A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication

Semiconductor lasers, one of the key components for optical communication systems, have been rapidly evolving to meet the requirements of next generation optical networks with respect to high speed, low power consumption, small form factor etc. However, these demands have brought severe challenges to the semiconductor laser reliability. Therefore, a great deal of attention has been devoted to improving it and thereby ensuring reliable transmission. In this paper, a predictive maintenance framework using machine learning techniques is proposed for real-time heath monitoring and prognosis of semiconductor laser and thus enhancing its reliability. The proposed approach is composed of three stages: i) real-time performance degradation prediction, ii) degradation detection, and iii) remaining useful life (RUL) prediction. First of all, an attention based gated recurrent unit (GRU) model is adopted for real-time prediction of performance degradation. Then, a convolutional autoencoder is used to detect the degradation or abnormal behavior of a laser, given the predicted degradation performance values. Once an abnormal state is detected, a RUL prediction model based on attention-based deep learning is utilized. Afterwards, the estimated RUL is input for decision making and maintenance planning. The proposed framework is validated using experimental data derived from accelerated aging tests conducted for semiconductor tunable lasers. The proposed approach achieves a very good degradation performance prediction capability with a small root mean square error (RMSE) of 0.01, a good anomaly detection accuracy of 94.24% and a better RUL estimation capability compared to the existing ML-based laser RUL prediction models.Comment: Published in Journal of Lightwave Technology (Volume: 40, Issue: 14, 15 July 2022

Fault Monitoring in Passive Optical Networks using Machine Learning Techniques

Passive optical network (PON) systems are vulnerable to a variety of failures, including fiber cuts and optical network unit (ONU) transmitter/receiver failures. Any service interruption caused by a fiber cut can result in huge financial losses for service providers or operators. Identifying the faulty ONU becomes difficult in the case of nearly equidistant branch terminations because the reflections from the branches overlap, making it difficult to distinguish the faulty branch given the global backscattering signal. With increasing network size, the complexity of fault monitoring in PON systems increases, resulting in less reliable monitoring. To address these challenges, we propose in this paper various machine learning (ML) approaches for fault monitoring in PON systems, and we validate them using experimental optical time domain reflectometry (OTDR) data.Comment: ICTON 202

Degradation Prediction of Semiconductor Lasers using Conditional Variational Autoencoder

Semiconductor lasers have been rapidly evolving to meet the demands of next-generation optical networks. This imposes much more stringent requirements on the laser reliability, which are dominated by degradation mechanisms (e.g., sudden degradation) limiting the semiconductor laser lifetime. Physics-based approaches are often used to characterize the degradation behavior analytically, yet explicit domain knowledge and accurate mathematical models are required. Building such models can be very challenging due to a lack of a full understanding of the complex physical processes inducing the degradation under various operating conditions. To overcome the aforementioned limitations, we propose a new data-driven approach, extracting useful insights from the operational monitored data to predict the degradation trend without requiring any specific knowledge or using any physical model. The proposed approach is based on an unsupervised technique, a conditional variational autoencoder, and validated using vertical-cavity surface-emitting laser (VCSEL) and tunable edge emitting laser reliability data. The experimental results confirm that our model (i) achieves a good degradation prediction and generalization performance by yielding an F1 score of 95.3%, (ii) outperforms several baseline ML based anomaly detection techniques, and (iii) helps to shorten the aging tests by early predicting the failed devices before the end of the test and thereby saving costsComment: Published in: Journal of Lightwave Technology (Volume: 40, Issue: 18, 15 September 2022

Incidence and developmental timing of endosperm failure in post-zygotic isolation between wild tomato lineages

Background and AimsDefective hybrid seed development in angiosperms might mediate the rapid establishment of intrinsic post-zygotic isolation between closely related species. Extensive crosses within and among three lineages of wild tomatoes (Solanum section Lycopersicon) were performed to address the incidence, developmental timing and histological manifestations of hybrid seed failure. These lineages encompass different, yet fairly recent, divergence times and both allopatric and partially sympatric pairs.MethodsMature seeds were scored visually 2 months after hand pollinations, and viable-looking seeds were assessed for germination success. Using histological sections from early-developing seeds from a sub-set of crosses, the growth of three major seed compartments (endosperm, embryo and seed coat) was measured at critical developmental stages up to 21 d after pollination, with a focus on the timing and histological manifestations of endosperm misdevelopment in abortive hybrid seeds.Key ResultsFor two of three interspecific combinations including the most closely related pair that was also studied histologically, almost all mature seeds appeared ‘flat’ and proved inviable; histological analyses revealed impaired endosperm proliferation at early globular embryo stages, concomitant with embryo arrest and seed abortion in both cross directions. The third interspecific combination yielded a mixture of flat, inviable and plump, viable seeds; many of the latter germinated and exhibited near-normal juvenile phenotypes or, in some instances, hybrid necrosis and impaired growth.ConclusionsThe overall results suggest that near-complete hybrid seed failure can evolve fairly rapidly and without apparent divergence in reproductive phenology/biology. While the evidence accrued here is largely circumstantial, early-acting disruptions of normal endosperm development are most probably the common cause of seed failure regardless of the type of endosperm (nuclear or cellular)

Vocal Interactivity in-and-between Humans, Animals, and Robots

Almost all animals exploit vocal signals for a range of ecologically motivated purposes: detecting predators/prey and marking territory, expressing emotions, establishing social relations, and sharing information. Whether it is a bird raising an alarm, a whale calling to potential partners, a dog responding to human commands, a parent reading a story with a child, or a business-person accessing stock prices using Siri, vocalization provides a valuable communication channel through which behavior may be coordinated and controlled, and information may be distributed and acquired. Indeed, the ubiquity of vocal interaction has led to research across an extremely diverse array of fields, from assessing animal welfare, to understanding the precursors of human language, to developing voice-based human–machine interaction. Opportunities for cross-fertilization between these fields abound; for example, using artificial cognitive agents to investigate contemporary theories of language grounding, using machine learning to analyze different habitats or adding vocal expressivity to the next generation of language-enabled autonomous social agents. However, much of the research is conducted within well-defined disciplinary boundaries, and many fundamental issues remain. This paper attempts to redress the balance by presenting a comparative review of vocal interaction within-and-between humans, animals, and artificial agents (such as robots), and it identifies a rich set of open research questions that may benefit from an interdisciplinary analysis

Incidence and developmental timing of endosperm failure in post-zygotic isolation between wild tomato lineages

Abstract Background and Aims Defective hybrid seed development in angiosperms might mediate the rapid establishment of intrinsic post-zygotic isolation between closely related species. Extensive crosses within and among three lineages of wild tomatoes (Solanum section Lycopersicon) were performed to address the incidence, developmental timing and histological manifestations of hybrid seed failure. These lineages encompass different, yet fairly recent, divergence times and both allopatric and partially sympatric pairs. Methods Mature seeds were scored visually 2 months after hand pollinations, and viable-looking seeds were assessed for germination success. Using histological sections from early-developing seeds from a sub-set of crosses, the growth of three major seed compartments (endosperm, embryo and seed coat) was measured at critical developmental stages up to 21 d after pollination, with a focus on the timing and histological manifestations of endosperm misdevelopment in abortive hybrid seeds. Key Results For two of three interspecific combinations including the most closely related pair that was also studied histologically, almost all mature seeds appeared ‘flat' and proved inviable; histological analyses revealed impaired endosperm proliferation at early globular embryo stages, concomitant with embryo arrest and seed abortion in both cross directions. The third interspecific combination yielded a mixture of flat, inviable and plump, viable seeds; many of the latter germinated and exhibited near-normal juvenile phenotypes or, in some instances, hybrid necrosis and impaired growth. Conclusions The overall results suggest that near-complete hybrid seed failure can evolve fairly rapidly and without apparent divergence in reproductive phenology/biology. While the evidence accrued here is largely circumstantial, early-acting disruptions of normal endosperm development are most probably the common cause of seed failure regardless of the type of endosperm (nuclear or cellular)

Amino acid-based polyphosphorodiamidates with hydrolytically labile bonds for degradation-tuned photopolymers

Photochemical additive manufacturing technologies can produce complex geometries in short production times and thus have considerable potential as a tool to fabricate medical devices such as individualised patient-specific implants, prosthetics and tissue engineering scaffolds. However, most photopolymer resins degrade only slowly under the mild conditions required for many biomedical applications. Herein we report novel amino acid-based polyphosphorodiamidate (APdA) monomers with hydrolytically cleavable bonds. The substituent on the α-amino acid can be used as a handle for facile control of hydrolysis rates of the monomers into their endogenous components, namely phosphate and the corresponding amino acid. Furthermore, monomer hydrolysis is considerably accelerated at lower pH values. The monomers underwent thiol-yne photopolymerization and could be 3D structured via multi-photon lithography. Copolymerization with commonly used hydrophobic thiols demonstrates not only their ability to regulate the ambient degradation rate of thiol-yne polyester photopolymer resins but also rare but highly desirable surface erosion behaviour. Such degradation profiles, in the appropriate timeframes in suitably mild conditions, combined with their good cytocompatibility and 3D printability, render these novel photomonomers of significant interest for a wide range of biomaterial applications

3D Inkjet Printing of Biomaterials with Solvent‐Free, Thiol‐Yne‐Based Photocurable Inks

Abstract 3D inkjet printing is a fast, reliable, and non‐contact bottom–up approach to printing small and large models and is one of the fastest additive manufacturing technologies available. These attributes position inkjet printing as a promising tool for the additive manufacturing of biomaterials, for example, tissue engineering scaffolds. However, due to the stringent technical rheological requirements of current inkjet technologies, there is a lack of photopolymer resins suitable for the inkjet printing of biomaterials. Hence, a novel ink engineered for 3D piezoelectric inkjet printing of biomaterials is designed and developed. The novel resin leverages a biodegradable amino acid phosphorodiamidate matrix copolymerized with a dialkyne ether to modulate the viscosity. Copolymerization with commercially available thiols facilitates the photochemical thiol‐yne curing reaction. The ink exhibits optimal viscosity, eliminating the need for solvents, as well as reliable jetting and sufficiently swift curing kinetics. Furthermore, the formulation is successfully demonstrated in an industrial inkjet printhead. Notably, the resulting materials have low cytotoxicity and, hence, have significant promise in advancing the applications of 3D inkjet printing of biological scaffolds