- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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High-Speed Plasmonic-Silicon Modulator Driven by Epsilon-near-zero Conductive Oxide
摘要： In this paper, closed-form expressions for the performance of the normalized matched ?lter (NMF) detector are developed speci?cally for the case of large time-bandwidth product, N . As a test case, the task of detecting underwater acoustic signals is considered. While the matched ?lter is the most common detector used, the NMF detector is used in cases where the ambient noise is fast time varying and is hard to estimate. While the performance of the NMF has been studied, no closed-form expressions are given for the detection and false alarm probabilities, and the accuracy of the available approximations greatly deteriorates with N . As a result, evaluating the detection threshold from the receiver operating characteristic requires signi?cant, and sometimes untraceable, numerical calculations. This is speci?cally important for underwater acoustic signals, where due to the low signal-to-noise ratio, N is very large. The analysis performed in this paper solves this problem. The analysis is based on the probability distribution of the NMF to give an exact closed-form (tabulized) expression for the false alarm probability, and a relatively accurate approximation for the probability of detection, both for the large N case. These approximations are found accurate in numerical simulations. Results from an experiment conducted in the Mediterranean sea at the depth of roughly 1000 m validate the analysis.
关键词： false alarm probability,detection probability,detection,Underwater acoustics,matched ?lter,receiver operating characteristic
更新于2025-09-23 15:19:57
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Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification
摘要： Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the final association to the positive or negative classes. This system has been developed and the classifier was trained on a database implemented by the AIDA (AutoImmunité, Diagnostic Assisté par ordinateur) project. The method proposed here has been tested on a public part of the same database, consisting of 2080 IIF images. The performance analysis showed an accuracy of fluorescent intensity around 93%. The results have been evaluated by comparing them with some of the most representative state-of-the-art works, demonstrating the quality of the system in the intensity classification of HEp-2 images.
关键词： autoimmune diseases,accuracy,SVM,receiver operating characteristic (ROC) curve,Convolutional Neural Network (CNN),IIF images
更新于2025-09-19 17:15:36
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[IEEE 2019 Computing, Communications and IoT Applications (ComComAp) - Shenzhen, China (2019.10.26-2019.10.28)] 2019 Computing, Communications and IoT Applications (ComComAp) - A Filtering Slot Antenna by the Air-Filled Annular Waveguide Structure
摘要： Receiver operating characteristic (ROC) curve is a plot traced out by pairs of false-positive rate and true-positive rate according to various decision threshold settings. The area under the ROC curve (AUC) is widely used as a figure of merit to summarize a diagnostic system’s performance, a binary classifier’s overall accuracy, or an energy detector’s power. Exploiting the equivalent relationship between the sample version of AUC and Mann Whitney U statistic (MWUS), in this paper, we develop an efficient algorithm of linearithmic order, based on dynamic programming, for unbiased estimation of the mean and variance of MWUS. Monte Carlo simulations verify our algorithmic findings.
关键词： Area under the curve (AUC),Mann-Whitney U statistic (MWUS),receiver operating characteristic (ROC),dynamic programming
更新于2025-09-19 17:13:59
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[IEEE 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) - Dubai, United Arab Emirates (2020.1.9-2020.1.10)] 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) - 35.83% Efficient Non-Toxic Perovskite Solar Cell using Copper Iodide and Tin-oxide
摘要： Receiver operating characteristic (ROC) curve is a plot traced out by pairs of false-positive rate and true-positive rate according to various decision threshold settings. The area under the ROC curve (AUC) is widely used as a figure of merit to summarize a diagnostic system’s performance, a binary classifier’s overall accuracy, or an energy detector’s power. Exploiting the equivalent relationship between the sample version of AUC and Mann Whitney U statistic (MWUS), in this paper, we develop an efficient algorithm of linearithmic order, based on dynamic programming, for unbiased estimation of the mean and variance of MWUS. Monte Carlo simulations verify our algorithmic findings.
关键词： receiver operating characteristic (ROC),dynamic programming,Area under the curve (AUC),Mann-Whitney U statistic (MWUS)
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Experimental and Theoretical Evidences of Hysteresis in Passive Mode-Locked Quantum Dots Lasers
摘要： In this paper, closed-form expressions for the performance of the normalized matched ?lter (NMF) detector are developed speci?cally for the case of large time-bandwidth product, N . As a test case, the task of detecting underwater acoustic signals is considered. While the matched ?lter is the most common detector used, the NMF detector is used in cases where the ambient noise is fast time varying and is hard to estimate. While the performance of the NMF has been studied, no closed-form expressions are given for the detection and false alarm probabilities, and the accuracy of the available approximations greatly deteriorates with N . As a result, evaluating the detection threshold from the receiver operating characteristic requires signi?cant, and sometimes untraceable, numerical calculations. This is speci?cally important for underwater acoustic signals, where due to the low signal-to-noise ratio, N is very large. The analysis performed in this paper solves this problem. The analysis is based on the probability distribution of the NMF to give an exact closed-form (tabulized) expression for the false alarm probability, and a relatively accurate approximation for the probability of detection, both for the large N case. These approximations are found accurate in numerical simulations. Results from an experiment conducted in the Mediterranean sea at the depth of roughly 1000 m validate the analysis.
关键词： receiver operating characteristic,matched ?lter,detection probability,detection,Underwater acoustics,false alarm probability
更新于2025-09-16 10:30:52