In daily life, there are a variety of complex sound sources. It is important to effectively detect certain sounds in some situations. With the outbreak of COVID-19, it is necessary to distinguish the sound of coughing, to estimate suspected patients in the population. In this paper, we propose a method for cough recognition based on a Mel-spectrogram and a Convolutional Neural Network called the Cough Recognition Network (CRN), which can effectively distinguish cough sounds.

Calibration is often an important and necessary step in the use of image-guided systems. In the case of the $AX=YB$ problem, the relative hand-eye ( $X$ ) and robot-world ( $Y$ ) transformations must be determined to provide accurate data for use in control. As an added difficulty, the exact correspondence between the streams of sensor data ( $A$ ’s and $B$ ’s) is typically unknown due to asynchrony in sampling rates and processing time. One common scenario is a constant shift between the two data streams. Therefore, in this paper, we present a probabilistic method to simultaneously solve for $X$ and $Y$ without a priori knowledge of the correspondence between the streams of $A$ ’s and $B$ ’s. We begin by discussing probability density functions on $SE(3)$ and then use Euclidean-group invariants to obtain an exact solution for $X$ and $Y$ . We then present a method to simultaneously recover $X$ and $Y$ and the correspondence between temporally shifted data sets using a correlation method. Following this, we show how to solve the problem in the case when the data are completely scrambled, corresponding to a complete loss of temporal information. Finally, we numerically simulated the proposed method with asynchronous data and noise added to the stream of $B$ ’s to verify its efficiency and robustness.

This paper presents a novel self-assembly modular robot (Sambot) that also shares characteristics with self-reconfigurable and self-assembly and swarm robots. Each Sambot can move autonomously and connect with the others. Multiple Sambot can be self-assembled to form a robotic structure, which can be reconfigured into different configurable robots and can locomote. A novel mechanical design is described to realize function of autonomous motion and docking. Introducing embedded mechatronics integrated technology, whole actuators, sensors, microprocessors, power and communication unit are embedded in the module. The Sambot is compact and flexible, the overall size is 80×80×102mm. The preliminary self-assembly and self-reconfiguration of Sambot is discussed, and several possible configurations consisting of multiple Sambot are designed in simulation environment. At last, the experiment of self-assembly and self-reconfiguration and locomotion of multiple Sambot has been implemented.