Use of Discrete Element Modelling to Evaluate the Parameters of the Sampling Theory in the Feed Grade Sampler of a Sulphide Gold Plant

Cross-stream cutters are widely used in the mining and resources industry to obtain representative samples of particulate flows. Discrete element modelling (DEM) and analysis can be used to investigate influences of operational parameters, sampler design and material physical properties in the generation of the Increment Extraction Error (IEE), which when present, results in a frequently biased, non-representative sample. The study investigates the practicality of the rules and recommendations proposed by Dr. Pierre Gy that were developed and established as principles for the correct extraction of samples in industrial sampling equipment. Results validate Pierre Gy’s sampling theory using DEM in a cross-stream cutter of a sulphide gold plant. Importantly, the outcomes indicate that careful consideration must be given to physical ore properties and, consequently, that sampling systems should be developed specifically to each application.

Extraction of Sunflower Lodging Information Based on UAV Multi-Spectral Remote Sensing and Deep Learning

The rapid and accurate identification of sunflower lodging is important for the assessment of damage to sunflower crops. To develop a fast and accurate method of extraction of information on sunflower lodging, this study improves the inputs to SegNet and U-Net to render them suitable for multi-band image processing. Random forest and two improved deep learning methods are combined with RGB, RGB + NIR, RGB + red-edge, and RGB + NIR + red-edge bands of multi-spectral images captured by a UAV (unmanned aerial vehicle) to construct 12 models to extract information on sunflower lodging. These models are then combined with the method used to ignore edge-related information to predict sunflower lodging. The results of experiments show that the deep learning methods were superior to the random forest method in terms of the obtained lodging information and accuracy. The predictive accuracy of the model constructed by using a combination of SegNet and RGB + NIR had the highest overall accuracy of 88.23%. Adding NIR to RGB improved the accuracy of extraction of the lodging information whereas adding red-edge reduced it. An overlay analysis of the results for the lodging area shows that the extraction error was mainly caused by the failure of the model to recognize lodging in mixed areas and low-coverage areas. The predictive accuracy of information on sunflower lodging when edge-related information was ignored was about 2% higher than that obtained by using the direct splicing method.

Consolidated EHR Workflow for Endoscopy Quality Reporting

Although colonoscopy is the most frequently performed endoscopic procedure, the lack of standardized reporting is impeding clinical and translational research. Inadequacies in data extraction from the raw, unstructured text in electronic health records (EHR) pose an additional challenge to procedure quality metric reporting, as vital details related to the procedure are stored in disparate documents. Currently, there is no EHR workflow that links these documents to the specific colonoscopy procedure, making the process of data extraction error prone. We hypothesize that extracting comprehensive colonoscopy quality metrics from consolidated procedure documents using computational linguistic techniques, and integrating it with discrete EHR data can improve quality of screening and cancer detection rate. As a first step, we developed an algorithm that links colonoscopy, pathology and imaging documents by analyzing the chronology of various orders placed relative to the colonoscopy procedure. The algorithm was installed and validated at the University of Arkansas for Medical Sciences (UAMS). The proposed algorithm in conjunction with Natural Language Processing (NLP) techniques can overcome current limitations of manual data abstraction.

An Automatic Method for Black Margin Elimination of Sentinel-1A Images over Antarctica

The Sentinel-1A satellite was launched in April 2014 with a primary C-Band terrain observation with progressive scans synthetic aperture radar (TOPSAR) onboard and has collected plenty of high-quality images for global change studies. However, low magnitude signals around image margins (black margins) does not preserve the normal standard level, influencing the potential usage with these data. Through image analysis, we find that the signal from black margin (BM) is highly dominated by the closest effective signals and the signal in BM shows an increasing trend along the direction from image boundary to image center. An edge detector is developed based on the signal characteristics of BM. Furthermore, an automatic method to discriminate and eliminate BM is designed. Images from both extra wide (EW) and interferometric wide (IW) swath observation modes, covering the land, ocean, and coast of the Antarctic, are taken to verify the robustness of our method. Through comparison with BM edges extracted by human interpretation, our method has the maximum BM edge extraction error of 1.9 ± 3.2 pixels. When considering perimeter (or area) difference along radial direction of BM edge, our method has an averaging extraction accuracy of −0.35 ± 0.11 (or 0.14 ± 1.38) pixels, which suggests that our method is effective and can be potentially used to eliminate BM for multidisciplinary applications of Sentinel-1 data.

Extraction method of position and posture information of robot arm picking up target based on RGB-D data

There is a big error in the traditional method to extract the position and attitude information of the robot. In the process of obtaining the target attitude, a method of extracting the target attitude information of robot arm based on RGB-D data is proposed. The position and attitude of the manipulator target are acquired by depth image processing, and the detected target position is sent to the manipulator control node, and the feature points of the manipulator are extracted. The 3-D mapping is carried out on the acquired RGB image, and the depth and RGB values of feature points, as well as position and attitude information are calculated by using the Gauss mixture model. Finally, the target is extracted by combining the covariance matrix of feature points. The experimental results show that the co-ordinate error and angle error of the robot arm extracted by this method are small. The maximum extraction error is only 28%, which is much lower than the traditional method, which shows that the proposed method is more applicable.

Digital watermarking method based on heteroassociative image compression and its realization with artificial neural networks

In this paper, we present a digital watermarking method and associated algorithms that use a heteroassociative compressive transformation to embed a digital watermark bit sequence into blocks (fragments) of container images. A principal feature of the proposed method is the use of the heteroassociative compressing transformation – a mutual mapping with the compression of two neighboring image regions of an arbitrary shape. We also present the results of our experiments, namely the dependencies of quality indicators of thus created digital watermarks, which show the container distortion level, and the probability of digital watermark extraction error. In the final section, we analyze the performance of the proposed digital watermarking algorithms under various distortions and transformations aimed at destroying the hidden data, and compare these algorithms with the existing ones.

Semifragile Speech Watermarking Based on Least Significant Bit Replacement of Line Spectral Frequencies

There are various techniques for speech watermarking based on modifying the linear prediction coefficients (LPCs); however, the estimated and modified LPCs vary from each other even without attacks. Because line spectral frequency (LSF) has less sensitivity to watermarking than LPC, watermark bits are embedded into the maximum number of LSFs by applying the least significant bit replacement (LSBR) method. To reduce the differences between estimated and modified LPCs, a checking loop is added to minimize the watermark extraction error. Experimental results show that the proposed semifragile speech watermarking method can provide high imperceptibility and that any manipulation of the watermark signal destroys the watermark bits since manipulation changes it to a random stream of bits.

Extraction of Feature Photoacoustic Spectroscopy Probed by a Sensor against a Large Background Noise

An algorithm of independent component analysis from blind source separation domain is used and a Δλ-model strong-noise immunity is proposed in this work. The test results showed that maximal extraction error is respectively 0.28% and 3.7% under a SNR of 1/886 and an excellent agreementbetween the numerical simulation and the actual detection value is found, and the detection limit of test sample is improved from 16 ppb with the model based on common linear equations to 2 ppb using the proposed model.