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Horticulturae ◽  
2022 ◽  
Vol 8 (1) ◽  
pp. 77
Author(s):  
Christian Höing ◽  
Sharvari Raut ◽  
Abozar Nasirahmadi ◽  
Barbara Sturm ◽  
Oliver Hensel

The state-of-the-art technique to control slug pests in agriculture is the spreading of slug pellets. This method has some downsides, because slug pellets also harm beneficials and often fail because their efficiency depends on the prevailing weather conditions. This study is part of a research project which is developing a pest control robot to monitor the field, detect slugs, and eliminate them. Robots represent a promising alternative to slug pellets. They work independent of weather conditions and can distinguish between pests and beneficials. As a prerequisite, a robot must be able to reliably identify slugs irrespective of the characteristics of the surrounding conditions. In this context, the utilization of computer vision and image analysis methods are challenging, because slugs look very similar to the soil, particularly in color images. Therefore, the goal of this study was to develop an optical filter-based system that distinguishes between slugs and soil. In this context, the spectral characteristics of both slugs and soil in the visible and visible near-infrared (VNIR) wavebands were measured. Conspicuous maxima followed by conspicuous local minima were found for the reflection spectra of slugs in the near infrared range from 850 nm to 990 nm]. Thus, this enabled differentiation between slugs and soils; soils showed a monotonic increase in the intensity of the relative reflection for this wavelength. The extrema determined in the reflection spectra of slugs were used to develop and set up a slug detector device consisting of a monochromatic camera, a filter changer and two narrow bandpass filters with nominal wavelengths of 925 nm and 975 nm. The developed optical system takes two photographs of the target area at night. By subtracting the pixel values of the images, the slugs are highlighted, and the soil is removed in the image due to the properties of the reflection spectra of soils and slugs. In the resulting image, the pixels of slugs were, on average, 12.4 times brighter than pixels of soil. This enabled the detection of slugs by a threshold method.


2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Qinghu Yan ◽  
Wuzhang Wang ◽  
Wenlong Zhao ◽  
Liping Zuo ◽  
Dongdong Wang ◽  
...  

Abstract Objective To differentiate nontuberculous mycobacteria (NTM) pulmonary diseases from pulmonary tuberculosis (PTB) by analyzing the CT radiomics features of their cavity. Methods 73 patients of NTM pulmonary diseases and 69 patients of PTB with the cavity in Shandong Province Chest Hospital and Qilu Hospital of Shandong University were retrospectively analyzed. 20 patients of NTM pulmonary diseases and 20 patients of PTB with the cavity in Jinan Infectious Disease Hospitall were collected for external validation of the model. 379 cavities as the region of interesting (ROI) from chest CT images were performed by 2 experienced radiologists. 80% of cavities were allocated to the training set and 20% to the validation set using a random number generated by a computer. 1409 radiomics features extracted from the Huiying Radcloud platform were used to analyze the two kinds of diseases' CT cavity characteristics. Feature selection was performed using analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) methods, and six supervised learning classifiers (KNN, SVM, XGBoost, RF, LR, and DT models) were used to analyze the features. Results 29 optimal features were selected by the variance threshold method, K best method, and Lasso algorithm.and the ROC curve values are obtained. In the training set, the AUC values of the six models were all greater than 0.97, 95% CI were 0.95–1.00, the sensitivity was greater than 0.92, and the specificity was greater than 0.92. In the validation set, the AUC values of the six models were all greater than 0.84, 95% CI were 0.76–1.00, the sensitivity was greater than 0.79, and the specificity was greater than 0.79. In the external validation set, The AUC values of the six models were all greater than 0.84, LR classifier has the highest precision, recall and F1-score, which were 0.92, 0.94, 0.93. Conclusion The radiomics features extracted from cavity on CT images can provide effective proof in distinguishing the NTM pulmonary disease from PTB, and the radiomics analysis shows a more accurate diagnosis than the radiologists. Among the six classifiers, LR classifier has the best performance in identifying two diseases.


2022 ◽  
Vol 17 (01) ◽  
pp. C01006
Author(s):  
Yuki Mitsuya ◽  
Kenji Shimazoe ◽  
Takeshi Fujiwara ◽  
Hiroyuki Takahashi

Abstract Energy-resolved neutron imaging with pulsed neutron source provides quantitative neutron imaging techniques such as Bragg-edge imaging, resonance absorption imaging, and polarized neutron imaging. Micro-pattern gaseous detectors (MPGDs) such as gas electron multipliers (GEMs) are widely used in neutron detection. In this research, we will report on the first demonstration of energy-resolved neutron imaging with a glass gas electron multiplier (G-GEM) and the dynamic time-over-threshold (dToT) signal processing method. We successfully performed energy-resolved neutron imaging at J-PARC MLF by measuring incident position and the Time-of-Flight (TOF) of each neutron simultaneously.


Author(s):  
Hao Li

Due to the influence of recognition parameters, image recognition has low recognition accuracy, long recognition time and large storage cost. Therefore, an automatic image recognition method based on Boltzmann machine is proposed. Based on threshold method and fuzzy set method, image malformation correction is performed. The mean filter and median filter are combined to eliminate the influence of image filtering, and the pre-processing of image is completed by using the fuzzy enhancement of image. Based on the restricted Boltzmann method, the network model is dynamically evolved, and the identification parameters of each shape and contour are obtained. Different shapes and contours are classified and recognized. Simulation results show that image recognition method based on human-computer interaction has high recognition ability, shortens the time cost and greatly reduces the space needed for node storage.


Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 519-539
Author(s):  
Aqeel Mohsin Hamad

Cardiovascular disease (CADs) is considered the primary leading cause of death. Irregular activity of heart, these disease can be detected and classified by Electrocardiogram (ECG), which is constructed from using electrodes placed on human skin to record the electrical activity of the heart. Because QRS complex represents the basic part of the ECG signal, these components should be recognized in order to analysis the other characteristics of the signal. Different methods and algorithms are proposed to analysis and processing the ECG signal. In this paper, a new QRS complex recognition method are proposed based on discrete cosine transform (DCT) with variable adaptive threshold method, which is used to determine threshold based on characteristic of each ECG signal to detect upper and lower levels of threshold to detect the peak of the signal. At first, the DCT is applied to the ECG signal to isolate it into different coefficients and eliminate or reduce the noises of the signal based on processing of high frequency components of DCT coefficients, which have less information, then the ECG is reconstructed by cropping the most important coefficients to be used in threshold determination. The basic idea is that the reconstructed signal have high differences between the components of the signal, and this facilitates the process of calculating the threshold value, which is used later to find peaks of ECG signal. The proposed method is tested and its performance are determined based on three different datasets, which are MITBIH Arrhythmia dataset, (LTSTDB) and (EDB) and the performance are evaluated using different metrics, which are Detection rate, accuracy, specificity and sensitivity. The experimental results show that the proposed method is performed or outperformed other works, therefore it can be used in peak detection applications.


2021 ◽  
Author(s):  
Lanyong Zhang ◽  
Ruixuan Zhang ◽  
Papavassiliou Christos

At present, there are many shortcomings in the discontinuity of wavelet threshold function and the constant threshold of different decomposition layers and the constant error it produced. The amplitude-frequency characteristics of wavelet filters are studied and analyzed by mathematical modeling. An improved wavelet threshold function with adjustable parameters is proposed. Particle swarm optimization (PSO) algorithm is used to find the optimal parameters of the improved threshold function in a background noise environment. The improved wavelet threshold function is combined with Bayesian threshold method to obtain the threshold based on Bayesian criterion, which makes the threshold adaptive in different layers and overcomes the shortcomings of fixed threshold. Finally, the speech signal with optimal wavelet coefficients is obtained after reconstruction. Compared with the traditional threshold function, Simulation results show that the improved threshold function achieves precise notch denoising, effectively retains the singularity and eigenvalues of the signal, and reduces the signal distortion.


2021 ◽  
Vol 13 (24) ◽  
pp. 5163
Author(s):  
Xiaofei Guo ◽  
Jianhua Wan ◽  
Shanwei Liu ◽  
Mingming Xu ◽  
Hui Sheng ◽  
...  

Sea fog is a precarious weather disaster affecting transportation on the sea. The accuracy of the threshold method for sea fog detection is limited by time and region. In comparison, the deep learning method learns features of objects through different network layers and can therefore accurately extract fog data and is less affected by temporal and spatial factors. This study proposes a scSE-LinkNet model for daytime sea fog detection that leverages residual blocks to encoder feature maps and attention module to learn the features of sea fog data by considering spectral and spatial information of nodes. With the help of satellite radar data from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a ground sample database was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B data. The scSE-LinkNet was trained on the training set, and quantitative evaluation was performed on the test set. Results showed the probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and Heidke skill scores (HSS) were 0.924, 0.143, 0.800, and 0.864, respectively. Compared with other neural networks (FCN, U-Net, and LinkNet), the CSI of scSE-LinkNet was improved, with a maximum increase of nearly 8%. Moreover, the sea fog detection results were consistent with the measured data and CALIOP products.


2021 ◽  
Vol 13 (24) ◽  
pp. 5028
Author(s):  
Linqi Liu ◽  
Yingchao Xie ◽  
Xiang Gao ◽  
Xiangfen Cheng ◽  
Hui Huang ◽  
...  

Canopy temperature (Tc) is used to characterize plant water physiology, and thermal infrared (TIR) remote sensing is a convenient technology for measuring Tc in forest ecosystems. However, the images produced through this method contain background pixels of forest gaps, thereby reducing the accuracy of Tc observations. Extracting Tc data from TIR images is of great significance for understanding changes in ecosystem water status. In this study, a temperature threshold method was developed to rapidly, accurately, and automatically extract forest canopy pixels for Tc data obtention. Specifically, this method takes the temperature corresponding to the point with a slope of 0.5 in the curve composed of the normalized average temperature and the normalized cumulative number of pixels as the segmentation threshold to separate the forest gap pixels from the forest canopy pixels in the TIR images and extract the separated forest canopy pixels based on the pixel coordinates for Tc data obtention. Taking the Tc values, measured using a thermocouple, as the standard, Tc extraction using the new temperature threshold method and traditional methods (the Otsu algorithm and direct extraction) was compared in cork oak plantations. The results showed that the temperature threshold method offered the highest extraction accuracy, followed by the direct extraction method and the Otsu algorithm. The temperature threshold method was determined to be the most suitable for extracting Tc data from the TIR images of cork oak plantations.


Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6196
Author(s):  
Tobias Greve ◽  
Liang Wang ◽  
Sophie Katzendobler ◽  
Lucas L. Geyer ◽  
Christian Schichor ◽  
...  

Facial muscle corticobulbar motor evoked potentials (FMcoMEPs) are used to monitor facial nerve integrity during vestibular schwannoma resections to increase maximal safe tumor resection. Established warning criteria, based on ipsilateral amplitude reduction, have the limitation that the rate of false positive alarms is high, in part because FMcoMEP changes occur on both sides, e.g., due to brain shift or pneumocephalus. We retrospectively compared the predictive value of ipsilateral-only warning criteria and actual intraoperative warnings with a novel candidate warning criterion, based on “ipsilateral versus contralateral difference in relative stimulation threshold increase, from baseline to end of resection” (BilatMT ≥ 20%), combined with an optimistic approach in which a warning would be triggered only if all facial muscles on the affected side deteriorated. We included 60 patients who underwent resection of vestibular schwannoma. The outcome variable was postoperative facial muscle function. Retrospectively applying BilatMT, with the optimistic approach, was found to have a significantly better false positive rate, which was much lower (9% at day 90) than the traditionally used ipsilateral warning criteria (>20%) and was also lower than actual intraoperative warnings. This is the first report combining the threshold method with an optimistic approach in a bilateral multi-facial muscle setup. This method could substantially reduce the rate of false positive alarms in FMcoMEP monitoring.


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