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Cannabis Indoor Growing Conditions, Management Practices, and Post-Harvest Treatment: A Review

American Journal of Plant Sciences 2019年第6期10卷 925-946页

作者： Dan Jin Shengxi Jin Jie Chen Biomedical Engineering Department University of Alberta Edmonton Canada Labs-Mart Inc. Edmonton Canada Electrical and Computer Engineering Department University of Alberta Edmonton Canada

Cannabis has attracted a new wave of research attention as an herbal medicine. To deliver compliant, uniform, and safe cannabis medicine, growers should optimize growing environments on a site-specific basis. Considering that environmental factors are interconnected, changes in a factor prompts adjustment of other factors. This paper reviews existing work that considers indoor growing conditions (light, temperature, CO2 concentration, humidity, growing media, and nutrient supply), management practices (irrigation, fertilization, pruning & training, and harvest timing), and post-harvest treatment (drying and storage) for cannabis indoor production.

关键词： Cannabis Environmental Factors Growing Conditions Post-Harvest Treatment Indoor Cultivation Agrology Agricultural Science

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Registration-Invariant Biomechanical Features for Disease Staging of COPD in SPIROMICS 2nd

Registration-Invariant Biomechanical Features for Disease St...

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2nd International Workshop on Thoracic Image Analysis, TIA 2020 Held in Conjunction with Medical Image Computing and computer-Assisted Intervention Conference, MICCAI 2020

作者： Chaudhary, Muhammad F. A. Pan, Yue Wang, Di Bodduluri, Sandeep Bhatt, Surya P. Comellas, Alejandro P. Hoffman, Eric A. Christensen, Gary E. Reinhardt, Joseph M. Roy J. Carver Department of Biomedical Engineering The University of Iowa Iowa CityIA52242 United States Department of Electrical and Computer Engineering The University of Iowa Iowa CityIA52242 United States UAB Lung Imaging Core University of Alabama at Birmingham BirminghamAL35294 United States Department of Radiology College of Medicine The University of Iowa Iowa CityIA52242 United States Division of Pulmonary Critical Care and Occupational Medicine The University of Iowa Iowa CityIA52242 United States

ISBN: (纸本)9783030624682

Chronic Obstructive Pulmonary Disease (COPD) manifests itself as a heterogeneous mixture of airways disease and parenchymal tissue damage. Computed tomography (CT) imaging has been used to detect, assess, and characterize COPD. One approach for using CT imaging to assess COPD relies on image registration to match inspiratory and expiratory images to extract biomarkers reflective of local tissue biomechanics. Despite the initial success of such biomarkers, their diagnostic performance and stability across registration methods is yet to be studied. In this study, we evaluated the ability of pulmonary biomechanical features to explain several clinical parameters across four state-of-the-art image registration algorithms. We extracted features from three registration-based measures of lung mechanics: the Jacobian determinant, the anisotropic deformation index, and the slab-rod index. Four state-of-the-art registration methods were used to derive these measures and consequently four different biomechanical feature sets were constructed. We analyzed and compared the stability of these features for predicting airflow obstruction, COPD severity, and different spirometric measures. Biomechanical features were consistent across all four registration methods and were independently able to explain most of the variance in the spirometric measures. These variables predicted airflow obstruction and COPD stage with an average area under receiver operating characteristic curve of 0.86 and 0.80, respectively. © 2020, Springer Nature Switzerland AG.

关键词： Image registration

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engineering the Resonant Cavities in Substrate Integrated Waveguide Technology

Engineering the Resonant Cavities in Substrate Integrated Wa...

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IEEE MTT-S International Microwave and RF Conference

作者： Maurizio Bozzi Lorenzo Silvestri Nicolò Delmonte Cristiano Tomassoni Luca Perregrini Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Department of Engineering University of Perugia Perugia Italy

ISBN: (数字)9781728140407

ISBN: (纸本)9781728140414

This paper presents a review of the major applications of substrate integrated waveguide (SIW) cavities and of the various techniques proposed in the last years to engineer the resonant cavities in SIW technology. The aim of engineering the SIW cavities is multifold, and includes the size reduction, the loss minimization, and the optimization of the resonant modes pattern. A variety of topologies of modified SIW cavity resonators are presented, together with their advantages and disadvantages, and several applications are described and discussed.

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iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry

arXiv

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arXiv 2020年

作者： Fu, Wanyi Sharma, Shobhit Abadi, Ehsan Iliopoulos, Alexandros-Stavros Wang, Qi Lo, Joseph Y. Sun, Xiaobai Segars, William P. Samei, Ehsan Department of Electrical and Computer Engineering Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Department of Physics Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Department of Computer Science Duke University DurhamNC27708 United States Department of Radiology Fourth Clinical Hospital of Hebei Medical University Heibei050011 China Departments of Electrical and Computer Engineering Biomedical Engineering Medical Physics Graduate Program Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Departments of Biomedical Engineering Medical Physics Graduate Program and Radiology Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Carl E. Ravin Advanced Imaging Laboratories Medical Physics Graduate Program Departments of Radiology Electrical and Computer Engineering Biomedical Engineering and Physics Duke University DurhamNC27705 United States

Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or "digital-twins (DT)" using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. Method: From patient CT images, iPhantom segments selected anchor organs (e.g. liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs challenging to segment (e.g. intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting full-patient phantoms are used to assess organ doses during routine CT exams. Result: iPhantom was validated on both the XCAT (n=50) and an independent clinical (n=10) dataset with similar accuracy. iPhantom precisely predicted all organ locations with good accuracy of Dice Similarity Coefficients (DSC) >0.6 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed Copyright © 2020, The Authors. All rights reserved.

关键词： Phantoms

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High-Resolution Ground No2 Estimation Using Deep Learning with Sentinel-2 and Sentinel-5p Data

SSRN

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SSRN 2025年

作者： Khan, Solaiman Ouadou, Anes Song, Xing Scott, Grant J. Institute for Data Science and Informatics University of Missouri 22 Heinkel Building ColumbiaMO65211 United States Department of Electrical Engineering and Computer Science University of Missouri Naka Hall ColumbiaMO65211 United States Department of Biomedical Informatics Biostatistics and Medical Epidemiology University of Missouri 5 Hospital Dr ColumbiaMO65201 United States Surescripts United States

Nitrogen dioxide (NO2) is a harmful air pollutant that can cause various health issues, including respiratory disease and lung infection. Monitoring of NO2 is primarily dependent on expensive ground-based sensor systems. This research explores the potential of integrating imagery from Sentinel-2 and Sentinel-5P to estimate high-resolution ground NO2 concentration at city and neighborhood levels. This study presents a two-stream deep learning model for NO2 estimation. The model is flexible regarding data input, allowing the use of both Sentinel-2 and Sentinel-5P combined or Sentinel-2 exclusively. The experimental results show that the model performs better if it utilizes data from both sources. The model performance is assessed on datasets aggregated in three temporal frequencies: daily, monthly, and quarterly. The estimated NO2 is found to be more reliable at the quarterly level, followed by the monthly level. The daily estimates are more variable due to short-term fluctuations in NO2 concentration. This study reinforces the application of deep learning and remote sensing for air quality monitoring, especially in the absence of expensive ground sensor-based monitoring systems. © 2025, The Authors. All rights reserved.

关键词： Air quality

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Leveraging LoRaWAN technology for smart agricultural monitoring of Malaysian palm oil plantation

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IOP Conference Series: Earth and Environmental Science 2021年第1期756卷

作者： R Anzum J Naeem Department of Electrical and Computer Engineering International Islamic University Malaysia Jalan Gombak 53100 Kuala Lumpur Selangor Malaysia Department of Biomedical Engineering Faculty of Engineering University of Malaya 50603 Kuala Lumpur Malaysia

Palm oil is a vital cash crop in tropical Asia. Implementation of technology in palm oil industry not only increase production but also it reduces the plantation management cost significantly. Affordability of smart devices of the farm owners is the major concern behind not using technologies in production and process. There are a lot of technologies are being used in agriculture sectors to ease the manual labour with reduced cost. Study on the challenges faced by the plantation owners is needed to introduce new technological solutions for the plantation management in Malaysia. This article reviews some problems on technology-based monitoring. A qualitative research has been conducted to address the problems faced by the authority of Palm oil plantation. Based on the problem statement researcher introduced a conceptual study of LoRaWAN (Low power wide area network) embedded system suitable for low cost energy efficient Palm oil plantation monitoring. Installation of IoT based device will ensure smart monitoring as well as handling the agriculture activities of the Palm oil plantation with reduced manpower resources.

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Low-Rank nonlinear decoding of µ-ecog from the primary auditory cortex

arXiv

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arXiv 2020年

作者： Emami, Melikasadat Ardakan, Mojtaba Sahraee Pandit, Parthe Fletcher, Alyson K. Rangan, Sundeep Trumpis, Michael Bent, Brinnae Chiang, Chia-Han Viventi, Jonathan Department of Electrical and Computer Engineering University of California Los Angeles 420 Westwood Plaza Los AngelesCA90095 United States Department of Statistics University of California Los Angeles 420 Westwood Plaza Los AngelesCA90095 Department of Electrical and Computer Engineering New York University 2 MetroTech Center BrooklynNY11201 United States Department of Biomedical Engineering Duke University 101 Science Drive DurhamNC27708 United States

This paper considers the problem of neural decoding from parallel neural measurements systems such as micro-electrocorticography (µ-ECoG). In systems with large numbers of array elements at very high sampling rates, the dimension of the raw measurement data may be large. Learning neural decoders for this high-dimensional data can be challenging, particularly when the number of training samples is limited. To address this challenge, this work presents a novel neural network decoder with a low-rank structure in the first hidden layer. The low-rank constraints dramatically reduce the number of parameters in the decoder while still enabling a rich class of nonlinear decoder maps. The low-rank decoder is illustrated on µ-ECoG data from the primary auditory cortex (A1) of awake rats. This decoding problem is particularly challenging due to the complexity of neural responses in the auditory cortex and the presence of confounding signals in awake animals. It is shown that the proposed low-rank decoder significantly outperforms models using standard dimensionality reduction techniques such as principal component analysis (PCA). Copyright © 2020, The Authors. All rights reserved.

关键词： Decoding

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Arterial spin-labeling spatial coefficient of variation is associated with cerebral small vessel disease

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Alzheimer's & Dementia 2023年第S17期19卷

作者： W Hudson Robb Darcie S Kim Michelle L Houston Kimberly R. Pechman Audrey R. Verde L. Taylor Davis Bennett A. Landman Timothy J. Hohman Angela L. Jefferson Vanderbilt Memory & Alzheimer’s Center Vanderbilt University Medical Center Nashville TN USA Correspondece Department of Radiology Vanderbilt University Medical Center Nashville TN USA Department of Biomedical Engineering Vanderbilt University Nashville TN USA Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA Department of Neurology Vanderbilt University Medical Center Nashville TN USA

Background Cerebral small vessel disease (SVD) is the most common pathology to co-occur with Alzheimer’s disease but pathophysiologic mechanisms leading to SVD are poorly understood. The arterial spin-labeling (ASL) spatial coefficient of variation (sCoV), a proxy marker of arterial transit time (ATT), may better reflect SVD-related hemodynamic disturbances than ASL-derived cerebral blood flow (CBF). We investigated whether grey matter CBF and grey matter sCoV each related to neuroimaging markers of SVD, including white matter hyperintensities (WMHs), basal ganglia enlarged perivascular spaces (ePVS), cerebral microbleeds (CMBs), and lacunes. Methods Vanderbilt Memory and Aging Project participants free of clinical dementia and stroke at study entry (n = 312, 73±7 years, 39% mild cognitive impairment, 41% female) underwent multimodal 3T brain magnetic resonance imaging to quantify ASL parameters and SVD burden. Pseudo-continuous ASL assessed CBF in total grey matter. Grey matter sCoV was calculated as σ(CBF Grey Matter )/μ(CBF Grey Matter ). WMHs and ePVS volumes were quantified using automated methods and ePVS volumes were standardized to basal ganglia volume. CMBs and lacune counts were quantified by an expert rater. Ordinary least squares regression models cross-sectionally related grey matter CBF and sCoV individually to log-transformed burden of each SVD marker. Models were adjusted for age, sex, race/ethnicity, education, cognitive status, Framingham Stroke Risk Profile (minus age), intracranial volume, grey matter volume, and apolipoprotein E -ε4 status. Results Grey matter CBF was unrelated to all SVD outcomes (p-values>0.25). Higher grey matter sCoV was associated with greater WMHs (β = 2.1, p = 0.001), ePVS (β = 0.004, p = 0.0003), and lacunar infarcts (β = 1.4, p = 0.0006) but not CMBs (β = 1.0, p = 0.06). Conclusions Our findings align with previous results showing that grey matter sCoV is a valuable marker of SVD. In contrast to previous work, we found

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Segmentation of infrared breast images using MultiResUnet neural networks

arXiv

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arXiv 2020年

作者： Lou, Ange Guan, Shuyue Kamona, Nada Loew, Murray Medical Imaging and Image Analysis Laboratory Department of Electrical and Computer Engineering George Washington University Medical Center WashingtonDC United States Medical Imaging and Image Analysis Laboratory Department of Biomedical Engineering George Washington University Medical Center WashingtonDC United States

Breast cancer is the second leading cause of death for women in the U.S. Early detection of breast cancer is key to higher survival rates of breast cancer patients. We are investigating infrared (IR) thermography as a noninvasive adjunct to mammography for breast cancer screening. IR imaging is radiation-free, pain-free, and non-contact. Automatic segmentation of the breast area from the acquired full-size breast IR images will help limit the area for tumor search, as well as reduce the time and effort costs of manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (C-DCNN) had been applied to automatically segment the breast area in IR images in previous studies. In this study, we applied a state-of-the-art deep-learning segmentation model, MultiResUnet, which consists of an encoder part to capture features and a decoder part for precise localization. It was used to segment the breast area by using a set of breast IR images, collected in our pilot study by imaging breast cancer patients and normal volunteers with a thermal infrared camera (N2 Imager). The database we used has 450 images, acquired from 14 patients and 16 volunteers. We used a thresholding method to remove interference in the raw images and remapped them from the original 16-bit to 8-bit, and then cropped and segmented the 8-bit images manually. Experiments using leave-one-out cross-validation (LOOCV) and comparison with the ground-truth images by using Tanimoto similarity show that the average accuracy of MultiResUnet is 91.47%, which is about 2% higher than that of the autoencoder. MultiResUnet offers a better approach to segment breast IR images than our previous model. Copyright © 2020, The Authors. All rights reserved.

关键词： computer aided diagnosis

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The Design of a Parkinson’s Tremor Predictor and Estimator Using a Hybrid Convolutional-Multilayer Perceptron Neural Network

The Design of a Parkinson’s Tremor Predictor and Estimator ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Anas Ibrahim Yue Zhou Mary E. Jenkins Ana Luisa Trejos Michael D. Naish Department of Electrical and Computer Engineering (ECE) Western University School of Biomedical Engineering (BME) Western University Movement Disorders Program Clinical logical Sciences Western University ECE and BME Western University Department of Mechanical and Materials neering ECE and BME Western University London ON Canada

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Parkinson's Disease (PD) is considered to be the second most common age-related neuroegenerative disorder, and it is estimated that seven to ten million people worldwide have PD. One of the symptoms of PD is tremor, and studies have shown that wearable assistive devices have the potential to assist in suppressing it. However, despite the progress in the development of these devices, their performance is limited by the tremor estimators they use. Thus, a need for a tremor model that helps the wearable assistive devices to increase tremor suppression without impeding voluntary motion remains. In this work, a user-independent and task-independent tremor and voluntary motion detection method based on neural networks is proposed. Inertial measurement units (IMUs) were used to measure acceleration and angular velocity from participants with PD, these data were then used to train the neural network. The achieved estimation percentage accuracy of voluntary motion was 99.0%, and the future prediction percentage accuracy was 97.3%, 93.7%, 91.4% and 90.3% for 10 ms, 20 ms, 50 ms and 100 ms ahead, respectively. The root mean squared error (RMSE) achieved for tremor estimation was an average of 0.00087 ° /s on new unseen data, and the future prediction average RMSE across the different tasks achieved was 0.001 ° /s, 0.002 ° /s, 0.020 ° /s and 0.049 ° /s for 1 ms, 2 ms, 5 ms, and 10 ms ahead, respectively. Therefore, the proposed method shows promise for use in wearable suppression devices.

关键词： Task analysis Neurons Convolution Feature extraction Estimation Neural networks Assistive devices

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