Abstract: Systems and methods for performing Optical Time-Domain Reflectometry (OTDR) tests on optical fibers are provided. In an embodiment, a method includes applying OTDR pulses to a fiber under test and receiving noisy acquisition sets of the fiber under test; processing the noisy acquisition sets with a denoising model that is a pre-trained machine learning model configured to simultaneously use spatial information and temporal information associated with the noisy acquisition sets to filter out noise; and analyzing outputs from the denoising model to determine characteristics of the fiber under test. This approach is performed in lieu of the conventional approach of mathematically averaging acquisitions, thereby resulting in proper results with fewer acquisitions, significantly speeding up the overall acquisition time.

Abstract: Systems and methods for inspecting optical fibers are provided. A method, according to one implementation, includes steps of obtaining an image of an end-face of an optical fiber; analyzing the image with a pre-trained neural network model to classify pixels therein as any of a defect, a scratch, or clean; aggregating the pixels based on proximity to obtain defect segments or scratch segments; characterizing these defect segments and scratch segments based on but not limited to size and location, and providing an output including any of the defect segments or scratch segments.

Abstract: Example embodiments are disclosed of systems and methods for predicting failure probabilities of future product tests of a testing sequence based on outcomes of prior tests. Predictions are made by a machine-learning-based model (MLM) trained with a set of test-result sequence records (TRSRs) including test values and pass/fail indicators (PRIs) of completed tests. Within training epochs over the set, iterations are carried out over each TRSR. Each iteration involves sub-iterations carried out successively over test results of the TRSR. Each sub-iteration involves (i) inputting to the MLM values of a given test and those of tests earlier in the sequence while masking those later in the sequence, (ii) computing probabilities of test failures for the masked tests found later in the sequence than the given test, and (iii) applying the PFIs of test results later in the sequence than the given test as ground-truths to update parameters of the MLM.

Abstract: Example embodiments are disclosed of systems and methods for predicting failure probabilities of future product tests of a testing sequence based on outcomes of prior tests. Predictions are made by a machine-learning-based model (MLM) trained with a set of test-result sequence records (TRSRs) including test values and pass/fail indicators (PRIs) of completed tests. Within training epochs over the set, iterations are carried out over each TRSR. Each iteration involves sub-iterations carried out successively over test results of the TRSR. Each sub-iteration involves (i) inputting to the MLM values of a given test and those of tests earlier in the sequence while masking those later in the sequence, (ii) computing probabilities of test failures for the masked tests found later in the sequence than the given test, and (iii) applying the PFIs of test results later in the sequence than the given test as ground-truths to update parameters of the MLM.