We demonstrate a single-beam three-axis parametric-resonance magnetometer operated in near-zero fields. By reflecting the incident laser beam at 90° in the vapor cell and applying three orthogonal parametric modulation fields, the three components of the magnetic field can be extracted from the transmitted light signal. Our vector magnetometer experimentally demonstrates magnetic-field sensitivities of 30 fT Hz^−1/2 along x- and y-axes and 70 fT Hz^−1/2 along the z-axis, and features a compact single-beam architecture, which is particularly attractive for applications requiring highly sensitive measurements of the vector components of magnetic fields with low power consumption and miniaturized magnetometers, such as magnetoencephalography and magnetocardiography.

Artificial intelligence (AI) promises to revolutionize the design, optimization and management of next-generation communication systems. In this article, we explore the integration of large AI models (LAMs) into semantic communications (SemCom) by leveraging their multi-modal data processing and generation capabilities. Although LAMs bring unprecedented abilities to extract semantics from raw data, this integration entails multifaceted challenges including high resource demands, model complexity, and the need for adaptability across diverse modalities and tasks. To overcome these challenges, we propose a LAM-based multi-task SemCom (MTSC) architecture, which includes an adaptive model compression strategy and a federated split fine-tuning approach to facilitate the efficient deployment of LAM-based semantic models in resource-limited networks. Furthermore, a retrieval-augmented generation scheme is implemented to synthesize the most recent local and global knowledge bases to enhance the accuracy of semantic extraction and content generation, thereby improving the inference performance. Finally, simulation results demonstrate the efficacy of the proposed LAM-based MTSC architecture, highlighting the performance enhancements across various downstream tasks under varying channel conditions.

In recent years, the Transformer architecture has achieved outstanding performance across a wide range of tasks and modalities. Token is the unified input and output representation in Transformer-based models, which has become a fundamental information unit. In this work, we consider the problem of token communication, studying how to transmit tokens efficiently and reliably. Point cloud, a prevailing three-dimensional format which exhibits a more complex spatial structure compared to image or video, is chosen to be the information source. We utilize the set abstraction method to obtain point tokens. Subsequently, to get a more informative and transmission-friendly representation based on tokens, we propose a joint semantic-channel and modulation (JSCCM) scheme for the token encoder, mapping point tokens to standard digital constellation points (modulated tokens). Specifically, the JSCCM consists of two parallel Point Transformer-based encoders and a differential modulator which combines the Gumel-softmax and soft quantization methods. Besides, the rate allocator and channel adapter are developed, facilitating adaptive generation of high-quality modulated tokens conditioned on both semantic information and channel conditions. Extensive simulations demonstrate that the proposed method outperforms both joint semantic-channel coding and traditional separate coding, achieving over 1dB gain in reconstruction and more than 6× compression ratio in modulated symbols.

Magnetoencephalography (MEG) detects very weak magnetic fields originating from the neurons so as to study human brain functions. The original detected MEG data always include interference generated by blinks, which can be called blink artifacts. Blink artifacts could cover the MEG signal we are interested in, and therefore need to be removed. Commonly used artifact cleaning algorithms are signal space projection (SSP) and independent component analysis (ICA). These algorithms need to locate the blink artifacts, which is typically done with the identification of the blink signals in the electrooculogram (EOG). The EOG needs to be measured by electrodes placed near the eye. In this work, a new algorithm is proposed for automatic and on-the-fly identification of the blink artifacts from the original detected MEG data based on machine learning; specifically, the artificial neural network (ANN). Seven hundred and one blink artifacts contained in eight MEG signal data sets are harnessed to verify the effect of the proposed blink artifacts identification algorithm. The results show that the method can recognize the blink artifacts from the original detected MEG data, providing a feasible MEG data-processing approach that can potentially be implemented automatically and simultaneously with MEG data measurement.

Magnetoencephalography (MEG) is a persuasive tool to study the human brain in physiology and psychology. It can be employed to obtain the inference of change between the external environment and the internal psychology, which requires us to recognize different single trial event‐related magnetic fields (ERFs) originated from different functional areas of the brain. Current recognition methods for the single trial data are mainly used for event‐related potentials (ERPs) in the electroencephalography (EEG). Although the MEG shares the same signal sources with the EEG, much less interference from the other brain tissues may give the MEG an edge in recognition of the ERFs. In this work, we propose a new recognition method for the single trial auditory evoked magnetic fields (AEFs) through enhancing the signal. We find that the signal strength of the single trial AEFs is concentrated in the primary auditory cortex of the temporal lobe, which can be clearly displayed in the 2D images. These 2D images are then recognized by an artificial neural network (ANN) with 100% accuracy, which realizes the automatic recognition for the single trial AEFs. The method not only may be combined with the source estimation algorithm to improve its accuracy but also paves the way for the implementation of the brain‐computer interface (BCI) with the MEG.

Despite the remarkable achievements of modern communication systems based on Shannon’s theory, there is still considerable room for exploration in information transmission capacity, and semantic communication technology has emerged as a promising approach in this regard. Nonetheless, the benefits of semantic communication remain elusive, and the absence of a unified system model has hindered practical implementation. In this context, we contend that semantic communication can benefit from data distortion and the incorporation of natural language modeling information, such that source coding with semantic modeling information does not compromise the performance of semantic communication systems. To fortify our stance, a novel Separated Data-Semantic Coding (SDSC) system is proposed, which disentangles the source coding and semantic coding. Furthermore, relevant experiments are conducted to validate the contention and the SDSC system. By illuminating the superiority of semantic communication, the research not only contributes to the advancement of semantic communication technologies but also facilitates the development of more practical communication systems.