pull-push computational model

Hybrid pull-push computational model can provide compelling results over either of single one for processing real-world graphs. Programmability and pipeline parallelism of FPGAs make it potential to process different stages of graph iterations. Nevertheless, considering the limited on-chip resources and streamline pipeline computation, the efficiency of hybrid model on FPGAs often suffers due to well-known random access feature of graph processing. In this paper, we present a hybrid graph processing system on FPGAs, which can achieve the best of both worlds. Our approach on FPGAs is unique and novel as follow. First, we propose to use edge block (consisting of edges with the same destination vertex set), which allows to sequentially access edges at block granularity for locality while still preserving the precision. Due to the independence of blocks in the sense that all edges in an inactive block are associated with inactive vertices, this also enables to skip invalid blocks for reducing redundant computation. Second, we consider a large number of vertices and their associated edge-blocks to maintain a predictable execution history. We also present to switch models in advance with few stalls using their state statistics. Our evaluation on a wide variety of graph algorithms for many real-world graphs shows that our approach achieves up to 3.69x speedup over state-of-the-art FPGA-based graph processing systems.

Progressive Photon Elimination With a Status Tree

We describe a modified progressive photon mapping (PPM) method applied with sample elimination, referred to as progressive photon elimination, to pursue accurate results and accelerated iterations. Meanwhile, an elimination status tree is proposed for progressive photon elimination, which retains the information used in the elimination and can be updated along with the multi-pass process to solve the challenge of the accuracy of local parameters. By using the status tree, our method can obtain a uniform photon distribution at each iteration by eliminating a certain number of photons. The tree is also improved to adapt to the complicated photon distributions. This strategy also facilitates the parallel elimination with a priori elimination ratio in the status tree, making the algorithm further accelerated. In parallel block processing, the equilibrium and edge problems are resolved. The experimental results show that our method requires about half of the number of iterations to achieve the same visual effect compared with progressive photon mapping.

A Video Steganalytic Approach Against Quantized Transform Coefficient-Based H.264 Steganography by Exploiting In-Loop Deblocking Filtering

This article presents a method for detection of H.264 steganographic algorithms that modulate quantized transform coefficients (QTCs) for data hiding. The in-loop deblocking filter specified in the H.264 standard is used for attenuating the discontinuities at coded block boundaries. However, our investigation indicates that the deblocking filtering process would probably be affected by QTC modification. Therefore, we suggest performing steganalysis by considering the influence of QTC modification on filtering decisions and filtering operations. In the filtering of luma block edges, for each luma sample, we try to obtain the cumulative amount of modification applied to it caused by filtering operations, and determine the frequency of the modification made to its sample value. Based on this, a 768-dimensional feature set is designed for training and classification. Extensive experiments were conducted under a range of setups to evaluate the effectiveness of the proposed method. The corresponding experimental results demonstrate that, compared with the prior art, our proposed method is generally capable of providing superior performance on detecting QTC modification in H.264 video, even at low embedding strengths.

Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment

Due to large volume and high variability of editing tools, protecting multimedia contents, and ensuring their privacy and authenticity has become an increasingly important issue in cyber-physical security of industrial environments, especially industrial surveillance. The approaches authenticating images using their principle content emerge as popular authentication techniques in industrial video surveillance applications. But maintaining a good tradeoff between perceptual robustness and discriminations is the key research challenge in image hashing approaches. In this paper, a robust image hashing method is proposed for efficient authentication of keyframes extracted from surveillance video data. A novel feature extraction strategy is employed in the proposed image hashing approach for authentication by extracting two important features: the positions of rich and nonzero low edge blocks and the dominant discrete cosine transform (DCT) coefficients of the corresponding rich edge blocks, keeping the computational cost at minimum. Extensive experiments conducted from different perspectives suggest that the proposed approach provides a trustworthy and secure way of multimedia data transmission over surveillance networks. Further, the results vindicate the suitability of our proposal for real-time authentication and embedded security in smart industrial applications compared to state-of-the-art methods.

Deep Origin of the Dome‐Shaped Hyblean Plateau, Southeastern Sicily: A New Tectono‐Magmatic Model

AbstractSoutheastern Sicily is characterized by a prominent topography known as the Hyblean Plateau. It is commonly considered as a flexural bulge formed by the northwest plunging of the Hyblean‐Malta platform beneath the Sicilian Fold and Thrust Belt. However its noncylindrical shape and multiphase uplift history correlated to the Late Miocene and Plio‐Pleistocene volcanism outline a possible magmatic control on the Hyblean Plateau topographic expression. We argue, using 2‐D flexural models, that the flexural bending alone is unlikely to explain the high anomalous elevation of the Hyblean Plateau. Moreover, topographic analysis show that the Hyblean Plateau has a dome shape composed of radially tilted preserved surfaces defining individual blocks. The block edges seem to be related to deep‐seated inherited faults. Using an analogue modeling approach, we show that magmatic intrusions beneath the thick Meso‐Cenozoic sedimentary cover can reproduce the first‐order morphology of the Hyblean Plateau when a structural inheritance is imposed. Accordingly, we propose a tectono‐magmatic model that favors mafic sill intrusions as the major uplift source for the topographic anomaly. However, to explain the strong positive Bouguer anomaly, a deeper high‐density intracrustal body is needed. Moho uprising related to Mesozoic rifting or mafic lower crust underplating/intraplating since the Late Cretaceous can be evoked. The small wavelength of the topography and the deep intracrustal mass anomaly makes the lithospheric elastic stresses sufficiently high to maintain a nonisostatic state over the geological time scales.

Transport gaps in ideal zigzag-edge graphene nanoribbons with chemical edge disorder

The effect of chemical edge disorder (CH and CH2) on the electron transport in ideal zigzag graphene nanoribbons (ZGNRs) is studied by using density functional theory (DFT) and the mean-field Hubbard model based quantum transport calculations. It is shown that a certain length of defective edge blocks can lead to a saturated suppression of the transmission through the lowest-energy bulk channel, where only two contiguous CH2 edge hybridizations at both edges is enough for ZGNRs narrower than ten zigzag chains. It suggests that the transport gap is established with the increasing heterogeneity of sp2 (CH edge) and sp3 (CH2 edge) hybridizations. With increasing the concentration (P) of sp3 over sp2 edge carbons, the transmission is shown to decrease exponentially by one order of magnitude until P ~ 0.3 with a saturated behavior around P = 0.5 and further decrease as P approaches 1. These findings show that the conductance can change more than 2 orders of magnitude with strong dependence on the edge chemistry even for structurally ideal ZGNRs.

Permanent, determinant, and rank of bi-block graphs

Computing the permanent of a (0, 1)-square matrix is a $$\# P$$-complete problem, whereas the determinant and rank are solved in polynomial time. A simple graph is called a bi-block graph if each block in it is a complete bipartite graph. In this paper, we consider the (0, 1)-adjacency matrix of a bi-block to find its permanent, determinant, and rank. These numbers are known for trees, so this work is a generalization of the results on trees. To compute the permanent and determinant we define two partitions namely block bipartite vertex covering and block edge perfect matching which are computable in linear time. For the rank, we give a linear time algorithm.