Nowadays, there is a greater need for energy efficient and stable underwater sensor networks (UWSNs). Underwater sensors usually do not have enough power, so the goal of underwater sensor networks is to make the network have a long lifetime. An underwater heterogeneous sensor network (UWHSN) is one way to cluster the sensors, and the application of UWHSNs is simple and fast, but robots, lifetime and energy-partition are all drawbacks of UWHSNs. In this paper we propose the underwater isomorphic sensor network (UWISN) clustering technology. By analyzing the characteristics of UWISNs, we determine that an UWISN has strong expansibility, mobility, energy-efficiency and long lifetime. An UWISN adopts normal sensor nodes to be cluster heads, and these cluster heads communicate with each other. This paper seeks the optimal number of clusters and uses FCM to elect cluster heads and establish the network. In addition, an idea of real cluster heads and the method to elect them have been proposed. Finally, the simulation results show that the solution is effective and UWISNs can improve the energy consumption of an UWSN.

Audio signals play a crucial role in our perception of our surroundings. People rely on sound to assess motion, distance, direction, and environmental conditions, aiding in danger avoidance and decision making. However, in real-world environments, during the acquisition and transmission of audio signals, we often encounter various types of noises that interfere with the intended signals. As a result, the essential features of audio signals become significantly obscured. Under the interference of strong noise, identifying noise segments or sound segments, and distinguishing audio types becomes pivotal for detecting specific events and sound patterns or isolating abnormal sounds. This study analyzes the characteristics of Mel’s acoustic spectrogram, explores the application of the deep learning ECAPA-TDNN method for audio type recognition, and substantiates its effectiveness through experiments. Ultimately, the experimental results demonstrate that the deep learning ECAPA-TDNN method for audio type recognition, utilizing Mel’s acoustic spectrogram as features, achieves a notably high recognition accuracy.

For laser ultrasound in the thermoelastic mechanism, excitation of ultrasonic body wave signal is weak, it is not easy to realize the detection of deep defects inside the workpiece. While the ablation mechanism produces a high and practical ultrasonic signal-to-noise ratio, this paper is based on the generation mechanism of laser ablation excitation of ultrasonic waves, the establishment of laser ultrasound in the ablation mechanism in the aluminum plate excitation and propagation of ultrasonic numerical model, through the solution, obtained the ultrasonic acoustic field map, discussed the ablation mechanism of the laser ultrasonic body wave acoustic field directionality. Additionally, the preliminary verification of the validity of the model is presented. Then, in order to explore the application potential of high signal-to-noise ratio longitudinal waves in defect detection, defects of different depths are preset in the model for simulation calculations, and waveform and acoustic field analyses are performed on the simulation results to study the ultrasonic propagation paths inside the member and the interaction with the defects, and the transverse position and depth of the internal defects are judged by using B-scan imaging. Finally, experimental validation is carried out. The experimental results are highly consistent with the simulation model, and the defect experiments can qualitatively determine the location of internal defects and verify the practicality and accuracy of the model.

Individuals suffering from motor dysfunction due to various diseases often face challenges in performing essential activities such as grasping objects using their upper limbs, eating, writing, and more. This limitation significantly impacts their ability to live independently. Brain–computer interfaces offer a promising solution, enabling them to interact with the external environment in a meaningful way. This exploration focused on decoding the electroencephalography of natural grasp tasks across three dimensions: movement-related cortical potentials, event-related desynchronization/synchronization, and brain functional connectivity, aiming to provide assistance for the development of intelligent assistive devices controlled by electroencephalography signals generated during natural movements. Furthermore, electrode selection was conducted using global coupling strength, and a random forest classification model was employed to decode three types of natural grasp tasks (palmar grasp, lateral grasp, and rest state). The results indicated that a noteworthy lateralization phenomenon in brain activity emerged, which is closely associated with the right or left of the executive hand. The reorganization of the frontal region is closely associated with external visual stimuli and the central and parietal regions play a crucial role in the process of motor execution. An overall average classification accuracy of 80.3% was achieved in a natural grasp task involving eight subjects.

The purpose of using electroencephalogram to explore the dynamic changes of brain functional connectivity during natural grasping tasks is to uncover the underlying mechanisms of information transmission between different brain regions during cognitive processing. This exploration aims to provide new insights for the development of brain-computer interface technology and contribute to the diagnosis and treatment of brain disorders. In this study, we used time-frequency cross mutual information to evaluate the brain functional connectivity during 3-class natural grasping tasks (palmar grasp, lateral grasp and rest state). Specifically, our analysis focused on the functional brain connectivity generated by the amplitude and phase of electroencephalogram signals within the alpha (8-13 Hz) and beta (20-30 Hz) frequency bands. To assess the differences in global coupling strength, we employed two-series correlation coefficients, between different motor periods and between different brain regions for the three motor tasks. Furthermore, it was compared that the differences in the global coupling strength between different motor periods in the same motor task. Finally, the analysis of topologic characteristics in brain functional connectivity networks between the three tasks was investigated. The findings of our study indicate that functional reorganization of frontal region closely related to external visual stimuli occurs during the motor preparation period. The onset of movement leads to a lateralized reorganization of brain functional connectivity, which is associated with the right or left of the executive hand. Both the central and parietal regions contribute prominently to motor execution, and the parietal region in particular plays an important role in the execution of fine motor movements. Further analysis revealed that it is the brain’s dynamic regulation of functional connectivity across frequency bands, amplitudes and phases, enabling it to perform multiple tasks with limited energy resources.

With the rapid advancement of biology and life sciences, there is an increasing demand for observing sub-cellular structures and molecular interactions at submicroscopic or even single-molecule levels, providing critical insights into life activities and disease diagnostics. Raman spectroscopy, which relies on molecular vibrational energy transitions, enables non-label and non-invasive cellular visualization, holding significant potential for modern medical technology. The microscopy method based on the coherent anti-Stokes Raman scattering effect, a novel visualization modality with superior signal intensity, chemical specificity, and label-free capability, demonstrates great promise in biomedical applications. Recently, dual-comb technology, consisting of two frequency combs with slightly different repetition rates, as a powerful light source has been successfully applied in CARS applications with the excellent performance characteristics of rapid acquisition, high resolution, and high signal-to-noise ratio. The dual-comb technique allows to clearly resolve sharp molecular lines and could suppress the non-resonant background in CARS. Through recent research progress, this work reviews the generation of dual-comb lasers based on a single cavity, the development of dual-comb CARS systems, and their biomedical applications. This review could provide further insights into high-resolution dual-comb CARS and potential ways to design such technology for potential biomedical applications.