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Based on the photoacoustic spectroscopy technology, this research designed a photoacoustic cell system for detecting gas concentration. By injecting a beam of periodically modulated light, the light reacts with the mixed gas. The gas molecules absorb the light and transition from the ground state to the excited state and then release heat. The periodically changing heat will generate periodically changing pressure, thereby generating sound waves. After receiving the sound wave through a microphone, the gas concentration can be calculated by analyzing the collected signal. This paper analyzes the principle of harmonic detection and analyzes the relationship between input thermal amplitude and sound pressure.

Based on the photoacoustic spectroscopy technology, this research designed a photoacoustic cell system for detecting gas concentration. By injecting a beam of periodically modulated light, the light reacts with the mixed gas. The gas molecules absorb the light and transition from the ground state to the excited state and then release heat. The periodically changing heat will generate periodically changing pressure, thereby generating sound waves. After receiving the sound wave through a microphone, the gas concentration can be calculated by analyzing the collected signal. This article analyzes the relationship between different gas concentration detection and amplitude output.

Additional file 2: Table S2. Characteristics ofpatients with gliomabased on CGGA RNA-seq data.

Additional file 4: Table S4. Characteristics ofpatients with gliomabased on TCGA RNA-seq data.

This article uses an optical fiber hydrogen sensor based on the light-emphasis system. It has a transmissive optical structure, and multiple sensor films can be placed to measure the change in optical performance under a hydrogen environment to improve the signal intensity of light intensity changes. When hydrogen gas contacts the surface of palladium, it is captured and decomposed into hydrogen atoms and enters the interior of the palladium lattice, causing physical properties of the palladium nanoparticles to change, which in turn affects the intensity of transmitted light. However, due to the very small size of the nanoparticles, the resulting changes in optical performance are limited, so this paper uses a multilayer sensitization method to enhance the intensity of the optical signal and improve the performance-to-noise ratio.

The filter designed by the analog circuit has a big problem. It is extremely convenient to design the FIR filter in the FPGA. In the article we will introduce the whole process of the entire FIR filter from design, verification, simulation to implementation. This paper mainly explains the FIR filter principle and filter coefficient calculation, and simulates the linear filter with the sampling rate of 1Mhz, the passband is 120Khz, and the order is 15th order. Finally, the experimental verification is carried out through the FPGA board, and the corresponding The design of the required FIR filter.

Efficient neuronal communication between brain regions through oscillatory synchronization at certain frequencies is necessary for cognition. Such synchronized networks are transient and dynamic, established on the timescale of milliseconds in order to support ongoing cognitive operations. However, few studies characterizing dynamic electrophysiological brain networks have simultaneously accounted for temporal non-stationarity, spectral structure, and spatial properties. Here, we propose an analysis framework for characterizing the large-scale phase-coupling network dynamics during task performance using magnetoencephalography (MEG). We exploit the high spatiotemporal resolution of MEG to measure time-frequency dynamics of connectivity between parcellated brain regions, yielding data in tensor format. We then use a tensor component analysis (TCA)-based procedure to identify the spatio-temporal-spectral modes of covariation among separate regions in the human brain. We validate our pipeline using MEG data recorded during a hand movement task, extracting a transient motor network with beta-dominant spectral mode, which is significantly modulated by the movement task. Next, we apply the proposed pipeline to explore brain networks that support cognitive operations during a working memory task. The derived results demonstrate the temporal formation and dissolution of multiple phase-coupled networks with specific spectral modes, which are associated with face recognition, vision, and movement. The proposed pipeline can characterize the spectro-temporal dynamics of functional connectivity in the brain on the subsecond timescale, commensurate with that of cognitive performance. peerReviewed

A new path-planning algorithm based on neural networks is proposed for mobile robots. The neural network is used in the algorithm to model the environment and calculate the collision energy function (CEF) which is the dominating term in the cost function. To implement the path-planning procedure, rather than calculating the minimum value of the cost function directly, a discrete method is used to approximate the minus gradient direction of the cost function in order to determine the motion tendency of the point set along the path. Finally, the performance and efficiency of the algorithm are estimated through computer simulation. The algorithm is very efficient in situations where real-time operation is required.