With the rapid evolution of societal and technological trends leading up to 2030, the limitations of existing 5G mobile communication networks have become increasingly evident. Emerging applications demand unprecedented performance from 6G networks, including extremely high data transfer rates, ultra-low latency, exceptional reliability, scalability, energy efficiency, and mobility. This paper explores relevant visions for 6G and comprehensively reviews its current developmental status. We specifically identify and analyze three key application scenarios driving 6G requirements: Integrated Sensing and Communication (ISAC), energy saving, and Artificial Intelligence(AI)-related technologies. Furthermore, the paper examines a range of potential future application scenarios and their associated demand directions. The analysis aims to provide a scientific basis and strategic guidance for the development of 6G technology, which is expected to pioneer new scenarios, offer innovative experiences, and support economic growth by enriching the global communication service.
Enhancing microvascular imaging improves tumor prediction and early assessment. Tissue harmonic imaging (THI) and super-harmonic imaging (SHI), leveraging the nonlinear effects of acoustic waves with dual-frequency transducers, enable high-precision imaging with improved contrast-to-tissue ratio (CTR). However, current dual-frequency systems are often bulky, expensive, and complex. In this paper, a portable ZYNQ-based dual-frequency ultrafast ultrasound imaging system is proposed, capable of simultaneously exciting and receiving signals. System performance was evaluated through low and high frequency pulse transmission tests and dual-frequency tissue harmonic imaging (DF-THI) experiments. Results demonstrate flexible pulse frequency adjustment, 80V excitation at both frequencies, and plane wave electronic beam steering. DF-THI experiments at 6000 frames per second achieved a CTR of 38.4 dB after 15angle coherent compounding. Utilizing only a ZYNQ-based acquisition board and a PC, the system demonstrates strong portability and leaves an ARM-based interface for future onboard RF processing and image display, showing potential for portable ultrafast nonlinear and super-harmonic imaging.
On-device learning is becoming increasingly important for enabling adaptive and privacy-preserving AI directly on resource-constrained edge devices. However, traditional deep learning approaches based on backpropagation (BP) remain computationally expensive and memory-intensive, making them impractical in such environments. To address these limitations, the Forward-Forward (FF) algorithm has been proposed as an alternative, replacing the backward pass with two forward passes to reduce memory overhead and computational complexity. In this paper, we introduce Sparse-FF, a novel variation of the FF algorithm that incorporates a stochastic selection mechanism to update only a subset of weights per training step. By dynamically selecting a fraction of neurons for activation and weight updates, Sparse-FF significantly reduces computational and memory requirements while maintaining competitive performance. We provide a formal analysis of its computational complexity, demonstrating its efficiency compared to both BP and FF. In addition, we conducted extensive experiments on the MNIST dataset to evaluate its accuracy and training speed. Our results show that Sparse-FF achieves a favorable balance between efficiency and model performance, making it a promising alternative for real-time edge learning applications where resources are limited.
This study introduces a wide-range tensile force detection system employing optoelectronic principles along the Z-direction, enabling accurate measurement of vertical mechanical signals through optoelectronic conversion mechanisms. The system comprises an elastic body, support layer, optoelectronic devices, and conditioning circuitry. Light signals emitted by light-emitting diodes (LEDs) are reflected by a metallic reflective coating on the inner wall of the elastic body and subsequently received by photodiodes (PDs). When tensile force induces deformation in the elastic body, the resulting photocurrent output reflects the deformation status of the inner wall, thereby enabling characterization of Z-axis tensile forces. The optoelectronic chip is fabricated using GaN-based semiconductor optoelectronic processes, with an active low-pass filter circuit converting optical signals to electrical signals. Experimental results demonstrate that at 25 degrees C, the system achieves tensile force characterization within 0-1.1 MPa, featuring a sensitivity of 0.36 mV/kPa, nonlinearity error < +/- 5.56% FS, repeatability error < +/- 6.83% FS, and overall accuracy < +/- 8.83% FS. These findings confirm that the optoelectronic sensing-based measurement system effectively characterizes tensile forces with excellent output stability and reliability.
Efficient and flexible wireless power transfer (WPT) is essential for the development of next-generation wearable electronics. This work presents a wireless power-sharing scheme based on magnetic resonance coupling (MRC-WPSS) that enables simultaneous and controllable power distribution between two passive loads using only passive receiving circuits. By adjusting the input resonant capacitance on the transmitter side and configuring for loose coupling, the system achieves tunable and overlapping resonance profiles, allowing independent control of the power delivered to each receiver. Both simulation and experimental results confirm that varying the input capacitance enables seamless power sharing, allowing one receiver to be selectively powered or the load to be equally distributed. Physical experiments with fabric-based resistive heaters demonstrate the system’s capabilities, achieving independent temperature control and validating power sharing in real-time. The average power transfer efficiency reaches 15.65%, sufficient for practical applications in multi-zone wearable heating. This fully passive approach eliminates the need for active or complex receiver-side circuitry, providing a simple and scalable solution for distributed wearable electronics and other remote actuation scenarios.
