Unmanned surface vehicles (USVs) have great value with their ability to execute hazardous and timeconsuming missions over water surfaces. Recently, USVs for inland waterways have attracted increasing attention for their potential application in autonomous monitoring, transportation, and cleaning. However, unlike sailing in open water, the challenges posed by scenes of inland waterways, such as the complex distribution of obstacles, the global positioning system (GPS) signal denial environment, the reflection of bank-side structures, and the fog over the water surface, all impede USV application in inland waterways. To address these problems and stimulate relevant research, we introduce USVInland, a multisensor dataset for USVs in inland waterways. The collection of USVInland spans a trajectory of more than 26 km in diverse real-world scenes of inland waterways using various modalities, including lidar, stereo cameras, millimeterwave radar, GPS, and inertial measurement units (IMUs). Based on the requirements and challenges in the perception and navigation of USVs for inland waterways, we build benchmarks for simultaneous localization and mapping (SLAM), stereo matching, and water segmentation. We evaluate common algorithms for the above tasks to determine the influence of unique inland waterway scenes on algorithm performance. Our dataset and the development tools are available online at https://www.orca-tech.cn/datasets.html.

Acetylcholine (ACh) is one of the important neurotransmitters, involved in signal transduction function in human and animal brain. However, the influence of ACh treatment on salt-stress tolerance in plants is yet unknown. Salt stress caused a reduction in gas-exchange parameters, chlorophyll content, antioxidant enzyme activities, and leaf relative water content of Nicotiana benthamiana plants. However, the above inhibitions could be significantly alleviated by application of leaf spray or root application of ACh. Exogenous ACh reduced the accumulation of malondialdehyde by enhancing activities of antioxidant enzymes such as peroxidase and superoxide dismutase. In addition, enhanced accumulation of organic osmolytes including soluble sugars and proline possibly regulated the signal mechanisms related to stress. Application of ACh could also improve gas-exchange parameters and photosynthetic pigment accumulation in leaves of salt-stressed plants. These effects of ACh were beneficial for maintaining better water status in plants, the concentration of 10 µM ACh applied both in the form of leaf spray or root application was the most effective. Therefore, our findings provided a stronger evidence for a physiological role of ACh and its potential use at optimal concentration by leaf or root application to alleviate damage caused by salt-stress in plants.

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This paper describes a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with an energy-efficient trilevel alternate switching capacitive digital-to-analog converter (CDAC). The switching scheme of this CDAC preserves the features of the asymmetric-switching CDAC. By narrowing and smoothing the dynamic variation of DAC voltage, the switching scheme diminishes the dynamic offset effect induced by the asymmetric-switching CDAC. The CDAC reduces the capacitor requirement by almost fourfold and improves the average switching energy efficiency by almost 86.5% when compared with the conventional switching CDACs. This SAR ADC was implemented using the 90-nm CMOS technology, and its measured performances were as follows: 1) spurious free dynamic range of 56.98 dB; 2) signal-to-noiseand-distortion ratio of 68.79 dB; and 3) power dissipation of 3.45 µW at an operation of 0.5 V and 1.28 MS/s. The ADC achieves a figure-of-merit of 4.68-fJ/conversion-step.Index Terms-Analog-to-digital converter (ADC), capacitive digital-to-analog converter (CDAC), charge redistribution, dynamic offset, successive approximation register (SAR), trilevel switching.

The millimeter-wave radar, as an important sensor, is widely used in autonomous driving. In recent years, to meet the requirement of high level autonomous driving applications, attentions have been paid to generate high-quality radar point clouds. However, in the complex roadway environment, the weaknesses of classical radar detectors are exposed, such as too much clutter points and sparse valid point clouds. Therefore, in this paper, we propose a new automotive radar detector based on deep learning using the spatial distribution feature of the real targets, in order to improve the performance of automotive radar detector in the real-world driving scene. Besides, aiming at the lack of radar data labels, we propose an autonomous labeling method by using synchronized Lidar data. Finally, we evaluate the detector on data collected in the real-world roadway scene and the result shows that the proposed radar detector out-performs the classical radar detectors in suppressing the clutter and generating denser point clouds.

Abstract-This paper presents a microstrip wideband antenna and its utilization in integration of multiple wireless communication systems. A simple fork-like strip antenna, fed by a coplanar-waveguide (CPW) transmission line, is designed to excite a right-hand circularly polarized wave at 1.57 GHz. A rectangular patch is added at the end of one prong to enhance the circular polarization performance. By modifying the geometry of the ground plane, a left-hand circularly polarized wave is excited at 2.33 GHz, and the wideband frequency response is derived. To reduce the lower resonant frequency, a stub is added at the left side of the ground plane. The measured impedance bandwidth of reflection coefficients (S 11 ) < −10 dB ranges from 1.49 to 2.92 GHz, which satisfies the system bandwidths of most of commercial wireless communication systems. The 3-dB axial-ratio bandwidths are approximately 40 MHz at the lower band (1.57 GHz) and 290 MHz at the upper band (2.33 GHz).