Sixth-generation (6G) networks are envisioned to achieve full-band cognition by jointly utilizing spectrum resources from Frequency Range 1 (FR1) to Frequency Range 3 (FR3, 7–24 GHz). Realizing this vision faces two challenges. First, physics-based ray tracing (RT), the standard tool for network planning and coverage modeling, becomes computationally prohibitive for multi-band and multi-directional analysis over large areas. Second, current 5G systems rely on inter-frequency measurement gaps for carrier aggregation and beam management, which reduce throughput, increase latency, and scale poorly as bands and beams proliferate. These limitations motivate a data-driven approach to infer high-frequency characteristics from low-frequency observations. This work proposes CommUNext, a unified deep learning framework for cross-band, multi-directional signal strength (SS) prediction. The framework leverages low-frequency coverage data and crowd-aided partial measurements at the target band to generate high-fidelity FR3 predictions. Two complementary architectures are introduced: Full CommUNext, which substitutes costly RT simulations for large-scale offline modeling, and Partial CommUNext, which reconstructs incomplete low-frequency maps to mitigate measurement gaps in real-time operation. Experimental results show that CommUNext delivers accurate and robust high-frequency SS prediction even with sparse supervision, substantially reducing both simulation and measurement overhead.

The trend toward using increasingly large arrays of antenna elements continues. However, fitting more antennas into the limited space available on user equipment (UE) within the currently popular Frequency Range 1 spectrum presents a significant challenge. This limitation constrains the capacity-scaling gains for end users, even when networks support a higher number of antennas. To address this issue, we explore a user-centric collaborative MIMO approach, termed UE-CoMIMO, which leverages several fixed or portable devices within a personal area to form a virtually expanded antenna array. This paper develops a comprehensive mathematical framework to analyze the performance of UE-CoMIMO. Our analytical results demonstrate that UE-CoMIMO can significantly enhance the system’s effective channel response within the current communication system without requiring extensive modifications. Further performance improvements can be achieved by optimizing the phase shifters on the expanded antenna arrays at the collaborative devices. These findings are corroborated by ray-tracing simulations. Beyond the simulations, we implemented these collaborative devices and successfully conducted over-the-air validation in a real 5G environment, showcasing the practical potential of UE-CoMIMO. Several practical perspectives are discussed, highlighting the feasibility and benefits of this approach in real-world scenarios.

State-of-the-art exoskeletons are typically limited by low control bandwidth and small range stiffness of actuators which are based on high gear ratios and elastic components (e.g., series elastic actuators). Furthermore, most exoskeletons are based on discrete gait phase detection and/or discrete stiffness control resulting in discontinuous torque profiles. To fill these two gaps, we developed a portable lightweight knee exoskeleton using quasi-direct drive (QDD) actuation that provides 14 Nm torque (36.8% biological joint moment for overground walking). This paper presents 1) stiffness modeling of torque-controlled QDD exoskeletons and 2) stiffness-based continuous torque controller that estimates knee joint moment in real-time. Experimental tests found the exoskeleton had high bandwidth of stiffness control (16 Hz under 100 Nm/rad) and high torque tracking accuracy with 0.34 Nm Root Mean Square (RMS) error (6.22%) across 0-350 Nm/rad large range stiffness. The continuous controller was able to estimate knee moments accurately and smoothly for three walking speeds and their transitions. Experimental results with 8 able-bodied subjects demonstrated that our exoskeleton was able to reduce the muscle activities of all 8 measured knee and ankle muscles by 8.60%-15.22% relative to unpowered condition, and two knee flexors and one ankle plantar flexor by 1.92%-10.24% relative to baseline (no exoskeleton) condition.

The growth of inbound travel is fully coordinate with the successful urban development. Increasing the number of inbound travelers not only creates more jobs and economic opportunities but also drives the country toward prosperity. Thus, inbound traveler analysis through trajectory pattern mining, a subfield of urban computing, is regarded as a promising solution. This paper introduces large-scale mobile ad requests as an alternative data source of trajectory pattern mining in order to eliminate the limitations of conventional data sources, such as GPS data, cellular data, and IP address data. In addition, to expedite a comprehensive inbound traveler analysis, we build TraVis, a real-world system for efficiently exploring the inbound travelers' activities through the interactive visualization interface. By incorporating various modules, such as mobile users' home country and travel intent prediction, frequent trajectory pattern mining, and interactive visualization, TraVis proves the capability of profiling the travelers' behavior pattern. We use Japan inbound travelers in the case study to present the mining insights, and we also demonstrate the extensive system functionalities. Our system has been assisting Japan government agencies to formulate travel marketing strategies, including tourist experience enhancement and attractions marketing.

Human intention estimation is important for assistive lower limb exoskeleton, and the task is realized mostly by the dynamics model or the EMG model. Although the dynamics model offers better estimation, it fails when unmodeled disturbances come into the system, such as the ground reaction force. In contrast, the EMG model is non-stationary, and therefore the offline calibrated EMG model is not satisfactory for long-time operation. In this paper, we propose the self-learning scheme with the sliding mode admittance control to overcome the deficiency. In the swing phase, the dynamics model is used to estimate the intention while teaching the EMG model; in the consecutive swing phase, the taught EMG model is used alternatively. In consequence, the self-learning control scheme provides better estimations during the whole operation. In addition, the admittance interface and the sliding mode controller ensure robust performance. The control scheme is justified by the knee orthosis with the backdrivable spring torsion actuator, and the experimental results are prominent.

Simultaneously considering the physical interaction between the user and the robot within safety and performance constraints in rehabilitation and human walking situations, this paper proposes a new backdrivable torsion spring actuator (BTSA) with hybrid control that switches between direct electromyography (EMG) biofeedback control and zero impedance control, to provide a novel rehabilitation training and walking assistance mechanism for humans. The proposed backdrivable 1-DOF serial elastic actuator is designed to achieve intrinsic safety, compliance properties, and control performance. The proposed mechanical system can provide desirable backdrivable property and softer stiffness than that of traditional robots. In additional, the proposed hybrid control not only considers the assistive function, when human assistance is required, but also the compliance property, when assistance is not needed. Compared to state-of-the-art assistive methods, the BTSA with the proposed hybrid control system is unique in that it can simultaneously achieve assistance control through EMG biofeedback and compliance control through zero impedance control. A simple human-robot interaction model is built to investigate performance and explain the whole control concept. Further, a knee exoskeleton is built and three kinds of controls are used on a human subject to demonstrate the difference between them. Both simulation and experimental results show that the proposed BTSA mechanism with hybrid control offers the desired properties.

Focusing on the physical interaction between people and machines within safety constraints in versatile situations, this paper proposes a new, efficient actuation approach, continuous-state coupled elastic actuation (CCEA), to provide oncoming human-machine systems with an intrinsic programmable stiffness capacity to shape output force corresponding to the deviation between human motions and set positions of the system. As one of all the possible CCEA systems, a prototype of a 2-DOF coupled elastic actuator is designed to provide a compromise between performance and safety. Using a pair of antagonistic four-bar linkages, the inherent stiffness of the system can be adjusted dynamically. Compared to the state-of-the-art variable stiffness actuators, the CCEA system is unique in that it can achieve near zero mechanical stiffness in an efficient way. In addition, a human-robot interaction model is built to investigate the controlled bandwidth and safety of the CCEA system. For the application of assistive exercises, this study also proposes two kinds of controls for assistive exercises. Finally, a CCEA exoskeleton is built for elbow rehabilitation. Both simulations and experiments are conducted to show some desired properties of the proposed CCEA system.