Highly sensitive devices such as the SHRIMP IIe/MC ion microprobe help scientists to make precise measurements of past time-scales, paleoclimatic temperatures, and much more.

The ultrasonic flowmeter which is described in this paper, measures the transit of time of an ultrasonic pulse. This device consists of two ultrasonic transducers and a high resolution time interval measurement module. An ultrasonic transducer emits a characteristic wave packet (transmit mode). When the transducer is in receive mode, a characteristic wave packet is formed and it is connected to the time interval measurement module inputs. The time interval measurement module allows registration of transit time differences of a few pulses in the packet. In practice, during a single measuring cycle a few time-stamps are registered. Moreover, the measurement process is also synchronous and, by applying the statistics, the time interval measurement uncertainty improves even in a single measurement. In this article, besides a detailed discussion on the principle of operation of the ultrasonic flowmeter implemented in the FPGA structure, also the test results are presented and discussed

In the external target experiment for heavy ion collisions in the HIRFL-CSR, Multi-Wire Drift Chambers are used to measure the drift time of charged particles to obtain the track information. This 128-channel high precision time measurement module is designed to perform the time digitization. The data transfer is based on a PXI interface to guarantee a high data rate. Test results show that a 100 ps resolution with a data transfer rate up to 40 MBps has been achieved; this module has also been proven to function well with the detector through a commissioning test.

Rat robots have great potential in rescue and search tasks because of their excellent motion ability. However, most of the current rat-robot systems relay on human guidance due to variable voluntary motor behaviour of rats, which limits their application. In this study, we developed a real-time system to detect a rat robot’s transient motion states, as the prerequisite for further study of automatic navigation. We built the detection model by using a wearable inertial sensor to capture acceleration and angular velocity data during the control of a rat robot. Various machine learning algorithms, including Decision Trees, Random Forests, Logistic Regression, and SupportVector Machines,were employed to performthe classification of motion states. This detection system was tested in manual navigation experiments, with detection accuracy achieving 96.70%. The sequence of transient motion states could be further used as a promising reference for offline behaviour analysis.