In a continuing effort to develop suitable methods for the surveillance of harmful algal blooms (HABs) of Karenia brevis using satellite radiometers, a new multi-algorithm method was developed to explore whether impro...
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In a continuing effort to develop suitable methods for the surveillance of harmful algal blooms (HABs) of Karenia brevis using satellite radiometers, a new multi-algorithm method was developed to explore whether improvements in the remote-sensing detection of the Florida Red Tide was possible. A Hybrid Scheme was introduced that sequentially applies the optimized versions of two pre-existing satellite-based algorithms: an Empirical Approach (using water-leaving radiance as a function of chlorophyll concentration) and a Bio-optical Technique (using particulate backscatter along with chlorophyll concentration). The long-term evaluation of the new multi-algorithm method was performed using a multi-year MODIS dataset (2002-2006;during the boreal Summer-Fall periods - July-December) along the Central West Florida Shelf between 25.75 degrees N and 28.25 degrees N. algorithm validation was done with in situ measurements of the abundances of K. brevis: cell counts >= 1.5 x 10(4) cells I-1 defined a detectable HAB. Encouraging statistical results were derived when either or both algorithms correctly flagged known samples. The majority of the valid match-ups were correctly identified (similar to 80% of both HABs and non-blooming conditions) and few false negatives or false positives were produced (similar to 20% of each). Additionally, most of the HAB-positive identifications in the satellite data were indeed HAB samples (positive predictive value: similar to 70%) and those classified as HAB-negative were almost all non-bloom cases (negative predictive value: similar to 86%). These results demonstrate an excellent detection capability, on average similar to 10% more accurate than the individual algorithms used separately. Thus, the new Hybrid Scheme could become a powerful tool for environmental monitoring of K. brevis blooms, with valuable consequences including leading to the more rapid and efficient use of ships to make in situ measurements of HABs. (C) 2010 Elsevier B.V.
Process and alarm data are usually available from industrial processes. It is common practice to use process data for process monitoring and diagnostics. In contrast, alarm data is typically used to determine the inst...
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Determining stream networks automatically from digital elevation models is an issue that is actively being studied. The quality of elevation models has increased over time, but many hydrologically critical features, s...
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Determining stream networks automatically from digital elevation models is an issue that is actively being studied. The quality of elevation models has increased over time, but many hydrologically critical features, such as culverts, are often missing from the elevation data. To analyze the surficial water flow, one must either prepare a special elevation model or post-process an already-existing model. This study builds on the traditional, well-established method of determining the stream network from digital elevation models. We have extended the traditional method by locating culverts automatically, using road network data as an input. We show, by comparison to the reference data, that the culverts being most relevant for the stream network can be found with good accuracy. We demonstrate that by including the automatically located culverts in the automatic stream network determination, the quality of the generated network can be noticeably improved.
During the past several years, significant effort has been made to develop a robotic system to harvest fresh market apples in a modern apple orchard at Washington State University (WSU). This system has gone through s...
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Introduction: Asthma is a common childhood respiratory disorder characterized by wheeze, cough and respiratory distress responsive to bronchodilator therapy. Asthma severity can be determined by subjective, manual sco...
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Introduction: Asthma is a common childhood respiratory disorder characterized by wheeze, cough and respiratory distress responsive to bronchodilator therapy. Asthma severity can be determined by subjective, manual scoring systems such as the Pulmonary Score (PS). These systems require significant medical training and expertise to rate clinical findings such as wheeze characteristics, and work of breathing. In this study, we report the development of an objective method of assessing acute asthma severity based on the automated analysis of cough sounds. Methods: We collected a cough sound dataset from 224 children;103 without acute asthma and 121 with acute asthma. Using this database coupled with clinical diagnoses and PS determined by a clinical panel, we developed a machine classifier algorithm to characterize the severity of airway constriction. The performance of our algorithm was then evaluated against the PS from a separate set of patients, independent of the training set. Results: The cough-only model discriminated no/mild disease (PS 0-1) from severe disease (PS 5,6) but required a modified respiratory rate calculation to separate very severe disease (PS > 6). Asymptomatic children (PS 0) were separated from moderate asthma (PS 2-4) by the cough-only model without the need for clinical inputs. Conclusions: The PS provides information in managing childhood asthma but is not readily usable by non-medical personnel. Our method offers an objective measurement of asthma severity which does not rely on clinician-dependent inputs. It holds potential for use in clinical settings including improving the performance of existing asthma-rating scales and in community-management programs.
While Artificial Intelligence (AI) has the potential to transform the field of diagnostic radiology, important obstacles still inhibit its integration into clinical environments. Foremost among them is the inability t...
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While Artificial Intelligence (AI) has the potential to transform the field of diagnostic radiology, important obstacles still inhibit its integration into clinical environments. Foremost among them is the inability to integrate clinical information and prior and concurrent imaging examinations, which can lead to diagnostic errors that could irreversibly alter patient care. For AI to succeed in modern clinical practice, model training and algorithm development need to account for relevant background information that may influence the presentation of the patient in question. While AI is often remarkably accurate in distinguishing binary outcomes-hemorrhage vs. no hemorrhage;fracture vs. no fracture-the narrow scope of current training datasets prevents AI from examining the entire clinical context of the image in question. In this article, we provide an overview of the ways in which failure to account for clinical data and prior imaging can adversely affect AI interpretation of imaging studies. We then showcase how emerging techniques such as multimodal fusion and combined neural networks can take advantage of both clinical and imaging data, as well as how development strategies like domain adaptation can ensure greater generalizability of AI algorithms across diverse and dynamic clinical environments.
We proposed a support vector machine (SVM) algorithm to retrieve colored dissolved organic matter (CDOM) concentration (using a(g)(443) as a proxy) in the highly turbid Pearl River estuary. Two band ratios, namely, R-...
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We proposed a support vector machine (SVM) algorithm to retrieve colored dissolved organic matter (CDOM) concentration (using a(g)(443) as a proxy) in the highly turbid Pearl River estuary. Two band ratios, namely, R-rs(443)/R-rs(547) and R-rs(488)/R-rs(547), were used as inputs. Comparisons between the estimated and measured a(g)(443) illustrated high accuracy of the SVM algorithm, yielding R(2)s of 0.98 and 0.89, mean absolute percentage errors of 5.18% and 13.1%, and root-mean-square deviations of 0.012 and 0.034m(-1) for the training and validation data sets, respectively. The SVM algorithm was also evaluated against existing ones for the study area and gave the best accuracy with a R-2 of 0.9, a mean absolute percentage error of 10.23%, and a root-mean-square deviation of 0.025m(-1). The diurnal dynamics of CDOM in the Pearl River estuary was revealed showing complicated variations and influenced by the combined effects of wind, tide, circulation, and river discharge. As for remote sensing applications, the SVM-based CDOM product exhibited great potential to trace the Pearl River plume and the satellite-derived plume area agreed well with the FVCOM model simulation result. SVM is an accurate and fast tool for retrieving CDOM concentration, especially in highly turbid estuarine coastal waters, and thus, river plume dynamics can be traced. Plain Language Summary Colored dissolved organic matter is an important optically active component in the water column. Together with others, it determines the water color. In this study, we developed an algorithm to retrieve colored dissolved organic matter from remote sensing measurements in the turbid Pearl River estuary. The algorithm performs with high accuracy and outcompetes existing ones. Based on the algorithm, the diurnal dynamics of colored dissolved organic matter and the forcing factors were analyzed. The algorithm was also applied to a satellite scene. The characteristics of the Pearl River plume were elucidated.
Improvements in sensing, connectivity and computing technologies mean that industrial processes now generate a vast amount of data from a variety of disparate sources. Data may take a number of different forms, from d...
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Commercial biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. This article addresses the prob...
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Herein we introduce a deep learning (DL) application engine (DLAE) system concept, present potential uses of it, and describe pathways for its integration in clinical workflows. An open-source software application was...
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Herein we introduce a deep learning (DL) application engine (DLAE) system concept, present potential uses of it, and describe pathways for its integration in clinical workflows. An open-source software application was developed to provide a code-free approach to DL for medical imaging applications. DLAE supports several DL techniques used in medical imaging, including convolutional neural networks, fully convolutional networks, generative adversarial networks, and bounding box detectors. Several example applications using clinical images were developed and tested to demonstrate the capabilities of DLAE. Additionally, a model deployment example was demonstrated in which DLAE was used to integrate two trained models into a commercial clinical software package. (C) 2019 The Authors. Published by Elsevier B.V.
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