Artificial intelligence (AI) has emerged as a significant driving force in the development of technology and industry. It is also integrated with music as music AI in music generation and analysis. It originated from early algorithmic composition techniques in the mid-20th century. Recent advancements in machine learning and neural networks have enabled innovative music generation and exploration. This article surveys the development history and technical route of music AI, analyzes the current status and limitations of music artificial intelligence across various areas, including music generation and composition, rehabilitation and treatment, as well as education and learning. It reveals that music AI has become a promising creator in the field of music generation. The influence of music AI on the music industry and the challenges it encounters are explored. Additionally, an emotional music generation system driven by multimodal signals is proposed. Although music artificial intelligence technology still needs to be further improved, with the continuous breakthroughs in technology, it will have a more profound impact on all areas of music.

Among the biomedical imaging modalities, photoacoustic computed tomography (PACT) is one of the emerging hybrid techniques in recent years. In designing the PACT imaging system, a finite bandwidth transducer is one of the limited factors for the overall performance. As the target size is inversely proportional to the dominant frequency components of the generated photoacoustic (PA) signal, a broad bandwidth transducer is desired for different scales imaging. In the current study, a monolithic multi-band CMUT array was designed and fabricated for the reception of the wide band PA signals so as to provide high resolution images with high frequency CMUT arrays and present the high signal-to-noise-ratio major structure with low frequency CMUT arrays. To demonstrate its performance, a phantom experiment was conducted to show and evaluate the various qualities of multi-resolution images. In addition, an in vivo mouse model experiment was also carried out for revealing the multi-scale photoacoustic imaging capability with the multi-band CMUTs on biological tissues. From the obtained results, the images from different CMUT arrays could show the structures of the mouse brain in different scales. In addition, the images from the high frequency CMUT arrays were able to reveal the major blood vasculatures, whereas, the images from low frequency CMUT arrays showed the gross macroscopic anatomy of the brain with higher contrast.

Capacitive micromachined ultrasonic transducers (CMUTs) have emerged as a competitive alternative to piezoelectric ultrasonic transducers, especially in medical ultrasound imaging and therapeutic ultrasound applications, which require high output pressure. However, as compared with piezoelectric ultrasonic transducers, the output pressure capability of CMUTs remains to be improved. In this paper, a novel structure is proposed by forming an embossed vibrating membrane on a CMUT cell operating in the collapse mode to increase the maximum output pressure. By using a beam model in undamped conditions and finite-element analysis simulations, the proposed embossed structure showed improvement on the maximum output pressure of the CMUT cell when the embossed pattern was placed on the estimated location of the peak deflection. As compared with a uniform membrane CMUT cell worked in the collapse mode, the proposed CMUT cell can yield the maximum output pressure by 51.1% and 88.1% enhancement with a single embossed pattern made of Si3N4 and nickel, respectively. The maximum output pressures were improved by 34.9% (a single Si3N4 embossed pattern) and 46.7% (a single nickel embossed pattern) with the uniform membrane when the center frequencies of both original and embossed CMUT designs were similar.

We review the research progress on noninvasive neural regulatory systems through system design and theoretical guidance. We provide an overview of the development history of noninvasive neuromodulation technology, focusing on system design. We also discuss typical cases of neuromodulation that use modern noninvasive electrical stimulation and the main limitations associated with this technology. In addition, we propose a closed-loop system design solution of the “time domain”, “space domain”, and “multi-electrode combination”. For theoretical guidance, this paper provides an overview of the “digital brain” development process used for noninvasive electrical-stimulation-targeted modeling and the development of “digital human” programs in various countries. We also summarize the core problems of the existing “digital brain” used for noninvasive electrical-stimulation-targeted modeling according to the existing achievements and propose segmenting the tissue. For this, the tissue parameters of a multimodal image obtained from a fresh cadaver were considered as an index. The digital projection of the multimodal image of the brain of a living individual was implemented, following which the segmented tissues could be reconstructed to obtain a “digital twin brain” model with personalized tissue structure differences. The “closed-loop system” and “personalized digital twin brain” not only enable the noninvasive electrical stimulation of neuromodulation to achieve the visualization of the results and adaptive regulation of the stimulation parameters but also enable the system to have individual differences and more accurate stimulation.

Analytical modeling of capacitive micromachined ultrasonic transducer (CMUT) is one of the commonly used modeling methods and has the advantages of intuitive understanding of the physics of CMUTs and convergent when modeling of collapse mode CMUT. This review article summarizes analytical modeling of the collapse voltage and shows that the collapse voltage of a CMUT correlates with the effective gap height and the electrode area. There are analytical expressions for the collapse voltage. Modeling of the membrane deflections are characterized by governing equations from Timoshenko, von Kármán equations and the 2D plate equation, and solved by various methods such as Galerkin’s method and perturbation method. Analytical expressions from Timoshenko’s equation can be used for small deflections, while analytical expression from von Kármán equations can be used for both small and large deflections.