A 25 Gbps VCSEL driving ASIC: an attempt for ultra-high-speed front-end readout applications

This paper presents the design and the test results of a 25 Gbps VCSEL driving ASIC fabricated in a 55 nm CMOS technology as an attempt for the future very high-speed optical links. The VCSEL driving ASIC is composed of an input equalizer stage, a pre-driver stage and a novel output driver stage. To achieve high bandwidth, the pre-driver stage combines the inductor-shared peaking structure and the active-feedback technique. A novel output driver stage uses the pseudo differential CML driver structure and the adjustable FFE pre-emphasis technique to improve the bandwidth. This VCSEL driver has been integrated in a customized optical module with a VCSEL array. Both the electrical function and optical performance have been fully evaluated. The output optical eye diagram has passed the eye mask test at the data rate of 25 Gbps. The peak-to-peak jitter of 25 Gbps optical eye is 19.5 ps and the RMS jitter is 2.9 ps.

Technical vision system for apple defects recognition: justification, development, testing

The most rational method for identifying the quality of fruits is the optical method using PPE, which has the accuracy and stability of measurement, as well as distance and high productivity. The paper presents classification of fruit quality recognition systems and substantiates the design and technological scheme of the vision system for sorting them, consisting of an optical module with installed structural illumination and a video camera, an electronic control unit with an interface and actuators for the sorter and conveyor for fruits. In the course of the study, a single-stream type of fruit flow in PPE with forced rotation was substantiated, a structural and technological scheme of an STZ with a feeding conveyor, an optical module and a control unit, an algorithm for functioning of the STZ software was developed based on algorithm for segmentation of fruit colors, tracking algorithm, etc. deep learning ANN, which provide recognition of the size and color of fruits, as well as damage from mechanical stress, pests and diseases. The developed STZ has been introduced into the processing line for sorting and packing apples, LSP-4 has successfully passed preliminary tests and production tests at OJSC Ostromechevo. In the course of preliminary tests of the LSP-4 line, it was found that it provided fruit recognition with a probability of at least 95%, while the labor productivity made 2.5 t/h.

Steering Wheel AGV Path Tracking Control Based on Improved Pure Pursuit Model

Aiming at the path tracking problem of the AGV transfer platform of an Optical module installing and calibrating system, this paper designs a pure pursuit control strategy in which the preview distance changes adaptively according to the current speed of AGV and the curvature of the reference path. Firstly, AGV kinematics model and pure pursuit model are established according to the geometric relationship. Then fitness function is established with tracking deviation and steering stability, and Particle swarm optimization (PSO) algorithm is used to optimize the preview distance of pure pursuit model of AGV under various working conditions. During the tracking process, AGV selects the optimal preview distance according to the curvature of the reference path and the current speed. The simulation experiment results show that the improved pure pursuit control strategy containing curvature information of reference path can improve the adaptability of AGV when it is tracking complex path, guaranteeing tracking accuracy and steering stability.

A multi-PMT optical sensor for IceCube-Gen2

A new long optical module (LOM) is under development for IceCube-Gen2, the proposed expansion to the IceCube neutrino observatory at the South Pole. The module is housed in an elongated borosilicate-glass pressure vessel, the size of which is constrained by the borehole diameter, which impacts drilling economy. The designs under consideration use either 16 or 18 4-inch PMTs, conditional on future performance tests, mounted so as to guarantee full angular coverage. Modular electronics have been custom-designed to fit into the available space and to minimize cost and power requirements for the ∼10000 modules to be installed. We will provide an overview of our approach to these design considerations and summarize the results of our tests and simulations. Prototype modules will be installed in the upcoming IceCube Upgrade.

Multifunctional fiber-optic sensors for space infrastructure

Sensors used in rocket and space technology are subject to extreme external influences in terms of temperature, vibration, and shock. Therefore, the choice of the type of sensors is justified precisely by the resistance to such factors, as well as the ability to ensure the temporal and parametric stability of measurements. A new type of sensors – fiber-optic ones – meets these conditions. The basis for the selection and further improvement of such sensors were such requirements as minimum power consumption, high accuracy and stability of measurements, the ability to combine several measurements in one sensor. It is noted that for space infrastructure the factor of the possibility of simultaneous measurement of several parameters with one sensor is one of the important quality indicators. This is due to the possibility of reducing the number of sensors themselves, which reduces the mass and size parameters of space technology. This applies, first of all, to measurements of pressure and temperature, since they, in aggregate, account for at least 40 % of all measurements in space products. The path of choos-ing the types of methods and sensor designs led to the combination of the amplitude conversion method and optical communication in one sensor. In this case, amplitude modulation of pressure and temperature is carried out by a microelec-tromechanical unit (module), and the modulated optical signal is transmitted by an optical module. Such a modular composition of the sensor makes it possible to dispense with optical ana-lyzers (interrogators) and carry out further pro-cessing based on standard interfaces. A limitation of the proposed methods and designs is the need for microelectromechanical structures that measure certain physical quantities. Such structures for fiber-optic sensors are not mass-produced; therefore, their manufacture can be established at instrument-making enterprises with microelectronic equipment

A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem: design, development, and application of assimilating Himawari-8 aerosol observations

This paper presents a three-dimensional variational (3DVAR) data assimilation (DA) system for aerosol optical properties, including aerosol optical depth (AOD) retrievals and lidar-based aerosol profiles, which was developed for the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) within the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. For computational efficiency, 32 model variables in the MOSAIC_4bin scheme are lumped into 20 aerosol state variables that are representative of mass concentrations in the DA system. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was employed, which was obtained by simplifying the optical module in WRF-Chem. The tangent linear (TL) and adjoint (AD) operators were then established and passed the TL/AD sensitivity test. The Himawari-8 derived aerosol optical thickness (AOT) data were assimilated to validate the system and investigate the effects of assimilation on both AOT and PM2.5 simulations. Two comparative experiments were performed with a cycle of 24 h from November 23 to 29, 2018, during which a heavy air pollution event occurred in North China. The DA performances of the model simulation were evaluated against independent aerosol observations, including the Aerosol Robotic Network (AERONET) AOT and surface PM2.5 measurements. The results show that Himawari-8 AOT assimilation can significantly improve model AOT analyses and forecasts. Generally, the control experiments without assimilation seriously underestimated AOTs compared with observed values and were therefore unable to describe real aerosol pollution. The analysis fields closer to observations improved AOT simulations, indicating that the system successfully assimilated AOT observations into the model. In terms of statistical metrics, assimilating Himawari-8 AOTs only limitedly improved PM2.5 analyses in the inner simulation domain (D02); however, the positive effect can last for over 24 h. Assimilation effectively enlarged the underestimated PM2.5 concentrations to be closer to the real distribution in North China, which is of great value for studying heavy air pollution events

Can assimilation of satellite observations improve subsurface biological properties in a numerical model? A case study for the Gulf of Mexico

Given current threats to ocean ecosystem health, there is a growing demand for accurate biogeochemical hindcasts, nowcasts, and predictions. Provision of such products requires data assimilation, i.e., a comprehensive strategy for incorporating observations into biogeochemical models, but current data streams of biogeochemical observations are generally considered insufficient for the operational provision of such products. This study investigates to what degree the assimilation of satellite observations in combination with a priori model calibration by sparse BGC-Argo profiles can improve subsurface biogeochemical properties. The multivariate deterministic ensemble Kalman filter (DEnKF) has been implemented to assimilate physical and biological observations into a three-dimensional coupled physical–biogeochemical model, the biogeochemical component of which has been calibrated by BGC-Argo float data for the Gulf of Mexico. Specifically, observations of sea surface height, sea surface temperature, and surface chlorophyll were assimilated, and profiles of both physical and biological variables were updated based on the surface information. We assessed whether this leads to improved subsurface distributions, especially of biological properties, using observations from five BGC-Argo floats that were not assimilated. An alternative light parameterization that was tuned a priori using BGC-Argo observations was also applied to test the sensitivity of data assimilation impact on subsurface biological properties. Results show that assimilation of the satellite data improves model representation of major circulation features, which translate into improved three-dimensional distributions of temperature and salinity. The multivariate assimilation also improves the agreement of subsurface nitrate through its tight correlation with temperature, but the improvements in subsurface chlorophyll were modest initially due to suboptimal choices of the model's optical module. Repeating the assimilation run by using the alternative light parameterization greatly improved the subsurface distribution of chlorophyll. Therefore, even sparse BGC-Argo observations can provide substantial benefits for biogeochemical prediction by enabling a priori model tuning. Given that, so far, the abundance of BGC-Argo profiles in the Gulf of Mexico and elsewhere has been insufficient for sequential assimilation, updating 3D biological properties in a model that has been well calibrated is an intermediate step toward full assimilation of the new data types.

Development of an Easy-to-Operate Underwater Raman System for Deep-Sea Cold Seep and Hydrothermal Vent In Situ Detection

As a powerful in situ detection technique, Raman spectroscopy is becoming a popular underwater investigation method, especially in deep-sea research. In this paper, an easy-to-operate underwater Raman system with a compact design and competitive sensitivity is introduced. All the components, including the optical module and the electronic module, were packaged in an L362 × Φ172 mm titanium capsule with a weight of 20 kg in the air (about 12 kg in water). By optimising the laser coupling mode and focusing lens parameters, a competitive sensitivity was achieved with the detection limit of SO42− being 0.7 mmol/L. The first sea trial was carried out with the aid of a 3000 m grade remotely operated vehicle (ROV) “FCV3000” in October 2018. Over 20,000 spectra were captured from the targets interested, including methane hydrate, clamshell in the area of cold seep, and bacterial mats around a hydrothermal vent, with a maximum depth of 1038 m. A Raman peak at 2592 cm−1 was found in the methane hydrate spectra, which revealed the presence of hydrogen sulfide in the seeping gas. In addition, we also found sulfur in the bacterial mats, confirming the involvement of micro-organisms in the sulfur cycle in the hydrothermal field. It is expected that the system can be developed as a universal deep-sea survey and detection equipment in the near future.