- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 20th International Conference on Transparent Optical Networks (ICTON) - Bucharest (2018.7.1-2018.7.5)] 2018 20th International Conference on Transparent Optical Networks (ICTON) - Soft Failure Localization in Elastic Optical Networks
摘要： Soft failure localization to early detect service level agreement violations is of paramount importance in elastic optical networks (EONs), while it allows anticipating possible hard failure events. Nowadays, effective and automated solutions for soft failure localization during lightpaths’ commissioning testing and operation are still missing. In this paper, we focus on presenting soft failure localization algorithms based on two different active monitoring techniques. First, the Testing optIcal Switching at connection SetUp timE (TISSUE) algorithm is proposed to localize soft failures during commissioning testing phase by elaborating the estimated bit-error rate (BER) values provided by low-cost optical testing channel (OTC) modules. Second, the FailurE causE Localization for optIcal NetworkinG (FEELING) algorithm is proposed to localize failures during lightpath operation using cost-effective optical spectrum analyzers (OSAs) widely deployed in network nodes. Results are presented to validate both algorithms in the event of several soft failures affecting lasers and filters.
关键词： machine learning algorithms,soft failure localization,monitoring and data analytics
更新于2025-09-23 15:22:29
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Low-Power Optical Sensor for Traffic Detection
摘要： 4-D-computed tomography (4DCT) provides not only a new dimension of patient-specific information for radiation therapy planning and treatment, but also a challenging scale of data volume to process and analyze. Manual analysis using existing 3-D tools is unable to keep up with vastly increased 4-D data volume, automated processing and analysis are thus needed to process 4DCT data effectively and efficiently. In this paper, we applied ideas and algorithms from image/signal processing, computer vision, and machine learning to 4DCT lung data so that lungs can be reliably segmented in a fully automated manner, lung features can be visualized and measured on the fly via user interactions, and data quality classifications can be computed in a robust manner. Comparisons of our results with an established treatment planning system and calculation by experts demonstrated negligible discrepancies (within ±2%) for volume assessment but one to two orders of magnitude performance enhancement. An empirical Fourier-analysis-based quality measure-delivered performances closely emulating human experts. Three machine learners are inspected to justify the viability of machine learning techniques used to robustly identify data quality of 4DCT images in the scalable manner. The resultant system provides a toolkit that speeds up 4-D tasks in the clinic and facilitates clinical research to improve current clinical practice.
关键词： classification algorithms,machine learning algorithms,image analysis,Biomedical image processing,data visualization,computed tomography,morphological operations
更新于2025-09-23 15:21:01
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[IEEE 2019 International Topical Meeting on Microwave Photonics (MWP) - Ottawa, ON, Canada (2019.10.7-2019.10.10)] 2019 International Topical Meeting on Microwave Photonics (MWP) - On-chip Photonic Method for Doppler Frequency Shift Measurement
摘要： 4-D-computed tomography (4DCT) provides not only a new dimension of patient-specific information for radiation therapy planning and treatment, but also a challenging scale of data volume to process and analyze. Manual analysis using existing 3-D tools is unable to keep up with vastly increased 4-D data volume, automated processing and analysis are thus needed to process 4DCT data effectively and efficiently. In this paper, we applied ideas and algorithms from image/signal processing, computer vision, and machine learning to 4DCT lung data so that lungs can be reliably segmented in a fully automated manner, lung features can be visualized and measured on the fly via user interactions, and data quality classifications can be computed in a robust manner. Comparisons of our results with an established treatment planning system and calculation by experts demonstrated negligible discrepancies (within ±2%) for volume assessment but one to two orders of magnitude performance enhancement. An empirical Fourier-analysis-based quality measure-delivered performances closely emulating human experts. Three machine learners are inspected to justify the viability of machine learning techniques used to robustly identify data quality of 4DCT images in the scalable manner. The resultant system provides a toolkit that speeds up 4-D tasks in the clinic and facilitates clinical research to improve current clinical practice.
关键词： Biomedical image processing,machine learning algorithms,classification algorithms,data visualization,computed tomography,morphological operations,image analysis
更新于2025-09-23 15:19:57
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The time response of plasmonic sensors due to binary adsorption: analytical versus numerical modeling
摘要： In order to allow for multiscale modeling of complex systems, we focus on various approaches to modeling binary adsorption. We consider multiple methods of modeling the temporal response of general plasmonic sensors. We start from the analytical approach. The kinetics of adsorption and desorption is modeled both as a first order reaction and as a second-order reaction. The criteria for their validity and the choice between them in the case of two-component adsorption are established. Due to the nonlinearities of the second-order reactions and the lack of their analytical solutions, computer-aided modeling is considered next, also in multiple ways: the employment of numerical solvers, fitting of experimental results, the stochastic simulation algorithms and the employment of artificial neural networks (ANN). The examples we present illustrate the advantages and disadvantages of the particular approaches. The goal is to aid the concurrent multiscale modeling of adsorption-based devices. Machine learning in ANN performed here is used to estimate the equilibrium values of adsorbed quantities. The obtained results show that to train an ANN for the estimation of the equilibrium adsorption quantities the Levenberg–Marquardt and the Bayesian regularization algorithms are less efficient than the quasi-Newton BFGS (Broyden–Fletcher–Goldfarb–Shanno) algorithm.
关键词： Kinetics,Stochastic simulation algorithms,Adsorption,Machine learning algorithms,Plasmonic sensing
更新于2025-09-23 15:19:57
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Remote sensing bio-control damage on aquatic invasive alien plant species
摘要： Aquatic Invasive Alien Plant (AIAP) species are a major threat to freshwater ecosystems, placing great strain on South Africa’s limited water resources. Bio-control programmes have been initiated in an effort to mitigate the negative environmental impacts associated with their presence in non-native areas. Remote sensing can be used as an effective tool to detect, map and monitor bio-control damage on AIAP species. This paper reconciles previous and current research concerning the application of remote sensing to detect and map bio-control damage on AIAP species. Initially, the spectral characteristics of bio-control damage are described. Thereafter, the potential of remote sensing chlorophyll content and chlorophyll fluorescence as pre-visual indicators of bio-control damage are reviewed and synthesised. The utility of multispectral and hyperspectral sensors for mapping different severities of bio-control damage are also discussed. Popular machine learning algorithms that offer operational potential to classify bio-control damage are proposed. This paper concludes with the challenges of remote sensing bio-control damage as well as proposes recommendations to guide future research to successfully detect and map bio-control damage on AIAP species.
关键词： machine learning algorithms,multispectral sensors,chlorophyll content,Aquatic Invasive Alien Plant (AIAP) species,chlorophyll fluorescence,hyperspectral sensors,Remote sensing,bio-control damage
更新于2025-09-23 15:19:57
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Detection of pepper fusarium disease using machine learning algorithms based on spectral reflectance
摘要： The development of computerized automated diagnostic systems ensures more effective health screening in plants. In this way, the damage caused by diseases can be reduced by early detection. Light reflections from plant leaves are known to carry information about plant health. In the study, healthy and fusarium diseased peppers (capsicum annuum) was detected from the reflections obtained from the pepper leaves with the aid of spectroradiometer. Reflections were taken from four groups of pepper leaves (healthy, fusarium-diseased, mycorrhizal fungus, fusarium-diseased and mycorrhizal fungus) grown in a closed environment at wavelengths between 350 nm and 2500 nm. Pepper disease detection takes place in two stages. In the first step, the feature vector is obtained. In the second step, the feature vectors of the input data are classified. The feature vector consist of the coefficients of wavelet decomposition and the statistical values of these coefficients. Artificial Neural Networks (ANN), Naive Bayes (NB) and K-nearest Neighbor (KNN) were used for classification. In detection the health case of pepper, the average success rates of different classification algorithms for the first two groups (diseased and healthy peppers) were calculated as 100% for KNN, 97.5% for ANN and 90% for NB. Likewise, these rates for the classification of all groups were calculated as 100% for KNN, 88.125% for ANN and 82% for NB. Overall, the results have shown that leaf reflections can be successfully used in disease detection.
关键词： Wavelet,Spectral reflectance,Machine learning algorithms,Pepper disease detection,Classification
更新于2025-09-19 17:15:36