Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)最新文献

Electrical stimulation and microneedle-mediated drug delivery emerge as promising therapies in gastrointestinal (GI) motility disorders and inflammatory conditions. However, on-demand intervention therapy in enclosed narrow GI remains a challenge. Herein, a magnetic-driven soft membrane robot is presented that synergistically combines microneedle-mediated electrical stimulation and drug delivery. The membrane robot's bipolar magnetization enables switching between two surfaces by external magnetic fields, where N-pole drives treatment surface with microneedle to penetrate GI wall and S-pole initiates smooth surface for low resistance locomotion. The membrane robot utilizes magnetically coupled resonant wireless transmission to enable regulated electrical stimulation with 86.7% efficiency at 6 cm distance, while providing tunable voltage (0–20 V) and programmable pulse waveforms (0.4–50 ms width) for adaptive bioelectrical modulation. The drug-loaded microneedle array serves dual roles as both a penetrating electrode and a therapeutic interface, delivering electrical stimulation while simultaneously releasing encapsulated agents upon tissue penetration. In vitro experiments of the multimode motion and multifunctional treatment are validated in a fresh pig gut. This integrated membrane magnetic robot offers great potential in GI diagnostics, personalized neuromodulation, and on-demand drug release applications.

Heterogeneous multirobot systems show great potential in complex tasks requiring coordinated hybrid cooperation. However, existing methods that rely on static or task-specific models often lack generalizability across diverse tasks and dynamic environments. This highlights the need for generalizable intelligence that can bridge high-level reasoning with low-level execution across heterogeneous agents. To address this, a hierarchical multimodal framework that integrates a prompted large language model (LLM) with a fine-tuned vision-language model (VLM) is proposed. At the system level, the LLM performs hierarchical task decomposition and constructs a global semantic map, while the VLM provides semantic perception and object localization, where the proposed GridMask significantly enhances the VLM's spatial accuracy for reliable fine-grained manipulation. The aerial robot leverages this global map to generate semantic paths and guide the ground robot's local navigation and manipulation, ensuring robust coordination even in target-absent or ambiguous scenarios. The framework is validated through extensive simulation and real-world experiments on long-horizon object arrangement tasks, demonstrating zero-shot adaptability, robust semantic navigation, and reliable manipulation in dynamic environments. To the best of our knowledge, this work presents the first heterogeneous aerial–ground robotic system that integrates VLM-based perception with LLM-driven reasoning for global high-level task planning and execution.

Dysarthric speech recognition faces significant challenges of acoustic variability and data scarcity, and this study proposes a robust system by integrating generative adversarial network enhancement and large language model correction to address these issues effectively. The system employs three key components, including a multimodal recognition core that combines whisper-medium encoder with LoRA-fine-tuned Llama-3.1-8B for end-to-end acoustic-to-semantic mapping, an improved CycleGAN module that generates synthetic dysarthric speech through Inception-ResNet fusion blocks, and an intelligent error correction mechanism using N-best hypothesis reranking with semantic constraints. Experiments on the UA-Speech dataset show that the complete system achieves a 20.61% word error rate representing a 73.9% relative improvement over traditional end-to-end transformer automatic speech recognition. Under very low intelligibility conditions it maintains a 48.69% word error rate demonstrating robust recognition for severe pathological speech. Ablation studies validate each module's effectiveness, providing significant advances for dysarthric patient communication technologies.

This article proposes a neural network-based parameter extraction methodology for the Berkeley Short-Channel IGFET Model–Common Multi-Gate (BSIM–CMG) model applied to gate-all-around field effect transistors (GAAFETs), capturing both current–voltage and capacitance–voltage characteristics to support compact model library development. Conventional BSIM parameter extraction is often complex and inefficient, requiring manual intervention and significant time to cover a wide range of device dimensions and temperatures. To address these limitations, a novel binning adaptive sampling strategy is integrated into the neural network-based extraction framework to efficiently generate training data across broad device dimension ranges. In addition, the transformer-based deep neural networks are designed to output only binnable parameters, ensuring compatibility with compact model library requirements. The trained networks are tested using 3 nm node GAAFET Technology Computer Aided Design (TCAD) data under various conditions, achieving mean absolute percentage errors below 5% for both drain current and gate capacitance. Consequently, the extracted parameters are integrated with corner model parameters through binning equations. This approach results in binning models that are readily deployable in compact model libraries while significantly reducing parameter extraction time and enabling automation across a wide range of GAAFET dimensions.

With the advancement of artificial intelligence (AI), there is an increasing demand for high-speed, energy-efficient hardware capable of running complex machine learning algorithms. Traditional hardware is constrained by the Von Neumann bottleneck, resulting in high power consumption and slower speeds. Inspired by the human brain, bio-mimicking the dynamic synaptic plasticity of the biological synapse using synaptic transistors is crucial to building the next generation of high-performance computing hardware-based neural networks. This study investigates neuromorphic behavior in 180 nm bulk complementary metal oxide semiconductor (CMOS) devices at 4 K, emphasizing memory properties and synapse-like characteristics. These findings position bulk CMOS as a scalable, energy-efficient, cryo-compatible platform for neuromorphic and quantum computing use. Gated-pulse measurements are used to study potentiation–depression behavior by quantifying conductance changes as functions of pulse amplitude and width. These results closely resemble biological synaptic plasticity, laying the groundwork for integrating cryo-CMOS technology into neuromorphic computing. The work reported here aims to work toward the development of hybrid computational systems by bridging the gap between conventional CMOS devices and emerging cryogenic technology, offering new avenues for scalable, energy-efficient, and high-performance cryogenic neuromorphic technologies.

Animal-Robot Interaction

The cover illustrates a gregarious locust interacting with a biomimetic agent inoculated with Beauveria bassiana on an robotic experimental platform, highlighting the dynamics of social immunity and pathogen information spread within the swarm, as explored through innovative biohybrid method of this study. More details can be found in article 2400763 by Donato Romano and Cesare Stefanini.

Siamese Network

This paper proposed a novel AI framework that enhances guidewire tip tracking in image-guided therapy for vascular diseases. Combining a Siamese network with attention mechanisms ensures robust tracking despite visual ambiguities and tissue deformations. Validated on clinical angiography sequences and robotic platforms, it improves diagnostic and therapeutic precision in endovascular interventions. More details can be found in the Research Article by Peng Qi and co-workers (DOI: 10.1002/aisy.202500425).

Bio-Intelligent Cyborg Insect

The cover image features a Bio-Intelligent Cyborg Insect (BCI) guided by non-invasive ultraviolet (UV) stimulation. A lightweight wireless backpack and UV helmet enable real-time feedback control based on the insect’s natural sensory perception, achieving autonomous navigation in complex environments. This work highlights the integration of biological intelligence with engineered systems for advanced biohybrid robotics. More details can be found in article 10.1002/aisy.202400838 by Keisuke Morishima and co-workers.

This paper explores trajectory planning for robotic arms in 3D operational spaces. To improve the adaptation to dynamic via-points in complex, confined environments, an enhanced dynamic movement primitives (DMP) approach is proposed and designed for dynamic planning under composite steering force field constraints. By incorporating steering attraction forces, this method enhances the generalization capability of DMP, allowing the robotic arm to navigate through dynamic via-points flexibly without altering the start and end positions. The trajectory shape is adjusted via regression attraction forces, which helps preserve the demonstrated trajectory, reduce free-space loss, and improve the system's adaptability to complex, dynamic environments. The convergence of the target state is rigorously proven using Lyapunov stability theory. Numerical simulations and experiments conducted with the Franka robotic arm validate the effectiveness of the proposed approach. Results show that in dynamic environments with multiple via-points, this method produces reliable trajectories for robotic arm movements, significantly enhancing the adaptation of DMP to dynamic contexts. The planning process requires no additional learning, and the generated trajectory closely resembles the original demonstrated path. This method enables effective via-point operations in confined spaces without requiring additional learning, while maintaining existing skills.