Neighboring Blocks Research Articles

Modelling spatiotemporal patterns of visceral leishmaniasis incidence in two endemic states in India using environment, bioclimatic and demographic data, 2013-2022.

As of 2021, the National Kala-azar Elimination Programme (NKAEP) in India has achieved visceral leishmaniasis (VL) elimination (<1 case / 10,000 population/year per block) in 625 of the 633 endemic blocks (subdistricts) in four states. The programme needs to sustain this achievement and target interventions in the remaining blocks to achieve the WHO 2030 target of VL elimination as a public health problem. An effective tool to analyse programme data and predict/ forecast the spatial and temporal trends of VL incidence, elimination threshold, and risk of resurgence will be of use to the programme management at this juncture. We employed spatiotemporal models incorporating environment, climatic and demographic factors as covariates to describe monthly VL cases for 8-years (2013-2020) in 491 and 27 endemic and non-endemic blocks of Bihar and Jharkhand states. We fitted 37 models of spatial, temporal, and spatiotemporal interaction random effects with covariates to monthly VL cases for 6-years (2013-2018, training data) using Bayesian inference via Integrated Nested Laplace Approximation (INLA) approach. The best-fitting model was selected based on deviance information criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) and was validated with monthly cases for 2019-2020 (test data). The model could describe observed spatial and temporal patterns of VL incidence in the two states having widely differing incidence trajectories, with >93% and 99% coverage probability (proportion of observations falling inside 95% Bayesian credible interval for the predicted number of VL cases per month) during the training and testing periods. PIT (probability integral transform) histograms confirmed consistency between prediction and observation for the test period. Forecasting for 2021-2023 showed that the annual VL incidence is likely to exceed elimination threshold in 16-18 blocks in 4 districts of Jharkhand and 33-38 blocks in 10 districts of Bihar. The risk of VL in non-endemic neighbouring blocks of both Bihar and Jharkhand are less than 0.5 during the training and test periods, and for 2021-2023, the probability that the risk greater than 1 is negligible (P<0.1). Fitted model showed that VL occurrence was positively associated with mean temperature, minimum temperature, enhanced vegetation index, precipitation, and isothermality, and negatively with maximum temperature, land surface temperature, soil moisture and population density. The spatiotemporal model incorporating environmental, bioclimatic, and demographic factors demonstrated that the KAMIS database of the national programmme can be used for block level predictions of long-term spatial and temporal trends in VL incidence and risk of outbreak / resurgence in endemic and non-endemic settings. The database integrated with the modelling framework and a dashboard facility can facilitate such analysis and predictions. This could aid the programme to monitor progress of VL elimination at least one-year ahead, assess risk of resurgence or outbreak in post-elimination settings, and implement timely and targeted interventions or preventive measures so that the NKAEP meet the target of achieving elimination by 2030.

Iterative Training Sample Augmentation for Enhancing Land Cover Change Detection Performance With Deep Learning Neural Network.

Labeled samples are important in achieving land cover change detection (LCCD) tasks via deep learning techniques with remote sensing images. However, labeling samples for change detection with bitemporal remote sensing images is labor-intensive and time-consuming. Moreover, manually labeling samples between bitemporal images requires professional knowledge for practitioners. To address this problem in this article, an iterative training sample augmentation (ITSA) strategy to couple with a deep learning neural network for improving LCCD performance is proposed here. In the proposed ITSA, we start by measuring the similarity between an initial sample and its four-quarter-overlapped neighboring blocks. If the similarity satisfies a predefined constraint, then a neighboring block will be selected as the potential sample. Next, a neural network is trained with renewed samples and used to predict an intermediate result. Finally, these operations are fused into an iterative algorithm to achieve the training and prediction of a neural network. The performance of the proposed ITSA strategy is verified with some widely used change detection deep learning networks using seven pairs of real remote sensing images. The excellent visual performance and quantitative comparisons from the experiments clearly indicate that detection accuracies of LCCD can be effectively improved when a deep learning network is coupled with the proposed ITSA. For example, compared with some state-of-the-art methods, the quantitative improvement is 0.38%-7.53% in terms of overall accuracy. Moreover, the improvement is robust, generic to both homogeneous and heterogeneous images, and universally adaptive to various neural networks of LCCD. The code will be available at https://github.com/ImgSciGroup/ITSA.

Nectarine Disease Identification Based on Color Features and Label Sparse Dictionary Learning with Hyperspectral Images

Fruit cracking and rust spots are common diseases of nectarines that seriously affect their yield and quality. Therefore, it is essential to construct fast and accurate disease-identification models for agricultural products. In this paper, a sparse dictionary learning method was proposed to realize the rapid and nondestructive identification of nectarine disease based on multiple color features combined with improved LK-SVD (Label K-Singular Value Decomposition). According to the color characteristics of the nectarine itself and the significant color differences existing in the three categories of nectarine (diseased, normal, and background parts), multiple color spaces of RGB, HSV, Lab, and YCbCr were studied. It was concluded that the G channel in RGB space, Y channel in YCbCr space, and L channel in Lab space can better distinguish the diseased part from the other parts. At the model-training stage, pixels of the diseased, normal, and background parts in the nectarine image were randomly selected as the initial training sets, and then, the neighboring image blocks of the pixels were selected to construct the feature vectors based on the above color space channels. An improved LK-SVD dictionary learning algorithm was proposed that integrated the category label into the process of dictionary learning, and thus, an over-complete feature dictionary with significant discrimination was obtained. At the model-testing stage, the orthogonal matching pursuit (OMP) algorithm was used for sparse reconstruction of the original data, which can obtain the classification categories based on the optimized feature dictionary. The experimental results show that the sparse dictionary learning method based on multi-color features combined with improved LK-SVD can identify fruit cracking and rust spot diseases of nectarines quickly and accurately, and the average overall classification accuracies were 92.06% and 88.98%, respectively, which were better than those of k-nearest neighbor (KNN), support vector machine (SVM), DeepLabV3+, and Unet++; the identification results of DeepLabV3+ and Unet++ were also relatively high, but their average time costs were much higher, requiring 126.46~265.65 s. It is demonstrated that this study can provide technical support for disease identification in agricultural products.

Comparison of sparse Gabor-based methods for detection of microseismic events

Event detection and selection is a challenging and time-consuming step in microseismic data processing because signals often are embedded in noisy recordings. Automated triggering-based algorithms can detect many potential events in the data. However, their precision rates often are low, thus requiring substantial manual labor to select desired events of interest. This study investigates two time-frequency methods which combine signal enhancement with automated event detection and selection, namely, (1) the sparse Gabor transform and (2) the neighboring block thresholding. Both methods use thresholding in the time-frequency domain to increase signal enhancement, followed by an energy detection criterion, leading to improved event detection with higher precision rates. However, the neighboring block thresholding causes amplitude fidelity issues and we observe changes in the relative and maximum amplitudes of the waveforms reconstructed from the thresholded coefficients. Conversely, the sparse Gabor transform attenuates the noise significantly while preserving the signals. Thus, this time-frequency method is suitable for enhanced event detection and subsequent processing, including magnitude estimation and moment tensor inversion.

Reversible demosaiced image authentication scheme with recoverability using clustering and matching techniques

In this paper, we propose a reversible and recoverable authentication technique for demosaiced images using the k-means clustering algorithm and side match technique. Prior methods generate the recovery codes directly using the most significant bits of sampled components or their compressed codes, which requires excessive bits to record them. This not only damages the image to a large extent, but also limits the quality of the recovered image. In this paper, the sampled components are clustered and their cluster indices are used as the recovery codes. A tampered block can mostly be recovered by other untampered ones with the same cluster index. For an unrecoverable block, the cluster indices of its neighboring untampered blocks are employed to predict the block, and the recovery is performed by using the side match technique. Since unrecoverable blocks are sparsely scattered in the tampered areas, the recovered blocks around them are mostly available, and therefore an accurate prediction can be made. The experimental results show that the average quality of marked and recovered images of our method increases by around 3.36 dB and 2.96 dB, respectively, compared to previous methods.

Paper] Memory Bandwidth Constrained Overlapped Block Motion Compensation for Video Coding

Overlapped block motion compensation (OBMC) is an inter prediction tool that improves coding performance by blending reference samples of the current and neighboring block across the block boundaries. For this mechanism, OBMC increases the reference samples fetched from the external memory on hardware decoders, i.e., memory bandwidth. This is a disadvantage when introducing OBMC to video coding standards such as VVC, especially for mobile devices with limited batteries, because the extended memory bandwidth increases decoders' power consumption. In this paper, we propose a memory bandwidth constrained OBMC method with adaptive number of motion vectors and interpolation filter taps of the neighboring blocks depending on the current block sizes. Simulation results show that the proposed method achieves -0.22 % performance improvements over VVC reference software without exceeding the maximum memory bandwidth of VVC, which is comparable to the full performance of OBMC (-0.33 %), requiring 3.8 times its memory bandwidth.

An Information Security Method Based on Optimized High-Fidelity Reversible Data Hiding

With the rapid development of the Industrial Internet of Things, the social and commercial value of digital information is greatly increased, and the sensitive information of groups and individuals faces more and more problems. Due to providing accurate prediction errors, pixel value ordering (PVO)-based reversible data hiding is an active research topic. In this article, a new PVO-based embedding method is proposed first, in which the adaptive data embedding is adopted according to the difference between the maximum/minimum three pixels. Second, a pixel collecting and sorting model is designed to increase expandable prediction errors. By converting a shifting prediction error into an expandable one, the proposed PVO-based embedding method creates conditions for data embedding. In this way, the space location of pixels in the block can be better utilized to achieve improved capacity-distortion performance. Finally, an adaptive embedding is designed based on the content of the target block and its neighboring blocks. By measuring the complexity of the target pixel block, smoothing blocks are preferentially used and the appropriate embedding is determined by the complexity. The experimental results show that the proposed method outperforms a series of PVO-based multipass embedding methods. The proposed method is expected to address some security issues in the industrial and privacy fields, such as enhancing confidentiality and security during the transmission of sensitive information.

NA-Resnet: neighbor block and optimized attention module for global-local feature extraction in facial expression recognition

NA-Resnet: neighbor block and optimized attention module for global-local feature extraction in facial expression recognition

Complex Color Space Segmentation to Classify Objects in Urban Environments

Color image segmentation divides the image into areas that represent different objects and focus points. One of the biggest problems in color image segmentation is the lack of homogeneity in the color of real urban images, which generates areas of over-segmentation when traditional color segmentation techniques are used. This article describes an approach to detecting and classifying objects in urban environments based on a new chromatic segmentation to locate focus points. Based on components a and b on the CIELab space, we define a chromatic map on the complex space to determine the highest threshold values by comparing neighboring blocks and thus divide various areas of the image automatically. Even though thresholds can result in broad segmentation areas, they suffice to locate centroids of patches on the color image that are then classified using a convolutional neural network (CNN). Thus, this broadly segmented image helps to crop only outlying areas instead of classifying the entire image. The CNN is trained to use six classes based on the patches drawn from the database of reference images from urban environments. Experimental results show a high score for classification accuracy that confirms the contribution of this segmentation approach.

Interblock Flow Prediction With Relation Graph Network for Cold Start on Bike-Sharing System

As the IoT technology becomes well established and sharing economy expands worldwide, the bike-sharing system (BSS) has spread fast in the last decade. When introducing the BSS in a new city, the operator often faces many challenges: e.g., optimizing station siting (physical or electric), constructing bike lanes, and making strategies for the initial distribution and rebalancing of bikes. These challenges require an accurate interblock flow prediction before deploying the BSS. In this article, we derive blocks from the urban road network and extract blocks’ features based on the distribution and types of POI. Then, the interblock flow can be predicted based on the features of both start/end block and neighbor blocks. We propose a unified architecture with a generalized attention mechanism named the relation graph network (RGN) to extract block features and make predictions across cities. The evaluations on real-world data sets show that RGN outperforms other popular graph neural networks in multiple metrics. We further simulate the applications of station site selection and bike lane planning based on the prediction from RGN, and the result provides a noticeable increase in the BSS’s deployment/operation efficiency.

E&amp;P Notes (March 2022)

E&amp;P Notes (March 2022)

Enabling efficient sub-block disabled caches using coarse grain spatial predictions

Reducing the supply voltage in today′s process technologies introduces significant reliability challenges for on-chip SRAM arrays. As a reaction, many Cache Fault-Tolerance (CFT) techniques have been developed to ensure error free and performance efficient execution in the presence of faults. A class of recent CFT techniques are based on the concept of disabling cache portions, such as (sub-)blocks or words that include defective bits, and reconfiguring operational ones (e.g., physical or logical neighbor (sub-)blocks). All those techniques require significant circuit modifications which may pose unacceptable overheads to time-sensitive L1 caches. In this work we propose to deal with the cache reliability problem at a very different level. We assume that faulty caches are enhanced with the ability of disabling their defective parts at different sub-block granularities. The key idea is to leverage the spatial reuse patterns of the memory blocks (not all the data fetched into the cache is accessed) in order to mask out the additional, due-to-faults, cache misses. To this end, we propose a new class of frugal spatial predictors (with 24-bytes storage requirements) to orchestrate the replacement decisions of the fully and partially functional cache blocks. Our evaluation results reveal that the proposed approach is able to offer 32.3% on average, among all studied percentage of failures, increase in hit ratio in first level data caches (64.6% in instruction caches) over a conventional faulty cache (based on block disabling). Further, experimental results reveal the superiority of the proposed method with respect to Instructions-Per-Cycle and Energy-Delay-Product.

Synthesis, structural and spectral characteristics of Cd3O2SO4

X-ray diffraction and thermogravimetric analysis methods were used to reveal conditions for the formation and thermal stability of cadmium oxosulfate Cd3O2SO4 synthesized by heating β-Cd2(OH)2SO4 in air. The crystal structure of Cd3O2SO4 was studied for the first time. It was shown that it is built by alternation of two types of blocks oriented parallel to the bc plane. The first type block consists of CdO8 clusters connected with each other by SO4 tetrahedra. The space between these blocks is occupied by blocks composed of tetragonal CdO5 pyramids connected with neighboring CdO8 blocks. The connections are carried out both through bridging oxygen atoms and oxygen atoms belonging to SO4 clusters. The unit cell parameters of Cd3O2SO4 are a=12.1762 (1) Å, b=6.85421 (5) Å, c=6.97322 (6) Å, β=106.5578 (6)°, V=557.840 (8) Å3, Z=4, sp. gr. C2/c. Vibrational spectroscopy data are in complete agreement with the crystal structure analysis. The UV-Vis-NIR spectra demonstrate the semiconducting nature of Cd3O2SO4 with the band gap of 3.45 eV stable at different heating temperatures. The energy band structure of Cd3O2SO4 has been studied by means of the first-principle pseudo-potential PAW method, VASP software package. The calculations performed point to a high probability of oxygen vacancies formation under the indicated synthesis conditions. However, the presence of vacancies does not lead to the appearance of vacancy bands inside the band gap, i.e., in accordance with experiment, they do not significantly affect the optical properties of Cd3O2SO4.

FISHing on a Budget.

Sensitive quantification of RNA transcripts via fluorescence in situ hybridization (FISH) is a ubiquitous part of understanding quantitative gene expression in single cells. Many techniques exist to identify and localize transcripts inside the cell, but often they are costly and labor intensive. Here we present a method to use a singly labeled short DNA oligo probe to perform FISH in yeast cells. This method is effective for highly constrained FISH applications where the target length is limited (<200 nucleotides). This method can quantify different RNA isoforms or enable the use of fluorescence resonance energy transfer (FRET) to detect co-transcription of neighboring sequence blocks. Since this method relies on a single probe, it is also more cost-effective than a multiple probe labeling strategy.

Early Intra CU Size Decision for Versatile Video Coding Based on a Tunable Decision Model

Versatile video coding (VVC) further improves video coding gain by introducing a new Quad-tree (QT) with nested multi-type tree (QTMT) partition structure, which includes QT partition, horizontal and vertical binary-tree (BT) partition, and horizontal and vertical ternary-tree (TT) partition. However, the coding gain is achieved at the cost of drastically increased coding complexity. We propose an early intra coding unit (CU) size partition method for VVC based on a tunable decision model. The distortion of the CU is firstly obtained by the difference between the original luminance pixel and the predicted one. By exploiting the distortion, two decision models are established for the early skipping of BT and TT partition for each CU, respectively. Then, a tunable decision model is presented to achieve different trade-off between encoding loss and complexity reduction. Extensive experimental results show that the proposed approach reduces the encoding time ranging from 22.6% to 67.6% with the BD-Rate increase ranging from 0.56% to 2.61%. It is also suitable for parallel implementation by exploiting the pipeline structure of the VVC encoder due to its independency with neighboring CU blocks.

Numerical analysis of linear and nonlinear buckling instability of plates made of topologically interlocked materials

Topologically interlocked materials (TIMs) are an emerging class of architectured materials that each of block is supported by their neighboring blocks without any connector, which is inspired by microstructures observed in nature. Therefore, they showed alluring attributes such as resistance to impact, toughness, high flexibility even when using brittle materials, and high crack propagation resistance. In these structures, the presence of lateral compressive force, applied on peripheral blocks, ensures the integrity and identified properties. Lateral load, however, can adversely affect TIM plates and cause instability, and initiate buckling. In the present study, to focus on the buckling behavior of TIM plates due to the lateral force, a finite element model of TIM plates, made of tetrahedral blocks, was first validated using numerical results available in the literature and then analyzed. In this regard, an appropriate procedure has been presented to simulate the possible instability of these structures. Unlike monolithic plates, geometric parameters of TIM plates are dependent and this makes their parametric study more challenging. Therefore, some case studies have been accomplished to illustrate the effect of diverse parameters. The current study indicates that increasing the assembly plate length and decreasing its thickness causes a reduction of buckling load; nevertheless, it is necessary to mention that the thickness variation is a more impressive factor.

Improving Data Security using Compression Integrated Pixel Interchange Model with Efficient Encryption Technique

We exist in a digital era in which communication is largely based on the transfer of digital knowledge over data networks. Disclosures are sometimes regarded as a transmitter that transmits a digital file. A system of encoding, efficient and easy yet secure model must be developed for quick and prompt transmission. The file is sent from source to destination where it is difficult to maintain the privacy of knowledge. The encryption of images is vital for securing sensitive information or images from unauthorized readers. By using a technique of selective encryption, the original image pixel values are completely obscured in this way and an intruder cannot retrieve statistical data from its original image. This paper introduces a new methodology for the use of a protective facility for the transmission of digital data over the public network. A highly established field with image compression is allowed for speedy transmission and efficient information storage. During the initial process, the original image is divided into blocks of the same size and sub segmentation is performed for accurate extraction of images within the boundaries. A random matrix is used to swap the pixels of the neighboring sub blocks. Afterwards, each pixel is randomly exchanged for the neighboring blocks with a random matrix, then each block is encrypted with the proposed function and then the encrypted data can be stored in cloud. The proposed method uses Image Segmentation based Compression Model with Pixel Interchange Encryption (ISbCPIE) Model for providing high security to the Image transmitted in the network. Compressor is needed to achieve rapid transmission and efficient storage. The proposed model is compared with the traditional models and the results show that the proposed model security levels are better than the existing models.

Blind MV-based video steganalysis based on joint inter-frame and intra-frame statistics

Despite all its irrefutable benefits, the development of steganography methods has sparked ever-increasing concerns over steganography abuse in recent decades. To prevent the inimical usage of steganography, steganalysis approaches have been introduced. Since motion vector manipulation leads to random and indirect changes in the statistics of videos, MV-based video steganography has been the center of attention in recent years. In this paper, we propose a 54-dimentional feature set exploiting spatio-temporal features of motion vectors to blindly detect MV-based stego videos. The idea behind the proposed features originates from two facts. First, there are strong dependencies among neighboring MVs due to utilizing rate-distortion optimization techniques and belonging to the same rigid object or static background. Accordingly, MV manipulation can leave important clues on the differences between each MV and the MVs belonging to the neighboring blocks. Second, a majority of MVs in original videos are locally optimal after decoding concerning the Lagrangian multiplier, notwithstanding the information loss during compression. Motion vector alteration during information embedding can affect these statistics that can be utilized for steganalysis. Experimental results have shown that our features’ performance far exceeds that of state-of-the-art steganalysis methods. This outstanding performance lies in the utilization of complementary spatio-temporal statistics affected by MV manipulation as well as feature dimensionality reduction applied to prevent overfitting. Moreover, unlike other existing MV-based steganalysis methods, our proposed features can be adjusted to various settings of the state-of-the-art video codec standards such as sub-pixel motion estimation and variable-block-size motion estimation.

Интерпретация результатов геохимических исследований нефти и воды на примере одного месторождения (Республика Казахстан)

46 oil samples were collected at the wellheads of different wells of a particular oil field and “oil fingerprinting” was performed by gas chromatographic analysis on LTM-MD-GC in order to understand the fluid connectivity of the reservoir within the field. This field located in the eastern edge of the Caspian Basin. According to the results of cluster analyzes, it was found that the studied samples of the north-eastern part of the oil field differ from those of the south-western part. Since the oil field has a massive reservoir height, all wells operate with minimum water-cut values, except for the production well. In this regard, the ionic composition of the water and the titration method were used to analyze the ionic composition of water, separated from the oil of producing well, two neighboring injection wells and block cluster pumping station of this field, to determine the ionic composition and identify differences and similarities of waters at the molecular level. According to the results of the analyzes, we came to the conclusion that all the studied formation water samples have common origin. The relatively high NaCl value in producing well water may be due to the high concentration of chloride in the oil.

Acceleration of contact detection between arbitrarily shaped polyhedra based on multi-cover methods in three dimensional discontinuous deformation analysis

Three-dimensional discontinuous deformation analysis (3D DDA) can address the large-displacement dynamic responses in rock engineering. However, the efficiency of contact detection is the bottleneck of 3D DDA. This study automatically generates a multi-shell cover (MSC) system and develops a novel multi-cover search (MCS) algorithm for 3D cases. MSC expedites contact detection by dividing the geometric elements of polyhedra into several groups, and MCS accelerates contact detection by efficiently selecting potential contacts using the neighbor relations between faces of polyhedral blocks. These methods increase the accuracy of the rough search in the neighbor search process and improve contact pattern identification. As a result, the number of delicate checks between the geometric elements of two neighbor blocks is greatly reduced. Some numerical tests in rock engineering are used to verify that these two multi-cover methods can accelerate contact detection between arbitrarily shaped polyhedra in 3D DDA.