Error Correction of Water Vapor Radiometers for VLBI Observations in Deep-Space Networks

In this paper, we present the design and implementation tests of a water vapor radiometer (WVR) suitable for very long baseline interferometry (VLBI) observation. We describe the calibration method with an analysis of the sources of measurement errors. The experimental results show that the long-term measurement accuracy of the brightness temperature of the water vapor radiometer can reach 0.2 K under arbitrary ambient conditions by absolute calibration, receiver gain error calibration, and antenna feeder system temperature noise error calibration. Furthermore, we present a method for measurements of the calibration error of the oblique path measurement. This results in an oblique path wet delay measurement accuracy of the water vapor radiometer reaching 20 mm (within one month).

Quantification and mitigation of the airborne limb imaging FTIR GLORIA instrument effects and uncertainties

The Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) is an infrared imaging FTS spectrometer with a 2-D infrared detector operated on two high flying research aircrafts. It has flown on eight campaigns and measured along more than 300 000 km of flight track. This paper details our instrument calibration and characterization efforts, which in particular leverage almost exclusively in-flight data. First, we present the framework of our new calibration scheme, which uses information from all three available calibration measurements (two blackbodies and upward pointing deep space measurements). Part of this scheme is a new correction algorithm correcting the erratically changing non-linearity of a subset of detector pixels and the identification of remaining bad pixels. Using this new calibration, we derive a 1-σ bound of 1 % on the instrumental gain error and a bound of 30 nW cm−2 sr−1 cm on the instrumental offset error. We show how we can examine the noise and spectral accuracy for all measured atmospheric spectra and derive a spectral accuracy of 5 ppm, on average. All these errors are compliant with the initial instrument requirements. We also discuss, for the first time, the pointing system of the GLORIA instrument. Combining laboratory calibration efforts with the measurement of astronomical bodies during the flight, we can derive a pointing accuracy of 0.032°, which corresponds to one detector pixel. The paper concludes with a brief study on how these newly characterised instrumental parameters affect temperature and ozone retrievals. We find that, first, the pointing uncertainty and, second, the instrumental gain uncertainty introduce the largest error in the result.

Unity-Gain Zero-Offset CMOS Buffer with Improved Feedforward Path

A voltage unity-gain zero-offset CMOS amplifier with reduced gain error and increased PSRR (power supply rejection ratio) is proposed. The amplifier uses two feed mechanisms, negative feedback and supporting positive feedforward, to achieve low deviation from unit gain over the entire input range. The circuit, designed in a standard 180-nanometer 1.8-voltage CMOS process, is compared with two known buffers of similar topology, also designed in the same process. Simulations show that, with the same supply (1.8 V), power (1.2 mW), load (12 pF), bandwidth (50 MHz), and similar area (600 µm2), the proposed buffer achieves the lowest gain error (0.3%) and the highest PSRR (72 dB).

Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN

This paper proposes a data-driven method-based fault diagnosis method using the deep convolutional neural network (DCNN). The DCNN is used to deal with sensor and actuator faults of robot joints, such as gain error, offset error, and malfunction for both sensors and actuators, and different fault types are diagnosed using the trained neural network. In order to achieve the above goal, the fused data of sensors and actuators are used, where both types of fault are described in one formulation. Then, the deep convolutional neural network is applied to learn characteristic features from the merged data to try to find discriminative information for each kind of fault. After that, the fully connected layer does prediction work based on learned features. In order to verify the effectiveness of the proposed deep convolutional neural network model, different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), conventional neural network (CNN) using the LeNet-5 method, and long-term memory network (LTMN) are investigated and compared with DCNN method. The results show that the DCNN fault diagnosis method can realize high fault recognition accuracy while needing less model training time.

Design Strategies and Architectures for Ultra-Low-Voltage Delta-Sigma ADCs

The design of ultra-low voltage analog CMOS integrated circuits requires ad hoc solutions to counteract the severe limitations introduced by the reduced voltage headroom. A popular approach is represented by inverter-based topologies, which however may suffer from reduced finite DC gain, thus limiting the accuracy and the resolutions of pivotal circuits like analog-to-digital converters. In this work, we discuss the effects of finite DC gain on ultra-low voltage ΔΣ modulators, focusing on the converter gain error. We propose an ultra-low voltage, ultra-low power, inverter-based ΔΣ modulator with reduced finite-DC-gain sensitivity. The modulator employs a two-stage, high DC-gain, switched-capacitor integrator that applies a correlated double sampling technique for offset cancellation and flicker noise reduction; it also makes use of an amplifier that implements a novel common-mode stabilization loop. The modulator was designed with the UMC 0.18 μm CMOS process to operate with a supply voltage of 0.3 V. It was validated by means of electrical simulations using the CadenceTM design environment. The achieved SNDR was 73 dB, with a bandwidth of 640 Hz, and a clock frequency of 164 kHz, consuming only 200.5 nW. It achieves a Schreier Figure of Merit of 168.1 dB. The proposed modulator is also able to work with lower supply voltages down to 0.15 V with the same resolution and a lower power consumption despite of a lower bandwidth. These characteristics make this design very appealing in sensor interfaces powered by energy harvesting sources.