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After palladium in palladium-EDTA was selectively released by thiourea, EDTA equal to the amount of palladium was analyzed by using automatic photometric titration. In this way, the determination of palladium in palladium compounds was realized. The wavelength of determination, the amount of thiourea, the value of end-point jump AE, the influence of coexisting ions and the conventional titration conditions were studied systematically. The results showed that when the palladium content was in the range of 19%~60%, the relative standard deviation (RSD, n = 11) was 0.11%~0.19% with a spiked recovery rate of 99.64%~100.27%, consistent with the related standard methods. Choosing thiourea instead of dimethylglyoxime as a releasing agent can solve the problem arising from the precipitation of dimethylglyoxime-Pd complex on the surface of the photometric electrode, and using automatic photometric titration can eliminate the error in judging the titration end point by the visual inspection. The automatic and rapid determination of palladium content can be realized by adopting the method developed in this study. [ABSTRACT FROM AUTHOR]

The development of multimode high-resolution Synthetic Aperture Radar (SAR) poses new challenges to information perception and feature abstraction of the space, ground, sea, and environment targets. The intersection of spatial remote sensing big data and artificial intelligence information technology is a new scientific research domain and major application area in Automatic Target Recognition (ATR). We emphasize that research on artificial intelligence information technology needs to be conducted under the physical background of the interaction between electromagnetic waves and targets, i.e., physical intelligence, to develop microwave vision of information perception on the electromagnetic spectrum that cannot be recognized by human eyes. This study is based on a keynote speech presented by author at the Fifth Young Scientists Forum of Journal of Radars on August 15, 2019.

Passive bistatic radar is a novel radar technology that passively detects targets without actively emitting signals. Since passive bistatic radar entails larger data volumes and computations compared to traditional active radiation radar, the development of hardware and software platforms capable of efficiently processing signals from passive bistatic radar has emerged as a research focus in this field. This research investigates the signal processing flow of passive bistatic radar based on its characteristics and devises a parallel signal processing scheme under graphic processing unit (GPU) architecture for computation-intensive tasks. The proposed scheme utilizes high-computing-power GPU as the hardware platform and compute unified device architecture (CUDA) as the software platform and optimizes the extensive cancellation algorithm batches (ECA-B), range Doppler and constant false alarm detection algorithms. The detection and tracking of a single target are realized on the passive bistatic radar dataset of natural scenarios, and experiments show that the design of this algorithm can achieve a maximum acceleration ratio of 113.13. Comparative experiments conducted with varying data volumes revealed that this method significantly enhances the signal processing rate for passive bistatic radar. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is an active microwave imaging sensor for high-resolution observation, with the ability of working in all-weather and all-day. Recently, SAR images have been widely used in many fields. Among them, ship detection in single-channel SAR images is a significant part of civilian and military fields. This article first discusses the characteristic of SAR images and the detectability of ships, then summarizes the recent advance of traditional and deep learning-based methods used for ship detection in single-channel SAR images. In addition, the characteristics and existing problems of various methods are discussed and their future development trends are predicted. Aiming at the problems of the large amount of calculation, multi-scale and densely docked ship detection in single-channel SAR images, an improved deep learning-based detection algorithm is proposed, which has achieved excellent performance on the SAR ship detection dataset (SSDD). [ABSTRACT FROM AUTHOR]

Deep learning such as deep neural networks has revolutionized the computer vision area. Deep learning-based algorithms have surpassed conventional algorithms in terms of performance by a significant margin. This paper reviews our works in the application of deep convolutional neural networks to target recognition and terrain classification using the SAR image. A convolutional neural network is employed to automatically extract a hierarchic feature representation from the data, based on which the target recognition and terrain classification can be conducted. Experimental results on the MSTAR benchmark dataset reveal that deep convolutional network could achieve a state-of-the-art classification accuracy of 99% for the 10-class task. For a polarimetric SAR image classification, we propose complex-valued convolutional neural networks for complex SAR images. This algorithm achieved a state-of-the-art accuracy of 95% for the 15-class task on the Flevoland benchmark dataset.