Reduce Data Volume Research Articles

Identification and mapping of soybean and maize crops based on Sentinel-2 data

Soybean and maize are important raw materials for the production of food and livestock feed. Accurate mapping of these two crops is of great significance to crop management, yield estimation, and crop-damage control. In this study, two towns in Guoyang County, Anhui Province, China, were selected as the study area, and Sentinel-2 images were adopted to map the distributions of both crops in the 2019 growing season. The data obtained on August 18 (early pod-setting stage of soybean) was determined to be the most applicable to soybean and maize mapping by means of the Jeffries–Matusita (JM) distance. Subsequently, three machine-learning algorithms, i.e., random forest (RF), support vector machine (SVM) and back-propagation neural network (BPNN) were employed and their respective performance in crop identification was evaluated with the aid of 254 ground truth plots. It appeared that RF with a Kappa of 0.83 was superior to the other two methods. Furthermore, twenty candidate features containing the reflectance of ten spectral bands (spatial resolution at 10 m or 20 m) and ten remote-sensing indices were input into the RF algorithm to conduct an important assessment. Seven features were screened out and served as the optimum subset, the mapping results of which were assessed based on the ground truth derived from the unmanned aerial vehicle (UAV) images covering six ground samples. The optimum feature-subset achieved high-accuracy crop mapping, with a reduction of data volume by 65% compared with the total twenty features, which also overrode the performance of ten spectral bands. Therefore, feature-optimization had great potential in the identification of the two crops. Generally, the findings of this study can provide a valuable reference for mapping soybean and maize in areas with a fragmented landscape of farmland and complex planting structure. Keywords: soybean and maize, crop identification, Sentinel-2 data, machine learning, feature selection DOI: 10.25165/j.ijabe.20201306.6183 Citation: She B, Yang Y Y, Zhao Z G, Huang L S, Liang D, Zhang D Y. Identification and mapping of soybean and maize crops based on Sentinel-2 data. Int J Agric & Biol Eng, 2020; 13(6): 171–182.

Primed Track: Reliable Volumetric Single-cell Tracking and Lineage Tracing of Living Specimen with Dual-labeling Approaches.

Mammalian embryonic development starts with a single fertilized zygote that develops into a blastocyst embryo consisting of three cell types that evolve into either embryonic or extra-embryonic tissues. Lineage tracing of these cells can provide important information about the molecular and cellular dynamics contributing to fate allocation during early development. While global labeling techniques allow for visualization of all cells at the same time, lineage tracing of cells over several divisions can become complicated due to embryo movement and rotation as well as increasing cell densities. Here, we use green-to-red photoconvertible proteins for both global and sparse labeling of cells of interest in the developing murine embryo. We use primed conversion to achieve precise photoconversion of single nuclei in 4-cell stage embryos followed by volumetric live imaging to capture development up to the blastocyst stage. We developed an image analysis pipeline, called primed Track, that uses the dual labeling strategy for both straightforward segmentation and registration of all cells in the embryo as well as correction of rotational and spatial drift. Together, this strategy allows for reliable and fast tracking and lineage tracing of individual cells, even over increased imaging time intervals that result in a major reduction in data volume, all essential conditions for volumetric long-term imaging techniques.

Модель подання мультимодальних даних для комплексного опису об’єктів спостереження

The paper is devoted to solving the problem of the complex presentation of multimodal data about an object of observation, the characteristics of which are measured and investigated taking into account the time stamps and the relationship between the data of various modalities. This problem is relevant for many engineering tasks. In particular, the comprehensive presentation of data on a physical object is important in the technology of digital twins, which has recently become more widely used. Based on the fact that a digital twin is determined by both a behavioral model and a visual model, the paper presents a data model based on the concept of a multimodal data element. This model makes possible a comprehensive and consistent presentation of data about the object of observation. The mathematical model of the complex presentation of multimodal data is based on the concepts of an aggregate and a multi-image defined in an algebraic system of aggregates. The article analyzes the relationship between the complex presentation and processing of multimodal data, which are determined in time, using the apparatus of the algebraic system of aggregates and the consideration of aggregated data as functions of several variables. Thus, the analysis of the data of the muxel model can be performed using logical operations, ordering operations, and relations defined in the algebraic system of aggregates as well as operations and approaches defined in other mathematical concepts. Since the muxel model presents large amounts of data, it is advisable to optimize the way how data is presented. This can be achieved by data compression, modification of the data structure, or a combination of the first and second approaches. In the first approach, it is advisable to apply muxel model data compression based on the RLE method. In the second approach, it is proposed to apply data structures similar to the Sparse Voxel Octree, which is used to reduce data volume of models in voxel graphics. The practical implementation of the processing of multimodal data presented using the muxel model can be performed by means of ASAMPL programming language.

Automatic Data Reduction and Quantification of X-Ray Computed Tomography Images of Sedimentary Cores: Method and Illustration

This paper presents a procedure from which information contained in 3-Dimensional single energy X-ray computed tomography (XR-CT) images of sedimentary rocks is converted into sub-mm scale resolution core scalar and core image logs. This new data provide a quantitative and compact (data volume reduction of ~90%) description of the XR-CT images. Density-related outputs are calibrated through automatic integration with continuous digital visual core description (VCD) and discrete moisture and density (MAD) property index measurements of selected samples. After lithology-based calibration of the X-ray attenuation coefficients into density values, quantitative displays include: 1) histogram of the distribution of density values and its related statistical parameters, 2) radial and angular distributions of core density values, 3) volume, average density and mass contributions of three core fractions defined by density thresholds corresponding to voids or vugs (VV, density ≤ ~1 g•cm−3), and a break in the histogram of distribution of the density values showing the limit between the damaged (DM) and non-damaged (ND) fractions of the core material, and so, 4) providing a sub-mm scale bulk density core log free of any drilling disturbance. The procedure is illustrated on data from the 365 m deep Hole C9001C drilled off-shore Shimokita (northeast coast of Honshu, Japan). Usage of the outputs include: 1) derivation of sub-mm scale porosity core log, 2) correction of volume sensitive measurements in case of poor core quality and partially filled core liner, and 3) seismic modeling and well ties.

Measurement of image degradation: a no-reference approach

In modern communication systems, transmission of data, particularly that of images is very important. Unfortunately, the process is prone to different kinds of degradations. For instance, the large traffic on the network makes the reduction of data volume and storage imperative. This is achieved through compression techniques such as JPEG and JPEG2000, at the expense of image quality, unfortunately. Image quality also deteriorates on account of the state of the transmission channel and the amount of losses occurring. Another common type of image degradation is blurring. At the receiving end, identifying the degradation is an important requirement for applications like blind image denoising so that appropriate techniques for removal may be adopted. Since the undegraded original image is usually not available, no-reference methods are not only attractive but also essential. This article proposes a novel no-reference method which can clearly distinguish between noise, compression and blurring. This is achieved in two stages – the first distinguishes between noise and compression/blurring while the second distinguishes between compression and blurring. Finally, a quantification of the degradation suffered is also carried out, which forms another important contribution of the approach. The primary tool employed is the Discrete Wavelet Transform (DWT) and the differential behaviour of the image DWT coefficients to noise, compression and blurring, at various levels of resolution. The use of the properties of the inter-level coefficients make this approach unique in nature. These properties have been explored and analyzed in detail. A large number of simulations using images from popular databases, establish the usefulness of the approach.

Extra Clocking of LFSR Seeds for Improved Path Delay Fault Coverage

Test data compression is based on storage of compressed tests and use of on-chip decompression logic for test application. Further reductions in the input test data volume are achieved by methods that apply several different tests based on every compressed test. This article makes the new observation that by keeping the same number of compressed tests and applying several different tests based on every compressed test, it is possible to improve the quality of the test set applied to the circuit. This article studies such an approach for path delay faults (PDFs) using a linear-feedback shift register (LFSR) as the decompression logic. Because of the nature of PDFs, targeting an extended subset of PDFs increases the confidence that important PDFs are detected. However, the benefit of detecting additional faults may not justify an increase in the number of stored tests. The approach suggested in this article is used for detecting an extended subset of target PDFs using the same set of LFSR seeds. Extra clocking of the LFSR is used for obtaining scan-in states for several new two-cycle tests based on the same seed. Experimental results for benchmark circuits demonstrate the effectiveness of this approach.

Energy-efficient scheme for target recognition and localization in wireless acoustic sensor networks

The development of wireless acoustic sensor networks has driven the use of acoustic signals for target monitoring. Most monitoring applications require continuous network connectivity and data transfers, which can rapidly exhaust nodes’ energy. Consequently, sensors must collaborate in an adequate architecture to perform target recognition and localization tasks and then to send the results to a remote server with a reduced data volume. The design of an energy-efficient scheme that achieves acoustic target recognition and localization remains an open research problem. Accordingly, this article proposes a low-energy acoustic-based sensing scheme for target recognition and localization to be implemented in a cluster-based sensing approach designed to appropriately balance energy consumption and local processing performed by sensor nodes. A reduced set of low-complexity feature extraction methods in the time domain signal are used in the recognition process. The scheme uses the received energy of the acoustic signals for the target localization. This article details the network architecture, the scheme specification, and its implementation. The results show that the scheme can classify targets with 81.34% accuracy. It requires 3.2 mJ of energy when executed in MICAz, achieving 99% energy savings compared to streaming 3 s of an acoustic signal to a remote server.

Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms

The aim of this study was to apply data mining algorithms to produce a landslide susceptibility map of the national-scale catchment called Bandar Torkaman in northern Iran. As it was impossible to directly use the advanced data mining methods due to the volume of data at this scale, an intermediate approach, called normalized frequency-ratio unique condition units (NFUC), was devised to reduce the data volume. With the aid of this technique, different data mining algorithms such as fuzzy gamma (FG), binary logistic regression (BLR), backpropagation artificial neural network (BPANN), support vector machine (SVM), and C5 decision tree (C5DT) were employed. The success and prediction rates of the models, which were calculated by receiver operating characteristic curve, were 0.859 and 0.842 for FG, 0.887 and 0.855 for BLR, 0.893 and 0.856 for C5DT, 0.891 and 0.875 for SVM, and 0.896 and 0.872 for BPANN that showed the highest validation rates as compared with the other methods. The proposed approach of NFUC proved highly efficient in data volume reduction, and therefore the application of computationally demanding algorithms for large areas with voluminous data was feasible.

Refocusing of Moving Targets Based on Low-Bit Quantized SAR Data via Parametric Quantized Iterative Hard Thresholding

Low-bit quantization of echo improves storage and leads to more efficient downlink transmission of spaceborne synthetic aperture radar (SAR) systems. In this paper, a new parametric quantized iterative hard thresholding (PQIHT) algorithm is proposed to refocus the images of moving targets with low-bit quantized SAR data, based on the combination of quantized iterative hard thresholding (QIHT) and the parametric sparse representation. The blurred and quantization-error-involved subimage of the region of interest (ROI) containing the moving target is represented in a sparse fashion through an adaptive parametric dictionary. The QIHT with a pruned searching method is performed for efficiently estimating the motion-adaptive parameter inside the dictionary, refocusing the ROI image and suppressing the quantization-induced error in an iterative way. Different from the conventional QIHT algorithm with a fixed dictionary that can only represent stationary targets, the proposed method exploits a parametric dictionary with a parameter related to target motion status, which is capable of adaptively representing the radar echo from a moving target with unknown motion status and, therefore, is suitable for moving target refocusing. Simulations and experiments on real GF-3 satellite SAR data demonstrate that, compared with the conventional parametric sparse representation framework for moving target refocusing based on purely precise data, the proposed algorithm can provide satisfactory quality of moving target refocusing with remarkably reduced data volume.

Wind Farm NWP Data Preprocessing Method Based on t-SNE

The operation prediction of wind farms will be accompanied by the need for massive data processing, especially the preprocessing of wind farm meteorological data or numerical weather prediction (NWP). Because NWP data are strongly correlated with wind farm operation, proper processing of NWP data could not only reduce data volume but also improve the correlations of wind farm operation predictions. For this purpose, this paper proposes a data preprocessing algorithm based on t-distributed stochastic neighbor embedding (t-SNE). Firstly, the data collected were normalized to eliminate the influence caused by different dimensions. The t-SNE algorithm is then used to reduce the dimensionality of the NWP data related to wind farm operation. Finally, the wind farm data visualization platform is established. In this paper, 22 index variables in NWP data were taken as objects. The t-SNE method was used to preprocess the NWP historical data of a wind farm, and the results were compared with the results of the principal component analysis (PCA) algorithm. It outperformed PCA in error precision; in addition, t-SNE dimension reduction preprocessing also had a visual effect, which could be applied to big data visualization platforms. A long short-term memory network (LSTM) was used to predict the operation of the wind farm by combining the preprocessed NWP data and the operation data. The simulation results proved that the effect of the preprocessed NWP data based on t-SNE on the wind power prediction was significantly improved.

Principles of Organization of the Human Eye Retina and Their Use in Computer Vision Systems

This paper provides a summary of principles of organization of the human eye retina. The following principles are considered: locality during interacting neurons, ring organization of receptive fields with on- and off-centers (center-surround organization), specialization of neuron layers, organization of feedbacks, adaptation to lighting and contrast levels, and data volume reduction in a video stream. It is shown that the perfect organization of the human retina being used as a prototype allows to significantly improve technical characteristics of computer vision systems. The results of this research were used in creating a family of intelligent video cameras and a number of systems based on them and also in constructing specialized neural networks for primary information processing directly on a sensor matrix.

Vision and Challenges for Knowledge Centric Networking

In the creation of a smart future information society, IoT and CCN break two key barriers for both front-end sensing and back-end networking. However, we still observe the missing piece of the research that dominates current networking traffic control and system management, for example, lacking the knowledge penetrated into both sensing and networking to glue them holistically. In this article, we propose leveraging emerging machine learning or deep learning techniques to create aspects of knowledge to facilitate the designs. In particular, we can extract knowledge from collected data to facilitate reduced data volume, enhanced system intelligence and interactivity, improved service quality, communication with better controllability and lower cost. We this knowledge-oriented traffic control and networking management paradigm Knowledge Centric Networking (KCN). This article presents the rationale for KCN, its benefits, related works and research opportunities.

Time-frequency localization using three-tap biorthogonal wavelet filter bank for electrocardiogram compressions.

A joint time-frequency localized three-band biorthogonal wavelet filter bank to compress Electrocardiogram signals is proposed in this work. Further, the use of adaptive thresholding and modified run-length encoding resulted in maximum data volume reduction while guaranteeing reconstructing quality. Using signal-to-noise ratio, compression ratio (CR), maximum absolute error (EMA), quality score (Qs), root mean square error, compression time (CT) and percentage root mean square difference the validity of the proposed approach is studied. The experimental results deduced that the performance of the proposed approach is better when compared to the two-band wavelet filter bank. The proposed compression method enables loss-less data transmission of medical signals to remote locations for therapeutic usage.

Secure image transmission based on visual cryptography scheme and artificial neural network-particle swarm optimization-guided adaptive vector quantization

Image transmission holds a major share in data communication, and thus secure image transmission is currently a challenging domain of research. A secure image transmission scheme is proposed that physically transmits the encrypted image employing visual cryptography scheme (VCS). During physical transmission, the meaningless shares may attract curious hackers and if captured and stacked, the secret may be revealed. Moreover, the increase in transmission overhead due to multiple share images resulted from a single secret image after encryption is another concern regarding the physical implementation of VCS. Focusing on both observations, vector quantization (VQ) is used to encode as well as to compress each of the shares before transmission. To utilize VQ, its two parameters, cell width and dimension of grid, are needed to be optimized for various kind of images without compromising the randomness property of the shares. Hence, a particle swarm optimization-guided VQ is proposed, and furthermore, a multilayer perceptron in conjunction with an autoencoder are also trained in synchronism with that to automatically obtain the optimal VQ for each image type during the transmission. The proposed scheme is successfully implemented with different types of images for secure physical transmission with a 62.8% data volume reduction and 98.07% image quality retrieval.

Improving Test and Diagnosis Efficiency through Ensemble Reduction and Learning

Machine learning is a powerful lever for developing, improving, and optimizing test methodologies to cope with the demand from the advanced nodes. Ensemble methods are a particular learning paradigm that uses multiple models to boost performance. In this work, ensemble reduction and learning is explored for integrated circuit test and diagnosis. For testing, the proposed method is able to reduce the number of system-level tests without incurring substantial increase in defect escapes or yield losses. Significant cost from test execution and set-up preparation can thereby be saved. Experiments are performed on two designs of commercially fabricated chips, for an overall population of >264,000 chips. The results demonstrate that our method is able to reduce 29.27% and 21.74% of the number of tests for the two chips, respectively, at the cost of very low defect escapes. For failure diagnosis, the framework is able to predict an adequate amount of test data necessary for accurate failure diagnosis. Experiments performed on five standard benchmarks demonstrate that our method outperforms a state-of-the-art work in terms of data-volume reduction. The proposed ensemble-based methodology creates opportunities for improving test and diagnosis efficiency.

A comparison of Satellite Image Compression methods in the Wavelet Domain

Better resolution of remotely sensed satellite images will make images clearer and interpretation easier but will increase the total volume of data that has to be managed. In order to reduce data volume for easier satellite communication transmission and reduce the total volume of data needed to be stores, the images should be compressed. Image compression in wavelet domain can be used for both lossy or lossless compression. Four major compression methods are available using the wavelet domain, i.e. CCSDS, Wavelet, Bandelet, and JPEG 2000. Some optical satellite images, were used as input data in simulation software which analyzed and compared the four compression methods in the wavelet domain. The result showed that the CCSDS method yielded the fastest compression and decompression time, but the Bandelet method retained better image quality when reconstructing original images or approximations of them compared to CCSDS. The JPEG 2000 method delivered better quality images than CCSDS for low bit rate. In summary at a rate of 0.25 bpp, CCSDS is 15 times faster than Bandelet and 3 times faster than JPEG2000. However, CCSDS quality is lower by up to 8.77% compared with Bandelet and up to 13.64% compared with JPEG2000.

Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models

With the development of 3D scanning technology, a huge volume of point cloud data has been collected at a lower cost. The huge data set is the main burden during the data processing of point clouds, so point cloud simplification is critical. The main aim of point cloud simplification is to reduce data volume while preserving the data features. Therefore, this paper provides a new method for point cloud simplification, named FPPS (feature-preserved point cloud simplification). In FPPS, point cloud simplification entropy is defined, which quantifies features hidden in point clouds. According to simplification entropy, the key points including the majority of the geometric features are selected. Then, based on the natural quadric shape, we introduce a point cloud matching model (PCMM), by which the simplification rules are set. Additionally, the similarity between PCMM and the neighbors of the key points is measured by the shape operator. This represents the criteria for the adaptive simplification parameters in FPPS. Finally, the experiment verifies the feasibility of FPPS and compares FPPS with other four-point cloud simplification algorithms. The results show that FPPS is superior to other simplification algorithms. In addition, FPPS can partially recognize noise.

Blockchain & Multi-Agent System: A New Promising Approach for Cloud Data Integrity Auditing with Deduplication

Recently, data storage represents one of the most important services in Cloud Computing. The cloud provider should ensure two major requirements which are data integrity and storage efficiency. Blockchain data structure and the efficient data deduplication represent possible solutions to address these exigencies. Several approaches have been proposed, some of them implement deduplication in Cloud server side, which involves a lot of computation to eliminate the redundant data and it becomes more and more complex. Therefore, this paper proposed an efficient, reliable and secure approach, in which the authors propose a Multi-Agent System in order to manipulate deduplication technique that permits to reduce data volumes thereby reduce storage overhead. On the other side, the loss of physical control over data introduces security challenges such as data loss, data tampering and data modification. To solve similar problems, the authors also propose Blockchain as a database for storing metadata of client files. This database serves as logging database that ensures data integrity auditing function.

SMAR3T©-anew time-saving diagnostic emergency room management algorithm

Primary computed tomography (CT) plays an increasingly important role in diagnosing life-threatening conditions in polytrauma patients; however, it is associated with two major problems: suboptimal interobserver reliability with unstructured reports especially when the reporting is undertaken by physicians in training during working hours and adelay in beginning urgent surgical interventions, which is mainly due to the time taken until the CT report is available and less to the technical time necessary for the CT. This is why the clinical benefits of aprimary CT scan in hemodynamically unstable patients after polytrauma is currently under interdisciplinary discussion. The present study focused on the development and evaluation of astandardized imaging and reporting protocol for initial CT diagnostics of injuries that need immediate treatment after polytrauma. In this study 30 patients after polytrauma were subjected to anovel imaging and reporting protocol, SMAR3T, consisting of an imaging protocol with decreased thin-slice axial scan sequences and astandardized structured reporting protocol. These were compared to conventional emergency room CT protocol with respect to time efficiency and quality of the results. The application of the SMAR3T algorithm significantly reduced the time from scan to reporting from an average of 59.6 ± 4.2 min to an average of 8.5 ± 0.6 min (p < 10-23). With the conventional reporting protocol as well as the novel SMAR3T reporting protocol, all life-threatening conditions and injuries requiring immediate treatment were detected. Based on the results of 30CT scans in polytraumatized patients, the SMAR3T algorithm significantly reduced the time to surgical intervention without compromising diagnostic accuracy with respect to life-threatening conditions. Additionally, the reduction in imaging data volume could facilitate telemedical transmission of data to superordinate centers.

A heuristics based global navigation satellite system data reduction algorithm integrated with map-matching

The transmission and storage of global navigation satellite system (GNSS) data places very high demands on mobile networks and centralised data processing systems. GNSS applications including community based navigation and fleet management require GNSS data to be transmitted from a vehicle to a centralised system and then processed by a map-matching algorithm to determine the location of a vehicle within a road segment. Various data compression techniques have been developed to reduce the volume of data transmitted. There is also an independent literature relating to map-matching algorithms. However, no previous research has integrated data compression with a map-matching algorithm that accepts compressed data as an input without the need for decompression. This paper develops a novel GNSS data reduction algorithm with deterministic error bounds, which was seamless integrated with a specifically designed map-matching algorithm. The approach significantly reduces the volume of GNSS data communicated and improves the performance of the map-matching algorithm. The data compression extracts critical points in the trajectory and velocity–time curve of a vehicle. During the process of selecting critical points, the error of restoring vehicle trajectories and velocity–time curves are used as parameters to control the number of critical points selected. By setting different error bound values prior to the execution of the algorithm, the accuracy and volume of reduced data is controlled precisely. The compressed GNSS data, particularly the critical points selected from the vehicle’s trajectory is directly input to the map-matching algorithm without the need for decompression. An experiment indicated that the data reduction algorithm is very effective in reducing data volume. This research will be useful in many fields including community driven navigation and fleet management.