Cell-free massive MIMO (CF-mMIMO) has emerged as a promising technology for next generation wireless systems, combining the benefits of distributed antenna systems (DAS) and traditional MIMO technology. In this work, we present the first extensive channel measurements for CF-mMIMO in the mid-band (FR3, 6-24 GHz), using a virtual widely distributed antenna array comprising 512 elements in the urban Macrocell (UMa) environment. Based on the measurement data, this paper compares the channel capacity of CF-mMIMO and Conventional mMIMO under both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions across a range of signal-to-noise ratios (SNRs). We then analyze how channel capacity varies with Rx positions from the perspectives of the full array and of individual subarrays. Finally, we conclude that the 64-element array configuration yields the greatest advantage in channel capacity for CF-mMIMO in the measurement environment considered, with gains of 14.02\% under LOS and 24.61\% under NLOS conditions. This in-depth analysis of channel capacity in the FR3 band provides critical insights for optimizing CF-mMIMO systems in next generation wireless networks.

Although the complex spectrum-based speech enhancement (SE) methods have achieved significant performance, coupling amplitude and phase can lead to a compensation effect, where amplitude information is sacrificed to compensate for the phase that is harmful to SE. In addition, to further improve the performance of SE, many modules are stacked onto SE, resulting in increased model complexity that limits the application of SE. To address these problems, we proposed a dual-path network based on compressed frequency using Mamba. First, we extract amplitude and phase information through parallel dual branches. This approach leverages structured complex spectra to implicitly capture phase information and solves the compensation effect by decoupling amplitude and phase, and the network incorporates an interaction module to suppress unnecessary parts and recover missing components from the other branch. Second, to reduce network complexity, the network introduces a band-split strategy to compress the frequency dimension. To further reduce complexity while maintaining good performance, we designed a Mamba-based module that models the time and frequency dimensions under linear complexity. Finally, compared to baselines, our model achieves an average 8.3 times reduction in computational complexity while maintaining superior performance. Furthermore, it achieves a 25 times reduction in complexity compared to transformer-based models.

Decoding speech from brain signals is a challenging research problem. Although existing technologies have made progress in reconstructing the mel spectrograms of auditory stimuli at the word or letter level, there remain core challenges in the precise reconstruction of minute-level continuous imagined speech: traditional models struggle to balance the efficiency of temporal dependency modeling and information retention in long-sequence decoding. To address this issue, this paper proposes the Dynamic Multiscale Fusion Network (DMF2Mel), which consists of four core components: the Dynamic Contrastive Feature Aggregation Module (DC-FAM), the Hierarchical Attention-Guided Multi-Scale Network (HAMS-Net), the SplineMap attention mechanism, and the bidirectional state space module (convMamba). Specifically, the DC-FAM separates speech-related ''foreground features'' from noisy ''background features'' through local convolution and global attention mechanisms, effectively suppressing interference and enhancing the representation of transient signals. HAMS-Net, based on the U-Net framework, achieves cross-scale fusion of high-level semantics and low-level details. The SplineMap attention mechanism integrates the Adaptive Gated Kolmogorov-Arnold Network (AGKAN) to combine global context modeling with spline-based local fitting. The convMamba captures long-range temporal dependencies with linear complexity and enhances nonlinear dynamic modeling capabilities. Results on the SparrKULee dataset show that DMF2Mel achieves a Pearson correlation coefficient of 0.074 in mel spectrogram reconstruction for known subjects (a 48% improvement over the baseline) and 0.048 for unknown subjects (a 35% improvement over the baseline).Code is available at: https://github.com/fchest/DMF2Mel.

In this study, we attempt to present a new way for high school students to explore the relations between empirical probability and theoretical probability and build conceptual understandings of probability by the means of Augmented Reality. Two classes of seventh grade students were selected as an experimental class and a control class. Students were assessed by the pretests and posttests handed to students at the beginning and the end of the class respectively. The quantitative analysis showed an improvement of the mean score between the two groups. Also, the qualitative analysis of the open-ended questions and interviews of students and the teacher showed their strong inclinations toward the Augmented Reality technology-equipped instruction.

This study assessed the effectiveness of rehabilitating children with autism with a series of Leap Motion-based applications in a special school setting. Experiment was carried out according to an AB sequence. Experimental result showed that the two participants' fine motor skills improved significantly, and their recognition of colors and fruits after the intervention was 100%.